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S3b_5_Hass-UNECE-OECD-CC Expenditure Definition-v3

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Climate Change Mitigation and Adaptation Expenditures in the Economy: Towards an Operational Definition

2024 OECD-UNECE SEEA Implementation Geneva, Switzerland

March 18-20, 2024

Julie L. Hass and Scott Wentland

Disclaimer: The opinions are those of the authors and do not necessarily reflect the official position of the Bureau of Economics Analysis, Department of Commerce, or United States Government.

Thematic accounts / “Special Topics”

• Thematic Accounts • “These supplementary statistics

allow in-depth analysis of special topics that aren’t easily seen within BEA’s core statistics… provide a deeper understanding of the U.S. economy.”

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2https://www.bea.gov/data/special-topics

Digital Economy

Small Business

Space Economy

Thematic accounts

• Primarily developed based on expert inputs, data availability, often established based on policy demand for these special topics.

• Difficult to put them into relation with one another since they are built in isolation.

• No definitive guidelines for how to decide what to include or exclude. • For example: There are overlaps between BEA’s

– “Outdoor Recreation” and the “Marine Economy” accounts;

– “Outdoor Recreation” and “Travel and Tourism” accounts

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How do we develop CC Expenditure Accounts?

Two ways forward: A. Based on the established criteria

for the SEEA-CF’s Environmental Protection Expenditure (EPE) and Resource Management(RM) Accounts and broaden to CCEA

B. Thematic/Special Topic – Based on experts and without consideration to the established criteria for the SEEA-CF EPE and RM accounts

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Is there a starting point in the SEEA-CF? Yes! Have EPE on mitigation Greenhouse gases

• Example from Statistics Norway Environmental Protection Expenditure, Reduce Greenhouse gas emissions, NACE B- E36: Mining, Manufacturing, Electricity and Water supply (NOK 1 000), 2019-2022.

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2019 2020 2021 2022

Environmental current expenditure 495 377 422 339 468 038 539 021

EP ancilliary ouput 384 478 283 022 253 200 234 052

Intermediate consumption of EP services 110 899 139 317 214 838 304 969

Investments 1 231 404 2 674 927 4 626 501 1 452 526

Source: https://www.ssb.no/en/statbank/table/13062/

Is there a starting point in the SEEA-CF? Yes! Research Agenda of SEEA-CF (Annex II)

Accounts and statistics relating to the minimization of natural hazards and the effects of climate change: • “A2.19 The SEEA Central Framework limits the scope of

economic activities considered to be environmental to environmental protection and resource management activity. However, it is recognized that there are a number of other economic activities that are related to the environment which may be of particular interest for policy and analytical purposes (see sect. 4.2). A specific set of activities encompasses efforts to minimize the impact of natural hazards (such as floods, cyclones and bush fires) and efforts to mitigate, or adapt to, the effects of climate change.”

(SEEA-CF, Annex II, pages 307-308, emphases added) 3/15/2024

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Is there a starting point thematic/Special Topic Accounts? Work related to Climate Change Expenditures

• UNECE: CES CC related statistics – Indicators but no definitions

• Eurostat: EPE/RM statistics + CC statistics

– Mitigation in EPE & expanded definition of mitigation but no data

– Ongoing project to delineate CC mitigation & adaptation activities – produce EU estimates

• OECD: CC Budget Tagging – Focus on projects and policy. Definitions no

more specific than “principal objective” and descriptions of expected results

• EU: Green Budgeting – Budgetary policymaking to achieve climate

goals

• EU: Taxonomy for Sustainable Activities – Unit of analysis is ‘projects’ – Currently only Mitigation Economic Activities are

described • IADB: Government CC Spending

– Proposes both main purpose and secondary tags which have no CC intent stated but have a measurable impact or are responses to CC impacts

– Proposes classification & methodology • Austrian project: Federal Government current

costs for CC Adaptation – Budget analysis & expert interviews – Provides some figures for 2014

• IMF Data Gaps Initiative 3: Recommendation 7 – Working definitions focus on impacts, adaptation

and resilience

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See London Group Paper for more detailed information and descriptions of these studies: https://seea.un.org/sites/seea.un.org/files/measuring_climate_mitigation_and_adaptation_exp enditures_in_the_economy_-_hass_and_wentland_u.s._bureau_of_economic_analysis_bea.pdf

Climate Change Mitigation & Adaptation Expenditures

• Are there internationally agreed upon definitions of Climate Change Mitigation and Adaptation Expenditures?

• Do we know what is included/excluded?

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IMF Data Gaps Initiative 3: Recommendation 7

• Climate Change Expenditure Accounts – CCEA • Claim: ‘Consistent with SNA, SEEA-CF and GFSM’ (i.e. boundaries)

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• “Working” definition for climate change mitigation expenditure: • Climate change mitigation expenditures are expenditures aimed at making the impacts of climate change less severe by preventing or reducing the emission of greenhouse gases (GHG) into the atmosphere and enhancing sinks of greenhouse gases.

• “Working” definition for climate change adaptation expenditure: • Climate change adaptation expenditures are expenditures aimed at adapting and building resilience of human and ecological systems to the changing climate conditions, reducing vulnerability, and minimizing the negative climate change impacts.

Source: IMF Concept Note for Recommendation 7. January 2024

Note: Emphases in red have been added

SEEA-CF uses ‘primary purpose’ to define EPE/RM

• SEEA-CF 4.15 While some economic activities may be undertaken only for a single purpose, many activities are undertaken for a variety of purposes. Following general principles of classification, activities are deemed to be environmental activities only if the primary purpose of the activity is consistent with the definitions of the two types of environmental activity listed as environmental, i.e., environmental protection and resource management. In practice, the primary purpose must be attributed to particular transactions or groups of transactions as recorded in the accounts.

• Definition of Environmental Protection Expenditure (EPE)

4.12 … Environmental protection activities are those activities whose primary purpose is the prevention, reduction and elimination of pollution and other forms of degradation of the environment.

• Definition of Resource Management Expenditures (RM)

4.13 Resource management activities are those activities whose primary purpose is preserving and maintaining the stock of natural resources and hence safeguarding against depletion.

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Decision criteria – Are NOT the same! SEEA (CEA), SNA (ex. COFOG): Primary Purpose • Primary purpose – used in different

statistical classification systems such as COFOG, SEEA-CF

• Easier to determine • Can make decision criteria

for what is ‘in’ or ‘out’

IMF: Reducing impacts of climate change • Impacts

– implies causality – not known for a long time – how to determine this?

• Further analyses are needed. Estimating net impacts on climate require expertise outside of what national statistical offices typically do.

• Comparability  will this create greater scope for some NSOs to disagree? (one NSO determines a net impact as positive and another determines the same activity as negative?) Will this reduce international comparability?

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IPCC Definition of Climate Change

• Mitigation A human intervention to reduce emissions or enhance the sinks of greenhouse gases.

• Adaptation In natural systems, the process of adjustment to actual climate and its effects; human intervention may facilitate adjustment to expected climate and its effects.

In human systems, the process of adjustment to actual or expected climate and its effects, in order to moderate harm or exploit beneficial opportunities.

(Source: https://apps.ipcc.ch/glossary/)

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Mitigation expenditures – included in EPE Reduce GHG emissions • Included in CEA 1: Protection of ambient air and climate

– Expenditures for the reduction of GHG emissions (technology, process improvements, etc.)

– Includes expenditures (human interventions) for Carbon Capture & Storage • Technically these should also be included:

– Expenditures on GHG emissions tradeable permits/credits – Expenditures imposed by Carbon taxes – Technically, expenditures on offsets should be included but these are not

always reliable projects – so need to take care when including => but often are not currently included due to data collection challenges

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Mitigation: “enhance the sinks of greenhouse gases”

Currently NOT included in EPE mitigation expenditures definitions

• What does “enhance the sinks” of GHGs include? – GHG sinks: sequester or store carbon

• Resource management expenditures that improve or preserve “GHG sinks” would also be CC mitigation expenditures.

• Examples include expenditures that improve or preserve: - Forested areas - Wetlands - Mangroves - Topsoil – soil carbon - Tropical ecosystems

• These types of RM expenditures would also be CC mitigation expenditures

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Climate Change mitigation and EPE/RM expenditures

• What is new to EPE/RM when GHG reduction focus is broadened to include CC mitigation?

• Human systems – Reducing GHG emissions – Tradeable permits & carbon

taxes – GHG offsets

(carbon/biofuel credits) • Natural Systems

– Enhance natural carbon sinks

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Environmental Protection Expenditure (EPE) Resource Management Expenditures (RM) Classification of Environmental Activities (CEA) Classification of Environmental Activities (CEA)

HUMAN SYSTEMS NATURAL SYSTEMS

CEA 1: Protection of Ambient Air & Climate CEA 10-16

CEA 1 Climate Change Mitigation Activities CC Mitigation Activities include expenditures on: include expenditures on:

1) Reducing GHG emissions Enhancing natural sinks for carbon 2) Carbon Capture & Sequestration 3) Tradeable permits 4) Carbon Taxes 5) Carbon offsets

CC mitigation in human and natural systems + CC Adaptation in natural systems in relation to EPE&RM Expenditures

Environmental Protection Expenditure (EPE) Resource Management Expenditures (RM) Classification of Environmental Activities

(CEA) Classification of Environmental Activities (CEA)

HUMAN SYSTEMS

NATURAL SYSTEMS

CEA 1: Protection of Ambient Air &

Climate CEA 10-16

CEA 1 Climate Change Mitigation Activities CC Mitigation Activities include expenditures on: include expenditures on: 1) Reducing GHG emissions Enhancing natural sinks for carbon 2) Carbon Capture & Sequestration 3) Tradeable

permits

4) Carbon Taxes

Mixed CC Mitigation & Adaptation Activities 5) Carbon offsets

include expenditures on: - Increase natural systems' resiliance for dealing with changes in environmental conditions

CC Adaptation Activities include expenditures on: - Increase natural systems' resiliance for dealing with changes in environmental conditions

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SEEA EPE&RM Expenditures as starting point for CC Expenditure Accounts

CC mitigation in human and natural systems + CC Adaptation in natural systems + CC Adaptation in Human systems

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Environmental Protection Expenditure (EPE) Resource Management Expenditures (RM)

Classification of Environmental Activities

(CEA) Classification of Environmental Activities (CEA)

HUMAN SYSTEMS

NATURAL SYSTEMS

CEA 1: Protection of Ambient Air & Climate CEA 10-16 CEA 1 Climate Change Mitigation Activities include expenditures on: CC Mitigation Activities 1) Reducing GHG emissions include expenditures on: 2) Carbon Capture & Sequestration Enhancing natural sinks for carbon

3) Tradeable

permits

4) Carbon Taxes

5) Carbon offsets

Mixed CC Mitigation & Adaptation Activities include expenditures on: - Increase natural systems' resilience for dealing with changes in environmental conditions HUMAN SYSTEMS CC Adaptation Expenditures CC Adaptation Activities Related to human systems include expenditures on: - Increase natural systems' resilience

(NOT part of EPE/RM expenditures) for dealing with changes in environmental

conditions

Climate Change Adaptation

• In natural systems… • The process of adjustment to actual

climate and its effects; human intervention may facilitate adjustment to expected climate and its effects. (IPCC definition)  should find these “human interventions” as part of RM expenditure since it has to do with ‘natural systems’

• In human systems… • In human systems, the process of

adjustment to actual or expected climate and its effects, in order to moderate harm or exploit beneficial opportunities. (IPCC definition) most applicable human systems are outside of EPE and RM expenditures

 need to identify and define what these are

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CC Adaptation – Nature-based solutions included in RM

• “Nature-based solutions” – part of resource management expenditures & ecosystem improvement and resilience

• For example: – In Coastal areas, the planting of mangroves or

other wetland species—can reduce the threat of inundation while also providing a habitat for marine life and improving water quality.

– Climate-smart agriculture, preserving and enhancing topsoil

– Decreasing deforestation – Constructing terraces on hillsides, using

vegetation at critical points to control soil erosion, increase soil moisture, and reduce runoff.

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Human systems: Adaptation / Resilience Examples

• Example: Miami, Florida “Beyond water management strategies, Miami must also develop considerably new or reinforced zoning and building codes. Homes, commercial buildings, and urban infrastructure will bear the brunt of climate effects, and there’s a host of potential solutions…” (Source: https://slate.com/technology/2022/05/miami-climate-change-survival.html)

• “Two Florida communities prove the power of climate adaptation” Punta Gorda: after 2007 storm…updated, climate-resilient building codes. Babcock Ranch: “is a new community designed with climate resilience as an underlying principle. …Strong building codes, floodable streets, native plantings in prolific greenspaces, building outside of the floodplain, undergrounding vulnerable utilities, and providing microgrids and energy independence” (Source: https://www.geiconsultants.com/thought_leadership/power-of-climate-adaptation/)

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Primary Purpose and Baselines?

• Consider a bridge built to higher specifications due to climate change. – The “primary purpose” of the bridge is for

transportation, but should we consider the additional expenditure above and beyond the old (baseline) standards as climate change adaptation?

• The primary purpose of this additional (but not entire) expenditure is climate change related.

– Is it the baseline that matters for primary purpose? • Similarly, we consider electric cars as mitigation-related

because the baseline car (or the standard alternative) is an internal combustion engine running on fossil fuel (emitting GHGs)… But what if the electricity production is from coal fired power plants?

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SEEA-CF Environmental Expenditure Accounts

Two pathways to development…

• SEEA-CF EPE/RM Expenditure accounts can form the foundation for Climate Change Expenditure Accounts (CCEA)

• Better to have CCEA related to the SEEA Environmental Expenditure Accounts than as separate, unrelated thematic accounts – especially since we already find parts of the CC Expenditures in the established SEEA-CF EPE/RM Expenditure accounts Must use same criteria: primary purpose and not impacts.

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Next Steps

• Need to further develop criteria for determining what to include / exclude from the different topics.

• Is “primary / main purpose” a good enough criterion? How should we consider secondary purpose and/or technical aspects?

• “Impacts” of expenditures are not useable for developing expenditure statistics.

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Next steps for USA – Learning by doing

• Develop preliminary criteria for what is in and out of CC expenditures – The Eurostat study may be helpful in this work.

• See how far we can get examining the different statistics and data sources to see what could be useable. – Examine products and services in the SUT internal system – Examine the construction and/or housing cost data – Examine the relevant federal government Agency budgets

For example: FEMA (Federal Emergency Management Agency)

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Thank You!

Additional questions/comments? Julie L. Hass

[email protected] Scott Wentland

[email protected] . .

Some Definitions of CC Mitigation and Adaptation

• Intergovernmental Panel on Climate Change (IPCC) IPCC. 2022. Annex I: Glossary. In: P Shukla, J Skea, R Slade, et al. (eds.). Climate Change 2022: Mitigation of Climate Change.

• OECD-Development Assistance Committee (DAC) OECD DAC Rio Markers for Climate: Handbook

• Multilateral Development Banks (MDBs) MDB-IDFC 2021, Common principles for climate mitigation finance tracking, version 3; MDB-IDFC 2015, Common principles for climate change adaptation finance tracking

• International Development Finance Club (IDFC) IDFC. 2021. IDFC Green Finance Mapping Report 2021

• Climate Policy Initiative (CPI) Buchner et al., 2021. Global Landscape of Climate Finance 2021.

• Climate Bonds Initiative (CBI) CBI 2019. Climate Resilience Principles: A framework for assessing climate resilience investments

• EU Sustainable finance taxonomy EU Commission. 2020. Regulation (EU) 2020/852 of the European Parliament and of the Council of 18 June 2020 on the establishment of a framework to facilitate sustainable investment, and amending Regulation (EU) 2019/2088

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26Source: Table 1. of IMF Concept Note for Recommendation 7. January 2024

EPE/RM + Climate Change + Disaster Expenditures

• Putting all three concepts together show that there are areas overlapping – even though the terminology use can be different.

• Disaster preventive/adaptive activities are relevant for both natural and human systems

• Same for Disaster recovery activities • There are also Non-Climate Change

disasters in both human and natural systems

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  • Climate Change Mitigation and Adaptation Expenditures in the Economy: Towards an Operational Definition ��
  • Thematic accounts / “Special Topics”
  • Thematic accounts
  • How do we develop CC Expenditure Accounts?
  • Is there a starting point in the SEEA-CF? �Yes! è Have EPE on mitigation Greenhouse gases
  • Is there a starting point in the SEEA-CF?�Yes! è Research Agenda of SEEA-CF (Annex II)
  • Is there a starting point thematic/Special Topic Accounts?�Work related to Climate Change Expenditures
  • Climate Change Mitigation & Adaptation Expenditures
  • IMF Data Gaps Initiative 3: Recommendation 7
  • SEEA-CF uses ‘primary purpose’ to define EPE/RM
  • Decision criteria – Are NOT the same!
  • IPCC Definition of Climate Change
  • Mitigation expenditures – included in EPE
  • Mitigation: “enhance the sinks of greenhouse gases”
  • Climate Change mitigation and EPE/RM expenditures
  • CC mitigation in human and natural systems + �CC Adaptation in natural systems in relation to EPE&RM Expenditures
  • SEEA EPE&RM Expenditures as starting point for �CC Expenditure Accounts ���CC mitigation in human and natural systems + �CC Adaptation in natural systems + �CC Adaptation in Human systems
  • Climate Change Adaptation
  • CC Adaptation – Nature-based solutions included in RM
  • Human systems: Adaptation / Resilience Examples
  • Primary Purpose and Baselines?
  • SEEA-CF Environmental Expenditure Accounts
  • Next Steps
  • Next steps for USA – Learning by doing
  • Thank You!
  • Some Definitions of CC Mitigation and Adaptation
  • EPE/RM + Climate Change + Disaster Expenditures

The Federal Interagency Forum on Child and Family Statistics: a unique model of collaborative data collection and reporting among federal agencies in the United States, Traci Cook (Centers for Disease Control and Prevention, United States of America)

Languages and translations
English

UNECE Conference, Geneva Switzerland

4 March – 6 March, 2024

The Federal Interagency Forum Model &

America’s Children: Key National Indicators of

Well-Being

TRACI COOK, FORUM STAFF DIRECTOR UNITED STATES UNECE WORKGROUP ON CHILD STATISTICS REPRSENTATIVE

• Mission and Overview

• Model

• Collaboration Process

• Participating Agencies

• America’s Children

- Visuals - Domains and Data Findings

• Forum Social Media

Forum Presentation Summary

The Children’s Forum

To foster coordination, cooperation, and collaboration among Federal agencies and improve federal data

related to children and families.

Founded in 1994 by six Federal member agencies and OMB

Formally established in April

1997 through Executive Order

13045

Currently includes 23 Federal

Agencies and Departments

Mission

Overview

The Forum Model: Federal Interagency Collaboration

July 8, 2015

Memo to Heads of Federal Agencies from Office of Information and Regulatory Affairs

• Establish frameworks for identifying report domains and key indicators

Reaching Consensus

Collaboration extends beyond the science

Standard Operating Procedures

• Establish frameworks for identifying report domains and key indicators

• Establish criteria for evaluating data measures

• Establish technical report guidelines and report parameters

Guiding Principles

• Broaden the scope of the report to extend beyond the usual population

• Modify data measures to reflect changes in methodology or to address more timely and relevant topics

• Engage for agency consensus

Forum Agencies

America’s Children Key National Indicators of Well-Being, 2023

Highlights selected indicators across the following domains:

Health

Family and Social Environment

Economic Circumstances

Physical Environment and

Safety

Behavior Education

Healthcare

Demographic Background

NUMBER OF CHILDREN AGES 0-17 IN THE UNITED STATES, 1950-2022 AND PROJECTED 2023-2050

Source: U.S. Census Bureau, Population Division

Family and Social Environment

NUMBER PERCENTAGE OF CHILDREN AGES 0-17 BY PRESENCE OF PARENTS IN HOUSEHOLD, 2010-2022

Source: U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplement.

Economic Circumstances

PERCENTAGE OF CHILDREN AGES 0-17 LIVING IN POVERTY BY RACE AND HISPANIC ORIGIN, 2000-2021

Source: U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplement.

Health Care

PERCENTAGE OF CHILDREN AGES 0-17 BY HEALTH INSURANCE COVERAGE STATUS AT THE TIME OF INTERVIEW, 2011-2021

Source: National Center for Health Statistics, National Health Interview Survey.

Physical Environment and Safety

PERCENTAGE OF CHIDLREN AGES 4-11 WITH SPECIFIED BLOOD COTININE LEVELS, SELECTED YEARS 1988-1994 THROUGH 2017-MARCH 2020

Source: National Center for Health Statistics, National Health and Nutrition Examination Survey

Behavior

NUMBER PERCENTAGE OF 8TH, 10TH, AND 12TH GRADE STUDENTS WHO REPORTED SMOKING CIGARETTES DAILY IN THE PAST 30 DAYS BY GRADE, 2000-2022

Source: National Institute on Drug Abuse, Monitoring the Future Survey.

Education NUMBER PERCENTAGE OF CHILDREN AGES 3-5 WHO WERE READ TO THREE OR MORE TIMES IN THE LAST WEEK BY A FAMILY MEMBER BY

MOTHER’S EDUCATION, SELECTED YEARS 1993-2019

Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program.

Health

PERCENTAGE OF INFANTS BORN PRETERM AND PERCENTAGE OF INFANTS BORN WITH LOW BIRTHWEIGHT, 2011-2021

Source: National Center for Health Statistics, National Vital Statistics System.

Outreach

Joint Forum Innovation Workgroup

• Collaborations with Forum Agencies

• America’s Children @childstats

Social Media Staff Director

Federal Interagency Forum on Child and Family Statistics

“The 2020 edition of America’s Children in Brief focuses on differences across indicators of children’s well-being by metropolitan status.”

Access the full report at childstats.gov.

• Charter, Mission & Vision

• Current and prior year reports, special publications and project deliverables

• Up-to-date data tables for each indicator

• Information on Forum members agencies and Forum committees

Closing Thoughts

THANK YOU I look forward to staying connected with you!

+1-301-458-4082

[email protected]

Traci Cook | Forum Staff Director

Digitalization in Energy: Case Study on "Cyber Resilience of Critical Energy Infrastructure"

Digitalization is gaining more and more attention as a way to support and complement the energy transition process. Digitalization entails the use of digital technologies for existing processes, as it helps address existing challenges in new ways.

While using an integrated energy system with intelligent connected devices has many advantages, it also causes challenges. One of these challenges is the increased surface of attack and thus the related cybersecurity risk.

Market Forecast Tables 2023

These tables show forest products production and trade forecasts for 2023 and 2024. These cover roundwood (logs, pulpwood and fuel wood), sawnwood (coniferous and non-coniferous), wood-based panels (plywood, particle board, OSB and fibreboard), pulp, paper and wood pellets.  The forecast data are provided by national correspondents and approved at the meeting of the Committee on Forests and the Forest Industry.

Languages and translations
English

List of tables

List of Tables and Notes
Table 1 - Sawn Softwood
Table 2 - Sawn Hardwood (total)
Table 2a - Sawn Hardwood (temperate)
Table 2b - Sawn Hardwood (tropical)
Table 3 - Veneer Sheets
Table 4 - Plywood
Table 5 - Particle Board (excluding OSB)
Table 5a - Oriented Strand Board
Table 6 - Fibreboard
Table 6a - Hardboard
Table 6b - MDF/HDF
Table 6c - Other Fibreboard
Table 7 - Wood Pulp
Table 8 - Paper and Paperboard
Table 9 - Removals of wood in the rough
Table 9a - Removals of wood in the rough (softwood)
Table 9b - Removals of wood in the rough (hardwood)
Table 10 - Softwood sawlogs
Table 11 - Hardwood sawlogs
Table 11a - Hardwood logs (temperate)
Table 11b - Hardwood logs (tropical)
Table 12 - Pulpwood
Table 12a - Pulpwood (softwood)
Table 12b - Pulpwood (hardwood)
Table 12c - Wood Residues, Chips and Particles
Table 13 - Wood Pellets
Table 14 - Europe: Summary table of market forecasts for 2023 and 2024
Table 15 - North America: Summary table of market forecasts for 2023 and 2024
Source: UNECE Committee on Forests and the Forest Industry , November 2023, http://www.unece.org/forests/fpm/timbercommittee.html
Notes: Data in italics are estimated by the secretariat. EECCA is Eastern Europe, Caucasus and Central Asia.
Data for the two latest years are forecasts.
In contrast to previous years, data are shown only for countries providing forecasts. Sub-regional totals are only for reporting countries.
In contrast to years prior to 2020, data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subregion.
Confidential data have not been included. Please inform secretariat in case you notice any confidential data which might have been included inadvertently.
Wherever the forecast data is incomplete, then data is repeated to avoid skewing.
For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.
Countries with nil, missing or confidential data for all years on a table are not shown.
Consumption figures are the sum of production and national imports minus national exports. Softwood = coniferous, hardwood = non-coniferous. United Kingdom production figures for OSB is secretariat estimate.
Uzbekistan – data extrapolated by the Secretariat based on national data for the first eight months 2023.
Poland - The trade turnover is based on data that includes the estimated value of trade turnover by entities exempt from the reporting obligation. These trade turnover figures are estimated at 3%. Roundwood: sawlogs and veneer logs and pulpwood and wood fuel - with removals from trees and shrubs outside the forest, including forest chips, with stump. Residues - production excluding recovered wood.
Softwood = coniferous, hardwood = non-coniferous
For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.
Countries with nil, missing or confidential data for all years on a table are not shown.

Table1

TABLE 1
SAWN SOFTWOOD SCIAGES CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 6,141 4,978 4,978 10,104 8,588 8,588 1,784 1,270 1,270 5,747 4,880 4,880 Autriche
Cyprus 33 34 34 1 1 1 32 33 33 0 0 0 Chypre
Czech Republic 2,965 2,343 2,470 4,720 3,776 4,040 583 414 350 2,338 1,847 1,920 République tchèque
Estonia 2,068 1,550 1,550 1,725 1,500 1,500 1,209 700 700 866 650 650 Estonie
Finland 2,938 2,420 2,420 11,200 10,300 10,400 305 20 20 8,567 7,900 8,000 Finlande
France 8,633 8,750 8,800 7,168 7,200 7,300 2,350 2,450 2,400 885 900 900 France
Germany 17,294 14,900 13,300 24,309 21,400 19,800 4,146 2,700 3,000 11,162 9,200 9,500 Allemagne
Hungary 788 902 918 85 96 86 717 821 842 14 15 11 Hongrie
Italy 4,790 4,302 4,302 400 400 400 4,608 4,157 4,157 217 255 255 Italie
Latvia 1,025 950 950 3,102 3,000 3,000 829 750 750 2,906 2,800 2,800 Lettonie
Luxembourg 71 122 122 39 39 39 43 91 91 11 8 8 Luxembourg
Malta 7 9 9 0 0 0 7 9 9 0 0 0 Malte
Montenegro 30 30 29 118 115 112 10 9 7 98 94 90 Monténégro
Netherlands 2,259 2,088 2,029 115 115 115 2,659 2,473 2,399 515 500 485 Pays-Bas
Poland 4,631 4,630 4,800 4,144 4,100 4,200 1,219 1,240 1,300 732 710 700 Pologne
Portugal 696 686 685 807 815 820 130 130 125 242 259 260 Portugal
Serbia 367 361 383 91 95 98 281 270 290 5 4 5 Serbie
Slovakia 847 810 860 1,430 1,360 1,400 480 450 460 1,063 1,000 1,000 Slovaquie
Slovenia 665 670 660 983 990 980 530 530 530 848 850 850 Slovénie
Spain 4,029 4,001 4,001 3,006 3,189 3,189 1,166 956 956 143 144 144 Espagne
Sweden 5,709 5,050 5,650 18,870 18,400 18,300 587 500 450 13,748 13,850 13,100 Suède
Switzerland 1,271 1,300 1,325 1,186 1,200 1,210 300 310 320 215 210 205 Suisse
United Kingdom 8,663 8,125 8,214 3,108 2,860 2,860 5,719 5,385 5,474 165 120 120 Royaume-Uni
Total Europe 75,919 69,011 68,490 96,712 89,540 88,439 29,694 25,668 25,934 50,487 46,197 45,883 Total Europe
Uzbekistan 2,256 1,498 1,498 0 0 0 2,256 1,498 1,498 0 0 0 Ouzbékistan
Total EECCA Total EOCAC
Canada a 3,707 2,691 2,242 36,398 33,228 31,331 891 988 948 33,581 31,525 30,037 Canada a
United States a 87,925 87,155 88,151 64,039 64,178 64,399 26,202 25,492 26,149 2,316 2,515 2,397 Etats-Unis a
Total North America 91,632 89,846 90,393 100,437 97,406 95,730 27,093 26,480 27,097 35,898 34,040 32,434 Total Amérique du Nord
a converted from nominal to actual size using factor of 0.72 a convertis du dimension nominale au véritable avec une facteur du 0.72

Table2

TABLE 2
SAWN HARDWOOD (total) SCIAGES NON-CONIFERES (total)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 310 222 222 238 202 202 217 140 140 145 120 120 Autriche
Cyprus 11 7 7 0 0 0 11 7 7 0 0 0 Chypre
Czech Republic 324 245 240 222 167 175 136 103 105 34 24 40 République tchèque
Estonia 232 125 125 175 125 125 147 60 60 90 60 60 Estonie
Finland 84 44 44 73 40 40 34 24 24 23 20 20 Finlande
France 1,124 1,140 1,150 1,446 1,300 1,400 264 420 350 586 580 600 France
Germany 693 650 650 997 800 800 395 300 300 699 450 450 Allemagne
Hungary 258 150 131 414 343 342 45 38 30 200 231 241 Hongrie
Italy 798 776 776 500 500 500 637 578 578 339 302 302 Italie
Latvia 5 105 105 720 800 800 54 55 55 769 750 750 Lettonie
Luxembourg 96 98 98 39 39 39 64 65 65 7 6 6 Luxembourg
Malta 7 8 9 0 0 0 7 8 9 0 0 0 Malte
Montenegro 11 8 10 39 35 34 2 1 1 30 28 25 Monténégro
Netherlands 238 213 203 34 34 34 314 289 279 110 110 110 Pays-Bas
Poland 495 470 500 487 450 460 267 270 300 259 250 260 Pologne
Portugal 369 295 290 182 185 190 287 200 190 100 90 90 Portugal
Serbia 172 215 225 343 370 385 64 60 70 235 215 230 Serbie
Slovakia 235 240 275 385 400 420 55 50 55 205 210 200 Slovaquie
Slovenia 106 145 145 143 145 145 83 80 80 121 80 80 Slovénie
Spain 425 467 467 302 321 321 175 193 193 53 47 47 Espagne
Sweden 142 140 140 100 100 100 83 80 80 41 40 40 Suède
Switzerland 78 79 81 52 53 54 50 51 52 24 25 25 Suisse
United Kingdom 807 810 810 37 40 40 787 790 790 17 20 20 Royaume-Uni
Total Europe 7,019 6,652 6,703 6,928 6,449 6,606 4,177 3,862 3,813 4,086 3,658 3,716 Total Europe
Uzbekistan 228 208 208 195 195 195 33 16 16 0 3 3 Ouzbékistan
Total EECCA Total EOCAC
Canada 1,208 1,324 1,242 859 893 815 793 826 738 444 395 311 Canada
United States 14,647 14,835 15,217 17,637 17,827 18,214 798 805 820 3,788 3,797 3,817 Etats-Unis
Total North America 15,855 16,159 16,459 18,496 18,720 19,029 1,591 1,631 1,558 4,231 4,192 4,128 Total Amérique du Nord

Table 2a

TABLE 2a
SAWN HARDWOOD (temperate) SCIAGES NON-CONIFERES (zone tempérée)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 306 219 219 238 202 202 213 136 136 144 119 119 Autriche
Cyprus 9 5 5 0 0 0 8 5 5 0 0 0 Chypre
Czech Republic 307 229 223 222 167 175 119 86 88 34 24 40 République tchèque
Estonia 230 122 122 175 125 125 142 56 56 87 59 59 Estonie
Finland 80 40 40 73 40 40 26 16 16 19 16 16 Finlande
France 960 988 988 1,420 1,285 1,375 123 280 210 583 577 597 France
Germany 664 630 630 997 800 800 315 240 240 649 410 410 Allemagne
Hungary 257 147 127 414 343 342 43 35 26 200 230 241 Hongrie
Italy 819 791 791 495 495 495 476 423 423 152 127 127 Italie
Latvia 5 105 105 720 800 800 54 55 55 769 750 750 Lettonie
Luxembourg 92 96 96 39 39 39 60 63 63 7 6 6 Luxembourg
Malta 6 7 8 0 0 0 6 7 8 0 0 0 Malte
Montenegro 11 8 10 39 35 34 2 1 1 30 28 25 Monténégro
Netherlands 89 80 77 27 27 27 117 108 105 55 55 55 Pays-Bas
Poland 484 459 488 487 450 460 254 257 286 257 248 258 Pologne
Portugal 319 272 268 170 172 178 180 150 140 31 50 50 Portugal
Serbia 167 211 220 342 369 384 59 57 66 234 215 230 Serbie
Slovakia 235 240 275 385 400 420 55 50 55 205 210 200 Slovaquie
Slovenia 104 143 143 143 145 145 81 78 78 120 80 80 Slovénie
Spain 383 417 417 300 318 318 128 142 142 45 43 43 Espagne
Sweden 142 139 139 100 100 100 83 79 79 41 40 40 Suède
Switzerland 69 70 72 49 50 51 44 45 46 24 25 25 Suisse
United Kingdom 716 720 720 37 40 40 693 700 700 14 20 20 Royaume-Uni
Total Europe 6,453 6,138 6,183 6,872 6,402 6,550 3,281 3,069 3,025 3,700 3,334 3,392 Total Europe
Uzbekistan 227 207 207 195 195 195 33 15 15 0 3 3 Ouzbékistan
Total EECCA Total EOCAC
Canada 1,191 1,316 1,236 859 893 815 762 805 715 430 382 294 Canada
United States 14,379 14,578 14,957 17,637 17,827 18,214 523 529 544 3,782 3,778 3,801 Etats-Unis
Total North America 15,569 15,893 16,193 18,496 18,720 19,029 1,285 1,334 1,259 4,212 4,160 4,095 Total Amérique du Nord

Table 2b

5.NC.T
TABLE 2b
SAWN HARDWOOD (tropical) SCIAGES NON-CONIFERES (tropicale)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 3 3 3 0 0 0 4 4 4 1 1 1 Autriche
Bulgaria 0 0 0 0 0 0 0 0 0 0 0 0 Bulgarie
Cyprus 3 2 2 0 0 0 3 2 2 0 0 0 Chypre
Czech Republic 17 17 17 0 0 0 17 17 17 0 0 0 République tchèque
Estonia 2 3 3 0 0 0 5 4 4 3 1 1 Estonie
Finland 4 4 4 0 0 0 8 8 8 4 4 4 Finlande
France 164 152 162 26 15 25 141 140 140 3 3 3 France
Germany 29 20 20 0 0 0 79 60 60 50 40 40 Allemagne
Hungary 2 3 4 0 0 0 2 4 4 0 0 0 Hongrie
Italy -21 -15 -15 5 5 5 161 154 154 187 175 175 Italie
Luxembourg 4 2 2 0 0 0 4 2 2 0 0 0 Luxembourg
Malta 1 1 1 0 0 0 1 1 1 0 0 0 Malte
Netherlands 149 133 126 7 7 7 197 181 174 55 55 55 Pays-Bas
Poland 10 11 12 0 0 0 12 13 14 2 2 2 Pologne
Portugal 50 23 22 12 13 12 107 50 50 69 40 40 Portugal
Serbia 5 4 5 1 1 1 5 3 4 1 0 0 Serbie
Slovenia 2 2 2 0 0 0 2 2 2 0 0 0 Slovénie
Spain 42 49 49 2 2 2 47 50 50 7 4 4 Espagne
Sweden 1 1 1 0 0 0 1 1 1 0 0 0 Suède
Switzerland 9 9 9 3 3 3 6 6 6 0 0 0 Suisse
United Kingdom 91 90 90 0 0 0 94 90 90 3 0 0 Royaume-Uni
Total Europe 566 515 519 56 46 55 896 793 788 386 324 324 Total Europe
Canada 17 8 7 0 0 0 31 21 23 14 13 16 Canada
United States 269 257 260 0 0 0 275 276 276 6 19 16 Etats-Unis
Total North America 286 266 266 0 0 0 305 297 299 20 31 32 Total Amérique du Nord

Table 3

TABLE 3
VENEER SHEETS FEUILLES DE PLACAGE
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 74 39 39 8 8 8 83 45 45 17 14 14 Autriche
Cyprus 1 1 1 0 0 0 1 1 1 0 0 0 Chypre
Czech Republic 28 28 27 28 16 17 58 53 50 58 41 40 République tchèque
Estonia 111 125 125 105 110 110 87 95 95 82 80 80 Estonie
Finland 27 21 21 190 160 160 12 10 10 175 149 149 Finlande
France 366 366 366 157 157 157 273 273 273 64 64 64 France
Germany 157 143 125 110 105 105 99 78 70 52 40 50 Allemagne
Hungary 23 25 20 13 18 13 39 39 39 28 31 32 Hongrie
Italy 344 308 308 107 107 107 274 234 234 37 33 33 Italie
Latvia 105 105 105 40 50 50 140 140 140 75 85 85 Lettonie
Luxembourg 1 0 0 0 0 0 1 0 0 0 0 0 Luxembourg
Malta 1 2 3 0 0 0 1 2 3 0 0 0 Malte
Netherlands 15 13 13 0 0 0 17 15 15 3 3 3 Pays-Bas
Poland 121 121 129 45 42 45 92 94 98 16 15 14 Pologne
Portugal 12 20 35 20 30 25 38 40 50 46 50 40 Portugal
Serbia 4 4 5 30 28 30 8 6 8 34 30 33 Serbie
Slovakia 17 25 25 21 25 25 27 30 30 31 30 30 Slovaquie
Slovenia 9 8 9 28 27 25 13 14 14 32 33 30 Slovénie
Spain 122 92 92 40 36 36 127 90 90 45 34 34 Espagne
Sweden 32 31 31 60 50 50 19 10 10 47 29 29 Suède
Switzerland 3 3 3 0 0 0 4 4 4 1 1 1 Suisse
United Kingdom 6 10 10 0 0 0 7 10 10 1 0 0 Royaume-Uni
Total Europe 1,577 1,490 1,491 1,002 969 962 1,419 1,283 1,288 843 762 760 Total Europe
Uzbekistan 4 4 4 3 3 3 2 1 1 0 0 0 Ouzbékistan
Total EECCA 0 Total EOCAC
Canada 204 262 267 581 581 581 212 218 230 590 537 544 Canada
United States 2,643 2,670 2,699 2,284 2,306 2,329 652 658 664 293 294 294 Etats-Unis
Total North America 2,847 2,932 2,966 2,866 2,887 2,910 864 876 894 883 831 838 Total Amérique du Nord
Note: Definition of veneers excludes domestic use for plywood.
La définition des placages exclus la conversion directe en contreplaqué.

Table 4

TABLE 4
PLYWOOD CONTREPLAQUES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 19 15 15 131 155 155 183 150 150 296 290 290 Autriche
Cyprus 14 15 15 0 0 0 14 15 15 0 0 0 Chypre
Czech Republic 193 116 123 240 236 238 230 115 115 277 235 230 République tchèque
Estonia 145 50 50 200 210 210 151 50 50 205 210 210 Estonie
Finland 297 240 240 1,110 940 940 87 60 60 900 760 760 Finlande
France 589 583 583 253 270 270 476 452 452 140 139 139 France
Germany 1,073 1,154 840 85 80 80 1,319 1,281 1,000 330 207 240 Allemagne
Hungary 136 110 107 60 61 63 138 138 138 62 90 94 Hongrie
Italy 602 537 537 288 290 290 525 442 442 211 195 195 Italie
Latvia 92 55 55 331 300 300 94 95 95 333 340 340 Lettonie
Luxembourg 33 29 29 0 0 0 33 29 29 0 0 0 Luxembourg
Malta 10 11 12 0 0 0 10 11 12 0 0 0 Malte
Montenegro 2 2 2 1 1 1 2 2 2 1 1 1 Monténégro
Netherlands 488 457 441 0 0 0 586 551 529 98 94 88 Pays-Bas
Poland 650 640 670 539 515 530 468 475 480 357 350 340 Pologne
Portugal 154 180 166 103 100 110 95 110 100 44 30 44 Portugal
Serbia 40 36 38 19 18 19 34 30 33 13 12 14 Serbie
Slovakia 67 63 63 153 150 150 59 59 59 146 146 146 Slovaquie
Slovenia 49 50 58 94 90 98 26 30 30 71 70 70 Slovénie
Spain 231 326 326 462 416 416 132 117 117 363 207 207 Espagne
Sweden 278 160 160 90 90 90 236 120 120 48 50 50 Suède
Switzerland 206 206 206 7 7 7 203 203 203 4 4 4 Suisse
United Kingdom 1,254 1,180 1,180 0 0 0 1,320 1,250 1,250 66 70 70 Royaume-Uni
Total Europe 6,623 6,215 5,916 4,166 3,930 3,967 6,422 5,786 5,482 3,965 3,501 3,532 Total Europe
Uzbekistan 62 46 46 0 0 0 63 47 47 0 0 0 Ouzbékistan
Total EECCA 0 Total EOCAC
Canada 2,174 2,028 2,123 1,604 1,557 1,526 1,224 1,058 1,241 654 587 644 Canada
United States 14,742 14,890 15,188 9,254 9,345 9,528 6,259 6,317 6,436 771 772 776 Etats-Unis
Total North America 16,916 16,918 17,311 10,858 10,902 11,054 7,483 7,375 7,677 1,425 1,359 1,420 Total Amérique du Nord

Table 5

TABLE 5
PARTICLE BOARD (excluding OSB) PANNEAUX DE PARTICULES (ne comprennent pas l'OSB)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 951 630 630 2,280 2,170 2,170 313 355 355 1,642 1,895 1,895 Autriche
Cyprus 49 46 46 0 0 0 49 46 46 0 0 0 Chypre
Czech Republic 793 811 835 962 866 910 530 484 485 699 538 560 République tchèque
Estonia 123 67 67 90 0 0 77 68 68 44 2 1 Estonie
Finland 113 75 75 54 54 54 85 44 44 26 23 23 Finlande
France 2,224 2,148 2,148 3,177 3,094 3,094 299 355 355 1,253 1,301 1,301 France
Germany 5,572 5,220 4,970 5,526 5,195 5,020 1,970 1,934 1,900 1,924 1,909 1,950 Allemagne
Hungary 408 384 379 447 428 438 264 282 272 303 326 331 Hongrie
Italy 3,070 2,813 2,813 2,646 2,500 2,500 956 821 821 532 508 508 Italie
Latvia 52 85 85 306 300 300 69 25 25 322 240 240 Lettonie
Luxembourg 20 12 12 0 0 0 21 13 13 1 1 1 Luxembourg
Malta 10 11 11 0 0 0 10 11 11 0 0 0 Malte
Montenegro 32 33 34 0 0 0 32 33 34 0 0 0 Monténégro
Netherlands 464 440 432 0 0 0 514 488 479 50 48 47 Pays-Bas
Poland 6,501 6,450 6,740 5,227 5,150 5,450 2,173 2,180 2,200 899 880 910 Pologne
Portugal 537 473 514 766 750 760 281 300 290 510 577 536 Portugal
Serbia 373 351 371 219 210 220 196 184 198 42 43 47 Serbie
Slovakia 352 343 340 676 675 675 148 140 137 473 473 472 Slovaquie
Slovenia 137 110 110 0 0 0 143 114 114 6 4 4 Slovénie
Spain 2,392 2,213 2,213 2,566 2,310 2,310 626 621 621 800 718 718 Espagne
Sweden 1,055 868 868 636 600 600 475 335 335 57 67 67 Suède
Switzerland 281 286 286 420 425 425 141 141 141 280 280 280 Suisse
United Kingdom 2,606 2,542 2,542 2,012 1,982 1,982 648 610 610 55 50 50 Royaume-Uni
Total Europe 28,115 26,410 26,521 28,012 26,710 26,908 10,021 9,584 9,555 9,917 9,883 9,942 Total Europe
Uzbekistan 880 542 542 252 252 252 654 317 317 26 27 27 Ouzbékistan
Total EECCA 27 Total EOCAC
Canada 1,466 1,886 1,894 1,625 2,032 2,012 552 504 491 710 650 609 Canada
United States 5,196 5,565 5,562 4,488 4,552 4,534 1,193 1,465 1,487 485 452 459 Etats-Unis
Total North America 6,663 7,451 7,456 6,113 6,584 6,546 1,745 1,969 1,978 1,195 1,102 1,068 Total Amérique du Nord
Data are calculated by subtracting OSB from the particleboard/OSB total - les données sont calculées en soustrayant les OSB du total des panneaux de particules et OSB.

Table 5a

TABLE 5a
ORIENTED STRAND BOARD (OSB) PANNEAUX STRUCTURAUX ORIENTES (OSB)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 205 135 135 0 0 0 212 140 140 7 5 5 Autriche
Cyprus 11 14 14 0 0 0 11 14 14 0 0 0 Chypre
Czech Republic 380 342 350 689 620 655 126 113 115 435 392 420 République tchèque
Estonia 55 32 32 0 0 0 55 32 32 1 0 0 Estonie
Finland 56 56 56 0 0 0 56 56 56 0 0 0 Finlande
France 427 522 522 302 406 406 222 165 165 96 49 49 France
Germany 1,316 1,238 1,130 1,164 1,105 1,080 679 669 600 526 536 550 Allemagne
Hungary 133 147 152 379 419 443 56 60 59 302 331 350 Hongrie
Italy 346 287 287 100 100 100 346 274 274 100 87 87 Italie
Latvia 196 165 165 674 650 650 76 75 75 554 560 560 Lettonie
Luxembourg 110 135 135 338 338 338 6 14 14 234 217 217 Luxembourg
Montenegro 2 2 2 0 0 0 2 2 2 0 0 0 Monténégro
Netherlands 222 222 227 0 0 0 286 286 292 64 64 65 Pays-Bas
Poland 655 650 760 647 650 750 302 320 350 294 320 340 Pologne
Portugal 46 37 41 0 0 0 50 40 45 4 3 4 Portugal
Serbia 40 35 41 0 0 0 41 36 42 1 1 1 Serbie
Slovakia 48 58 60 0 0 0 48 60 63 1 3 3 Slovaquie
Slovenia 31 24 24 0 0 0 33 26 26 2 2 2 Slovénie
Spain 26 15 15 3 3 3 35 33 33 12 20 20 Espagne
Sweden 94 92 92 0 0 0 97 95 95 3 3 3 Suède
Switzerland 95 95 95 0 0 0 96 96 96 1 1 1 Suisse
United Kingdom 773 758 758 598 598 598 365 350 350 190 190 190 Royaume-Uni
Total Europe 5,268 5,060 5,092 4,894 4,888 5,023 3,200 2,956 2,938 2,826 2,784 2,868 Total Europe
Uzbekistan 7 5 5 0 0 0 7 5 5 0 0 0 Ouzbékistan
Total EECCA 0 Total EOCAC
Canada 1,546 1,253 1,153 7,270 6,820 6,798 82 65 61 5,806 5,632 5,706 Canada
United States 19,658 19,834 20,197 13,592 13,783 14,059 6,198 6,236 6,326 132 185 188 Etats-Unis
Total North America 21,204 21,087 21,350 20,862 20,603 20,857 6,280 6,301 6,387 5,938 5,817 5,894 Total Amérique du Nord

Table 6

TABLE 6
FIBREBOARD PANNEAUX DE FIBRES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 421 386 386 470 395 395 331 308 308 381 316 316 Autriche
Cyprus 20 15 16 0 0 0 20 15 16 0 0 0 Chypre
Czech Republic 328 276 280 41 41 42 438 347 360 151 112 122 République tchèque
Estonia 70 46 47 75 40 40 65 46 47 70 40 40 Estonie
Finland 139 105 105 44 44 44 141 102 102 46 41 41 Finlande
France 828 915 915 1,238 1,035 1,035 721 772 772 1,130 892 892 France
Germany 3,791 3,437 3,325 5,194 4,900 4,800 1,590 1,543 1,470 2,993 3,006 2,945 Allemagne
Hungary 9 -17 -13 21 0 0 204 235 244 215 253 258 Hongrie
Italy 1,862 1,661 1,661 827 818 818 1,281 974 974 245 131 131 Italie
Latvia 60 50 40 48 50 50 62 65 65 50 65 75 Lettonie
Luxembourg 100 90 90 147 147 147 34 19 19 80 76 76 Luxembourg
Malta 6 7 7 0 0 0 6 7 7 0 0 0 Malte
Montenegro 32 32 33 0 0 0 32 32 33 0 0 0 Monténégro
Netherlands 332 310 296 29 29 29 465 431 412 162 150 145 Pays-Bas
Poland 3,808 3,765 4,020 4,960 4,920 5,080 590 585 630 1,743 1,740 1,690 Pologne
Portugal 534 485 529 526 520 560 338 315 335 330 350 366 Portugal
Serbia 74 74 88 19 20 22 71 73 88 16 19 22 Serbie
Slovakia 210 218 223 0 0 0 248 256 262 39 38 39 Slovaquie
Slovenia 24 15 15 132 120 125 28 25 30 136 130 140 Slovénie
Spain 920 894 894 1,430 1,287 1,287 462 355 355 972 748 748 Espagne
Sweden 301 260 260 0 0 0 425 360 360 124 100 100 Suède
Switzerland 238 238 238 97 97 97 308 308 308 167 167 167 Suisse
United Kingdom 1,692 1,630 1,630 856 850 850 895 840 840 60 60 60 Royaume-Uni
Total Europe 15,799 14,892 15,085 16,153 15,313 15,421 8,755 8,013 8,037 9,110 8,434 8,373 Total Europe
Uzbekistan 1,092 809 809 47 47 47 1,057 771 771 13 9 9 Ouzbékistan
Total EECCA Total EOCAC
Canada 1,236 1,183 1,181 1,277 1,288 1,299 818 628 605 859 733 723 Canada
United States 8,684 8,749 8,888 6,362 6,420 6,571 3,359 3,289 3,310 1,038 960 993 Etats-Unis
Total North America 9,920 9,932 10,069 7,639 7,708 7,870 4,177 3,917 3,915 1,896 1,693 1,716 Total Amérique du Nord

Table 6a

TABLE 6a
HARDBOARD PANNEAUX DURS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 29 28 28 54 43 43 18 16 16 43 32 32 Autriche
Cyprus 2 1 2 0 0 0 2 1 2 0 0 0 Chypre
Czech Republic 43 45 45 0 0 0 61 59 60 18 14 15 République tchèque
Estonia 23 15 19 0 0 0 30 16 20 7 1 1 Estonie
Finland 23 21 21 44 44 44 21 15 15 41 38 38 Finlande
France 55 55 55 221 221 221 207 207 207 373 373 373 France
Germany 176 183 165 0 0 0 200 203 180 23 20 15 Allemagne
Hungary 27 41 45 2 0 0 65 81 85 40 40 40 Hongrie
Italy 280 280 280 16 16 16 283 283 283 19 19 19 Italie
Latvia 1 5 5 15 15 15 18 20 20 32 30 30 Lettonie
Luxembourg -31 -12 -12 0 0 0 3 8 8 34 20 20 Luxembourg
Montenegro 1 1 1 0 0 0 1 1 1 0 0 0 Monténégro
Netherlands 44 41 39 0 0 0 63 58 56 19 17 17 Pays-Bas
Poland -179 -120 -50 80 80 80 88 100 120 347 300 250 Pologne
Portugal 50 30 39 0 0 0 61 40 50 11 10 11 Portugal
Serbia 39 35 38 19 20 22 33 31 34 13 16 18 Serbie
Slovakia 21 20 21 0 0 0 21 21 22 1 1 1 Slovaquie
Slovenia -1 0 1 0 0 0 4 2 4 4 2 3 Slovénie
Spain 17 15 15 32 29 29 46 46 46 61 60 60 Espagne
Sweden 47 30 30 0 0 0 116 110 110 70 80 80 Suède
Switzerland 19 19 19 0 0 0 24 24 24 5 5 5 Suisse
United Kingdom 101 90 90 0 0 0 110 100 100 9 10 10 Royaume-Uni
Total Europe 787 822 895 482 468 470 1,474 1,441 1,463 1,169 1,087 1,037 Total Europe
Uzbekistan 89 50 50 0 0 0 90 50 50 0 0 0 Ouzbékistan
Total EECCA Total EOCAC
Canada 33 47 42 90 90 90 52 27 28 109 70 76 Canada
United States 481 509 514 437 504 509 259 255 258 215 250 253 Etats-Unis
Total North America 514 556 556 527 594 599 311 282 286 324 320 329 Total Amérique du Nord

Table 6b

TABLE 6b
MDF/HDF
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 260 230 230 416 351 351 177 160 160 333 281 281 Autriche
Cyprus 16 12 12 0 0 0 16 12 12 0 0 0 Chypre
Czech Republic 199 157 160 41 41 42 180 135 140 22 19 22 République tchèque
Estonia 18 21 18 0 0 0 33 28 25 15 7 7 Estonie
Finland 82 67 67 0 0 0 86 70 70 4 3 3 Finlande
France 708 794 794 954 751 751 337 388 388 583 345 345 France
Germany 1,870 1,728 1,720 3,792 3,700 3,650 424 395 370 2,345 2,367 2,300 Allemagne
Hungary -39 -65 -62 0 0 0 136 148 156 175 213 218 Hongrie
Italy 1,501 1,299 1,299 809 800 800 913 606 606 221 107 107 Italie
Latvia 52 40 30 33 35 35 22 25 25 2 20 30 Lettonie
Luxembourg 128 98 98 147 147 147 27 7 7 46 56 56 Luxembourg
Malta 5 5 5 0 0 0 5 5 5 0 0 0 Malte
Montenegro 31 31 32 0 0 0 31 31 32 0 0 0 Monténégro
Netherlands 220 205 196 0 0 0 361 336 322 141 131 126 Pays-Bas
Poland 3,066 3,020 3,130 3,052 3,030 3,100 470 450 470 456 460 440 Pologne
Portugal 447 440 465 494 500 530 257 260 265 305 320 330 Portugal
Serbia 31 35 46 0 0 0 34 38 50 3 3 4 Serbie
Slovakia 135 135 135 0 0 0 170 170 170 35 35 35 Slovaquie
Slovenia 24 15 14 132 120 125 24 23 26 131 128 137 Slovénie
Spain 835 821 821 1,334 1,201 1,201 397 302 302 897 682 682 Espagne
Sweden 254 225 225 0 0 0 284 230 230 30 5 5 Suède
Switzerland 24 24 24 97 97 97 88 88 88 161 161 161 Suisse
United Kingdom 1,553 1,510 1,510 856 850 850 739 700 700 42 40 40 Royaume-Uni
Total Europe 11,419 10,847 10,969 12,157 11,623 11,679 5,210 4,606 4,618 5,948 5,382 5,328 Total Europe
Uzbekistan 671 513 513 46 46 46 629 469 469 3 2 2 Ouzbékistan
Total EECCA Total EOCAC
Canada 1,053 999 1,005 1,087 1,098 1,109 608 472 449 641 570 553 Canada
United States 5,156 5,228 5,226 2,746 2,778 2,786 2,939 2,874 2,866 529 424 426 Etats-Unis
Total North America 6,209 6,227 6,231 3,833 3,876 3,895 3,547 3,346 3,315 1,170 994 979 Total Amérique du Nord

Table 6c

TABLE 6c
OTHER FIBREBOARD AUTRES PANNEAUX DE FIBRES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 131 128 128 0 0 0 136 132 132 4 3 3 Autriche
Cyprus 2 2 2 0 0 0 3 2 2 0 0 0 Chypre
Czech Republic 86 74 75 0 0 0 197 154 160 111 80 85 République tchèque
Estonia 29 10 10 75 40 40 3 2 2 49 32 32 Estonie
Finland 33 17 17 0 0 0 34 17 17 0 0 0 Finlande
France 65 66 66 63 63 63 177 177 177 174 174 174 France
Germany 1,745 1,526 1,440 1,402 1,200 1,150 966 945 920 624 619 630 Allemagne
Hungary 21 7 4 19 0 0 3 7 4 0 0 0 Hongrie
Italy 82 82 82 3 3 3 85 85 85 6 6 6 Italie
Latvia 7 5 5 0 0 0 23 20 20 16 15 15 Lettonie
Luxembourg 4 4 4 0 0 0 4 4 4 0 0 0 Luxembourg
Malta 1 2 2 0 0 0 1 2 2 0 0 0 Malte
Netherlands 68 64 61 29 29 29 41 37 34 2 2 2 Pays-Bas
Poland 920 865 940 1,828 1,810 1,900 33 35 40 940 980 1,000 Pologne
Portugal 37 15 25 32 20 30 20 15 20 15 20 25 Portugal
Serbia 4 4 4 0 0 0 4 4 4 0 0 0 Serbie
Slovakia 54 63 67 0 0 0 57 65 70 3 2 3 Slovaquie
Slovenia 0 0 0 0 0 0 0 0 0 0 0 0 Slovénie
Spain 69 59 59 64 58 58 20 7 7 15 6 6 Espagne
Sweden 0 5 5 0 0 0 25 20 20 24 15 15 Suède
Switzerland 195 195 195 0 0 0 196 196 196 1 1 1 Suisse
United Kingdom 38 30 30 0 0 0 47 40 40 9 10 10 Royaume-Uni
Total Europe 3,592 3,223 3,221 3,514 3,222 3,272 2,071 1,965 1,956 1,993 1,965 2,007 Total Europe
Uzbekistan 331 246 246 2 2 2 339 252 252 10 7 7 Ouzbékistan
Total EECCA Total EOCAC
Canada 150 137 134 100 100 100 158 129 128 108 92 94 Canada
United States 3,047 3,012 3,148 3,179 3,138 3,276 161 160 186 294 286 314 Etats-Unis
Total North America 3,196 3,149 3,282 3,279 3,238 3,376 319 289 314 402 378 408 Total Amérique du Nord

