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Recording of Data in the German National Accounts

Recording of Data in the German National Accounts

Languages and translations
English

Recording of Data in the German National Accounts Meeting of the Group of Experts on National Accounts

23-25 April 2024

Benedikt Kuckelkorn

destatis.deRecording of Data in the German National Accounts

Source: The Economist

23.04.2024Federal Statistical Office (Destatis) 2

Introduction Rapidly expanding

volumes of data expenditures spent on recording data

→Need for NA to explicitly reflect investments in data → Recording data as an asset part of SNA 2025

revision. Recommendations by DITT in Guidance Note DZ.06

First feasibility study by Destatis 2023

Follow-up project currently ongoing

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 3

Data is “information content that is produced by accessing and observing phenomena; and recording, organizing and storing information elements from these phenomena in a digital format, which provide an economic benefit when used in productive activities.”

- Separate asset category “Data and Databases”

- Only data that provides an economic benefit when used in the productive activities should be included

- Own account production

- is considered as capital formation

- Should be valued at sum of cost

Data as an asset in GN DZ.06

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 4

Estimation of GFCF in data

Number of employees Involvement

Rate

Gross earnings Mark-Up

Structure of Earnings survey

Microcensus

Classification of occupations

Data Output

Production on own-account

destatis.deRecording of Data in the German National Accounts

Purchased data

23.04.2024Federal Statistical Office (Destatis) 5

No data sources available

→Necessary assumption: data assets are purchased on exclusive agreements

→ Allows estimation of total data assets in the economy as:

Absent better information, exports and imports of IT services as approximation

Estimation of GFCF in data

Data Output Data ExportsData Imports Investment in

Data

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 6

Calculated using Perpetual Inventory Method

Requirement: Retirement and depreciation profiles and average service lives (ASL)

- Test of multiple specifications:

- Straight-line depreciation in combination with a bell-shaped retirement function

Density function of the gamma distribution

- Geometric depreciation method

- ASL of 2, 5, 10 years

Depreciation and Capital Stocks of Data Assets

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 7

Results

0

20

40

60

80

100

120

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

bn . €

GFCF in Data

GFCF in Data (high involvement) GFCF in Data (low involvement)

0

100

200

300

400

500

600

700

800

900

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

bn . €

GFCF with and without Data

GFCF without Data GFCF incl. Data (low involvement) GFCF incl. Data (high involvement)

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 8

Results

0

20

40

60

80

100

120

140

160

180

200

2011 2012 2013 2014 2015 2016 2017 2018

bn . €

Net Capital Stock, ASL = 5

Linear DDB DBR

0

10

20

30

40

50

60

70

80

2011 2012 2013 2014 2015 2016 2017 2018

bn . €

Consumption of Fixed Capital, ASL = 5

Linear DDB DBR

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 9

Results

-0,50%

-0,40%

-0,30%

-0,20%

-0,10%

0,00%

0,10%

0,20%

0,30%

0,40%

-8,00%

-6,00%

-4,00%

-2,00%

0,00%

2,00%

4,00%

6,00%

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Impact of Data on GDP Growth

GDP Growth Rate (incl. Data) GDP Growth Rate Difference

0

500

1000

1500

2000

2500

3000

3500

4000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

bn . €

Impact of Data on GDP in Current Prices

GDP (current prices) GDP incl. Data (current prices)

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 10

- Alternative estimation of involvement rates using ESCO skills-occupation matrix

- Identify relevant skills and competencies for data producing jobs

- Automatically assign involvement rate based on number of relevant skills and number of number of required skills

- Still work in progress

Way forward at Destatis

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 11

❖ Evaluation of possibilities to survey expenditures on data

❖ Further development of appropriate price indices

❖ How to record copies

❖ Treatment of data purchases in data-intensive industries

Way forward at Destatis

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 12

❖ Especially involvement rates drive magnitude of results without any statistical basis

❖ High degree of freedom and potentially high impact of recording data assets can damage international comparability of NA results

→Without harmonization efforts inclusion of data assets could hurt the role of GDP/GNI in policy discussion

❖ Concerns on validity of measurement method, economic importance of data inherently driven by advances in soft- and hardware. Measurement approach based on labour input seems counterintuitive.

Conclusion

destatis.deRecording of Data in the German National Accounts

23.04.2024Federal Statistical Office (Destatis) 13

❖ How can an estimation model based on labour input measure the dynamics of an asset mainly driven by automatisation?

❖ How to measure quarterly investment in data without distorting quarterly NA results?

❖ How can we correctly assign economic ownership of data assets in internationally active MNE groups?

❖ Are there alternatives to the sum of cost approach feasible for NA purposes?

Open Questions

Thank you for your attention!

Statistisches Bundesamt 65180 Wiesbaden Germany

Contact Person Benedikt Kuckelkorn [email protected] Phone +49 611 75-3852

www.destatis.de

www.destatis.de/kontakt

Just Transition in Coal Regions of Germany - case study of Lausatia, by Mr. Hans Rüdiger Lange, UNECE International Consultant

Languages and translations
English

Just Transition Case Study Lusatia Germany

1

Race of Speed Climate change vs Adaptation

Race of Speed Climate change vs Adaptation

The experience I want to share with you so that we can learn together ...

Lusatia Case

2015 2021

re gi

on al

w ea

lth

2038

Crisis-Response-Ecosystem-Adaptation-Curve (CREA)

Source: HR Lange

2

Intense conflicts between workers, the region and the central state as well as public opinion

3

Case Study Lusatia Phase I

2015 2021

re gi

on al

w ea

lth

2038

Crisis-Response-Ecosystem-Adaptation-Curve (CREA)

Source: HR Lange

Lusatia Case

Phase I: Regional entrepreneurial transformation process (RETP)

dialogue &

analysis

projects &

markets

innovation system

• Growth projects entrepreneurs, revenue- or employee growth in 5 years

• Workshops– focus on strategy, products and business models of firms

• Growth potentials – emerging as a combination of several projects

• Starting point and boundary conditions for structural change

• Studies, structured interviews with actors and stakeholders

• Cooperation with researchers, experts and other regions.

• Regional organization – a regional coordination and strategy for the region

• Initiatives to strengthen the innovation system – infrastructure, institutes, incubator etc.

• Capital for company projects, initiatives and Analyses

? X X

X 1 2 3 developsearchunderstand

Lusatia Case – Practice Mission (2016)

1. Develop a strategy for the region in order to address the challenge of transformation

2. Help the affected firms of the industrial cluster in their adaptation through strategy consulting and workshops

3. Identify and promote strategic projects for the region

Networking and cooperation

Source:HR Lange, M Tomenendal, adapted from Geels (2011) - Multi-Level Perspective on Socio-Economic Transformations

Increasing structuration of activities in local practices

Time

Socio-technical landscape (exogenous context)

Socio-technical regime (regional specialization)

Niche innovations

Small networks of actors support novelties on the basis of expectations and visions. Learning processes take place on multiple dimensions (co-construction). Efforts to link different elements in a seamless web.

External influences on niches (via expectations and networks)

Landscape developments put pressure on existing regime,

which opens up, creating windows of opportunity for novelties

Elements become aligned, and stabilise in a dominant design.

Internal momentum increases.

New configuration breaks through, taking advantage of ‘windows of opportunity’.

New Ecosystem influences landscape

Policy

Finance

Culture

Markets

Human Capital

Sup- ports

Markets, user preferences

Industry

Policy

Technology

Science

Culture

Strategy Development – Theory Matters Build Appropriate Theoretical Understanding

Buffalo, New York Dortmund, Deutschland

Malmö, Schweden

Oulu, Finnland Manchester, GB

Source: H.R. Lange, Brandenburgische Technische Universität (unpublished)

Strategy Development - in depth analysis International, Selected Case Studies

Strategy Development - in depth analysis Structural analysis of the target region Lusatia

Source: own research, Bundesministerium für Wirtschaft (RWI Study, 2017)

Patents / Mio. inhabitants

60

205 230

Research Spending % of GDP

0,5 1,04

2,01

0,34 0,86

1,32 R&D Personal / Total Employment

Lusatia Rheinland

Leipzig

Analysis of Competitiveness Innovation Indicators

Competing Mining

Regions

Direct and Indirect Employments in the Disrupted Sector (Lusatia 2013) Direct 7.430

Indirect 7.158

4.953

2.477

2.677

1.864

1.689 384

14.581

TechnologyBuilding Works

Services and Logistics

Mining Metal works

537 Power Plant

Total Other

Strategy Development - in depth analysis Detailed Capability Analysis

Source: H.R. Lange, Brandenburgische Technische Universität (unpublished)

Result: The Regional Entrepreneurial Transformation Process (RETP)

dialogue &

analysis

projects &

markets

innovation system

• Growth projects entrepreneurs, revenue- or employee growth in 5 years

• Workshops– focus on strategy, products and business models of firms

• Growth potentials – emerging as a combination of several projects

• Starting point and boundary conditions for structural change

• Studies, structured interviews with actors and stakeholders

• Cooperation with researchers, experts and other regions.

• Regional organization – a regional coordination and strategy for the region

• Initiatives to strengthen the innovation system – infrastructure, institutes, incubator etc.

• Capital for company projects, initiatives and Analyses

? X X

X 1 2 3 developsearchunderstand

H.R. Lange, M. Tomenendal (2017).

3

Case Study Lusatia Phase II

2015 2021

re gi

on al

w ea

lth

2038

Crisis-Response-Ecosystem-Adaptation-Curve (CREA)

Source: HR Lange

Lusatia Case

Regional entrepreneurial transformation process (RETP)

dialogue &

analysis

projects &

markets

innovation system

• Growth projects entrepreneurs, revenue- or employee growth in 5 years

• Workshops– focus on strategy, products and business models of firms

• Growth potentials – emerging as a combination of several projects

• Starting point and boundary conditions for structural change

• Studies, structured interviews with actors and stakeholders

• Cooperation with researchers, experts and other regions.

• Regional organization – a regional coordination and strategy for the region

• Initiatives to strengthen the innovation system – infrastructure, institutes, incubator etc.

• Capital for company projects, initiatives and Analyses

? X X

X 1 2 3 developsearchunderstand

LausitzLab

ESMT Training & Partners

• Kasachstan / GIZ / Czech / ESMT (!!)

Hackathons / Summer Academy

A new DNA of cooperation

Over 100 innovation projects from Bio-Food to Block-Chain-Energy-Contracts elaborated. Over 400 participants, over 100 firms have taken part in our workshops.

Regional Entrepreneurial Transformation Process (RETP)

5 strategical initiatives

Ø 100 initiated projects

29 + 12 structured interviews

6 potential growth markets

2016 2017

1 2 3 developsearchunderstand

2018

4

Case Study Lusatia Phase III

2015 2021

re gi

on al

w ea

lth

2038

Crisis-Response-Ecosystem-Adaptation-Curve (CREA)

Source: HR Lange

Lusatia Case

Regional entrepreneurial transformation process (RETP)

dialogue &

analysis

projects &

markets

innovation system

• Growth projects entrepreneurs, revenue- or employee growth in 5 years

• Workshops– focus on strategy, products and business models of firms

• Growth potentials – emerging as a combination of several projects

• Starting point and boundary conditions for structural change

• Studies, structured interviews with actors and stakeholders

• Cooperation with researchers, experts and other regions.

• Regional organization – a regional coordination and strategy for the region

• Initiatives to strengthen the innovation system – infrastructure, institutes, incubator etc.

• Capital for company projects, initiatives and Analyses

? X X

X 1 2 3 developsearchunderstand

State + Districts Brandenburg and Saxony Investing into the Innovation Ecosystem

• Government shut down decision of 2.7 GW (2015) • Change Management Support for regions (2017) • Kohlekommission Coal Exit 2038 (2019) • Speed Projects in every region (2020) • Kohleausstiegs- und Strukturstärkungsgesetz (2020) • Regional Development Agencies established (2020) • Regional Prioritization Process for Projects started (2021)

Investment and Transformation Activities on multiple levels: Firms, Municipalities, Regions, State & EU

Jänschwalde Mine + Power Plant (central) Waste Incineration Plant

Jänschwalde Mine + Power Plant (central) „Waste Incineration Plant“

Jänschwalde Mine + Power Plant (decentral) „From Maintenance to Production“

Boxberg Mine + Power Plant (decentral) Shift to new markets outside mining

Conveyor systems builder

Steam Turbine Production (decentral) „Product Development & Innovation Centre“

Faleminderit – Thank you!

Invitation to share experience & Invitation to learn together

Market Forecast Tables 2023

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

Languages and translations
English

List of tables

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

Table1

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

Table2

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

Table 2a

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

Table 2b

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

Table 3

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

Table 4

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

Table 5

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

Table 5a

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

Table 6

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

Table 6a

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

Table 6b

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

Table 6c

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

Table 7

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

Table 8

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

Table 9

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

Table 9a

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

Table 9b

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

Table 10

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

Table 11

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

Table 11a

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

Table 11b

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

Table12

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

Table 12a

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

Table 12b

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

Table 12c

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

Table 13

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

Table 14

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

Table 15

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

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

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

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

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

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

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

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

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

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

Imports Exports

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

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

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 1 SAWN SOFTWOOD SCIAGES CONIFERES

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

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

Apparent Consumption

Imports Exports

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

1000 m3

Apparent Consumption Country Consommation Apparente Production Imports - Importations

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

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

Exports - Exportations Pays

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 3 VENEER SHEETS FEUILLES DE PLACAGE

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 4 PLYWOOD CONTREPLAQUES

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6 FIBREBOARD PANNEAUX DE FIBRES

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6a HARDBOARD PANNEAUX DURS

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6b MDF/HDF

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6c OTHER FIBREBOARD AUTRES PANNEAUX DE FIBRES

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

Apparent Consumption

Imports Exports

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

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

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 7 WOOD PULP PATE DE BOIS

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 8 PAPER AND PAPERBOARD PAPIERS ET CARTONS

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

Apparent Consumption

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

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

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

used for energy purposes à des fins energétiques

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

Industrial wood - Bois industriels

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

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

Wood fuel c

Bois de chauffage c Pays

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

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

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

used for energy purposes à des fins energétiques

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

Industrial wood - Bois industriels

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

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

Wood fuel c

Bois de chauffage c Pays

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

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

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

used for energy purposes à des fins energétiques

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

Industrial wood - Bois industriels

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

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

Wood fuel c

Bois de chauffage c Pays

Imports Exports

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

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

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

TABLE 10 SOFTWOOD SAWLOGS GRUMES DE SCIAGES DES CONIFERES

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

Apparent Consumption a

Imports Exports

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

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

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

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

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

Apparent Consumption a

Imports Exports

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

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

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

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

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

Apparent Consumption a

Imports Exports

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

Country Commerce Net Production Imports - Importations Exports - Exportations Pays

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

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

Net Trade

Imports Exports

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

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

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

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

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

Apparent Consumption a

Imports Exports

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

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

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

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

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

Imports Exports

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

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

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

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

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

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

1000 m3

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 13 WOOD PELLETS GRANULES DE BOIS

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

Apparent Consumption

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

TABLE 14

Europe: Summary table of market forecasts for 2023 and 2024

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

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

Consommation Apparente Production Imports - Importations Exports - Exportations

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

TABLE 15

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

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

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

Consommation Apparente Production Imports - Importations Exports - Exportations

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

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

JQ2022DEU

JFSQ2022 Country Replies Germany

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 Germany
Contact organisation Contact organisation Thünen Institute
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? NO
If yes, please provide details below.
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? Please select YES or NO
If yes, please provide details.
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? Please select YES or NO
If yes, please provide details (which products, units, etc.).
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? Please select YES or NO
If yes, please provide details.
Do you use felling reports? Please select YES or NO
If yes, please provide details.
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.

Cover

Joint Forest Sector Questionnaire
2022
DATA INPUT FILE
Correspondent country: DE
Reference year: 2022 Fill in the year
Name of person responsible for reply:
Official address (in full): Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
Telephone:
Fax:
E-mail:

Removals over bark

Country: DE Date:
Name of Official responsible for reply: 0
Check Table
Official Address (in full):
Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
FOREST SECTOR QUESTIONNAIRE
EU JQ1 OB Telephone: 0 0 Discrepancies
Removals E-mail: 0 Please verify, if there's an error!
Year 1 Year 2 Flag Flag Note Note
Product Product Unit 2021 2022 2021 2022 2021 2022 Product Product Unit 2021 2022
Code Quantity Quantity Code Quantity Quantity
ROUNDWOOD REMOVALS OVERBARK ROUNDWOOD REMOVALS OVERBARK
1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ob 1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ob OK OK
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ob 1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ob OK OK
1.1.C Coniferous 1000 m3ob 1.1.C Coniferous 1000 m3ob
1.1.NC Non-Coniferous 1000 m3ob 1.1.NC Non-Coniferous 1000 m3ob
1.2 INDUSTRIAL ROUNDWOOD 1000 m3ob 1.2 INDUSTRIAL ROUNDWOOD 1000 m3ob OK OK
1.2.C Coniferous 1000 m3ob 1.2.C Coniferous 1000 m3ob OK OK
1.2.NC Non-Coniferous 1000 m3ob 1.2.NC Non-Coniferous 1000 m3ob OK OK
1.2.NC.T of which: Tropical 1000 m3ob 1.2.NC.T of which: Tropical 1000 m3ob OK OK
1.2.1 SAWLOGS AND VENEER LOGS 1000 m3ob 1.2.1 SAWLOGS AND VENEER LOGS 1000 m3ob OK OK
1.2.1.C Coniferous 1000 m3ob 1.2.1.C Coniferous 1000 m3ob
1.2.1.NC Non-Coniferous 1000 m3ob 1.2.1.NC Non-Coniferous 1000 m3ob
1.2.2 PULPWOOD, ROUND AND SPLIT 1000 m3ob 1.2.2 PULPWOOD, ROUND AND SPLIT 1000 m3ob OK OK
1.2.2.C Coniferous 1000 m3ob 1.2.2.C Coniferous 1000 m3ob
1.2.2.NC Non-Coniferous 1000 m3ob 1.2.2.NC Non-Coniferous 1000 m3ob
1.2.3 OTHER INDUSTRIAL ROUNDWOOD 1000 m3ob 1.2.3 OTHER INDUSTRIAL ROUNDWOOD 1000 m3ob OK OK
1.2.3.C Coniferous 1000 m3ob 1.2.3.C Coniferous 1000 m3ob
1.2.3.NC Non-Coniferous 1000 m3ob 1.2.3.NC Non-Coniferous 1000 m3ob
To fill: 17 17
Product Product Unit 2021 2022
Code CF CF
OVERBARK/UNDERBARK CONVERSION FACTORS
1 ROUNDWOOD (WOOD IN THE ROUGH) m3/m3 0.000 0.000
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) m3/m3 0.000 0.000
1.1.C Coniferous m3/m3 0.000 0.000
1.1.NC Non-Coniferous m3/m3 0.000 0.000
1.2 INDUSTRIAL ROUNDWOOD m3/m3 1.100 1.100
1.2.C Coniferous m3/m3 1.200 1.200
1.2.NC Non-Coniferous m3/m3 0.000 0.000
1.2.NC.T of which: Tropical m3/m3 ERROR:#DIV/0! ERROR:#DIV/0!
1.2.1 SAWLOGS AND VENEER LOGS m3/m3 0.000 0.000
1.2.1.C Coniferous m3/m3 0.000 0.000
1.2.1.NC Non-Coniferous m3/m3 1.100 1.100
1.2.2 PULPWOOD, ROUND AND SPLIT m3/m3 1.200 1.200
1.2.2.C Coniferous m3/m3 0.000 0.000
1.2.2.NC Non-Coniferous m3/m3 1.100 1.100
1.2.3 OTHER INDUSTRIAL ROUNDWOOD m3/m3 1.200 1.200
1.2.3.C Coniferous m3/m3 0.000 0.000
1.2.3.NC Non-Coniferous m3/m3 1.100 1.100

