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Market Forecast Tables 2023

Read more about 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

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

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

Presentation, Nadja Lamei

Read more about Presentation, Nadja Lamei

Discussant's reflections

Languages and translations

English

www.statistik.at

Unabhängige Statistiken für faktenbasierte Entscheidungen

A. Social policies, social transfers, and

data - Discussion

Nadja Lamei, Statistics Austria

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS Group of Experts

on Measuring Poverty and Inequality

28-29 November 2023

www.statistik.at Folie 2

Social policies, social transfers, and data Three presentations - Much in common, some specifics

Alessandro Albano Jörg Neugschwender Óscar Ramírez

Data used EU-SILC 2018-2022 LIS 1960ies/2000/2014-2020 INEGI (ENIGH) 2022 and 2016- 2022

Perspective Comparative for EU+ Comparative global perspective Mexico

How to achieve comparability?

Countries have different social systems – this is accounted for by output harmonization.

Key message 1 Social transfers affect the distribution of income (but differently for countries/years), they need to

be considered!

What is considered as social transfers?

Monetary transfers Monetary transfers + STIKs Monetary transfers + STIKs

Key message 2 It needs more than one indicator to tell the story!

Indicators, analysis

AROP before/after social transfers AROP anchored 2019 Median equiv. hh income in PPS + share of social transfers AROPE

AROP before family transfers/ before all social transfers/ after social transfers different thresholds (40, 50, 60% of median equiv. hh inc.) and absolute threshold (less than $ 6.85 per day) single parent households

Income sources Gini coefficient before/ after social transfers Expenditure groups

www.statistik.at Folie 3

Further discussions

• Social policies in the national perspective

− Evaluation of specific social transfers

− Effect on poverty reduction (in total/ for target groups)

Which data and methods to use?

• Social policies in a multi-national perspecive

− How to compare social systems

− Social transfers: advantages of combining / keeping separate

monetary and in-kind transfers

pensions and social transfers other than pensions

− Analysing social transfers by function

Slide 1
Slide 2: Social policies, social transfers, and data
Slide 3: Further discussions

JQ2022AUT

Read more about JQ2022AUT

JFSQ2022 Country Replies Austria

Languages and translations

English

Guidelines

Dear Correspondent, Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines. Please use only this questionnaire to report your data. Use this questionnaire also to revise any historical data - fill in the correct year and your name on the cover page. The total number of sheets to be filled in is seven core sheets (green tabs - to be validated by Eurostat) plus three for ITTO (brown tabs - not validated by Eurostat). Four sheets containing cross-references are included at the end. The flat file is for Eurostat for validation purposes, please do not change any cells here. Also, please do not add / delete rows in any of the sheets, because this will affect the functioning of the flatfile. Put all your data into one Excel file. If you send some data in later, give your file a new version number and date (see A.1. below) and notify us of the changes with respect to the previous version. Only send us completely filled-in sheets, highlighting the changes in yellow. Do not delete worksheets. Each sheet has a working area for your input. Most sheets have checking cells and tables. Each working area has white cells and shaded green cells. Eurostat has highlighted the variables it considers most important for its publications - please fill those in as a priority. When you submit a revision, please highlight changes in yellow and explain them in the appropriate 'Note' column, but please fill in all the cells that were filled in previously. Please use flags and notes (see A.6 below). This information is important for Eurostat. A. General recommendations A.1 Please use eDAMIS to send your questionnaire to Eurostat. Choose the correct domain ("FOREST_A_A") and the correct reference year (for this data collection: 2022). A.2 Fill in the JFSQ quality report each year. A.3 The cover page is for your contact details, which are automatically copied to the other worksheets • Check your country code • If necessary change the reference year as appropriate - the previous year will appear automatically If you distribute worksheets to various experts, they can each put their contact details into the sheets. It will then be your job to put all the information together again and to verify the checking tables, since some of them will not work as designed in isolation. A.4 Look at the unit of measurement to be used for each item and report in this unit if possible, using the conversion factors on the last page of the JFSQ definitions. Please report the monetary values in the same unit for both reporting years. Only report data or modify cells in the working areas. Please do not delete checking areas or checking sheets. • Look at the checking areas and make the necessary corrections to your data to remove all warnings (see the specific recommendations) before sending in your data. Fill in real zeros '0' in the worksheets if there is no production or trade. Empty cells will be interpreted as 'Data not available'. • There are counters at the bottom of the tables to indicate the number of cells left to be filled in. Report all data with at least three decimals. Do not use a separator for thousands; for the decimal point, please use the one set up by default. A.5 Report numbers only. If data are confidential, please provide them if possible, appropriately flagged (see A.6). • Eurostat has a right to all confidential data necessary for its work. It has an obligation to use such data only in aggregates and to respect all the legal obligations. • If you cannot provide confidential data, a good option is to send in your own estimate flagged as a national estimate '9'. • As a last resort, leave the cell empty, flag it and write a note indicating data sources and links. Checking tables contain formulae to sum up the totals for sub-items. A.6 Flag cells and write notes as appropriate. Flags should be entered in the 'Flag' columns and notes in the 'Note' columns for the appropriate year and item. The flags to use are: • 3 for break in time series, see metadata (please explain in the notes and in the quality report the reasons) • 4 for definition differs, see metadata (please explain in the notes and in the quality report the reasons) • 5 for repeating the data of a previous year • 6 for confidential data • 7 for provisional data • 9 for national estimate B Specific recommendations B.1 Sheet 'Removals over bark' is for volumes of wood products measured over bark. General over bark/under bark conversion factors are calculated automatically. • Should you use different conversion factor(s) please delete the ones provided and insert your own • If you only have under bark data, please leave this worksheet empty, but revise the table with the conversion factors. • Unchanged conversion factors will be considered revised. A checking table verifies that sums of sub-items agree with the totals. B.2 Checking tables on worksheets improve data quality, verifying that: • The sum of the sub-items equals the total. • The sum of 'of which' items is not larger than the total. All cells in a checking table should be zero or empty. If this is not the case, please check your numbers for the sub-items and totals. The checking table indicates the difference, so if you see a negative value, you will have to decide which number should be increased by that amount. The only exception is when no data are entered due to confidentiality. B.3 Worksheets 'JQ2' contains a checking table for apparent consumption. Apparent consumption = Production + Imports – Exports. It should be positive or nil. If this is not the case, the cell will change colour and indicate the difference. • Please correct the data in the sheets until checking results are positive or nil. One solution is to increase production. • If the data are correct but apparent consumption is still negative, please explain why in the 'Note' column provided in the apparent consumption checking table. B.4 Sheets 'JQ2', 'ECE-EU Species' and 'EU1' on trade have checking tables to verify data consistency. Both quantity and value must be present. When something is missing, messages or coloured cells appear in the checking tables. Please correct your data until all warnings disappear. The meaning of the messages is: • 0: both value and quantity are zero – all is well, there is no trade • ZERO Q: value is reported, quantity is zero - please correct • ZERO V: quantity is reported, value is zero - please correct • REPORT: both quantity and value are blank - please fill in • NO Q: blank cell for quantity – please fill in • NO V: blank cell for value – please fill in Please enter even very small numbers to resolve problems, using as many decimal places as necessary. If there is no way to correct the problem, please write an explanation in the 'Note' column. If there is no trade for a product, please enter 0 for both quantity and value. Thank you for collecting data for the JFSQ, Eurostat's Forestry Team

JFSQ quality report

Joint Forest Sector Questionnaire Quality Report
		Quality information	Country reply
1	Contact
	Country name	Country name	Austria
	Contact organisation	Contact organisation	Federal Ministry of Agriculture, Forestry, Regions and Water Management
	Contact name	Contact name
	Contact email address	Contact email address
2	Changes to previous year
	Necessity of update	Are there any changes to the quality report of the last data collection?	YES
		If yes, please provide details below.	Changed cells are highlighted in yellow.
3	Statistical processing
	Overview of the source data	Please provide an overview of the sources used to produce JFSQ data.
		Do you use a dedicated survey (of the industry, of households, of forest owners, etc.)?	Please select YES or NO
		If yes, please provide details (e.g., who are the respondents, what is its frequency?).
		Do you use forestry statistics?	YES
		If yes, please provide details.	Removals statistics: Holzeinschlagsmeldung 2022, BML 2023, https://info.bml.gv.at/themen/wald/wald-in-oesterreich/wald-und-zahlen/holzeinschlagsmeldung-2022.html
		Do you use national forest inventory?	Please select YES or NO
		If yes, please provide details.
		Do you use national PRODCOM data compiled according to the CPA classification?	YES
		If yes, please provide details (which products, units, etc.).	For CLT, X-lam and plywood production (JQ1).
		Do you use any other national production statistics?	Please select YES or NO
		If yes, please provide details.
		Do you use data collected by associations of industry?	Please select YES or NO
		If yes, please provide details.
		Do you collect data from direct contacts with manufacturing companies?	Please select YES or NO
		If yes, please provide details.
		Do you use estimates of roundwood use (in manufacturing)?	Please select YES or NO
		If yes, please provide details.
		Do you use national trade data?	YES
		If yes, please provide details.	Austrian foreign trade (final data for 2021, provisional data for 2022), Statistics Austria 2023, 8-digit CN, https://www.statistik.at/statistiken/internationaler-handel/internationaler-warenhandel/importe-und-exporte-von-guetern
		Do you use felling reports?	YES
		If yes, please provide details.	Removals statstistics: Holzeinschlagsmeldung 2022, BML 2023 https://info.bml.gv.at/themen/wald/wald-in-oesterreich/wald-und-zahlen/holzeinschlagsmeldung-2022.html
		Do you use forestry companies' accounting network?	Please select YES or NO
		If yes, please provide details.
		Do you use administrative data (e.g. tax records, business registers)?	Please select YES or NO
		If yes, please provide details.
		Do you use data from national accounts?	Please select YES or NO
		If yes, please provide details (e.g. for which data, from which account tables?).
		Do you use SBS (Structural business statistics)?	Please select YES or NO
		If yes, please provide details (e.g. for which data?).
		Do you use other environmental accounts?	Please select YES or NO
		If yes, please provide details.
		Do you use other statistics (e.g. agriculture statistics)?	Please select YES or NO
		If yes, please specify them.
		Do you use any other sources?	Please select YES or NO
		If yes, please specify them.
	Methodological issues	Are there any pending classification or measurement issues?	Please select YES or NO
		If yes, please specify them.
	Data validation	Do you check the quality of the data collected to compile JFSQ?	Please select YES or NO
		If yes, please explain the quality assurance procedure.
		Do you compare JFSQ data with different data sources or do you perform other cross-checks?	Please select YES or NO
		If yes, please explain your approach.
		Do you have validation rules and other plausibility checks for the outputs of your JFSQ data compilation process?	Please select YES or NO
		If yes, please briefly describe them.
4	Relevance
	User needs	Please provide references to the relevance of JFSQ at national level e.g. main users, national indicator sets, quantitative policy targets etc.
5	Coherence and comparability
	Coherence - cross domain	Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?	Please select YES or NO
		It not, please explain.
		Do you cross-check the JFSQ data with the results of European Forest Accounts?	Please select YES or NO
		If yes, please indicate for which reporting items, and comments on the discrepancies observed, if any. It not, please explain.
	Coherence - internal	Are there any other consistency issues related to your JFSQ data?	Please select YES or NO
		If yes, please explain them.
6	Accessibility and clarity
	Publications	Do you disseminate JFSQ data nationally (e.g. in news releases or other documents)?	Please select YES or NO
		If yes, please provide URLs and/or the reference to the relevant publications.
	Online database	Do you publish your JFSQ accounts in an online data base?	Please select YES or NO
		If yes, please provide URLs.
	Documentation on methodology	Did you prepare a description of your national JFSQ methodology or metadata?	Please select YES or NO
		If yes, please provide URLs.
	Quality documentation	Do you have national quality documentation?	Please select YES or NO
		If yes, please provide URLs.
7	Other comments
	Other comments	Please provide any further feedback you might have on the quality of the reported data, sources and methods used and/or Eurostat's validation and quality report templates.

https://www.statistik.at/statistiken/internationaler-handel/internationaler-warenhandel/importe-und-exporte-von-guetern

Cover

Joint Forest Sector Questionnaire
			2022
	DATA INPUT FILE
		Correspondent country:			AT
		Reference year:			2022	Fill in the year
Name of person responsible for reply:

Official address (in full):				BML, 1030 Vienna, Marxerg. 2

	Telephone:
	Fax:
	E-mail:

Removals over bark

Country:

Date: 14.06.2023

Name of Official responsible for reply:

Check Table

Official Address (in full):

BML, 1030 Vienna, Marxerg. 2

FOREST SECTOR QUESTIONNAIRE

EU JQ1 OB

Telephone:

Discrepancies

Removals

E-mail:

Please verify, if there's an error!

Year 1

Year 2

Flag

Note

Product

Unit

2021

2022

2021

2022

2021

2022

Product

Unit

2021

2022

Code

Quantity

Code

Quantity

ROUNDWOOD REMOVALS OVERBARK

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ob

20630.697

21680.887

includes only removals from FOWL

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ob

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ob

5487.449

6074.551

includes only removals from FOWL

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ob

1.1.C

Coniferous

1000 m3ob

3352.042

3637.462

includes only removals from FOWL

1.1.C

Coniferous

1000 m3ob

1.1.NC

Non-Coniferous

1000 m3ob

2135.407

2437.089

includes only removals from FOWL

1.1.NC

Non-Coniferous

1000 m3ob

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ob

15143.248

15606.336

includes only removals from FOWL

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.C

Coniferous

1000 m3ob

14190.984

14512.298

includes only removals from FOWL

1.2.C

Coniferous

1000 m3ob

1.2.NC

Non-Coniferous

1000 m3ob

952.264

1094.038

includes only removals from FOWL

1.2.NC

Non-Coniferous

1000 m3ob

1.2.NC.T

of which: Tropical

1000 m3ob

0.000

includes only removals from FOWL

1.2.NC.T

of which: Tropical

1000 m3ob

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ob

11669.877

11996.321

includes only removals from FOWL

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ob

1.2.1.C

Coniferous

1000 m3ob

11355.835

11627.376

includes only removals from FOWL

1.2.1.C

Coniferous

1000 m3ob

1.2.1.NC

Non-Coniferous

1000 m3ob

314.042

368.945

includes only removals from FOWL

1.2.1.NC

Non-Coniferous

1000 m3ob

1.2.2

PULPWOOD, ROUND AND SPLIT

1000 m3ob

3473.371

3610.015

includes only removals from FOWL

1.2.2

PULPWOOD, ROUND AND SPLIT

1000 m3ob

1.2.2.C

Coniferous

1000 m3ob

2835.149

2884.922

includes only removals from FOWL

1.2.2.C

Coniferous

1000 m3ob

1.2.2.NC

Non-Coniferous

1000 m3ob

638.222

725.094

includes only removals from FOWL

1.2.2.NC

Non-Coniferous

1000 m3ob

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ob

0.000

includes only removals from FOWL

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.3.C

Coniferous

1000 m3ob

0.000

includes only removals from FOWL

1.2.3.C

Coniferous

1000 m3ob

1.2.3.NC

Non-Coniferous

1000 m3ob

0.000

includes only removals from FOWL

1.2.3.NC

Non-Coniferous

1000 m3ob

To fill:

Product

Unit

2021

2022

Code

OVERBARK/UNDERBARK CONVERSION FACTORS

ROUNDWOOD (WOOD IN THE ROUGH)

m3/m3

1.120

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

m3/m3

1.120

1.1.C

Coniferous

m3/m3

1.120

1.1.NC

Non-Coniferous

m3/m3

1.120

1.2

INDUSTRIAL ROUNDWOOD

m3/m3

1.120

1.2.C

Coniferous

m3/m3

1.120

1.2.NC

Non-Coniferous

m3/m3

1.120

1.2.NC.T

of which: Tropical

m3/m3

ERROR:#DIV/0!

1.2.1

SAWLOGS AND VENEER LOGS

m3/m3

1.120

1.2.1.C

Coniferous

m3/m3

1.120

1.2.1.NC

Non-Coniferous

m3/m3

1.120

1.2.2

PULPWOOD, ROUND AND SPLIT

m3/m3

1.120

1.2.2.C

Coniferous

m3/m3

1.120

1.2.2.NC

Non-Coniferous

m3/m3

1.120

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

m3/m3

ERROR:#DIV/0!

1.2.3.C

Coniferous

m3/m3

ERROR:#DIV/0!

1.2.3.NC

Non-Coniferous

m3/m3

ERROR:#DIV/0!

JQ1 Production

Country:

7/4/23

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE JQ1

BML, 1030 Vienna, Marxerg. 2

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

Year 1

Year 2

Flag

Note

Product

Unit

2021

2022

2021

2022

2021

2022

Product

Unit

2021

2022

2021

2022

% change

Conversion factors

Code

Quantity

Code

Quantity

Roundwood

Industrial roundwood availability

23,331

729,982

3029%

m3 of wood in m3 or t of product

ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)

Recovered wood used in particle board

893

798

-11%

Solid wood equivalent

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

18420.265

19357.935

includes only removals from FOWL

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

Solid Wood Demand

agglomerate production

1,675

1,743

2.4

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

4899.508

5423.706

includes only removals from FOWL

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

Sawnwood production

10,764

10,342

-4%

1.1.C

Coniferous

1000 m3ub

2992.895

3247.734

includes only removals from FOWL

1.1.C

Coniferous

1000 m3ub

veneer production

1.1.NC

Non-Coniferous

1000 m3ub

1906.613

2175.972

includes only removals from FOWL

1.1.NC

Non-Coniferous

1000 m3ub

plywood production

184

131

-29%

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

13520.757

13934.229

includes only removals from FOWL

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

particle board production (incl OSB)

2,550

2,280

-11%

1.58

1.2.C

Coniferous

1000 m3ub

12670.521

12957.409

includes only removals from FOWL

1.2.C

Coniferous

1000 m3ub

fibreboard production

690

470

-32%

1.8

1.2.NC

Non-Coniferous

1000 m3ub

850.236

976.820

includes only removals from FOWL

1.2.NC

Non-Coniferous

1000 m3ub

mechanical/semi-chemical pulp production

1,306

1,304

-0%

2.5

1.2.NC.T

of which: Tropical

1000 m3ub

0.000

includes only removals from FOWL

1.2.NC.T

of which: Tropical

1000 m3ub

chemical pulp production

missing data

4.9

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

10419.533

10711.001

includes only removals from FOWL

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

dissolving pulp production

385

419

5.7

1.2.1.C

Coniferous

1000 m3ub

10139.138

10381.586

includes only removals from FOWL

1.2.1.C

Coniferous

1000 m3ub

Availability

Solid Wood Demand

missing data

1.2.1.NC

Non-Coniferous

1000 m3ub

280.395

329.415

includes only removals from FOWL

1.2.1.NC

Non-Coniferous

1000 m3ub

Difference (roundwood-demand)

missing data

positive = surplus

1.2.2

PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)

1000 m3ub

3101.224

3223.228

includes only removals from FOWL

1.2.2

PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)

1000 m3ub

gap (demand/availability)

missing data

Negative number means not enough roundwood available

1.2.2.C

Coniferous

1000 m3ub

2531.383

2575.823

includes only removals from FOWL

1.2.2.C

Coniferous

1000 m3ub

Positive number means more roundwood available than demanded

1.2.2.NC

Non-Coniferous

1000 m3ub

569.841

647.405

includes only removals from FOWL

1.2.2.NC

Non-Coniferous

1000 m3ub

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

0.000

includes only removals from FOWL

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2.3.C

Coniferous

1000 m3ub

0.000

includes only removals from FOWL

1.2.3.C

Coniferous

1000 m3ub

% of particle board that is from recovered wood

35%

1.2.3.NC

Non-Coniferous

1000 m3ub

0.000

includes only removals from FOWL

1.2.3.NC

Non-Coniferous

1000 m3ub

share of agglomerates produced from industrial roundwood residues

100%

PRODUCTION

usable industrial roundwood - amount of roundwood that is used, remainder leaves industry

98.5%

WOOD CHARCOAL

1000 t

1.463

1.096

WOOD CHARCOAL

1000 t

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

7,466.500

7,823.600

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3,903.900

3,994.200

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

3,562.600

3,829.400

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

3.2.1

of which: Sawdust

1000 m3

3,120.600

3,308.400

3.2.1

of which: Sawdust

1000 m3

RECOVERED POST-CONSUMER WOOD

1000 t

1,131.220

2022 numbers not available yet

RECOVERED POST-CONSUMER WOOD

1000 t

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

1,675.300

1,743.000

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

5.1

WOOD PELLETS

1000 t

1,607.000

1,691.000

5.1

WOOD PELLETS

1000 t

5.2

OTHER AGGLOMERATES

1000 t

68.300

52.000

5.2

OTHER AGGLOMERATES

1000 t

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

10,764.000

10,342.000

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

6.C

Coniferous

1000 m3

10,582.000

10,104.000

6.C

Coniferous

1000 m3

6.NC

Non-Coniferous

1000 m3

182.000

238.000

6.NC

Non-Coniferous

1000 m3

6.NC.T

of which: Tropical

1000 m3

estimate by NC

6.NC.T

of which: Tropical

1000 m3

VENEER SHEETS

1000 m3

7.500

VENEER SHEETS

1000 m3

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

estimate by NC

7.NC.T

of which: Tropical

1000 m3

WOOD-BASED PANELS

1000 m3

3,423.800

2,886.388

WOOD-BASED PANELS

1000 m3

8.1

PLYWOOD

1000 m3

183.800

131.388

8.1

PLYWOOD

1000 m3

8.1.C

Coniferous

1000 m3

179.500

127.582

8.1.C

Coniferous

1000 m3

8.1.NC

Non-Coniferous

1000 m3

4.300

3.806

8.1.NC

Non-Coniferous

1000 m3

8.1.NC.T

of which: Tropical

1000 m3

estimate by NC

8.1.NC.T

of which: Tropical

1000 m3

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

8.1.1.C

Coniferous

1000 m3

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

2,550.000

2,280.000

8.2

PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD

1000 m3

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

8.3

FIBREBOARD

1000 m3

690.000

470.000

8.3

FIBREBOARD

1000 m3

8.3.1

HARDBOARD

1000 m3

75.000

54.000

8.3.1

HARDBOARD

1000 m3

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

615.000

416.000

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

WOOD PULP

1000 t

2,004.481

1,977.160

WOOD PULP

1000 t

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

314.049

254.362

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.2

CHEMICAL WOOD PULP

1000 t

1,305.542

1,303.670

9.2

CHEMICAL WOOD PULP

1000 t

9.2.1

SULPHATE PULP

1000 t

9.2.1

SULPHATE PULP

1000 t

9.2.1.1

of which: BLEACHED

1000 t

9.2.1.1

of which: BLEACHED

1000 t

9.2.2

SULPHITE PULP

1000 t

9.2.2

SULPHITE PULP

1000 t

9.3

DISSOLVING GRADES

1000 t

384.890

419.128

9.3

DISSOLVING GRADES

1000 t

OTHER PULP

1000 t

2,197.166

2,007.557

OTHER PULP

1000 t

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

- 0

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

10.2

RECOVERED FIBRE PULP

1000 t

2,197.166

2,007.557

10.2

RECOVERED FIBRE PULP

1000 t

RECOVERED PAPER

1000 t

1,694.000

AustroPapier changed their reporting on Recover Paper from 2021 onwards, from calculated to reported (source: Umweltbundesamt).

2022 figures are not yet available.

RECOVERED PAPER

1000 t

PAPER AND PAPERBOARD

1000 t

5,065.256

4,633.352

PAPER AND PAPERBOARD

1000 t

12.1

GRAPHIC PAPERS

1000 t

2,247.582

1,869.263

12.1

GRAPHIC PAPERS

1000 t

12.1.1

NEWSPRINT

1000 t

12.1.1

NEWSPRINT

1000 t

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.3

UNCOATED WOODFREE

1000 t

12.1.3

UNCOATED WOODFREE

1000 t

12.1.4

COATED PAPERS

1000 t

1,270.000

1,100.000

12.1.4

COATED PAPERS

1000 t

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

125.000

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.3

PACKAGING MATERIALS

1000 t

2,492.708

2,474.466

12.3

PACKAGING MATERIALS

1000 t

12.3.1

CASE MATERIALS

1000 t

1,700.000

1,600.000

revised

12.3.1

CASE MATERIALS

1000 t

12.3.2

CARTONBOARD

1000 t

12.3.2

CARTONBOARD

1000 t

12.3.3

WRAPPING PAPERS

1000 t

12.3.3

WRAPPING PAPERS

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

199.966

164.623

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

1000 m3

2,578.036

2,457.228

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

1000 m3

15.1

GLULAM

1000 m3

2,063.135

1,991.451

0,45 t/m³

15.1

GLULAM

1000 m3

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

514.901

465.777

0,48 t/m³

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

I BEAMS (I-JOISTS)1

1000 t

VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): zero deleted based on email exchange between UNECE and AT 02082023

estimate by NC

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

To fill:

m3ub = cubic metres solid volume underbark (i.e. excluding bark)

m3 = cubic metres solid volume

t = metric tonnes

https://www.fao.org/3/cb8216en/cb8216en.pdf

JQ2 Trade

			61	62	61	62	91	92	91	92
		FOREST SECTOR QUESTIONNAIRE JQ2				Country:	AT		Date:	6-Jul																																								0	both VALUE and quantity reported ZERO
						Name of Official responsible for reply:		0																																										ZERO Q	quantity ZERO when VALUE is reported										INTRA-EU	The difference might be caused by Intra-EU trade
		PRIMARY PRODUCTS				Official Address (in full):		BML, 1030 Vienna, Marxerg. 2																															This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data!											ZERO V	Value ZERO when quantity is reported										CHECK
		Trade				Telephone:	0		Fax:	0																		This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!																				ZERO CHECK 1 - if no value please CHECK		NO Q	no quantity reported							ZERO CHECK 2 - if no value in Zero Check 1
						E-mail:	0																																			Country:	AT							NO V	no value reported			Treshold:	2			verifies whether the JQ2 figures refers only to intra-EU trade
	Value must always be in 1000 NAC (national currency)			1000 EUR							Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note		Trade		Discrepancies																				REPORT	no figures reported
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		Column1	Column2		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	IMPORT	EXPORT		code	Product	unit	2021	2022	2021	2022
			Quantity	Value	Quantity	Value	Quantity VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): whole column updated based on email 01082023	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	11,072.732	829,053	8,700.760	842,359	1,105.553	98,279	1,318.690	145,004			7	7			7	7									1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK		1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	28,387	26,740				1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3	75	97	89	110	ACCEPT	ACCEPT		1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	169.696	15,776	181.851	26,310	12.724	1,050	11.984	1,564			7	7			7	7									1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK		1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	5,056	5,594				1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3	93	145	83	130	ACCEPT	ACCEPT		1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3
1.1.C	Coniferous	1000 m3ub	90.769	6,760	95.641	9,947	1.821	122	4.558	404			7	7			7	7	The conversion factor used (1m³ = 900 kg) seems too high. A revision is considered.		The conversion factor used (1m³ = 900 kg) seems too high. A revision is considered.		The conversion factor used (1m³ = 900 kg) seems too high. A revision is considered.		The conversion factor used (1m³ = 900 kg) seems too high. A revision is considered.		1.1.C	Coniferous	1000 m3ub										1.1.C	Coniferous	1000 m3ub	3,082	3,339				1.1.C	Coniferous	NAC/m3	74	104	67	89	ACCEPT	ACCEPT		1.1.C	Coniferous	NAC/m3
1.1.NC	Non-Coniferous	1000 m3ub	78.927	9,016	86.208	16,363	10.903	928	7.426	1,160			7	7			7	7	The conversion factor used (1m³ = 900 kg) seems too high. A revision is considered.		The conversion factor used (1m³ = 900 kg) seems too high. A revision is considered.		The conversion factor used (1m³ = 900 kg) seems too high. A revision is considered.		The conversion factor used (1m³ = 900 kg) seems too high. A revision is considered.		1.1.NC	Non-Coniferous	1000 m3ub										1.1.NC	Non-Coniferous	1000 m3ub	1,975	2,255				1.1.NC	Non-Coniferous	NAC/m3	114	190	85	156	ACCEPT	ACCEPT		1.1.NC	Non-Coniferous	NAC/m3
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	10,903.036	813,277	8,518.909	816,049	1,092.829	97,229	1,306.706	143,440			7	7			7	7									1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK		1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	23,331	21,146				1.2	INDUSTRIAL ROUNDWOOD	NAC/m3	75	96	89	110	ACCEPT	ACCEPT		1.2	INDUSTRIAL ROUNDWOOD	NAC/m3
1.2.C	Coniferous	1000 m3ub	10,017.644	736,655	7,975.906	743,290	954.007	77,677	1,151.168	114,972			7	7			7	7									1.2.C	Coniferous	1000 m3ub										1.2.C	Coniferous	1000 m3ub	21,734	19,782				1.2.C	Coniferous	NAC/m3	74	93	81	100	ACCEPT	ACCEPT		1.2.C	Coniferous	NAC/m3
1.2.NC	Non-Coniferous	1000 m3ub	885.392	76,622	543.003	72,759	138.822	19,552	155.538	28,468			7	7			7	7									1.2.NC	Non-Coniferous	1000 m3ub										1.2.NC	Non-Coniferous	1000 m3ub	1,597	1,364				1.2.NC	Non-Coniferous	NAC/m3	87	134	141	183	ACCEPT	ACCEPT		1.2.NC	Non-Coniferous	NAC/mt
1.2.NC.T	of which: Tropical1	1000 m3ub	0.054	99	0.081	84	0.000	0	0.032	17			7	7			7	7									1.2.NC.T	of which: Tropical1	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK		1.2.NC.T	of which: Tropical1	1000 m3ub	0	0				1.2.NC.T	of which: Tropical	NAC/m3	1831	1036	0	544	ACCEPT	CHECK		1.2.NC.T	of which: Tropical	1000 m3
2	WOOD CHARCOAL	1000 t	15.872	10,139	13.446	9,358	1.464	1,295	1.315	1,057			7	7			7	7									2	WOOD CHARCOAL	1000 t										2	WOOD CHARCOAL	1000 t	16	13				2	WOOD CHARCOAL	NAC / t	639	696	884	804	ACCEPT	ACCEPT		2	WOOD CHARCOAL	1000 m3
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	1,907.067	75,723	2,271.895	152,462	768.628	36,172	650.261	47,880			7	7			7	7	revised	revised	provisional, includes 4	provisional, includes 4	revised	revised	provisional, includes 4	provisional, includes 4	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	8,605	9,445				3	WOOD CHIPS, PARTICLES AND RESIDUES	NAC/m3	40	67	47	74	ACCEPT	ACCEPT		3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3
3.1	WOOD CHIPS AND PARTICLES	1000 m3	818.429	40,625	907.115	74,020	313.995	15,693	240.650	19,642			7	7			7	7	Conversion factor used: 1 t = 1,16667 m³		Conversion factor used: 1 t = 1,16667 m³		Conversion factor used: 1 t = 1,16667 m³		Conversion factor used: 1 t = 1,16667 m³		3.1	WOOD CHIPS AND PARTICLES	1000 m3										3.1	WOOD CHIPS AND PARTICLES	1000 m3	4,408	4,661				3.1	WOOD CHIPS AND PARTICLES	NAC/m3	50	82	50	82	ACCEPT	ACCEPT		3.1	WOOD CHIPS AND PARTICLES	1000 mt
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	1,088.638	35,098	1,364.780	78,442	454.633	20,479	409.611	28,238			7	7			7	7	revised, conversion factor used: 1 m³ = 0,866666 t	revised	provisional, includes 4; conversion factor used: 1 m³ = 0,866666 t	provisional, includes 4	revised, conversion factor used: 1 m³ = 0,866666 t	revised	provisional, includes 4; conversion factor used: 1 m³ = 0,866666 t	provisional, includes 4	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3										3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	4,197	4,785				3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	NAC/m3	32	57	45	69	ACCEPT	ACCEPT		3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 mt
3.2.1	of which: Sawdust	1000 m3			192.410	18,215			256.116	23,143			7	7			7	7									3.2.1	of which: Sawdust	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		3.2.1	of which: Sawdust	1000 m3	3,121	3,245				3.2.1	of which: Sawdust	NAC/m3	REPORT	95	REPORT	90	CHECK	CHECK
4	RECOVERED POST-CONSUMER WOOD	1000 t	333.201	10,250	included in 3.2	included in 3.2	141.021	5,291	included in 3.2	included in 3.2									revised	revised	not yet available		revised	revised	not yet available		4	RECOVERED POST-CONSUMER WOOD	1000 t										4	RECOVERED POST-CONSUMER WOOD	1000 t	1,323	ERROR:#VALUE!				4	RECOVERED POST-CONSUMER WOOD	NAC / t	31	REPORT	38	REPORT	CHECK	CHECK		4	RECOVERED POST-CONSUMER WOOD	1000 mt		CHECK		CHECK
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	509.159	80,414	432.930	137,115	901.202	178,316	759.737	278,476			7	7			7	7									5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	OK	OK	OK	OK	OK	OK	OK	OK		5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	1,283	1,416				5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC / t	158	317	198	367	CHECK	ACCEPT		5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/m3
5.1	WOOD PELLETS	1000 t	412.744	61,104	345.648	106,356	875.445	173,381	748.723	275,257			7	7			7	7									5.1	WOOD PELLETS	1000 t										5.1	WOOD PELLETS	1000 t	1,144	1,288				5.1	WOOD PELLETS	NAC / t	148	308	198	368	CHECK	ACCEPT		5.1	WOOD PELLETS	NAC/m3
5.2	OTHER AGGLOMERATES	1000 t	96.415	19,310	87.282	30,760	25.757	4,934	11.014	3,218			7	7			7	7									5.2	OTHER AGGLOMERATES	1000 t										5.2	OTHER AGGLOMERATES	1000 t	139	128				5.2	OTHER AGGLOMERATES	NAC / t	200	352	192	292	ACCEPT	ACCEPT		5.2	OTHER AGGLOMERATES	NAC/m3
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	2,088.125	697,948	2,552.218	748,526	6,119.574	1,985,783	6,099.443	2,026,472			7	7			7	7									6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	6,733	6,795				6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3	334	293	324	332	ACCEPT	ACCEPT		6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3
6.C	Coniferous	1000 m3	1,911.235	567,134	1,785.572	552,086	5,946.608	1,870,911	5,730.805	1,895,011			7	7			7	7									6.C	Coniferous	1000 m3										6.C	Coniferous	1000 m3	6,547	6,159				6.C	Coniferous	NAC/m3	297	309	315	331	ACCEPT	ACCEPT		6.C	Coniferous	NAC/m3
6.NC	Non-Coniferous	1000 m3	176.890	130,814	214.696	196,440	172.966	114,872	145.138	131,461			7	7			7	7									6.NC	Non-Coniferous	1000 m3										6.NC	Non-Coniferous	1000 m3	186	308				6.NC	Non-Coniferous	NAC/m3	740	915	664	906	ACCEPT	ACCEPT		6.NC	Non-Coniferous	NAC/m3
6.NC.T	of which: Tropical1	1000 m3	5.209	5,516	3.948	5,622	1.117	1,697	0.927	1,687			7	7			7	7									6.NC.T	of which: Tropical1	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		6.NC.T	of which: Tropical1	1000 m3	4	3				6.NC.T	of which: Tropical	NAC/m3	1059	1424	1519	1820	ACCEPT	ACCEPT		6.NC.T	of which: Tropical	NAC/m3
7	VENEER SHEETS	1000 m3	69.640	143,254	75.673	187,928	17.744	56,390	16.952	65,332			7	7			7	7									7	VENEER SHEETS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		7	VENEER SHEETS	1000 m3	59	66				7	VENEER SHEETS	NAC/m3	2057	2483	3178	3854	ACCEPT	ACCEPT		7	VENEER SHEETS	NAC/m3
7.C	Coniferous	1000 m3	20.887	13,263	23.349	16,243	3.014	8,419	2.653	8,543			7	7			7	7									7.C	Coniferous	1000 m3										7.C	Coniferous	1000 m3	18	21				7.C	Coniferous	NAC/m3	635	696	2793	3220	ACCEPT	ACCEPT		7.C	Coniferous	NAC/m3
7.NC	Non-Coniferous	1000 m3	48.753	129,991	52.324	171,685	14.730	47,971	14.299	56,790			7	7			7	7									7.NC	Non-Coniferous	1000 m3										7.NC	Non-Coniferous	1000 m3	34	38				7.NC	Non-Coniferous	NAC/m3	2666	3281	3257	3972	ACCEPT	ACCEPT		7.NC	Non-Coniferous	NAC/m3
7.NC.T	of which: Tropical	1000 m3	1.130	2,121	1.260	4,979	0.307	1,200	0.596	1,054			7	7			7	7									7.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		7.NC.T	of which: Tropical	1000 m3	1	1				7.NC.T	of which: Tropical	NAC/m3	1877	3952	3908	1768	CHECK	CHECK		7.NC.T	of which: Tropical	NAC/m3
8	WOOD-BASED PANELS	1000 m3	1,190.778	509,733	1,028.253 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected to the right sum	519,037 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected to the right sum	2,962.339	1,446,999	2,356.429 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected to the right sum	1,472,778			7	7			7	7									8	WOOD-BASED PANELS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		8	WOOD-BASED PANELS	1000 m3	1,652	1,558				8	WOOD-BASED PANELS	NAC/m3	428	505	488	625	ACCEPT	ACCEPT		8	WOOD-BASED PANELS	NAC/m3
8.1	PLYWOOD	1000 m3	267.176	202,042	190.121 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): of which category added. Based on email 02082023	190,555 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected based on email between UNECE and AT 02082023			VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected to the right sum	356.923	320,512	295.904 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected based on email 02082023	309,247			7	7			7	7									8.1	PLYWOOD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		8.1	PLYWOOD	1000 m3	94	26				8.1	PLYWOOD	NAC/m3	756	1002	898	1045	ACCEPT	ACCEPT		8.1	PLYWOOD	NAC/m3
8.1.C	Coniferous	1000 m3			75.374	59,133			263.965	252,322			7	7			7	7									8.1.C	Coniferous	1000 m3										8.1.C	Coniferous	1000 m3	180	-61				8.1.C	Coniferous	NAC/m3	REPORT	785	REPORT	956	CHECK	CHECK		8.1.C	Coniferous	NAC/m3	CHECK		CHECK
8.1.NC	Non-Coniferous	1000 m3			110.082	127,044			31.434	56,925			7	7			7	7									8.1.NC	Non-Coniferous	1000 m3										8.1.NC	Non-Coniferous	1000 m3	4	82				8.1.NC	Non-Coniferous	NAC/m3	REPORT	1154	REPORT	1811	CHECK	CHECK		8.1.NC	Non-Coniferous	NAC/m3	CHECK		CHECK
8.1.NC.T	of which: Tropical	1000 m3	15.611	15,345	25.575	23,939	0.292	852	1.378	2,583			7	7			7	7									8.1.NC.T	of which: Tropical	1000 m3	Error	Error	OK	OK	Error	Error	OK	OK		8.1.NC.T	of which: Tropical	1000 m3	15	24				8.1.NC.T	of which: Tropical	NAC/m3	983	936	2919	1875	ACCEPT	ACCEPT		8.1.NC.T	of which: Tropical	NAC/m3
8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3			4.665	4,378			0.505	2,318			7	7			7	7									8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	0	4				8.1.1	of which: Laminated Veneer Lumber (LVL)	NAC/m3	REPORT	939	REPORT	4591	CHECK	CHECK
8.1.1.C	Coniferous	1000 m3			1.150	1,091			0.199	1,035			7	7			7	7									8.1.1.C	Coniferous	1000 m3										8.1.1.C	Coniferous	1000 m3	0	1				8.1.1.C	Coniferous	NAC/m3	REPORT	949	REPORT	5199	CHECK	CHECK
8.1.1.NC	Non-Coniferous	1000 m3			3.515	3,287			0.306	1,284			7	7			7	7									8.1.1.NC	Non-Coniferous	1000 m3										8.1.1.NC	Non-Coniferous	1000 m3	0	3				8.1.1.NC	Non-Coniferous	NAC/m3	REPORT	935	REPORT	4196	CHECK	CHECK
8.1.1.NC.T	of which: Tropical	1000 m3			2.436	2,080			0.164	225			7	7			7	7									8.1.1.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		8.1.1.NC.T	of which: Tropical	1000 m3	0	2				8.1.1.NC.T	of which: Tropical	NAC/m3	REPORT	854	REPORT	1370	CHECK	CHECK
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	553.182	182,262	526.783	182,187	2,049.439	744,738	1,680.026	831,654			7	7			7	7									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	1,054	1,127				8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3	329	346	363	495	ACCEPT	ACCEPT		8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	192.199	74,866	211.923	62,204	5.960	2,850	6.824	3,261			7	7			7	7									8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	186	205				8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3	390	294	478	478	ACCEPT	ACCEPT		8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3
8.3	FIBREBOARD	1000 m3	370.420	125,429	311.349	146,295	555.977	381,749	380.499	331,876			7	7			7	7									8.3	FIBREBOARD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		8.3	FIBREBOARD	1000 m3	504	401				8.3	FIBREBOARD	NAC/m3	339	470	687	872	ACCEPT	ACCEPT		8.3	FIBREBOARD	NAC/m3
8.3.1	HARDBOARD	1000 m3	17.049	14,047	18.568	21,840	60.206	36,242	43.023	32,599			7	7			7	7									8.3.1	HARDBOARD	1000 m3										8.3.1	HARDBOARD	1000 m3	32	30				8.3.1	HARDBOARD	NAC/m3	824	1176	602	758	ACCEPT	ACCEPT		8.3.1	HARDBOARD	NAC/mt
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	191.844	85,511	159.373	97,168	490.961	343,777	333.141	297,034			7	7			7	7									8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3										8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	316	242				8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/m3	446	610	700	892	ACCEPT	ACCEPT		8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/mt
8.3.3	OTHER FIBREBOARD	1000 m3	161.527	25,870	133.408	27,286	4.810	1,729	4.335	2,243			7	7			7	7									8.3.3	OTHER FIBREBOARD	1000 m3										8.3.3	OTHER FIBREBOARD	1000 m3	157	129				8.3.3	OTHER FIBREBOARD	NAC/m3	160	205	360	517	ACCEPT	ACCEPT		8.3.3	OTHER FIBREBOARD	NAC/mt
9	WOOD PULP	1000 t	578.391	388,955	559.363	498,789	321.447	210,214	385.924	300,796			7	7			7	7									9	WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK		9	WOOD PULP	1000 t	2,261	2,151				9	WOOD PULP	NAC/t	672	892	654	779	ACCEPT	ACCEPT		9	WOOD PULP	NAC/mt
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	10.176	4,644	7.288	4,497	0.074	46	0.045	19			7	7			7	7									9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t										9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	1,316	1,311				9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/t	456	617	621	422	ACCEPT	ACCEPT		9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/mt
9.2	CHEMICAL WOOD PULP	1000 t	487.516	322,117	465.158	404,064	254.452	168,695	271.278	222,375			7	7			7	7									9.2	CHEMICAL WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK		9.2	CHEMICAL WOOD PULP	1000 t	ERROR:#REF!	ERROR:#REF!				9.2	CHEMICAL WOOD PULP	NAC/t	661	869	663	820	ACCEPT	ACCEPT		9.2	CHEMICAL WOOD PULP	NAC/mt
9.2.1	SULPHATE PULP	1000 t	463.300	303,272	444.085	382,924	246.206	163,108	258.028	210,667			7	7			7	7									9.2.1	SULPHATE PULP	1000 t										9.2.1	SULPHATE PULP	1000 t	217	186				9.2.1	SULPHATE PULP	NAC/t	655	862	662	816	ACCEPT	ACCEPT		9.2.1	SULPHATE PULP	NAC/mt
9.2.1.1	of which: BLEACHED	1000 t	461.880	302,186	443.731	382,601	223.609	151,140	223.036	187,211			7	7			7	7									9.2.1.1	of which: BLEACHED	1000 t	OK	OK	OK	OK	OK	OK	OK	OK		9.2.1.1	of which: BLEACHED	1000 t	238	221				9.2.1.1	of which: BLEACHED	NAC/t	654	862	676	839	ACCEPT	ACCEPT		9.2.1.1	of which: BLEACHED	NAC/mt
9.2.2	SULPHITE PULP	1000 t	24.216	18,846	21.073	21,140	8.246	5,587	13.250	11,708			7	7			7	7									9.2.2	SULPHITE PULP	1000 t										9.2.2	SULPHITE PULP	1000 t	16	8				9.2.2	SULPHITE PULP	NAC/t	778	1003	678	884	ACCEPT	ACCEPT		9.2.2	SULPHITE PULP	NAC/mt
9.3	DISSOLVING GRADES	1000 t	80.699	62,194	86.917	90,229	66.922	41,473	114.601	78,402			7	7			7	7									9.3	DISSOLVING GRADES	1000 t										9.3	DISSOLVING GRADES	1000 t	399	391				9.3	DISSOLVING GRADES	NAC/t	771	1038	620	684	ACCEPT	ACCEPT		9.3	DISSOLVING GRADES	NAC/mt
10	OTHER PULP	1000 t	36.112	30,268	32.987	34,337	10.873	9,282	9.828	11,415			7	7			7	7									10	OTHER PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK		10	OTHER PULP	1000 t	2,222	2,031				10	OTHER PULP	NAC/t	838	1041	854	1161	ACCEPT	ACCEPT		10	OTHER PULP	NAC/mt
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	26.396	26,604	23.298	29,736	0.919	2,733	1.288	4,428			7	7			7	7									10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t										10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	25	22				10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/t	1008	1276	2974	3438	ACCEPT	ACCEPT		10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/mt
10.2	RECOVERED FIBRE PULP	1000 t	9.715	3,664	9.689	4,601	9.954	6,549	8.540	6,987			7	7			7	7									10.2	RECOVERED FIBRE PULP	1000 t										10.2	RECOVERED FIBRE PULP	1000 t	2,197	2,009				10.2	RECOVERED FIBRE PULP	NAC/t	377	475	658	818	ACCEPT	ACCEPT		10.2	RECOVERED FIBRE PULP	NAC/mt
11	RECOVERED PAPER	1000 t	1,675.573	326,061	1,451.000	342,624	248.774	48,134	260.346	58,553			7	7			7	7									11	RECOVERED PAPER	1000 t										11	RECOVERED PAPER	1000 t	3,121	1,191				11	RECOVERED PAPER	NAC/t	195	236	193	225	ACCEPT	ACCEPT		11	RECOVERED PAPER	NAC/mt
12	PAPER AND PAPERBOARD	1000 t	1,296.015	1,039,401	1,235.669	1,340,355	4,027.585	2,824,375	3,695.052	3,889,356			7	7			7	7									12	PAPER AND PAPERBOARD	1000 t	OK	OK	OK	OK	OK	OK	OK	OK		12	PAPER AND PAPERBOARD	1000 t	2,334	2,174				12	PAPER AND PAPERBOARD	NAC/t	802	1085	701	1053	ACCEPT	ACCEPT		12	PAPER AND PAPERBOARD	NAC/mt
12.1	GRAPHIC PAPERS	1000 t	415.471	302,425	388.537	421,849	1,911.124	1,355,927	1,661.780	1,975,938			7	7			7	7									12.1	GRAPHIC PAPERS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK		12.1	GRAPHIC PAPERS	1000 t	752	596				12.1	GRAPHIC PAPERS	NAC/t	728	1086	709	1189	ACCEPT	ACCEPT		12.1	GRAPHIC PAPERS	NAC/mt
12.1.1	NEWSPRINT	1000 t	77.209	33,809	81.789	64,147	305.872	125,405	234.523	190,704			7	7			7	7									12.1.1	NEWSPRINT	1000 t										12.1.1	NEWSPRINT	1000 t	-229	-153				12.1.1	NEWSPRINT	NAC/t	438	784	410	813	ACCEPT	ACCEPT		12.1.1	NEWSPRINT	NAC/mt
12.1.2	UNCOATED MECHANICAL	1000 t	55.275	30,205	54.763	46,829	218.351	112,597	216.596	192,892			7	7			7	7									12.1.2	UNCOATED MECHANICAL	1000 t										12.1.2	UNCOATED MECHANICAL	1000 t	-163	-162				12.1.2	UNCOATED MECHANICAL	NAC/t	546	855	516	891	ACCEPT	ACCEPT		12.1.2	UNCOATED MECHANICAL	NAC/mt
12.1.3	UNCOATED WOODFREE	1000 t	97.888	96,878	103.186	137,377	335.162	336,354	276.970	440,891			7	7			7	7									12.1.3	UNCOATED WOODFREE	1000 t										12.1.3	UNCOATED WOODFREE	1000 t	-237	-174				12.1.3	UNCOATED WOODFREE	NAC/t	990	1331	1004	1592	ACCEPT	ACCEPT		12.1.3	UNCOATED WOODFREE	NAC/mt
12.1.4	COATED PAPERS	1000 t	185.098	141,533	148.798	173,495	1,051.739	781,571	933.691	1,151,450			7	7			7	7									12.1.4	COATED PAPERS	1000 t										12.1.4	COATED PAPERS	1000 t	403	315				12.1.4	COATED PAPERS	NAC/t	765	1166	743	1233	ACCEPT	ACCEPT		12.1.4	COATED PAPERS	NAC/mt
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	9.356	13,710	11.321	22,621	5.382	9,738	5.191	10,860			7	7			7	7									12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t										12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	129	131				12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/t	1465	1998	1810	2092	ACCEPT	ACCEPT		12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/mt
12.3	PACKAGING MATERIALS	1000 t	857.945	695,474	822.419	861,858	2,105.454	1,395,977	2,022.706	1,841,898			7	7			7	7									12.3	PACKAGING MATERIALS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK		12.3	PACKAGING MATERIALS	1000 t	1,245	1,274				12.3	PACKAGING MATERIALS	NAC/t	811	1048	663	911	ACCEPT	ACCEPT		12.3	PACKAGING MATERIALS	NAC/mt
12.3.1	CASE MATERIALS	1000 t	446.690	254,507	414.619	308,240	1,243.919	670,618	1,196.577	867,198			7	7			7	7									12.3.1	CASE MATERIALS	1000 t										12.3.1	CASE MATERIALS	1000 t	903	818				12.3.1	CASE MATERIALS	NAC/t	570	743	539	725	ACCEPT	ACCEPT		12.3.1	CASE MATERIALS	NAC/mt
12.3.2	CARTONBOARD	1000 t	209.680	251,730	214.936	307,857	353.220	332,238	311.639	409,580			7	7			7	7									12.3.2	CARTONBOARD	1000 t										12.3.2	CARTONBOARD	1000 t	-144	-97				12.3.2	CARTONBOARD	NAC/t	1201	1432	941	1314	ACCEPT	ACCEPT		12.3.2	CARTONBOARD	NAC/mt
12.3.3	WRAPPING PAPERS	1000 t	163.040	168,493	160.531	221,705	497.841	385,637	503.301	554,440			7	7			7	7									12.3.3	WRAPPING PAPERS	1000 t										12.3.3	WRAPPING PAPERS	1000 t	-335	-343				12.3.3	WRAPPING PAPERS	NAC/t	1033	1381	775	1102	ACCEPT	ACCEPT		12.3.3	WRAPPING PAPERS	NAC/mt
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	38.535	20,744	32.333	24,056	10.473	7,485	11.189	10,680			7	7			7	7									12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t										12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	28	21				12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/t	538	744	715	955	ACCEPT	ACCEPT		12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/mt
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	13.243	27,793	13.392	34,027	5.625	62,732	5.375	60,660			7	7			7	7									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	208	173				12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/t	2099	2541	11152	11286	ACCEPT	ACCEPT		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			66.929	48,541			1,728.471	1,127,564			7	7			7	7									15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK		15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	2,578	796				15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	NAC/m3	REPORT	725	REPORT	652	CHECK	CHECK
15.1	GLULAM	1000 m3			41.478	27,991			1,340.822	836,890			7	7			7	7									15.1	GLULAM	1000 m3										15.1	GLULAM	1000 m3	2,063	692				15.1	GLULAM	NAC/m3	REPORT	675	REPORT	624	CHECK	CHECK
15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3			25.451	20,550			387.649	290,675			7	7			7	7									15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3										15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3	515	104				15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	NAC/m3	REPORT	807	REPORT	750	CHECK	CHECK
16	I BEAMS (I-JOISTS)2	1000 t			0.031	52			0.320	2,150			7	7			7	7									16	I BEAMS (I-JOISTS)1	1000 t										16	I BEAMS (I-JOISTS)1	1000 t	0	-0				16	I BEAMS (I-JOISTS)1	NAC/t	REPORT	1678	REPORT	6719	CHECK	CHECK
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
		To fill:	11	11	0	0	11	11	0	0
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)
	t = metric tonnes

https://www.fao.org/3/cb8216en/cb8216en.pdf

JQ3 Secondary PP Trade

			62	91	91
			Country:	AT	Date: 03.07.23
			Name of Official responsible for reply:		0
			Official Address (in full):
	FOREST SECTOR QUESTIONNAIRE JQ3		BML, 1030 Vienna, Marxerg. 2
	SECONDARY PROCESSED PRODUCTS		Telephone/Fax:	0	0
	Trade		E-mail:	0
															This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
	Value must always be in 1000 NAC (national currency)	1000 EUR														Discrepancies
	Eurozone countries may use the old national currency, but only in both years					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	2,124,454	2,286,128 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected to the right sum (rounding problem)	2,666,948	1,801,381		7		7					13	SECONDARY WOOD PRODUCTS	OK	OK	OK	OK
13.1	FURTHER PROCESSED SAWNWOOD	114,221	107,868	135,568	147,612		7		7					13.1	FURTHER PROCESSED SAWNWOOD	OK	OK	OK	OK
13.1.C	Coniferous	85,552	79,440	105,680	116,979		7		7					13.1.C	Coniferous
13.1.NC	Non-coniferous	28,669	28,428	29,887	30,633		7		7					13.1.NC	Non-coniferous
13.1.NC.T	of which: Tropical	1,412	2,338	483	765		7		7					13.1.NC.T	of which: Tropical	OK	OK	OK	OK
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	187,864	256,196	87,007	124,804		7		7					13.2	WOODEN WRAPPING AND PACKAGING MATERIAL
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	42,768	44,841	11,818	13,395		7		7					13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1	356,147	337,875	1,756,524	745,414		7		7					13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1
13.5	WOODEN FURNITURE	1,263,095	1,339,512	572,923	632,017		7		7					13.5	WOODEN FURNITURE
13.6	PREFABRICATED BUILDINGS OF WOOD	31,305	46,447	48,619	69,304		7		7					13.6	PREFABRICATED BUILDINGS OF WOOD
13.7	OTHER MANUFACTURED WOOD PRODUCTS	129,053	153,388	54,490	68,835		7		7					13.7	OTHER MANUFACTURED WOOD PRODUCTS
14	SECONDARY PAPER PRODUCTS	1,059,809	1,242,828	1,207,040	1,396,363		7		7					14	SECONDARY PAPER PRODUCTS	OK	OK	OK	OK
14.1	COMPOSITE PAPER AND PAPERBOARD	11,166	13,283	393	343		7		7					14.1	COMPOSITE PAPER AND PAPERBOARD
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	164,323	188,905	93,835	111,443		7		7					14.2	SPECIAL COATED PAPER AND PULP PRODUCTS
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	217,798	306,833	146,382	192,916		7		7					14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4	PACKAGING CARTONS, BOXES ETC.	394,385	468,293	781,761	891,718		7		7					14.4	PACKAGING CARTONS, BOXES ETC.
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	272,137	265,515	184,670	199,942		7		7					14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	OK	OK	OK	OK
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	5,638	7,253	3,356	3,512		7		7					14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	16,223	17,410	2,939	6,101		7		7					14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	23,528	23,234	5,841	8,538		7		7					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.
	To fill:	0	0	0	0