Table 7

TABLE 7
WOOD PULP PATE DE BOIS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 mt
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 2,209 1,950 2,030 1,977 1,700 1,800 630 610 630 399 360 400 Autriche
Czech Republic 847 688 700 640 525 540 324 259 260 117 96 100 République tchèque
Estonia 70 75 80 227 180 180 42 50 50 199 155 150 Estonie
Finland a 5,468 4,483 4,614 9,200 8,690 9,360 355 150 150 4,087 4,357 4,896 Finlande a
France 2,898 2,420 2,500 1,666 1,300 1,350 1,715 1,450 1,500 483 330 350 France
Germany 5,092 4,600 5,000 2,172 1,850 2,000 4,173 3,900 4,200 1,253 1,150 1,200 Allemagne
Hungary 205 206 214 66 77 87 141 133 131 3 3 4 Hongrie
Italy 3,466 3,466 3,466 223 223 223 3,536 3,536 3,536 293 293 293 Italie
Latvia 7 7 7 12 13 13 7 7 7 12 13 13 Lettonie
Netherlands 443 442 442 37 37 37 1,717 1,717 1,717 1,312 1,312 1,312 Pays-Bas
Poland 2,836 2,830 2,930 1,729 1,710 1,750 1,291 1,300 1,320 183 180 140 Pologne
Portugal 1,757 1,735 1,760 2,869 2,870 2,870 140 145 150 1,252 1,280 1,260 Portugal
Serbia 82 88 92 0 0 0 82 88 92 0 0 0 Serbie
Slovakia 700 700 715 692 700 725 173 170 170 166 170 180 Slovaquie
Slovenia 322 321 316 73 63 68 249 260 250 1 2 2 Slovénie
Spain 1,520 1,328 1,328 1,120 1,120 1,120 1,176 976 976 775 768 768 Espagne
Sweden 8,438 7,600 7,950 11,631 10,900 11,400 641 600 600 3,834 3,900 4,050 Suède
Switzerland 188 188 188 87 87 87 101 101 101 0 0 0 Suisse
United Kingdom 1,057 940 950 220 200 200 838 740 750 1 0 0 Royaume-Uni
Total Europe 37,604 34,067 35,282 34,641 32,244 33,809 17,333 16,193 16,590 14,369 14,369 15,118 Total Europe
Uzbekistan 38 28 28 1 1 1 37 28 28 0 0 0 Ouzbékistan
Total EECCA Total EOCAC
Canada 6,007 5,851 5,616 14,200 13,102 12,638 472 582 640 8,665 7,833 7,662 Canada
United States 39,787 42,269 42,815 40,822 41,230 41,478 6,948 7,643 8,254 7,983 6,603 6,917 Etats-Unis
Total North America 45,794 48,121 48,431 55,022 54,332 54,116 7,420 8,224 8,894 16,648 14,436 14,579 Total Amérique du Nord
a imports exclude dissolving pulp a les importations excluent pâte à dissoudre

Table 8

TABLE 8
PAPER AND PAPERBOARD PAPIERS ET CARTONS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 mt
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 2,133 1,750 2,050 4,633 3,500 4,000 1,231 1,050 1,150 3,730 2,800 3,100 Autriche
Cyprus 56 48 48 0 0 0 56 48 48 0 0 0 Chypre
Czech Republic 1,467 1,234 1,258 938 769 785 1,531 1,286 1,312 1,002 822 838 République tchèque
Estonia 120 111 111 57 35 35 123 102 102 59 26 26 Estonie
Finland 514 475 460 7,200 5,990 6,150 333 275 280 7,019 5,790 5,970 Finlande
France 8,272 7,290 7,400 7,092 6,240 6,600 4,845 4,650 4,600 3,665 3,600 3,800 France
Germany 17,836 14,600 17,000 21,612 17,500 21,000 9,302 8,000 9,500 13,078 10,900 13,500 Allemagne
Hungary 1,213 1,167 1,212 1,057 1,003 1,034 877 892 898 720 727 721 Hongrie
Italy 11,390 11,390 11,390 8,696 8,696 8,696 5,800 5,800 5,800 3,106 3,106 3,106 Italie
Latvia 168 175 175 29 30 30 173 180 180 33 35 35 Lettonie
Luxembourg 26 14 14 0 0 0 27 15 15 1 1 1 Luxembourg
Malta 26 27 28 0 0 0 26 27 28 0 0 0 Malte
Netherlands 2,814 2,760 2,760 2,884 2,827 2,827 2,180 2,096 2,096 2,250 2,163 2,163 Pays-Bas
Poland 7,532 7,400 7,550 5,237 5,130 5,250 4,869 4,870 4,950 2,574 2,600 2,650 Pologne
Portugal 1,090 1,200 1,240 2,123 2,200 2,240 948 940 945 1,981 1,940 1,945 Portugal
Serbia 790 762 778 483 481 490 514 490 500 207 209 212 Serbie
Slovakia 565 575 600 967 975 1,000 457 450 475 859 850 875 Slovaquie
Slovenia 412 390 390 591 500 500 367 390 390 545 500 500 Slovénie
Spain 7,060 6,778 6,778 6,355 6,355 6,355 2,997 2,577 2,577 2,291 2,154 2,154 Espagne
Sweden 834 700 750 8,531 7,300 8,100 894 700 750 8,591 7,300 8,100 Suède
Switzerland 1,020 1,015 1,010 1,160 1,155 1,150 640 635 630 780 775 770 Suisse
United Kingdom 7,420 6,280 6,440 3,460 3,190 3,250 5,015 4,150 4,250 1,055 1,060 1,060 Royaume-Uni
Total Europe 72,758 66,140 69,442 83,103 73,876 79,492 43,204 39,623 41,476 53,549 47,358 51,526 Total Europe
Uzbekistan 335 297 297 142 142 142 214 171 171 21 17 17 Ouzbékistan
Total EECCA Total EOCAC
Canada 5,505 6,069 6,231 9,094 9,124 9,155 2,516 2,242 2,235 6,105 5,298 5,159 Canada
United States 64,243 62,896 63,029 65,959 64,476 64,476 8,202 8,180 8,159 9,917 9,761 9,606 Etats-Unis
Total North America 69,748 68,964 69,260 75,053 73,600 73,631 10,718 10,423 10,395 16,023 15,059 14,765 Total Amérique du Nord

Table 9

TABLE 9
REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT
TOTAL TOTAL
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country Industrial wood - Bois industriels Wood fuel c Bois de chauffage c Pays
Total Logs Pulpwood a Other b Total
Grumes Bois de trituration a Autre b
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 13,935 11,716 12,225 10,711 8,904 9,338 3,223 2,812 2,887 0 0 0 5,424 5,115 5,234 19,359 16,831 17,459 Autriche
Cyprus 3 2 2 2 2 2 0 0 0 0 0 0 11 9 8 14 11 10 Chypre
Czech Republic 20,708 15,535 14,897 14,635 10,617 10,106 5,965 4,804 4,675 108 113 115 4,405 3,965 3,900 25,113 19,499 18,797 République tchèque
Estonia 6,474 6,401 6,401 4,276 4,200 4,200 2,148 2,150 2,150 51 51 51 4,066 3,800 3,800 10,541 10,201 10,201 Estonie
Finland 56,246 53,397 55,435 25,699 22,749 23,412 30,547 30,648 32,023 0 0 0 9,340 9,340 9,340 65,586 62,737 64,775 Finlande
France 25,648 25,270 25,070 17,198 17,200 17,300 7,891 7,500 7,200 559 570 570 24,173 24,500 25,600 49,821 49,770 50,670 France
Germany 56,534 53,930 49,630 44,756 41,200 39,500 11,644 12,600 10,000 135 130 130 22,338 22,700 22,700 78,872 76,630 72,330 Allemagne
Hungary 2,901 2,881 2,881 1,410 1,374 1,399 912 995 1,008 579 512 475 3,626 3,284 3,397 6,527 6,165 6,278 Hongrie
Italy 2,838 3,540 3,540 1,890 1,890 1,890 316 1,018 1,018 632 632 632 10,839 10,839 10,839 13,677 14,379 14,379 Italie
Latvia 12,491 12,150 12,350 7,603 7,250 7,450 3,868 3,800 3,800 1,020 1,100 1,100 2,936 3,000 3,000 15,427 15,150 15,350 Lettonie
Luxembourg 231 197 193 147 144 133 56 38 38 27 15 22 40 45 43 271 242 235 Luxembourg
Montenegro 751 697 678 515 492 487 201 198 186 35 7 5 194 193 190 945 890 868 Monténégro
Netherlands 614 599 589 221 220 215 352 340 335 41 39 39 2,382 2,380 2,385 2,996 2,979 2,974 Pays-Bas
Poland 38,735 39,880 40,850 18,533 18,800 19,150 19,350 20,100 20,550 852 980 1,150 6,958 7,420 7,750 45,693 47,300 48,600 Pologne
Portugal 12,235 12,330 12,190 2,038 2,040 2,060 9,799 9,850 9,700 399 440 430 2,383 2,380 2,300 14,619 14,710 14,490 Portugal
Serbia 1,478 1,520 1,561 1,077 1,104 1,130 265 275 283 136 141 148 6,574 6,646 6,760 8,052 8,166 8,321 Serbie
Slovakia 6,827 6,820 6,880 4,130 4,080 4,100 2,672 2,710 2,750 25 30 30 609 610 650 7,435 7,430 7,530 Slovaquie
Slovenia 2,928 3,752 3,482 2,184 2,780 2,600 698 920 830 45 52 52 1,149 1,290 1,270 4,076 5,042 4,752 Slovénie
Spain 14,366 15,244 15,244 4,150 4,404 4,404 9,813 10,413 10,413 403 427 427 3,555 3,772 3,772 17,921 19,016 19,016 Espagne
Sweden 71,165 69,076 69,310 38,280 37,480 37,080 32,585 31,296 31,930 300 300 300 6,000 6,016 6,016 77,165 75,092 75,326 Suède
Switzerland 3,011 3,082 3,142 2,555 2,625 2,680 444 445 450 12 12 12 1,938 2,000 2,025 4,949 5,082 5,167 Suisse
United Kingdom 7,604 7,193 7,193 5,509 5,236 5,236 1,646 1,529 1,529 448 428 428 2,184 2,184 2,184 9,788 9,377 9,377 Royaume-Uni
Total Europe 357,723 345,212 343,742 207,519 194,791 193,872 144,397 144,441 143,754 5,807 5,980 6,116 121,124 121,488 123,163 478,847 466,699 466,905 Total Europe
Canada 142,131 140,499 140,499 124,900 123,350 123,350 15,040 14,864 14,864 2,190 2,285 2,285 1,683 1,908 1,908 143,814 142,407 142,407 Canada
United States 382,544 384,963 388,611 186,157 188,221 191,211 182,650 182,996 183,637 13,737 13,746 13,763 76,230 76,240 76,278 458,774 461,203 464,889 Etats-Unis
Total North America 524,675 525,462 529,110 311,057 311,571 314,561 197,690 197,861 198,501 15,927 16,031 16,048 77,913 78,148 78,186 602,587 603,610 607,296 Total Amérique du Nord
a Pulpwood, round and split, as well as chips and particles produced directly a Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
therefrom and used as pulpwood directement à partir des rondins et quartiers et utilisées comme bois de trituration
b Pitprops, poles, piling, posts etc. b Bois de mine, poteaux, pilotis, piquets etc.
c Including chips and particles produced from wood in the rough and c Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
used for energy purposes à des fins energétiques

Table 9a

TABLE 9a
REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT
SOFTWOOD CONIFERES
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country Industrial wood - Bois industriels Wood fuel c Bois de chauffage c Pays
Total Logs Pulpwood a Other b Total
Grumes Bois de trituration a Autre b
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 12,958 10,873 11,338 10,382 8,638 9,038 2,576 2,235 2,300 0 0 0 3,248 3,069 3,140 16,206 13,942 14,478 Autriche
Cyprus 2 2 2 2 2 2 0 0 0 0 0 0 10 8 7 12 10 9 Chypre
Czech Republic 19,440 14,455 13,825 14,019 10,094 9,589 5,316 4,253 4,125 105 109 111 3,610 3,249 3,200 23,050 17,704 17,025 République tchèque
Estonia 4,023 3,927 3,927 3,118 3,000 3,000 878 900 900 26 27 27 1,486 1,400 1,400 5,509 5,327 5,327 Estonie
Finland 47,408 45,464 47,590 24,662 21,700 22,351 22,746 23,764 25,239 0 0 0 4,593 4,593 4,593 52,001 50,057 52,183 Finlande
France 17,300 17,070 16,770 12,491 12,500 12,500 4,559 4,300 4,000 250 270 270 2,417 2,500 2,600 19,717 19,570 19,370 France
Germany 52,425 50,120 46,120 41,761 38,500 37,000 10,541 11,500 9,000 123 120 120 8,834 9,200 9,200 61,259 59,320 55,320 Allemagne
Hungary 688 759 743 175 201 208 411 488 481 102 70 53 383 294 333 1,071 1,053 1,076 Hongrie
Italy 1,797 2,502 2,502 1,169 1,169 1,169 148 853 853 480 480 480 1,180 1,180 1,180 2,977 3,682 3,682 Italie
Latvia 8,253 7,900 8,100 5,873 5,500 5,700 1,850 1,800 1,800 530 600 600 298 300 300 8,551 8,200 8,400 Lettonie
Luxembourg 162 143 145 124 122 115 10 6 8 27 15 22 17 11 12 178 154 158 Luxembourg
Montenegro 573 553 537 372 352 349 201 198 186 0 3 2 66 65 63 639 618 600 Monténégro
Netherlands 449 440 430 173 170 165 244 240 235 32 30 30 457 450 450 906 890 880 Pays-Bas
Poland 31,941 32,800 33,470 15,775 16,000 16,250 15,411 15,950 16,250 754 850 970 3,627 3,820 3,950 35,568 36,620 37,420 Pologne
Portugal 3,045 3,210 3,150 1,682 1,710 1,700 1,213 1,350 1,300 150 150 150 996 990 980 4,041 4,200 4,130 Portugal
Serbia 279 290 301 178 184 190 66 70 73 35 36 38 141 146 160 420 436 461 Serbie
Slovakia 3,325 3,160 3,120 2,559 2,430 2,400 748 710 700 18 20 20 259 260 275 3,584 3,420 3,395 Slovaquie
Slovenia 1,966 2,586 2,386 1,687 2,150 2,000 275 430 380 4 6 6 191 240 220 2,157 2,826 2,606 Slovénie
Spain 7,435 7,889 7,889 3,420 3,629 3,629 3,754 3,984 3,984 261 277 277 2,243 2,380 2,380 9,678 10,269 10,269 Espagne
Sweden 64,603 62,760 62,873 38,100 37,300 36,900 26,353 25,310 25,823 150 150 150 3,000 3,008 3,008 67,603 65,768 65,881 Suède
Switzerland 2,578 2,639 2,689 2,290 2,350 2,400 279 280 280 9 9 9 769 770 775 3,347 3,409 3,464 Suisse
United Kingdom 7,486 7,076 7,076 5,453 5,180 5,180 1,633 1,516 1,516 400 380 380 1,571 1,571 1,571 9,058 8,647 8,647 Royaume-Uni
Total Europe 288,136 276,619 274,984 185,467 172,881 171,836 99,212 100,136 99,433 3,458 3,602 3,715 39,396 39,504 39,798 327,533 316,123 314,781 Total Europe
Canada 114,659 112,907 112,907 110,046 108,424 108,424 4,229 4,021 4,021 384 462 462 806 946 946 115,465 113,853 113,853 Canada
United States 306,119 309,360 313,639 152,799 154,479 156,695 141,226 142,779 144,827 12,094 12,102 12,117 37,619 37,609 37,606 343,738 346,969 351,245 Etats-Unis
Total North America 420,778 422,267 426,546 262,845 262,903 265,119 145,455 146,800 148,848 12,478 12,564 12,579 38,425 38,555 38,552 459,203 460,822 465,098 Total Amérique du Nord
a Pulpwood, round and split, as well as chips and particles produced directly a Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
therefrom and used as pulpwood directement à partir des rondins et quartiers et utilisées comme bois de trituration
b Pitprops, poles, piling, posts etc. b Bois de mine, poteaux, pilotis, piquets etc.
c Including chips and particles produced from wood in the rough and c Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
used for energy purposes à des fins energétiques

Table 9b

TABLE 9b
REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT
HARDWOOD NON-CONIFERES
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country Industrial wood - Bois industriels Wood fuel c Bois de chauffage c Pays
Total Logs Pulpwood a Other b Total
Grumes Bois de trituration a Autre b
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 977 843 887 329 266 300 647 577 587 0 0 0 2,176 2,046 2,094 3,153 2,889 2,981 Autriche
Cyprus 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 Chypre
Czech Republic 1,268 1,079 1,071 616 524 517 649 552 550 3 4 4 795 716 700 2,063 1,795 1,771 République tchèque
Estonia 2,452 2,474 2,474 1,158 1,200 1,200 1,270 1,250 1,250 24 24 24 2,580 2,400 2,400 5,032 4,874 4,874 Estonie
Finland 8,838 7,933 7,845 1,037 1,049 1,061 7,801 6,884 6,784 0 0 0 4,747 4,747 4,747 13,585 12,680 12,592 Finlande
France 8,348 8,200 8,300 4,707 4,700 4,800 3,332 3,200 3,200 309 300 300 21,756 22,000 23,000 30,104 30,200 31,300 France
Germany 4,110 3,810 3,510 2,995 2,700 2,500 1,103 1,100 1,000 12 10 10 13,504 13,500 13,500 17,613 17,310 17,010 Allemagne
Hungary 2,213 2,122 2,138 1,234 1,173 1,191 502 507 526 477 442 421 3,244 2,990 3,064 5,456 5,112 5,202 Hongrie
Italy 1,041 1,038 1,038 721 721 721 168 166 166 152 152 152 9,659 9,659 9,659 10,700 10,697 10,697 Italie
Latvia 4,238 4,250 4,250 1,730 1,750 1,750 2,018 2,000 2,000 490 500 500 2,638 2,700 2,700 6,876 6,950 6,950 Lettonie
Luxembourg 69 54 47 23 22 18 46 32 30 0 0 0 23 34 30 92 89 78 Luxembourg
Montenegro 178 144 141 143 140 138 0 0 0 35 4 3 128 128 127 306 272 268 Monténégro
Netherlands 165 159 159 48 50 50 108 100 100 9 9 9 1,925 1,930 1,935 2,090 2,089 2,094 Pays-Bas
Poland 6,794 7,080 7,380 2,757 2,800 2,900 3,939 4,150 4,300 98 130 180 3,331 3,600 3,800 10,125 10,680 11,180 Pologne
Portugal 9,190 9,120 9,040 356 330 360 8,586 8,500 8,400 249 290 280 1,387 1,390 1,320 10,578 10,510 10,360 Portugal
Serbia 1,199 1,230 1,260 899 920 940 199 205 210 101 105 110 6,433 6,500 6,600 7,632 7,730 7,860 Serbie
Slovakia 3,502 3,660 3,760 1,570 1,650 1,700 1,924 2,000 2,050 8 10 10 350 350 375 3,851 4,010 4,135 Slovaquie
Slovenia 962 1,166 1,096 497 630 600 424 490 450 41 46 46 957 1,050 1,050 1,919 2,216 2,146 Slovénie
Spain 6,931 7,354 7,354 730 775 775 6,059 6,429 6,429 142 151 151 1,312 1,392 1,392 8,243 8,746 8,746 Espagne
Sweden 6,562 6,316 6,437 180 180 180 6,232 5,986 6,107 150 150 150 3,000 3,008 3,008 9,562 9,324 9,445 Suède
Switzerland 433 443 453 265 275 280 165 165 170 3 3 3 1,169 1,230 1,250 1,602 1,673 1,703 Suisse
United Kingdom 118 117 117 56 56 56 13 13 13 48 48 48 613 613 613 730 730 730 Royaume-Uni
Total Europe 69,587 68,593 68,759 22,052 21,910 22,036 45,185 44,305 44,322 2,350 2,377 2,401 81,728 81,984 83,365 151,314 150,576 152,124 Total Europe
Canada 27,472 27,592 27,592 14,854 14,926 14,926 10,812 10,843 10,843 1,806 1,823 1,823 877 961 961 28,349 28,554 28,554 Canada
United States 76,425 75,603 74,972 33,358 33,742 34,516 41,424 40,217 38,810 1,643 1,644 1,646 38,611 38,631 38,672 115,036 114,234 113,644 Etats-Unis
Total North America 103,897 103,196 102,564 48,212 48,668 49,442 52,236 51,060 49,653 3,449 3,467 3,469 39,488 39,592 39,633 143,385 142,788 142,197 Total Amérique du Nord
a Pulpwood, round and split, as well as chips and particles produced directly a Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
therefrom and used as pulpwood directement à partir des rondins et quartiers et utilisées comme bois de trituration
b Pitprops, poles, piling, posts etc. b Bois de mine, poteaux, pilotis, piquets etc.
c Including chips and particles produced from wood in the rough and c Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
used for energy purposes à des fins energétiques

Table 10

TABLE 10
SOFTWOOD SAWLOGS GRUMES DE SCIAGES DES CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption a Imports Exports
Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 16,101 13,943 13,638 10,382 8,638 9,038 6,664 5,710 5,000 945 405 400 Autriche
Cyprus 2 2 2 2 2 2 0 0 0 0 0 0 Chypre
Czech Republic 8,002 6,511 6,962 14,019 10,094 9,589 411 596 715 6,428 4,178 3,343 République tchèque
Estonia 3,533 3,270 3,270 3,118 3,000 3,000 522 450 450 107 180 180 Estonie
Finland 24,310 21,336 21,991 24,662 21,700 22,351 127 79 83 479 443 443 Finlande
France 12,053 12,120 12,120 12,491 12,500 12,500 335 360 360 773 740 740 France
Germany 39,391 35,800 34,900 41,761 38,500 37,000 3,300 3,000 3,100 5,670 5,700 5,200 Allemagne
Hungary 175 201 208 175 201 208 0 0 0 0 0 0 Hongrie
Italy 1,645 1,396 1,396 1,169 1,169 1,169 580 457 457 104 230 230 Italie
Latvia 6,471 5,830 6,200 5,873 5,500 5,700 1,147 900 900 549 570 400 Lettonie
Luxembourg 465 403 396 124 122 115 693 424 424 352 143 143 Luxembourg
Montenegro 382 361 357 372 352 349 10 9 8 0 0 0 Monténégro
Netherlands 133 145 145 173 170 165 77 80 80 117 105 100 Pays-Bas
Poland 14,243 14,500 14,800 15,775 16,000 16,250 1,245 1,400 1,550 2,777 2,900 3,000 Pologne
Portugal 1,880 1,905 1,900 1,682 1,710 1,700 241 230 240 43 35 40 Portugal
Serbia 188 187 194 178 184 190 12 9 12 2 6 8 Serbie
Slovakia 3,059 3,030 3,100 2,559 2,430 2,400 900 950 1,000 400 350 300 Slovaquie
Slovenia 1,643 1,650 1,630 1,687 2,150 2,000 239 150 180 283 650 550 Slovénie
Spain 3,223 3,307 3,307 3,420 3,629 3,629 240 185 185 437 507 507 Espagne
Sweden 38,103 37,725 37,325 38,100 37,300 36,900 964 1,128 1,128 961 703 703 Suède
Switzerland 2,035 2,100 2,155 2,290 2,350 2,400 55 60 65 310 310 310 Suisse
United Kingdom 5,810 5,538 5,538 5,453 5,180 5,180 457 457 457 99 99 99 Royaume-Uni
Total Europe 182,849 171,260 171,534 185,467 172,881 171,836 18,218 16,634 16,394 20,836 18,255 16,696 Total Europe
Canada 105,870 103,492 103,916 110,046 108,424 108,424 1,346 1,402 1,309 5,522 6,333 5,816 Canada
United States 148,043 150,509 153,391 152,799 154,479 156,695 586 570 555 5,342 4,540 3,859 Etats-Unis
Total North America 253,913 254,001 257,307 262,845 262,903 265,119 1,931 1,972 1,864 10,863 10,873 9,675 Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11

TABLE 11
HARDWOOD SAWLOGS (total) GRUMES DE SCIAGES DES NON-CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption a Imports Exports
Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 406 311 300 329 266 300 134 90 50 57 45 50 Autriche
Czech Republic 544 457 447 616 524 517 144 120 125 216 186 195 République tchèque
Estonia 1,187 1,244 1,244 1,158 1,200 1,200 46 60 60 16 16 16 Estonie
Finland 1,068 1,041 1,061 1,037 1,049 1,061 32 1 9 1 9 9 Finlande
France 3,453 4,020 4,120 4,707 4,700 4,800 116 120 120 1,370 800 800 France
Germany 2,532 2,290 2,130 2,995 2,700 2,500 111 110 110 574 520 480 Allemagne
Hungary 1,234 1,173 1,191 1,234 1,173 1,191 0 0 0 0 0 0 Hongrie
Italy 2,088 1,718 1,718 721 721 721 1,413 1,055 1,055 47 59 59 Italie
Latvia 1,221 1,190 1,410 1,730 1,750 1,750 87 40 60 596 600 400 Lettonie
Luxembourg 226 148 144 23 22 18 221 160 160 18 34 34 Luxembourg
Montenegro 143 140 138 143 140 138 0 0 0 0 0 0 Monténégro
Netherlands 54 60 60 48 50 50 54 60 60 48 50 50 Pays-Bas
Poland 2,687 2,730 2,830 2,757 2,800 2,900 80 80 80 150 150 150 Pologne
Portugal 997 885 925 356 330 360 663 580 590 22 25 25 Portugal
Serbia 894 922 946 899 920 940 15 20 28 20 18 22 Serbie
Slovakia 1,670 1,700 1,750 1,570 1,650 1,700 500 450 450 400 400 400 Slovaquie
Slovenia 281 290 280 497 630 600 31 30 30 247 370 350 Slovénie
Spain 833 854 854 730 775 775 164 174 174 61 94 94 Espagne
Sweden 217 217 217 180 180 180 37 37 37 0 0 0 Suède
Switzerland 145 155 160 265 275 280 35 40 40 155 160 160 Suisse
United Kingdom 78 77 77 56 56 56 26 26 26 5 5 5 Royaume-Uni
Total Europe 21,959 21,622 22,002 22,052 21,910 22,036 3,910 3,253 3,265 4,003 3,541 3,299 Total Europe
Canada 15,890 15,923 15,895 14,854 14,926 14,926 1,106 1,060 1,027 70 64 59 Canada
United States 31,550 32,311 33,431 33,358 33,742 34,516 221 156 156 2,028 1,587 1,241 Etats-Unis
Total North America 47,441 48,234 49,326 48,212 48,668 49,442 1,327 1,216 1,183 2,098 1,650 1,300 Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11a

TABLE 11a
HARDWOOD LOGS (temperate) GRUMES DE NON-CONIFERES (zone tempérée)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption a Imports Exports
Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 406 311 300 329 266 300 134 90 50 57 45 50 Autriche
Czech Republic 544 457 447 616 524 517 144 120 125 216 186 195 République tchèque
Estonia 1,187 1,244 1,244 1,158 1,200 1,200 46 60 60 16 16 16 Estonie
Finland 1,068 1,041 1,061 1,037 1,049 1,061 32 1 9 1 9 9 Finlande
France 3,412 3,978 4,078 4,707 4,700 4,800 72 75 75 1,367 797 797 France
Germany 2,527 2,285 2,125 2,995 2,700 2,500 101 100 100 569 515 475 Allemagne
Hungary 1,234 1,173 1,191 1,234 1,173 1,191 0 0 0 0 0 0 Hongrie
Italy 2,068 1,729 1,729 721 721 721 1,389 1,047 1,047 42 39 39 Italie
Latvia 1,221 1,190 1,410 1,730 1,750 1,750 87 40 60 596 600 400 Lettonie
Luxembourg 226 148 144 23 22 18 221 160 160 18 34 34 Luxembourg
Montenegro 143 140 138 143 140 138 0 0 0 0 0 0 Monténégro
Netherlands 46 55 55 48 50 50 42 50 50 44 45 45 Pays-Bas
Poland 2,685 2,727 2,827 2,757 2,800 2,900 78 77 77 150 150 150 Pologne
Portugal 981 870 912 356 330 360 642 560 571 17 20 19 Portugal
Serbia 893 921 945 899 920 940 14 19 27 20 18 22 Serbie
Slovakia 1,670 1,700 1,750 1,570 1,650 1,700 500 450 450 400 400 400 Slovaquie
Slovenia 280 290 280 497 630 600 30 30 30 247 370 350 Slovénie
Spain 827 847 847 730 775 775 158 167 167 61 94 94 Espagne
Sweden 217 217 217 180 180 180 37 37 37 0 0 0 Suède
Switzerland 145 155 160 265 275 280 35 40 40 155 160 160 Suisse
United Kingdom 76 75 75 56 56 56 24 24 24 5 5 5 Royaume-Uni
Total Europe 21,857 21,553 21,935 22,052 21,910 22,036 3,786 3,146 3,158 3,980 3,503 3,260 Total Europe
Canada 15,890 15,923 15,895 14,854 14,926 14,926 1,106 1,060 1,027 70 64 59 Canada
United States 31,549 32,308 33,429 33,358 33,742 34,516 219 152 154 2,027 1,586 1,240 Etats-Unis
Total North America 47,440 48,231 49,324 48,212 48,668 49,442 1,325 1,212 1,181 2,097 1,649 1,299 Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11b

TABLE 11b
HARDWOOD LOGS (tropical) GRUMES DE NON-CONIFERES (tropicale)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Net Trade Imports Exports
Country Commerce Net Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
France -41 -42 -42 44 45 45 3 3 3 France
Germany -5 -5 -5 10 10 10 5 5 5 Allemagne
Italy -20 11 11 25 9 9 4 20 20 Italie
Netherlands -8 -5 -5 12 10 10 4 5 5 Pays-Bas
Poland -2 -3 -3 2 3 3 0 0 0 Pologne
Portugal -16 -15 -13 21 20 19 5 5 6 Portugal
Serbia -1 -1 -1 1 1 1 0 0 0 Serbie
Slovenia -1 -0 -0 1 0 1 0 0 0 Slovénie
Spain -6 -7 -7 6 7 7 0 0 0 Espagne
United Kingdom -2 -2 -2 2 2 2 0 0 0 Royaume-Uni
Total Europe -102 -69 -67 124 107 106 22 38 39 Total Europe
United States -1 -3 -1 2 4 2 1 1 1 Etats-Unis
Total North America -1 -3 -1 2 4 2 1 1 1 Total Amérique du Nord

Table12

TABLE 12
PULPWOOD (total) BOIS DE TRITURATION (total)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption a Imports Exports
Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 13,844 12,627 12,592 11,047 9,212 9,287 3,676 4,070 4,020 879 655 715 Autriche
Cyprus 8 9 10 7 8 9 1 1 1 0 0 0 Chypre
Czech Republic 5,559 5,135 5,154 7,664 6,164 6,130 1,270 1,146 1,162 3,375 2,175 2,138 République tchèque
Estonia 3,117 2,380 2,435 6,548 6,550 6,550 256 330 285 3,687 4,500 4,400 Estonie
Finland 48,404 47,241 49,358 44,923 44,026 45,568 5,037 4,969 5,545 1,556 1,755 1,755 Finlande
France 24,495 24,350 24,050 24,257 24,000 23,700 2,527 2,600 2,600 2,289 2,250 2,250 France
Germany 26,555 26,580 23,090 27,936 27,100 23,500 4,474 3,870 3,770 5,855 4,390 4,180 Allemagne
Hungary 2,122 2,017 2,065 2,049 1,984 2,023 112 73 82 39 39 39 Hongrie
Italy 4,508 5,210 5,210 3,916 4,618 4,618 1,288 1,288 1,288 696 696 696 Italie
Latvia 5,540 5,150 5,150 9,484 8,800 8,800 1,084 950 950 5,028 4,600 4,600 Lettonie
Luxembourg 583 589 589 577 559 559 182 130 130 176 100 100 Luxembourg
Malta 2 3 3 0 0 0 2 3 3 0 0 0 Malte
Montenegro 245 241 227 245 241 227 0 0 0 0 0 0 Monténégro
Netherlands 604 1,100 1,095 1,267 1,240 1,230 289 100 105 952 240 240 Pays-Bas
Poland 35,250 36,265 37,135 33,531 34,600 35,450 3,652 3,660 3,710 1,933 1,995 2,025 Pologne
Portugal 15,954 15,330 15,365 11,664 11,720 11,590 4,657 4,000 4,140 368 390 365 Portugal
Serbia 981 1,007 1,045 967 1,000 1,033 15 8 13 1 1 1 Serbie
Slovakia 3,634 3,650 3,760 3,821 3,860 3,950 1,023 1,030 1,050 1,210 1,240 1,240 Slovaquie
Slovenia 926 770 790 2,058 2,280 2,230 625 490 530 1,757 2,000 1,970 Slovénie
Spain 13,959 14,358 14,358 14,383 15,261 15,261 1,435 1,564 1,564 1,859 2,467 2,467 Espagne
Sweden 55,632 54,193 54,727 50,015 48,196 48,730 7,036 7,750 7,750 1,419 1,753 1,753 Suède
Switzerland 1,823 1,824 1,829 1,216 1,217 1,222 795 795 795 188 188 188 Suisse
United Kingdom 4,590 4,471 4,471 4,293 4,175 4,175 406 405 405 109 109 109 Royaume-Uni
Total Europe 268,336 264,500 264,508 261,870 256,811 255,841 39,843 39,232 39,898 33,377 31,543 31,231 Total Europe
Canada 37,044 35,822 35,734 35,326 32,985 32,975 2,578 3,462 3,467 860 625 708 Canada
United States 238,450 239,587 240,850 244,912 246,110 247,536 348 324 308 6,809 6,848 6,994 Etats-Unis
Total North America 275,495 275,409 276,585 280,238 279,096 280,511 2,926 3,786 3,776 7,670 7,473 7,702 Total Amérique du Nord
Includes wood residues, chips and particles for all purposes Comprend les dechets de bois, plaquettes et particules pour toute utilisation
a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12a

TABLE 12a
PULPWOOD LOGS (ROUND AND SPLIT) BOIS DE TRITURATION (RONDINS ET QUARTIERS)
Softwood Conifères
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Apparent Consumption a Imports Exports
Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 3,681 3,895 3,850 2,576 2,235 2,300 1,312 1,750 1,700 206 90 150 Autriche
Czech Republic 3,927 3,744 3,675 5,316 4,253 4,125 811 811 830 2,200 1,320 1,280 République tchèque
Estonia 476 245 245 878 900 900 56 45 45 458 700 700 Estonie
Finland 22,913 24,189 25,835 22,746 23,764 25,239 1,163 1,410 1,581 996 985 985 Finlande
France 4,689 4,400 4,100 4,559 4,300 4,000 608 550 550 478 450 450 France
Germany 10,311 11,900 9,500 10,541 11,500 9,000 2,200 2,100 2,000 2,430 1,700 1,500 Allemagne
Hungary 411 488 481 411 488 481 0 0 0 0 0 0 Hongrie
Italy 148 853 853 148 853 853 0 0 0 0 0 0 Italie
Latvia 1,775 1,700 1,700 1,850 1,800 1,800 374 400 400 449 500 500 Lettonie
Luxembourg -16 -18 -16 10 6 8 9 3 3 35 27 27 Luxembourg
Montenegro 201 198 186 201 198 186 0 0 0 0 0 0 Monténégro
Netherlands 146 150 145 244 240 235 70 80 85 168 170 175 Pays-Bas
Poland 15,378 15,900 16,300 15,411 15,950 16,250 1,428 1,500 1,650 1,462 1,550 1,600 Pologne
Portugal 1,323 1,430 1,375 1,213 1,350 1,300 122 100 90 12 20 15 Portugal
Serbia 66 70 74 66 70 73 0 0 1 0 0 0 Serbie
Slovakia 598 600 610 748 710 700 600 630 650 750 740 740 Slovaquie
Slovenia 264 200 220 275 430 380 268 170 200 278 400 360 Slovénie
Spain 3,369 3,467 3,467 3,754 3,984 3,984 179 138 138 564 655 655 Espagne
Sweden 28,513 27,431 27,944 26,353 25,310 25,823 3,114 3,269 3,269 954 1,148 1,148 Suède
Switzerland 209 210 210 279 280 280 20 20 20 90 90 90 Suisse
United Kingdom 1,894 1,776 1,776 1,633 1,516 1,516 291 291 291 31 31 31 Royaume-Uni
Total Europe 100,275 102,827 102,530 99,212 100,136 99,433 12,625 13,267 13,503 11,562 10,576 10,406 Total Europe
Canada 4,531 4,347 4,410 4,229 4,021 4,021 324 336 401 22 10 12 Canada
United States 141,231 142,785 144,831 141,226 142,779 144,827 5 6 4 0 0 0 Etats-Unis
Total North America 145,762 147,132 149,241 145,455 146,800 148,848 329 341 405 22 10 12 Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12b

TABLE 12b
PULPWOOD LOGS (ROUND AND SPLIT) BOIS DE TRITURATION (RONDINS ET QUARTIERS)
Hardwood Non-conifères
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Apparent Consumption a Imports Exports
Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 1,217 997 1,007 647 577 587 668 500 500 98 80 80 Autriche
Czech Republic 450 380 384 649 552 550 3 2 2 202 174 168 République tchèque
Estonia 363 200 250 1,270 1,250 1,250 154 250 200 1,060 1,300 1,200 Estonie
Finland 8,997 7,940 8,052 7,801 6,884 6,784 1,550 1,633 1,845 354 577 577 Finlande
France 2,386 2,250 2,250 3,332 3,200 3,200 43 50 50 989 1,000 1,000 France
Germany 1,116 1,180 1,090 1,103 1,100 1,000 259 270 270 246 190 180 Allemagne
Hungary 502 507 526 502 507 526 0 0 0 0 0 0 Hongrie
Italy 168 166 166 168 166 166 0 0 0 0 0 0 Italie
Latvia 172 200 200 2,018 2,000 2,000 244 100 100 2,090 1,900 1,900 Lettonie
Luxembourg 77 71 69 46 32 30 36 48 48 5 9 9 Luxembourg
Netherlands 62 50 55 108 100 100 21 20 20 67 70 65 Pays-Bas
Poland 4,424 4,635 4,785 3,939 4,150 4,300 560 560 560 75 75 75 Pologne
Portugal 10,495 10,300 10,260 8,586 8,500 8,400 2,100 2,000 2,050 191 200 190 Portugal
Serbia 199 205 210 199 205 210 0 0 0 0 0 0 Serbie
Slovakia 1,874 1,950 2,000 1,924 2,000 2,050 100 100 100 150 150 150 Slovaquie
Slovenia 137 120 130 424 490 450 84 80 90 371 450 410 Slovénie
Spain 5,422 5,288 5,288 6,059 6,429 6,429 269 291 291 906 1,432 1,432 Espagne
Sweden 8,517 8,412 8,533 6,232 5,986 6,107 2,313 2,481 2,481 28 55 55 Suède
Switzerland 128 128 133 165 165 170 3 3 3 40 40 40 Suisse
United Kingdom 23 22 22 13 13 13 18 18 18 9 9 9 Royaume-Uni
Total Europe 46,729 45,001 45,410 45,185 44,305 44,322 8,426 8,406 8,628 6,881 7,711 7,540 Total Europe
Canada 10,554 10,654 10,644 10,812 10,843 10,843 38 36 30 296 225 228 Canada
United States 41,407 40,200 38,795 41,424 40,217 38,810 58 32 18 75 50 33 Etats-Unis
Total North America 51,961 50,854 49,439 52,236 51,060 49,653 96 68 48 371 275 261 Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12c

TABLE 12c
WOOD RESIDUES, CHIPS AND PARTICLES DECHETS DE BOIS, PLAQUETTES ET PARTICULES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 8,945 7,735 7,735 7,824 6,400 6,400 1,696 1,820 1,820 575 485 485 Autriche
Cyprus 8 9 10 7 8 9 1 1 1 0 0 0 Chypre
Czech Republic 1,182 1,011 1,094 1,699 1,359 1,454 456 333 330 973 681 690 République tchèque
Estonia 2,278 1,935 1,940 4,400 4,400 4,400 47 35 40 2,169 2,500 2,500 Estonie
Finland 16,494 15,112 15,471 14,376 13,378 13,545 2,324 1,926 2,119 206 193 193 Finlande
France 17,420 17,700 17,700 16,366 16,500 16,500 1,876 2,000 2,000 822 800 800 France
Germany 15,128 13,500 12,500 16,292 14,500 13,500 2,015 1,500 1,500 3,179 2,500 2,500 Allemagne
Hungary 1,209 1,022 1,057 1,137 989 1,015 112 73 82 39 39 39 Hongrie
Italy 4,192 4,192 4,192 3,600 3,600 3,600 1,288 1,288 1,288 696 696 696 Italie
Latvia 3,593 3,250 3,250 5,616 5,000 5,000 466 450 450 2,489 2,200 2,200 Lettonie
Luxembourg 522 536 536 521 521 521 137 79 79 136 64 64 Luxembourg
Malta 2 3 3 0 0 0 2 3 3 0 0 0 Malte
Montenegro 44 43 41 44 43 41 0 0 0 0 0 0 Monténégro
Netherlands 396 900 895 915 900 895 198 0 0 717 0 0 Pays-Bas
Poland 15,448 15,730 16,050 14,181 14,500 14,900 1,664 1,600 1,500 396 370 350 Pologne
Portugal 4,136 3,600 3,730 1,865 1,870 1,890 2,435 1,900 2,000 165 170 160 Portugal
Serbia 716 732 761 702 725 750 15 8 12 1 1 1 Serbie
Slovakia 1,162 1,100 1,150 1,149 1,150 1,200 323 300 300 310 350 350 Slovaquie
Slovenia 525 450 440 1,360 1,360 1,400 273 240 240 1,107 1,150 1,200 Slovénie
Spain 5,169 5,603 5,603 4,570 4,849 4,849 987 1,135 1,135 388 380 380 Espagne
Sweden 18,602 18,350 18,250 17,430 16,900 16,800 1,609 2,000 2,000 437 550 550 Suède
Switzerland 1,486 1,486 1,486 772 772 772 772 772 772 58 58 58 Suisse
United Kingdom 2,673 2,673 2,673 2,646 2,646 2,646 96 96 96 69 69 69 Royaume-Uni
Total Europe 121,332 116,673 116,568 117,472 112,370 112,087 18,793 17,559 17,767 14,933 13,256 13,285 Total Europe
Canada 21,959 20,821 20,680 20,285 18,121 18,111 2,216 3,090 3,037 542 390 467 Canada
United States 55,812 56,602 57,224 62,262 63,114 63,899 285 286 286 6,734 6,798 6,961 Etats-Unis
Total North America 77,771 77,423 77,904 82,547 81,235 82,010 2,500 3,376 3,323 7,277 7,188 7,428 Total Amérique du Nord

Table 13

TABLE 13
WOOD PELLETS GRANULES DE BOIS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 mt
Apparent Consumption Imports Exports
Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
Austria 1,290 1,497 1,450 1,691 1,938 2,050 344 309 300 745 750 900 Autriche
Cyprus 8 5 5 0 0 0 8 5 5 0 0 0 Chypre
Czech Republic 234 215 225 540 459 482 38 38 40 344 282 296 République tchèque
Estonia 284 300 230 1,650 1,350 1,300 12 50 30 1,378 1,100 1,100 Estonie
Finland 530 541 562 360 380 405 188 163 160 18 2 3 Finlande
France 2,735 3,260 3,660 2,050 2,250 2,450 775 1,100 1,300 90 90 90 France
Germany 3,328 3,540 3,720 3,569 3,700 3,900 443 480 420 684 640 600 Allemagne
Hungary 63 44 50 62 43 49 11 13 12 11 12 12 Hongrie
Italy 2,359 2,359 2,359 450 450 450 1,916 1,916 1,916 7 7 7 Italie
Latvia 621 750 750 1,980 2,000 2,000 326 350 350 1,685 1,600 1,600 Lettonie
Luxembourg 61 72 72 63 63 63 17 11 11 19 2 2 Luxembourg
Malta 1 1 1 0 0 0 1 1 1 0 0 0 Malte
Montenegro 18 25 26 83 84 84 0 0 0 65 59 58 Monténégro
Netherlands 5,354 5,354 5,354 268 268 268 5,551 5,551 5,551 465 465 465 Pays-Bas
Poland 842 920 1,100 1,152 1,200 1,350 366 370 380 677 650 630 Pologne
Portugal 228 225 220 747 740 735 4 5 5 523 520 520 Portugal
Serbia 478 460 485 418 450 480 83 70 80 23 60 75 Serbie
Slovakia 22 175 175 390 450 450 47 75 75 415 350 350 Slovaquie
Slovenia 125 155 150 164 175 180 126 120 130 165 140 160 Slovénie
Spain 867 907 907 1,007 1,007 1,007 65 46 46 206 146 146 Espagne
Sweden 1,776 1,800 1,850 1,809 1,750 1,800 199 210 210 232 160 160 Suède
Switzerland 410 415 420 330 335 340 80 80 80 0 0 0 Suisse
United Kingdom 7,819 7,830 7,830 327 330 330 7,516 7,520 7,520 23 20 20 Royaume-Uni
Total Europe 29,451 30,850 31,601 19,110 19,422 20,173 18,114 18,482 18,621 7,774 7,055 7,194 Total Europe
Canada 368 420 179 3,830 3,830 3,830 31 52 56 3,493 3,462 3,707 Canada
United States 761 273 152 9,544 9,744 9,948 194 174 155 8,977 9,644 9,951 Etats-Unis
Total North America 1,129 694 331 13,374 13,574 13,778 225 226 211 12,470 13,106 13,659 Total Amérique du Nord

Table 14

TABLE 14
Europe: Summary table of market forecasts for 2023 and 2024
Europe: Tableau récapitulatif des prévisions du marché pour 2023 et 2024
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
Apparent Consumption
Consommation Apparente Production Imports - Importations Exports - Exportations
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
actual forecasts actual forecasts actual forecasts actual forecasts
réels prévisions réels prévisions réels prévisions réels prévisions
Sawn softwood 75.92 69.01 68.49 96.71 89.54 88.44 29.69 25.67 25.93 50.49 46.20 45.88 Sciages conifères
Softwood logs a 182.85 171.26 171.53 185.47 172.88 171.84 18.22 16.63 16.39 20.84 18.25 16.70 Grumes de conifères a
Sawn hardwood 7.02 6.65 6.70 6.93 6.45 6.61 4.18 3.86 3.81 4.09 3.66 3.72 Sciages non-conifères
– temperate zone b 6.45 6.14 6.18 6.87 6.40 6.55 3.28 3.07 3.02 3.70 3.33 3.39 – zone tempérée b
– tropical zone b 0.57 0.51 0.52 0.06 0.05 0.06 0.90 0.79 0.79 0.39 0.32 0.32 – zone tropicale b
Hardwood logs a 21.96 21.62 22.00 22.05 21.91 22.04 3.91 3.25 3.26 4.00 3.54 3.30 Grumes de non-conifères a
– temperate zone b 21.86 21.55 21.93 22.05 21.91 22.04 3.79 3.15 3.16 3.98 3.50 3.26 – zone tempérée b
– tropical zone b 0.10 0.07 0.07 0.12 0.11 0.11 0.02 0.04 0.04 – zone tropicale b
Veneer sheets 1.58 1.49 1.49 1.00 0.97 0.96 1.42 1.28 1.29 0.84 0.76 0.76 Feuilles de placage
Plywood 6.62 6.21 5.92 4.17 3.93 3.97 6.42 5.79 5.48 3.96 3.50 3.53 Contreplaqués
Particle board (excluding OSB) 28.12 26.41 26.52 28.01 26.71 26.91 10.02 9.58 9.55 9.92 9.88 9.94 Pann. de particules (sauf OSB)
OSB 5.27 5.06 5.09 4.89 4.89 5.02 3.20 2.96 2.94 2.83 2.78 2.87 OSB
Fibreboard 15.80 14.89 15.09 16.15 15.31 15.42 8.76 8.01 8.04 9.11 8.43 8.37 Panneaux de fibres
– Hardboard 0.79 0.82 0.90 0.48 0.47 0.47 1.47 1.44 1.46 1.17 1.09 1.04 – Durs
– MDF 11.42 10.85 10.97 12.16 11.62 11.68 5.21 4.61 4.62 5.95 5.38 5.33 – MDF
– Other board 3.59 3.22 3.22 3.51 3.22 3.27 2.07 1.97 1.96 1.99 1.96 2.01 – Autres panneaux
Pulpwood a 268.34 264.50 264.51 261.87 256.81 255.84 39.84 39.23 39.90 33.38 31.54 31.23 Bois de trituration a
– Pulp logs 147.00 147.83 147.94 144.40 144.44 143.75 21.05 21.67 22.13 18.44 18.29 17.95 – Bois ronds de trituration
– softwood 100.28 102.83 102.53 99.21 100.14 99.43 12.63 13.27 13.50 11.56 10.58 10.41 – conifères
– hardwood 46.73 45.00 45.41 45.18 44.31 44.32 8.43 8.41 8.63 6.88 7.71 7.54 – non-conifères
– Residues, chips and particles 121.33 116.67 116.57 117.47 112.37 112.09 18.79 17.56 17.77 14.93 13.26 13.29 – Déchets, plaquettes et part.
Wood pulp 37.60 34.07 35.28 34.64 32.24 33.81 17.33 16.19 16.59 14.37 14.37 15.12 Pâte de bois
Paper and paperboard 72.76 66.14 69.44 83.10 73.88 79.49 43.20 39.62 41.48 53.55 47.36 51.53 Papiers et cartons
Wood Pellets 29.45 30.85 31.60 19.11 19.42 20.17 18.11 18.48 18.62 7.77 7.05 7.19 Granulés de bois
a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fourni des données sur le commerce
b Trade figures by zone do not equal the total as some countries cannot provide data for both zones b Les chiffres du commerce par zone ne correspondent pas aux totaux
en raison du fait que certains pays ne peuvent les différencier.

Table 15

TABLE 15
North America: Summary table of market forecasts for 2023 and 2024
Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2023 et 2024
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
Apparent Consumption
Consommation Apparente Production Imports - Importations Exports - Exportations
2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024
actual forecasts actual forecasts actual forecasts actual forecasts
réels prévisions réels prévisions réels prévisions réels prévisions
Sawn softwood 91.63 89.85 90.39 100.44 97.41 95.73 27.09 26.48 27.10 35.90 34.04 32.43 Sciages conifères
Softwood logs 253.91 254.00 257.31 262.84 262.90 265.12 1.93 1.97 1.86 10.86 10.87 9.68 Grumes de conifères
Sawn hardwood 15.85 16.16 16.46 18.50 18.72 19.03 1.59 1.63 1.56 4.23 4.19 4.13 Sciages non-conifères
– temperate zone 15.57 15.89 16.19 18.50 18.72 19.03 1.29 1.33 1.26 4.21 4.16 4.10 – zone tempérée
– tropical zone 0.29 0.27 0.27 0.00 0.00 0.00 0.31 0.30 0.30 0.02 0.03 0.03 – zone tropicale
Hardwood logs 47.44 48.23 49.33 48.21 48.67 49.44 1.33 1.22 1.18 2.10 1.65 1.30 Grumes de non-conifères
– temperate zone 47.44 48.23 49.32 48.21 48.67 49.44 1.32 1.21 1.18 2.10 1.65 1.30 – zone tempérée
– tropical zone 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 – zone tropicale
Veneer sheets 2.85 2.93 2.97 2.87 2.89 2.91 0.86 0.88 0.89 0.88 0.83 0.84 Feuilles de placage
Plywood 16.92 16.92 17.31 10.86 10.90 11.05 7.48 7.37 7.68 1.43 1.36 1.42 Contreplaqués
Particle board (excluding OSB) 6.66 7.45 7.46 6.11 6.58 6.55 1.75 1.97 1.98 1.19 1.10 1.07 Pann. de particules (sauf OSB)
OSB 21.20 21.09 21.35 20.86 20.60 20.86 6.28 6.30 6.39 5.94 5.82 5.89 OSB
Fibreboard 9.92 9.93 10.07 7.64 7.71 7.87 4.18 3.92 3.92 1.90 1.69 1.72 Panneaux de fibres
– Hardboard 0.51 0.56 0.56 0.53 0.59 0.60 0.31 0.28 0.29 0.32 0.32 0.33 – Durs
– MDF 6.21 6.23 6.23 3.83 3.88 3.89 3.55 3.35 3.32 1.17 0.99 0.98 – MDF
– Other board 3.20 3.15 3.28 3.28 3.24 3.38 0.32 0.29 0.31 0.40 0.38 0.41 – Autres panneaux
Pulpwood 275.49 275.41 276.58 280.24 279.10 280.51 2.93 3.79 3.78 7.67 7.47 7.70 Bois de trituration
– Pulp logs 197.72 197.99 198.68 197.69 197.86 198.50 0.43 0.41 0.45 0.39 0.28 0.27 – Bois ronds de trituration
– softwood 145.76 147.13 149.24 145.45 146.80 148.85 0.33 0.34 0.41 0.02 0.01 0.01 – conifères
– hardwood 51.96 50.85 49.44 52.24 51.06 49.65 0.10 0.07 0.05 0.37 0.27 0.26 – non-conifères
– Residues, chips and particles 77.77 77.42 77.90 82.55 81.23 82.01 2.50 3.38 3.32 7.28 7.19 7.43 – Déchets, plaquettes et part.
Wood pulp 45.79 48.12 48.43 55.02 54.33 54.12 7.42 8.22 8.89 16.65 14.44 14.58 Pâte de bois
Paper and paperboard 69.75 68.96 69.26 75.05 73.60 73.63 10.72 10.42 10.39 16.02 15.06 14.77 Papiers et cartons
Wood pellets 1.13 0.69 0.33 13.37 13.57 13.78 0.23 0.23 0.21 12.47 13.11 13.66 Granulés de bois

List of Tables and Notes Table 1 - Sawn Softwood Table 2 - Sawn Hardwood (total) Table 2a - Sawn Hardwood (temperate) Table 2b - Sawn Hardwood (tropical) Table 3 - Veneer Sheets Table 4 - Plywood Table 5 - Particle Board (excluding OSB) Table 5a - Oriented Strand Board Table 6 - Fibreboard Table 6a - Hardboard Table 6b - MDF/HDF Table 6c - Other Fibreboard Table 7 - Wood Pulp Table 8 - Paper and Paperboard Table 9 - Removals of wood in the rough Table 9a - Removals of wood in the rough (softwood) Table 9b - Removals of wood in the rough (hardwood) Table 10 - Softwood sawlogs Table 11 - Hardwood sawlogs Table 11a - Hardwood logs (temperate) Table 11b - Hardwood logs (tropical) Table 12 - Pulpwood Table 12a - Pulpwood (softwood) Table 12b - Pulpwood (hardwood) Table 12c - Wood Residues, Chips and Particles Table 13 - Wood Pellets Table 14 - Europe: Summary table of market forecasts for 2023 and 2024 Table 15 - North America: Summary table of market forecasts for 2023 and 2024

Source: UNECE Committee on Forests and the Forest Industry , November 2023, http://www.unece.org/forests/fpm/timbercommittee.html

Notes: Data in italics are estimated by the secretariat. EECCA is Eastern Europe, Caucasus and Central Asia. Data for the two latest years are forecasts. In contrast to previous years, data are shown only for countries providing forecasts. Sub-regional totals are only for reporting countries.

For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.

Softwood = coniferous, hardwood = non-coniferous For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year. Countries with nil, missing or confidential data for all years on a table are not shown.

Uzbekistan – data extrapolated by the Secretariat based on national data for the first eight months 2023. Poland - The trade turnover is based on data that includes the estimated value of trade turnover by entities exempt from the reporting obligation. These trade turnover figures are estimated at 3%. Roundwood: sawlogs and veneer logs and pulpwood and wood fuel - with removals from trees and shrubs outside the forest, including forest chips, with stump. Residues - production excluding recovered wood.

In contrast to years prior to 2020, data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subreg Confidential data have not been included. Please inform secretariat in case you notice any confidential data which might have been included inadvertently.

Wherever the forecast data is incomplete, then data is repeated to avoid skewing.

Countries with nil, missing or confidential data for all years on a table are not shown. Consumption figures are the sum of production and national imports minus national exports. Softwood = coniferous, hardwood = non-coniferous. United Kingdom production figures for OSB is secretariat estimate.