JQ1 Production

Country: DE Date:
Name of Official responsible for reply: 0
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ1 Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
Industrial Roundwood Balance
PRIMARY PRODUCTS Telephone: 0 0 This table highlights discrepancies between items and sub-items. Please verify your data if there's an error! Discrepancies
Removals and Production E-mail: 0 test for good numbers, missing number, bad number, negative number
Year 1 Year 2 Flag Flag Note Note
Product Product Unit 2021 2022 2021 2022 2021 2022 Product Product Unit 2021 2022 2021 2022 % change Conversion factors
Code Quantity Quantity Code Quantity Quantity Roundwood Industrial roundwood availability 53,530 -507,305 -1048% m3 of wood in m3 or t of product
ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH) ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH) Recovered wood used in particle board 2113 1934 -8% Solid wood equivalent
1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub 82,178.018 78,871.947 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub OK OK Solid Wood Demand agglomerate production 4,333 4,015 -7% 2.4
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub 22,799.330 22,337.621 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub OK OK Sawnwood production 26,438 25,342 -4% 1
1.1.C Coniferous 1000 m3ub 9,095.635 8,833.851 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.1.C Coniferous 1000 m3ub veneer production 116 110 -5% 1
1.1.NC Non-Coniferous 1000 m3ub 13,703.695 13,503.770 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.1.NC Non-Coniferous 1000 m3ub plywood production 103 85 -18% 1
1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 59,378.688 56,534.326 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub OK OK particle board production (incl OSB) 7,318 6,690 -9% 1.58
1.2.C Coniferous 1000 m3ub 55,494.760 52,424.742 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.C Coniferous 1000 m3ub OK OK fibreboard production 6,105 5,194 -15% 1.8
1.2.NC Non-Coniferous 1000 m3ub 3,883.928 4,109.584 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.NC Non-Coniferous 1000 m3ub OK OK mechanical/semi-chemical pulp production 756 667 -12% 2.5
1.2.NC.T of which: Tropical 1000 m3ub 0.000 0.000 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.NC.T of which: Tropical 1000 m3ub OK OK chemical pulp production 1,571 1,505 -4% 4.9
1.2.1 SAWLOGS AND VENEER LOGS 1000 m3ub 47,427.825 44,755.719 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.1 SAWLOGS AND VENEER LOGS 1000 m3ub OK OK dissolving pulp production 0 0 missing data 5.7
1.2.1.C Coniferous 1000 m3ub 44,666.194 41,760.742 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.1.C Coniferous 1000 m3ub Availability Solid Wood Demand 69,197 64,133 -7%
1.2.1.NC Non-Coniferous 1000 m3ub 2,761.631 2,994.976 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.1.NC Non-Coniferous 1000 m3ub Difference (roundwood-demand) -14,358 -561,895 3814% positive = surplus
1.2.2 PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD) 1000 m3ub 11,789.987 11,644.100 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.2 PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD) 1000 m3ub OK OK gap (demand/availability) -29% 113% Negative number means not enough roundwood available
1.2.2.C Coniferous 1000 m3ub 10,675.334 10,541.107 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.2.C Coniferous 1000 m3ub Positive number means more roundwood available than demanded
1.2.2.NC Non-Coniferous 1000 m3ub 1,114.653 1,102.993 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.2.NC Non-Coniferous 1000 m3ub
1.2.3 OTHER INDUSTRIAL ROUNDWOOD 1000 m3ub 160.876 134.507 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.3 OTHER INDUSTRIAL ROUNDWOOD 1000 m3ub OK OK
1.2.3.C Coniferous 1000 m3ub 153.232 122.893 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.3.C Coniferous 1000 m3ub % of particle board that is from recovered wood 35%
1.2.3.NC Non-Coniferous 1000 m3ub 7.644 11.614 9 9 Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. Official data are underestimating domestic removals. For this national estimate we use an calculation approach based on the amount of used roundwood. 1.2.3.NC Non-Coniferous 1000 m3ub share of agglomerates produced from industrial roundwood residues 100%
PRODUCTION PRODUCTION usable industrial roundwood - amount of roundwood that is used, remainder leaves industry 98.5%
2 WOOD CHARCOAL 1000 t 30.000 30.000 9 9 2 WOOD CHARCOAL 1000 t
3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 16703.191 16292.084 9 9 3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 OK OK
3.1 WOOD CHIPS AND PARTICLES 1000 m3 11805.134 11315.436 9 9 3.1 WOOD CHIPS AND PARTICLES 1000 m3
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 4898.058 4976.648 9 9 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3
3.2.1 of which: Sawdust 1000 m3 3.2.1 of which: Sawdust 1000 m3 OK OK
4 RECOVERED POST-CONSUMER WOOD 1000 t 8035.000 8035.000 9 9 4 RECOVERED POST-CONSUMER WOOD 1000 t
5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t 4333.229 4015.060 9 9 5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t OK OK
5.1 WOOD PELLETS 1000 t 3353.000 3569.000 9 9 5.1 WOOD PELLETS 1000 t
5.2 OTHER AGGLOMERATES 1000 t 980.229 446.060 5.2 OTHER AGGLOMERATES 1000 t
6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 26438.296 25341.588 9 9 6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 OK OK
6.C Coniferous 1000 m3 25335.412 24314.052 9 9 6.C Coniferous 1000 m3
6.NC Non-Coniferous 1000 m3 1102.884 1027.536 9 9 6.NC Non-Coniferous 1000 m3
6.NC.T of which: Tropical 1000 m3 2.029 3.577 9 9 6.NC.T of which: Tropical 1000 m3 OK OK
7 VENEER SHEETS 1000 m3 115.937 110.049 9 9 7 VENEER SHEETS 1000 m3 OK OK
7.C Coniferous 1000 m3 13.544 14.415 9 9 7.C Coniferous 1000 m3
7.NC Non-Coniferous 1000 m3 102.393 95.634 9 9 7.NC Non-Coniferous 1000 m3
7.NC.T of which: Tropical 1000 m3 1.788 3.153 9 9 7.NC.T of which: Tropical 1000 m3 OK OK
8 WOOD-BASED PANELS 1000 m3 13525.395 11968.002 9 9 8 WOOD-BASED PANELS 1000 m3 OK OK
8.1 PLYWOOD 1000 m3 103.012 84.600 9 9 8.1 PLYWOOD 1000 m3 OK OK
8.1.C Coniferous 1000 m3 44.100 27.809 9 9 8.1.C Coniferous 1000 m3
8.1.NC Non-Coniferous 1000 m3 58.912 56.791 5 5 8.1.NC Non-Coniferous 1000 m3
8.1.NC.T of which: Tropical 1000 m3 0.170 0.140 9 9 8.1.NC.T of which: Tropical 1000 m3 OK OK
8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 OK OK
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 OK OK
8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD 1000 m3 7317.814 6689.886 9 9 8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD 1000 m3
8.2.1 of which: ORIENTED STRAND BOARD (OSB) 1000 m3 1281.538 1163.557 9 9 8.2.1 of which: ORIENTED STRAND BOARD (OSB) 1000 m3 OK OK
8.3 FIBREBOARD 1000 m3 6104.569 5193.516 9 9 8.3 FIBREBOARD 1000 m3 OK OK
8.3.1 HARDBOARD 1000 m3 0.000 0.000 9 9 8.3.1 HARDBOARD 1000 m3
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 4692.624 3791.548 9 9 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3
8.3.3 OTHER FIBREBOARD 1000 m3 1411.945 1401.968 9 9 8.3.3 OTHER FIBREBOARD 1000 m3
9 WOOD PULP 1000 t 2327.399 2171.994 9 9 9 WOOD PULP 1000 t OK OK
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 755.958 666.676 9 9 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t
9.2 CHEMICAL WOOD PULP 1000 t 1571.441 1505.318 9 9 9.2 CHEMICAL WOOD PULP 1000 t OK OK
9.2.1 SULPHATE PULP 1000 t 1065.213 1017.864 9 9 9.2.1 SULPHATE PULP 1000 t
9.2.1.1 of which: BLEACHED 1000 t 1065.213 1017.864 9 9 9.2.1.1 of which: BLEACHED 1000 t OK OK
9.2.2 SULPHITE PULP 1000 t 506.228 487.454 9 9 9.2.2 SULPHITE PULP 1000 t
9.3 DISSOLVING GRADES 1000 t 0.000 0.000 9 9 9.3 DISSOLVING GRADES 1000 t
10 OTHER PULP 1000 t 15352.477 14280.498 9 9 10 OTHER PULP 1000 t OK OK
10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 56.477 42.498 9 9 10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t
10.2 RECOVERED FIBRE PULP 1000 t 15296.000 14238.000 9 9 10.2 RECOVERED FIBRE PULP 1000 t
11 RECOVERED PAPER 1000 t 14487.498 13187.683 9 9 11 RECOVERED PAPER 1000 t
12 PAPER AND PAPERBOARD 1000 t 23127.532 21611.516 9 9 12 PAPER AND PAPERBOARD 1000 t OK OK
12.1 GRAPHIC PAPERS 1000 t 6822.530 6194.000 9 9 12.1 GRAPHIC PAPERS 1000 t OK OK
12.1.1 NEWSPRINT 1000 t 1052.197 938.978 9 9 12.1.1 NEWSPRINT 1000 t
12.1.2 UNCOATED MECHANICAL 1000 t 1808.782 1680.538 9 9 12.1.2 UNCOATED MECHANICAL 1000 t
12.1.3 UNCOATED WOODFREE 1000 t 1447.551 1327.085 9 9 12.1.3 UNCOATED WOODFREE 1000 t
12.1.4 COATED PAPERS 1000 t 2514.000 2247.399 9 9 12.1.4 COATED PAPERS 1000 t
12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 1479.322 1465.565 9 9 12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t
12.3 PACKAGING MATERIALS 1000 t 13343.425 12535.866 9 9 12.3 PACKAGING MATERIALS 1000 t OK OK
12.3.1 CASE MATERIALS 1000 t 9978.423 9515.774 9 9 12.3.1 CASE MATERIALS 1000 t
12.3.2 CARTONBOARD 1000 t 1830.530 1545.427 9 9 12.3.2 CARTONBOARD 1000 t
12.3.3 WRAPPING PAPERS 1000 t 494.551 469.451 9 9 12.3.3 WRAPPING PAPERS 1000 t
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 1039.921 1005.214 9 9 12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t
12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t 1481.785 1416.085 9 9 12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t
15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1 1000 m3 2335.000 2112.000 9 9 15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1 1000 m3 OK OK
15.1 GLULAM 1000 m3 1289.000 1166.000 9 9 15.1 GLULAM 1000 m3
15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 1046.000 946.000 9 9 15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3
16 I BEAMS (I-JOISTS)1 1000 t 16 I BEAMS (I-JOISTS)1 1000 t
1 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here
To fill: 6 6
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: DE Date: 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): Thünen Institute, Leuschnerstr. 91, 21031 Hamburg 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: DE 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) 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 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 6,535.5 472,933.0 5,860.2 604,100.0 12,157.7 1,013,808.0 10,096.0 1,061,988.0 1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub OK OK Error OK OK OK OK OK 1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3ub 76,556 74,636 1 ROUNDWOOD (WOOD IN THE ROUGH) NAC/m3 72 103 83 105 ACCEPT ACCEPT 1 ROUNDWOOD (WOOD IN THE ROUGH) NAC/m3
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub 270.4 34,851.0 283.9 61,242.0 204.5 12,021.0 246.8 19,346.0 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 22,865 22,375 1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) NAC/m3 129 216 59 78 ACCEPT ACCEPT 1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) NAC/m3
1.1.C Coniferous 1000 m3ub 94.2 10,711.0 72.9 13,630.0 122.2 6,602.0 180.4 13,480.0 1.1.C Coniferous 1000 m3ub 1.1.C Coniferous 1000 m3ub 9,068 8,726 1.1.C Coniferous NAC/m3 114 187 54 75 ACCEPT ACCEPT 1.1.C Coniferous NAC/m3
1.1.NC Non-Coniferous 1000 m3ub 176.2 24,140.0 211.1 47,612.0 82.3 5,419.0 66.4 5,866.0 1.1.NC Non-Coniferous 1000 m3ub 1.1.NC Non-Coniferous 1000 m3ub 13,798 13,648 1.1.NC Non-Coniferous NAC/m3 137 226 66 88 ACCEPT ACCEPT 1.1.NC Non-Coniferous NAC/m3
1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 6,265.2 438,082.0 5,576.3 542,858.0 11,953.2 1,001,787.0 9,849.3 1,042,642.0 1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub OK OK OK OK OK OK OK OK 1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 53,691 52,261 1.2 INDUSTRIAL ROUNDWOOD NAC/m3 70 97 84 106 ACCEPT ACCEPT 1.2 INDUSTRIAL ROUNDWOOD NAC/m3
1.2.C Coniferous 1000 m3ub 5,875.8 387,080.0 5,188.2 470,624.0 10,927.4 873,711.0 8,978.2 911,744.0 1.2.C Coniferous 1000 m3ub 1.2.C Coniferous 1000 m3ub 50,443 48,635 1.2.C Coniferous NAC/m3 66 91 80 102 ACCEPT ACCEPT 1.2.C Coniferous NAC/m3
1.2.NC Non-Coniferous 1000 m3ub 389.4 51,002.0 388.1 72,234.0 1,025.8 128,076.0 871.0 130,898.0 1.2.NC Non-Coniferous 1000 m3ub 1.2.NC Non-Coniferous 1000 m3ub 3,248 3,627 1.2.NC Non-Coniferous NAC/m3 131 186 125 150 ACCEPT ACCEPT 1.2.NC Non-Coniferous NAC/mt
1.2.NC.T of which: Tropical1 1000 m3ub 11.6 6,185.0 15.8 9,447.0 5.0 2,848.0 4.3 2,504.0 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 7 11 1.2.NC.T of which: Tropical NAC/m3 533 598 570 577 ACCEPT ACCEPT 1.2.NC.T of which: Tropical 1000 m3
2 WOOD CHARCOAL 1000 t 147.6 82,055.0 133.6 87,395.0 30.0 30,385.0 29.7 29,497.0 2 WOOD CHARCOAL 1000 t 2 WOOD CHARCOAL 1000 t 148 134 2 WOOD CHARCOAL NAC / t 556 654 1014 995 ACCEPT ACCEPT 2 WOOD CHARCOAL 1000 m3
3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 1,108.5 38,208.0 2,698.0 128,166.0 2,550.6 125,163.0 3,570.6 236,929.0 3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 OK OK OK OK OK OK OK OK 3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 15,261 15,420 3 WOOD CHIPS, PARTICLES AND RESIDUES NAC/m3 34 48 49 66 ACCEPT ACCEPT 3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3
3.1 WOOD CHIPS AND PARTICLES 1000 m3 393.1 18,090.0 639.2 34,068.0 1,616.9 78,007.0 2,102.7 123,271.0 3.1 WOOD CHIPS AND PARTICLES 1000 m3 3.1 WOOD CHIPS AND PARTICLES 1000 m3 10,581 9,852 3.1 WOOD CHIPS AND PARTICLES NAC/m3 46 53 48 59 ACCEPT ACCEPT 3.1 WOOD CHIPS AND PARTICLES 1000 mt
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 715.3 20,118.0 1,148.2 55,994.0 933.7 47,156.0 945.7 79,785.3 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 4,680 5,179 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) NAC/m3 28 49 51 84 ACCEPT ACCEPT 3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 mt
3.2.1 of which: Sawdust 1000 m3 693.0 36,942.0 684.6 62,849.0 3.2.1 of which: Sawdust 1000 m3 OK OK OK OK OK OK OK OK 3.2.1 of which: Sawdust 1000 m3 0 8 3.2.1 of which: Sawdust NAC/m3 REPORT 53 REPORT 92 CHECK CHECK
4 RECOVERED POST-CONSUMER WOOD 1000 t 935.5 43,453.0 569.1 38,104.0 684.3 45,844.0 326.3 33,872.7 4 RECOVERED POST-CONSUMER WOOD 1000 t 4 RECOVERED POST-CONSUMER WOOD 1000 t 8,286 8,278 4 RECOVERED POST-CONSUMER WOOD NAC / t 46 67 67 104 ACCEPT ACCEPT 4 RECOVERED POST-CONSUMER WOOD 1000 mt
5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t 761.7 112,165.0 813.7 213,052.0 915.1 168,097.0 882.4 278,546.0 5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t OK OK Error OK OK OK OK OK 5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t 4,180 3,946 5 WOOD PELLETS AND OTHER AGGLOMERATES NAC / t 147 262 184 316 ACCEPT ACCEPT 5 WOOD PELLETS AND OTHER AGGLOMERATES NAC/m3
5.1 WOOD PELLETS 1000 t 403.6 65,599.0 477.2 128,199.0 817.3 149,091.0 683.4 244,945.0 5.1 WOOD PELLETS 1000 t 5.1 WOOD PELLETS 1000 t 2,939 3,363 5.1 WOOD PELLETS NAC / t 163 269 182 358 ACCEPT ACCEPT 5.1 WOOD PELLETS NAC/m3
5.2 OTHER AGGLOMERATES 1000 t 358.1 46,566.0 336.4 84,853.0 97.8 19,006.0 198.9 33,601.0 5.2 OTHER AGGLOMERATES 1000 t 5.2 OTHER AGGLOMERATES 1000 t 1,241 584 5.2 OTHER AGGLOMERATES NAC / t 130 252 194 169 ACCEPT ACCEPT 5.2 OTHER AGGLOMERATES NAC/m3
6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 5,819.3 1,939,756.0 4,182.7 1,598,111.0 11,333.8 3,712,759.0 11,502.5 3,984,881.0 6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 OK OK OK OK OK OK OK OK 6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 20,924 18,022 6 SAWNWOOD (INCLUDING SLEEPERS) NAC/m3 333 382 328 346 ACCEPT ACCEPT 6 SAWNWOOD (INCLUDING SLEEPERS) NAC/m3
6.C Coniferous 1000 m3 5,317.3 1,646,970.0 3,762.6 1,277,924.0 10,552.1 3,277,684.0 10,781.4 3,526,187.0 6.C Coniferous 1000 m3 6.C Coniferous 1000 m3 20,101 17,295 6.C Coniferous NAC/m3 310 340 311 327 ACCEPT ACCEPT 6.C Coniferous NAC/m3
6.NC Non-Coniferous 1000 m3 502.0 292,786.0 420.1 320,187.0 781.7 435,075.0 721.1 458,694.0 6.NC Non-Coniferous 1000 m3 6.NC Non-Coniferous 1000 m3 823 727 6.NC Non-Coniferous NAC/m3 583 762 557 636 ACCEPT ACCEPT 6.NC Non-Coniferous NAC/m3
6.NC.T of which: Tropical1 1000 m3 74.7 63,910.0 79.5 67,741.0 37.6 55,775.0 50.3 42,635.0 6.NC.T of which: Tropical1 1000 m3 OK OK OK OK OK OK OK OK 6.NC.T of which: Tropical1 1000 m3 39 33 6.NC.T of which: Tropical NAC/m3 855 852 1483 847 ACCEPT ACCEPT 6.NC.T of which: Tropical NAC/m3
7 VENEER SHEETS 1000 m3 113.9 172,647.0 98.8 206,531.0 62.0 151,921.0 51.9 155,726.0 7 VENEER SHEETS 1000 m3 OK OK Error OK OK OK OK OK 7 VENEER SHEETS 1000 m3 168 157 7 VENEER SHEETS NAC/m3 1515 2091 2449 3001 ACCEPT ACCEPT 7 VENEER SHEETS NAC/m3
7.C Coniferous 1000 m3 27.4 20,761.0 20.2 20,131.0 0.6 2,799.0 0.9 3,690.0 7.C Coniferous 1000 m3 7.C Coniferous 1000 m3 40 34 7.C Coniferous NAC/m3 759 998 4634 4306 ACCEPT ACCEPT 7.C Coniferous NAC/m3
7.NC Non-Coniferous 1000 m3 86.6 151,886.0 78.6 186,400.0 61.4 149,122.0 51.0 152,036.0 7.NC Non-Coniferous 1000 m3 7.NC Non-Coniferous 1000 m3 128 123 7.NC Non-Coniferous NAC/m3 1755 2372 2427 2979 ACCEPT ACCEPT 7.NC Non-Coniferous NAC/m3
7.NC.T of which: Tropical 1000 m3 9.7 11,872.0 8.3 11,631.0 2.2 10,388.0 2.1 9,772.0 7.NC.T of which: Tropical 1000 m3 OK OK OK OK OK OK OK OK 7.NC.T of which: Tropical 1000 m3 9 9 7.NC.T of which: Tropical NAC/m3 1227 1410 4803 4658 ACCEPT ACCEPT 7.NC.T of which: Tropical NAC/m3
8 WOOD-BASED PANELS 1000 m3 6,467.4 2,420,877.0 5,557.8 2,661,839.0 6,786.4 2,968,511.0 5,772.9 3,100,216.0 8 WOOD-BASED PANELS 1000 m3 OK OK OK OK OK OK OK OK 8 WOOD-BASED PANELS 1000 m3 13,206 11,753 8 WOOD-BASED PANELS NAC/m3 374 479 437 537 ACCEPT ACCEPT 8 WOOD-BASED PANELS NAC/m3
8.1 PLYWOOD 1000 m3 1,482.9 942,995.0 1,318.7 1,096,848.0 387.9 326,202.0 330.1 344,032.0 8.1 PLYWOOD 1000 m3 OK OK OK OK OK OK Error OK 8.1 PLYWOOD 1000 m3 1,198 1,073 8.1 PLYWOOD NAC/m3 636 832 841 1042 ACCEPT ACCEPT 8.1 PLYWOOD NAC/m3
8.1.C Coniferous 1000 m3 540.6 290,796.0 495.4 304,255.0 168.3 114,471.5 153.2 125,214.0 8.1.C Coniferous 1000 m3 8.1.C Coniferous 1000 m3 416 370 8.1.C Coniferous NAC/m3 538 614 680 817 ACCEPT ACCEPT 8.1.C Coniferous NAC/m3
8.1.NC Non-Coniferous 1000 m3 942.3 652,199.0 823.3 792,593.0 219.6 211,730.5 176.9 218,818.0 8.1.NC Non-Coniferous 1000 m3 8.1.NC Non-Coniferous 1000 m3 782 703 8.1.NC Non-Coniferous NAC/m3 692 963 964 1237 ACCEPT ACCEPT 8.1.NC Non-Coniferous NAC/m3
8.1.NC.T of which: Tropical 1000 m3 134.1 103,857.0 156.0 154,982.0 38.3 46,070.0 59.7 77,017.0 8.1.NC.T of which: Tropical 1000 m3 OK OK OK OK OK OK OK OK 8.1.NC.T of which: Tropical 1000 m3 96 96 8.1.NC.T of which: Tropical NAC/m3 774 993 1204 1289 ACCEPT ACCEPT 8.1.NC.T of which: Tropical NAC/m3
8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 39.4 34,670.0 44.2 47,780.0 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 -5 8.1.1 of which: Laminated Veneer Lumber (LVL) NAC/m3 REPORT 879 REPORT 1081 CHECK CHECK
8.1.1.C Coniferous 1000 m3 14.9 10,917.0 37.0 39,742.0 8.1.1.C Coniferous 1000 m3 8.1.1.C Coniferous 1000 m3 0 -22 8.1.1.C Coniferous NAC/m3 REPORT 731 REPORT 1075 CHECK CHECK
8.1.1.NC Non-Coniferous 1000 m3 24.5 23,753.0 7.2 8,038.0 8.1.1.NC Non-Coniferous 1000 m3 8.1.1.NC Non-Coniferous 1000 m3 0 17 8.1.1.NC Non-Coniferous NAC/m3 REPORT 969 REPORT 1110 CHECK CHECK
8.1.1.NC.T of which: Tropical 1000 m3 13.7 12,534.0 0.6 773.0 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 13 8.1.1.NC.T of which: Tropical NAC/m3 REPORT 916 REPORT 1214 CHECK CHECK
8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD 1000 m3 2,958.7 910,456.0 2,649.2 990,789.0 2,744.8 783,764.0 2,450.2 878,679.0 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 7,532 6,889 8.2 PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD NAC/m3 308 374 286 359 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 758.6 287,924.0 679.0 264,062.0 554.9 196,022.0 526.2 205,773.0 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 1,485 1,316 8.2.1 of which: ORIENTED STRANDBOARD (OSB) NAC/m3 380 389 353 391 ACCEPT ACCEPT 8.2.1 of which: ORIENTED STRANDBOARD (OSB) NAC/m3
8.3 FIBREBOARD 1000 m3 2,025.7 567,426.0 1,589.9 574,202.0 3,653.7 1,858,545.0 2,992.5 1,877,505.0 8.3 FIBREBOARD 1000 m3 OK OK OK OK OK OK OK OK 8.3 FIBREBOARD 1000 m3 4,477 3,791 8.3 FIBREBOARD NAC/m3 280 361 509 627 ACCEPT ACCEPT 8.3 FIBREBOARD NAC/m3
8.3.1 HARDBOARD 1000 m3 249.3 102,503.0 199.6 122,521.0 30.5 21,089.0 23.5 20,948.3 8.3.1 HARDBOARD 1000 m3 8.3.1 HARDBOARD 1000 m3 219 176 8.3.1 HARDBOARD NAC/m3 411 614 692 892 ACCEPT ACCEPT 8.3.1 HARDBOARD NAC/mt
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 653.6 326,419.0 424.2 300,959.0 2,936.1 1,772,071.0 2,345.5 1,784,724.7 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 2,410 1,870 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) NAC/m3 499 710 604 761 ACCEPT ACCEPT 8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) NAC/mt
8.3.3 OTHER FIBREBOARD 1000 m3 1,122.8 138,504.0 966.1 150,722.0 687.2 65,385.0 623.6 71,832.0 8.3.3 OTHER FIBREBOARD 1000 m3 8.3.3 OTHER FIBREBOARD 1000 m3 1,848 1,745 8.3.3 OTHER FIBREBOARD NAC/m3 123 156 95 115 ACCEPT ACCEPT 8.3.3 OTHER FIBREBOARD NAC/mt
9 WOOD PULP 1000 t 4,534.0 2,690,191.0 4,173.0 3,311,965.6 1,177.0 757,124.0 1,253.0 1,047,942.2 9 WOOD PULP 1000 t OK OK OK OK OK OK OK OK 9 WOOD PULP 1000 t 5,684 5,092 9 WOOD PULP NAC/t 593 794 643 836 ACCEPT ACCEPT 9 WOOD PULP NAC/mt
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 155.0 89,579.0 221.0 168,137.6 97.0 46,724.0 139.0 99,223.2 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 814 749 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP NAC/t 578 761 482 714 ACCEPT ACCEPT 9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP NAC/mt
9.2 CHEMICAL WOOD PULP 1000 t 3,968.0 2,259,470.0 3,551.0 2,763,368.0 1,070.0 700,916.0 1,092.0 922,551.0 9.2 CHEMICAL WOOD PULP 1000 t OK OK OK OK OK OK OK OK 9.2 CHEMICAL WOOD PULP 1000 t 4,469 3,964 9.2 CHEMICAL WOOD PULP NAC/t 569 778 655 845 ACCEPT ACCEPT 9.2 CHEMICAL WOOD PULP NAC/mt
9.2.1 SULPHATE PULP 1000 t 3,897.0 2,182,569.0 3,484.0 2,676,476.0 959.0 575,034.0 987.0 778,688.0 9.2.1 SULPHATE PULP 1000 t 9.2.1 SULPHATE PULP 1000 t 4,003 3,515 9.2.1 SULPHATE PULP NAC/t 560 768 600 789 ACCEPT ACCEPT 9.2.1 SULPHATE PULP NAC/mt
9.2.1.1 of which: BLEACHED 1000 t 3,774.0 2,115,094.0 1,102.0 906,587.0 948.0 567,595.0 977.0 770,202.0 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 3,891 1,143 9.2.1.1 of which: BLEACHED NAC/t 560 823 599 788 ACCEPT ACCEPT 9.2.1.1 of which: BLEACHED NAC/mt
9.2.2 SULPHITE PULP 1000 t 71.0 76,901.0 67.0 86,892.0 111.0 125,882.0 105.0 143,863.0 9.2.2 SULPHITE PULP 1000 t 9.2.2 SULPHITE PULP 1000 t 466 449 9.2.2 SULPHITE PULP NAC/t 1083 1297 1134 1370 ACCEPT ACCEPT 9.2.2 SULPHITE PULP NAC/mt
9.3 DISSOLVING GRADES 1000 t 411.0 341,142.0 401.0 380,460.0 10.0 9,484.0 22.0 26,168.0 9.3 DISSOLVING GRADES 1000 t 9.3 DISSOLVING GRADES 1000 t 401 379 9.3 DISSOLVING GRADES NAC/t 830 949 948 1189 ACCEPT ACCEPT 9.3 DISSOLVING GRADES NAC/mt
10 OTHER PULP 1000 t 144.0 41,001.0 143.0 63,772.0 126.0 67,000.0 85.0 58,740.0 10 OTHER PULP 1000 t OK OK OK OK OK OK OK OK 10 OTHER PULP 1000 t 15,370 14,338 10 OTHER PULP NAC/t 285 446 532 691 ACCEPT ACCEPT 10 OTHER PULP NAC/mt
10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 15.0 20,522.0 19.0 37,763.0 5.0 7,178.0 4.0 8,012.0 10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 66 57 10.1 PULP FROM FIBRES OTHER THAN WOOD NAC/t 1368 1988 1436 2003 ACCEPT ACCEPT 10.1 PULP FROM FIBRES OTHER THAN WOOD NAC/mt
10.2 RECOVERED FIBRE PULP 1000 t 129.0 20,479.0 124.0 26,009.0 121.0 59,822.0 81.0 50,728.0 10.2 RECOVERED FIBRE PULP 1000 t 10.2 RECOVERED FIBRE PULP 1000 t 15,304 14,281 10.2 RECOVERED FIBRE PULP NAC/t 159 210 494 626 ACCEPT ACCEPT 10.2 RECOVERED FIBRE PULP NAC/mt
11 RECOVERED PAPER 1000 t 5,639.0 1,045,180.0 5,462.0 1,235,544.0 1,829.0 306,254.0 1,612.0 325,414.2 11 RECOVERED PAPER 1000 t 11 RECOVERED PAPER 1000 t 18,297 17,038 11 RECOVERED PAPER NAC/t 185 226 167 202 ACCEPT ACCEPT 11 RECOVERED PAPER NAC/mt
12 PAPER AND PAPERBOARD 1000 t 10,114.5 7,559,121.2 9,302.0 9,860,429.1 14,166.0 11,358,944.8 13,078.0 14,518,949.1 12 PAPER AND PAPERBOARD 1000 t OK OK OK OK OK OK OK OK 12 PAPER AND PAPERBOARD 1000 t 19,076 17,836 12 PAPER AND PAPERBOARD NAC/t 747 1060 802 1110 ACCEPT ACCEPT 12 PAPER AND PAPERBOARD NAC/mt
12.1 GRAPHIC PAPERS 1000 t 4,052.8 2,696,754.0 3,585.0 3,817,186.1 5,007.1 3,740,936.0 4,466.8 5,206,671.8 12.1 GRAPHIC PAPERS 1000 t OK OK OK OK OK OK OK OK 12.1 GRAPHIC PAPERS 1000 t 5,868 5,312 12.1 GRAPHIC PAPERS NAC/t 665 1065 747 1166 ACCEPT ACCEPT 12.1 GRAPHIC PAPERS NAC/mt
12.1.1 NEWSPRINT 1000 t 602.3 259,492.0 609.4 490,055.5 523.8 219,742.0 525.0 433,172.7 12.1.1 NEWSPRINT 1000 t 12.1.1 NEWSPRINT 1000 t 1,131 1,023 12.1.1 NEWSPRINT NAC/t 431 804 420 825 ACCEPT ACCEPT 12.1.1 NEWSPRINT NAC/mt
12.1.2 UNCOATED MECHANICAL 1000 t 441.7 283,840.0 423.2 387,227.8 816.9 401,590.0 763.4 656,298.2 12.1.2 UNCOATED MECHANICAL 1000 t 12.1.2 UNCOATED MECHANICAL 1000 t 1,434 1,340 12.1.2 UNCOATED MECHANICAL NAC/t 643 915 492 860 ACCEPT ACCEPT 12.1.2 UNCOATED MECHANICAL NAC/mt
12.1.3 UNCOATED WOODFREE 1000 t 1,114.5 914,800.0 1,055.3 1,284,915.0 899.2 987,776.0 831.6 1,288,419.0 12.1.3 UNCOATED WOODFREE 1000 t 12.1.3 UNCOATED WOODFREE 1000 t 1,663 1,551 12.1.3 UNCOATED WOODFREE NAC/t 821 1218 1099 1549 ACCEPT ACCEPT 12.1.3 UNCOATED WOODFREE NAC/mt
12.1.4 COATED PAPERS 1000 t 1,894.3 1,238,622.0 1,497.2 1,654,987.8 2,767.2 2,131,828.0 2,346.7 2,828,781.8 12.1.4 COATED PAPERS 1000 t 12.1.4 COATED PAPERS 1000 t 1,641 1,398 12.1.4 COATED PAPERS NAC/t 654 1105 770 1205 ACCEPT ACCEPT 12.1.4 COATED PAPERS NAC/mt
12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 156.0 229,085.2 200.0 401,692.6 147.0 266,992.8 106.0 257,863.8 12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 1,488 1,560 12.2 HOUSEHOLD AND SANITARY PAPERS NAC/t 1468 2008 1816 2433 ACCEPT ACCEPT 12.2 HOUSEHOLD AND SANITARY PAPERS NAC/mt
12.3 PACKAGING MATERIALS 1000 t 5,711.6 4,128,534.0 5,346.0 5,119,976.1 8,679.2 6,490,616.0 8,189.0 8,080,788.2 12.3 PACKAGING MATERIALS 1000 t OK OK OK OK OK OK OK OK 12.3 PACKAGING MATERIALS 1000 t 10,376 9,693 12.3 PACKAGING MATERIALS NAC/t 723 958 748 987 ACCEPT ACCEPT 12.3 PACKAGING MATERIALS NAC/mt
12.3.1 CASE MATERIALS 1000 t 2,956.2 1,404,557.0 2,618.6 1,743,655.5 5,057.9 2,527,252.0 4,770.2 3,212,675.6 12.3.1 CASE MATERIALS 1000 t 12.3.1 CASE MATERIALS 1000 t 7,877 7,364 12.3.1 CASE MATERIALS NAC/t 475 666 500 673 ACCEPT ACCEPT 12.3.1 CASE MATERIALS NAC/mt
12.3.2 CARTONBOARD 1000 t 1,409.9 1,463,445.0 1,458.6 1,733,506.2 2,260.9 2,560,279.0 2,039.8 3,036,746.3 12.3.2 CARTONBOARD 1000 t 12.3.2 CARTONBOARD 1000 t 980 964 12.3.2 CARTONBOARD NAC/t 1038 1188 1132 1489 ACCEPT ACCEPT 12.3.2 CARTONBOARD NAC/mt
12.3.3 WRAPPING PAPERS 1000 t 1,045.6 989,508.0 1,020.4 1,330,790.0 1,040.8 1,128,331.0 1,097.7 1,489,013.4 12.3.3 WRAPPING PAPERS 1000 t 12.3.3 WRAPPING PAPERS 1000 t 499 392 12.3.3 WRAPPING PAPERS NAC/t 946 1304 1084 1357 ACCEPT ACCEPT 12.3.3 WRAPPING PAPERS NAC/mt
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 299.9 271,024.0 248.3 312,024.3 319.6 274,754.0 281.4 342,352.9 12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 1,020 972 12.3.4 OTHER PAPERS MAINLY FOR PACKAGING NAC/t 904 1256 860 1217 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 194.2 504,748.0 171.0 521,574.3 332.7 860,400.0 316.2 973,625.3 12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t 12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t 1,343 1,271 12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) NAC/t 2599 3050 2586 3079 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 384 264,416 492 350,350 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 2,335 2,004 15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1 NAC/m3 REPORT 688 REPORT 711 CHECK CHECK
15.1 GLULAM 1000 m3 313 217,920 438 305,849 15.1 GLULAM 1000 m3 15.1 GLULAM 1000 m3 1,289 1,041 15.1 GLULAM NAC/m3 REPORT 697 REPORT 698 CHECK CHECK
15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 72 46,496 54 44,501 15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 1,046 963 15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) NAC/m3 REPORT 648 REPORT 817 CHECK CHECK
16 I BEAMS (I-JOISTS)2 1000 t 5.5 7,221.0 0.5 589.0 16 I BEAMS (I-JOISTS)1 1000 t 16 I BEAMS (I-JOISTS)1 1000 t 0 5 16 I BEAMS (I-JOISTS)1 NAC/t REPORT 1311 REPORT 1197 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: 9 9 0 0 9 9 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: DE Date:
Name of Official responsible for reply: 0
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ3 Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
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) 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 10,753,037.0 11,295,654.0 8,630,216.0 9,079,231.0 13 SECONDARY WOOD PRODUCTS OK OK OK OK
13.1 FURTHER PROCESSED SAWNWOOD 387,107.0 356,791.0 292,299.0 277,284.0 13.1 FURTHER PROCESSED SAWNWOOD OK OK OK OK
13.1.C Coniferous 259,680.0 233,788.0 238,954.0 225,743.0 13.1.C Coniferous
13.1.NC Non-coniferous 127,427.0 123,003.0 53,345.0 51,541.0 13.1.NC Non-coniferous
13.1.NC.T of which: Tropical 62,911.0 57,216.0 7,426.0 9,378.0 13.1.NC.T of which: Tropical OK OK OK OK
13.2 WOODEN WRAPPING AND PACKAGING MATERIAL 843,657.0 1,067,780.0 442,323.0 588,306.0 13.2 WOODEN WRAPPING AND PACKAGING MATERIAL
13.3 WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE 363,938.0 413,181.0 166,870.0 176,570.0 13.3 WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4 BUILDER’S JOINERY AND CARPENTRY OF WOOD1 1,381,341.0 1,260,753.0 1,449,530.0 1,220,187.0 13.4 BUILDER’S JOINERY AND CARPENTRY OF WOOD1
13.5 WOODEN FURNITURE 6,436,567.0 6,748,736.0 5,591,032.0 6,089,953.0 13.5 WOODEN FURNITURE
13.6 PREFABRICATED BUILDINGS OF WOOD 311,088.0 310,401.0 95,051.0 96,214.0 13.6 PREFABRICATED BUILDINGS OF WOOD
13.7 OTHER MANUFACTURED WOOD PRODUCTS 1,029,339.0 1,138,012.0 593,111.0 630,717.0 13.7 OTHER MANUFACTURED WOOD PRODUCTS
14 SECONDARY PAPER PRODUCTS 4,198,897.0 5,211,668.0 7,971,213.0 9,428,423.0 14 SECONDARY PAPER PRODUCTS OK OK OK OK
14.1 COMPOSITE PAPER AND PAPERBOARD 49,169.0 59,557.0 95,507.0 137,521.0 14.1 COMPOSITE PAPER AND PAPERBOARD
14.2 SPECIAL COATED PAPER AND PULP PRODUCTS 619,904.0 710,133.0 1,692,316.0 1,991,107.0 14.2 SPECIAL COATED PAPER AND PULP PRODUCTS
14.3 HOUSEHOLD AND SANITARY PAPER, READY FOR USE 730,604.0 1,057,745.0 1,112,912.0 1,532,677.0 14.3 HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4 PACKAGING CARTONS, BOXES ETC. 1,566,725.0 1,894,075.0 3,223,608.0 3,698,286.0 14.4 PACKAGING CARTONS, BOXES ETC.
14.5 OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE 1,232,495.0 1,490,158.0 1,846,870.0 2,068,832.0 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 19,830.0 28,120.0 66,687.0 92,715.0 14.5.1 of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2 of which: ARTICLES, MOULDED OR PRESSED FROM PULP 129,048.0 140,460.0 87,833.0 98,731.0 14.5.2 of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3 of which: FILTER PAPER AND PAPERBOARD, READY FOR USE 48,913.0 63,918.0 199,012.0 217,039.0 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: DE Date:
Name of Official responsible for reply: 0
FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade Official Address (in full): Check Table
Thünen Institute, Leuschnerstr. 91, 21031 Hamburg 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 1000NAC 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 5,875.8 387,080.0 5,188.2 470,624.0 10,927.4 873,711.0 8,978.2 911,744.0 OK OK OK OK OK OK OK OK 4403.11/21/22/23/24/25/26 Industrial Roundwood, Coniferous NAC/m3 66 91 80 102 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 1,460.8 59,448.0 1,338.9 90,098.0 559.5 33,345.0 883.2 82,901.0 OK OK OK OK OK OK OK OK 4403.21/22 of which: Pine (Pinus spp.) NAC/m3 41 67 60 94 ACCEPT ACCEPT Product Classification Classification Unit of 2021 2022 2021 2022 2021 2022
4403 21 10 sawlogs and veneer logs 1000 m3ub 341.9 26,592.0 373.2 33,528.0 245.9 18,336.0 612.2 61,225.0 4403 21 10 sawlogs and veneer logs NAC/m3 78 90 75 100 ACCEPT ACCEPT 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 1,118.9 32,856.0 965.8 56,570.0 313.5 15,009.0 271.0 21,676.0 4403 21 90 4403 22 00 pulpwood and other industrial roundwood NAC/m3 29 59 48 80 ACCEPT ACCEPT 1 4401.11/12 44.03 Roundwood production 1000 m3 JQ1 82,178 78,872
4403.23/24 of which: Fir/Spruce (Abies spp., Picea spp.) 1000 m3ub 3,881.7 290,097.0 3,469.4 348,772.0 9,387.7 776,111.0 7,490.8 778,798.0 OK OK OK OK OK OK OK OK 4403.23/24 of which: Fir/Spruce (Abies spp., Picea spp.) NAC/m3 75 101 83 104 ACCEPT ACCEPT EU2 0 0
4403 23 10 sawlogs and veneer logs 1000 m3ub 2,708.2 229,991.0 2,278.2 255,258.0 7,807.4 692,724.0 5,561.3 627,640.0 4403 23 10 sawlogs and veneer logs NAC/m3 85 112 89 113 ACCEPT ACCEPT dif 82,178 78,872
4403 23 90 4403 24 00 pulpwood and other industrial roundwood 1000 m3ub 1,173.5 60,106.0 1,191.2 93,514.0 1,580.3 83,387.0 1,929.6 151,158.0 4403 23 90 4403 24 00 pulpwood and other industrial roundwood NAC/m3 51 79 53 78 ACCEPT ACCEPT 1.2.C 4403.11/21/22/23/24/25/26 Industrial Roundwood (wood in the rough), Coniferous 1000 m3 JQ2 5,876 387,080 5,188 470,624 10,927 873,711 8,978 911,744
1.2.NC 4403.12/41/42/49/91/93/94 4403.95/96/97/98/99 Industrial Roundwood, Non-Coniferous 1000 m3ub 389.4 51,002.0 388.1 72,234.0 1,025.8 128,076.0 871.0 130,898.0 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 131 186 125 150 ACCEPT ACCEPT ECE/EU 5,876 387,080 5,188 470,624 10,927 873,711 8,978 911,744
ex4403.12 4403.91 of which: Oak (Quercus spp.) 1000 m3ub 23.8 8,476.0 25.1 13,631.0 158.0 32,576.0 124.2 32,801.0 ex4403.12 4403.91 of which: Oak (Quercus spp.) NAC/m3 356 543 206 264 ACCEPT ACCEPT dif 0 0 0 0 0 0 0 0
ex4403.12 4403.93/94 of which: Beech (Fagus spp.) 1000 m3ub 85.5 5,340.0 104.6 7,977.0 604.3 62,141.0 537.7 63,932.0 ex4403.12 4403.93/94 of which: Beech (Fagus spp.) NAC/m3 62 76 103 119 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 389 51,002 388 72,234 1,026 128,076 871 130,898
ex4403.12 4403.95/96 of which: Birch (Betula spp.) 1000 m3ub 27.6 2,702.0 54.0 4,884.0 9.7 649.0 25.2 3,477.0 OK OK OK OK OK OK OK OK ex4403.12 4403.95/96 of which: Birch (Betula spp.) NAC/m3 98 90 67 138 ACCEPT CHECK ECE/EU 389 51,002 388 72,234 1,026 128,076 871 130,898
4403 95 10 sawlogs and veneer logs 1000 m3ub 0.5 460.0 2.3 450.0 1.8 131.0 1.4 116.0 4403 95 10 sawlogs and veneer logs NAC/m3 853 192 73 85 CHECK ACCEPT 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 27.0 2,242.0 51.7 4,434.0 7.9 518.0 23.8 3,361.0 ex4403 12 00 4403 95 90 4403 96 00 pulpwood and other industrial roundwood NAC/m3 83 86 66 141 ACCEPT CHECK 6.C 4406.11/91 4407.11/12/13/14/19 Sawnwood, Coniferous 1000 m3 JQ2 5,317 1,646,970 3,763 1,277,924 10,552 3,277,684 10,781 3,526,187
ex4403.12 4403.97 of which: Poplar/Aspen (Populus spp.) 1000 m3ub 14.8 557.0 5.0 281.0 9.9 601.0 17.0 1,373.0 ex4403.12 4403.97 of which: Poplar/Aspen (Populus spp.) NAC/m3 38 56 61 81 ACCEPT ACCEPT ECE/EU 5,317 1,646,970 3,763 1,277,924 10,552 3,277,684 10,781 3,526,187
ex4403.12 4403.98 of which: Eucalyptus (Eucalyptus spp.) 1000 m3ub 0.1 139.0 11.5 551.0 0.0 1.0 0.1 13.0 ex4403.12 4403.98 of which: Eucalyptus (Eucalyptus spp.) NAC/m3 939 48 1000 117 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 5,317.3 1,646,970.0 3,762.6 1,277,924.0 10,552.1 3,277,684.0 10,781.4 3,526,187.0 OK OK OK OK OK OK OK OK 4406.11/91 4407.11/12/13/14/19 Sawnwood, Coniferous NAC/m3 310 340 311 327 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 502 292,786 420 320,187 782 435,075 721 458,694
4407.11 ex4407.13 ex4406.11/91 of which: Pine (Pinus spp.) 1000 m3 786.0 224,985.0 502.5 172,856.0 1,392.5 448,426.0 1,568.3 537,740.0 4407.11 ex4407.13 ex4406.11/91 of which: Pine (Pinus spp.) NAC/m3 286 344 322 343 ACCEPT ACCEPT ECE/EU 502 292,786 420 320,187 782 435,075 721 458,694
4407.12 ex4407.13/14 ex4406.11/91 of which: Fir/Spruce (Abies spp., Picea spp.) 1000 m3 3,822.8 1,153,620.0 2,688.8 838,287.0 8,108.4 2,597,398.0 8,387.2 2,747,987.0 4407.12 ex4407.13/14 ex4406.11/91 of which: Fir/Spruce (Abies spp., Picea spp.) NAC/m3 302 312 320 328 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 502.0 292,786.0 420.1 320,187.0 781.7 435,075.0 721.1 458,694.0 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 583 762 557 636 ACCEPT ACCEPT
ex4406.12/92 4407.91 of which: Oak (Quercus spp.) 1000 m3 124.7 108,638.0 114.3 130,193.0 143.6 109,803.0 137.1 113,960.0 ex4406.12/92 4407.91 of which: Oak (Quercus spp.) NAC/m3 871 1139 764 831 ACCEPT ACCEPT
ex4406.12/92 4407.92 of which: Beech (Fagus spp.) 1000 m3 20.9 10,449.0 16.8 9,630.0 527.4 223,879.0 458.6 246,980.0 ex4406.12/92 4407.92 of which: Beech (Fagus spp.) NAC/m3 501 572 425 539 ACCEPT ACCEPT
ex4406.12/92 4407.93 of which: Maple (Acer spp.) 1000 m3 3.8 3,614.0 3.3 4,724.0 3.5 3,222.0 3.5 3,937.0 ex4406.12/92 4407.93 of which: Maple (Acer spp.) NAC/m3 958 1443 930 1131 ACCEPT ACCEPT
ex4406.12/92 4407.94 of which: Cherry (Prunus spp.) 1000 m3 2.1 1,744.0 1.2 1,284.0 0.7 741.0 0.6 689.0 ex4406.12/92 4407.94 of which: Cherry (Prunus spp.) NAC/m3 848 1040 1005 1222 ACCEPT ACCEPT
ex4406.12/92 4407.95 of which: Ash (Fraxinus spp.) 1000 m3 13.4 7,478.0 13.1 7,904.0 20.6 8,446.0 26.7 12,339.0 ex4406.12/92 4407.95 of which: Ash (Fraxinus spp.) NAC/m3 557 601 409 463 ACCEPT ACCEPT
ex4406.12/92 4407.96 of which: Birch (Betula spp.) 1000 m3 32.8 7,884.0 12.0 3,520.0 5.4 1,487.0 3.4 1,131.0 ex4406.12/92 4407.96 of which: Birch (Betula spp.) NAC/m3 241 292 278 338 ACCEPT ACCEPT
ex4406.12/92 4407.97 of which: Poplar/Aspen (Populus spp.) 1000 m3 39.4 9,117.0 15.2 4,510.0 3.7 1,183.0 3.1 1,536.0 ex4406.12/92 4407.97 of which: Poplar/Aspen (Populus spp.) NAC/m3 231 296 321 492 ACCEPT ACCEPT
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: DE Date: 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): Thünen Institute, Leuschnerstr. 91, 21031 Hamburg 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 NAC 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 848.6 91,796.0 936.9 124,971.0 4,831.6 542,552.0 3,773.9 509,854.0 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 108 133 112 135 ACCEPT CHECK
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 1000 m3ub 130.0 17,829.0 101.7 22,327.0 4.1 1,215.0 14.8 2,094.0 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 137 219 297 141 ACCEPT CHECK
1.1.C Coniferous 1000 m3ub 13.9 2,132.0 7.2 1,593.0 0.5 129.0 6.6 1,029.0 OK OK OK OK OK OK OK OK 1.1.C Coniferous 1000 m3ub 1.1.C Coniferous NAC/ m3 154 222 261 156 ACCEPT CHECK
1.1.NC Non-Coniferous 1000 m3ub 116.2 15,697.0 94.6 20,734.0 3.6 1,086.0 8.2 1,065.0 OK OK OK OK OK OK OK OK 1.1.NC Non-Coniferous 1000 m3ub 1.1.NC Non-Coniferous NAC/ m3 135 219 302 130 ACCEPT CHECK
1.2 INDUSTRIAL ROUNDWOOD 1000 m3ub 718.6 73,967.0 835.2 102,644.0 4,827.5 541,337.0 3,759.1 507,760.0 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 103 123 112 135 ACCEPT CHECK
1.2.C Coniferous 1000 m3ub 664.7 54,799.0 750.9 72,810.0 4,365.6 464,336.0 3,296.7 420,997.0 OK OK OK OK OK OK OK OK 1.2.C Coniferous 1000 m3ub 1.2.C Coniferous NAC/ m3 82 97 106 128 ACCEPT CHECK
1.2.NC Non-Coniferous 1000 m3ub 53.9 19,168.0 84.3 29,834.0 462.0 77,001.0 462.4 86,763.0 OK OK OK OK OK OK OK OK 1.2.NC Non-Coniferous 1000 m3ub 1.2.NC Non-Coniferous NAC/ m3 356 354 167 188 CHECK CHECK
1.2.NC.T of which: Tropical 1000 m3ub 11.6 6,185.0 15.8 9,447.0 0.1 253.0 1.2 543.0 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 533 598 4148 461 CHECK CHECK
2 WOOD CHARCOAL 1000 t 80.4 42,847.0 75.5 49,495.0 5.5 4,969.0 4.8 4,843.0 OK OK OK OK OK OK OK OK 2 WOOD CHARCOAL 1000 t 2 WOOD CHARCOAL NAC/ t 533 655 904 1004 ACCEPT CHECK
3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3 42.0 1,422.0 206.6 6,867.0 268.5 19,637.0 406.7 40,449.0 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 34 33 73 99 CHECK CHECK
3.1 WOOD CHIPS AND PARTICLES 1000 m3 3.9 478.0 35.4 2,940.0 125.2 9,913.0 214.5 18,123.0 OK OK OK OK OK OK OK OK 3.1 WOOD CHIPS AND PARTICLES 1000 m3 3.1 WOOD CHIPS AND PARTICLES NAC/ m3 121 83 79 84 ACCEPT CHECK
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 1000 m3 38.1 944.0 80.3 2,105.7 143.3 9,724.0 161.6 18,293.3 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 25 26 68 113 CHECK CHECK
3.2.1 of which: Sawdust 1000 m3 34.8 1,195.0 146.3 16,277.0 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 34 REPORT 111 CHECK CHECK
4 RECOVERED POST-CONSUMER WOOD 1000 t 104.3 3,914.0 56.8 1,821.3 35.1 6,631.0 19.2 4,032.7 OK OK OK OK OK OK OK OK 4 RECOVERED POST-CONSUMER WOOD 1000 t 4 RECOVERED POST-CONSUMER WOOD NAC/ t 38 32 189 211 CHECK CHECK
5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 t 231.6 30,262.0 201.5 53,411.0 70.5 17,673.0 68.7 25,935.0 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 131 265 251 378 ACCEPT CHECK
5.1 WOOD PELLETS 1000 t 92.4 13,362.0 93.0 24,461.0 40.6 9,756.0 42.4 15,872.0 OK OK OK OK OK OK OK OK 5.1 WOOD PELLETS 1000 t 5.1 WOOD PELLETS NAC/ t 145 263 240 375 ACCEPT CHECK
5.2 OTHER AGGLOMERATES 1000 t 139.2 16,900.0 108.5 28,950.0 29.9 7,917.0 26.3 10,063.0 OK OK OK OK OK OK OK OK 5.2 OTHER AGGLOMERATES 1000 t 5.2 OTHER AGGLOMERATES NAC/ t 121 267 265 382 ACCEPT CHECK
6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 1,793.9 601,398.0 947.5 447,746.0 4,874.5 1,737,382.0 5,950.8 2,120,945.0 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 335 473 356 356 ACCEPT CHECK
6.C Coniferous 1000 m3 1,545.2 466,691.0 771.3 300,749.0 4,430.4 1,522,492.0 5,542.6 1,878,863.0 OK OK OK OK OK OK OK OK 6.C Coniferous 1000 m3 6.C Coniferous NAC/ m3 302 390 344 339 ACCEPT CHECK
6.NC Non-Coniferous 1000 m3 248.7 134,707.0 176.2 146,997.0 444.2 214,890.0 408.2 242,082.0 OK OK OK OK OK OK OK OK 6.NC Non-Coniferous 1000 m3 6.NC Non-Coniferous NAC/ m3 542 834 484 593 ACCEPT CHECK
6.NC.T of which: Tropical 1000 m3 61.3 44,329.0 69.8 59,089.0 7.7 11,854.0 7.3 11,510.0 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 723 846 1538 1586 ACCEPT CHECK
7 VENEER SHEETS 1000 m3 37.8 54,257.0 30.5 72,325.0 13.1 52,297.0 14.4 62,872.0 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 1437 2371 3985 4372 ACCEPT CHECK
7.C Coniferous 1000 m3 4.1 6,146.0 3.3 7,928.0 0.3 1,012.0 0.5 1,550.0 OK OK OK OK OK OK OK OK 7.C Coniferous 1000 m3 7.C Coniferous NAC/ m3 1499 2416 3478 3027 ACCEPT CHECK
7.NC Non-Coniferous 1000 m3 33.7 48,111.0 27.2 64,397.0 12.8 51,285.0 13.9 61,322.0 OK OK OK OK OK OK OK OK 7.NC Non-Coniferous 1000 m3 7.NC Non-Coniferous NAC/ m3 1430 2366 3996 4422 ACCEPT CHECK
7.NC.T of which: Tropical 1000 m3 7.3 5,810.0 6.6 7,006.0 1.1 3,162.0 1.0 3,456.0 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 797 1056 2846 3336 CHECK CHECK
8 WOOD-BASED PANELS 1000 m3 1,017.4 460,845.0 885.0 520,849.0 1,744.2 882,390.0 1,430.4 860,763.0 OK OK OK OK OK OK OK OK 8 WOOD-BASED PANELS 1000 m3 OK OK Error OK OK OK OK OK 8 WOOD-BASED PANELS NAC/ m3 453 589 506 602 ACCEPT CHECK
8.1 PLYWOOD 1000 m3 626.5 306,588.0 560.1 356,197.0 105.0 99,822.0 103.2 111,368.0 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 489 636 950 1079 ACCEPT CHECK
8.1.C Coniferous 1000 m3 224.8 82,057.0 240.0 108,982.0 74.3 62,349.0 77.0 75,438.0 OK OK OK OK OK OK OK OK 8.1.C Coniferous 1000 m3 8.1.C Coniferous NAC/ m3 365 454 839 980 ACCEPT CHECK
8.1.NC Non-Coniferous 1000 m3 401.7 224,531.0 320.1 247,215.0 30.7 37,473.0 26.2 35,930.0 OK OK OK OK OK OK OK OK 8.1.NC Non-Coniferous 1000 m3 8.1.NC Non-Coniferous NAC/ m3 559 772 1221 1370 ACCEPT CHECK
8.1.NC.T of which: Tropical 1000 m3 38.3 26,909.0 37.4 40,371.0 4.3 4,281.0 5.1 6,365.0 OK OK OK OK OK OK OK OK 8.1.NC.T of which: Tropical 1000 m3 OK OK OK OK OK OK OK OK 8.1.NC.T of which: Tropical NAC/ m3 703 1078 1004 1242 ACCEPT CHECK
8.1.1 of which: Laminated Veneer Lumber (LVL) 1000 m3 7.3 6,302.0 24.2 29,192.0 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 868 REPORT 1207 CHECK CHECK
8.1.1.C Coniferous 1000 m3 0.3 143.0 21.4 25,634.0 OK OK OK OK OK OK OK OK 8.1.1.C Coniferous 1000 m3 8.1.1.C Coniferous NAC/ m3 REPORT 447 REPORT 1195 CHECK CHECK
8.1.1.NC Non-Coniferous 1000 m3 6.9 6,159.0 2.7 3,558.0 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 887 REPORT 1299 CHECK CHECK
8.1.1.NC.T of which: Tropical 1000 m3 1.8 1,881.0 0.1 151.0 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 1040 REPORT 1678 CHECK CHECK
8.2 PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD 1000 m3 227.5 66,575.0 226.6 81,770.0 506.5 161,960.0 483.8 180,724.0 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 293 361 320 374 ACCEPT CHECK
8.2.1 of which: ORIENTED STRANDBOARD (OSB) 1000 m3 14.5 5,615.0 5.1 1,584.0 195.4 70,788.0 204.6 86,135.0 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 388 308 362 421 ACCEPT CHECK
8.3 FIBREBOARD 1000 m3 163.4 87,682.0 98.2 82,882.0 880.1 471,133.8 625.2 389,225.4 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 536 844 535 623 ACCEPT CHECK
8.3.1 HARDBOARD 1000 m3 26.4 12,596.0 28.0 26,510.0 13.6 9,454.9 10.5 9,513.5 OK OK OK OK OK OK OK OK 8.3.1 HARDBOARD 1000 m3 8.3.1 HARDBOARD NAC/ m3 477 948 693 910 ACCEPT CHECK
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 125.2 72,451.0 62.5 54,069.0 621.3 436,406.9 387.4 349,783.9 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 579 865 702 903 ACCEPT CHECK
8.3.3 OTHER FIBREBOARD 1000 m3 11.8 2,635.0 7.8 2,303.0 245.2 25,272.0 227.3 29,928.0 OK OK OK OK OK OK OK OK 8.3.3 OTHER FIBREBOARD 1000 m3 8.3.3 OTHER FIBREBOARD NAC/ m3 223 297 103 132 CHECK CHECK
9 WOOD PULP 1000 t 1,882.4 1,141,247.1 1,835.0 1,544,183.9 307.2 210,463.0 255.3 222,874.5 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 606 842 685 873 ACCEPT CHECK
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 1000 t 26.5 13,578.1 54.1 42,708.1 26.2 12,917.0 24.1 15,521.9 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 513 790 493 643 ACCEPT CHECK
9.2 CHEMICAL WOOD PULP 1000 t 1,740.9 1,014,842.0 1,672.3 1,366,823.6 280.0 197,033.0 226.6 203,158.0 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 583 817 704 896 ACCEPT CHECK
9.2.1 SULPHATE PULP 1000 t 1,711.5 984,093.0 1,649.4 1,336,205.0 245.7 143,401.0 196.9 148,563.1 OK OK OK OK OK OK OK OK 9.2.1 SULPHATE PULP 1000 t 9.2.1 SULPHATE PULP NAC/ t 575 810 584 754 ACCEPT CHECK
9.2.1.1 of which: BLEACHED 1000 t 1,678.4 962,622.0 538.5 463,253.1 239.6 139,340.0 194.3 146,533.0 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 574 860 582 754 ACCEPT CHECK
9.2.2 SULPHITE PULP 1000 t 29.4 30,749.0 22.9 30,618.7 34.3 53,632.0 29.7 54,594.9 OK OK OK OK OK OK OK OK 9.2.2 SULPHITE PULP 1000 t 9.2.2 SULPHITE PULP NAC/ t 1044 1338 1563 1837 ACCEPT CHECK
9.3 DISSOLVING GRADES 1000 t 115.1 112,827.0 108.6 134,652.2 1.1 513.0 4.5 4,194.6 OK OK OK OK OK OK OK OK 9.3 DISSOLVING GRADES 1000 t 9.3 DISSOLVING GRADES NAC/ t 980 1240 479 924 CHECK CHECK
10 OTHER PULP 1000 t 105.8 16,331.7 105.8 31,212.3 49.0 27,995.2 33.5 23,486.3 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 154 295 571 701 ACCEPT CHECK
10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 t 8.0 13,310.7 9.4 27,007.1 2.4 3,290.2 1.5 2,820.7 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 1657 2867 1387 1874 CHECK CHECK
10.2 RECOVERED FIBRE PULP 1000 t 97.8 3,021.0 96.4 4,205.2 46.7 24,705.0 32.0 20,665.6 OK OK OK OK OK OK OK OK 10.2 RECOVERED FIBRE PULP 1000 t 10.2 RECOVERED FIBRE PULP NAC/ t 31 44 529 646 CHECK CHECK
11 RECOVERED PAPER 1000 t 427.9 81,418.0 376.1 94,875.6 147.7 25,086.0 186.0 37,625.9 OK OK OK OK OK OK OK OK 11 RECOVERED PAPER 1000 t 11 RECOVERED PAPER NAC/ t 190 252 170 202 ACCEPT CHECK
12 PAPER AND PAPERBOARD 1000 t 1,201.8 898,191.3 1,243.2 1,317,815.3 3,750.9 3,007,726.4 3,591.9 3,987,610.8 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 747 1060 802 1110 ACCEPT CHECK
12.1 GRAPHIC PAPERS 1000 t 490.2 339,614.1 489.5 537,053.2 1,335.7 1,043,697.8 1,230.7 1,480,536.0 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 693 1097 781 1203 ACCEPT CHECK
12.1.1 NEWSPRINT 1000 t 76.0 32,519.6 82.7 68,218.9 138.6 59,787.8 138.0 117,818.0 OK OK OK OK OK OK OK OK 12.1.1 NEWSPRINT 1000 t 12.1.1 NEWSPRINT NAC/ t 428 825 431 854 ACCEPT CHECK
12.1.2 UNCOATED MECHANICAL 1000 t 55.7 35,570.9 56.8 53,394.2 216.2 109,265.4 200.3 178,345.4 OK OK OK OK OK OK OK OK 12.1.2 UNCOATED MECHANICAL 1000 t 12.1.2 UNCOATED MECHANICAL NAC/ t 639 940 505 890 ACCEPT CHECK
12.1.3 UNCOATED WOODFREE 1000 t 140.6 114,643.0 149.1 185,633.2 237.9 268,755.9 222.3 353,822.7 OK OK OK OK OK OK OK OK 12.1.3 UNCOATED WOODFREE 1000 t 12.1.3 UNCOATED WOODFREE NAC/ t 816 1245 1130 1591 ACCEPT CHECK
12.1.4 COATED PAPERS 1000 t 218.0 156,880.6 201.0 229,806.9 743.0 605,888.7 670.1 830,549.9 OK OK OK OK OK OK OK OK 12.1.4 COATED PAPERS 1000 t 12.1.4 COATED PAPERS NAC/ t 720 1144 815 1240 ACCEPT CHECK
12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 18.8 25,342.5 27.8 51,846.9 38.7 62,013.4 29.0 72,078.4 OK OK OK OK OK OK OK OK 12.2 HOUSEHOLD AND SANITARY PAPERS 1000 t 12.2 HOUSEHOLD AND SANITARY PAPERS NAC/ t 1348 1866 1604 2486 ACCEPT CHECK
12.3 PACKAGING MATERIALS 1000 t 684.2 505,985.4 715.9 695,927.0 2,353.1 1,815,901.0 2,308.2 2,334,478.0 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 740 972 772 1011 ACCEPT CHECK
12.3.1 CASE MATERIALS 1000 t 355.0 187,623.8 359.0 251,528.8 1,392.4 744,457.3 1,398.9 1,000,535.0 OK OK OK OK OK OK OK OK 12.3.1 CASE MATERIALS 1000 t 12.3.1 CASE MATERIALS NAC/ t 528 701 535 715 ACCEPT CHECK
12.3.2 CARTONBOARD 1000 t 176.6 182,345.2 196.2 241,997.8 566.1 673,769.6 538.0 823,778.0 OK OK OK OK OK OK OK OK 12.3.2 CARTONBOARD 1000 t 12.3.2 CARTONBOARD NAC/ t 1032 1234 1190 1531 ACCEPT CHECK
12.3.3 WRAPPING PAPERS 1000 t 127.9 123,331.7 135.0 182,068.6 317.5 349,966.1 304.2 447,198.8 OK OK OK OK OK OK OK OK 12.3.3 WRAPPING PAPERS 1000 t 12.3.3 WRAPPING PAPERS NAC/ t 965 1349 1102 1470 ACCEPT CHECK
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 t 24.7 12,684.8 25.8 20,331.8 77.2 47,707.9 67.0 62,966.2 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 514 787 618 939 ACCEPT CHECK
12.4 OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED) 1000 t 8.6 27,249.3 10.0 32,988.2 23.5 86,114.2 23.9 100,518.4 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 3184 3309 3671 4197 ACCEPT CHECK
15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1 1000 m3 6.5 6,715.0 210.2 143,752.0 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 1039 REPORT 684 CHECK CHECK
15.1 GLULAM 1000 m3 6.3 6,518.0 204.5 137,870.0 OK OK OK OK OK OK OK OK 15.1 GLULAM 1000 m3 15.1 GLULAM NAC/ m3 REPORT 1034 REPORT 674 CHECK CHECK
15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 1000 m3 0.2 197.0 5.7 5,882.0 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 1247 REPORT 1025 CHECK CHECK
16 I BEAMS (I-JOISTS)1 1000 t 0.0 4.0 0.0 5.0 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 2000 REPORT 556 CHECK CHECK
To fill: 9 9 0 0 9 9 0 0