ECE-EU Species


							Country:	AT		Date:	9/12/23
							Name of Official responsible for reply:			0

		FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade					Official Address (in full):																														Check Table
							BML, 1030 Vienna, Marxerg. 2																																		0	both VALUE and quantity reported ZERO
			Trade in Roundwood and Sawnwood by species				Telephone:	0		Fax:	0																		DISCREPANCIES												ZERO Q	quantity ZERO when VALUE is reported
							E-mail:	0																																	ZERO V	Value ZERO when quantity is reported
																													Checks whether the sum of subitems is bigger than the total										Zero check - if no value please CHECK		NO Q	no quantity reported
		Value must always be in 1000 NAC ( national currency)																																							NO V	no value reported			Treshold:	2
		Eurozone countries may use the old national currency, but only in both years			1000 EUR								Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note													REPORT	no figures reported
					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							Value per	I M P O R T		E X P O R T		Unit price check
Product	Classification	Classification		Unit of	2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		Classification	Classification		unit	2021	2022	2021	2022	IMPORT	EXPORT
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	HS2022	CN2022	Product
1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	1000 m3ub	10017.644	736,655	7975.906	743,290	954.007	77,677	1151.168	114,972			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	NAC/m3	74	93	81	100	ACCEPT	ACCEPT							PRODUCTION		I M P O R T				E X P O R T
	4403.21/22		of which: Pine (Pinus spp.)	1000 m3ub	628.423	32,329	625.888	40,806	108.191	8,285	228.780	21,542			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK	4403.21/22		of which: Pine (Pinus spp.)	NAC/m3	51	65	77	94	ACCEPT	ACCEPT	Product	Classification	Classification		Unit of		2021	2022	2021		2022		2021		2022
		4403 21 10	sawlogs and veneer logs	1000 m3ub	298.946	17,843	291.036	20,543	91.183	6,978	194.043	19,980			7	7			7	7																		4403 21 10	sawlogs and veneer logs	NAC/m3	60	103	77	REPORT	ACCEPT	CHECK	Code	HS2022	CN2022	Product	Quantity		Quantity	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
		4403 21 90 4403 22 00	pulpwood and other industrial roundwood	1000 m3ub	329.477	14,486	334.852	20,263	17.008	1,307	34.737	1,562			7	7			7	7																		4403 21 90 4403 22 00	pulpwood and other industrial roundwood	NAC/m3	44	45	77	REPORT	ACCEPT	CHECK	1	4401.11/12 44.03		Roundwood production	1000 m3	JQ1	18,420	19,358
	4403.23/24		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3ub	8634.032	641,042	6826.536	644,490	737.844	60,104	818.579	82,680			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK	4403.23/24		of which: Fir/Spruce (Abies spp., Picea spp.)	NAC/m3	74	94	81	101	ACCEPT	ACCEPT						EU2	18420.265	19357.935
		4403 23 10	sawlogs and veneer logs	1000 m3ub	7190.744	577,796	6012.710	594,719	409.278	33,744	660.249	68,723			7	7			7	7																		4403 23 10	sawlogs and veneer logs	NAC/m3	80	99	82	104	ACCEPT	ACCEPT						dif	0	0
		4403 23 90 4403 24 00	pulpwood and other industrial roundwood	1000 m3ub	1443.288	63,247	813.826	49,771	328.566	26,359	158.330	13,957			7	7			7	7																		4403 23 90 4403 24 00	pulpwood and other industrial roundwood	NAC/m3	44	61	80	88	ACCEPT	ACCEPT	1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood (wood in the rough), Coniferous	1000 m3	JQ2			10,018	736,655	7,976	743,290	954	77,677	1,151	114,972
1.2.NC	4403.12/41/42/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	1000 m3ub	885.392	76,622	543.003	72,759	138.822	19,552	155.538	28,468			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK	4403.12/41/42/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	NAC/m3	87	134	141	183	ACCEPT	ACCEPT						ECE/EU			10,018	736,655	7,976	743,290	954	77,677	1,151	114,972
	ex4403.12 4403.91		of which: Oak (Quercus spp.)	1000 m3ub	57.844	19,581	74.526	33,441	41.310	9,375	37.988	12,939			7	7			7	7																	ex4403.12 4403.91		of which: Oak (Quercus spp.)	NAC/m3	339	449	227	341	ACCEPT	ACCEPT						dif			0	0	0	0	0	0	0	0
	ex4403.12 4403.93/94		of which: Beech (Fagus spp.)	1000 m3ub	702.534	44,481	386.900	29,627	33.847	2,842	48.665	5,102			7	7			7	7																	ex4403.12 4403.93/94		of which: Beech (Fagus spp.)	NAC/m3	63	77	84	105	ACCEPT	ACCEPT	1.2.NC	4403.12/41/42/49/91/93/94/95/96/97/98/99		Industrial Roundwood (wood in the rough), Non-Coniferous	1000 m3	JQ2			885	76,622	543	72,759	139	19,552	156	28,468
	ex4403.12 4403.95/96		of which: Birch (Betula spp.)	1000 m3ub	5.099	414	4.449	301	0.329	153	0.542	35			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK	ex4403.12 4403.95/96		of which: Birch (Betula spp.)	NAC/m3	81	68	464	65	ACCEPT	CHECK						ECE/EU			885	76,622	543	72,759	139	19,552	156	28,468
		4403 95 10	sawlogs and veneer logs	1000 m3ub	0.376	126	1.278	102	0.002	0	0.000	0			7	7			7	7																		4403 95 10	sawlogs and veneer logs	NAC/m3	335	80	235	ZERO Q	CHECK	CHECK						dif			0	0	0	0	0	0	0	0
		ex4403 12 00 4403 95 90 4403 96 00	pulpwood and other industrial roundwood	1000 m3ub	4.723	288	3.171	199	0.327	152	0.542	35			7	7			7	7																		ex4403 12 00 4403 95 90 4403 96 00	pulpwood and other industrial roundwood	NAC/m3	61	63	466	65	ACCEPT	CHECK	6.C	4406.11/91 4407.11/12/13/14/19		Sawnwood, Coniferous	1000 m3	JQ2			1,911	567,134	1,786	552,086	5,947	1,870,911	5,731	1,895,011
	ex4403.12 4403.97		of which: Poplar/Aspen (Populus spp.)	1000 m3ub	10.291	503	16.869	700	17.072	1,404	31.001	3,195			7	7			7	7																	ex4403.12 4403.97		of which: Poplar/Aspen (Populus spp.)	NAC/m3	49	41	82	103	ACCEPT	ACCEPT						ECE/EU			1,911	567,134	1,786	552,086	5,947	1,870,911	5,731	1,895,011
	ex4403.12 4403.98		of which: Eucalyptus (Eucalyptus spp.)	1000 m3ub	0.001	4	0.000	0	0.000	0	0.000	0			7	7			7	7																	ex4403.12 4403.98		of which: Eucalyptus (Eucalyptus spp.)	NAC/m3	7058	0	0	0	CHECK	CHECK						dif			0	0	0	0	0	0	0	0
6.C	4406.11/91 4407.11/12/13/14/19		Sawnwood, Coniferous	1000 m3	1911.235	567,134	1785.572	552,086	5946.608	1,870,911	5730.805	1,895,011			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK	4406.11/91 4407.11/12/13/14/19		Sawnwood, Coniferous	NAC/m3	297	309	315	331	ACCEPT	ACCEPT	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	JQ2			177	130,814	215	196,440	173	114,872	145	131,461
	4407.11 ex4407.13 ex4406.11/91		of which: Pine (Pinus spp.)	1000 m3	70.746	20,141	203.339	35,252	154.494	41,115	215.480	66,790			7	7			7	7																	4407.11 ex4407.13 ex4406.11/91		of which: Pine (Pinus spp.)	NAC/m3	285	173	266	310	ACCEPT	ACCEPT						ECE/EU			177	130,814	215	196,440	173	114,872	145	131,461
	4407.12 ex4407.13/14 ex4406.11/91		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3	1634.831	462,206	1442.308	442,689	5458.563	1,689,487	5251.969	1,686,686			7	7			7	7																	4407.12 ex4407.13/14 ex4406.11/91		of which: Fir/Spruce (Abies spp., Picea spp.)	NAC/m3	283	307	310	321	ACCEPT	ACCEPT						dif			0	0	0	0	0	0	0	0
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	176.890	130,814	214.696	196,440	172.966	114,872	145.138	131,461			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK	4406.12/92 4407.21/22/23/25/26/27/28/29/ 91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	NAC/m3	740	915	664	906	ACCEPT	ACCEPT
	ex4406.12/92 4407.91		of which: Oak (Quercus spp.)	1000 m3	105.695	84,118	130.285	128,899	54.081	55,750	50.318	76,610			7	7			7	7																	ex4406.12/92 4407.91		of which: Oak (Quercus spp.)	NAC/m3	796	989	1031	1523	ACCEPT	ACCEPT
	ex4406.12/92 4407.92		of which: Beech (Fagus spp.)	1000 m3	15.966	7,249	19.261	8,634	55.373	23,204	49.399	22,592			7	7			7	7																	ex4406.12/92 4407.92		of which: Beech (Fagus spp.)	NAC/m3	454	448	419	457	ACCEPT	ACCEPT
	ex4406.12/92 4407.93		of which: Maple (Acer spp.)	1000 m3	1.514	1,288	1.768	2,146	1.662	999	1.337	1,064			7	7			7	7																	ex4406.12/92 4407.93		of which: Maple (Acer spp.)	NAC/m3	851	1214	601	796	ACCEPT	ACCEPT
	ex4406.12/92 4407.94		of which: Cherry (Prunus spp.)	1000 m3	0.640	520	1.004	799	0.434	431	0.496	455			7	7			7	7																	ex4406.12/92 4407.94		of which: Cherry (Prunus spp.)	NAC/m3	812	796	993	918	ACCEPT	ACCEPT
	ex4406.12/92 4407.95		of which: Ash (Fraxinus spp.)	1000 m3	6.953	4,526	8.861	6,592	14.120	9,206	16.380	11,132			7	7			7	7																	ex4406.12/92 4407.95		of which: Ash (Fraxinus spp.)	NAC/m3	651	744	652	680	ACCEPT	ACCEPT
	ex4406.12/92 4407.96		of which: Birch (Betula spp.)	1000 m3	2.578	1,142	3.183	2,015	1.142	307	0.768	529			7	7			7	7																	ex4406.12/92 4407.96		of which: Birch (Betula spp.)	NAC/m3	443	633	269	689	ACCEPT	CHECK
	ex4406.12/92 4407.97		of which: Poplar/Aspen (Populus spp.)	1000 m3	0.990	670	1.075	1,355	0.507	633	0.717	340			7	7			7	7																	ex4406.12/92 4407.97		of which: Poplar/Aspen (Populus spp.)	NAC/m3	677	1260	1249	474	ACCEPT	CHECK
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				To fill:	0	0	0	0	0	0	0	0
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

EU1 ExtraEU Trade

		FOREST SECTOR QUESTIONNAIRE				Country:	AT		Date: 12.09.23																																												0	both VALUE and quantity reported ZERO
		EU1				Name of Official responsible for reply:		0																																													ZERO Q	quantity ZERO when VALUE is reported
						Official Address (in full):		BML, 1030 Vienna, Marxerg. 2																																													ZERO V	Value ZERO when quantity is reported
		Trade with countries outside EU				Telephone:	0		Fax:	0																	JQ2/EU1 comparison																								Zero check - if no value please CHECK		NO Q	no quantity reported
	Value must always be in 1000 NAC (national currency)					E-mail:	0																				JQ2>=EU1																										NO V	no value reported				Treshold:	2
	Eurozone countries may use the old national currency, but only in both years			1000 EUR							Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note											Trade		Discrepancies														REPORT	no figures reported
Product		Unit of	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			I M P O R T				E X P O R T							Product		Value per	I M P O R T		E X P O R T			Column1	Column2
code	Product	quantity	2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022			code			2021		2022		2021		2022					code	Product	unit	2021	2022	2021	2022		IMPORT	EXPORT
			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					Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	156.678	15,472	170.340	22,094	9.261	2,185	11.025	3,566			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK				1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/ m3	99	130	236	323		ACCEPT	CHECK
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	44.633	5,394	40.706	7,931	0.078	40	0.105	48			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK				1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/ m3	121	195	505	457		CHECK	CHECK
1.1.C	Coniferous	1000 m3ub	1.553	223	0.510	82	0.000	0					7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		1.1.C	Coniferous	1000 m3ub												1.1.C	Coniferous	NAC/ m3	143	160	0	REPORT		CHECK	CHECK
1.1.NC	Non-Coniferous	1000 m3ub	43.079	5,171	40.196	7,850	0.078	40	0.105	48			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		1.1.NC	Non-Coniferous	1000 m3ub												1.1.NC	Non-Coniferous	NAC/ m3	120	195	505	457		CHECK	CHECK
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	112.045	10,079	129.634	14,163	9.183	2,145	10.919	3,518			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK				1.2	INDUSTRIAL ROUNDWOOD	NAC/ m3	90	109	234	322		CHECK	CHECK
1.2.C	Coniferous	1000 m3ub	90.717	7,314	96.166	9,317	5.680	645	5.809	839			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		1.2.C	Coniferous	1000 m3ub												1.2.C	Coniferous	NAC/ m3	81	97	113	144		ACCEPT	CHECK
1.2.NC	Non-Coniferous	1000 m3ub	21.328	2,765	33.468	4,846	3.503	1,500	5.110	2,679			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		1.2.NC	Non-Coniferous	1000 m3ub												1.2.NC	Non-Coniferous	NAC/ m3	130	145	428	524		CHECK	CHECK
1.2.NC.T	of which: Tropical	1000 m3ub	0.049	88	0.077	72	0.000	0	0.000	0			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		1.2.NC.T	of which: Tropical	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK				1.2.NC.T	of which: Tropical	NAC/ m3	1800	940	0	0		CHECK	ACCEPT
2	WOOD CHARCOAL	1000 t	10.453	6,198	7.619	5,271	0.275	172	0.943	495			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		2	WOOD CHARCOAL	1000 t												2	WOOD CHARCOAL	NAC/ t	593	692	627	525		ACCEPT	CHECK
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	64.120	2,601	32.598	1,449	16.286	1,068	17.141	1,429	4	4	4	4	4	4	4	4	includes 4	includes 4	includes 4	includes 4	includes 4	includes 4	includes 3	includes 4	OK	OK	OK	OK	OK	OK	OK	OK		3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				3	WOOD CHIPS, PARTICLES AND RESIDUES	NAC/ m3	41	44	66	83		ACCEPT	CHECK
3.1	WOOD CHIPS AND PARTICLES	1000 m3	19.267	1,683	6.908	789	4.180	500	8.197	818			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		3.1	WOOD CHIPS AND PARTICLES	1000 m3												3.1	WOOD CHIPS AND PARTICLES	NAC/ m3	87	114	120	100		ACCEPT	CHECK
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	44.853	918	25.690	660	12.107	568	8.944	611	4	4	4	4	4	4	4	4	includes 4	includes 4	includes 4	includes 4	includes 4	includes 4	includes 3	includes 4	OK	OK	OK	OK	OK	OK	OK	OK		3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3												3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	NAC/ m3	20	26	47	68		ACCEPT	CHECK
3.2.1	of which: Sawdust	1000 m3			0.258	40			6.200	536			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		3.2.1	of which: Sawdust	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				3.2.1	of which: Sawdust	NAC/ m3	REPORT	157	REPORT	86		CHECK	CHECK
4	RECOVERED POST-CONSUMER WOOD	1000 t	included in 3.2	included in 3.2	included in 3.2	included in 3.2	included in 3.2	included in 3.2	included in 3.2	included in 3.2																	WRONG	WRONG	OK	OK	WRONG	WRONG	OK	OK		4	RECOVERED POST-CONSUMER WOOD	1000 t												4	RECOVERED POST-CONSUMER WOOD	NAC/ t	REPORT	REPORT	REPORT	REPORT		CHECK	CHECK
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	24.201	3,503	25.690	9,488	27.264	5,008	39.789	15,350			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	OK	OK	OK	OK	OK	OK	OK	OK				5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/ t	145	369	184	386		CHECK	CHECK
5.1	WOOD PELLETS	1000 t	2.355	402	7.366	3,435	27.106	4,963	39.734	15,320			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		5.1	WOOD PELLETS	1000 t												5.1	WOOD PELLETS	NAC/ t	171	466	183	386		CHECK	CHECK
5.2	OTHER AGGLOMERATES	1000 t	21.846	3,101	18.324	6,053	0.158	45	0.055	30			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		5.2	OTHER AGGLOMERATES	1000 t												5.2	OTHER AGGLOMERATES	NAC/ t	142	330	283	545		ACCEPT	CHECK
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	155.590	66,246	144.273	73,074	1,056.149	421,685	1,050.766	453,823			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/ m3	426	506	399	432		ACCEPT	CHECK
6.C	Coniferous	1000 m3	131.433	46,389	78.815	40,458	1,010.012	393,758	986.551	422,009			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		6.C	Coniferous	1000 m3												6.C	Coniferous	NAC/ m3	353	513	390	428		ACCEPT	CHECK
6.NC	Non-Coniferous	1000 m3	24.157	19,857	25.232	32,617	46.137	27,927	42.774	31,814			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		6.NC	Non-Coniferous	1000 m3												6.NC	Non-Coniferous	NAC/ m3	822	1293	605	744		CHECK	CHECK
6.NC.T	of which: Tropical	1000 m3	4.301	4,505	2.657	3,933	0.224	188	0.084	146			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		6.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				6.NC.T	of which: Tropical	NAC/ m3	1047	1480	838	1734		ACCEPT	CHECK
7	VENEER SHEETS	1000 m3	25.562	77,926	27.745	108,061	3.483	14,171	4.653	15,925			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		7	VENEER SHEETS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				7	VENEER SHEETS	NAC/ m3	3048	3895	4069	3423		ACCEPT	CHECK
7.C	Coniferous	1000 m3	0.143	424	0.139	429	0.319	1,460	0.373	1,359			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		7.C	Coniferous	1000 m3												7.C	Coniferous	NAC/ m3	2968	3085	4577	3644		ACCEPT	CHECK
7.NC	Non-Coniferous	1000 m3	25.419	77,501	27.606	107,633	3.164	12,711	4.280	14,566			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		7.NC	Non-Coniferous	1000 m3												7.NC	Non-Coniferous	NAC/ m3	3049	3899	4017	3403		ACCEPT	CHECK
7.NC.T	of which: Tropical	1000 m3	0.256	409	0.439	681	0.092	541	0.470	509			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		7.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				7.NC.T	of which: Tropical	NAC/ m3	1598	1551	5886	1082		CHECK	CHECK
8	WOOD-BASED PANELS	1000 m3	52.299	29,153	37.705	35,044	621.119	375,332	509.345	380,580			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8	WOOD-BASED PANELS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				8	WOOD-BASED PANELS	NAC/ m3	557	929	604	747		ACCEPT	CHECK
8.1	PLYWOOD	1000 m3	30.817	21,882	19.283	19,249	101.152	91,279	92.401	95,514			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.1	PLYWOOD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				8.1	PLYWOOD	NAC/ m3	710	998	902	1034		ACCEPT	CHECK
8.1.C	Coniferous	1000 m3			5.327	4,726			87.088	81,763			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.1.C	Coniferous	1000 m3												8.1.C	Coniferous	NAC/ m3	REPORT	887	REPORT	939		CHECK	CHECK
8.1.NC	Non-Coniferous	1000 m3			13.708	14,330			5.165	12,913			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.1.NC	Non-Coniferous	1000 m3												8.1.NC	Non-Coniferous	NAC/ m3	REPORT	1045	REPORT	2500		CHECK	CHECK
8.1.NC.T	of which: Tropical	1000 m3	0.531	482	0.142	194	0.113	402	0.148	838			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.1.NC.T	of which: Tropical	1000 m3	Error	Error	OK	OK	Error	Error	OK	OK				8.1.NC.T	of which: Tropical	NAC/ m3	907	1367	3560	5663		CHECK	CHECK
8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3			0.076	52			0.062	112			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				8.1.1	of which: Laminated Veneer Lumber (LVL)	NAC/ m3	REPORT	682	REPORT	1808		CHECK	CHECK
8.1.1.C	Coniferous	1000 m3			0.052	23			0.000	0			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.1.1.C	Coniferous	1000 m3												8.1.1.C	Coniferous	NAC/ m3	REPORT	446	REPORT	0		CHECK	ACCEPT
8.1.1.NC	Non-Coniferous	1000 m3			0.022	27			0.051	112 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.1.1.NC	Non-Coniferous	1000 m3												8.1.1.NC	Non-Coniferous	NAC/ m3	REPORT	1227	REPORT	2196		CHECK	CHECK
8.1.1.NC.T	of which: Tropical	1000 m3			0.002	1			0.011	112			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.1.1.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				8.1.1.NC.T	of which: Tropical	NAC/ m3	REPORT	463	REPORT	10182		CHECK	CHECK
8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	1000 m3	15.710	5,781	7.135	3,735	344.047	148,895	302.309	163,899			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	1000 m3												8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/ m3	368	523	433	542		ACCEPT	CHECK
8.2.1	of which: ORIENTED STRANDBOARD (OSB)	1000 m3	5.610	3,271	3.806	2,425	1.188	550	1.675	852			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.2.1	of which: ORIENTED STRANDBOARD (OSB)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/ m3	583	637	463	509		ACCEPT	CHECK
8.3	FIBREBOARD	1000 m3	5.772	1,489	11.287	12,060	175.920	135,157	114.635	121,167 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected to the right sum			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.3	FIBREBOARD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				8.3	FIBREBOARD	NAC/ m3	258	1068	768	1057		ACCEPT	CHECK
8.3.1	HARDBOARD	1000 m3	0.044	105	5.597	9,590	6.405	3,242	5.543	3,128			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.3.1	HARDBOARD	1000 m3												8.3.1	HARDBOARD	NAC/ m3	2379	1713	506	564		CHECK	CHECK
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	1.418	737	4.056	2,069	169.396	131,854	108.795	117,897			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3												8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/ m3	519	510	778	1084		ACCEPT	CHECK
8.3.3	OTHER FIBREBOARD	1000 m3	4.310	648	1.634	401	0.119	61	0.297	140			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		8.3.3	OTHER FIBREBOARD	1000 m3												8.3.3	OTHER FIBREBOARD	NAC/ m3	150	245	517	473		CHECK	CHECK
9	WOOD PULP	1000 t	219.809	141,718	213.627	186,718	17.931	12,098	28.999	23,428			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		9	WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK				9	WOOD PULP	NAC/ t	645	874	675	808		ACCEPT	CHECK
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	2.527	1,176	1.758	1,249	0.004	10	0.019	6			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t												9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/ t	466	711	2382	327		CHECK	CHECK
9.2	CHEMICAL WOOD PULP	1000 t	193.194	119,943	184.642	155,747	15.185	9,815	25.222	20,018			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		9.2	CHEMICAL WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK				9.2	CHEMICAL WOOD PULP	NAC/ t	621	844	646	794		ACCEPT	CHECK
9.2.1	SULPHATE PULP	1000 t	193.191	119,934	184.642	155,745	15.163	9,772	24.437	19,250			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		9.2.1	SULPHATE PULP	1000 t												9.2.1	SULPHATE PULP	NAC/ t	621	844	644	788		ACCEPT	CHECK
9.2.1.1	of which: BLEACHED	1000 t	192.155	119,048	184.546	155,648	9.772	6,685	13.372	11,665			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		9.2.1.1	of which: BLEACHED	1000 t	OK	OK	OK	OK	OK	OK	OK	OK				9.2.1.1	of which: BLEACHED	NAC/ t	620	843	684	872		ACCEPT	CHECK
9.2.2	SULPHITE PULP	1000 t	0.004	9	0.000	1	0.022	43	0.785	767			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		9.2.2	SULPHITE PULP	1000 t												9.2.2	SULPHITE PULP	NAC/ t	2528	8369	1894	978		CHECK	CHECK
9.3	DISSOLVING GRADES	1000 t	24.088	20,598	27.228	29,723	2.742	2,272	3.757	3,404			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		9.3	DISSOLVING GRADES	1000 t												9.3	DISSOLVING GRADES	NAC/ t	855	1092	829	906		ACCEPT	CHECK
10	OTHER PULP	1000 t	2.966	400	4.926	3,147	0.301	288	0.323	419			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		10	OTHER PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK				10	OTHER PULP	NAC/ t	135	639	957	1298		ACCEPT	CHECK
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	0.128	168	1.406	2,407	0.013	83	0.022	155			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t												10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/ t	1309	1712	6229	7001		CHECK	CHECK
10.2	RECOVERED FIBRE PULP	1000 t	2.838	232	3.520	740	0.288	205	0.300	263			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		10.2	RECOVERED FIBRE PULP	1000 t												10.2	RECOVERED FIBRE PULP	NAC/ t	82	210	712	877		CHECK	CHECK
11	RECOVERED PAPER	1000 t	40.649	8,666	36.251	9,365	1.336	241	5.037	987			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		11	RECOVERED PAPER	1000 t												11	RECOVERED PAPER	NAC/ t	213	258	180	196		ACCEPT	CHECK
12	PAPER AND PAPERBOARD	1000 t	48.639	50,259	48.867 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected to the right sum	67,904 VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected the decimal				VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected	VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): corrected to the right sum	638.858	472,303	512.838	605,176			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12	PAPER AND PAPERBOARD	1000 t	OK	OK	OK	OK	OK	OK	OK	OK				12	PAPER AND PAPERBOARD	NAC/ t	1033	1390	739	1180	ACCEPT	CHECK
12.1	GRAPHIC PAPERS	1000 t	20.218	12,882	17.391	17,842	475.773	340,933	378.363	444,185			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.1	GRAPHIC PAPERS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK				12.1	GRAPHIC PAPERS	NAC/ t	637	1026	717	1174		ACCEPT	CHECK
12.1.1	NEWSPRINT	1000 t	13.078	5,798	11.199	9,085	57.154	20,826	52.685	39,589			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.1.1	NEWSPRINT	1000 t												12.1.1	NEWSPRINT	NAC/ t	443	811	364	751		CHECK	CHECK
12.1.2	UNCOATED MECHANICAL	1000 t	1.605	1,066	1.897	1,886	21.586	9,688	4.136	3,873			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.1.2	UNCOATED MECHANICAL	1000 t												12.1.2	UNCOATED MECHANICAL	NAC/ t	664	994	449	936		CHECK	CHECK
12.1.3	UNCOATED WOODFREE	1000 t	1.522	2,604	2.116	3,825	96.749	106,956	75.249	132,465			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.1.3	UNCOATED WOODFREE	1000 t												12.1.3	UNCOATED WOODFREE	NAC/ t	1711	1808	1105	1760		ACCEPT	CHECK
12.1.4	COATED PAPERS	1000 t	4.013	3,415	2.179	3,045	300.284	203,463	246.293	268,258			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.1.4	COATED PAPERS	1000 t												12.1.4	COATED PAPERS	NAC/ t	851	1398	678	1089		CHECK	CHECK
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	0.459	1,392	0.481	2,098	0.010	49	0.037	149			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t												12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/ t	3034	4362	4835	4034		ACCEPT	CHECK
12.3	PACKAGING MATERIALS	1000 t	27.707	33,595	30.789	44,480	162.907	129,499	134.193	158,554			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.3	PACKAGING MATERIALS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK				12.3	PACKAGING MATERIALS	NAC/ t	1212	1445	795	1182		ACCEPT	CHECK
12.3.1	CASE MATERIALS	1000 t	16.003	11,551	16.507	14,835	78.465	40,999	63.938	47,511			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.3.1	CASE MATERIALS	1000 t												12.3.1	CASE MATERIALS	NAC/ t	722	899	523	743		ACCEPT	CHECK
12.3.2	CARTONBOARD	1000 t	8.406	16,379	11.049	23,686	39.472	50,407	25.788	56,009			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.3.2	CARTONBOARD	1000 t												12.3.2	CARTONBOARD	NAC/ t	1948	2144	1277	2172		ACCEPT	CHECK
12.3.3	WRAPPING PAPERS	1000 t	3.114	5,186	2.818	5,227	42.913	36,528	42.413	52,865			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.3.3	WRAPPING PAPERS	1000 t												12.3.3	WRAPPING PAPERS	NAC/ t	1665	1855	851	1246		CHECK	CHECK
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	0.184	479	0.415	732	2.056	1,565	2.054	2,169			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t												12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/ t	2604	1763	761	1056		CHECK	CHECK
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	0.255	2,390	0.206	3,484	0.168	1,822	0.245	2,287			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		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)	NAC/ t	9368	16910	10833	9335		ACCEPT	CHECK
15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3			1.106	906			300.920	227,065			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK				15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	NAC/ m3	REPORT	819	REPORT	755		CHECK	CHECK
15.1	GLULAM	1000 m3			0.493	503			232.580	167,216			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		15.1	GLULAM	1000 m3												15.1	GLULAM	NAC/ m3	REPORT	1020	REPORT	719		CHECK	CHECK
15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3			0.613	403			68.340	59,849			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3												15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	NAC/ m3	REPORT	658	REPORT	876		CHECK	CHECK
16	I BEAMS (I-JOISTS)1	1000 t			0.001	1			0.000	1			7	7			7	7									OK	OK	OK	OK	OK	OK	OK	OK		16	I BEAMS (I-JOISTS)1	1000 t												16	I BEAMS (I-JOISTS)1	NAC/ t	REPORT	887	REPORT	8730		CHECK	CHECK
		To fill:	11	11	0	0	11	11	1	1

EU2 Removals

Country:

14-Jun

Name of Official responsible for reply:

Official Address (in full):

BML, 1030 Vienna, Marxerg. 2

Phone/Fax:

E-mail:

FOREST SECTOR QUESTIONNAIRE

EU2

Removals by type of ownership

Discrepancies

Product code

Ownership

Flag

Note

Product code

Ownership

Unit

2021

2022

2021

2022

2021

2022

Unit

2021

2022

Quantity

ROUNDWOOD REMOVALS (under bark)

ROUNDWOOD REMOVALS

ROUNDWOOD

1000 m3

18420.265

19357.935

ROUNDWOOD

1000 m3

1.C

Coniferous

1000 m3

15663.416

16205.144

1.C

Coniferous

1000 m3

1.NC

Non-coniferous

1000 m3

2756.849

3152.791

1.NC

Non-coniferous

1000 m3

State forests

1000 m3

1836.812

1981.003

includes only ÖBf AG

State forests

1000 m3

Coniferous

1000 m3

1583.238

1630.582

includes only ÖBf AG

Coniferous

1000 m3

Non-coniferous

1000 m3

253.574

350.421

includes only ÖBf AG

Non-coniferous

1000 m3

Other publicly owned forests

1000 m3

VEREBELYINE DOSA Melinda (ESTAT): VEREBELYINE DOSA Melinda (ESTAT): zeros deleted, data not available

included in private forests

Other publicly owned forests

1000 m3

Coniferous

1000 m3

included in private forests

Coniferous

1000 m3

Non-coniferous

1000 m3

included in private forests

Non-coniferous

1000 m3

Private forest

1000 m3

16583.453

17376.932

including other publicly owned forests

Private forest

1000 m3

Coniferous

1000 m3

14080.178

14574.562

including other publicly owned forests

Coniferous

1000 m3

Non-coniferous

1000 m3

2503.275

2802.37

including other publicly owned forests

Non-coniferous

1000 m3

To fill:

Note:

–

Ownership categories correspond to those of the TBFRA.

State forests: Forests owned by national, state and regional governments, or government-owned corporations; Crown forests.

Other publicly owned forests: Forests belonging to cities, municipalities, villages and communes.

Private forests: Forests owned by individuals, co-operatives, enterprises and industries and other private institutions.

–

The unit should be solid cubic metres, under bark.

ITTO1-Estimates

				Country:	AT	Date:
				Name of Official responsible for reply:		0

				Official Address (in full):		BML, 1030 Vienna, Marxerg. 2
	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)

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:

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:

Comments (if any):

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.

Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.

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?

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.

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.).

Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.

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.

ITTO2-Species


						Country:	AT	Date:
		ITTO2				Name of Official responsible for reply:		0

						Official Address (in full):		BML, 1030 Vienna, Marxerg. 2
		FOREST SECTOR QUESTIONNAIRE
		Trade in Tropical Species				Telephone:	0	Fax:	0
						E-mail:		0
	Specify Currency and Unit of Value (e.g.:1000 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
	HS2017:
	ex4403.12 4403.41/49
	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
	HS2017:
	ex4406.12/92 4407.21/22/25/26/27/28/29
	HS2012/2007:
	ex4406.10/90 4407.21/22/25/26/27/28/30
7.NC.T	HS2022:
Veneer Sheets, Tropical	4408.31/39
	HS2017:
	4408.31/39
	HS2012/2007:
	4408.31/39 ex4408.90
8.1.NC.T	HS2022:
Plywood, Tropical	4412.31/41/51/91
	HS2017:
	4412.31 ex4412.94/99
	HS2012/2007:
	4412.31 ex4412.32/94/99
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.

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).

TS-OB

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

P.OB

1000 m3

ERROR:#REF!

P.OB

1000 m3

1_C

ERROR:#REF!

P.OB

1000 m3

1_NC

ERROR:#REF!

P.OB

1000 m3

1_1

ERROR:#REF!

P.OB

1000 m3

1_1_C

ERROR:#REF!

P.OB

1000 m3

1_1_NC

ERROR:#REF!

P.OB

1000 m3

1_2

ERROR:#REF!

P.OB

1000 m3

1_2_C

ERROR:#REF!

P.OB

1000 m3

1_2_NC

ERROR:#REF!

P.OB

1000 m3

1_2_1

ERROR:#REF!

P.OB

1000 m3

1_2_1_C

ERROR:#REF!

P.OB

1000 m3

1_2_1_NC

ERROR:#REF!

P.OB

1000 m3

1_2_2

ERROR:#REF!

P.OB

1000 m3

1_2_2_C

ERROR:#REF!

P.OB

1000 m3

1_2_2_NC

ERROR:#REF!

P.OB

1000 m3

1_2_3

ERROR:#REF!

P.OB

1000 m3

1_2_3_C

ERROR:#REF!

P.OB

1000 m3

1_2_3_NC

ERROR:#REF!

TS-JQ1

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 m3

ERROR:#REF!

1000 m3

1_C

ERROR:#REF!

1000 m3

1_NC

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_1_C

ERROR:#REF!

1000 m3

1_1_NC

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_1

ERROR:#REF!

1000 m3

1_2_1_C

ERROR:#REF!

1000 m3

1_2_1_NC

ERROR:#REF!

1000 m3

1_2_2

ERROR:#REF!

1000 m3

1_2_2_C

ERROR:#REF!

1000 m3

1_2_2_NC

ERROR:#REF!

1000 m3

1_2_3

ERROR:#REF!

1000 m3

1_2_3_C

ERROR:#REF!

1000 m3

1_2_3_NC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

TS-JQ2

Min:

80%

Max:

120%

Notes

JQ2

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

€

1000 NAC

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

€

1000 NAC

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

€

1000 NAC

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

€

1000 NAC

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

€

1000 NAC

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

€

1000 NAC

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

€

1000 NAC

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

€

1000 NAC

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

€

1000 NAC

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

€

1000 NAC

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

€

1000 NAC

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

€

1000 NAC

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

€

1000 NAC

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

€

1000 NAC

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

€

1000 NAC

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

€

1000 NAC

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

€

1000 NAC

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

€

1000 NAC

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

€

1000 NAC

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

€

1000 NAC

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

€

1000 NAC

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

€

1000 NAC

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

€

1000 NAC

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

€

1000 NAC

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

€

1000 NAC

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

€

1000 NAC

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

€

1000 NAC

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

€

1000 NAC

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

€

1000 NAC

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

€

1000 NAC

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

€

1000 NAC

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

€

1000 NAC

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

€

1000 NAC

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

€

1000 NAC

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

€

1000 NAC

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

€

1000 NAC

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

€

1000 NAC

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

€

1000 NAC

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

€

1000 NAC

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

€

1000 NAC

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

€

1000 NAC

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

€

1000 NAC

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

€

1000 NAC

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

€

1000 NAC

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

€

1000 NAC

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

€

1000 NAC

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

€

1000 NAC

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

€

1000 NAC

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

€

1000 NAC

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

€

1000 NAC

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

€

1000 NAC

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

€

1000 NAC

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

€

1000 NAC

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

€

1000 NAC

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

€

1000 NAC

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

€

1000 NAC

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

€

1000 NAC

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

€

1000 NAC

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

€

1000 NAC

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

€

1000 NAC

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

€

1000 NAC

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

€

1000 NAC

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

€

1000 NAC

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

€

1000 NAC

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

€

1000 NAC

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

€

1000 NAC

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

€

1000 NAC

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

€

1000 NAC

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

€

1000 NAC

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

€

1000 NAC

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

€

1000 NAC

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

€

1000 NAC

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

€

1000 NAC

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

€

1000 NAC

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

€

1000 NAC

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

€

1000 NAC

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

€

1000 NAC

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

€

1000 NAC

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

€

1000 NAC

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

€

1000 NAC

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

€

1000 NAC

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

€

1000 NAC

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

€

1000 NAC

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

€

1000 NAC

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

€

1000 NAC

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

€

1000 NAC

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

€

1000 NAC

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

€

1000 NAC

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

€

1000 NAC

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

€

1000 NAC

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

€

1000 NAC

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

€

1000 NAC

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

€

1000 NAC

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

€

1000 NAC

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

€

1000 NAC

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

€

1000 NAC

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

TS-JQ3

Min:

80%

Max:

120%

Notes

JQ3

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 NAC

11_1

ERROR:#REF!

1000 NAC

11_1_C

ERROR:#REF!

1000 NAC

11_1_NC

ERROR:#REF!

1000 NAC

11_1_NC_T

ERROR:#REF!

1000 NAC

11_2

ERROR:#REF!

1000 NAC

11_3

ERROR:#REF!

1000 NAC

11_4

ERROR:#REF!

1000 NAC

11_5

ERROR:#REF!

1000 NAC

11_6

ERROR:#REF!

1000 NAC

11_7

ERROR:#REF!

1000 NAC

11_7_1

ERROR:#REF!

1000 NAC

12_1

ERROR:#REF!

1000 NAC

12_2

ERROR:#REF!

1000 NAC

12_3

ERROR:#REF!

1000 NAC

12_4

ERROR:#REF!

1000 NAC

12_5

ERROR:#REF!

1000 NAC

12_6

ERROR:#REF!

1000 NAC

12_6_1

ERROR:#REF!

1000 NAC

12_6_2

ERROR:#REF!

1000 NAC

12_6_3

ERROR:#REF!

1000 NAC

12_7

ERROR:#REF!

1000 NAC

12_7_1

ERROR:#REF!

1000 NAC

12_7_2

ERROR:#REF!

1000 NAC

12_7_3

ERROR:#REF!

1000 NAC

11_1

ERROR:#REF!

1000 NAC

11_1_C

ERROR:#REF!

1000 NAC

11_1_NC

ERROR:#REF!

1000 NAC

11_1_NC_T

ERROR:#REF!

1000 NAC

11_2

ERROR:#REF!

1000 NAC

11_3

ERROR:#REF!

1000 NAC

11_4

ERROR:#REF!

1000 NAC

11_5

ERROR:#REF!

1000 NAC

11_6

ERROR:#REF!

1000 NAC

11_7

ERROR:#REF!

1000 NAC

11_7_1

ERROR:#REF!

1000 NAC

12_1

ERROR:#REF!

1000 NAC

12_2

ERROR:#REF!

1000 NAC

12_3

ERROR:#REF!

1000 NAC

12_4

ERROR:#REF!

1000 NAC

12_5

ERROR:#REF!

1000 NAC

12_6

ERROR:#REF!

1000 NAC

12_6_1

ERROR:#REF!

1000 NAC

12_6_2

ERROR:#REF!

1000 NAC

12_6_3

ERROR:#REF!

TS-ECEEU

Min:

80%

Max:

120%

Notes

ECEEU

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 m3

ST_1_2_C

ERROR:#REF!

€

1000 NAC

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

€

1000 NAC

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

€

1000 NAC

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

€

1000 NAC

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

€

1000 NAC

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

€

1000 NAC

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

€

1000 NAC

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

€

1000 NAC

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

€

1000 NAC

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

€

1000 NAC

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

€

1000 NAC

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

€

1000 NAC

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

€

1000 NAC

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

€

1000 NAC

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

€

1000 NAC

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

€

1000 NAC

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

€

1000 NAC

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

€

1000 NAC

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

€

1000 NAC

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

€

1000 NAC

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

€

1000 NAC

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

€

1000 NAC

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

€

1000 NAC

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

€

1000 NAC

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

€

1000 NAC

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

€

1000 NAC

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

TS-EU1

Min:

80%

Max:

120%

Notes

EU1

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

EX_M

1000 m3

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

1_1

ERROR:#REF!

€

EX_M

1000 NAC

1_1

ERROR:#REF!

EX_M

1000 m3

1_1

ERROR:#REF!

EX_X

1000 m3

1_1

ERROR:#REF!

€

EX_X

1000 NAC

1_1

ERROR:#REF!

EX_X

1000 m3

1_1

ERROR:#REF!

EX_M

1000 m3

1_2

ERROR:#REF!

€

EX_M

1000 NAC

1_2

ERROR:#REF!

EX_M

1000 m3

1_2

ERROR:#REF!

EX_X

1000 m3

1_2

ERROR:#REF!

€

EX_X

1000 NAC

1_2

ERROR:#REF!

EX_X

1000 m3

1_2

ERROR:#REF!

EX_M

1000 m3

1_2_C

ERROR:#REF!

€

EX_M

1000 NAC

1_2_C

ERROR:#REF!

EX_M

1000 m3

1_2_C

ERROR:#REF!

EX_X

1000 m3

1_2_C

ERROR:#REF!

€

EX_X

1000 NAC

1_2_C

ERROR:#REF!

EX_X

1000 m3

1_2_C

ERROR:#REF!

EX_M

1000 m3

1_2_NC

ERROR:#REF!

€

EX_M

1000 NAC

1_2_NC

ERROR:#REF!

EX_M

1000 m3

1_2_NC

ERROR:#REF!

EX_X

1000 m3

1_2_NC

ERROR:#REF!

€

EX_X

1000 NAC

1_2_NC

ERROR:#REF!

EX_X

1000 m3

1_2_NC

ERROR:#REF!

EX_M

1000 m3

1_2_NC_T

ERROR:#REF!

€

EX_M

1000 NAC

1_2_NC_T

ERROR:#REF!

EX_M

1000 m3

1_2_NC_T

ERROR:#REF!

EX_X

1000 m3

1_2_NC_T

ERROR:#REF!

€

EX_X

1000 NAC

1_2_NC_T

ERROR:#REF!

EX_X

1000 m3

1_2_NC_T

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

3_1

ERROR:#REF!

€

EX_M

1000 NAC

3_1

ERROR:#REF!

EX_M

1000 m3

3_1

ERROR:#REF!

EX_X

1000 m3

3_1

ERROR:#REF!

€

EX_X

1000 NAC

3_1

ERROR:#REF!

EX_X

1000 m3

3_1

ERROR:#REF!

EX_M

1000 m3

3_2

ERROR:#REF!

€

EX_M

1000 NAC

3_2

ERROR:#REF!

EX_M

1000 m3

3_2

ERROR:#REF!

EX_X

1000 m3

3_2

ERROR:#REF!

€

EX_X

1000 NAC

3_2

ERROR:#REF!

EX_X

1000 m3

3_2

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

4_1

ERROR:#REF!

€

EX_M

1000 NAC

4_1

ERROR:#REF!

EX_M

1000 mt

4_1

ERROR:#REF!

EX_X

1000 mt

4_1

ERROR:#REF!

€

EX_X

1000 NAC

4_1

ERROR:#REF!

EX_X

1000 mt

4_1

ERROR:#REF!

EX_M

1000 mt

4_2

ERROR:#REF!

€

EX_M

1000 NAC

4_2

ERROR:#REF!

EX_M

1000 mt

4_2

ERROR:#REF!

EX_X

1000 mt

4_2

ERROR:#REF!

€

EX_X

1000 NAC

4_2

ERROR:#REF!

EX_X

1000 mt

4_2

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

5_C

ERROR:#REF!

€

EX_M

1000 NAC

5_C

ERROR:#REF!

EX_M

1000 m3

5_C

ERROR:#REF!

EX_X

1000 m3

5_C

ERROR:#REF!

€

EX_X

1000 NAC

5_C

ERROR:#REF!

EX_X

1000 m3

5_C

ERROR:#REF!

EX_M

1000 m3

5_NC

ERROR:#REF!

€

EX_M

1000 NAC

5_NC

ERROR:#REF!

EX_M

1000 m3

5_NC

ERROR:#REF!

EX_X

1000 m3

5_NC

ERROR:#REF!

€

EX_X

1000 NAC

5_NC

ERROR:#REF!

EX_X

1000 m3

5_NC

ERROR:#REF!

EX_M

1000 m3

5_NC_T

ERROR:#REF!

€

EX_M

1000 NAC

5_NC_T

ERROR:#REF!

EX_M

1000 m3

5_NC_T

ERROR:#REF!

EX_X

1000 m3

5_NC_T

ERROR:#REF!

€

EX_X

1000 NAC

5_NC_T

ERROR:#REF!

EX_X

1000 m3

5_NC_T

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

6_1

ERROR:#REF!

€

EX_M

1000 NAC

6_1

ERROR:#REF!

EX_M

1000 m3

6_1

ERROR:#REF!

EX_X

1000 m3

6_1

ERROR:#REF!

€

EX_X

1000 NAC

6_1

ERROR:#REF!

EX_X

1000 m3

6_1

ERROR:#REF!

EX_M

1000 m3

6_1_C

ERROR:#REF!

€

EX_M

1000 NAC

6_1_C

ERROR:#REF!

EX_M

1000 m3

6_1_C

ERROR:#REF!

EX_X

1000 m3

6_1_C

ERROR:#REF!

€

EX_X

1000 NAC

6_1_C

ERROR:#REF!

EX_X

1000 m3

6_1_C

ERROR:#REF!

EX_M

1000 m3

6_1_NC

ERROR:#REF!

€

EX_M

1000 NAC

6_1_NC

ERROR:#REF!

EX_M

1000 m3

6_1_NC

ERROR:#REF!

EX_X

1000 m3

6_1_NC

ERROR:#REF!

€

EX_X

1000 NAC

6_1_NC

ERROR:#REF!

EX_X

1000 m3

6_1_NC

ERROR:#REF!

EX_M

1000 m3

6_1_NC_T

ERROR:#REF!

€

EX_M

1000 NAC

6_1_NC_T

ERROR:#REF!

EX_M

1000 m3

6_1_NC_T

ERROR:#REF!

EX_X

1000 m3

6_1_NC_T

ERROR:#REF!

€

EX_X

1000 NAC

6_1_NC_T

ERROR:#REF!

EX_X

1000 m3

6_1_NC_T

ERROR:#REF!

EX_M

1000 m3

6_2

ERROR:#REF!

€

EX_M

1000 NAC

6_2

ERROR:#REF!

EX_M

1000 m3

6_2

ERROR:#REF!

EX_X

1000 m3

6_2

ERROR:#REF!

€

EX_X

1000 NAC

6_2

ERROR:#REF!

EX_X

1000 m3

6_2

ERROR:#REF!

EX_M

1000 m3

6_2_C

ERROR:#REF!

€

EX_M

1000 NAC

6_2_C

ERROR:#REF!

EX_M

1000 m3

6_2_C

ERROR:#REF!

EX_X

1000 m3

6_2_C

ERROR:#REF!

€

EX_X

1000 NAC

6_2_C

ERROR:#REF!

EX_X

1000 m3

6_2_C

ERROR:#REF!

EX_M

1000 m3

6_2_NC

ERROR:#REF!

€

EX_M

1000 NAC

6_2_NC

ERROR:#REF!

EX_M

1000 m3

6_2_NC

ERROR:#REF!

EX_X

1000 m3

6_2_NC

ERROR:#REF!

€

EX_X

1000 NAC

6_2_NC

ERROR:#REF!

EX_X

1000 m3

6_2_NC

ERROR:#REF!

EX_M

1000 m3

6_2_NC_T

ERROR:#REF!

€

EX_M

1000 NAC

6_2_NC_T

ERROR:#REF!

EX_M

1000 m3

6_2_NC_T

ERROR:#REF!

EX_X

1000 m3

6_2_NC_T

ERROR:#REF!

€

EX_X

1000 NAC

6_2_NC_T

ERROR:#REF!

EX_X

1000 m3

6_2_NC_T

ERROR:#REF!

EX_M

1000 m3

6_3

ERROR:#REF!

€

EX_M

1000 NAC

6_3

ERROR:#REF!

EX_M

1000 m3

6_3

ERROR:#REF!

EX_X

1000 m3

6_3

ERROR:#REF!

€

EX_X

1000 NAC

6_3

ERROR:#REF!

EX_X

1000 m3

6_3

ERROR:#REF!

EX_M

1000 m3

6_3_1

ERROR:#REF!

€

EX_M

1000 NAC

6_3_1

ERROR:#REF!

EX_M

1000 m3

6_3_1

ERROR:#REF!

EX_X

1000 m3

6_3_1

ERROR:#REF!

€

EX_X

1000 NAC

6_3_1

ERROR:#REF!

EX_X

1000 m3

6_3_1

ERROR:#REF!

EX_M

1000 m3

6_4

ERROR:#REF!

€

EX_M

1000 NAC

6_4

ERROR:#REF!

EX_M

1000 m3

6_4

ERROR:#REF!

EX_X

1000 m3

6_4

ERROR:#REF!

€

EX_X

1000 NAC

6_4

ERROR:#REF!

EX_X

1000 m3

6_4

ERROR:#REF!

EX_M

1000 m3

6_4_1

ERROR:#REF!

€

EX_M

1000 NAC

6_4_1

ERROR:#REF!

EX_M

1000 m3

6_4_1

ERROR:#REF!

EX_X

1000 m3

6_4_1

ERROR:#REF!

€

EX_X

1000 NAC

6_4_1

ERROR:#REF!

EX_X

1000 m3

6_4_1

ERROR:#REF!

EX_M

1000 m3

6_4_2

ERROR:#REF!

€

EX_M

1000 NAC

6_4_2

ERROR:#REF!

EX_M

1000 m3

6_4_2

ERROR:#REF!

EX_X

1000 m3

6_4_2

ERROR:#REF!

€

EX_X

1000 NAC

6_4_2

ERROR:#REF!

EX_X

1000 m3

6_4_2

ERROR:#REF!

EX_M

1000 m3

6_4_3

ERROR:#REF!

€

EX_M

1000 NAC

6_4_3

ERROR:#REF!

EX_M

1000 m3

6_4_3

ERROR:#REF!

EX_X

1000 m3

6_4_3

ERROR:#REF!

€

EX_X

1000 NAC

6_4_3

ERROR:#REF!

EX_X

1000 m3

6_4_3

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

7_1

ERROR:#REF!

€

EX_M

1000 NAC

7_1

ERROR:#REF!

EX_M

1000 mt

7_1

ERROR:#REF!

EX_X

1000 mt

7_1

ERROR:#REF!

€

EX_X

1000 NAC

7_1

ERROR:#REF!

EX_X

1000 mt

7_1

ERROR:#REF!

EX_M

1000 mt

7_2

ERROR:#REF!

€

EX_M

1000 NAC

7_2

ERROR:#REF!

EX_M

1000 mt

7_2

ERROR:#REF!

EX_X

1000 mt

7_2

ERROR:#REF!

€

EX_X

1000 NAC

7_2

ERROR:#REF!

EX_X

1000 mt

7_2

ERROR:#REF!

EX_M

1000 mt

7_3

ERROR:#REF!

€

EX_M

1000 NAC

7_3

ERROR:#REF!

EX_M

1000 mt

7_3

ERROR:#REF!

EX_X

1000 mt

7_3

ERROR:#REF!

€

EX_X

1000 NAC

7_3

ERROR:#REF!

EX_X

1000 mt

7_3

ERROR:#REF!

EX_M

1000 mt

7_3_1

ERROR:#REF!

€

EX_M

1000 NAC

7_3_1

ERROR:#REF!

EX_M

1000 mt

7_3_1

ERROR:#REF!

EX_X

1000 mt

7_3_1

ERROR:#REF!

€

EX_X

1000 NAC

7_3_1

ERROR:#REF!

EX_X

1000 mt

7_3_1

ERROR:#REF!

EX_M

1000 mt

7_3_2

ERROR:#REF!

€

EX_M

1000 NAC

7_3_2

ERROR:#REF!

EX_M

1000 mt

7_3_2

ERROR:#REF!

EX_X

1000 mt

7_3_2

ERROR:#REF!

€

EX_X

1000 NAC

7_3_2

ERROR:#REF!

EX_X

1000 mt

7_3_2

ERROR:#REF!

EX_M

1000 mt

7_3_3

ERROR:#REF!

€

EX_M

1000 NAC

7_3_3

ERROR:#REF!

EX_M

1000 mt

7_3_3

ERROR:#REF!

EX_X

1000 mt

7_3_3

ERROR:#REF!

€

EX_X

1000 NAC

7_3_3

ERROR:#REF!

EX_X

1000 mt

7_3_3

ERROR:#REF!

EX_M

1000 mt

7_3_4

ERROR:#REF!

€

EX_M

1000 NAC

7_3_4

ERROR:#REF!

EX_M

1000 mt

7_3_4

ERROR:#REF!

EX_X

1000 mt

7_3_4

ERROR:#REF!

€

EX_X

1000 NAC

7_3_4

ERROR:#REF!

EX_X

1000 mt

7_3_4

ERROR:#REF!

EX_M

1000 mt

7_4

ERROR:#REF!

€

EX_M

1000 NAC

7_4

ERROR:#REF!

EX_M

1000 mt

7_4

ERROR:#REF!

EX_X

1000 mt

7_4

ERROR:#REF!

€

EX_X

1000 NAC

7_4

ERROR:#REF!

EX_X

1000 mt

7_4

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

8_1

ERROR:#REF!

€

EX_M

1000 NAC

8_1

ERROR:#REF!

EX_M

1000 mt

8_1

ERROR:#REF!

EX_X

1000 mt

8_1

ERROR:#REF!

€

EX_X

1000 NAC

8_1

ERROR:#REF!

EX_X

1000 mt

8_1

ERROR:#REF!

EX_M

1000 mt

8_2

ERROR:#REF!

€

EX_M

1000 NAC

8_2

ERROR:#REF!

EX_M

1000 mt

8_2

ERROR:#REF!

EX_X

1000 mt

8_2

ERROR:#REF!

€

EX_X

1000 NAC

8_2

ERROR:#REF!

EX_X

1000 mt

8_2

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

10_1

ERROR:#REF!

€

EX_M

1000 NAC

10_1

ERROR:#REF!

EX_M

1000 mt

10_1

ERROR:#REF!