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 6,141 4,978 4,978 10,104 8,588 8,588 1,784 1,270 1,270 5,747 4,880 4,880 Autriche Cyprus 33 34 34 1 1 1 32 33 33 0 0 0 Chypre Czech Republic 2,965 2,343 2,470 4,720 3,776 4,040 583 414 350 2,338 1,847 1,920 République tchèque Estonia 2,068 1,550 1,550 1,725 1,500 1,500 1,209 700 700 866 650 650 Estonie Finland 2,938 2,420 2,420 11,200 10,300 10,400 305 20 20 8,567 7,900 8,000 Finlande France 8,633 8,750 8,800 7,168 7,200 7,300 2,350 2,450 2,400 885 900 900 France Germany 17,294 14,900 13,300 24,309 21,400 19,800 4,146 2,700 3,000 11,162 9,200 9,500 Allemagne Hungary 788 902 918 85 96 86 717 821 842 14 15 11 Hongrie Italy 4,790 4,302 4,302 400 400 400 4,608 4,157 4,157 217 255 255 Italie Latvia 1,025 950 950 3,102 3,000 3,000 829 750 750 2,906 2,800 2,800 Lettonie Luxembourg 71 122 122 39 39 39 43 91 91 11 8 8 Luxembourg Malta 7 9 9 0 0 0 7 9 9 0 0 0 Malte Montenegro 30 30 29 118 115 112 10 9 7 98 94 90 Monténégro Netherlands 2,259 2,088 2,029 115 115 115 2,659 2,473 2,399 515 500 485 Pays-Bas Poland 4,631 4,630 4,800 4,144 4,100 4,200 1,219 1,240 1,300 732 710 700 Pologne Portugal 696 686 685 807 815 820 130 130 125 242 259 260 Portugal Serbia 367 361 383 91 95 98 281 270 290 5 4 5 Serbie Slovakia 847 810 860 1,430 1,360 1,400 480 450 460 1,063 1,000 1,000 Slovaquie Slovenia 665 670 660 983 990 980 530 530 530 848 850 850 Slovénie Spain 4,029 4,001 4,001 3,006 3,189 3,189 1,166 956 956 143 144 144 Espagne Sweden 5,709 5,050 5,650 18,870 18,400 18,300 587 500 450 13,748 13,850 13,100 Suède Switzerland 1,271 1,300 1,325 1,186 1,200 1,210 300 310 320 215 210 205 Suisse United Kingdom 8,663 8,125 8,214 3,108 2,860 2,860 5,719 5,385 5,474 165 120 120 Royaume-Uni Total Europe 75,919 69,011 68,490 96,712 89,540 88,439 29,694 25,668 25,934 50,487 46,197 45,883 Total Europe Uzbekistan 2,256 1,498 1,498 0 0 0 2,256 1,498 1,498 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada a 3,707 2,691 2,242 36,398 33,228 31,331 891 988 948 33,581 31,525 30,037 Canada a

United States a 87,925 87,155 88,151 64,039 64,178 64,399 26,202 25,492 26,149 2,316 2,515 2,397 Etats-Unis a

Total North America 91,632 89,846 90,393 100,437 97,406 95,730 27,093 26,480 27,097 35,898 34,040 32,434 Total Amérique du Nord a converted from nominal to actual size using factor of 0.72 a convertis du dimension nominale au véritable avec une facteur du 0.72

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 1 SAWN SOFTWOOD SCIAGES CONIFERES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 310 222 222 238 202 202 217 140 140 145 120 120 Autriche Cyprus 11 7 7 0 0 0 11 7 7 0 0 0 Chypre Czech Republic 324 245 240 222 167 175 136 103 105 34 24 40 République tchèque Estonia 232 125 125 175 125 125 147 60 60 90 60 60 Estonie Finland 84 44 44 73 40 40 34 24 24 23 20 20 Finlande France 1,124 1,140 1,150 1,446 1,300 1,400 264 420 350 586 580 600 France Germany 693 650 650 997 800 800 395 300 300 699 450 450 Allemagne Hungary 258 150 131 414 343 342 45 38 30 200 231 241 Hongrie Italy 798 776 776 500 500 500 637 578 578 339 302 302 Italie Latvia 5 105 105 720 800 800 54 55 55 769 750 750 Lettonie Luxembourg 96 98 98 39 39 39 64 65 65 7 6 6 Luxembourg Malta 7 8 9 0 0 0 7 8 9 0 0 0 Malte Montenegro 11 8 10 39 35 34 2 1 1 30 28 25 Monténégro Netherlands 238 213 203 34 34 34 314 289 279 110 110 110 Pays-Bas Poland 495 470 500 487 450 460 267 270 300 259 250 260 Pologne Portugal 369 295 290 182 185 190 287 200 190 100 90 90 Portugal Serbia 172 215 225 343 370 385 64 60 70 235 215 230 Serbie Slovakia 235 240 275 385 400 420 55 50 55 205 210 200 Slovaquie Slovenia 106 145 145 143 145 145 83 80 80 121 80 80 Slovénie Spain 425 467 467 302 321 321 175 193 193 53 47 47 Espagne Sweden 142 140 140 100 100 100 83 80 80 41 40 40 Suède Switzerland 78 79 81 52 53 54 50 51 52 24 25 25 Suisse United Kingdom 807 810 810 37 40 40 787 790 790 17 20 20 Royaume-Uni Total Europe 7,019 6,652 6,703 6,928 6,449 6,606 4,177 3,862 3,813 4,086 3,658 3,716 Total Europe Uzbekistan 228 208 208 195 195 195 33 16 16 0 3 3 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,208 1,324 1,242 859 893 815 793 826 738 444 395 311 Canada United States 14,647 14,835 15,217 17,637 17,827 18,214 798 805 820 3,788 3,797 3,817 Etats-Unis Total North America 15,855 16,159 16,459 18,496 18,720 19,029 1,591 1,631 1,558 4,231 4,192 4,128 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 2 SAWN HARDWOOD (total) SCIAGES NON-CONIFERES (total)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 306 219 219 238 202 202 213 136 136 144 119 119 Autriche Cyprus 9 5 5 0 0 0 8 5 5 0 0 0 Chypre Czech Republic 307 229 223 222 167 175 119 86 88 34 24 40 République tchèque Estonia 230 122 122 175 125 125 142 56 56 87 59 59 Estonie Finland 80 40 40 73 40 40 26 16 16 19 16 16 Finlande France 960 988 988 1,420 1,285 1,375 123 280 210 583 577 597 France Germany 664 630 630 997 800 800 315 240 240 649 410 410 Allemagne Hungary 257 147 127 414 343 342 43 35 26 200 230 241 Hongrie Italy 819 791 791 495 495 495 476 423 423 152 127 127 Italie Latvia 5 105 105 720 800 800 54 55 55 769 750 750 Lettonie Luxembourg 92 96 96 39 39 39 60 63 63 7 6 6 Luxembourg Malta 6 7 8 0 0 0 6 7 8 0 0 0 Malte Montenegro 11 8 10 39 35 34 2 1 1 30 28 25 Monténégro Netherlands 89 80 77 27 27 27 117 108 105 55 55 55 Pays-Bas Poland 484 459 488 487 450 460 254 257 286 257 248 258 Pologne Portugal 319 272 268 170 172 178 180 150 140 31 50 50 Portugal Serbia 167 211 220 342 369 384 59 57 66 234 215 230 Serbie Slovakia 235 240 275 385 400 420 55 50 55 205 210 200 Slovaquie Slovenia 104 143 143 143 145 145 81 78 78 120 80 80 Slovénie Spain 383 417 417 300 318 318 128 142 142 45 43 43 Espagne Sweden 142 139 139 100 100 100 83 79 79 41 40 40 Suède Switzerland 69 70 72 49 50 51 44 45 46 24 25 25 Suisse United Kingdom 716 720 720 37 40 40 693 700 700 14 20 20 Royaume-Uni Total Europe 6,453 6,138 6,183 6,872 6,402 6,550 3,281 3,069 3,025 3,700 3,334 3,392 Total Europe Uzbekistan 227 207 207 195 195 195 33 15 15 0 3 3 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,191 1,316 1,236 859 893 815 762 805 715 430 382 294 Canada United States 14,379 14,578 14,957 17,637 17,827 18,214 523 529 544 3,782 3,778 3,801 Etats-Unis Total North America 15,569 15,893 16,193 18,496 18,720 19,029 1,285 1,334 1,259 4,212 4,160 4,095 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 2a SAWN HARDWOOD (temperate) SCIAGES NON-CONIFERES (zone tempérée)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 3 3 3 0 0 0 4 4 4 1 1 1 Autriche Bulgaria 0 0 0 0 0 0 0 0 0 0 0 0 Bulgarie Cyprus 3 2 2 0 0 0 3 2 2 0 0 0 Chypre Czech Republic 17 17 17 0 0 0 17 17 17 0 0 0 République tchèque Estonia 2 3 3 0 0 0 5 4 4 3 1 1 Estonie Finland 4 4 4 0 0 0 8 8 8 4 4 4 Finlande France 164 152 162 26 15 25 141 140 140 3 3 3 France Germany 29 20 20 0 0 0 79 60 60 50 40 40 Allemagne Hungary 2 3 4 0 0 0 2 4 4 0 0 0 Hongrie Italy -21 -15 -15 5 5 5 161 154 154 187 175 175 Italie Luxembourg 4 2 2 0 0 0 4 2 2 0 0 0 Luxembourg Malta 1 1 1 0 0 0 1 1 1 0 0 0 Malte Netherlands 149 133 126 7 7 7 197 181 174 55 55 55 Pays-Bas Poland 10 11 12 0 0 0 12 13 14 2 2 2 Pologne Portugal 50 23 22 12 13 12 107 50 50 69 40 40 Portugal Serbia 5 4 5 1 1 1 5 3 4 1 0 0 Serbie Slovenia 2 2 2 0 0 0 2 2 2 0 0 0 Slovénie Spain 42 49 49 2 2 2 47 50 50 7 4 4 Espagne Sweden 1 1 1 0 0 0 1 1 1 0 0 0 Suède Switzerland 9 9 9 3 3 3 6 6 6 0 0 0 Suisse United Kingdom 91 90 90 0 0 0 94 90 90 3 0 0 Royaume-Uni Total Europe 566 515 519 56 46 55 896 793 788 386 324 324 Total Europe Canada 17 8 7 0 0 0 31 21 23 14 13 16 Canada United States 269 257 260 0 0 0 275 276 276 6 19 16 Etats-Unis Total North America 286 266 266 0 0 0 305 297 299 20 31 32 Total Amérique du Nord

1000 m3

Apparent Consumption Country Consommation Apparente Production Imports - Importations

TABLE 2b SAWN HARDWOOD (tropical) SCIAGES NON-CONIFERES (tropicale)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Exports - Exportations Pays

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 74 39 39 8 8 8 83 45 45 17 14 14 Autriche Cyprus 1 1 1 0 0 0 1 1 1 0 0 0 Chypre Czech Republic 28 28 27 28 16 17 58 53 50 58 41 40 République tchèque Estonia 111 125 125 105 110 110 87 95 95 82 80 80 Estonie Finland 27 21 21 190 160 160 12 10 10 175 149 149 Finlande France 366 366 366 157 157 157 273 273 273 64 64 64 France Germany 157 143 125 110 105 105 99 78 70 52 40 50 Allemagne Hungary 23 25 20 13 18 13 39 39 39 28 31 32 Hongrie Italy 344 308 308 107 107 107 274 234 234 37 33 33 Italie Latvia 105 105 105 40 50 50 140 140 140 75 85 85 Lettonie Luxembourg 1 0 0 0 0 0 1 0 0 0 0 0 Luxembourg Malta 1 2 3 0 0 0 1 2 3 0 0 0 Malte Netherlands 15 13 13 0 0 0 17 15 15 3 3 3 Pays-Bas Poland 121 121 129 45 42 45 92 94 98 16 15 14 Pologne Portugal 12 20 35 20 30 25 38 40 50 46 50 40 Portugal Serbia 4 4 5 30 28 30 8 6 8 34 30 33 Serbie Slovakia 17 25 25 21 25 25 27 30 30 31 30 30 Slovaquie Slovenia 9 8 9 28 27 25 13 14 14 32 33 30 Slovénie Spain 122 92 92 40 36 36 127 90 90 45 34 34 Espagne Sweden 32 31 31 60 50 50 19 10 10 47 29 29 Suède Switzerland 3 3 3 0 0 0 4 4 4 1 1 1 Suisse United Kingdom 6 10 10 0 0 0 7 10 10 1 0 0 Royaume-Uni Total Europe 1,577 1,490 1,491 1,002 969 962 1,419 1,283 1,288 843 762 760 Total Europe Uzbekistan 4 4 4 3 3 3 2 1 1 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 204 262 267 581 581 581 212 218 230 590 537 544 Canada United States 2,643 2,670 2,699 2,284 2,306 2,329 652 658 664 293 294 294 Etats-Unis Total North America 2,847 2,932 2,966 2,866 2,887 2,910 864 876 894 883 831 838 Total Amérique du Nord Note: Definition of veneers excludes domestic use for plywood. La définition des placages exclus la conversion directe en contreplaqué.

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 3 VENEER SHEETS FEUILLES DE PLACAGE

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 19 15 15 131 155 155 183 150 150 296 290 290 Autriche Cyprus 14 15 15 0 0 0 14 15 15 0 0 0 Chypre Czech Republic 193 116 123 240 236 238 230 115 115 277 235 230 République tchèque Estonia 145 50 50 200 210 210 151 50 50 205 210 210 Estonie Finland 297 240 240 1,110 940 940 87 60 60 900 760 760 Finlande France 589 583 583 253 270 270 476 452 452 140 139 139 France Germany 1,073 1,154 840 85 80 80 1,319 1,281 1,000 330 207 240 Allemagne Hungary 136 110 107 60 61 63 138 138 138 62 90 94 Hongrie Italy 602 537 537 288 290 290 525 442 442 211 195 195 Italie Latvia 92 55 55 331 300 300 94 95 95 333 340 340 Lettonie Luxembourg 33 29 29 0 0 0 33 29 29 0 0 0 Luxembourg Malta 10 11 12 0 0 0 10 11 12 0 0 0 Malte Montenegro 2 2 2 1 1 1 2 2 2 1 1 1 Monténégro Netherlands 488 457 441 0 0 0 586 551 529 98 94 88 Pays-Bas Poland 650 640 670 539 515 530 468 475 480 357 350 340 Pologne Portugal 154 180 166 103 100 110 95 110 100 44 30 44 Portugal Serbia 40 36 38 19 18 19 34 30 33 13 12 14 Serbie Slovakia 67 63 63 153 150 150 59 59 59 146 146 146 Slovaquie Slovenia 49 50 58 94 90 98 26 30 30 71 70 70 Slovénie Spain 231 326 326 462 416 416 132 117 117 363 207 207 Espagne Sweden 278 160 160 90 90 90 236 120 120 48 50 50 Suède Switzerland 206 206 206 7 7 7 203 203 203 4 4 4 Suisse United Kingdom 1,254 1,180 1,180 0 0 0 1,320 1,250 1,250 66 70 70 Royaume-Uni Total Europe 6,623 6,215 5,916 4,166 3,930 3,967 6,422 5,786 5,482 3,965 3,501 3,532 Total Europe Uzbekistan 62 46 46 0 0 0 63 47 47 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 2,174 2,028 2,123 1,604 1,557 1,526 1,224 1,058 1,241 654 587 644 Canada United States 14,742 14,890 15,188 9,254 9,345 9,528 6,259 6,317 6,436 771 772 776 Etats-Unis Total North America 16,916 16,918 17,311 10,858 10,902 11,054 7,483 7,375 7,677 1,425 1,359 1,420 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 4 PLYWOOD CONTREPLAQUES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 951 630 630 2,280 2,170 2,170 313 355 355 1,642 1,895 1,895 Autriche Cyprus 49 46 46 0 0 0 49 46 46 0 0 0 Chypre Czech Republic 793 811 835 962 866 910 530 484 485 699 538 560 République tchèque Estonia 123 67 67 90 0 0 77 68 68 44 2 1 Estonie Finland 113 75 75 54 54 54 85 44 44 26 23 23 Finlande France 2,224 2,148 2,148 3,177 3,094 3,094 299 355 355 1,253 1,301 1,301 France Germany 5,572 5,220 4,970 5,526 5,195 5,020 1,970 1,934 1,900 1,924 1,909 1,950 Allemagne Hungary 408 384 379 447 428 438 264 282 272 303 326 331 Hongrie Italy 3,070 2,813 2,813 2,646 2,500 2,500 956 821 821 532 508 508 Italie Latvia 52 85 85 306 300 300 69 25 25 322 240 240 Lettonie Luxembourg 20 12 12 0 0 0 21 13 13 1 1 1 Luxembourg Malta 10 11 11 0 0 0 10 11 11 0 0 0 Malte Montenegro 32 33 34 0 0 0 32 33 34 0 0 0 Monténégro Netherlands 464 440 432 0 0 0 514 488 479 50 48 47 Pays-Bas Poland 6,501 6,450 6,740 5,227 5,150 5,450 2,173 2,180 2,200 899 880 910 Pologne Portugal 537 473 514 766 750 760 281 300 290 510 577 536 Portugal Serbia 373 351 371 219 210 220 196 184 198 42 43 47 Serbie Slovakia 352 343 340 676 675 675 148 140 137 473 473 472 Slovaquie Slovenia 137 110 110 0 0 0 143 114 114 6 4 4 Slovénie Spain 2,392 2,213 2,213 2,566 2,310 2,310 626 621 621 800 718 718 Espagne Sweden 1,055 868 868 636 600 600 475 335 335 57 67 67 Suède Switzerland 281 286 286 420 425 425 141 141 141 280 280 280 Suisse United Kingdom 2,606 2,542 2,542 2,012 1,982 1,982 648 610 610 55 50 50 Royaume-Uni Total Europe 28,115 26,410 26,521 28,012 26,710 26,908 10,021 9,584 9,555 9,917 9,883 9,942 Total Europe Uzbekistan 880 542 542 252 252 252 654 317 317 26 27 27 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 27 Total EOCAC Canada 1,466 1,886 1,894 1,625 2,032 2,012 552 504 491 710 650 609 Canada United States 5,196 5,565 5,562 4,488 4,552 4,534 1,193 1,465 1,487 485 452 459 Etats-Unis Total North America 6,663 7,451 7,456 6,113 6,584 6,546 1,745 1,969 1,978 1,195 1,102 1,068 Total Amérique du Nord Data are calculated by subtracting OSB from the particleboard/OSB total - les données sont calculées en soustrayant les OSB du total des panneaux de particules et OSB.

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 5 PARTICLE BOARD (excluding OSB) PANNEAUX DE PARTICULES (ne comprennent pas l'OSB)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 205 135 135 0 0 0 212 140 140 7 5 5 Autriche Cyprus 11 14 14 0 0 0 11 14 14 0 0 0 Chypre Czech Republic 380 342 350 689 620 655 126 113 115 435 392 420 République tchèque Estonia 55 32 32 0 0 0 55 32 32 1 0 0 Estonie Finland 56 56 56 0 0 0 56 56 56 0 0 0 Finlande France 427 522 522 302 406 406 222 165 165 96 49 49 France Germany 1,316 1,238 1,130 1,164 1,105 1,080 679 669 600 526 536 550 Allemagne Hungary 133 147 152 379 419 443 56 60 59 302 331 350 Hongrie Italy 346 287 287 100 100 100 346 274 274 100 87 87 Italie Latvia 196 165 165 674 650 650 76 75 75 554 560 560 Lettonie Luxembourg 110 135 135 338 338 338 6 14 14 234 217 217 Luxembourg Montenegro 2 2 2 0 0 0 2 2 2 0 0 0 Monténégro Netherlands 222 222 227 0 0 0 286 286 292 64 64 65 Pays-Bas Poland 655 650 760 647 650 750 302 320 350 294 320 340 Pologne Portugal 46 37 41 0 0 0 50 40 45 4 3 4 Portugal Serbia 40 35 41 0 0 0 41 36 42 1 1 1 Serbie Slovakia 48 58 60 0 0 0 48 60 63 1 3 3 Slovaquie Slovenia 31 24 24 0 0 0 33 26 26 2 2 2 Slovénie Spain 26 15 15 3 3 3 35 33 33 12 20 20 Espagne Sweden 94 92 92 0 0 0 97 95 95 3 3 3 Suède Switzerland 95 95 95 0 0 0 96 96 96 1 1 1 Suisse United Kingdom 773 758 758 598 598 598 365 350 350 190 190 190 Royaume-Uni Total Europe 5,268 5,060 5,092 4,894 4,888 5,023 3,200 2,956 2,938 2,826 2,784 2,868 Total Europe Uzbekistan 7 5 5 0 0 0 7 5 5 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 1,546 1,253 1,153 7,270 6,820 6,798 82 65 61 5,806 5,632 5,706 Canada United States 19,658 19,834 20,197 13,592 13,783 14,059 6,198 6,236 6,326 132 185 188 Etats-Unis Total North America 21,204 21,087 21,350 20,862 20,603 20,857 6,280 6,301 6,387 5,938 5,817 5,894 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 5a ORIENTED STRAND BOARD (OSB) PANNEAUX STRUCTURAUX ORIENTES (OSB)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 421 386 386 470 395 395 331 308 308 381 316 316 Autriche Cyprus 20 15 16 0 0 0 20 15 16 0 0 0 Chypre Czech Republic 328 276 280 41 41 42 438 347 360 151 112 122 République tchèque Estonia 70 46 47 75 40 40 65 46 47 70 40 40 Estonie Finland 139 105 105 44 44 44 141 102 102 46 41 41 Finlande France 828 915 915 1,238 1,035 1,035 721 772 772 1,130 892 892 France Germany 3,791 3,437 3,325 5,194 4,900 4,800 1,590 1,543 1,470 2,993 3,006 2,945 Allemagne Hungary 9 -17 -13 21 0 0 204 235 244 215 253 258 Hongrie Italy 1,862 1,661 1,661 827 818 818 1,281 974 974 245 131 131 Italie Latvia 60 50 40 48 50 50 62 65 65 50 65 75 Lettonie Luxembourg 100 90 90 147 147 147 34 19 19 80 76 76 Luxembourg Malta 6 7 7 0 0 0 6 7 7 0 0 0 Malte Montenegro 32 32 33 0 0 0 32 32 33 0 0 0 Monténégro Netherlands 332 310 296 29 29 29 465 431 412 162 150 145 Pays-Bas Poland 3,808 3,765 4,020 4,960 4,920 5,080 590 585 630 1,743 1,740 1,690 Pologne Portugal 534 485 529 526 520 560 338 315 335 330 350 366 Portugal Serbia 74 74 88 19 20 22 71 73 88 16 19 22 Serbie Slovakia 210 218 223 0 0 0 248 256 262 39 38 39 Slovaquie Slovenia 24 15 15 132 120 125 28 25 30 136 130 140 Slovénie Spain 920 894 894 1,430 1,287 1,287 462 355 355 972 748 748 Espagne Sweden 301 260 260 0 0 0 425 360 360 124 100 100 Suède Switzerland 238 238 238 97 97 97 308 308 308 167 167 167 Suisse United Kingdom 1,692 1,630 1,630 856 850 850 895 840 840 60 60 60 Royaume-Uni Total Europe 15,799 14,892 15,085 16,153 15,313 15,421 8,755 8,013 8,037 9,110 8,434 8,373 Total Europe Uzbekistan 1,092 809 809 47 47 47 1,057 771 771 13 9 9 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,236 1,183 1,181 1,277 1,288 1,299 818 628 605 859 733 723 Canada United States 8,684 8,749 8,888 6,362 6,420 6,571 3,359 3,289 3,310 1,038 960 993 Etats-Unis Total North America 9,920 9,932 10,069 7,639 7,708 7,870 4,177 3,917 3,915 1,896 1,693 1,716 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6 FIBREBOARD PANNEAUX DE FIBRES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 29 28 28 54 43 43 18 16 16 43 32 32 Autriche Cyprus 2 1 2 0 0 0 2 1 2 0 0 0 Chypre Czech Republic 43 45 45 0 0 0 61 59 60 18 14 15 République tchèque Estonia 23 15 19 0 0 0 30 16 20 7 1 1 Estonie Finland 23 21 21 44 44 44 21 15 15 41 38 38 Finlande France 55 55 55 221 221 221 207 207 207 373 373 373 France Germany 176 183 165 0 0 0 200 203 180 23 20 15 Allemagne Hungary 27 41 45 2 0 0 65 81 85 40 40 40 Hongrie Italy 280 280 280 16 16 16 283 283 283 19 19 19 Italie Latvia 1 5 5 15 15 15 18 20 20 32 30 30 Lettonie Luxembourg -31 -12 -12 0 0 0 3 8 8 34 20 20 Luxembourg Montenegro 1 1 1 0 0 0 1 1 1 0 0 0 Monténégro Netherlands 44 41 39 0 0 0 63 58 56 19 17 17 Pays-Bas Poland -179 -120 -50 80 80 80 88 100 120 347 300 250 Pologne Portugal 50 30 39 0 0 0 61 40 50 11 10 11 Portugal Serbia 39 35 38 19 20 22 33 31 34 13 16 18 Serbie Slovakia 21 20 21 0 0 0 21 21 22 1 1 1 Slovaquie Slovenia -1 0 1 0 0 0 4 2 4 4 2 3 Slovénie Spain 17 15 15 32 29 29 46 46 46 61 60 60 Espagne Sweden 47 30 30 0 0 0 116 110 110 70 80 80 Suède Switzerland 19 19 19 0 0 0 24 24 24 5 5 5 Suisse United Kingdom 101 90 90 0 0 0 110 100 100 9 10 10 Royaume-Uni Total Europe 787 822 895 482 468 470 1,474 1,441 1,463 1,169 1,087 1,037 Total Europe Uzbekistan 89 50 50 0 0 0 90 50 50 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 33 47 42 90 90 90 52 27 28 109 70 76 Canada United States 481 509 514 437 504 509 259 255 258 215 250 253 Etats-Unis Total North America 514 556 556 527 594 599 311 282 286 324 320 329 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6a HARDBOARD PANNEAUX DURS

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 260 230 230 416 351 351 177 160 160 333 281 281 Autriche Cyprus 16 12 12 0 0 0 16 12 12 0 0 0 Chypre Czech Republic 199 157 160 41 41 42 180 135 140 22 19 22 République tchèque Estonia 18 21 18 0 0 0 33 28 25 15 7 7 Estonie Finland 82 67 67 0 0 0 86 70 70 4 3 3 Finlande France 708 794 794 954 751 751 337 388 388 583 345 345 France Germany 1,870 1,728 1,720 3,792 3,700 3,650 424 395 370 2,345 2,367 2,300 Allemagne Hungary -39 -65 -62 0 0 0 136 148 156 175 213 218 Hongrie Italy 1,501 1,299 1,299 809 800 800 913 606 606 221 107 107 Italie Latvia 52 40 30 33 35 35 22 25 25 2 20 30 Lettonie Luxembourg 128 98 98 147 147 147 27 7 7 46 56 56 Luxembourg Malta 5 5 5 0 0 0 5 5 5 0 0 0 Malte Montenegro 31 31 32 0 0 0 31 31 32 0 0 0 Monténégro Netherlands 220 205 196 0 0 0 361 336 322 141 131 126 Pays-Bas Poland 3,066 3,020 3,130 3,052 3,030 3,100 470 450 470 456 460 440 Pologne Portugal 447 440 465 494 500 530 257 260 265 305 320 330 Portugal Serbia 31 35 46 0 0 0 34 38 50 3 3 4 Serbie Slovakia 135 135 135 0 0 0 170 170 170 35 35 35 Slovaquie Slovenia 24 15 14 132 120 125 24 23 26 131 128 137 Slovénie Spain 835 821 821 1,334 1,201 1,201 397 302 302 897 682 682 Espagne Sweden 254 225 225 0 0 0 284 230 230 30 5 5 Suède Switzerland 24 24 24 97 97 97 88 88 88 161 161 161 Suisse United Kingdom 1,553 1,510 1,510 856 850 850 739 700 700 42 40 40 Royaume-Uni Total Europe 11,419 10,847 10,969 12,157 11,623 11,679 5,210 4,606 4,618 5,948 5,382 5,328 Total Europe Uzbekistan 671 513 513 46 46 46 629 469 469 3 2 2 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,053 999 1,005 1,087 1,098 1,109 608 472 449 641 570 553 Canada United States 5,156 5,228 5,226 2,746 2,778 2,786 2,939 2,874 2,866 529 424 426 Etats-Unis Total North America 6,209 6,227 6,231 3,833 3,876 3,895 3,547 3,346 3,315 1,170 994 979 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6b MDF/HDF

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 131 128 128 0 0 0 136 132 132 4 3 3 Autriche Cyprus 2 2 2 0 0 0 3 2 2 0 0 0 Chypre Czech Republic 86 74 75 0 0 0 197 154 160 111 80 85 République tchèque Estonia 29 10 10 75 40 40 3 2 2 49 32 32 Estonie Finland 33 17 17 0 0 0 34 17 17 0 0 0 Finlande France 65 66 66 63 63 63 177 177 177 174 174 174 France Germany 1,745 1,526 1,440 1,402 1,200 1,150 966 945 920 624 619 630 Allemagne Hungary 21 7 4 19 0 0 3 7 4 0 0 0 Hongrie Italy 82 82 82 3 3 3 85 85 85 6 6 6 Italie Latvia 7 5 5 0 0 0 23 20 20 16 15 15 Lettonie Luxembourg 4 4 4 0 0 0 4 4 4 0 0 0 Luxembourg Malta 1 2 2 0 0 0 1 2 2 0 0 0 Malte Netherlands 68 64 61 29 29 29 41 37 34 2 2 2 Pays-Bas Poland 920 865 940 1,828 1,810 1,900 33 35 40 940 980 1,000 Pologne Portugal 37 15 25 32 20 30 20 15 20 15 20 25 Portugal Serbia 4 4 4 0 0 0 4 4 4 0 0 0 Serbie Slovakia 54 63 67 0 0 0 57 65 70 3 2 3 Slovaquie Slovenia 0 0 0 0 0 0 0 0 0 0 0 0 Slovénie Spain 69 59 59 64 58 58 20 7 7 15 6 6 Espagne Sweden 0 5 5 0 0 0 25 20 20 24 15 15 Suède Switzerland 195 195 195 0 0 0 196 196 196 1 1 1 Suisse United Kingdom 38 30 30 0 0 0 47 40 40 9 10 10 Royaume-Uni Total Europe 3,592 3,223 3,221 3,514 3,222 3,272 2,071 1,965 1,956 1,993 1,965 2,007 Total Europe Uzbekistan 331 246 246 2 2 2 339 252 252 10 7 7 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 150 137 134 100 100 100 158 129 128 108 92 94 Canada United States 3,047 3,012 3,148 3,179 3,138 3,276 161 160 186 294 286 314 Etats-Unis Total North America 3,196 3,149 3,282 3,279 3,238 3,376 319 289 314 402 378 408 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6c OTHER FIBREBOARD AUTRES PANNEAUX DE FIBRES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 2,209 1,950 2,030 1,977 1,700 1,800 630 610 630 399 360 400 Autriche Czech Republic 847 688 700 640 525 540 324 259 260 117 96 100 République tchèque Estonia 70 75 80 227 180 180 42 50 50 199 155 150 Estonie Finland a 5,468 4,483 4,614 9,200 8,690 9,360 355 150 150 4,087 4,357 4,896 Finlande a

France 2,898 2,420 2,500 1,666 1,300 1,350 1,715 1,450 1,500 483 330 350 France Germany 5,092 4,600 5,000 2,172 1,850 2,000 4,173 3,900 4,200 1,253 1,150 1,200 Allemagne Hungary 205 206 214 66 77 87 141 133 131 3 3 4 Hongrie Italy 3,466 3,466 3,466 223 223 223 3,536 3,536 3,536 293 293 293 Italie Latvia 7 7 7 12 13 13 7 7 7 12 13 13 Lettonie Netherlands 443 442 442 37 37 37 1,717 1,717 1,717 1,312 1,312 1,312 Pays-Bas Poland 2,836 2,830 2,930 1,729 1,710 1,750 1,291 1,300 1,320 183 180 140 Pologne Portugal 1,757 1,735 1,760 2,869 2,870 2,870 140 145 150 1,252 1,280 1,260 Portugal Serbia 82 88 92 0 0 0 82 88 92 0 0 0 Serbie Slovakia 700 700 715 692 700 725 173 170 170 166 170 180 Slovaquie Slovenia 322 321 316 73 63 68 249 260 250 1 2 2 Slovénie Spain 1,520 1,328 1,328 1,120 1,120 1,120 1,176 976 976 775 768 768 Espagne Sweden 8,438 7,600 7,950 11,631 10,900 11,400 641 600 600 3,834 3,900 4,050 Suède Switzerland 188 188 188 87 87 87 101 101 101 0 0 0 Suisse United Kingdom 1,057 940 950 220 200 200 838 740 750 1 0 0 Royaume-Uni Total Europe 37,604 34,067 35,282 34,641 32,244 33,809 17,333 16,193 16,590 14,369 14,369 15,118 Total Europe Uzbekistan 38 28 28 1 1 1 37 28 28 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 6,007 5,851 5,616 14,200 13,102 12,638 472 582 640 8,665 7,833 7,662 Canada United States 39,787 42,269 42,815 40,822 41,230 41,478 6,948 7,643 8,254 7,983 6,603 6,917 Etats-Unis Total North America 45,794 48,121 48,431 55,022 54,332 54,116 7,420 8,224 8,894 16,648 14,436 14,579 Total Amérique du Nord

a imports exclude dissolving pulp a les importations excluent pâte à dissoudre

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 7 WOOD PULP PATE DE BOIS

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 mt

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 2,133 1,750 2,050 4,633 3,500 4,000 1,231 1,050 1,150 3,730 2,800 3,100 Autriche Cyprus 56 48 48 0 0 0 56 48 48 0 0 0 Chypre Czech Republic 1,467 1,234 1,258 938 769 785 1,531 1,286 1,312 1,002 822 838 République tchèque Estonia 120 111 111 57 35 35 123 102 102 59 26 26 Estonie Finland 514 475 460 7,200 5,990 6,150 333 275 280 7,019 5,790 5,970 Finlande France 8,272 7,290 7,400 7,092 6,240 6,600 4,845 4,650 4,600 3,665 3,600 3,800 France Germany 17,836 14,600 17,000 21,612 17,500 21,000 9,302 8,000 9,500 13,078 10,900 13,500 Allemagne Hungary 1,213 1,167 1,212 1,057 1,003 1,034 877 892 898 720 727 721 Hongrie Italy 11,390 11,390 11,390 8,696 8,696 8,696 5,800 5,800 5,800 3,106 3,106 3,106 Italie Latvia 168 175 175 29 30 30 173 180 180 33 35 35 Lettonie Luxembourg 26 14 14 0 0 0 27 15 15 1 1 1 Luxembourg Malta 26 27 28 0 0 0 26 27 28 0 0 0 Malte Netherlands 2,814 2,760 2,760 2,884 2,827 2,827 2,180 2,096 2,096 2,250 2,163 2,163 Pays-Bas Poland 7,532 7,400 7,550 5,237 5,130 5,250 4,869 4,870 4,950 2,574 2,600 2,650 Pologne Portugal 1,090 1,200 1,240 2,123 2,200 2,240 948 940 945 1,981 1,940 1,945 Portugal Serbia 790 762 778 483 481 490 514 490 500 207 209 212 Serbie Slovakia 565 575 600 967 975 1,000 457 450 475 859 850 875 Slovaquie Slovenia 412 390 390 591 500 500 367 390 390 545 500 500 Slovénie Spain 7,060 6,778 6,778 6,355 6,355 6,355 2,997 2,577 2,577 2,291 2,154 2,154 Espagne Sweden 834 700 750 8,531 7,300 8,100 894 700 750 8,591 7,300 8,100 Suède Switzerland 1,020 1,015 1,010 1,160 1,155 1,150 640 635 630 780 775 770 Suisse United Kingdom 7,420 6,280 6,440 3,460 3,190 3,250 5,015 4,150 4,250 1,055 1,060 1,060 Royaume-Uni Total Europe 72,758 66,140 69,442 83,103 73,876 79,492 43,204 39,623 41,476 53,549 47,358 51,526 Total Europe Uzbekistan 335 297 297 142 142 142 214 171 171 21 17 17 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 5,505 6,069 6,231 9,094 9,124 9,155 2,516 2,242 2,235 6,105 5,298 5,159 Canada United States 64,243 62,896 63,029 65,959 64,476 64,476 8,202 8,180 8,159 9,917 9,761 9,606 Etats-Unis Total North America 69,748 68,964 69,260 75,053 73,600 73,631 10,718 10,423 10,395 16,023 15,059 14,765 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 8 PAPER AND PAPERBOARD PAPIERS ET CARTONS

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 mt

Apparent Consumption

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 13,935 11,716 12,225 10,711 8,904 9,338 3,223 2,812 2,887 0 0 0 5,424 5,115 5,234 19,359 16,831 17,459 Autriche Cyprus 3 2 2 2 2 2 0 0 0 0 0 0 11 9 8 14 11 10 Chypre Czech Republic 20,708 15,535 14,897 14,635 10,617 10,106 5,965 4,804 4,675 108 113 115 4,405 3,965 3,900 25,113 19,499 18,797 République tchèque Estonia 6,474 6,401 6,401 4,276 4,200 4,200 2,148 2,150 2,150 51 51 51 4,066 3,800 3,800 10,541 10,201 10,201 Estonie Finland 56,246 53,397 55,435 25,699 22,749 23,412 30,547 30,648 32,023 0 0 0 9,340 9,340 9,340 65,586 62,737 64,775 Finlande France 25,648 25,270 25,070 17,198 17,200 17,300 7,891 7,500 7,200 559 570 570 24,173 24,500 25,600 49,821 49,770 50,670 France Germany 56,534 53,930 49,630 44,756 41,200 39,500 11,644 12,600 10,000 135 130 130 22,338 22,700 22,700 78,872 76,630 72,330 Allemagne Hungary 2,901 2,881 2,881 1,410 1,374 1,399 912 995 1,008 579 512 475 3,626 3,284 3,397 6,527 6,165 6,278 Hongrie Italy 2,838 3,540 3,540 1,890 1,890 1,890 316 1,018 1,018 632 632 632 10,839 10,839 10,839 13,677 14,379 14,379 Italie Latvia 12,491 12,150 12,350 7,603 7,250 7,450 3,868 3,800 3,800 1,020 1,100 1,100 2,936 3,000 3,000 15,427 15,150 15,350 Lettonie Luxembourg 231 197 193 147 144 133 56 38 38 27 15 22 40 45 43 271 242 235 Luxembourg Montenegro 751 697 678 515 492 487 201 198 186 35 7 5 194 193 190 945 890 868 Monténégro Netherlands 614 599 589 221 220 215 352 340 335 41 39 39 2,382 2,380 2,385 2,996 2,979 2,974 Pays-Bas Poland 38,735 39,880 40,850 18,533 18,800 19,150 19,350 20,100 20,550 852 980 1,150 6,958 7,420 7,750 45,693 47,300 48,600 Pologne Portugal 12,235 12,330 12,190 2,038 2,040 2,060 9,799 9,850 9,700 399 440 430 2,383 2,380 2,300 14,619 14,710 14,490 Portugal Serbia 1,478 1,520 1,561 1,077 1,104 1,130 265 275 283 136 141 148 6,574 6,646 6,760 8,052 8,166 8,321 Serbie Slovakia 6,827 6,820 6,880 4,130 4,080 4,100 2,672 2,710 2,750 25 30 30 609 610 650 7,435 7,430 7,530 Slovaquie Slovenia 2,928 3,752 3,482 2,184 2,780 2,600 698 920 830 45 52 52 1,149 1,290 1,270 4,076 5,042 4,752 Slovénie Spain 14,366 15,244 15,244 4,150 4,404 4,404 9,813 10,413 10,413 403 427 427 3,555 3,772 3,772 17,921 19,016 19,016 Espagne Sweden 71,165 69,076 69,310 38,280 37,480 37,080 32,585 31,296 31,930 300 300 300 6,000 6,016 6,016 77,165 75,092 75,326 Suède Switzerland 3,011 3,082 3,142 2,555 2,625 2,680 444 445 450 12 12 12 1,938 2,000 2,025 4,949 5,082 5,167 Suisse United Kingdom 7,604 7,193 7,193 5,509 5,236 5,236 1,646 1,529 1,529 448 428 428 2,184 2,184 2,184 9,788 9,377 9,377 Royaume-Uni Total Europe 357,723 345,212 343,742 207,519 194,791 193,872 144,397 144,441 143,754 5,807 5,980 6,116 121,124 121,488 123,163 478,847 466,699 466,905 Total Europe Canada 142,131 140,499 140,499 124,900 123,350 123,350 15,040 14,864 14,864 2,190 2,285 2,285 1,683 1,908 1,908 143,814 142,407 142,407 Canada United States 382,544 384,963 388,611 186,157 188,221 191,211 182,650 182,996 183,637 13,737 13,746 13,763 76,230 76,240 76,278 458,774 461,203 464,889 Etats-Unis Total North America 524,675 525,462 529,110 311,057 311,571 314,561 197,690 197,861 198,501 15,927 16,031 16,048 77,913 78,148 78,186 602,587 603,610 607,296 Total Amérique du Nord

a Pulpwood, round and split, as well as chips and particles produced directly a Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées therefrom and used as pulpwood directement à partir des rondins et quartiers et utilisées comme bois de trituration

b Pitprops, poles, piling, posts etc. b Bois de mine, poteaux, pilotis, piquets etc. c Including chips and particles produced from wood in the rough and c Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées

used for energy purposes à des fins energétiques

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

TABLE 9 REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT

TOTAL TOTAL 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Wood fuel c

Bois de chauffage c Pays

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 12,958 10,873 11,338 10,382 8,638 9,038 2,576 2,235 2,300 0 0 0 3,248 3,069 3,140 16,206 13,942 14,478 Autriche Cyprus 2 2 2 2 2 2 0 0 0 0 0 0 10 8 7 12 10 9 Chypre Czech Republic 19,440 14,455 13,825 14,019 10,094 9,589 5,316 4,253 4,125 105 109 111 3,610 3,249 3,200 23,050 17,704 17,025 République tchèque Estonia 4,023 3,927 3,927 3,118 3,000 3,000 878 900 900 26 27 27 1,486 1,400 1,400 5,509 5,327 5,327 Estonie Finland 47,408 45,464 47,590 24,662 21,700 22,351 22,746 23,764 25,239 0 0 0 4,593 4,593 4,593 52,001 50,057 52,183 Finlande France 17,300 17,070 16,770 12,491 12,500 12,500 4,559 4,300 4,000 250 270 270 2,417 2,500 2,600 19,717 19,570 19,370 France Germany 52,425 50,120 46,120 41,761 38,500 37,000 10,541 11,500 9,000 123 120 120 8,834 9,200 9,200 61,259 59,320 55,320 Allemagne Hungary 688 759 743 175 201 208 411 488 481 102 70 53 383 294 333 1,071 1,053 1,076 Hongrie Italy 1,797 2,502 2,502 1,169 1,169 1,169 148 853 853 480 480 480 1,180 1,180 1,180 2,977 3,682 3,682 Italie Latvia 8,253 7,900 8,100 5,873 5,500 5,700 1,850 1,800 1,800 530 600 600 298 300 300 8,551 8,200 8,400 Lettonie Luxembourg 162 143 145 124 122 115 10 6 8 27 15 22 17 11 12 178 154 158 Luxembourg Montenegro 573 553 537 372 352 349 201 198 186 0 3 2 66 65 63 639 618 600 Monténégro Netherlands 449 440 430 173 170 165 244 240 235 32 30 30 457 450 450 906 890 880 Pays-Bas Poland 31,941 32,800 33,470 15,775 16,000 16,250 15,411 15,950 16,250 754 850 970 3,627 3,820 3,950 35,568 36,620 37,420 Pologne Portugal 3,045 3,210 3,150 1,682 1,710 1,700 1,213 1,350 1,300 150 150 150 996 990 980 4,041 4,200 4,130 Portugal Serbia 279 290 301 178 184 190 66 70 73 35 36 38 141 146 160 420 436 461 Serbie Slovakia 3,325 3,160 3,120 2,559 2,430 2,400 748 710 700 18 20 20 259 260 275 3,584 3,420 3,395 Slovaquie Slovenia 1,966 2,586 2,386 1,687 2,150 2,000 275 430 380 4 6 6 191 240 220 2,157 2,826 2,606 Slovénie Spain 7,435 7,889 7,889 3,420 3,629 3,629 3,754 3,984 3,984 261 277 277 2,243 2,380 2,380 9,678 10,269 10,269 Espagne Sweden 64,603 62,760 62,873 38,100 37,300 36,900 26,353 25,310 25,823 150 150 150 3,000 3,008 3,008 67,603 65,768 65,881 Suède Switzerland 2,578 2,639 2,689 2,290 2,350 2,400 279 280 280 9 9 9 769 770 775 3,347 3,409 3,464 Suisse United Kingdom 7,486 7,076 7,076 5,453 5,180 5,180 1,633 1,516 1,516 400 380 380 1,571 1,571 1,571 9,058 8,647 8,647 Royaume-Uni Total Europe 288,136 276,619 274,984 185,467 172,881 171,836 99,212 100,136 99,433 3,458 3,602 3,715 39,396 39,504 39,798 327,533 316,123 314,781 Total Europe Canada 114,659 112,907 112,907 110,046 108,424 108,424 4,229 4,021 4,021 384 462 462 806 946 946 115,465 113,853 113,853 Canada United States 306,119 309,360 313,639 152,799 154,479 156,695 141,226 142,779 144,827 12,094 12,102 12,117 37,619 37,609 37,606 343,738 346,969 351,245 Etats-Unis Total North America 420,778 422,267 426,546 262,845 262,903 265,119 145,455 146,800 148,848 12,478 12,564 12,579 38,425 38,555 38,552 459,203 460,822 465,098 Total Amérique du Nord

a Pulpwood, round and split, as well as chips and particles produced directly a Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées therefrom and used as pulpwood directement à partir des rondins et quartiers et utilisées comme bois de trituration

b Pitprops, poles, piling, posts etc. b Bois de mine, poteaux, pilotis, piquets etc. c Including chips and particles produced from wood in the rough and c Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées

used for energy purposes à des fins energétiques

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

TABLE 9a REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT

SOFTWOOD CONIFERES 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Wood fuel c

Bois de chauffage c Pays

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 977 843 887 329 266 300 647 577 587 0 0 0 2,176 2,046 2,094 3,153 2,889 2,981 Autriche Cyprus 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 Chypre Czech Republic 1,268 1,079 1,071 616 524 517 649 552 550 3 4 4 795 716 700 2,063 1,795 1,771 République tchèque Estonia 2,452 2,474 2,474 1,158 1,200 1,200 1,270 1,250 1,250 24 24 24 2,580 2,400 2,400 5,032 4,874 4,874 Estonie Finland 8,838 7,933 7,845 1,037 1,049 1,061 7,801 6,884 6,784 0 0 0 4,747 4,747 4,747 13,585 12,680 12,592 Finlande France 8,348 8,200 8,300 4,707 4,700 4,800 3,332 3,200 3,200 309 300 300 21,756 22,000 23,000 30,104 30,200 31,300 France Germany 4,110 3,810 3,510 2,995 2,700 2,500 1,103 1,100 1,000 12 10 10 13,504 13,500 13,500 17,613 17,310 17,010 Allemagne Hungary 2,213 2,122 2,138 1,234 1,173 1,191 502 507 526 477 442 421 3,244 2,990 3,064 5,456 5,112 5,202 Hongrie Italy 1,041 1,038 1,038 721 721 721 168 166 166 152 152 152 9,659 9,659 9,659 10,700 10,697 10,697 Italie Latvia 4,238 4,250 4,250 1,730 1,750 1,750 2,018 2,000 2,000 490 500 500 2,638 2,700 2,700 6,876 6,950 6,950 Lettonie Luxembourg 69 54 47 23 22 18 46 32 30 0 0 0 23 34 30 92 89 78 Luxembourg Montenegro 178 144 141 143 140 138 0 0 0 35 4 3 128 128 127 306 272 268 Monténégro Netherlands 165 159 159 48 50 50 108 100 100 9 9 9 1,925 1,930 1,935 2,090 2,089 2,094 Pays-Bas Poland 6,794 7,080 7,380 2,757 2,800 2,900 3,939 4,150 4,300 98 130 180 3,331 3,600 3,800 10,125 10,680 11,180 Pologne Portugal 9,190 9,120 9,040 356 330 360 8,586 8,500 8,400 249 290 280 1,387 1,390 1,320 10,578 10,510 10,360 Portugal Serbia 1,199 1,230 1,260 899 920 940 199 205 210 101 105 110 6,433 6,500 6,600 7,632 7,730 7,860 Serbie Slovakia 3,502 3,660 3,760 1,570 1,650 1,700 1,924 2,000 2,050 8 10 10 350 350 375 3,851 4,010 4,135 Slovaquie Slovenia 962 1,166 1,096 497 630 600 424 490 450 41 46 46 957 1,050 1,050 1,919 2,216 2,146 Slovénie Spain 6,931 7,354 7,354 730 775 775 6,059 6,429 6,429 142 151 151 1,312 1,392 1,392 8,243 8,746 8,746 Espagne Sweden 6,562 6,316 6,437 180 180 180 6,232 5,986 6,107 150 150 150 3,000 3,008 3,008 9,562 9,324 9,445 Suède Switzerland 433 443 453 265 275 280 165 165 170 3 3 3 1,169 1,230 1,250 1,602 1,673 1,703 Suisse United Kingdom 118 117 117 56 56 56 13 13 13 48 48 48 613 613 613 730 730 730 Royaume-Uni Total Europe 69,587 68,593 68,759 22,052 21,910 22,036 45,185 44,305 44,322 2,350 2,377 2,401 81,728 81,984 83,365 151,314 150,576 152,124 Total Europe Canada 27,472 27,592 27,592 14,854 14,926 14,926 10,812 10,843 10,843 1,806 1,823 1,823 877 961 961 28,349 28,554 28,554 Canada United States 76,425 75,603 74,972 33,358 33,742 34,516 41,424 40,217 38,810 1,643 1,644 1,646 38,611 38,631 38,672 115,036 114,234 113,644 Etats-Unis Total North America 103,897 103,196 102,564 48,212 48,668 49,442 52,236 51,060 49,653 3,449 3,467 3,469 39,488 39,592 39,633 143,385 142,788 142,197 Total Amérique du Nord

a Pulpwood, round and split, as well as chips and particles produced directly a Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées therefrom and used as pulpwood directement à partir des rondins et quartiers et utilisées comme bois de trituration

b Pitprops, poles, piling, posts etc. b Bois de mine, poteaux, pilotis, piquets etc. c Including chips and particles produced from wood in the rough and c Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées

used for energy purposes à des fins energétiques

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

TABLE 9b REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT

HARDWOOD NON-CONIFERES 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Wood fuel c

Bois de chauffage c Pays

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 16,101 13,943 13,638 10,382 8,638 9,038 6,664 5,710 5,000 945 405 400 Autriche Cyprus 2 2 2 2 2 2 0 0 0 0 0 0 Chypre Czech Republic 8,002 6,511 6,962 14,019 10,094 9,589 411 596 715 6,428 4,178 3,343 République tchèque Estonia 3,533 3,270 3,270 3,118 3,000 3,000 522 450 450 107 180 180 Estonie Finland 24,310 21,336 21,991 24,662 21,700 22,351 127 79 83 479 443 443 Finlande France 12,053 12,120 12,120 12,491 12,500 12,500 335 360 360 773 740 740 France Germany 39,391 35,800 34,900 41,761 38,500 37,000 3,300 3,000 3,100 5,670 5,700 5,200 Allemagne Hungary 175 201 208 175 201 208 0 0 0 0 0 0 Hongrie Italy 1,645 1,396 1,396 1,169 1,169 1,169 580 457 457 104 230 230 Italie Latvia 6,471 5,830 6,200 5,873 5,500 5,700 1,147 900 900 549 570 400 Lettonie Luxembourg 465 403 396 124 122 115 693 424 424 352 143 143 Luxembourg Montenegro 382 361 357 372 352 349 10 9 8 0 0 0 Monténégro Netherlands 133 145 145 173 170 165 77 80 80 117 105 100 Pays-Bas Poland 14,243 14,500 14,800 15,775 16,000 16,250 1,245 1,400 1,550 2,777 2,900 3,000 Pologne Portugal 1,880 1,905 1,900 1,682 1,710 1,700 241 230 240 43 35 40 Portugal Serbia 188 187 194 178 184 190 12 9 12 2 6 8 Serbie Slovakia 3,059 3,030 3,100 2,559 2,430 2,400 900 950 1,000 400 350 300 Slovaquie Slovenia 1,643 1,650 1,630 1,687 2,150 2,000 239 150 180 283 650 550 Slovénie Spain 3,223 3,307 3,307 3,420 3,629 3,629 240 185 185 437 507 507 Espagne Sweden 38,103 37,725 37,325 38,100 37,300 36,900 964 1,128 1,128 961 703 703 Suède Switzerland 2,035 2,100 2,155 2,290 2,350 2,400 55 60 65 310 310 310 Suisse United Kingdom 5,810 5,538 5,538 5,453 5,180 5,180 457 457 457 99 99 99 Royaume-Uni Total Europe 182,849 171,260 171,534 185,467 172,881 171,836 18,218 16,634 16,394 20,836 18,255 16,696 Total Europe Canada 105,870 103,492 103,916 110,046 108,424 108,424 1,346 1,402 1,309 5,522 6,333 5,816 Canada United States 148,043 150,509 153,391 152,799 154,479 156,695 586 570 555 5,342 4,540 3,859 Etats-Unis Total North America 253,913 254,001 257,307 262,845 262,903 265,119 1,931 1,972 1,864 10,863 10,873 9,675 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

TABLE 10 SOFTWOOD SAWLOGS GRUMES DE SCIAGES DES CONIFERES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption a

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 406 311 300 329 266 300 134 90 50 57 45 50 Autriche Czech Republic 544 457 447 616 524 517 144 120 125 216 186 195 République tchèque Estonia 1,187 1,244 1,244 1,158 1,200 1,200 46 60 60 16 16 16 Estonie Finland 1,068 1,041 1,061 1,037 1,049 1,061 32 1 9 1 9 9 Finlande France 3,453 4,020 4,120 4,707 4,700 4,800 116 120 120 1,370 800 800 France Germany 2,532 2,290 2,130 2,995 2,700 2,500 111 110 110 574 520 480 Allemagne Hungary 1,234 1,173 1,191 1,234 1,173 1,191 0 0 0 0 0 0 Hongrie Italy 2,088 1,718 1,718 721 721 721 1,413 1,055 1,055 47 59 59 Italie Latvia 1,221 1,190 1,410 1,730 1,750 1,750 87 40 60 596 600 400 Lettonie Luxembourg 226 148 144 23 22 18 221 160 160 18 34 34 Luxembourg Montenegro 143 140 138 143 140 138 0 0 0 0 0 0 Monténégro Netherlands 54 60 60 48 50 50 54 60 60 48 50 50 Pays-Bas Poland 2,687 2,730 2,830 2,757 2,800 2,900 80 80 80 150 150 150 Pologne Portugal 997 885 925 356 330 360 663 580 590 22 25 25 Portugal Serbia 894 922 946 899 920 940 15 20 28 20 18 22 Serbie Slovakia 1,670 1,700 1,750 1,570 1,650 1,700 500 450 450 400 400 400 Slovaquie Slovenia 281 290 280 497 630 600 31 30 30 247 370 350 Slovénie Spain 833 854 854 730 775 775 164 174 174 61 94 94 Espagne Sweden 217 217 217 180 180 180 37 37 37 0 0 0 Suède Switzerland 145 155 160 265 275 280 35 40 40 155 160 160 Suisse United Kingdom 78 77 77 56 56 56 26 26 26 5 5 5 Royaume-Uni Total Europe 21,959 21,622 22,002 22,052 21,910 22,036 3,910 3,253 3,265 4,003 3,541 3,299 Total Europe Canada 15,890 15,923 15,895 14,854 14,926 14,926 1,106 1,060 1,027 70 64 59 Canada United States 31,550 32,311 33,431 33,358 33,742 34,516 221 156 156 2,028 1,587 1,241 Etats-Unis Total North America 47,441 48,234 49,326 48,212 48,668 49,442 1,327 1,216 1,183 2,098 1,650 1,300 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

TABLE 11 HARDWOOD SAWLOGS (total) GRUMES DE SCIAGES DES NON-CONIFERES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption a

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 406 311 300 329 266 300 134 90 50 57 45 50 Autriche Czech Republic 544 457 447 616 524 517 144 120 125 216 186 195 République tchèque Estonia 1,187 1,244 1,244 1,158 1,200 1,200 46 60 60 16 16 16 Estonie Finland 1,068 1,041 1,061 1,037 1,049 1,061 32 1 9 1 9 9 Finlande France 3,412 3,978 4,078 4,707 4,700 4,800 72 75 75 1,367 797 797 France Germany 2,527 2,285 2,125 2,995 2,700 2,500 101 100 100 569 515 475 Allemagne Hungary 1,234 1,173 1,191 1,234 1,173 1,191 0 0 0 0 0 0 Hongrie Italy 2,068 1,729 1,729 721 721 721 1,389 1,047 1,047 42 39 39 Italie Latvia 1,221 1,190 1,410 1,730 1,750 1,750 87 40 60 596 600 400 Lettonie Luxembourg 226 148 144 23 22 18 221 160 160 18 34 34 Luxembourg Montenegro 143 140 138 143 140 138 0 0 0 0 0 0 Monténégro Netherlands 46 55 55 48 50 50 42 50 50 44 45 45 Pays-Bas Poland 2,685 2,727 2,827 2,757 2,800 2,900 78 77 77 150 150 150 Pologne Portugal 981 870 912 356 330 360 642 560 571 17 20 19 Portugal Serbia 893 921 945 899 920 940 14 19 27 20 18 22 Serbie Slovakia 1,670 1,700 1,750 1,570 1,650 1,700 500 450 450 400 400 400 Slovaquie Slovenia 280 290 280 497 630 600 30 30 30 247 370 350 Slovénie Spain 827 847 847 730 775 775 158 167 167 61 94 94 Espagne Sweden 217 217 217 180 180 180 37 37 37 0 0 0 Suède Switzerland 145 155 160 265 275 280 35 40 40 155 160 160 Suisse United Kingdom 76 75 75 56 56 56 24 24 24 5 5 5 Royaume-Uni Total Europe 21,857 21,553 21,935 22,052 21,910 22,036 3,786 3,146 3,158 3,980 3,503 3,260 Total Europe Canada 15,890 15,923 15,895 14,854 14,926 14,926 1,106 1,060 1,027 70 64 59 Canada United States 31,549 32,308 33,429 33,358 33,742 34,516 219 152 154 2,027 1,586 1,240 Etats-Unis Total North America 47,440 48,231 49,324 48,212 48,668 49,442 1,325 1,212 1,181 2,097 1,649 1,299 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

TABLE 11a HARDWOOD LOGS (temperate) GRUMES DE NON-CONIFERES (zone tempérée)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption a

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 France -41 -42 -42 44 45 45 3 3 3 France Germany -5 -5 -5 10 10 10 5 5 5 Allemagne Italy -20 11 11 25 9 9 4 20 20 Italie Netherlands -8 -5 -5 12 10 10 4 5 5 Pays-Bas Poland -2 -3 -3 2 3 3 0 0 0 Pologne Portugal -16 -15 -13 21 20 19 5 5 6 Portugal Serbia -1 -1 -1 1 1 1 0 0 0 Serbie Slovenia -1 0 0 1 0 1 0 0 0 Slovénie Spain -6 -7 -7 6 7 7 0 0 0 Espagne United Kingdom -2 -2 -2 2 2 2 0 0 0 Royaume-Uni Total Europe -102 -69 -67 124 107 106 22 38 39 Total Europe United States -1 -3 -1 2 4 2 1 1 1 Etats-Unis Total North America -1 -3 -1 2 4 2 1 1 1 Total Amérique du Nord

Country Commerce Net Production Imports - Importations Exports - Exportations Pays

TABLE 11b HARDWOOD LOGS (tropical) GRUMES DE NON-CONIFERES (tropicale)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Net Trade