EU2 Removals

Country: DE Date:
Name of Official responsible for reply: 0
Official Address (in full):
Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
Phone/Fax: 0 0
E-mail: 0
FOREST SECTOR QUESTIONNAIRE EU2
Removals by type of ownership
Discrepancies
Product code Ownership Flag Flag Note Note Product code Ownership
Unit 2021 2022 2021 2022 2021 2022 Unit 2021 2022
Quantity Quantity Quantity Quantity
ROUNDWOOD REMOVALS (under bark) ROUNDWOOD REMOVALS
1 ROUNDWOOD 1000 m3 1 ROUNDWOOD 1000 m3 OK OK
1.C Coniferous 1000 m3 1.C Coniferous 1000 m3 OK OK
1.NC Non-coniferous 1000 m3 1.NC Non-coniferous 1000 m3 OK OK
State forests 1000 m3 State forests 1000 m3 OK OK
Coniferous 1000 m3 Coniferous 1000 m3
Non-coniferous 1000 m3 Non-coniferous 1000 m3
Other publicly owned forests 1000 m3 Other publicly owned forests 1000 m3 OK OK
Coniferous 1000 m3 Coniferous 1000 m3
Non-coniferous 1000 m3 Non-coniferous 1000 m3
Private forest 1000 m3 Private forest 1000 m3 OK OK
Coniferous 1000 m3 Coniferous 1000 m3
Non-coniferous 1000 m3 Non-coniferous 1000 m3
To fill: 12 12
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: DE Date:
Name of Official responsible for reply: 0
Official Address (in full): Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
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 56,534 5,576 542,858 9,849 1,042,642
1.2.C Coniferous 1000 m3ub 52,425 5,188 470,624 8,978 911,744
1.2.NC Non-Coniferous 1000 m3ub 4,110 388 72,234 871 130,898
1.2.NC.T of which: Tropical1 1000 m3ub 0 16 9,447 4 2,504
6 SAWNWOOD (INCLUDING SLEEPERS) 1000 m3 446 4,183 1,598,111 11,502 3,984,881
6.C Coniferous 1000 m3 25,342 3,763 1,277,924 10,781 3,526,187
6.NC Non-Coniferous 1000 m3 24,314 420 320,187 721 458,694
6.NC.T of which: Tropical1 1000 m3 1,028 79 67,741 50 42,635
7 VENEER SHEETS 1000 m3 4 99 206,531 52 155,726
7.C Coniferous 1000 m3 110 20 20,131 1 3,690
7.NC Non-Coniferous 1000 m3 14 79 186,400 51 152,036
7.NC.T of which: Tropical 1000 m3 96 8 11,631 2 9,772
8.1 PLYWOOD 1000 m3 11,968 1,319 1,096,848 330 344,032
8.1.C Coniferous 1000 m3 85 495 304,255 153 125,214
8.1.NC Non-Coniferous 1000 m3 28 823 792,593 177 218,818
8.1.NC.T of which: Tropical 1000 m3 57 156 154,982 60 77,017
1 Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
m3 = cubic metres solid volume
m3ub = cubic metres solid volume underbark (i.e. excluding bark)

ITTO2-Species

Country: DE Date:
ITTO2 Name of Official responsible for reply: 0
Official Address (in full): Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
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 44034100 0.0 2.0 0.9 40
44034200 1.0 1,154.0 0.2 83
HS2017: 44034910 5.2 2,700.0 5.1 2,811.0 0.2 189.0 0.2 197
ex4403.12 4403.41/49 44034935 1.5 796.0 3.1 1,676.0 0.1 58.0 0.1 131
HS2012/2007: 44034985 4.9 2,689.0 6.6 3,804.0 4.7 2,599.0 2.9 2,053
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 44072110 0.0 50.0 0.0 59.0 0.0 52.0 0.0 58.0
44072191 0.1 6.0 0.1 1.0
HS2017: 44072199 0.7 56.0 1.4 113.0 0.1 77.0 0.1 191.0
ex4406.12/92 4407.21/22/25/26/27/28/29 44072210 0.8 877.0 1.0 1,110.0 0.9 842.0 0.9 971.0
44072291 0.2 162.0 0.3 300.0 0.0 6.0 0.0 7.0
HS2012/2007: 44072299 0.5 456.0 0.5 498.0 0.1 92.0
ex4406.10/90 4407.21/22/25/26/27/28/30 44072310 0.0 97.0 0.0 1.0
44072320 0.0 15.0 0.0 2.0
44072390 0.4 1,724.0 0.1 750.0
44072510 0.1 115.0 0.3 281.0 0.1 95.0
44072530 0.3 457.0 0.3 535.0 0.1 154.0 0.2 241.0
44072590 12.0 10,271.0 12.3 14,330.0 2.6 2,379.0 2.8 3,170.0
44072690 0.2 179.0 0.0 38.0 0.2 153.0 0.0 38.0
44072710 0.0 36.0 0.1 60.0
44072791 0.3 877.0
44072799 9.2 6,858.0 12.0 9,950.0 7.2 6,803.0 6.6 6,959.0
44072810 0.1 49.0
44072891 0.0 2.0
44072899 2.3 1,804.0 4.1 3,571.0 3.1 3,293.0 3.0 3,621.0
44072915 2.5 1,472.0 1.6 1,179.0 0.1 206.0 0.1 189.0
44072983 3.0 3,273.0 2.6 2,852.0 2.4 4,772.0 17.7 3,407.0
44072985 0.0 3.0 0.1 0.0
44072995 37.0 31,482.0 36.2 23,105.0 18.1 30,209.0 15.7 15,627.0
44072996 0.6 572.0 1.8 1,778.0 0.0 102.0 0.4 354.0
44072997 0.4 544.0 0.0 3.0 0.0 11.0
44072998 5.1 5,881.0 4.0 5,791.0 2.2 5,474.0 2.2 5,583.0
7.NC.T HS2022:
Veneer Sheets, Tropical 4408.31/39 44083121 0.0 15.0
HS2017: 44083130 0.0 18.0 0.0 71.0 0.0 39.0
4408.31/39 44083915 0.0 115.0 0.0 109.0 0.0 399.0 0.1 461.0
HS2012/2007: 44083921 0.1 252.0
4408.31/39 ex4408.90 44083930 0.7 2,871.0 0.9 2,759.0 0.7 1,853.0 0.7 2,102.0
44083955 0.0 2.0 0.1 109.0 0.0 155.0 0.2 298.0
44083970 1.5 2,004.0 1.2 1,915.0
44083985 1.1 3,711.0 0.8 3,774.0 1.0 5,680.0 1.0 5,767.0
44083995 6.3 3,169.0 5.2 2,932.0 0.3 1,978.0 0.1 1,105.0
8.1.NC.T HS2022:
Plywood, Tropical 4412.31/41/51/91 44123110 21 22,220 19 26,437 1 2,217 0.8 1,764.0
HS2017: 44123190 94 66,279 90 87,192 33 39,316 28.7 43,161.0
4412.31 ex4412.94/99 44124191 3 3,030 0.6 717.0
HS2012/2007: 44124199 11 9,504 0.1 56.0
4412.31 ex4412.32/94/99 44125110 11 9,336 24.8 26,333.0
44125190 17 15,117 3.4 3,649.0
44129110 3 2,414 0.5 20.0
44129191 1 858 0.4 737.0
44129199 1 1,094 0.5 580.0
44129410 6 3,733 0 3
44129930 0 0 0 0
44129950 13 11,625 4 4,534
Note: List the major species traded in each category. Use additional sheet if more species are to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.

ITTO3-Miscellaneous

Country: Date:
Name of Official responsible for reply:
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE ITTO3
Miscellaneous Items Telephone: Fax:
(use additional paper if necessary) E-mail:
1 Please enter current import tariff rates applied to tropical and non-tropical timber products. If available, please provide tariffs by the relevant customs classification category. If tariff levels have been reported in previous years, enter changes only. (Logs = JQ code 1.2, Sawn = JQ code 6, Veneer = JQ code 7, and Plywood = JQ code 8.1)
Current import tariff Logs Tropical: Sawn Tropical: Veneer Tropical: Plywood Tropical:
Non-Tropical: Non-Tropical: Non-Tropical: Non-Tropical:
Comments (if any):
2 Please comment on any quotas, incentives, disincentives, tariff/non-tariff barriers or other related factors which now or in future will significantly affect your production and trade of tropical timber products.
3 Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.
4 Please indicate any trends or changes expected in the species composition of your trade. How important are lesser-used tropical timber species and/or minor tropical forest products?
5 Please indicate trends in domestic building activity, housing starts, mortgage/interest rates, substitution of non-tropical wood and/or non-wood products for tropical timbers, and any other domestic factors having a significant impact on tropical timber consumption in your country.
6 Please indicate the extent of foreign involvement in your timber sector (e.g. number and nationalities of concessionaires/mill (joint) owners, area of forest allocated, scale of investment, etc.).
7 Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.
8 Please indicate the current extent of forest plantations in your country (ha), annual establishment rate (ha/yr) and proportion of industrial roundwood production from plantations.

TS-OB

% Min: 80% Max: 120% Notes
JQ1 Country Flow Unit Product 2017 2018 2019 2020 2021 2021 2022 17/18 18/19 19/20 20/21 21/21 21/22 2017 2018 2019 2020
DE P.OB 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_3_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P.OB 1000 m3 1_2_3_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-JQ1

% Min: 80% Max: 120% Notes
JQ1 Country Flow Unit Product 2017 2018 2019 2020 2021 2021 2022 17/18 18/19 19/20 20/21 21/21 21/22 2017 2018 2019 2020
DE P 1000 m3 1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_3_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 1_2_3_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 5_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 5_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 5_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_2_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_4_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_4_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 6_4_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 7_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 8 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 8_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_3_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_3_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_3_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 mt 10_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-JQ2

% Min: 80% Max: 120% Notes
JQ2 Country Flow Unit Product 2017 2018 2019 2020 2021 2021 2022 17/18 18/19 19/20 20/21 21/21 21/22 2017 2018 2019 2020
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TS-JQ3

% Min: 80% Max: 120% Notes
JQ3 Country Flow Unit Product 2017 2018 2019 2020 2021 2021 2022 17/18 18/19 19/20 20/21 21/21 21/22 2017 2018 2019 2020
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DE M 1000 NAC 11_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 11_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 11_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 11_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 11_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 11_5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 11_6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 11_7 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 11_7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 12_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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DE M 1000 NAC 12_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 12_5 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE M 1000 NAC 12_6 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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DE M 1000 NAC 12_7_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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DE X 1000 NAC 11_1_NC_T ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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DE X 1000 NAC 11_7_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE X 1000 NAC 12_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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DE X 1000 NAC 12_6_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE X 1000 NAC 12_6_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!

TS-ECEEU

% Min: 80% Max: 120% Notes
ECEEU Country Flow Unit Product 2017 2018 2019 2020 2021 2021 2022 17/18 18/19 19/20 20/21 21/21 21/22 2017 2018 2019 2020
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TS-EU1

% Min: 80% Max: 120% Notes
EU1 Country Flow Unit Product 2017 2018 2019 2020 2021 2021 2022 17/18 18/19 19/20 20/21 21/21 21/22 2017 2018 2019 2020
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Q DE EX_X 1000 mt 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_X 1000 NAC 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_X 1000 mt 8_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_M 1000 NAC 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_M 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_X 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_X 1000 NAC 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_X 1000 mt 9 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_M 1000 NAC 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_M 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_X 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_X 1000 NAC 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_X 1000 mt 10 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_M 1000 NAC 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_M 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_X 1000 mt 10_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_M 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_X 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_X 1000 mt 10_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_M 1000 NAC 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_M 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_X 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_X 1000 NAC 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_X 1000 mt 10_1_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_M 1000 NAC 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_M 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_X 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_X 1000 mt 10_1_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_M 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_X 1000 mt 10_1_4 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
€ DE EX_M 1000 NAC 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
UV DE EX_M 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_X 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_X 1000 mt 10_2 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_M 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_X 1000 mt 10_3 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
Q DE EX_M 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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UV DE EX_X 1000 mt 10_3_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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TS-EU2

% Min: 80% Max: 120% Notes
EU2 Country Flow Unit Product 2017 2018 2019 2020 2021 2021 2022 17/18 18/19 19/20 20/21 21/21 21/22 2017 2018 2019 2020
DE P 1000 m3 EU2_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 EU2_1_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 EU2_1_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 EU2_1_1 ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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DE P 1000 m3 EU2_1_2_C ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
DE P 1000 m3 EU2_1_2_NC ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! ERROR:#REF! !! !! !! !! !! !!
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Annex1 | JQ1-Corres.

Last updated in 2016
FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
PRIMARY PRODUCTS
Removals and Production
CORRESPONDENCES to CPC Ver.2.1
Central Product Classification Version 2.1 (CPC Ver. 2.1)
Product Product
Code
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1 ROUNDWOOD (WOOD IN THE ROUGH) 031
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 0313
1.1.C Coniferous 03131
1.1.NC Non-Coniferous 03132
1.2 INDUSTRIAL ROUNDWOOD 0311 0312
1.2.C Coniferous 0311
1.2.NC Non-Coniferous 0312
1.2.NC.T of which: Tropical ex0312
1.2.1 SAWLOGS AND VENEER LOGS ex03110 ex03120
1.2.1.C Coniferous ex03110
1.2.1.NC Non-Coniferous ex03120
1.2.2 PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD) ex03110 ex03120
1.2.2.C Coniferous ex03110
1.2.2.NC Non-Coniferous ex03120
1.2.3 OTHER INDUSTRIAL ROUNDWOOD ex03110 ex03120
1.2.3.C Coniferous ex03110
1.2.3.NC Non-Coniferous ex03120
PRODUCTION
2 WOOD CHARCOAL ex34510
3 WOOD CHIPS, PARTICLES AND RESIDUES ex31230 ex39283
3.1 WOOD CHIPS AND PARTICLES ex31230
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) ex39283
4 RECOVERED POST-CONSUMER WOOD ex39283
5 WOOD PELLETS AND OTHER AGGLOMERATES 39281 39282
5.1 WOOD PELLETS 39281
5.2 OTHER AGGLOMERATES 39282
6 SAWNWOOD (INCLUDING SLEEPERS) 311 3132
6.C Coniferous 31101 ex31109 ex3132
6.NC Non-Coniferous 31102 ex31109 ex3132
6.NC.T of which: Tropical ex31102 ex31109 ex3132
7 VENEER SHEETS 3151
7.C Coniferous 31511
7.NC Non-Coniferous 31512
7.NC.T of which: Tropical ex31512
8 WOOD-BASED PANELS 3141 3142 3143 3144
8.1 PLYWOOD 3141 3142
8.1.C Coniferous 31411 31421
8.1.NC Non-Coniferous 31412 31422
8.1.NC.T of which: Tropical ex31412 ex31422
8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD 3143
8.2.1 of which: ORIENTED STRAND BOARD (OSB) 31432
8.3 FIBREBOARD 3144
8.3.1 HARDBOARD 31442
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 31441
8.3.3 OTHER FIBREBOARD 31449
9 WOOD PULP 32111 32112 ex32113
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP ex32113
9.2 CHEMICAL WOOD PULP 32112
9.2.1 SULPHATE PULP ex32112
9.2.1.1 of which: BLEACHED ex32112
9.2.2 SULPHITE PULP ex32112
9.3 DISSOLVING GRADES 32111
10 OTHER PULP ex32113
10.1 PULP FROM FIBRES OTHER THAN WOOD ex32113
10.2 RECOVERED FIBRE PULP ex32113
11 RECOVERED PAPER 3924
12 PAPER AND PAPERBOARD 3212 3213 32142 32143 ex32149 32151 32198 ex32199
12.1 GRAPHIC PAPERS 3212 ex32143 ex32149
12.1.1 NEWSPRINT 32121
12.1.2 UNCOATED MECHANICAL ex32122 ex32129
12.1.3 UNCOATED WOODFREE 32122 ex32129
12.1.4 COATED PAPERS ex32143 ex32149
12.2 HOUSEHOLD AND SANITARY PAPERS 32131
12.3 PACKAGING MATERIALS 32132 ex32133 32134 32135 ex32136 ex32137 32142 32151 ex32143 ex32149
12.3.1 CASE MATERIALS 32132 32134 32135 ex32136
12.3.2 CARTONBOARD ex32133 ex32136 ex32143 ex32149
12.3.3 WRAPPING PAPERS ex32133 ex32136 ex32137 32142 32151
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING ex32136
12.4 OTHER PAPER AND PAPERBOARD N.E.S. ex32149 ex32133 ex32136 ex32137 32198 ex32199
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the CPC Ver.2.1 code is applicable.
For instance "ex31512" under product 7.NC.T means that only a part of CPC Ver.2.1 code 31512 refers to non-coniferous tropical veneer sheets.
In CPC, if only 3 or 4 digits are shown, then all sub-codes at lower degrees of aggregation are included (for example, 0313 includes 03131 and 03132).

Annex2 | JQ2-Corres.