EX_X

1000 mt

10_1

ERROR:#REF!

€

EX_X

1000 NAC

10_1

ERROR:#REF!

EX_X

1000 mt

10_1

ERROR:#REF!

EX_M

1000 mt

10_1_1

ERROR:#REF!

€

EX_M

1000 NAC

10_1_1

ERROR:#REF!

EX_M

1000 mt

10_1_1

ERROR:#REF!

EX_X

1000 mt

10_1_1

ERROR:#REF!

€

EX_X

1000 NAC

10_1_1

ERROR:#REF!

EX_X

1000 mt

10_1_1

ERROR:#REF!

EX_M

1000 mt

10_1_2

ERROR:#REF!

€

EX_M

1000 NAC

10_1_2

ERROR:#REF!

EX_M

1000 mt

10_1_2

ERROR:#REF!

EX_X

1000 mt

10_1_2

ERROR:#REF!

€

EX_X

1000 NAC

10_1_2

ERROR:#REF!

EX_X

1000 mt

10_1_2

ERROR:#REF!

EX_M

1000 mt

10_1_3

ERROR:#REF!

€

EX_M

1000 NAC

10_1_3

ERROR:#REF!

EX_M

1000 mt

10_1_3

ERROR:#REF!

EX_X

1000 mt

10_1_3

ERROR:#REF!

€

EX_X

1000 NAC

10_1_3

ERROR:#REF!

EX_X

1000 mt

10_1_3

ERROR:#REF!

EX_M

1000 mt

10_1_4

ERROR:#REF!

€

EX_M

1000 NAC

10_1_4

ERROR:#REF!

EX_M

1000 mt

10_1_4

ERROR:#REF!

EX_X

1000 mt

10_1_4

ERROR:#REF!

€

EX_X

1000 NAC

10_1_4

ERROR:#REF!

EX_X

1000 mt

10_1_4

ERROR:#REF!

EX_M

1000 mt

10_2

ERROR:#REF!

€

EX_M

1000 NAC

10_2

ERROR:#REF!

EX_M

1000 mt

10_2

ERROR:#REF!

EX_X

1000 mt

10_2

ERROR:#REF!

€

EX_X

1000 NAC

10_2

ERROR:#REF!

EX_X

1000 mt

10_2

ERROR:#REF!

EX_M

1000 mt

10_3

ERROR:#REF!

€

EX_M

1000 NAC

10_3

ERROR:#REF!

EX_M

1000 mt

10_3

ERROR:#REF!

EX_X

1000 mt

10_3

ERROR:#REF!

€

EX_X

1000 NAC

10_3

ERROR:#REF!

EX_X

1000 mt

10_3

ERROR:#REF!

EX_M

1000 mt

10_3_1

ERROR:#REF!

€

EX_M

1000 NAC

10_3_1

ERROR:#REF!

EX_M

1000 mt

10_3_1

ERROR:#REF!

EX_X

1000 mt

10_3_1

ERROR:#REF!

€

EX_X

1000 NAC

10_3_1

ERROR:#REF!

EX_X

1000 mt

10_3_1

ERROR:#REF!

EX_M

1000 mt

10_3_2

ERROR:#REF!

€

EX_M

1000 NAC

10_3_2

ERROR:#REF!

EX_M

1000 mt

10_3_2

ERROR:#REF!

EX_X

1000 mt

10_3_2

ERROR:#REF!

€

EX_X

1000 NAC

10_3_2

ERROR:#REF!

EX_X

1000 mt

10_3_2

ERROR:#REF!

EX_M

1000 mt

10_3_3

ERROR:#REF!

€

EX_M

1000 NAC

10_3_3

ERROR:#REF!

EX_M

1000 mt

10_3_3

ERROR:#REF!

EX_X

1000 mt

10_3_3

ERROR:#REF!

€

EX_X

1000 NAC

10_3_3

ERROR:#REF!

EX_X

1000 mt

10_3_3

ERROR:#REF!

EX_M

1000 mt

10_3_4

ERROR:#REF!

€

EX_M

1000 NAC

10_3_4

ERROR:#REF!

EX_M

1000 mt

10_3_4

ERROR:#REF!

EX_X

1000 mt

10_3_4

ERROR:#REF!

€

EX_X

1000 NAC

10_3_4

ERROR:#REF!

EX_X

1000 mt

10_3_4

ERROR:#REF!

EX_M

1000 mt

10_4

ERROR:#REF!

€

EX_M

1000 NAC

10_4

ERROR:#REF!

EX_M

1000 mt

10_4

ERROR:#REF!

EX_X

1000 mt

10_4

ERROR:#REF!

€

EX_X

1000 NAC

10_4

ERROR:#REF!

EX_X

1000 mt

10_4

ERROR:#REF!

TS-EU2

Min:

80%

Max:

120%

Notes

EU2

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 m3

EU2_1

ERROR:#REF!

1000 m3

EU2_1_C

ERROR:#REF!

1000 m3

EU2_1_NC

ERROR:#REF!

1000 m3

EU2_1_1

ERROR:#REF!

1000 m3

EU2_1_1_C

ERROR:#REF!

1000 m3

EU2_1_1_NC

ERROR:#REF!

1000 m3

EU2_1_2

ERROR:#REF!

1000 m3

EU2_1_2_C

ERROR:#REF!

1000 m3

EU2_1_2_NC

ERROR:#REF!

1000 m3

EU2_1_3

ERROR:#REF!

1000 m3

EU2_1_3_C

ERROR:#REF!

1000 m3

EU2_1_3_NC

ERROR:#REF!

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.

Annex3 | JQ3-Corres.



			FOREST SECTOR QUESTIONNAIRE JQ3 (Supp. 1)
			SECONDARY PROCESSED PRODUCTS
			Trade
			CORRESPONDENCES to HS 2022, 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

13	SECONDARY WOOD PRODUCTS
13.1	FURTHER PROCESSED SAWNWOOD	4409.10/22/29	4409.10/22/29	4409.10/29	248.3 248.5
13.1.C	Coniferous	4409.10	4409.10	4409.10	248.3
13.1.NC	Non-coniferous	4409.22/29	4409.22/29	4409.29	248.5
13.1.NC.T	of which: Tropical	4409.22	4409.22	ex4409.29	ex248.5
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	44.15/16	44.15/16	44.15/16	635.1 635.2
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	44.14 4419.20 4419.90 44.20	44.14 4419.90 44.20	44.14 ex4419.00 44.20	635.41 ex635.42 635.49
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1	4418.11/19/21/29/30/40/50/74/75/79/89/92/99	4418.10/20/40/50/60/74/75/79/99	4418.10/20/40/50/60 ex4418.71 ex4418.72 ex4418.79 ex4418.90	635.31/32/33 ex635.34 ex635.39
13.5	WOODEN FURNITURE	9401.31/41 9401.61/69/91 9403.30/40/50/60/91	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	821.16 ex821.19 821.51/53/55/59 ex821.8
13.6	PREFABRICATED BUILDINGS OF WOOD	9406.10	9406.10	ex94.06	ex811.0
13.7	OTHER MANUFACTURED WOOD PRODUCTS	44.04/05/13/17 4421.10/20/99	44.04/05/13/17 4421.10/99	44.04/05/13/17 4421.10 ex4421.90	634.21/91/93 635.91 ex635.99
14	SECONDARY PAPER PRODUCTS
14.1	COMPOSITE PAPER AND PAPERBOARD	48.07	48.07	48.07	641.92
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	4811.10/41/49/60/90	4811.10/41/49/60/90	4811.10/41/49/60/90	641.73/78/79
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	48.18	48.18	48.18	642.43/94
14.4	PACKAGING CARTONS, BOXES ETC.	48.19	48.19	48.19	642.1
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	48.14/16/17/20/21/22/23	48.14/16/17/20/21/22/23	48.14/16/17/20/21/22/23	641.94 642.2 642.3 642.42/45/91/93/99 892.81
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	ex4823.90	ex4823.90	ex4823.90	ex642.99
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	4823.70	4823.70	4823.70	ex642.99
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	4823.20	4823.20	4823.20	642.45
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.
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017/2022 or SITC Rev.4 code is applicable.
For instance "ex811.00" under "Prefabricated buildings of wood" means that only a part of SITC code 811.00 refers to buildings prefabricated from wood, as that code does not distinguish between the materials buildings were prefabricated from.
In SITC Rev.4, if only 4 digits are shown, then all subheadings at lower degrees of aggregation are included (for example, 892.2 includes 892.21 and 892.29).

Annex4 |JQ2-JQ3-Corres.

JQ Product code	Nomenclature	HS Code	Remarks on HS codes
1	HS2002	440110		Annex 4 does not include HS2022 codes
1	HS2002	4403
1	HS2007	440110
1	HS2007	4403
1	HS2012	440110
1	HS2012	4403
1	HS2017	440111
1	HS2017	440112
1	HS2017	4403
1.1	HS2002	440110
1.1	HS2007	440110
1.1	HS2012	440110
1.1	HS2017	440111
1.1	HS2017	440112
1.1C	HS2002	440110	Only some part of it
1.1C	HS2007	440110	Only some part of it
1.1C	HS2012	440110	Only some part of it
1.1C	HS2017	440111
1.1NC	HS2002	440110	Only some part of it
1.1NC	HS2007	440110	Only some part of it
1.1NC	HS2012	440110	Only some part of it
1.1NC	HS2017	440112
1.2	HS2002	4403
1.2	HS2007	4403
1.2	HS2012	4403
1.2	HS2017	4403
1.2.C	HS2002	440310	Only some part of it
1.2.C	HS2002	440320
1.2.C	HS2007	440310	Only some part of it
1.2.C	HS2007	440320
1.2.C	HS2012	440310	Only some part of it
1.2.C	HS2012	440320
1.2.C	HS2017	440311
1.2.C	HS2017	440321
1.2.C	HS2017	440322
1.2.C	HS2017	440323
1.2.C	HS2017	440324
1.2.C	HS2017	440325
1.2.C	HS2017	440326
1.2.NC	HS2002	440310	Only some part of it
1.2.NC	HS2002	440341
1.2.NC	HS2002	440349
1.2.NC	HS2002	440391
1.2.NC	HS2002	440392
1.2.NC	HS2002	440399
1.2.NC	HS2007	440310	Only some part of it
1.2.NC	HS2007	440341
1.2.NC	HS2007	440349
1.2.NC	HS2007	440391
1.2.NC	HS2007	440392
1.2.NC	HS2007	440399
1.2.NC	HS2012	440310	Only some part of it
1.2.NC	HS2012	440341
1.2.NC	HS2012	440349
1.2.NC	HS2012	440391
1.2.NC	HS2012	440392
1.2.NC	HS2012	440399
1.2.NC	HS2017	440312
1.2.NC	HS2017	440341
1.2.NC	HS2017	440349
1.2.NC	HS2017	440391
1.2.NC	HS2017	440393
1.2.NC	HS2017	440394
1.2.NC	HS2017	440395
1.2.NC	HS2017	440396
1.2.NC	HS2017	440397
1.2.NC	HS2017	440398
1.2.NC	HS2017	440399
1.2.NC.T	HS2002	440310	Only some part of it
1.2.NC.T	HS2002	440341
1.2.NC.T	HS2002	440349
1.2.NC.T	HS2002	440399	Only some part of it
1.2.NC.T	HS2007	440310	Only some part of it
1.2.NC.T	HS2007	440341
1.2.NC.T	HS2007	440349
1.2.NC.T	HS2007	440399	Only some part of it
1.2.NC.T	HS2012	440310	Only some part of it
1.2.NC.T	HS2012	440341
1.2.NC.T	HS2012	440349
1.2.NC.T	HS2012	440399	Only some part of it
1.2.NC.T	HS2017	440312	Only some part of it
1.2.NC.T	HS2017	440341
1.2.NC.T	HS2017	440349
2	HS2002	440200	Only some part of it
2	HS2007	440290
2	HS2012	440290
2	HS2017	440290
3	HS2002	440121
3	HS2002	440122
3	HS2002	440130	Only some part of it
3	HS2007	440121
3	HS2007	440122
3	HS2007	440130	Only some part of it
3	HS2012	440121
3	HS2012	440122
3	HS2012	440139	Only some part of it
3	HS2017	440121
3	HS2017	440122
3	HS2017	440140
3.1	HS2002	440121
3.1	HS2002	440122
3.1	HS2007	440121
3.1	HS2007	440122
3.1	HS2012	440121
3.1	HS2012	440122
3.1	HS2017	440121
3.1	HS2017	440122
3.2	HS2002	440130	Only some part of it
3.2	HS2012	440130	Only some part of it
3.2	HS2012	440139	Only some part of it
3.2	HS2017	440140	Only some part of it
4	HS2002	440130	Only some part of it
4	HS2007	440130	Only some part of it
4	HS2012	440139	Only some part of it
4	HS2017	440140	Only some part of it
5	HS2002	440130	Only some part of it
5	HS2007	440130	Only some part of it
5	HS2012	440131
5	HS2012	440139	Only some part of it
5	HS2017	440131
5	HS2017	440139
5.1	HS2002	440130	Only some part of it
5.1	HS2007	440130	Only some part of it
5.1	HS2012	440131
5.1	HS2017	440131
5.2	HS2002	440130	Only some part of it
5.2	HS2007	440130	Only some part of it
5.2	HS2012	440139	Only some part of it
5.2	HS2017	440139
6	HS2002	4406
6	HS2002	4407
6	HS2007	4406
6	HS2007	4407
6	HS2012	4406
6	HS2012	4407
6	HS2017	4406
6	HS2017	4407
6.C	HS2002	440610	Only some part of it
6.C	HS2002	440690	Only some part of it
6.C	HS2002	440710
6.C	HS2007	440610	Only some part of it
6.C	HS2007	440690	Only some part of it
6.C	HS2007	440710
6.C	HS2012	440610	Only some part of it
6.C	HS2012	440690	Only some part of it
6.C	HS2012	440710
6.C	HS2017	440611
6.C	HS2017	440691
6.C	HS2017	440711
6.C	HS2017	440712
6.C	HS2017	440719
6.NC	HS2002	440610	Only some part of it
6.NC	HS2002	440690	Only some part of it
6.NC	HS2002	440724
6.NC	HS2002	440725
6.NC	HS2002	440726
6.NC	HS2002	440729
6.NC	HS2002	440791
6.NC	HS2002	440792
6.NC	HS2002	440799
6.NC	HS2007	440610	Only some part of it
6.NC	HS2007	440690	Only some part of it
6.NC	HS2007	440721
6.NC	HS2007	440722
6.NC	HS2007	440725
6.NC	HS2007	440726
6.NC	HS2007	440727
6.NC	HS2007	440728
6.NC	HS2007	440729
6.NC	HS2007	440791
6.NC	HS2007	440792
6.NC	HS2007	440793
6.NC	HS2007	440794
6.NC	HS2007	440795
6.NC	HS2007	440799
6.NC	HS2012	440610	Only some part of it
6.NC	HS2012	440690	Only some part of it
6.NC	HS2012	440721
6.NC	HS2012	440722
6.NC	HS2012	440725
6.NC	HS2012	440726
6.NC	HS2012	440727
6.NC	HS2012	440728
6.NC	HS2012	440729
6.NC	HS2012	440791
6.NC	HS2012	440792
6.NC	HS2012	440793
6.NC	HS2012	440794
6.NC	HS2012	440795
6.NC	HS2012	440799
6.NC	HS2017	4406.12
6.NC	HS2017	4406.92
6.NC	HS2017	4407.21
6.NC	HS2017	4407.22
6.NC	HS2017	4407.25
6.NC	HS2017	4407.26
6.NC	HS2017	4407.27
6.NC	HS2017	4407.28
6.NC	HS2017	4407.29
6.NC	HS2017	4407.91
6.NC	HS2017	4407.92
6.NC	HS2017	4407.93
6.NC	HS2017	4407.94
6.NC	HS2017	4407.95
6.NC	HS2017	4407.96
6.NC	HS2017	4407.97
6.NC	HS2017	4407.99
6.NC.T	HS2002	440610	Only some part of it
6.NC.T	HS2002	440690	Only some part of it
6.NC.T	HS2002	440724
6.NC.T	HS2002	440725
6.NC.T	HS2002	440726
6.NC.T	HS2002	440729
6.NC.T	HS2002	440799	Only some part of it
6.NC.T	HS2007	440610	Only some part of it
6.NC.T	HS2007	440690	Only some part of it
6.NC.T	HS2007	440721
6.NC.T	HS2007	440722
6.NC.T	HS2007	440725
6.NC.T	HS2007	440726
6.NC.T	HS2007	440727
6.NC.T	HS2007	440728
6.NC.T	HS2007	440729
6.NC.T	HS2007	440799	Only some part of it
6.NC.T	HS2012	440610	Only some part of it
6.NC.T	HS2012	440690	Only some part of it
6.NC.T	HS2012	440721
6.NC.T	HS2012	440722
6.NC.T	HS2012	440725
6.NC.T	HS2012	440726
6.NC.T	HS2012	440727
6.NC.T	HS2012	440728
6.NC.T	HS2012	440729
6.NC.T	HS2012	440799	Only some part of it
6.NC.T	HS2017	440612	Only some part of it
6.NC.T	HS2017	440692	Only some part of it
6.NC.T	HS2017	440721
6.NC.T	HS2017	440722
6.NC.T	HS2017	440725
6.NC.T	HS2017	440726
6.NC.T	HS2017	440727
6.NC.T	HS2017	440728
6.NC.T	HS2017	440729
7	HS2002	4408
7	HS2007	4408
7	HS2012	4408
7	HS2017	4408
7.C	HS2002	440810
7.C	HS2007	440810
7.C	HS2012	440810
7.C	HS2017	440810
7.NC	HS2002	440831
7.NC	HS2002	440839
7.NC	HS2002	440890
7.NC	HS2007	440831
7.NC	HS2007	440839
7.NC	HS2007	440890
7.NC	HS2012	440831
7.NC	HS2012	440839
7.NC	HS2012	440890
7.NC	HS2017	440831
7.NC	HS2017	440839
7.NC	HS2017	440890
7.NC.T	HS2002	440831
7.NC.T	HS2002	440839
7.NC.T	HS2002	440890	Only some part of it
7.NC.T	HS2007	440831
7.NC.T	HS2007	440839
7.NC.T	HS2007	440890	Only some part of it
7.NC.T	HS2012	440831
7.NC.T	HS2012	440839
7.NC.T	HS2012	440890	Only some part of it
7.NC.T	HS2017	440831
7.NC.T	HS2017	440839
8	HS2002	4410
8	HS2002	4411
8	HS2002	441213
8	HS2002	441214
8	HS2002	441219
8	HS2002	441299	Only some part of it
8	HS2007	4410
8	HS2007	4411
8	HS2007	441231
8	HS2007	441232
8	HS2007	441239
8	HS2007	441294
8	HS2007	441299
8	HS2012	4410
8	HS2012	4411
8	HS2012	441231
8	HS2012	441232
8	HS2012	441239
8	HS2012	441294
8	HS2012	441299
8	HS2017	4410
8	HS2017	4411
8	HS2017	441231
8	HS2017	441233
8	HS2017	441234
8	HS2017	441239
8	HS2017	441294
8	HS2017	441299
8.1	HS2002	441213
8.1	HS2002	441214
8.1	HS2002	441219
8.1	HS2002	441299	Only some part of it
8.1	HS2007	441231
8.1	HS2007	441232
8.1	HS2007	441239
8.1	HS2007	441294
8.1	HS2007	441299
8.1	HS2012	441231
8.1	HS2012	441232
8.1	HS2012	441239
8.1	HS2012	441294
8.1	HS2012	441299
8.1	HS2017	441231
8.1	HS2017	441233
8.1	HS2017	441234
8.1	HS2017	441239
8.1	HS2017	441294
8.1	HS2017	441299
8.1.C	HS2002	441219
8.1.C	HS2002	441299	Only some part of it
8.1.C	HS2007	441239
8.1.C	HS2007	441294	Only some part of it
8.1.C	HS2007	441299	Only some part of it
8.1.C	HS2012	441239
8.1.C	HS2012	441294	Only some part of it
8.1.C	HS2012	441299	Only some part of it
8.1.C	HS2017	441239
8.1.C	HS2017	441294	Only some part of it
8.1.C	HS2017	441299	Only some part of it
8.1.NC	HS2002	441213
8.1.NC	HS2002	441214
8.1.NC	HS2002	441299	Only some part of it
8.1.NC	HS2007	441231
8.1.NC	HS2007	441232
8.1.NC	HS2007	441294	Only some part of it
8.1.NC	HS2007	441299	Only some part of it
8.1.NC	HS2012	441231
8.1.NC	HS2012	441232
8.1.NC	HS2012	441294	Only some part of it
8.1.NC	HS2012	441299	Only some part of it
8.1.NC	HS2017	441231
8.1.NC	HS2017	441233
8.1.NC	HS2017	441234
8.1.NC	HS2017	441294	Only some part of it
8.1.NC	HS2017	441299	Only some part of it
8.1.NC.T	HS2002	441213
8.1.NC.T	HS2002	441214	Only some part of it
8.1.NC.T	HS2002	441299	Only some part of it
8.1.NC.T	HS2007	441231
8.1.NC.T	HS2007	441232	Only some part of it
8.1.NC.T	HS2007	441294	Only some part of it
8.1.NC.T	HS2007	441299	Only some part of it
8.1.NC.T	HS2012	441231
8.1.NC.T	HS2012	441232	Only some part of it
8.1.NC.T	HS2012	441294	Only some part of it
8.1.NC.T	HS2012	441299	Only some part of it
8.1.NC.T	HS2017	441231
8.1.NC.T	HS2017	441294	Only some part of it
8.1.NC.T	HS2017	441299	Only some part of it
8.2	HS2002	4410
8.2	HS2007	4410
8.2	HS2012	4410
8.2	HS2017	4410
8.2.1	HS2002	441021	Only some part of it
8.2.1	HS2002	441029	Only some part of it
8.2.1	HS2007	441012
8.2.1	HS2012	441012
8.2.1	HS2017	441012
8.3	HS2002	4411
8.3	HS2007	4411
8.3	HS2012	4411
8.3	HS2017	4411
8.3.1	HS2002	441111	Only some part of it
8.3.1	HS2002	441119	Only some part of it
8.3.1	HS2007	441192
8.3.1	HS2012	441192
8.3.1	HS2017	441192
8.3.2	HS2002	441111	Only some part of it
8.3.2	HS2002	441119	Only some part of it
8.3.2	HS2002	441121	Only some part of it
8.3.2	HS2002	441129	Only some part of it
8.3.2	HS2007	441112
8.3.2	HS2007	441113
8.3.2	HS2007	441114	Only some part of it
8.3.2	HS2012	441112
8.3.2	HS2012	441113
8.3.2	HS2012	441114	Only some part of it
8.3.2	HS2017	441112
8.3.2	HS2017	441113
8.3.2	HS2017	441114	Only some part of it
8.3.3	HS2002	441131
8.3.3	HS2002	441139
8.3.3	HS2002	441191
8.3.3	HS2002	441199
8.3.3	HS2007	441114	Only some part of it
8.3.3	HS2007	441193
8.3.3	HS2007	441194
8.3.3	HS2012	441114	Only some part of it
8.3.3	HS2012	441193
8.3.3	HS2012	441194
8.3.3	HS2017	441114	Only some part of it
8.3.3	HS2017	441193
8.3.3	HS2017	441194
9	HS2002	4701
9	HS2002	4702
9	HS2002	4703
9	HS2002	4704
9	HS2002	4705
9	HS2007	4701
9	HS2007	4702
9	HS2007	4703
9	HS2007	4704
9	HS2007	4705
9	HS2012	4701
9	HS2012	4702
9	HS2012	4703
9	HS2012	4704
9	HS2012	4705
9	HS2017	4701
9	HS2017	4702
9	HS2017	4703
9	HS2017	4704
9	HS2017	4705
9.1	HS2002	4701
9.1	HS2002	4705
9.1	HS2007	4701
9.1	HS2007	4705
9.1	HS2012	4701
9.1	HS2012	4705
9.1	HS2017	4701
9.1	HS2017	4705
9.2	HS2002	4703
9.2	HS2002	4704
9.2	HS2007	4703
9.2	HS2007	4704
9.2	HS2012	4703
9.2	HS2012	4704
9.2	HS2017	4703
9.2	HS2017	4704
9.2.1	HS2002	4703
9.2.1	HS2007	4703
9.2.1	HS2012	4703
9.2.1	HS2017	4703
9.2.1.1	HS2002	470321
9.2.1.1	HS2002	470329
9.2.1.1	HS2007	470321
9.2.1.1	HS2007	470329
9.2.1.1	HS2012	470321
9.2.1.1	HS2012	470329
9.2.1.1	HS2017	470321
9.2.1.1	HS2017	470329
9.2.2	HS2002	4704
9.2.2	HS2007	4704
9.2.2	HS2012	4704
9.2.2	HS2017	4704
9.3	HS2002	4702
9.3	HS2007	4702
9.3	HS2012	4702
9.3	HS2017	4702
10	HS2002	4706
10	HS2007	4706
10	HS2012	4706
10	HS2017	4706
10.1	HS2002	470610
10.1	HS2002	470691
10.1	HS2002	470692
10.1	HS2002	470693
10.1	HS2007	470610
10.1	HS2007	470630
10.1	HS2007	470691
10.1	HS2007	470692
10.1	HS2007	470693
10.1	HS2012	470610
10.1	HS2012	470630
10.1	HS2012	470691
10.1	HS2012	470692
10.1	HS2012	470693
10.1	HS2017	470610
10.1	HS2017	470630
10.1	HS2017	470691
10.1	HS2017	470692
10.1	HS2017	470693
10.2	HS2002	470620
10.2	HS2007	470620
10.2	HS2012	470620
10.2	HS2017	470620
11	HS2002	4707
11	HS2007	4707
11	HS2012	4707
11	HS2017	4707
12	HS2002	4801
12	HS2002	4802
12	HS2002	4803
12	HS2002	4804
12	HS2002	4805
12	HS2002	4806
12	HS2002	4808
12	HS2002	4809
12	HS2002	4810
12	HS2002	481151
12	HS2002	481159
12	HS2002	4812
12	HS2002	4813
12	HS2007	4801
12	HS2007	4802
12	HS2007	4803
12	HS2007	4804
12	HS2007	4805
12	HS2007	4806
12	HS2007	4808
12	HS2007	4809
12	HS2007	4810
12	HS2007	481151
12	HS2007	481159
12	HS2007	4812
12	HS2007	4813
12	HS2012	4801
12	HS2012	4802
12	HS2012	4803
12	HS2012	4804
12	HS2012	4805
12	HS2012	4806
12	HS2012	4808
12	HS2012	4809
12	HS2012	4810
12	HS2012	481151
12	HS2012	481159
12	HS2012	4812
12	HS2012	4813
12	HS2017	4801
12	HS2017	4802
12	HS2017	4803
12	HS2017	4804
12	HS2017	4805
12	HS2017	4806
12	HS2017	4808
12	HS2017	4809
12	HS2017	4810
12	HS2017	481151
12	HS2017	481159
12	HS2017	4812
12	HS2017	4813
12.1	HS2002	4801
12.1	HS2002	480210
12.1	HS2002	480220
12.1	HS2002	480254
12.1	HS2002	480255
12.1	HS2002	480256
12.1	HS2002	480257
12.1	HS2002	480258
12.1	HS2002	480261
12.1	HS2002	480262
12.1	HS2002	480269
12.1	HS2002	4809
12.1	HS2002	481013
12.1	HS2002	481014
12.1	HS2002	481019
12.1	HS2002	481022
12.1	HS2002	481029
12.1	HS2007	4801
12.1	HS2007	480210
12.1	HS2007	480220
12.1	HS2007	480254
12.1	HS2007	480255
12.1	HS2007	480256
12.1	HS2007	480257
12.1	HS2007	480258
12.1	HS2007	480261
12.1	HS2007	480262
12.1	HS2007	480269
12.1	HS2007	4809
12.1	HS2007	481013
12.1	HS2007	481014
12.1	HS2007	481019
12.1	HS2007	481022
12.1	HS2007	481029
12.1	HS2012	4801
12.1	HS2012	480210
12.1	HS2012	480220
12.1	HS2012	480254
12.1	HS2012	480255
12.1	HS2012	480256
12.1	HS2012	480257
12.1	HS2012	480258
12.1	HS2012	480261
12.1	HS2012	480262
12.1	HS2012	480269
12.1	HS2012	4809
12.1	HS2012	481013
12.1	HS2012	481014
12.1	HS2012	481019
12.1	HS2012	481022
12.1	HS2012	481029
12.1	HS2017	4801
12.1	HS2017	480210
12.1	HS2017	480220
12.1	HS2017	480254
12.1	HS2017	480255
12.1	HS2017	480256
12.1	HS2017	480257
12.1	HS2017	480258
12.1	HS2017	480261
12.1	HS2017	480262
12.1	HS2017	480269
12.1	HS2017	4809
12.1	HS2017	481013
12.1	HS2017	481014
12.1	HS2017	481019
12.1	HS2017	481022
12.1	HS2017	481029
12.1.1	HS2002	4801
12.1.1	HS2007	4801
12.1.1	HS2012	4801
12.1.1	HS2017	4801
12.1.2	HS2002	480261
12.1.2	HS2002	480262
12.1.2	HS2002	480269
12.1.2	HS2007	480261
12.1.2	HS2007	480262
12.1.2	HS2007	480269
12.1.2	HS2012	480261
12.1.2	HS2012	480262
12.1.2	HS2012	480269
12.1.2	HS2017	480261
12.1.2	HS2017	480262
12.1.2	HS2017	480269
12.1.3	HS2002	480210
12.1.3	HS2002	480220
12.1.3	HS2002	480254
12.1.3	HS2002	480255
12.1.3	HS2002	480256
12.1.3	HS2002	480257
12.1.3	HS2002	480258
12.1.3	HS2007	480210
12.1.3	HS2007	480220
12.1.3	HS2007	480254
12.1.3	HS2007	480255
12.1.3	HS2007	480256
12.1.3	HS2007	480257
12.1.3	HS2007	480258
12.1.3	HS2012	480210
12.1.3	HS2012	480220
12.1.3	HS2012	480254
12.1.3	HS2012	480255
12.1.3	HS2012	480256
12.1.3	HS2012	480257
12.1.3	HS2012	480258
12.1.3	HS2017	480210
12.1.3	HS2017	480220
12.1.3	HS2017	480254
12.1.3	HS2017	480255
12.1.3	HS2017	480256
12.1.3	HS2017	480257
12.1.3	HS2017	480258
12.1.4	HS2002	4809
12.1.4	HS2002	481013
12.1.4	HS2002	481014
12.1.4	HS2002	481019
12.1.4	HS2002	481022
12.1.4	HS2002	481029
12.1.4	HS2007	4809
12.1.4	HS2007	481013
12.1.4	HS2007	481014
12.1.4	HS2007	481019
12.1.4	HS2007	481022
12.1.4	HS2007	481029
12.1.4	HS2012	4809
12.1.4	HS2012	481013
12.1.4	HS2012	481014
12.1.4	HS2012	481019
12.1.4	HS2012	481022
12.1.4	HS2012	481029
12.1.4	HS2017	4809
12.1.4	HS2017	481013
12.1.4	HS2017	481014
12.1.4	HS2017	481019
12.1.4	HS2017	481022
12.1.4	HS2017	481029
12.2	HS2002	4803
12.2	HS2007	4803
12.2	HS2012	4803
12.2	HS2017	4803
12.3	HS2002	480411
12.3	HS2002	480419
12.3	HS2002	480421
12.3	HS2002	480429
12.3	HS2002	480431
12.3	HS2002	480439
12.3	HS2002	480442
12.3	HS2002	480449
12.3	HS2002	480451
12.3	HS2002	480452
12.3	HS2002	480459
12.3	HS2002	480511
12.3	HS2002	480512
12.3	HS2002	480519
12.3	HS2002	480524
12.3	HS2002	480525
12.3	HS2002	480530
12.3	HS2002	480591
12.3	HS2002	480592
12.3	HS2002	480593
12.3	HS2002	480610
12.3	HS2002	480620
12.3	HS2002	480640
12.3	HS2002	4808
12.3	HS2002	481031
12.3	HS2002	481032
12.3	HS2002	481039
12.3	HS2002	481092
12.3	HS2002	481099
12.3	HS2002	481151
12.3	HS2002	481159
12.3	HS2007	480411
12.3	HS2007	480419
12.3	HS2007	480421
12.3	HS2007	480429
12.3	HS2007	480431
12.3	HS2007	480439
12.3	HS2007	480442
12.3	HS2007	480449
12.3	HS2007	480451
12.3	HS2007	480452
12.3	HS2007	480459
12.3	HS2007	480511
12.3	HS2007	480512
12.3	HS2007	480519
12.3	HS2007	480524
12.3	HS2007	480525
12.3	HS2007	480530
12.3	HS2007	480591
12.3	HS2007	480592
12.3	HS2007	480593
12.3	HS2007	480610
12.3	HS2007	480620
12.3	HS2007	480640
12.3	HS2007	4808
12.3	HS2007	481031
12.3	HS2007	481032
12.3	HS2007	481039
12.3	HS2007	481092
12.3	HS2007	481099
12.3	HS2007	481151
12.3	HS2007	481159
12.3	HS2012	480411
12.3	HS2012	480419
12.3	HS2012	480421
12.3	HS2012	480429
12.3	HS2012	480431
12.3	HS2012	480439
12.3	HS2012	480442
12.3	HS2012	480449
12.3	HS2012	480451
12.3	HS2012	480452
12.3	HS2012	480459
12.3	HS2012	480511
12.3	HS2012	480512
12.3	HS2012	480519
12.3	HS2012	480524
12.3	HS2012	480525
12.3	HS2012	480530
12.3	HS2012	480591
12.3	HS2012	480592
12.3	HS2012	480593
12.3	HS2012	480610
12.3	HS2012	480620
12.3	HS2012	480640
12.3	HS2012	4808
12.3	HS2012	481031
12.3	HS2012	481032
12.3	HS2012	481039
12.3	HS2012	481092
12.3	HS2012	481099
12.3	HS2012	481151
12.3	HS2012	481159
12.3	HS2017	480411
12.3	HS2017	480419
12.3	HS2017	480421
12.3	HS2017	480429
12.3	HS2017	480431
12.3	HS2017	480439
12.3	HS2017	480442
12.3	HS2017	480449
12.3	HS2017	480451
12.3	HS2017	480452
12.3	HS2017	480459
12.3	HS2017	480511
12.3	HS2017	480512
12.3	HS2017	480519
12.3	HS2017	480524
12.3	HS2017	480525
12.3	HS2017	480530
12.3	HS2017	480591
12.3	HS2017	480592
12.3	HS2017	480593
12.3	HS2017	480610
12.3	HS2017	480620
12.3	HS2017	480640
12.3	HS2017	4808
12.3	HS2017	481031
12.3	HS2017	481032
12.3	HS2017	481039
12.3	HS2017	481092
12.3	HS2017	481099
12.3	HS2017	481151
12.3	HS2017	481159
12.3.1	HS2002	480411
12.3.1	HS2002	480419
12.3.1	HS2002	480511
12.3.1	HS2002	480512
12.3.1	HS2002	480519
12.3.1	HS2002	480524
12.3.1	HS2002	480525
12.3.1	HS2002	480591
12.3.1	HS2007	480411
12.3.1	HS2007	480419
12.3.1	HS2007	480511
12.3.1	HS2007	480512
12.3.1	HS2007	480519
12.3.1	HS2007	480524
12.3.1	HS2007	480525
12.3.1	HS2007	480591
12.3.1	HS2012	480411
12.3.1	HS2012	480419
12.3.1	HS2012	480511
12.3.1	HS2012	480512
12.3.1	HS2012	480519
12.3.1	HS2012	480524
12.3.1	HS2012	480525
12.3.1	HS2012	480591
12.3.2	HS2002	480442
12.3.2	HS2002	480449
12.3.2	HS2002	480451
12.3.2	HS2002	480452
12.3.2	HS2002	480459
12.3.2	HS2002	480592
12.3.2	HS2002	481032
12.3.2	HS2002	481039
12.3.2	HS2002	481092
12.3.2	HS2002	481151
12.3.2	HS2002	481159
12.3.2	HS2007	480442
12.3.2	HS2007	480449
12.3.2	HS2007	480451
12.3.2	HS2007	480452
12.3.2	HS2007	480459
12.3.2	HS2007	480592
12.3.2	HS2007	481032
12.3.2	HS2007	481039
12.3.2	HS2007	481092
12.3.2	HS2007	481151
12.3.2	HS2007	481159
12.3.2	HS2012	480442
12.3.2	HS2012	480449
12.3.2	HS2012	480451
12.3.2	HS2012	480452
12.3.2	HS2012	480459
12.3.2	HS2012	480592
12.3.2	HS2012	481032
12.3.2	HS2012	481039
12.3.2	HS2012	481092
12.3.2	HS2012	481151
12.3.2	HS2012	481159
12.3.2	HS2017	480442
12.3.2	HS2017	480449
12.3.2	HS2017	480451
12.3.2	HS2017	480452
12.3.2	HS2017	480459
12.3.2	HS2017	480592
12.3.2	HS2017	481032
12.3.2	HS2017	481039
12.3.2	HS2017	481092
12.3.2	HS2017	481151
12.3.2	HS2017	481159
12.3.3	HS2002	480421
12.3.3	HS2002	480429
12.3.3	HS2002	480431
12.3.3	HS2002	480439
12.3.3	HS2002	480530
12.3.3	HS2002	480610
12.3.3	HS2002	480620
12.3.3	HS2002	480640
12.3.3	HS2002	4808
12.3.3	HS2002	481031
12.3.3	HS2002	481099
12.3.3	HS2007	480421
12.3.3	HS2007	480429
12.3.3	HS2007	480431
12.3.3	HS2007	480439
12.3.3	HS2007	480530
12.3.3	HS2007	480610
12.3.3	HS2007	480620
12.3.3	HS2007	480640
12.3.3	HS2007	4808
12.3.3	HS2007	481031
12.3.3	HS2007	481099
12.3.3	HS2012	480421
12.3.3	HS2012	480429
12.3.3	HS2012	480431
12.3.3	HS2012	480439
12.3.3	HS2012	480530
12.3.3	HS2012	480610
12.3.3	HS2012	480620
12.3.3	HS2012	480640
12.3.3	HS2012	4808
12.3.3	HS2012	481031
12.3.3	HS2012	481099
12.3.3	HS2017	480421
12.3.3	HS2017	480429
12.3.3	HS2017	480431
12.3.3	HS2017	480439
12.3.3	HS2017	480530
12.3.3	HS2017	480610
12.3.3	HS2017	480620
12.3.3	HS2017	480640
12.3.3	HS2017	4808
12.3.3	HS2017	481031
12.3.3	HS2017	481099
12.3.4	HS2002	480593
12.3.4	HS2007	480593
12.3.4	HS2012	480593
12.3.4	HS2017	480593
12.4	HS2002	480240
12.4	HS2002	480441
12.4	HS2002	480540
12.4	HS2002	480550
12.4	HS2002	480630
12.4	HS2002	4812
12.4	HS2002	4813
12.4	HS2007	480240
12.4	HS2007	480441
12.4	HS2007	480540
12.4	HS2007	480550
12.4	HS2007	480630
12.4	HS2007	4812
12.4	HS2007	4813
12.4	HS2012	480240
12.4	HS2012	480441
12.4	HS2012	480540
12.4	HS2012	480550
12.4	HS2012	480630
12.4	HS2012	4812
12.4	HS2012	4813
12.4	HS2017	480240
12.4	HS2017	480441
12.4	HS2017	480540
12.4	HS2017	480550
12.4	HS2017	480630
12.4	HS2017	4812
12.4	HS2017	4813
13.1	HS2002	440910
13.1	HS2002	440920	Only some part of it
13.1	HS2007	440910
13.1	HS2007	440929
13.1	HS2012	440910
13.1	HS2012	440929
13.1	HS2017	440910
13.1	HS2017	440922
13.1	HS2017	440929
13.1.C	HS2002	440910
13.1.C	HS2007	440910
13.1.C	HS2012	440910
13.1.C	HS2017	440910
13.1.NC	HS2002	440920	Only some part of it
13.1.NC	HS2007	440929
13.1.NC	HS2012	440929
13.1.NC	HS2017	440922
13.1.NC	HS2017	440929
13.1.NC.T	HS2002	440920	Only some part of it
13.1.NC.T	HS2007	440929	Only some part of it
13.1.NC.T	HS2012	440929	Only some part of it
13.1.NC.T	HS2017	440922
13.2	HS2002	4415
13.2	HS2002	4416
13.2	HS2007	4415
13.2	HS2007	4416
13.2	HS2012	4415
13.2	HS2012	4416
13.2	HS2017	4415
13.2	HS2017	4416
13.3	HS2002	4414
13.3	HS2002	4419	Only some part of it
13.3	HS2002	4420
13.3	HS2007	4414
13.3	HS2007	4419	Only some part of it
13.3	HS2007	4420
13.3	HS2012	4414
13.3	HS2012	4419	Only some part of it
13.3	HS2012	4420
13.3	HS2017	4414
13.3	HS2017	441990
13.3	HS2017	4420
13.4	HS2002	441810
13.4	HS2002	441820
13.4	HS2002	441830
13.4	HS2002	441840
13.4	HS2002	441850
13.4	HS2002	441890	Only some part of it
13.4	HS2007	441810
13.4	HS2007	481820
13.4	HS2007	441840
13.4	HS2007	441850
13.4	HS2007	441860
13.4	HS2007	441871	Only some part of it
13.4	HS2007	441872	Only some part of it
13.4	HS2007	441879	Only some part of it
13.4	HS2007	441890	Only some part of it
13.4	HS2012	441810
13.4	HS2012	441820
13.4	HS2012	441840
13.4	HS2012	441850
13.4	HS2012	441860
13.4	HS2012	441871	Only some part of it
13.4	HS2012	441872	Only some part of it
13.4	HS2012	441879	Only some part of it
13.4	HS2012	441890	Only some part of it
13.4	HS2017	441810
13.4	HS2017	441820
13.4	HS2017	441840
13.4	HS2017	441850
13.4	HS2017	441860
13.4	HS2017	441874
13.4	HS2017	441875
13.4	HS2017	441879
13.4	HS2017	441899
13.5	HS2002	940161
13.5	HS2002	940169
13.5	HS2002	940190	Only some part of it
13.5	HS2002	940330
13.5	HS2002	940340
13.5	HS2002	940350
13.5	HS2002	940360
13.5	HS2002	940390	Only some part of it
13.5	HS2007	940161
13.5	HS2007	940169
13.5	HS2007	940190	Only some part of it
13.5	HS2007	940330
13.5	HS2007	940340
13.5	HS2007	940350
13.5	HS2007	940360
13.5	HS2007	940390	Only some part of it
13.5	HS2012	940161
13.5	HS2012	940169
13.5	HS2012	940190	Only some part of it
13.5	HS2012	940330
13.5	HS2012	940340
13.5	HS2012	940350
13.5	HS2012	940360
13.5	HS2012	940390	Only some part of it
13.5	HS2017	940161
13.5	HS2017	940169
13.5	HS2017	940190	Only some part of it
13.5	HS2017	940330
13.5	HS2017	940340
13.5	HS2017	940350
13.5	HS2017	940360
13.5	HS2017	940390	Only some part of it
13.6	HS2002	9406	Only some part of it
13.6	HS2007	9406	Only some part of it
13.6	HS2012	9406	Only some part of it
13.6	HS2017	940610
13.7	HS2002	4404
13.7	HS2002	4405
13.7	HS2002	4413
13.7	HS2002	4417
13.7	HS2002	442110
13.7	HS2002	442190	Only some part of it
13.7	HS2007	4404
13.7	HS2007	4405
13.7	HS2007	4413
13.7	HS2007	4417
13.7	HS2007	442110
13.7	HS2007	442190	Only some part of it
13.7	HS2012	4404
13.7	HS2012	4405
13.7	HS2012	4413
13.7	HS2012	4417
13.7	HS2012	442110
13.7	HS2012	442190	Only some part of it
13.7	HS2017	4404
13.7	HS2017	4405
13.7	HS2017	4413
13.7	HS2017	4417
13.7	HS2017	442110
13.7	HS2017	442199
14.1	HS2002	4807
14.1	HS2007	4807
14.1	HS2012	4807
14.1	HS2017	4807
14.2	HS2002	481110
14.2	HS2002	481141
14.2	HS2002	481149
14.2	HS2002	481160
14.2	HS2002	481190
14.2	HS2007	481110
14.2	HS2007	481141
14.2	HS2007	481149
14.2	HS2007	481160
14.2	HS2007	481190
14.2	HS2012	481110
14.2	HS2012	481141
14.2	HS2012	481149
14.2	HS2012	481160
14.2	HS2012	481190
14.2	HS2017	481110
14.2	HS2017	481141
14.2	HS2017	481149
14.2	HS2017	481160
14.2	HS2017	481190
14.3	HS2002	4818
14.3	HS2007	4818
14.3	HS2012	4818
14.3	HS2017	4818
14.4	HS2002	4819
14.4	HS2007	4819
14.4	HS2012	4819
14.4	HS2017	4819
14.5	HS2002	4814
14.5	HS2002	4816
14.5	HS2002	4817
14.5	HS2002	4820
14.5	HS2002	4821
14.5	HS2002	4822
14.5	HS2002	4823
14.5	HS2007	4814
14.5	HS2007	4816
14.5	HS2007	4817
14.5	HS2007	4820
14.5	HS2007	4821
14.5	HS2007	4822
14.5	HS2007	4823
14.5	HS2012	4814
14.5	HS2012	4816
14.5	HS2012	4817
14.5	HS2012	4820
14.5	HS2012	4821
14.5	HS2012	4822
14.5	HS2012	4823
14.5	HS2017	4814
14.5	HS2017	4816
14.5	HS2017	4817
14.5	HS2017	4820
14.5	HS2017	4821
14.5	HS2017	4822
14.5	HS2017	4823
14.5.1	HS2002	482390	Only some part of it
14.5.1	HS2007	482390	Only some part of it
14.5.1	HS2012	482390	Only some part of it
14.5.1	HS2017	482390	Only some part of it
14.5.2	HS2002	482370
14.5.2	HS2007	482370
14.5.2	HS2012	482370
14.5.2	HS2017	482370
14.5.3	HS2002	482320
14.5.3	HS2007	482320
14.5.3	HS2012	482320
14.5.3	HS2017	482320
12.6	HS2002	482110	Only some part of it
12.6	HS2002	482190	Only some part of it
12.6	HS2002	482210	Only some part of it
12.6	HS2002	482290	Only some part of it
12.6	HS2002	482312	Only some part of it
12.6	HS2002	482319	Only some part of it
12.6	HS2002	482320	Only some part of it
12.6	HS2002	482340	Only some part of it
12.6	HS2002	482360	Only some part of it
12.6	HS2002	482370	Only some part of it
12.6	HS2002	482390	Only some part of it
12.6	HS2002	480210	Only some part of it
12.6	HS2002	480220	Only some part of it
12.6	HS2002	480230	Only some part of it
12.6	HS2002	480240	Only some part of it
12.6	HS2002	480254	Only some part of it
12.6	HS2002	480255	Only some part of it
12.6	HS2002	480256	Only some part of it
12.6	HS2002	480257	Only some part of it
12.6	HS2002	480258	Only some part of it
12.6	HS2002	480261	Only some part of it
12.6	HS2002	480262	Only some part of it
12.6	HS2002	480269	Only some part of it
12.6	HS2002	481013	Only some part of it
12.6	HS2002	481014	Only some part of it
12.6	HS2002	481019	Only some part of it
12.6	HS2002	481022	Only some part of it
12.6	HS2002	481029	Only some part of it
12.6	HS2002	481031	Only some part of it
12.6	HS2002	481032	Only some part of it
12.6	HS2002	481039	Only some part of it
12.6	HS2002	481092	Only some part of it
12.6	HS2002	481099	Only some part of it
12.6	HS2007	481410
12.6	HS2007	481420
12.6	HS2007	481490
12.6	HS2007	481710
12.6	HS2007	481720
12.6	HS2007	481730
12.6	HS2007	482010
12.6	HS2007	482020
12.6	HS2007	482030
12.6	HS2007	482040
12.6	HS2007	482050
12.6	HS2007	482090
12.6	HS2007	482110
12.6	HS2007	482190
12.6	HS2007	482210
12.6	HS2007	482290
12.6	HS2007	482320
12.6	HS2007	482340
12.6	HS2007	482361
12.6	HS2007	482369
12.6	HS2007	482370
12.6	HS2007	482390
12.6	HS2012	481420
12.6	HS2012	481490
12.6	HS2012	481710
12.6	HS2012	481720
12.6	HS2012	481730
12.6	HS2012	482020
12.6	HS2012	482030
12.6	HS2012	482040
12.6	HS2012	482050
12.6	HS2012	482090
12.6	HS2012	482110
12.6	HS2012	482190
12.6	HS2012	482210
12.6	HS2012	482290
12.6	HS2012	482320
12.6	HS2012	482340
12.6	HS2012	482361
12.6	HS2012	482369
12.6	HS2012	482370
12.6	HS2012	482390
12.6.1	HS2002	480210	Only some part of it
12.6.1	HS2002	480220	Only some part of it
12.6.1	HS2002	480230	Only some part of it
12.6.1	HS2002	480240	Only some part of it
12.6.1	HS2002	480254	Only some part of it
12.6.1	HS2002	480255	Only some part of it
12.6.1	HS2002	480256	Only some part of it
12.6.1	HS2002	480257	Only some part of it
12.6.1	HS2002	480258	Only some part of it
12.6.1	HS2002	480261	Only some part of it
12.6.1	HS2002	480262	Only some part of it
12.6.1	HS2002	480269	Only some part of it
12.6.1	HS2002	481013	Only some part of it
12.6.1	HS2002	481014	Only some part of it
12.6.1	HS2002	481019	Only some part of it
12.6.1	HS2002	481022	Only some part of it
12.6.1	HS2002	481029	Only some part of it
12.6.1	HS2002	481031	Only some part of it
12.6.1	HS2002	481032	Only some part of it
12.6.1	HS2002	481039	Only some part of it
12.6.1	HS2002	481092	Only some part of it
12.6.1	HS2002	481099	Only some part of it
12.6.1	HS2002	482390	Only some part of it
12.6.1	HS2007	482390	Only some part of it
12.6.1	HS2012	482390	Only some part of it
12.6.2	HS2002	482370
12.6.2	HS2007	482370
12.6.2	HS2012	482370
12.6.3	HS2002	482320
12.6.3	HS2007	482320
12.6.3	HS2012	482320

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

Roundwood equivalent

m3 per MT

Roundwood equivalent

m3 per MT

m3 per m2

Roundwood

equivalent

Europe

NA**

EECCA**

Europe

NA**

EECCA**

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

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

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

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)

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

2.32

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)

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

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

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)