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 13,844 12,627 12,592 11,047 9,212 9,287 3,676 4,070 4,020 879 655 715 Autriche Cyprus 8 9 10 7 8 9 1 1 1 0 0 0 Chypre Czech Republic 5,559 5,135 5,154 7,664 6,164 6,130 1,270 1,146 1,162 3,375 2,175 2,138 République tchèque Estonia 3,117 2,380 2,435 6,548 6,550 6,550 256 330 285 3,687 4,500 4,400 Estonie Finland 48,404 47,241 49,358 44,923 44,026 45,568 5,037 4,969 5,545 1,556 1,755 1,755 Finlande France 24,495 24,350 24,050 24,257 24,000 23,700 2,527 2,600 2,600 2,289 2,250 2,250 France Germany 26,555 26,580 23,090 27,936 27,100 23,500 4,474 3,870 3,770 5,855 4,390 4,180 Allemagne Hungary 2,122 2,017 2,065 2,049 1,984 2,023 112 73 82 39 39 39 Hongrie Italy 4,508 5,210 5,210 3,916 4,618 4,618 1,288 1,288 1,288 696 696 696 Italie Latvia 5,540 5,150 5,150 9,484 8,800 8,800 1,084 950 950 5,028 4,600 4,600 Lettonie Luxembourg 583 589 589 577 559 559 182 130 130 176 100 100 Luxembourg Malta 2 3 3 0 0 0 2 3 3 0 0 0 Malte Montenegro 245 241 227 245 241 227 0 0 0 0 0 0 Monténégro Netherlands 604 1,100 1,095 1,267 1,240 1,230 289 100 105 952 240 240 Pays-Bas Poland 35,250 36,265 37,135 33,531 34,600 35,450 3,652 3,660 3,710 1,933 1,995 2,025 Pologne Portugal 15,954 15,330 15,365 11,664 11,720 11,590 4,657 4,000 4,140 368 390 365 Portugal Serbia 981 1,007 1,045 967 1,000 1,033 15 8 13 1 1 1 Serbie Slovakia 3,634 3,650 3,760 3,821 3,860 3,950 1,023 1,030 1,050 1,210 1,240 1,240 Slovaquie Slovenia 926 770 790 2,058 2,280 2,230 625 490 530 1,757 2,000 1,970 Slovénie Spain 13,959 14,358 14,358 14,383 15,261 15,261 1,435 1,564 1,564 1,859 2,467 2,467 Espagne Sweden 55,632 54,193 54,727 50,015 48,196 48,730 7,036 7,750 7,750 1,419 1,753 1,753 Suède Switzerland 1,823 1,824 1,829 1,216 1,217 1,222 795 795 795 188 188 188 Suisse United Kingdom 4,590 4,471 4,471 4,293 4,175 4,175 406 405 405 109 109 109 Royaume-Uni Total Europe 268,336 264,500 264,508 261,870 256,811 255,841 39,843 39,232 39,898 33,377 31,543 31,231 Total Europe Canada 37,044 35,822 35,734 35,326 32,985 32,975 2,578 3,462 3,467 860 625 708 Canada United States 238,450 239,587 240,850 244,912 246,110 247,536 348 324 308 6,809 6,848 6,994 Etats-Unis Total North America 275,495 275,409 276,585 280,238 279,096 280,511 2,926 3,786 3,776 7,670 7,473 7,702 Total Amérique du Nord

Includes wood residues, chips and particles for all purposes Comprend les dechets de bois, plaquettes et particules pour toute utilisation a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

TABLE 12 PULPWOOD (total) BOIS DE TRITURATION (total)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption a

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 3,681 3,895 3,850 2,576 2,235 2,300 1,312 1,750 1,700 206 90 150 Autriche Czech Republic 3,927 3,744 3,675 5,316 4,253 4,125 811 811 830 2,200 1,320 1,280 République tchèque Estonia 476 245 245 878 900 900 56 45 45 458 700 700 Estonie Finland 22,913 24,189 25,835 22,746 23,764 25,239 1,163 1,410 1,581 996 985 985 Finlande France 4,689 4,400 4,100 4,559 4,300 4,000 608 550 550 478 450 450 France Germany 10,311 11,900 9,500 10,541 11,500 9,000 2,200 2,100 2,000 2,430 1,700 1,500 Allemagne Hungary 411 488 481 411 488 481 0 0 0 0 0 0 Hongrie Italy 148 853 853 148 853 853 0 0 0 0 0 0 Italie Latvia 1,775 1,700 1,700 1,850 1,800 1,800 374 400 400 449 500 500 Lettonie Luxembourg -16 -18 -16 10 6 8 9 3 3 35 27 27 Luxembourg Montenegro 201 198 186 201 198 186 0 0 0 0 0 0 Monténégro Netherlands 146 150 145 244 240 235 70 80 85 168 170 175 Pays-Bas Poland 15,378 15,900 16,300 15,411 15,950 16,250 1,428 1,500 1,650 1,462 1,550 1,600 Pologne Portugal 1,323 1,430 1,375 1,213 1,350 1,300 122 100 90 12 20 15 Portugal Serbia 66 70 74 66 70 73 0 0 1 0 0 0 Serbie Slovakia 598 600 610 748 710 700 600 630 650 750 740 740 Slovaquie Slovenia 264 200 220 275 430 380 268 170 200 278 400 360 Slovénie Spain 3,369 3,467 3,467 3,754 3,984 3,984 179 138 138 564 655 655 Espagne Sweden 28,513 27,431 27,944 26,353 25,310 25,823 3,114 3,269 3,269 954 1,148 1,148 Suède Switzerland 209 210 210 279 280 280 20 20 20 90 90 90 Suisse United Kingdom 1,894 1,776 1,776 1,633 1,516 1,516 291 291 291 31 31 31 Royaume-Uni Total Europe 100,275 102,827 102,530 99,212 100,136 99,433 12,625 13,267 13,503 11,562 10,576 10,406 Total Europe Canada 4,531 4,347 4,410 4,229 4,021 4,021 324 336 401 22 10 12 Canada United States 141,231 142,785 144,831 141,226 142,779 144,827 5 6 4 0 0 0 Etats-Unis Total North America 145,762 147,132 149,241 145,455 146,800 148,848 329 341 405 22 10 12 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

TABLE 12a PULPWOOD LOGS (ROUND AND SPLIT) BOIS DE TRITURATION (RONDINS ET QUARTIERS)

Softwood Conifères 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 1,217 997 1,007 647 577 587 668 500 500 98 80 80 Autriche Czech Republic 450 380 384 649 552 550 3 2 2 202 174 168 République tchèque Estonia 363 200 250 1,270 1,250 1,250 154 250 200 1,060 1,300 1,200 Estonie Finland 8,997 7,940 8,052 7,801 6,884 6,784 1,550 1,633 1,845 354 577 577 Finlande France 2,386 2,250 2,250 3,332 3,200 3,200 43 50 50 989 1,000 1,000 France Germany 1,116 1,180 1,090 1,103 1,100 1,000 259 270 270 246 190 180 Allemagne Hungary 502 507 526 502 507 526 0 0 0 0 0 0 Hongrie Italy 168 166 166 168 166 166 0 0 0 0 0 0 Italie Latvia 172 200 200 2,018 2,000 2,000 244 100 100 2,090 1,900 1,900 Lettonie Luxembourg 77 71 69 46 32 30 36 48 48 5 9 9 Luxembourg Netherlands 62 50 55 108 100 100 21 20 20 67 70 65 Pays-Bas Poland 4,424 4,635 4,785 3,939 4,150 4,300 560 560 560 75 75 75 Pologne Portugal 10,495 10,300 10,260 8,586 8,500 8,400 2,100 2,000 2,050 191 200 190 Portugal Serbia 199 205 210 199 205 210 0 0 0 0 0 0 Serbie Slovakia 1,874 1,950 2,000 1,924 2,000 2,050 100 100 100 150 150 150 Slovaquie Slovenia 137 120 130 424 490 450 84 80 90 371 450 410 Slovénie Spain 5,422 5,288 5,288 6,059 6,429 6,429 269 291 291 906 1,432 1,432 Espagne Sweden 8,517 8,412 8,533 6,232 5,986 6,107 2,313 2,481 2,481 28 55 55 Suède Switzerland 128 128 133 165 165 170 3 3 3 40 40 40 Suisse United Kingdom 23 22 22 13 13 13 18 18 18 9 9 9 Royaume-Uni Total Europe 46,729 45,001 45,410 45,185 44,305 44,322 8,426 8,406 8,628 6,881 7,711 7,540 Total Europe Canada 10,554 10,654 10,644 10,812 10,843 10,843 38 36 30 296 225 228 Canada United States 41,407 40,200 38,795 41,424 40,217 38,810 58 32 18 75 50 33 Etats-Unis Total North America 51,961 50,854 49,439 52,236 51,060 49,653 96 68 48 371 275 261 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

TABLE 12b PULPWOOD LOGS (ROUND AND SPLIT) BOIS DE TRITURATION (RONDINS ET QUARTIERS)

Hardwood Non-conifères 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 8,945 7,735 7,735 7,824 6,400 6,400 1,696 1,820 1,820 575 485 485 Autriche Cyprus 8 9 10 7 8 9 1 1 1 0 0 0 Chypre Czech Republic 1,182 1,011 1,094 1,699 1,359 1,454 456 333 330 973 681 690 République tchèque Estonia 2,278 1,935 1,940 4,400 4,400 4,400 47 35 40 2,169 2,500 2,500 Estonie Finland 16,494 15,112 15,471 14,376 13,378 13,545 2,324 1,926 2,119 206 193 193 Finlande France 17,420 17,700 17,700 16,366 16,500 16,500 1,876 2,000 2,000 822 800 800 France Germany 15,128 13,500 12,500 16,292 14,500 13,500 2,015 1,500 1,500 3,179 2,500 2,500 Allemagne Hungary 1,209 1,022 1,057 1,137 989 1,015 112 73 82 39 39 39 Hongrie Italy 4,192 4,192 4,192 3,600 3,600 3,600 1,288 1,288 1,288 696 696 696 Italie Latvia 3,593 3,250 3,250 5,616 5,000 5,000 466 450 450 2,489 2,200 2,200 Lettonie Luxembourg 522 536 536 521 521 521 137 79 79 136 64 64 Luxembourg Malta 2 3 3 0 0 0 2 3 3 0 0 0 Malte Montenegro 44 43 41 44 43 41 0 0 0 0 0 0 Monténégro Netherlands 396 900 895 915 900 895 198 0 0 717 0 0 Pays-Bas Poland 15,448 15,730 16,050 14,181 14,500 14,900 1,664 1,600 1,500 396 370 350 Pologne Portugal 4,136 3,600 3,730 1,865 1,870 1,890 2,435 1,900 2,000 165 170 160 Portugal Serbia 716 732 761 702 725 750 15 8 12 1 1 1 Serbie Slovakia 1,162 1,100 1,150 1,149 1,150 1,200 323 300 300 310 350 350 Slovaquie Slovenia 525 450 440 1,360 1,360 1,400 273 240 240 1,107 1,150 1,200 Slovénie Spain 5,169 5,603 5,603 4,570 4,849 4,849 987 1,135 1,135 388 380 380 Espagne Sweden 18,602 18,350 18,250 17,430 16,900 16,800 1,609 2,000 2,000 437 550 550 Suède Switzerland 1,486 1,486 1,486 772 772 772 772 772 772 58 58 58 Suisse United Kingdom 2,673 2,673 2,673 2,646 2,646 2,646 96 96 96 69 69 69 Royaume-Uni Total Europe 121,332 116,673 116,568 117,472 112,370 112,087 18,793 17,559 17,767 14,933 13,256 13,285 Total Europe Canada 21,959 20,821 20,680 20,285 18,121 18,111 2,216 3,090 3,037 542 390 467 Canada United States 55,812 56,602 57,224 62,262 63,114 63,899 285 286 286 6,734 6,798 6,961 Etats-Unis Total North America 77,771 77,423 77,904 82,547 81,235 82,010 2,500 3,376 3,323 7,277 7,188 7,428 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 12c WOOD RESIDUES, CHIPS AND PARTICLES DECHETS DE BOIS, PLAQUETTES ET PARTICULES Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 1,290 1,497 1,450 1,691 1,938 2,050 344 309 300 745 750 900 Autriche Cyprus 8 5 5 0 0 0 8 5 5 0 0 0 Chypre Czech Republic 234 215 225 540 459 482 38 38 40 344 282 296 République tchèque Estonia 284 300 230 1,650 1,350 1,300 12 50 30 1,378 1,100 1,100 Estonie Finland 530 541 562 360 380 405 188 163 160 18 2 3 Finlande France 2,735 3,260 3,660 2,050 2,250 2,450 775 1,100 1,300 90 90 90 France Germany 3,328 3,540 3,720 3,569 3,700 3,900 443 480 420 684 640 600 Allemagne Hungary 63 44 50 62 43 49 11 13 12 11 12 12 Hongrie Italy 2,359 2,359 2,359 450 450 450 1,916 1,916 1,916 7 7 7 Italie Latvia 621 750 750 1,980 2,000 2,000 326 350 350 1,685 1,600 1,600 Lettonie Luxembourg 61 72 72 63 63 63 17 11 11 19 2 2 Luxembourg Malta 1 1 1 0 0 0 1 1 1 0 0 0 Malte Montenegro 18 25 26 83 84 84 0 0 0 65 59 58 Monténégro Netherlands 5,354 5,354 5,354 268 268 268 5,551 5,551 5,551 465 465 465 Pays-Bas Poland 842 920 1,100 1,152 1,200 1,350 366 370 380 677 650 630 Pologne Portugal 228 225 220 747 740 735 4 5 5 523 520 520 Portugal Serbia 478 460 485 418 450 480 83 70 80 23 60 75 Serbie Slovakia 22 175 175 390 450 450 47 75 75 415 350 350 Slovaquie Slovenia 125 155 150 164 175 180 126 120 130 165 140 160 Slovénie Spain 867 907 907 1,007 1,007 1,007 65 46 46 206 146 146 Espagne Sweden 1,776 1,800 1,850 1,809 1,750 1,800 199 210 210 232 160 160 Suède Switzerland 410 415 420 330 335 340 80 80 80 0 0 0 Suisse United Kingdom 7,819 7,830 7,830 327 330 330 7,516 7,520 7,520 23 20 20 Royaume-Uni Total Europe 29,451 30,850 31,601 19,110 19,422 20,173 18,114 18,482 18,621 7,774 7,055 7,194 Total Europe Canada 368 420 179 3,830 3,830 3,830 31 52 56 3,493 3,462 3,707 Canada United States 761 273 152 9,544 9,744 9,948 194 174 155 8,977 9,644 9,951 Etats-Unis Total North America 1,129 694 331 13,374 13,574 13,778 225 226 211 12,470 13,106 13,659 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 13 WOOD PELLETS GRANULES DE BOIS

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 mt

Apparent Consumption

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 actual actual actual actual réels réels réels réels

Sawn softwood 75.92 69.01 68.49 96.71 89.54 88.44 29.69 25.67 25.93 50.49 46.20 45.88 Sciages conifères

Softwood logs a 182.85 171.26 171.53 185.47 172.88 171.84 18.22 16.63 16.39 20.84 18.25 16.70 Grumes de conifères a

Sawn hardwood 7.02 6.65 6.70 6.93 6.45 6.61 4.18 3.86 3.81 4.09 3.66 3.72 Sciages non-conifères

– temperate zone b 6.45 6.14 6.18 6.87 6.40 6.55 3.28 3.07 3.02 3.70 3.33 3.39 – zone tempérée b

– tropical zone b 0.57 0.51 0.52 0.06 0.05 0.06 0.90 0.79 0.79 0.39 0.32 0.32 – zone tropicale b

Hardwood logs a 21.96 21.62 22.00 22.05 21.91 22.04 3.91 3.25 3.26 4.00 3.54 3.30 Grumes de non-conifères a

– temperate zone b 21.86 21.55 21.93 22.05 21.91 22.04 3.79 3.15 3.16 3.98 3.50 3.26 – zone tempérée b

– tropical zone b 0.10 0.07 0.07 0.12 0.11 0.11 0.02 0.04 0.04 – zone tropicale b

Veneer sheets 1.58 1.49 1.49 1.00 0.97 0.96 1.42 1.28 1.29 0.84 0.76 0.76 Feuilles de placage

Plywood 6.62 6.21 5.92 4.17 3.93 3.97 6.42 5.79 5.48 3.96 3.50 3.53 Contreplaqués

Particle board (excluding OSB) 28.12 26.41 26.52 28.01 26.71 26.91 10.02 9.58 9.55 9.92 9.88 9.94 Pann. de particules (sauf OSB)

OSB 5.27 5.06 5.09 4.89 4.89 5.02 3.20 2.96 2.94 2.83 2.78 2.87 OSB

Fibreboard 15.80 14.89 15.09 16.15 15.31 15.42 8.76 8.01 8.04 9.11 8.43 8.37 Panneaux de fibres

– Hardboard 0.79 0.82 0.90 0.48 0.47 0.47 1.47 1.44 1.46 1.17 1.09 1.04 – Durs

– MDF 11.42 10.85 10.97 12.16 11.62 11.68 5.21 4.61 4.62 5.95 5.38 5.33 – MDF

– Other board 3.59 3.22 3.22 3.51 3.22 3.27 2.07 1.97 1.96 1.99 1.96 2.01 – Autres panneaux Pulpwood a 268.34 264.50 264.51 261.87 256.81 255.84 39.84 39.23 39.90 33.38 31.54 31.23 Bois de trituration a

– Pulp logs 147.00 147.83 147.94 144.40 144.44 143.75 21.05 21.67 22.13 18.44 18.29 17.95 – Bois ronds de trituration

– softwood 100.28 102.83 102.53 99.21 100.14 99.43 12.63 13.27 13.50 11.56 10.58 10.41 – conifères

– hardwood 46.73 45.00 45.41 45.18 44.31 44.32 8.43 8.41 8.63 6.88 7.71 7.54 – non-conifères

– Residues, chips and particles 121.33 116.67 116.57 117.47 112.37 112.09 18.79 17.56 17.77 14.93 13.26 13.29 – Déchets, plaquettes et part. Wood pulp 37.60 34.07 35.28 34.64 32.24 33.81 17.33 16.19 16.59 14.37 14.37 15.12 Pâte de bois

Paper and paperboard 72.76 66.14 69.44 83.10 73.88 79.49 43.20 39.62 41.48 53.55 47.36 51.53 Papiers et cartons

Wood Pellets 29.45 30.85 31.60 19.11 19.42 20.17 18.11 18.48 18.62 7.77 7.05 7.19 Granulés de bois a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fourni des données sur le commerce b Trade figures by zone do not equal the total as some countries cannot provide data for both zones b Les chiffres du commerce par zone ne correspondent pas aux totaux

en raison du fait que certains pays ne peuvent les différencier.

TABLE 14

Europe: Summary table of market forecasts for 2023 and 2024

Europe: Tableau récapitulatif des prévisions du marché pour 2023 et 2024 Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques) Apparent Consumption

Consommation Apparente Production Imports - Importations Exports - Exportations

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 actual actual actual actual réels réels réels réels

Sawn softwood 91.63 89.85 90.39 100.44 97.41 95.73 27.09 26.48 27.10 35.90 34.04 32.43 Sciages conifères

Softwood logs 253.91 254.00 257.31 262.84 262.90 265.12 1.93 1.97 1.86 10.86 10.87 9.68 Grumes de conifères

Sawn hardwood 15.85 16.16 16.46 18.50 18.72 19.03 1.59 1.63 1.56 4.23 4.19 4.13 Sciages non-conifères

– temperate zone 15.57 15.89 16.19 18.50 18.72 19.03 1.29 1.33 1.26 4.21 4.16 4.10 – zone tempérée

– tropical zone 0.29 0.27 0.27 0.00 0.00 0.00 0.31 0.30 0.30 0.02 0.03 0.03 – zone tropicale

Hardwood logs 47.44 48.23 49.33 48.21 48.67 49.44 1.33 1.22 1.18 2.10 1.65 1.30 Grumes de non-conifères

– temperate zone 47.44 48.23 49.32 48.21 48.67 49.44 1.32 1.21 1.18 2.10 1.65 1.30 – zone tempérée

– tropical zone 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 – zone tropicale Veneer sheets 2.85 2.93 2.97 2.87 2.89 2.91 0.86 0.88 0.89 0.88 0.83 0.84 Feuilles de placage

Plywood 16.92 16.92 17.31 10.86 10.90 11.05 7.48 7.37 7.68 1.43 1.36 1.42 Contreplaqués

Particle board (excluding OSB) 6.66 7.45 7.46 6.11 6.58 6.55 1.75 1.97 1.98 1.19 1.10 1.07 Pann. de particules (sauf OSB)

OSB 21.20 21.09 21.35 20.86 20.60 20.86 6.28 6.30 6.39 5.94 5.82 5.89 OSB

Fibreboard 9.92 9.93 10.07 7.64 7.71 7.87 4.18 3.92 3.92 1.90 1.69 1.72 Panneaux de fibres

– Hardboard 0.51 0.56 0.56 0.53 0.59 0.60 0.31 0.28 0.29 0.32 0.32 0.33 – Durs

– MDF 6.21 6.23 6.23 3.83 3.88 3.89 3.55 3.35 3.32 1.17 0.99 0.98 – MDF

– Other board 3.20 3.15 3.28 3.28 3.24 3.38 0.32 0.29 0.31 0.40 0.38 0.41 – Autres panneaux Pulpwood 275.49 275.41 276.58 280.24 279.10 280.51 2.93 3.79 3.78 7.67 7.47 7.70 Bois de trituration

– Pulp logs 197.72 197.99 198.68 197.69 197.86 198.50 0.43 0.41 0.45 0.39 0.28 0.27 – Bois ronds de trituration

– softwood 145.76 147.13 149.24 145.45 146.80 148.85 0.33 0.34 0.41 0.02 0.01 0.01 – conifères

– hardwood 51.96 50.85 49.44 52.24 51.06 49.65 0.10 0.07 0.05 0.37 0.27 0.26 – non-conifères

– Residues, chips and particles 77.77 77.42 77.90 82.55 81.23 82.01 2.50 3.38 3.32 7.28 7.19 7.43 – Déchets, plaquettes et part. Wood pulp 45.79 48.12 48.43 55.02 54.33 54.12 7.42 8.22 8.89 16.65 14.44 14.58 Pâte de bois

Paper and paperboard 69.75 68.96 69.26 75.05 73.60 73.63 10.72 10.42 10.39 16.02 15.06 14.77 Papiers et cartons

Wood pellets 1.13 0.69 0.33 13.37 13.57 13.78 0.23 0.23 0.21 12.47 13.11 13.66 Granulés de bois

TABLE 15

North America: Summary table of market forecasts for 2023 and 2024

Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2023 et 2024 Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques) Apparent Consumption

Consommation Apparente Production Imports - Importations Exports - Exportations

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

  • List of tables
  • Table1
  • Table2
  • Table 2a
  • Table 2b
  • Table 3
  • Table 4
  • Table 5
  • Table 5a
  • Table 6
  • Table 6a
  • Table 6b
  • Table 6c
  • Table 7
  • Table 8
  • Table 9
  • Table 9a
  • Table 9b
  • Table 10
  • Table 11
  • Table 11a
  • Table 11b
  • Table12
  • Table 12a
  • Table 12b
  • Table 12c
  • Table 13
  • Table 14
  • Table 15

Paper, Experimental CPI for lower and higher income households (U.S. Bureau of Labor Statistics)

This paper examines CPI indexes for subsets of the target population defined by the bottom and top of the income distribution and compares results with the target population. We use data from the Consumer Expenditure Surveys (CE) to construct biennial and monthly market basket shares for groups of respondents based on their reported income, in order to calculate CPIs using modified Laspeyres and Tornqvist formulas respectively. From 2003 to 2018, we find the Laspeyres index for the lowest income quartile population rose faster than the index for all urban consumers.

Languages and translations
English

BLS WORKING PAPERS U.S. Department of Labor U.S. Bureau of Labor Statistics Office of Prices and Living Conditions

Experimental CPI for lower and higher income households

Josh Klick Anya Stockburger U.S. Bureau of Labor Statistics

Working Paper 537 March 8, 2021

1

Experimental CPI for lower and higher income households1 Josh Klick, Anya Stockburger

Abstract This paper examines CPI indexes for subsets of the target population defined by the bottom and top of

the income distribution and compares results with the target population. We use data from the

Consumer Expenditure Surveys (CE) to construct biennial and monthly market basket shares for groups

of respondents based on their reported income, in order to calculate CPIs using modified Laspeyres and

Tornqvist formulas respectively. From 2003 to 2018, we find the Laspeyres index for the lowest income

quartile population rose faster than the index for all urban consumers. The Laspeyres index for the

highest income quartile population rose slower than the index for all urban consumers. Chained CPI

indexes for the income quartile populations rose slower than their Laspeyres counterparts. The measure

of consumer substitution was lowest for the lowest income quartile population; the difference between

the Laspeyres and Tornqvist index for the lowest income quartile population was less than half the

difference for all urban consumers.

Introduction The Consumer Price Index (CPI) measures the change in the cost of goods and services purchased by

consumers between two time periods. The target population for the headline CPI is the urban

population (CPI-U), however BLS also calculates estimates of price change for subsets of the target,

including those aged 62 years and older (R-CPI-E) and those earning most of their income from a select

list of wage-earning and clerical worker occupations (CPI-W). There is a lot of user interest in CPI indexes

for lower income households. This paper examines CPI indexes for subsets of the target population

defined by the bottom and top of the income distribution and compares results with the target

population. We use data from the Consumer Expenditure Surveys (CE) to construct biennial and monthly

market basket shares for groups of respondents based on their reported income, in order to calculate

CPIs using modified Laspeyres and Tornqvist formulas respectively.

Almost 25 years ago, BLS researchers Thesia Garner, David Johnson, and Mary Kokowski published

results for an experimental index for lower income households.2 The authors used CE Interview Survey

data from 1982-1984 and 1992-1994 to generate shares for lower income and lower expenditure

households to calculate Laspeyres, Paasche, and Fisher indexes from 1984 to 1994. They found little

difference in inflation between urban consumers and both lower income and lower expenditure

households. In order to register differences between urban consumers and any subset of the target

population, there must be significant differences in budget shares and price change differentials for

1 Many thanks to David Popko for his contributions to earlier versions of this research and to Chris Miller

and Greg Barbieri for their assistance compiling the data. We are also grateful to Robert Cage, Thesia

Garner, and Sara Stanley for their insightful comments that improved the paper.

2 https://www.bls.gov/opub/mlr/1996/09/art5full.pdf

2

those item categories. Without budget share differences, and relative price differences for those items,

any measure of price change will be the same across different population definitions.

In a 2002 BLS working paper, Rob Cage, Thesia Garner, and Javier Ruiz-Castillo constructed household

specific price indexes.3 Compared to the earlier Garner et al. results, they found greater differences in

inflation rates between urban consumers and the lower income population subset, perhaps because of

the inclusion of budget shares for categories, particularly food, collected in the CE Diary Survey. Leslie

McGranahan and Anna Paulson of the Federal Reserve Bank of Chicago conducted research over a

longer time period to study inflation for lower income consumers and found little long-term

differences.4 Similar to prior BLS studies, this research focused on changing weights to reflect different

consumption patterns across different population subsets.

More recently, several academic researchers have used scanner data linked with consumer information

to account for differences in consumer behavior at a much finer level than possible with BLS data. 5

When accounting for the heterogeneity across consumers at the lowest levels, these studies generally

find lower inflation rates for lower income consumers in the 1990s and early 2000s, and then higher

inflation rates for more recent time periods.

In this paper we review the background and issues with calculating CPIs for population subsets, define

two income-based populations (lowest and highest quartiles), and describe differences in their

demographic characteristics. In the results section, we present (i) a comparison of expenditure share

differences across the income-based populations, (ii) index results for both Laspeyres and Tornqvist

formulas, and (iii) a comparison of upper level substitution bias. We conclude with final observations

and remarks.

Background and Issues This section begins with a brief explanation of the methods to construct the CPI for the target

population, all-urban consumers. This foundation is helpful to explain how this methodology has been

adapted to construct CPIs for subsets of the target population, and the various drawbacks due to those

adaptations.

BLS selects cities to represent geographic strata (index areas) and sample units (goods or services) to

represent consumption item strata. With market basket revisions, BLS may change the number of strata

over time. As of January 2018, there were 32 index areas and 243 item strata. The product of these

strata create 7,776 elementary index cells for which prices are collected and then aggregated in two

stages.

At the first stage, changes in price are averaged across sampled units in each elementary index cell using

either a geometric mean or modified Laspeyres formula.6 The elementary index cells form the building

3 https://www.bls.gov/osmr/research-papers/2002/pdf/ec020030.pdf 4 https://www.chicagofed.org/publications/working-papers/2005/2005-20 5 Many examples include Broda and Romalis (2009), Broda, Leibtag, and Weinstein (2009), Agente and Lee (2017), Jaravel (2017), and Kaplan and Schulhofer-Wohl (2017). 6 The formula choice at the first stage of aggregation is based on the level of consumer substitution for that item category. Most goods and services use the geometric mean formula because consumers are generally able to substitute away from any particular item whose price is rising relative to others. Rent and Owner’s Equivalent Rent

3

blocks for the second stage of estimation. The same calculated elementary index cells are used as

building blocks to calculate the target (CPI-U) and subset (CPI-W and R-CPI-E) population indexes, as well

as the chained CPI (C-CPI-U) which uses a different aggregation formula and weights. The building blocks

for the target population are used as proxies for other populations. Building blocks are not produced

independently for the consumption patterns of the population subset of interest.

At the second stage, BLS uses market basket shares to combine price changes across elementary index

cells to calculate measures of aggregate price change. Market basket shares are calculated using data

collected by the Census Bureau on behalf of BLS in the CE Diary and Interview surveys. Several

adjustments are needed to modify CE data for CPI definitions of consumption, the most important of

which is an adjustment for expenditures on owned homes to estimate a consumption value.

Expenditures on the shelter component of the CPI include rent paid by renters and an estimate of the

rent homeowners would pay to live in their home (Owners’ Equivalent Rent). 7 BLS calculates market

basket shares independently for each population, and these shares are the only index construction

difference between populations. BLS uses the modified Laspeyres aggregation to calculate CPI-U indexes

(as well as the CPI-W and R-CPI-E indexes described below). It computes an arithmetic average price

change weighted by base period quantities. BLS uses the Tornqvist index formula to calculate the final

version of the C-CPI-U. It is a geometric average of component price changes weighted using the average

budget share for the previous and current month.

BLS has a long history of calculating indexes for subsets of the target population. The CPI-W is the oldest

measure of consumer inflation calculated by BLS.8 In the 1978 revision of the CPI, the urban population

was introduced as the target. The wage-earner and clerical worker population is a subset of the urban

population, where only CE respondents who work full-time and earn most of their income from a select

list of occupations are eligible for inclusion. Hence, when the CPI-U was introduced as the target

population the wage earner population became a subset of the target population. BLS produces another

index for a subset of the target population to measure price change for older consumers (R-CPI-E). This

series began in 1988 at the request of Congress and is published on a research basis. The reasons why

the R-CPI-E is published as a research index are listed in Table 1. These same caveats also apply to the

CPI-W, which was not reclassified as an experimental index when the CPI-U was introduced, or any other

population subset index calculated using the same methodology.

are calculated using a Laspeyres formula because consumers cannot easily move in response to changes in rent. There are a few other categories that use a Laspeyres formula due to the limited ability to substitute (such as prescription drugs). 7 For more information on the calculation of price change for rent and owners’ equivalent rent, see the factsheet https://www.bls.gov/cpi/factsheets/owners-equivalent-rent-and-rent.pdf 8 The First 100 Years of the Consumer Price Index: a methodological and political history. Darren Rippy. Monthly Labor Review. April 2014. https://www.bls.gov/opub/mlr/2014/article/the-first-hundred-years-of-the-consumer- price-index.htm

4

Table 1: Primary caveats with BLS approach to calculating indexes for subsets of the target population

Experimental weights: the CE sample is designed to produce reliable weights for the population living in urban areas. The smaller sample of CE respondents used to calculate weights for subsets of the target population are expected to have higher sampling error compared to the full sample of respondents used to calculate urban population weights. The CE sample is also designed to produce expenditure weights for Laspeyres indexes that pool data over 24 months. Tornqvist indexes require spending estimates every month and data limitations constrain the ability to construct reliable monthly weights for demographic subsets of the CE sample.

Areas and outlets priced: the sample of cities is designed to represent the population living in urban areas. Within cities, the sample of retail establishments and rental units are designed to represent the total population. To the extent that subsets of the target population live in different cities (or in different parts of cities) and shop at different stores, the urban samples may not be representative.

Items priced: for goods and services sold in a retail establishment, the unique items selected for pricing are based on sales data within the store. If a subset of the target population purchases different items than the general population, then the items selected for pricing may not be representative.

Rental units priced: the realized sample of rental units may have rent-determining characteristics that are not representative of a subset of the target population.9

Prices collected: there is only one set of prices collected. Any discount given to particular groups (such as senior-citizens or veterans) are used in the CPI only in proportion to their use by the urban population as a whole. This could understate the prevalence of this type of discount in an index specifically designed for a population subset.

BLS is researching improvements to methodology to measure price change for a subset of the target

population, drawing on the recent work in the international statistical community and academia. 10 In

particular, BLS is investigating a different treatment of owner occupied housing. While the concept of

owner’s equivalent rent is an appropriate conceptual approach for aggregate economic measurement, it

does not reflect price change experienced by individuals or households which is most useful for

escalation purposes. For homeowners with a mortgage, the imputed rent is used in place of mortgage

payments or other out-of-pocket expenses associated with owning a home. For populations with a large

9 Research by BLS in 2019 and 2020 have shown there is a statistically significant different in rent changes by type of structure of the housing unit, for example whether it is a single family home or an apartment building. Beyond geographic differences, there could be rental unit characteristics that should be controlled to produce unbiased estimates of price change for subpopulations. 10 The United Kingdom’s Office on National Statistics in particular has made several improvements in the calculation of subpopulation indexes that BLS is investigating, including democratic aggregation and a payments approach to expenditures on owner occupied housing. Academic research on consumer heterogeneity, such as work by Greg Kaplan and Xavier Jaravel, also provide valuable insights into potential biases in subpopulation indexes. A summary of this work is outside the scope of this paper.

5

share of home owners with no mortgage (such as the E population), this process imputes a larger

expense of owning a home than out-of-pocket spending.

BLS has been limited in its ability to assess the impact of the drawbacks listed in Table 1 on any

particular population subset. In particular, BLS does not have the data needed to research issues related

to the first stage of aggregation. Each of these caveats are unique and must be studied separately for

each population of interest. For example, using a single set of prices collected might be the most

important issue for the older subset, while the areas and outlets priced might be the most important

issue for the lower income subset. BLS has produced population subset indexes with these caveats for

many years, and there is growing interest in assessing the impact and potentially addressing these

drawbacks.

Methodology Price index number formula We calculate Laspeyres and Tornqvist indexes following BLS methodology as described in Formulas 1

and 2, respectively.11 The modified Laspeyres formula is a weighted arithmetic average of constituent

elementary index cell price changes. The weights as described in Formula 1 as aggregation weights can

be roughly interpreted as quantities corresponding to a 24 month reference period of consumer

expenditures. For example, monthly indexes calculated from January 2018 to December 2019 use

aggregation weights constructed from consumer spending in 2015 and 2016.

Formula 1: Modified Laspeyres Formula

𝐼𝑋𝑡 [𝐼,𝐴]

= 𝐼𝑋𝑡−1 [𝐼,𝐴]

∗ ∑ 𝐴𝑊

𝑏 [𝑖,𝑎] 𝐼𝑋𝑡

[𝑖,𝑎] [𝑖,𝑎]∈[𝐼,𝐴]

∑ 𝐴𝑊𝑏 [𝑖,𝑎] 𝐼𝑋𝑡−1

[𝑖,𝑎] [𝑖,𝑎]∈[𝐼,𝐴]

Where:

𝐼𝑋[𝐼,𝐴] is the All-Items, All-US aggregate index

𝐼𝑋[𝑖,𝑎]are the elementary index cells

t and t-1 are the current and previous months

𝐴𝑊𝑏 𝑖,𝑎 are the aggregation weights for elementary index cells, [i,a], based on a biennial

reference period, b

The Tornqvist index differs in both aggregation method and weights. The formula is a geometric average

of price change weighted by average budget shares from the current and previous month.

11 CPI Handbook of Methods, index calculation section. https://www.bls.gov/opub/hom/cpi/calculation.htm#index-calculation

6

Formula 2: Tornqvist formula

𝐼𝑋𝑡 [𝐼,𝐴] = 𝐼𝑋𝑡−1

[𝐼,𝐴] ∗ ∏ ( 𝐼𝑋𝑡

[𝑖,𝑎]

𝐼𝑋𝑡−1 [𝑖,𝑎]

)

𝑠𝑡 [𝑖,𝑎]

+𝑠𝑡−1 [𝑖,𝑎]

2

[𝑖,𝑎]∈[𝐼,𝐴]

Where 𝐼𝑋[𝐼,𝐴], 𝐼𝑋[𝑖,𝑎], t, and t-1 are defined as in Formula 1 and 𝑠[𝑖,𝑎] are the monthly expenditure

shares for the elementary index cells.

As noted in the background section, the CE sample is designed to produce expenditure estimates pooled

over a 24 month biennial reference period, b. To calculate monthly spending estimates used in the

Tornqvist index calculation (𝑠[𝑖,𝑎]), BLS uses a ratio allocation approach to allocate national spending on

an item category to index areas in order to minimize the number of elementary index cells with missing

expenditure data. Where cells are still missing after this procedure, annual expenditures are set to $0.01

(or monthly expenditures of 1/12th of a penny) to synthesize with the CPI-U procedure.12 The sparsity of

data is the primary reason Tornqvist indexes for W and E populations are currently not produced.

The different aggregation method and weights in the Laspeyres and Tornqvist formulas result in

different measures of price change. The resulting difference in inflation rates can be referred to as a

measure of consumer substitution bias. This bias in the CPI-U index is one of many summarized in

various reviews of CPI methodology.13 In short, consumers tend to respond to price changes by

substituting away from (or towards) items whose prices are rising (or falling) faster than average. Since

the modified Laspeyres formula holds quantities fixed for two years (as captured by 𝐴𝑊𝑏 𝑖,𝑎 in Formula

1), that index tends to overstate a true cost of living index when consumers exhibit substitution

behavior. A Tornqvist index uses an average budget share from the current and previous time period,

and reflects consumer substitution in response to relative price change. Tornqvist indexes (and other

indexes that use both current and previous period weights) are closer approximations of a cost of living

index than Laspeyres indexes (and other indexes with fixed weights). The generally upward bias in a

Laspeyres index is called substitution bias, and at the upper level is measured by the difference in

Tornqvist and Laspeyres indexes.14

Data and definitions In this study we use CE data from both the Diary and Interview surveys. Although expenditures for some

items are collected in both surveys, the CPI program selects one survey as the source for a particular

reference year. We use the same survey source as was used in the production calculation of weights for

the CPI-U index.

The time period of study is 2004 to 2018. BLS added an income imputation in 2004 that makes results

prior to that time period not comparable. Also, as of this research, BLS had published Tornqvist indexes

through July 2019, so we selected December 2018 as a terminus. The base period quantities used in the

12 The CPI Handbook of Methods, Final C-CPI-U calculation section https://www.bls.gov/opub/hom/cpi/calculation.htm#final-c-cpi-u 13 The Boskin Commission, CNSTAT At What Price, Moulton’s NBER paper are a few references. 14 Since there are two stages of index calculation, there are also two stages where consumer substitution bias can overstate inflation in a Laspeyres index. This measure of substitution bias is at the second stage, or upper level substitution bias.

7

modified Laspeyres formula are constructed using two years of CE data and updated in January of even

years. For example, data from 2001 and 2002 are compiled to create aggregation weights used in index

calculation from January 2004 through December 2005. We calculate Laspeyres indexes from December

2003 (2001/2002 weights) through December 2018 (2015/2016 weights). In order to preserve the same

base period, we calculate Tornqvist indexes starting in December 2003, and ending in December 2018.15

We made several adjustments to the data to account for minor CPI item structure changes over this

longitudinal time period.

In this paper we define lowest and highest income populations by income quartiles. There are many

other possible definitions of low and high income. We focused on a simple definition that ensures a

quarter of CE respondents nationally are classified in the populations of interest. In order to define the

income quartiles, we pooled respondents from each survey (Diary and Interview) by reference year,

then ranked by income, and then divided into income quartiles. Our definition of income is total before-

tax income, after imputation. We did not exclude households with incomes equal or below zero, but

that is a definitional change that could be considered in future research. The populations we present in

this report are the lowest income quartile and the highest income quartile.

Table 2 shows a comparison between these lowest and highest income quartile populations as well as

the urban, wage earners, and elderly populations. The median annual income of urban CE respondents

over this time period is $48,816. The wage earner and elderly subset of the urban population have

slightly lower median annual incomes. By tautology, there are larger differences in annual income when

grouping CE respondents by that variable. Looking at the quarter of CE respondents with the lowest and

highest income reported, the median annual income was $13,500 and $122,800 respectively.

Table 2: Annual income for Population Cohorts: 2004-2018

Variable Urban Wage

earner

Elderly Lowest

Income

Quartile

Highest

Income

Quartile

Mean Annual Income $67,109 $55,802 $51,156 $12,705 $155,045

Median Annual Income $47,920 $46,099 $33,313 $13,570 $124,362

Source: CE integrated data from 2004-2018, population weighted to represent consumer units in the

U.S.

We define the income bounds for the lowest and highest income quartile groupings in this paper based

on CE data. Alternatively, one could define the income thresholds using an external source of

information, for example to reflect a different benchmark income distribution of the population.

According to an analysis conducted in 2019 to study nonresponse bias in the CE, the population earning

less than $50,000 a year was over-represented by five to 20 percent when compared with the American

15 Tornqvist indexes were also calculated from December 1999 through December 2001, but are not presented here for ease of explication. The results in these two early years are similar to the rest of the time period presented in this paper.

8

Community Survey (ACS).16 The lowest and highest income quartiles in this paper might reflect lower

incomes than corresponding income distribution levels as measured by the ACS, even after adjusting for

different definitions of income. For example, CE respondents reporting an annual income in 2016 of less

than $25,000 were included in the lowest income quartile in this paper. Using ACS data, the lowest

quartile income cutoff is around $28,000. Similarly, based on the creation of the income quartile

variable, CE respondents reporting an annual income greater than about $93,000 were include in the

highest income quartile, compared to an ACS cutoff of around $110,000. This research could be

repeated for other definitions of income.

A comparison of other demographic information across populations is also helpful context to explain

differences in market basket shares. As shown in Table 3, relative to higher income respondents, lower

income respondents have lower rates of home ownership and educational attainment and lower rates

of labor force participation. Other demographic comparisons reveal the overlap in respondents included

in the elderly and the lowest income populations. The lowest income population is by definition 25

percent of the urban population. The elderly population is around 30 percent of the urban population,

36 percent of the lowest income population, and 16 percent of the highest income population. This is

likely the driving factor behind why, relative to higher income respondents, lower income respondents

are older, more likely to be retired, and have higher rates of home ownership without a mortgage.

Household size differences across populations are important to note and likely play an important role in

the median income differences in Table 2. Income was not adjusted for household size and future

research should control for household size to improve comparability across populations.17

16 A Nonresponse Bias Study of the Consumer Expenditure Survey for the Ten-Year Period 2007-2016; Krieger et al. https://www.reginfo.gov/public/do/DownloadDocument?objectID=101978401 17 There is a long literature using equivalence scales to adjust household income to account for different characteristics across households. Angela Daley, Thesia Garner, Shelley Phipps, Eva Sierminska, “Differences Across Place and Time in Household Expenditure Patterns: Implications for the Estimation of Equivalence Scales,” BLS Working Paper, 2020 https://www.bls.gov/osmr/research-papers/2020/pdf/ec200010.pdf

9

Table 3: Demographic Comparisons between Population Cohorts

Variable Urban Wage

earner

Elderly Lowest

income

quartile

Highest

income

quartile

Home Ownership

Percent Owner (incl. unknown mortgage status) 64.3% 56.0% 79.0% 41.3% 87.4%

Percent Owner with Mortgage 39.8% 39.8% 26.7% 13.0% 69.5%

Percent Owner no Mortgage 23.1% 14.4% 50.4% 26.6% 17.3%

Age and Household Size

Median Age of Householder 49 43 70 55 48

Mean Household Size 2.5 2.9 1.8 1.8 3.1

Education Level

Percent High School Diploma or Above 87.4% 83.5% 82.9% 75.6% 97.1%

Percent Associate's Degree or Above 42.0% 26.8% 35.5% 21.5% 67.8%

Employment Status

Percent Not Working/Any Reason 31.4% 12.8% 69.7% 58.0% 12.1%

Person Not Working Disabled or Taking Care

of Family 10.7% 8.5% 7.6% 20.2% 5.8%

Percent Not Working/Retired 19.0% 3.4% 61.9% 33.5% 5.8%

Source: CE integrated data from 2004-2018, population weighted to represent consumer units in the

U.S.

Results Using these examples, we constructed expenditure weights for lower and higher income populations

and used them as input to calculate Laspeyres and Tornqvist indexes. First we present a comparison of

expenditure weights for the population definitions, and then index results.

Expenditure Weights Recall a caveat to the method BLS uses to calculate indexes for subsets of the target is the potential for

increased sampling error of expenditure weights. This caution is particularly relevant for populations

defined by income. As we show in Table 4, biennial expenditure weights calculated for the 7,776

elementary index cells are rarely missing for the urban population (3 percent of the time during the

study period). The rate of missing cells is higher for subsets of the target population, and the highest for

the lowest income quartile population. The item structure is defined for the urban population, and some

10

item categories might be less relevant for a subset of the target population. For example there are

missing expenditures for the item category Sports vehicles (which includes bicycles, boats, and

snowmobiles) in 39 percent of the areas for the urban population and 86 percent of the areas for the

lowest income population. Very low (or no) expenditures might be an appropriate proxy for spending by

some populations on certain item categories. Nonetheless, the high rate of missing cells is a concerning

quality metric that should be further studied.

Table 4: Rate at which expenditure data are missing for elementary index cells (average 2004-2018)

Type of weights Urban Wage

earner

Elderly Lowest

Income

(Q1)

Highest

Income

(Q4)

Biennial expenditure weight 3% 9% 11% 17% 6%

Monthly expenditure weight 19% 44% 45% 55% 36%

Source: CPI expenditure weights from 2004 to 2018

As we stretch CE data further to calculate monthly expenditure weights for the Tornqvist index, the

number of elementary index cells with missing expenditure data increases substantially. BLS publishes a

Tornqvist index for the urban population, with a missing rate of 19%. In the past, the high rate of missing

data for subsets of the target population monthly expenditures has been a primary reason Tornqvist

indexes for the W and E populations have not been explored. This same caveat applies to populations

defined by income. Indeed, on average over half of the elementary index cells for monthly expenditure

weights are missing expenditure data for the lowest income quartile. Here, the imputation techniques

used for the urban population are applied to population subsets to enable calculation of Tornqvist

indexes. Imputation of missing expenditure data is another area that could be improved upon in future

research.

After imputing missing expenditures to fully populate the elementary index cells, there are several

notable differences in market basket shares between populations as displayed in Table 5. The eight

major group categories are presented, along with some notable subcategories. Although the market

basket shares vary over the time period of study, the comparison of 2015-2016 differences is illustrative

of general differences. Note these shares are not identical to CPI relative importances published on the

BLS website, which are inflation adjusted to reflect snapshots of weights used in CPI index calculation.

Food: Although spending shares on food in total by the lowest income quartile population is

similar to all households, more of their budget is spent on food at home rather than food away

from home (such as restaurants) and alcoholic beverages.

Housing: The share of spending (or consumption in the case of owners) on shelter (rent and

owner’s equivalent rent) is highest for the lowest income quartile population. Although there

are roughly twice as many renters in the lowest quartile compared to the highest quartile, their

budget share allocated to rent is more than four times as high18. Recall from the background

18 Recent research by BLS and Census Bureau linking CE data with rent subsidy information collected by the Department of Housing and Urban Development could have interesting implications for the budget share of rent

11

section that owner’s equivalent rent is an imputed cost of the shelter services provided by

owned homes. The imputed budget share for owner’s equivalent rent is the lowest for the

lowest income quartile population, but more than proportional to their smaller share of

homeowners. The lowest income quartile population also spends more on household utilities

than the other populations in Table 5 and less on household furnishings and lodging away from

home (including hotels and motels).

Recreation: The lowest income quartile population spends more of their budget share on

televisions than any other population presented in Table 5. With that one exception, spending

shares on all other recreation categories were lowest for the lowest income quartile than any

other population.

Education and communication: Spending shares on these item categories are very similar

between the urban population and the lowest income quartile population. The highest income

quartile population spends more of their budget share on education than the other populations

listed and the spending share of the older population is the lowest.

Apparel: Spending shares on jewelry and watches had the largest dispersion across the income

distribution. Spending shares on other apparel categories were fairly similar across the income

distribution and lowest for the older population.

Medical care: The older population spends the highest budget shares on all medical care

categories. The lowest income quartile population spends the least share on physician’s services

and health insurance.19 The impact of programs such as Medicaid and Medicare on the budget

shares for different populations is an interesting area for future research.

Transportation: Spending shares on transportation goods and services are the lowest for the

lowest income quartile population mostly due to differences in expenditures on vehicles and

vehicle maintenance and public transportation which includes all forms of non-private

transportation (such as fares for air, bus, train, ship, taxis, and ride sharing).

Other goods and services: Overall spending shares on other goods and services are highest for

the lowest income quartile population. This is due to larger spending shares on cigarettes and

for the lower income population. Future research should explore this particular impact of CE data quality on the calculation of weights specifically for a lower income population. Garret Christensen, Laura Erhard, Thesia Garner, Brett McBride, Nikolas Pharris-Ciurej, John Voorheis, “The promises and challenges of l inked rent data from the Consumer Expenditure Survey and Housing and Urban Development,” paper presented at the Joint Statistical Meetings Annual Conference 2019, Denver, Colorado, July 27–August 1, 2019 (U.S. Census Bureau, 2019). See https://www.census.gov/newsroom/press- kits/2019/jsm.html for conference proceedings, including links to all of the papers presented at the conference. 19 BLS uses an indirect method to measure the price change for health insurance. CE respondents report out-of- pocket spending on health insurance which is mostly allocated to the health care services that are covered by health insurance. The remainder is included in a health insurance retained earnings category which also includes the costs incurred by insurance companies to process claims. Since the factors used to allocate health insurance spending are fixed across populations, the lower overall budget shares of the lowest income quartile population on health insurance retained earnings can be accurately described as lower shares on out-of-pocket health insurance.

12

miscellaneous personal services (a category that includes legal, funeral, laundry, and banking

services).

Table 5: Distribution of total CPI market basket expenditures, snapshot of 2015-201620

Item Category All urban households (U)

62 years or older (E)

Lowest income quartile

Highest income quartile

Food, total 14.6% 12.4% 15.6% 14.2% Food at home 7.7% 7.1% 9.5% 6.7%

Food away from home 5.9% 4.5% 5.4% 6.3% Alcoholic beverages 1.0% 0.8% 0.7% 1.2%

Housing, total 41.0% 45.8% 45.2% 39.5% Shelter 30.5% 34.4% 34.6% 28.8%

Rent 7.5% 4.7% 15.6% 3.4%

Owner’s equivalent rent 23.0% 29.7% 19.1% 25.5% Household utilities 4.7% 5.0% 5.9% 3.8%

House furnishings and other household services 4.5% 4.8% 3.7% 5.1%

Lodging away from home 1.0% 1.0% 0.6% 1.4% Recreation 5.9% 5.7% 4.6% 6.7%

Education and communication, total 7.1% 4.4% 6.8% 8.0% Education 3.0% 0.8% 2.7% 4.4%

Communication 4.1% 3.7% 4.1% 3.6%

Apparel 3.2% 2.1% 2.9% 3.5% Medical care 8.5% 12.0% 8.2% 7.9%

Health insurance 1.0% 1.2% 0.8% 1.1% Professional services 3.3% 4.6% 3.0% 3.2%

Transportation, total 16.6% 14.7% 13.0% 17.2% Motor Fuel 3.7% 2.9% 3.4% 3.3%

Vehicles and vehicle maintenance 9.0% 8.0% 6.0% 9.7%

Motor vehicle insurance 2.1% 2.1% 2.4% 1.9% Public transportation 1.3% 1.2% 0.9% 1.7%

Other goods and services 3.2% 3.0% 3.6% 3.0%

Source: CE integrated data with CPI division adjustments based on CE data from 2015 – 2016.

Price Indexes We calculated Laspeyres indexes using biennial budget shares (expenditure weights), like the 2015-2016

shares described in the previous section. Indexes for the lowest and highest income quartile populations

are shown in graph 1, along with the indexes for the CPI-W and R-CPI-E for comparison purposes. We

show index results at the all items and major group levels in Table 6. The annualized percent change

over the time period of study (December 2001 to December 2018) is defined in Formula 3.

20 Due to rounding, the figures presented may not add to exactly 100%. The component items displayed are not exhaustive so the sum of their market basket shares may not equal the major group.

13

At the all items level, the annualized change in the lowest income quartile index is larger than that for

the urban population (and R-CPI-E index) and the annualized percent change for the highest income

quartile index is lower than that for the urban population. The annualized percent change in the lowest

income quartile index is greater than the urban population index for the education and communication,

other goods and services, housing, recreation, and transportation major groups. The annualized percent

change in the highest income quartile index is less than the urban population index for the other goods

and services, housing, recreation, and transportation major groups. As a reminder, these indexes differ

only in the market basket shares at the elementary index level.

Between 2002 and 2018, the 12-month change in the lowest income quartile index is consistently

greater than the urban population index, in 152 out of 169 months. The remaining 17 months occur in

2006, 2009, 2010, and 2011. Further study is needed to understand the cause of these months that are

different than the rest. Similarly, the 12-month change in the highest income quartile index is

consistently less than the urban population index. Future research should include variance estimation so

confidence intervals can be calculated to statistically compare these index results.

Formula 3: Annualized percent change

𝐴𝑛𝑛𝑢𝑎𝑙𝑖𝑧𝑒𝑑 𝑝𝑒𝑟𝑐𝑒𝑛𝑡 𝑐ℎ𝑎𝑛𝑔𝑒 = ( 𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 𝐼𝑛𝑑𝑒𝑥 𝑉𝑎𝑙𝑢𝑒

100 )

12 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑛𝑡ℎ𝑠⁄

14

Graph 1: Monthly Laspeyres indexes for lowest and highest income quartiles: December 2003 to

December 2018

Table 6: Laspeyres index annualized percent changes from December 2003 to December 2018

Item Category All urban households

(U)

62 years or older (E)

Wage earner (W)

Lowest income quartile

Highest income quartile

All items 2.07 2.17 2.06 2.25 1.97 Apparel 0.14 0.05 0.10 -0.09 0.23 Education and communication 1.39 0.69 0.86 1.84 1.77

Food and beverages 2.19 2.14 2.18 2.13 2.23 Other goods and services 2.65 2.52 3.07 3.03 2.25

Housing 2.31 2.32 2.36 2.45 2.17 Medical care 3.21 3.08 3.29 3.11 3.29 Recreation 0.70 1.17 0.54 0.92 0.63

Transportation 1.85 1.92 1.93 2.11 1.68

15

Indexes for subset populations differ from the urban population when there are meaningful differences

in budget shares and price change. The scatterplot in graph 2 displays the relationship between long

term price change (the percent change in indexes from December 2003 to December 2018) and the

difference in market basket shares (the ratio of 2015-2016 biennial shares for the lower income and

urban populations) for expenditures classes at the national level.21 The bolded x-axis shows the percent

change of the All Items, US City Average index over this time period (41.6%). They bolded y-axis shows

budget share ratios equal to one, where observations greater than one reflect greater spending shares

for the lowest income quartile compared to the urban population.

Graph 2: Relationship between price change and budget share differences for the lowest income

quartile and urban population

Source: Consumer expenditure survey data from 2015-2016, Consumer Price Index data from December

2001 and December 2018

The population in the lowest income quartile spent more than the urban population on rent and energy

services (which includes electricity), as a share of total spending, and the indexes for both of those items

rose faster than average over this time period (upper right quadrant of the graph). Conversely, the lower

income population had lower budget shares for items whose indexes rose slower than average (or fell)

such as private transportation, which includes new and used vehicles (lower left quadrant of the graph).

21 The 243 item strata that form the building blocks of CPI estimation are grouped into 70 expenditure classes.

Alcoholic beverages away from home

Rent of primary residence

Owners' equivalent rent of residences

Energy services

Appliances

Medicinal drugs

Private transportation

-100%

-50%

0%

50%

100%

150%

200%

0 0.5 1 1.5 2 2.5

20 15

/2 01

6 b

u d

ge t s

h ar

e r

at io

Lo w

e st

in co

m e

q u

ar ti

le /

u rb

an p

o p

u la

ti o

n

Price change

2001-2018

16

These indexes likely contributed to a larger measure of price change for the lowest income quartile for

the housing and transportation major groups as well as the all items level.

Item categories in the upper left or lower right quadrants of the graph likely contributed to a smaller

measure of price change for the lowest income quartile. For example, the lowest income quartile

population spent less on owner’s equivalent rent and alcohol away from home whose prices rose faster

than average over the time period (upper left quadrant) and spent more on medicinal drugs and

appliances whose prices fell faster than the average over the time period (lower right quadrant). The

distribution of item categories across the four quadrants does not reveal a clear pattern between price

change and budget share differences.

The general patterns of price change between populations using a Laspeyres index also hold true for the

Tornqvist indexes. The lowest income quartile index displays the highest rate of inflation, and the

highest income quartile displays the lowest. Tornqvist indexes for the E and W populations were also

calculated, however note these are research indexes as opposed to the Laspeyres indexes for the E and

W populations which are produced by the BLS production systems. Graph 3 shows the Tornqvist indexes

and Table 7 shows the annualized percent changes at the all items level.22

22 Results at the major group level were presented for Laspeyres indexes to explain the differences at the all items level. This is possible because the Laspeyres index formula is consistent in aggregation, meaning the weighted sum of the major group level is equal to a direct calculation of the all items level. The Tornqvist index formula is not consistent in aggregation, therefore a presentation of major group level indexes would not necessarily explain differences at the all items level.

17

Graph 3: Monthly Tornqvist indexes for lowest and highest income quartiles: December 2003 to

December 2018

Table 7: Tornqvist index annualized percent changes: December 2003 – December 2018

All urban households

(U)

62 years or older (E)

Wage earner (W)

Lowest income quartile

(I1)

Highest income quartile

(I4)

Index value December 2018 (December 2003 = 100) 131.7 133.0 131.5 137.6 130.0 Annualized percent change 1.84% 1.91% 1.83% 2.14% 1.76%

For each population, the Tornqvist formula generally displays a lower measure of price change than the

Laspeyres index. The graphs in Appendix 1 and Table 8 show the difference in substitution bias between

the populations, defined as the difference in annual inflation rates measured by the Tornqvist and

Laspeyres indexes. The graphs in Appendix 1 shows the difference in the annual rate of change each

month for each population, and Table 8 shows the difference in the annualized percent changes over

the 16 year period.