FOREST SECTOR QUESTIONNAIRE JQ2 (Supp. 1)
PRIMARY PRODUCTS
Trade
CORRESPONDENCES to HS2022, HS2017, HS2012 and SITC Rev.4
C l a s s i f i c a t i o n s
Product Product
Code HS2022 HS2017 HS2012 SITC Rev.4
1 ROUNDWOOD (WOOD IN THE ROUGH) 4401.11/12 44.03 4401.11/12 44.03 4401.10 44.03 245.01 247
1.1 WOOD FUEL (INCLUDING WOOD FOR CHARCOAL) 4401.11/12 4401.11/12 4401.10 245.01
1.1.C Coniferous 4401.11 4401.11 ex4401.10 ex245.01
1.1.NC Non-Coniferous 4401.12 4401.12 ex4401.10 ex245.01
1.2 INDUSTRIAL ROUNDWOOD 44.03 44.03 44.03 247
1.2.C Coniferous 4403.11/21/22/23/24/25/26 4403.11/21/22/23/24/25/26 ex4403.10 4403.20 ex247.3 247.4
1.2.NC Non-Coniferous 4403.12/41/42/49/91/93/94/95/96/97/98/99 4403.12/41/49/91/93/94/95/96/97/98/99 ex4403.10 4403.41/49/91/92/99 ex247.3 247.5 247.9
1.2.NC.T of which: Tropical1 ex4403.12 4403.41/42/49 4403.41/49 ex4403.10 4403.41/49 ex4403.99 ex247.3 247.5 ex247.9
2 WOOD CHARCOAL 4402.90 4402.90 4402.90 ex245.02
3 WOOD CHIPS, PARTICLES AND RESIDUES 4401.21/22 4401.41 ex4401.49 4401.21/22 ex4401.40 4401.21/22 ex4401.39 246.1 ex246.2
3.1 WOOD CHIPS AND PARTICLES 4401.21/22 4401.21/22 4401.21/22 246.1
3.2 WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES) 4401.41 ex4401.49++ ex4401.40++ ex4401.39 ex246.2
3.2.1 of which: Sawdust 4401.41 ex4401.40++ ex4401.39 ex246.2
4 RECOVERED POST-CONSUMER WOOD ex4401.49++ ex4401.40++ ex4401.39 ex246.2
5 WOOD PELLETS AND OTHER AGGLOMERATES 4401.31/32/39 4401.31/39 4401.31 ex4401.39 ex246.2
5.1 WOOD PELLETS 4401.31 4401.31 4401.31 ex246.2
5.2 OTHER AGGLOMERATES 4401.32/39 4401.39 ex4401.39 ex246.2
6 SAWNWOOD (INCLUDING SLEEPERS) 44.06 44.07 44.06 44.07 44.06 44.07 248.1 248.2 248.4
6.C Coniferous 4406.11/91 4407.11/12/13/14/19 4406.11/91 4407.11/12/19 ex4406.10/90 4407.10 ex248.11 ex248.19 248.2
6.NC Non-Coniferous 4406.12/92 4407.21/22/23/25/26/27/28/29/91/92/93/94/95/96/97/99 4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99 ex4406.10/90 4407.21/22/25/26/27/28/29/91/92/93/94/95/99 ex248.11 ex248.19 248.4
6.NC.T of which: Tropical1 ex4406.12/92 4407.21/22/23/25/26/27/28/29 4407.21/22/25/26/27/28/29 ex4406.10/90 4407.21/22/25/26/27/28/29 ex4407.99 ex248.11 ex248.19 ex248.4
7 VENEER SHEETS 44.08 44.08 44.08 634.1
7.C Coniferous 4408.10 4408.10 4408.10 634.11
7.NC Non-Coniferous 4408.31/39/90 4408.31/39/90 4408.31/39/90 634.12
7.NC.T of which: Tropical 4408.31/39 4408.31/39 4408.31/39 ex4408.90 ex634.12
8 WOOD-BASED PANELS 44.10 44.11 4412.31/33/34/39/41/42/49/51/52/59/91/92/99 44.10 44.11 4412.31/33/34/39/94/99 44.10 44.11 4412.31/32/39/94/99 634.22/23/31/33/39 634.5
8.1 PLYWOOD 4412.31/33/34/39/41/42/49/51/52/59/91/92/99 4412.31/33/34/39/94/99 4412.31/32/39/94/99 634.31/33/39
8.1.C Coniferous 4412.39/49/59/99 4412.39 ex4412.94 ex4412.99 4412.39 ex4412.94 ex.4412.99 ex634.31 ex634.33 ex634.39
8.1.NC Non-Coniferous 4412.33/34/42/52/92 4412.31/33/34 ex4412.94 ex4412.99 4412.31/32 ex4412.94 ex4412.99 ex634.31 ex634.33 ex634.39
8.1.NC.T of which: Tropical 4412.31/41/51/91 4412.31 ex4412.94 ex4412.99 4412.31 ex4412.32 ex4412.94 ex4412.99 ex634.31 ex634.33 ex634.39
8.1.1 of which: Laminated Veneer Lumber (LVL) 4412.41/42/49 ex4412.99 ex4412.99 ex634.39
8.1.1.C Coniferous 4412.49 ex4412.99 ex4412.99 ex634.39
8.1.1.NC Non-Coniferous 4412.41/42 ex4412.99 ex4412.99 ex634.39
8.1.1.NC.T of which: Tropical 4412.41 ex4412.99 ex4412.99 ex634.39
8.2 PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD 44.10 44.10 44.10 634.22/23
8.2.1 of which: ORIENTED STRAND BOARD (OSB) 4410.12 4410.12 4410.12 ex634.22
8.3 FIBREBOARD 44.11 44.11 44.11 634.5
8.3.1 HARDBOARD 4411.92 4411.92 4411.92 ex634.54 ex634.55
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 4411.12/13 ex4411.14* 4411.12/13 ex4411.14* 4411.12/13 ex4411.14* ex634.54 ex634.55
8.3.3 OTHER FIBREBOARD ex4411.14* 4411.93/94 ex4411.14* 4411.93/94 ex4411.14 4411.93/94 ex634.54 ex634.55
9 WOOD PULP 47.01/02/03/04/05 47.01/02/03/04/05 47.01/02/03/04/05 251.2 251.3 251.4 251.5 251.6 251.91
9.1 MECHANICAL AND SEMI-CHEMICAL WOOD PULP 47.01 47.05 47.01 47.05 47.01 47.05 251.2 251.91
9.2 CHEMICAL WOOD PULP 47.03 47.04 47.03 47.04 47.03 47.04 251.4 251.5 251.6
9.2.1 SULPHATE PULP 47.03 47.03 47.03 251.4 251.5
9.2.1.1 of which: BLEACHED 4703.21/29 4703.21/29 4703.21/29 251.5
9.2.2 SULPHITE PULP 47.04 47.04 47.04 251.6
9.3 DISSOLVING GRADES 47.02 47.02 47.02 251.3
10 OTHER PULP 47.06 47.06 47.06 251.92
10.1 PULP FROM FIBRES OTHER THAN WOOD 4706.10/30/91/92/93 4706.10/30/91/92/93 4706.10/30/91/92/93 ex251.92
10.2 RECOVERED FIBRE PULP 4706.20 4706.20 4706.20 ex251.92
11 RECOVERED PAPER 47.07 47.07 47.07 251.1
12 PAPER AND PAPERBOARD 48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13 48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13 48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13 641.1 641.2 641.3 641.4 641.5 641.62/63/64/69/71/72/74/75/76/77/93 642.41
12.1 GRAPHIC PAPERS 48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29 48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29 48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29 641.1 641.21/22/26/29 641.3
12.1.1 NEWSPRINT 48.01 48.01 48.01 641.1
12.1.2 UNCOATED MECHANICAL 4802.61/62/69 4802.61/62/69 4802.61/62/69 641.29
12.1.3 UNCOATED WOODFREE 4802.10/20/54/55/56/57/58 4802.10/20/54/55/56/57/58 4802.10/20/54/55/56/57/58 641.21/22/26
12.1.4 COATED PAPERS 48.09 4810.13/14/19/22/29 48.09 4810.13/14/19/22/29 48.09 4810.13/14/19/22/29 641.3
12.2 HOUSEHOLD AND SANITARY PAPERS 48.03 48.03 48.03 641.63
12.3 PACKAGING MATERIALS 4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59 4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59 4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59 641.41/42/46 ex641.47 641.48/51/52 ex641.53 641.54/59/62/64/69/71/72/74/75/76/77
12.3.1 CASE MATERIALS 4804.11/19 4805.11/12/19/24/25/91 4804.11/19 4805.11/12/19/24/25/91 4804.11/19 4805.11/12/19/24/25/91 641.41/51/54 ex641.59
12.3.2 CARTONBOARD 4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59 4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59 4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59 ex641.47 641.48 ex641.59 641.75/76 ex641.77 641.71/72
12.3.3 WRAPPING PAPERS 4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99 4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99 4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99 641.42/46/52 ex641.53 641.62/64/69/74 ex641.77
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 4805.93 4805.93 4805.93 ex641.59
12.4 OTHER PAPER AND PAPERBOARD N.E.S. 4802.40 4804.41 4805.40/50 4806.30 48.12 48.13 4802.40 4804.41 4805.40/50 4806.30 48.12 48.13 4802.40 4804.41 4805.40/50 4806.30 48.12 48.13 641.24 ex641.47 641.56 ex641.53 641.55/93 642.41
15 GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)2 4418.81/82 ex4418.60 ex4418.60 ex635.39
15.1 GLULAM 4418.81 ex4418.60 ex4418.60 ex635.39
15.2 CROSS-LAMINATED TIMBER (CLT or X-LAM) 4418.82 ex4418.60 ex4418.60 ex635.39
16 I BEAMS (I-JOISTS)2 4418.83 ex4418.60 ex4418.60 ex635.39
1Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included in JQ1 and JQ2
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017/HS2022 or SITC Rev.4 code is applicable.
For instance "ex4401.49" under product 3.2 means that only a part of HS2022 code 4401.49 refers to wood residues coming from wood processing (the other part coded under 4401.49 is recovered post-consumer wood).
++ Please use your judgement or, as a default, assign half of 4401.49 to item 3.2 and half to item 4 (note different quantity units)
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.

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 m3 per MT m3 per MT Roundwood equivalent Roundwood equivalent Roundwood equivalent m3 per MT m3 per MT Roundwood equivalent m3 per MT m3 per m2 Roundwood
equivalent
Europe NA** EECCA** Europe NA** EECCA**
1 ROUNDWOOD (WOOD IN THE ROUGH) 1000 m3 ub
1.1 WOOD FUEL, INCLUDING WOOD FOR CHARCOAL 1000 m3 ub 1.38
1.1.C Coniferous 1000 m3 ub 1.64 typical shipping weight Green = 1.12 Based on 891 kg/m3 green, basic density of .41, and 20% moisture seasoned 1.60
1000 m3 ub Seasoned = 1.82 Based on 407 kg/m3 dry, assuming 20% moisture
1.1.NC Non-Coniferous 1000 m3 ub 1.11 typical shipping weight Green=1.05 Based on 1137 kg/m3 green, specific gravity of .55, and 20% moisture seasoned 1.33
1000 m3 ub Seasoned=1.43
1.2 INDUSTRIAL ROUNDWOOD 1000 m3 ub
1.2.C Coniferous 1000 m3 ub 1.11 1.08 1.27 Averaged pulp and log 1.10 Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood
1.2.C.Fir Fir (and Spruce) 1000 m3 ub 1.21 Austrian Energy Agency, 2009. weighted by share of standing inventory of European speices (57% spruce, 10% silver fir and remaining species)
1.2.C.Pine Pine 1000 m3 ub 1.08 Austrian Energy Agency, 2009, weighted 25% Scots Pine, 2% maritime pine, 2% black pine and remaining species
1.2.NC Non-Coniferous 1000 m3 ub 0.98 1.02 1.15 0.91 Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood
1.2.NC.T of which:Tropical 1000 m3 ub AFRICA=1.31, ASIA=0.956, LA. AM= 0.847, World=1.12 Source: Fonseca "Measurement of Roundwood" 2005, ITTO Annual Review 2007, table 3-2-a Species weight averaged using m3/tonne from Fonseca 2005 and volume exported by species from each region as shown in ITTO 2007 (assumes that bark is removed) 1.37
1.2.1 SAWLOGS AND VENEER LOGS 1000 m3 ub 1.04 0.96 1.12 Averaged C & NC 1.05 Based on 950 kg/m3 green. Bark is included in weight but not in volume.
1.2.1.C Coniferous 1000 m3 ub 1.10 1.00 1.19 1.07 Based on 935 kg/m3 green. Bark is included in weight but not in volume. 1.43
1.2.1.NC Non-Coniferous 1000 m3 ub 0.97 0.92 1.04 0.91 Based on 1093 kg/m3 green. Bark is included in weight but not in volume. 1.25
1.2.NC.Beech Beech 1000 m3 ub 0.92 Austrian Energy Agency, 2009
1.2.NC.Birch Birch 1000 m3 ub 0.88 Austrian Energy Agency, 2009
1.2.NC.Eucalyptus Eucalyptus 1000 m3 ub 0.77 ATIBT, 1982
1.2.NC.Oak Oak 1000 m3 ub 0.88 Austrian Energy Agency, 2009
1.2.NC.Poplar Poplar 1000 m3 ub 1.06 Austrian Energy Agency, 2009
1.2.2 PULPWOOD (ROUND & SPLIT) 1000 m3 ub 1.05 1.14 1.30 Averaged C & NC 1.08 Based on 930 kg/m3 green. Bark is included in weight but not in volume. 1.48
1.2.2.C Coniferous 1000 m3 ub 1.11 1.16 1.35 1.12 Based on 891 kg/m3 green. Bark is included in weight but not in volume. 1.54
1.2.2.NC Non-Coniferous 1000 m3 ub 0.98 1.11 1.25 0.91 Based on 1095 kg/m3 green. Bark is included in weight but not in volume. 1.33
1.2.3 OTHER INDUSTRIAL ROUNDWOOD 1000 m3 ub 1.07 1.33
1.2.3.C Coniferous 1000 m3 ub 1.11 1.16 1.35 used pulpwood data 1.12 same as 1.2.2.C 1.43
1.2.3.NC Non-Coniferous 1000 m3 ub 0.98 1.11 1.25 0.91 same as 1.2.2.NC 1.25
2 WOOD CHARCOAL 1000 MT 6 m3rw/tonne 5.35 Does not include the use of any of the wood fiber to generate the heat to make (add about 30% if inputted wood fiber used to provide heat) 6.00
3 WOOD CHIPS, PARTICLES AND RESIDUES 1000 m3
3.1 WOOD CHIPS AND PARTICLES 1000 m3 1.205 1.07 1.21 1.08 m3 /MT = green swe per odmt / avg delivered tonne/odmt, rwe= +1% softwood=1.19 1.205 Based on swe/odmt of 2.41 and avg delivered mt / odmt of 2.0 in solid m3 1.60
1000 m3 hardwood = 1.05 1.123 Based on swe/odmt of 2.01 and avg delivered mt / odmt of 1.79 in solid m3
1000 m3 Woodchip, Green swe to oven-dry tonne m3/odmt mix = 1.15
3.2 WOOD RESIDUES 1000 m3 1.205 1.07 1.21 1.08 Based on wood chips Green=1.15 Based on wood chips 1.50
1000 m3 2.12 2.07 Seasoned = 2.12 2.07 Assumption for seasoned is based on average basic density of .42 from questionnaire and assumes 15% moisture content
3.2.1 of which: SAWDUST 1000 m3 1.205 1.07 1.21 1.08 Based on wood chips
4 RECOVERED POST-CONSUMER WOOD 1000 mt Delivered MT (12-20% atmospheric moisture). Convert to dry weight for energy purposes (multiply by 0.88 - 0.80)
5 WOOD PELLETS AND OTHER AGGLOMERATES 1000 MT
5.1 WOOD PELLETS 1000 MT 1.54 1.45 1.54 1.51 1.44 nodata m3/ton - bulk density, loose volume, 5-10% mcw- Equivalent - solid wood imput to bulk m3 pellets 1.51 1.44 Bulk (loose) volume, 5-10% moisture
5.2 OTHER AGGLOMERATES 1000 MT 1.12 nodata nodata 2.32 nodata nodata m3/ton - Pressed logs and briquettes, bulk density, loose volume. Equivalent - m3rw/odmt 1.31 2.29 roundwood equivalent is m3rw/odmt, volume to weight is bulk (loose volume)
6 SAWNWOOD 1000 m3 1.6 / 1.82*
6.C Coniferous 1000 m3 1.202 1.69 1.62 1.85 m3/ton - Average Sawnwood shipping weight. Equivalent - Sawnwood green rough Green=1.202 RoughGreen=1.67 Green sawnwood based on basic density of .94, less bark (11%) 1.82
1000 m3 1.82 1.72 Nodata 2 1.69 2.05 Sawnwood dry rough Dry = 1.99 RoughDry=1.99 Dry sawnwood weight based on basic density of .42, 4% shrinkage and 15% moisture content
1000 m3 2.26 2.08 nodata Sawnwood dry planed PlanedDry=2.13
6.C.Fir Fir and Spruce 1000 m3 2.16 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.
6.C.Pine Pine 1000 m3 1.72 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.
6.NC Non-Coniferous 1000 m3 1.04 1.89 1.79 nodata Sawnwood green rough Green=1.04 RoughGreen=1.86 Green sawnwood based on basic density of 1.09, less bark (12%) 1.43
1000 m3 1.43 nodata nodata 2.01 1.92 nodata m3/ton - Average Sawnwood shipping weight. Equivalent - Sawnwood green rough Seasoned=1.50 RoughDry=2.01 Dry sawnwood weight based on basic density of .55, 5% shrinkage and 15% moisture content
1000 m3 3.25 3.38 nodata Sawnwood dry planed PlanedDry=2.81
6.NC.Ash Ash 1000 m3 1.47 Wood Database (wood-database.com). Air-dry.
6.NC.Beech Beech 1000 m3 1.42 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Birch Birch 1000 m3 1.47 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Cherry Cherry 1000 m3 1.62 Giordano, 1976, Tecnologia del legno. Air-dry. Prunus avium.
6.NC.Maple Maple 1000 m3 1.35 Giordano, 1976, Tecnologia del legno. Air-dry
6.NC.Oak Oak 1000 m3 1.38 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.Poplar Poplar 1000 m3 2.29 Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).
6.NC.T of which:Tropical 1000 m3 1.38 Based on FP Conversion Factors (2019), Asia (720 kg / m3)
7 VENEER SHEETS 1000 m3 1.33 0.0025 1.9*
7.C Coniferous 1000 m3 1.05 1.95 1.5 Green veneer based on the ratio from the old conversion factors Green=1.20 1.5*** Green veneer based on basic density of .94, less bark (11%) 0.003
1000 m3 1.8 nodata nodata 2.08 1.6 nodata m3/ton - Average panel shipping weight; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product Seasoned=2.06 1.6*** Dry veneer weight based on basic density of .42, 9% shrinkage and 5% moisture content
7.NC Non-Coniferous 1000 m3 1.15 nodata nodata 2.11 1.89 Green veneer based on the ratio from the old conversion factors Green=1.04 1.5*** Green veneer based on basic density of 1.09, less bark (11%) 0.001
1000 m3 1.7 nodata nodata 2.25 2 nodata m3/ton - Average panel shipping weight; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product Seasoned=1.53 1.6*** Dry veneer weight based on basic density of .55, 11.5% shrinkage and 5% moisture content
7.NC.T of which:Tropical 1000 m3
8 WOOD-BASED PANELS 1000 m3 1.6
8.1 PLYWOOD 1000 m3 1.54 0.105 2.3*
8,1.C Coniferous 1000 m3 1.67 Nodata Nodata 2.16 1.92 nodata 1.69 2.12 dried, sanded, peeled 0.0165***
8.1.NC Non-Coniferous 1000 m3 1.54 Nodata Nodata 2.54 2.14 nodata 1.54 1.92 dried, sanded, sliced 0.0215***
8.1.NC.T of which:Tropical 1000 m3
8.1.1 of which: LAMINATED VENEER LUMBER 1000 m3 1.69 Same as coniferous plywood
8.1.1.C Coniferous 1000 m3 1.69 Same as coniferous plywood
8.1.1.NC Non-Coniferous 1000 m3 no data
8.1.1.NC.T of which:Tropical 1000 m3 no data
8.2 PARTICLE BOARD (including OSB) 1000 m3 1.54
8.2x PARTICLE BOARD (excluding OSB) 1000 m3 1.54 Nodata Nodata 1.51 1.54 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 1.53 1.50 0.018***
8.2.1 of which: OSB 1000 m3 1.64 Nodata Nodata 1.72 1.63 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 1.67 1.63 0.018***
8.3 FIBREBOARD 1000 m3 nodata nodata nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.
8.3.1 HARDBOARD 1000 m3 1.06 Nodata Nodata 2.2 1.77 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 1.06 1.93 solid wood per m3 of product 1.05 0.005
Alex McCusker: Alex McCusker: 0.003 per Conversion Factors Study
8.3.2 MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF) 1000 m3 1.35 Nodata Nodata 1.80 1.53 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 1.37 1.70 solid wood per m3 of product 2.00 0.016
8.3.3 OTHER FIBREBOARD 1000 m3 3.85 Nodata Nodata 0.68 0.71 nodata m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product. 3.44 0.71 solid wood per m3 of product, mostly insulating board 4.00 0.025
9 WOOD PULP 1000 MT 3.7 nodata 3.76 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.86 3.37
9.1 MECHANICAL AND SEMI-CHEMICAL 1000 MT 2.59 2.45 2.94 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 2.60 air-dried metric ton (mechanical 2.50, semi-chemical 2.70)
9..2 CHEMICAL 1000 MT 4.80 4.29 4.10 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.90
9.2.1 SULPHATE 1000 MT 4.50 nodata 4.60 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.57 air-dried metric ton (unbleached 4.63, bleached 4.50)
9.2.1.1 of which: bleached 1000 MT 4.50 nodata 4.90 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.50 air-dried metric ton
9.2.2 SULPHITE 1000 MT 4.73 nodata 4.15 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.83 air-dried metric ton (unbleached 4.64 and bleached 5.01)
9.3 DISSOLVING GRADES 1000 MT 4.46 nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 5.65 air-dried metric ton
10 OTHER PULP 1000 MT nodata nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)
10.1 PULP FROM FIBRES OTHER THAN WOOD 1000 MT nodata nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)
10.2 RECOVERED FIBRE PULP 1000 MT nodata nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)
11 RECOVERED PAPER 1000 MT nodata nodata nodata m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 1.28 MT in per MT out
12 PAPER AND PAPERBOARD 1000 MT 3.85 nodata 4.15 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.6 3.37
12.1 GRAPHIC PAPERS 1000 MT nodata nodata nodata
12.1.1 NEWSPRINT 1000 MT 2.80 2.50 3.15 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 2.80 air-dried metric ton
12.1.2 UNCOATED MECHANICAL 1000 MT 3.50 nodata 4.00 3.50 air-dried metric ton
12.1.3 UNCOATED WOODFREE 1000 MT nodata nodata nodata
12.1.4 COATED PAPERS 1000 MT 3.50 nodata 4.00 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.95 air-dried metric ton
12.2 SANITARY AND HOUSEHOLD PAPERS 1000 MT 4.60 nodata 4.20 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.90 air-dried metric ton
12.3 PACKAGING MATERIALS 1000 MT 3.25 nodata 4.30 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.25 air-dried metric ton
12.3.1 CASE MATERIALS 1000 MT 4.20 nodata 4.00 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.20 air-dried metric ton
12.3.2 CARTONBOARD 1000 MT 4.00 nodata 4.30 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.00 air-dried metric ton
12.3.3 WRAPPING PAPERS 1000 MT 4.10 nodata 4.40 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.10 air-dried metric ton
12.3.4 OTHER PAPERS MAINLY FOR PACKAGING 1000 MT 4.00 nodata 3.30 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 4.00 air-dried metric ton
12.4 OTHER PAPER AND PAPERBOARD N.E.S 1000 MT 3.48 nodata 3.30 m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis) 3.48 air-dried metric ton
15 GLULAM AND CROSS-LAMINATED TIMBER 1000 m3
15.1 GLULAM 1000 m3 1.69 same as coniferous plywood
15.2 CROSS-LAMINATED TIMBER 1000 m3 2.00
16 I-BEAMS 1000 MT 1.68 222 linear meters per MT
For inverse relationships divide 1 by the factor given, e.g. to convert m3 of wood charcoal to mt divide 1 by m3/mt factor of 6 = 0.167
Notes: Forest Measures
MT = metric tonnes (1000 kg) Unit m3/unit
m3 = cubic meters (solid volume) 1000 board feet (sawlogs) 4.53**** **** = obsolete - more recent figures would be:
m2 = square meters 1000 board feet (sawnwood - nominal) 2.36 for Oregon, Washington State, Alaska (west of Cascades), South East United States (Doyle region): 6.3
(s) = solid volume 1000 board feet (sawnwood - actual) 1.69 Inland Western North America, Great Lakes (North America), Eastern Canada: 5.7
1000 square feet (1/8 inch thickness) 0.295 Northeast United States Int 1/4": 5
Unit Conversion cord 3.625
1 inch = 25.4 millimetres cord (pulpwood) 2.55
1 square foot = 0.0929 square metre cord (wood fuel) 2.12
1 pound = 0.454 kilograms cubic foot 0.02832
1 short ton (2000 pounds) = 0.9072 metric ton cubic foot (stacked) 0.01841
1 long ton (2240 pounds) = 1.016 metric ton cunit 2.83
Bold = FAO published figure fathom 6.1164
hoppus cubic foot 0.0222
* = ITTO hoppus super(ficial) foot 0.00185
hoppus ton (50 hoppus cubic feet) 1.11
** NA = North America; EECCA = Eastern Europe, Caucasus and Central Asia Petrograd Standard 4.672
stere 1
*** = Conversion Factor Study, US figures, rotary for conifer and sliced for non-conifer stere (pulpwood) 0.72
stere (wood fuel) 0.65
Fonseca "Measurement of Roundwood" 2005. Estimated by Matt Fonseca based on regional knowledge of the scaling methods and timber types
prepared February 2004
updated 2007 with RWE factors
updated 2009 with provisional results of forest products conversion factors study
updated 2011 with results of forest products conversion factors study (DP49)
updated 2023 with results of 2019 UNECE/FAO/ITTO study - https://www.fao.org/documents/card/en/c/ca7952en