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

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

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

2.25

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

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

2.16

1.92

nodata

1.69

2.12

dried, sanded, peeled

0.0165***

8.1.NC

Non-Coniferous

1000 m3

1.54

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

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

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

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

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

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

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

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

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

OTHER PULP

1000 MT

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

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

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

RECOVERED PAPER

1000 MT

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

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

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

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

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

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

*** = 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

Flatfile

geo	stk_flow	time	prod_wd	treespec	unit	obs_value	obs_flag
AT	PRD	2021	RW_OB	TOTAL	THS_M3	20630.6968	4
AT	PRD	2021	RW_FW_OB	TOTAL	THS_M3	5487.44896	4
AT	PRD	2021	RW_FW_OB	CONIF	THS_M3	3352.0424	4
AT	PRD	2021	RW_FW_OB	NCONIF	THS_M3	2135.40656	4
AT	PRD	2021	RW_IN_OB	TOTAL	THS_M3	15143.24784	4
AT	PRD	2021	RW_IN_OB	CONIF	THS_M3	14190.98352	4
AT	PRD	2021	RW_IN_OB	NCONIF	THS_M3	952.26432	4
AT	PRD	2021	RW_IN_OB	NC_TRO	THS_M3	0	4
AT	PRD	2021	RW_IN_LG_OB	TOTAL	THS_M3	11669.87696	4
AT	PRD	2021	RW_IN_LG_OB	CONIF	THS_M3	11355.83456	4
AT	PRD	2021	RW_IN_LG_OB	NCONIF	THS_M3	314.0424	4
AT	PRD	2021	RW_IN_PW_OB	TOTAL	THS_M3	3473.37088	4
AT	PRD	2021	RW_IN_PW_OB	CONIF	THS_M3	2835.14896	4
AT	PRD	2021	RW_IN_PW_OB	NCONIF	THS_M3	638.22192	4
AT	PRD	2021	RW_IN_O_OB	TOTAL	THS_M3	0	4
AT	PRD	2021	RW_IN_O_OB	CONIF	THS_M3	0	4
AT	PRD	2021	RW_IN_O_OB	NCONIF	THS_M3	0	4
AT	PRD	2022	RW_OB	TOTAL	THS_M3	21680.8872	4
AT	PRD	2022	RW_FW_OB	TOTAL	THS_M3	6074.55072	4
AT	PRD	2022	RW_FW_OB	CONIF	THS_M3	3637.46208	4
AT	PRD	2022	RW_FW_OB	NCONIF	THS_M3	2437.08864	4
AT	PRD	2022	RW_IN_OB	TOTAL	THS_M3	15606.33648	4
AT	PRD	2022	RW_IN_OB	CONIF	THS_M3	14512.29808	4
AT	PRD	2022	RW_IN_OB	NCONIF	THS_M3	1094.0384	4
AT	PRD	2022	RW_IN_OB	NC_TRO	THS_M3	0	4
AT	PRD	2022	RW_IN_LG_OB	TOTAL	THS_M3	11996.32112	4
AT	PRD	2022	RW_IN_LG_OB	CONIF	THS_M3	11627.37632	4
AT	PRD	2022	RW_IN_LG_OB	NCONIF	THS_M3	368.9448	4
AT	PRD	2022	RW_IN_PW_OB	TOTAL	THS_M3	3610.01536	4
AT	PRD	2022	RW_IN_PW_OB	CONIF	THS_M3	2884.92176	4
AT	PRD	2022	RW_IN_PW_OB	NCONIF	THS_M3	725.0936	4
AT	PRD	2022	RW_IN_O_OB	TOTAL	THS_M3	0	4
AT	PRD	2022	RW_IN_O_OB	CONIF	THS_M3	0	4
AT	PRD	2022	RW_IN_O_OB	NCONIF	THS_M3	0	4
AT	PRD	2021	RW	TOTAL	THS_M3	18420.265	4
AT	PRD	2021	RW_FW	TOTAL	THS_M3	4899.508	4
AT	PRD	2021	RW_FW	CONIF	THS_M3	2992.895	4
AT	PRD	2021	RW_FW	NCONIF	THS_M3	1906.613	4
AT	PRD	2021	RW_IN	TOTAL	THS_M3	13520.757	4
AT	PRD	2021	RW_IN	CONIF	THS_M3	12670.521	4
AT	PRD	2021	RW_IN	NCONIF	THS_M3	850.236	4
AT	PRD	2021	RW_IN	NC_TRO	THS_M3	0	4
AT	PRD	2021	RW_IN_LG	TOTAL	THS_M3	10419.533	4
AT	PRD	2021	RW_IN_LG	CONIF	THS_M3	10139.138	4
AT	PRD	2021	RW_IN_LG	NCONIF	THS_M3	280.395	4
AT	PRD	2021	RW_IN_PW	TOTAL	THS_M3	3101.224	4
AT	PRD	2021	RW_IN_PW	CONIF	THS_M3	2531.383	4
AT	PRD	2021	RW_IN_PW	NCONIF	THS_M3	569.841	4
AT	PRD	2021	RW_IN_O	TOTAL	THS_M3	0	4
AT	PRD	2021	RW_IN_O	CONIF	THS_M3	0	4
AT	PRD	2021	RW_IN_O	NCONIF	THS_M3	0	4
AT	PRD	2021	CHA	TOTAL	THS_T	1.463
AT	PRD	2021	CHP_RES	TOTAL	THS_M3	7466.5
AT	PRD	2021	CHP	TOTAL	THS_M3	3903.9
AT	PRD	2021	RES	TOTAL	THS_M3	3562.6
AT	PRD	2021	RES_SWD	TOTAL	THS_M3	3120.6
AT	PRD	2021	RCW	TOTAL	THS_T	1131.22
AT	PRD	2021	PEL_AGG	TOTAL	THS_T	1675.3
AT	PRD	2021	PEL	TOTAL	THS_T	1607
AT	PRD	2021	AGG	TOTAL	THS_T	68.3
AT	PRD	2021	SN	TOTAL	THS_M3	10764
AT	PRD	2021	SN	CONIF	THS_M3	10582
AT	PRD	2021	SN	NCONIF	THS_M3	182
AT	PRD	2021	SN	NC_TRO	THS_M3	0	9
AT	PRD	2021	PN_VN	TOTAL	THS_M3	7.5	5
AT	PRD	2021	PN_VN	CONIF	THS_M3
AT	PRD	2021	PN_VN	NCONIF	THS_M3
AT	PRD	2021	PN_VN	NC_TRO	THS_M3	0	9
AT	PRD	2021	PN	TOTAL	THS_M3	3423.8
AT	PRD	2021	PN_PY	TOTAL	THS_M3	183.8
AT	PRD	2021	PN_PY	CONIF	THS_M3	179.5
AT	PRD	2021	PN_PY	NCONIF	THS_M3	4.3
AT	PRD	2021	PN_PY	NC_TRO	THS_M3	0	9
AT	PRD	2021	PN_PY_LVL	TOTAL	THS_M3
AT	PRD	2021	PN_PY_LVL	CONIF	THS_M3
AT	PRD	2021	PN_PY_LVL	NCONIF	THS_M3
AT	PRD	2021	PN_PY_LVL	NC_TRO	THS_M3
AT	PRD	2021	PN_PB	TOTAL	THS_M3	2550
AT	PRD	2021	PN_PB_OSB	TOTAL	THS_M3	0
AT	PRD	2021	PN_FB	TOTAL	THS_M3	690
AT	PRD	2021	PN_FB_HB	TOTAL	THS_M3	75
AT	PRD	2021	PN_FB_MDF	TOTAL	THS_M3	615
AT	PRD	2021	PN_FB_O	TOTAL	THS_M3	0
AT	PRD	2021	PL	TOTAL	THS_T	2004.481
AT	PRD	2021	PL_MC_SCH	TOTAL	THS_T	314.049
AT	PRD	2021	PL_CH	TOTAL	THS_T	1305.542
AT	PRD	2021	PL_CH_SA	TOTAL	THS_T
AT	PRD	2021	PL_CH_SAB	TOTAL	THS_T
AT	PRD	2021	PL_CH_SI	TOTAL	THS_T
AT	PRD	2021	PL_DS	TOTAL	THS_T	384.89
AT	PRD	2021	PLO	TOTAL	THS_T	2197.166
AT	PRD	2021	PLO_NW	TOTAL	THS_T	0
AT	PRD	2021	PLO_RC	TOTAL	THS_T	2197.166
AT	PRD	2021	RCP	TOTAL	THS_T	1694	3
AT	PRD	2021	PP	TOTAL	THS_T	5065.256
AT	PRD	2021	PP_GR	TOTAL	THS_T	2247.582
AT	PRD	2021	PP_GR_NP	TOTAL	THS_T
AT	PRD	2021	PP_GR_MC	TOTAL	THS_T
AT	PRD	2021	PP_GR_NW	TOTAL	THS_T
AT	PRD	2021	PP_GR_CO	TOTAL	THS_T	1270
AT	PRD	2021	PP_HS	TOTAL	THS_T	125
AT	PRD	2021	PP_PK	TOTAL	THS_T	2492.708
AT	PRD	2021	PP_PK_CS	TOTAL	THS_T	1700
AT	PRD	2021	PP_PK_CB	TOTAL	THS_T
AT	PRD	2021	PP_PK_WR	TOTAL	THS_T
AT	PRD	2021	PP_PK_O	TOTAL	THS_T
AT	PRD	2021	PP_O	TOTAL	THS_T	199.966
AT	PRD	2021	GLT_CLT	TOTAL	THS_M3	2578.0363504167
AT	PRD	2021	GLT	TOTAL	THS_M3	2063.1351733333
AT	PRD	2021	CLT	TOTAL	THS_M3	514.9011770833
AT	PRD	2021	I_BEAMS	TOTAL	THS_T
AT	PRD	2022	RW	TOTAL	THS_M3	19357.935	4
AT	PRD	2022	RW_FW	TOTAL	THS_M3	5423.706	4
AT	PRD	2022	RW_FW	CONIF	THS_M3	3247.734	4
AT	PRD	2022	RW_FW	NCONIF	THS_M3	2175.972	4
AT	PRD	2022	RW_IN	TOTAL	THS_M3	13934.229	4
AT	PRD	2022	RW_IN	CONIF	THS_M3	12957.409	4
AT	PRD	2022	RW_IN	NCONIF	THS_M3	976.82	4
AT	PRD	2022	RW_IN	NC_TRO	THS_M3	0	4
AT	PRD	2022	RW_IN_LG	TOTAL	THS_M3	10711.001	4
AT	PRD	2022	RW_IN_LG	CONIF	THS_M3	10381.586	4
AT	PRD	2022	RW_IN_LG	NCONIF	THS_M3	329.415	4
AT	PRD	2022	RW_IN_PW	TOTAL	THS_M3	3223.228	4
AT	PRD	2022	RW_IN_PW	CONIF	THS_M3	2575.823	4
AT	PRD	2022	RW_IN_PW	NCONIF	THS_M3	647.405	4
AT	PRD	2022	RW_IN_O	TOTAL	THS_M3	0	4
AT	PRD	2022	RW_IN_O	CONIF	THS_M3	0	4
AT	PRD	2022	RW_IN_O	NCONIF	THS_M3	0	4
AT	PRD	2022	CHA	TOTAL	THS_T	1.096
AT	PRD	2022	CHP_RES	TOTAL	THS_M3	7823.6
AT	PRD	2022	CHP	TOTAL	THS_M3	3994.2
AT	PRD	2022	RES	TOTAL	THS_M3	3829.4
AT	PRD	2022	RES_SWD	TOTAL	THS_M3	3308.4
AT	PRD	2022	RCW	TOTAL	THS_T
AT	PRD	2022	PEL_AGG	TOTAL	THS_T	1743
AT	PRD	2022	PEL	TOTAL	THS_T	1691
AT	PRD	2022	AGG	TOTAL	THS_T	52
AT	PRD	2022	SN	TOTAL	THS_M3	10342
AT	PRD	2022	SN	CONIF	THS_M3	10104
AT	PRD	2022	SN	NCONIF	THS_M3	238
AT	PRD	2022	SN	NC_TRO	THS_M3	0	9
AT	PRD	2022	PN_VN	TOTAL	THS_M3	7.5	5
AT	PRD	2022	PN_VN	CONIF	THS_M3
AT	PRD	2022	PN_VN	NCONIF	THS_M3
AT	PRD	2022	PN_VN	NC_TRO	THS_M3	0	9
AT	PRD	2022	PN	TOTAL	THS_M3	2886.388
AT	PRD	2022	PN_PY	TOTAL	THS_M3	131.388
AT	PRD	2022	PN_PY	CONIF	THS_M3	127.582
AT	PRD	2022	PN_PY	NCONIF	THS_M3	3.806
AT	PRD	2022	PN_PY	NC_TRO	THS_M3	0	9
AT	PRD	2022	PN_PY_LVL	TOTAL	THS_M3
AT	PRD	2022	PN_PY_LVL	CONIF	THS_M3
AT	PRD	2022	PN_PY_LVL	NCONIF	THS_M3
AT	PRD	2022	PN_PY_LVL	NC_TRO	THS_M3
AT	PRD	2022	PN_PB	TOTAL	THS_M3	2280
AT	PRD	2022	PN_PB_OSB	TOTAL	THS_M3	0
AT	PRD	2022	PN_FB	TOTAL	THS_M3	470
AT	PRD	2022	PN_FB_HB	TOTAL	THS_M3	54
AT	PRD	2022	PN_FB_MDF	TOTAL	THS_M3	416
AT	PRD	2022	PN_FB_O	TOTAL	THS_M3	0
AT	PRD	2022	PL	TOTAL	THS_T	1977.16
AT	PRD	2022	PL_MC_SCH	TOTAL	THS_T	254.362
AT	PRD	2022	PL_CH	TOTAL	THS_T	1303.67
AT	PRD	2022	PL_CH_SA	TOTAL	THS_T
AT	PRD	2022	PL_CH_SAB	TOTAL	THS_T
AT	PRD	2022	PL_CH_SI	TOTAL	THS_T
AT	PRD	2022	PL_DS	TOTAL	THS_T	419.128
AT	PRD	2022	PLO	TOTAL	THS_T	2007.557
AT	PRD	2022	PLO_NW	TOTAL	THS_T	0
AT	PRD	2022	PLO_RC	TOTAL	THS_T	2007.557
AT	PRD	2022	RCP	TOTAL	THS_T
AT	PRD	2022	PP	TOTAL	THS_T	4633.352
AT	PRD	2022	PP_GR	TOTAL	THS_T	1869.263
AT	PRD	2022	PP_GR_NP	TOTAL	THS_T
AT	PRD	2022	PP_GR_MC	TOTAL	THS_T
AT	PRD	2022	PP_GR_NW	TOTAL	THS_T
AT	PRD	2022	PP_GR_CO	TOTAL	THS_T	1100
AT	PRD	2022	PP_HS	TOTAL	THS_T	125
AT	PRD	2022	PP_PK	TOTAL	THS_T	2474.466
AT	PRD	2022	PP_PK_CS	TOTAL	THS_T	1600
AT	PRD	2022	PP_PK_CB	TOTAL	THS_T
AT	PRD	2022	PP_PK_WR	TOTAL	THS_T
AT	PRD	2022	PP_PK_O	TOTAL	THS_T
AT	PRD	2022	PP_O	TOTAL	THS_T	164.623
AT	PRD	2022	GLT_CLT	TOTAL	THS_M3	2457.227885
AT	PRD	2022	GLT	TOTAL	THS_M3	1991.4507266667
AT	PRD	2022	CLT	TOTAL	THS_M3	465.7771583333
AT	PRD	2022	I_BEAMS	TOTAL	THS_T
AT	IMP	2021	RW	TOTAL	THS_M3	11072.732
AT	IMP	2021	RW_FW	TOTAL	THS_M3	169.696
AT	IMP	2021	RW_FW	CONIF	THS_M3	90.7685555556
AT	IMP	2021	RW_FW	NCONIF	THS_M3	78.9274444444
AT	IMP	2021	RW_IN	TOTAL	THS_M3	10903.036
AT	IMP	2021	RW_IN	CONIF	THS_M3	10017.644
AT	IMP	2021	RW_IN	NCONIF	THS_M3	885.392
AT	IMP	2021	RW_IN	NC_TRO	THS_M3	0.054
AT	IMP	2021	CHA	TOTAL	THS_T	15.8724
AT	IMP	2021	CHP_RES	TOTAL	THS_M3	1907.0669825606
AT	IMP	2021	CHP	TOTAL	THS_M3	818.4287989928
AT	IMP	2021	RES	TOTAL	THS_M3	1088.6381835678
AT	IMP	2021	RES_SWD	TOTAL	THS_M3
AT	IMP	2021	RCW	TOTAL	THS_T	333.201
AT	IMP	2021	PEL_AGG	TOTAL	THS_T	509.1592
AT	IMP	2021	PEL	TOTAL	THS_T	412.744
AT	IMP	2021	AGG	TOTAL	THS_T	96.4152
AT	IMP	2021	SN	TOTAL	THS_M3	2088.125229
AT	IMP	2021	SN	CONIF	THS_M3	1911.235
AT	IMP	2021	SN	NCONIF	THS_M3	176.890229
AT	IMP	2021	SN	NC_TRO	THS_M3	5.209
AT	IMP	2021	PN_VN	TOTAL	THS_M3	69.64
AT	IMP	2021	PN_VN	CONIF	THS_M3	20.887
AT	IMP	2021	PN_VN	NCONIF	THS_M3	48.753
AT	IMP	2021	PN_VN	NC_TRO	THS_M3	1.13
AT	IMP	2021	PN	TOTAL	THS_M3	1190.778
AT	IMP	2021	PN_PY	TOTAL	THS_M3	267.176
AT	IMP	2021	PN_PY	CONIF	THS_M3
AT	IMP	2021	PN_PY	NCONIF	THS_M3
AT	IMP	2021	PN_PY	NC_TRO	THS_M3	15.611
AT	IMP	2021	PN_PY_LVL	TOTAL	THS_M3
AT	IMP	2021	PN_PY_LVL	CONIF	THS_M3
AT	IMP	2021	PN_PY_LVL	NCONIF	THS_M3
AT	IMP	2021	PN_PY_LVL	NC_TRO	THS_M3
AT	IMP	2021	PN_PB	TOTAL	THS_M3	553.182
AT	IMP	2021	PN_PB_OSB	TOTAL	THS_M3	192.199
AT	IMP	2021	PN_FB	TOTAL	THS_M3	370.42
AT	IMP	2021	PN_FB_HB	TOTAL	THS_M3	17.049
AT	IMP	2021	PN_FB_MDF	TOTAL	THS_M3	191.844
AT	IMP	2021	PN_FB_O	TOTAL	THS_M3	161.527
AT	IMP	2021	PL	TOTAL	THS_T	578.391401
AT	IMP	2021	PL_MC_SCH	TOTAL	THS_T	10.176325
AT	IMP	2021	PL_CH	TOTAL	THS_T	487.515753
AT	IMP	2021	PL_CH_SA	TOTAL	THS_T	463.299666
AT	IMP	2021	PL_CH_SAB	TOTAL	THS_T	461.880405
AT	IMP	2021	PL_CH_SI	TOTAL	THS_T	24.216087
AT	IMP	2021	PL_DS	TOTAL	THS_T	80.699323
AT	IMP	2021	PLO	TOTAL	THS_T	36.111609
AT	IMP	2021	PLO_NW	TOTAL	THS_T	26.396422
AT	IMP	2021	PLO_RC	TOTAL	THS_T	9.715187
AT	IMP	2021	RCP	TOTAL	THS_T	1675.5731
AT	IMP	2021	PP	TOTAL	THS_T	1296.0147
AT	IMP	2021	PP_GR	TOTAL	THS_T	415.4706
AT	IMP	2021	PP_GR_NP	TOTAL	THS_T	77.2091
AT	IMP	2021	PP_GR_MC	TOTAL	THS_T	55.2754
AT	IMP	2021	PP_GR_NW	TOTAL	THS_T	97.8884
AT	IMP	2021	PP_GR_CO	TOTAL	THS_T	185.0977
AT	IMP	2021	PP_HS	TOTAL	THS_T	9.3556
AT	IMP	2021	PP_PK	TOTAL	THS_T	857.9451
AT	IMP	2021	PP_PK_CS	TOTAL	THS_T	446.6904
AT	IMP	2021	PP_PK_CB	TOTAL	THS_T	209.6801
AT	IMP	2021	PP_PK_WR	TOTAL	THS_T	163.0399
AT	IMP	2021	PP_PK_O	TOTAL	THS_T	38.5347
AT	IMP	2021	PP_O	TOTAL	THS_T	13.2434
AT	IMP	2021	GLT_CLT	TOTAL	THS_M3
AT	IMP	2021	GLT	TOTAL	THS_M3
AT	IMP	2021	CLT	TOTAL	THS_M3
AT	IMP	2021	I_BEAMS	TOTAL	THS_T
AT	IMP	2021	RW	TOTAL	THS_NAC	829053.407
AT	IMP	2021	RW_FW	TOTAL	THS_NAC	15776.141
AT	IMP	2021	RW_FW	CONIF	THS_NAC	6760.457
AT	IMP	2021	RW_FW	NCONIF	THS_NAC	9015.684
AT	IMP	2021	RW_IN	TOTAL	THS_NAC	813277.266
AT	IMP	2021	RW_IN	CONIF	THS_NAC	736655.091
AT	IMP	2021	RW_IN	NCONIF	THS_NAC	76622.175
AT	IMP	2021	RW_IN	NC_TRO	THS_NAC	98.892
AT	IMP	2021	CHA	TOTAL	THS_NAC	10138.882
AT	IMP	2021	CHP_RES	TOTAL	THS_NAC	75722.516270836
AT	IMP	2021	CHP	TOTAL	THS_NAC	40624.556
AT	IMP	2021	RES	TOTAL	THS_NAC	35097.960270836
AT	IMP	2021	RES_SWD	TOTAL	THS_NAC
AT	IMP	2021	RCW	TOTAL	THS_NAC	10250.339729164
AT	IMP	2021	PEL_AGG	TOTAL	THS_NAC	80413.631
AT	IMP	2021	PEL	TOTAL	THS_NAC	61103.552
AT	IMP	2021	AGG	TOTAL	THS_NAC	19310.079
AT	IMP	2021	SN	TOTAL	THS_NAC	697948.403
AT	IMP	2021	SN	CONIF	THS_NAC	567134.475
AT	IMP	2021	SN	NCONIF	THS_NAC	130813.928
AT	IMP	2021	SN	NC_TRO	THS_NAC	5516.127
AT	IMP	2021	PN_VN	TOTAL	THS_NAC	143253.64
AT	IMP	2021	PN_VN	CONIF	THS_NAC	13262.585
AT	IMP	2021	PN_VN	NCONIF	THS_NAC	129991.055
AT	IMP	2021	PN_VN	NC_TRO	THS_NAC	2121.346
AT	IMP	2021	PN	TOTAL	THS_NAC	509732.742
AT	IMP	2021	PN_PY	TOTAL	THS_NAC	202041.843
AT	IMP	2021	PN_PY	CONIF	THS_NAC
AT	IMP	2021	PN_PY	NCONIF	THS_NAC
AT	IMP	2021	PN_PY	NC_TRO	THS_NAC	15344.632
AT	IMP	2021	PN_PY_LVL	TOTAL	THS_NAC
AT	IMP	2021	PN_PY_LVL	CONIF	THS_NAC
AT	IMP	2021	PN_PY_LVL	NCONIF	THS_NAC
AT	IMP	2021	PN_PY_LVL	NC_TRO	THS_NAC
AT	IMP	2021	PN_PB	TOTAL	THS_NAC	182262.003
AT	IMP	2021	PN_PB_OSB	TOTAL	THS_NAC	74866.011
AT	IMP	2021	PN_FB	TOTAL	THS_NAC	125428.896
AT	IMP	2021	PN_FB_HB	TOTAL	THS_NAC	14047.224
AT	IMP	2021	PN_FB_MDF	TOTAL	THS_NAC	85511.393
AT	IMP	2021	PN_FB_O	TOTAL	THS_NAC	25870.279
AT	IMP	2021	PL	TOTAL	THS_NAC	388955.369
AT	IMP	2021	PL_MC_SCH	TOTAL	THS_NAC	4644.245
AT	IMP	2021	PL_CH	TOTAL	THS_NAC	322117.425
AT	IMP	2021	PL_CH_SA	TOTAL	THS_NAC	303271.57
AT	IMP	2021	PL_CH_SAB	TOTAL	THS_NAC	302185.673
AT	IMP	2021	PL_CH_SI	TOTAL	THS_NAC	18845.855
AT	IMP	2021	PL_DS	TOTAL	THS_NAC	62193.699
AT	IMP	2021	PLO	TOTAL	THS_NAC	30268.35
AT	IMP	2021	PLO_NW	TOTAL	THS_NAC	26604.304
AT	IMP	2021	PLO_RC	TOTAL	THS_NAC	3664.046
AT	IMP	2021	RCP	TOTAL	THS_NAC	326060.51
AT	IMP	2021	PP	TOTAL	THS_NAC	1039400.877
AT	IMP	2021	PP_GR	TOTAL	THS_NAC	302424.625
AT	IMP	2021	PP_GR_NP	TOTAL	THS_NAC	33808.58
AT	IMP	2021	PP_GR_MC	TOTAL	THS_NAC	30205.271
AT	IMP	2021	PP_GR_NW	TOTAL	THS_NAC	96878.247
AT	IMP	2021	PP_GR_CO	TOTAL	THS_NAC	141532.527
AT	IMP	2021	PP_HS	TOTAL	THS_NAC	13709.523
AT	IMP	2021	PP_PK	TOTAL	THS_NAC	695474.188
AT	IMP	2021	PP_PK_CS	TOTAL	THS_NAC	254507.325
AT	IMP	2021	PP_PK_CB	TOTAL	THS_NAC	251729.552
AT	IMP	2021	PP_PK_WR	TOTAL	THS_NAC	168493.331
AT	IMP	2021	PP_PK_O	TOTAL	THS_NAC	20743.98
AT	IMP	2021	PP_O	TOTAL	THS_NAC	27792.541
AT	IMP	2021	GLT_CLT	TOTAL	THS_NAC
AT	IMP	2021	GLT	TOTAL	THS_NAC
AT	IMP	2021	CLT	TOTAL	THS_NAC
AT	IMP	2021	I_BEAMS	TOTAL	THS_NAC
AT	IMP	2022	RW	TOTAL	THS_M3	8700.75967	7
AT	IMP	2022	RW_FW	TOTAL	THS_M3	181.850667	7
AT	IMP	2022	RW_FW	CONIF	THS_M3	95.641	7
AT	IMP	2022	RW_FW	NCONIF	THS_M3	86.208	7
AT	IMP	2022	RW_IN	TOTAL	THS_M3	8518.909	7
AT	IMP	2022	RW_IN	CONIF	THS_M3	7975.906	7
AT	IMP	2022	RW_IN	NCONIF	THS_M3	543.003	7
AT	IMP	2022	RW_IN	NC_TRO	THS_M3	0.081	7
AT	IMP	2022	CHA	TOTAL	THS_T	13.446	7
AT	IMP	2022	CHP_RES	TOTAL	THS_M3	2271.8951316482	7
AT	IMP	2022	CHP	TOTAL	THS_M3	907.1151316482	7
AT	IMP	2022	RES	TOTAL	THS_M3	1364.78	7
AT	IMP	2022	RES_SWD	TOTAL	THS_M3	192.41	7
AT	IMP	2022	RCW	TOTAL	THS_T
AT	IMP	2022	PEL_AGG	TOTAL	THS_T	432.93	7
AT	IMP	2022	PEL	TOTAL	THS_T	345.648	7
AT	IMP	2022	AGG	TOTAL	THS_T	87.282	7
AT	IMP	2022	SN	TOTAL	THS_M3	2552.218	7
AT	IMP	2022	SN	CONIF	THS_M3	1785.572	7
AT	IMP	2022	SN	NCONIF	THS_M3	214.696	7
AT	IMP	2022	SN	NC_TRO	THS_M3	3.948	7
AT	IMP	2022	PN_VN	TOTAL	THS_M3	75.673	7
AT	IMP	2022	PN_VN	CONIF	THS_M3	23.349	7
AT	IMP	2022	PN_VN	NCONIF	THS_M3	52.324	7
AT	IMP	2022	PN_VN	NC_TRO	THS_M3	1.26	7
AT	IMP	2022	PN	TOTAL	THS_M3	1028.253	7
AT	IMP	2022	PN_PY	TOTAL	THS_M3	190.121	7
AT	IMP	2022	PN_PY	CONIF	THS_M3	75.374	7
AT	IMP	2022	PN_PY	NCONIF	THS_M3	110.082	7
AT	IMP	2022	PN_PY	NC_TRO	THS_M3	25.575	7
AT	IMP	2022	PN_PY_LVL	TOTAL	THS_M3	4.665	7
AT	IMP	2022	PN_PY_LVL	CONIF	THS_M3	1.15	7
AT	IMP	2022	PN_PY_LVL	NCONIF	THS_M3	3.515	7
AT	IMP	2022	PN_PY_LVL	NC_TRO	THS_M3	2.436	7
AT	IMP	2022	PN_PB	TOTAL	THS_M3	526.783	7
AT	IMP	2022	PN_PB_OSB	TOTAL	THS_M3	211.923	7
AT	IMP	2022	PN_FB	TOTAL	THS_M3	311.349	7
AT	IMP	2022	PN_FB_HB	TOTAL	THS_M3	18.568	7
AT	IMP	2022	PN_FB_MDF	TOTAL	THS_M3	159.373	7
AT	IMP	2022	PN_FB_O	TOTAL	THS_M3	133.408	7
AT	IMP	2022	PL	TOTAL	THS_T	559.363	7
AT	IMP	2022	PL_MC_SCH	TOTAL	THS_T	7.288	7
AT	IMP	2022	PL_CH	TOTAL	THS_T	465.158	7
AT	IMP	2022	PL_CH_SA	TOTAL	THS_T	444.085	7
AT	IMP	2022	PL_CH_SAB	TOTAL	THS_T	443.731	7
AT	IMP	2022	PL_CH_SI	TOTAL	THS_T	21.073	7
AT	IMP	2022	PL_DS	TOTAL	THS_T	86.917	7
AT	IMP	2022	PLO	TOTAL	THS_T	32.986776	7
AT	IMP	2022	PLO_NW	TOTAL	THS_T	23.297606	7
AT	IMP	2022	PLO_RC	TOTAL	THS_T	9.68917	7
AT	IMP	2022	RCP	TOTAL	THS_T	1451	7
AT	IMP	2022	PP	TOTAL	THS_T	1235.669	7
AT	IMP	2022	PP_GR	TOTAL	THS_T	388.537	7
AT	IMP	2022	PP_GR_NP	TOTAL	THS_T	81.789	7
AT	IMP	2022	PP_GR_MC	TOTAL	THS_T	54.763	7
AT	IMP	2022	PP_GR_NW	TOTAL	THS_T	103.186	7
AT	IMP	2022	PP_GR_CO	TOTAL	THS_T	148.798	7
AT	IMP	2022	PP_HS	TOTAL	THS_T	11.321	7
AT	IMP	2022	PP_PK	TOTAL	THS_T	822.419	7
AT	IMP	2022	PP_PK_CS	TOTAL	THS_T	414.619	7
AT	IMP	2022	PP_PK_CB	TOTAL	THS_T	214.936	7
AT	IMP	2022	PP_PK_WR	TOTAL	THS_T	160.531	7
AT	IMP	2022	PP_PK_O	TOTAL	THS_T	32.333	7
AT	IMP	2022	PP_O	TOTAL	THS_T	13.392	7
AT	IMP	2022	GLT_CLT	TOTAL	THS_M3	66.9292777778	7
AT	IMP	2022	GLT	TOTAL	THS_M3	41.4784444444	7
AT	IMP	2022	CLT	TOTAL	THS_M3	25.4508333333	7
AT	IMP	2022	I_BEAMS	TOTAL	THS_T	0.031	7
AT	IMP	2022	RW	TOTAL	THS_NAC	842358.654	7
AT	IMP	2022	RW_FW	TOTAL	THS_NAC	26309.873	7
AT	IMP	2022	RW_FW	CONIF	THS_NAC	9946.706	7
AT	IMP	2022	RW_FW	NCONIF	THS_NAC	16363.167	7
AT	IMP	2022	RW_IN	TOTAL	THS_NAC	816048.781	7
AT	IMP	2022	RW_IN	CONIF	THS_NAC	743290.23	7
AT	IMP	2022	RW_IN	NCONIF	THS_NAC	72758.551	7
AT	IMP	2022	RW_IN	NC_TRO	THS_NAC	83.939	7
AT	IMP	2022	CHA	TOTAL	THS_NAC	9358.494	7
AT	IMP	2022	CHP_RES	TOTAL	THS_NAC	152461.669	7
AT	IMP	2022	CHP	TOTAL	THS_NAC	74020.062	7
AT	IMP	2022	RES	TOTAL	THS_NAC	78441.607	7
AT	IMP	2022	RES_SWD	TOTAL	THS_NAC	18214.944	7
AT	IMP	2022	RCW	TOTAL	THS_NAC
AT	IMP	2022	PEL_AGG	TOTAL	THS_NAC	137115.241	7
AT	IMP	2022	PEL	TOTAL	THS_NAC	106355.506	7
AT	IMP	2022	AGG	TOTAL	THS_NAC	30759.735	7
AT	IMP	2022	SN	TOTAL	THS_NAC	748526.153	7
AT	IMP	2022	SN	CONIF	THS_NAC	552085.779	7
AT	IMP	2022	SN	NCONIF	THS_NAC	196440.374	7
AT	IMP	2022	SN	NC_TRO	THS_NAC	5622.454	7
AT	IMP	2022	PN_VN	TOTAL	THS_NAC	187927.781	7
AT	IMP	2022	PN_VN	CONIF	THS_NAC	16243.009	7
AT	IMP	2022	PN_VN	NCONIF	THS_NAC	171684.772	7
AT	IMP	2022	PN_VN	NC_TRO	THS_NAC	4979.045	7
AT	IMP	2022	PN	TOTAL	THS_NAC	519037	7
AT	IMP	2022	PN_PY	TOTAL	THS_NAC	190555.378	7
AT	IMP	2022	PN_PY	CONIF	THS_NAC	59132.576	7
AT	IMP	2022	PN_PY	NCONIF	THS_NAC	127044.133	7
AT	IMP	2022	PN_PY	NC_TRO	THS_NAC	23939.063	7
AT	IMP	2022	PN_PY_LVL	TOTAL	THS_NAC	4378.336	7
AT	IMP	2022	PN_PY_LVL	CONIF	THS_NAC	1090.86	7
AT	IMP	2022	PN_PY_LVL	NCONIF	THS_NAC	3287.476	7
AT	IMP	2022	PN_PY_LVL	NC_TRO	THS_NAC	2079.611	7
AT	IMP	2022	PN_PB	TOTAL	THS_NAC	182186.728	7
AT	IMP	2022	PN_PB_OSB	TOTAL	THS_NAC	62204.247	7
AT	IMP	2022	PN_FB	TOTAL	THS_NAC	146294.746	7
AT	IMP	2022	PN_FB_HB	TOTAL	THS_NAC	21840.398	7
AT	IMP	2022	PN_FB_MDF	TOTAL	THS_NAC	97168.361	7
AT	IMP	2022	PN_FB_O	TOTAL	THS_NAC	27285.987	7
AT	IMP	2022	PL	TOTAL	THS_NAC	498789.403	7
AT	IMP	2022	PL_MC_SCH	TOTAL	THS_NAC	4496.57	7
AT	IMP	2022	PL_CH	TOTAL	THS_NAC	404064.314	7
AT	IMP	2022	PL_CH_SA	TOTAL	THS_NAC	382924.46	7
AT	IMP	2022	PL_CH_SAB	TOTAL	THS_NAC	382600.627	7
AT	IMP	2022	PL_CH_SI	TOTAL	THS_NAC	21139.854	7
AT	IMP	2022	PL_DS	TOTAL	THS_NAC	90228.519	7
AT	IMP	2022	PLO	TOTAL	THS_NAC	34337.322	7
AT	IMP	2022	PLO_NW	TOTAL	THS_NAC	29736.17	7
AT	IMP	2022	PLO_RC	TOTAL	THS_NAC	4601.152	7
AT	IMP	2022	RCP	TOTAL	THS_NAC	342624.247	7
AT	IMP	2022	PP	TOTAL	THS_NAC	1340354.78	7
AT	IMP	2022	PP_GR	TOTAL	THS_NAC	421848.919	7
AT	IMP	2022	PP_GR_NP	TOTAL	THS_NAC	64146.972	7
AT	IMP	2022	PP_GR_MC	TOTAL	THS_NAC	46829.498	7
AT	IMP	2022	PP_GR_NW	TOTAL	THS_NAC	137377.247	7
AT	IMP	2022	PP_GR_CO	TOTAL	THS_NAC	173495.202	7
AT	IMP	2022	PP_HS	TOTAL	THS_NAC	22621.126	7
AT	IMP	2022	PP_PK	TOTAL	THS_NAC	861857.784	7
AT	IMP	2022	PP_PK_CS	TOTAL	THS_NAC	308240.279	7
AT	IMP	2022	PP_PK_CB	TOTAL	THS_NAC	307856.53	7
AT	IMP	2022	PP_PK_WR	TOTAL	THS_NAC	221705.385	7
AT	IMP	2022	PP_PK_O	TOTAL	THS_NAC	24055.59	7
AT	IMP	2022	PP_O	TOTAL	THS_NAC	34026.947	7
AT	IMP	2022	GLT_CLT	TOTAL	THS_NAC	48541.452	7
AT	IMP	2022	GLT	TOTAL	THS_NAC	27991.269	7
AT	IMP	2022	CLT	TOTAL	THS_NAC	20550.183	7
AT	IMP	2022	I_BEAMS	TOTAL	THS_NAC	52.008	7
AT	EXP	2021	RW	TOTAL	THS_M3	1105.553
AT	EXP	2021	RW_FW	TOTAL	THS_M3	12.724
AT	EXP	2021	RW_FW	CONIF	THS_M3	1.821
AT	EXP	2021	RW_FW	NCONIF	THS_M3	10.903
AT	EXP	2021	RW_IN	TOTAL	THS_M3	1092.829
AT	EXP	2021	RW_IN	CONIF	THS_M3	954.007
AT	EXP	2021	RW_IN	NCONIF	THS_M3	138.822
AT	EXP	2021	RW_IN	NC_TRO	THS_M3	0
AT	EXP	2021	CHA	TOTAL	THS_T	1.4643
AT	EXP	2021	CHP_RES	TOTAL	THS_M3	768.6277792664
AT	EXP	2021	CHP	TOTAL	THS_M3	313.994737241
AT	EXP	2021	RES	TOTAL	THS_M3	454.6330420254
AT	EXP	2021	RES_SWD	TOTAL	THS_M3
AT	EXP	2021	RCW	TOTAL	THS_T	141.021
AT	EXP	2021	PEL_AGG	TOTAL	THS_T	901.202
AT	EXP	2021	PEL	TOTAL	THS_T	875.445
AT	EXP	2021	AGG	TOTAL	THS_T	25.757
AT	EXP	2021	SN	TOTAL	THS_M3	6119.574148
AT	EXP	2021	SN	CONIF	THS_M3	5946.608
AT	EXP	2021	SN	NCONIF	THS_M3	172.966148
AT	EXP	2021	SN	NC_TRO	THS_M3	1.117
AT	EXP	2021	PN_VN	TOTAL	THS_M3	17.744
AT	EXP	2021	PN_VN	CONIF	THS_M3	3.014
AT	EXP	2021	PN_VN	NCONIF	THS_M3	14.73
AT	EXP	2021	PN_VN	NC_TRO	THS_M3	0.307
AT	EXP	2021	PN	TOTAL	THS_M3	2962.339
AT	EXP	2021	PN_PY	TOTAL	THS_M3	356.923
AT	EXP	2021	PN_PY	CONIF	THS_M3
AT	EXP	2021	PN_PY	NCONIF	THS_M3
AT	EXP	2021	PN_PY	NC_TRO	THS_M3	0.292
AT	EXP	2021	PN_PY_LVL	TOTAL	THS_M3
AT	EXP	2021	PN_PY_LVL	CONIF	THS_M3
AT	EXP	2021	PN_PY_LVL	NCONIF	THS_M3
AT	EXP	2021	PN_PY_LVL	NC_TRO	THS_M3
AT	EXP	2021	PN_PB	TOTAL	THS_M3	2049.439
AT	EXP	2021	PN_PB_OSB	TOTAL	THS_M3	5.96
AT	EXP	2021	PN_FB	TOTAL	THS_M3	555.977
AT	EXP	2021	PN_FB_HB	TOTAL	THS_M3	60.206
AT	EXP	2021	PN_FB_MDF	TOTAL	THS_M3	490.961
AT	EXP	2021	PN_FB_O	TOTAL	THS_M3	4.81
AT	EXP	2021	PL	TOTAL	THS_T	321.447066
AT	EXP	2021	PL_MC_SCH	TOTAL	THS_T	0.073992
AT	EXP	2021	PL_CH	TOTAL	THS_T	254.4515
AT	EXP	2021	PL_CH_SA	TOTAL	THS_T	246.205522
AT	EXP	2021	PL_CH_SAB	TOTAL	THS_T	223.608906
AT	EXP	2021	PL_CH_SI	TOTAL	THS_T	8.245978
AT	EXP	2021	PL_DS	TOTAL	THS_T	66.921574
AT	EXP	2021	PLO	TOTAL	THS_T	10.872601
AT	EXP	2021	PLO_NW	TOTAL	THS_T	0.918774
AT	EXP	2021	PLO_RC	TOTAL	THS_T	9.953827
AT	EXP	2021	RCP	TOTAL	THS_T	248.7739
AT	EXP	2021	PP	TOTAL	THS_T	4027.5845
AT	EXP	2021	PP_GR	TOTAL	THS_T	1911.1241
AT	EXP	2021	PP_GR_NP	TOTAL	THS_T	305.8724
AT	EXP	2021	PP_GR_MC	TOTAL	THS_T	218.3511
AT	EXP	2021	PP_GR_NW	TOTAL	THS_T	335.1617
AT	EXP	2021	PP_GR_CO	TOTAL	THS_T	1051.7389
AT	EXP	2021	PP_HS	TOTAL	THS_T	5.3816
AT	EXP	2021	PP_PK	TOTAL	THS_T	2105.4535
AT	EXP	2021	PP_PK_CS	TOTAL	THS_T	1243.9191
AT	EXP	2021	PP_PK_CB	TOTAL	THS_T	353.2203
AT	EXP	2021	PP_PK_WR	TOTAL	THS_T	497.8413
AT	EXP	2021	PP_PK_O	TOTAL	THS_T	10.4728
AT	EXP	2021	PP_O	TOTAL	THS_T	5.6253
AT	EXP	2021	GLT_CLT	TOTAL	THS_M3
AT	EXP	2021	GLT	TOTAL	THS_M3
AT	EXP	2021	CLT	TOTAL	THS_M3
AT	EXP	2021	I_BEAMS	TOTAL	THS_T
AT	EXP	2021	RW	TOTAL	THS_NAC	98279.099
AT	EXP	2021	RW_FW	TOTAL	THS_NAC	1050.048
AT	EXP	2021	RW_FW	CONIF	THS_NAC	122.117
AT	EXP	2021	RW_FW	NCONIF	THS_NAC	927.931
AT	EXP	2021	RW_IN	TOTAL	THS_NAC	97229.051
AT	EXP	2021	RW_IN	CONIF	THS_NAC	77676.779
AT	EXP	2021	RW_IN	NCONIF	THS_NAC	19552.272
AT	EXP	2021	RW_IN	NC_TRO	THS_NAC	0
AT	EXP	2021	CHA	TOTAL	THS_NAC	1294.801
AT	EXP	2021	CHP_RES	TOTAL	THS_NAC	36172.3431197599
AT	EXP	2021	CHP	TOTAL	THS_NAC	15692.899
AT	EXP	2021	RES	TOTAL	THS_NAC	20479.4441197599
AT	EXP	2021	RES_SWD	TOTAL	THS_NAC
AT	EXP	2021	RCW	TOTAL	THS_NAC	5290.6648802401
AT	EXP	2021	PEL_AGG	TOTAL	THS_NAC	178315.793
AT	EXP	2021	PEL	TOTAL	THS_NAC	173381.456
AT	EXP	2021	AGG	TOTAL	THS_NAC	4934.337
AT	EXP	2021	SN	TOTAL	THS_NAC	1985783.059
AT	EXP	2021	SN	CONIF	THS_NAC	1870911.483
AT	EXP	2021	SN	NCONIF	THS_NAC	114871.576
AT	EXP	2021	SN	NC_TRO	THS_NAC	1696.693
AT	EXP	2021	PN_VN	TOTAL	THS_NAC	56389.66
AT	EXP	2021	PN_VN	CONIF	THS_NAC	8418.815
AT	EXP	2021	PN_VN	NCONIF	THS_NAC	47970.845
AT	EXP	2021	PN_VN	NC_TRO	THS_NAC	1199.84
AT	EXP	2021	PN	TOTAL	THS_NAC	1446998.663
AT	EXP	2021	PN_PY	TOTAL	THS_NAC	320511.706
AT	EXP	2021	PN_PY	CONIF	THS_NAC
AT	EXP	2021	PN_PY	NCONIF	THS_NAC
AT	EXP	2021	PN_PY	NC_TRO	THS_NAC	852.271
AT	EXP	2021	PN_PY_LVL	TOTAL	THS_NAC
AT	EXP	2021	PN_PY_LVL	CONIF	THS_NAC
AT	EXP	2021	PN_PY_LVL	NCONIF	THS_NAC
AT	EXP	2021	PN_PY_LVL	NC_TRO	THS_NAC
AT	EXP	2021	PN_PB	TOTAL	THS_NAC	744738.395
AT	EXP	2021	PN_PB_OSB	TOTAL	THS_NAC	2849.891
AT	EXP	2021	PN_FB	TOTAL	THS_NAC	381748.562
AT	EXP	2021	PN_FB_HB	TOTAL	THS_NAC	36242.421
AT	EXP	2021	PN_FB_MDF	TOTAL	THS_NAC	343776.794
AT	EXP	2021	PN_FB_O	TOTAL	THS_NAC	1729.347
AT	EXP	2021	PL	TOTAL	THS_NAC	210213.55
AT	EXP	2021	PL_MC_SCH	TOTAL	THS_NAC	45.97
AT	EXP	2021	PL_CH	TOTAL	THS_NAC	168694.894
AT	EXP	2021	PL_CH_SA	TOTAL	THS_NAC	163108.096
AT	EXP	2021	PL_CH_SAB	TOTAL	THS_NAC	151139.89
AT	EXP	2021	PL_CH_SI	TOTAL	THS_NAC	5586.798
AT	EXP	2021	PL_DS	TOTAL	THS_NAC	41472.686
AT	EXP	2021	PLO	TOTAL	THS_NAC	9282.097
AT	EXP	2021	PLO_NW	TOTAL	THS_NAC	2732.688
AT	EXP	2021	PLO_RC	TOTAL	THS_NAC	6549.409
AT	EXP	2021	RCP	TOTAL	THS_NAC	48133.721
AT	EXP	2021	PP	TOTAL	THS_NAC	2824374.984
AT	EXP	2021	PP_GR	TOTAL	THS_NAC	1355927.041
AT	EXP	2021	PP_GR_NP	TOTAL	THS_NAC	125405.012
AT	EXP	2021	PP_GR_MC	TOTAL	THS_NAC	112596.501
AT	EXP	2021	PP_GR_NW	TOTAL	THS_NAC	336354.074
AT	EXP	2021	PP_GR_CO	TOTAL	THS_NAC	781571.454
AT	EXP	2021	PP_HS	TOTAL	THS_NAC	9738.323
AT	EXP	2021	PP_PK	TOTAL	THS_NAC	1395977.397
AT	EXP	2021	PP_PK_CS	TOTAL	THS_NAC	670617.573
AT	EXP	2021	PP_PK_CB	TOTAL	THS_NAC	332238.32
AT	EXP	2021	PP_PK_WR	TOTAL	THS_NAC	385636.683
AT	EXP	2021	PP_PK_O	TOTAL	THS_NAC	7484.821
AT	EXP	2021	PP_O	TOTAL	THS_NAC	62732.223
AT	EXP	2021	GLT_CLT	TOTAL	THS_NAC
AT	EXP	2021	GLT	TOTAL	THS_NAC
AT	EXP	2021	CLT	TOTAL	THS_NAC
AT	EXP	2021	I_BEAMS	TOTAL	THS_NAC
AT	EXP	2022	RW	TOTAL	THS_M3	1318.69	7
AT	EXP	2022	RW_FW	TOTAL	THS_M3	11.984	7
AT	EXP	2022	RW_FW	CONIF	THS_M3	4.558	7
AT	EXP	2022	RW_FW	NCONIF	THS_M3	7.426	7
AT	EXP	2022	RW_IN	TOTAL	THS_M3	1306.706	7
AT	EXP	2022	RW_IN	CONIF	THS_M3	1151.168	7
AT	EXP	2022	RW_IN	NCONIF	THS_M3	155.538	7
AT	EXP	2022	RW_IN	NC_TRO	THS_M3	0.032	7
AT	EXP	2022	CHA	TOTAL	THS_T	1.315	7
AT	EXP	2022	CHP_RES	TOTAL	THS_M3	650.2614304703	7
AT	EXP	2022	CHP	TOTAL	THS_M3	240.65	7
AT	EXP	2022	RES	TOTAL	THS_M3	409.6114304703	7
AT	EXP	2022	RES_SWD	TOTAL	THS_M3	256.116	7
AT	EXP	2022	RCW	TOTAL	THS_T
AT	EXP	2022	PEL_AGG	TOTAL	THS_T	759.737	7
AT	EXP	2022	PEL	TOTAL	THS_T	748.723	7
AT	EXP	2022	AGG	TOTAL	THS_T	11.014	7
AT	EXP	2022	SN	TOTAL	THS_M3	6099.443	7
AT	EXP	2022	SN	CONIF	THS_M3	5730.805	7
AT	EXP	2022	SN	NCONIF	THS_M3	145.138	7
AT	EXP	2022	SN	NC_TRO	THS_M3	0.927	7
AT	EXP	2022	PN_VN	TOTAL	THS_M3	16.952	7
AT	EXP	2022	PN_VN	CONIF	THS_M3	2.653	7
AT	EXP	2022	PN_VN	NCONIF	THS_M3	14.299	7
AT	EXP	2022	PN_VN	NC_TRO	THS_M3	0.596	7
AT	EXP	2022	PN	TOTAL	THS_M3	2356.429	7
AT	EXP	2022	PN_PY	TOTAL	THS_M3	295.904	7
AT	EXP	2022	PN_PY	CONIF	THS_M3	263.965	7
AT	EXP	2022	PN_PY	NCONIF	THS_M3	31.434	7
AT	EXP	2022	PN_PY	NC_TRO	THS_M3	1.378	7
AT	EXP	2022	PN_PY_LVL	TOTAL	THS_M3	0.505	7
AT	EXP	2022	PN_PY_LVL	CONIF	THS_M3	0.199	7
AT	EXP	2022	PN_PY_LVL	NCONIF	THS_M3	0.306	7
AT	EXP	2022	PN_PY_LVL	NC_TRO	THS_M3	0.164	7
AT	EXP	2022	PN_PB	TOTAL	THS_M3	1680.026	7
AT	EXP	2022	PN_PB_OSB	TOTAL	THS_M3	6.824	7
AT	EXP	2022	PN_FB	TOTAL	THS_M3	380.499	7
AT	EXP	2022	PN_FB_HB	TOTAL	THS_M3	43.023	7
AT	EXP	2022	PN_FB_MDF	TOTAL	THS_M3	333.141	7
AT	EXP	2022	PN_FB_O	TOTAL	THS_M3	4.335	7
AT	EXP	2022	PL	TOTAL	THS_T	385.924	7
AT	EXP	2022	PL_MC_SCH	TOTAL	THS_T	0.045	7
AT	EXP	2022	PL_CH	TOTAL	THS_T	271.278	7
AT	EXP	2022	PL_CH_SA	TOTAL	THS_T	258.028	7
AT	EXP	2022	PL_CH_SAB	TOTAL	THS_T	223.036	7
AT	EXP	2022	PL_CH_SI	TOTAL	THS_T	13.25	7
AT	EXP	2022	PL_DS	TOTAL	THS_T	114.601	7
AT	EXP	2022	PLO	TOTAL	THS_T	9.828	7
AT	EXP	2022	PLO_NW	TOTAL	THS_T	1.288	7
AT	EXP	2022	PLO_RC	TOTAL	THS_T	8.54	7
AT	EXP	2022	RCP	TOTAL	THS_T	260.3464	7
AT	EXP	2022	PP	TOTAL	THS_T	3695.052	7
AT	EXP	2022	PP_GR	TOTAL	THS_T	1661.78	7
AT	EXP	2022	PP_GR_NP	TOTAL	THS_T	234.523	7
AT	EXP	2022	PP_GR_MC	TOTAL	THS_T	216.596	7
AT	EXP	2022	PP_GR_NW	TOTAL	THS_T	276.97	7
AT	EXP	2022	PP_GR_CO	TOTAL	THS_T	933.691	7
AT	EXP	2022	PP_HS	TOTAL	THS_T	5.191	7
AT	EXP	2022	PP_PK	TOTAL	THS_T	2022.706	7
AT	EXP	2022	PP_PK_CS	TOTAL	THS_T	1196.577	7
AT	EXP	2022	PP_PK_CB	TOTAL	THS_T	311.639	7
AT	EXP	2022	PP_PK_WR	TOTAL	THS_T	503.301	7
AT	EXP	2022	PP_PK_O	TOTAL	THS_T	11.189	7
AT	EXP	2022	PP_O	TOTAL	THS_T	5.375	7
AT	EXP	2022	GLT_CLT	TOTAL	THS_M3	1728.4705416667	7
AT	EXP	2022	GLT	TOTAL	THS_M3	1340.822	7
AT	EXP	2022	CLT	TOTAL	THS_M3	387.6485416667	7
AT	EXP	2022	I_BEAMS	TOTAL	THS_T	0.32	7
AT	EXP	2022	RW	TOTAL	THS_NAC	145003.898	7
AT	EXP	2022	RW_FW	TOTAL	THS_NAC	1563.896	7
AT	EXP	2022	RW_FW	CONIF	THS_NAC	403.803	7
AT	EXP	2022	RW_FW	NCONIF	THS_NAC	1160.093	7
AT	EXP	2022	RW_IN	TOTAL	THS_NAC	143440.002	7
AT	EXP	2022	RW_IN	CONIF	THS_NAC	114972.276	7
AT	EXP	2022	RW_IN	NCONIF	THS_NAC	28467.726	7
AT	EXP	2022	RW_IN	NC_TRO	THS_NAC	17.392	7
AT	EXP	2022	CHA	TOTAL	THS_NAC	1057.357	7
AT	EXP	2022	CHP_RES	TOTAL	THS_NAC	47879.56	7
AT	EXP	2022	CHP	TOTAL	THS_NAC	19641.656	7
AT	EXP	2022	RES	TOTAL	THS_NAC	28237.904	7
AT	EXP	2022	RES_SWD	TOTAL	THS_NAC	23143.165	7
AT	EXP	2022	RCW	TOTAL	THS_NAC
AT	EXP	2022	PEL_AGG	TOTAL	THS_NAC	278475.939	7
AT	EXP	2022	PEL	TOTAL	THS_NAC	275257.498	7
AT	EXP	2022	AGG	TOTAL	THS_NAC	3218.441	7
AT	EXP	2022	SN	TOTAL	THS_NAC	2026472.18	7
AT	EXP	2022	SN	CONIF	THS_NAC	1895011.47	7
AT	EXP	2022	SN	NCONIF	THS_NAC	131460.702	7
AT	EXP	2022	SN	NC_TRO	THS_NAC	1687.321	7
AT	EXP	2022	PN_VN	TOTAL	THS_NAC	65332.48	7
AT	EXP	2022	PN_VN	CONIF	THS_NAC	8542.801	7
AT	EXP	2022	PN_VN	NCONIF	THS_NAC	56789.679	7
AT	EXP	2022	PN_VN	NC_TRO	THS_NAC	1053.541	7
AT	EXP	2022	PN	TOTAL	THS_NAC	1472778	7
AT	EXP	2022	PN_PY	TOTAL	THS_NAC	309247	7
AT	EXP	2022	PN_PY	CONIF	THS_NAC	252322	7
AT	EXP	2022	PN_PY	NCONIF	THS_NAC	56925	7
AT	EXP	2022	PN_PY	NC_TRO	THS_NAC	2583.122	7
AT	EXP	2022	PN_PY_LVL	TOTAL	THS_NAC	2318.432	7
AT	EXP	2022	PN_PY_LVL	CONIF	THS_NAC	1034.514	7
AT	EXP	2022	PN_PY_LVL	NCONIF	THS_NAC	1283.918	7
AT	EXP	2022	PN_PY_LVL	NC_TRO	THS_NAC	224.647	7
AT	EXP	2022	PN_PB	TOTAL	THS_NAC	831654	7
AT	EXP	2022	PN_PB_OSB	TOTAL	THS_NAC	3261.304	7
AT	EXP	2022	PN_FB	TOTAL	THS_NAC	331876	7
AT	EXP	2022	PN_FB_HB	TOTAL	THS_NAC	32599.416	7
AT	EXP	2022	PN_FB_MDF	TOTAL	THS_NAC	297033.885	7
AT	EXP	2022	PN_FB_O	TOTAL	THS_NAC	2242.525	7
AT	EXP	2022	PL	TOTAL	THS_NAC	300796	7
AT	EXP	2022	PL_MC_SCH	TOTAL	THS_NAC	19	7
AT	EXP	2022	PL_CH	TOTAL	THS_NAC	222375	7
AT	EXP	2022	PL_CH_SA	TOTAL	THS_NAC	210667	7
AT	EXP	2022	PL_CH_SAB	TOTAL	THS_NAC	187211	7
AT	EXP	2022	PL_CH_SI	TOTAL	THS_NAC	11708	7
AT	EXP	2022	PL_DS	TOTAL	THS_NAC	78402	7
AT	EXP	2022	PLO	TOTAL	THS_NAC	11415.113	7
AT	EXP	2022	PLO_NW	TOTAL	THS_NAC	4428.226	7
AT	EXP	2022	PLO_RC	TOTAL	THS_NAC	6986.887	7
AT	EXP	2022	RCP	TOTAL	THS_NAC	58553.351	7
AT	EXP	2022	PP	TOTAL	THS_NAC	3889356	7
AT	EXP	2022	PP_GR	TOTAL	THS_NAC	1975938	7
AT	EXP	2022	PP_GR_NP	TOTAL	THS_NAC	190703.731	7
AT	EXP	2022	PP_GR_MC	TOTAL	THS_NAC	192892.213	7
AT	EXP	2022	PP_GR_NW	TOTAL	THS_NAC	440891.382	7
AT	EXP	2022	PP_GR_CO	TOTAL	THS_NAC	1151450.2	7
AT	EXP	2022	PP_HS	TOTAL	THS_NAC	10860.18	7
AT	EXP	2022	PP_PK	TOTAL	THS_NAC	1841897.97	7
AT	EXP	2022	PP_PK_CS	TOTAL	THS_NAC	867197.851	7
AT	EXP	2022	PP_PK_CB	TOTAL	THS_NAC	409579.908	7
AT	EXP	2022	PP_PK_WR	TOTAL	THS_NAC	554440.137	7
AT	EXP	2022	PP_PK_O	TOTAL	THS_NAC	10680.07	7
AT	EXP	2022	PP_O	TOTAL	THS_NAC	60659.745	7
AT	EXP	2022	GLT_CLT	TOTAL	THS_NAC	1127564.39	7
AT	EXP	2022	GLT	TOTAL	THS_NAC	836889.832	7
AT	EXP	2022	CLT	TOTAL	THS_NAC	290674.558	7
AT	EXP	2022	I_BEAMS	TOTAL	THS_NAC	2149.928	7
AT	IMP_XEU	2021	RW	TOTAL	THS_M3	156.6775555556
AT	IMP_XEU	2021	RW_FW	TOTAL	THS_M3	44.6325555556
AT	IMP_XEU	2021	RW_FW	CONIF	THS_M3	1.5532222222
AT	IMP_XEU	2021	RW_FW	NCONIF	THS_M3	43.0793333333
AT	IMP_XEU	2021	RW_IN	TOTAL	THS_M3	112.045
AT	IMP_XEU	2021	RW_IN	CONIF	THS_M3	90.717
AT	IMP_XEU	2021	RW_IN	NCONIF	THS_M3	21.328
AT	IMP_XEU	2021	RW_IN	NC_TRO	THS_M3	0.049
AT	IMP_XEU	2021	CHA	TOTAL	THS_T	10.4529
AT	IMP_XEU	2021	CHP_RES	TOTAL	THS_M3	64.1200529802	4
AT	IMP_XEU	2021	CHP	TOTAL	THS_M3	19.2666723238
AT	IMP_XEU	2021	RES	TOTAL	THS_M3	44.8533806564	4
AT	IMP_XEU	2021	RES_SWD	TOTAL	THS_M3
AT	IMP_XEU	2021	RCW	TOTAL	THS_T
AT	IMP_XEU	2021	PEL_AGG	TOTAL	THS_T	24.2013
AT	IMP_XEU	2021	PEL	TOTAL	THS_T	2.3549
AT	IMP_XEU	2021	AGG	TOTAL	THS_T	21.8464
AT	IMP_XEU	2021	SN	TOTAL	THS_M3	155.590101
AT	IMP_XEU	2021	SN	CONIF	THS_M3	131.433
AT	IMP_XEU	2021	SN	NCONIF	THS_M3	24.157101
AT	IMP_XEU	2021	SN	NC_TRO	THS_M3	4.301
AT	IMP_XEU	2021	PN_VN	TOTAL	THS_M3	25.562
AT	IMP_XEU	2021	PN_VN	CONIF	THS_M3	0.143
AT	IMP_XEU	2021	PN_VN	NCONIF	THS_M3	25.419
AT	IMP_XEU	2021	PN_VN	NC_TRO	THS_M3	0.256
AT	IMP_XEU	2021	PN	TOTAL	THS_M3	52.299
AT	IMP_XEU	2021	PN_PY	TOTAL	THS_M3	30.817
AT	IMP_XEU	2021	PN_PY	CONIF	THS_M3
AT	IMP_XEU	2021	PN_PY	NCONIF	THS_M3
AT	IMP_XEU	2021	PN_PY	NC_TRO	THS_M3	0.531
AT	IMP_XEU	2021	PN_PY_LVL	TOTAL	THS_M3
AT	IMP_XEU	2021	PN_PY_LVL	CONIF	THS_M3
AT	IMP_XEU	2021	PN_PY_LVL	NCONIF	THS_M3
AT	IMP_XEU	2021	PN_PY_LVL	NC_TRO	THS_M3
AT	IMP_XEU	2021	PN_PB	TOTAL	THS_M3	15.71
AT	IMP_XEU	2021	PN_PB_OSB	TOTAL	THS_M3	5.61
AT	IMP_XEU	2021	PN_FB	TOTAL	THS_M3	5.772
AT	IMP_XEU	2021	PN_FB_HB	TOTAL	THS_M3	0.044
AT	IMP_XEU	2021	PN_FB_MDF	TOTAL	THS_M3	1.418
AT	IMP_XEU	2021	PN_FB_O	TOTAL	THS_M3	4.31
AT	IMP_XEU	2021	PL	TOTAL	THS_T	219.808647
AT	IMP_XEU	2021	PL_MC_SCH	TOTAL	THS_T	2.52674
AT	IMP_XEU	2021	PL_CH	TOTAL	THS_T	193.194373
AT	IMP_XEU	2021	PL_CH_SA	TOTAL	THS_T	193.1908
AT	IMP_XEU	2021	PL_CH_SAB	TOTAL	THS_T	192.155257
AT	IMP_XEU	2021	PL_CH_SI	TOTAL	THS_T	0.003573
AT	IMP_XEU	2021	PL_DS	TOTAL	THS_T	24.087534
AT	IMP_XEU	2021	PLO	TOTAL	THS_T	2.965707
AT	IMP_XEU	2021	PLO_NW	TOTAL	THS_T	0.127988
AT	IMP_XEU	2021	PLO_RC	TOTAL	THS_T	2.837719
AT	IMP_XEU	2021	RCP	TOTAL	THS_T	40.649
AT	IMP_XEU	2021	PP	TOTAL	THS_T	48.639
AT	IMP_XEU	2021	PP_GR	TOTAL	THS_T	20.2177
AT	IMP_XEU	2021	PP_GR_NP	TOTAL	THS_T	13.0777
AT	IMP_XEU	2021	PP_GR_MC	TOTAL	THS_T	1.6053
AT	IMP_XEU	2021	PP_GR_NW	TOTAL	THS_T	1.5219
AT	IMP_XEU	2021	PP_GR_CO	TOTAL	THS_T	4.0128
AT	IMP_XEU	2021	PP_HS	TOTAL	THS_T	0.459
AT	IMP_XEU	2021	PP_PK	TOTAL	THS_T	27.7072
AT	IMP_XEU	2021	PP_PK_CS	TOTAL	THS_T	16.0033
AT	IMP_XEU	2021	PP_PK_CB	TOTAL	THS_T	8.4064
AT	IMP_XEU	2021	PP_PK_WR	TOTAL	THS_T	3.1137
AT	IMP_XEU	2021	PP_PK_O	TOTAL	THS_T	0.1838
AT	IMP_XEU	2021	PP_O	TOTAL	THS_T	0.2551
AT	IMP_XEU	2021	GLT_CLT	TOTAL	THS_M3
AT	IMP_XEU	2021	GLT	TOTAL	THS_M3
AT	IMP_XEU	2021	CLT	TOTAL	THS_M3
AT	IMP_XEU	2021	I_BEAMS	TOTAL	THS_T
AT	IMP_XEU	2021	RW	TOTAL	THS_NAC	15472.016
AT	IMP_XEU	2021	RW_FW	TOTAL	THS_NAC	5393.509