100

105

110

115

120

125

130

135

140

145 2

00 31

2

2 00

40 7

2 00

50 2

2 00

50 9

2 00

60 4

2 00

61 1

2 00

70 6

2 00

80 1

2 00

80 8

2 00

90 3

2 00

91 0

2 01

00 5

2 01

01 2

2 01

10 7

2 01

20 2

2 01

20 9

2 01

30 4

2 01

31 1

2 01

40 6

2 01

50 1

2 01

50 8

2 01

60 3

2 01

61 0

2 01

70 5

2 01

71 2

2 01

80 7

C-CPI- lowest quartile C-CPI-U

C-CPI- highest quartile C-CPI-E

C-CPI-W

18

Table 8: Difference in Laspeyres and Tornqvist annualized percent change: December 2003 – December

2019

Annualized percent change All urban households

(U)

62 years or older (E)

Wage earner (W)

Lowest income quartile

Highest income quartile

Tornqvist 1.84% 1.91% 1.83% 2.14% 1.76% Laspeyres 2.07% 2.17% 2.06% 2.25% 1.97%

Substitution bias 0.23% 0.26% 0.23% 0.11% 0.21%

While the Tornqvist index for each population rises more slowly than its Laspeyres counterpart, the

difference in the rate of change is smallest for the lowest income quartile population. Indeed, the

measure of consumer substitution bias over the 2003 to 2018 time period for the lowest income quartile

population is less than half that of all urban consumers. The highest income quartile population had a

similar consumer substitution effect as all urban consumers. Future research should explore the extent

of consumer substitution (and the elasticity of substitution) across populations.

Summary and conclusion In this paper we present results of estimating CPI indexes for the lowest and highest income quartiles of

CE respondents. From 2003 to 2018, the Laspeyres index for the lowest income quartile population rose

faster than the index for all urban consumers. The Laspeyres index for the highest income quartile

population rose slower than the index for all urban consumers. Chained CPI indexes for the income

quartile populations rose slower than their Laspeyres counterparts. The measure of consumer

substitution was lowest for the lowest income quartile population; the difference between the

Laspeyres and Tornqvist index for the lowest income quartile population was less than half the

difference for all urban consumers.

We present these results with many caveats. Future research can improve upon the work of this paper

by redefining the income groups, either by using an equivalence scale to adjust for varying household

sizes, using externally defined income bands that are more representative of the population, or defining

income quartiles at the index area level (as opposed to nationally). Other improvements include using a

more sophisticated imputation methodology for missing expenditure weights (ideally sensitive to

population spending patterns) and calculation of variances to enable a statistical comparison of index

results. Additionally, the lowest income quartile population exhibits the largest number of missing

expenditure weights and the lowest measure of consumer substitution bias. Further research is needed

to understand the spending patterns of the lower income quartile subpopulation, which appear to be

unique from the W and E subpopulations previously defined.

19

Appendix 1: Difference in Laspeyres and Tornqvist annual percent change: December 2003 – December 2019

20

Paper, Household Cost Indexes: Prototype Methods and Results (U.S. Bureau of Labor Statistics)

We estimate a family of price indexes known as Household Cost Indexes (HCI) using U.S. data. HCIs aim to measure the average inflation experiences of households as they purchase goods and services for consumption, and similar indexes are produced in the United Kingdom and New Zealand. These differ from the Bureau of Labor Statistics’ headline Consumer Price Index (CPI) products in two main respects. First, the upper-level aggregation of the HCIs weights households equally, unlike most headline CPIs which implicitly give more weight to higher expenditure households.

Languages and translations
English

BLS WORKING PAPERS U.S. Department of Labor U.S. Bureau of Labor Statistics Office of Prices and Living Conditions

Household Cost Indexes: Prototype Methods and Results

Robert S. Martin, U.S. Bureau of Labor Statistics Joshua Klick, U.S. Bureau of Labor Statistics William Johnson, U.S. Bureau of Labor Statistics Paul Liegey, U.S. Bureau of Labor Statistics

Working Paper 604 August 2023

1

Household Cost Indexes: Prototype Methods and

Results1

Robert S. Martin, Joshua Klick, William Johnson, Paul Liegey2

August 2023

Abstract

We estimate a family of price indexes known as Household Cost Indexes (HCI) using U.S.

data. HCIs aim to measure the average inflation experiences of households as they purchase

goods and services for consumption, and similar indexes are produced in the United Kingdom

and New Zealand. These differ from the Bureau of Labor Statistics’ headline Consumer Price

Index (CPI) products in two main respects. First, the upper-level aggregation of the HCIs weights

households equally, unlike most headline CPIs which implicitly give more weight to higher-

expenditure households. Second, the HCIs use the payments approach to value owner-occupied

housing services explicitly using household outlays. In contrast, the U.S. CPIs use rental

equivalence. The HCI for all urban consumers has an average 12-month change of 1.51% over

December 2011 to December 2021, compared to 1.86% for the CPI-U. Roughly 95% of the

difference is due to the payments approach.

Key Words: Price index; inflation; democratic aggregation; payments approach

JEL Codes: C43, E31

1 We thank Anya Stockburger, Robert Cage, Thesia I. Garner, and many others at the Bureau of Labor Statistics for helpful comments and guidance. 2 Division of Price and Index Number Research (Martin), Division of Consumer Price Indexes (Klick, Liegey), Division of Price Statistical Methods (Johnson), Bureau of Labor Statistics, 2 Massachusetts Ave., NE, Washington, DC 20212, USA. Emails: [email protected], [email protected], [email protected], [email protected]

2

1. Introduction

This article estimates Household Cost Indexes (HCIs) using U.S. data. Similar price

indexes are already produced in the United Kingdom (Office for National Statistics, 2017) and

New Zealand (Statistics New Zealand, 2020). HCIs measure the change in cash outflows

required, on average, for households to access the goods and services they purchase at a

constant quality. Like the headline and subpopulation Consumer Price Indexes (CPIs) produced

by the Bureau of Labor Statistics (BLS), the HCIs aim to capture price change for consumer

goods and services. However, the HCIs differ in two important methodological respects from

the CPIs. First, the upper-level aggregation of the HCIs weights households equally, whereas the

CPI market baskets implicitly give higher weight to higher-expenditure households.3 Second,

the HCIs use the payments approach to value services from owner-occupied housing, using

outlays on mortgage interest, property taxes, and the full reported value of insurance,

appliances, maintenance and repairs (i.e., what the household pays and when they pay it). The

CPIs, in contrast, use an implicit measure of owner-occupied housing consumption called rental

equivalence, all other goods and services (besides owner-occupied housing) are valued using

acquisition prices and expenditures (i.e., when the household acquired or took possession of

the good). For HCIs in principle, the payments approach should be applied more broadly, but

this paper focuses only on owner-occupied housing. In many cases, such as food, acquisition

and payment occur at the same time and involve the same values. We are ignoring household

outlays for the purchase of vehicles and other durable goods and instead are including the full

3 Households are still weighted by their sampling weight so that averages represent the population.

3

acquisition expenditures for these regardless of financing; including these in an HCI is left for a

future study.

We compute an HCI for the urban U.S. population covering the period December 2011

to December 2021. The HCI is based on the Lowe (modified Laspeyres) formula using average

annual household weights with about a two-year lag. From December 2012 to December 2021,

we find an average twelve-month inflation rate of 1.51 percent for the HCI-U, compared to 1.86

for the CPI-U and 1.73 for the Chained CPI-U. We find these empirical differences between the

HCIs and CPIs are primarily due to the HCI’s use of the payments approach, which we estimate

subtracts 0.39 percentage points per year on average relative to an index that uses rental

equivalence. This difference reflects both a lower weight for owner-occupied housing in the HCI

as well as lower inflation in explicit housing costs when compared to owner’s equivalent rent

inflation (as imputed from actual rent changes). In contrast, we estimate that equal household

weighting increases the index only about 0.05 percentage points per year on average compared

to an index which uses the standard expenditure weighting, but otherwise uses the same

methodology as the HCI.

CPIs are used in a wide variety of economic applications—as an overall macroeconomic

indicator, to deflate national accounts, to adjust marginal tax rates, and measure changes in the

cost-of-living representative of the entire economy. In such applications, measuring the change

in purchasing power of the average dollar of expenditure using an implicit consumption

concept like owner equivalent rent may be appropriate. In other cases, such comparing the

economic conditions of population subgroups, a measure tied to explicit outlays may be

4

attractive. One index cannot usually satisfy all needs, and in this sense the HCIs can provide

useful complementary information about the average household inflation experience.

2. Literature Review

Current BLS CPI methodology is based on market-level expenditure weights and the

rental equivalence approach to owner-occupied housing (Bureau of Labor Statistics, 2020).

Household-weighted aggregation and the payments approach differ substantially from current

BLS CPI methodology, though neither is new to the price index literature. Astin and Leyland

(2015) propose using these methods to better capture the inflation experiences of households.

They argue such a measurement is more credible for indexing monetary values, while a

traditional CPI is superior for macroeconomic analysis and inflation targeting. Based in part on

their research, the Office of National Statistics developed a set of HCIs for the United Kingdom

(Office for National Statistics, 2017). Statistics New Zealand publishes a similar set of indexes

called the Household Living-Costs Price Indexes. Research on a similar set of indexes for the U.S.

began with Cage, et. al. (2018).

Household-weighted aggregation (also known as democratic aggregation) has been

considered at least since Prais (1958). The topic has been developed and reviewed in Pollak

(1989), National Research Council (2002), International Labor Organization (2004, Chapter 18),

Ley (2005), and Martin (2022), among others. Spending patterns differ across the distribution of

total expenditure. To the extent that these differences coincide with expenditure categories

that have higher or lower inflation than average, a household-weighted index will differ from a

traditional expenditure-weighted one. Equally weighted indexes have been studied with U.S.

5

data in Kokoski (2000) and Hobijn, et. al. (2009). The latter is notable for statistically matching

the interview and diary components of the Consumer Expenditure Survey (CE), and we follow

many aspects of its approach. Our paper also builds on work from Cage, et. al. (2018) and

Martin (2022), the latter of which finds that household-weighted aggregation adds about 0.08

percentage points per year to inflation measured by a Lowe-type CPI from December 2001 to

June 2021.

Based in part on the observation from Boskin, et. al. (1998) that increases in the owner

equivalent rent component of the CPI could correspond to housing value appreciation, and that

owner-occupiers "should not be compensated for capital gains on their housing", Cage, et. al.

(2018) began exploring alternative methods for the BLS. The payments approach to owner-

occupied housing has been discussed at least since the 1989 version of the International Labor

Organization (ILO) CPI manual (as cited by Goodhart, 2001), and much of our initial approach

follows the 2004 version (International Labor Organization, 2004, Chapter 10). The payments

approach to owner-occupied housing focuses on the month-to-month outlays by households

rather than an upfront purchase price (the acquisition approach) or the implicit consumption

value (the use approach).4 In addition to the HCIs for the United Kingdom and New Zealand, the

payments approach is also used in the CPI for Ireland (Central Statistics Office, 2016). Mortgage

interest is also included in the housing component of the CPI for Canada (Statistics Canada,

2019), and was a part of the U.S. CPI housing component prior to 1983 (Gillingham and Lane,

1982). Diewert and Nakamura (2009) contains a conceptual comparison of the payments

4 Rental equivalence and user cost are both flavors of the use approach.

6

approach against other methods like the user cost approach and rental equivalence, while

Garner and Verbrugge (2009) compare methods empirically using the CE.

Astin and Leyland (2015) argue that the payments approach is superior for comparing

household inflation experiences and escalating payments. They make the case that because

rental equivalence is not tied to explicit outlays, an index which includes it as a large

component may be less tethered to the actual price movements that affect household budgets.

For some subpopulations, there can be large differences between implicit rents and explicit

cash flows. For instance, in Cage et. al. (2018), the subpopulation of households which receives

at least 50% of its before-tax income from Social Security has higher relative expenditures on

shelter (35-39%) when measured using rental equivalence than the overall urban population

(32%), but lower relative expenditures when measured using payments (16-23%). This is

because these households are disproportionately likely to be owner-occupiers without

mortgages, meaning their explicit housing outlays are limited to items like property taxes,

insurance, and maintenance.

Astin and Leyland (2015, 2023), as well as ILO (2003) advocate such an index for

escalation purposes, but this position is not universally held. Diewert and Shimzu (2021) argue

“it is not an index that can measure household consumption of the services of durable goods

because it focuses on the immediate costs associated with the purchase of durable goods and

ignores possible future benefits of these purchases.” The payments approach has also been

criticized in Goodhart (2001), Poole, Ptacek, and Verbrugge (2005), and elsewhere on the basis

that it doesn’t reflect consumption in an economic sense. We agree that a flow-of-service

method like rental equivalence is more appropriate for a macro-focused CPI or a representative

7

consumer’s cost-of-living index (See, e.g., Diewert 1976). However, we study the HCIs as

complementary series intended to capture explicit outlays of households rather than the

implicit consumption prices (in an economic theoretic sense) reflected in a traditional CPI,

though initially the distinction is limited to owner-occupied housing. The objective of our paper

is primarily to compare owner-occupied housing and household aggregation methods.

3. Methods and Data

Our methods for this paper are preliminary and based on utilizing existing BLS surveys or

publicly available data sources. Like the CPIs, the HCIs are constructed in two stages. First, basic

indexes are constructed for item-area strata (e.g., coffee in Washington, DC). These are then

aggregated using expenditure weights from the CE. As our initial version only applies the

payments approach to owner-occupied housing, the elementary indexes and underlying

household expenditures used in upper-level aggregation are largely the same. See Bureau of

Labor Statistics (2020) for more details. For housing, the owner equivalent rent elementary

indexes are replaced with indexes for property taxes, mortgage interest, and property

management services. In addition, to reflect payment amounts, we use the full reported value

of household expenditures on household appliances, maintenance and repair, and insurance

when constructing upper-level aggregation weights.5 Finally, we estimate equally weighted

averages of household expenditure shares based on matched CE Interview and Diary data and

use these in the second-stage aggregation.

5 This is different from the published CPI and C-CPI, which adjust these expenditures downward to reflect the likelihood they would be made by a renter.

8

3.A. Payments Approach Item Structure and Elementary Indexes

The payments approach for owner occupied housing reflects the housing-related cash

outflows of households. Compared to the CPI, the HCI item structure excludes owner’s

equivalent rent and includes three additional expenditure classes—property taxes, mortgage

interest, and other primary residence expenses. The payments approach also removes several

adjustments CPI makes to other category weights, which we discuss more later in this section.

Within property taxes and mortgage interest, we create new elementary item indexes

representing primary residences. These also serve as proxies for secondary residences. In the

CPI, the price index for owner’s equivalent rent of primary residences (numbered “01”) also

serves as the proxy for the unpriced item (numbered “09”) representing secondary residences.

A further item classification (see Table 1 for details) for other primary residence expenses

consists of ground rent, parking, and property management services. This category comprises

less than one half of one percent of the overall index weight, and we provisionally measure its

price change using the producer price index for final demand property management services as

a proxy. Finally, our objective, where possible, is to limit expenditures to those pertaining to

primary residences and vacation homes and exclude investment properties.

The rest of this section details the construction of the property tax and mortgage

interest payment indexes. We follow what is (to our knowledge) international practice by

including the interest component of mortgage payments (excluding second mortgages or home

equity lines of credit) and excluding the portion that goes toward principal reduction (and by

this reasoning down payments and cash purchases). From the 2004 ILO manual, only the

interest portion is considered a pure cash outflow; the principal portion immediately shows up

9

on the household’s balance sheet as an increase in assets, so it may be considered more like an

investment with a potential future return (International Labor Organization 2004, Chapter 10).

This view is not universal (see Astin and Leyland, 2015). However, including mortgage principal

presents additional technical challenges.6

Also following international practice, the mortgage interest and property tax payments

indexes derive conceptually from two sources of potential change: a rate (an interest rate or an

effective property tax rate) and the base to which the rate is applied (the debt level or the

dwelling value). Changes in rates alone do not capture changes in purchasing power

(International Labor Organization 2004, Chapter 10). Some users could be concerned about

allowing the effects of home prices given these could be associated with (eventual) financial

returns to households. In our view, there is a tradeoff between representing the explicit outlays

of households and controlling for investment using economic theory. Indeed, as noted by

Poole, Ptacek, and Verbrugge (2005), adjusting housing payments to account for investment

results in the user cost approach, which is another implicit housing cost concept. Empirically,

Garner and Verbrugge (2009) show that user costs can differ greatly from explicit payments.7

Our initial strategy, following international practice, aims to exclude the investment aspect of

housing ownership by excluding mortgage principal. Appendix A shows the decision to

6 The most straightforward method to estimate the proportional impact of changing interest rates on mortgage principal payments would involve plugging in aggregate (i.e., average) interest rates into a nonlinear function. In the sense of measuring a change in average payments across households, the potential bias of such a plug-in procedure from Jensen’s Inequality is unknown. 7 Garner and Verbrugge (2009) also find that user cost measures based on different underlying assumptions can differ greatly from each other and from implicit rents.

10

indirectly include home prices is significantly inflationary for the housing payments indexes and

suggests the decision to exclude mortgage principal is somewhat deflationary.

Finally, our preliminary results compute a single set of payments approach elementary

item indexes representing the U.S. urban population. We leave it to future research to extend

these methods to create elementary indexes by CPI geographic areas.

3.A.1. Mortgage Interest Payment Index

The mortgage interest payments index measures the proportional change in the interest

payment amount that would occur holding fixed the financing conditions—such as the loan

term and proportion of principal remaining. We aim to follow the recommendations in the

2004 ILO manual (Chapter 10), which is to use both a representative basket of interest rates

and a debt index, which holds “constant the age of the debt” between index periods

(International Labor Organization 2004, Chapter 10). Payments in each period are determined

by transactions occurring at many previous points in time, as mortgage loans are long-term

contracts. Consequently, our index is based on weighted averages of interest rates and house

prices corresponding to loans or debt of different ages. A fixed-basket approach has the

advantage of being feasible with aggregate interest rate and house price data, but the

disadvantage of not being micro-founded.8

8 We considered such a micro-founded approach which could, for example, average proportional changes in rates actually paid by households between the reference and comparison periods without fixing the loan age. Such an approach may be more appropriate for the U.S. market, which is dominated by 30-year fixed rate mortgages. However, basing such an approach on CE interest rate microdata misses any variation which occurs when a consumer unit moves from one house to another since consumer units are not followed.

11

Similar to Canada (Statistics Canada, 2019), we define the index as the product of a debt

index (which is influenced by home prices) and an interest rate index which compare payments

in the comparison period 𝑡 against the reference period 𝑠.9 The index is based on the model of

a thirty-year fixed rate mortgage, which dominates the U.S. market (about 75% of existing loans

as reported in the CE).10 It is written:

𝑃𝑀𝐼𝑃 = 𝑃𝐷𝑃𝑟 , (1)

where 𝑃𝐷 is the debt index and 𝑃𝑟 is the interest rate index. They are written

𝑃𝐷 = ∏ 𝐻

𝑡−𝑗

𝜓𝑏𝑗�̅� 𝑗=0

∏ 𝐻 𝑠−𝑗

𝜓𝑏𝑗�̅� 𝑗=0

(2)

and

𝑃𝑟 = ∏ 𝑟

𝑡−𝑗

𝜑𝑏𝑗𝜃−1 𝑗=0

∏ 𝑟 𝑠−𝑗

𝜑𝑏𝑗𝜃−1 𝑗=0

. (3)

The indexes measure change from period 𝑠 to period 𝑡 by weighting past home prices (relative

to a common base) and interest rates according to the relative importance of loans or debt

initiated in those months to the index periods 𝑡 and 𝑠.11

In these expressions, 𝐻𝜏 is a home price index for month 𝜏, 𝑟𝜏 is an average interest rate

for month 𝜏, 𝜓𝑏𝑗 is the population-weighted proportion of mortgagor-month observations with

debt of age 𝑗 (measured as the number of months since the property was acquired), and 𝜑𝑏𝑗 is

9 While our debt index is similar to the housing component of Canada’s mortgage interest index, their interest rate component is based on unit value-like averages using administrative banking data. 10 We ignore preferential treatment of mortgage interest in the tax code. 11 While the product of two geometric means with identical weights could be written as one geometric mean, writing the index as a product of two components makes for convenient discussion and analysis.

12

the population-weighted proportion of mortgagor-month observations with current loans of

age 𝑗 (measured as the number of months since the first payment) during the reference period

𝑏. The 𝜓 and 𝜑 parameters differ due to refinances. We use the proportion of mortgagors

(rather than the proportion of debt, which is closer to what Statistics Canada uses) in keeping

with the equal-weighting objective of the HCI. The parameter 𝜃 equals 360 to reflect the

number of potential payments in a thirty-year loan, while �̅� is set higher to allow for acquisition

periods to be earlier on refinanced properties. While not well bounded in theory, we set �̅�

equal to 408 to accommodate the beginning of our house price indexes in January 1975. This

covers about 97.5% of observations in our sample. We evaluate adjacent months 𝑡 and 𝑠. We

set 𝑏 as the fourth quarterly lag of the quarter containing month 𝑡. This reflects a realistic

production constraint for using CE data to construct the weights while keeping them as current

as possible. We use CE microdata on mortgage expenses and keep those observations with 30-

year fixed rate first mortgages on primary residences. We drop loan records that likely pertain

to non-housing expenditures (second mortgages and home equity lines of credit).

We use monthly averages of the weekly 30-year fixed mortgage rate averages from the

Freddie Mac Primary Mortgage Market Survey (PMMS), which are available only for the U.S.

market. We also use the Federal Housing Finance Agency’s (FHFA) All Transactions House Price

Index. This index is quarterly, and we interpolate monthly values using the natural spline in

SAS’s PROC EXPAND. The FHFA’s purchase only house price index is monthly and superior

conceptually for a debt index representing past home purchases. However, this series only goes

back to 1991, and would not be long enough to cover all loan ages in our sample.

13

3.A.2. Property Tax Payment Index

The property tax payment index measures the change in average property tax payments

for households. Our proposed method attempts to hold the aggregate quality of the housing

stock constant and uses annual data from the CE.12 Let 𝑋𝑠,𝑡 and 𝑉𝑠,𝑡 denote proportional growth

in population aggregates for property tax payments and owner-occupied housing unit values

between years 𝑠 and 𝑡, and let 𝐻𝑠,𝑡 be a constant-quality home price index between years 𝑠 and

𝑡. We use timeseries representing the entire U.S. and leave it for future research to extend the

method to geographic areas, which require more granular tax data than we currently have. We

compute the following:

𝑃𝑃𝑇𝑃 = 𝑋𝑠,𝑡 𝑉𝑠,𝑡

𝐻𝑠,𝑡 . (4)

Our method is similar to that of Statistics Canada and the Office for National Statistics,

which compute unit value indexes, or ratios of average property tax payments, though they do

so for different geographic areas. Let 𝑁𝑠,𝑡 be the growth in the number of owner-occupied

housing units between 𝑠 and 𝑡. A similar approach we explored with CE data computes

𝑃𝑃𝑇𝑈𝑉 = 𝑋𝑠,𝑡 𝑁𝑠,𝑡

. (5)

12 The CE asks homeowners the annual property taxes owed on their primary residence and adjusts these amounts if the property is partly used as a business. The CE also asks the consumer unit to estimate the market value of their primary residence. Investigating potentially more timely sources of property tax data is a task for future research.

14

where we use the number of owner-occupier consumer units to proxy for the number of

owner-occupied housing units.13 Equation (4) is equal to equation (5) divided by (𝑉𝑠,𝑡/𝑁𝑠,𝑡)/𝐻𝑠,𝑡

which is the growth in average home values deflated by the constant-quality home price index.

We interpret this ratio as a measure of change in dwelling quality which is relevant under the

assumption that the total housing market valuations 𝑉𝑠,𝑡 and the house price indexes 𝐻𝑠,𝑡

approximate changes in value and price as would be measured by tax assessors. We found that

the long-term trends of Eq. (4) and (5) were very similar. As in Canada and the U.K., we do not

attempt to control for potential differences in quality of municipal services.

Our preliminary efforts use annual property tax aggregates from the CE, as the survey

asks about annual tax obligations rather than monthly payments. The monthly expenditure

microdata include these figures divided by 12. We find that that using Equations (4) and (5) on

this average monthly data leads to substantial short-term sampling variation. For this reason,

we compute the property tax index at an annual frequency and interpolate monthly values

using a spline function. Statistics Canada and the Office for National Statistics, for instance,

update their property tax indexes once per year. The CE is not the ideal source for property tax

and housing value data, as data for a calendar year are released about nine months after that

year ends. For this reason, this paper’s analysis only covers through the end of 2021. Finding

timelier and larger samples using alternative data is an objective for future research.

13 In the CE, consumer units are equivalent to households in the vast majority of cases, but are defined by joint economic decision making rather than residence or familiar relationships.

15

3.B. Upper-level Aggregation

As in the CPI, we use CE data to derive upper-level aggregation weights, with some

important differences. As shown in Table 1, the set of eligible elementary item strata now

includes property taxes and mortgage interest and excludes owner equivalent rent. The

property tax and mortgage interest weight are derived from the monthly expenditures on those

items as collected by the CE. In addition, we use the full reported values of expenditures on

items like maintenance and repair, homeowner’s insurance, appliances, and household

furnishings. Under the rental equivalence approach, these items are scaled down for owner-

occupiers to reflect the likelihood of a renter making the same purchase. Table 2 compares

average housing-related relative importance across consumer units in different subpopulations

—by housing tenure, an indicator for being a wage earner or clerical worker (as in the CPI-W),

and an indicator for being elderly (age greater than or equal to 62, as in the R-CPI-E)14—both

under the payments approach and rental equivalence. In general, housing payments make up a

smaller share of overall spending under the payments approach than under rental equivalence.

For the urban population, for instance, housing under the payments approach amounts to

34.3% of the market basket on average, versus 42.9% on average under rental equivalence.

Interestingly, patterns of spending across some subpopulations differ by housing approach. For

instance, under rental equivalence, the average share going to housing among the elderly is

relatively high at 46.8%. Under the payments approach, however, the elderly have a high

proportion going to insurance, appliances, maintenance, and repairs (“other housing”), but

14 Consumer units were classified according to their reported demographic in their last interview in the sample.

16

relatively less going to mortgage interest, resulting in a total housing weight of 34.1%, slightly

less than the overall urban population (34.3%).

Table 1: Weights for Select Housing Items for the HCI Subsample in 2019

Payments Rental

Equivalence Code Description $ Bil. % RI* $ Bil. % RI*

HC01 Owner’s Equivalent Rent of Primary Residence NA NA 1,144.36 22.40 HC09 Unsampled Own. Equiv. Rent of Second. Res. NA NA 56.29 0.75 HD01 Tenants’ and Household Insurance 38.02 1.01 17.24 0.38 HH01 Floor Coverings 8.29 0.18 2.54 0.05 HK01 Major Appliances 17.05 0.39 2.38 0.06 HK09 Other Appliances 0.08 0.00 0.07 0.00 HM01 Tools, Hardware, and Supplies 17.23 0.43 11.67 0.26 HM09 Unsamp. Tools, Hardw., Outdoor Equip, Supp. 58.44 1.31 9.35 0.20 HP04 Repair of Household Items 46.52 0.83 4.14 0.08 HP09 Unsampled Household Operations 10.69 0.23 4.29 0.07 HR01 Property Tax of Primary Residence 199.70 4.51 NA NA HR09 Property Tax of Secondary Residence 8.61 0.16 NA NA HS01 Mortgage Interest of Primary Residence 211.64 4.26 NA NA HS09 Mortgage Interest of Secondary Residence 4.55 0.08 NA NA HT01 Other Owner Payments for Primary Residence 14.10 0.42 NA NA HT09 Other Owner Payments for Secondary Res. 1.29 0.02 NA NA * Average (equally-weighted) relative importance across consumer units.

17

Table 2: Average Household Relative Importance for Housing by Subpopulation (percent)

Category Urban Wage- earner Elderly

Own. w/ Mortgage

Own. w/o Mortgage Renter

Payments Approach Rent 9.2 13.0 6.3 0.1 0.2 31.8 Property Tax (Primary) 4.5 4.2 5.5 6.0 6.8 0.1 Property Tax (Secondary) 0.2 0.1 0.3 0.2 0.2 0.1 Mortgage Interest (Primary) 4.3 5.1 2.6 10.1 0.1 0.0 Mortgage Interest (Secondary) 0.1 0.1 0.1 0.1 0.2 0.0 Other Housing 16.0 14.8 19.4 16.9 22.0 8.8 Total Housing 34.3 37.2 34.1 33.2 29.5 40.9 Rental Equivalence Approach Rent 9.2 13.0 6.3 0.1 0.2 31.7 Owner’s Equiv. Rent (Primary) 22.4 20.5 28.2 30.4 32.8 0.4 Owner’s Equiv. Rent (Secondary) 0.7 0.4 1.2 0.7 1.2 0.4 Other Housing 10.6 10.4 11.1 10.9 12.0 8.7 Total Housing 42.9 44.3 46.8 42.1 46.1 41.2 Note: Cells show average December 2020 relative importance (2019 reference period weights price-updated to December 2020 values) across households meeting the HCI sample requirement. While expenditures cover a year, consumer units are classified according by attribute from their last collection quarter.

Our upper-level aggregation uses the Lowe formula, and same as the CPI (as of January

2023) the quantity weights pertain to annual expenditure reference periods which are updated

each year. The household-weighted aggregation starts from the CE Interview sample, as

consumer units contribute up to one year of data and the Interview comprises most eligible

expenditures. Eligible expenditures from the Diary survey are imputed to the Interview sample

using a matching procedure based on Hobijn, et. al. (2009), which is described further later in

this section and similar to that used in Martin (2022). The procedure matches eligible Diary

consumer units to an Interview consumer unit based on demographic characteristics that are

predictive of total expenditure. The second-stage aggregation is then based on the Lowe

formula with lagged expenditure weights.

𝑃𝐻𝐶𝐼 = ∑∑�̅�𝑎,𝑖,𝑣,𝑏𝑃𝑎,𝑖,𝑡,𝑣

𝑖∈ℐ𝑎∈𝒜

(6)

18

�̅�𝑎,𝑖{𝑣,𝑏} = (

𝐻𝑎,𝑏

𝐻𝑏 )𝐻𝑎,𝑏

−1 ∑ 𝜔ℎ

ℎ∈ℋ𝑎,𝑏

𝑠𝑖,𝑣,𝑏,ℎ

(7)

𝐻𝑎 = ∑ 𝜔ℎ

ℎ∈ℋ𝑎,𝑏

, 𝐻𝑏 = ∑ ∑ 𝜔ℎ

ℎ∈ℋ𝑎,𝑏𝑎∈𝒜

,

(8)

where 𝑎 indexes the geographic area, 𝑖 the item stratum, 𝑣 the index pivot month, 𝑏 the weight

reference period, and ℎ the consumer unit. The set of areas is 𝒜, the set of items ℐ, and the

set of consumer units in area 𝑎 during period 𝑏 is ℋ𝑎,𝑏. The elementary index between pivot

month 𝑣 and period 𝑡 for item 𝑖 in area 𝑎 is given by 𝑃𝑎,𝑖,𝑡,𝑣. The associated household-weighted

expenditure shares are �̅�𝑎,𝑖,𝑣,𝑏. These are equally (with respect to the population) weighted

averages of individual consumer unit annual expenditure shares 𝑠𝑖,𝑣,𝑏,ℎ, with 𝜔ℎbeing

household ℎ’s sampling weight. The weight reference period 𝑏 is the calendar year two years

prior to the calendar year containing month 𝑡, and the expenditure shares 𝑠𝑖,𝑣,𝑏,ℎ are price-

updated to represent period 𝑣 values using the ratio of the elementary index in month 𝑣 to its

average over period 𝑏.

Consumer units participate in the CE for up to four collection quarters, providing up to

twelve months of expenditures. Because participation is on a rolling basis and there is unit

nonresponse and occasional attrition, the number of observations exactly lining up with a single

calendar year is relatively small, often only a few hundred. Therefore, for the HCI, we define a

“reference year” sample differently than does either the CE or CPI. We assign a consumer unit

to a reference year 𝑏 if its last month of expenditure occurred during year 𝑏. So that each ℎ’s

expenditure basket reflects a whole year, we include only observations which completed all

four quarterly interviews, even if some of their expenditures occurred in the prior calendar

19

year. For the 2019 reference year, for instance, (used for indexes in 2021), we include

consumer units with at least one month occurring in 2019, meaning we include some

observations whose sample tenure started as early February 2018. With the four-quarter

requirement, this amounts to a sample of 3,063 unique consumer units (12,252 collection

quarters) representing our 2019 reference year. In comparison, 11,740 unique consumer units

(comprising 22,957 collection quarters) in the CE have expenditures recorded for the calendar

year 2019.15 For index subgroup definitions, we use consumer unit characteristics from their

final collection quarter.

As discussed in Martin (2022), including observations with periods less than one year

can distort household-weighted indexes due to greater variability in total expenditures and

lower average expenditure shares for less frequently purchased items. However, there is a

potential trade-off with the four-quarter requirement due to representativity. Table 3 shows

differences in the relative frequencies of a few consumer unit demographics. For the 2019

reference year, the HCI subsample has a greater proportion of owners and elderly than the full

sample of urban consumer units. At the same time, Table 2 shows there are differences in the

average expenditure shares on housing-related payments across these groups, suggesting

potential consequences for price indexes. For instance, the elderly spend relatively more on

property taxes than on mortgage interest, reflecting that they are disproportionately owners

without mortgages.

15 These sample sizes were calculated by counting the number of unique FAMID (or the consumer-unit specific portion of the FAMID) for a given expenditure reference period.

20

Table 3: Frequency of Consumer Unit Characteristics by Sample in 2019 (percent)

All Urban HCI Subsample

Owner with mortgage 37.3 41.4 Owner without mortgage 23.6 29.1 Renter 39.2 29.6 Wage earner 27.0 25.3 Elderly 30.8 37.7

Nevertheless, we find little evidence of a sample selection bias stemming from our HCI

eligibility criteria, at least over during sample period. Table 4 shows (comparing columns 2 and

3) the impact of using the CE subsample on major group-level weights is small relative to the

effect of using the payments approach or household aggregation. Additionally, we find

(Appendix C) that the sample selection impact on an expenditure-weighted version of the HCI-U

(corresponding to column 4 of Table 4) is minimal, about 0.01 percentage points per year.

Furthermore, our results show a CPI-like index calculated from these subsamples (with Diary

expenditures imputed as described in the next subsection), corresponding to column 3 of Table

4 closely matches the published CPI-U. These together imply our results are driven by the

payments approach and household-weighted aggregation, and not the reference period or CE

subsample. Our current method makes no adjustments to the CE sampling weights, which we

leave to future research. Such adjustments may be more important with more recent data than

our sample period, particularly with recent surges in mortgage interest rates.

There are a few other differences between our research indexes and official CPI

methods. Since the HCI is based on consumer unit-specific shares, which must be weakly

21

positive, we censor negative annual expenditures at zero.16 We also make some small item-

structure changes to simplify calculations using historical data. Finally, we omit weight-

smoothing procedures used in the CPI, including composite estimation for the item-area

weights, which are designed to lower their sampling variance across geographic areas. Our all

items, all areas CPI-U replications closely match the published indexes even without these

procedures, and our prototype procedure only estimates property tax and mortgage interest at

the national level. We leave it to future research to extend weight-smoothing procedures to the

HCIs.

Figure 1 below shows the December 2020 relative importance by major expenditure

group and select housing categories and compares them with the published shares for the CPI-

U. The HCI shares correspond to the 2019 weight reference year, while for the CPI they

correspond to the 2017-18 reference period. Table 4 tracks the change in relative importance

by major group as different HCI elements are activated. The effects of the payments approach

and household-weighted aggregation on the relative weights are significant, but sometimes

have offsetting effects. For instance, the overall housing weight in the HCI is smaller than the

CPI, as property tax, mortgage interest, and the increase in other housing outlays amounts to

less than the decrease due to the exclusion of OER. By itself, this decrease in housing weight

increases the weight allocated to other categories, like medical and recreation. At the same

time, however, household-weighted aggregation shifts weight toward households with lower

16 This affects items RC01 “Sports Vehicles, Including Bicycles”, TA02 “Used Cars and Trucks”, and TA09 “Unsampled New and Used Motor Vehicles.” The CPI counts returns or sales as negative expenditures.

22

total expenditures, further increasing the relative importance of rent and food while decreasing

that of transportation.

Figure 1: December 2020 Relative Importance for HCI-U and CPI-U

Panel a: HCI-U (2019 weights)

Panel b: CPI-U (2017-18 weights)

20.2%

9.2%

4.7%

4.3%

16.0%3.1%

14.2%

11.1%

6.6%

6.8% 3.8%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Housing: Other Apparel

Transportation Medical

Recreation Educ. & Comm.

Other

15.2%

7.9%

24.3%

10.3% 2.7%

15.2%

8.9%

5.8%

6.8% 3.2%

Food & Bev. Housing: Rent

Housing: OER Housing: Other

Apparel Transportation

Medical Recreation

Educ. & Comm. Other

23

Table 4: December 2020 Relative Importance for Different Index Types (percent)

Major Group CPI-U (2) (3) (4) HCI-U

Food and Beverages 15.16 15.68 15.60 17.96 20.16 Housing 42.39 41.84 42.13 33.34 34.26 Apparel 2.66 2.70 2.67 3.07 3.15 Transportation 15.16 15.43 14.60 16.80 14.23 Medical 8.87 8.79 9.18 10.58 11.09 Recreation 5.80 5.80 6.16 7.08 6.59 Education and Comm. 6.81 6.72 6.57 7.61 6.76 Other 3.16 3.04 3.09 3.56 3.76 Methods* Reference Period 2017-18 2018-19 2019** 2019** 2019** CE Sample Full Full 4-quarter 4-quarter 4-quarter Aggregation Expenditure Expenditure Expenditure Expenditure Household Owner Occ. Housing REQ*** REQ*** REQ*** Payments Payments * Columns 2-5 also reflect other methodology changes and simplifications described in text. ** Under our sample eligibility criteria, this includes spending back to February 2018. *** REQ = Rental Equivalence

3.B.1. Interview-Diary Matching Procedure

As mentioned, the basis of our household average expenditure weights is the CE

Interview sample, which covers about three-quarters of the expenditure basket as traditionally

sourced by the CPI. We implement a statistical matching procedure based on Hobijn et al.

(2009) to impute the remaining proportion which CPI sources from the Diary.17 Similar

observations from the Diary sample provide the remaining expenditure data for each Interview

consumer unit, according to a model of expenditures as a function of demographic

characteristics. The dependent variable is expenditures on items which HCI (and the CPI)

sources from the Diary, but for which the Interview either collects the same item or has more

17 Garner, et. al. (2022) and Martin (2022) also use matching processes based on Hobijn, et. al. (2009).

24

aggregate data.18 The model is a convenient way of combining many characteristics according

to which linear combination most strongly predicts expenditures. We then use the predicted

values to form measures of distance between an Interview recipient and its potential Diary

donors. For our main results, the only attribute guaranteed to match between donor and

recipient is quintile group membership based on the distribution of annual before-tax income.19

For our results on housing tenure subpopulations, we also guarantee this attribute matches.

The matching procedure is many-to-one, as we draw four donor Diaries for each Interview in

each month with replacement. The procedure is implemented separately by month so that

weekly Diary donors are evenly distributed temporally over the recipient Interview’s sample

tenure. Due to the sample selection criteria outlined earlier, for reference year 2019, for

example, that means we are running monthly regressions from February 2018 to December

2019. The stratification and model estimation are done on the full Interview sample, not just

the four-quarter subsample.

First, we stratify both Interview and Diary consumer unit samples for the reference

period by the sample quintiles of annual before-tax income. For each month 𝑡 and quintile

grouping 𝑞, we use the Interview sample to estimate the regression

𝑦ℎ𝑡 = 𝒙ℎ𝑡𝜷𝑞𝑡 + 𝑢ℎ𝑡 ,

(9)

18 From Martin (2022), Table A2, these amount to about 80% of Diary-sourced expenditures in 2019. Alternatively, it might seem attractive to use the Diary sample to estimate Diary expenditures as a function of demographic characteristics, as we intend to impute these expenditures for the Interview sample. However, we find that characteristics explain relatively little variation in Diary expenditures, perhaps due to the short (week-long) recall period. 19 The Diary samples are small enough that conditioning on multiple characteristics quickly leads to empty cells. See Hobijn, et al. (2009) for more discussion.

25

where 𝑦ℎ𝑡 is logged expenditure of consumer unit h. The term 𝑢ℎ𝑡 is an error term, and 𝒙ℎ𝑡

include Census region, urban/rural, age, race, sex, and education of the reference person,

consumer unit size, the log of annual before-tax income (if positive), and an indicator for

whether income was negative.20 We use the least squares estimator weighted by the CE

sampling weight, finlwt21. Over the sample period, R-squared values for the quintile and

month-specific regressions averaged 0.17, while income quintile itself explained about 0.31 of

the variation in the dependent variable.

Let �̂�𝑞𝑡 be the slope estimate for quintile 𝑞 in month 𝑡. As household characteristics are

available and comparably defined in both surveys, we calculate predicted values �̂�ℎ𝑡 = 𝒙ℎ𝑡�̂�𝑞𝑡

for each Diary and Interview observation. For a given Interview observation ℎ and Diary

observation 𝑘, the distance metric is defined as

𝛿𝑡(ℎ, 𝑘) = |�̂�ℎ𝑡 − �̂�𝑘𝑡|.

(10)

Within each month and income quintile, we calculate 𝛿𝑡(ℎ, 𝑘) for all {ℎ, 𝑘} pairs. Then for each

Interview observation ℎ, we randomly select (with replacement) four 𝑘 from the twenty

smallest 𝛿𝑡(ℎ, 𝑘) out of all the Diary observations from the same month and income quintile.

The random component is intended to ensure a more even distribution of matches across Diary

observations. The detailed set of expenditures of the donor Diary is then assigned to the

recipient Interview. As one donor Diary is intended to represent one quarter of one month of

expenditure, but Diaries correspond to a one-week recall period, the donor Diary expenditures

20 These demographic variables technically pertain to the collection quarter or some other reference period, so we implicitly assume they represent the associated reference months. For the matching regressions, we allow a consumer unit’s attributes to vary by collection quarter.

26

are scaled by 13/12. This process is repeated for each Interview observation, for each month it

is in the sample.21 Since the Interview sample is much larger than the Diary on a per-month

basis, each Diary is matched with several Interviews. Further analysis of the matching

procedure is in Appendix B.

4. Results

We find the HCI-U follows similar patterns of acceleration and deceleration as the CPI-U,

but it has significantly lower average rates of growth during our sample period. The average 12-

month change in the HCI-U averages 1.51% versus 1.86% for the CPI-U, as shown in Table 5.

Figure 2 plots the index levels, showing markedly different trends between the CPI-U and HCI-U

from 2012-2020. The two indexes increased at a similar rate in 2021, averaging 4.6-4.7% year-

over-year growth throughout the year. Table 5 includes an index (U-EW-REQ) which uses

expenditure weighting and the rental equivalence approach but uses our CE subsample and

processing methods. It also includes a comparable series (U-EW-PAY) which instead uses the

payments approach but uses expenditure weighting as in the CPI. Comparisons of these indexes

and the HCI-U show the difference in trends and average growth reflects primarily the impact

of the payments approach. U-EW-PAY averages about 0.39 percentage points per year less than

U-EW-REQ, and in a single year (2016) averages 0.74 percentage points lower. In 2021, the

impact of the payments approach is to add 0.15 percentage points to the average 12-month

percent change, reflecting increasing home prices and interest rates. In 2022, we also expect

21 In the CPI, diary expenditures are multiplied by 13 to account for the difference in recall periods between weekly diaries and quarterly interviews. The scaling in our procedure is analogous in that an interview is matched with a total of 12 diaries each quarter, and with the scaling these also represent 13 weeks.

27

this effect to be positive and much larger in magnitude due to the large increase in mortgage

interest rates. In contrast, comparing HCI-U to U-EW-PAY shows the household-weighted

aggregation adding only slight amount to the overall average 12-month percent change

(0.05%), but yearly average differences are as high as 0.16 percentage points in 2017. In 2021,

household-weighted aggregation lowers HCI-U by 0.1 percentage points on average.

Figure 2: HCI-U and CPI-U Index Levels

1

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

lowe-u (ew, req) lowe-u (ew, pay) cpi-u hci-u

28

Table 5: HCI and CPI Average 12-month Percent Changes by Year

Year HCI-U CPI-U U-EW-

REQ U-EW-

PAY HCI-OM HCI-ONM HCI-RNT

2013 0.99% 1.47% 1.43% 0.86% 0.52% 1.22% 1.57% 2014 1.41% 1.62% 1.63% 1.27% 1.02% 1.65% 1.77% 2015 -0.44% 0.12% 0.15% -0.44% -0.88% -0.52% 0.27% 2016 0.56% 1.26% 1.24% 0.51% 0.10% 0.55% 1.19% 2017 1.76% 2.13% 2.13% 1.60% 1.41% 1.79% 2.24% 2018 2.36% 2.44% 2.42% 2.33% 2.32% 2.23% 2.52% 2019 1.39% 1.81% 1.81% 1.43% 1.30% 1.02% 1.80% 2020 0.93% 1.24% 1.21% 0.84% 0.65% 0.89% 1.31% 2021 4.62% 4.69% 4.58% 4.73% 4.54% 4.95% 4.44%

Average 1.51% 1.86% 1.84% 1.46% 1.22% 1.53% 1.90% Notes: U signifies urban population. U-EW-REQ is a CPI-like replication using the HCI sample and simplified expenditure processing methods, but expenditure-weighting and rental equivalence. Similarly, U-EW-PAY uses expenditure-weighting, but the payments approach. “OM” is owners with a mortgage, “ONM” is owners without a mortgage, and “RNT” is renters.

Table 6: International HCI and CPI Comparison, Average 12-month Percent Changes

Year UK-HCI* UK-CPIH* NZ-HLPI† NZ-CPI‡

2013 2.53% 2.31% 1.36% 1.13% 2014 1.48% 1.45% 1.65% 1.23% 2015 -0.08% 0.37% 0.72% 0.29% 2016 0.73% 1.00% 0.35% 0.65% 2017 2.67% 2.57% 1.78% 1.85% 2018 2.49% 2.30% 1.99% 1.60% 2019 1.99% 1.75% 1.43% 1.62% 2020 0.63% 1.00% 1.25% 1.72% 2021 2.51% 2.49% 3.11% 3.94%

Average 1.66% 1.69% 1.51% 1.56% *Office for National Statistics (2022). †Statistics New Zealand (May 2023). ‡Statistics New Zealand (June 2023).

For comparison, in Table 6, we list the average yearly inflation for headline HCI and CPI

inflation in the United Kingdom (U.K.) and New Zealand over our study period. We expect some

variation across these measures due to differences in methods and country-specific economic

conditions. New Zealand’s price index which is comparable to the HCI is known as the

29

Household Living-costs Price Index (HLPI). Both it and the UK’s HCI use a payments approach as

well as household-weighted aggregation. While the U.S. HCI-U tends to be among the lower

yearly averages, its long-term average is about the same as New Zealand’s HLPI. We are

particularly interested in the differences between a country’s HCI and CPI. For this, the U.K. is

the more comparable case, as its Consumer Price Index Including Owner-Occupied Housing

(CPIH) uses rental equivalence (Office for National Statistics, 2019). New Zealand’s CPI, on the

other hand uses the net acquisitions approach, which measures net additions to the housing

stock, including new construction and a excluding the value of land (Statistics New Zealand,

May 2023). The magnitudes of the yearly average HCI-CPI inflation differences for the U.S. (0.36

percentage points) are similar to the U.K. (0.21) and New Zealand (0.37). On the other hand,

the U.S. appears different in that the HCI (in terms of annual averages) is uniformly lower than

the CPI by 0.36 percentage points. In contrast, the U.K. and New Zealand’s HCI and HLPI

average 0.03 and 0.04 percentage points lower than their respective CPIs.

Figure 3 describes further how in the U.S., the actual outlays for owner-occupiers are

associated with lower inflation than would be implied by rental equivalence. Over the sample

period, the official index for owner’s equivalent rent increases 33.8% cumulatively, while our

sub-aggregate for owner’s payments (combining property tax, mortgage interest, and other

owner payments) increased only 11.5%. Within owner’s payments, the two major components,

the trend in the property tax index is similar to owner’s equivalent rent for most of the sample

period. However, the mortgage interest index trends flat, not yet picking up the sharp increases

30

in interest rates occurring in 2022 after our sample period ends.22 We also note that evolution

of the mortgage interest index is smoother than current average mortgage interest rates (from

the Freddie Mac PMMS), because the index is averaging over 30 years of past mortgage rates in

order to reflect current payments.

Figure 3: Owner’s Equivalent Rent vs. Owner’s Payments

Finally, we further illustrate the treatment of owned housing outlays by estimating HCI’s

for three subpopulations, owners with a mortgage (OM), owners without a mortgage (ONM),

and renters (RNT). We define these using the housing tenure value reported by the consumer

unit in their final interview. The final three columns of Table 5 show the average 12-month

percent changes, while Figure 4 plots the index levels. HCI-RNT, has average inflation of 1.9%

22 Our analysis is constrained by sourcing property tax payments from the CE, which as of June 2023 are only available through the first half of 2022. The average 12-month change for the mortgage interest index is 8.2% in 2022. Using the first half of 2022 property tax burden (X/V) as a crude forecast, we find an average change in the owner’s payments index of 10.0% in 2022 (versus 5.7% for owner’s equivalent rent), and an average change in the HCI-U of 8.7% (versus 8% for the CPI-U).

0

1

2

3

4

5

6

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

1.3

1.35

1.4

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Owner's Equiv. Rent (HC) Owner's Payments (HR, HS, HT)

Property Tax (HR) Mortgage Interest (HS)

Other Owner Payments (HT) 30-yr fix. rate (r. axis, %, PMMS)

31

and is closest to the CPI-U. While there may be overall weight differences between the urban

population and the subpopulation of renters, the evolution of owner’s equivalent rent is close

enough to the evolution of actual rent that this result is not surprising. In contrast, the HCI

inflation for owners is significantly lower, averaging 1.53% per year for those without a

mortgage and 1.22% per year for those with a mortgage. As with the urban indexes, the relative

rankings are not the same year to year. For instance, owners without mortgages had the

highest average inflation in 2021, 4.95%, versus 4.54% for owners with a mortgage and 4.44%

for renters.

Figure 4: HCIs for Housing Tenure Subpopulations

4.A. Alternative Treatments of Owner Payments for Housing

As discussed in Section 3.A, we follow international practice in excluding mortgage

principal and basing mortgage interest and property tax index changes on two sources: a

change in a rate (the interest rate or the effective property tax rate), and the change in a

monetary base (the debt level and the housing value). The appendix, including Figure 6 and

1

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

hci-om hci-onm hci-rnt

32

Figure 7, explore the sensitivity of the indexes to these decisions. Including mortgage principal

would raise the owner’s payments subindex (combining mortgage interest, property tax, and

other payments as in Figure 3) by 0.8 percentage points per year. Combined with the associated

weight increase to mortgages, this would result in an all-items HCI-U that is higher by 0.10

percentage points per year. The effect of home prices would be more substantial, lowering the

owner’s payments index by 4.0 percentage points per year and the all-items HCI-U by 0.38

percentage points per year.

5. Conclusions and Future Research

To the extent feasible with existing CPI and publicly available data, we compute HCIs for

the urban population and housing tenure subpopulations. Data constraints (specifically for the

property tax index) would prevent timely production of an HCI using these methods, but our

results still shed light on the relative impacts of different elements of HCI methodology. In

particular, we find the HCI differs from the CPI mainly because it uses the payments approach

for owner occupied housing, and only slightly because it weights households equally in its

upper-level aggregation. The payments approach tracks the actual outlays of homeowners,

which over our sample period of 2012 to 2021 have escalated at a lower trend than (imputed)

owner’s equivalent rent, resulting in lower inflation as measured by the HCI than as measured

by the CPI. We do not argue that the payments approach is superior from the standpoint of

measuring the cost-of-living as an economic theoretic concept or for use in monetary policy.

Rather, by reflecting the explicit outlays of owners, we show the HCI offers a measurement of

the household inflation experience which is empirically different than the CPI.

33

Future research could focus on many areas. First, the inclusion of principal reduction

payments in a mortgage index (or the capital component of housing more generally) remains an

area of debate and discussion (e.g., Astin and Leland 2023), which should be evaluated

empirically. In addition, our measures of price change for mortgage and property tax payments

use only national-level data. A natural next step would be to extend these to subnational

geographic areas, if relevant and feasible. Further down the road, exploring mortgage

microdata of the sort described by Bhutta, et. al. (2020) could be informative on different

experiences of subpopulations, to the extent that long enough histories can be obtained to

account for the long lives of mortgage loans. More timely and granular property tax data would

also improve the HCI. In addition, in principle, the payments approach could be extended to any

durable good where payment occurs over a long timeframe, with automobiles in particular

being a high priority. Martin (2022) suggests treating automobiles under an approach

consistent with the target of the index (payments, in our case) is critical if higher-frequency

household weights are to be taken seriously, such as for a monthly weighted superlative like

the C-CPI-U. Custom sampling weights should also be created to account for demographic

differences for the four-quarter sample of consumer units used for the HCIs, but further

analysis may also be warranted related to weight frequency and subsample selection. With

payments approach weighting of automobiles, for instance, perhaps infrequent purchase issue

discussed in Martin (2022) is less salient. Finally, the impact household-weighted aggregation

on the all-items index’s sampling variation or the potential of weight-smoothing techniques

have yet to be explored.

34

References

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Freddie Mac. (2022). Primary Mortgage Market Survey - About. Retrieved April 29, 2022, from Primary

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Garner, T. I., & Verbrugge, R. (2009). Reconciling user costs and rental equivalence: Evidence from the

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Gillingham, R., & Lane, W. (1982). Changing the treatment of shelter costs for homeowners in the CPI.

Monthly Labor Review, 9-14. Retrieved from

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Goodhart, C. (2001). What Weight Should be Given to Asset Prices in the Measurement of Inflation? The

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Ed.) Geneva: International Labor Organization. Retrieved from

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Retrieved June 21, 2023, from Stats NZ: https://www.stats.govt.nz/information-

releases/household-living-costs-price-indexes-march-2023-quarter/

37

Appendix

A. Alternative Mortgage Interest and Property Tax Indexes

The mortgage payments index which includes mortgage principal replaces the interest

rate component, Eq. (3), with the following representing change in full mortgage payments

between months 𝑠 and 𝑡:

𝑃𝑓 =

∏ [𝑓(𝑟𝑡−𝑗 , 𝜃 − 𝑗)] 𝜑𝑏𝑗𝜃−1

𝑗=0

∏ [𝑟𝑠−𝑗 , 𝜃 − 𝑗)] 𝜑𝑏𝑗𝜃−1

𝑗=0

. (11)

where 𝑓(𝑟, 𝜔) = 𝑟𝑅𝜔 (𝑅𝜔 − 1)⁄ , 𝜔 > 1, where 𝑅 = 1 + 𝑟. The function 𝑓 represents the

fixed mortgage payment as a proportion of the current debt amount. In this expression, the

interest rate 𝑟 is the annualized rate divided by 12 so that it corresponds to one month. Note,

when estimated using aggregate data, even if 𝑟𝑡−𝑗 equals an average interest rate across

households with loans of age 𝑗, the amount 𝑓(𝑟𝑡−𝑗 , 𝜃 − 𝑗) cannot be interpreted as an average

mortgage payment ratio across households due to Jensen’s inequality. The relationship

between 𝑓(𝑟𝑡−𝑗 , 𝜃 − 𝑗) and a true household average is unknown (at least to the authors) but

using such an average in a price index would require microdata tracking individual mortgagors

across loan changes including refinances (which we can observe in the CE) and new loans

(which we often do not observe due to address-based sampling). The mortgage payment

indexes without home prices remove the debt index component, Eq. (2), while the property tax

index without home prices is just the effective tax rate component, 𝑋𝑠,𝑡 𝑉𝑠,𝑡⁄ from Eq. (4).

38

Figure 5 shows the December 2020 relative importance for the baseline HCI-U (2019

reference period) along with the three versions which either include mortgage principal,

exclude home prices, or do both things simultaneously. Note, the relative importance reflects

not only the 2019 reference period expenditure weight, but also the price-updating to reflect

spending in the December 2020 pivot month. Including mortgage principal leads to an increase

in the mortgage payment weight from 4.3% to 7.2% when home prices are excluded, or from

4.1% to 6.9% when home prices are excluded. Accordingly, weight on all other spending

categories decreases slightly when mortgage principal is included. On net, the increase in the

total housing relative importance from including mortgage principal is only about 2 percentage

points. Additionally, even when including mortgage principal, the total weight for the housing

major group (36.3%) is still lower than when using rental equivalence (42.4%). As the inclusion

of home prices in the mortgage and property taxes affects only the price-updating, its effects

on the relative importance are much smaller.

Figure 6 plots the different Owner’s Payment subindexes (combining mortgage interest,

property taxes, etc., as in Figure 3) and compares them again against owner’s equivalent rent.

Adding mortgage principal increases the owner’s payments index by about 0.8 percentage

points per year when home prices are included, and about 1 percentage point per year when

home prices are excluded. Given the strong upward trend of home prices over the past several

decades, removing their lowers the payments index by 4.0 percentage points per year when

mortgage principal is excluded and by 6.6 percentage points per year when mortgage principal

is included, resulting in downward trends. Figure 7 tracks these payments indexes changes on

the all-items HCI-U, accounting for changes in both the elementary indexes and the aggregation

39

weights. The overall effect of mortgage principal is modest, adding 0.10 or 0.03 percentage

points per year depending on whether house prices are included. Home prices themselves have

a larger impact on the all-items index, decreasing it by either 0.38 or 0.45 percentage points per

year depending on whether mortgage principal is included.

40

Figure 5: December 2020 Relative Importance for HCI-U under Alternative Owner Payments

Panel a: HCI-U (baseline)

Panel b: HCI-U (with mortgage principal)

Panel c: HCI-U (no house prices)

Panel d: HCI-U (with mortgage principal, no

house prices)

20.2%

9.2%

4.7%

4.3%

16.0%3.1%

14.2%

11.1%

6.6%

6.8% 3.8%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Housing: Other Apparel

Transportation Medical

Recreation Educ. & Comm.

Other

19.5%

9.2%

4.4%

7.2%

15.5% 3.0%

13.8%

10.8%

6.4%

6.5% 3.7%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Apparel Transportation

Medical Recreation

Educ. & Comm. Other

20.3%

9.2%

4.2%

4.1%

16.1%3.2%

14.3%

11.2%

6.6%

6.8% 3.8%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Housing: Other Apparel

Transportation Medical

Recreation Educ. & Comm.