Flatfile

geo stk_flow time prod_wd treespec unit obs_value obs_flag
DE PRD 2021 RW_OB TOTAL THS_M3
DE PRD 2021 RW_FW_OB TOTAL THS_M3
DE PRD 2021 RW_FW_OB CONIF THS_M3
DE PRD 2021 RW_FW_OB NCONIF THS_M3
DE PRD 2021 RW_IN_OB TOTAL THS_M3
DE PRD 2021 RW_IN_OB CONIF THS_M3
DE PRD 2021 RW_IN_OB NCONIF THS_M3
DE PRD 2021 RW_IN_OB NC_TRO THS_M3
DE PRD 2021 RW_IN_LG_OB TOTAL THS_M3
DE PRD 2021 RW_IN_LG_OB CONIF THS_M3
DE PRD 2021 RW_IN_LG_OB NCONIF THS_M3
DE PRD 2021 RW_IN_PW_OB TOTAL THS_M3
DE PRD 2021 RW_IN_PW_OB CONIF THS_M3
DE PRD 2021 RW_IN_PW_OB NCONIF THS_M3
DE PRD 2021 RW_IN_O_OB TOTAL THS_M3
DE PRD 2021 RW_IN_O_OB CONIF THS_M3
DE PRD 2021 RW_IN_O_OB NCONIF THS_M3
DE PRD 2022 RW_OB TOTAL THS_M3
DE PRD 2022 RW_FW_OB TOTAL THS_M3
DE PRD 2022 RW_FW_OB CONIF THS_M3
DE PRD 2022 RW_FW_OB NCONIF THS_M3
DE PRD 2022 RW_IN_OB TOTAL THS_M3
DE PRD 2022 RW_IN_OB CONIF THS_M3
DE PRD 2022 RW_IN_OB NCONIF THS_M3
DE PRD 2022 RW_IN_OB NC_TRO THS_M3
DE PRD 2022 RW_IN_LG_OB TOTAL THS_M3
DE PRD 2022 RW_IN_LG_OB CONIF THS_M3
DE PRD 2022 RW_IN_LG_OB NCONIF THS_M3
DE PRD 2022 RW_IN_PW_OB TOTAL THS_M3
DE PRD 2022 RW_IN_PW_OB CONIF THS_M3
DE PRD 2022 RW_IN_PW_OB NCONIF THS_M3
DE PRD 2022 RW_IN_O_OB TOTAL THS_M3
DE PRD 2022 RW_IN_O_OB CONIF THS_M3
DE PRD 2022 RW_IN_O_OB NCONIF THS_M3
DE PRD 2021 RW TOTAL THS_M3 82178.0183607038 9
DE PRD 2021 RW_FW TOTAL THS_M3 22799.3303073151 9
DE PRD 2021 RW_FW CONIF THS_M3 9095.6350489807 9
DE PRD 2021 RW_FW NCONIF THS_M3 13703.6952583344 9
DE PRD 2021 RW_IN TOTAL THS_M3 59378.6880533887 9
DE PRD 2021 RW_IN CONIF THS_M3 55494.7603266046 9
DE PRD 2021 RW_IN NCONIF THS_M3 3883.9277267841 9
DE PRD 2021 RW_IN NC_TRO THS_M3 0 9
DE PRD 2021 RW_IN_LG TOTAL THS_M3 47427.8250198875 9
DE PRD 2021 RW_IN_LG CONIF THS_M3 44666.1941260633 9
DE PRD 2021 RW_IN_LG NCONIF THS_M3 2761.6308938242 9
DE PRD 2021 RW_IN_PW TOTAL THS_M3 11789.9870335012 9
DE PRD 2021 RW_IN_PW CONIF THS_M3 10675.3341203132 9
DE PRD 2021 RW_IN_PW NCONIF THS_M3 1114.652913188 9
DE PRD 2021 RW_IN_O TOTAL THS_M3 160.876 9
DE PRD 2021 RW_IN_O CONIF THS_M3 153.2320802281 9
DE PRD 2021 RW_IN_O NCONIF THS_M3 7.6439197719 9
DE PRD 2021 CHA TOTAL THS_T 30 9
DE PRD 2021 CHP_RES TOTAL THS_M3 16703.191440378 9
DE PRD 2021 CHP TOTAL THS_M3 11805.133840378 9
DE PRD 2021 RES TOTAL THS_M3 4898.0576 9
DE PRD 2021 RES_SWD TOTAL THS_M3
DE PRD 2021 RCW TOTAL THS_T 8035 9
DE PRD 2021 PEL_AGG TOTAL THS_T 4333.229 9
DE PRD 2021 PEL TOTAL THS_T 3353 9
DE PRD 2021 AGG TOTAL THS_T 980.229
DE PRD 2021 SN TOTAL THS_M3 26438.2958 9
DE PRD 2021 SN CONIF THS_M3 25335.4118 9
DE PRD 2021 SN NCONIF THS_M3 1102.884 9
DE PRD 2021 SN NC_TRO THS_M3 2.028624 9
DE PRD 2021 PN_VN TOTAL THS_M3 115.937 9
DE PRD 2021 PN_VN CONIF THS_M3 13.5440883182 9
DE PRD 2021 PN_VN NCONIF THS_M3 102.3929116818 9
DE PRD 2021 PN_VN NC_TRO THS_M3 1.78805 9
DE PRD 2021 PN TOTAL THS_M3 13525.394565203 9
DE PRD 2021 PN_PY TOTAL THS_M3 103.0116521595 9
DE PRD 2021 PN_PY CONIF THS_M3 44.0996521595 9
DE PRD 2021 PN_PY NCONIF THS_M3 58.912 5
DE PRD 2021 PN_PY NC_TRO THS_M3 0.1702642678 9
DE PRD 2021 PN_PY_LVL TOTAL THS_M3
DE PRD 2021 PN_PY_LVL CONIF THS_M3
DE PRD 2021 PN_PY_LVL NCONIF THS_M3
DE PRD 2021 PN_PY_LVL NC_TRO THS_M3
DE PRD 2021 PN_PB TOTAL THS_M3 7317.814 9
DE PRD 2021 PN_PB_OSB TOTAL THS_M3 1281.538 9
DE PRD 2021 PN_FB TOTAL THS_M3 6104.5689130435 9
DE PRD 2021 PN_FB_HB TOTAL THS_M3 0 9
DE PRD 2021 PN_FB_MDF TOTAL THS_M3 4692.623786961 9
DE PRD 2021 PN_FB_O TOTAL THS_M3 1411.9451260825 9
DE PRD 2021 PL TOTAL THS_T 2327.399 9
DE PRD 2021 PL_MC_SCH TOTAL THS_T 755.958 9
DE PRD 2021 PL_CH TOTAL THS_T 1571.441 9
DE PRD 2021 PL_CH_SA TOTAL THS_T 1065.213 9
DE PRD 2021 PL_CH_SAB TOTAL THS_T 1065.213 9
DE PRD 2021 PL_CH_SI TOTAL THS_T 506.228 9
DE PRD 2021 PL_DS TOTAL THS_T 0 9
DE PRD 2021 PLO TOTAL THS_T 15352.477 9
DE PRD 2021 PLO_NW TOTAL THS_T 56.477 9
DE PRD 2021 PLO_RC TOTAL THS_T 15296 9
DE PRD 2021 RCP TOTAL THS_T 14487.498 9
DE PRD 2021 PP TOTAL THS_T 23127.532 9
DE PRD 2021 PP_GR TOTAL THS_T 6822.53 9
DE PRD 2021 PP_GR_NP TOTAL THS_T 1052.197 9
DE PRD 2021 PP_GR_MC TOTAL THS_T 1808.782 9
DE PRD 2021 PP_GR_NW TOTAL THS_T 1447.551 9
DE PRD 2021 PP_GR_CO TOTAL THS_T 2514 9
DE PRD 2021 PP_HS TOTAL THS_T 1479.322 9
DE PRD 2021 PP_PK TOTAL THS_T 13343.425 9
DE PRD 2021 PP_PK_CS TOTAL THS_T 9978.423 9
DE PRD 2021 PP_PK_CB TOTAL THS_T 1830.53 9
DE PRD 2021 PP_PK_WR TOTAL THS_T 494.551 9
DE PRD 2021 PP_PK_O TOTAL THS_T 1039.921 9
DE PRD 2021 PP_O TOTAL THS_T 1481.785 9
DE PRD 2021 GLT_CLT TOTAL THS_M3 2335 9
DE PRD 2021 GLT TOTAL THS_M3 1289 9
DE PRD 2021 CLT TOTAL THS_M3 1046 9
DE PRD 2021 I_BEAMS TOTAL THS_T
DE PRD 2022 RW TOTAL THS_M3 78871.9473433676
DE PRD 2022 RW_FW TOTAL THS_M3 22337.6213683933
DE PRD 2022 RW_FW CONIF THS_M3 8833.8514800557 9
DE PRD 2022 RW_FW NCONIF THS_M3 13503.7698883376 ERROR:#REF!
DE PRD 2022 RW_IN TOTAL THS_M3 56534.3259749743 9
DE PRD 2022 RW_IN CONIF THS_M3 52424.7423181195 9
DE PRD 2022 RW_IN NCONIF THS_M3 4109.5836568548 9
DE PRD 2022 RW_IN NC_TRO THS_M3 0 9
DE PRD 2022 RW_IN_LG TOTAL THS_M3 44755.7185127789 9
DE PRD 2022 RW_IN_LG CONIF THS_M3 41760.7421681041 9
DE PRD 2022 RW_IN_LG NCONIF THS_M3 2994.9763446747 9
DE PRD 2022 RW_IN_PW TOTAL THS_M3 11644.1004621954 9
DE PRD 2022 RW_IN_PW CONIF THS_M3 10541.1073553038 9
DE PRD 2022 RW_IN_PW NCONIF THS_M3 1102.9931068916 9
DE PRD 2022 RW_IN_O TOTAL THS_M3 134.507 9
DE PRD 2022 RW_IN_O CONIF THS_M3 122.8927947115 9
DE PRD 2022 RW_IN_O NCONIF THS_M3 11.6142052885 9
DE PRD 2022 CHA TOTAL THS_T 30 9
DE PRD 2022 CHP_RES TOTAL THS_M3 16292.0836835167 9
DE PRD 2022 CHP TOTAL THS_M3 11315.4356835167 9
DE PRD 2022 RES TOTAL THS_M3 4976.648 9
DE PRD 2022 RES_SWD TOTAL THS_M3
DE PRD 2022 RCW TOTAL THS_T 8035 9
DE PRD 2022 PEL_AGG TOTAL THS_T 4015.06 9
DE PRD 2022 PEL TOTAL THS_T 3569 9
DE PRD 2022 AGG TOTAL THS_T 446.06
DE PRD 2022 SN TOTAL THS_M3 25341.5878 9
DE PRD 2022 SN CONIF THS_M3 24314.0518 9
DE PRD 2022 SN NCONIF THS_M3 1027.536 9
DE PRD 2022 SN NC_TRO THS_M3 3.577392 9
DE PRD 2022 PN_VN TOTAL THS_M3 110.049 9
DE PRD 2022 PN_VN CONIF THS_M3 14.4151806192 9
DE PRD 2022 PN_VN NCONIF THS_M3 95.6338193808 9
DE PRD 2022 PN_VN NC_TRO THS_M3 3.15315 9
DE PRD 2022 PN TOTAL THS_M3 11968.0024347826 9
DE PRD 2022 PN_PY TOTAL THS_M3 84.6 9
DE PRD 2022 PN_PY CONIF THS_M3 27.809 9
DE PRD 2022 PN_PY NCONIF THS_M3 56.791 5
DE PRD 2022 PN_PY NC_TRO THS_M3 0.1398323079 9
DE PRD 2022 PN_PY_LVL TOTAL THS_M3
DE PRD 2022 PN_PY_LVL CONIF THS_M3
DE PRD 2022 PN_PY_LVL NCONIF THS_M3
DE PRD 2022 PN_PY_LVL NC_TRO THS_M3
DE PRD 2022 PN_PB TOTAL THS_M3 6689.886 9
DE PRD 2022 PN_PB_OSB TOTAL THS_M3 1163.557 9
DE PRD 2022 PN_FB TOTAL THS_M3 5193.5164347826 9
DE PRD 2022 PN_FB_HB TOTAL THS_M3 0 9
DE PRD 2022 PN_FB_MDF TOTAL THS_M3 3791.5481787667 9
DE PRD 2022 PN_FB_O TOTAL THS_M3 1401.9682560159 9
DE PRD 2022 PL TOTAL THS_T 2171.994 9
DE PRD 2022 PL_MC_SCH TOTAL THS_T 666.676 9
DE PRD 2022 PL_CH TOTAL THS_T 1505.318 9
DE PRD 2022 PL_CH_SA TOTAL THS_T 1017.864 9
DE PRD 2022 PL_CH_SAB TOTAL THS_T 1017.864 9
DE PRD 2022 PL_CH_SI TOTAL THS_T 487.454 9
DE PRD 2022 PL_DS TOTAL THS_T 0 9
DE PRD 2022 PLO TOTAL THS_T 14280.498 9
DE PRD 2022 PLO_NW TOTAL THS_T 42.498 9
DE PRD 2022 PLO_RC TOTAL THS_T 14238 9
DE PRD 2022 RCP TOTAL THS_T 13187.683 9
DE PRD 2022 PP TOTAL THS_T 21611.516 9
DE PRD 2022 PP_GR TOTAL THS_T 6194 9
DE PRD 2022 PP_GR_NP TOTAL THS_T 938.9775146281 9
DE PRD 2022 PP_GR_MC TOTAL THS_T 1680.5382461748 9
DE PRD 2022 PP_GR_NW TOTAL THS_T 1327.0850048636 9
DE PRD 2022 PP_GR_CO TOTAL THS_T 2247.3992343335 9
DE PRD 2022 PP_HS TOTAL THS_T 1465.565 9
DE PRD 2022 PP_PK TOTAL THS_T 12535.866 9
DE PRD 2022 PP_PK_CS TOTAL THS_T 9515.774 9
DE PRD 2022 PP_PK_CB TOTAL THS_T 1545.427 9
DE PRD 2022 PP_PK_WR TOTAL THS_T 469.451 9
DE PRD 2022 PP_PK_O TOTAL THS_T 1005.214 9
DE PRD 2022 PP_O TOTAL THS_T 1416.085 9
DE PRD 2022 GLT_CLT TOTAL THS_M3 2112 9
DE PRD 2022 GLT TOTAL THS_M3 1166 9
DE PRD 2022 CLT TOTAL THS_M3 946 9
DE PRD 2022 I_BEAMS TOTAL THS_T
DE IMP 2021 RW TOTAL THS_M3 6535.529
DE IMP 2021 RW_FW TOTAL THS_M3 270.36
DE IMP 2021 RW_FW CONIF THS_M3 94.203
DE IMP 2021 RW_FW NCONIF THS_M3 176.157
DE IMP 2021 RW_IN TOTAL THS_M3 6265.169
DE IMP 2021 RW_IN CONIF THS_M3 5875.775
DE IMP 2021 RW_IN NCONIF THS_M3 389.394
DE IMP 2021 RW_IN NC_TRO THS_M3 11.603
DE IMP 2021 CHA TOTAL THS_T 147.625
DE IMP 2021 CHP_RES TOTAL THS_M3 1108.451
DE IMP 2021 CHP TOTAL THS_M3 393.139
DE IMP 2021 RES TOTAL THS_M3 715.312
DE IMP 2021 RES_SWD TOTAL THS_M3
DE IMP 2021 RCW TOTAL THS_T 935.471
DE IMP 2021 PEL_AGG TOTAL THS_T 761.709
DE IMP 2021 PEL TOTAL THS_T 403.576
DE IMP 2021 AGG TOTAL THS_T 358.133
DE IMP 2021 SN TOTAL THS_M3 5819.319
DE IMP 2021 SN CONIF THS_M3 5317.309
DE IMP 2021 SN NCONIF THS_M3 502.01
DE IMP 2021 SN NC_TRO THS_M3 74.727
DE IMP 2021 PN_VN TOTAL THS_M3 113.923
DE IMP 2021 PN_VN CONIF THS_M3 27.366
DE IMP 2021 PN_VN NCONIF THS_M3 86.557
DE IMP 2021 PN_VN NC_TRO THS_M3 9.676
DE IMP 2021 PN TOTAL THS_M3 6467.367
DE IMP 2021 PN_PY TOTAL THS_M3 1482.895
DE IMP 2021 PN_PY CONIF THS_M3 540.55
DE IMP 2021 PN_PY NCONIF THS_M3 942.345
DE IMP 2021 PN_PY NC_TRO THS_M3 134.101
DE IMP 2021 PN_PY_LVL TOTAL THS_M3
DE IMP 2021 PN_PY_LVL CONIF THS_M3
DE IMP 2021 PN_PY_LVL NCONIF THS_M3
DE IMP 2021 PN_PY_LVL NC_TRO THS_M3
DE IMP 2021 PN_PB TOTAL THS_M3 2958.745
DE IMP 2021 PN_PB_OSB TOTAL THS_M3 758.636
DE IMP 2021 PN_FB TOTAL THS_M3 2025.727
DE IMP 2021 PN_FB_HB TOTAL THS_M3 249.303
DE IMP 2021 PN_FB_MDF TOTAL THS_M3 653.581
DE IMP 2021 PN_FB_O TOTAL THS_M3 1122.843
DE IMP 2021 PL TOTAL THS_T 4534
DE IMP 2021 PL_MC_SCH TOTAL THS_T 155
DE IMP 2021 PL_CH TOTAL THS_T 3968
DE IMP 2021 PL_CH_SA TOTAL THS_T 3897
DE IMP 2021 PL_CH_SAB TOTAL THS_T 3774
DE IMP 2021 PL_CH_SI TOTAL THS_T 71
DE IMP 2021 PL_DS TOTAL THS_T 411
DE IMP 2021 PLO TOTAL THS_T 144
DE IMP 2021 PLO_NW TOTAL THS_T 15
DE IMP 2021 PLO_RC TOTAL THS_T 129
DE IMP 2021 RCP TOTAL THS_T 5639
DE IMP 2021 PP TOTAL THS_T 10114.5456
DE IMP 2021 PP_GR TOTAL THS_T 4052.769
DE IMP 2021 PP_GR_NP TOTAL THS_T 602.255
DE IMP 2021 PP_GR_MC TOTAL THS_T 441.672
DE IMP 2021 PP_GR_NW TOTAL THS_T 1114.5216
DE IMP 2021 PP_GR_CO TOTAL THS_T 1894.3204
DE IMP 2021 PP_HS TOTAL THS_T 156
DE IMP 2021 PP_PK TOTAL THS_T 5711.5758
DE IMP 2021 PP_PK_CS TOTAL THS_T 2956.1588
DE IMP 2021 PP_PK_CB TOTAL THS_T 1409.9316
DE IMP 2021 PP_PK_WR TOTAL THS_T 1045.5548
DE IMP 2021 PP_PK_O TOTAL THS_T 299.9306
DE IMP 2021 PP_O TOTAL THS_T 194.2008
DE IMP 2021 GLT_CLT TOTAL THS_M3
DE IMP 2021 GLT TOTAL THS_M3
DE IMP 2021 CLT TOTAL THS_M3
DE IMP 2021 I_BEAMS TOTAL THS_T
DE IMP 2021 RW TOTAL THS_NAC 472933
DE IMP 2021 RW_FW TOTAL THS_NAC 34851
DE IMP 2021 RW_FW CONIF THS_NAC 10711
DE IMP 2021 RW_FW NCONIF THS_NAC 24140
DE IMP 2021 RW_IN TOTAL THS_NAC 438082
DE IMP 2021 RW_IN CONIF THS_NAC 387080
DE IMP 2021 RW_IN NCONIF THS_NAC 51002
DE IMP 2021 RW_IN NC_TRO THS_NAC 6185
DE IMP 2021 CHA TOTAL THS_NAC 82055
DE IMP 2021 CHP_RES TOTAL THS_NAC 38208
DE IMP 2021 CHP TOTAL THS_NAC 18090
DE IMP 2021 RES TOTAL THS_NAC 20118
DE IMP 2021 RES_SWD TOTAL THS_NAC
DE IMP 2021 RCW TOTAL THS_NAC 43453
DE IMP 2021 PEL_AGG TOTAL THS_NAC 112165
DE IMP 2021 PEL TOTAL THS_NAC 65599
DE IMP 2021 AGG TOTAL THS_NAC 46566
DE IMP 2021 SN TOTAL THS_NAC 1939756
DE IMP 2021 SN CONIF THS_NAC 1646970
DE IMP 2021 SN NCONIF THS_NAC 292786
DE IMP 2021 SN NC_TRO THS_NAC 63910
DE IMP 2021 PN_VN TOTAL THS_NAC 172647
DE IMP 2021 PN_VN CONIF THS_NAC 20761
DE IMP 2021 PN_VN NCONIF THS_NAC 151886
DE IMP 2021 PN_VN NC_TRO THS_NAC 11872
DE IMP 2021 PN TOTAL THS_NAC 2420877
DE IMP 2021 PN_PY TOTAL THS_NAC 942995
DE IMP 2021 PN_PY CONIF THS_NAC 290796
DE IMP 2021 PN_PY NCONIF THS_NAC 652199
DE IMP 2021 PN_PY NC_TRO THS_NAC 103857
DE IMP 2021 PN_PY_LVL TOTAL THS_NAC
DE IMP 2021 PN_PY_LVL CONIF THS_NAC
DE IMP 2021 PN_PY_LVL NCONIF THS_NAC
DE IMP 2021 PN_PY_LVL NC_TRO THS_NAC
DE IMP 2021 PN_PB TOTAL THS_NAC 910456
DE IMP 2021 PN_PB_OSB TOTAL THS_NAC 287924
DE IMP 2021 PN_FB TOTAL THS_NAC 567426
DE IMP 2021 PN_FB_HB TOTAL THS_NAC 102503
DE IMP 2021 PN_FB_MDF TOTAL THS_NAC 326419
DE IMP 2021 PN_FB_O TOTAL THS_NAC 138504
DE IMP 2021 PL TOTAL THS_NAC 2690191
DE IMP 2021 PL_MC_SCH TOTAL THS_NAC 89579
DE IMP 2021 PL_CH TOTAL THS_NAC 2259470
DE IMP 2021 PL_CH_SA TOTAL THS_NAC 2182569
DE IMP 2021 PL_CH_SAB TOTAL THS_NAC 2115094
DE IMP 2021 PL_CH_SI TOTAL THS_NAC 76901
DE IMP 2021 PL_DS TOTAL THS_NAC 341142
DE IMP 2021 PLO TOTAL THS_NAC 41001
DE IMP 2021 PLO_NW TOTAL THS_NAC 20522
DE IMP 2021 PLO_RC TOTAL THS_NAC 20479
DE IMP 2021 RCP TOTAL THS_NAC 1045180
DE IMP 2021 PP TOTAL THS_NAC 7559121.20770379
DE IMP 2021 PP_GR TOTAL THS_NAC 2696754
DE IMP 2021 PP_GR_NP TOTAL THS_NAC 259492
DE IMP 2021 PP_GR_MC TOTAL THS_NAC 283840
DE IMP 2021 PP_GR_NW TOTAL THS_NAC 914800
DE IMP 2021 PP_GR_CO TOTAL THS_NAC 1238622
DE IMP 2021 PP_HS TOTAL THS_NAC 229085.20770379
DE IMP 2021 PP_PK TOTAL THS_NAC 4128534
DE IMP 2021 PP_PK_CS TOTAL THS_NAC 1404557
DE IMP 2021 PP_PK_CB TOTAL THS_NAC 1463445
DE IMP 2021 PP_PK_WR TOTAL THS_NAC 989508
DE IMP 2021 PP_PK_O TOTAL THS_NAC 271024
DE IMP 2021 PP_O TOTAL THS_NAC 504748
DE IMP 2021 GLT_CLT TOTAL THS_NAC
DE IMP 2021 GLT TOTAL THS_NAC
DE IMP 2021 CLT TOTAL THS_NAC
DE IMP 2021 I_BEAMS TOTAL THS_NAC
DE IMP 2022 RW TOTAL THS_M3 5860.231
DE IMP 2022 RW_FW TOTAL THS_M3 283.935
DE IMP 2022 RW_FW CONIF THS_M3 72.879
DE IMP 2022 RW_FW NCONIF THS_M3 211.056
DE IMP 2022 RW_IN TOTAL THS_M3 5576.296
DE IMP 2022 RW_IN CONIF THS_M3 5188.185
DE IMP 2022 RW_IN NCONIF THS_M3 388.111
DE IMP 2022 RW_IN NC_TRO THS_M3 15.807
DE IMP 2022 CHA TOTAL THS_T 133.617
DE IMP 2022 CHP_RES TOTAL THS_M3 2697.993
DE IMP 2022 CHP TOTAL THS_M3 639.237
DE IMP 2022 RES TOTAL THS_M3 1148.2440920962
DE IMP 2022 RES_SWD TOTAL THS_M3 692.987
DE IMP 2022 RCW TOTAL THS_T 569.0703270105
DE IMP 2022 PEL_AGG TOTAL THS_T 813.664
DE IMP 2022 PEL TOTAL THS_T 477.216
DE IMP 2022 AGG TOTAL THS_T 336.448
DE IMP 2022 SN TOTAL THS_M3 4182.693
DE IMP 2022 SN CONIF THS_M3 3762.574
DE IMP 2022 SN NCONIF THS_M3 420.119
DE IMP 2022 SN NC_TRO THS_M3 79.464
DE IMP 2022 PN_VN TOTAL THS_M3 98.768
DE IMP 2022 PN_VN CONIF THS_M3 20.168
DE IMP 2022 PN_VN NCONIF THS_M3 78.6
DE IMP 2022 PN_VN NC_TRO THS_M3 8.251
DE IMP 2022 PN TOTAL THS_M3 5557.796
DE IMP 2022 PN_PY TOTAL THS_M3 1318.706
DE IMP 2022 PN_PY CONIF THS_M3 495.374
DE IMP 2022 PN_PY NCONIF THS_M3 823.332
DE IMP 2022 PN_PY NC_TRO THS_M3 156.009
DE IMP 2022 PN_PY_LVL TOTAL THS_M3 39.438
DE IMP 2022 PN_PY_LVL CONIF THS_M3 14.935
DE IMP 2022 PN_PY_LVL NCONIF THS_M3 24.503
DE IMP 2022 PN_PY_LVL NC_TRO THS_M3 13.677
DE IMP 2022 PN_PB TOTAL THS_M3 2649.168
DE IMP 2022 PN_PB_OSB TOTAL THS_M3 679
DE IMP 2022 PN_FB TOTAL THS_M3 1589.922
DE IMP 2022 PN_FB_HB TOTAL THS_M3 199.623
DE IMP 2022 PN_FB_MDF TOTAL THS_M3 424.18
DE IMP 2022 PN_FB_O TOTAL THS_M3 966.119
DE IMP 2022 PL TOTAL THS_T 4173
DE IMP 2022 PL_MC_SCH TOTAL THS_T 221
DE IMP 2022 PL_CH TOTAL THS_T 3551
DE IMP 2022 PL_CH_SA TOTAL THS_T 3484
DE IMP 2022 PL_CH_SAB TOTAL THS_T 1102
DE IMP 2022 PL_CH_SI TOTAL THS_T 67
DE IMP 2022 PL_DS TOTAL THS_T 401
DE IMP 2022 PLO TOTAL THS_T 143
DE IMP 2022 PLO_NW TOTAL THS_T 19
DE IMP 2022 PLO_RC TOTAL THS_T 124
DE IMP 2022 RCP TOTAL THS_T 5462
DE IMP 2022 PP TOTAL THS_T 9302.0003
DE IMP 2022 PP_GR TOTAL THS_T 3585
DE IMP 2022 PP_GR_NP TOTAL THS_T 609.4009361047
DE IMP 2022 PP_GR_MC TOTAL THS_T 423.1855219339
DE IMP 2022 PP_GR_NW TOTAL THS_T 1055.2532367415
DE IMP 2022 PP_GR_CO TOTAL THS_T 1497.16030522
DE IMP 2022 PP_HS TOTAL THS_T 200
DE IMP 2022 PP_PK TOTAL THS_T 5346
DE IMP 2022 PP_PK_CS TOTAL THS_T 2618.5652710113
DE IMP 2022 PP_PK_CB TOTAL THS_T 1458.638765648
DE IMP 2022 PP_PK_WR TOTAL THS_T 1020.4460460514
DE IMP 2022 PP_PK_O TOTAL THS_T 248.3499172893
DE IMP 2022 PP_O TOTAL THS_T 171.0003
DE IMP 2022 GLT_CLT TOTAL THS_M3 384.275
DE IMP 2022 GLT TOTAL THS_M3 312.556
DE IMP 2022 CLT TOTAL THS_M3 71.719
DE IMP 2022 I_BEAMS TOTAL THS_T 5.507
DE IMP 2022 RW TOTAL THS_NAC 604100
DE IMP 2022 RW_FW TOTAL THS_NAC 61242
DE IMP 2022 RW_FW CONIF THS_NAC 13630
DE IMP 2022 RW_FW NCONIF THS_NAC 47612
DE IMP 2022 RW_IN TOTAL THS_NAC 542858
DE IMP 2022 RW_IN CONIF THS_NAC 470624
DE IMP 2022 RW_IN NCONIF THS_NAC 72234
DE IMP 2022 RW_IN NC_TRO THS_NAC 9447
DE IMP 2022 CHA TOTAL THS_NAC 87395
DE IMP 2022 CHP_RES TOTAL THS_NAC 128166
DE IMP 2022 CHP TOTAL THS_NAC 34068
DE IMP 2022 RES TOTAL THS_NAC 55994
DE IMP 2022 RES_SWD TOTAL THS_NAC 36942
DE IMP 2022 RCW TOTAL THS_NAC 38104
DE IMP 2022 PEL_AGG TOTAL THS_NAC 213052
DE IMP 2022 PEL TOTAL THS_NAC 128199
DE IMP 2022 AGG TOTAL THS_NAC 84853
DE IMP 2022 SN TOTAL THS_NAC 1598111
DE IMP 2022 SN CONIF THS_NAC 1277924
DE IMP 2022 SN NCONIF THS_NAC 320187
DE IMP 2022 SN NC_TRO THS_NAC 67741
DE IMP 2022 PN_VN TOTAL THS_NAC 206531
DE IMP 2022 PN_VN CONIF THS_NAC 20131
DE IMP 2022 PN_VN NCONIF THS_NAC 186400
DE IMP 2022 PN_VN NC_TRO THS_NAC 11631
DE IMP 2022 PN TOTAL THS_NAC 2661839
DE IMP 2022 PN_PY TOTAL THS_NAC 1096848
DE IMP 2022 PN_PY CONIF THS_NAC 304255
DE IMP 2022 PN_PY NCONIF THS_NAC 792593
DE IMP 2022 PN_PY NC_TRO THS_NAC 154982
DE IMP 2022 PN_PY_LVL TOTAL THS_NAC 34670
DE IMP 2022 PN_PY_LVL CONIF THS_NAC 10917
DE IMP 2022 PN_PY_LVL NCONIF THS_NAC 23753
DE IMP 2022 PN_PY_LVL NC_TRO THS_NAC 12534
DE IMP 2022 PN_PB TOTAL THS_NAC 990789
DE IMP 2022 PN_PB_OSB TOTAL THS_NAC 264062
DE IMP 2022 PN_FB TOTAL THS_NAC 574202
DE IMP 2022 PN_FB_HB TOTAL THS_NAC 122521
DE IMP 2022 PN_FB_MDF TOTAL THS_NAC 300959
DE IMP 2022 PN_FB_O TOTAL THS_NAC 150722
DE IMP 2022 PL TOTAL THS_NAC 3311965.58409509
DE IMP 2022 PL_MC_SCH TOTAL THS_NAC 168137.584095093
DE IMP 2022 PL_CH TOTAL THS_NAC 2763368
DE IMP 2022 PL_CH_SA TOTAL THS_NAC 2676476
DE IMP 2022 PL_CH_SAB TOTAL THS_NAC 906587
DE IMP 2022 PL_CH_SI TOTAL THS_NAC 86892
DE IMP 2022 PL_DS TOTAL THS_NAC 380460
DE IMP 2022 PLO TOTAL THS_NAC 63772
DE IMP 2022 PLO_NW TOTAL THS_NAC 37763
DE IMP 2022 PLO_RC TOTAL THS_NAC 26009
DE IMP 2022 RCP TOTAL THS_NAC 1235544.01257609
DE IMP 2022 PP TOTAL THS_NAC 9860429.08485834
DE IMP 2022 PP_GR TOTAL THS_NAC 3817186.12686602
DE IMP 2022 PP_GR_NP TOTAL THS_NAC 490055.542015218
DE IMP 2022 PP_GR_MC TOTAL THS_NAC 387227.759170923
DE IMP 2022 PP_GR_NW TOTAL THS_NAC 1284914.99140338
DE IMP 2022 PP_GR_CO TOTAL THS_NAC 1654987.8342765
DE IMP 2022 PP_HS TOTAL THS_NAC 401692.569499558
DE IMP 2022 PP_PK TOTAL THS_NAC 5119976.05030058
DE IMP 2022 PP_PK_CS TOTAL THS_NAC 1743655.54349096
DE IMP 2022 PP_PK_CB TOTAL THS_NAC 1733506.18993731
DE IMP 2022 PP_PK_WR TOTAL THS_NAC 1330789.98586266
DE IMP 2022 PP_PK_O TOTAL THS_NAC 312024.331009649
DE IMP 2022 PP_O TOTAL THS_NAC 521574.338192179
DE IMP 2022 GLT_CLT TOTAL THS_NAC 264416
DE IMP 2022 GLT TOTAL THS_NAC 217920
DE IMP 2022 CLT TOTAL THS_NAC 46496
DE IMP 2022 I_BEAMS TOTAL THS_NAC 7221
DE EXP 2021 RW TOTAL THS_M3 12157.684
DE EXP 2021 RW_FW TOTAL THS_M3 204.486
DE EXP 2021 RW_FW CONIF THS_M3 122.212
DE EXP 2021 RW_FW NCONIF THS_M3 82.274
DE EXP 2021 RW_IN TOTAL THS_M3 11953.198
DE EXP 2021 RW_IN CONIF THS_M3 10927.383
DE EXP 2021 RW_IN NCONIF THS_M3 1025.815
DE EXP 2021 RW_IN NC_TRO THS_M3 4.999
DE EXP 2021 CHA TOTAL THS_T 29.97
DE EXP 2021 CHP_RES TOTAL THS_M3 2550.63
DE EXP 2021 CHP TOTAL THS_M3 1616.939
DE EXP 2021 RES TOTAL THS_M3 933.691
DE EXP 2021 RES_SWD TOTAL THS_M3
DE EXP 2021 RCW TOTAL THS_T 684.257
DE EXP 2021 PEL_AGG TOTAL THS_T 915.091
DE EXP 2021 PEL TOTAL THS_T 817.257
DE EXP 2021 AGG TOTAL THS_T 97.834
DE EXP 2021 SN TOTAL THS_M3 11333.809
DE EXP 2021 SN CONIF THS_M3 10552.063
DE EXP 2021 SN NCONIF THS_M3 781.746
DE EXP 2021 SN NC_TRO THS_M3 37.601
DE EXP 2021 PN_VN TOTAL THS_M3 62.036
DE EXP 2021 PN_VN CONIF THS_M3 0.604
DE EXP 2021 PN_VN NCONIF THS_M3 61.432
DE EXP 2021 PN_VN NC_TRO THS_M3 2.163
DE EXP 2021 PN TOTAL THS_M3 6786.394
DE EXP 2021 PN_PY TOTAL THS_M3 387.868
DE EXP 2021 PN_PY CONIF THS_M3 168.271
DE EXP 2021 PN_PY NCONIF THS_M3 219.597
DE EXP 2021 PN_PY NC_TRO THS_M3 38.255
DE EXP 2021 PN_PY_LVL TOTAL THS_M3
DE EXP 2021 PN_PY_LVL CONIF THS_M3
DE EXP 2021 PN_PY_LVL NCONIF THS_M3
DE EXP 2021 PN_PY_LVL NC_TRO THS_M3
DE EXP 2021 PN_PB TOTAL THS_M3 2744.779
DE EXP 2021 PN_PB_OSB TOTAL THS_M3 554.892
DE EXP 2021 PN_FB TOTAL THS_M3 3653.747
DE EXP 2021 PN_FB_HB TOTAL THS_M3 30.486238226
DE EXP 2021 PN_FB_MDF TOTAL THS_M3 2936.088761774
DE EXP 2021 PN_FB_O TOTAL THS_M3 687.172
DE EXP 2021 PL TOTAL THS_T 1177
DE EXP 2021 PL_MC_SCH TOTAL THS_T 97
DE EXP 2021 PL_CH TOTAL THS_T 1070
DE EXP 2021 PL_CH_SA TOTAL THS_T 959
DE EXP 2021 PL_CH_SAB TOTAL THS_T 948
DE EXP 2021 PL_CH_SI TOTAL THS_T 111
DE EXP 2021 PL_DS TOTAL THS_T 10
DE EXP 2021 PLO TOTAL THS_T 126
DE EXP 2021 PLO_NW TOTAL THS_T 5
DE EXP 2021 PLO_RC TOTAL THS_T 121
DE EXP 2021 RCP TOTAL THS_T 1829
DE EXP 2021 PP TOTAL THS_T 14165.9832
DE EXP 2021 PP_GR TOTAL THS_T 5007.0716
DE EXP 2021 PP_GR_NP TOTAL THS_T 523.79
DE EXP 2021 PP_GR_MC TOTAL THS_T 816.9402
DE EXP 2021 PP_GR_NW TOTAL THS_T 899.1536
DE EXP 2021 PP_GR_CO TOTAL THS_T 2767.1878
DE EXP 2021 PP_HS TOTAL THS_T 147
DE EXP 2021 PP_PK TOTAL THS_T 8679.2344
DE EXP 2021 PP_PK_CS TOTAL THS_T 5057.9108
DE EXP 2021 PP_PK_CB TOTAL THS_T 2260.8974
DE EXP 2021 PP_PK_WR TOTAL THS_T 1040.8295
DE EXP 2021 PP_PK_O TOTAL THS_T 319.5967
DE EXP 2021 PP_O TOTAL THS_T 332.6772
DE EXP 2021 GLT_CLT TOTAL THS_M3
DE EXP 2021 GLT TOTAL THS_M3
DE EXP 2021 CLT TOTAL THS_M3
DE EXP 2021 I_BEAMS TOTAL THS_T
DE EXP 2021 RW TOTAL THS_NAC 1013808
DE EXP 2021 RW_FW TOTAL THS_NAC 12021
DE EXP 2021 RW_FW CONIF THS_NAC 6602
DE EXP 2021 RW_FW NCONIF THS_NAC 5419
DE EXP 2021 RW_IN TOTAL THS_NAC 1001787
DE EXP 2021 RW_IN CONIF THS_NAC 873711
DE EXP 2021 RW_IN NCONIF THS_NAC 128076
DE EXP 2021 RW_IN NC_TRO THS_NAC 2848
DE EXP 2021 CHA TOTAL THS_NAC 30385
DE EXP 2021 CHP_RES TOTAL THS_NAC 125163
DE EXP 2021 CHP TOTAL THS_NAC 78007
DE EXP 2021 RES TOTAL THS_NAC 47156
DE EXP 2021 RES_SWD TOTAL THS_NAC
DE EXP 2021 RCW TOTAL THS_NAC 45844
DE EXP 2021 PEL_AGG TOTAL THS_NAC 168097
DE EXP 2021 PEL TOTAL THS_NAC 149091
DE EXP 2021 AGG TOTAL THS_NAC 19006
DE EXP 2021 SN TOTAL THS_NAC 3712759
DE EXP 2021 SN CONIF THS_NAC 3277684
DE EXP 2021 SN NCONIF THS_NAC 435075
DE EXP 2021 SN NC_TRO THS_NAC 55775
DE EXP 2021 PN_VN TOTAL THS_NAC 151921