AT	IMP_XEU	2021	RW_FW	CONIF	THS_NAC	222.539
AT	IMP_XEU	2021	RW_FW	NCONIF	THS_NAC	5170.97
AT	IMP_XEU	2021	RW_IN	TOTAL	THS_NAC	10078.507
AT	IMP_XEU	2021	RW_IN	CONIF	THS_NAC	7313.844
AT	IMP_XEU	2021	RW_IN	NCONIF	THS_NAC	2764.663
AT	IMP_XEU	2021	RW_IN	NC_TRO	THS_NAC	88.197
AT	IMP_XEU	2021	CHA	TOTAL	THS_NAC	6197.622
AT	IMP_XEU	2021	CHP_RES	TOTAL	THS_NAC	2600.685	4
AT	IMP_XEU	2021	CHP	TOTAL	THS_NAC	1682.811
AT	IMP_XEU	2021	RES	TOTAL	THS_NAC	917.874	4
AT	IMP_XEU	2021	RES_SWD	TOTAL	THS_NAC
AT	IMP_XEU	2021	RCW	TOTAL	THS_NAC
AT	IMP_XEU	2021	PEL_AGG	TOTAL	THS_NAC	3503.275
AT	IMP_XEU	2021	PEL	TOTAL	THS_NAC	401.853
AT	IMP_XEU	2021	AGG	TOTAL	THS_NAC	3101.422
AT	IMP_XEU	2021	SN	TOTAL	THS_NAC	66246.208
AT	IMP_XEU	2021	SN	CONIF	THS_NAC	46389.458
AT	IMP_XEU	2021	SN	NCONIF	THS_NAC	19856.75
AT	IMP_XEU	2021	SN	NC_TRO	THS_NAC	4505.195
AT	IMP_XEU	2021	PN_VN	TOTAL	THS_NAC	77925.674
AT	IMP_XEU	2021	PN_VN	CONIF	THS_NAC	424.425
AT	IMP_XEU	2021	PN_VN	NCONIF	THS_NAC	77501.249
AT	IMP_XEU	2021	PN_VN	NC_TRO	THS_NAC	408.97
AT	IMP_XEU	2021	PN	TOTAL	THS_NAC	29152.593
AT	IMP_XEU	2021	PN_PY	TOTAL	THS_NAC	21882.167
AT	IMP_XEU	2021	PN_PY	CONIF	THS_NAC
AT	IMP_XEU	2021	PN_PY	NCONIF	THS_NAC
AT	IMP_XEU	2021	PN_PY	NC_TRO	THS_NAC	481.719
AT	IMP_XEU	2021	PN_PY_LVL	TOTAL	THS_NAC
AT	IMP_XEU	2021	PN_PY_LVL	CONIF	THS_NAC
AT	IMP_XEU	2021	PN_PY_LVL	NCONIF	THS_NAC
AT	IMP_XEU	2021	PN_PY_LVL	NC_TRO	THS_NAC
AT	IMP_XEU	2021	PN_PB	TOTAL	THS_NAC	5781.203
AT	IMP_XEU	2021	PN_PB_OSB	TOTAL	THS_NAC	3270.849
AT	IMP_XEU	2021	PN_FB	TOTAL	THS_NAC	1489.223
AT	IMP_XEU	2021	PN_FB_HB	TOTAL	THS_NAC	104.697
AT	IMP_XEU	2021	PN_FB_MDF	TOTAL	THS_NAC	736.572
AT	IMP_XEU	2021	PN_FB_O	TOTAL	THS_NAC	647.954
AT	IMP_XEU	2021	PL	TOTAL	THS_NAC	141717.647
AT	IMP_XEU	2021	PL_MC_SCH	TOTAL	THS_NAC	1176.315
AT	IMP_XEU	2021	PL_CH	TOTAL	THS_NAC	119943.478
AT	IMP_XEU	2021	PL_CH_SA	TOTAL	THS_NAC	119934.446
AT	IMP_XEU	2021	PL_CH_SAB	TOTAL	THS_NAC	119047.912
AT	IMP_XEU	2021	PL_CH_SI	TOTAL	THS_NAC	9.032
AT	IMP_XEU	2021	PL_DS	TOTAL	THS_NAC	20597.854
AT	IMP_XEU	2021	PLO	TOTAL	THS_NAC	399.638
AT	IMP_XEU	2021	PLO_NW	TOTAL	THS_NAC	167.565
AT	IMP_XEU	2021	PLO_RC	TOTAL	THS_NAC	232.073
AT	IMP_XEU	2021	RCP	TOTAL	THS_NAC	8666.01
AT	IMP_XEU	2021	PP	TOTAL	THS_NAC	50259.017
AT	IMP_XEU	2021	PP_GR	TOTAL	THS_NAC	12882.258
AT	IMP_XEU	2021	PP_GR_NP	TOTAL	THS_NAC	5797.731
AT	IMP_XEU	2021	PP_GR_MC	TOTAL	THS_NAC	1065.559
AT	IMP_XEU	2021	PP_GR_NW	TOTAL	THS_NAC	2604.024
AT	IMP_XEU	2021	PP_GR_CO	TOTAL	THS_NAC	3414.944
AT	IMP_XEU	2021	PP_HS	TOTAL	THS_NAC	1392.442
AT	IMP_XEU	2021	PP_PK	TOTAL	THS_NAC	33594.547
AT	IMP_XEU	2021	PP_PK_CS	TOTAL	THS_NAC	11551.078
AT	IMP_XEU	2021	PP_PK_CB	TOTAL	THS_NAC	16379.222
AT	IMP_XEU	2021	PP_PK_WR	TOTAL	THS_NAC	5185.545
AT	IMP_XEU	2021	PP_PK_O	TOTAL	THS_NAC	478.702
AT	IMP_XEU	2021	PP_O	TOTAL	THS_NAC	2389.77
AT	IMP_XEU	2021	GLT_CLT	TOTAL	THS_NAC
AT	IMP_XEU	2021	GLT	TOTAL	THS_NAC
AT	IMP_XEU	2021	CLT	TOTAL	THS_NAC
AT	IMP_XEU	2021	I_BEAMS	TOTAL	THS_NAC
AT	IMP_XEU	2022	RW	TOTAL	THS_M3	170.34	7
AT	IMP_XEU	2022	RW_FW	TOTAL	THS_M3	40.706	7
AT	IMP_XEU	2022	RW_FW	CONIF	THS_M3	0.51	7
AT	IMP_XEU	2022	RW_FW	NCONIF	THS_M3	40.196	7
AT	IMP_XEU	2022	RW_IN	TOTAL	THS_M3	129.634	7
AT	IMP_XEU	2022	RW_IN	CONIF	THS_M3	96.166	7
AT	IMP_XEU	2022	RW_IN	NCONIF	THS_M3	33.468	7
AT	IMP_XEU	2022	RW_IN	NC_TRO	THS_M3	0.077	7
AT	IMP_XEU	2022	CHA	TOTAL	THS_T	7.619	7
AT	IMP_XEU	2022	CHP_RES	TOTAL	THS_M3	32.598	4
AT	IMP_XEU	2022	CHP	TOTAL	THS_M3	6.908	7
AT	IMP_XEU	2022	RES	TOTAL	THS_M3	25.69	4
AT	IMP_XEU	2022	RES_SWD	TOTAL	THS_M3	0.258	7
AT	IMP_XEU	2022	RCW	TOTAL	THS_T
AT	IMP_XEU	2022	PEL_AGG	TOTAL	THS_T	25.69	7
AT	IMP_XEU	2022	PEL	TOTAL	THS_T	7.366	7
AT	IMP_XEU	2022	AGG	TOTAL	THS_T	18.324	7
AT	IMP_XEU	2022	SN	TOTAL	THS_M3	144.273	7
AT	IMP_XEU	2022	SN	CONIF	THS_M3	78.815	7
AT	IMP_XEU	2022	SN	NCONIF	THS_M3	25.232	7
AT	IMP_XEU	2022	SN	NC_TRO	THS_M3	2.657	7
AT	IMP_XEU	2022	PN_VN	TOTAL	THS_M3	27.745	7
AT	IMP_XEU	2022	PN_VN	CONIF	THS_M3	0.139	7
AT	IMP_XEU	2022	PN_VN	NCONIF	THS_M3	27.606	7
AT	IMP_XEU	2022	PN_VN	NC_TRO	THS_M3	0.439	7
AT	IMP_XEU	2022	PN	TOTAL	THS_M3	37.705	7
AT	IMP_XEU	2022	PN_PY	TOTAL	THS_M3	19.283	7
AT	IMP_XEU	2022	PN_PY	CONIF	THS_M3	5.327	7
AT	IMP_XEU	2022	PN_PY	NCONIF	THS_M3	13.708	7
AT	IMP_XEU	2022	PN_PY	NC_TRO	THS_M3	0.142	7
AT	IMP_XEU	2022	PN_PY_LVL	TOTAL	THS_M3	0.076	7
AT	IMP_XEU	2022	PN_PY_LVL	CONIF	THS_M3	0.052	7
AT	IMP_XEU	2022	PN_PY_LVL	NCONIF	THS_M3	0.022	7
AT	IMP_XEU	2022	PN_PY_LVL	NC_TRO	THS_M3	0.002	7
AT	IMP_XEU	2022	PN_PB	TOTAL	THS_M3	7.135	7
AT	IMP_XEU	2022	PN_PB_OSB	TOTAL	THS_M3	3.806	7
AT	IMP_XEU	2022	PN_FB	TOTAL	THS_M3	11.287	7
AT	IMP_XEU	2022	PN_FB_HB	TOTAL	THS_M3	5.597	7
AT	IMP_XEU	2022	PN_FB_MDF	TOTAL	THS_M3	4.056	7
AT	IMP_XEU	2022	PN_FB_O	TOTAL	THS_M3	1.634	7
AT	IMP_XEU	2022	PL	TOTAL	THS_T	213.627231	7
AT	IMP_XEU	2022	PL_MC_SCH	TOTAL	THS_T	1.757789	7
AT	IMP_XEU	2022	PL_CH	TOTAL	THS_T	184.64187	7
AT	IMP_XEU	2022	PL_CH_SA	TOTAL	THS_T	184.641729	7
AT	IMP_XEU	2022	PL_CH_SAB	TOTAL	THS_T	184.546	7
AT	IMP_XEU	2022	PL_CH_SI	TOTAL	THS_T	0.000141	7
AT	IMP_XEU	2022	PL_DS	TOTAL	THS_T	27.227572	7
AT	IMP_XEU	2022	PLO	TOTAL	THS_T	4.926176	7
AT	IMP_XEU	2022	PLO_NW	TOTAL	THS_T	1.405976	7
AT	IMP_XEU	2022	PLO_RC	TOTAL	THS_T	3.5202	7
AT	IMP_XEU	2022	RCP	TOTAL	THS_T	36.251	7
AT	IMP_XEU	2022	PP	TOTAL	THS_T	48.867	7
AT	IMP_XEU	2022	PP_GR	TOTAL	THS_T	17.391	7
AT	IMP_XEU	2022	PP_GR_NP	TOTAL	THS_T	11.199	7
AT	IMP_XEU	2022	PP_GR_MC	TOTAL	THS_T	1.897	7
AT	IMP_XEU	2022	PP_GR_NW	TOTAL	THS_T	2.116	7
AT	IMP_XEU	2022	PP_GR_CO	TOTAL	THS_T	2.179	7
AT	IMP_XEU	2022	PP_HS	TOTAL	THS_T	0.481	7
AT	IMP_XEU	2022	PP_PK	TOTAL	THS_T	30.789	7
AT	IMP_XEU	2022	PP_PK_CS	TOTAL	THS_T	16.507	7
AT	IMP_XEU	2022	PP_PK_CB	TOTAL	THS_T	11.049	7
AT	IMP_XEU	2022	PP_PK_WR	TOTAL	THS_T	2.818	7
AT	IMP_XEU	2022	PP_PK_O	TOTAL	THS_T	0.415	7
AT	IMP_XEU	2022	PP_O	TOTAL	THS_T	0.206	7
AT	IMP_XEU	2022	GLT_CLT	TOTAL	THS_M3	1.106	7
AT	IMP_XEU	2022	GLT	TOTAL	THS_M3	0.493	7
AT	IMP_XEU	2022	CLT	TOTAL	THS_M3	0.613	7
AT	IMP_XEU	2022	I_BEAMS	TOTAL	THS_T	0.001	7
AT	IMP_XEU	2022	RW	TOTAL	THS_NAC	22094.269	7
AT	IMP_XEU	2022	RW_FW	TOTAL	THS_NAC	7931.349	7
AT	IMP_XEU	2022	RW_FW	CONIF	THS_NAC	81.726	7
AT	IMP_XEU	2022	RW_FW	NCONIF	THS_NAC	7849.623	7
AT	IMP_XEU	2022	RW_IN	TOTAL	THS_NAC	14162.92	7
AT	IMP_XEU	2022	RW_IN	CONIF	THS_NAC	9316.555	7
AT	IMP_XEU	2022	RW_IN	NCONIF	THS_NAC	4846.365	7
AT	IMP_XEU	2022	RW_IN	NC_TRO	THS_NAC	72.367	7
AT	IMP_XEU	2022	CHA	TOTAL	THS_NAC	5271.425	7
AT	IMP_XEU	2022	CHP_RES	TOTAL	THS_NAC	1448.89	4
AT	IMP_XEU	2022	CHP	TOTAL	THS_NAC	788.665	7
AT	IMP_XEU	2022	RES	TOTAL	THS_NAC	660.225	4
AT	IMP_XEU	2022	RES_SWD	TOTAL	THS_NAC	40.394	7
AT	IMP_XEU	2022	RCW	TOTAL	THS_NAC
AT	IMP_XEU	2022	PEL_AGG	TOTAL	THS_NAC	9487.734	7
AT	IMP_XEU	2022	PEL	TOTAL	THS_NAC	3435.232	7
AT	IMP_XEU	2022	AGG	TOTAL	THS_NAC	6052.502	7
AT	IMP_XEU	2022	SN	TOTAL	THS_NAC	73074.233	7
AT	IMP_XEU	2022	SN	CONIF	THS_NAC	40457.641	7
AT	IMP_XEU	2022	SN	NCONIF	THS_NAC	32616.592	7
AT	IMP_XEU	2022	SN	NC_TRO	THS_NAC	3933.09	7
AT	IMP_XEU	2022	PN_VN	TOTAL	THS_NAC	108061.499	7
AT	IMP_XEU	2022	PN_VN	CONIF	THS_NAC	428.856	7
AT	IMP_XEU	2022	PN_VN	NCONIF	THS_NAC	107632.643	7
AT	IMP_XEU	2022	PN_VN	NC_TRO	THS_NAC	681.08	7
AT	IMP_XEU	2022	PN	TOTAL	THS_NAC	35044	7
AT	IMP_XEU	2022	PN_PY	TOTAL	THS_NAC	19249	7
AT	IMP_XEU	2022	PN_PY	CONIF	THS_NAC	4725.667	7
AT	IMP_XEU	2022	PN_PY	NCONIF	THS_NAC	14329.965	7
AT	IMP_XEU	2022	PN_PY	NC_TRO	THS_NAC	194.102	7
AT	IMP_XEU	2022	PN_PY_LVL	TOTAL	THS_NAC	51.857	7
AT	IMP_XEU	2022	PN_PY_LVL	CONIF	THS_NAC	23.198	7
AT	IMP_XEU	2022	PN_PY_LVL	NCONIF	THS_NAC	27	7
AT	IMP_XEU	2022	PN_PY_LVL	NC_TRO	THS_NAC	0.925	7
AT	IMP_XEU	2022	PN_PB	TOTAL	THS_NAC	3734.888	7
AT	IMP_XEU	2022	PN_PB_OSB	TOTAL	THS_NAC	2424.733	7
AT	IMP_XEU	2022	PN_FB	TOTAL	THS_NAC	12059.687	7
AT	IMP_XEU	2022	PN_FB_HB	TOTAL	THS_NAC	9590.035	7
AT	IMP_XEU	2022	PN_FB_MDF	TOTAL	THS_NAC	2069.148	7
AT	IMP_XEU	2022	PN_FB_O	TOTAL	THS_NAC	400.504	7
AT	IMP_XEU	2022	PL	TOTAL	THS_NAC	186718.113	7
AT	IMP_XEU	2022	PL_MC_SCH	TOTAL	THS_NAC	1248.997	7
AT	IMP_XEU	2022	PL_CH	TOTAL	THS_NAC	155746.613	7
AT	IMP_XEU	2022	PL_CH_SA	TOTAL	THS_NAC	155745.433	7
AT	IMP_XEU	2022	PL_CH_SAB	TOTAL	THS_NAC	155647.669	7
AT	IMP_XEU	2022	PL_CH_SI	TOTAL	THS_NAC	1.18	7
AT	IMP_XEU	2022	PL_DS	TOTAL	THS_NAC	29722.503	7
AT	IMP_XEU	2022	PLO	TOTAL	THS_NAC	3147.353	7
AT	IMP_XEU	2022	PLO_NW	TOTAL	THS_NAC	2407.098	7
AT	IMP_XEU	2022	PLO_RC	TOTAL	THS_NAC	740.255	7
AT	IMP_XEU	2022	RCP	TOTAL	THS_NAC	9365.014	7
AT	IMP_XEU	2022	PP	TOTAL	THS_NAC	67903.5	7
AT	IMP_XEU	2022	PP_GR	TOTAL	THS_NAC	17841.5	7
AT	IMP_XEU	2022	PP_GR_NP	TOTAL	THS_NAC	9085.39	7
AT	IMP_XEU	2022	PP_GR_MC	TOTAL	THS_NAC	1885.549	7
AT	IMP_XEU	2022	PP_GR_NW	TOTAL	THS_NAC	3825.274	7
AT	IMP_XEU	2022	PP_GR_CO	TOTAL	THS_NAC	3045.229	7
AT	IMP_XEU	2022	PP_HS	TOTAL	THS_NAC	2098.082	7
AT	IMP_XEU	2022	PP_PK	TOTAL	THS_NAC	44480.377	7
AT	IMP_XEU	2022	PP_PK_CS	TOTAL	THS_NAC	14835.309	7
AT	IMP_XEU	2022	PP_PK_CB	TOTAL	THS_NAC	23686.093	7
AT	IMP_XEU	2022	PP_PK_WR	TOTAL	THS_NAC	5227.201	7
AT	IMP_XEU	2022	PP_PK_O	TOTAL	THS_NAC	731.774	7
AT	IMP_XEU	2022	PP_O	TOTAL	THS_NAC	3483.521	7
AT	IMP_XEU	2022	GLT_CLT	TOTAL	THS_NAC	906.115	7
AT	IMP_XEU	2022	GLT	TOTAL	THS_NAC	502.724	7
AT	IMP_XEU	2022	CLT	TOTAL	THS_NAC	403.391	7
AT	IMP_XEU	2022	I_BEAMS	TOTAL	THS_NAC	0.887	7
AT	EXP_XEU	2021	RW	TOTAL	THS_M3	9.2613333333
AT	EXP_XEU	2021	RW_FW	TOTAL	THS_M3	0.0783333333
AT	EXP_XEU	2021	RW_FW	CONIF	THS_M3	0
AT	EXP_XEU	2021	RW_FW	NCONIF	THS_M3	0.0783333333
AT	EXP_XEU	2021	RW_IN	TOTAL	THS_M3	9.183
AT	EXP_XEU	2021	RW_IN	CONIF	THS_M3	5.68
AT	EXP_XEU	2021	RW_IN	NCONIF	THS_M3	3.503
AT	EXP_XEU	2021	RW_IN	NC_TRO	THS_M3	0
AT	EXP_XEU	2021	CHA	TOTAL	THS_T	0.2753
AT	EXP_XEU	2021	CHP_RES	TOTAL	THS_M3	16.2864402575	4
AT	EXP_XEU	2021	CHP	TOTAL	THS_M3	4.1799309448
AT	EXP_XEU	2021	RES	TOTAL	THS_M3	12.1065093127	4
AT	EXP_XEU	2021	RES_SWD	TOTAL	THS_M3
AT	EXP_XEU	2021	RCW	TOTAL	THS_T
AT	EXP_XEU	2021	PEL_AGG	TOTAL	THS_T	27.2639
AT	EXP_XEU	2021	PEL	TOTAL	THS_T	27.1055
AT	EXP_XEU	2021	AGG	TOTAL	THS_T	0.1584
AT	EXP_XEU	2021	SN	TOTAL	THS_M3	1056.149471
AT	EXP_XEU	2021	SN	CONIF	THS_M3	1010.012
AT	EXP_XEU	2021	SN	NCONIF	THS_M3	46.137471
AT	EXP_XEU	2021	SN	NC_TRO	THS_M3	0.224
AT	EXP_XEU	2021	PN_VN	TOTAL	THS_M3	3.483
AT	EXP_XEU	2021	PN_VN	CONIF	THS_M3	0.319
AT	EXP_XEU	2021	PN_VN	NCONIF	THS_M3	3.164
AT	EXP_XEU	2021	PN_VN	NC_TRO	THS_M3	0.092
AT	EXP_XEU	2021	PN	TOTAL	THS_M3	621.119
AT	EXP_XEU	2021	PN_PY	TOTAL	THS_M3	101.152
AT	EXP_XEU	2021	PN_PY	CONIF	THS_M3
AT	EXP_XEU	2021	PN_PY	NCONIF	THS_M3
AT	EXP_XEU	2021	PN_PY	NC_TRO	THS_M3	0.113
AT	EXP_XEU	2021	PN_PY_LVL	TOTAL	THS_M3
AT	EXP_XEU	2021	PN_PY_LVL	CONIF	THS_M3
AT	EXP_XEU	2021	PN_PY_LVL	NCONIF	THS_M3
AT	EXP_XEU	2021	PN_PY_LVL	NC_TRO	THS_M3
AT	EXP_XEU	2021	PN_PB	TOTAL	THS_M3	344.047
AT	EXP_XEU	2021	PN_PB_OSB	TOTAL	THS_M3	1.188
AT	EXP_XEU	2021	PN_FB	TOTAL	THS_M3	175.92
AT	EXP_XEU	2021	PN_FB_HB	TOTAL	THS_M3	6.405
AT	EXP_XEU	2021	PN_FB_MDF	TOTAL	THS_M3	169.396
AT	EXP_XEU	2021	PN_FB_O	TOTAL	THS_M3	0.119
AT	EXP_XEU	2021	PL	TOTAL	THS_T	17.931273
AT	EXP_XEU	2021	PL_MC_SCH	TOTAL	THS_T	0.004277
AT	EXP_XEU	2021	PL_CH	TOTAL	THS_T	15.185229
AT	EXP_XEU	2021	PL_CH_SA	TOTAL	THS_T	15.162742
AT	EXP_XEU	2021	PL_CH_SAB	TOTAL	THS_T	9.772025
AT	EXP_XEU	2021	PL_CH_SI	TOTAL	THS_T	0.022487
AT	EXP_XEU	2021	PL_DS	TOTAL	THS_T	2.741767
AT	EXP_XEU	2021	PLO	TOTAL	THS_T	0.301329
AT	EXP_XEU	2021	PLO_NW	TOTAL	THS_T	0.013358
AT	EXP_XEU	2021	PLO_RC	TOTAL	THS_T	0.287971
AT	EXP_XEU	2021	RCP	TOTAL	THS_T	1.3359
AT	EXP_XEU	2021	PP	TOTAL	THS_T	638.8577
AT	EXP_XEU	2021	PP_GR	TOTAL	THS_T	475.7728
AT	EXP_XEU	2021	PP_GR_NP	TOTAL	THS_T	57.1537
AT	EXP_XEU	2021	PP_GR_MC	TOTAL	THS_T	21.5859
AT	EXP_XEU	2021	PP_GR_NW	TOTAL	THS_T	96.7492
AT	EXP_XEU	2021	PP_GR_CO	TOTAL	THS_T	300.284
AT	EXP_XEU	2021	PP_HS	TOTAL	THS_T	0.0101
AT	EXP_XEU	2021	PP_PK	TOTAL	THS_T	162.9066
AT	EXP_XEU	2021	PP_PK_CS	TOTAL	THS_T	78.465
AT	EXP_XEU	2021	PP_PK_CB	TOTAL	THS_T	39.4723
AT	EXP_XEU	2021	PP_PK_WR	TOTAL	THS_T	42.9131
AT	EXP_XEU	2021	PP_PK_O	TOTAL	THS_T	2.0562
AT	EXP_XEU	2021	PP_O	TOTAL	THS_T	0.1682
AT	EXP_XEU	2021	GLT_CLT	TOTAL	THS_M3
AT	EXP_XEU	2021	GLT	TOTAL	THS_M3
AT	EXP_XEU	2021	CLT	TOTAL	THS_M3
AT	EXP_XEU	2021	I_BEAMS	TOTAL	THS_T
AT	EXP_XEU	2021	RW	TOTAL	THS_NAC	2184.52
AT	EXP_XEU	2021	RW_FW	TOTAL	THS_NAC	39.555
AT	EXP_XEU	2021	RW_FW	CONIF	THS_NAC	0
AT	EXP_XEU	2021	RW_FW	NCONIF	THS_NAC	39.555
AT	EXP_XEU	2021	RW_IN	TOTAL	THS_NAC	2144.965
AT	EXP_XEU	2021	RW_IN	CONIF	THS_NAC	644.638
AT	EXP_XEU	2021	RW_IN	NCONIF	THS_NAC	1500.327
AT	EXP_XEU	2021	RW_IN	NC_TRO	THS_NAC	0
AT	EXP_XEU	2021	CHA	TOTAL	THS_NAC	172.486
AT	EXP_XEU	2021	CHP_RES	TOTAL	THS_NAC	1067.87	4
AT	EXP_XEU	2021	CHP	TOTAL	THS_NAC	500.093
AT	EXP_XEU	2021	RES	TOTAL	THS_NAC	567.777	4
AT	EXP_XEU	2021	RES_SWD	TOTAL	THS_NAC
AT	EXP_XEU	2021	RCW	TOTAL	THS_NAC
AT	EXP_XEU	2021	PEL_AGG	TOTAL	THS_NAC	5008.155
AT	EXP_XEU	2021	PEL	TOTAL	THS_NAC	4963.346
AT	EXP_XEU	2021	AGG	TOTAL	THS_NAC	44.809
AT	EXP_XEU	2021	SN	TOTAL	THS_NAC	421685.341
AT	EXP_XEU	2021	SN	CONIF	THS_NAC	393758.219
AT	EXP_XEU	2021	SN	NCONIF	THS_NAC	27927.122
AT	EXP_XEU	2021	SN	NC_TRO	THS_NAC	187.773
AT	EXP_XEU	2021	PN_VN	TOTAL	THS_NAC	14171.12
AT	EXP_XEU	2021	PN_VN	CONIF	THS_NAC	1459.935
AT	EXP_XEU	2021	PN_VN	NCONIF	THS_NAC	12711.185
AT	EXP_XEU	2021	PN_VN	NC_TRO	THS_NAC	541.48
AT	EXP_XEU	2021	PN	TOTAL	THS_NAC	375331.708
AT	EXP_XEU	2021	PN_PY	TOTAL	THS_NAC	91278.952
AT	EXP_XEU	2021	PN_PY	CONIF	THS_NAC
AT	EXP_XEU	2021	PN_PY	NCONIF	THS_NAC
AT	EXP_XEU	2021	PN_PY	NC_TRO	THS_NAC	402.24
AT	EXP_XEU	2021	PN_PY_LVL	TOTAL	THS_NAC
AT	EXP_XEU	2021	PN_PY_LVL	CONIF	THS_NAC
AT	EXP_XEU	2021	PN_PY_LVL	NCONIF	THS_NAC
AT	EXP_XEU	2021	PN_PY_LVL	NC_TRO	THS_NAC
AT	EXP_XEU	2021	PN_PB	TOTAL	THS_NAC	148895.353
AT	EXP_XEU	2021	PN_PB_OSB	TOTAL	THS_NAC	550.323
AT	EXP_XEU	2021	PN_FB	TOTAL	THS_NAC	135157.403
AT	EXP_XEU	2021	PN_FB_HB	TOTAL	THS_NAC	3241.801
AT	EXP_XEU	2021	PN_FB_MDF	TOTAL	THS_NAC	131854.122
AT	EXP_XEU	2021	PN_FB_O	TOTAL	THS_NAC	61.48
AT	EXP_XEU	2021	PL	TOTAL	THS_NAC	12097.602
AT	EXP_XEU	2021	PL_MC_SCH	TOTAL	THS_NAC	10.186
AT	EXP_XEU	2021	PL_CH	TOTAL	THS_NAC	9814.979
AT	EXP_XEU	2021	PL_CH_SA	TOTAL	THS_NAC	9772.385
AT	EXP_XEU	2021	PL_CH_SAB	TOTAL	THS_NAC	6684.887
AT	EXP_XEU	2021	PL_CH_SI	TOTAL	THS_NAC	42.594
AT	EXP_XEU	2021	PL_DS	TOTAL	THS_NAC	2272.437
AT	EXP_XEU	2021	PLO	TOTAL	THS_NAC	288.296
AT	EXP_XEU	2021	PLO_NW	TOTAL	THS_NAC	83.202
AT	EXP_XEU	2021	PLO_RC	TOTAL	THS_NAC	205.094
AT	EXP_XEU	2021	RCP	TOTAL	THS_NAC	240.829
AT	EXP_XEU	2021	PP	TOTAL	THS_NAC	472302.65
AT	EXP_XEU	2021	PP_GR	TOTAL	THS_NAC	340932.696
AT	EXP_XEU	2021	PP_GR_NP	TOTAL	THS_NAC	20825.809
AT	EXP_XEU	2021	PP_GR_MC	TOTAL	THS_NAC	9688.151
AT	EXP_XEU	2021	PP_GR_NW	TOTAL	THS_NAC	106955.803
AT	EXP_XEU	2021	PP_GR_CO	TOTAL	THS_NAC	203462.933
AT	EXP_XEU	2021	PP_HS	TOTAL	THS_NAC	48.83
AT	EXP_XEU	2021	PP_PK	TOTAL	THS_NAC	129499.004
AT	EXP_XEU	2021	PP_PK_CS	TOTAL	THS_NAC	40998.964
AT	EXP_XEU	2021	PP_PK_CB	TOTAL	THS_NAC	50407.09
AT	EXP_XEU	2021	PP_PK_WR	TOTAL	THS_NAC	36527.768
AT	EXP_XEU	2021	PP_PK_O	TOTAL	THS_NAC	1565.182
AT	EXP_XEU	2021	PP_O	TOTAL	THS_NAC	1822.12
AT	EXP_XEU	2021	GLT_CLT	TOTAL	THS_NAC
AT	EXP_XEU	2021	GLT	TOTAL	THS_NAC
AT	EXP_XEU	2021	CLT	TOTAL	THS_NAC
AT	EXP_XEU	2021	I_BEAMS	TOTAL	THS_NAC
AT	EXP_XEU	2022	RW	TOTAL	THS_M3	11.025	7
AT	EXP_XEU	2022	RW_FW	TOTAL	THS_M3	0.105	7
AT	EXP_XEU	2022	RW_FW	CONIF	THS_M3		7
AT	EXP_XEU	2022	RW_FW	NCONIF	THS_M3	0.105	7
AT	EXP_XEU	2022	RW_IN	TOTAL	THS_M3	10.919	7
AT	EXP_XEU	2022	RW_IN	CONIF	THS_M3	5.809	7
AT	EXP_XEU	2022	RW_IN	NCONIF	THS_M3	5.11	7
AT	EXP_XEU	2022	RW_IN	NC_TRO	THS_M3	0	7
AT	EXP_XEU	2022	CHA	TOTAL	THS_T	0.943	7
AT	EXP_XEU	2022	CHP_RES	TOTAL	THS_M3	17.141	4
AT	EXP_XEU	2022	CHP	TOTAL	THS_M3	8.197	7
AT	EXP_XEU	2022	RES	TOTAL	THS_M3	8.944	4
AT	EXP_XEU	2022	RES_SWD	TOTAL	THS_M3	6.2	7
AT	EXP_XEU	2022	RCW	TOTAL	THS_T
AT	EXP_XEU	2022	PEL_AGG	TOTAL	THS_T	39.789	7
AT	EXP_XEU	2022	PEL	TOTAL	THS_T	39.734	7
AT	EXP_XEU	2022	AGG	TOTAL	THS_T	0.055	7
AT	EXP_XEU	2022	SN	TOTAL	THS_M3	1050.766	7
AT	EXP_XEU	2022	SN	CONIF	THS_M3	986.551	7
AT	EXP_XEU	2022	SN	NCONIF	THS_M3	42.774	7
AT	EXP_XEU	2022	SN	NC_TRO	THS_M3	0.084	7
AT	EXP_XEU	2022	PN_VN	TOTAL	THS_M3	4.653	7
AT	EXP_XEU	2022	PN_VN	CONIF	THS_M3	0.373	7
AT	EXP_XEU	2022	PN_VN	NCONIF	THS_M3	4.28	7
AT	EXP_XEU	2022	PN_VN	NC_TRO	THS_M3	0.47	7
AT	EXP_XEU	2022	PN	TOTAL	THS_M3	509.345	7
AT	EXP_XEU	2022	PN_PY	TOTAL	THS_M3	92.401	7
AT	EXP_XEU	2022	PN_PY	CONIF	THS_M3	87.088	7
AT	EXP_XEU	2022	PN_PY	NCONIF	THS_M3	5.165	7
AT	EXP_XEU	2022	PN_PY	NC_TRO	THS_M3	0.148	7
AT	EXP_XEU	2022	PN_PY_LVL	TOTAL	THS_M3	0.062	7
AT	EXP_XEU	2022	PN_PY_LVL	CONIF	THS_M3	0	7
AT	EXP_XEU	2022	PN_PY_LVL	NCONIF	THS_M3	0.051	7
AT	EXP_XEU	2022	PN_PY_LVL	NC_TRO	THS_M3	0.011	7
AT	EXP_XEU	2022	PN_PB	TOTAL	THS_M3	302.309	7
AT	EXP_XEU	2022	PN_PB_OSB	TOTAL	THS_M3	1.675	7
AT	EXP_XEU	2022	PN_FB	TOTAL	THS_M3	114.635	7
AT	EXP_XEU	2022	PN_FB_HB	TOTAL	THS_M3	5.543	7
AT	EXP_XEU	2022	PN_FB_MDF	TOTAL	THS_M3	108.795	7
AT	EXP_XEU	2022	PN_FB_O	TOTAL	THS_M3	0.297	7
AT	EXP_XEU	2022	PL	TOTAL	THS_T	28.998851	7
AT	EXP_XEU	2022	PL_MC_SCH	TOTAL	THS_T	0.019415	7
AT	EXP_XEU	2022	PL_CH	TOTAL	THS_T	25.222095	7
AT	EXP_XEU	2022	PL_CH_SA	TOTAL	THS_T	24.437168	7
AT	EXP_XEU	2022	PL_CH_SAB	TOTAL	THS_T	13.371568	7
AT	EXP_XEU	2022	PL_CH_SI	TOTAL	THS_T	0.784927	7
AT	EXP_XEU	2022	PL_DS	TOTAL	THS_T	3.757341	7
AT	EXP_XEU	2022	PLO	TOTAL	THS_T	0.322551	7
AT	EXP_XEU	2022	PLO_NW	TOTAL	THS_T	0.022189	7
AT	EXP_XEU	2022	PLO_RC	TOTAL	THS_T	0.300362	7
AT	EXP_XEU	2022	RCP	TOTAL	THS_T	5.037	7
AT	EXP_XEU	2022	PP	TOTAL	THS_T	512.838	7
AT	EXP_XEU	2022	PP_GR	TOTAL	THS_T	378.363	7
AT	EXP_XEU	2022	PP_GR_NP	TOTAL	THS_T	52.685	7
AT	EXP_XEU	2022	PP_GR_MC	TOTAL	THS_T	4.136	7
AT	EXP_XEU	2022	PP_GR_NW	TOTAL	THS_T	75.249	7
AT	EXP_XEU	2022	PP_GR_CO	TOTAL	THS_T	246.293	7
AT	EXP_XEU	2022	PP_HS	TOTAL	THS_T	0.037	7
AT	EXP_XEU	2022	PP_PK	TOTAL	THS_T	134.193	7
AT	EXP_XEU	2022	PP_PK_CS	TOTAL	THS_T	63.938	7
AT	EXP_XEU	2022	PP_PK_CB	TOTAL	THS_T	25.788	7
AT	EXP_XEU	2022	PP_PK_WR	TOTAL	THS_T	42.413	7
AT	EXP_XEU	2022	PP_PK_O	TOTAL	THS_T	2.054	7
AT	EXP_XEU	2022	PP_O	TOTAL	THS_T	0.245	7
AT	EXP_XEU	2022	GLT_CLT	TOTAL	THS_M3	300.92	7
AT	EXP_XEU	2022	GLT	TOTAL	THS_M3	232.58	7
AT	EXP_XEU	2022	CLT	TOTAL	THS_M3	68.34	7
AT	EXP_XEU	2022	I_BEAMS	TOTAL	THS_T	0.0001	7
AT	EXP_XEU	2022	RW	TOTAL	THS_NAC	3565.683	7
AT	EXP_XEU	2022	RW_FW	TOTAL	THS_NAC	47.967	7
AT	EXP_XEU	2022	RW_FW	CONIF	THS_NAC		7
AT	EXP_XEU	2022	RW_FW	NCONIF	THS_NAC	47.967	7
AT	EXP_XEU	2022	RW_IN	TOTAL	THS_NAC	3517.716	7
AT	EXP_XEU	2022	RW_IN	CONIF	THS_NAC	839.027	7
AT	EXP_XEU	2022	RW_IN	NCONIF	THS_NAC	2678.689	7
AT	EXP_XEU	2022	RW_IN	NC_TRO	THS_NAC	0	7
AT	EXP_XEU	2022	CHA	TOTAL	THS_NAC	495.212	7
AT	EXP_XEU	2022	CHP_RES	TOTAL	THS_NAC	1429.413	4
AT	EXP_XEU	2022	CHP	TOTAL	THS_NAC	818.386	7
AT	EXP_XEU	2022	RES	TOTAL	THS_NAC	611.027	4
AT	EXP_XEU	2022	RES_SWD	TOTAL	THS_NAC	535.828	7
AT	EXP_XEU	2022	RCW	TOTAL	THS_NAC
AT	EXP_XEU	2022	PEL_AGG	TOTAL	THS_NAC	15349.542	7
AT	EXP_XEU	2022	PEL	TOTAL	THS_NAC	15319.593	7
AT	EXP_XEU	2022	AGG	TOTAL	THS_NAC	29.949	7
AT	EXP_XEU	2022	SN	TOTAL	THS_NAC	453823.308	7
AT	EXP_XEU	2022	SN	CONIF	THS_NAC	422009.168	7
AT	EXP_XEU	2022	SN	NCONIF	THS_NAC	31814.14	7
AT	EXP_XEU	2022	SN	NC_TRO	THS_NAC	145.641	7
AT	EXP_XEU	2022	PN_VN	TOTAL	THS_NAC	15925.124	7
AT	EXP_XEU	2022	PN_VN	CONIF	THS_NAC	1359.063	7
AT	EXP_XEU	2022	PN_VN	NCONIF	THS_NAC	14566.061	7
AT	EXP_XEU	2022	PN_VN	NC_TRO	THS_NAC	508.694	7
AT	EXP_XEU	2022	PN	TOTAL	THS_NAC	380580.16	7
AT	EXP_XEU	2022	PN_PY	TOTAL	THS_NAC	95514.131	7
AT	EXP_XEU	2022	PN_PY	CONIF	THS_NAC	81762.709	7
AT	EXP_XEU	2022	PN_PY	NCONIF	THS_NAC	12913	7
AT	EXP_XEU	2022	PN_PY	NC_TRO	THS_NAC	838.122	7
AT	EXP_XEU	2022	PN_PY_LVL	TOTAL	THS_NAC	112.108	7
AT	EXP_XEU	2022	PN_PY_LVL	CONIF	THS_NAC	0	7
AT	EXP_XEU	2022	PN_PY_LVL	NCONIF	THS_NAC	112	7
AT	EXP_XEU	2022	PN_PY_LVL	NC_TRO	THS_NAC	112	7
AT	EXP_XEU	2022	PN_PB	TOTAL	THS_NAC	163899.029	7
AT	EXP_XEU	2022	PN_PB_OSB	TOTAL	THS_NAC	852.223	7
AT	EXP_XEU	2022	PN_FB	TOTAL	THS_NAC	121167	7
AT	EXP_XEU	2022	PN_FB_HB	TOTAL	THS_NAC	3128.449	7
AT	EXP_XEU	2022	PN_FB_MDF	TOTAL	THS_NAC	117897.358	7
AT	EXP_XEU	2022	PN_FB_O	TOTAL	THS_NAC	140.346	7
AT	EXP_XEU	2022	PL	TOTAL	THS_NAC	23428.451	7
AT	EXP_XEU	2022	PL_MC_SCH	TOTAL	THS_NAC	6.349	7
AT	EXP_XEU	2022	PL_CH	TOTAL	THS_NAC	20017.829	7
AT	EXP_XEU	2022	PL_CH_SA	TOTAL	THS_NAC	19250.365	7
AT	EXP_XEU	2022	PL_CH_SAB	TOTAL	THS_NAC	11664.728	7
AT	EXP_XEU	2022	PL_CH_SI	TOTAL	THS_NAC	767.464	7
AT	EXP_XEU	2022	PL_DS	TOTAL	THS_NAC	3404.273	7
AT	EXP_XEU	2022	PLO	TOTAL	THS_NAC	418.786	7
AT	EXP_XEU	2022	PLO_NW	TOTAL	THS_NAC	155.352	7
AT	EXP_XEU	2022	PLO_RC	TOTAL	THS_NAC	263.434	7
AT	EXP_XEU	2022	RCP	TOTAL	THS_NAC	986.733	7
AT	EXP_XEU	2022	PP	TOTAL	THS_NAC	605175.904	7
AT	EXP_XEU	2022	PP_GR	TOTAL	THS_NAC	444185.148	7
AT	EXP_XEU	2022	PP_GR_NP	TOTAL	THS_NAC	39589.141	7
AT	EXP_XEU	2022	PP_GR_MC	TOTAL	THS_NAC	3873.153	7
AT	EXP_XEU	2022	PP_GR_NW	TOTAL	THS_NAC	132464.636	7
AT	EXP_XEU	2022	PP_GR_CO	TOTAL	THS_NAC	268258.218	7
AT	EXP_XEU	2022	PP_HS	TOTAL	THS_NAC	149.243	7
AT	EXP_XEU	2022	PP_PK	TOTAL	THS_NAC	158554.429	7
AT	EXP_XEU	2022	PP_PK_CS	TOTAL	THS_NAC	47511.042	7
AT	EXP_XEU	2022	PP_PK_CB	TOTAL	THS_NAC	56009.381	7
AT	EXP_XEU	2022	PP_PK_WR	TOTAL	THS_NAC	52864.637	7
AT	EXP_XEU	2022	PP_PK_O	TOTAL	THS_NAC	2169.369	7
AT	EXP_XEU	2022	PP_O	TOTAL	THS_NAC	2287.084	7
AT	EXP_XEU	2022	GLT_CLT	TOTAL	THS_NAC	227065.153	7
AT	EXP_XEU	2022	GLT	TOTAL	THS_NAC	167215.859	7
AT	EXP_XEU	2022	CLT	TOTAL	THS_NAC	59849.294	7
AT	EXP_XEU	2022	I_BEAMS	TOTAL	THS_NAC	0.873	7
AT	IMP	2021	SW	TOTAL	THS_NAC	2124453.761
AT	IMP	2021	SW_SN	TOTAL	THS_NAC	114220.664
AT	IMP	2021	SW_SN	CONIF	THS_NAC	85551.615
AT	IMP	2021	SW_SN	NCONIF	THS_NAC	28669.049
AT	IMP	2021	SW_SN	NC_TRO	THS_NAC	1412.087
AT	IMP	2021	SW_WR	TOTAL	THS_NAC	187864.428
AT	IMP	2021	SW_DM	TOTAL	THS_NAC	42768.477
AT	IMP	2021	SW_JN	TOTAL	THS_NAC	356146.624
AT	IMP	2021	SW_FU	TOTAL	THS_NAC	1263095.169
AT	IMP	2021	SW_BL_W	TOTAL	THS_NAC	31305.051
AT	IMP	2021	SW_O	TOTAL	THS_NAC	129053.348
AT	IMP	2021	SP	TOTAL	THS_NAC	1059809.2
AT	IMP	2021	SP_CM	TOTAL	THS_NAC	11165.749
AT	IMP	2021	SP_SCO	TOTAL	THS_NAC	164323.138
AT	IMP	2021	SP_HS	TOTAL	THS_NAC	217798.251
AT	IMP	2021	SP_PK	TOTAL	THS_NAC	394384.648
AT	IMP	2021	SP_O	TOTAL	THS_NAC	272137.414
AT	IMP	2021	SP_O_PR	TOTAL	THS_NAC	5637.838
AT	IMP	2021	SP_O_AR	TOTAL	THS_NAC	16223.446
AT	IMP	2021	SP_O_FL	TOTAL	THS_NAC	23528.346
AT	IMP	2022	SW	TOTAL	THS_NAC	2286128	7
AT	IMP	2022	SW_SN	TOTAL	THS_NAC	107867.53	7
AT	IMP	2022	SW_SN	CONIF	THS_NAC	79439.533	7
AT	IMP	2022	SW_SN	NCONIF	THS_NAC	28427.997	7
AT	IMP	2022	SW_SN	NC_TRO	THS_NAC	2337.977	7
AT	IMP	2022	SW_WR	TOTAL	THS_NAC	256196.296	7
AT	IMP	2022	SW_DM	TOTAL	THS_NAC	44840.896	7
AT	IMP	2022	SW_JN	TOTAL	THS_NAC	337874.9	7
AT	IMP	2022	SW_FU	TOTAL	THS_NAC	1339512.31	7
AT	IMP	2022	SW_BL_W	TOTAL	THS_NAC	46447.461	7
AT	IMP	2022	SW_O	TOTAL	THS_NAC	153388.352	7
AT	IMP	2022	SP	TOTAL	THS_NAC	1242828.47	7
AT	IMP	2022	SP_CM	TOTAL	THS_NAC	13283.425	7
AT	IMP	2022	SP_SCO	TOTAL	THS_NAC	188904.706	7
AT	IMP	2022	SP_HS	TOTAL	THS_NAC	306832.627	7
AT	IMP	2022	SP_PK	TOTAL	THS_NAC	468293.169	7
AT	IMP	2022	SP_O	TOTAL	THS_NAC	265514.542	7
AT	IMP	2022	SP_O_PR	TOTAL	THS_NAC	7253.393	7
AT	IMP	2022	SP_O_AR	TOTAL	THS_NAC	17409.815	7
AT	IMP	2022	SP_O_FL	TOTAL	THS_NAC	23234.355	7
AT	EXP	2021	SW	TOTAL	THS_NAC	2666947.743
AT	EXP	2021	SW_SN	TOTAL	THS_NAC	135567.676
AT	EXP	2021	SW_SN	CONIF	THS_NAC	105680.377
AT	EXP	2021	SW_SN	NCONIF	THS_NAC	29887.299
AT	EXP	2021	SW_SN	NC_TRO	THS_NAC	483.081
AT	EXP	2021	SW_WR	TOTAL	THS_NAC	87006.612
AT	EXP	2021	SW_DM	TOTAL	THS_NAC	11817.777
AT	EXP	2021	SW_JN	TOTAL	THS_NAC	1756523.897
AT	EXP	2021	SW_FU	TOTAL	THS_NAC	572922.681
AT	EXP	2021	SW_BL_W	TOTAL	THS_NAC	48618.649
AT	EXP	2021	SW_O	TOTAL	THS_NAC	54490.451
AT	EXP	2021	SP	TOTAL	THS_NAC	1207039.512
AT	EXP	2021	SP_CM	TOTAL	THS_NAC	392.55
AT	EXP	2021	SP_SCO	TOTAL	THS_NAC	93834.593
AT	EXP	2021	SP_HS	TOTAL	THS_NAC	146381.592
AT	EXP	2021	SP_PK	TOTAL	THS_NAC	781761.234
AT	EXP	2021	SP_O	TOTAL	THS_NAC	184669.543
AT	EXP	2021	SP_O_PR	TOTAL	THS_NAC	3355.899
AT	EXP	2021	SP_O_AR	TOTAL	THS_NAC	2938.542
AT	EXP	2021	SP_O_FL	TOTAL	THS_NAC	5840.864
AT	EXP	2022	SW	TOTAL	THS_NAC	1801380.51	7
AT	EXP	2022	SW_SN	TOTAL	THS_NAC	147611.962	7
AT	EXP	2022	SW_SN	CONIF	THS_NAC	116978.656	7
AT	EXP	2022	SW_SN	NCONIF	THS_NAC	30633.306	7
AT	EXP	2022	SW_SN	NC_TRO	THS_NAC	764.692	7
AT	EXP	2022	SW_WR	TOTAL	THS_NAC	124804.304	7
AT	EXP	2022	SW_DM	TOTAL	THS_NAC	13394.893	7
AT	EXP	2022	SW_JN	TOTAL	THS_NAC	745413.87	7
AT	EXP	2022	SW_FU	TOTAL	THS_NAC	632016.607	7
AT	EXP	2022	SW_BL_W	TOTAL	THS_NAC	69303.757	7
AT	EXP	2022	SW_O	TOTAL	THS_NAC	68835.112	7
AT	EXP	2022	SP	TOTAL	THS_NAC	1396362.95	7
AT	EXP	2022	SP_CM	TOTAL	THS_NAC	343.24	7
AT	EXP	2022	SP_SCO	TOTAL	THS_NAC	111442.968	7
AT	EXP	2022	SP_HS	TOTAL	THS_NAC	192916.283	7
AT	EXP	2022	SP_PK	TOTAL	THS_NAC	891718.299	7
AT	EXP	2022	SP_O	TOTAL	THS_NAC	199942.155	7
AT	EXP	2022	SP_O_PR	TOTAL	THS_NAC	3512.436	7
AT	EXP	2022	SP_O_AR	TOTAL	THS_NAC	6101.051	7
AT	EXP	2022	SP_O_FL	TOTAL	THS_NAC	8537.882	7
AT	IMP	2021	ST_1_2	CONIF	THS_M3	10017.644
AT	IMP	2021	ST_1_2	C_PIN	THS_M3	628.423
AT	IMP	2021	ST_1_2_1	C_PIN	THS_M3	298.946
AT	IMP	2021	ST_1_2_2	C_PIN	THS_M3	329.477
AT	IMP	2021	ST_1_2	C_FIR	THS_M3	8634.032
AT	IMP	2021	ST_1_2_1	C_FIR	THS_M3	7190.744
AT	IMP	2021	ST_1_2_2	C_FIR	THS_M3	1443.288
AT	IMP	2021	ST_1_2	NCONIF	THS_M3	885.392
AT	IMP	2021	ST_1_2	NC_OAK	THS_M3	57.844
AT	IMP	2021	ST_1_2	NC_BEE	THS_M3	702.534
AT	IMP	2021	ST_1_2	NC_BIR	THS_M3	5.099
AT	IMP	2021	ST_1_2_1	NC_BIR	THS_M3	0.376
AT	IMP	2021	ST_1_2_2	NC_BIR	THS_M3	4.723
AT	IMP	2021	ST_1_2	NC_POP	THS_M3	10.291
AT	IMP	2021	ST_1_2	NC_EUC	THS_M3	0.0005
AT	IMP	2021	ST_6	CONIF	THS_M3	1911.235
AT	IMP	2021	ST_6	C_PIN	THS_M3	70.746
AT	IMP	2021	ST_6	C_FIR	THS_M3	1634.831
AT	IMP	2021	ST_6	NCONIF	THS_M3	176.890229
AT	IMP	2021	ST_6	NC_OAK	THS_M3	105.695229
AT	IMP	2021	ST_6	NC_BEE	THS_M3	15.966
AT	IMP	2021	ST_6	NC_MAP	THS_M3	1.514
AT	IMP	2021	ST_6	NC_CHE	THS_M3	0.64
AT	IMP	2021	ST_6	NC_ASH	THS_M3	6.953
AT	IMP	2021	ST_6	NC_BIR	THS_M3	2.578
AT	IMP	2021	ST_6	NC_POP	THS_M3	0.99
AT	IMP	2021	ST_1_2	CONIF	THS_NAC	736655.091
AT	IMP	2021	ST_1_2	C_PIN	THS_NAC	32329.197
AT	IMP	2021	ST_1_2_1	C_PIN	THS_NAC	17842.816
AT	IMP	2021	ST_1_2_2	C_PIN	THS_NAC	14486.381
AT	IMP	2021	ST_1_2	C_FIR	THS_NAC	641042.451
AT	IMP	2021	ST_1_2_1	C_FIR	THS_NAC	577795.661
AT	IMP	2021	ST_1_2_2	C_FIR	THS_NAC	63246.79
AT	IMP	2021	ST_1_2	NCONIF	THS_NAC	76622.175
AT	IMP	2021	ST_1_2	NC_OAK	THS_NAC	19580.758
AT	IMP	2021	ST_1_2	NC_BEE	THS_NAC	44480.815
AT	IMP	2021	ST_1_2	NC_BIR	THS_NAC	414.17
AT	IMP	2021	ST_1_2_1	NC_BIR	THS_NAC	126.012
AT	IMP	2021	ST_1_2_2	NC_BIR	THS_NAC	288.158
AT	IMP	2021	ST_1_2	NC_POP	THS_NAC	502.682
AT	IMP	2021	ST_1_2	NC_EUC	THS_NAC	3.529
AT	IMP	2021	ST_6	CONIF	THS_NAC	567134.475
AT	IMP	2021	ST_6	C_PIN	THS_NAC	20140.503
AT	IMP	2021	ST_6	C_FIR	THS_NAC	462206.308
AT	IMP	2021	ST_6	NCONIF	THS_NAC	130813.928
AT	IMP	2021	ST_6	NC_OAK	THS_NAC	84117.74
AT	IMP	2021	ST_6	NC_BEE	THS_NAC	7249.112
AT	IMP	2021	ST_6	NC_MAP	THS_NAC	1288.034
AT	IMP	2021	ST_6	NC_CHE	THS_NAC	519.966
AT	IMP	2021	ST_6	NC_ASH	THS_NAC	4526.401
AT	IMP	2021	ST_6	NC_BIR	THS_NAC	1142.228
AT	IMP	2021	ST_6	NC_POP	THS_NAC	670.176
AT	IMP	2022	ST_1_2	CONIF	THS_M3	7975.906	7
AT	IMP	2022	ST_1_2	C_PIN	THS_M3	625.888	7
AT	IMP	2022	ST_1_2_1	C_PIN	THS_M3	194.043	7
AT	IMP	2022	ST_1_2_2	C_PIN	THS_M3	34.737	7
AT	IMP	2022	ST_1_2	C_FIR	THS_M3	6826.536	7
AT	IMP	2022	ST_1_2_1	C_FIR	THS_M3	6012.71	7
AT	IMP	2022	ST_1_2_2	C_FIR	THS_M3	813.826	7
AT	IMP	2022	ST_1_2	NCONIF	THS_M3	543.003	7
AT	IMP	2022	ST_1_2	NC_OAK	THS_M3	74.526	7
AT	IMP	2022	ST_1_2	NC_BEE	THS_M3	386.9	7
AT	IMP	2022	ST_1_2	NC_BIR	THS_M3	4.449	7
AT	IMP	2022	ST_1_2_1	NC_BIR	THS_M3	1.278	7
AT	IMP	2022	ST_1_2_2	NC_BIR	THS_M3	3.171	7
AT	IMP	2022	ST_1_2	NC_POP	THS_M3	16.869	7
AT	IMP	2022	ST_1_2	NC_EUC	THS_M3	0	7
AT	IMP	2022	ST_6	CONIF	THS_M3	1785.572	7
AT	IMP	2022	ST_6	C_PIN	THS_M3	203.339	7
AT	IMP	2022	ST_6	C_FIR	THS_M3	1442.308	7
AT	IMP	2022	ST_6	NCONIF	THS_M3	214.696	7
AT	IMP	2022	ST_6	NC_OAK	THS_M3	130.285	7
AT	IMP	2022	ST_6	NC_BEE	THS_M3	19.261	7
AT	IMP	2022	ST_6	NC_MAP	THS_M3	1.768	7
AT	IMP	2022	ST_6	NC_CHE	THS_M3	1.004	7
AT	IMP	2022	ST_6	NC_ASH	THS_M3	8.861	7
AT	IMP	2022	ST_6	NC_BIR	THS_M3	3.183	7
AT	IMP	2022	ST_6	NC_POP	THS_M3	1.075	7
AT	IMP	2022	ST_1_2	CONIF	THS_NAC	743290.23	7
AT	IMP	2022	ST_1_2	C_PIN	THS_NAC	40805.951	7
AT	IMP	2022	ST_1_2_1	C_PIN	THS_NAC	19980.168	7
AT	IMP	2022	ST_1_2_2	C_PIN	THS_NAC	1562	7
AT	IMP	2022	ST_1_2	C_FIR	THS_NAC	644490.088	7
AT	IMP	2022	ST_1_2_1	C_FIR	THS_NAC	594719.38	7
AT	IMP	2022	ST_1_2_2	C_FIR	THS_NAC	49770.708	7
AT	IMP	2022	ST_1_2	NCONIF	THS_NAC	72758.551	7
AT	IMP	2022	ST_1_2	NC_OAK	THS_NAC	33441.255	7
AT	IMP	2022	ST_1_2	NC_BEE	THS_NAC	29627.371	7
AT	IMP	2022	ST_1_2	NC_BIR	THS_NAC	301.194	7
AT	IMP	2022	ST_1_2_1	NC_BIR	THS_NAC	102.429	7
AT	IMP	2022	ST_1_2_2	NC_BIR	THS_NAC	198.765	7
AT	IMP	2022	ST_1_2	NC_POP	THS_NAC	699.791	7
AT	IMP	2022	ST_1_2	NC_EUC	THS_NAC	0	7
AT	IMP	2022	ST_6	CONIF	THS_NAC	552085.779	7
AT	IMP	2022	ST_6	C_PIN	THS_NAC	35252.426	7
AT	IMP	2022	ST_6	C_FIR	THS_NAC	442688.823	7
AT	IMP	2022	ST_6	NCONIF	THS_NAC	196440.374	7
AT	IMP	2022	ST_6	NC_OAK	THS_NAC	128898.823	7
AT	IMP	2022	ST_6	NC_BEE	THS_NAC	8634.28	7
AT	IMP	2022	ST_6	NC_MAP	THS_NAC	2146.041	7
AT	IMP	2022	ST_6	NC_CHE	THS_NAC	798.764	7
AT	IMP	2022	ST_6	NC_ASH	THS_NAC	6592.176	7
AT	IMP	2022	ST_6	NC_BIR	THS_NAC	2015.277	7
AT	IMP	2022	ST_6	NC_POP	THS_NAC	1354.952	7
AT	EXP	2021	ST_1_2	CONIF	THS_M3	954.007
AT	EXP	2021	ST_1_2	C_PIN	THS_M3	108.191
AT	EXP	2021	ST_1_2_1	C_PIN	THS_M3	91.183
AT	EXP	2021	ST_1_2_2	C_PIN	THS_M3	17.008
AT	EXP	2021	ST_1_2	C_FIR	THS_M3	737.844
AT	EXP	2021	ST_1_2_1	C_FIR	THS_M3	409.278
AT	EXP	2021	ST_1_2_2	C_FIR	THS_M3	328.566
AT	EXP	2021	ST_1_2	NCONIF	THS_M3	138.822
AT	EXP	2021	ST_1_2	NC_OAK	THS_M3	41.31
AT	EXP	2021	ST_1_2	NC_BEE	THS_M3	33.847
AT	EXP	2021	ST_1_2	NC_BIR	THS_M3	0.329
AT	EXP	2021	ST_1_2_1	NC_BIR	THS_M3	0.002
AT	EXP	2021	ST_1_2_2	NC_BIR	THS_M3	0.327
AT	EXP	2021	ST_1_2	NC_POP	THS_M3	17.072
AT	EXP	2021	ST_1_2	NC_EUC	THS_M3	0
AT	EXP	2021	ST_6	CONIF	THS_M3	5946.608
AT	EXP	2021	ST_6	C_PIN	THS_M3	154.494
AT	EXP	2021	ST_6	C_FIR	THS_M3	5458.563
AT	EXP	2021	ST_6	NCONIF	THS_M3	172.966148
AT	EXP	2021	ST_6	NC_OAK	THS_M3	54.081148
AT	EXP	2021	ST_6	NC_BEE	THS_M3	55.373
AT	EXP	2021	ST_6	NC_MAP	THS_M3	1.662
AT	EXP	2021	ST_6	NC_CHE	THS_M3	0.434
AT	EXP	2021	ST_6	NC_ASH	THS_M3	14.12
AT	EXP	2021	ST_6	NC_BIR	THS_M3	1.142
AT	EXP	2021	ST_6	NC_POP	THS_M3	0.507
AT	EXP	2021	ST_1_2	CONIF	THS_NAC	77676.779
AT	EXP	2021	ST_1_2	C_PIN	THS_NAC	8284.854
AT	EXP	2021	ST_1_2_1	C_PIN	THS_NAC	6977.939
AT	EXP	2021	ST_1_2_2	C_PIN	THS_NAC	1306.915
AT	EXP	2021	ST_1_2	C_FIR	THS_NAC	60103.725
AT	EXP	2021	ST_1_2_1	C_FIR	THS_NAC	33744.427
AT	EXP	2021	ST_1_2_2	C_FIR	THS_NAC	26359.298
AT	EXP	2021	ST_1_2	NCONIF	THS_NAC	19552.272
AT	EXP	2021	ST_1_2	NC_OAK	THS_NAC	9375.45
AT	EXP	2021	ST_1_2	NC_BEE	THS_NAC	2841.754
AT	EXP	2021	ST_1_2	NC_BIR	THS_NAC	152.788
AT	EXP	2021	ST_1_2_1	NC_BIR	THS_NAC	0.469
AT	EXP	2021	ST_1_2_2	NC_BIR	THS_NAC	152.319
AT	EXP	2021	ST_1_2	NC_POP	THS_NAC	1404.349
AT	EXP	2021	ST_1_2	NC_EUC	THS_NAC	0
AT	EXP	2021	ST_6	CONIF	THS_NAC	1870911.483
AT	EXP	2021	ST_6	C_PIN	THS_NAC	41115.102
AT	EXP	2021	ST_6	C_FIR	THS_NAC	1689486.885
AT	EXP	2021	ST_6	NCONIF	THS_NAC	114871.576
AT	EXP	2021	ST_6	NC_OAK	THS_NAC	55749.894
AT	EXP	2021	ST_6	NC_BEE	THS_NAC	23204.147
AT	EXP	2021	ST_6	NC_MAP	THS_NAC	998.966
AT	EXP	2021	ST_6	NC_CHE	THS_NAC	431.167
AT	EXP	2021	ST_6	NC_ASH	THS_NAC	9205.586
AT	EXP	2021	ST_6	NC_BIR	THS_NAC	307.339
AT	EXP	2021	ST_6	NC_POP	THS_NAC	633.017
AT	EXP	2022	ST_1_2	CONIF	THS_M3	1151.168	7
AT	EXP	2022	ST_1_2	C_PIN	THS_M3	228.78	7
AT	EXP	2022	ST_1_2_1	C_PIN	THS_M3	194.043	7
AT	EXP	2022	ST_1_2_2	C_PIN	THS_M3	34.737	7
AT	EXP	2022	ST_1_2	C_FIR	THS_M3	818.579	7
AT	EXP	2022	ST_1_2_1	C_FIR	THS_M3	660.249	7
AT	EXP	2022	ST_1_2_2	C_FIR	THS_M3	158.33	7
AT	EXP	2022	ST_1_2	NCONIF	THS_M3	155.538	7
AT	EXP	2022	ST_1_2	NC_OAK	THS_M3	37.988	7
AT	EXP	2022	ST_1_2	NC_BEE	THS_M3	48.665	7
AT	EXP	2022	ST_1_2	NC_BIR	THS_M3	0.542	7
AT	EXP	2022	ST_1_2_1	NC_BIR	THS_M3	0	7
AT	EXP	2022	ST_1_2_2	NC_BIR	THS_M3	0.542	7
AT	EXP	2022	ST_1_2	NC_POP	THS_M3	31.001	7
AT	EXP	2022	ST_1_2	NC_EUC	THS_M3	0	7
AT	EXP	2022	ST_6	CONIF	THS_M3	5730.805	7
AT	EXP	2022	ST_6	C_PIN	THS_M3	215.48	7
AT	EXP	2022	ST_6	C_FIR	THS_M3	5251.969	7
AT	EXP	2022	ST_6	NCONIF	THS_M3	145.138	7
AT	EXP	2022	ST_6	NC_OAK	THS_M3	50.318	7
AT	EXP	2022	ST_6	NC_BEE	THS_M3	49.399	7
AT	EXP	2022	ST_6	NC_MAP	THS_M3	1.337	7
AT	EXP	2022	ST_6	NC_CHE	THS_M3	0.496	7
AT	EXP	2022	ST_6	NC_ASH	THS_M3	16.38	7
AT	EXP	2022	ST_6	NC_BIR	THS_M3	0.768	7
AT	EXP	2022	ST_6	NC_POP	THS_M3	0.717	7
AT	EXP	2022	ST_1_2	CONIF	THS_NAC	114972.276	7
AT	EXP	2022	ST_1_2	C_PIN	THS_NAC	21542.45	7
AT	EXP	2022	ST_1_2_1	C_PIN	THS_NAC	19980.168	7
AT	EXP	2022	ST_1_2_2	C_PIN	THS_NAC	1562	7
AT	EXP	2022	ST_1_2	C_FIR	THS_NAC	82679.638	7
AT	EXP	2022	ST_1_2_1	C_FIR	THS_NAC	68722.611	7
AT	EXP	2022	ST_1_2_2	C_FIR	THS_NAC	13957	7
AT	EXP	2022	ST_1_2	NCONIF	THS_NAC	28467.726	7
AT	EXP	2022	ST_1_2	NC_OAK	THS_NAC	12939.068	7
AT	EXP	2022	ST_1_2	NC_BEE	THS_NAC	5102	7
AT	EXP	2022	ST_1_2	NC_BIR	THS_NAC	35.379	7
AT	EXP	2022	ST_1_2_1	NC_BIR	THS_NAC	0.207	7
AT	EXP	2022	ST_1_2_2	NC_BIR	THS_NAC	35.172	7
AT	EXP	2022	ST_1_2	NC_POP	THS_NAC	3194.816	7
AT	EXP	2022	ST_1_2	NC_EUC	THS_NAC	0	7
AT	EXP	2022	ST_6	CONIF	THS_NAC	1895011.47	7
AT	EXP	2022	ST_6	C_PIN	THS_NAC	66789.845	7
AT	EXP	2022	ST_6	C_FIR	THS_NAC	1686686.09	7
AT	EXP	2022	ST_6	NCONIF	THS_NAC	131460.702	7
AT	EXP	2022	ST_6	NC_OAK	THS_NAC	76609.608	7
AT	EXP	2022	ST_6	NC_BEE	THS_NAC	22592.468	7
AT	EXP	2022	ST_6	NC_MAP	THS_NAC	1064.456	7
AT	EXP	2022	ST_6	NC_CHE	THS_NAC	455.322	7
AT	EXP	2022	ST_6	NC_ASH	THS_NAC	11132.397	7
AT	EXP	2022	ST_6	NC_BIR	THS_NAC	529.046	7
AT	EXP	2022	ST_6	NC_POP	THS_NAC	339.588	7
AT	PRD	2021	EU2_1	TOTAL	THS_M3	18420.265
AT	PRD	2021	EU2_1	CONIF	THS_M3	15663.416
AT	PRD	2021	EU2_1	NCONIF	THS_M3	2756.849
AT	PRD	2021	EU2_1_1	TOTAL	THS_M3	1836.812
AT	PRD	2021	EU2_1_1	CONIF	THS_M3	1583.238
AT	PRD	2021	EU2_1_1	NCONIF	THS_M3	253.574
AT	PRD	2021	EU2_1_2	TOTAL	THS_M3
AT	PRD	2021	EU2_1_2	CONIF	THS_M3
AT	PRD	2021	EU2_1_2	NCONIF	THS_M3
AT	PRD	2021	EU2_1_3	TOTAL	THS_M3	16583.453	4
AT	PRD	2021	EU2_1_3	CONIF	THS_M3	14080.178	4
AT	PRD	2021	EU2_1_3	NCONIF	THS_M3	2503.275	4
AT	PRD	2022	EU2_1	TOTAL	THS_M3	19357.935
AT	PRD	2022	EU2_1	CONIF	THS_M3	16205.144
AT	PRD	2022	EU2_1	NCONIF	THS_M3	3152.791
AT	PRD	2022	EU2_1_1	TOTAL	THS_M3	1981.003
AT	PRD	2022	EU2_1_1	CONIF	THS_M3	1630.582
AT	PRD	2022	EU2_1_1	NCONIF	THS_M3	350.421
AT	PRD	2022	EU2_1_2	TOTAL	THS_M3
AT	PRD	2022	EU2_1_2	CONIF	THS_M3
AT	PRD	2022	EU2_1_2	NCONIF	THS_M3
AT	PRD	2022	EU2_1_3	TOTAL	THS_M3	17376.932	4
AT	PRD	2022	EU2_1_3	CONIF	THS_M3	14574.562	4
AT	PRD	2022	EU2_1_3	NCONIF	THS_M3	2802.37	4