Other

19.7%

9.2%

4.0%

6.9%

15.6% 3.1%

13.9%

10.9%

6.4%

6.6% 3.7%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Apparel Transportation

Medical Recreation

Educ. & Comm. Other

41

Figure 6: Alternative Versions of Owner’s Payments

Figure 7: HCI-U Under Alternative Versions of Owner’s Payments

B. Interview-Diary Matching Details

We base our household-averaged weights on the CE Interview sample but use a

statistical matching procedure to assign sets of weekly Diary expenditures to each Interview

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Owner's Equiv. Rent (HC) Paym. (HR, HS, HT) Paym. (with principal) Paym. (no home prices) Paym. (with principal, no home prices)

1

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

cpi-u hci-u hci-u (with principal) hci-u (no home prices) hci-u (with principal, no home prices)

42

consumer unit. Our procedure is similar in spirit to that of Hobijn, et. al. (2009), though that

paper models expenditure change (implied by a consumer-unit specific price index) rather than

expenditure levels. Modeling expenditure changes is attractive given the ultimate use of the

matched dataset for price indexes, but Martin (2022) finds demographics explain much less of

the variation in expenditure changes. We limit the dependent variable to categories collected in

both the Interview and the Diary to ensure that the correlations picked up by the model are

relevant to the expenditures we ultimately wish to impute. Over the sample period, R-squared

values for the quintile and month-specific regressions averaged 0.17, while income quintile

itself explained about 0.31 of the variation in the dependent variable. Figure 8 below plots the

average regression R-squared for each quintile, where the averaging is over the 23 months used

for each reference period. The figure shows that average R-squared for the income quintiles are

fairly stable over time, averaging about 0.23 for the 1st quintile, 0.17 for the second quintile,

0.13 for the third quintile, 0.11 for the fourth quintile, and 0.15 for the fifth quintile. The fits

(conditional on income quintile) are not particularly strong, which motivates matching an actual

diary’s expenditure set to an interview consumer unit rather than using regression fitted values.

43

Figure 8: Average R-Squared by Reference Period and Income Quintile

The rest of this section presents figures comparing the imputed weekly diary

expenditures to the actual. Figure 9 shows average imputed weekly expenditures for the

reference period track the actual averages well over time, always falling within 1% of the true

averages. Figure 10 compares average weekly Diary expenditures over time by major group. For

food and beverages, which is by far the largest category sourced from the Diary, the imputed

averages fall within 1% of the actual averages, and they fall within 10% for all other categories.

Figure 11 compares the deciles of weekly imputed Diary expenditures to those of the actual

Diary expenditures for the 2019 reference period (results are similar for other periods). The two

marginal distributions line up well—the imputed deciles are within a few dollars of the actual

deciles.

0

0.05

0.1

0.15

0.2

0.25

2010 2011 2012 2010 2013 2014 2015 2016 2010 2017 2018 2019

IQ1 IQ2 IQ3 IQ4 IQ5

44

Figure 9: Actual and Imputed Average Weekly Diary Expenditures by Reference Period

220

230

240

250

260

270

280

290

300

310

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

actual imputed

45

Figure 10: Average Weekly Diary Expenditures by Reference Period and Major Group

Panel a: Food and Beverages

Panel b: Housing

Panel c: Apparel

Panel d: Transportation

Panel e: Medical

Panel f: Recreation

Panel g: Education and Communication

Panel h: Other

0

50

100

150

200

actual imputed

0

10

20

30

40

actual imputed

0

10

20

30

40

actual imputed

0

5

10

15

20

25

30

actual imputed

0

2

4

6

8

actual imputed

0

10

20

30

40

actual imputed

0

1

2

3

4

actual imputed

0

5

10

15

actual imputed

46

Figure 11: Deciles of Actual and Imputed Weekly Diary Expenditures for 2019 Reference Year

In terms of joint distributions, the matching procedure also does a good job at

replicating average diary expenditures by several demographic characteristics, as shown in

Figure 12 for 2019. Not surprisingly, because income quintile is conditioned on, the procedure

replicates average expenditures by income quintile quite well. The procedure also does well

replicating average differences by housing tenure, age categories, Census region, presence of

children, and education categories, even though these characteristics are not explicitly

conditioned on in the matching process. In these cases, the match quality is being driven by the

correlation between these characteristics and income, as well as the extent to which similarity

in these characteristics across surveys is predictive of expenditures, and so leading to lower

distance between similarly attributed observations.

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9

actual imputed

47

Figure 12: Average Weekly Diary Expenditures by Attribute, 2019 Reference Period

Panel a: Income Quintile

Panel b: Housing Tenure

Panel c: Age

Panel d: Presence of Children

Panel e: Census Region

Panel f: Education

0

100

200

300

400

500

600

1 2 3 4 5

actual imputed

0

100

200

300

400

Own w/ Mort.

Own w/o Mort.

Renter No cash rent

Student

actual imputed

0

50

100

150

200

250

300

350

<=61 >61

actual imputed

0

100

200

300

400

No kids Kids

actual imputed

260

270

280

290

300

310

320

330

NE MW S W

actual imputed

0

100

200

300

400

< H.S. H.S. & Some Coll.

>= Bachelors

actual imputed

48

C. All-items Indexes Using Different CE subsamples

Figure 13: Twelve-month inflation of CPI and indexes using payments approach by subsample

As a check of our sample requirement that consumer units contributing to the HCI have

four quarters of data in the CE survey, we compare all-items indexes (all using the payments

approach) with this eligibility requirement against all-items indexes without. For this

comparison, we examine expenditure-weighted aggregates across households, as equally-

weighted aggregates can be sensitive to weight frequency and overall dispersion in total

expenditures (Ley, 2005; Martin, 2022). We consider both the full CE sample for the reference

year, as well as for the full CE sample for the biennial period ending in the reference year, as

our HCI subsample also includes four-quarter households who entered the CE in the year prior

to the reference year. Figure 13 plots the twelve-month percent changes of these indexes as

well as the CPI-U for reference. Over this period, average inflation of the CPI-U is 1.86% per

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08 D

ec -1

2

M ay

-1 3

O ct

-1 3

M ar

-1 4

A u

g- 1

4

Ja n

-1 5

Ju n

-1 5

N o

v- 1

5

A p

r- 1

6

Se p

-1 6

Fe b

-1 7

Ju l-

1 7

D ec

-1 7

M ay

-1 8

O ct

-1 8

M ar

-1 9

A u

g- 1

9

Ja n

-2 0

Ju n

-2 0

N o

v- 2

0

A p

r- 2

1

Se p

-2 1

lowe-u (ew, pay, 4Q) lowe-u (ew, pay, full-be)

lowe-u (ew, pay, full-a) cpi-u

49

year. The payments approach index using the four-quarter sample averaged 1.46%, while the

indexes using the full annual and biennial samples averaged 1.47% and 1.46%, respectively.

Figure 14: Twelve-month inflation of HCI and indexes using payments approach by subsample

Figure 14 repeats the analysis in Figure 13, but compares the HCI-U and comparable

household-weighted indexes using the full annual or biennial CE samples. The HCI-U averaged

1.51% year-over-year, while the index using the full annual and full biennial samples averaged

1.50% and 1.51%, respectively, though larger differences occurred in 2021. Here, index

differences could reflect sample selection effects, but also likely reflect the mixed frequencies

of household weights underlying the full-sample indexes, as some consumer units have only a

few months or quarters of expenditure due to normal sample rotations and unit nonresponse.

Higher frequency expenditure shares tend to give less weight to less frequently purchased

items and more weight to more frequently purchased items (Martin, 2022). We do not want to

capture this latter effect because, in the case of the HCI’s, it is an artifact of using CPI weights

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

D ec

-1 2

M ay

-1 3

O ct

-1 3

M ar

-1 4

A u

g- 1

4

Ja n

-1 5

Ju n

-1 5

N o

v- 1

5

A p

r- 1

6

Se p

-1 6

Fe b

-1 7

Ju l-

1 7

D ec

-1 7

M ay

-1 8

O ct

-1 8

M ar

-1 9

A u

g- 1

9

Ja n

-2 0

Ju n

-2 0

N o

v- 2

0

A p

r- 2

1

Se p

-2 1

hci-u lowe-u (hw, pay, full-be) lowe-u (hw, pay, full-a)

50

for automobiles, which are measured by full purchase price at the time of acquisition, rather

than ongoing monthly payments. In 2021, when HCI-U (over the four-quarter sample) has

slightly higher inflation than the two full sample indexes. In 2021, vehicle price inflation was

high relative to the average inflation across all items, and the comparison in the figure is

consistent with the full-sample indexes giving too little weight to vehicles. A payments

approach for vehicles should mitigate this effect in the full samples.

Presentation, Thesia Garner (U.S. Bureau of Labor Statistics)

Languages and translations
English

1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Progress Report of the UNECE Task Force on Subjective Poverty Measures

Thesia I. Garner, PhD

Chair of UNECE Task Force on Subjective Poverty Measurement

and Chief Researcher, Office of Prices and Living Conditions

28–29 November: Meeting of the UNECE Group of Experts on Measuring Poverty and Inequality

Session D. “Subjective poverty” 28 November 2023 16:05 - 17:30

Geneva, Switzerland

2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Appreciation to UNECE Expert Group on Poverty and Inequality

Task Force Members & Vania

3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Progress Details ◼ Meeting of Task Force members (throughout 2023)

 Finalized what to include

 Identified leadership and contributors for writing chapters

◼ Final draft report

 Introduction

 Chapter 2. Focus on Subjective Poverty

 Chapter 3. Approaches for Measurement and Analysis

 Chapter 4. Methods for Data Collection and Guidance

 Chapter 5. Recommendations

 Appendices

– Appendix A. Survey of countries summary

– Appendix B. R computer code to produce Subjective Poverty Line as intersection of MIQ & income based on econometric estimation

4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapters 1 & 2

◼ Chapter 1 Introduction -- background

◼ Chapter 2. Focus on Subjective Poverty

 Introduction

Definitions of subjective poverty

– Contrast to objective poverty

– Frameworks for subjective poverty

– Collection and analysis of subjective poverty at NSOs

– Collection and analysis of subjective poverty at International Agencies

Why measure

Evolution of subjective poverty measurement (literature review)

5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 3 ◼ Approaches to measurement

Qualitative questions not focused on specific level of income (or consumption) – Identification

– Evaluation

– Prediction

Qualitative categorical focused on specific level of income (or consumption) – Evaluation

– Prediction

Money metric valuation question

◼ Analysis

Relationships

 Subjective poverty lines

Country/international organization examples

6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Box 7. Example of Qualitative Categorical Evaluation Questions Focused on Income (Deleeck)

[EU-SILC participating countries] A household may have different sources of income and more than

one household member may contribute to it. Thinking of your household’s total income, is your

household able to make ends meet, namely, to pay for its usual necessary expenses?

• With great difficulty

• With difficulty

• With some difficulties

• Fairly easily

• Easily

• Very easily

7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Box 10. Examples of Money Metric Valuation Questions, Minimum Income (MIQ)

[Brazil] Taking into account the current situation of your family, what would be the minimum

monthly income needed to “make ends meet”?

[Ukraine] What do you think: how much money (according to today’s price level) for one of your

household members is needed in order to not feel poor?

[Kyrgyz Republic] What is your opinion, how much money on average per month at today's price are

needed for the family with the same number of people as you have in order to avoid poverty?

[Moldova] What monthly cash income would meet the minimum needs of one person in order to

'live from day to day’?

[Belarus] In your opinion, what amount of money does your household need to have monthly to

meet[satisfy] the minimum needs of all its members?

8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 4. Methods for Data Collection and Guidance - 1

◼ Survey frame and sample consideration

◼ Surveys – traditional versus alternative (e.g., rapid response)

◼ Administrative and registry data

◼ Sources of error – responses and representativeness

◼ Validity and relationship to other measure of poverty and economic well-being

◼ Time frame for data collection and release

◼ Cross sectional versus longitudinal data collection

◼ OECD subjective well-being guidelines

9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 4. Methods for Data Collection and Guidance - 2

◼ Hypothetical assessments of subjective poverty

 Importance of question wording and examples

Frame and mode effects

What impacts responses (e.g., demographics, culture STiK)

◼ Lessons learned from COVID 19

 Subjective poverty in SEIA Questionnaires and Comparability Analysis

Overview of UNDP Socio-Economic Impact Assessments (SEIAs) for countries

in households of UNECE region

 Implications regarding COVID experience outbreak

10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Money Metric Valuation and Use

◼ To meet the expenses you consider

necessary, what do you think is the minimum

income, before tax, a family like yours needs, on

a yearly basis, to make ends meet (if you are not

living with relatives, what are the minimum needs,

before tax, of an individual like you)?

◼ In your opinion, how much do you have to

spend each year in order to provide the basic

needs for your family? By basic needs I mean

barely adequate food, shelter, clothing and other

essential items required for daily living.

Version 1 (MIQ, 1988)

Version 2 (MSQ, 1988)

Su b

je ct

iv e

m in

im u

m in

co m

e

Actual income

Z*

Z*45

Intersection of MIQ and Income

11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 5. Recommendations 1-3 Subjective measures of poverty should be included among the set of assessment tools used by countries …

▪ NOT to replace objective measures or multidimensional measures

▪ Serve as complements

▪ Countries with dashboards of poverty indicators should include subj assessments

Given their inclusion in EU-SILC, and their utility in identifying subjective poverty, NSOs use as standard for international comparisons...

▪ Deleeck questions - refer to level of financial difficulty (categorical)

▪ Minimum Income Question (money metric valuation)

Primary method to estimate subjective poverty lines …

▪ Utilize Minimum Income Question with

▪ Intersection approach (econometric estimation)

12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 5. Recommendations 4-6 NSOs and analysts should consider the possible impacts of …

▪ Survey mode, context (framing), sampling methods, and working differences

▪ When analyzing subjective indicators like subjective poverty

▪ Countries with dashboards of poverty indicators should include subj assessments

NSOs and analysts should continue to demonstrate the utility of subjective poverty measures, considering...

▪ Issues of overlap with objective poverty measures and

▪ Policy applications

Subjective poverty measures should be …

▪ Disaggregated to at-risk groups

▪ Follow recommendations in UNECE’s guide to disaggregation

13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Table A.1: Question Types Reported Being Asked by Country in UNECE (2021) Study

Qualitative Categorical Money

Metric Total # of

Subjective

Poverty

Questions

Other

Country Identification Evaluation Prediction Evaluation

Deprivation,

Social

Exclusion,

Well-being

Total # across

all Countries 4 42 6 40 45 22

14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Appendix B. Estimation Example - Sample

◼ Sample data (N=1000):  income: monthly household income

 miq: response to the Minimum Income Question

 ordered: response to a categorical question (categories: 1-6)

 size: household size

 urban: degree of urbanisation with categories: capital city; other cities; towns; rural areas

 LNincome; LNmiq, and LNsize refer to natural logs of income, miq, and size variables.

0

100

200

300

400

1 2 3 4 5 6

Ordered response

F re

q u e n c y

Self-reported category

0

100

200

300

1 2 3 4 5

Household size

F re

q u e n c y

Household size

0

100

200

300

capital city town rural

Type of location

F re

q u e n c y

Type of location

0.00000

0.00025

0.00050

0.00075

0.00100

0 1000 2000 3000 4000

Income (eur/month)

D e n s ity

Actual income

0.00000

0.00025

0.00050

0.00075

0.00100

0 1000 2000 3000 4000

Minimum income (eur/month)

D e n s ity

Subjective minimum income

0

100

200

300

400

1 2 3 4 5 6

Ordered response

F re

q u e n c y

Self-reported category

0

100

200

300

1 2 3 4 5

Household size

F re

q u e n c y

Household size

0

100

200

300

capital city town rural

Type of location

F re

q u e n c y

Type of location

15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Appendix B. R Code Provided to Produce SPL

16 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Remaining Timeline

Time Task Status 20 Nov 2023 Draft report posted on wiki Completed

15 Dec 2023 Report finalized

Early Jan-Feb 2024

Send full report to Conference of European Statisticians (CES) Bureau for review

Mar-Apr 2024 Electronic UNECE wide consultation with all UN Member States – here is where we expect to receive comments from the countries

End of April- May 2024

Integrate comments and submit final report to the CES Bureau (CES Bureau meeting held in June)

May 2024 Submit final report to the CES plenary session for endorsement

17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Discussion & Questions

18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Contact

Thesia I. Garner

Chair of the UNECE Task Force on Subjective Poverty Measures and

Chief Researcher, Office of Prices and Living Conditions Bureau of Labor Statistics

Washington, DC 20212

[email protected]

  • Slide 1: Progress Report of the UNECE Task Force on Subjective Poverty Measures
  • Slide 2: Appreciation to UNECE Expert Group on Poverty and Inequality Task Force Members & Vania
  • Slide 3: Progress Details
  • Slide 4: Chapters 1 & 2
  • Slide 5: Chapter 3
  • Slide 6: Box 7. Example of Qualitative Categorical Evaluation Questions Focused on Income (Deleeck)
  • Slide 7: Box 10. Examples of Money Metric Valuation Questions, Minimum Income (MIQ)
  • Slide 8: Chapter 4. Methods for Data Collection and Guidance - 1
  • Slide 9: Chapter 4. Methods for Data Collection and Guidance - 2
  • Slide 10: Money Metric Valuation and Use
  • Slide 11
  • Slide 12
  • Slide 13: Table A.1: Question Types Reported Being Asked by Country in UNECE (2021) Study
  • Slide 14: Appendix B. Estimation Example - Sample
  • Slide 15: Appendix B. R Code Provided to Produce SPL
  • Slide 16: Remaining Timeline
  • Slide 17: Discussion & Questions
  • Slide 18: Contact
Russian

1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Отчет о работе Целевой группы ЕЭК ООН по субъективным показателям бедности

Тесия И. Гарнер, доктор наук Председатель Целевой группы ЕЭК ООН по измерению субъективной бедностии главный научный

сотрудник Управления по ценам и условиям жизни

28-29 ноября: Совещание Группы экспертов ЕЭК ООН по измерению бедности и неравенства

Сессия D. "Субъективная бедность" 28 ноября 2023 г. 16:05 - 17:30

Женева, Швейцария

2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Благодарность экспертной группе ЕЭК ООН по проблемам бедности и неравенства Члены целевой

группы и Ване

3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Подробности о ходе работ ◼ Встреча членов рабочей группы (в течение 2023 г.)

 Окончательно определились с тем, что включить

 Определены руководители и исполнители для написания глав

◼ Окончательный проект отчета

 Введение

 Глава 2. Фокус на субъективную бедность

 Глава 3. Подходы к измерению и анализу

 Глава 4. Методы сбора данных и руководства

 Глава 5. Рекомендации

 Приложения

– Приложение A. Резюме опроса стран

– Приложение B. Компьютерный код на языке R для получения субъективной черты бедности как пересечения MIQ и дохода на

основе эконометрической оценки

4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Главы 1 и 2

◼ Глава 1. Введение - история вопроса

◼ Глава 2. Фокус на субъективной бедности

Введение

Определения субъективной бедности

– Противопоставление объективной бедности

– Рамки субъективной бедности

– Сбор и анализ субъективной бедности в НСО

– Сбор и анализ субъективной бедности в международных агентствах

Зачем измерять

Эволюция измерения субъективной бедности (обзор литературы)

5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 3 ◼ Подходы к измерению

Качественные вопросы, не ориентированные на конкретный уровень дохода (или потребления)

– Идентификация – Оценка – Прогнозирование

Качественные категориальные, ориентированные на конкретный уровень дохода (или потребления)

– Оценка – Прогнозирование

Вопрос об оценке в денежной метрике

◼ Анализ Взаимоотношения Субъективные границы бедности Примеры стран/международных организаций

6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Вставка 7. Пример вопросов качественной категориальной оценки, ориентированных на доход (Deleeck)

[Страны-участницы ЕС-СИЛК] Домохозяйство может иметь различные источники дохода, и

несколько членов домохозяйства могут вносить в него свой вклад. Если подумать о совокупном

доходе Вашего домохозяйства, способно ли оно сводить концы с концами, то есть оплачивать

свои обычные необходимые расходы? • С большим трудом

• С трудом

• С некоторыми трудностями

• Достаточно легко

• Легко

• Очень легко

7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Вставка 10. Примеры вопросов по оценке денежной метрики, минимальный доход (MIQ)

[Бразилия] Принимая во внимание текущее положение Вашей семьи, каков должен быть

минимальный ежемесячный доход, необходимый для того, чтобы "свести концы с концами"?

[Украина] Как Вы считаете, сколько денег (по сегодняшнему уровню цен) необходимо для одного из

членов Вашего домохозяйства, чтобы не чувствовать себя бедным?

[Кыргызская Республика] Как Вы считаете, сколько денег в среднем в месяц по сегодняшним ценам

необходимо семье с таким же количеством человек, как у Вас, чтобы избежать бедности?

[Молдова] Какой ежемесячный денежный доход удовлетворял бы минимальные потребности

одного человека, чтобы «жить изо дня в день»?

[Беларусь] По Вашему мнению, какую сумму денег должно ежемесячно иметь Ваше домохозяйство,

чтобы удовлетворять минимальные потребности всех его членов?

8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 4. Методы сбора данных и рекомендации - 1

◼ Рассмотрение структуры и выборки опроса

◼ Опросы - традиционные и альтернативные (например, быстрого

реагирования)

◼ Административные и регистрационные данные

◼ Источники ошибок - ответы и репрезентативность

◼ Валидность и связь с другими показателями бедности и экономического

благосостояния

◼ Сроки сбора и выпуска данных

◼ Поперечный и продольный сбор данных

◼ Руководство по субъективному благополучию Организации экономического

сотрудничества и развития

9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 4. Методы сбора данных и рекомендации - 2

◼ Гипотетические оценки субъективной бедности

Важность формулировки вопроса и примеры

Эффекты рамок и режимов

Что влияет на ответы (например, демографические характеристики,

культура STiK)

◼ Уроки, извлеченные из COVID 19

Субъективная бедность в анкетах SEIA и анализ сопоставимости

Обзор оценок социально-экономического воздействия (ОСПВ) ПРООН для

стран региона ЕЭК ООН по домохозяйствам

Последствия вспышки заболевания, вызванной опытом COVID

10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Оценка и использование денежной метрики

◼ Как Вы считаете, какой минимальный доход

до вычета налогов необходим такой семье,

как Ваша, в год, чтобы свести концы с концами

(если Вы не живете с родственниками, каковы

минимальные потребности до вычета налогов

такого человека, как Вы)?

◼ Как Вы считаете, сколько Вам приходится

тратить в год, чтобы обеспечить основные

потребности своей семьи? Под основными

потребностями я понимаю едва ли

достаточное количество пищи, жилья, одежды

и других предметов первой необходимости,

необходимых для повседневной жизни.

Версия 1 (MIQ, 1988)

Версия 2 (MSQ, 1988)

Su b

je ct

iv e

m in

im u

m in

co m

e

Actual income

Z*

Z*45

Пересечение MIQ и дохода

11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 5. Рекомендации 1-3 Субъективные показатели бедности должны быть включены в набор инструментов оценки, используемых странами…

▪ НЕ должны заменять объективные или многомерные измерения

▪ Служат в качестве дополнения

▪ Страны, имеющие панели показателей бедности, должны включать субъективные оценки

Учитывая их включение в EU-SILC и полезность для определения субъективной бедности, НСО используют их в качестве стандарта для международных

сопоставлений...

▪ Вопросы Делека - относятся к уровню финансовых трудностей (категорические)

▪ Вопрос о минимальном доходе (оценка денежной метрики)

Первичный метод оценки субъективных границ бедности…

▪ Использовать вопрос о минимальном доходе с

▪ Пересекающимся подходом (эконометрическая оценка)

12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 5. Рекомендации 4-6 НСО и аналитики должны учитывать возможные последствия…

▪ Способ проведения исследования, контекст (фрейминг), методы выборки и рабочие различия

▪ При анализе субъективных показателей, таких как субъективная бедность

▪ Страны, имеющие панели показателей бедности, должны включать в них субъективные оценки

НСО и аналитики должны продолжать демонстрировать полезность субъективных показателей бедности, учитывая...

▪ Вопросы совпадения с объективными показателями бедности и

▪ Применение политики

Субъективные показатели бедности должны быть…

▪ Дезагрегировано по группам риска

▪ Следовать рекомендациям руководства ЕЭК ООН по дезагрегированию

13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Таблица А.1: Типы вопросов, задаваемых по странам в исследовании ЕЭК ООН (2021)

Качественные Категориальные Денежная

метрика Общее

количество

вопросов о

субъективной

бедности

Прочее

Страна Идентификация Оценка Прогноз Оценка

Депривация,

социальное

отчуждение,

благосостоя

ние Общее

количест

во по

всем

странам

4 42 6 40 45 22

14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Приложение Б. Пример оценки - Образец

◼ Выборочные данные (N=1000):  доход: ежемесячный доход домохозяйства

 miq: ответ на вопрос о минимальном доходе

 упорядоченный: ответ на категорический вопрос (категории: 1-6)

 размер: размер домохозяйства

 город: степень урбанизации с категориями: столица; другие города; поселки; сельская местность

 LNincome; LNmiq и LNsize означают натуральные логарифмы переменных дохода, miq и размера.

0

100

200

300

400

1 2 3 4 5 6

Ordered response

F re

q u e n c y

Self-reported category

0

100

200

300

1 2 3 4 5

Household size

F re

q u e n c y

Household size

0

100

200

300

capital city town rural

Type of location

F re

q u e n c y

Type of location

0.00000

0.00025

0.00050

0.00075

0.00100

0 1000 2000 3000 4000

Income (eur/month)

D e n s ity

Actual income

0.00000

0.00025

0.00050

0.00075

0.00100

0 1000 2000 3000 4000

Minimum income (eur/month)

D e n s ity

Subjective minimum income

0

100

200

300

400

1 2 3 4 5 6

Ordered response

F re

q u e n c y

Self-reported category

0

100

200

300

1 2 3 4 5

Household size

F re

q u e n c y

Household size

0

100

200

300

capital city town rural

Type of location

F re

q u e n c y

Type of location

15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Приложение В. R-код, используемый для получения SPL

16 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Оставшиеся сроки

Время Задача Статус 20 ноября 2023 Проект отчета размещен в Вики Завершено

15 декабря 2023 Подготовка отчета завершена

Начало января - февраль 2024

Направить полный текст отчета на рассмотрение Бюро Конференции европейских статистиков (CES)

Март-апрель 2024

Электронные консультации с участием всех стран- членов ЕЭК ООН - здесь мы ожидаем получить комментарии от стран

Конец апреля - май 2024

Интеграция комментариев и представление окончательного отчета в Бюро КЕС (заседание Бюро КЕС состоялось в июне)

май 2024 Представить итоговый отчет на пленарное заседание КЕС для утверждения

17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Обсуждение и вопросы

18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Контакт

Thesia I. Garner

Chair of the UNECE Task Force on Subjective Poverty Measures and

Chief Researcher, Office of Prices and Living Conditions Bureau of Labor Statistics

Washington, DC 20212

[email protected]

  • Slide 1: Отчет о работе Целевой группы ЕЭК ООН по субъективным показателям бедности
  • Slide 2: Благодарность экспертной группе ЕЭК ООН по проблемам бедности и неравенства Члены целевой группы и Ване
  • Slide 3: Подробности о ходе работ
  • Slide 4: Главы 1 и 2
  • Slide 5: Глава 3
  • Slide 6: Вставка 7. Пример вопросов качественной категориальной оценки, ориентированных на доход (Deleeck)
  • Slide 7: Вставка 10. Примеры вопросов по оценке денежной метрики, минимальный доход (MIQ)
  • Slide 8: Глава 4. Методы сбора данных и рекомендации - 1
  • Slide 9: Глава 4. Методы сбора данных и рекомендации - 2
  • Slide 10: Оценка и использование денежной метрики
  • Slide 11
  • Slide 12
  • Slide 13: Таблица А.1: Типы вопросов, задаваемых по странам в исследовании ЕЭК ООН (2021)
  • Slide 14: Приложение Б. Пример оценки - Образец
  • Slide 15: Приложение В. R-код, используемый для получения SPL
  • Slide 16: Оставшиеся сроки
  • Slide 17: Обсуждение и вопросы
  • Slide 18: Контакт

JQ2022USA

JFSQ2022 Country Replies USA

Languages and translations
English

Cover

Joint Forest Sector Questionnaire
2022
DATA INPUT FILE
Correspondent country:
Reference year: 2022 Fill in the year
Name of person responsible for reply:
Official address (in full):
Telephone:
Fax:
E-mail:

Manual

The UNECE manual for the JFSQ for 2022 data is available on the UNECE website:
https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-manual
The definitions for the JFSQ for 2022 data are available on the UNECE website:
https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-definitions
https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-definitions https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-manual

conversion factors

JFSQ
JOINT FOREST SECTOR QUESTIONNAIRE
Conversion Factors
NOTE THESE ARE ONLY GENERAL FACTORS. IT WOULD BE PREFERABLE TO USE SPECIES- OR COUNTRY-SPECIFIC FACTORS
Multiply the quantity expressed in units on the right side of "per" with the factor to get the value expressed in units on left side of "per".
Items in BOLD RED text were added to the JFSQ in February 2023
Product Code Product JFSQ Quantity Unit Results from UNECE/FAO/ITTO 2020 publication "Forest Product Conversion Factors" UNECE/FAO Engineered Wood Products Questionnaire (last revised 2020) Results from UNECE/FAO 2009 Conversion Factors Questionnaire (median) FAO and UNECE Statistical Publications (Pre-2009)
volume to weight volume/weight of finished product to volume of roundwood Notes to Results volume to weight Notes to Results volume to weight volume/weight of finished product to volume of roundwood Notes to Results volume to weight volume to area volume/weight of finished product to volume of roundwood
m3 per MT m3 per MT m3 per MT Roundwood equivalent Roundwood equivalent Roundwood equivalent m3 per MT m3 per MT Roundwood equivalent m3 per MT m3 per m2 Roundwood
equivalent
Europe NA** EECCA** Europe NA** EECCA**
1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3 ub
1.1 WOOD FUEL, INCLUDING WOOD FOR CHARCOAL 1000 m3 ub 1.38
1.1.C Coniferous 1000 m3 ub 1.64 typical shipping weight Green = 1.12 Based on 891 kg/m3 green, basic density of .41, and 20% moisture seasoned 1.60
1000 m3 ub Seasoned = 1.82 Based on 407 kg/m3 dry, assuming 20% moisture
1.1.NC Non-Coniferous 1000 m3 ub 1.11 typical shipping weight Green=1.05 Based on 1137 kg/m3 green, specific gravity of .55, and 20% moisture seasoned 1.33
1000 m3 ub Seasoned=1.43
1.2 INDUSTRIAL ROUNDWOOD 1000 m3 ub
1.2.C Coniferous 1000 m3 ub 1.11 1.08 1.27 Averaged pulp and log 1.10 Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood
1.2.C.Fir Fir (and Spruce) 1000 m3 ub 1.21 Austrian Energy Agency, 2009. weighted by share of standing inventory of European speices (57% spruce, 10% silver fir and remaining species)
1.2.C.Pine Pine 1000 m3 ub 1.08 Austrian Energy Agency, 2009, weighted 25% Scots Pine, 2% maritime pine, 2% black pine and remaining species
1.2.NC Non-Coniferous 1000 m3 ub 0.98 1.02 1.15 0.91 Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood
1.2.NC.T of which:Tropical 1000 m3 ub AFRICA=1.31, ASIA=0.956, LA. AM= 0.847, World=1.12 Source: Fonseca "Measurement of Roundwood" 2005, ITTO Annual Review 2007, table 3-2-a Species weight averaged using m3/tonne from Fonseca 2005 and volume exported by species from each region as shown in ITTO 2007 (assumes that bark is removed) 1.37
1.2.1 SAWLOGS AND VENEER LOGS 1000 m3 ub 1.04 0.96 1.12 Averaged C & NC 1.05 Based on 950 kg/m3 green. Bark is included in weight but not in volume.
1.2.1.C Coniferous 1000 m3 ub 1.10 1.00 1.19 1.07 Based on 935 kg/m3 green. Bark is included in weight but not in volume. 1.43
1.2.1.NC Non-Coniferous 1000 m3 ub 0.97 0.92 1.04 0.91 Based on 1093 kg/m3 green. Bark is included in weight but not in volume. 1.25
1.2.NC.Beech Beech 1000 m3 ub 0.92 Austrian Energy Agency, 2009
1.2.NC.Birch Birch 1000 m3 ub 0.88 Austrian Energy Agency, 2009
1.2.NC.Eucalyptus Eucalyptus 1000 m3 ub 0.77 ATIBT, 1982
1.2.NC.Oak Oak 1000 m3 ub 0.88 Austrian Energy Agency, 2009
1.2.NC.Poplar Poplar 1000 m3 ub 1.06 Austrian Energy Agency, 2009
1.2.2 PULPWOOD (ROUND & SPLIT) 1000 m3 ub 1.05 1.14 1.30 Averaged C & NC 1.08 Based on 930 kg/m3 green. Bark is included in weight but not in volume. 1.48
1.2.2.C Coniferous 1000 m3 ub 1.11 1.16 1.35 1.12 Based on 891 kg/m3 green. Bark is included in weight but not in volume. 1.54
1.2.2.NC Non-Coniferous 1000 m3 ub 0.98 1.11 1.25 0.91 Based on 1095 kg/m3 green. Bark is included in weight but not in volume. 1.33
1.2.3 OTHER INDUSTRIAL ROUNDWOOD 1000 m3 ub 1.07 1.33
1.2.3.C Coniferous 1000 m3 ub 1.11 1.16 1.35 used pulpwood data 1.12 same as 1.2.2.C 1.43
1.2.3.NC Non-Coniferous 1000 m3 ub 0.98 1.11 1.25 0.91 same as 1.2.2.NC 1.25
2 WOOD CHARCOAL 1000 MT 6 m3rw/tonne 5.35 Does not include the use of any of the wood fiber to generate the heat to make (add about 30% if inputted wood fiber used to provide heat) 6.00
3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3
3.1 WOOD CHIPS AND PARTICLES 1000 m3 1.205 1.07 1.21 1.08 m3 /MT = green swe per odmt / avg delivered tonne/odmt, rwe= +1% softwood=1.19 1.205 Based on swe/odmt of 2.41 and avg delivered mt / odmt of 2.0 in solid m3 1.60
1000 m3 hardwood = 1.05 1.123 Based on swe/odmt of 2.01 and avg delivered mt / odmt of 1.79 in solid m3
1000 m3 Woodchip, Green swe to oven-dry tonne m3/odmt mix = 1.15
3.2 WOOD RESIDUES 1000 m3 1.205 1.07 1.21 1.08 Based on wood chips Green=1.15 Based on wood chips 1.50
1000 m3 2.12 2.07 Seasoned = 2.12 2.07 Assumption for seasoned is based on average basic density of .42 from questionnaire and assumes 15% moisture content
3.2.1 of which: SAWDUST 1000 m3 1.205 1.07 1.21 1.08 Based on wood chips
4 RECOVERED POST-CONSUMER WOOD 1000 mt Delivered MT (12-20% atmospheric moisture). Convert to dry weight for energy purposes (multiply by 0.88 - 0.80)
5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 MT
5.1 WOOD PELLETS 1000 MT 1.54 1.45 1.54 1.51 1.44 nodata m3/ton - bulk density, loose volume, 5-10% mcw- Equivalent - solid wood imput to bulk m3 pellets 1.51 1.44 Bulk (loose) volume, 5-10% moisture
5.2 OTHER AGGLOMERATES 1000 MT 1.12 nodata nodata 2.32 nodata nodata m3/ton - Pressed logs and briquettes, bulk density, loose volume. Equivalent - m3rw/odmt 1.31 2.29 roundwood equivalent is m3rw/odmt, volume to weight is bulk (loose volume)
6 SAWNWOOD 1000 m3 1.6 / 1.82*
6.C Coniferous 1000 m3 1.202 1.69 1.62 1.85 m3/ton - Average Sawnwood shipping weight. Equivalent - Sawnwood green rough Green=1.202 RoughGreen=1.67 Green sawnwood based on basic density of .94, less bark (11%) 1.82
1000 m3 1.82 1.72 Nodata 2 1.69 2.05 Sawnwood dry rough Dry = 1.99 RoughDry=1.99 Dry sawnwood weight based on basic density of .42, 4% shrinkage and 15% moisture content
1000 m3 2.26 2.08 nodata Sawnwood dry planed PlanedDry=2.13
6.C.Fir Fir and Spruce 1000 m3 2.16 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.
6.C.Pine Pine 1000 m3 1.72 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.
6.NC Non-Coniferous 1000 m3 1.04 1.89 1.79 nodata Sawnwood green rough Green=1.04 RoughGreen=1.86 Green sawnwood based on basic density of 1.09, less bark (12%) 1.43
1000 m3 1.43 nodata nodata 2.01 1.92 nodata m3/ton - Average Sawnwood shipping weight. Equivalent - Sawnwood green rough Seasoned=1.50 RoughDry=2.01 Dry sawnwood weight based on basic density of .55, 5% shrinkage and 15% moisture content
1000 m3 3.25 3.38 nodata Sawnwood dry planed PlanedDry=2.81
6.NC.Ash Ash 1000 m3 1.47 Wood Database (wood-database.com). Air-dry.
6.NC.Beech Beech 1000 m3 1.42 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Birch Birch 1000 m3 1.47 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Cherry Cherry 1000 m3 1.62 Giordano, 1976, Tecnologia del legno. Air-dry. Prunus avium.
6.NC.Maple Maple 1000 m3 1.35 Giordano, 1976, Tecnologia del legno. Air-dry
6.NC.Oak Oak 1000 m3 1.38 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Poplar Poplar 1000 m3 2.29 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.T of which:Tropical 1000 m3 1.38 Based on FP Conversion Factors (2019), Asia (720 kg / m3)
7 VENEER SHEETS 1000 m3 1.33 0.0025 1.9*
7.C Coniferous 1000 m3 1.05 1.95 1.5 Green veneer based on the ratio from the old conversion factors Green=1.20 1.5*** Green veneer based on basic density of .94, less bark (11%) 0.003
1000 m3 1.8 nodata nodata 2.08 1.6 nodata m3/ton - Average panel shipping weight; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product Seasoned=2.06 1.6*** Dry veneer weight based on basic density of .42, 9% shrinkage and 5% moisture content
7.NC Non-Coniferous 1000 m3 1.15 nodata nodata 2.11 1.89 Green veneer based on the ratio from the old conversion factors Green=1.04 1.5*** Green veneer based on basic density of 1.09, less bark (11%) 0.001
1000 m3 1.7 nodata nodata 2.25 2 nodata m3/ton - Average panel shipping weight; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product Seasoned=1.53 1.6*** Dry veneer weight based on basic density of .55, 11.5% shrinkage and 5% moisture content
7.NC.T of which:Tropical 1000 m3
8 WOOD-BASED PANELS 1000 m3 1.6
8.1 PLYWOOD 1000 m3 1.54 0.105 2.3*
8,1.C Coniferous 1000 m3 1.67 Nodata Nodata 2.16 1.92 nodata 1.69 2.12 dried, sanded, peeled 0.0165***
8.1.NC Non-Coniferous 1000 m3 1.54 Nodata Nodata 2.54 2.14 nodata 1.54 1.92 dried, sanded, sliced 0.0215***
8.1.NC.T of which:Tropical 1000 m3
8.1.1 of which: LAMINATED VENEER LUMBER 1000 m3 1.69 Same as coniferous plywood
8.1.1.C Coniferous 1000 m3 1.69 Same as coniferous plywood
8.1.1.NC Non-Coniferous 1000 m3 no data
8.1.1.NC.T of which:Tropical 1000 m3 no data
8.2 PARTICLE BOARD (including OSB) 1000 m3 1.54
8.2x PARTICLE BOARD (excluding OSB) 1000 m3 1.54 Nodata Nodata 1.51 1.54 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 1.53 1.50 0.018***
8.2.1 of which: OSB 1000 m3 1.64 Nodata Nodata 1.72 1.63 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 1.67 1.63 0.018***
8.3 FIBREBOARD 1000 m3 nodata nodata nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.
8.3.1 HARDBOARD 1000 m3 1.06 Nodata Nodata 2.2 1.77 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 1.06 1.93 solid wood per m3 of product 1.05 0.005
Alex McCusker: Alex McCusker: 0.003 per Conversion Factors Study
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 1.35 Nodata Nodata 1.80 1.53 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 1.37 1.70 solid wood per m3 of product 2.00 0.016
8.3.3 OTHER FIBREBOARD 1000 m3 3.85 Nodata Nodata 0.68 0.71 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 3.44 0.71 solid wood per m3 of product, mostly insulating board 4.00 0.025
9 WOOD PULP 1000 MT 3.7 nodata 3.76 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.86 3.37
9.1 MECHANICAL AND SEMI-CHEMICAL 1000 MT 2.59 2.45 2.94 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 2.60 air-dried metric ton (mechanical 2.50, semi-chemical 2.70)
9..2 CHEMICAL 1000 MT 4.80 4.29 4.10 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.90
9.2.1 SULPHATE 1000 MT 4.50 nodata 4.60 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.57 air-dried metric ton (unbleached 4.63, bleached 4.50)
9.2.1.1 of which: bleached 1000 MT 4.50 nodata 4.90 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.50 air-dried metric ton
9.2.2 SULPHITE 1000 MT 4.73 nodata 4.15 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.83 air-dried metric ton (unbleached 4.64 and bleached 5.01)
9.3 DISSOLVING GRADES 1000 MT 4.46 nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 5.65 air-dried metric ton
10 OTHER PULP 1000 MT nodata nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)
10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 MT nodata nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)
10.2 RECOVERED FIBRE PULP 1000 MT nodata nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)
11 RECOVERED PAPER 1000 MT nodata nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 1.28 MT in per MT out
12 PAPER AND PAPERBOARD 1000 MT 3.85 nodata 4.15 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.6 3.37
12.1 GRAPHIC PAPERS 1000 MT nodata nodata nodata
12.1.1 NEWSPRINT 1000 MT 2.80 2.50 3.15 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 2.80 air-dried metric ton
12.1.2 UNCOATED MECHANICAL 1000 MT 3.50 nodata 4.00 3.50 air-dried metric ton
12.1.3 UNCOATED WOODFREE 1000 MT nodata nodata nodata
12.1.4 COATED PAPERS 1000 MT 3.50 nodata 4.00 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.95 air-dried metric ton
12.2 SANITARY AND HOUSEHOLD PAPERS 1000 MT 4.60 nodata 4.20 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.90 air-dried metric ton
12.3 PACKAGING MATERIALS 1000 MT 3.25 nodata 4.30 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.25 air-dried metric ton
12.3.1 CASE MATERIALS 1000 MT 4.20 nodata 4.00 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.20 air-dried metric ton
12.3.2 CARTONBOARD 1000 MT 4.00 nodata 4.30 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.00 air-dried metric ton
12.3.3 WRAPPING PAPERS 1000 MT 4.10 nodata 4.40 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.10 air-dried metric ton
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 MT 4.00 nodata 3.30 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.00 air-dried metric ton
12.4 OTHER PAPER AND PAPERBOARD N.E.S 1000 MT 3.48 nodata 3.30 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.48 air-dried metric ton
15 GLULAM AND CROSS-LAMINATED TIMBER 1000 m3
15.1 GLULAM 1000 m3 1.69 same as coniferous plywood
15.2 CROSS-LAMINATED TIMBER 1000 m3 2.00
16 I-BEAMS 1000 MT 1.68 222 linear meters per MT
For inverse relationships divide 1 by the factor given, e.g. to convert m3 of wood charcoal to mt divide 1 by m3/mt factor of 6 = 0.167
Notes: Forest Measures
MT = metric tonnes (1000 kg) Unit m3/unit
m3 = cubic meters (solid volume) 1000 board feet (sawlogs) 4.53**** **** = obsolete - more recent figures would be:
m2 = square meters 1000 board feet (sawnwood - nominal) 2.36 for Oregon, Washington State, Alaska (west of Cascades), South East United States (Doyle region): 6.3
(s) = solid volume 1000 board feet (sawnwood - actual) 1.69 Inland Western North America, Great Lakes (North America), Eastern Canada: 5.7
1000 square feet (1/8 inch thickness) 0.295 Northeast United States Int 1/4": 5
Unit Conversion cord 3.625
1 inch = 25.4 millimetres cord (pulpwood) 2.55
1 square foot = 0.0929 square metre cord (wood fuel) 2.12
1 pound = 0.454 kilograms cubic foot 0.02832
1 short ton (2000 pounds) = 0.9072 metric ton cubic foot (stacked) 0.01841
1 long ton (2240 pounds) = 1.016 metric ton cunit 2.83
Bold = FAO published figure fathom 6.1164
hoppus cubic foot 0.0222
* = ITTO hoppus super(ficial) foot 0.00185
hoppus ton (50 hoppus cubic feet) 1.11
** NA = North America; EECCA = Eastern Europe, Caucasus and Central Asia Petrograd Standard 4.672
stere 1
*** = Conversion Factor Study, US figures, rotary for conifer and sliced for non-conifer stere (pulpwood) 0.72
stere (wood fuel) 0.65
Fonseca "Measurement of Roundwood" 2005. Estimated by Matt Fonseca based on regional knowledge of the scaling methods and timber types
prepared February 2004
updated 2007 with RWE factors
updated 2009 with provisional results of forest products conversion factors study
updated 2011 with results of forest products conversion factors study (DP49)
updated 2023 with results of 2019 UNECE/FAO/ITTO study - https://www.fao.org/documents/card/en/c/ca7952en

JQ1 Production

Country: USA Date:
Name of Official responsible for reply:
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ1 USDA Forest Service
4700 Old Kington Pike, Knoxville, TN 37919 Industrial Roundwood Balance
PRIMARY PRODUCTS Telephone: This table highlights discrepancies between items and sub-items. Please verify your data if there's an error! Discrepancies
Removals and Production E-mail: test for good numbers, missing number, bad number, negative number
Flag Flag Note Note
Product Product Unit 2021 2022 2021 2022 2021 2022 Product Product Unit 2021 2022 2021 2022 % change Conversion factors
Code Quantity Quantity Code Quantity Quantity Roundwood Industrial roundwood availability
McCusker 14/6/07: McCusker 14/6/07: minus 1.2.3 (other ind. RW) production
Missing data Missing data missing data m3 of wood in m3 or t of product
ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH) ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH) Recovered wood used in particle board 1448 -4757 -429% Solid wood equivalent
1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub 453,530 458,774 1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub 0 0 Solid Wood Demand agglomerate production 8,449 9,544 13% 2.4
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub 71,111 76,230 1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub 0 0 Sawnwood production 80,705 81,676 1% 1
1.1.C Coniferous 1000 m3ub 33,760 37,619 1.1.C Coniferous 1000 m3ub veneer production Missing data Missing data missing data 1
1.1.NC Non-Coniferous 1000 m3ub 37,351 38,611 1.1.NC Non-Coniferous 1000 m3ub plywood production 9,705 9,020 -7% 1
1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 382,420 382,544 1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 0 0 particle board production (incl OSB) 17,975 missing data missing data 1.58
1.2.C Coniferous 1000 m3ub 305,851 306,119 1.2.C Coniferous 1000 m3ub 0 0 fibreboard production missing data missing data missing data 1.8
1.2.NC Non-Coniferous 1000 m3ub 76,569 76,425 1.2.NC Non-Coniferous 1000 m3ub 0 0 mechanical/semi-chemical pulp production 4,046 missing data missing data 2.5
1.2.NC.T of which: Tropical 1000 m3ub 0 0 1.2.NC.T of which: Tropical 1000 m3ub chemical pulp production 44,411 missing data missing data 4.9
1.2.1 SAWLOGS AND VENEER LOGS 1000 m3ub 183,401 186,157 1.2.1 SAWLOGS AND VENEER LOGS 1000 m3ub 0 0 dissolving pulp production 1,228 missing data missing data 5.7
1.2.1.C Coniferous 1000 m3ub 150,702 152,799 1.2.1.C Coniferous 1000 m3ub Availability Solid Wood Demand missing data missing data missing data
1.2.1.NC Non-Coniferous 1000 m3ub 32,699 33,358 1.2.1.NC Non-Coniferous 1000 m3ub Difference (roundwood-demand) missing data missing data missing data positive = surplus
1.2.2 PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD) 1000 m3ub 185,686 182,650 1.2.2 PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD) 1000 m3ub 0 0 gap (demand/availability) missing data missing data Negative number means not enough roundwood available
1.2.2.C Coniferous 1000 m3ub 143,462 141,226 1.2.2.C Coniferous 1000 m3ub Positive number means more roundwood available than demanded
1.2.2.NC Non-Coniferous 1000 m3ub 42,224 41,424 1.2.2.NC Non-Coniferous 1000 m3ub
1.2.3 OTHER INDUSTRIAL ROUNDWOOD 1000 m3ub 13,333 13,737 1.2.3 OTHER INDUSTRIAL ROUNDWOOD 1000 m3ub 0 0
1.2.3.C Coniferous 1000 m3ub 11,687 12,094 1.2.3.C Coniferous 1000 m3ub % of particle board that is from recovered wood 35%
1.2.3.NC Non-Coniferous 1000 m3ub 1,646 1,643 1.2.3.NC Non-Coniferous 1000 m3ub share of agglomerates produced from industrial roundwood residues 100%
PRODUCTION PRODUCTION usable industrial roundwood - amount of roundwood that is used, remainder leaves industry 98.5%
2 WOOD CHARCOAL 1000 t 2 WOOD CHARCOAL 1000 t
3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 60,485 62,262 3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 0 0
3.1 WOOD CHIPS AND PARTICLES 1000 m3 44,209 45,900 3.1 WOOD CHIPS AND PARTICLES 1000 m3
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 16,276 16,362 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3
3.2.1 of which: Sawdust 1000 m3 8,890 8,937 3.2.1 of which: Sawdust 1000 m3
4 RECOVERED POST-CONSUMER WOOD 1000 t ... 4 RECOVERED POST-CONSUMER WOOD 1000 t
5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t 8,449 9,544 5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t ERROR:#VALUE! ERROR:#VALUE!
5.1 WOOD PELLETS 1000 t 8,449 9,544 5.1 WOOD PELLETS 1000 t
5.2 OTHER AGGLOMERATES 1000 t ... 5.2 OTHER AGGLOMERATES 1000 t
6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 80,705 81,676 6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 0 0
6.C Coniferous 1000 m3 63,417 64,039 6.C Coniferous 1000 m3
6.NC Non-Coniferous 1000 m3 17,288 17,637 6.NC Non-Coniferous 1000 m3
6.NC.T of which: Tropical 1000 m3 6.NC.T of which: Tropical 1000 m3
7 VENEER SHEETS 1000 m3 7 VENEER SHEETS 1000 m3 0 0
7.C Coniferous 1000 m3 7.C Coniferous 1000 m3
7.NC Non-Coniferous 1000 m3 7.NC Non-Coniferous 1000 m3
7.NC.T of which: Tropical 1000 m3 7.NC.T of which: Tropical 1000 m3
8 WOOD-BASED PANELS 1000 m3 27,680 9,020 8 WOOD-BASED PANELS 1000 m3 0 0
8.1 PLYWOOD 1000 m3 9,705 9,020 8.1 PLYWOOD 1000 m3 0 ERROR:#VALUE!
8.1.C Coniferous 1000 m3 9,471 9,020 8.1.C Coniferous 1000 m3
8.1.NC Non-Coniferous 1000 m3 234 8.1.NC Non-Coniferous 1000 m3
8.1.NC.T of which: Tropical 1000 m3 0 0 8.1.NC.T of which: Tropical 1000 m3
8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 2,093 2,030 8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 ERROR:#VALUE! ERROR:#VALUE!
8.1.1.C Coniferous 1000 m3 2,093 2,030 8.1.1.C Coniferous 1000 m3
8.1.1.NC Non-Coniferous 1000 m3 ... 8.1.1.NC Non-Coniferous 1000 m3
8.1.1.NC.T of which: Tropical 1000 m3 8.1.1.NC.T of which: Tropical 1000 m3
8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD 1000 m3 17,975 8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD 1000 m3
8.2.1 of which: ORIENTED STRAND BOARD (OSB) 1000 m3 13,839 13,592 8.2.1 of which: ORIENTED STRAND BOARD (OSB) 1000 m3
8.3 FIBREBOARD 1000 m3 8.3 FIBREBOARD 1000 m3 0 0
8.3.1 HARDBOARD 1000 m3 8.3.1 HARDBOARD 1000 m3
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3
8.3.3 OTHER FIBREBOARD 1000 m3 8.3.3 OTHER FIBREBOARD 1000 m3
9 WOOD PULP 1000 t 49,685 9 WOOD PULP 1000 t 0 0
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 4,046 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t
9.2 CHEMICAL WOOD PULP 1000 t 44,411 9.2 CHEMICAL WOOD PULP 1000 t 0 0
9.2.1 SULPHATE PULP 1000 t 44,167 9.2.1 SULPHATE PULP 1000 t
9.2.1.1 of which: BLEACHED 1000 t 20,262 9.2.1.1 of which: BLEACHED 1000 t
9.2.2 SULPHITE PULP 1000 t 244 9.2.2 SULPHITE PULP 1000 t
9.3 DISSOLVING GRADES 1000 t 1,228 9.3 DISSOLVING GRADES 1000 t
10 OTHER PULP 1000 t 30,072 31,250 10 OTHER PULP 1000 t 0 0
10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 108 96 10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t
10.2 RECOVERED FIBRE PULP 1000 t 29,964 31,154 10.2 RECOVERED FIBRE PULP 1000 t
11 RECOVERED PAPER 1000 t 45,037 44,828 11 RECOVERED PAPER 1000 t
12 PAPER AND PAPERBOARD 1000 t 67,475 12 PAPER AND PAPERBOARD 1000 t 0 0
12.1 GRAPHIC PAPERS 1000 t 8,296 12.1 GRAPHIC PAPERS 1000 t -0 0
12.1.1 NEWSPRINT 1000 t 370 12.1.1 NEWSPRINT 1000 t
12.1.2 UNCOATED MECHANICAL 1000 t 394.6 12.1.2 UNCOATED MECHANICAL 1000 t
12.1.3 UNCOATED WOODFREE 1000 t 4,775 12.1.3 UNCOATED WOODFREE 1000 t
12.1.4 COATED PAPERS 1000 t 2,757 12.1.4 COATED PAPERS 1000 t
12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 6,928 12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t
12.3 PACKAGING MATERIALS 1000 t 50,948 12.3 PACKAGING MATERIALS 1000 t 0 0
12.3.1 CASE MATERIALS 1000 t 36,169 12.3.1 CASE MATERIALS 1000 t
12.3.2 CARTONBOARD 1000 t 8,482 12.3.2 CARTONBOARD 1000 t
12.3.3 WRAPPING PAPERS 1000 t 2,542 12.3.3 WRAPPING PAPERS 1000 t
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 3,755 12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t
12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t 1,303 12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t
15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1 1000 m3 396 386 15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1 1000 m3 ERROR:#VALUE! ERROR:#VALUE!
15.1 GLULAM 1000 m3 396 386 15.1 GLULAM 1000 m3
15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 ... 15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3
16 I BEAMS (I-JOISTS)1 1000 t 776 647 16 I BEAMS (I-JOISTS)1 1000 t
1 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here
m3ub = cubic metres solid volume underbark (i.e. excluding bark) Please complete each cell if possible with
m3 = cubic metres solid volume data (numerical value)
t = metric tonnes or "…" for not available
or "0" for zero data
Notes: Sawnwood, nominal
https://www.fao.org/3/cb8216en/cb8216en.pdf