DE EXP 2021 PN_VN CONIF THS_NAC 2799
DE EXP 2021 PN_VN NCONIF THS_NAC 149122
DE EXP 2021 PN_VN NC_TRO THS_NAC 10388
DE EXP 2021 PN TOTAL THS_NAC 2968511
DE EXP 2021 PN_PY TOTAL THS_NAC 326202
DE EXP 2021 PN_PY CONIF THS_NAC 114471.5
DE EXP 2021 PN_PY NCONIF THS_NAC 211730.5
DE EXP 2021 PN_PY NC_TRO THS_NAC 46070
DE EXP 2021 PN_PY_LVL TOTAL THS_NAC
DE EXP 2021 PN_PY_LVL CONIF THS_NAC
DE EXP 2021 PN_PY_LVL NCONIF THS_NAC
DE EXP 2021 PN_PY_LVL NC_TRO THS_NAC
DE EXP 2021 PN_PB TOTAL THS_NAC 783764
DE EXP 2021 PN_PB_OSB TOTAL THS_NAC 196022
DE EXP 2021 PN_FB TOTAL THS_NAC 1858545
DE EXP 2021 PN_FB_HB TOTAL THS_NAC 21088.9850168194
DE EXP 2021 PN_FB_MDF TOTAL THS_NAC 1772071.01498318
DE EXP 2021 PN_FB_O TOTAL THS_NAC 65385
DE EXP 2021 PL TOTAL THS_NAC 757124
DE EXP 2021 PL_MC_SCH TOTAL THS_NAC 46724
DE EXP 2021 PL_CH TOTAL THS_NAC 700916
DE EXP 2021 PL_CH_SA TOTAL THS_NAC 575034
DE EXP 2021 PL_CH_SAB TOTAL THS_NAC 567595
DE EXP 2021 PL_CH_SI TOTAL THS_NAC 125882
DE EXP 2021 PL_DS TOTAL THS_NAC 9484
DE EXP 2021 PLO TOTAL THS_NAC 67000
DE EXP 2021 PLO_NW TOTAL THS_NAC 7178
DE EXP 2021 PLO_RC TOTAL THS_NAC 59822
DE EXP 2021 RCP TOTAL THS_NAC 306254
DE EXP 2021 PP TOTAL THS_NAC 11358944.7739666
DE EXP 2021 PP_GR TOTAL THS_NAC 3740936
DE EXP 2021 PP_GR_NP TOTAL THS_NAC 219742
DE EXP 2021 PP_GR_MC TOTAL THS_NAC 401590
DE EXP 2021 PP_GR_NW TOTAL THS_NAC 987776
DE EXP 2021 PP_GR_CO TOTAL THS_NAC 2131828
DE EXP 2021 PP_HS TOTAL THS_NAC 266992.773966585
DE EXP 2021 PP_PK TOTAL THS_NAC 6490616
DE EXP 2021 PP_PK_CS TOTAL THS_NAC 2527252
DE EXP 2021 PP_PK_CB TOTAL THS_NAC 2560279
DE EXP 2021 PP_PK_WR TOTAL THS_NAC 1128331
DE EXP 2021 PP_PK_O TOTAL THS_NAC 274754
DE EXP 2021 PP_O TOTAL THS_NAC 860400
DE EXP 2021 GLT_CLT TOTAL THS_NAC
DE EXP 2021 GLT TOTAL THS_NAC
DE EXP 2021 CLT TOTAL THS_NAC
DE EXP 2021 I_BEAMS TOTAL THS_NAC
DE EXP 2022 RW TOTAL THS_M3 10096.015
DE EXP 2022 RW_FW TOTAL THS_M3 246.758
DE EXP 2022 RW_FW CONIF THS_M3 180.404
DE EXP 2022 RW_FW NCONIF THS_M3 66.354
DE EXP 2022 RW_IN TOTAL THS_M3 9849.257
DE EXP 2022 RW_IN CONIF THS_M3 8978.226
DE EXP 2022 RW_IN NCONIF THS_M3 871.031
DE EXP 2022 RW_IN NC_TRO THS_M3 4.341
DE EXP 2022 CHA TOTAL THS_T 29.66
DE EXP 2022 CHP_RES TOTAL THS_M3 3570.563
DE EXP 2022 CHP TOTAL THS_M3 2102.693
DE EXP 2022 RES TOTAL THS_M3 945.7166801816
DE EXP 2022 RES_SWD TOTAL THS_M3 684.637
DE EXP 2022 RCW TOTAL THS_T 326.3464707857
DE EXP 2022 PEL_AGG TOTAL THS_T 882.391
DE EXP 2022 PEL TOTAL THS_T 683.443
DE EXP 2022 AGG TOTAL THS_T 198.948
DE EXP 2022 SN TOTAL THS_M3 11502.466
DE EXP 2022 SN CONIF THS_M3 10781.401
DE EXP 2022 SN NCONIF THS_M3 721.065
DE EXP 2022 SN NC_TRO THS_M3 50.317
DE EXP 2022 PN_VN TOTAL THS_M3 51.889
DE EXP 2022 PN_VN CONIF THS_M3 0.857
DE EXP 2022 PN_VN NCONIF THS_M3 51.032
DE EXP 2022 PN_VN NC_TRO THS_M3 2.098
DE EXP 2022 PN TOTAL THS_M3 5772.913
DE EXP 2022 PN_PY TOTAL THS_M3 330.131
DE EXP 2022 PN_PY CONIF THS_M3 153.231
DE EXP 2022 PN_PY NCONIF THS_M3 176.9
DE EXP 2022 PN_PY NC_TRO THS_M3 59.74
DE EXP 2022 PN_PY_LVL TOTAL THS_M3 44.193
DE EXP 2022 PN_PY_LVL CONIF THS_M3 36.953
DE EXP 2022 PN_PY_LVL NCONIF THS_M3 7.24
DE EXP 2022 PN_PY_LVL NC_TRO THS_M3 0.637
DE EXP 2022 PN_PB TOTAL THS_M3 2450.242
DE EXP 2022 PN_PB_OSB TOTAL THS_M3 526.214
DE EXP 2022 PN_FB TOTAL THS_M3 2992.54
DE EXP 2022 PN_FB_HB TOTAL THS_M3 23.4808874946
DE EXP 2022 PN_FB_MDF TOTAL THS_M3 2345.4891125054
DE EXP 2022 PN_FB_O TOTAL THS_M3 623.57
DE EXP 2022 PL TOTAL THS_T 1253
DE EXP 2022 PL_MC_SCH TOTAL THS_T 139
DE EXP 2022 PL_CH TOTAL THS_T 1092
DE EXP 2022 PL_CH_SA TOTAL THS_T 987
DE EXP 2022 PL_CH_SAB TOTAL THS_T 977
DE EXP 2022 PL_CH_SI TOTAL THS_T 105
DE EXP 2022 PL_DS TOTAL THS_T 22
DE EXP 2022 PLO TOTAL THS_T 85
DE EXP 2022 PLO_NW TOTAL THS_T 4
DE EXP 2022 PLO_RC TOTAL THS_T 81
DE EXP 2022 RCP TOTAL THS_T 1612
DE EXP 2022 PP TOTAL THS_T 13078.00046986
DE EXP 2022 PP_GR TOTAL THS_T 4466.77642476
DE EXP 2022 PP_GR_NP TOTAL THS_T 525.01902804
DE EXP 2022 PP_GR_MC TOTAL THS_T 763.44257028
DE EXP 2022 PP_GR_NW TOTAL THS_T 831.56699808
DE EXP 2022 PP_GR_CO TOTAL THS_T 2346.74782836
DE EXP 2022 PP_HS TOTAL THS_T 106
DE EXP 2022 PP_PK TOTAL THS_T 8189.0494929
DE EXP 2022 PP_PK_CS TOTAL THS_T 4770.1823598
DE EXP 2022 PP_PK_CB TOTAL THS_T 2039.83895856
DE EXP 2022 PP_PK_WR TOTAL THS_T 1097.65628622
DE EXP 2022 PP_PK_O TOTAL THS_T 281.37188832
DE EXP 2022 PP_O TOTAL THS_T 316.1745522
DE EXP 2022 GLT_CLT TOTAL THS_M3 492.412
DE EXP 2022 GLT TOTAL THS_M3 437.924
DE EXP 2022 CLT TOTAL THS_M3 54.488
DE EXP 2022 I_BEAMS TOTAL THS_T 0.492
DE EXP 2022 RW TOTAL THS_NAC 1061988
DE EXP 2022 RW_FW TOTAL THS_NAC 19346
DE EXP 2022 RW_FW CONIF THS_NAC 13480
DE EXP 2022 RW_FW NCONIF THS_NAC 5866
DE EXP 2022 RW_IN TOTAL THS_NAC 1042642
DE EXP 2022 RW_IN CONIF THS_NAC 911744
DE EXP 2022 RW_IN NCONIF THS_NAC 130898
DE EXP 2022 RW_IN NC_TRO THS_NAC 2504
DE EXP 2022 CHA TOTAL THS_NAC 29497
DE EXP 2022 CHP_RES TOTAL THS_NAC 236929
DE EXP 2022 CHP TOTAL THS_NAC 123271
DE EXP 2022 RES TOTAL THS_NAC 79785.3333333333
DE EXP 2022 RES_SWD TOTAL THS_NAC 62849
DE EXP 2022 RCW TOTAL THS_NAC 33872.6666666667
DE EXP 2022 PEL_AGG TOTAL THS_NAC 278546
DE EXP 2022 PEL TOTAL THS_NAC 244945
DE EXP 2022 AGG TOTAL THS_NAC 33601
DE EXP 2022 SN TOTAL THS_NAC 3984881
DE EXP 2022 SN CONIF THS_NAC 3526187
DE EXP 2022 SN NCONIF THS_NAC 458694
DE EXP 2022 SN NC_TRO THS_NAC 42635
DE EXP 2022 PN_VN TOTAL THS_NAC 155726
DE EXP 2022 PN_VN CONIF THS_NAC 3690
DE EXP 2022 PN_VN NCONIF THS_NAC 152036
DE EXP 2022 PN_VN NC_TRO THS_NAC 9772
DE EXP 2022 PN TOTAL THS_NAC 3100216
DE EXP 2022 PN_PY TOTAL THS_NAC 344032
DE EXP 2022 PN_PY CONIF THS_NAC 125214
DE EXP 2022 PN_PY NCONIF THS_NAC 218818
DE EXP 2022 PN_PY NC_TRO THS_NAC 77017
DE EXP 2022 PN_PY_LVL TOTAL THS_NAC 47780
DE EXP 2022 PN_PY_LVL CONIF THS_NAC 39742
DE EXP 2022 PN_PY_LVL NCONIF THS_NAC 8038
DE EXP 2022 PN_PY_LVL NC_TRO THS_NAC 773
DE EXP 2022 PN_PB TOTAL THS_NAC 878679
DE EXP 2022 PN_PB_OSB TOTAL THS_NAC 205773
DE EXP 2022 PN_FB TOTAL THS_NAC 1877505
DE EXP 2022 PN_FB_HB TOTAL THS_NAC 20948.3076667116
DE EXP 2022 PN_FB_MDF TOTAL THS_NAC 1784724.69233329
DE EXP 2022 PN_FB_O TOTAL THS_NAC 71832
DE EXP 2022 PL TOTAL THS_NAC 1047942.22456672
DE EXP 2022 PL_MC_SCH TOTAL THS_NAC 99223.2245667196
DE EXP 2022 PL_CH TOTAL THS_NAC 922551
DE EXP 2022 PL_CH_SA TOTAL THS_NAC 778688
DE EXP 2022 PL_CH_SAB TOTAL THS_NAC 770202
DE EXP 2022 PL_CH_SI TOTAL THS_NAC 143863
DE EXP 2022 PL_DS TOTAL THS_NAC 26168
DE EXP 2022 PLO TOTAL THS_NAC 58740
DE EXP 2022 PLO_NW TOTAL THS_NAC 8012
DE EXP 2022 PLO_RC TOTAL THS_NAC 50728
DE EXP 2022 RCP TOTAL THS_NAC 325414.212934182
DE EXP 2022 PP TOTAL THS_NAC 14518949.073494
DE EXP 2022 PP_GR TOTAL THS_NAC 5206671.7660141
DE EXP 2022 PP_GR_NP TOTAL THS_NAC 433172.695540005
DE EXP 2022 PP_GR_MC TOTAL THS_NAC 656298.206249189
DE EXP 2022 PP_GR_NW TOTAL THS_NAC 1288419.03278504
DE EXP 2022 PP_GR_CO TOTAL THS_NAC 2828781.83143987
DE EXP 2022 PP_HS TOTAL THS_NAC 257863.790165322
DE EXP 2022 PP_PK TOTAL THS_NAC 8080788.18591075
DE EXP 2022 PP_PK_CS TOTAL THS_NAC 3212675.64707739
DE EXP 2022 PP_PK_CB TOTAL THS_NAC 3036746.3066195
DE EXP 2022 PP_PK_WR TOTAL THS_NAC 1489013.38067095
DE EXP 2022 PP_PK_O TOTAL THS_NAC 342352.851542911
DE EXP 2022 PP_O TOTAL THS_NAC 973625.331403847
DE EXP 2022 GLT_CLT TOTAL THS_NAC 350350
DE EXP 2022 GLT TOTAL THS_NAC 305849
DE EXP 2022 CLT TOTAL THS_NAC 44501
DE EXP 2022 I_BEAMS TOTAL THS_NAC 589
DE IMP_XEU 2021 RW TOTAL THS_M3 848.623
DE IMP_XEU 2021 RW_FW TOTAL THS_M3 130.017
DE IMP_XEU 2021 RW_FW CONIF THS_M3 13.866
DE IMP_XEU 2021 RW_FW NCONIF THS_M3 116.151
DE IMP_XEU 2021 RW_IN TOTAL THS_M3 718.606
DE IMP_XEU 2021 RW_IN CONIF THS_M3 664.741
DE IMP_XEU 2021 RW_IN NCONIF THS_M3 53.865
DE IMP_XEU 2021 RW_IN NC_TRO THS_M3 11.603
DE IMP_XEU 2021 CHA TOTAL THS_T 80.39
DE IMP_XEU 2021 CHP_RES TOTAL THS_M3 42.005
DE IMP_XEU 2021 CHP TOTAL THS_M3 3.949
DE IMP_XEU 2021 RES TOTAL THS_M3 38.056
DE IMP_XEU 2021 RES_SWD TOTAL THS_M3
DE IMP_XEU 2021 RCW TOTAL THS_T 104.289
DE IMP_XEU 2021 PEL_AGG TOTAL THS_T 231.56
DE IMP_XEU 2021 PEL TOTAL THS_T 92.351
DE IMP_XEU 2021 AGG TOTAL THS_T 139.209
DE IMP_XEU 2021 SN TOTAL THS_M3 1793.875
DE IMP_XEU 2021 SN CONIF THS_M3 1545.157
DE IMP_XEU 2021 SN NCONIF THS_M3 248.718
DE IMP_XEU 2021 SN NC_TRO THS_M3 61.316
DE IMP_XEU 2021 PN_VN TOTAL THS_M3 37.752
DE IMP_XEU 2021 PN_VN CONIF THS_M3 4.099
DE IMP_XEU 2021 PN_VN NCONIF THS_M3 33.653
DE IMP_XEU 2021 PN_VN NC_TRO THS_M3 7.287
DE IMP_XEU 2021 PN TOTAL THS_M3 1017.425
DE IMP_XEU 2021 PN_PY TOTAL THS_M3 626.524
DE IMP_XEU 2021 PN_PY CONIF THS_M3 224.841
DE IMP_XEU 2021 PN_PY NCONIF THS_M3 401.683
DE IMP_XEU 2021 PN_PY NC_TRO THS_M3 38.301
DE IMP_XEU 2021 PN_PY_LVL TOTAL THS_M3
DE IMP_XEU 2021 PN_PY_LVL CONIF THS_M3
DE IMP_XEU 2021 PN_PY_LVL NCONIF THS_M3
DE IMP_XEU 2021 PN_PY_LVL NC_TRO THS_M3
DE IMP_XEU 2021 PN_PB TOTAL THS_M3 227.456
DE IMP_XEU 2021 PN_PB_OSB TOTAL THS_M3 14.471
DE IMP_XEU 2021 PN_FB TOTAL THS_M3 163.445
DE IMP_XEU 2021 PN_FB_HB TOTAL THS_M3 26.407
DE IMP_XEU 2021 PN_FB_MDF TOTAL THS_M3 125.204
DE IMP_XEU 2021 PN_FB_O TOTAL THS_M3 11.834
DE IMP_XEU 2021 PL TOTAL THS_T 1882.4499392847
DE IMP_XEU 2021 PL_MC_SCH TOTAL THS_T 26.4653146457
DE IMP_XEU 2021 PL_CH TOTAL THS_T 1740.9015023295
DE IMP_XEU 2021 PL_CH_SA TOTAL THS_T 1711.4532882688
DE IMP_XEU 2021 PL_CH_SAB TOTAL THS_T 1678.3972419737
DE IMP_XEU 2021 PL_CH_SI TOTAL THS_T 29.4482140607
DE IMP_XEU 2021 PL_DS TOTAL THS_T 115.0831223094
DE IMP_XEU 2021 PLO TOTAL THS_T 105.8402127723
DE IMP_XEU 2021 PLO_NW TOTAL THS_T 8.0353773965
DE IMP_XEU 2021 PLO_RC TOTAL THS_T 97.8048353758
DE IMP_XEU 2021 RCP TOTAL THS_T 427.8960019829
DE IMP_XEU 2021 PP TOTAL THS_T 1201.793
DE IMP_XEU 2021 PP_GR TOTAL THS_T 490.2496145377
DE IMP_XEU 2021 PP_GR_NP TOTAL THS_T 75.9566791476
DE IMP_XEU 2021 PP_GR_MC TOTAL THS_T 55.7034733217
DE IMP_XEU 2021 PP_GR_NW TOTAL THS_T 140.5635821696
DE IMP_XEU 2021 PP_GR_CO TOTAL THS_T 218.0258798988
DE IMP_XEU 2021 PP_HS TOTAL THS_T 18.7985396574
DE IMP_XEU 2021 PP_PK TOTAL THS_T 684.1871105703
DE IMP_XEU 2021 PP_PK_CS TOTAL THS_T 355.0174961222
DE IMP_XEU 2021 PP_PK_CB TOTAL THS_T 176.6454577955
DE IMP_XEU 2021 PP_PK_WR TOTAL THS_T 127.8533513918
DE IMP_XEU 2021 PP_PK_O TOTAL THS_T 24.6708052608
DE IMP_XEU 2021 PP_O TOTAL THS_T 8.5577352345
DE IMP_XEU 2021 GLT_CLT TOTAL THS_M3
DE IMP_XEU 2021 GLT TOTAL THS_M3
DE IMP_XEU 2021 CLT TOTAL THS_M3
DE IMP_XEU 2021 I_BEAMS TOTAL THS_T
DE IMP_XEU 2021 RW TOTAL THS_NAC 91796
DE IMP_XEU 2021 RW_FW TOTAL THS_NAC 17829
DE IMP_XEU 2021 RW_FW CONIF THS_NAC 2132
DE IMP_XEU 2021 RW_FW NCONIF THS_NAC 15697
DE IMP_XEU 2021 RW_IN TOTAL THS_NAC 73967
DE IMP_XEU 2021 RW_IN CONIF THS_NAC 54799
DE IMP_XEU 2021 RW_IN NCONIF THS_NAC 19168
DE IMP_XEU 2021 RW_IN NC_TRO THS_NAC 6185
DE IMP_XEU 2021 CHA TOTAL THS_NAC 42847
DE IMP_XEU 2021 CHP_RES TOTAL THS_NAC 1422
DE IMP_XEU 2021 CHP TOTAL THS_NAC 478
DE IMP_XEU 2021 RES TOTAL THS_NAC 944
DE IMP_XEU 2021 RES_SWD TOTAL THS_NAC
DE IMP_XEU 2021 RCW TOTAL THS_NAC 3914
DE IMP_XEU 2021 PEL_AGG TOTAL THS_NAC 30262
DE IMP_XEU 2021 PEL TOTAL THS_NAC 13362
DE IMP_XEU 2021 AGG TOTAL THS_NAC 16900
DE IMP_XEU 2021 SN TOTAL THS_NAC 601398
DE IMP_XEU 2021 SN CONIF THS_NAC 466691
DE IMP_XEU 2021 SN NCONIF THS_NAC 134707
DE IMP_XEU 2021 SN NC_TRO THS_NAC 44329
DE IMP_XEU 2021 PN_VN TOTAL THS_NAC 54257
DE IMP_XEU 2021 PN_VN CONIF THS_NAC 6146
DE IMP_XEU 2021 PN_VN NCONIF THS_NAC 48111
DE IMP_XEU 2021 PN_VN NC_TRO THS_NAC 5810
DE IMP_XEU 2021 PN TOTAL THS_NAC 460845
DE IMP_XEU 2021 PN_PY TOTAL THS_NAC 306588
DE IMP_XEU 2021 PN_PY CONIF THS_NAC 82057
DE IMP_XEU 2021 PN_PY NCONIF THS_NAC 224531
DE IMP_XEU 2021 PN_PY NC_TRO THS_NAC 26909
DE IMP_XEU 2021 PN_PY_LVL TOTAL THS_NAC
DE IMP_XEU 2021 PN_PY_LVL CONIF THS_NAC
DE IMP_XEU 2021 PN_PY_LVL NCONIF THS_NAC
DE IMP_XEU 2021 PN_PY_LVL NC_TRO THS_NAC
DE IMP_XEU 2021 PN_PB TOTAL THS_NAC 66575
DE IMP_XEU 2021 PN_PB_OSB TOTAL THS_NAC 5615
DE IMP_XEU 2021 PN_FB TOTAL THS_NAC 87682
DE IMP_XEU 2021 PN_FB_HB TOTAL THS_NAC 12596
DE IMP_XEU 2021 PN_FB_MDF TOTAL THS_NAC 72451
DE IMP_XEU 2021 PN_FB_O TOTAL THS_NAC 2635
DE IMP_XEU 2021 PL TOTAL THS_NAC 1141247.10622474
DE IMP_XEU 2021 PL_MC_SCH TOTAL THS_NAC 13578.1062247399
DE IMP_XEU 2021 PL_CH TOTAL THS_NAC 1014842
DE IMP_XEU 2021 PL_CH_SA TOTAL THS_NAC 984093
DE IMP_XEU 2021 PL_CH_SAB TOTAL THS_NAC 962622
DE IMP_XEU 2021 PL_CH_SI TOTAL THS_NAC 30749
DE IMP_XEU 2021 PL_DS TOTAL THS_NAC 112827
DE IMP_XEU 2021 PLO TOTAL THS_NAC 16331.7326988492
DE IMP_XEU 2021 PLO_NW TOTAL THS_NAC 13310.7326988492
DE IMP_XEU 2021 PLO_RC TOTAL THS_NAC 3021
DE IMP_XEU 2021 RCP TOTAL THS_NAC 81418
DE IMP_XEU 2021 PP TOTAL THS_NAC 898191.293319477
DE IMP_XEU 2021 PP_GR TOTAL THS_NAC 339614.090278597
DE IMP_XEU 2021 PP_GR_NP TOTAL THS_NAC 32519.6116688322
DE IMP_XEU 2021 PP_GR_MC TOTAL THS_NAC 35570.9099936851
DE IMP_XEU 2021 PP_GR_NW TOTAL THS_NAC 114642.997682579
DE IMP_XEU 2021 PP_GR_CO TOTAL THS_NAC 156880.570933501
DE IMP_XEU 2021 PP_HS TOTAL THS_NAC 25342.5188864959
DE IMP_XEU 2021 PP_PK TOTAL THS_NAC 505985.41711557
DE IMP_XEU 2021 PP_PK_CS TOTAL THS_NAC 187623.767547512
DE IMP_XEU 2021 PP_PK_CB TOTAL THS_NAC 182345.151778614
DE IMP_XEU 2021 PP_PK_WR TOTAL THS_NAC 123331.703755303
DE IMP_XEU 2021 PP_PK_O TOTAL THS_NAC 12684.7940341418
DE IMP_XEU 2021 PP_O TOTAL THS_NAC 27249.2670388138
DE IMP_XEU 2021 GLT_CLT TOTAL THS_NAC
DE IMP_XEU 2021 GLT TOTAL THS_NAC
DE IMP_XEU 2021 CLT TOTAL THS_NAC
DE IMP_XEU 2021 I_BEAMS TOTAL THS_NAC
DE IMP_XEU 2022 RW TOTAL THS_M3 936.898
DE IMP_XEU 2022 RW_FW TOTAL THS_M3 101.726
DE IMP_XEU 2022 RW_FW CONIF THS_M3 7.166
DE IMP_XEU 2022 RW_FW NCONIF THS_M3 94.56
DE IMP_XEU 2022 RW_IN TOTAL THS_M3 835.172
DE IMP_XEU 2022 RW_IN CONIF THS_M3 750.881
DE IMP_XEU 2022 RW_IN NCONIF THS_M3 84.291
DE IMP_XEU 2022 RW_IN NC_TRO THS_M3 15.807
DE IMP_XEU 2022 CHA TOTAL THS_T 75.514
DE IMP_XEU 2022 CHP_RES TOTAL THS_M3 206.63
DE IMP_XEU 2022 CHP TOTAL THS_M3 35.412
DE IMP_XEU 2022 RES TOTAL THS_M3 80.2602661845
DE IMP_XEU 2022 RES_SWD TOTAL THS_M3 34.78
DE IMP_XEU 2022 RCW TOTAL THS_T 56.8495313638
DE IMP_XEU 2022 PEL_AGG TOTAL THS_T 201.485
DE IMP_XEU 2022 PEL TOTAL THS_T 93.026
DE IMP_XEU 2022 AGG TOTAL THS_T 108.459
DE IMP_XEU 2022 SN TOTAL THS_M3 947.501
DE IMP_XEU 2022 SN CONIF THS_M3 771.31
DE IMP_XEU 2022 SN NCONIF THS_M3 176.191
DE IMP_XEU 2022 SN NC_TRO THS_M3 69.82
DE IMP_XEU 2022 PN_VN TOTAL THS_M3 30.505
DE IMP_XEU 2022 PN_VN CONIF THS_M3 3.282
DE IMP_XEU 2022 PN_VN NCONIF THS_M3 27.223
DE IMP_XEU 2022 PN_VN NC_TRO THS_M3 6.637
DE IMP_XEU 2022 PN TOTAL THS_M3 884.968
DE IMP_XEU 2022 PN_PY TOTAL THS_M3 560.131
DE IMP_XEU 2022 PN_PY CONIF THS_M3 239.987
DE IMP_XEU 2022 PN_PY NCONIF THS_M3 320.144
DE IMP_XEU 2022 PN_PY NC_TRO THS_M3 37.434
DE IMP_XEU 2022 PN_PY_LVL TOTAL THS_M3 7.264
DE IMP_XEU 2022 PN_PY_LVL CONIF THS_M3 0.32
DE IMP_XEU 2022 PN_PY_LVL NCONIF THS_M3 6.944
DE IMP_XEU 2022 PN_PY_LVL NC_TRO THS_M3 1.809
DE IMP_XEU 2022 PN_PB TOTAL THS_M3 226.617
DE IMP_XEU 2022 PN_PB_OSB TOTAL THS_M3 5.137
DE IMP_XEU 2022 PN_FB TOTAL THS_M3 98.22
DE IMP_XEU 2022 PN_FB_HB TOTAL THS_M3 27.955
DE IMP_XEU 2022 PN_FB_MDF TOTAL THS_M3 62.5
DE IMP_XEU 2022 PN_FB_O TOTAL THS_M3 7.765
DE IMP_XEU 2022 PL TOTAL THS_T 1834.9677485729
DE IMP_XEU 2022 PL_MC_SCH TOTAL THS_T 54.0948516499
DE IMP_XEU 2022 PL_CH TOTAL THS_T 1672.2842305509
DE IMP_XEU 2022 PL_CH_SA TOTAL THS_T 1649.4045621272
DE IMP_XEU 2022 PL_CH_SAB TOTAL THS_T 538.4835946236
DE IMP_XEU 2022 PL_CH_SI TOTAL THS_T 22.8796684237
DE IMP_XEU 2022 PL_DS TOTAL THS_T 108.5886663722
DE IMP_XEU 2022 PLO TOTAL THS_T 105.8083998759
DE IMP_XEU 2022 PLO_NW TOTAL THS_T 9.4194834979
DE IMP_XEU 2022 PLO_RC TOTAL THS_T 96.388916378
DE IMP_XEU 2022 RCP TOTAL THS_T 376.1254937336
DE IMP_XEU 2022 PP TOTAL THS_T 1243.183
DE IMP_XEU 2022 PP_GR TOTAL THS_T 489.5162866918
DE IMP_XEU 2022 PP_GR_NP TOTAL THS_T 82.6636404562
DE IMP_XEU 2022 PP_GR_MC TOTAL THS_T 56.8138636363
DE IMP_XEU 2022 PP_GR_NW TOTAL THS_T 149.0790882083
DE IMP_XEU 2022 PP_GR_CO TOTAL THS_T 200.959694391
DE IMP_XEU 2022 PP_HS TOTAL THS_T 27.7922725796
DE IMP_XEU 2022 PP_PK TOTAL THS_T 715.9058008715
DE IMP_XEU 2022 PP_PK_CS TOTAL THS_T 358.9533571829
DE IMP_XEU 2022 PP_PK_CB TOTAL THS_T 196.1541638487
DE IMP_XEU 2022 PP_PK_WR TOTAL THS_T 134.9743878758
DE IMP_XEU 2022 PP_PK_O TOTAL THS_T 25.8238919642
DE IMP_XEU 2022 PP_O TOTAL THS_T 9.9686398571
DE IMP_XEU 2022 GLT_CLT TOTAL THS_M3 6.463
DE IMP_XEU 2022 GLT TOTAL THS_M3 6.305
DE IMP_XEU 2022 CLT TOTAL THS_M3 0.158
DE IMP_XEU 2022 I_BEAMS TOTAL THS_T 0.002
DE IMP_XEU 2022 RW TOTAL THS_NAC 124971
DE IMP_XEU 2022 RW_FW TOTAL THS_NAC 22327
DE IMP_XEU 2022 RW_FW CONIF THS_NAC 1593
DE IMP_XEU 2022 RW_FW NCONIF THS_NAC 20734
DE IMP_XEU 2022 RW_IN TOTAL THS_NAC 102644
DE IMP_XEU 2022 RW_IN CONIF THS_NAC 72810
DE IMP_XEU 2022 RW_IN NCONIF THS_NAC 29834
DE IMP_XEU 2022 RW_IN NC_TRO THS_NAC 9447
DE IMP_XEU 2022 CHA TOTAL THS_NAC 49495
DE IMP_XEU 2022 CHP_RES TOTAL THS_NAC 6867
DE IMP_XEU 2022 CHP TOTAL THS_NAC 2940
DE IMP_XEU 2022 RES TOTAL THS_NAC 2105.6666666667
DE IMP_XEU 2022 RES_SWD TOTAL THS_NAC 1195
DE IMP_XEU 2022 RCW TOTAL THS_NAC 1821.3333333333
DE IMP_XEU 2022 PEL_AGG TOTAL THS_NAC 53411
DE IMP_XEU 2022 PEL TOTAL THS_NAC 24461
DE IMP_XEU 2022 AGG TOTAL THS_NAC 28950
DE IMP_XEU 2022 SN TOTAL THS_NAC 447746
DE IMP_XEU 2022 SN CONIF THS_NAC 300749
DE IMP_XEU 2022 SN NCONIF THS_NAC 146997
DE IMP_XEU 2022 SN NC_TRO THS_NAC 59089
DE IMP_XEU 2022 PN_VN TOTAL THS_NAC 72325
DE IMP_XEU 2022 PN_VN CONIF THS_NAC 7928
DE IMP_XEU 2022 PN_VN NCONIF THS_NAC 64397
DE IMP_XEU 2022 PN_VN NC_TRO THS_NAC 7006
DE IMP_XEU 2022 PN TOTAL THS_NAC 520849
DE IMP_XEU 2022 PN_PY TOTAL THS_NAC 356197
DE IMP_XEU 2022 PN_PY CONIF THS_NAC 108982
DE IMP_XEU 2022 PN_PY NCONIF THS_NAC 247215
DE IMP_XEU 2022 PN_PY NC_TRO THS_NAC 40371
DE IMP_XEU 2022 PN_PY_LVL TOTAL THS_NAC 6302
DE IMP_XEU 2022 PN_PY_LVL CONIF THS_NAC 143
DE IMP_XEU 2022 PN_PY_LVL NCONIF THS_NAC 6159
DE IMP_XEU 2022 PN_PY_LVL NC_TRO THS_NAC 1881
DE IMP_XEU 2022 PN_PB TOTAL THS_NAC 81770
DE IMP_XEU 2022 PN_PB_OSB TOTAL THS_NAC 1584
DE IMP_XEU 2022 PN_FB TOTAL THS_NAC 82882
DE IMP_XEU 2022 PN_FB_HB TOTAL THS_NAC 26510
DE IMP_XEU 2022 PN_FB_MDF TOTAL THS_NAC 54069
DE IMP_XEU 2022 PN_FB_O TOTAL THS_NAC 2303
DE IMP_XEU 2022 PL TOTAL THS_NAC 1544183.94824269
DE IMP_XEU 2022 PL_MC_SCH TOTAL THS_NAC 42708.093018499
DE IMP_XEU 2022 PL_CH TOTAL THS_NAC 1366823.63598897
DE IMP_XEU 2022 PL_CH_SA TOTAL THS_NAC 1336204.95287398
DE IMP_XEU 2022 PL_CH_SAB TOTAL THS_NAC 463253.075310552
DE IMP_XEU 2022 PL_CH_SI TOTAL THS_NAC 30618.6831149877
DE IMP_XEU 2022 PL_DS TOTAL THS_NAC 134652.219235218
DE IMP_XEU 2022 PLO TOTAL THS_NAC 31212.3305446465
DE IMP_XEU 2022 PLO_NW TOTAL THS_NAC 27007.1198454268
DE IMP_XEU 2022 PLO_RC TOTAL THS_NAC 4205.2106992198
DE IMP_XEU 2022 RCP TOTAL THS_NAC 94875.6145192071
DE IMP_XEU 2022 PP TOTAL THS_NAC 1317815.28821774
DE IMP_XEU 2022 PP_GR TOTAL THS_NAC 537053.181528004
DE IMP_XEU 2022 PP_GR_NP TOTAL THS_NAC 68218.852572598
DE IMP_XEU 2022 PP_GR_MC TOTAL THS_NAC 53394.2345005275
DE IMP_XEU 2022 PP_GR_NW TOTAL THS_NAC 185633.20942574
DE IMP_XEU 2022 PP_GR_CO TOTAL THS_NAC 229806.885029139
DE IMP_XEU 2022 PP_HS TOTAL THS_NAC 51846.9370132294
DE IMP_XEU 2022 PP_PK TOTAL THS_NAC 695926.990266667
DE IMP_XEU 2022 PP_PK_CS TOTAL THS_NAC 251528.795546229
DE IMP_XEU 2022 PP_PK_CB TOTAL THS_NAC 241997.762054883
DE IMP_XEU 2022 PP_PK_WR TOTAL THS_NAC 182068.62465205
DE IMP_XEU 2022 PP_PK_O TOTAL THS_NAC 20331.8080135061
DE IMP_XEU 2022 PP_O TOTAL THS_NAC 32988.1794098427
DE IMP_XEU 2022 GLT_CLT TOTAL THS_NAC 6715
DE IMP_XEU 2022 GLT TOTAL THS_NAC 6518
DE IMP_XEU 2022 CLT TOTAL THS_NAC 197
DE IMP_XEU 2022 I_BEAMS TOTAL THS_NAC 4
DE EXP_XEU 2021 RW TOTAL THS_M3 4831.626
DE EXP_XEU 2021 RW_FW TOTAL THS_M3 4.085
DE EXP_XEU 2021 RW_FW CONIF THS_M3 0.494
DE EXP_XEU 2021 RW_FW NCONIF THS_M3 3.591
DE EXP_XEU 2021 RW_IN TOTAL THS_M3 4827.541
DE EXP_XEU 2021 RW_IN CONIF THS_M3 4365.589
DE EXP_XEU 2021 RW_IN NCONIF THS_M3 461.952
DE EXP_XEU 2021 RW_IN NC_TRO THS_M3 0.061
DE EXP_XEU 2021 CHA TOTAL THS_T 5.495
DE EXP_XEU 2021 CHP_RES TOTAL THS_M3 268.496
DE EXP_XEU 2021 CHP TOTAL THS_M3 125.239
DE EXP_XEU 2021 RES TOTAL THS_M3 143.257
DE EXP_XEU 2021 RES_SWD TOTAL THS_M3
DE EXP_XEU 2021 RCW TOTAL THS_T 35.105
DE EXP_XEU 2021 PEL_AGG TOTAL THS_T 70.511
DE EXP_XEU 2021 PEL TOTAL THS_T 40.595
DE EXP_XEU 2021 AGG TOTAL THS_T 29.916
DE EXP_XEU 2021 SN TOTAL THS_M3 4874.529
DE EXP_XEU 2021 SN CONIF THS_M3 4430.369
DE EXP_XEU 2021 SN NCONIF THS_M3 444.16
DE EXP_XEU 2021 SN NC_TRO THS_M3 7.705
DE EXP_XEU 2021 PN_VN TOTAL THS_M3 13.125
DE EXP_XEU 2021 PN_VN CONIF THS_M3 0.291
DE EXP_XEU 2021 PN_VN NCONIF THS_M3 12.834
DE EXP_XEU 2021 PN_VN NC_TRO THS_M3 1.111
DE EXP_XEU 2021 PN TOTAL THS_M3 1744.166
DE EXP_XEU 2021 PN_PY TOTAL THS_M3 105.04
DE EXP_XEU 2021 PN_PY CONIF THS_M3 74.347
DE EXP_XEU 2021 PN_PY NCONIF THS_M3 30.693
DE EXP_XEU 2021 PN_PY NC_TRO THS_M3 4.264
DE EXP_XEU 2021 PN_PY_LVL TOTAL THS_M3
DE EXP_XEU 2021 PN_PY_LVL CONIF THS_M3
DE EXP_XEU 2021 PN_PY_LVL NCONIF THS_M3
DE EXP_XEU 2021 PN_PY_LVL NC_TRO THS_M3
DE EXP_XEU 2021 PN_PB TOTAL THS_M3 506.522
DE EXP_XEU 2021 PN_PB_OSB TOTAL THS_M3 195.369
DE EXP_XEU 2021 PN_FB TOTAL THS_M3 880.1243911936
DE EXP_XEU 2021 PN_FB_HB TOTAL THS_M3 13.6491775916
DE EXP_XEU 2021 PN_FB_MDF TOTAL THS_M3 621.3092136021
DE EXP_XEU 2021 PN_FB_O TOTAL THS_M3 245.166
DE EXP_XEU 2021 PL TOTAL THS_T 307.2314583924
DE EXP_XEU 2021 PL_MC_SCH TOTAL THS_T 26.1816903209
DE EXP_XEU 2021 PL_CH TOTAL THS_T 279.9779020037
DE EXP_XEU 2021 PL_CH_SA TOTAL THS_T 245.6578069901
DE EXP_XEU 2021 PL_CH_SAB TOTAL THS_T 239.5599167667
DE EXP_XEU 2021 PL_CH_SI TOTAL THS_T 34.3200950136
DE EXP_XEU 2021 PL_DS TOTAL THS_T 1.0718660678
DE EXP_XEU 2021 PLO TOTAL THS_T 49.0468203942
DE EXP_XEU 2021 PLO_NW TOTAL THS_T 2.3726064538
DE EXP_XEU 2021 PLO_RC TOTAL THS_T 46.6742139404
DE EXP_XEU 2021 RCP TOTAL THS_T 147.7323451298
DE EXP_XEU 2021 PP TOTAL THS_T 3750.938
DE EXP_XEU 2021 PP_GR TOTAL THS_T 1335.7067011297
DE EXP_XEU 2021 PP_GR_NP TOTAL THS_T 138.594330597
DE EXP_XEU 2021 PP_GR_MC TOTAL THS_T 216.1643329411