ACCC/C/2019/163_Austria

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Association Justice and Environment, z.s.

European Network of Environmental Law Organizations

Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

[email protected] ■ www.justiceandenvironment.org ■ IČO: 75141892

Statement of Observer Justice and Environment concerning

Communication ACCC/C/2019/163 (Austria)

7 September, 2023

1. Justice and Environment, a network composed of 15 European NGOs, submits the present

statement for the Committee´s consideration in advance of the hearing to discuss the substance of

communication ACCC/C/2019/163 (Austria) (C163).

2. The communication concerns the construction of underground transportation infrastructure in the

city of Feldkirch, located in the province of Voralberg (Stadttunnel Feldkirch, the Project).1 The

Project falls under point 9(h) of annex 1 to the Austrian EIA Act2 and thus is a “column 3” project for

which an EIA in the “simplified form” was required.3 It is accordingly not in dispute that article 6 of

the Convention is engaged.

3. It is not clear from reading Austria´s response whether or not it acknowledges that the

communicant is a member of the public concerned. We submit at any event that it would be

difficult to deny the communicant this status inter alia because of the fact that the communicant is

composed of individuals residing in municipalities that are located directly adjacent to the

municipality where the Project is to be located, and Austria could not exclude potential negative

impacts and thus undertook a transboundary assessment.4 Austria appears rather to rely on three

arguments in support of its position that it has not violated article 3(9) in conjunction with article 6,

and 9(2) of the Convention.

4. First, Austria submits it could not violate the rights of the public concerned from Liechtenstein as

that country is not a Party to the Convention.5 Second, Austria claims it provides sufficient rights for

the public concerned through its provisions under the Austrian EIA Act concerning the rights of

individually affected persons, such as neighbors, and NGOs, both of which can be foreign.6 Third,

Austria makes a number of statements regarding citizen initiatives as an institution, its place within

national, EU, and international frameworks, yet submits that there is no mechanism by which to

verify that the foreign public concerned has formed such an association.7 Throughout, Austria

1 Communication, p. 1, Party´s response, p. 1. 2 Annex 5 to the communication, p. 1. Point 9(h) of annex 1 to the EIA Act reads: “Expansion measures of other types on

expressways, new construction of other roads or their sections with a continuous length of at least 500 m, in each case if an area

worthy of protection of category B or D is touched and an annual average daily traffic volume (JDTV) of at least 2,000 motor

vehicles in one forecast period of five years is to be expected.” Protection categories B and D are listed in annex 2 of the Austrian

EIA Act and correspond to protected alpine areas and those areas affected by air pollution, respectively. 3 See communication, para. 7, and Party´s response, p. 9. Simplified EIA procedures differ from normal EIA procedures in that

simplified procedures do not involve the creation of an environmental assessment report pursuant to article 12 of the Austrian EIA

Act, but only a summary of the environmental impacts under article 12(a) of that Act. Simplified procedures furthermore historically

involve limitations on public participation rights and the ability to bring a legal appeal under article 19(2) of the Austrian EIA Act,

which is discussed below. 4 Communication, paras. 5 and 11, Parties response, pp. 7-8. 5 Party´s response, p. 2. 6 Party´s response, pp. 4-7 7 Party´s statement on admissibility, pp. 1-2, Parties response, pp. 3-5, and 10.

Association Justice and Environment, z.s.

European Network of Environmental Law Organizations

Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

[email protected] ■ www.justiceandenvironment.org ■ IČO: 75141892

suggests it discharged its duties to the public concerned in the present case, including through the

transboundary procedure under the Espoo Convention. 8 We address these points below.

I. Obligations to the public (concerned) in a non-Party to the Convention

5. Contrary to Austria´s contention, the obligations Austria assumed as a Party to the Convention

towards the public (concerned) are not dependent on obligations stemming from other

international instruments, including the Espoo Convention, a point which the Committee has made

clear in its findings on communication ACCC/C/2012/71 (Czechia) (C71). Moreover, even where a

Party of origin and affected Party share joint responsibility for ensuring public participation in the

territory of the affected Party (as under the Espoo Convention), or even where an affected Party

has sole responsibility for this, the obligation to ensure that the requirements of article 6 always

rests with the Party of origin. 9

6. Moreover, as the Committee clarified in its findings on communication ACCC/C/2013/91 (UK), the

definitions of the public and public concerned in article 2(4) and (5), respectively, “must be seen in

the context of the requirements set out in article 3, paragraph 9, of the Convention, which requires

that the public shall have access to information, have the possibility to participate in decision-

making, and have access to justice in environmental matters without discrimination as to

citizenship, nationality or domicile […] the scope of obligations related to public participation in

decision-making with respect to proposed activities subject to article 6 of the Aarhus Convention is

not limited to the public only in the Party concerned…[In] cases where the area potentially affected

by a proposed activity crosses an international border, members of the public in the neighbouring

country will be members of the “public concerned” for the purposes of article 6.” 10

7. Crucial to the present case is the fact that, Parties “to the Convention have obligations under article

2(5) and article 6 of the Convention to ensure the effective participation of the public `affected or

likely to be affected by, or having an interest in, the environmental decision-making,´ irrespective

of whether those persons reside in an Aarhus signatory state or not […] article 1 of the Convention

expressly convey the rights of the Convention to every person, not every citizen of a party to the

Convention. Likewise, article 3(9) of the Convention makes clear that the public has the right to

participate in decision-making without discrimination as to citizenship, nationality or domicile.

Moreover, the right to participate applies to the public concerned from affected countries that are

not Party to either the Aarhus Convention or Espoo Convention is explicitly stated in paragraph 23

of the Maastricht Recommendations on Promoting Effective Public Participation in Decision-making

in Environmental Matters.”11

8 Party´s response, pp. 7-8. 99 ECE/MP/PP/C.1/2017/3, para. 67. 10 ECE/MP/PP/C.1/2017/14, paras. 68-69

11 Committee´s first progress review concerning decision VI/8k (UK), para. 114. See also Committee´s report concerning decision

VI/8k (UK), ECE/MP/2021/60, para. 48.

Association Justice and Environment, z.s.

European Network of Environmental Law Organizations

Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

[email protected] ■ www.justiceandenvironment.org ■ IČO: 75141892

8. In light of the above, Austria´s argument that it owes no duties to the public (concerned) in

Liechtenstein due to the fact that the latter is not a Party to the Convention must fail. The

communicant is a member of the public affected or likely to be affected, whose members reside

directly across the border between Austria and Liechtenstein in an area where negative impacts

can occur. Accordingly, it must be asked whether the means by which Austria ensures rights to the

public (concerned) in Liechtenstein are adequate, and the communicant was afforded its rights.

II. The rights granted to foreign persons and organizations

9. To be clear at the outset we do not agree with Austria´s suggestion that, because it affords rights to

foreign persons and foreign organizations, this means that it fulfilled its obligations to the

communicant and members of the public (concerned) like it.12 Even were Austria to perfectly fufill

its obligations with respect to foreign persons and foreign organizations – a point we dispute – this

would satisfy the requirements concerning but two subsets, and not the entire scope of, the public

(concerned).

10. However, for the sake of completeness and to address any questions as to why the communicant

could not avail itself of either of the two ways in which Austria claims to provide rights to the

foreign public (concerned), or could not do so adequately, we provide the below. We stress,

however, that we are not seeking to expand the scope of the communication. Rather, our aim is to

provide background information that may assist the Committee in its deliberations.

a. Foreign persons

11. It is correct that article 19(1) of the Austrian EIA Act provides party standing in EIA procedures.

Neighbors are specifically:

“People who are endangered or bothered by the construction, operation or existence of

the project, or whose rights in rem could be endangered at home or abroad, as well as the

owners of facilities in which regularly stop persons temporarily, with regard to the

protection of these persons; Neighbors are not people who are temporarily in the vicinity of

the project and who do not have rights in rem; with regard to neighbors abroad, the

principle of reciprocity applies to states that are not contracting parties to the Agreement

on the European Economic Area.”

12. This is similar to many provisions concerning the rights of individuals in other jurisdictions that, like

Austria, follow a strict impairment of rights legal regime. From this flow two significant points.

13. First, an individual seeking to participate in an EIA procedure and bring any legal challenge

thereafter must prove that the project would impair their subjective rights. To fail to do so means

12 Party´s response, p. 7.

Association Justice and Environment, z.s.

European Network of Environmental Law Organizations

Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

[email protected] ■ www.justiceandenvironment.org ■ IČO: 75141892

that individual would lack standing in the public participation procedure and before a court. As the

communicant is composed of residents located in an area directly adjacent to the project,13 we

presume they could show they are people who would be endangered or bothered by the

construction, operation or existence of the project, or whose rights in rem could be endangered.

The main standing hurdle in article 19(1) of the EIA Act is generally the “temporarily” criterion,

about which the Committee voiced some concerns in its findings on communication

ACCC/C/2010/48 (Austria).14 However, according to jurisprudence, this criterion is examined

regardless of the legal grounds upon which the person is situated there.15 Thus tenants can attain

standing16 provided that the stay is “not simply temporary“.17 Whilst there may be compliance

issues with these provisions, the communicant, being residents, would presumably not have

difficulties with the “temporarily criterion”. Nonetheless, they would have to actively prove they

satisfy the criteria of being a neighbor.

14. The second issue would, by contrast, pose a significant problem in the present case, in that the

scope of an individual´s rights, and thus the scope of arguments it can put forth, are limited to

subjective rights.18 This means for example that an owner of a property bordering a project can

intervene in an EIA permitting procedure and bring a legal appeal due to potential noise impacts on

this piece of property, but cannot intervene or appeal on the basis of potential impacts on regional

flora or fauna, or environmental interests in general, which is an objective right that individuals do

not possess. These scope restrictions for individuals were alleged to fall short of the Convention´s

requirements in communication ACCC/C/2010/48 (Austria). In its findings, the Committee

considered those allegations to not be sufficiently substantiated, by way of jurisprudence, for

example, but expressed concerns that the scope might not allow for challenges as to procedural

and substantive legality.19 We are very much of the view that these scope restrictions fall short of

what the Convention requires. We are not attacking this, however, as we view the communication

does not raise this issue as such. Nonetheless, we highlight that this was a major factor driving why

status as a citizen initiative was beneficial, and thus was sought, and why the Committee should

bear this in mind.

15. By contrast, citizen initiatives do not have to prove their standing by alleging an infringement of

their subjective rights. Rather, their affectedness is presumed as a matter of law provided that they

13 See the illustrative map on p. 2 of the communication.

14 ECE/MP/PP/C.1/2012/4, paras. 60-66.

15 VwGH 20.10.1999, Zl 99/04/0016.

16 VwGH Slg 4007A, 5154A.

17 US 16. 02. 2009, 3B/2005/19-72 NÖ 380 kV-Leitung Etzersdorf-Theiß II

18 Wendl, in Stolzlechner/Wendl/Bergthaler (editors), Die gewerblice Betriebsanlage3 (2008), recital 249

19 ECE/MP/PP/C.1/2012/4, paras. 60-66.

Association Justice and Environment, z.s.

European Network of Environmental Law Organizations

Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

[email protected] ■ www.justiceandenvironment.org ■ IČO: 75141892

fulfill the criteria in article 19(4) of the Austrian EIA Act.20 And crucially, citizen initiatives in Austria

have the right to enforce all environmental provisions, and thus the scope of their rights, and

arguments they can put forth, is considerably broader.21 This is a huge advantage over standing as

an individual under article 19(1) of the Austrian EIA Act and must be seen on top other many

factors, such as costs, capacity, procedural economy, safety, which are discussed in detail below.

b. Foreign NGOs

16. As regards foreign NGOs, Austria correctly points out article 19(11) of the EIA Act. We consider,

however, that Austria´s description of that provision and thus its scope, is incomplete.22 This

provision reads in full:

“An environmental organization from another State can exercise the rights under

paragraph 1023 if a notification of another State pursuant to section 10, para, 1, point 1 has

occurred24 and the environmental organization from another State could participate in the

procedures for the EIA and permitting, if the project would be realized in that State.”

17. There are multiple points to unpack from in provision. We note first it is clear that the Austrian

system makes the rights of foreign NGOs dependent on Espoo procedures and the willingness of

Austria to notify potential affected States. In light of the arguments in paras. 5-8 above, and the

Committee´s jurisprudence cited therein, we submit this restriction is incompatible with the

Convention. Second, it makes such rights conditional, limited to those NGOs in such affected States

which meet their own domestic requirements, and there is no guarantee, obviously, that those

requirements are sufficiently broad to satisfy the Convention.

18. In this regard, we would like to point out that the Liechtenstein EIA Law, specifically its article 5(c)

translates “the public“ as one or more natural or legal persons or their associations, organizations

or groups. In this regard it repeats virtually verbatim article 2(4) of the Aarhus Convention, merely

omitting “in accordance with national legislation or practice”. Its article 17 governs public

participation, and specifically article 17(h) gives “every person” the right to provide comments on

the environmental report within an appropriate period of time. Moreover, other provisions

concerning participation25 use the term “the public” without any further restriction, implying this

20 Communication, para. 12.

21 Ennöckl, Daniel/Raschauer Nicolas (2006): Umweltverträglichkeitsprüfungsgesetz, Kommentar, 2. Auflage Springer

Wien New York

22 Party´s response, p. 6. 23 This paragraph accords environmental organizations party standing in the article 6 permitting procedures and the ability to bring

legal challenges. 24 Section 10 of the Austrian EIA Act is Austria´s implementation of article 7 of the EIA Directive, relating essentially to the Espoo

Conventions and duties in the transboundary context. 25 Such as those concerning notification, consideration of the concerns and opinions submitted during the public participation

procedure, etc.

Association Justice and Environment, z.s.

European Network of Environmental Law Organizations

Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

[email protected] ■ www.justiceandenvironment.org ■ IČO: 75141892

corresponds directly to what is contained in article 5(c) of the Act. Moreover, unlike in Austria,

there does not appear to be a specific provision concerning party standing in the article 6 context in

the Liechtenstein EIA Act. Given this, it seems clear NGOs would be able to submit comments and

enjoy other participatory rights, but in doing so would have no special privileges beyond those

accorded to other members of the public. Accordingly, it seems an absurd proposition to expect

from members of the public such as the communicant that it organize itself as an NGO in

Liechtenstein for the purpose a project such as the one at issue, were it to be planned on

Liechtenstein´s territory. It is doubtful that the communicant could achieve this in the time allotted

for comments, and there would be no advantage in being an NGO.

19. Similarly, there would be no reason for the communicant to organize itself in Liechtenstein as an

NGO if the Project were to be realized in Liechtenstein for purposes of bringing a legal challenge

within the meaning of article 9(2) of the Convention. Pursuant to article 32(1)(c) of the

Liechtenstein EIA Act, among other requirements, only NGOs that have existed for five years prior

to the EIA procedure be entitled to go to court, and have had that entitlement independent of the

procedure in question. At the same time, article 32(1)(d) “Persons which are affected or likely to be

affected by, or have an interest in, the EIA procedure” are also entitled to go to court, and can

apply for this right easily, submitting a short statement as to their reasons, and do so at different

times in the procedure. Thus again, there is no reason to set up an NGO where the article 9(2)

standing rights are so broad in Liechtenstein for members of the public concerned. Accordingly, it is

unreasonable, if not impossible, to have expected the communicant to found an NGO in

Liechtenstein so that, presuming all requirements were met, it could exercise its Aarhus rights using

article 19(11) of the EIA Act.

20. For the sake of completeness we note that, whilst theoretically a foreign NGO operating within the

territory of Austria could under some circumstances qualify for recognition directly under article

19(7) of the Austrian EIA Act, as Austria suggests,26 this avenue would be entirely be blocked for the

communicant due to the criterion that the NGO in question must have been in existence for three

years, an impossible hurdle for an ad hoc group like the communicant needing to timely intervene

in an article 6 procedure. This defies the wording and very purpose of article 6.27

21. The role and status of citizen initiatives

22. Citizen initiatives have a long history in Austria and were already regulated and given full party

rights for all procedures in the original Austrian EIA Act dating back to 1993, that is, prior to

Austria´s accession to the EU and prior to the Convention´s adoption.28 In the course of the

26 Party´s response, p. 7.

27 Jendroska, J., ‘Access to Justice in the Aarhus Convention – Genesis, Legislative History and Overview of the Main

Interpretation Dilemmas’, Journal for European Environmental & Planning Law, 2020 (17).

28 Communication, annex 4, p. 3.

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comprehensive amendment of the EIA Act in 2000 a distinction between normal and simplified EIA

procedures (see para. 2 above) was introduced, and the rights of citizen initiatives were reduced.

From then on, citizen initiatives only had party standing as to normal procedures. As to simplified

procedures, they were only accorded the status of “participants.” Participants only have the right

to inspect the administrative file.29 They do not have the same rights to submit proper comments

(Einwendungen) as parties to the procedure do, and those limited comments they can provide are

given less weight. They have, moreover, no right to challenge the decision-making in court. As the

first instance authority for the Project observed, the national legislator thus granted citizen´s

initiatives a special procedural status that differs from that of “the public concerned” as

implemented and regulated in the Austrian legal system. 30

23. Yet, as the authority and the Austrian Supreme Administrative Court confirmed in case Ro

201510610008-7,31 both normal and simplified procedures are “environmental decision-making”

which must fulfill the requirements of the EIA Directive (and article 6 and 9(2) of the Convention).32

Moreover, citizen initiatives are members of the public, specifically “groups” within the meaning of

the second clause of article 2(4) of the Convention. By virtue of the fact that they are a collective

group of individuals who reside in the siting municipalities or those directly neighboring, meaning

that a close geographical relationship is established and affectedness or likely affectedness by the

project´s permit is to be affirmed, they are also members of the public concerned. 33 Accordingly, a

“properly formed” citizen´s initiative must be accorded full party rights in the article 6 procedure,

and concomittant article 9(2) rights. To fail to do so would, as the authority suggested, be an

“incomplete implementation of the Convention.” 34 In other words, it would fail to comply with

article 6 and 9(2) of the Convention.

24. In light of the above, we find Austria´s observation that “the institution of citizen groups is neither

mentioned in the Aarhus Convention nor in the Espoo Convention nor at the European level,”

misplaced. Yes, it is true that this particular institution is a national construct, but if this is intended

to indicate that to reduce the rights of citizen initiatives or even abolish this institution entirely

would not run afoul of EU or international law is dangerously mistaken (and contrary to what the

Supreme Court said in case Ro 201510610008-7). Failing to accord standing to ad-hoc groups of

individuals coming together to share their concerns about an article 6 procedure and, where

needed, bring a challenge as to both procedure and substance, falls short of the Convention´s

29 Comunicant´s update of 14.11.2018, annex, p. 9, referring to the then relevant provision of the Austrian EIA Act, which has since

been removed following the Supreme Administrative Court´s ruling.

30 Communication, annex 4, p. 3.

31 Communicant´s update of 14.11.2018, annex.

32 Communication, annex 4, p. 6, communicant´s update of 14.11.2018, annex, pp.13-14.

33 Communication, annex 4, pp. 5-6, communicant´s update of 14.11.2018, annex, pp.15-16.

34 Communication, annex 4, p. 3.

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requirements. Thus, “citizen groups” may be the modality Austria has chosen to (partially)35 fulfill

its obligations towards such ad-hoc groups, but the fact that EU legislation and international

instruments fail to use this specific term in no way changes the ultimate nature of these

obligations.

25. As Austria itself observes, citizen groups help build a common platform for individuals to argue

their interests in relation to a specific project approval […] bundling similar interests of individuals

concerned by a project in order to allow the local population to submit their comments in an

aggregated way to the competent authority. Especially with respect to major projects or projects of

large scale their participation may well ensure the acceptance of project approvals.36 It is thus a

special institution in the interest of procedural economy. 37 All of this is true, and other advantages

can be added to this list. Individual members of citizen initiatives can pool their financial and other

resources in order to make more effective interventions, they can act more safely, with diminished

fear of reprisals for their engagement in what can often be controversial projects, and they can

ensure that environmental laws are properly enforced when, for reasons of a lack of capacity, or

undue political pressure, established NGOs fail to act.

26. Austria is in no way unique. Many groups in other countries, be they citizen initiatives in

Germany,38 partnerships in Romania,39 unregistered environmental associations in Italy,40 or

unincorporated environmental NGOs representing residents in Ireland41 come together as ad hoc

groups to exercise article 6 and 9(2) rights. They do so for the very reasons sketched out in the

paragraph above. Crucially, all Parties to the Convention must find appropriate ways to fulfil the

same obligations, despite the fact that they come from a vast range of jurisdictions, each with their

own legal histories and specificities.

35 The author is not of the opinion that article 19(4) of the Austrian EIA Act is adequate for a number of reasons, but these are not

discussed in detail as they are outside the scope of the present communication.

36 Party´s Response, pp. 3-4

37 Party´s Response, p. 4.

38 Such as the Aarhus Konvention Initiative, which is very active in environmental cases at the national level and a communicant in

cases ACCC/C/2012/71 (Czechia), ACCC/C/2016/143 (Czechia), and ACCC/C/2020/178 (Germany).

39 See the Opinion of AG Medina in case C-252/22

40 ACCC/C/2023/200 (Italy)

41 See Dublins 8 residents association, which is currently on appeal to the CJEU: https://eur-lex.europa.eu/legal-

content/EN/TXT/?uri=CELEX%3A62022CN0613

Association Justice and Environment, z.s.

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Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

[email protected] ■ www.justiceandenvironment.org ■ IČO: 75141892

27. Nor is the Committee alone in having to grapple with the rights of such organizations. The CJEU has

been asked to weigh in about such rights, and certainly more questions will come.42 The Committee

will surely have to confront more questions concerning the rights of adhoc groups as well, and we

invite the Committee to take a forward-looking approach that does not unduly close off options

due to the narrow scope of the present communication.

Application in the present case

28. The core issue in the present communication is this: Did Austria violate the communicant´s rights

under article 6 and 9(2) of the Convention, either as stand-alone provisions, and/or in conjunction

with its article 3(9). We submit the answer is “yes.”

29. Following the Committee´s approach in case C7143 we first turn to the article 6 and 9(2)

allegations,44 since, without assessing the extent to which the communicant in Liechtenstein had

the possibility to effectively participate in the decision-making and have access to justice, it is not

possible to properly assess whether discrimination has occurred or not.

Article 2(5)

30. As noted in para. 3, it is not clear that Austria disputes that the communicant is a member of the

public (concerned). The first instance authority notably did consider that the communicant could be

affected by the environmentally relevant decision-making procedure for the Project and did grant

the communicant full party rights accordingly.45 Both court instances did not truly address the

issue. The communicant would appear to be a member of the public, namely a group in accordance

with national legislation and practice, presuming the voting requirement, the one criterion the

communicant was unable to fulfill by virtue of being located in Liechtenstein, should be stricken. At

any event, considering the description of the Project´s scale, and the map provided,46 it is hard to

imagine that the communicant, which is all composed of local residents, would not be affected, or

likely to be affected, by the Project and thus, like its Austrian citizen initiative counterpart, is a

member of the public concerned. This affectedness was never in dispute.47

Article 6

42 Sandymount and residents association, available here:. https://www.courts.ie/acc/alfresco/4bb24962-9d63-492b-

a588-a37cf1915b71/2013_IEHC_542_1.pdf/pdf#view=fitH; and the Dublin case: https://eur-lex.europa.eu/legal-

content/EN/TXT/?uri=CELEX%3A62022CN0613

43 See para. 61.

44 Following the Committee´s practice, we are not independently examining article 2(4) and (5) as violations as such, but rather as a

threshold issue to establish whether or not the communicant was entitled to article 6 and 9 rights. The article 3(1) allegations are

briefly addressed below.

45 Communication, annex 4, pp. 8-9.

46 Communication, pp. 1-2.

47 Communication, para. 15.

https://www.courts.ie/acc/alfresco/4bb24962-9d63-492b-a588-a37cf1915b71/2013_IEHC_542_1.pdf/pdf#view=fitH

https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A62022CN0613

Association Justice and Environment, z.s.

European Network of Environmental Law Organizations

Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

[email protected] ■ www.justiceandenvironment.org ■ IČO: 75141892

31. It bears repeating that the authority in the first instance thought that the status of a participant in

Austria fell below what article 6 requires in that it insufficiently guaranteed the rights of the public

concerned, and the Supreme Administrative Court agreed insofar as domestic members of the

public concerned form as Austrian citizen initiatives are concerned (see para. 23 above). Yet what

happened in the satellite litigation concerning the communicant´s status is that it was stripped of

all rights entirely. It was no longer even a participant. Rather, it was rendered essentially a non-

entity for purposes of the ongoing and re-opened EIA procedure48 because the courts determined

that, having failed to evidence signatures of those with voting rights in the Austrian municipality in

which the Project was to be located or in an adjacent Austrian municipality, no citizen´s initiative

was formed at all. 49 This means that the communicant could not exercise any article 6 rights in the

ongoing EIA procedure, nor were any further means to obtain these rights in court.50 The EIA

procedure meanwhile concluded in 2019 and, indeed, in light of the judicial rulings at issue in the

present communication, the communicant was denied critical article 6 rights, a situation which

stands in direct contrast to its Austrian counterpart.

32. For the sake of completeness we would also like to clarify that it is no answer to the complaints

alleged in this communication, that an Austrian citizen initiative submitted comments that were

similar, or the same to those submitted by the communicant. For the reasons elaborated above and

in particular as to article 6(7) in paras. 36-47 below, this is not the same as submitting comments

that will be effective within an article 6 procedure in Austria. Austria also mentions the fact that

Liechtenstein NGOs took part in the EIA procedure. This, too, is irrelevant. That a subset of the

public concerned took part in the procedure has no bearing whatever on the fact that the

communicant, also a member of the public concerned, was denied its rights to participate and

enjoy concomitant rights to appeal.

Article 6(4)

33. As the Committee has emphasized, the obligation to ensure opportunities for the public to

participate effectively is the fundamental standard against which all aspects of a public

participation procedure under the Convention should be measured.51

34. We submit that this fundamental standard has been violated in the present case. Whilst the

communicant specifically alleged violations of this provision, together with article 6(7) of the

Convention, the cross-cutting standard of article 6(4) of the Convention cannot be ignored.

48 Party´s response, p. 10.

49 Party´s response, p.

50 Communication, para. 27.

51 ACCC/A/2020/2 (Kazakhstan).

Association Justice and Environment, z.s.

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Justice and Environment – European Network of Environmental Law Organizations Udolni 33, 602 00 Brno, Czech Republic

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35. The Party concerned submits that the communicant has been afforded its article 6 rights. We

disagree. Austria´s support for this assertion is vague and unclear, based primarily on the bare

statement that “all comments by the public presented in the oral hearing, also comments by the

citizens group “mobil ohnr Stadttunnel”, were taken into account by the authorities in its EIA

decision. Also the concerns raised by the Communicant were similar to those raised by the Austrian

citizens group established according to the EIA Act and named “statt Tunnel.”52

Article 6(7)

36. The Austrian courts´ ruling that the communicant had failed to properly constitute a citizen´s

initiative in accordance with article 19(4) of the Austrian EIA Act meant the communicant could not

submit comments in a manner that would satisfy article 6(7).

37. It is true that the communicant submitted some comments (a Stellungnahme) attached to the list

of signatures of residents in Liechtenstein with voting rights in adjacent, affected municipalities.53 It

did so because, unlike for forming citizen initiatives in Liechtenstein, this is a requirement for the

proper formation of a citizen initiative in accordance with Austrian law, specifically article 19(4) of

its EIA Act. However, these “comments” are broad, and within the Austrian legal system merely

serve the purpose to demonstrate to the authorities that a group of people have chosen to come

together due to certain common concerns and achieve a status in the article 6 procedure. They are

not the same as proper comments submitted within the course of the EIA procedure

(Einwendungen), within the meaning of article 6(7) of the Convention. Indeed, even if a legitimate

citizen´s initiative is formed and Einwendungen are not provided within the deadline for comments,

the status obtained through the formation of the citizen´s initiative is lost.54

38. Bearing in mind the above, what happened in the present case is the following:

39. First, the public authority could not accept and consider the comments (Stellungnahme) the

communicant submitted to form a citizen´s initiative as a matter of law in the ongoing (and re-

opened) EIA procedure, which subsequently concluded in 2019. Second, the communicant was

barred from submitting further comments entirely, and specifically those which would not only be

aimed at attaining status in the EIA procedure, but also elaborated comments within the meaning

of article 6(7). Following the judicial rulings, in particular that of the Supreme Administrative Court,

the communicant, as a non-entity could submit no comments, including at hearings. The Party

concerned´s assertion that „all comments by the public presented in the oral hearing, also the

coments by [the] communicant, were taken into account by the authority in its EIA decision,“55 is

accordingly misleading. This ignores the fact that the courts later not only voided the

52 Party´s response, p. 7.

53 Communication, annex 2, Party´s response, p. 8.

54 ÖKOBÜRO: „Voraussetzungen für die Parteistellung von Bürgerinitiativen im UVP-Verfahren“, pp. 6-7.

55 Party´s response, p. 7.

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communicant´s party status, but the communicant as an entity at all. This meant that no comments

previously submitted by the communicant could be considered by authorities; nor could any future

comments be submitted or considered, in clear violation of article 6(7) of the Convention.

40. As a final remark we acknowledge that the communicant specifically invokes article 6(7) of the

Convention, yet fails to mention article 6(8) in its communication. We consider that this omission is

attributable to the communicant´s seeing through the lens of the particular legal system in Austria,

which couples the right to make comments with the duty for these to be properly considered. We

invite the Committee to, in accordance with its long-standing practice, not be bound by the specific

pleadings by communicants, but rather examine the issues raised within the scope of the

communication itself so that it can address the heart of the matter.

41. Thus, in the present case, and in all cases involving Austria´s compliance, restrictions on who can

submit comments directly entail restrictions on if and to what extent such comments are taken into

account, and in particular whether they “weigh more” than other comments. Here the Committee

has emphasized that “a system whereby only the comments of certain members of the public are

duly taken into account, while others are disregarded or considered to ´count less´by the decision-

making authorities, would not be consistent with the Convention.“56 We submit that the present

case indeed exemplifies precisely the system the Committee has already identified as

noncompliant.

Article 9(2)

42. By the court decisions in the present case and specifically the Supreme Administrative Court´s

ruling, the communicant was denied standing to challenge either the substantive or procedural

legality of the decision to permit the Project, which is article 6 decision-making. This is not

disputed. As described above, the communicant is a member of the public concerned, and no other

failure to meet the requirements of article 9(2) have been put forth, other than that the

communicant failed to constitute a citizen´s initiative under Austrian law by virtue of the fact that

its members, affected residents in adjacent municipalities, do not have voting rights in Austria. This

is not disputed.

43. We submit this constitutes a clear violation of article 9(2) of the Convention.

Article 3(9)

44. We consider the above already entails violations of article 6 and 9(2), even without reference to

article 3(9) of the Convention. That being said, the present case exemplifies the problems that

discriminatory, arbitrary and absurd formalities can cause. Specifically we note that the rights of

the Austrian citizen initiative was acknowledged and confirmed, which includes the rights to:

56 C-96, para. 97, and references cited therein. For example C, 76 Bulgaria, Czech report….

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• Enjoy effective participation within the meaning of article 6(4)

• Make comments (Einwendungen) in article 6 procedures, that would furthermore be considered, or

in other terms: accorded “weight” in the procedure, in accordance with article 6(7)

• Bring a legal appeal as to substantive or procedural failings with respect to the article 6 permitting,

in accordance with article 9(2).

45. By contrast precisely these rights were denied to the communicant. This is arbitrary and absurd,

and fails to respect the clear and obvious fact that the communicant is a member of the affected,

or likely to be affected, public concerned and as such, should enjoy precisely the same rights as its

Austrian counterparts.

III. Conclusion and remarks relevant to recommendations

46. We consider the above has established that Austria, by failing to accord full article 6 participatory

rights and the right to appeal article 6 decision-making to foreign members of the public

concerned, fails to comply with its obligations under article 6 and 9(2) of the Convention, in

conjunction with araticle 3(9). Specifcally:

a. We submit that the Party concerned fails to comply with the article 6(4) in conjunction with

article 3(9) of the Convention by denying the exercise of effective public participation of

the foreign public (concerned);

b. We submit further that the Party concerned fails to comply with article 6(7) in conjunction

with article 3(9) of the Convention by denying the opportunity for the foreign public

(concerned) to properly submit comments;

c. We also submit that the Party concerned fails to comply with article 9(2) in conjunction

with article 3(9) of the Convention by denying the standing of foreign members of the

public concerned to appeal decisions affecting them as to procedure or substance.

47. We underscore that the Committee´s findings, and thus its recommendations, must crucially not

be understood merely through the lens of Austrian law. It would be a grave miscarriage of justice

and waste years of litigation, were the Committee to base any findings of noncompliance due to

the sheer fact that Liechtenstein members of the public concerned were treated differently than

the domestic (Austrian) public concerned. The simple fact is: The communicant is a member of the

public concerned, despite borders. Was it discriminated in ways that impinged upon its rights under

the Convention? Yes. But so were any number of other domestic members of the public.

48. As the law (article 19(4) of the Austrian EIA Act) stands, it discriminates not only against foreign

members of the public concerned, but also domestic members of the public concerned. In Vienna

alone, approximately one third of the public who reside there and are registered there, are not

entitled to vote and thus entirely shut out of any rights under this provision. Despite this it should

be beyond all dispute that these people living in Vienna, despite their voting privileges or lack

thereof, qualify as the public (concerned) as regards certain projects which could affect them.

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49. We consider moreover the limitation in article 19(4) that only those (with voting rights) in

municipalities where the project is to be realized, or in an adjacent municipality is problematic

under the Convention. At any event we vigorously dispute Austria´s suggestion that to “check“ with

foreign authorities would constitute an undue burden.57 Indeed in the present case the

Liechtenstein authority also provided evidence that, in accordance with law and practice in

Liechtenstein, the communicant qualified as a member of the public concerned and would be

accorded full rights, including the right to bring a legal challenge within the meaning of article 9(2)

of the Convention. It is common practice throughout the region that people, regardless of their

citizenship, register in the areas in which they live. It is also common practice that countries share

this information amongst one another. It is accordingly a non-starter to suggest that it is in some

way difficult or untenable to inquire or otherwise acquire such information. The present case, in

which the Lichtenstein authorities were most helpful in clarifying to their Austrian counterparts

that the communicant would enjoy both article 6 and 9(2) rights, merely confirms this.

50. Finally, in light of the above we wish to repeat that, were Austria to “solve” its problem

discriminating against foreign citizen initiatives by abolishing this institution entirely, this would be

a grave breach of Austrian, EU, and international law. There is no “solution“ there and any litigation

surrounding this would be a waste of time. Austria fails to adequately provide article 6 and 9(2)

rights to the public concerned which chooses to come together as ad hoc groups. Full stop. The

foreign public concerned is merely a part of this. By reducing the rights of the public concerned, be

they foreign or domestic, Austria only moves further away from complying with the Convention;

here in particular article 6 and 9(2) of the Convention.