JQ2 Trade

61 62 61 62 91 92 91 92
FOREST SECTOR QUESTIONNAIRE JQ2 Country: USA Date:
Name of Official responsible for reply: INTRA-EU The difference might be caused by Intra-EU trade
PRIMARY PRODUCTS Official Address (in full): This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data! CHECK
Trade Telephone: Fax: This table highlights discrepancies between items and sub-items. Please verify your data if there's an error! ZERO CHECK 2 - if no value in Zero Check 1
E-mail: Country: USA verifies whether the JQ2 figures refers only to intra-EU trade
Specify Currency and Unit of Value (e.g.:1000 USD): 1,000 US$ Flag Flag Flag Flag Flag Flag Flag Flag Note Note Note Note Note Note Note Note Trade Discrepancies
Product Unit of I M P O R T E X P O R T Import Export Import Export Product I M P O R T E X P O R T Product Apparent Consumption Related Notes Product Value per I M P O R T E X P O R T
code Product quantity 2021 2022 2021 2022 2021 2022 2021 2022 2021 2022 2021 2022 code 2021 2022 2021 2022 code 2021 2022 2021 2022 code Product unit 2021 2022 2021 2022
Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value
1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub 898 205,803 1,021 245,538 9,415 2,149,547 7,425 1,948,240 1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub 0 0 0 0 0 0 0 0 1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub 445,013 452,371 1 ROUNDWOOD (WOOD IN THE ROUGH) NAC/m3
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub 126 30,914 81 22,463 1 1,146 4 5,230 1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub 0 0 0 0 0 0 0 0 1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub 71,236 76,306 1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) NAC/m3
1.1.C Coniferous
Subashini NARASIMHAN: Subashini NARASIMHAN: All highlighted blue data have been changed in the Excel processed sheets and also in the DB on 31.7.2023
1000 m3ub 34 8,537 29 8,153 0 581 4 4,482 1.1.C Coniferous 1000 m3ub 1.1.C Coniferous 1000 m3ub 33,793 37,644 1.1.C Coniferous NAC/m3
1.1.NC Non-Coniferous 1000 m3ub 92 22,377 52 14,310 0 565 0 748 1.1.NC Non-Coniferous 1000 m3ub 1.1.NC Non-Coniferous 1000 m3ub 37,443 38,662 1.1.NC Non-Coniferous NAC/m3
1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 772 174,889 941 223,075 9,415 2,148,401 7,420 1,943,010 1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 0 0 0 0 0 0 0 0 1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 373,777 376,064 1.2 INDUSTRIAL ROUNDWOOD NAC/m3
1.2.C Coniferous 1000 m3ub 484 149,132 600 198,107 7,280 1,316,788 5,286 1,039,206 1.2.C Coniferous 1000 m3ub 1.2.C Coniferous 1000 m3ub 299,055 301,432 1.2.C Coniferous NAC/m3
1.2.NC Non-Coniferous 1000 m3ub 288 25,756 341 24,968 2,135 831,613 2,134 903,803 1.2.NC Non-Coniferous 1000 m3ub 1.2.NC Non-Coniferous 1000 m3ub 74,722 74,632 1.2.NC Non-Coniferous NAC/mt
1.2.NC.T of which: Tropical1 1000 m3ub 8 1,420 18 1,950 6 2,053 4 1,183 1.2.NC.T of which: Tropical1 1000 m3ub 1.2.NC.T of which: Tropical1 1000 m3ub 2 14 1.2.NC.T of which: Tropical 1000 m3
2 WOOD CHARCOAL 1000 t 183 107,439 121 81,295 28 25,960 18 17,287 2 WOOD CHARCOAL 1000 t 2 WOOD CHARCOAL 1000 t ERROR:#VALUE! ERROR:#VALUE! 2 WOOD CHARCOAL 1000 m3
3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 216 40,081 139 244,880 5,794 241,355 6,733 4,910,315 3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 0 0 -146 188,420 0 0 -2 4,598,885 3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 54,907 55,668 3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3
3.1 WOOD CHIPS AND PARTICLES 1000 m3 80 17,732 116 26,045 5,773 232,637 6,721 303,738 3.1 WOOD CHIPS AND PARTICLES 1000 m3 3.1 WOOD CHIPS AND PARTICLES 1000 m3 38,517 39,294 3.1 WOOD CHIPS AND PARTICLES 1000 mt
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 136 22,348 169 30,415 22 8,718 13 7,692 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 16,391 16,518 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 mt
3.2.1 of which: Sawdust 1000 m3 1 214 8 3,131 3.2.1 of which: Sawdust 1000 m3 3.2.1 of which: Sawdust 1000 m3 8,890 8,929
4 RECOVERED POST-CONSUMER WOOD 1000 t 85 25,336 2 1,476 4 RECOVERED POST-CONSUMER WOOD 1000 t 4 RECOVERED POST-CONSUMER WOOD 1000 t ERROR:#VALUE! ERROR:#VALUE! 4 RECOVERED POST-CONSUMER WOOD 1000 mt
5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t 363 88,384 352 90,562 7,535 1,070,184 8,989 1,554,944 5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t 0 0 0 0 0 0 0 0 5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t 1,276 906 5 WOOD PELLETS AND OTHER AGGLOMERATES NAC/m3
5.1 WOOD PELLETS 1000 t 196 42,997 194 46,788 7,523 1,059,261 8,977 1,545,409 5.1 WOOD PELLETS 1000 t 5.1 WOOD PELLETS 1000 t 1,122 761 5.1 WOOD PELLETS NAC/m3
5.2 OTHER AGGLOMERATES 1000 t 167 45,387 157 43,774 13 10,923 12 9,535 5.2 OTHER AGGLOMERATES 1000 t 5.2 OTHER AGGLOMERATES 1000 t ERROR:#VALUE! ERROR:#VALUE! 5.2 OTHER AGGLOMERATES NAC/m3
6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 38,164 13,728,885 37,190 12,273,652 7,254 3,559,309 7,005 3,533,102 6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 0 0 -0 0 0 0 -0 0 6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 111,615 111,861 6 SAWNWOOD (INCLUDING SLEEPERS) NAC/m3
6.C Coniferous 1000 m3 37,447 13,196,263 36,392 11,552,380 3,559 1,231,043 3,217 1,144,776 6.C Coniferous 1000 m3 6.C Coniferous 1000 m3 97,305 97,214 6.C Coniferous NAC/m3
6.NC Non-Coniferous 1000 m3 717 532,622 798 721,272 3,695 2,328,267 3,788 2,388,326 6.NC Non-Coniferous 1000 m3 6.NC Non-Coniferous 1000 m3 14,310 14,647 6.NC Non-Coniferous NAC/m3
6.NC.T of which: Tropical1 1000 m3 226 264,016 275 419,419 39 27,984 39 25,505 6.NC.T of which: Tropical1 1000 m3 6.NC.T of which: Tropical1 1000 m3 186 236 6.NC.T of which: Tropical NAC/m3
7 VENEER SHEETS 1000 m3 671 484,150 0 560,912 281 360,099 0 392,195 7 VENEER SHEETS 1000 m3 0 0 -652 0 0 0 -294 0 7 VENEER SHEETS 1000 m3 391 0 7 VENEER SHEETS NAC/m3
7.C Coniferous 1000 m3 606 306,918 585 319,117 81 37,424 82 38,911 7.C Coniferous 1000 m3 7.C Coniferous 1000 m3 525 503 7.C Coniferous NAC/m3
7.NC Non-Coniferous 1000 m3 65 177,232 67 241,795 200 322,675 211 353,284 7.NC Non-Coniferous 1000 m3 7.NC Non-Coniferous 1000 m3 -134 -144 7.NC Non-Coniferous NAC/m3
7.NC.T of which: Tropical 1000 m3 8 21,894 9 34,349 9 16,754 8 15,152 7.NC.T of which: Tropical 1000 m3 7.NC.T of which: Tropical 1000 m3 -1 1 7.NC.T of which: Tropical NAC/m3
8 WOOD-BASED PANELS 1000 m3 20,455 9,839,884 16,927 9,966,936 2,134 830,395 2,278 898,403 8 WOOD-BASED PANELS 1000 m3 0 0 -0 0 0 0 0 0 8 WOOD-BASED PANELS 1000 m3 46,001 23,669 8 WOOD-BASED PANELS NAC/m3
8.1 PLYWOOD 1000 m3 8,086 4,066,342 6,259 4,318,139 759 363,164 771 358,879 8.1 PLYWOOD 1000 m3 0 0 0 0 0 0 0 -0 8.1 PLYWOOD 1000 m3 17,031 14,508 8.1 PLYWOOD NAC/m3
8.1.C Coniferous 1000 m3 2,280 1,228,392 2,356 1,220,531 541 233,440 593 254,332 8.1.C Coniferous 1000 m3 8.1.C Coniferous 1000 m3 11,210 10,783 8.1.C Coniferous NAC/m3
8.1.NC Non-Coniferous 1000 m3 5,806 2,837,950 3,904 3,097,608 218 129,723 179 104,547 8.1.NC Non-Coniferous 1000 m3 8.1.NC Non-Coniferous 1000 m3 5,821 ERROR:#VALUE! 8.1.NC Non-Coniferous NAC/m3
8.1.NC.T of which: Tropical 1000 m3 786 516,649 1,013 831,465 21 11,360 43 20,703 8.1.NC.T of which: Tropical 1000 m3 8.1.NC.T of which: Tropical 1000 m3 765 971 8.1.NC.T of which: Tropical NAC/m3
8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 159 135,667 62 33,345 8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 ERROR:#VALUE! ERROR:#VALUE! 0 0.01 ERROR:#VALUE! ERROR:#VALUE! 0 -0.01 8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 ERROR:#VALUE! 2,126
8.1.1.C Coniferous 1000 m3 130 107,344 60 32,294 8.1.1.C Coniferous 1000 m3 8.1.1.C Coniferous 1000 m3 ERROR:#VALUE! 2,100
8.1.1.NC Non-Coniferous 1000 m3 28 28,323 2 1,051 8.1.1.NC Non-Coniferous 1000 m3 8.1.1.NC Non-Coniferous 1000 m3 ERROR:#VALUE! ERROR:#VALUE!
8.1.1.NC.T of which: Tropical 1000 m3 26 24,658 0 184 8.1.1.NC.T of which: Tropical 1000 m3 8.1.1.NC.T of which: Tropical 1000 m3 ERROR:#VALUE! ERROR:#VALUE!
8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD 1000 m3 7,590 4,397,773 7,391 3,602,412 634 234,114 617 269,214 8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD 1000 m3 8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD 1000 m3 24,932 6,775 8.2 PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD NAC/m3
8.2.1 of which: ORIENTED STRAND BOARD (OSB) 1000 m3 6,128 4,065,184 6,198 3,222,448 146 58,742 132 59,284 8.2.1 of which: ORIENTED STRAND BOARD (OSB) 1000 m3 8.2.1 of which: ORIENTED STRAND BOARD (OSB) 1000 m3 19,821 19,658 8.2.1 of which: ORIENTED STRANDBOARD (OSB) NAC/m3
8.3 FIBREBOARD 1000 m3 4,780 1,375,769 3,277 2,046,385 741 233,117 890 270,310 8.3 FIBREBOARD 1000 m3 0 0 -0 0 0 0 0 0 8.3 FIBREBOARD 1000 m3 4,038 2,387 8.3 FIBREBOARD NAC/m3
8.3.1 HARDBOARD 1000 m3 243 141,339 249 180,815 255 94,419 224 85,718 8.3.1 HARDBOARD 1000 m3 8.3.1 HARDBOARD 1000 m3 -12 25 8.3.1 HARDBOARD NAC/mt
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 2,498 1,191,819 2,866 1,814,461 315 90,901 372 104,062 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 2,183 2,494 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) NAC/mt
8.3.3 OTHER FIBREBOARD 1000 m3 2,038 42,611 161 51,109 171 47,797 294 80,531 8.3.3 OTHER FIBREBOARD 1000 m3 8.3.3 OTHER FIBREBOARD 1000 m3 1,867 -133 8.3.3 OTHER FIBREBOARD NAC/mt
9 WOOD PULP 1000 t 6,036 3,858,758 6,948 4,801,537 7,621 6,054,396 7,983 7,283,702 9 WOOD PULP 1000 t 0 0 0 0 0 0 0 0 9 WOOD PULP 1000 t 48,100 47,983 9 WOOD PULP NAC/mt
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 137 47,577 207 123,673 211 124,222 239 142,122 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 3,973 3,912 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP NAC/mt
9.2 CHEMICAL WOOD PULP 1000 t 5,652 3,574,714 6,457 4,392,114 6,620 5,023,298 6,899 6,081,834 9.2 CHEMICAL WOOD PULP 1000 t 0 0 0 0 0 0 0 0 9.2 CHEMICAL WOOD PULP 1000 t 43,443 43,431 9.2 CHEMICAL WOOD PULP NAC/mt
9.2.1 SULPHATE PULP 1000 t 5,186 3,415,369 5,756 4,155,342 6,580 5,002,265 6,865 6,064,771 9.2.1 SULPHATE PULP 1000 t 9.2.1 SULPHATE PULP 1000 t 42,773 42,565 9.2.1 SULPHATE PULP NAC/mt
9.2.1.1 of which: BLEACHED 1000 t 5,043 3,316,872 5,592 4,051,886 6,218 4,767,898 6,607 5,896,472 9.2.1.1 of which: BLEACHED 1000 t 9.2.1.1 of which: BLEACHED 1000 t 19,087 18,887 9.2.1.1 of which: BLEACHED NAC/mt
9.2.2 SULPHITE PULP 1000 t 466 159,345 702 236,772 40 21,033 34 17,064 9.2.2 SULPHITE PULP 1000 t 9.2.2 SULPHITE PULP 1000 t 670 866 9.2.2 SULPHITE PULP NAC/mt
9.3 DISSOLVING GRADES 1000 t 247 236,467 283 285,750 790 906,875 845 1,059,746 9.3 DISSOLVING GRADES 1000 t 9.3 DISSOLVING GRADES 1000 t 684 640 9.3 DISSOLVING GRADES NAC/mt
10 OTHER PULP 1000 t 81 27,595 71 33,388 516 320,014 600 381,082 10 OTHER PULP 1000 t 0 0 0 0 0 0 0 0 10 OTHER PULP 1000 t 29,637 30,721 10 OTHER PULP NAC/mt
10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 35 23,572 44 31,024 87 93,024 85 105,372 10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 56 55 10.1 PULP FROM FIBRES OTHER THAN WOOD NAC/mt
10.2 RECOVERED FIBRE PULP 1000 t 46 4,023 27 2,364 429 226,990 515 275,710 10.2 RECOVERED FIBRE PULP 1000 t 10.2 RECOVERED FIBRE PULP 1000 t 29,581 30,666 10.2 RECOVERED FIBRE PULP NAC/mt
11 RECOVERED PAPER 1000 t 878 134,696 828 138,208 16,334 3,302,623 14,990 3,186,042 11 RECOVERED PAPER 1000 t 11 RECOVERED PAPER 1000 t 29,581 30,666 11 RECOVERED PAPER NAC/mt
12 PAPER AND PAPERBOARD 1000 t 8,223 8,031,344 8,202 10,372,687 10,077 9,137,947 9,917 10,037,653 12 PAPER AND PAPERBOARD 1000 t 0 0 -0 0 0 0 0 0 12 PAPER AND PAPERBOARD 1000 t 65,621 63,780 12 PAPER AND PAPERBOARD NAC/mt
12.1 GRAPHIC PAPERS 1000 t 4,609 3,635,105 4,536 5,024,247 1,283 1,288,913 957 1,116,460 12.1 GRAPHIC PAPERS 1000 t 0 0 0 -0 0 0 0 0 12.1 GRAPHIC PAPERS 1000 t 11,622 11,757 12.1 GRAPHIC PAPERS NAC/mt
12.1.1 NEWSPRINT 1000 t 1,065 557,210 286 184,077 120 68,813 67 43,961 12.1.1 NEWSPRINT 1000 t 12.1.1 NEWSPRINT 1000 t 1,315 547 12.1.1 NEWSPRINT NAC/mt
12.1.2 UNCOATED MECHANICAL 1000 t 1,195 787,757 1,204 1,028,678 59 62,303 60 69,424 12.1.2 UNCOATED MECHANICAL 1000 t 12.1.2 UNCOATED MECHANICAL 1000 t 1,531 1,510 12.1.2 UNCOATED MECHANICAL NAC/mt
12.1.3 UNCOATED WOODFREE 1000 t 986 1,011,189 1,152 1,412,886 331 423,467 282 431,055 12.1.3 UNCOATED WOODFREE 1000 t 12.1.3 UNCOATED WOODFREE 1000 t 5,430 5,645 12.1.3 UNCOATED WOODFREE NAC/mt
12.1.4 COATED PAPERS 1000 t 1,363 1,278,949 1,895 2,398,608 774 734,331 547 572,020 12.1.4 COATED PAPERS 1000 t 12.1.4 COATED PAPERS 1000 t 3,346 4,055 12.1.4 COATED PAPERS NAC/mt
12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 275 375,570 322 516,593 172 223,342 176 264,718 12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 7,031 7,104 12.2 HOUSEHOLD AND SANITARY PAPERS NAC/mt
12.3 PACKAGING MATERIALS 1000 t 3,284 3,672,684 3,287 4,403,084 8,316 7,328,648 8,426 8,329,281 12.3 PACKAGING MATERIALS 1000 t 0 0 0 -0 0 0 -0 0 12.3 PACKAGING MATERIALS 1000 t 45,917 43,934 12.3 PACKAGING MATERIALS NAC/mt
12.3.1 CASE MATERIALS 1000 t 1,358 1,062,697 1,261 1,142,438 5,029 3,454,575 5,169 4,085,718 12.3.1 CASE MATERIALS 1000 t 12.3.1 CASE MATERIALS 1000 t 32,498 30,381 12.3.1 CASE MATERIALS NAC/mt
12.3.2 CARTONBOARD 1000 t 1,267 1,679,581 1,362 2,088,774 2,104 2,591,605 2,120 2,864,440 12.3.2 CARTONBOARD 1000 t 12.3.2 CARTONBOARD 1000 t 7,645 5,779 12.3.2 CARTONBOARD NAC/mt
12.3.3 WRAPPING PAPERS 1000 t 547 839,063 569 1,072,837 1,090 1,208,280 1,018 1,268,734 12.3.3 WRAPPING PAPERS 1000 t 12.3.3 WRAPPING PAPERS 1000 t 1,998 1,995 12.3.3 WRAPPING PAPERS NAC/mt
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 112 91,344 95 99,035 92 74,188 119 110,388 12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 3,775 5,780 12.3.4 OTHER PAPERS MAINLY FOR PACKAGING NAC/mt
12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t 55 347,984 57 428,762 306 297,043 358 327,195 12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t 12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t 1,052 986 12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) NAC/mt
15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)2 1000 m3 ... 37 65,253 2 2,887 15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1 1000 m3 ERROR:#VALUE! ERROR:#VALUE! 0 0 ERROR:#VALUE! ERROR:#VALUE! 0 0 15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1 1000 m3 ERROR:#VALUE! 421
15.1 GLULAM 1000 m3 18 41,410 2 2,816 15.1 GLULAM 1000 m3 15.1 GLULAM 1000 m3 ERROR:#VALUE! 402
15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 19 23,843 0 72 15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 ERROR:#VALUE! ERROR:#VALUE!
16 I BEAMS (I-JOISTS)2 1000 t 191 288,746 42 107,440 16 I BEAMS (I-JOISTS)1 1000 t 16 I BEAMS (I-JOISTS)1 1000 t ERROR:#VALUE! 796
1 Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here
m3 = cubic metres solid volume Please complete each cell if possible with
m3ub = cubic metres solid volume underbark (i.e. excluding bark) data (numerical value)
t = metric tonnes or "…" for not available
or "0" for zero data
https://www.fao.org/3/cb8216en/cb8216en.pdf

JQ3 Secondary PP Trade

62 91 91
Country: USA Date:
Name of Official responsible for reply:
ERROR:#REF!
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ3
SECONDARY PROCESSED PRODUCTS Telephone/Fax:
Trade E-mail: ERROR:#REF!
This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
Specify Currency and Unit of Value (e.g.:1000 US $): 1,000 US$ Discrepancies
Flag Flag Flag Flag Note Note Note Note
Product Product I M P O R T V A L U E E X P O R T V A L U E Import Export Import Export Product Product I M P O R T V A L U E E X P O R T V A L U E
code 2021 2022 2021 2022 2021 2022 2021 2022 2021 2022 2021 2022 Code 2021 2022 2021 2022
13 SECONDARY WOOD PRODUCTS 13 SECONDARY WOOD PRODUCTS
13.1 FURTHER PROCESSED SAWNWOOD 1,780,058 2,265,920 271,452 321,594 13.1 FURTHER PROCESSED SAWNWOOD 0 0 0 0
13.1.C Coniferous 1,444,981 1,831,434 52,587 57,841 13.1.C Coniferous
13.1.NC Non-coniferous 335,078 434,486 218,865 263,753 13.1.NC Non-coniferous
13.1.NC.T of which: Tropical 72,628 111,968 3,668 3,843 13.1.NC.T of which: Tropical
13.2 WOODEN WRAPPING AND PACKAGING MATERIAL 433,094 532,798 370,077 514,900 13.2 WOODEN WRAPPING AND PACKAGING MATERIAL
13.3 WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE 1,399,953 1,428,139 71,996 73,367 13.3 WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4 BUILDER’S JOINERY AND CARPENTRY OF WOOD1 3,052,487 3,273,534 538,751 511,169 13.4 BUILDER’S JOINERY AND CARPENTRY OF WOOD1
13.5 WOODEN FURNITURE 25,434,020 27,484,945 1,862,401 2,224,718 13.5 WOODEN FURNITURE
13.6 PREFABRICATED BUILDINGS OF WOOD 152,392 211,759 35,038 43,016 13.6 PREFABRICATED BUILDINGS OF WOOD
13.7 OTHER MANUFACTURED WOOD PRODUCTS 1,861,141 1,919,923 209,009 223,679 13.7 OTHER MANUFACTURED WOOD PRODUCTS
14 SECONDARY PAPER PRODUCTS 14 SECONDARY PAPER PRODUCTS
14.1 COMPOSITE PAPER AND PAPERBOARD 78,670 117,361 49,954 60,700 14.1 COMPOSITE PAPER AND PAPERBOARD
14.2 SPECIAL COATED PAPER AND PULP PRODUCTS 930,331 1,120,178 1,124,808 1,233,112 14.2 SPECIAL COATED PAPER AND PULP PRODUCTS
14.3 HOUSEHOLD AND SANITARY PAPER, READY FOR USE 1,401,154 1,557,780 826,261 930,630 14.3 HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4 PACKAGING CARTONS, BOXES ETC. 3,041,464 3,506,777 2,257,086 2,409,528 14.4 PACKAGING CARTONS, BOXES ETC.
14.5 OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE 2,966,365 3,674,828 1,943,314 2,081,488 14.5 OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE 2,479,418 2778405 1759537.11 1752296
14.5.1 of which: PRINTING AND WRITING PAPER, READY FOR USE 503 16,616 10,606 69,038 14.5.1 of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2 of which: ARTICLES, MOULDED OR PRESSED FROM PULP 349,590 536,539 75,064 90,800 14.5.2 of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3 of which: FILTER PAPER AND PAPERBOARD, READY FOR USE 136,853 343,268 98,106 169,354 14.5.3 of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
1 In February 2023 this definition was updated to exclude Glulam, Cross-Laminated Timber and I-Beams which are now distinct items in the JFSQ (15.1, 15.2 and 16). This change was made to reflect the update of HS2022.
Please complete each cell with
data (numerical value)
or "…" for not available
or "0" for zero data

ECE-EU Species

Country: Date:
Name of Official responsible for reply: DISCREPANCIES
FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade Official Address (in full): Checks
- Checks that values reported on JQ2 match values reported on this sheet
Trade in Roundwood and Sawnwood by species Telephone: Fax: - Checks that subitems are < or = to aggregate
E-mail:
Specify Currency and Unit of Value (e.g.:1000 national currency): _______________________________
Flag Flag Flag Flag Flag Flag Flag Flag Note Note Note Note Note Note Note Note
I M P O R T E X P O R T Import Export Import Export I M P O R T E X P O R T
Product Classification Classification Unit of 2021 2022 2021 2022 2021 2022 2021 2022 2021 2022 2021 2022 0 0 0 0
Code HS2022 CN2022 Product Quantity Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value Quantity Value
1.2.C 4403.11/21/22/23/24/25/26 Industrial Roundwood, Coniferous 1000 m3ub ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
4403.21/22 of which: Pine (Pinus spp.) 1000 m3ub OK OK OK OK OK OK OK OK
4403 21 10 sawlogs and veneer logs 1000 m3ub
4403 21 90 4403 22 00 pulpwood and other industrial roundwood 1000 m3ub
4403.23/24 of which: Fir/Spruce (Abies spp., Picea spp.) 1000 m3ub OK OK OK OK OK OK OK OK
4403 23 10 sawlogs and veneer logs 1000 m3ub
4403 23 90 4403 24 00 pulpwood and other industrial roundwood 1000 m3ub
1.2.NC 4403.12/41/42/49/91/93/94 4403.95/96/97/98/99 Industrial Roundwood, Non-Coniferous 1000 m3ub ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
ex4403.12 4403.91 of which: Oak (Quercus spp.) 1000 m3ub
ex4403.12 4403.93/94 of which: Beech (Fagus spp.) 1000 m3ub
ex4403.12 4403.95/96 of which: Birch (Betula spp.) 1000 m3ub OK OK OK OK OK OK OK OK
4403 95 10 sawlogs and veneer logs 1000 m3ub
ex4403 12 00 4403 95 90 4403 96 00 pulpwood and other industrial roundwood 1000 m3ub
ex4403.12 4403.97 of which: Poplar/Aspen (Populus spp.) 1000 m3ub
ex4403.12 4403.98 of which: Eucalyptus (Eucalyptus spp.) 1000 m3ub
6.C 4406.11/91 4407.11/12/13/14/19 Sawnwood, Coniferous 1000 m3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
4407.11 ex4407.13 ex4406.11/91 of which: Pine (Pinus spp.) 1000 m3
4407.12 ex4407.13/14 ex4406.11/91 of which: Fir/Spruce (Abies spp., Picea spp.) 1000 m3
6.NC 4406.12/92 4407.21/22/23/25/26/27/28/29/ 91/92/93/94/95/96/97/99 Sawnwood, Non-coniferous 1000 m3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
ex4406.12/92 4407.91 of which: Oak (Quercus spp.) 1000 m3
ex4406.12/92 4407.92 of which: Beech (Fagus spp.) 1000 m3
ex4406.12/92 4407.93 of which: Maple (Acer spp.) 1000 m3
ex4406.12/92 4407.94 of which: Cherry (Prunus spp.) 1000 m3
ex4406.12/92 4407.95 of which: Ash (Fraxinus spp.) 1000 m3
ex4406.12/92 4407.96 of which: Birch (Betula spp.) 1000 m3
ex4406.12/92 4407.97 of which: Poplar/Aspen (Populus spp.) 1000 m3
Light blue cells are requested only for EU members using the Combined Nomenclature to fill in - other countries are welcome to do so if their trade classification nomenclature permits
Please note that information on tropical species trade is requested in questionnaire ITTO2 for ITTO member countries
"ex" codes indicate that only part of that trade classication code is used
m3ub = cubic metres underbark (i.e. excluding bark)
Please complete each cell if possible with
data (numerical value)
or "…" for not available
or "0" for zero data

ITTO1-Estimates

Country: 0 Date:
Name of Official responsible for reply: 0
Official Address (in full): 0
ITTO1
Telephone: 0 Fax: 0
FOREST SECTOR QUESTIONNAIRE E-mail: 0
Production and Trade Estimates for 2023
Specify Currency and Unit of Value (e.g.:1000 US $): __________
Product Unit of Production Imports Exports
Code Product quantity Quantity Quantity Value Quantity Value
1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub
1.2.C Coniferous 1000 m3ub
1.2.NC Non-Coniferous 1000 m3ub
1.2.NC.T of which: Tropical1 1000 m3ub
6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3
6.C Coniferous 1000 m3
6.NC Non-Coniferous 1000 m3
6.NC.T of which: Tropical1 1000 m3
7 VENEER SHEETS 1000 m3
7.C Coniferous 1000 m3
7.NC Non-Coniferous 1000 m3
7.NC.T of which: Tropical 1000 m3
8.1 PLYWOOD 1000 m3
8.1.C Coniferous 1000 m3
8.1.NC Non-Coniferous 1000 m3
8.1.NC.T of which: Tropical 1000 m3
1 Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
m3 = cubic metres solid volume
m3ub = cubic metres solid volume underbark (i.e. excluding bark)

ITTO2-Species

Country: 0 Date:
ITTO2 Name of Official responsible for reply: 0
Official Address (in full): 0
FOREST SECTOR QUESTIONNAIRE
Trade in Tropical Species Telephone: 0 Fax: 0
E-mail: 0
Specify Currency and Unit of Value (e.g.:1000 US $): 1,000 US $
I M P O R T E X P O R T
Product Classifications 2021 2022 2021 2022
HS2022/HS2017/HS2012/HS2007 Scientific Name Local/Trade Name Quantity Value Quantity Value Quantity Value Quantity Value
(1000 m3) (1000 m3) (1000 m3) (1000 m3)
1.2.NC.T HS2022:
Industrial Roundwood, Tropical ex4403.12 4403.41/42/49 Shorea spp. Dark/light red meranti and meranti bakau 0 0 0 3 1 159 1 81
HS2017: Tectona grandis Teak 0 0 0 119 0 0 0 62
ex4403.12 4403.41/49 Other tropical Other tropical 8 1,420 18 1,831 5 1,894 3 1,040
HS2012/2007:
ex4403.10 4403.41/49 ex4403.99
6.NC.T HS2022:
Sawnwood, Tropical ex4406.12/92 4407.21/22/23/25/26/27/28/29 Swietenia spp. Mahogany 10 9,235 14 16,582 4 3,179 2 2,470
Ocotea porosa & Ochroma pyramidale Virola and Imbuia 7 4,200 7 4,193 19 14,263 15 11,961
HS2017: Shorea spp. Dark/light red, white and yellow meranty, white luan/seraya, and bakau 7 8,880 9 14,121 0 146 1 443
ex4406.12/92 4407.21/22/25/26/27/28/29 Ochroma pyramidale Balsa 10 10,276 6 4,362
Entandrophragma cylindricum Sapelli /Sapele 27 26,988 42 43,240 4 3,084 4 2,536
HS2012/2007: Milicia excelsa, M. regia (syn. Chlorophora excelsa, C. regia) Iroko 2 1,139 2 1,826 0 33 0 160
ex4406.10/90 4407.21/22/25/26/27/28/30 Hymenaea courbaril Jatoba/ Brazilian cherry 2 1,487 2 3,264
Dipterocarpus spp. Keruing 16 14,882 27 30,302
Khaya spp. Acajou d'afrique/ African mahogany 10 11,045 15 16,856
Pouteria spp. Aningre / Aniegre/ Anegre 0 87 0 67
Tectona grandis Teak 5 19,500 0 119 0 62
Handroanthus spp.  Ipe 34 89,257 41 158,381
Carapa spp.  Andiroba/ Padauk 1 943 2 2,644
Cedrela odorata Cedro/ Spanish cedar 5 4,901 6 5,629
Other tropical Other tropical 90 61,194 96 98,428 12 7,279 15 7,217
7.NC.T HS2022:
Veneer Sheets, Tropical 4408.31/39 Shorea spp. Dark/light red meranti and meranti bakau 0 387 14 683 3 5,136 2,574 3,918
HS2017: Other tropical Other tropical 8 21,507 9,010 33,666 6 11,618 5,514 11,235
4408.31/39
HS2012/2007:
4408.31/39 ex4408.90
8.1.NC.T HS2022:
Plywood, Tropical 4412.31/41/51/91 Swietenia spp. Mahogany 0 751 0 276
HS2017: Cedrela odorata Cedro/ Spanish cedar 1 1,203 1 1,096 1 521
4412.31 ex4412.94/99 Other tropical Other tropical 784 514,695 1,012 829,947 21 10,838 42 20,092
HS2012/2007:
4412.31 ex4412.32/94/99 Other tropical Other tropical 1000 square meters 172 2,931
Note: List the major species traded in each category. Use additional sheet if more species are to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.

ITTO3-Miscellaneous

Country: Date:
Name of Official responsible for reply:
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE ITTO3
Miscellaneous Items Telephone: Fax:
(use additional paper if necessary) E-mail:
1 Please enter current import tariff rates applied to tropical and non-tropical timber products. If available, please provide tariffs by the relevant customs classification category. If tariff levels have been reported in previous years, enter changes only. (Logs = JQ code 1.2, Sawn = JQ code 6, Veneer = JQ code 7, and Plywood = JQ code 8.1)
Current import tariff Logs Tropical: Sawn Tropical: Veneer Tropical: Plywood Tropical:
Non-Tropical: Non-Tropical: Non-Tropical: Non-Tropical:
Comments (if any):
2 Please comment on any quotas, incentives, disincentives, tariff/non-tariff barriers or other related factors which now or in future will significantly affect your production and trade of tropical timber products.
No icentive program for trade of tropical timber products
3 Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.
None
4 Please indicate any trends or changes expected in the species composition of your trade. How important are lesser-used tropical timber species and/or minor tropical forest products?
No change expected
5 Please indicate trends in domestic building activity, housing starts, mortgage/interest rates, substitution of non-tropical wood and/or non-wood products for tropical timbers, and any other domestic factors having a significant impact on tropical timber consumption in your country.
6 Please indicate the extent of foreign involvement in your timber sector (e.g. number and nationalities of concessionaires/mill (joint) owners, area of forest allocated, scale of investment, etc.).
7 Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.
8 Please indicate the current extent of forest plantations in your country (ha), annual establishment rate (ha/yr) and proportion of industrial roundwood production from plantations.

TS-OB

% Min: 80% Max: 120% Notes
JQ1 Country Flow Unit Product ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_3_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P.OB 1000 m3 1_2_3_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-JQ1

% Min: 80% Max: 120% Notes
JQ1 Country Flow Unit Product ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
ERROR:#REF! P 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_3_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 1_2_3_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 6_4_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 7_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 8 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 8_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-JQ2

% Min: 80% Max: 120% Notes
JQ2 Country Flow Unit Product ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
Q ERROR:#REF! M 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! X 1000 m3 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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ERROR:#REF! X 1000 NAC 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! X 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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ERROR:#REF! X 1000 NAC 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! X 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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ERROR:#REF! M 1000 NAC 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-JQ3

% Min: 80% Max: 120% Notes
JQ3 Country Flow Unit Product ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 1000 NAC 11_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 11_7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC 12_7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 11_7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC 12_6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-ECEEU

% Min: 80% Max: 120% Notes
ECEEU Country Flow Unit Product ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
Q ERROR:#REF! M 1000 m3 ST_1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC ST_1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 ST_1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 ST_1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC ST_1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 ST_1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 ST_1_2_C_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 m3 ST_1_2_C_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 ST_1_2_C_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC ST_1_2_C_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 ST_1_2_C_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 ST_1_2_C_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC ST_1_2_C_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 ST_1_2_C_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 ST_1_2_C_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC ST_1_2_C_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 ST_1_2_C_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 ST_1_2_C_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC ST_1_2_C_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 ST_1_2_C_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 ST_1_2_C_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC ST_1_2_C_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 ST_1_2_C_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 m3 ST_1_2_C_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 m3 ST_1_2_C_2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! X 1000 m3 ST_1_2_C_2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! X 1000 NAC ST_1_2_C_2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! X 1000 m3 ST_1_2_C_2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 ST_1_2_C_2_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 m3 ST_1_2_C_2_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 m3 ST_1_2_C_2_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! X 1000 m3 ST_1_2_C_2_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! M 1000 m3 ST_1_2_C_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 m3 ST_1_2_C_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! X 1000 m3 ST_1_2_C_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 ST_1_2_NC_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! M 1000 m3 ST_1_2_NC_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 ST_1_2_NC_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! M 1000 NAC ST_1_2_NC_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! M 1000 m3 ST_1_2_NC_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! M 1000 m3 ST_1_2_NC_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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ERROR:#REF! M 1000 NAC ST_1_2_NC_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! X 1000 m3 ST_5_NC_7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-EU1

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UV ERROR:#REF! EX_M 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! EX_M 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! EX_X 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! EX_M 1000 m3 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! EX_M 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! EX_X 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! EX_M 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Q ERROR:#REF! EX_X 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV ERROR:#REF! EX_M 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 m3 6_4_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 6_4_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 m3 6_4_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 m3 6_4_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 6_4_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 m3 6_4_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 7_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 7_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 7_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 7_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 7_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 7_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 8 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 8 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 8 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 8 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 8 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 8 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 8_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 8_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 8_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 8_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 8_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 8_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_M 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_M 1000 NAC 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_M 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q ERROR:#REF! EX_X 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! EX_X 1000 NAC 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV ERROR:#REF! EX_X 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-EU2

% Min: 80% Max: 120% Notes
EU2 Country Flow Unit Product ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF!
ERROR:#REF! P 1000 m3 EU2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_3_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
ERROR:#REF! P 1000 m3 EU2_1_3_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

Annex1 | JQ1-Corres.

Last updated in 2016
FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
PRIMARY PRODUCTS
Removals and Production
CORRESPONDENCES to CPC Ver.2.1
Central Product Classification Version 2.1 (CPC Ver. 2.1)
Product Product
Code
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1 ROUNDWOOD (WOOD IN THE ROUGH) 031
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 0313
1.1.C Coniferous 03131
1.1.NC Non-Coniferous 03132
1.2 INDUSTRIAL ROUNDWOOD 0311 0312
1.2.C Coniferous 0311
1.2.NC Non-Coniferous 0312
1.2.NC.T of which: Tropical ex0312
1.2.1 SAWLOGS AND VENEER LOGS ex03110 ex03120
1.2.1.C Coniferous ex03110
1.2.1.NC Non-Coniferous ex03120
1.2.2 PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD) ex03110 ex03120
1.2.2.C Coniferous ex03110
1.2.2.NC Non-Coniferous ex03120
1.2.3 OTHER INDUSTRIAL ROUNDWOOD ex03110 ex03120
1.2.3.C Coniferous ex03110
1.2.3.NC Non-Coniferous ex03120
PRODUCTION
2 WOOD CHARCOAL ex34510
3 WOOD CHIPS, PARTICLES AND RESIDUES ex31230 ex39283
3.1 WOOD CHIPS AND PARTICLES ex31230
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) ex39283
4 RECOVERED POST-CONSUMER WOOD ex39283
5 WOOD PELLETS AND OTHER AGGLOMERATES 39281 39282
5.1 WOOD PELLETS 39281
5.2 OTHER AGGLOMERATES 39282
6 SAWNWOOD (INCLUDING SLEEPERS) 311 3132
6.C Coniferous 31101 ex31109 ex3132
6.NC Non-Coniferous 31102 ex31109 ex3132
6.NC.T of which: Tropical ex31102 ex31109 ex3132
7 VENEER SHEETS 3151
7.C Coniferous 31511
7.NC Non-Coniferous 31512
7.NC.T of which: Tropical ex31512
8 WOOD-BASED PANELS 3141 3142 3143 3144
8.1 PLYWOOD 3141 3142
8.1.C Coniferous 31411 31421
8.1.NC Non-Coniferous 31412 31422
8.1.NC.T of which: Tropical ex31412 ex31422
8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD 3143
8.2.1 of which: ORIENTED STRAND BOARD (OSB) 31432
8.3 FIBREBOARD 3144
8.3.1 HARDBOARD 31442
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 31441
8.3.3 OTHER FIBREBOARD 31449
9 WOOD PULP 32111 32112 ex32113
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP ex32113
9.2 CHEMICAL WOOD PULP 32112
9.2.1 SULPHATE PULP ex32112
9.2.1.1 of which: BLEACHED ex32112
9.2.2 SULPHITE PULP ex32112
9.3 DISSOLVING GRADES 32111
10 OTHER PULP ex32113
10.1 PULP FROM FIBRES OTHER THAN WOOD ex32113
10.2 RECOVERED FIBRE PULP ex32113
11 RECOVERED PAPER 3924
12 PAPER AND PAPERBOARD 3212 3213 32142 32143 ex32149 32151 32198 ex32199
12.1 GRAPHIC PAPERS 3212 ex32143 ex32149
12.1.1 NEWSPRINT 32121
12.1.2 UNCOATED MECHANICAL ex32122 ex32129
12.1.3 UNCOATED WOODFREE 32122 ex32129
12.1.4 COATED PAPERS ex32143 ex32149
12.2 HOUSEHOLD AND SANITARY PAPERS 32131
12.3 PACKAGING MATERIALS 32132 ex32133 32134 32135 ex32136 ex32137 32142 32151 ex32143 ex32149
12.3.1 CASE MATERIALS 32132 32134 32135 ex32136
12.3.2 CARTONBOARD ex32133 ex32136 ex32143 ex32149
12.3.3 WRAPPING PAPERS ex32133 ex32136 ex32137 32142 32151
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING ex32136
12.4 OTHER PAPER AND PAPERBOARD N.E.S. ex32149 ex32133 ex32136 ex32137 32198 ex32199
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the CPC Ver.2.1 code is applicable.
For instance "ex31512" under product 7.NC.T means that only a part of CPC Ver.2.1 code 31512 refers to non-coniferous tropical veneer sheets.
In CPC, if only 3 or 4 digits are shown, then all sub-codes at lower degrees of aggregation are included (for example, 0313 includes 03131 and 03132).

Annex2 | JQ2-Corres.

FOREST SECTOR QUESTIONNAIRE JQ2 (Supp. 1)
PRIMARY PRODUCTS
Trade
CORRESPONDENCES to HS2022, HS2017, HS2012 and SITC Rev.4
C l a s s i f i c a t i o n s
Product Product
Code HS2022 HS2017 HS2012 SITC Rev.4
1 ROUNDWOOD (WOOD IN THE ROUGH) 4401.11/12 44.03 4401.11/12 44.03 4401.10 44.03 245.01 247
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 4401.11/12 4401.11/12 4401.10 245.01
1.1.C Coniferous 4401.11 4401.11 ex4401.10 ex245.01
1.1.NC Non-Coniferous 4401.12 4401.12 ex4401.10 ex245.01
1.2 INDUSTRIAL ROUNDWOOD 44.03 44.03 44.03 247
1.2.C Coniferous 4403.11/21/22/23/24/25/26 4403.11/21/22/23/24/25/26 ex4403.10 4403.20 ex247.3 247.4
1.2.NC Non-Coniferous 4403.12/41/42/49/91/93/94/95/96/97/98/99 4403.12/41/49/91/93/94/95/96/97/98/99 ex4403.10 4403.41/49/91/92/99 ex247.3 247.5 247.9
1.2.NC.T of which: Tropical1 ex4403.12 4403.41/42/49 4403.41/49 ex4403.10 4403.41/49 ex4403.99 ex247.3 247.5 ex247.9
2 WOOD CHARCOAL 4402.90 4402.90 4402.90 ex245.02
3 WOOD CHIPS, PARTICLES AND RESIDUES 4401.21/22 4401.41 ex4401.49 4401.21/22 ex4401.40 4401.21/22 ex4401.39 246.1 ex246.2
3.1 WOOD CHIPS AND PARTICLES 4401.21/22 4401.21/22 4401.21/22 246.1
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 4401.41 ex4401.49++ ex4401.40++ ex4401.39 ex246.2
3.2.1 of which: Sawdust 4401.41 ex4401.40++ ex4401.39 ex246.2
4 RECOVERED POST-CONSUMER WOOD ex4401.49++ ex4401.40++ ex4401.39 ex246.2
5 WOOD PELLETS AND OTHER AGGLOMERATES 4401.31/32/39 4401.31/39 4401.31 ex4401.39 ex246.2
5.1 WOOD PELLETS 4401.31 4401.31 4401.31 ex246.2
5.2 OTHER AGGLOMERATES 4401.32/39 4401.39 ex4401.39 ex246.2
6 SAWNWOOD (INCLUDING SLEEPERS) 44.06 44.07 44.06 44.07 44.06 44.07 248.1 248.2 248.4
6.C Coniferous 4406.11/91 4407.11/12/13/14/19 4406.11/91 4407.11/12/19 ex4406.10/90 4407.10 ex248.11 ex248.19 248.2
6.NC Non-Coniferous 4406.12/92 4407.21/22/23/25/26/27/28/29/91/92/93/94/95/96/97/99 4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99 ex4406.10/90 4407.21/22/25/26/27/28/29/91/92/93/94/95/99 ex248.11 ex248.19 248.4
6.NC.T of which: Tropical1 ex4406.12/92 4407.21/22/23/25/26/27/28/29 4407.21/22/25/26/27/28/29 ex4406.10/90 4407.21/22/25/26/27/28/29 ex4407.99 ex248.11 ex248.19 ex248.4
7 VENEER SHEETS 44.08 44.08 44.08 634.1
7.C Coniferous 4408.10 4408.10 4408.10 634.11
7.NC Non-Coniferous 4408.31/39/90 4408.31/39/90 4408.31/39/90 634.12
7.NC.T of which: Tropical 4408.31/39 4408.31/39 4408.31/39 ex4408.90 ex634.12
8 WOOD-BASED PANELS 44.10 44.11 4412.31/33/34/39/41/42/49/51/52/59/91/92/99 44.10 44.11 4412.31/33/34/39/94/99 44.10 44.11 4412.31/32/39/94/99 634.22/23/31/33/39 634.5
8.1 PLYWOOD 4412.31/33/34/39/41/42/49/51/52/59/91/92/99 4412.31/33/34/39/94/99 4412.31/32/39/94/99 634.31/33/39
8.1.C Coniferous 4412.39/49/59/99 4412.39 ex4412.94 ex4412.99 4412.39 ex4412.94 ex.4412.99 ex634.31 ex634.33 ex634.39
8.1.NC Non-Coniferous 4412.33/34/42/52/92 4412.31/33/34 ex4412.94 ex4412.99 4412.31/32 ex4412.94 ex4412.99 ex634.31 ex634.33 ex634.39
8.1.NC.T of which: Tropical 4412.31/41/51/91 4412.31 ex4412.94 ex4412.99 4412.31 ex4412.32 ex4412.94 ex4412.99 ex634.31 ex634.33 ex634.39
8.1.1 of which: Laminated Veneer Lumber (LVL) 4412.41/42/49 ex4412.99 ex4412.99 ex634.39
8.1.1.C Coniferous 4412.49 ex4412.99 ex4412.99 ex634.39
8.1.1.NC Non-Coniferous 4412.41/42 ex4412.99 ex4412.99 ex634.39
8.1.1.NC.T of which: Tropical 4412.41 ex4412.99 ex4412.99 ex634.39
8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD 44.10 44.10 44.10 634.22/23
8.2.1 of which: ORIENTED STRAND BOARD (OSB) 4410.12 4410.12 4410.12 ex634.22
8.3 FIBREBOARD 44.11 44.11 44.11 634.5
8.3.1 HARDBOARD 4411.92 4411.92 4411.92 ex634.54 ex634.55
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 4411.12/13 ex4411.14* 4411.12/13 ex4411.14* 4411.12/13 ex4411.14* ex634.54 ex634.55
8.3.3 OTHER FIBREBOARD ex4411.14* 4411.93/94 ex4411.14* 4411.93/94 ex4411.14 4411.93/94 ex634.54 ex634.55
9 WOOD PULP 47.01/02/03/04/05 47.01/02/03/04/05 47.01/02/03/04/05 251.2 251.3 251.4 251.5 251.6 251.91
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 47.01 47.05 47.01 47.05 47.01 47.05 251.2 251.91
9.2 CHEMICAL WOOD PULP 47.03 47.04 47.03 47.04 47.03 47.04 251.4 251.5 251.6
9.2.1 SULPHATE PULP 47.03 47.03 47.03 251.4 251.5
9.2.1.1 of which: BLEACHED 4703.21/29 4703.21/29 4703.21/29 251.5
9.2.2 SULPHITE PULP 47.04 47.04 47.04 251.6
9.3 DISSOLVING GRADES 47.02 47.02 47.02 251.3
10 OTHER PULP 47.06 47.06 47.06 251.92
10.1 PULP FROM FIBRES OTHER THAN WOOD 4706.10/30/91/92/93 4706.10/30/91/92/93 4706.10/30/91/92/93 ex251.92
10.2 RECOVERED FIBRE PULP 4706.20 4706.20 4706.20 ex251.92
11 RECOVERED PAPER 47.07 47.07 47.07 251.1
12 PAPER AND PAPERBOARD 48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13 48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13 48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13 641.1 641.2 641.3 641.4 641.5 641.62/63/64/69/71/72/74/75/76/77/93 642.41
12.1 GRAPHIC PAPERS 48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29 48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29 48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29 641.1 641.21/22/26/29 641.3
12.1.1 NEWSPRINT 48.01 48.01 48.01 641.1
12.1.2 UNCOATED MECHANICAL 4802.61/62/69 4802.61/62/69 4802.61/62/69 641.29
12.1.3 UNCOATED WOODFREE 4802.10/20/54/55/56/57/58 4802.10/20/54/55/56/57/58 4802.10/20/54/55/56/57/58 641.21/22/26
12.1.4 COATED PAPERS 48.09 4810.13/14/19/22/29 48.09 4810.13/14/19/22/29 48.09 4810.13/14/19/22/29 641.3
12.2 HOUSEHOLD AND SANITARY PAPERS 48.03 48.03 48.03 641.63
12.3 PACKAGING MATERIALS 4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59 4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59 4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59 641.41/42/46 ex641.47 641.48/51/52 ex641.53 641.54/59/62/64/69/71/72/74/75/76/77
12.3.1 CASE MATERIALS 4804.11/19 4805.11/12/19/24/25/91 4804.11/19 4805.11/12/19/24/25/91 4804.11/19 4805.11/12/19/24/25/91 641.41/51/54 ex641.59
12.3.2 CARTONBOARD 4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59 4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59 4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59 ex641.47 641.48 ex641.59 641.75/76 ex641.77 641.71/72
12.3.3 WRAPPING PAPERS 4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99 4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99 4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99 641.42/46/52 ex641.53 641.62/64/69/74 ex641.77
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 4805.93 4805.93 4805.93 ex641.59
12.4 OTHER PAPER AND PAPERBOARD N.E.S. 4802.40 4804.41 4805.40/50 4806.30 48.12 48.13 4802.40 4804.41 4805.40/50 4806.30 48.12 48.13 4802.40 4804.41 4805.40/50 4806.30 48.12 48.13 641.24 ex641.47 641.56 ex641.53 641.55/93 642.41
15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)2 4418.81/82 ex4418.60 ex4418.60 ex635.39
15.1 GLULAM 4418.81 ex4418.60 ex4418.60 ex635.39
15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 4418.82 ex4418.60 ex4418.60 ex635.39
16 I BEAMS (I-JOISTS)2 4418.83 ex4418.60 ex4418.60 ex635.39
1Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included in JQ1 and JQ2
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017/HS2022 or SITC Rev.4 code is applicable.
For instance "ex4401.49" under product 3.2 means that only a part of HS2022 code 4401.49 refers to wood residues coming from wood processing (the other part coded under 4401.49 is recovered post-consumer wood).
++ Please use your judgement or, as a default, assign half of 4401.49 to item 3.2 and half to item 4 (note different quantity units)
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.