DE EXP_XEU 2021 PP_GR_NW TOTAL THS_T 237.9249994467
DE EXP_XEU 2021 PP_GR_CO TOTAL THS_T 743.0230381448
DE EXP_XEU 2021 PP_HS TOTAL THS_T 38.661433185
DE EXP_XEU 2021 PP_PK TOTAL THS_T 2353.1125869031
DE EXP_XEU 2021 PP_PK_CS TOTAL THS_T 1392.40297464
DE EXP_XEU 2021 PP_PK_CB TOTAL THS_T 566.0527772709
DE EXP_XEU 2021 PP_PK_WR TOTAL THS_T 317.4956389437
DE EXP_XEU 2021 PP_PK_O TOTAL THS_T 77.1611960485
DE EXP_XEU 2021 PP_O TOTAL THS_T 23.4572787823
DE EXP_XEU 2021 GLT_CLT TOTAL THS_M3
DE EXP_XEU 2021 GLT TOTAL THS_M3
DE EXP_XEU 2021 CLT TOTAL THS_M3
DE EXP_XEU 2021 I_BEAMS TOTAL THS_T
DE EXP_XEU 2021 RW TOTAL THS_NAC 542552
DE EXP_XEU 2021 RW_FW TOTAL THS_NAC 1215
DE EXP_XEU 2021 RW_FW CONIF THS_NAC 129
DE EXP_XEU 2021 RW_FW NCONIF THS_NAC 1086
DE EXP_XEU 2021 RW_IN TOTAL THS_NAC 541337
DE EXP_XEU 2021 RW_IN CONIF THS_NAC 464336
DE EXP_XEU 2021 RW_IN NCONIF THS_NAC 77001
DE EXP_XEU 2021 RW_IN NC_TRO THS_NAC 253
DE EXP_XEU 2021 CHA TOTAL THS_NAC 4969
DE EXP_XEU 2021 CHP_RES TOTAL THS_NAC 19637
DE EXP_XEU 2021 CHP TOTAL THS_NAC 9913
DE EXP_XEU 2021 RES TOTAL THS_NAC 9724
DE EXP_XEU 2021 RES_SWD TOTAL THS_NAC
DE EXP_XEU 2021 RCW TOTAL THS_NAC 6631
DE EXP_XEU 2021 PEL_AGG TOTAL THS_NAC 17673
DE EXP_XEU 2021 PEL TOTAL THS_NAC 9756
DE EXP_XEU 2021 AGG TOTAL THS_NAC 7917
DE EXP_XEU 2021 SN TOTAL THS_NAC 1737382
DE EXP_XEU 2021 SN CONIF THS_NAC 1522492
DE EXP_XEU 2021 SN NCONIF THS_NAC 214890
DE EXP_XEU 2021 SN NC_TRO THS_NAC 11854
DE EXP_XEU 2021 PN_VN TOTAL THS_NAC 52297
DE EXP_XEU 2021 PN_VN CONIF THS_NAC 1012
DE EXP_XEU 2021 PN_VN NCONIF THS_NAC 51285
DE EXP_XEU 2021 PN_VN NC_TRO THS_NAC 3162
DE EXP_XEU 2021 PN TOTAL THS_NAC 882390
DE EXP_XEU 2021 PN_PY TOTAL THS_NAC 99822
DE EXP_XEU 2021 PN_PY CONIF THS_NAC 62349
DE EXP_XEU 2021 PN_PY NCONIF THS_NAC 37473
DE EXP_XEU 2021 PN_PY NC_TRO THS_NAC 4281
DE EXP_XEU 2021 PN_PY_LVL TOTAL THS_NAC
DE EXP_XEU 2021 PN_PY_LVL CONIF THS_NAC
DE EXP_XEU 2021 PN_PY_LVL NCONIF THS_NAC
DE EXP_XEU 2021 PN_PY_LVL NC_TRO THS_NAC
DE EXP_XEU 2021 PN_PB TOTAL THS_NAC 161960
DE EXP_XEU 2021 PN_PB_OSB TOTAL THS_NAC 70788
DE EXP_XEU 2021 PN_FB TOTAL THS_NAC 471133.803927435
DE EXP_XEU 2021 PN_FB_HB TOTAL THS_NAC 9454.9360192202
DE EXP_XEU 2021 PN_FB_MDF TOTAL THS_NAC 436406.867908214
DE EXP_XEU 2021 PN_FB_O TOTAL THS_NAC 25272
DE EXP_XEU 2021 PL TOTAL THS_NAC 210463.029893205
DE EXP_XEU 2021 PL_MC_SCH TOTAL THS_NAC 12917.0298932052
DE EXP_XEU 2021 PL_CH TOTAL THS_NAC 197033
DE EXP_XEU 2021 PL_CH_SA TOTAL THS_NAC 143401
DE EXP_XEU 2021 PL_CH_SAB TOTAL THS_NAC 139340
DE EXP_XEU 2021 PL_CH_SI TOTAL THS_NAC 53632
DE EXP_XEU 2021 PL_DS TOTAL THS_NAC 513
DE EXP_XEU 2021 PLO TOTAL THS_NAC 27995.1623388597
DE EXP_XEU 2021 PLO_NW TOTAL THS_NAC 3290.1623388597
DE EXP_XEU 2021 PLO_RC TOTAL THS_NAC 24705
DE EXP_XEU 2021 RCP TOTAL THS_NAC 25086
DE EXP_XEU 2021 PP TOTAL THS_NAC 3007726.42467212
DE EXP_XEU 2021 PP_GR TOTAL THS_NAC 1043697.83586142
DE EXP_XEU 2021 PP_GR_NP TOTAL THS_NAC 59787.8111267449
DE EXP_XEU 2021 PP_GR_MC TOTAL THS_NAC 109265.352415057
DE EXP_XEU 2021 PP_GR_NW TOTAL THS_NAC 268755.927057784
DE EXP_XEU 2021 PP_GR_CO TOTAL THS_NAC 605888.745261832
DE EXP_XEU 2021 PP_HS TOTAL THS_NAC 62013.440706055
DE EXP_XEU 2021 PP_PK TOTAL THS_NAC 1815900.96870596
DE EXP_XEU 2021 PP_PK_CS TOTAL THS_NAC 744457.31353162
DE EXP_XEU 2021 PP_PK_CB TOTAL THS_NAC 673769.631185224
DE EXP_XEU 2021 PP_PK_WR TOTAL THS_NAC 349966.103090028
DE EXP_XEU 2021 PP_PK_O TOTAL THS_NAC 47707.9208990906
DE EXP_XEU 2021 PP_O TOTAL THS_NAC 86114.1793986852
DE EXP_XEU 2021 GLT_CLT TOTAL THS_NAC
DE EXP_XEU 2021 GLT TOTAL THS_NAC
DE EXP_XEU 2021 CLT TOTAL THS_NAC
DE EXP_XEU 2021 I_BEAMS TOTAL THS_NAC
DE EXP_XEU 2022 RW TOTAL THS_M3 3773.949
DE EXP_XEU 2022 RW_FW TOTAL THS_M3 14.83
DE EXP_XEU 2022 RW_FW CONIF THS_M3 6.613
DE EXP_XEU 2022 RW_FW NCONIF THS_M3 8.217
DE EXP_XEU 2022 RW_IN TOTAL THS_M3 3759.119
DE EXP_XEU 2022 RW_IN CONIF THS_M3 3296.674
DE EXP_XEU 2022 RW_IN NCONIF THS_M3 462.445
DE EXP_XEU 2022 RW_IN NC_TRO THS_M3 1.178
DE EXP_XEU 2022 CHA TOTAL THS_T 4.822
DE EXP_XEU 2022 CHP_RES TOTAL THS_M3 406.728
DE EXP_XEU 2022 CHP TOTAL THS_M3 214.484
DE EXP_XEU 2022 RES TOTAL THS_M3 161.5916770784
DE EXP_XEU 2022 RES_SWD TOTAL THS_M3 146.264
DE EXP_XEU 2022 RCW TOTAL THS_T 19.1573997657
DE EXP_XEU 2022 PEL_AGG TOTAL THS_T 68.691
DE EXP_XEU 2022 PEL TOTAL THS_T 42.375
DE EXP_XEU 2022 AGG TOTAL THS_T 26.316
DE EXP_XEU 2022 SN TOTAL THS_M3 5950.768
DE EXP_XEU 2022 SN CONIF THS_M3 5542.616
DE EXP_XEU 2022 SN NCONIF THS_M3 408.152
DE EXP_XEU 2022 SN NC_TRO THS_M3 7.258
DE EXP_XEU 2022 PN_VN TOTAL THS_M3 14.38
DE EXP_XEU 2022 PN_VN CONIF THS_M3 0.512
DE EXP_XEU 2022 PN_VN NCONIF THS_M3 13.868
DE EXP_XEU 2022 PN_VN NC_TRO THS_M3 1.036
DE EXP_XEU 2022 PN TOTAL THS_M3 1430.417
DE EXP_XEU 2022 PN_PY TOTAL THS_M3 103.197
DE EXP_XEU 2022 PN_PY CONIF THS_M3 76.968
DE EXP_XEU 2022 PN_PY NCONIF THS_M3 26.229
DE EXP_XEU 2022 PN_PY NC_TRO THS_M3 5.125
DE EXP_XEU 2022 PN_PY_LVL TOTAL THS_M3 24.182
DE EXP_XEU 2022 PN_PY_LVL CONIF THS_M3 21.443
DE EXP_XEU 2022 PN_PY_LVL NCONIF THS_M3 2.739
DE EXP_XEU 2022 PN_PY_LVL NC_TRO THS_M3 0.09
DE EXP_XEU 2022 PN_PB TOTAL THS_M3 483.826
DE EXP_XEU 2022 PN_PB_OSB TOTAL THS_M3 204.61
DE EXP_XEU 2022 PN_FB TOTAL THS_M3 625.215420717
DE EXP_XEU 2022 PN_FB_HB TOTAL THS_M3 10.4538508334
DE EXP_XEU 2022 PN_FB_MDF TOTAL THS_M3 387.4375698835
DE EXP_XEU 2022 PN_FB_O TOTAL THS_M3 227.324
DE EXP_XEU 2022 PL TOTAL THS_T 255.3102679737
DE EXP_XEU 2022 PL_MC_SCH TOTAL THS_T 24.1351011409
DE EXP_XEU 2022 PL_CH TOTAL THS_T 226.6332801052
DE EXP_XEU 2022 PL_CH_SA TOTAL THS_T 196.9138152429
DE EXP_XEU 2022 PL_CH_SAB TOTAL THS_T 194.3275033985
DE EXP_XEU 2022 PL_CH_SI TOTAL THS_T 29.7194648623
DE EXP_XEU 2022 PL_DS TOTAL THS_T 4.5418867276
DE EXP_XEU 2022 PLO TOTAL THS_T 33.4864306259
DE EXP_XEU 2022 PLO_NW TOTAL THS_T 1.5053440646
DE EXP_XEU 2022 PLO_RC TOTAL THS_T 31.9810865613
DE EXP_XEU 2022 RCP TOTAL THS_T 186.0225861189
DE EXP_XEU 2022 PP TOTAL THS_T 3591.856
DE EXP_XEU 2022 PP_GR TOTAL THS_T 1230.6852269016
DE EXP_XEU 2022 PP_GR_NP TOTAL THS_T 137.9815770861
DE EXP_XEU 2022 PP_GR_MC TOTAL THS_T 200.3148022331
DE EXP_XEU 2022 PP_GR_NW TOTAL THS_T 222.3274825558
DE EXP_XEU 2022 PP_GR_CO TOTAL THS_T 670.0613650265
DE EXP_XEU 2022 PP_HS TOTAL THS_T 28.9976429356
DE EXP_XEU 2022 PP_PK TOTAL THS_T 2308.2244193262
DE EXP_XEU 2022 PP_PK_CS TOTAL THS_T 1398.9408623416
DE EXP_XEU 2022 PP_PK_CB TOTAL THS_T 538.0476321036
DE EXP_XEU 2022 PP_PK_WR TOTAL THS_T 304.2002997035
DE EXP_XEU 2022 PP_PK_O TOTAL THS_T 67.0356251775
DE EXP_XEU 2022 PP_O TOTAL THS_T 23.9487108366
DE EXP_XEU 2022 GLT_CLT TOTAL THS_M3 210.203
DE EXP_XEU 2022 GLT TOTAL THS_M3 204.465
DE EXP_XEU 2022 CLT TOTAL THS_M3 5.738
DE EXP_XEU 2022 I_BEAMS TOTAL THS_T 0.009
DE EXP_XEU 2022 RW TOTAL THS_NAC 509854
DE EXP_XEU 2022 RW_FW TOTAL THS_NAC 2094
DE EXP_XEU 2022 RW_FW CONIF THS_NAC 1029
DE EXP_XEU 2022 RW_FW NCONIF THS_NAC 1065
DE EXP_XEU 2022 RW_IN TOTAL THS_NAC 507760
DE EXP_XEU 2022 RW_IN CONIF THS_NAC 420997
DE EXP_XEU 2022 RW_IN NCONIF THS_NAC 86763
DE EXP_XEU 2022 RW_IN NC_TRO THS_NAC 543
DE EXP_XEU 2022 CHA TOTAL THS_NAC 4843
DE EXP_XEU 2022 CHP_RES TOTAL THS_NAC 40449
DE EXP_XEU 2022 CHP TOTAL THS_NAC 18123
DE EXP_XEU 2022 RES TOTAL THS_NAC 18293.3333333333
DE EXP_XEU 2022 RES_SWD TOTAL THS_NAC 16277
DE EXP_XEU 2022 RCW TOTAL THS_NAC 4032.6666666667
DE EXP_XEU 2022 PEL_AGG TOTAL THS_NAC 25935
DE EXP_XEU 2022 PEL TOTAL THS_NAC 15872
DE EXP_XEU 2022 AGG TOTAL THS_NAC 10063
DE EXP_XEU 2022 SN TOTAL THS_NAC 2120945
DE EXP_XEU 2022 SN CONIF THS_NAC 1878863
DE EXP_XEU 2022 SN NCONIF THS_NAC 242082
DE EXP_XEU 2022 SN NC_TRO THS_NAC 11510
DE EXP_XEU 2022 PN_VN TOTAL THS_NAC 62872
DE EXP_XEU 2022 PN_VN CONIF THS_NAC 1550
DE EXP_XEU 2022 PN_VN NCONIF THS_NAC 61322
DE EXP_XEU 2022 PN_VN NC_TRO THS_NAC 3456
DE EXP_XEU 2022 PN TOTAL THS_NAC 860763
DE EXP_XEU 2022 PN_PY TOTAL THS_NAC 111368
DE EXP_XEU 2022 PN_PY CONIF THS_NAC 75438
DE EXP_XEU 2022 PN_PY NCONIF THS_NAC 35930
DE EXP_XEU 2022 PN_PY NC_TRO THS_NAC 6365
DE EXP_XEU 2022 PN_PY_LVL TOTAL THS_NAC 29192
DE EXP_XEU 2022 PN_PY_LVL CONIF THS_NAC 25634
DE EXP_XEU 2022 PN_PY_LVL NCONIF THS_NAC 3558
DE EXP_XEU 2022 PN_PY_LVL NC_TRO THS_NAC 151
DE EXP_XEU 2022 PN_PB TOTAL THS_NAC 180724
DE EXP_XEU 2022 PN_PB_OSB TOTAL THS_NAC 86135
DE EXP_XEU 2022 PN_FB TOTAL THS_NAC 389225.367963447
DE EXP_XEU 2022 PN_FB_HB TOTAL THS_NAC 9513.505982411
DE EXP_XEU 2022 PN_FB_MDF TOTAL THS_NAC 349783.861981036
DE EXP_XEU 2022 PN_FB_O TOTAL THS_NAC 29928
DE EXP_XEU 2022 PL TOTAL THS_NAC 222874.514708239
DE EXP_XEU 2022 PL_MC_SCH TOTAL THS_NAC 15521.8613934525
DE EXP_XEU 2022 PL_CH TOTAL THS_NAC 203158.016436752
DE EXP_XEU 2022 PL_CH_SA TOTAL THS_NAC 148563.134882814
DE EXP_XEU 2022 PL_CH_SAB TOTAL THS_NAC 146533.019852942
DE EXP_XEU 2022 PL_CH_SI TOTAL THS_NAC 54594.8815539376
DE EXP_XEU 2022 PL_DS TOTAL THS_NAC 4194.6368780345
DE EXP_XEU 2022 PLO TOTAL THS_NAC 23486.2794376042
DE EXP_XEU 2022 PLO_NW TOTAL THS_NAC 2820.7058222821
DE EXP_XEU 2022 PLO_RC TOTAL THS_NAC 20665.573615322
DE EXP_XEU 2022 RCP TOTAL THS_NAC 37625.9060125966
DE EXP_XEU 2022 PP TOTAL THS_NAC 3987610.82300993
DE EXP_XEU 2022 PP_GR TOTAL THS_NAC 1480536.03965429
DE EXP_XEU 2022 PP_GR_NP TOTAL THS_NAC 117817.998670969
DE EXP_XEU 2022 PP_GR_MC TOTAL THS_NAC 178345.420427473
DE EXP_XEU 2022 PP_GR_NW TOTAL THS_NAC 353822.743485296
DE EXP_XEU 2022 PP_GR_CO TOTAL THS_NAC 830549.877070548
DE EXP_XEU 2022 PP_HS TOTAL THS_NAC 72078.3740874347
DE EXP_XEU 2022 PP_PK TOTAL THS_NAC 2334478.00600675
DE EXP_XEU 2022 PP_PK_CS TOTAL THS_NAC 1000534.99283892
DE EXP_XEU 2022 PP_PK_CB TOTAL THS_NAC 823777.98050332
DE EXP_XEU 2022 PP_PK_WR TOTAL THS_NAC 447198.842928568
DE EXP_XEU 2022 PP_PK_O TOTAL THS_NAC 62966.1897359439
DE EXP_XEU 2022 PP_O TOTAL THS_NAC 100518.403261461
DE EXP_XEU 2022 GLT_CLT TOTAL THS_NAC 143752
DE EXP_XEU 2022 GLT TOTAL THS_NAC 137870
DE EXP_XEU 2022 CLT TOTAL THS_NAC 5882
DE EXP_XEU 2022 I_BEAMS TOTAL THS_NAC 5
DE IMP 2021 SW TOTAL THS_NAC 10753037
DE IMP 2021 SW_SN TOTAL THS_NAC 387107
DE IMP 2021 SW_SN CONIF THS_NAC 259680
DE IMP 2021 SW_SN NCONIF THS_NAC 127427
DE IMP 2021 SW_SN NC_TRO THS_NAC 62911
DE IMP 2021 SW_WR TOTAL THS_NAC 843657
DE IMP 2021 SW_DM TOTAL THS_NAC 363938
DE IMP 2021 SW_JN TOTAL THS_NAC 1381341
DE IMP 2021 SW_FU TOTAL THS_NAC 6436567
DE IMP 2021 SW_BL_W TOTAL THS_NAC 311088
DE IMP 2021 SW_O TOTAL THS_NAC 1029339
DE IMP 2021 SP TOTAL THS_NAC 4198897
DE IMP 2021 SP_CM TOTAL THS_NAC 49169
DE IMP 2021 SP_SCO TOTAL THS_NAC 619904
DE IMP 2021 SP_HS TOTAL THS_NAC 730604
DE IMP 2021 SP_PK TOTAL THS_NAC 1566725
DE IMP 2021 SP_O TOTAL THS_NAC 1232495
DE IMP 2021 SP_O_PR TOTAL THS_NAC 19830
DE IMP 2021 SP_O_AR TOTAL THS_NAC 129048
DE IMP 2021 SP_O_FL TOTAL THS_NAC 48913
DE IMP 2022 SW TOTAL THS_NAC 11295654
DE IMP 2022 SW_SN TOTAL THS_NAC 356791
DE IMP 2022 SW_SN CONIF THS_NAC 233788
DE IMP 2022 SW_SN NCONIF THS_NAC 123003
DE IMP 2022 SW_SN NC_TRO THS_NAC 57216
DE IMP 2022 SW_WR TOTAL THS_NAC 1067780
DE IMP 2022 SW_DM TOTAL THS_NAC 413181
DE IMP 2022 SW_JN TOTAL THS_NAC 1260753
DE IMP 2022 SW_FU TOTAL THS_NAC 6748736
DE IMP 2022 SW_BL_W TOTAL THS_NAC 310401
DE IMP 2022 SW_O TOTAL THS_NAC 1138012
DE IMP 2022 SP TOTAL THS_NAC 5211668
DE IMP 2022 SP_CM TOTAL THS_NAC 59557
DE IMP 2022 SP_SCO TOTAL THS_NAC 710133
DE IMP 2022 SP_HS TOTAL THS_NAC 1057745
DE IMP 2022 SP_PK TOTAL THS_NAC 1894075
DE IMP 2022 SP_O TOTAL THS_NAC 1490158
DE IMP 2022 SP_O_PR TOTAL THS_NAC 28120
DE IMP 2022 SP_O_AR TOTAL THS_NAC 140460
DE IMP 2022 SP_O_FL TOTAL THS_NAC 63918
DE EXP 2021 SW TOTAL THS_NAC 8630216
DE EXP 2021 SW_SN TOTAL THS_NAC 292299
DE EXP 2021 SW_SN CONIF THS_NAC 238954
DE EXP 2021 SW_SN NCONIF THS_NAC 53345
DE EXP 2021 SW_SN NC_TRO THS_NAC 7426
DE EXP 2021 SW_WR TOTAL THS_NAC 442323
DE EXP 2021 SW_DM TOTAL THS_NAC 166870
DE EXP 2021 SW_JN TOTAL THS_NAC 1449530
DE EXP 2021 SW_FU TOTAL THS_NAC 5591032
DE EXP 2021 SW_BL_W TOTAL THS_NAC 95051
DE EXP 2021 SW_O TOTAL THS_NAC 593111
DE EXP 2021 SP TOTAL THS_NAC 7971213
DE EXP 2021 SP_CM TOTAL THS_NAC 95507
DE EXP 2021 SP_SCO TOTAL THS_NAC 1692316
DE EXP 2021 SP_HS TOTAL THS_NAC 1112912
DE EXP 2021 SP_PK TOTAL THS_NAC 3223608
DE EXP 2021 SP_O TOTAL THS_NAC 1846870
DE EXP 2021 SP_O_PR TOTAL THS_NAC 66687
DE EXP 2021 SP_O_AR TOTAL THS_NAC 87833
DE EXP 2021 SP_O_FL TOTAL THS_NAC 199012
DE EXP 2022 SW TOTAL THS_NAC 9079231
DE EXP 2022 SW_SN TOTAL THS_NAC 277284
DE EXP 2022 SW_SN CONIF THS_NAC 225743
DE EXP 2022 SW_SN NCONIF THS_NAC 51541
DE EXP 2022 SW_SN NC_TRO THS_NAC 9378
DE EXP 2022 SW_WR TOTAL THS_NAC 588306
DE EXP 2022 SW_DM TOTAL THS_NAC 176570
DE EXP 2022 SW_JN TOTAL THS_NAC 1220187
DE EXP 2022 SW_FU TOTAL THS_NAC 6089953
DE EXP 2022 SW_BL_W TOTAL THS_NAC 96214
DE EXP 2022 SW_O TOTAL THS_NAC 630717
DE EXP 2022 SP TOTAL THS_NAC 9428423
DE EXP 2022 SP_CM TOTAL THS_NAC 137521
DE EXP 2022 SP_SCO TOTAL THS_NAC 1991107
DE EXP 2022 SP_HS TOTAL THS_NAC 1532677
DE EXP 2022 SP_PK TOTAL THS_NAC 3698286
DE EXP 2022 SP_O TOTAL THS_NAC 2068832
DE EXP 2022 SP_O_PR TOTAL THS_NAC 92715
DE EXP 2022 SP_O_AR TOTAL THS_NAC 98731
DE EXP 2022 SP_O_FL TOTAL THS_NAC 217039
DE IMP 2021 ST_1_2 CONIF THS_M3 5875.775
DE IMP 2021 ST_1_2 C_PIN THS_M3 1460.809
DE IMP 2021 ST_1_2_1 C_PIN THS_M3 341.944
DE IMP 2021 ST_1_2_2 C_PIN THS_M3 1118.865
DE IMP 2021 ST_1_2 C_FIR THS_M3 3881.691
DE IMP 2021 ST_1_2_1 C_FIR THS_M3 2708.234
DE IMP 2021 ST_1_2_2 C_FIR THS_M3 1173.457
DE IMP 2021 ST_1_2 NCONIF THS_M3 389.394
DE IMP 2021 ST_1_2 NC_OAK THS_M3 23.817
DE IMP 2021 ST_1_2 NC_BEE THS_M3 85.513
DE IMP 2021 ST_1_2 NC_BIR THS_M3 27.565
DE IMP 2021 ST_1_2_1 NC_BIR THS_M3 0.539
DE IMP 2021 ST_1_2_2 NC_BIR THS_M3 27.026
DE IMP 2021 ST_1_2 NC_POP THS_M3 14.768
DE IMP 2021 ST_1_2 NC_EUC THS_M3 0.148
DE IMP 2021 ST_6 CONIF THS_M3 5317.309
DE IMP 2021 ST_6 C_PIN THS_M3 786.023
DE IMP 2021 ST_6 C_FIR THS_M3 3822.767
DE IMP 2021 ST_6 NCONIF THS_M3 502.01
DE IMP 2021 ST_6 NC_OAK THS_M3 124.736
DE IMP 2021 ST_6 NC_BEE THS_M3 20.863
DE IMP 2021 ST_6 NC_MAP THS_M3 3.774
DE IMP 2021 ST_6 NC_CHE THS_M3 2.057
DE IMP 2021 ST_6 NC_ASH THS_M3 13.417
DE IMP 2021 ST_6 NC_BIR THS_M3 32.767
DE IMP 2021 ST_6 NC_POP THS_M3 39.386
DE IMP 2021 ST_1_2 CONIF THS_NAC 387080
DE IMP 2021 ST_1_2 C_PIN THS_NAC 59448
DE IMP 2021 ST_1_2_1 C_PIN THS_NAC 26592
DE IMP 2021 ST_1_2_2 C_PIN THS_NAC 32856
DE IMP 2021 ST_1_2 C_FIR THS_NAC 290097
DE IMP 2021 ST_1_2_1 C_FIR THS_NAC 229991
DE IMP 2021 ST_1_2_2 C_FIR THS_NAC 60106
DE IMP 2021 ST_1_2 NCONIF THS_NAC 51002
DE IMP 2021 ST_1_2 NC_OAK THS_NAC 8476
DE IMP 2021 ST_1_2 NC_BEE THS_NAC 5340
DE IMP 2021 ST_1_2 NC_BIR THS_NAC 2702
DE IMP 2021 ST_1_2_1 NC_BIR THS_NAC 460
DE IMP 2021 ST_1_2_2 NC_BIR THS_NAC 2242
DE IMP 2021 ST_1_2 NC_POP THS_NAC 557
DE IMP 2021 ST_1_2 NC_EUC THS_NAC 139
DE IMP 2021 ST_6 CONIF THS_NAC 1646970
DE IMP 2021 ST_6 C_PIN THS_NAC 224985
DE IMP 2021 ST_6 C_FIR THS_NAC 1153620
DE IMP 2021 ST_6 NCONIF THS_NAC 292786
DE IMP 2021 ST_6 NC_OAK THS_NAC 108638
DE IMP 2021 ST_6 NC_BEE THS_NAC 10449
DE IMP 2021 ST_6 NC_MAP THS_NAC 3614
DE IMP 2021 ST_6 NC_CHE THS_NAC 1744
DE IMP 2021 ST_6 NC_ASH THS_NAC 7478
DE IMP 2021 ST_6 NC_BIR THS_NAC 7884
DE IMP 2021 ST_6 NC_POP THS_NAC 9117
DE IMP 2022 ST_1_2 CONIF THS_M3 5188.185
DE IMP 2022 ST_1_2 C_PIN THS_M3 1338.914
DE IMP 2022 ST_1_2_1 C_PIN THS_M3 373.154
DE IMP 2022 ST_1_2_2 C_PIN THS_M3 965.76
DE IMP 2022 ST_1_2 C_FIR THS_M3 3469.395
DE IMP 2022 ST_1_2_1 C_FIR THS_M3 2278.193
DE IMP 2022 ST_1_2_2 C_FIR THS_M3 1191.202
DE IMP 2022 ST_1_2 NCONIF THS_M3 388.111
DE IMP 2022 ST_1_2 NC_OAK THS_M3 25.102
DE IMP 2022 ST_1_2 NC_BEE THS_M3 104.563
DE IMP 2022 ST_1_2 NC_BIR THS_M3 54.015
DE IMP 2022 ST_1_2_1 NC_BIR THS_M3 2.345
DE IMP 2022 ST_1_2_2 NC_BIR THS_M3 51.67
DE IMP 2022 ST_1_2 NC_POP THS_M3 4.981
DE IMP 2022 ST_1_2 NC_EUC THS_M3 11.492
DE IMP 2022 ST_6 CONIF THS_M3 3762.574
DE IMP 2022 ST_6 C_PIN THS_M3 502.463
DE IMP 2022 ST_6 C_FIR THS_M3 2688.827
DE IMP 2022 ST_6 NCONIF THS_M3 420.119
DE IMP 2022 ST_6 NC_OAK THS_M3 114.339
DE IMP 2022 ST_6 NC_BEE THS_M3 16.825
DE IMP 2022 ST_6 NC_MAP THS_M3 3.274
DE IMP 2022 ST_6 NC_CHE THS_M3 1.235
DE IMP 2022 ST_6 NC_ASH THS_M3 13.143
DE IMP 2022 ST_6 NC_BIR THS_M3 12.046
DE IMP 2022 ST_6 NC_POP THS_M3 15.217
DE IMP 2022 ST_1_2 CONIF THS_NAC 470624
DE IMP 2022 ST_1_2 C_PIN THS_NAC 90098
DE IMP 2022 ST_1_2_1 C_PIN THS_NAC 33528
DE IMP 2022 ST_1_2_2 C_PIN THS_NAC 56570
DE IMP 2022 ST_1_2 C_FIR THS_NAC 348772
DE IMP 2022 ST_1_2_1 C_FIR THS_NAC 255258
DE IMP 2022 ST_1_2_2 C_FIR THS_NAC 93514
DE IMP 2022 ST_1_2 NCONIF THS_NAC 72234
DE IMP 2022 ST_1_2 NC_OAK THS_NAC 13631
DE IMP 2022 ST_1_2 NC_BEE THS_NAC 7977
DE IMP 2022 ST_1_2 NC_BIR THS_NAC 4884
DE IMP 2022 ST_1_2_1 NC_BIR THS_NAC 450
DE IMP 2022 ST_1_2_2 NC_BIR THS_NAC 4434
DE IMP 2022 ST_1_2 NC_POP THS_NAC 281
DE IMP 2022 ST_1_2 NC_EUC THS_NAC 551
DE IMP 2022 ST_6 CONIF THS_NAC 1277924
DE IMP 2022 ST_6 C_PIN THS_NAC 172856
DE IMP 2022 ST_6 C_FIR THS_NAC 838287
DE IMP 2022 ST_6 NCONIF THS_NAC 320187
DE IMP 2022 ST_6 NC_OAK THS_NAC 130193
DE IMP 2022 ST_6 NC_BEE THS_NAC 9630
DE IMP 2022 ST_6 NC_MAP THS_NAC 4724
DE IMP 2022 ST_6 NC_CHE THS_NAC 1284
DE IMP 2022 ST_6 NC_ASH THS_NAC 7904
DE IMP 2022 ST_6 NC_BIR THS_NAC 3520
DE IMP 2022 ST_6 NC_POP THS_NAC 4510
DE EXP 2021 ST_1_2 CONIF THS_M3 10927.383
DE EXP 2021 ST_1_2 C_PIN THS_M3 559.472
DE EXP 2021 ST_1_2_1 C_PIN THS_M3 245.941
DE EXP 2021 ST_1_2_2 C_PIN THS_M3 313.531
DE EXP 2021 ST_1_2 C_FIR THS_M3 9387.732
DE EXP 2021 ST_1_2_1 C_FIR THS_M3 7807.436
DE EXP 2021 ST_1_2_2 C_FIR THS_M3 1580.296
DE EXP 2021 ST_1_2 NCONIF THS_M3 1025.815
DE EXP 2021 ST_1_2 NC_OAK THS_M3 157.998
DE EXP 2021 ST_1_2 NC_BEE THS_M3 604.25
DE EXP 2021 ST_1_2 NC_BIR THS_M3 9.7
DE EXP 2021 ST_1_2_1 NC_BIR THS_M3 1.795
DE EXP 2021 ST_1_2_2 NC_BIR THS_M3 7.905
DE EXP 2021 ST_1_2 NC_POP THS_M3 9.911
DE EXP 2021 ST_1_2 NC_EUC THS_M3 0.001
DE EXP 2021 ST_6 CONIF THS_M3 10552.063
DE EXP 2021 ST_6 C_PIN THS_M3 1392.48
DE EXP 2021 ST_6 C_FIR THS_M3 8108.447
DE EXP 2021 ST_6 NCONIF THS_M3 781.746
DE EXP 2021 ST_6 NC_OAK THS_M3 143.636
DE EXP 2021 ST_6 NC_BEE THS_M3 527.37
DE EXP 2021 ST_6 NC_MAP THS_M3 3.464
DE EXP 2021 ST_6 NC_CHE THS_M3 0.737
DE EXP 2021 ST_6 NC_ASH THS_M3 20.638
DE EXP 2021 ST_6 NC_BIR THS_M3 5.358
DE EXP 2021 ST_6 NC_POP THS_M3 3.686
DE EXP 2021 ST_1_2 CONIF THS_NAC 873711
DE EXP 2021 ST_1_2 C_PIN THS_NAC 33345
DE EXP 2021 ST_1_2_1 C_PIN THS_NAC 18336
DE EXP 2021 ST_1_2_2 C_PIN THS_NAC 15009
DE EXP 2021 ST_1_2 C_FIR THS_NAC 776111
DE EXP 2021 ST_1_2_1 C_FIR THS_NAC 692724
DE EXP 2021 ST_1_2_2 C_FIR THS_NAC 83387
DE EXP 2021 ST_1_2 NCONIF THS_NAC 128076
DE EXP 2021 ST_1_2 NC_OAK THS_NAC 32576
DE EXP 2021 ST_1_2 NC_BEE THS_NAC 62141
DE EXP 2021 ST_1_2 NC_BIR THS_NAC 649
DE EXP 2021 ST_1_2_1 NC_BIR THS_NAC 131
DE EXP 2021 ST_1_2_2 NC_BIR THS_NAC 518
DE EXP 2021 ST_1_2 NC_POP THS_NAC 601
DE EXP 2021 ST_1_2 NC_EUC THS_NAC 1
DE EXP 2021 ST_6 CONIF THS_NAC 3277684
DE EXP 2021 ST_6 C_PIN THS_NAC 448426
DE EXP 2021 ST_6 C_FIR THS_NAC 2597398
DE EXP 2021 ST_6 NCONIF THS_NAC 435075
DE EXP 2021 ST_6 NC_OAK THS_NAC 109803
DE EXP 2021 ST_6 NC_BEE THS_NAC 223879
DE EXP 2021 ST_6 NC_MAP THS_NAC 3222
DE EXP 2021 ST_6 NC_CHE THS_NAC 741
DE EXP 2021 ST_6 NC_ASH THS_NAC 8446
DE EXP 2021 ST_6 NC_BIR THS_NAC 1487
DE EXP 2021 ST_6 NC_POP THS_NAC 1183
DE EXP 2022 ST_1_2 CONIF THS_M3 8978.226
DE EXP 2022 ST_1_2 C_PIN THS_M3 883.236
DE EXP 2022 ST_1_2_1 C_PIN THS_M3 612.231
DE EXP 2022 ST_1_2_2 C_PIN THS_M3 271.005
DE EXP 2022 ST_1_2 C_FIR THS_M3 7490.847
DE EXP 2022 ST_1_2_1 C_FIR THS_M3 5561.251
DE EXP 2022 ST_1_2_2 C_FIR THS_M3 1929.596
DE EXP 2022 ST_1_2 NCONIF THS_M3 871.031
DE EXP 2022 ST_1_2 NC_OAK THS_M3 124.205
DE EXP 2022 ST_1_2 NC_BEE THS_M3 537.7
DE EXP 2022 ST_1_2 NC_BIR THS_M3 25.157
DE EXP 2022 ST_1_2_1 NC_BIR THS_M3 1.372
DE EXP 2022 ST_1_2_2 NC_BIR THS_M3 23.785
DE EXP 2022 ST_1_2 NC_POP THS_M3 16.992
DE EXP 2022 ST_1_2 NC_EUC THS_M3 0.111
DE EXP 2022 ST_6 CONIF THS_M3 10781.401
DE EXP 2022 ST_6 C_PIN THS_M3 1568.324
DE EXP 2022 ST_6 C_FIR THS_M3 8387.165
DE EXP 2022 ST_6 NCONIF THS_M3 721.065
DE EXP 2022 ST_6 NC_OAK THS_M3 137.128
DE EXP 2022 ST_6 NC_BEE THS_M3 458.579
DE EXP 2022 ST_6 NC_MAP THS_M3 3.48
DE EXP 2022 ST_6 NC_CHE THS_M3 0.564
DE EXP 2022 ST_6 NC_ASH THS_M3 26.661
DE EXP 2022 ST_6 NC_BIR THS_M3 3.351
DE EXP 2022 ST_6 NC_POP THS_M3 3.119
DE EXP 2022 ST_1_2 CONIF THS_NAC 911744
DE EXP 2022 ST_1_2 C_PIN THS_NAC 82901
DE EXP 2022 ST_1_2_1 C_PIN THS_NAC 61225
DE EXP 2022 ST_1_2_2 C_PIN THS_NAC 21676
DE EXP 2022 ST_1_2 C_FIR THS_NAC 778798
DE EXP 2022 ST_1_2_1 C_FIR THS_NAC 627640
DE EXP 2022 ST_1_2_2 C_FIR THS_NAC 151158
DE EXP 2022 ST_1_2 NCONIF THS_NAC 130898
DE EXP 2022 ST_1_2 NC_OAK THS_NAC 32801
DE EXP 2022 ST_1_2 NC_BEE THS_NAC 63932
DE EXP 2022 ST_1_2 NC_BIR THS_NAC 3477
DE EXP 2022 ST_1_2_1 NC_BIR THS_NAC 116
DE EXP 2022 ST_1_2_2 NC_BIR THS_NAC 3361
DE EXP 2022 ST_1_2 NC_POP THS_NAC 1373
DE EXP 2022 ST_1_2 NC_EUC THS_NAC 13
DE EXP 2022 ST_6 CONIF THS_NAC 3526187
DE EXP 2022 ST_6 C_PIN THS_NAC 537740
DE EXP 2022 ST_6 C_FIR THS_NAC 2747987
DE EXP 2022 ST_6 NCONIF THS_NAC 458694
DE EXP 2022 ST_6 NC_OAK THS_NAC 113960
DE EXP 2022 ST_6 NC_BEE THS_NAC 246980
DE EXP 2022 ST_6 NC_MAP THS_NAC 3937
DE EXP 2022 ST_6 NC_CHE THS_NAC 689
DE EXP 2022 ST_6 NC_ASH THS_NAC 12339
DE EXP 2022 ST_6 NC_BIR THS_NAC 1131
DE EXP 2022 ST_6 NC_POP THS_NAC 1536
DE PRD 2021 EU2_1 TOTAL THS_M3
DE PRD 2021 EU2_1 CONIF THS_M3
DE PRD 2021 EU2_1 NCONIF THS_M3
DE PRD 2021 EU2_1_1 TOTAL THS_M3
DE PRD 2021 EU2_1_1 CONIF THS_M3
DE PRD 2021 EU2_1_1 NCONIF THS_M3
DE PRD 2021 EU2_1_2 TOTAL THS_M3
DE PRD 2021 EU2_1_2 CONIF THS_M3
DE PRD 2021 EU2_1_2 NCONIF THS_M3
DE PRD 2021 EU2_1_3 TOTAL THS_M3
DE PRD 2021 EU2_1_3 CONIF THS_M3
DE PRD 2021 EU2_1_3 NCONIF THS_M3
DE PRD 2022 EU2_1 TOTAL THS_M3
DE PRD 2022 EU2_1 CONIF THS_M3
DE PRD 2022 EU2_1 NCONIF THS_M3
DE PRD 2022 EU2_1_1 TOTAL THS_M3
DE PRD 2022 EU2_1_1 CONIF THS_M3
DE PRD 2022 EU2_1_1 NCONIF THS_M3
DE PRD 2022 EU2_1_2 TOTAL THS_M3
DE PRD 2022 EU2_1_2 CONIF THS_M3
DE PRD 2022 EU2_1_2 NCONIF THS_M3
DE PRD 2022 EU2_1_3 TOTAL THS_M3
DE PRD 2022 EU2_1_3 CONIF THS_M3
DE PRD 2022 EU2_1_3 NCONIF THS_M3