51. A meaningful step towards implementing such recommendations would be to amend article 19(4)

of the Austrian EIA Act as follows:

• (4)Eine Stellungnahme gemäß § 9 Abs. 5 kann durch Eintragung in eine Unterschriftenliste

unterstützt werden, wobei Name, Anschrift und Geburtsdatum anzugeben und die datierte

Unterschrift beizufügen ist. Die Unterschriftenliste ist gleichzeitig mit der Stellungnahme

einzubringen. Wurde eine Stellungnahme von mindestens 200 Personen, die zum Zeitpunkt

der Unterstützung in der Standortgemeinde oder in einer an diese unmittelbar

angrenzenden Gemeinde für Gemeinderatswahlen wahlberechtigt waren in dem

beftroffenen Gebiet oder in einem Gebiet wo mögliche negative Einwirkungen möglich

sind, ihren Wohnsitz haben, oder nicht bloß vorübergehend in dem betffenen Gebiet sind,

unterstützt, dann nimmt diese Personengruppe (Bürgerinitiative) am Verfahren zur

Erteilung der Genehmigung für das Vorhaben und nach § 20 als Partei teil.

• 4) A statement in accordance with Section 9 Paragraph 5 can be supported by entry in a

signature list, whereby the name, address and date of birth must be stated and the dated

57 Party´s response, pp. 3-4.

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signature must be attached. The list of signatures must be submitted at the same time as

the statement. Was there a statement from at least 200 people who, at the time of

support, were eligible to vote for local council elections in the location municipality or in a

municipality immediately adjacent to it, had their registered residence in the affected area

or in an area where possible negative impacts are possible, or not just are temporarily in

the affected area, then this group of people (citizens' initiative) takes part in the procedure

for granting approval for the project and in accordance with Section 20 as a party.

Spatial SDC experiments and evaluations – multiple countries comparison, Statistics Austria

Read more about Spatial SDC experiments and evaluations – multiple countries comparison, Statistics Austria

standardized dataset, disclosure risk, spatial SDC methods, kernel density smoothing, comparison across countries

Languages and translations

English

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Expert meeting on Statistical Data Confidentiality

26–28 September 2023, Wiesbaden

Spatial SDC experiments and evaluations with multiple countries comparison Johannes Gussenbauer (Statistics Austria), Julien Jamme (Insee), Edwin de Jonge (CBS), Peter-Paul de Wolf (CBS), Martin Möhler (Destatis)

[email protected], [email protected], [email protected], [email protected], [email protected]

Abstract This study utilizes a standardized "census-like" dataset that is structured uniformly across all participating countries to assess disclosure risk based on grid data. We begin by evaluating and comparing the risk using this approach. Next, we apply spatial SDC methods from the R package sdcSpatial, including kernel density smoothing and quad tree aggregation. We re-evaluate the disclosure risk using these methods and analyze the resulting utility loss. Our analysis will be conducted across multiple countries, allowing for a comprehensive comparison of the utility loss between them.

1 Introduction

Population grids are by now well-established products of National Statistical Institutes. They map the distribution of population units (persons, families, households, or similar) in geographic space, using as domain a set of regular, small-scale squares, the grid cells. For our purposes it will be useful to view the gridded map as akin to a two-dimensional histogram and each cell as a bin. One advantage over irregular spatial references, like administrative areas, is then that spatial patterns can, in principle, be observed in a comparable manner over multiple countries. A disadvantage is that grid cells, as small areas, raise the potential for disclosing information on population units, as we may find many cells with only a few inhabitants. To address this problem, methods of Statistical Disclosure Control (SDC) for grid maps have been suggested by Behnisch et al. (2013) and de Jonge and de Wolf (2016), among others. Several such methods have been implemented in the R package sdcSpatial by de Jonge and de Wolf (2022), which we employ here. Our contribution in this paper is two-fold: For once we present the results of experiments with SDC methods for grid data, conducted over multiple contributing countries. We utilize "census-like" population grids from Austria (Statistics Austria), France (Insee), Germany (Destatis) and the Netherlands (CBS). Risk measures will be computed before and after SDC. Secondly, we test and compare two metrics for the utility loss resulting from SDC: the Hellinger distance and Kantorovich-Wasserstein distance. The paper proceeds as follows: In section 2 we outline methods for disclosure control applicable to grid data. In section 3 we consider metrics for measuring the utility loss in protected grids. Section 4 describes the setup and results of our multiple-country analysis. We finish in section 5 with some lessons learned and an outlook on areas that may deserve further investigation.

2 SDC Methods

Geographical grid data can be viewed as table cells, so such data could, in theory, be published as a large table. However most often grid data is plotted on a cartographic map, to show spatial patterns and to make more explicitly use of the geo-referencing character of the data. Because of this dual character of publishing grid data, different SDC methods are available. Some of these methods can be categorized as tabular approaches, where grid data is first represented as table cells, and then the resulting secure table is plotted on the map. One example of such a method is cell-suppression. However, tabular methods often neglect the geographical nature of the grid data, failing to use the spatial neighborhood of a unsafe cell to solve the SDC problem. Spatial statistical disclosure methods try to preserve the geographical utility of grid data. Examples of such methods are the quad tree and smoothing methods. Spatial sdc methods can be applied on a spatial population distribution, variable distribution, probability distribution or mean variable distribution. The experiments in this paper are restricted to the spatial population distribution. In this section we will describe the three methods that can be used to protect the grid data that will be used in our experiments.

2.1 Cell removal

Whenever cells are unsafe to publish (for definition of the risk we used in the experiments, see section 3), the cell is not published. Essentially this is a method that is applied to a tabular representation of the grid data, it suppresses the sensitive cell and its value. When plotted on a map, it means that an unsafe cell does not get a colour corresponding to its value. It is suggested to use a specific colour for ‘not available’ (NA), to distinguish between cells without any observation and cells that are not published.

2.2 Quad tree

The quad tree method implemented in the R package sdcSpatial generalizes the method as described by Suñé et al. (2017). The method reduces sensitivity by aggregating sensitive cells with its three neighbours, and does this recursively until no sensitive cells are left or when the specified maximum zoom level has been reached. Given the origin of the raster, grid cells are defined for each level of detail a priori. Each grid cell at a certain level is constructed by combining four grid cells of one level down (more detailed). See figure 1 for an example of a priori defined grid cells at three levels of detail.

𝐴3

𝐴1

𝐴4

𝐴2

𝐵3

𝐵1

𝐵4

𝐵2

𝐶3

𝐶1

𝐶4

𝐶2

𝐷3

𝐷1

𝐷4

𝐷2

A B

C D

Figure 1. Example of three levels of grid cells

Assume that grid cell number 24 at the most detailed level is unsafe (the grayed cell in figure 1). The quad tree method then looks one level up to find a less detailed grid cell that contains the problematic lower level grid cell. In this case it would find grid cell 𝐴4 that consists of the four grid cells 21, . . . , 24 at the more detailed level. In case cell 𝐴4 is still unsafe, the method again goes one level up and finds grid cell A that consists of grid cells 𝐴1, . . . , 𝐴4 of the previous level of detail. This process continues until either a safe grid cell is found or the highest level of the a priori defined grid cells is reached. The most detailed level cells belonging to a safe less detailed cell will share the same adjusted value, which aggregates to the sum of the original cell values. Note that the aggregation process thus depends on the a priori chosen grid cells at the different levels.

2.3 Smoothing

Spatial smoothing uses the spatial structure of grid data so that the values of neighboring cells help to protect sensitive values. In the examples of this paper we are interested in the population density as function of the location. We will denote this density as 𝑓 (𝑥, 𝑦), where (𝑥, 𝑦) is a location, i.e., a point in an area A ⊂ R2. The population in region A can then be seen as a sample from that population distribution, resulting in 𝑁 observations (𝑥𝑖 , 𝑦𝑖) ∈ R2 for 𝑖 = 1, . . . , 𝑁 . Each observation is then the location of an individual. A non-parametric estimator of the population density can be obtained using kernel smoothing (see e.g., Wand and Jones, 1994). The approach used in this paper follows the kernel density smoothing implementation of sdcSpatial. That is, the mass of the observed population is spread out over a neighbouring region by means of a Gaussian kernel. The estimate of the population density at point (𝑥, 𝑦) is then the sum of the spread out mass at that location:

𝑓ℎ (𝑥, 𝑦) = 1 ℎ2

𝑁∑︁ 𝑖=1

𝐾

(𝑥 − 𝑥𝑖 ℎ

, 𝑦 − 𝑦𝑖 ℎ

) (1)

where 𝐾 (𝑥, 𝑦) = (1/2𝜋) exp ( −(𝑥2 + 𝑦2)/2

) is the bivariate Gaussian kernel. The bandwidth ℎ determines the

size of the region over which the mass is smoothed out. 3

Note that in the current implementation in sdcSpatial the bandwidth is a constant value for all locations, and that the same bandwidth is used in both dimensions resulting in a symmetrically scaled kernel.

3 Risk and Utility measures

3.1 Risk assessment

Denote the area of interest by A ⊂ R 2 (for example the national territory or a subsection thereof). We

consider 𝑁 population units, geographically identified by their planar coordinates (𝑥𝑖 , 𝑦𝑖) ∈ A, 𝑖 = 1, . . . , 𝑁 . The geographic grid constitutes a tiling of A into very small subareas of grid cells, which we denote by C𝑗 , 𝑗 = 1, . . . , 𝑀 . The cell-level count is the number of population units located in a given cell, i.e.

𝑟 𝑗 =

𝑁∑︁ 𝑖=1 1 [ (𝑥𝑖 , 𝑦𝑖) ∈ C𝑗

] ∀ 𝑗 = 1, . . . , 𝑀

where 1[·] is the indicator function. We consider here a straightforward minimum count criterion, by which a cell is considered at risk, if it contains fewer than 𝑘 ∈ N+ units (cf. de Wolf and de Jonge, 2017). The risk indicator for a cell is 𝑅 𝑗 (𝑘) = 1

[ 𝑟 𝑗 < 𝑘

] ∀ 𝑗 = 1, . . . , 𝑀 . Two global risk measures can then be defined as the

share of cells at risk 𝑅 (C) and the share of population at risk 𝑅 (𝑁 ) for which we have:

𝑅 (C) (𝑘) := 1 𝑀

𝑀∑︁ 𝑗=1

𝑅 𝑗 (𝑘) and 𝑅 (𝑁 ) (𝑘) := 1 𝑁

𝑀∑︁ 𝑗=1

𝑅 𝑗 (𝑘) · 𝑟 𝑗 (2)

3.2 Utility assessment

3.2.1 A map seen as a table. To assess the loss of information due to a protection process, we have to compare the original map with the protected one. In a first approximation, a map can be seen as a table where the cells are described by the polygons and filled in with the count (population or households for instance) displayed in the map. Then, each utility metric relevant for frequency tables is relevant for maps. We choose the Hellinger distance for this purpose (See Shlomo (2007) for instance). A small distance implies small distortion and is therefore generally desirable. Denote the original (unprotected) raster by R and the protected version by R′, each having the same 𝑀 cells with values 𝑟 𝑗 and 𝑟 ′

𝑗 , 𝑗 = 1, . . . , 𝑀 respectively. The Hellinger distance on the interval [0 , 1] is:

𝐻𝐷 (R,R′) = 1 √

√√√√√ 𝑀∑︁ 𝑗=1

©« √√

𝑟 ′ 𝑗∑𝑀

𝑗=1 𝑟 ′ 𝑗

− √︄

𝑟 𝑗∑𝑀 𝑗=1 𝑟 𝑗

ª®¬ 2

(3)

3.2.2 How to assess the distortion of spatial patterns? A metric such as the Hellinger distance does not take into account the spatial distribution of the units. And, while releasing perturbed maps, to ensure that we haven’t perturbed too much the spatial distribution is at least as important as to ensure a good preservation of the values cell by cell. When the original phenomenon displayed on the map is characterized by a spatial dependency, we’d like to ensure that this dependency is not broken by the protection process. For this purpose, M. Buron & M. Fontaine suggest to compare the Moran’s I for the two maps (See Buron and Fontaine (2018)). The Moran’s I measures the intensity of the spatial autocorrelation of the phenomenon. Then, the more the coefficient is distorted, the more the spatial information of the phenomenon is lost. With some protection process, the spatial autocorrelation will be automatically reduced (noise injection) and with other ones it will be automatically reinforced (smoothing).

It is less unpredictable for other ones (cell suppression for instance). We, then, tend to prefer SDC methods that preserve this coefficient as well as possible. In the same perspective to focus on the main spatial patterns of the map, de Wolf and de Jonge (2017) suggest a utility metric able to monitor the distortion of cold and hot spots due to the protection process. Actually, three utility measures are suggested, one related to the location of the spots, one related to their shape and the last one related to their size. For this paper, we suggest to use the Kantorovic-Wasserstein Distance (KWD) as it is implemented in the SpatialKWD R package. This distance, also called Earth Mover Distance, comes from the transportation problem: What is the minimal cost to transport a mass from one distribution to another? Whereas the Hellinger distance, as many other ones, could be considered as a "bin-by-bin" distance, the KWD can be viewed as a "cross-bin" distance: each bin (our grid cells) is put in relation with all other ones (Ricciato (2023)). Formally, KWD is defined as the solution to an optimization problem: We shift around distribution mass of Δ𝑟 𝑗𝑘 between the 𝑗 th and 𝑘th grid cell, until R′ is transformed into R (or the other way around).1 Each such shift is evaluated with costs equivalent to the distance covered, denoted by 𝑑 𝑗𝑘 . Our implementation uses the Euclidean distance between cell centroids, measured in multiples of the cell width. We need to solve:

𝐾𝑊𝐷 (R,R′) = min Δ𝑟 𝑗𝑘

1∑𝑀 𝑗=1 𝑟 𝑗

𝑀∑︁ 𝑗=1

𝑀∑︁ 𝑘=1

Δ𝑟 𝑗𝑘𝑑 𝑗𝑘

s.t. 𝑀∑︁ 𝑘=1

Δ𝑟 𝑗𝑘 = 𝑟 𝑗 ∀ 𝑗 = 1, . . . , 𝑀

𝑀∑︁ 𝑗=1

Δ𝑟 𝑗𝑘 = 𝑟 ′𝑘 ∀𝑘 = 1, . . . , 𝑀

Δ𝑟 𝑗𝑘 ≥ 0 ∀ 𝑗 , 𝑘 = 1, . . . , 𝑀

(4)

The following example gives an intuition on how KWD takes the spatial distribution of error into account. Consider a 4 × 4 grid, symbolized by A, which maps the ground truth.

A =

0 1 0 0 1 1 1 0 0 1 0 0 0 0 0 0

B1, . . . ,B4 are different alterations, loosely corresponding to protection mechanisms. For instance, B1 and B2 are aggregations, whereas B3 and B4 are randomly shifting some cell values.

B1 =

0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0

, B2 =

0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0

, B3 =

0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0

, B4 =

0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1

Intuitively, we will tend to prefer B1 over B2, since while both come with the same level of aggregation, the former preserves the original’s center of gravity, while the latter does not. Similarly, considerations of utility would lead us to prefer, in most cases, B3 over B4: while both shift two cell values, the first shifts each of them only to direct neighbors, the other relocates to the edges of the map. It is easy to verify, however, that bin-by-bin utility measures, such as the Hellinger distance, do not cover this rationale as we get 𝐻𝐷 (A,B1) = 𝐻𝐷 (A,B2) and 𝐻𝐷 (A,B3) = 𝐻𝐷 (A,B4). They are ignorant of geographic considerations. The Kantorovic-Wasserstein distance, on the other hand, gives for the above cases the desired 𝐾𝑊𝐷 (A,B1) < 𝐾𝑊𝐷 (A,B2) as well as 𝐾𝑊𝐷 (A,B3) < 𝐾𝑊𝐷 (A,B4).

1Being a proper distance, we have 𝐾𝑊𝐷 (R,R′) = 𝐾𝑊𝐷 (R′,R). 5

Our choice of the KWD was in part inspired by Ricciato and Coluccia (2023). The SpatialKWD package provides an implementation of such a distance to compare two maps, a map being seen as a 2D-distribution. As its exact computation is very time-demanding, the package implements a "very tight approximation", following the work of Bassetti et al. (2020). Several considerations have to be made before using this tool in an effective and consistent way.

• Which distance to use for assessing the cost of transporting a value from point 𝐴 to point 𝐵? The Euclidean distance is the default, but actually the choice depends on the physical interpretation of this transportation. For instance, if we were comparing a map of residential locations with a map of working locations, the physical interpretation of the transportation problem is related to commuting and a travel time distance could be more appropriate. In the case of an SDC protection process, the physical interpretation is not so obvious. Since we consider the use case of a thematic map, we choose, as a first approximation, to interpret transportation analogous to eye movement of an observer, for which the Euclidean distance seems a sensible pick.

• Geographical areas are not always convex. In that case, the Euclidean distance could be replaced by the least internal path to join two points of the area. Here, we choose to keep the Euclidean distance even in non-convex areas.

• The KWD is fitted to compare two maps displaying the same total mass. Some protection methods (suppression for instance) modify the total mass displayed. One way to deal with this is to set a fixed per-unit mass penalty cost as the maximum distance between two points in the map for the remaining or lacking mass. Another is to virtually re-insert missing mass before computing KWD at one or more sensible proxy-locations.

3.3 Focus areas

The spatial patterns to compare depend on the extent of the map. The larger the map the more complex the spatial patterns. Furthermore, a user will rarely view the whole map, but rather show interest in some subsection, for instance their home region. It therefore makes sense to base utility measures not (only) on large-scale maps, but to consider a selection of smaller maps. We call any subarea A𝑖 ⊆ A a focus area. When assessing risk and utility metrics for one such focus area, we consider only the corresponding part of the population grid, i.e. the set of cells

{ 𝑗 = 1, . . . , 𝑀 :

( 𝑥 𝑗 , 𝑦 𝑗

) ∈ A𝑖

} , where

( 𝑥 𝑗 , 𝑦 𝑗

) are the planar coordinates of the center point

of the 𝑗 th grid cell. For simplicity, we employ here only square focus areas that are at the same grid resolution as the overall area. The R package lets us choose to compute the Kantorovic-Wasserstein distance only on some A𝑖 , while taking into account the whole A. Hence, the transportation of masses is computed only within the focus area, but, if needed, some masses can be transported from inside to outside and vice versa.

4 Experiments

The aim of the experiment is to protect a map of the number of persons or households per grid cell. The grid is based on the INSPIRE (2014) standard ETRS89-LAEA for geographic grid systems. This is done for different countries. The protection will be applied using the R package sdcSpatial and results are compared across countries using risk and utility measures.

4.1 Datasets

We start from a microdata set containing persons or households as well as X and Y coordinates and raster cells which follow the INSPIRE (2014) standard. Depending on the country the grid cells are either 500m × 500m or smaller. Table 1 shows example data from the Austrian use case. For reasons suggested above, we focus our

analysis on selected regions with specific characteristics regarding terrain and population distribution. Table 2 gives an overview of the focus areas used for each contributing country. Below we give a short overview of the characteristics of each contributing microdata set by source country.

Table 1. Example of microdata from Austrian use case

PID L000500 Y X 00000001 500mN28215E46275 2821500 4627500 00000002 500mN28085E47890 2808500 4789000 00000003 500mN28025E47925 2802500 4792500 00000004 500mN28120E47985 2812000 4798500 00000005 500mN27605E47900 2760500 4790000 00000006 500mN28040E47940 2804000 4794000

4.1.1 CBS. For the CBS application the population register for 2020 was used, which was extracted from the social statistical system of Statistics Netherlands (CBS). The dataset includes amongst other variables age, sex and educational attainment on a 100m × 100m geographic raster. Each registered inhabitant is assigned to a raster cell. To facilitate analysis the raster was coarsened to a grid of 500m × 500m. Spatial distributions for urbanized and rural areas are very different and often take different tuning parameters. To indicate how the statistical disclosure methods differs between different regions, four different regions within the Netherlands were selected. First, the capital city Amsterdam, which is a densely populated and urbanized area. Second, the medium sized and young city of Almere where many inhabitants are commuters to different cities. Urbanized, but enclosed by nature and rural areas. Third the rural area of Drenthe, which is sparsely populated and last the region Parkstad, which contains cities and rurals area near the Aachen region in Germany.

4.1.2 Destatis. For the German application Census 2011 results for persons were used. Demographic variables like age, sex or religion are collected on a fine-grained 100m × 100m geographic raster. Household addresses are used for the assignment. Subsequently, each person is located at the centroid of its assigned raster cell. The cells are coarsened to the 500m × 500m resolution for analysis. Focus areas are chosen to reflect a range of spatial structures. The first is the Ruhr valley area, composed of a cluster of multiple, integrated cities and homogeneously high population density. The second are the twin cities of Mainz and Wiesbaden with their corresponding surroundings. They form a diverse collection of urban, rural, forest and river parts. The third focus area is centered between the town of Stralsund and the island of Rügen at the Baltic Sea coast; it is characterised by an intricate mix of settlements and uninhabitable water areas. Finally, a map section close to the Alps in the historical region of Allgäu is chosen, in which farming and small settlements create an overall homogeneous, low population density.

4.1.3 INSEE. For the French use case, we used the 2017 tax data from the so-called Filosofi system. The data are available on the website of Insee2. Socio-demographic information are displayed on a 200m × 200m squares grid. We focused our analysis on the department of La Réunion. We chose to focus our attention on 4 areas, three dense areas picked along the coast of the island (Saint-Denis, Saint-Gilles and Saint-Pierre) and one sparse area picked in its center (La Plaine) (See figure 2). Saint-Denis in the north-east of the island is the most dense area with nearly 700 inhabitants per 𝑘𝑚2 whereas the density of the area called La Plaine, in the rural and steeper center of the island, is less than 40 inhabitants per 𝑘𝑚2. The areas of Saint-Gilles (north-east) and Saint-Pierre (south-east) have been chosen so as to include a large conurbation of cities around a populated center. Hence, both are quite larger than the two others and mix urban centers and some rural areas connected to them.

2https://www.insee.fr/fr/statistiques/6215138?sommaire=6215217

https://www.insee.fr/fr/statistiques/6215138?sommaire=6215217

Table 2. Focus areas

country focus area size size initial risk (cells) (km2) % cells % pop.

Vienna & Suburbs 85 × 85 1806.25 10.09 0.03 Bregenz 39 × 39 380.25 9.57 0.08 Alps in Tyrol 73 × 73 1332.25 17.10 0.59 Krems an der Donau 41 × 41 420.25 12.38 0.28

Ruhr valley 55 × 55 756.25 3.9 0.02 Mainz & Wiesbaden 41 × 41 420.25 9.2 0.04 Strelasund region 75 × 75 1406.25 22.2 0.64 German Allgäu 55 × 55 756.25 24.4 1.06

Saint-Denis 45 × 45 81.00 31.4 2.43 Saint-Pierre 109 × 109 475.24 49.1 8.63 La Plaine 41 × 41 67.24 72.2 31.63 Saint-Gilles 71 × 71 201.64 51.0 10.31

Amsterdam 59 × 46 678.50 12.1 0.04 Almere 47 × 42 493.50 13.9 0.07 Drenthe 89 × 111 2469.75 21.7 0.64 Parkstad 31 × 44 341.50 11.1 0.09

Figure 2. Number of Households by 200 × 200 meters grid squares in La Réunion. Red squares are the focus areas

4.1.4 Statistics Austria. For the use case at Statistics Austria we used the Austrian Rich-Frame. This internally compiled data set contains the reference frame for persons and households and is used as the sampling frame to draw a number of surveys. The data set is compiled from various administrative data sources and holds information for each person from the Central Population Register (such as age, gender, citizenship), the Building and Housing register as well as income-, tax-, education- or employment related information. Furthermore, it contains regional information at a very detailed geographical level. The data set is pseudo-anonymized which means that no direct identifying variables or coordinates are contained in the data set. The frame is updated quarterly and it takes around 6 weeks after a quarter change until the data set for the reference quarter has been finalized. For our use case we chose 4 focus areas. The first is the capital Vienna including the surrounding suburbs. The second is Bregenz including bordering municipalities containing the country border and the border to the Bodensee. Thrid and furth are regions with lower population density, namely part of the Alps in Tyrol and rural area in lower Austria including Krems an der Donau and surrounding municipalities.

4.2 Application

For applying the protection methods we mainly used the R package sdcSpatial. Example code can be found on https://github.com/sdcTools/sdcSpatialExperiment. Our cases applied the following protection methods:

• Cell removal ∼ sdcSpatial:::remove_sensitive • Quad tree protection ∼ sdcSpatial:::protect_quadtree • Kernel density smoothing ∼ sdcSpatial:::protect_smooth

There are slight differences in the use cases of the different countries: • CBS For the Dutch (NL) focus regions, a minimum of 5 contributors in a grid cell was used to test for

sensitivity. Four protection methods have been applied – Suppression of sensitive cells (‘removal’) – Quadtree with zoom levels 2 (‘quad tree I’) and 3 (‘quad tree II’) – Spatial smoothing with a Gaussian kernel and a bandwidth of 500m (‘smoothing’)

• Destatis Risk was assessed by the minimum count criterion, where a cell is considered sensitive if it contains fewer than 5 persons. Four protection methods were considered for the German data set: – Suppression of the sensitive cells (‘removal’); – Quadtree with a maximum zoom of 2 (‘quad tree I’) and 3 (‘quad tree II’); – Spatial smoothing with a Gaussian kernel and a bandwidth of 500m (‘smoothing’).

• INSEE For the French case (FR), four protection methods have been considered: – Suppression of the sensitive cells (‘removal’); – Quadtree with two different maximum of zoom of 3 (‘quad tree I’) and 4 (‘quad tree II’); – Spatial smoothing with a Gaussian kernel and a bandwidth of 200m (‘smoothing’).

• Statistics Austria Grid cells with a cell count below 5 were considered sensitive. For the use case at Statistics Austria the following protection methods were considered: – Suppression of the sensitive cells (‘removal’); – Quadtree with two different maximum of zoom of 2 (‘quad tree I’) and 3 (‘quad tree II’); – Spatial smoothing with a Gaussian kernel and a bandwidth of 500m (‘smoothing’).

4.3 Results

The initial risk assessment for focus areas is included in table 2 in the two rightmost columns. Tables 3 to 6 show risk and utility measures after applying SDC methods. Columns ’% cells’ and ’% pop.’ also include grid cells which were not populated in the original data set. Notably, while computation times for the Hellinger Distance were negligible, calculating the Kantarovich- Wasserstein Distance for a large number of grid cells will typically be more time-consuming. Other than with

https://github.com/sdcTools/sdcSpatialExperiment

Table 3. Results for the Dutch data set by focus area

focus area (NL) method residual risk utility % cells % pop. HD KWD

Amsterdam

removal 0 0 .01 .004 quad tree I 8.1 0.01 .08 .015 quad tree II 0.8 < .01 .13 .054 smoothing 1.1 < .01 .22 .257

Almere

removal 0 0 .02 .009 quad tree I 15.9 0.03 .09 .018 quad tree II 1.8 < .01 .13 .054 smoothing 1.3 < .01 .25 .316

Drenthe

removal 0 0 .06 .080 quad tree I 13.2 .13 .16 .062 quad tree II 0.3 < .01 .23 .164 smoothing 0.6 < .01 .31 .407

Parkstad

removal 0 0 .02 .007 quad tree I 6.6 0.01 .13 .039 quad tree II 0 0 .20 .124 smoothing 0 < .01 .27 .352

Table 4. Results for the German data set by focus area

focus area (DE) method residual risk utility % cells % pop. HD KWD

Ruhr valley

removal 0 0 .009 .004 quad tree I 0.7 < .001 .079 .015 quad tree II 0 0 .095 .025 smoothing 0 0 .280 .381

Mainz & Wiesbaden

removal 0 0 .014 .006 quad tree I 9.5 .012 .124 .034 quad tree II 0.3 < .001 .227 .162 smoothing 0 0 .365 .493

Strelasund region

removal 0 0 .057 .176 quad tree I 21.1 .207 .165 .064 quad tree II 2.2 .003 .222 .136 smoothing 0 0 .451 .627

German Allgäu

removal 0 0 .073 .229 quad tree I 10.6 .161 .188 .083 quad tree II 0 0 .239 .161 smoothing 0 0 .415 .625

bin-by-bin measures, it takes longer to calculate a large KWD than a short one. The approximation we used guarantees a result within 1.29% of the true value as per Gualandi (2022).

Table 5. Results for the French data set by focus area

focus area (FR) method residual risk utility % cells % pop. HD KWD

St-Denis

removal 0 0 0.111 0.284 quad tree I 3.48 0.01 0.242 0.196 quad tree II 0 0 0.244 0.201 smoothing 0 0 0.237 0.273

St-Pierre

removal 0 0 0.210 1.340 quad tree I 5.64 0.06 0.310 0.338 quad tree II 0 0 0.334 0.451 smoothing 0 0 0.248 0.267

La Plaine

removal 0 0 0.416 1.547 quad tree I 11.98 0.36 0.429 0.616 quad tree II 0 0 0.456 0.851 smoothing 0 0 0.304 0.311

St-Gilles

removal 0 0 0.230 1.167 quad tree I 10.54 0.1 0.359 0.467 quad tree II 0 0 0.394 0.655 smoothing 0 0 0.286 0.340

Table 6. Results for the Austrian data set by focus area

focus area (AT) method residual risk utility % cells % pop. HD KWD

Vienna & Suburbs

removal 0 0 0.014 0.008 quad tree I 0.030 < .001 0.086 0.022 quad tree II 0.002 < .001 0.125 0.062 smoothing < .001 < .001 0.273 0.351

Bregenz

removal 0 0 0.042 0.009 quad tree I 0.032 < .001 0.097 0.050 quad tree II 0.002 < .001 0.154 0.178 smoothing 0 0 0.304 0.419

Alps in Tyrol

removal 0 0 0.056 0.053 quad tree I 0.042 < .001 0.151 0.061 quad tree II 0.004 < .001 0.210 0.147 smoothing 0.001 < .001 0.381 0.577

Krems an der Donau

removal 0 0 0.042 0.029 quad tree I 0.037 < .001 0.174 0.078 quad tree II 0.002 < .001 0.261 0.213 smoothing 0 0 0.471 0.664

4.4 Discussion

We notice that KWD and HD yield the same rank-ordering of SDC methods and parameterizations between quad tree and smoothing, but judge removal quite differently. Indeed, KWD seems to penalize more the removal method than the quad tree or the smoothing, whereas HD penalizes more the quad tree and the smoothing than the removal. To understand the different behavior of the two utility measures, consider moving distribution mass of amount Δ𝑟 between cells. The KWD associated with this change is proportional to the minimum-cost way of reversing it, ’costing’ 𝑑Δ𝑟 . On the other hand, HD and other bin-by-bin measures scale only with Δ𝑟 , independent of distance. If SDC methods cause changes with wide variations in 𝑑, we expect no clear connection between the two types of measures. If, however, 𝑑 is within predictable bounds, which is always the case when an SDC mechanism acts locally, i.e. shifts mass exclusively (or preferably) within geographic neighborhood, we will see a close association. Consider, for instance, a single aggregation step of the quad tree method, described in 2.2. It consists of redistributing mass between cells of a four-cell square. The KWD associated with reversing such a step is proportional to 𝑑Δ𝑟 , where 𝑑 ∈ [1 ,

√ 2] is tightly bounded. Such a small variation of 𝑑 can basically be treated

as noise, compared to the amount of mass shifted Δ𝑟 . A similar intuition applies for the smoothing approach: Distribution mass is ’smeared’ out locally, so that shifting it back can be viewed as a localized transport problem, where the weighted distance 𝑑 is again closely bounded. The bound depends on the kernel bandwidth and tail of the kernel function. Typically we find that for both protection mechanisms (quad tree and smoothing) KWD scales mostly with Δ𝑟 . Judging them via bin-by-bin utility measures versus the cross-bin KWD metric therefore yields overall similar rankings of SDC methods. With cell removal, on the other hand, distribution mass is deleted at various points of the map. The KWD depends on how this missing mass is treated. Applying a constant cost 𝑐 per removed unit of mass implies total KWD of 𝑐Δ𝑟 , which again would scale up with Δ𝑟 . The ranking of cell removal in comparison to other SDC method depends then entirely on how we set 𝑐, i.e. how we judge the information loss from removing mass compared to shifting it. If we consider instead virtually re-inserting the missing mass, KWD depends on the distances of the virtual bin to the points of the map where mass was deleted. In that case, 𝑑 can vary widely and we do not expect to see a correlation of bin-by-bin measures and KWD. For instance, in the results for Germany (table 4), KWD preferred cell removal for the two more densely populated focus areas (Ruhr valley, Mainz & Wiesbaden), but judged it second-worst for the two sparsely populated focus areas. In comparison, HD favored removal throughout. Hence, we get a divergence between what difference utility metrics recommend. Throughout, we find that KWD after smoothing is highest. This does not necessarily disqualify the smoothing approach, however. To see why, consider the setting shown below. Let C be the ground truth; D1 is a situation as would result from the quad tree method, D2 might result from smoothing.

C =

0 0 0 0 0 50 0 0 0 0 50 0 0 0 0 0

, D1 =

0 0 0 0 0 25 25 0 0 25 25 0 0 0 0 0

, D2 =

4 4 4 0 4 25 5 4 4 5 25 4 0 4 4 4

Upon visual inspection, we can claim that D2 preserves some qualitative properties of C better: The statement ’distribution mass is centered along the main diagonal’, for instance, is easily learned from D2, but hidden in D1. The small masses in the former would easily be filtered out visually in a heat map, but they do influence utility metrics. Specifically, for the given example we will find that 𝐾𝑊𝐷 (C,D1) < 𝐾𝑊𝐷 (C,D2). Generally speaking, the more tail-heavy the kernel and the broader the bandwidth, the higher the measured utility loss from smoothing as compared to, for instance, the quad tree method. Generally speaking, we do not find that our utility metrics cover such qualitative aspects well. Finally, some practical issues relating to spatial SDC can be learned from Fig.3, which maps a small part of the German data set after protection by three different methods. Overlayed in grey are zones that count as generally uninhabited: rivers and lakes as well as some forms of vegetation (forests, swamps, marshes). We

Figure 3. Raster maps of a subset of German data after SDC methods have been applied; shaded sections indicate forest and river areas.

can see that quad tree aggregation may intrude into these zones, populating previously unpopulated locations. It can, for instance, aggregate cells from different sides of a river, creating artificial crossings, or make forest areas seem more inhabited than they are. Smoothing, similarly, will often assign positive distribution mass to these implausible locations. Whether this constitutes a problem of consistency for users will depend on the planned application. A more serious question from the point of view of disclosure protection is, in how far knowledge of implausible locations can be used to attack and partially reverse protection methods. In Fig.3 we have used shape files of uninhabited areas, but the opposite is also feasible: many states offer Open Data terrain models that explicitly demarcate settlement areas. Outside of these areas, valid person addresses may often be implausible. Overlaying protected raster maps with such auxiliary geographic data could be used to revert some of the changes made. These risks should be further investigated.

5 Conclusion

The experiment we carried out enabled us to test and compare several methods implemented in the sdcSpatial package for protecting geo-referenced grid data against the risk of disclosure. The risk we measure here is essentially a re-identification risk, considering a cell to be sensitive as soon as it doesn’t reach a certain population threshold. The finer the geographical level of distribution, the more serious the risk. Other types of risk could be investigated in the future. For example, the distribution of several maps on different categories of the population could generate problems of disclosure of group attributes. Another very realistic risk is the risk of differentiation with other maps displaying the same information on different zonings, such as administrative zonings. As these are not, in general, an exact sum of tiles, differentiation cannot be reduced to a problem of nested hierarchies. Costemalle (2019) provides an elegant way of detecting such problems. Future work could involve integrating the suggested analysis of differentiation problems into risk measurement. The three protection methods suggested by the sdcSpatial package (cell suppression, quad tree and smoothing) all have advantages and disadvantages, as summarized in table 7. A suppressive method seems reasonable when the number of sensitive cells is low, particularly in densely populated areas. The quad tree and smoothing methods protect sensitive information by diluting it in the neighborhood. Despite the creation of implausible locations that they both generate, if the zoom factor for the quad tree or the smoothing radius are not too large, the usefulness of the outputs, qualitatively speaking, remains interesting. In addition, smoothing generates less

Table 7. Advantages and disadvantages of SDC methods

SDC method advantages disadvantages Cell removal straightforward and irreversible, hot

spots kept intact by design, no artifi- cially inhabited cells

loses mass, low-density regions are deleted from the map

Quadtree resulting cells assure 𝑘-anonymity, small measured distance metrics

overly blocky structure, can artificially enlarge hot spots, can result in implau- sible locations

Smoothing secondary utility as a visualization aid, diminishes spatial noise

may lose mass at edges, can result in implausible locations, potential of re- versal attacks

noisy maps that are easier to read. It is not so obvious for quad tree. In the future, we could also explore the possibility of combining the different protection methods to increase the usefulness of the outputs. The sdcSpatial::protect_smooth function displays a smoothing with a Gaussian kernel which doesn’t take into account the borders or the presence of natural barriers. This could be implemented as in the btb package which uses a quadratic kernel, which takes into account only points within the bandwidth. In addition, an edge-correction (a Diggle correction) is implemented in the btb::btb_smooth function, to deal with edge- effects (see Sémécurbe et al. (2018)). The method is then more conservative than the one implemented in the sdcSpatial package and could lead to improve the utility of the resulting map. In a future work, we could also try other SDC methods, beginning with some classical ones as swapping or the cell key method for instance. Note that swapping and the cell key method could both be considered as ‘pre-map’ methods: they are applied to get safe data before using a method to plot the data on a map. The Quadtree and Smoothing methods act on the unprotected data directly and supply protection when plotting the data on a map. Moreover, swapping and the cell key method in their basic forms do not take the spatial characteristics into account. Any method of protection generates a loss of utility. It is therefore a question of choosing the method which, while protecting sufficiently, will be able to preserve the most original information. However, geo-referenced data cannot be assimilated to simple tables because the spatial distribution of the data is information as important as the data themselves. In this work, we thus used two metrics, one appropriate for comparing tables (the Hellinger distance), the other more suitable for comparing maps (the Kantorovic-Wasserstein distance). It was the first opportunity for us to use the, latter which requires a pronounced attention to certain details such as the mass-mismatching or the convexity of the zonings. We could think about other utility metrics, especially from the ones that can grasp the spatial patterns information, as the Moran’s I (Buron and Fontaine (2018)) or as the characteristics of cold and hot spots (de Wolf and de Jonge (2017)). An additional problem that should be addressed in future work is the relation between risk and utility measures and the resolution of the map in question. The resolution of a map in some sense determines the level to which a user could zoom in on the map. Obviously, the more zoomed-in a user is looking at the map, the more detailed locations could be determined. Thus the identification risk becomes higher. Future work should include recommendations on how to deal with this feature of being able to zoom in on the map, in relation to the disclosure risk and the utility. See for some discussion in this direction e.g., de Wolf and de Jonge (2018).

References

Bassetti, F., S. Gualandi, and M. Veneroni (2020). On the computation of Kantorovich–Wasserstein distances between two-dimensional histograms by uncapacitated minimum cost flows. SIAM Journal on Optimiza- tion 30(3), 2441–2469.

Behnisch, M., G. Meinel, S. Tramsen, and M. Diesselmann (2013). Using quadtree representation in building stock visualization and analysis. Erdkunde 67(2), 151–166.

Buron, M. and M. Fontaine (2018, Oct). Confidentiality of spatial data (Insee Methodes ed.)., Chapter 14, pp. 349–373. Paris.

Costemalle, V. (2019, Dec). Detecting geographical differencing problems in the context of spatial data dissemination. Statistical Journal of the IAOS 35(4), 559–568.

de Jonge, E. and P.-P. de Wolf (2016). Spatial smoothing and statistical disclosure control. In J. Domingo-Ferrer and M. Pejić-Bach (Eds.), Privacy in Statistical Databases. UNESCO Chair in Data Privacy International Conference, PSD ’16, Dubrovnic, Croatia, September 14-16, Proceedings, Springer Lecture Notes in Com- puter Science, LNCS 9867, pp. 107–117.

de Jonge, E. and P.-P. de Wolf (2022). sdcSpatial: Statistical Disclosure Control for Spatial Data. R package version 0.5.2.

de Wolf, P.-P. and E. de Jonge (2017). Location related risk and utility. In UNECE - Expert Meeting on Statistical Data Confidentiality.

de Wolf, P.-P. and E. de Jonge (2018). Safely plotting continuous variables on a map. In J. Domingo-Ferrer and F. Montes (Eds.), Privacy in Statistical Databases. UNESCO Chair in Data Privacy International Conference, PSD ’18, Valencia, Spain, September 26-28, Proceedings, Springer Lecture Notes in Computer Science, LNCS 11126, pp. 347–359.

Gualandi, S. (2022). SpatialKWD: Spatial KWD for Large Spatial Maps. R package version 0.4.1. INSPIRE (2014). Thematic Working Group Coordinate Reference Systems & Geographical Grid Systems,

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1. Introduction
2. SDC Methods

2.1. Cell removal
2.2. Quad tree
2.3. Smoothing

3. Risk and Utility measures

3.1. Risk assessment
3.2. Utility assessment
3.3. Focus areas

4. Experiments

4.1. Datasets
4.2. Application
4.3. Results
4.4. Discussion

5. Conclusion
References

Spatial SDC experiments and evaluations with multiple countries comparison Johannes Gussenbauer (STAT), Julien Jamme (INSEE), Edwin de Jonge (CBS),

Peter-Paul de Wolf (CBS), Martin Möhler (Destatis)

UNECE Expert meeting on Statistical Data Confidentiality 2023 26.09.2023-28.09.2023 Weisbaden, Germany

This work is part of the Centre of Excellence on SDC (CoE on SDC) and is co-funded by the European Commission by means of grant agreement number 899218, 2019-BG-Methodology.

Overview

• Experiment Setup

• Risk and Utility Measures

• Results and Discussion

Experiment Setup

• ‘Census-like’ datasets from 4 countries – Austria, France, Germany, Netherlands

• Tabular data on person level with coordinates of residence and grid cells – INSPIRE2014 standard ETRS89-LAEA

• Aim: test and compare several methods for protecting grid data

‘Census-like’ dataset and map

Person ID Grid cell ID Ys Xs … 00000001 500mN28215E

46275 2821500 4627500 …

00000002 500mN28085E 47890

2808500 4789000 …

00000003 500mN28025E 47925

2802500 4792500 …

00000004 500mN28120E 47985

2812000 4798500 …

… … … … …

Experiment Setup

1. Build table on count data (~number of people) by grid cells (L000500)

2. Calculate risk measures

3. Apply SDC methods using the R-Package sdcSpatial

4. Re-evaluate risk measures and calculate information loss

Protection Methods

• Cell removal: suppresses the sensitive cell

Original map

Protection Methods

• Cell removal: suppresses the sensitive cell

Protected map

Protection Methods

• Quad tree: aggregate sensitive cells with its three neighbours

• Can zoom-out multiple times

Original map

Protection Methods

• Quad tree: aggregate sensitive cells with its three neighbours

• Can zoom-out multiple times

Protected map

Protection Methods

• Kernel density smoothing: mass of population is spread out over a neighbouring region

𝑓𝑓ℎ 𝑥𝑥,𝑦𝑦 = 1 ℎ2 � 𝑖𝑖=1

𝑁𝑁

𝐾𝐾 𝑥𝑥 − 𝑥𝑥𝑖𝑖 ℎ

, 𝑦𝑦 − 𝑦𝑦𝑖𝑖 ℎ

𝐾𝐾 𝑥𝑥,𝑦𝑦 bivariate Gaussian kernel

Original map

Protection Methods

• Kernel density smoothing: mass of population is spread out over a neighbouring region

𝑓𝑓ℎ 𝑥𝑥,𝑦𝑦 = 1 ℎ2 � 𝑖𝑖=1

𝑁𝑁

𝐾𝐾 𝑥𝑥 − 𝑥𝑥𝑖𝑖 ℎ

, 𝑦𝑦 − 𝑦𝑦𝑖𝑖 ℎ

𝐾𝐾 𝑥𝑥,𝑦𝑦 bivariate Gaussian kernel

Protected map

Risk and Utility Measures

• Grid cell 𝒞𝒞𝑗𝑗 is at risk if it contains fewer than 𝑘𝑘 people

• Risk measure ~ share of grid cells/population which are at risk

𝑅𝑅 𝒞𝒞 𝑘𝑘 : = 1 𝑀𝑀 � 𝑗𝑗=1

𝑀𝑀

𝑅𝑅𝑗𝑗 𝑘𝑘 𝑅𝑅 𝑁𝑁 𝑘𝑘 : = 1 𝑁𝑁 � 𝑗𝑗=1

𝑀𝑀

𝑅𝑅𝑗𝑗 𝑘𝑘 ⋅ 𝑟𝑟𝑗𝑗

with 𝑅𝑅𝑗𝑗 𝑘𝑘 = 𝕀𝕀 𝑟𝑟𝑗𝑗 < 𝑘𝑘 ∀𝑗𝑗 = 1, … ,𝑀𝑀

𝑟𝑟𝑗𝑗 = � 𝑖𝑖=1

𝑁𝑁

𝕀𝕀 𝑥𝑥𝑖𝑖 ,𝑦𝑦𝑖𝑖 ∈ 𝒞𝒞𝑗𝑗 ∀𝑗𝑗 = 1, … ,𝑀𝑀

𝑥𝑥𝑖𝑖 ,𝑦𝑦𝑖𝑖 coordinates of person 𝑖𝑖

Risk and Utility Measures • (normalised) Hellinger's distance between raster 𝐑𝐑 and 𝐑𝐑𝐑

𝐻𝐻𝐻𝐻 𝐑𝐑,𝐑𝐑𝐑 = 1

2 � 𝑗𝑗=1

𝑀𝑀 𝑟𝑟𝐑𝑗𝑗

∑𝑗𝑗=1𝑀𝑀 𝑟𝑟 𝐑𝑗𝑗 −

𝑟𝑟𝑗𝑗 ∑𝑗𝑗=1𝑀𝑀 𝑟𝑟𝑗𝑗

• Easy calculation • Applicable to tabular data

• Does not account for spatial distribution

Risk and Utility Measures • Kantorovic-Wasserstein Distance (KWD) or Earth Mover Distance • Minimal cost to transport a mass from one distribution to another

Shift around distribution mass of 𝛥𝛥𝑟𝑟𝑗𝑗𝑗𝑗 between the 𝑗𝑗th and 𝑘𝑘th grid cell, until 𝐑𝐑𝐑 is transformed into 𝐑𝐑

• Considers spatial distribution • Intuitive interpretation

• Difficult to compute ~ R-Package SpatialKWD

• Needs methodological choices – How to deal with different mass in 𝐑𝐑𝐑 and 𝐑𝐑 – Focus Area – Convex hull true/false

Results • Depending on the country slightly

different setup – CBS, DESTATIS, STAT: 500m × 500m, 𝑘𝑘 = 5 – INSEE: 200m × 200m, 𝑘𝑘 = 11

• Each country selected 4 specific focus areas – Protection applied on whole data set

beforehand → focus in on area of interest • Can deal with mass missmatch

– Focus areas contain different population distributions

– Homogeneously populated, hot spots, country borders and uninhabitable terrain. Island of La Réunion, use case INSEE; Red

squares are the focus areas

Results

Conclusions and Discussion

• Cons and Pros of protection methods

Method Pros Cons

Cell removal No artificially inhabitable cells low density regions might be deleted

hot spots kept intact reidentification risk through differencing

Quadtree easy to apply overly blocky structure

utility loss rather small can enlarge hot spots

can populate uninhabitable cells

Smoothing Hot spots are usually kept intact Applied to whole data

can populate uninhabitable cells

Conclusions and Discussion • HD and KWD usually rank methods similar

– Protection was only applied very locally – Some methodological choices needed before applying KWD

• Impact of different specifications needs more investigation – Looking at focus areas instead of whole country more insightful

• Possible additions/improvements to sdcSpatial: – Respect borders or natural barriers during protection

• Further analysis needed for – Differencing attacks – Compare more utility measures (Moran’s I, Spatial K-function, Hotspot preservation, preservation of

population by type of land cover, …) – Compare with more classical methods like record swapping or cell key

Code for running experiment on dummy data on github: https://github.com/sdcTools/sdcSpatialExperiment

https://github.com/sdcTools/sdcSpatialExperiment

Spatial SDC experiments and evaluations with multiple countries comparison
Overview
Experiment Setup
‘Census-like’ dataset and map
Experiment Setup
Protection Methods
Protection Methods
Protection Methods
Protection Methods
Protection Methods
Protection Methods
Risk and Utility Measures
Risk and Utility Measures
Risk and Utility Measures
Results
Results
Conclusions and Discussion
Conclusions and Discussion

Introduction of Scanner Data into Austrian CPI and HICP – practical implementation experience, with a focus on window length options

Read more about Introduction of Scanner Data into Austrian CPI and HICP – practical implementation experience, with a focus on window length options

Languages and translations

English

Introduction of Scanner Data into Austrian CPI and HICP – practical

implementation experience, with a focus on window length options

Adam Tardos

Statistics Austria

Department of Price and Parity

Summary

After several years of preparation and a two-year transition period, scanner data have been introduced

into the Austrian CPI and HICP in January 2022. A significant factor was the amendment of the

Austrian national CPI-Regulation in December 2019, which since then regulates the scanner data

requirements and ensures the weekly scanner data deliveries by most important retailers, initially by

the grocery and drugstore retail trade (NACE classes 47.11 and 47.75).

During the implementation of the project, pragmatic decisions had to be taken on a number of issues

ranging from the way to establish a good relationship with data providers through the method of data

access, to the classification of products, and the choice of the appropriate index calculation and

aggregation method. One small, but not insignificant subset of these decisions, is the time window

length chosen when adopting a multilateral approach, i.e. based on how many consecutive months

of data the index is compiled. Although a two-year transition period in which traditionally collected

price data and scanner data can be compared seems to be comfortably long, it is too short to test the

commonly used window length of 25 months. That is why Statistics Austria introduced scanner data

into production with a 13-month window length.

After an extra year, however, we started to study the benefits of possibly more precise data resulting

from a longer window length at the overall index level and at lower aggregation levels. We also

assessed the additional resource use (computational capacity) that would be required to move from

a 13-month window to a 25-month window. On this basis, we have carried out a cost-benefit analysis

to determine whether it is more reasonable to choose a shorter or longer window length. On the whole

it seems that in most cases the 13-month window length provides similarly good data quality as a 25-

month window and saves plenty of resources, however there are specific conditions (e.g. seasonality)

in which a longer window length has a positive impact on data quality.

Keywords:

CPI, HICP, Scanner data, Multilateral method, GEKS, Windows length, Seasonality

Table of Contents Background ..................................................................................................................................... 3

Description of the data .................................................................................................................... 4

Data preparation and verification ................................................................................................ 5

Product classification .................................................................................................................. 5

Index calculation ............................................................................................................................. 5

Temporal basis for the indices .................................................................................................... 6

Content data basis for the indices ............................................................................................... 6

Outlier filtering ........................................................................................................................... 6

Regionality and aggregation level ............................................................................................... 7

Index calculation: method and window length ........................................................................... 7

Linking index chains of the old method with index chains of the new method ......................... 9

Alternative window length: 25-month vs. 13-month windows length ............................................ 9

Impact of 25-month windows length on the overall index ....................................................... 10

Impact of 25-month windows length on CPI food and on food and non-alcoholic beverages . 12

Impact of 25-month windows length in an environment of rising inflation ............................. 13

Impact of 25-month windows and seasonality .......................................................................... 15

Conclusion .................................................................................................................................... 19

Background

New technical developments and the continuous diversification in retail in form of considerably

higher assortment ranges and a stronger segmentation of product groups as well as changes in pricing

are essential aspects that currently pose new challenges for the price survey of the consumer price

index. In view of the challenges, the use of scanner data in consumer price statistics represents a

major qualitative advance. The use of sales volume and turnover values as well as the comprehensive

coverage of the reporting periods and the range of goods will further ensure the quality of the CPI in

the future.

Since 2010, Statistics Austria had been working on obtaining scanner data and calculating price

indices from them. Initial negotiations with potential data providers to provide data on a voluntary

basis failed and therefore a legal obligation for mandatory scanner data deliveries had to be

introduced. In December 2019, the Austrian national CPI-Regulation defines the scanner data

requirements and ensures scanner data deliveries by the major retailers, initially by the grocery and

drugstore retail trade. After a two-year test period, scanner data were introduced into the Austrian

CPI and HICP in January 2022, mainly for food and drugstore products.

During the scanner data implementation phase, and particularly during the testing phase, many

decisions have to be taken, and sometimes conflicting methodological and practical considerations

need to be considered. Such decisions include the selection of data providers, the storage of data, the

classification of products, the filtering of data by product or over time and, of course, the choice of

the appropriate index calculation methodology.

It is known that one of the advantages of scanner data is that the time coverage of the data is much

more comprehensive than the spot data from the conventional price surveys in the outlets. Ideally,

scanner data are available for every week of the month. Obviously, from a theoretical point of view,

the more weeks of data we build our index on, the better the representation of the given month.

However, from a practical point of view, given the tight publication deadlines, it is questionable

whether there is enough time to calculate the indices and implement thoroughly all quality control

mechanisms, if one waits until the data of the last calendar week of a given month arrives.

When selecting an index method, a decision has to be made whether to choose between one of the

well-established bilateral methods or a multilateral method that is more suitable for scanner data and

more resistant to chain-drift effects.

Even if a multilateral index is chosen, there are several methods with different advantages and

disadvantages. Once the appropriate method has been selected, the process is still not complete, as

each method can be used under different parameters. It has to be decided which splicing method

should be used each month to link the multilateral index chains, and last but not least, it has to be

decided on how many subsequent months the multilateral index should be based.

This brings us to the focus of the present study, namely the choice of the window length, i.e. the

number of consecutive months on which to base the index. Due to seasonal effects, it seems advisable

to cover a period of at least one year (window = 13) or a multiple of this (2 years, window = 25).

The appropriate window lengths have been tested by several experts. Chessa1 found that the

use of 13-month windows can be sensitive to downward drift, especially in case of seasonal items.

Kevin J. Fox, Peter Levell and Martin O’Connell2 concluded that chain drift bias falls significantly

as the window size increases.

It seems that if methodological considerations alone are taken into account, it is preferable to use a

time-window as long as possible, but at least 25 months. However, it should be considered that even

if a two-year test period precedes the introduction of a new methodology, there may not be sufficient

1 Chessa, A.G. (2021) Extension of multilateral index series over time: Analysis and comparison of methods, Paper

written for the 2021 Meeting of the Group of Experts on Consumer Price Indices 2 Fox, K. J., Levell, P., O’Connell, M. (2022) Multilateral index number methods for Consumer Price Statistics

https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=20002846

data available. The availability of historical data depends on the willingness of data providers, their

technical capabilities and the regulatory environment. If no historical data is provided, it will take 25

months of scanner data deliveries before testing with 25-month window lengths can begin. In

practice, the length of a test-period before the introduction of scanner data is limited in order to avoid

parallel data collection procedures and to reduce the burden on respondents. Another important factor

is the extent to which the new sector to be covered by the scanner data is characterised by the presence

of seasonal products. According to the literature, primarily indices for seasonal items benefit from

longer window lengths. And it should also be mentioned that longer window lengths require more

computing resources, with a 4-fold difference between 25- and 13-months window length.