SentData

Country Flow Year Unit Product Conc Data value
ERROR:#REF! P 2021 1000 m3 1 ERROR:#REF! 453530.087745588 JQ1
ERROR:#REF! P 2021 1000 m3 1_C ERROR:#REF! 71110.54
ERROR:#REF! P 2021 1000 m3 1_NC ERROR:#REF! 33759.55
ERROR:#REF! P 2021 1000 m3 1_1 ERROR:#REF! 37350.99
ERROR:#REF! P 2021 1000 m3 1_1_C ERROR:#REF! 382419.547745588
ERROR:#REF! P 2021 1000 m3 1_1_NC ERROR:#REF! 305851
ERROR:#REF! P 2021 1000 m3 1_2 ERROR:#REF! 76568.5477455878
ERROR:#REF! P 2021 1000 m3 1_2_C ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 1_2_NC ERROR:#REF! 183400.547745588
ERROR:#REF! P 2021 1000 m3 1_2_1 ERROR:#REF! 150702
ERROR:#REF! P 2021 1000 m3 1_2_1_C ERROR:#REF! 32698.5477455878
ERROR:#REF! P 2021 1000 m3 1_2_1_NC ERROR:#REF! 185686
ERROR:#REF! P 2021 1000 m3 1_2_2 ERROR:#REF! 143462
ERROR:#REF! P 2021 1000 m3 1_2_2_C ERROR:#REF! 42224
ERROR:#REF! P 2021 1000 m3 1_2_2_NC ERROR:#REF! 13333
ERROR:#REF! P 2021 1000 m3 1_2_3 ERROR:#REF! 11687
ERROR:#REF! P 2021 1000 m3 1_2_3_C ERROR:#REF! 1646
ERROR:#REF! P 2021 1000 m3 1_2_3_NC ERROR:#REF! 0
ERROR:#REF! P 2021 1000 mt 2 ERROR:#REF! 60485.27
ERROR:#REF! P 2021 1000 m3 3 ERROR:#REF! 44209.21
ERROR:#REF! P 2021 1000 m3 3_1 ERROR:#REF! 16276.06
ERROR:#REF! P 2021 1000 m3 3_2 ERROR:#REF! ...
ERROR:#REF! P 2021 1000 mt 4 ERROR:#REF! 8448.6
ERROR:#REF! P 2021 1000 mt 4_1 ERROR:#REF! 8448.6
ERROR:#REF! P 2021 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! P 2021 1000 m3 5 ERROR:#REF! 80705
ERROR:#REF! P 2021 1000 m3 5_C ERROR:#REF! 63417
ERROR:#REF! P 2021 1000 m3 5_NC ERROR:#REF! 17288
ERROR:#REF! P 2021 1000 m3 5_NC_T ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6 ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6_1 ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6_1_C ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6_1_NC ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6_1_NC_T ERROR:#REF! 27679.8579911906
ERROR:#REF! P 2021 1000 m3 6_2 ERROR:#REF! 9704.8579911906
ERROR:#REF! P 2021 1000 m3 6_2_C ERROR:#REF! 9470.8579911906
ERROR:#REF! P 2021 1000 m3 6_2_NC ERROR:#REF! 234
ERROR:#REF! P 2021 1000 m3 6_2_NC_T ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6_3 ERROR:#REF! 17975
ERROR:#REF! P 2021 1000 m3 6_3_1 ERROR:#REF! 13839
ERROR:#REF! P 2021 1000 m3 6_4 ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6_4_1 ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6_4_2 ERROR:#REF! 0
ERROR:#REF! P 2021 1000 m3 6_4_3 ERROR:#REF! 0
ERROR:#REF! P 2021 1000 mt 7 ERROR:#REF! 49685
ERROR:#REF! P 2021 1000 mt 7_1 ERROR:#REF! 4046
ERROR:#REF! P 2021 1000 mt 7_2 ERROR:#REF! 44411
ERROR:#REF! P 2021 1000 mt 7_3 ERROR:#REF! 44167
ERROR:#REF! P 2021 1000 mt 7_3_1 ERROR:#REF! 20262
ERROR:#REF! P 2021 1000 mt 7_3_2 ERROR:#REF! 244
ERROR:#REF! P 2021 1000 mt 7_3_3 ERROR:#REF! 1228
ERROR:#REF! P 2021 1000 mt 7_3_4 ERROR:#REF! 30072
ERROR:#REF! P 2021 1000 mt 7_4 ERROR:#REF! 108
ERROR:#REF! P 2021 1000 mt 8 ERROR:#REF! 29964
ERROR:#REF! P 2021 1000 mt 8_1 ERROR:#REF! 45037
ERROR:#REF! P 2021 1000 mt 8_2 ERROR:#REF! 67475.31
ERROR:#REF! P 2021 1000 mt 9 ERROR:#REF! 8296
ERROR:#REF! P 2021 1000 mt 10 ERROR:#REF! 370
ERROR:#REF! P 2021 1000 mt 10_1 ERROR:#REF! 394.6
ERROR:#REF! P 2021 1000 mt 10_1_1 ERROR:#REF! 4774.51
ERROR:#REF! P 2021 1000 mt 10_1_2 ERROR:#REF! 2756.93
ERROR:#REF! P 2021 1000 mt 10_1_3 ERROR:#REF! 6928.17
ERROR:#REF! P 2021 1000 mt 10_1_4 ERROR:#REF! 50948.42
ERROR:#REF! P 2021 1000 mt 10_2 ERROR:#REF! 36169.47
ERROR:#REF! P 2021 1000 mt 10_3 ERROR:#REF! 8482.18
ERROR:#REF! P 2021 1000 mt 10_3_1 ERROR:#REF! 2541.93
ERROR:#REF! P 2021 1000 mt 10_3_2 ERROR:#REF! 3754.84
ERROR:#REF! P 2021 1000 mt 10_3_3 ERROR:#REF! 1302.72
ERROR:#REF! P 2021 1000 mt 10_3_4 ERROR:#REF! 0
ERROR:#REF! P 2021 1000 mt 10_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P 2022 1000 m3 1 ERROR:#REF! 458773.695388199
ERROR:#REF! P 2022 1000 m3 1_C ERROR:#REF! 76230
ERROR:#REF! P 2022 1000 m3 1_NC ERROR:#REF! 37619
ERROR:#REF! P 2022 1000 m3 1_1 ERROR:#REF! 38611
ERROR:#REF! P 2022 1000 m3 1_1_C ERROR:#REF! 382543.695388199
ERROR:#REF! P 2022 1000 m3 1_1_NC ERROR:#REF! 306118.695388199
ERROR:#REF! P 2022 1000 m3 1_2 ERROR:#REF! 76425
ERROR:#REF! P 2022 1000 m3 1_2_C ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 1_2_NC ERROR:#REF! 186156.695388199
ERROR:#REF! P 2022 1000 m3 1_2_1 ERROR:#REF! 152798.695388199
ERROR:#REF! P 2022 1000 m3 1_2_1_C ERROR:#REF! 33358
ERROR:#REF! P 2022 1000 m3 1_2_1_NC ERROR:#REF! 182650
ERROR:#REF! P 2022 1000 m3 1_2_2 ERROR:#REF! 141226
ERROR:#REF! P 2022 1000 m3 1_2_2_C ERROR:#REF! 41424
ERROR:#REF! P 2022 1000 m3 1_2_2_NC ERROR:#REF! 13737
ERROR:#REF! P 2022 1000 m3 1_2_3 ERROR:#REF! 12094
ERROR:#REF! P 2022 1000 m3 1_2_3_C ERROR:#REF! 1643
ERROR:#REF! P 2022 1000 m3 1_2_3_NC ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 2 ERROR:#REF! 62262
ERROR:#REF! P 2022 1000 m3 3 ERROR:#REF! 45900
ERROR:#REF! P 2022 1000 m3 3_1 ERROR:#REF! 16362
ERROR:#REF! P 2022 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! P 2022 1000 mt 4 ERROR:#REF! 9544
ERROR:#REF! P 2022 1000 mt 4_1 ERROR:#REF! 9544
ERROR:#REF! P 2022 1000 mt 4_2 ERROR:#REF! ...
ERROR:#REF! P 2022 1000 m3 5 ERROR:#REF! 81676
ERROR:#REF! P 2022 1000 m3 5_C ERROR:#REF! 64039
ERROR:#REF! P 2022 1000 m3 5_NC ERROR:#REF! 17637
ERROR:#REF! P 2022 1000 m3 5_NC_T ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_1 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_1_C ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_1_NC ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_1_NC_T ERROR:#REF! 9020.0459873934
ERROR:#REF! P 2022 1000 m3 6_2 ERROR:#REF! 9020.0459873934
ERROR:#REF! P 2022 1000 m3 6_2_C ERROR:#REF! 9020.0459873934
ERROR:#REF! P 2022 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! P 2022 1000 m3 6_2_NC_T ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_3 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_3_1 ERROR:#REF! 13592
ERROR:#REF! P 2022 1000 m3 6_4 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_4_1 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_4_2 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 m3 6_4_3 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 7 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 7_1 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 7_2 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 7_3 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 7_3_1 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 7_3_2 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 7_3_3 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 7_3_4 ERROR:#REF! 31250
ERROR:#REF! P 2022 1000 mt 7_4 ERROR:#REF! 96
ERROR:#REF! P 2022 1000 mt 8 ERROR:#REF! 31154
ERROR:#REF! P 2022 1000 mt 8_1 ERROR:#REF! 44828
ERROR:#REF! P 2022 1000 mt 8_2 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 9 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_1 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_1_1 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_1_2 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_1_3 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_1_4 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_2 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_3 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_3_1 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_3_2 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_3_3 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_3_4 ERROR:#REF! 0
ERROR:#REF! P 2022 1000 mt 10_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 m3 1 ERROR:#REF! ERROR:#REF! JQ2
ERROR:#REF! M 2021 1000 m3 1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 m3 1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 m3 1_2_C ERROR:#REF! ERROR:#REF!
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ERROR:#REF! X 2022 1000 mt 7 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! X 2022 1000 NAC 1 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! X 2022 1000 NAC 1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 1_2_NC_T ERROR:#REF! ERROR:#REF!
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ERROR:#REF! X 2022 1000 NAC 5 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 5_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 5_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 5_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_1_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 6_4_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 7_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 7_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 7_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 7_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 7_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 7_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 7_3_4 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! X 2022 1000 NAC 8 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! X 2022 1000 NAC 8_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 9 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_1_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2022 1000 NAC 10_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 0 1000 NAC 11_1 ERROR:#REF! 1444980.53 JQ3
ERROR:#REF! M 0 1000 NAC 11_1_C ERROR:#REF! 335077.53
ERROR:#REF! M 0 1000 NAC 11_1_NC ERROR:#REF! 72627.89
ERROR:#REF! M 0 1000 NAC 11_1_NC_T ERROR:#REF! 433093.8
ERROR:#REF! M 0 1000 NAC 11_2 ERROR:#REF! 1399952.62
ERROR:#REF! M 0 1000 NAC 11_3 ERROR:#REF! 3052486.93
ERROR:#REF! M 0 1000 NAC 11_4 ERROR:#REF! 25434019.73
ERROR:#REF! M 0 1000 NAC 11_5 ERROR:#REF! 152392.3
ERROR:#REF! M 0 1000 NAC 11_6 ERROR:#REF! 1861141.27
ERROR:#REF! M 0 1000 NAC 11_7 ERROR:#REF! 0
ERROR:#REF! M 0 1000 NAC 11_7_1 ERROR:#REF! 78670.24
ERROR:#REF! M 0 1000 NAC 12_1 ERROR:#REF! 1401153.82
ERROR:#REF! M 0 1000 NAC 12_2 ERROR:#REF! 3041463.97
ERROR:#REF! M 0 1000 NAC 12_3 ERROR:#REF! 2966364.71
ERROR:#REF! M 0 1000 NAC 12_4 ERROR:#REF! 503.39
ERROR:#REF! M 0 1000 NAC 12_5 ERROR:#REF! 349590.35
ERROR:#REF! M 0 1000 NAC 12_6 ERROR:#REF! 136853.01
ERROR:#REF! M 0 1000 NAC 12_6_1 ERROR:#REF! 0
ERROR:#REF! M 0 1000 NAC 12_6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 0 1000 NAC 12_6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 0 1000 NAC 11_1 ERROR:#REF! 1831434
ERROR:#REF! M 0 1000 NAC 11_1_C ERROR:#REF! 434486
ERROR:#REF! M 0 1000 NAC 11_1_NC ERROR:#REF! 111968
ERROR:#REF! M 0 1000 NAC 11_1_NC_T ERROR:#REF! 532798
ERROR:#REF! M 0 1000 NAC 11_2 ERROR:#REF! 1428139
ERROR:#REF! M 0 1000 NAC 11_3 ERROR:#REF! 3273534
ERROR:#REF! M 0 1000 NAC 11_4 ERROR:#REF! 27484945
ERROR:#REF! M 0 1000 NAC 11_5 ERROR:#REF! 211759
ERROR:#REF! M 0 1000 NAC 11_6 ERROR:#REF! 1919923
ERROR:#REF! M 0 1000 NAC 11_7 ERROR:#REF! 0
ERROR:#REF! M 0 1000 NAC 11_7_1 ERROR:#REF! 117361
ERROR:#REF! M 0 1000 NAC 12_1 ERROR:#REF! 1557780
ERROR:#REF! M 0 1000 NAC 12_2 ERROR:#REF! 3506777
ERROR:#REF! M 0 1000 NAC 12_3 ERROR:#REF! 3674828
ERROR:#REF! M 0 1000 NAC 12_4 ERROR:#REF! 16616
ERROR:#REF! M 0 1000 NAC 12_5 ERROR:#REF! 536539
ERROR:#REF! M 0 1000 NAC 12_6 ERROR:#REF! 343268
ERROR:#REF! M 0 1000 NAC 12_6_1 ERROR:#REF! 0
ERROR:#REF! M 0 1000 NAC 12_6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 0 1000 NAC 12_6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 0 1000 NAC 11_1 ERROR:#REF! 52587.01
ERROR:#REF! X 0 1000 NAC 11_1_C ERROR:#REF! 218864.53
ERROR:#REF! X 0 1000 NAC 11_1_NC ERROR:#REF! 3667.65
ERROR:#REF! X 0 1000 NAC 11_1_NC_T ERROR:#REF! 370076.93
ERROR:#REF! X 0 1000 NAC 11_2 ERROR:#REF! 71996.3
ERROR:#REF! X 0 1000 NAC 11_3 ERROR:#REF! 538750.66
ERROR:#REF! X 0 1000 NAC 11_4 ERROR:#REF! 1862400.66
ERROR:#REF! X 0 1000 NAC 11_5 ERROR:#REF! 35037.9
ERROR:#REF! X 0 1000 NAC 11_6 ERROR:#REF! 209009.15
ERROR:#REF! X 0 1000 NAC 11_7 ERROR:#REF! 0
ERROR:#REF! X 0 1000 NAC 11_7_1 ERROR:#REF! 49953.75
ERROR:#REF! X 0 1000 NAC 12_1 ERROR:#REF! 826261.36
ERROR:#REF! X 0 1000 NAC 12_2 ERROR:#REF! 2257086.44
ERROR:#REF! X 0 1000 NAC 12_3 ERROR:#REF! 1943313.85
ERROR:#REF! X 0 1000 NAC 12_4 ERROR:#REF! 10605.98
ERROR:#REF! X 0 1000 NAC 12_5 ERROR:#REF! 75064.42
ERROR:#REF! X 0 1000 NAC 12_6 ERROR:#REF! 98106.34
ERROR:#REF! X 0 1000 NAC 12_6_1 ERROR:#REF! 0
ERROR:#REF! X 0 1000 NAC 12_6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 0 1000 NAC 12_6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 0 1000 NAC 11_1 ERROR:#REF! 57841
ERROR:#REF! X 0 1000 NAC 11_1_C ERROR:#REF! 263753
ERROR:#REF! X 0 1000 NAC 11_1_NC ERROR:#REF! 3843
ERROR:#REF! X 0 1000 NAC 11_1_NC_T ERROR:#REF! 514900
ERROR:#REF! X 0 1000 NAC 11_2 ERROR:#REF! 73367
ERROR:#REF! X 0 1000 NAC 11_3 ERROR:#REF! 511169
ERROR:#REF! X 0 1000 NAC 11_4 ERROR:#REF! 2224718
ERROR:#REF! X 0 1000 NAC 11_5 ERROR:#REF! 43016
ERROR:#REF! X 0 1000 NAC 11_6 ERROR:#REF! 223679
ERROR:#REF! X 0 1000 NAC 11_7 ERROR:#REF! 0
ERROR:#REF! X 0 1000 NAC 11_7_1 ERROR:#REF! 60700
ERROR:#REF! X 0 1000 NAC 12_1 ERROR:#REF! 930630
ERROR:#REF! X 0 1000 NAC 12_2 ERROR:#REF! 2409528
ERROR:#REF! X 0 1000 NAC 12_3 ERROR:#REF! 2081488
ERROR:#REF! X 0 1000 NAC 12_4 ERROR:#REF! 69038
ERROR:#REF! X 0 1000 NAC 12_5 ERROR:#REF! 90800
ERROR:#REF! X 0 1000 NAC 12_6 ERROR:#REF! 169354
ERROR:#REF! X 0 1000 NAC 12_6_1 ERROR:#REF! 0
ERROR:#REF! X 0 1000 NAC 12_6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 0 1000 NAC 12_6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 m3 ST_1_2_C ERROR:#REF! 0 ECEEU
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_1_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_2_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_1_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_2_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 m3 ST_1_2_C_2_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_1_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_2_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_1_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_2_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_3 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_1_3 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_2_3 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_4 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_1_2_NC_5 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_C ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_C_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_C_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_NC ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_NC_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_NC_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_NC_3 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_NC_4 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_NC_5 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_NC_6 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 m3 ST_5_NC_7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 NAC ST_1_2_C ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_1_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_2_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_1_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_2_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 NAC ST_1_2_C_2_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_1_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_2_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_1_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_2_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_3 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_1_3 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_2_3 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_4 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_1_2_NC_5 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_C ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_C_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_C_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_NC ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_NC_1 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_NC_2 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_NC_3 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_NC_4 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_NC_5 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_NC_6 ERROR:#REF! 0
ERROR:#REF! M 2021 1000 NAC ST_5_NC_7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2022 1000 m3 ST_1_2_C ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_1_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_2_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_1_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_2_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2022 1000 m3 ST_1_2_C_2_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_1_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_2_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_1_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_2_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_3 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_1_3 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_2_3 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_4 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_1_2_NC_5 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_C ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_C_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_C_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_NC ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_NC_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_NC_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_NC_3 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_NC_4 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_NC_5 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_NC_6 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 m3 ST_5_NC_7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2022 1000 NAC ST_1_2_C ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_1_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_2_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_1_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_2_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2022 1000 NAC ST_1_2_C_2_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_1_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_2_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_1_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_2_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_3 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_1_3 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_2_3 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_4 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_1_2_NC_5 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_C ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_C_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_C_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_NC ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_NC_1 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_NC_2 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_NC_3 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_NC_4 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_NC_5 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_NC_6 ERROR:#REF! 0
ERROR:#REF! M 2022 1000 NAC ST_5_NC_7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2021 1000 m3 ST_1_2_C ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_1_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_2_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_2 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_1_2 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_2_2 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2021 1000 m3 ST_1_2_C_2_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_1_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_2_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_2 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_1_2 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_2_2 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_3 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_1_3 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_2_3 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_4 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_1_2_NC_5 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_C ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_C_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_C_2 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_NC ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_NC_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_NC_2 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_NC_3 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_NC_4 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_NC_5 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_NC_6 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 m3 ST_5_NC_7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! X 2021 1000 NAC ST_1_2_C ERROR:#REF! 0
ERROR:#REF! X 2021 1000 NAC ST_1_2_C_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 NAC ST_1_2_C_1_1 ERROR:#REF! 0
ERROR:#REF! X 2021 1000 NAC ST_1_2_C_2_1 ERROR:#REF! 0
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ERROR:#REF! EX_M 2021 1000 m3 1 ERROR:#REF! ERROR:#REF! EU1
ERROR:#REF! EX_M 2021 1000 m3 1_1 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 4 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 5 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 7 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 8 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 9 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2021 1000 NAC 10 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 10_1_1 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 10_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2021 1000 NAC 10_1_4 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 10_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2021 1000 NAC 10_3_1 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2021 1000 NAC 10_3_4 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 m3 1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 mt 2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 mt 4 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 mt 4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 5 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 5_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_M 2022 1000 m3 5_NC ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 m3 6 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 m3 6_1_C ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 mt 7 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 mt 8 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 NAC 1 ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 NAC 6_1_C ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_M 2022 1000 NAC 6_2_C ERROR:#REF! ERROR:#REF!
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ERROR:#REF! EX_X 2021 1000 m3 1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 5 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 5_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 5_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_1_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 m3 6_4_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 7_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 8 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 8_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 8_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 9 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 mt 10_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 1_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 5 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 5_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 5_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 5_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_1_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 6_4_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 7_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 8 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 8_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 8_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 9 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_1_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2021 1000 NAC 10_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 1_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 5 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 5_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 5_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_1_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 m3 6_4_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 7_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 8 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 8_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 8_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 9 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 mt 10_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 1_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 5 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 5_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 5_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 5_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_1_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_2_NC_T ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_4_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_4_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 6_4_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 7_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 8 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 8_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 8_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 9 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_1_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_3_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_3_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_3_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_3_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! EX_X 2022 1000 NAC 10_4 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1 ERROR:#REF! ERROR:#REF! EU2
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_3_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_3_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_3_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P ERROR:#REF! 1000 m3 EU2_1_3_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1 ERROR:#REF! ERROR:#REF! OB
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_3_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_3_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_1 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_1_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_1_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_2 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_2_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_2_NC ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_3 ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_3_C ERROR:#REF! ERROR:#REF!
ERROR:#REF! P.OB ERROR:#REF! 1000 m3 1_2_3_NC ERROR:#REF! ERROR:#REF!

Database

Country Flow Year Unit Product conc
ERROR:#REF! P 2015 1000 m3 1 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_1_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_1_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_1 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_1_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_1_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_2 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_2_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_2_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_3 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_3_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 1_2_3_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 mt 2 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 3 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! P 2015 1000 mt 4 ERROR:#REF!
ERROR:#REF! P 2015 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! P 2015 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 5 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 5_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 6_4_1 ERROR:#REF!
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ERROR:#REF! M 2015 1000 m3 ST_1_2_C ERROR:#REF!
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ERROR:#REF! M 2015 1000 m3 ST_1_2_C_2 ERROR:#REF!
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ERROR:#REF! M 2015 1000 m3 ST_1_2_NC ERROR:#REF!
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ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_5_C ERROR:#REF!
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ERROR:#REF! M 2015 1000 m3 ST_5_NC ERROR:#REF!
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ERROR:#REF! M 2015 1000 m3 ST_5_NC_4 ERROR:#REF!
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ERROR:#REF! M 2015 1000 m3 ST_5_NC_6 ERROR:#REF!
ERROR:#REF! M 2015 1000 m3 ST_5_NC_7 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_C ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_C_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_C_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_NC ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_NC_1 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_NC_2 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_NC_3 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_NC_4 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_NC_5 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_NC_6 ERROR:#REF!
ERROR:#REF! M 2015 1000 NAC ST_5_NC_7 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_C ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_C_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_C_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_NC ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_NC_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_NC_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_NC_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_NC_4 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_NC_5 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_NC_6 ERROR:#REF!
ERROR:#REF! X 2015 1000 m3 ST_5_NC_7 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_NC ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! X 2015 1000 NAC ST_1_2_NC_2_1 ERROR:#REF!
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ERROR:#REF! X 2015 1000 NAC ST_5_NC_6 ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 m3 1 ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 NAC 1 ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 NAC 6_1_C ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! EX_M 2015 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2015 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2015 1000 NAC 6_3 ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 NAC 8 ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 NAC 9 ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! EX_M 2015 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! EX_M 2015 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! EX_M 2015 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! EX_M 2015 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! EX_M 2015 1000 NAC 10_2 ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 NAC 10_3_1 ERROR:#REF!
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ERROR:#REF! EX_M 2015 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 mt 2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 3 ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 mt 4 ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 5 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 5_C ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 6 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 m3 6_1_NC_T ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 m3 6_2_C ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 m3 6_2_NC_T ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 mt 9 ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 3 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 4 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 5 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 7 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 7_3_1 ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 NAC 7_3_4 ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 NAC 8 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 9 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 10 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! EX_X 2015 1000 NAC 10_3 ERROR:#REF!
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ERROR:#REF! EX_X 2015 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_1 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_1_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_1_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_2 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_2_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_2_NC ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_3 ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_3_C ERROR:#REF!
ERROR:#REF! P 2015 1000 m3 EU2_1_3_NC ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1 ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_C ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_NC ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_1_C ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_1_NC ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_1 ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_1_C ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_1_NC ERROR:#REF!
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ERROR:#REF! P.OB 2015 1000 m3 1_2_2_C ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_2_NC ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_3 ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_3_C ERROR:#REF!
ERROR:#REF! P.OB 2015 1000 m3 1_2_3_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_1_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_1_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_1_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_1_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_2_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_2_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_3 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_3_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 1_2_3_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 2 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 3 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 4 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 5 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 5_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 8 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 9 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10_2 ERROR:#REF!
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ERROR:#REF! P 2014 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10_3_2 ERROR:#REF!
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ERROR:#REF! P 2014 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! P 2014 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 4 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 5 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 5_C ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 7 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 7_3_2 ERROR:#REF!
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ERROR:#REF! M 2014 1000 mt 7_3_4 ERROR:#REF!
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ERROR:#REF! M 2014 1000 mt 8 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 9 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_3 ERROR:#REF!
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ERROR:#REF! M 2014 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 mt 10_3_4 ERROR:#REF!
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ERROR:#REF! M 2014 1000 NAC 1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 5 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 8 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 9 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 4 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 5 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 5_C ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 8 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 9 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 5 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 8 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 9 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_1_C ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_1_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_1_NC_T ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_5 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_6 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_7 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 11_7_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_5 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_6 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_6_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_6_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_6_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_7 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_7_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_7_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC 12_7_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_1_C ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_1_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_1_NC_T ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_5 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_6 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_7 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 11_7_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_5 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_6 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_6_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_6_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_6_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_7 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_7_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_7_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC 12_7_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_C ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_C_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_C_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_NC_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_NC_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_NC_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_NC_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_NC_5 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_NC_6 ERROR:#REF!
ERROR:#REF! M 2014 1000 m3 ST_5_NC_7 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_C ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_C_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_C_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_NC ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_NC_1 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_NC_2 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_NC_3 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_NC_4 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_NC_5 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_NC_6 ERROR:#REF!
ERROR:#REF! M 2014 1000 NAC ST_5_NC_7 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_C ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_C_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_C_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_NC_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_NC_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_NC_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_NC_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_NC_5 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_NC_6 ERROR:#REF!
ERROR:#REF! X 2014 1000 m3 ST_5_NC_7 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_C ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_C_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_C_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_NC ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_NC_1 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_NC_2 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_NC_3 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_NC_4 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_NC_5 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_NC_6 ERROR:#REF!
ERROR:#REF! X 2014 1000 NAC ST_5_NC_7 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 5 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 5_C ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 8 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 9 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 1 ERROR:#REF!
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ERROR:#REF! EX_M 2014 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 1_2_NC_T ERROR:#REF!
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ERROR:#REF! EX_M 2014 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 6 ERROR:#REF!
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ERROR:#REF! EX_M 2014 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 6_1_NC_T ERROR:#REF!
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ERROR:#REF! EX_M 2014 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 6_2_NC_T ERROR:#REF!
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ERROR:#REF! EX_M 2014 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 8 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 9 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2014 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 5 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 5_C ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 8 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 9 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 5 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 8 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 9 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2014 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_1 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_1_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_1_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_2 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_2_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_2_NC ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_3 ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_3_C ERROR:#REF!
ERROR:#REF! P 2014 1000 m3 EU2_1_3_NC ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1 ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_C ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_NC ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_1_C ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_1_NC ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_1 ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_1_C ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_1_NC ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_2 ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_2_C ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_2_NC ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_3 ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_3_C ERROR:#REF!
ERROR:#REF! P.OB 2014 1000 m3 1_2_3_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_1_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_1_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_1_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_1_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_2_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_2_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_3_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 1_2_3_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 2 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 3 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 4 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 5 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 5_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 8 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 9 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! P 2013 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 4 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 5 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 5_C ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 8 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 9 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 5 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 8 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 9 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 4 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 5 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 5_C ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 8 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 9 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 5 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 8 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 9 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_1_C ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_1_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_1_NC_T ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_5 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_6 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_7 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 11_7_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_5 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_6 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_6_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_6_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_6_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_7 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_7_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_7_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC 12_7_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_1_C ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_1_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_1_NC_T ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_5 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_6 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_7 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 11_7_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_5 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_6 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_6_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_6_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_6_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_7 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_7_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_7_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC 12_7_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_C ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_C_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_C_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_NC_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_NC_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_NC_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_NC_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_NC_5 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_NC_6 ERROR:#REF!
ERROR:#REF! M 2013 1000 m3 ST_5_NC_7 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_C ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_C_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_C_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_NC ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_NC_1 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_NC_2 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_NC_3 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_NC_4 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_NC_5 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_NC_6 ERROR:#REF!
ERROR:#REF! M 2013 1000 NAC ST_5_NC_7 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_C ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_C_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_C_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_NC_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_NC_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_NC_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_NC_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_NC_5 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_NC_6 ERROR:#REF!
ERROR:#REF! X 2013 1000 m3 ST_5_NC_7 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_C ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_C_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_C_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_NC ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_NC_1 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_NC_2 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_NC_3 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_NC_4 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_NC_5 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_NC_6 ERROR:#REF!
ERROR:#REF! X 2013 1000 NAC ST_5_NC_7 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 5 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 5_C ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 8 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 9 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 5 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 8 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 9 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2013 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 5 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 5_C ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_4 ERROR:#REF!
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ERROR:#REF! EX_X 2013 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 7 ERROR:#REF!
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ERROR:#REF! EX_X 2013 1000 mt 9 ERROR:#REF!
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ERROR:#REF! EX_X 2013 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 10_2 ERROR:#REF!
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ERROR:#REF! EX_X 2013 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 5 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 7 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 7_3_4 ERROR:#REF!
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ERROR:#REF! EX_X 2013 1000 NAC 8 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 9 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2013 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_1 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_1_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_1_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_2 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_2_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_2_NC ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_3 ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_3_C ERROR:#REF!
ERROR:#REF! P 2013 1000 m3 EU2_1_3_NC ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1 ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_C ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_NC ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_1_C ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_1_NC ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_1 ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_1_C ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_1_NC ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_2 ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_2_C ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_2_NC ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_3 ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_3_C ERROR:#REF!
ERROR:#REF! P.OB 2013 1000 m3 1_2_3_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_1_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_1_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_1_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_1_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_2 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_2_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_2_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_3 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_3_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 1_2_3_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 2 ERROR:#REF!
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ERROR:#REF! P 2012 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 4 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 5 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 5_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 m3 6_4_2 ERROR:#REF!
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ERROR:#REF! P 2012 1000 mt 7 ERROR:#REF!
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ERROR:#REF! P 2012 1000 mt 7_2 ERROR:#REF!
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ERROR:#REF! P 2012 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 7_3_2 ERROR:#REF!
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ERROR:#REF! P 2012 1000 mt 8 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 9 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_2 ERROR:#REF!
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ERROR:#REF! P 2012 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! P 2012 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 4 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 5 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 5_C ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 8 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 9 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_2 ERROR:#REF!
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ERROR:#REF! M 2012 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 5 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 8 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 9 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 4 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 5 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 5_C ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 8 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 9 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 5 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 8 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 9 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_1_C ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_1_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_1_NC_T ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_5 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_6 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_7 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 11_7_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_5 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_6 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_6_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_6_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_6_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_7 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_7_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_7_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC 12_7_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_1_C ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_1_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_1_NC_T ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_5 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_6 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_7 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 11_7_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_5 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_6 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_6_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_6_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_6_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_7 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_7_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_7_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC 12_7_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_C ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_C_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_C_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_NC_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_NC_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_NC_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_NC_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_NC_5 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_NC_6 ERROR:#REF!
ERROR:#REF! M 2012 1000 m3 ST_5_NC_7 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_C ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_C_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_C_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_NC ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_NC_1 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_NC_2 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_NC_3 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_NC_4 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_NC_5 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_NC_6 ERROR:#REF!
ERROR:#REF! M 2012 1000 NAC ST_5_NC_7 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_C ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_C_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_C_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_NC_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_NC_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_NC_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_NC_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_NC_5 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_NC_6 ERROR:#REF!
ERROR:#REF! X 2012 1000 m3 ST_5_NC_7 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_1_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_2_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_1_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_2_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_1_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_C_2_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_1_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_2_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_1_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_2_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_1_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_2_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_1_2_NC_5 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_C ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_C_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_C_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_NC ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_NC_1 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_NC_2 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_NC_3 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_NC_4 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_NC_5 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_NC_6 ERROR:#REF!
ERROR:#REF! X 2012 1000 NAC ST_5_NC_7 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 5 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 5_C ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 8 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 9 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 5 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 8 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 9 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_1_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_1_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_1_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_1_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_3_1 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_3_2 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_3_3 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_3_4 ERROR:#REF!
ERROR:#REF! EX_M 2012 1000 NAC 10_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 1_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 1_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 1_2_C ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 1_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 3_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 3_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 4_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 4_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 5 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 5_C ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 5_NC ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 5_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_1_C ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_1_NC ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_2_C ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_4_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_4_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 m3 6_4_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 7_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 8 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 8_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 8_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 9 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_1_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_1_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_1_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_1_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 mt 10_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 1_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 1_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 1_2_C ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 1_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 1_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 3_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 3_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 4_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 4_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 5 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 5_C ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 5_NC ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 5_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_1_C ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_1_NC ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_1_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_2_C ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_2_NC ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_2_NC_T ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_4_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_4_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 6_4_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7_3_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7_3_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7_3_3 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7_3_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 7_4 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 8 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 8_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 8_2 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 9 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 10 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 10_1 ERROR:#REF!
ERROR:#REF! EX_X 2012 1000 NAC 10_1_1 ERROR:#REF!
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Presentation, Liana Fox (United States Census Bureau)

National Experimental Wellbeing Statistics, Liana Fox, United States Census Bureau

Languages and translations
English

National Experimental Well-being Statistics (NEWS) Combining Survey and Administrative Data to Improve Income and Poverty Statistics

Liana E. Fox U.S. Census Bureau

UNECE Group of Experts on Measuring Poverty and Inequality November 28-29, 2023

Any opinions and conclusions expressed herein are those of the authors and do not reflect the views of the U.S. Census Bureau. The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data used to produce this product (Data Management System (DMS) number: P-7524052, Disclosure Review Board (DRB) approval number: CDRB-FY23-SEHSD003-025).

1

Attribution

• Adam Bee, Joshua Mitchell, Nikolas Mittag, Jonathan Rothbaum, Carl Sanders, Lawrence Schmidt, and Matthew Unrath

2

Income and Poverty Estimates

• Household survey nonresponse is increasing • 11% in 2013 to 31% in 2023 (March Current Population Survey)

• For those that respond to the survey, many do not answer income questions • ~45% of income in official poverty estimate imputed for nonresponse

• For those that answer income questions, many underreport • We estimate 1.1 percentage points fewer people in poverty (~3.5 million

people) than official estimates

3

What is NEWS?

4

• Rethink how we produce income and resource statistics • What is the best possible estimate given all the data currently

available at Census for a given income/resource statistic?

• Address multiple sources of bias simultaneously • Apply research on addressing each

How Does NEWS Do This?

• Pull together all available data: survey, census, administrative records, commercial (third-party) data • Often need linked data to address bias correctly

• Do everything in a transparent, replicable, evidence-based manner

• Engage research community • Will create linked microdata and code database for access in FSRDCs

• Code will be shared publicly (subject to disclosure constraints)

5

What Have We Done?

6

• Version 1 Release – February 14 • Proof of concept

• 1 year

• Mirror income and poverty releases – money income (no taxes, credits, in- kind benefits)

• Present methods and approach for feedback

• Paper and estimates available at • https://www.census.gov/data/experimental-data-products/national-

experimental-wellbeing-statistics.html

Measurement Challenges Survey Data 1. Unit Nonresponse Bias

• Not answering the survey • Poverty biased down by 0.3-0.5 percentage points during the pandemic (Bee and Rothbaum,

2022)

2. Item Nonresponse Bias • Not answering income questions (~45 percent of income in the CPS ASEC is imputed!) • Poverty biased down by 0.5-1 percentage points (Bollinger et al., 2019; Hokayem et al., 2022)

3. Mis- and underreporting • Not answering accurately • Poverty biased up by 2.5 percentage points for individuals 65+ (Bee and Mitchell, 2017)

Biases can have different signs and magnitudes which can vary by group

7

Measurement Challenges Administrative Data 1. Selection into administrative data

• Not everyone has to file taxes or gets a W-2 or other information return

• Larrimore, Mortenson, and Splinter (2020) estimate poverty from administrative data, but must impute the existence and poverty status of 4-6 million people

2. Administrative data “nonresponse” • Some information not reported that should have been

• Under-the-table jobs without a W-2, for example – 5% of adults in CPS ASEC report wage and salary earnings on the survey with no W-2

3. Administrative mis- and underreporting • Not always 100% accurate

• Unreported tips, underreported self-employment earnings (refer to IRS tax gap analyses)

8

Measurement Challenges Administrative Data 4. Conceptual misalignment

• Administrative not always measuring what we want

• W-2s historically do not have earnings used to pay for health insurance premiums – understate true earnings (Census also doesn’t get this information when it’s available)

5. Incomplete data coverage • Data not available for individuals or places

6. Selection into linkage • Not all individuals can be linked across data sources (refer to Bond et al., 2014)

9

Addressing the Measurement Challenges

10

Step Description Measurement Challenge Related Work

Weighting Use address-level data for all occupied housing units to weight respondent, linked sample to be representative of the target universe of households

Survey unit nonresponse Selection into administrative data Administrative data “nonresponse” Selection into linkage

Rothbaum et al. (2021) Rothbaum and Bee (2022)

Imputation

Survey earnings Impute survey earnings conditional on survey and administrative information

Survey item nonresponse Hokayem et al. (2022)

Admin gross earnings Impute gross earnings when missing in administrative data

Administrative data “nonresponse” Conceptual misalignment Incomplete data coverage

Means-tested program data Impute means-tested program data for states for which administrative data is not available

Incomplete data coverage Fox et al. (2022)

Nonfiler income Impute unemployment insurance compensation, interest, and dividends for nonfilers

Selection into administrative data Incomplete data coverage

Rothbaum (2023)

Estimation

Combine survey and admin earnings Combine survey and administrative wage and salary earnings according to the NEWS earnings measurement error model

Survey mis- and underreporting Administrative mis- and underreporting

Bee et al. (2023)

Income replacement Use survey and administrative data, imputed income, and earnings from the measurement error model to construct household and family income

Survey mis- and underreporting Administrative mis- and underreporting

Bee and Mitchell (2017)

Address-Linked Data (Weighting)

11

Survey Housing Units (Occupied)

Master Address File Black Knight

IRMF

Link Addresses to People (MAFID→PIK)

MAFARF

1040 Tax Returns

MAFID

Linked Individuals at Occupied Units

W-2s

1040 Tax Returns

Information Returns (IRMF)

IRS Data

SSA Data

Social Security/OASDI Payments (PHUS)

SSI Payments (SSR)

State Data (from partner states)

LEHDPIK

Firm Data (LBD)

EIN

Job-Level Match

Decennial Censuses

Geographic Summaries of Characteristics

ACS 5-Year Files

IRMF MAFARF

Numident

MAFID

Housing Unit Information EIN

EIN

EIN EIN

Numident

PIK

W-2s

PIK

1040 Tax Returns

Geographic ID (State, County, Tract)

1099-Rs

PIK

Decennial Censuses

PIK

Links by Geography

Links by Address

Links to People in Adrecs at the Addresses

Links jobs to each other and to firms

Estimation Combining Survey and Admin Earnings • Five sources of wage and salary earnings information

1. Survey

2. W-2s

3. Detailed Earnings Records

4. LEHD

5. 1040 wage and salary

12

The Full Picture – Wage and Salary Earnings

13

1. Use job-level Information to get “best possible” administrative job-level earnings

2. Compare to 1040 to check for missing earnings (at tax-unit level)

1040

W-2

DER

LEHD

Best Job Earnings

Best Adrec

Earnings

Final Earnings Estimate

Survey

3. Compare to survey and decide for which individuals to use adrec or survey earnings

4. Final “best” estimate of earnings for each individual/household

If LEHD is missing (or has apparent data quality issues), impute gross earnings conditional on administrative and survey information for each job (up to 2)

How to combine survey and administrative earnings? Improve survey imputes

Different Earnings Sources W-2 vs. Survey Responses

14

Source: O'Hara et al. (2017) using the 2011 ACS linked to 2010 W-2 records.

Cluster around 45° line Noisy

“Mean-reverting”

Survey Earnings Use

• 21 percent of individuals

• More often for: • Workers in real estate and construction

• Younger workers (25-44 year-olds)

• Less often for: • Workers in retail, education, management, and health care

• Older workers (65+)

• Black workers

15

Household Income in 2018: NEWS Estimate Relative to Survey

16

Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and third-party data.

Household Income in 2018: NEWS Relative to Survey by Age

17 Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and third-party data.

Results

• Overall, median household income was 6.3 percent higher than in the survey estimate, and poverty was 1.1 percentage points lower.

• Results driven by individuals age 65 and over: • Median household income was 27.3 percent higher than in the survey estimate

• Poverty is 3.3 percentage points lower than the survey estimate.

• No significant impact on median household income for householders under 65 or on child poverty.

18

Future Plans • More years

• Not all adrecs are available in all years • Not all survey variables are available in all years

• More geographies • Use ACS – less detailed information makes combining surveys and adrecs more difficult

• More income/resource concepts • Include taxes, credits, and in-kind transfers • Supplemental Poverty Measure

• Address more sources of measurement error • Self-employment earnings • Income at the very top of the distribution (top 0.1%, 0.01%,...)

• Further investigate assumptions, issues for other subgroups of interest • Non-citizens, homeless/unhoused (or those with unstable living arrangements), group quarters

• Feedback into surveys to improve questions and processing

19

Feedback

Paper and estimates available at:

https://www.census.gov/data/experimental-data-products/national- experimental-wellbeing-statistics.html

Please e-mail any comments, concerns, suggestions, and feedback to:

[email protected]

20

  • Slide 1: National Experimental Well-being Statistics (NEWS) Combining Survey and Administrative Data to Improve Income and Poverty Statistics
  • Slide 2: Attribution
  • Slide 3: Income and Poverty Estimates
  • Slide 4: What is NEWS?
  • Slide 5: How Does NEWS Do This?
  • Slide 6: What Have We Done?
  • Slide 7: Measurement Challenges Survey Data
  • Slide 8: Measurement Challenges Administrative Data
  • Slide 9: Measurement Challenges Administrative Data
  • Slide 10: Addressing the Measurement Challenges
  • Slide 11: Address-Linked Data (Weighting)
  • Slide 12: Estimation Combining Survey and Admin Earnings
  • Slide 13: The Full Picture – Wage and Salary Earnings
  • Slide 14: Different Earnings Sources W-2 vs. Survey Responses
  • Slide 15: Survey Earnings Use
  • Slide 16: Household Income in 2018: NEWS Estimate Relative to Survey
  • Slide 17: Household Income in 2018: NEWS Relative to Survey by Age
  • Slide 18: Results
  • Slide 19: Future Plans
  • Slide 20: Feedback
Russian

Национальная экспериментальная статистика благосостояния (NEWS) Объединение данных обследований и административных данных для

улучшения статистики доходов и бедности

Лиана Э. Фокс Бюро переписи населения США

Группа экспертов ЕЭК ООН по измерению бедности и неравенства 28-29 ноября 2023 г.

Любые мнения и выводы, выраженные в данном документе, принадлежат авторам и не отражают точку зрения Бюро переписи населения США. Бюро переписи населения проверило данный информационный продукт с целью обеспечения надлежащего доступа, использования и защиты от разглашения конфиденциальных исходных данных, использованных для создания данного продукта (номер системы управления данными (DMS): P-7524052, номер одобрения Disclosure Review Board (DRB): CDRB-FY23- SEHSD003-025). 1

Атрибуция

• Адам Би, Джошуа Митчелл, Николас Миттаг, Джонатан Ротбаум, Карл Сандерс, Лоренс Шмидт и Мэтью Унрат

2

Оценки доходов и уровня бедности

• Неотвечаемость при обследовании домохозяйств растетот 11% в 2013 году до 31% в 2023 году (мартовское текущее обследование населения)

• Среди тех, кто ответил на вопросы обследования, многие не отвечают на вопросы о доходах • ~ 45% дохода в официальной оценке бедности, вмененного за отсутствие

ответов

• Среди тех, кто отвечает на вопросы о доходах, многие занижают данные • По нашим оценкам, число людей, живущих в бедности, на 1,1

процентного пункта (~3,5 млн. человек) меньше, чем по официальным оценкам

3

Что такое NEWS?

4

• Переосмысление методов подготовки статистики доходов и ресурсов • Какова наилучшая возможная оценка с учетом всех данных,

имеющихся в настоящее время в распоряжении Census, для данной статистики доходов/ресурсов?

• Одновременное устранение нескольких источников предубеждений • Применять исследования для решения каждой

Kак это делает NEWS?

• Собирает воедино все имеющиеся данные: опросы, переписи, административные записи, коммерческие (сторонние) данные • Для корректного решения проблемы смещения часто требуются связанные данные

• Делает все прозрачно, воспроизводимо, на основе фактических данных

• Привлекает исследовательское сообщество • Будет создана связанная база микроданных и кодов для доступа к ней в

федеральных центрах данных статистических исследований • Код будет размещен в открытом доступе (с учетом ограничений на

раскрытие информации)

5

Что мы сделали?

6

• Выпуск версии 1 - 14 февраля 14 • Proof of concept

• 1 год

• Зеркало доходов и релизы бедности - денежные доходы (без налогов, кредитов, неденежных льгот)

• Представить методы и подход к обратной связи

• Документ и расчеты доступны по адресу https://www.census.gov/data/experimental-data-products/national- experimental-wellbeing-statistics.html

Проблемы, связанные с измерениями Данные опроса 1. Непредвзятость ответов подразделений

• Отказ от ответа на опрос • Снижение уровня бедности на 0,3-0,5 процентных пункта во время пандемии (Bee and Rothbaum, 2022)

2. Непредвзятость ответов на вопросы • Отказ от ответов на вопросы о доходах (~45% доходов в CPS ASEC являются вымышленными!)

• Снижение уровня бедности на 0,5-1 процентный пункт (Bollinger et al., 2019; Hokayem et al., 2022)

3. Искажение и занижение данных • Неточные ответы

• Бедность смещена в сторону увеличения на 2,5 процентных пункта для лиц 65+ (Bee and Mitchell, 2017)

Предвзятость может иметь различные знаки и величины, которые могут варьироваться в зависимости от группы

7

Проблемы измерения Административные данные 1. Выборка по административным данным

• Не все должны подавать налоги или получать W-2 или другую информационную декларацию

• Ларримор, Мортенсон и Сплинтер (2020) оценивают уровень бедности на основе административных данных, но при этом им приходится вменять существование и статус бедности 4-6 млн. человек

2. Административные данные "неответы" • Не представлена некоторая информация, которая должна была бы быть представлена • Работа "под столом" без W-2, например - 5% взрослых в CPS ASEC сообщают о

заработках в рамках опроса без W-2

3. Административные искажения и занижения • Не всегда 100% точность • Незарегистрированные чаевые, заниженные доходы от самозанятости (см. анализ

налоговых пробелов IRS)

8

Проблемы измерения Административные данные 4. Концептуальное рассогласование

• Администрация не всегда измеряет то, что мы хотим

• В W-2 исторически не указываются доходы, использованные для уплаты взносов на медицинское страхование, что занижает истинные доходы (перепись населения также не получает эту информацию, когда она доступна)

5. Неполный охват данных • Данные по отдельным лицам или местам отсутствуют

6. Отбор в систему связи • Не все лица могут быть связаны между собой в разных источниках данных (см. Bond et

al., 2014)

9

Решение проблем, связанных с измерениями

10

Шаг Описание Проблема измерения Похожие работы

Взвешивание Использование данных об адресах всех занятых единиц жилья для взвешивания респондентов, связанной выборки для обеспечения репрезентативности целевой совокупности домохозяйств

Неотвечающие единицы обследования Выбор в административные данные"Неотвечающие" административные данные Выборка в систему связей

Ротбаум и др. (2021) Ротбаум и Би (2022)

Импутация

Доходы от проведения опроса Вмененный заработок по результатам опроса, обусловленный данными опроса и административной информацией

Неотвечающие элементы обследования

Хокайем и др. (2022)

Валовой заработок администратора

Исчисление валового заработка в случае его отсутствия в административных данных

"Неответы" административных данныхКонцептуальное несоответствиеНеполный охват данных

Данные по программам с выплатой пособий

Вмененные данные по программам с оплатой по средствам для штатов, по которым отсутствуют административные данные

Неполный охват данных Фокс и др. (2022)

Доходы неплательщиков Вычет компенсации по страхованию от безработицы, процентов и дивидендов для неплательщиков

Выборка в административных данныхНеполный охват данных

Ротбаум и др. (2023)

Оценка

Совмещайте заработок на опросах и администрировании

Объединение данных обследования и административных доходов от заработной платы в соответствии с моделью ошибки измерения доходов NEWS

Искажение и занижение данных в обследованияхАдминистративные искажения и занижения данных

Би и др. (2023)

Замещение дохода Использование данных обследований и Искажение и занижение данных в Би и Митчелл (2017)

Адресно-связанные данные (взвешивание)

11

Опрос Жилищные единицы(занято)

Главная адресная картотека

Черный рыцарь

IRMF

Link Addresses to People (MAFID→PIK)

MAFARF

Налоговые декларации 1040

MAFID

Связанные физические лица в Занятые единицы

W-2s

Налоговые декларации 1040

Возврат информации (IRMF)

Данные налоговой службы

SSA Data

Платежи по социальному обеспечению/OASDI

(PHUS)

Выплаты по SSI (SSR)

Данные по государствам(от государств-партнеров)

LEHDPIK

Firm Data (LBD)

EIN

Соответствие должности и

уровня квалификаци

и

Десятилет ние

переписи

Краткие географические данныехарактеристик

Файлы ACS за 5

лет IRMF MAFARF

Numident

MAFID

Жилищное подразделение Информация EIN

EIN

EIN EIN

Numident

PIK

W-2s

PIK

Налоговые декларации

1040

Географический идентификатор(штат,

округ, район)

1099-Rs

PIK

Десятилетние переписи

PIK

Ссылки по географическому

принципу

Ссылки по адресу

Ссылки на людей в Adrecs по адресам

Связывает рабочие места друг с другом между собой и с фирмами

Оценка Объединение результатов опроса и административного заработка • Пять источников информации о заработной плате

1. Обследование

2. W-2s

3. Подробный учет доходов

4. LEHD - Продольная динамика "работодатель – домохозяйство”

5. 1040 заработная плата и оклад

12

Полная картина - заработная плата и оклад

13

1. Использование информации

об уровне должности для получения "максимально возможного" административного заработка на уровне должности

2. Сравнение с 1040 для проверки отсутствующих доходов (на уровне налоговых единиц)

1040

W-2

DER

LEHD

Лучший заработо

к на работе

Лучший заработо к Adrec

Окончат ельная оценка

прибыли

Обследование

3. Сравните с результатами опроса и решите, для каких индивидуумов использовать adrec или доходы от опроса

4. Окончательная "наилучшая" оценка доходов для каждого человека/домохозяйства

Если LEHD отсутствует (или имеются явные проблемы с качеством данных), то для каждого рабочего места (до 2) произведите интерполяцию валового заработка на основе административной и опросной информации

Как совместить опрос и административный заработок?Улучшение вменений

при обследовании

Различные источники доходовW-2 в сравнении с ответами на вопросы анкеты

14

Источник: O'Hara et al. (2017) с использованием данных ACS 2011 года, связанных с записями W-2 2010 года.

Кластер вокруг линии 45° Шумно

“Среднереверсивный"

Использование заработков по результатам опроса • 21% лиц

• Чаще всего для: • Работников, занятых в сфере недвижимости и строительства

• Более молодых работников (25-44 года)

• Реже для: • Работников розничной торговли, образования, управления и

здравоохранения

• Пожилых работников (65+)

• Темнокожих работников

15

Доходы населения в 2018 году: Оценка NEWS относительно опроса

16

Источник: Ежегодное социально-экономическое приложение к обследованию населения (2019 Current Population Survey Annual Social and Economic Supplement), связанное с административными данными, данными десятилетней переписи населения и данными сторонних организаций.

Доходы населения в 2018 г: НОВИНКИ относительно опроса по возрасту

17 Источник: Ежегодное социально-экономическое приложение к обследованию населения (2019 Current Population Survey Annual Social and Economic Supplement), связанное с административными данными, данными десятилетней переписи населения и данными сторонних организаций.

Результаты

• В целом медианный доход домохозяйств был на 6,3% выше, чем в оценочном исследовании, а уровень бедности - на 1,1 процентного пункта ниже.

• Результаты обусловлены лицами в возрасте 65 лет и старше: • Медианный доход домохозяйства был на 27,3% выше, чем в оценочном

исследовании

• Уровень бедности на 3,3 процентных пункта ниже, чем в оценке исследования.

• Существенного влияния на медианный доход домохозяйств для лиц моложе 65 лет и на уровень детской бедности не было.

18

Планы на будущее • Несколько лет

• Не все адресаты доступны во все годы • Не все переменные исследования доступны во все годы

• Больше географий • Использование ACS - менее подробная информация затрудняет объединение опросов и адресов

• Больше концепций доходов/ресурсов • Включает налоги, кредиты и трансферты в натуральной форме • Дополнительный показатель бедности

• Устранение дополнительных источников ошибок измерения • Доходы от самозанятости • Доходы на самом верху распределения (верхние 0,1%, 0,01%,..)

• Дальнейшее исследование допущений и проблем для других интересующих подгрупп • Неграждане, бездомные/не имеющие жилья (или лица с нестабильными жилищными условиями),

групповые квартиры

• Обратная связь с опросами для улучшения вопросов и обработки

19

Обратная связь

Документ и расчеты доступны по адресу :

https://www.census.gov/data/experimental-data-products/national- experimental-wellbeing-statistics.html

Все комментарии, замечания, предложения и отзывы направляйте по адресу :

[email protected]

20

  • Slide 1: Национальная экспериментальная статистика благосостояния (NEWS) Объединение данных обследований и административных данных для улучшения статистики доходов и бедности
  • Slide 2: Атрибуция
  • Slide 3: Оценки доходов и уровня бедности
  • Slide 4: Что такое NEWS?
  • Slide 5: Kак это делает NEWS?
  • Slide 6: Что мы сделали?
  • Slide 7: Проблемы, связанные с измерениями Данные опроса
  • Slide 8: Проблемы измерения Административные данные
  • Slide 9: Проблемы измерения Административные данные
  • Slide 10: Решение проблем, связанных с измерениями
  • Slide 11: Адресно-связанные данные (взвешивание)
  • Slide 12: Оценка Объединение результатов опроса и административного заработка
  • Slide 13: Полная картина - заработная плата и оклад
  • Slide 14: Различные источники доходовW-2 в сравнении с ответами на вопросы анкеты
  • Slide 15: Использование заработков по результатам опроса
  • Slide 16: Доходы населения в 2018 году: Оценка NEWS относительно опроса
  • Slide 17: Доходы населения в 2018 г: НОВИНКИ относительно опроса по возрасту
  • Slide 18: Результаты
  • Slide 19: Планы на будущее
  • Slide 20: Обратная связь

Presentation, Thesia Garner (U.S. Bureau of Labor Statistics)

Languages and translations
English

1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Expanding the family of U.S. Consumer Price Indexes

Thesia I. Garner,

Bill Johnson, Joshua Klick, Paul Liegey, Robert Martin, Anya Stockburger

U.S. Bureau of Labor Statistics

UNECE Group of Experts on Measuring Poverty and Inequality

November 28, 2023

2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Outline

Introduction and Motivation

Income-based indexes

Household Cost Indexes

Next steps

3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

CPI Family of Indexes Official Indexes

CPI-U Chained

CPI-U CPI-W

Research Indexes-https://www.bls.gov/cpi/research-series/

R-CPI-U-RS R-CPI-E R-HICP

R-COICOP

R-CPI-Income Household Cost

Index? R-C-CPI-Income

4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Motivation

◼Headline consumer price indexes summarize a range of household experiences

◼ Increased need for data granularity pertaining to demographic groups in particular

Recent recommendations by Committee on National Statistics, interest from Federal Reserve Bank, data users, and media

◼ Interest in inflation from the household perspective rather than the “macro” perspective

Inspired by the United Kingdom, New Zealand, and Australia

5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Methods Overview

Index Prices / Rents Expenditure Weights

CPI-U, C-CPI-U (official)

- Outlets and items selected to represent urban households - Owned housing is measured using owner equivalent rent (OER)

- Consumer Expenditure Surveys (CE) Diary and Interview: sum expenditures for urban households

CPIs by Income - Same as CPI-U and C-CPI-U - Group CE respondents by quintile of equivalized income, sum expenditures separately for each group

HCI-U - Same as CPI-U except owned housing is measured using payments approach

- Create weights for each CE respondent and average equally (“democratic”) over urban population.

6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Key Results

◼ CPIs by equivalized income quintiles: Over 2005-2022, average annual inflation for the lowest quintile was about 0.3 percentage points higher than for the highest quintile.

◼Household cost index: using a payments approach and household-weighted (“democratic”) aggregation, average annual inflation for the urban population was about 0.35 percentage lower than the CPI.

7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Snapshot of spending weights by population, 2019-2020 biennial expenditure weight share, equivalized income

0% 5% 10% 15% 20% 25% 30%

Rent

Food at home

Motor fuel

Owner's equivalent rent

Vehicles and maintenance

Food away from home

Recreation

Q1 U Q5

8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Annualized Inflation Rates by Income Quintile Based on CPIs Lowe Formula, December 2005 - December 2022

2.60

2.54

2.47

2.41

2.33

2.43

2.1

2.2

2.3

2.4

2.5

2.6

2.7

Q1 Q2 Q3 Q4 Q5

Income Quintiles Urban

9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Items contributing to inflation gap (2022) CPI-U 8%; Q1 8.2%; Q5 7.7%

-10 -5 0 5 10 15 20

Rent primary residence(HA01)

Gasoline (all types)(TB01)

Electricity(HF01)

Utility (piped) gas service(HF02)

Cigarettes(GA01)

Motor vehicle insurance(TE01)

Limited service meals/snacks(FV02)

Juices and drinks(FN03)

Cable & satellite tv/radio(RA02)

Chicken(FF01)

Club membership (RB02)

Child care & nursery school(EB03)

Owners' rent secondary res.(HC09)

Leased cars and trucks(TA03)

Full service meals and snacks(FV01)

Commercial Health Insurance(ME01)

Owners' rent primary residence(HC01)

Lodging away from home(HB02)

Airline fare(TG01)

New vehicles(TA01)

Q1 > Q5

Q1 < Q5

10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

HCI-U and CPI-U index levels

Average 12-month % change

CPI-U 1.86%

HCI-U (Payments Approach + Household-weighted Aggregation)

1.51%

HCI-U (Payments Approach Only) 1.46%

1

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

December 2020 relative importance

20.2%

9.2%

4.7%

4.3%

16.0%3.1%

14.2%

11.1%

6.6%

6.8% 3.8%

Food & Bev. Housing: Rent Housing: OER Housing: Prop. Tax

Housing: Mortgage Housing: Other Apparel Transportation

Medical Recreation Educ. & Comm. Other

15.2%

7.9%

24.3%

10.3% 2.7%

15.2%

8.9%

5.8%

6.8% 3.2%

HCI-U (2019 weights) CPI-U (2017-18 weights)

12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

HCI-U housing components versus OER

0.9

1

1.1

1.2

1.3

1.4

1.5

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Owner's Payments (HR, HS, HT) Mortgage Interest (HS) Owner's Equiv. Rent (HC)

Property Tax (HR) Other Owner Payments (HT)

13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Limitations and Future Research

◼Data does not reflect lower-level heterogeneity (e.g. specific prices paid by households or groups)

◼Ongoing discussions on payments approach methods for HCI

e.g., should mortgage payments reflect principal as well as interest?

◼ Continued refinement of methods

◼What is the impact on poverty measurement?

14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Further Reading

◼ CPI by Income Publications: Initial working paper, Spotlight on Statistics

Home page: https://www.bls.gov/cpi/research-series/r-cpi-i.htm

◼ Household Cost Index

Working paper

15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Thank you!

Thesia I. Garner Chief Researcher, Office of Prices and Living Conditions

U.S. Bureau of Labor Statistics [email protected]

  • Slide 1: Expanding the family of U.S. Consumer Price Indexes
  • Slide 2: Outline
  • Slide 3: CPI Family of Indexes
  • Slide 4: Motivation
  • Slide 5: Methods Overview
  • Slide 6: Key Results
  • Slide 7: Snapshot of spending weights by population, 2019-2020 biennial expenditure weight share, equivalized income
  • Slide 8: Annualized Inflation Rates by Income Quintile Based on CPIs Lowe Formula, December 2005 - December 2022
  • Slide 9: Items contributing to inflation gap (2022) CPI-U 8%; Q1 8.2%; Q5 7.7%
  • Slide 10: HCI-U and CPI-U index levels
  • Slide 11: December 2020 relative importance
  • Slide 12: HCI-U housing components versus OER
  • Slide 13: Limitations and Future Research
  • Slide 14: Further Reading
  • Slide 15: Thank you! Thesia I. Garner Chief Researcher, Office of Prices and Living Conditions U.S. Bureau of Labor Statistics [email protected]
Russian

1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Расширяя семейство индексов потребительских цен США

Thesia I. Garner,

Bill Johnson, Joshua Klick, Paul Liegey, Robert Martin, Anya Stockburger

Федеральное Бюро Статистики Труда США

Группа экспертов ЕЭК ООН по измерению бедности и неравенства

28 ноября 2023

2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Содержание

Вступление и мотивация

Индексы на основании доходов

Индексы расходов домохозяйства

Дальнейшие шаги

3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Семейство ИПЦ (CPI) (индексов потребительских цен)

Официальные индексы

ИПЦ-U Сцепленные

ИПЦ-U ИПЦ-W

Исследование индексов-https://www.bls.gov/cpi/research-series/

R-ИПЦ-U-RS R-ИПЦ-E R-HICP

R-COICOP

R-ИПЦ-Доход Индекс расходов домохозяйства?

R-C-ИПЦ-Доход

4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Мотивация

◼Индексы потребительских цен обобщают спект расходов домохозяйства

◼ Возросла потребность в подробных данных, особенно в отношении демографических групп

Недавние рекомендации Комитета национальной статистики, процентная ставка Федерального банка резервов, пользователей данных и СМИ

◼Процентная ставка инфляции с точки зрения именно домохозяйств в отличии от «макро» переспективы

Вдохновленные примером Соединенного Королевства, Новой Зеландии и Австралии

5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Обзор методов

Индекс Цены/ Ренты Вес расходов

ИПЦ-U, C-ИПЦ-U (официальный)

- Магазины и товары выбранные для представления городских домохозяйств - Собственное жилье измеряется с использованием эквивалентной аренды для собственника (OER)

- Обследование потребительских расходов (CE) Дневники и интервью: сумма расходов для городских домохозяйств

ИПЦ по доходам - Также как в ИПЦ-U и C-ИПЦ-U - Респонденты группы CE по квинтилям эквивалентного дохода, сумма расходов отдельно по каждой группе

HCI-U (индекс расходов

домохозяйства)

- Также как в ИПЦ-U только собственное жилье измеряется с использованием платежного подхода

- Создать весы для каждого респондента CE, распределить поровну (демократически) на городское население.

6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Ключевые результаты

◼ИПЦ по квинтилям эквивалентного дохода: В течение 2005- 2022гг средняя годовая инфляция для самого низкого квинтиля составила примерно на 0,3 процентных пункта выше, чем для самого высокого квинтиля.

◼Индекс расходов домохозяйства: используя платежный подход и взвешенное по домохозяйствам (демократическое) обобщение, средняя годовая инфляция городского населения была примерно на 0,35% ниже, чем в ИПЦ

7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

График весов расходов по группам населения, 2019-2020гг доля двухлетних расходов, эквивалентный доход

0% 5% 10% 15% 20% 25% 30%

Rent

Food at home

Motor fuel

Owner's equivalent rent

Vehicles and maintenance

Food away from home

Recreation

Q1 U Q5

8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Ежегодный уровень инфляции по квинтилям дохода На основании ИПЦ, индекс Лоу, декабрь 2005 – декабрь 2022

2.60

2.54

2.47

2.41

2.33

2.43

2.1

2.2

2.3

2.4

2.5

2.6

2.7

Q1 Q2 Q3 Q4 Q5

Income Quintiles Urban

9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Что влияет на инфляционный разрыв (2022) ИПЦ-U 8%; Q1 8.2%; Q5 7.7%

-10 -5 0 5 10 15 20

Rent primary residence(HA01)

Gasoline (all types)(TB01)

Electricity(HF01)

Utility (piped) gas service(HF02)

Cigarettes(GA01)

Motor vehicle insurance(TE01)

Limited service meals/snacks(FV02)

Juices and drinks(FN03)

Cable & satellite tv/radio(RA02)

Chicken(FF01)

Club membership (RB02)

Child care & nursery school(EB03)

Owners' rent secondary res.(HC09)

Leased cars and trucks(TA03)

Full service meals and snacks(FV01)

Commercial Health Insurance(ME01)

Owners' rent primary residence(HC01)

Lodging away from home(HB02)

Airline fare(TG01)

New vehicles(TA01)

Q1 > Q5

Q1 < Q5

10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Уровни индексов HCI-U и CPI-U

Average 12-month % change

CPI-U 1.86%

HCI-U (Payments Approach + Household-weighted Aggregation)

1.51%

HCI-U (Payments Approach Only) 1.46%

1

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Декабрь 2020г, относительная важность

20.2%

9.2%

4.7%

4.3%

16.0%3.1%

14.2%

11.1%

6.6%

6.8% 3.8%

Food & Bev. Housing: Rent Housing: OER Housing: Prop. Tax

Housing: Mortgage Housing: Other Apparel Transportation

Medical Recreation Educ. & Comm. Other

15.2%

7.9%

24.3%

10.3% 2.7%

15.2%

8.9%

5.8%

6.8% 3.2%

HCI-U (2019 weights) CPI-U (2017-18 weights)

12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

HCI-U компоненты жилья в противовес OER

0.9

1

1.1

1.2

1.3

1.4

1.5

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Owner's Payments (HR, HS, HT) Mortgage Interest (HS) Owner's Equiv. Rent (HC)

Property Tax (HR) Other Owner Payments (HT)

13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Ограничения и дальнейшие исследования

◼Данные не отражают низкоуровневую неоднородность (напр. конкретные цены, которые оплачивают домохозяйства или группы)

◼Идут дискуссии по методу платежного подхода для HCI

Напр. должны ли выплаты за ипотеку отражать основную задолженность наравне с процентами?

◼Продолжается работа над улучшением методологии

◼ Как это отражается на измерении бедности?

14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Что еще почитать

◼ИПЦ по доходу Публикации: Initial working paper, Spotlight on Statistics

Домашняя страница: https://www.bls.gov/cpi/research-series/r-cpi-i.htm

◼ Индекс расходов домохозяйства (HCI)

Рабочий доклад

15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Спасибо!

Thesia I. Garner Chief Researcher, Office of Prices and Living Conditions

U.S. Bureau of Labor Statistics [email protected]

  • Slide 1: Расширяя семейство индексов потребительских цен США
  • Slide 2: Содержание
  • Slide 3: Семейство ИПЦ (CPI) (индексов потребительских цен)
  • Slide 4: Мотивация
  • Slide 5: Обзор методов
  • Slide 6: Ключевые результаты
  • Slide 7: График весов расходов по группам населения, 2019-2020гг доля двухлетних расходов, эквивалентный доход
  • Slide 8: Ежегодный уровень инфляции по квинтилям дохода На основании ИПЦ, индекс Лоу, декабрь 2005 – декабрь 2022
  • Slide 9: Что влияет на инфляционный разрыв (2022) ИПЦ-U 8%; Q1 8.2%; Q5 7.7%
  • Slide 10: Уровни индексов HCI-U и CPI-U
  • Slide 11: Декабрь 2020г, относительная важность
  • Slide 12: HCI-U компоненты жилья в противовес OER
  • Slide 13: Ограничения и дальнейшие исследования
  • Slide 14: Что еще почитать
  • Slide 15: Спасибо! Thesia I. Garner Chief Researcher, Office of Prices and Living Conditions U.S. Bureau of Labor Statistics [email protected]