For these practical reasons, Statistics Austria has introduced the scanner data into the CPI with a

window length of 13 months. Given the potential advantages and disadvantages of this decision, the

aim of this study is to compare, one year after the introduction of the Scanner data, how the index

would have evolved if a longer, 25-month-window-length had been chosen. Whether there is a

difference, and if so, whether it is significant. The results may provide guidance to other NSIs, who

are still in the early stages of scanner data implementation, on the conditions under which it is

relatively safe to opt for a shorter window length.

All the other decisions along the path of compiling CPIs with scanner data would merit a separate

paper, apparently the window length seems to have the most practical relevance, so after a brief

methodological overview we will look at this topic in more detail.

Description of the data

The Austrian CPI Regulation regulates the periodicity of the delivery of scanner data including shares

of turnover and the survey period. In contrast to the traditional survey, which usually only records

the current prices on a certain day (reference date), the use of scanner data has the character of a data

provision over a certain period of time, for which the achieved turnovers and sold quantities per

article are determined and from all this a so-called unit value (average value) is calculated. In order

to ensure a high degree of homogeneity, the data is required at least on a weekly basis, as well as (for

processing reasons) a prompt transmission of this data. The scope and characteristics of scanner data

require a change of CPI/HICP calculation processes and methods. For this reason, a gradual

introduction of scanner data into the CPI/HICP production process was foreseen, starting with the

scanner data of the enterprises classified in (Ö)NACE classes 47.11 (Retail sale in non-specialised

stores with food, beverages or tobacco predominating) and 47.75 (Retail sale of cosmetic and toiletry

articles in specialised stores), which are selected by cut-off sampling according to the Regulation

(Small and Medium-Sized Enterprises are excluded). The data of the enterprises in these (Ö)NACE

classes were particularly suitable for the introduction of scanner data, as the largest five retailers in

the food and drugstore sectors have a cumulated market share of more than 85% and as the product

groups primarily traded by them have a large weight of approx. 16% in the CPI shopping basket

(including food, beverages, daily consumer goods, drugstore goods).

Table 1 describes the properties and characteristics of scanner data as provided by the obliged

retailers for each item sold per postcode and calendar week. Table 1 - Scanner data variables and values

Variables Example(s)

Article number and EAN/GTIN (if available) 130404 (Art-nr.); 9100000742175 (GTIN)

Article name or description Red Bull 250 ml DS

Content quantity and unit 250 ml

Classification code and name of the article-related product group,

in as much detail as available.

Drinks/alcohol-free drinks/energy drinks

Sales volume 235

Sales value 315 EUR

Date (from - to, or calendar week) 07.11.22-13.11.22; (2022_45)

Postcode to which the local shop relates 1060

In 2023, it was decided to extend the scanner data project to include (Ö)NACE classes 47.71

(Retail sale of clothing in specialised stores) and 47.72 (Retail sale of footwear and leather goods in

specialised stores). These markets are more fragmented and therefore traditional data collection in

smaller shops and automated online price collection (web scraping) will continue to be important

alongside scanner data. These areas provide an excellent platform for exploring the potential for

synergy and combination of these three methods. As project in these fields are still in the early

stages, this document focuses on the areas already in production.

Data preparation and verification

The supplied files from the data providers are automatically transmitted, imported and checked.

Reports are created to verify the incoming data. These contain, among other things, the weekly

turnover per data provider, the number of postcodes from which data was delivered during the current

week, the number of product groups sold and the number of new products sold. In the case of

inconsistent data patterns, the data provider is contacted and either the plausibility of the data is

confirmed or the data delivery is repeated.

After the data have undergone all the checking mechanisms, the data are loaded into a DB2 database.

From the article data, an article master data file is created for each supplier. During the weekly data

deliveries from the individual suppliers, it can happen that not only new articles are added, but in

some cases existing article descriptions, product groups, etc. are modified. These changes are

adjusted in the course of the updates/synchronisation.

Product classification

Product classification is one of the most complex tasks of the scanner-data-based method. During

the test period, a blended classification system was developed, based partly on an automated

matching procedure using GTINs and product names, partly on several machine-learning methods

and partly on a manual procedure. At COICOP-5 level, 90-95% of products are classified fully

automated based on three models: Support Vector Machine, Random Forest and Naive Bayes or

more recently on Long Short-Term Memory Neural Network. A disagreement between models

indicates products that are particularly difficult to classify and where a higher probability

misclassification should be expected. Agreement between models, on the other hand, indicates

reliable classification. The COICOP-5 classification of such problematic products, as well as the

classification into finer categories than COICOP-5, is done manually.

Index calculation

There are different approaches - bilateral and multilateral methods - to calculate a price index with

scanner data at the elementary aggregate level.

Bilateral concepts are based on the comparison of two periods (base and comparison period). Such

approaches are based on the standard theory of bilateral price indices. This approach is well

understood, transparent and can be easily explained to users.

However, bilateral indices with scanner data face one or more limitations and drawbacks: limited

product coverage due to decreasing product matches over time because of product discontinuations,

a lack of consideration of item sales in the sample, and also the risk of chain drift in case of updating

the base period or monthly chaining or because of the over-consideration of items with promotional

prices.

These disadvantages of bilateral approaches can be avoided by multilateral methods. In fact, chain

drift is a violation of the multi-period identity test that must be prevented. This test requires that if

all prices and quantities in a period T return to their values observed in the base period 0, the index

should show no price change. Multilateral indices satisfy this test3.

During the transition period in 2020 and 2021, we compared a number of bilateral and multilateral

index calculation methods, which allowed us to choose the most suitable solution for us according

to theoretical and practical criteria.

Temporal basis for the indices

An important question is how much data should be used for the index calculation. Since data

providers deliver data on a weekly basis, using data from one, two and three calendar weeks per

month is optional. Four calendar weeks were out of the question, as not every month contains four

full calendar weeks, and the aim was of course to cover the same length of time each month.

Initial test calculations showed that the scanner data indices are somewhat more volatile than

traditional CPI indices. However, the more calendar weeks the index is based on, the more moderate

the fluctuations are. Therefore, it is intended to use as many calendar weeks as possible, i.e. three

calendar weeks per month.

It should also be noted that there is a lead time of several days between the reception and processing

of the data. This may cause practical difficulties in production, especially for meeting publication

deadlines.

To avoid this, the Austrian CPI/HCIP Flash Estimate, which is already published at the end of a

reporting month, is based on scanner data from two calendar weeks of the current month and the

final index is completed with data from the third week.

Content data basis for the indices

Scanner data provides comprehensive data of the entire product range. It may therefore be possible

not to restrict the index calculation to the narrowly defined CPI basket positions (elementary

aggregates), but to compile the index at COICOP-5 level, considering all products belonging to the

respective COICOP category.

It would be attractive to head in this direction, as the indices could then be based on much more

product data, not to mention practical aspects such as the possible simplification of the classification.

However, such a change would also have meant that long time series of elementary aggregate indices

(going back many years) could not be continued, so a transition to COICOP-5-digit level was not

carried out. The index calculation is therefore based on products that correspond to the narrowly

defined CPI basket position descriptions (elementary aggregates). However, the quantity criteria and

other rather narrow product descriptions, that used to help price collectors in shops to select

representative items, are no longer applied. This means for example, that the long grain rice position

does not only consider products in 1 kg packages, but all long grain rice products, regardless of

weight.

Outlier filtering

In addition to the control mechanisms during data entry, an outlier search is carried out among the

calculated unit values to exclude unrealistically high or low unit values before the index calculation.

3 Practical Guide on Multilateral Methods in the HICP Version September 2020, EUROPEAN COMMISSION

EUROSTAT, Directorate C: Macro-economic statistics, Unit C-4: Price statistics. Purchasing Power Parities. Housing

statistics

Regionality and aggregation level

The CPI Regulation in Austria defines "survey regions for scanner data deliveries [...] by postcodes

[...] ". The areas which are defined by the 346 postcodes listed in the annex to the CPI Regulation

were selected to ensure representativeness at regional level. This way, the elementary aggregate used

to calculate the index is the unit value of products by retail chain and by region. At this level of

aggregation, nine regional indices are compiled at the federal state level and then aggregated into a

national index. By doing so, the procedure is harmonised with the index calculation methodology of

the other survey types, the calculations of which are still based on a traditional, likewise hierarchical

methodology: cities, regions (federal states) and country. For the regional weights, the same values

are used for all items, regardless of whether it is the traditional or the new methodology.

Figure 1 – Aggregation levels of the CPI/HICP-Index

Index calculation: bilateral vs. multilateral method and window length

Multilateral methods are a special type of index compilation method that can be applied to scanner

data. A price index usually measures the aggregate price change (at CPI basket position or COICOP

5-digit level) of the current period compared to a base period.

In multilateral methods, the aggregate price change between two comparison periods is determined

from prices and quantities observed in several periods, not only in the two comparison periods. This

is the great advantage of multilateral methods: they consider all products that are available in at least

two periods of the observed time interval (time window). Multilateral methods have been used for

many years for geographical price comparisons (e.g. between different countries or regions) of

purchase price parities and have been adapted for temporal comparisons. Scanner data is typically

dynamic. New products are constantly being added to the product range, while obsolete products that

were previously available are removed. Bilateral price index methods compare the prices of products

in the current period with prices in a past base period. However, as time passes, the overlap of

products decreases, making it difficult to calculate price comparisons. One way to increase the

overlap of products is to frequently update the base period and chain the resulting bilateral price

indices. However, it has been shown that such an approach can be subject to chain drift, especially

when products are explicitly weighted. Chained indices often lead to systematic distortions and

therefore do not measure a plausible price change over longer periods.

Multilateral methods offer a solution to the problems of bilateral approaches. They take into account

all products that are available in the different periods. They allow the explicit weighting of each

product according to its importance in each period. Finally, they avoid the chain drift problems that

arise with chained bilateral indices. Given these advantages, multilateral methods have been

recommended as appropriate price index compilation methods for transaction data, despite their

additional complexity compared to bilateral methods4.

In order to use multilateral methods in the compilation of price indices, some data requirements must

be met:

• Access to historical data: since multilateral approaches use the data of many months at the

same time (time window), sufficiently long data series from the past are required to test and

implement these methods (therefore the relatively long test period and implementation phase

from December 2019 to December 2021).

• The raw data received must be pre-processed and classified (see check and classification steps

above). As the multilateral methods are essentially based on all transactions, it is not

necessary to select items by means of random sampling or to filter them out due to low

turnover. Each product is included according to its importance. In practice, however, item

records will still be excluded during processing and data control mechanism, if important

information is missing (e.g. the turnover or commodity group code) or if they contain

inconsistent values.

A multilateral index is constructed over a given time window length T consisting of a sequence of

consecutive months. The index formula takes as input the prices (unit values) and quantities or

turnover of the individual products available in the months of the given time window.

The first step in the calculation of all multilateral indices is to determine the length of the time window,

which in practice means how many months of data a particular calculation should take into account.

Given the seasonality of certain products, one of the most commonly used time window length is the

number of months in the year plus 1, i.e. 13. This time window allows products that are only sold in

one month of the year to be linked and thus have an impact on the index. Of course, it is possible to

calculate with a longer time window (e.g. two years + 1 = 25), but this implies a longer data series

and more calculation effort. Our calculations were tested with different time windows, but for the

reasons given in the background chapter (lack of historical data, not sufficiently long transition

period) we considered 13 to be the optimal choice.

We tested the three theoretically well-founded methods recommended by Eurostat4, the Gini, Eltetö

and Köves, and Szulc (GEKS), the Weighted Time Product Dummy (WTPD), and the Geary-Khamis

(GK) index, respectively.

As we found only minor differences between the indices for most items, we have opted for the GEKS

index for practical reasons. Although all multilateral indices are based on a relatively complex

methodological background, the logic of the GEKS index is most similar to that of the traditional

bilateral indices and is therefore the easiest to communicate and to comprehend.

To calculate the GEKS index5, a matrix of bilateral indices at a given time window must be

constructed, and the corresponding bilateral index must be calculated for all possible pairs of

4 Guide on Multilateral Methods in the Harmonised Index of Consumer Prices, 2022 edition, Luxembourg:

Publications Office of the European Union

https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-gq-21-020 5 Whenever a GEKS index is calculated, it is linked to a bilateral index method. This leads to many variants of the

GEKS (e.g. GEKS-Fisher, GEKS-Törnqvist, GEKS-Jevons). The different variants are usually close to each other. It

https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-gq-21-020

months. This implies 13x13 = 169 index calculations for a time window of 13. If we consider the

symmetry of the matrix and the fact that the diagonals of the matrix are all equally 1, this means in

practice that 78 bilateral indices are calculated. At a window length of 25 months, the number of

bilateral indices to be calculated increases by a factor of almost four (25x25-25)/2 = 300. The value

of the GEKS index for a given time is the geometric mean of the corresponding bilateral indices.

The GEKS index between time periods 0 and t is calculated for a given time window W as follows:

I 𝐺𝐸𝐾𝑆 𝑊 0,𝑡 = ∏ (𝐼0,𝑘

𝑘∈𝑊 ∗ 𝐼𝑘,𝑡 )

1 |𝑤|

Linking index chains of the old method with index chains of the new method

Three approaches are available for linking chain indices based on different calculation methods:

• One-month overlap: where a single month, the last month of the old method, is used as the

overlap

• Annual overlap: where a whole year is used as overlap

• Over the year: where always the equivalent month of the previous year is used as overlap

For annual overlap, the aim is for the change in the linked index over the year to be as similar as

possible to the new index. As the average annual index is an important analytical value for users,

we would have preferred to make the linking based on the annual overlap.

However, given the current European legal framework, monthly overlap is the standard method for

linking the conventional and the new index. Both the traditional surveyed data and the new scanner

data index were calculated simultaneously during the test period and in the last month before the

introduction of the new method into the production, the December index 2021 of the old and new

methods were set equal.

In January 2022, scanner data were successfully introduced into the Austrian CPI along these

parameters. In the following, we will turn to the subject of our analysis, i.e. what happens if one of

the parameters, the window length, is changed.

Alternative window length: 25-month vs. 13-month windows length

For the comparison, the GEKS index was calculated with exactly the same parameters and using

the same data, only the window length was modified from 13 to 25 months. The resulting index

was linked to the old index using exactly the same linking method as the index with 13-month

window lengths. We calculated annual inflation rates from the two indices for each month in 2022

and compared these annual inflation rates and their averages. The differences were compared at

different COICOP levels, starting from 1-digit level (total CPI) up to COICOP 5-igit level. The

comparison has been restricted, as appropriate, to the COICOP groups involved in the introduction

of the scanner data.

was decided to use the GEKS method with the Törnqvist index, accordingly by GEKS we actually mean GEKS-

Törnqvist.

Impact of 25-month windows length on the overall index

Table 2 - Number of COICOP categories affected by introduction of scanner data at different COICOP levels

COICOP level Number of categories Average weight of the

scanner data

1 1 16%

2 6 30%

3 7 65%

4 19 99%

5 62 100%

COICOP 1-digit level covers the entire consumer basket. The coverage of the scanner data on this

level is 16%. At 2-digit level, the scanner data covers for instance division 01 (food and non-

alcoholic beverages), and partly division 02 (alcoholic beverages, tobacco), or division 12

(miscellaneous goods and services)

The coverage for food is close to 100%, while for example the coverage for group 12 is 15%. The

average for the 6 groups is 30% as shown in the table. Once again it is important to note that

groups not covered at all by the scanner data (e.g. 07 Transport) are not included in the average.

The lower the COICOP level, the higher the coverage of the groups. At COICOP 5-igit level, the

coverage of the groups concerned is 100%.

Of course, if the indices in a given group are calculated using not only scanner data, this reduces

the impact of the 25-month index calculation, as the sub-indices calculated using the traditional

method are not affected by the method applied to the scanner data. Still, it is very important to see

what impact the 25-month window length would have had on the overall index.

Figure 2 – Difference in average annual inflation by COICOP level: window length 25 vs. 13 (2022)

Ø WL = 25 8,64 5,44 6,84 8,52 9,86

Ø WL = 13 8,63 5,42 6,81 8,45 9,80

∆(25-13) +0,01 +0,02 +0,03 +0,07 +0,06

The box-plot in figure 2 shows the differences in average inflation in 2022 at different COICOP

levels depending on whether a 13- or 22-month window length is used. The grey dots show the

differences between each COICOP category. A positive difference means that inflation calculated

with a 25-month window length is bigger, and a negative difference means the opposite. The

horizontal jittering of the points along the symmetry axes of the box plots is for illustrative reasons

purposes only, so that the overlapping points can be seen. The lower the level of COICOP, the

greater the dispersion of differences around 0. The points are spread in both positive and negative

directions around 0, but there are more categories of COICOPs with a positive spread. Of 62

COICOP 5-digit sub-classes, 40 have positive differences and only 22 have negative differences

The table below the plot in figure 2 shows that the average difference at COICOP 5-digit level is

only +0,06 percentage points. Differences at this level range from -1,16 to +0,9 percentage points.

At lower COICOP levels the difference is even smaller: the average annual inflation would have

been 0,01 percentage points higher (8,64% instead of 8,63%) if the longer 25-month window

length had been used at the time of implementation.

Figure 3 – Difference in annual inflation by COICOP level and by month (2022)

COICOP 1 2 3 4 5

January 2022

Ø WL = 25 4,96 2,89 2,65 3,17 3,31

Ø WL = 13 4,95 2,87 2,57 3,10 3,23

∆(25-13) +0,01 +0,02 +0,08 +0,07 +0,08

December 2022

Ø WL = 25 10,40 7,98 10,55 14,03 16,74

Ø WL = 13 10,37 7,95 10,50 13,81 16,62

∆(25-13) +0,03 +0,03 +0,05 +0,22 +0,12

If we express the difference between the two methods in terms of the monthly value of annual

inflation instead of the average annual inflation (see Figure 3), we see that the difference at

COICOP 1 level increases from +0,01 in January to +0,03 percentage points in December. The

magnitude of the average difference increases more significantly at the lower COICOP levels (4 to

5), from 0,07 to 0,08 percentage points to 0,12 to 0,22 percentage points, i.e. annual inflation with a

25-month window length is generally higher than its counterpart with a 13-month window length.

More than the average difference is revealed by the increasing variances in the monthly charts. At

COICIOP 5 level, the differences in January vary between -0,58 and 1,16 percentage points, in

December they range between -2,03 and 2,63.

It is important to note that in January, annual inflation in the COICOP 5 categories involved was

only 3,2-3,3 percent, depending on the window-length, while at the end of the year it was 16,6-16,7

percent. In other words, the difference between the two methodologies seems to be related to the

rate of price increases.

Impact of 25-month windows length on CPI food and on food and non-alcoholic beverages

Although it is very important to see how the length of the 25-month window would have affected

the overall index, it is nevertheless a logical step to limit our analysis to the COICOP categories

that were fully covered by scanner data after the methodological change. Since the coverage of

scanner data is complete in Division 01 (food and non-alcoholic beverages), we focus our analysis

on this division.

Table 3 - Number of COICOP categories affected by scanner data at different COICOP levels

COICOP level Number of categories Average weight of the

scanner data

2 1 100%

3 2 100%

4 11 100%

5 50 100%

In Table 3 we see that we have fewer categories in the analysis, but they are all fully covered with

scanner data. In this case, it should be noted that the lowest level of examination is the division, so

in the following figures and tables we will show four COICOP levels instead of the previous five.

Figure 4 – Difference in average annual inflation by COICOP level: window length 25 vs. 13 food only (2022)

Ø WL = 25 11,85 12,11 11,97 11,38

Ø WL = 13 11,79 12,07 11,97 11,33

∆(25-13) +0,06 0,04 0,00 +0,05

The average annual inflation in division 01 (food and non-alcoholic beverages) calculated with 25-

month window lengths is +0,06 percentage points higher than the inflation calculated with 13-

month window lengths. At COICOP 5-digit level, we again see relatively larger differences in the

range -1,16 to +0,76 percentage points.

Figure 5 – Difference in annual inflation by COICOP level and by month – food only (2022)

COICOP 2 3 4 5

January 2022

Ø WL = 25 4,83 4,98 4,93 4,01

Ø WL = 13 4,75 4,92 4,87 3,94

∆(25-13) +0,08 +0,02 +0,06 +0,07

December 2022

Ø WL = 25 18,53 17,62 18,26 18,88

Ø WL = 13 18,40 17,58 18,18 18,81

∆(25-13) +0,13 +0,04 +0,08 +0,07

The monthly annual inflation values obtained by the two methods show the same picture as before

for the average annual inflation: the average differences are very close to zero, but the spread

around 0 increases over COICOP levels and time, i.e., as inflation increases over the period we

examine. In December, the difference between the two methods ranges between -2,03 and 1,60

percentage points, while the average difference remains close to zero at +0,07 percentage points.

Below, we examine the relationship between the magnitude of inflation and the magnitude of

differences between method results. Later, we examine which COICOP groups are responsible for

the larger differences. For this purpose, we use December as a base, when we the largest

differences could be observed.

Impact of 25-month windows length in an environment of rising inflation

In the chart below, each point represents a COICOP 5 category for 12 consecutive months (January

to December 2022). The x-axis shows the extent of inflation for the respective month (calculated at

window length 13) for the respective COICOP 5 category, and the y-axis shows the differences

between annual inflation at window length 25 and 13. The relationship is not very obvious visually,

but it is clear that below 5% inflation, the vast majority of points are close to 0, while at high

inflation, above 20%, points close to 0 are relatively less frequent.

Figure 6 – The difference according to the level of annual inflation

If the graph is slightly rearranged to take the absolute value of both the x-axis and the y-axis, i.e., to

remove the sign of both the price change and the difference, the relationship between the two

variables becomes somewhat clearer.

Figure 7 – The absolute difference according to the absolute value of annual inflation

The regression line, albeit with a low R2 shows that there is a weak positive relationship between

the magnitude of the price change and the magnitude of the difference between the methods.

This is illustrated in the table below, where price changes are broken down into categories and

differences are evaluated accordingly. If the price change is between 0 and 5 percent, the average

difference is 0,23 percentage points, increasing to 0,45 percentage points if the annual price change

is 20 percent or higher.

Table 4 - The absolute difference by absolute value of annual inflation, split by categories

Annual inflation

(absolute value of change)

Absolute value of

difference

0-5 0,23

5-10 0,29

10-20 0,34

20+ 0,45

Impact of 25-month windows and seasonality

The largest positive difference in the December inflation data is for ice cream, where annual

inflation is 1,60 percentage points higher at 25-month window lengths than at shorter window

lengths. The second largest positive difference is for yogurt and the third largest is for preserved

fish. The largest negative difference is for edible oils, where annual inflation is 2,03 percentage

points lower at 25-month window lengths than at the 13-month window lengths. For vegetable oils,

annual inflation was well above average, but this is not true for all COICOP categories shown in

the figure.

Figure 8 – Top and Bottom COICOP Subclasses at 5-digit level according to magnitude of difference of annual

inflation in December 2022

COICOP 5-digit level is not the elementary aggregate where the index calculation is done, so it is

worth looking at the top differences at this lowest elementary level to see the negative and positive

differences. The 50 COICOP 5 categories (see in Table 3) contain a total of 130 elementary

aggregates.

Figure 9 – Top and Bottom elementary aggregates according to magnitude of difference of annual inflation in

December 2022

We see that at the elementary aggregate level five of the eleven categories shown are some kind of

fruit, but at the higher COICOP 5-digit level the category fruit (01161) do not appear in the top

places because the positive and negative differences neutralize each other. The most COICOP 5

categories contain only a maximum of 3 elementary aggregates, but fruit is one of the exceptions

with 13 positions, so such a balancing mechanism may rather play a role. Besides fruit, there are

other products such as ice cream and canned peaches that are also seasonal. This is in line with the

literature, which shows that index calculation with long time windows can gain importance,

especially for seasonal products. To avoid balancing mechanisms it is appropriate to continue our

analysis at this more detailed elementary aggregate level.

If we can express seasonality in terms of some quantifiable indicator, we can get a more accurate

picture of the strength of the relationship between seasonality and the deviation of annual inflation

calculated over a 25- and 13-month window. Two indicators have been defined to express

seasonality. One of these is based on the volatility of income data per elementary aggregate over

the 25-month window length. This was defined using the standard deviation of revenues. Since

each elementary aggregate generates different revenue magnitudes, we finally chose as one of the

indicators the coefficient of variation (CV), also known as relative standard deviation (RSD),

defined as the ratio of the standard deviation to the mean. The other indicator measuring

seasonality measures the average number of months per elementary aggregate that products are in

supply over the period defined by the 25-month window length. For seasonal products, this value is

lower because the products are not in supply out of season or are substituted by alternative products

(e.g. imported products for fruit).

The strength of the relationship between these variables was measured using Pearson's correlation.

In addition to seasonality, we have also included in our analysis the magnitude of annual inflation,

which we have already seen is slightly positive related to the magnitude of the difference between

the methods. Our aim is to put this weak relationship in context once again by understanding the

strength of the relationship between seasonality and the difference between the methods. We

express both the difference and annual inflation in absolute terms, as before at Figure 7.

Table 5 - Pearson's correlation matrix at elementary aggregate level

data based on 12 Month from January to December

Difference

(abs)

Revenue

(RSD)

Number of

months on sale

Annual Inflation

(abs)

Difference (abs) 1,00 0,55

<,0001 -0,33 <,0001

0,18 <,0001

Revenue relative

standard deviation (RSD)

0,55 <,0001

1,00 -0,50 <,0001

0,02 0,4418

Number of months on sale -0,33 <,0001

-0,50 <,0001

1,00 0,03 0,2014

Annual Inflation (abs) 0,18 <,0001

0,02 0,4418

0,03 0,2014

1,00

The correlation matrix shows the pairwise correlations between each variable. In the first matrix,

all 12 months considered are included.

There is a strong positive linear relationship between the absolute value of the difference

(difference abs) in yearly inflation rates calculated with a 25-month and a 13-month window length

and the relative standard deviation of the revenues of each elementary aggregate. This means that

the higher the monthly volatility of revenues, the larger the difference between the two methods.

There is also a significant linear relationship between our other indicator of seasonality and the

absolute difference, but the direction is negative and the relationship is less strong. The negative

direction is consistent with our expectations, since the fewer months on average a product is on

sale, the more we can assume the seasonal character of the elementary aggregate, which is

associated with a larger absolute difference. Consistent with the above, our two seasonal indicators

are also strongly negatively correlated.

There is also a positive relationship between the magnitude of the annual price change, currently

defined as the absolute value of annual inflation measured by the 13-window-length method, and

the magnitude of the difference between the two methods, but the strength of the relationship is not

robust. This is consistent with Figure 5, which showed that in the first half of the year, when annual

inflation was typically lower, we measured smaller differences between the methods than in the

second half of the year when inflation was higher.

Table 6 - Pearson's correlation matrix at elementary aggregate level

data based on one-month December 2022

Difference

(abs)

Revenue

(RSD)

Number of

months on sale

Annual Inflation

(abs)

Difference (abs) 1,00 0,31

0,0003 -0,17 0,06

0,01 0,9116

Revenue relative

standard deviation (RSD))

0,31 0,0003

1,00 -0,50 <,0001

-0,10 0,2787

Number of months on sale -0,17 0,06

-0,50 <,0001

1,00 0,26 0,0025

Annual Inflation (abs) 0,01 0,9116

-0,10 0,2787

0,26 0,0025

1,00

If only December data are used, the seasonality indicators show a similar relationship with the

difference between the method as for 12 months, but the strength of the relationship is weaker.

However, the magnitude of annual inflation in December is not correlated with the difference in

methods.

Summarising what we have observed so far, the annual inflation rates derived from the 25-month

and 13-month window indices do not differ significantly. There are some small positive and

negative differences, but these almost completely neutralize each other, especially at higher levels

of aggregation. Nevertheless, overall, we have measured higher annual inflation for more

categories than lower annual inflation using the 25-month windows. We also found that when an

elementary aggregate is seasonal, the difference between the two methods becomes larger.

However, we are not yet able to conclude whether the difference will be more positive or negative

in the case of seasonality, i.e. whether annual inflation calculated with a 25-month window length

will be higher or lower. Among the Top aggregates on Figure 6, we have seen examples of both the

former and the latter.

To determine whether the difference is positive or negative, we used an additional seasonality

indicator formed from our two previous seasonal variables. This indicator takes into account both

the relative standard deviation of revenues and the number of months in which products are on sale.

Saisonality = σ(revenue)

µ(revenue) X (1 −

µ(number of months on sale)

25 )

We divided the 130 elementary aggregates into 5 quintiles along this new seasonality variable and

evaluated the differences between the methods. To identify the signs, this time we used the original

differences rather than the absolute values.

Figure 10 – Seasonality and difference in annual inflation on elementary aggregate level for food,

December 2022

Apparently, the top 20 percent of elementary aggregates (quintile 5), which according to our

indicator for seasonality can be considered as most likely to be seasonal, show on average a larger

positive difference than the other less seasonal elementary aggregates. This top group includes

strawberries, peaches, oranges, chocolate, veal, melons, or ice cream, among others. Thus, the

analysis shows that while there may be differences between the two methods at the level of certain

elemental aggregates for non-seasonal products, these differences almost completely compensate

each other.

For seasonal products, the overall picture is that the method with 25-month window length,

although dependent on elementary aggregates, tends to measure higher inflation. The average

deviation is +0,34 percentage points for the 25-month window length, while the deviation of

quintiles 1-3 is much closer to 0, ranging from +0,08 to +0,19 percentage points. The fourth

quintile shows a deviation of -0,11 percentage points.

Conclusion

• The use of scanner data in consumer price statistics is seen as a major qualitative

improvement. After several years of preparation, scanner data have been introduced into the

Austrian CPI and HICP in January 2022. In this paper we focused on the decision-making

process involved in selecting the appropriate index calculation methodology, specifically the

choice of the window length. For practical reasons, Statistics Austria introduced scanner data

into the CPI with a 13-month window length using the GEKS index methodology. The aim of

this study was to compare, one year after the introduction of the scanner data, how the index

would have evolved if a longer, 25-month window length had been chosen, to provide

guidance to other NSIs in the early stages of implementation.

• We compared two consumer price indices calculated using different window lengths (13

months and 25 months) to see the impact of the window length on the annual inflation rates.

Annual inflation rates were calculated for each month in 2022 and compared at different

COICOP levels, ranging from 1-digit level (total CPI) to 5-digit level. The scanner data

covered only 16% of the consumer basket at COICOP 1-digit level, while the coverage was

100% at 5-digit level within the division 01 for food. We found that the difference between the

two methodologies seems to be slightly related to the rate of price increases, and the impact of

the 25-month window length on the overall index was small. The difference in average annual

inflation was only +0,01 percentage points higher if the longer window length had been used

at the time of implementation. The differences at the COICOP 5-digit level ranged from -1,16

to +0,9 percentage points, with an average difference of only +0,06 percentage points.

• Later we limited our analysis to COICOP categories that are fully covered by scanner data,

and focused on food and non-alcoholic beverages. The average annual inflation in COICOP

division 01 (food and non-alcoholic beverages) calculated with 25-month window lengths was

+0,06 percentage points higher than the inflation calculated with 13-month window lengths.

• As 2022 showed a gradually increasing inflation path each month, the question arose as to

whether the increasing inflation had an impact on the difference in methods. We find that there

is a weak positive relationship between the magnitude of the price change and the magnitude

of the difference between the methods at COICOP 5-digit level, however, this relationship

does not tell us whether the difference is negative or positive.

• When listing the COICOP 5-digit categories with the largest differences, it was found that,

contrary to expectations, the most seasonal category "fruit" was not among the top categories.

This prompted us to continue the analysis at an even finer level, namely at the elementary

aggregate level. Here we have already found that many fruit elementary aggregates have the

largest positive and negative differences between the methods, but at the higher COICOP 5-

digit level the positive and negative differences neutralize each other.

• We used two indicators to express seasonality in relation to the deviation of annual inflation

calculated over a 25- and 13-month window. The indicators were the relative standard

deviation of the revenues and the average number of months per elementary aggregate that

products were in supply. Pearson's correlation was used to measure the strength of the

relationship between these variables. When we studied the differences between the methods in

all 12 months at all food elementary aggregates the correlation matrix showed a strong

positive linear relationship between the absolute value of the differences in yearly inflation

rates and the relative standard deviation of the revenue. There was also a significant linear

relationship between the other indicator of seasonality and the absolute difference, but the

direction was negative and the relationship was less strong. Finally, there was a positive

relationship between the magnitude of the annual price change and the magnitude of the

difference between the two methods, but the strength of the relationship was not robust. Using

only one-month December data, the seasonality indicators showed a similar relationship with

the difference between the method as for the 12 months, but the strength of the relationship is

weaker.

• Finally, a correlation between seasonality and the difference in methods was found that

provides also information on the direction of the difference. The elementary aggregates were

grouped into quintiles of five equal groups based on seasonality. For the most seasonal

products (quintile 5), the general picture is that the 25-month window length method, although

dependent on the elementary aggregates, generally measures higher annual inflation. The

average deviation is +0,34 percentage points for the 25-month window length, while the

deviation for quintiles 1-3 is much closer to 0, ranging between +0,08 and +0,19 percentage

points. The fourth quintile shows a deviation of -0,11 percentage points.

• The annual inflation rates derived from the 25-month and 13-month window indices do not

differ significantly. There are some small positive and negative differences, but these almost

completely compensate each other, especially at higher levels of aggregation. Overall,

however, the annual inflation of the lower COICOP level categories is more often higher than

lower for a 25-month window than for a 13-month window. We also found that when an

elementary aggregate is seasonal, the difference between the two methods becomes larger and

mostly the method with longer window length measures higher inflation.

• Overall, the differences that we found between the two methods are small enough to

recommend the introduction of scanner data with 13-month window length for saving time and

resources.

Introduction of Scanner Data into Austrian CPI and HICP: Practical implementation experience

Read more about Introduction of Scanner Data into Austrian CPI and HICP: Practical implementation experience

Languages and translations

English

www.statistik.at

Independent statistics for fact-based decisions

Introduction of Scanner Data into

Austrian CPI and HICP:

Practical implementation

experience

Window length 13 vs. 25 month

Adam Tardos

Statistics Austria

Department of Price and Parity

Meeting of the Group of Experts on CPI

07. Juni 2023

Page 2www.statistik.at

Why Window-length?

www.statistik.at Page 3

• Multilateral methods (GEKS, WTPD, GK)

• voluntary data provision is not the reality

• legislation or regulation is required to ensure the regular provision of data

• regulation is not enough, a constructive and cooperative relationship with data providers is needed to ensure the right quality of data

• an appropriate infrastructure is needed to store and process the data

• machine-learning methods

• manuel supervision

Challenges faced when implementing scanner data

Lack of regulation

Counter- interest of

potential data providers

Big data volume

Mass Product Classification

New data source requires

new index calculation methods

P ro

b le

m s

So lu

ti o

n s

www.statistik.at Page 4

Why window-length?

• It has of great practical importance when introducing scanner data, namely how many months of data are needed to be collected in advance.

• While other methodological choices can be made relatively freely, there is a trade-off in choosing the length of the time window: a longer time window is recommended in the literature, but in practice there is often not enough time to collect long data series.

Page 5www.statistik.at

Implementation of Scanner Data

www.statistik.at Page 6

Timeline of Austrian scanner data project

2010

Start of discussions with stakeholders and potential

data providers about voluntary data transmission

Transition period

12.2019

The new CPI regulation enters into force:

the start of regular scanner data delivery

in the food and drugstore sector

01.2022

Introduction of food and drugstore scanner data into the Austrian CPI

and HICP

2012

Data acquisition from AC Nielsen, start of

scanner data analysis

2016

Drafting first regulatory proposals

2013

Start of continuous (unfruitful) negotiations about voluntary

data transmissions (until 2017)

www.statistik.at Page 7

Regulation: a national compromise was reached between all stakeholders. Since December 2019, the CPI Regulation governs data collection by means of scanner data. This is not

the best possible regulation, but it could win the cooperation of the companies.

Austrian regulation defines:

The size of enterprise obliged to provide data: cut-off sampling excluding Small and Medium Enterprises.

The periodicity of the delivery of scanner data: weekly

The survey regions for the scanner data deliveries: 346 postcodes were selected in such a way as to ensure representativeness at regional level. Around 43% of the Austrian population live in the selected areas

Each country will have to negotiate a compromise with stakeholders that is acceptable to all.

www.statistik.at Page 8

Selected sectors: food, beverages, and cosmetic and toiletry articles

These sectors are highly concentrated in Austria, where the top 5 players have a market share of 80-90%, it is an ideal choice for the introduction of scanner data.

Relatively few data providers need to be involved, while these commodity groups have 15% weight in the CPI index basket.

There is a significant saving of resources, as in these areas regional price collection was carried out, involving a significant number of price collectors.

www.statistik.at Page 9

• Secure servers

• Stroarge privacy

• Data protection

Data processing

Automated data transfer

Weekly tasks:

• Check for completeness

• Search for anomaly

• Outlier check

Data Import, Review, Quality control

• verified data is loaded in database

Database upload,

syn- chronisation

Monthly task:

• automated matching procedure

• machine-learning methods

• manual procedure

Classification

www.statistik.at Page 10

Index-method Methodology

Bilateral Multilateral

Static annual weight

Dynamic monthly weight

GEKSWTPD GK

The two-year transition period did not allow us to experiment with the longer 25-month window, but we did not want to wait another year to introduce scanner data.

Country level Regional level

Windows length: 25 month

Windows length: 13 month

Page 11www.statistik.at

Alternative Window Length:

25-month vs. 13-month Windows

Length

www.statistik.at Page 12

Average annual inflation would have been only 0,01 percentage points higher with a longer time-window

Ø WL = 25 8,64 5,44 6,84 8,52 9,86

Ø WL = 13 8,63 5,42 6,81 8,45 9,80

∆(25-13) +0,01 +0,02 +0,03 +0,07 +0,06

The lower the level of COICOP groups we look at, the greater the dispersion of differences around 0.

The average difference at COICOP level 5 is only +0,06 percentage points.

Of the 62 COICOP 5 categories, 40 have positive differences and only 22 have negative differences.

Difference in average annual inflation by COICOP level: window length 25 vs. 13 (2022)

www.statistik.at Page 13

Monthly annual inflation shows increasing differences, with inflation rising as the year progresses.

1 2 3 4 5 January 2022

Ø WL = 25 4,96 2,89 2,65 3,17 3,31 Ø WL = 13 4,95 2,87 2,57 3,10 3,23 ∆(25-13) +0,01 +0,02 +0,08 +0,07 +0,08

December 2022

Ø WL = 25 10,40 7,98 10,55 14,03 16,74 Ø WL = 13 10,37 7,95 10,50 13,81 16,62 ∆(25-13) +0,03 +0,03 +0,05 +0,22 +0,12

The monthly annual inflation data indicate a larger difference compared to the annual average inflation.

At COICIOP 5 level, the differences in January vary between -0,58 and 1,16 percentage points, in December they range between -2,03 and 2,63.

The annual inflation in the COICOP 5 categories involved was only 3,2 percent in January, while at the end of the year it was 16,6 percent.

The extent of the difference between the two methods is somewhat influenced by the rate of inflation, but at COICOP 1 level, even with high inflation in December, the difference is not large, only +0,03 percentage points.

www.statistik.at Page 14

Average annual inflation by food would have been 0,06 percentage points higher with a longer time-window

Ø WL = 25 11,85 12,11 11,97 11,38

Ø WL = 13 11,79 12,07 11,97 11,33

∆(25-13) +0,06 0,04 0,00 +0,05

This chart represents fewer categories, but they are all fully covered with scanner data.

At COICOP Level 5, the average difference between the results of the methods is +0,05 percentage points, but again the results for each category show a relatively larger difference of between -1,16 and +0,76 percentage points.

Difference in average annual inflation by COICOP

level: window length 25 vs. 13 food only (2022)

www.statistik.at Page 15

There is only a weak positive relationship between the level of inflation and the size of the difference between the methods.

Annual inflation (absolute value of change)

Absolute value of difference

0-5 0,23

5-10 0,29

10-20 0,34

20+ 0,45

The regression line, albeit with a low R2 shows that there is a weak positive relationship between the magnitude of the price change and the magnitude of the difference between the methods.

In the table the price changes are broken down into categories and differences are evaluated accordingly. If the price change is between 0 and 5 percent, the average difference is 0,23 percentage points, increasing to 0,45 percentage points if the annual price change is 20 percent or higher.

Absolute difference according to the absolute value of annual inflation

www.statistik.at Page 16

Contrary to our expectations, there are no classic seasonal products at COICOP 5 level in the categories with the greatest differences.

The largest positive difference in the December inflation data is for ice cream, where annual inflation is 1,60 percentage points higher at 25-month window lengths than at shorter window lengths. The second largest positive difference is for yogurt and the third largest is for preserved fish.

The largest negative difference is for edible oils, where annual inflation is 2,03 percentage points lower at 25- month window lengths than at the 13-month window lengths. For vegetable oils, annual inflation was well above average, but this is not true for all COICOP categories shown in the figure.

COICOP level 5 is not the elementary aggregate where the index calculation is done, so it is worth looking at the top differences at this lowest elementary. It is conceivable that the positive and negative differences cancel each other out in the COICOP 5 groups covering several seasonal elementary aggregates.

Top and bottom COICOP subclasses at level 5 according to the size of the difference in annual inflation in December 2022

www.statistik.at Page 17

As expected, at the level of elementary aggregates, seasonal products dominate the categories with the largest differences.

At the elementary aggregate level five of the eleven categories shown are some kind of fruit, but at the higher COICOP 5-digit level the category fruit (01161) do not appear in the top places because the positive and negative differences neutralize each other.

Besides fruit, there are other products such as ice cream and canned peaches that are also seasonal.

The frequent appearance of seasonal elementary aggregates is in line with the literature which shows that index calculation with long time windows can become more important, especially for seasonal products.

Top and bottom elementary aggregates by size of annual inflation differential in December 2022

www.statistik.at Page 18

In the case of the most seasonal elementary aggregates, the window lengths chosen make a difference.

Seasonality was expressed in terms of some quantifiable indicator: This indicator takes into account both the relative standard deviation of revenues and the number of months in which products are on sale in the window.

130 elementary aggregates calculated based on scanner data were devided into 5 quintiles along this new seasonality variable.

Apparently, the top 20 percent of elementary aggregates (quintile 5), which according to the indicator for seasonality can be considered as most likely to be seasonal, show on average a larger positive difference than the other less seasonal elementary aggregates. This top group includes strawberries, peaches, oranges, chocolate, veal, melons, or ice cream, among others.

www.statistik.at Page 19

Conclusion

• The annual inflation rates derived from the 25-month and 13-month window indices do not

differ significantly. There are some small positive and negative differences, but these almost

completely compensate each other, especially at higher levels of aggregation.

• However, the annual inflation of the lower COICOP level categories is more often higher

than lower for a 25-month window than for a 13-month window.

• We also found that when an elementary aggregate is seasonal, the difference between the

two methods becomes larger and mostly the method with longer window length measures

higher inflation.

• Overall, the differences that we found between the two methods are small enough to

recommend the introduction of scanner data with 13-month window length for saving time

and resources.

www.statistik.at

Independent statistics for fact-based decisions

Questions and Discussion

Adam Tardos

Statistics Austria, Team Price

Contact: [email protected]

Introduction of Scanner Data into Austrian CPI and HICP: Practical implementation experience

Read more about Introduction of Scanner Data into Austrian CPI and HICP: Practical implementation experience

Languages and translations

English

www.statistik.at

Independent statistics for fact-based decisions

Introduction of Scanner Data into Austrian CPI and HICP: Practical implementation experience

Window length 13 vs. 25 month

Adam Tardos Statistics Austria

Department of Price and Parity

Meeting of the Group of Experts on CPI 07. Juni 2023

Page 2www.statistik.at

Why Window-length?

www.statistik.at Page 3

• Multilateral methods (GEKS, WTPD, GK)

• voluntary data provision is not the reality

• legislation or regulation is required to ensure the regular provision of data

• regulation is not enough, a constructive and cooperative relationship with data providers is needed to ensure the right quality of data

• an appropriate infrastructure is needed to store and process the data

• machine-learning methods

• manuel supervision

Challenges faced when implementing scanner data Lack of

regulation Counter-

interest of potential data

providers

Big data volume

Mass Product Classification

New data source requires

new index calculation methods

Pr ob

le m

s So

lu tio

www.statistik.at Page 4

Why window-length?

ĞẼȈẼ İ ḠḤĞI İ ḠḤĞǦỊ ḠḤĜḪẼȈḠI Ḥ

Î GẼḤḤǦĬ

ĞḠĜĜǦĬǦḤGǦ

ḪǦḤĢȈĠ

GI ḠGI ĲḪǦḮǦḪ

ẼḤḤÏ ẼḪ

ĤǦȈĠI ĞÎ ĲĬI ĞÏ GȈÎ

ĀǺÄĤI ḤȈĠ

ĞḠĜĜǦĬǦḤGǦÎ ĲĬḠGǦ

ȈḠĤǦĤI ḤȈĠÎ

ḠḤĞḠGǦÎ

ĲI ḠḤȈÎ ĤǦȈĠI Ğ

ǦḪǦĤǦḤȈẼĬÌ

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ĬǦḪẼȈḠI ḤÎ ĠḠĲ

ĲǦĬGǦḤȈẼĢǦ

ĲǦĬḠI ĞGẼḪGÏ ḪẼȈǦĞ

GĲḠ

ĠḠĢĠǦĬ ĲI Î ḠȈḠḮǦ

GẼȈǦĢI ĬḠǦÎ

ĤI ḤȈĠ ĤÏ ḪȈḠḪẼȈǦĬẼḪ

ĲĬI ĞÏ GȈ

Î ǦẼÎ I ḤẼḪḠȈÌ ÏÎǦĞ

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GẼḪGÏḪẼȈḠIḤ

ĞǦGǦĤFǦĬ

bilateral

13-month

deviation

number

seasonal

change

food

magnitude

negative

different

figure

• It has of great practical importance when introducing scanner data, namely how many months of data are needed to be collected in advance.

Page 5www.statistik.at

Implementation of Scanner Data

www.statistik.at Page 6

Timeline of Austrian scanner data project

2010

Start of discussions with stakeholders and potential

data providers about voluntary data transmission

Transition period

12.2019

The new CPI regulation enters into force:

the start of regular scanner data delivery

in the food and drugstore sector

01.2022

Introduction of food and drugstore scanner data into the Austrian CPI

and HICP

2012

Data acquisition from AC Nielsen, start of

scanner data analysis

2016

Drafting first regulatory proposals

2013

Start of continuous (unfruitful) negotiations about voluntary

data transmissions (until 2017)

www.statistik.at Page 7

Regulation: a national compromise was reached between all stakeholders. Since December 2019, the CPI Regulation governs data collection by means of scanner data. This is not the best possible regulation, but it could win the cooperation of the companies. Austrian regulation defines:

The size of enterprise obliged to provide data: cut-off sampling excluding Small and Medium Enterprises.

The periodicity of the delivery of scanner data: weekly

Each country will have to negotiate a compromise with stakeholders that is acceptable to all.

www.statistik.at Page 8

Selected sectors: food, beverages, and cosmetic and toiletry articles

These sectors are highly concentrated in Austria, where the top 5 players have a market share of 80-90%, it is an ideal choice for the introduction of scanner data.

Relatively few data providers need to be involved, while these commodity groups have 15% weight in the CPI index basket.

There is a significant saving of resources, as in these areas regional price collection was carried out, involving a significant number of price collectors.

www.statistik.at Page 9

• Secure servers

• Stroarge privacy

• Data protection

Data processing

Automated data transfer

Weekly tasks:

• Check for completeness

• Search for anomaly

• Outlier check

Data Import, Review, Quality control

• verified data is loaded in database

Database upload,

syn- chronisation

Monthly task:

• automated matching procedure

• machine-learning methods

• manual procedure

Classification

www.statistik.at Page 10

Index-method Methodology

Bilateral Multilateral

Static annual weight

Dynamic monthly weight GEKSWTPD GK

The two-year transition period did not allow us to experiment with the longer 25-month window, but we did not want to wait another year to introduce scanner data.

Country level Regional level

Windows length: 25 month

Windows length: 13 month

Page 11www.statistik.at

Alternative Window Length: 25-month vs. 13-month Windows Length

www.statistik.at Page 12

Average annual inflation would have been only 0,01 percentage points higher with a longer time-window

-1.0

-0.5

0.0

0.5

1.0

1 Total-CPI 2 Division 3 Group 4 Class 5 Subclass COICOP Level

D iff

er en

ce in

A nn

ua l I

nf la

tio n:

W in

do w

s le

ng th

2 5

- 1 3

Ø WL = 25 8,64 5,44 6,84 8,52 9,86

Ø WL = 13 8,63 5,42 6,81 8,45 9,80

∆(25-13) +0,01 +0,02 +0,03 +0,07 +0,06

The lower the level of COICOP groups we look at, the greater the dispersion of differences around 0.

The average difference at COICOP level 5 is only +0,06 percentage points.

Of the 62 COICOP 5 categories, 40 have positive differences and only 22 have negative differences.

Difference in average annual inflation by COICOP level: window length 25 vs. 13 (2022)

www.statistik.at Page 13

Monthly annual inflation shows increasing differences, with inflation rising as the year progresses.

1 2 3 4 5 January 2022

Ø WL = 25 4,96 2,89 2,65 3,17 3,31 Ø WL = 13 4,95 2,87 2,57 3,10 3,23

∆(25-13) +0,01 +0,02 +0,08 +0,07 +0,08 December 2022

Ø WL = 25 10,40 7,98 10,55 14,03 16,74 Ø WL = 13 10,37 7,95 10,50 13,81 16,62

∆(25-13) +0,03 +0,03 +0,05 +0,22 +0,12

The monthly annual inflation data indicate a larger difference compared to the annual average inflation.

At COICIOP 5 level, the differences in January vary between -0,58 and 1,16 percentage points, in December they range between -2,03 and 2,63.

The annual inflation in the COICOP 5 categories involved was only 3,2 percent in January, while at the end of the year it was 16,6 percent.

09 10 11 12

05 06 07 08

01 02 03 04

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0

COICOP Level

D iff

er en

ce in

A nn

ua l I

nf la

tio n:

W in

do w

s le

ng th

2 5

- 1 3

www.statistik.at Page 14

Average annual inflation by food would have been 0,06 percentage points higher with a longer time-window

Ø WL = 25 11,85 12,11 11,97 11,38

Ø WL = 13 11,79 12,07 11,97 11,33

∆(25-13) +0,06 0,04 0,00 +0,05

This chart represents fewer categories, but they are all fully covered with scanner data.

-0.5

0.0

0.5

1.0

2 Division 3 Group 4 Class 5 Subclass COICOP Level

D iff

er en

ce in

A nn

ua l I

nf la

tio n:

W in

do w

s le

ng th

2 5

- 1 3 Difference in average annual inflation by COICOP

level: window length 25 vs. 13 food only (2022)

www.statistik.at Page 15

There is only a weak positive relationship between the level of inflation and the size of the difference between the methods.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 10.0 20.0 30.0 40.0 50.0 Annual Inflation - absolute value (window length 13)

Ab so

lu te

va lu

e of

d iff

er en

ce in

A nn

ua l I

nf la

tio n Annual inflation

(absolute value of change) Absolute value of

difference 0-5 0,23

5-10 0,29

10-20 0,34

20+ 0,45

The regression line, albeit with a low R2 shows that there is a weak positive relationship between the magnitude of the price change and the magnitude of the difference between the methods.

Absolute difference according to the absolute value of annual inflation

www.statistik.at Page 16

Contrary to our expectations, there are no classic seasonal products at COICOP 5 level in the categories with the greatest differences.

01185 - Ice Cream

01144 - Yogurt

01136 - Preserved fish

01122 - Beef

01183 - Chocolate

01135 - Smoked fish

01153 - Olive oil

01131 - Fresh fish

01182 - Jam, honey, etc.

01143 - Pres. milk

01154 - Edible oil

-2 -1 0 1 2 Difference

Top and bottom COICOP subclasses at level 5 according to the size of the difference in annual inflation in December 2022

www.statistik.at Page 17

As expected, at the level of elementary aggregates, seasonal products dominate the categories with the largest differences.

01161 - Grapes

01122 - Filet of pork

01185 - Ice cream

01144 - Fruit yogurt

01161 - Peach

01161 - Kiwi

01143 - Pre. milk

01161 - Orange

01161 - Strawberry

01154 - Edible oils

01182 - Canned peaches

-3 -2 -1 0 1 2 3 Difference

Besides fruit, there are other products such as ice cream and canned peaches that are also seasonal.

Top and bottom elementary aggregates by size of annual inflation differential in December 2022

www.statistik.at Page 18

In the case of the most seasonal elementary aggregates, the window lengths chosen make a difference.

0.08 0.19

0.11

-0.11

0.34

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 Quintiles of seasonality

Di ff

er en

ce in

A nn

ua l In

fla tio

n: W

in do

w s

le ng

th 2

5 - 1

130 elementary aggregates calculated based on scanner data were devided into 5 quintiles along this new seasonality variable.

www.statistik.at Page 19

Conclusion

• The annual inflation rates derived from the 25-month and 13-month window indices do not differ significantly. There are some small positive and negative differences, but these almost completely compensate each other, especially at higher levels of aggregation.

• However, the annual inflation of the lower COICOP level categories is more often higher than lower for a 25-month window than for a 13-month window.

• We also found that when an elementary aggregate is seasonal, the difference between the two methods becomes larger and mostly the method with longer window length measures higher inflation.

• Overall, the differences that we found between the two methods are small enough to recommend the introduction of scanner data with 13-month window length for saving time and resources.

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Questions and Discussion

Adam Tardos Statistics Austria, Team Price Contact: [email protected]

Slide Number 1
Slide Number 2
Challenges faced when implementing scanner data
Why window-length?
Slide Number 5
Timeline of Austrian scanner data project
Regulation: a national compromise was reached between all stakeholders.
Selected sectors: food, beverages, and cosmetic and toiletry articles
Data processing
Index-method
Slide Number 11
Average annual inflation would have been only 0,01 percentage points higher with a longer time-window
Monthly annual inflation shows increasing differences, with inflation rising as the year progresses.
Average annual inflation by food would have been 0,06 percentage points higher with a longer time-window
There is only a weak positive relationship between the level of inflation and the size of the difference between the methods.
Contrary to our expectations, there are no classic seasonal products at COICOP 5 level in the categories with the greatest differences.
As expected, at the level of elementary aggregates, seasonal products dominate the categories with the largest differences.
In the case of the most seasonal elementary aggregates, the window lengths chosen make a difference.
Conclusion
Slide Number 20

Introduction of Scanner Data into Austrian CPI and HICP – practical implementation experience, with a focus on window length options

Read more about Introduction of Scanner Data into Austrian CPI and HICP – practical implementation experience, with a focus on window length options

After several years of preparation and a two-year transition period, scanner data have been introduced into the Austrian CPI and HICP in January 2022. A significant factor was the amendment of the Austrian national CPI-Regulation in December 2019, which since then regulates the scanner data requirements and ensures the weekly scanner data deliveries by most important retailers, initially by the grocery and drugstore retail trade (NACE classes 47.11 and 47.75).