Who-to-whom matrices (ECB)

Who-to-whom matrices (European Central Bank)

  1. Who-to-whom concept
  2. Who-to-whom: main data sources
  3. Compilation of who-to-whom: two cases
  4. Who-to-whom balancing in practice
  5. Main features euro area/national who-to-whom tables
  6. Data access and visualisation
  7. Exercise 1 & 2
Languages and translations
English

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Who-to-whom matrices

Pierre Sola European Central Bank

Workshop on Financial Accounts 9 to 11 October 2023

9 to 11 October 2023 - Brussels

This document should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.

www.ecb.europa.eu ©

Overview

2

1

2 Who-to-whom: main data sources

3 Compilation of who-to-whom: two cases

“Who-to-whom” concept

5 Main features of euro area/national who-to-whom tables

6 Data access and visualisation

7 Exercises

4 Who-to-whom balancing in practice

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1. Who-to-whom concept

3

Financial accounts basic data show total assets and liabilities by sector, instrument by instrument:

S1 S11 S12K S124 S12O S128 S129 S13 S1M S2 S1 S11 S12K S124 S12O S128 S129 S13 S1M S2

F1 Monetary gold and SDRs F2 Currency and deposits F3 Debt securities F4 Loans F51 Equity F52 Investment fund shares F62 Life insurance F6O Standardized guarantees F6N Pension schemes F7 Derivatives

F8 Other accounts, trade credit

ASSETS LIABILITIES

S1: all resident sectors; S11: non-financial corporations; S12K Monetary Financial Institutions; S124: investment funds; S12O: other financial institutions; S128: insurance corporations; S129: pension funds; S13: general government; S1M: households and non-profit institutions serving households; S2: Rest-of-the-world

For each instrument, the sum of assets held by all sectors is equal to the sum of liabilities (in stocks and flow data)

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With who-to-whom data, positions and flows (transactions, revaluations, others) are broken down by counterpart sectors

Liabilities -> Assets

S11 S12 S13 S1M S2 Total

S11 S12 S13 S1M S2 Total

1. Who-to-whom concept

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Columns break down a sector’s liabilities by counterparty.

Rows break down its assets.

1. Who-to-whom concept

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• In other words, who-to-whom data identify creditors (=holders) and debtors (=issuers) simultaneously.

• They therefore provide a complete overview on sectoral interlinkages for the entire economy, consistent with macroeconomic aggregates.

• Only resident counterpart sectors are identified, i.e. non-resident counterparts are aggregated into one sector [which has some drawbacks, in the context of globalisation)]

1. Who-to-whom concept

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How useful is who-to-whom • It adds analytical value to the accounts, as showing the relations between

sectors (e.g. MFI lending to NFCs)

• In 2009, the International Monetary Fund (IMF) and the Financial Stability Board (FSB) issued The Financial Crisis and Information Gaps report => to explore information gaps and provide appropriate proposals for strengthening data collection (IMF and FSB, 2009).

This initial Data Gaps Initiative (DGI-I), endorsed by the G-20, comprised 20 recommendations focusing on three key statistical domains: i) the build-up of risks in the financial sector; ii) international financial network connections; and iii) vulnerabilities to shocks.

1. Who-to-whom concept

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Role of financing providing sectors in the external financing of euro area NFCs By type of financial instrument (left panel) and as a share in total euro area NFC liabilities (right panel)

(percent; annual data; 2013 to 2019)

Sources: ECB (EEA) and ECB calculations.

0% 5%

10% 15% 20% 25% 30% 35% 40% 45% 50%

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MFIs NFCs Other OFIs IFs ICPFs RoW Other

2013 2014 2015 2016

2017 2018 2019

0%

2%

4%

6%

8%

10%

12%

14%

16%

MFIs NFCs Other OFIs IFs ICPFs RoW Other

2013 2014 2015 2016

2017 2018 2019

1. Who-to-whom concept Example of use in monetary analysis: financing of non-financial corporations

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Data collection perspective • A new dimension: in business accounting, the institutional sector of the

counterparty is not specified

• From a compilation point of view, it entails a further challenge, but also an opportunity for enhancing quality

• For who-to-whom to be feasible, source data need to keep track of the sector of the counterparty

1. Who-to-whom concept

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Example: transactions in long term debt securities

S12K: MFIs including Eurosystem; S12O: other financial sub-sectors; S1M households and non-profit institutions serving households

1. Who-to-whom concept

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX S 66.2 -79.0 -1.4 79.1 0.7 0.0 184.5 0.0 388.3 638.4

S11 1.5 2.9 -6.4 0.0 -0.3 0.2 0.0 5.9 0.0 -0.8 0.0 S12K 394.2 72.6 -4.7 0.0 3.6 -1.2 0.0 341.7 0.0 -17.7 -0.1 S124 365.5 19.7 24.8 0.0 32.9 1.5 0.0 -36.1 0.0 322.7 0.0 S12O 50.9 -2.5 0.0 0.0 35.5 0.1 0.0 -24.7 0.0 42.5 0.0 S128 19.8 13.0 -14.4 0.0 -2.4 0.7 0.0 1.4 0.0 21.5 0.0 S129 69.2 1.2 5.7 0.0 -1.3 0.0 0.0 38.4 0.0 25.2 0.0

S13 -26.9 -0.8 -0.7 0.0 -7.8 -0.5 0.0 -14.2 0.0 -2.9 0.0 S1M -91.7 -6.7 -65.5 0.0 -5.9 -0.1 0.0 -11.3 0.0 -2.2 0.0

S2 -144.2 -33.2 -17.8 -1.4 24.8 0.0 0.0 -116.6 0.0 0.0 SX 638.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1

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2. Who-to-whom: main data sources

11

Loans (F4) and deposits (F2M) can be obtained to a large extent via who-to-whom data from banks

Actively traded securities, i.e. F3 listed shares, F511 listed shares, F52 investment fund shares, may be obtained from securities holdings statistics and securities issues statistics

Currently, most EU countries show no who-to-who data for other instruments, including: - F21 Currency - F6 insurance, pensions and standardized guarantee schemes - F7 Financial derivatives - F8 Other accounts receivable/payable

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3. Compilation of who-to-whom: two cases

12

Case 1: full information on bilateral links

Totals are the simple sum of the components

Liabilities -> Assets

S11 S12 S13 S1M S2 Total

S11 S12 S13 S1M S2 Total

This is mostly the case in euro area accounts for deposits and loans.

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Case 2: no full information on bilateral links

Sometimes only totals are known for some rows or columns

and/or totals do not come from the same source as components

and/or some bilateral links are missing

=> Need to estimate

3. Compilation of who-to-whom: two cases

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This is the case in euro area accounts, covering (i) short and long term debt securities, (ii) listed shares, and (iii) investment funds shares

In particular:

- Full details matching with totals are available only for the MFI sector

- Totals are available for Government assets and liabilities

- Details (from NCB reporting, generally based on Securities Holdings Statistics) are available for other components but they do not necessarily match with the other available totals

3. Compilation of who-to-whom: two cases

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4. Who-to-whom balancing in practice

Starting point: already balanced, but still possibly wrong!

CASE 1: totals are the simple sum of the interior

S S11 S12K S124 S12O S128 S129 S13 S1M S2 S 110.3 0.0 -20.5 -139.2 -4.7 5.4 78.3 53.2 -213.7 -131.1

S11 -20.6 13.8 0.0 -0.1 -9.6 0.2 0.0 -1.1 0.1 -24.0 0.0 S12K 20.6 116.0 0.0 -4.6 -70.4 -4.3 -1.3 55.2 48.9 -118.9 0.0 S124 -17.0 -0.9 0.0 0.9 -6.7 0.0 0.0 3.1 1.4 -14.8 0.0 S12O -117.4 6.2 0.0 -2.9 -76.9 1.8 6.5 1.2 1.3 -54.6 0.0 S128 1.7 0.5 0.0 0.0 -0.6 2.9 0.1 -1.2 0.8 -0.8 0.0 S129 1.4 0.3 0.0 0.0 4.5 0.0 0.0 -4.4 0.3 0.7 0.0

S13 41.2 17.8 0.0 0.1 2.2 0.2 0.1 21.7 0.4 -1.4 0.0 S1M 1.3 1.1 0.0 0.0 0.5 -0.1 0.0 -0.1 -0.1 0.0 0.0

S2 -42.2 -44.5 0.0 -14.0 17.8 -5.4 0.0 3.8 0.0 0.0 SX -131.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Loans: wrong version

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4. Who-to-whom balancing in practice Be cautious in particular with:

- inter-company loans - sector allocation from each data source (esp. where estimations are made) - instrument allocation: e.g. trade credits versus loans

Loans: improved version S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 145.8 0.0 -22.4 -125.9 -6.4 5.4 82.8 58.1 -152.6 -15.1 S11 16.2 -6.6 0.0 0.1 -23.4 0.3 0.1 -0.9 0.1 46.5 0.0

S12K 15.7 114.3 0.0 -4.5 -70.8 -3.6 -1.3 58.5 49.0 -125.9 0.0 S124 2.6 -0.6 0.0 1.0 -3.9 0.0 0.0 2.8 3.3 0.0 0.0 S12O -89.9 -2.0 0.0 -3.5 -24.3 0.3 6.5 0.9 3.2 -70.9 0.0 S128 4.0 2.8 0.0 -0.1 0.3 2.0 0.1 -1.1 1.8 -1.8 0.0 S129 1.7 0.3 0.0 0.0 4.5 0.0 0.0 -4.3 0.3 0.8 0.0

S13 40.4 17.9 0.0 0.1 0.4 0.1 0.1 22.9 0.3 -1.3 0.0 S1M 0.7 0.4 0.0 0.0 0.4 -0.1 0.0 0.0 0.1 0.0 0.0

S2 -6.6 19.1 0.0 -15.3 -9.1 -5.4 0.0 4.1 0.0 0.0 SX -15.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

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S S11 S12K S124 S12O S128 S129 S13 S1M S2

S 18.5 23.0 1.0 -59.1 -0.5 0.0 63.5 0.0 69.5 Liab:116.0

S11 0.9 -0.8 -0.1 0.0 0.9 0.0 0.0 0.4 0.0 -1.0 1.3

S12K 54.9 6.8 16.3 0.0 1.4 0.0 0.0 35.7 0.0 30.4 -35.7

S124 37.8 8.2 8.7 0.0 2.5 -0.2 0.0 -0.9 0.0 19.4 0.0

S12O -81.0 1.1 -3.5 0.0 -87.8 0.2 0.0 5.7 0.0 14.3 -11.0

S128 20.0 0.1 2.8 0.0 6.3 -0.1 0.0 7.0 0.0 4.0 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.5 0.0 1.6 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.1 0.0 -0.8 0.1

S1M -7.3 -0.3 -4.7 0.0 -1.2 0.0 0.0 -2.5 0.0 1.5 0.0

S2 30.4 1.6 -0.9 1.0 8.9 0.0 0.0 19.7 0.0 0.0

SX Total assets: 55.7 1.2 3.0 0.0 11.2 -0.4 0.0 0.1 0.0 0.0

Sum of interior components: 101.0

4. Who-to-whom balancing in practice

Starting point: components and totals from various sources

CASE 2: totals and interior have different sources

Debt securities

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4. Who-to-whom balancing in practice

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.5 23.0 1.0 -59.1 -0.5 0.0 63.5 0.0 69.5 116.0

S11 0.9

S12K 54.9

S124 37.8

S12O -81.0

S128 20.0

S129 4.5

S13 -4.3

S1M -7.3

S2 30.4

SX 55.7

First step: balancing total assets and liabilities by sector

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4. Who-to-whom balancing in practice

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 0.0 0.0 0.0 -20.0 0.0 0.0 0.0 0.0 -15.0 -35.0

S11 0.0

S12K 0.0

S124 0.0

S12O 20.0

S128 0.0

S129 0.0

S13 0.0

S1M 0.0

S2 0.0

SX 20.0

First step: balancing total assets and liabilities by sector – 3 adjustments

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4. Who-to-whom balancing in practice First step: balancing total assets and liabilities by sector – 3 adjustments

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.5 23.0 1.0 -79.1 -0.5 0.0 63.5 0.0 54.5 81.0

S11 0.9 -0.8 -0.1 0.0 0.9 0.0 0.0 0.4 0.0 -1.0 1.3

S12K 54.9 6.8 16.3 0.0 1.4 0.0 0.0 35.7 0.0 30.4 -35.7

S124 37.8 8.2 8.7 0.0 2.5 -0.2 0.0 -0.9 0.0 19.4 0.0

S12O -61.0 1.1 -3.5 0.0 -87.8 0.2 0.0 5.7 0.0 14.3 9.0

S128 20.0 0.1 2.8 0.0 6.3 -0.1 0.0 7.0 0.0 4.0 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.5 0.0 1.6 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.1 0.0 -0.8 0.1

S1M -7.3 -0.3 -4.7 0.0 -1.2 0.0 0.0 -2.5 0.0 1.5 0.0

S2 30.4 1.6 -0.9 1.0 8.9 0.0 0.0 19.7 0.0 0.0

SX 75.7 1.2 3.0 0.0 -8.8 -0.4 0.0 0.1 0.0 -15.0

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4. Who-to-whom balancing in practice First step: balancing total assets and liabilities by sector – 3 adjustments

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.5 23.0 1.0 -79.1 -0.5 0.0 63.5 0.0 54.5 81.0

S11 0.9 -0.8 -0.1 0.0 0.9 0.0 0.0 0.4 0.0 -1.0 1.3

S12K 54.9 6.8 16.3 0.0 1.4 0.0 0.0 35.7 0.0 30.4 -35.7

S124 37.8 8.2 8.7 0.0 2.5 -0.2 0.0 -0.9 0.0 19.4 0.0

S12O -61.0 1.1 -3.5 0.0 -87.8 0.2 0.0 5.7 0.0 14.3 9.0

S128 20.0 0.1 2.8 0.0 6.3 -0.1 0.0 7.0 0.0 4.0 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.5 0.0 1.6 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.1 0.0 -0.8 0.1

S1M -7.3 -0.3 -4.7 0.0 -1.2 0.0 0.0 -2.5 0.0 1.5 0.0

S2 30.4 1.6 -0.9 1.0 8.9 0.0 0.0 19.7 0.0 0.0

SX 75.7 1.2 3.0 0.0 -8.8 -0.4 0.0 0.1 0.0 -15.0

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S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 0.0 0.0 0.0 -20.0 0.0 0.0 0.0 0.0 -15.0 -35.0

S11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S12K 0.0 0.0 0.0 0.0 -15.0 0.0 0.0 0.0 0.0 -15.0 30.0

S124 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S12O 20.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.0

S128 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S129 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S1M 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

SX 20.0 0.0 0.0 0.0 -5.0 0.0 0.0 0.0 0.0 0.0

4. Who-to-whom balancing in practice Second step: adjustments within the matrix, where main gaps are identified

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S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.5 23.0 1.0 -79.1 -0.5 0.0 63.5 0.0 54.5 81.0

S11 0.9 -0.8 -0.1 0.0 0.9 0.0 0.0 0.4 0.0 -1.0 1.3

S12K 54.9 6.8 16.3 0.0 -13.6 0.0 0.0 35.7 0.0 15.4 -5.7

S124 37.8 8.2 8.7 0.0 2.5 -0.2 0.0 -0.9 0.0 19.4 0.0

S12O -61.0 1.1 -3.5 0.0 -87.8 0.2 0.0 5.7 0.0 14.3 9.0

S128 20.0 0.1 2.8 0.0 6.3 -0.1 0.0 7.0 0.0 4.0 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.5 0.0 1.6 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.1 0.0 -0.8 0.1

S1M -7.3 -0.3 -4.7 0.0 -1.2 0.0 0.0 -2.5 0.0 1.5 0.0

S2 30.4 1.6 -0.9 1.0 8.9 0.0 0.0 19.7 0.0 0.0

SX 75.7 1.2 3.0 0.0 6.2 -0.4 0.0 0.1 0.0 0.0

4. Who-to-whom balancing in practice Outcome of the second step: only small gaps remain

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S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S -0.1 0.0 0.0 -0.7 0.0 0.0 0.0 0.0 -1.1 -1.9

S11 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.6 -1.3

S12K 0.0 0.0 0.0 0.0 -0.8 0.0 0.0 -4.1 0.0 -0.8 5.7

S124 1.4 0.3 0.3 0.0 0.2 0.0 0.0 0.8 0.0 -0.1 0.0

S12O 0.1 0.2 0.4 0.0 7.1 -0.4 0.0 0.8 0.0 1.0 -9.0

S128 0.7 0.5 0.6 0.0 -0.1 0.0 0.0 1.3 0.0 -1.6 0.0

S129 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 -0.1 0.0

S13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1

S1M 0.0 0.0 0.3 0.0 -0.1 0.0 0.0 -0.1 0.0 -0.1 0.0

S2 1.1 0.1 1.4 0.0 -0.9 0.0 0.0 0.5 0.0 0.0

SX 3.4 -1.2 -3.0 0.0 -6.2 0.4 0.0 -0.1 0.0 0.0

4. Who-to-whom balancing in practice Third step: automated adjustments to close remaining discrepancies

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S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.4 23.0 1.0 -79.7 -0.5 0.0 63.5 0.0 53.4 79.1

S11 0.9 -0.7 0.0 0.0 1.0 0.0 0.0 1.1 0.0 -0.4 0.0

S12K 54.9 6.8 16.3 0.0 -14.4 0.0 0.0 31.6 0.0 14.6 0.0

S124 39.2 8.5 9.0 0.0 2.7 -0.2 0.0 -0.1 0.0 19.3 0.0

S12O -60.9 1.3 -3.1 0.0 -80.7 -0.2 0.0 6.5 0.0 15.3 0.0

S128 20.8 0.5 3.4 0.0 6.2 -0.1 0.0 8.3 0.0 2.4 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.6 0.0 1.5 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.0 0.0 -0.8 0.0

S1M -7.3 -0.3 -4.4 0.0 -1.3 0.0 0.0 -2.7 0.0 1.5 0.0

S2 31.4 1.7 0.5 1.0 8.0 0.0 0.0 20.2 0.0 0.0 0.0

SX 79.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

4. Who-to-whom balancing in practice Outcome of the third step: balanced who-to-whom table

Remark: some algorithms can also be used to balance matrices – with some caution

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4. Who-to-whom balancing in practice CASE 3: balancing price/other changes

Listed shares

Consider ratio price change/initial position

But take into account possible wrong allocation of volume changes

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX S 716.9 58.7 0.0 123.9 21.3 0.0 0.0 0.0 440.4 1,361.2

S11 181.4 176.2 1.2 0.0 2.7 1.3 0.0 0.0 0.0 0.0 0.0 S12K 18.0 7.2 2.4 0.0 3.3 0.9 0.0 0.0 0.0 4.2 0.0 S124 485.1 96.4 8.0 0.0 6.0 5.5 0.0 0.0 0.0 369.2 0.0 S12O 63.8 35.9 7.3 0.0 18.2 1.4 0.0 0.0 0.0 1.0 0.0 S128 19.4 9.9 0.5 0.0 1.8 0.8 0.0 0.0 0.0 6.3 0.0 S129 31.2 5.8 0.3 0.0 0.6 0.2 0.0 0.0 0.0 24.3 0.0

S13 41.1 34.1 2.4 0.0 1.2 0.4 0.0 0.0 0.0 3.0 0.0 S1M 131.1 74.4 9.1 0.0 4.9 10.4 0.0 0.0 0.0 32.3 0.0

S2 390.2 276.9 27.7 0.0 85.4 0.2 0.0 0.0 0.0 0.0 0.0 SX 1,361.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

POSITIONS S S11 S12K S124 S12O S128 S129 S13 S1M S2 S 4,986.7 386.1 0.0 1,037.3 177.4 NA 0.2 NA 2,958.3

S11 1,514.0 1157.7 29.5 0.0 54.8 9.7 NA 0.0 NA 105.2 S12K 247.4 137.1 19.6 0.0 27.2 7.9 NA 0.0 NA 232.1 S124 3,195.6 764.7 72.0 5.0 68.8 37.8 NA 0.0 NA 2168.0 S12O 867.1 223.8 24.5 0.0 114.1 5.3 NA 7.8 NA 388.6 S128 182.9 101.6 4.6 0.0 14.6 20.1 NA 0.0 NA 54.5 S129 217.9 49.8 2.3 0.0 3.9 1.6 NA 0.2 NA 167.3

S13 295.9 240.4 25.0 0.0 9.4 3.3 NA 0.0 NA 16.0 S1M 891.2 570.8 53.1 0.0 84.2 31.0 NA 0.0 NA 174.6

S2 2,729.4 1830.5 132.4 0.0 548.3 23.1 NA 0.1 NA PERCENTAGES S S11 S12K S124 S12O S128 S129 S13 S1M S2

S 14.4 15.2 0.0 11.9 12.0 NA 0.0 NA 14.9 S11 12.0 15.2 3.9 NA 4.9 13.5 NA -62.3 NA 0.0

S12K 7.3 5.3 12.0 NA 12.1 11.3 NA 18.8 NA 1.8 S124 15.2 12.6 11.0 0.0 8.7 14.6 NA 0.4 NA 17.0 S12O 7.4 16.0 29.7 NA 16.0 25.9 NA 0.0 NA 0.3 S128 10.6 9.8 12.0 NA 12.0 4.2 NA NA NA 11.6 S129 14.3 11.7 12.4 NA 14.2 15.0 NA 0.0 NA 14.5

S13 13.9 14.2 9.5 NA 12.5 12.3 NA 0.0 NA 18.9 S1M 14.7 13.0 17.1 NA 5.8 33.7 NA NA NA 18.5

S2 14.3 15.1 20.9 NA 15.6 1.0 NA 0.0 NA

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5. Main features euro area/national who-to-whom tables

27

Data types Stocks, transactions, other changes

Holder residency Euro area, and 27 EU countries

Holder sectors 11 to 12 sectors (central banks are only available for some instruments)

Issuer residency Euro area / non-euro area

Issuer sectors 10 to 11 sectors for euro area issuers No sector detail for non-euro area issuers

Instruments Securities (except unlisted shares), loans and deposits

Series length 13Q4 to 23Q1 (securities) 99Q1 to 23Q1 (loans and deposits)

Timeliness T+120 (securities – euro area accounts) T+102: country data T+ 94 (deposits and loans – euro area accounts)

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Quarterly press release on euro area economic and financial developments by institutional sector - Full release - Annex Table 2.2 (for households) and Table 3.2 (for non-financial corporations) http://www.ecb.europa.eu/press/pr/stats/ffi/html/index.en.html

6. Data access and visualisation

Who-to-whom data lead to a significant increase of data volume. This requires statisticians / institutions to develop data visualisation tools to help the users

http://www.ecb.europa.eu/press/pr/stats/ffi/html/index.en.html

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6. Data access and visualisation

29

Euro Area Accounts Report in SDW: http://sdw.ecb.europa.eu/reports.do?node=1000005335

http://sdw.ecb.europa.eu/reports.do?node=1000005335

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Which questions may be answered by who-to-whom data?

• Did Government lend much to NFCs in 2020?

• Did Government issue large amount of debt securities?

• Did NFCs obtain much financing from non-banking financial institutions?

• Did Households increase their deposits in 2020?

7. Exercise 1

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Transactions in long term debt securities 7. Exercise 2

S11: NFCs; S12K: Banking sector including Central Bank; S124: Investment Funds; S12O: Other financial sub-sectors; S128: Insurance corporations; S129: Pension Funds; S13 Government; S1M Households and non-profit institutions serving households; S2: RoW

S S11 S12K S124 S12O S128 S129 S13 S1M S2

S 64.0 -78.4 -1.2 84.3 0.7 0.0 186.1 0.0 363.0 S11 -10.2 3.0 -6.4 0.0 -1.5 0.2 0.0 1.1 0.0 -6.6

S12K 410.1 70.8 -4.1 0.0 32.5 -1.0 0.0 332.9 0.0 -21.0 S124 348.0 16.0 31.6 0.0 30.9 1.5 0.0 -47.2 0.0 315.1 S12O 32.6 2.7 1.3 0.0 33.8 -0.1 0.0 -42.1 0.0 36.9 S128 10.7 8.3 -24.8 0.0 -1.8 0.8 0.0 11.9 0.0 16.3 S129 70.6 1.2 5.6 0.0 -1.2 0.0 0.0 39.8 0.0 25.1

S13 -26.5 -1.0 -0.7 0.0 -7.1 -0.4 0.0 -15.3 0.0 -1.9 S1M -80.9 -3.1 -79.7 0.0 -1.1 -0.3 0.0 4.1 0.0 -0.8

S2 -135.9 -34.1 -1.3 -1.2 -0.3 0.0 0.0 -99.1 0.0 0.0

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Questions on the table in the previous slide: • Which sectors have been the main net buyers of debt securities, and which

were the (euro area) sectors from whom they bought?

• Conversely, which sectors have been net sellers of debt securities over this period?

• Have net sellers sold to net buyers?

• How much was issued over this period by euro area residents?

• How large were the purchases of euro area debt securities by euro area residents?

7. Exercise 2

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Answers:

• Banks (including central banks) have purchased high amounts of securities, issued mainly by government.

• Investment funds (S124) purchased a lot of long term debt securities, mainly issued by non-euro area residents (S2)

• Conversely, non-euro area investors and households (S1M) have been net sellers of debt securities over this period.

7. Exercise 2

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Answers:

• Non-residents sold mainly government debt securities, while households sold mainly securities issued by the banking sector.

• However, we do not know which sectors “transacted” with whom

• Total net issues of debt securities by euro area residents reached EUR 255 billion, while net purchases by residents of securities issued by euro area residents reached EUR 391 billion

7. Exercise 2

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  • Who-to-whom matrices
  • Overview
  • 1. Who-to-whom concept
  • 1. Who-to-whom concept
  • 1. Who-to-whom concept
  • 1. Who-to-whom concept
  • 1. Who-to-whom concept
  • Slide Number 8
  • 1. Who-to-whom concept
  • 1. Who-to-whom concept
  • 2. Who-to-whom: main data sources
  • 3. Compilation of who-to-whom: two cases
  • 3. Compilation of who-to-whom: two cases
  • Slide Number 14
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 4. Who-to-whom balancing in practice
  • 5. Main features euro area/national who-to-whom tables�
  • 6. Data access and visualisation�
  • 6. Data access and visualisation�
  • 7. Exercise 1
  • 7. Exercise 2
  • 7. Exercise 2
  • 7. Exercise 2
  • 7. Exercise 2
  • Slide Number 35
Russian

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Матрицы «от кого к кому»

Пьер Сола Европейский центральный банк

Рабочее совещание по финансовым счетам9 to 11 October 2023

9 – 11 октября 2023 г. – Брюссель

Настоящий документ не должен рассматриваться как отражающий точку зрения Европейского центрального банка (ЕЦБ). Высказанные мнения принадлежат авторам и не обязательно отражают точку зрения ЕЦБ.

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Резюме

2

1

2 «От кого к кому»: основные источники данных

3 Составление «От кого к кому»: два случая

Концепт «От кого к кому»

5 Основные особенности таблиц зоны евро / национальных таблиц «От кого к кому»

6 Доступ к данным и визуализация

7 Упражнения

4 Балансирование «От кого к кому» на практике

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1. Концепт «От кого к кому»

3

Основные данные финансовых счетов показывают суммарные активы и обязательства по секторам, инструменты по их типам:

S1: все секторы резиденты; S11: нефинансовые корпорации; S12K: денежно-кредитные финансовые учреждения; S124: инвестиционные фонды; S12O: другие финансовые учреждения; S128: страховые корпорации; S129: пенсионные фонды; S13: органы государственного управления; S1M: домашние хозяйства и некоммерческие организации, обслуживающие домохозяйства; S2: остальной мир

По каждому инструменту сумма активов, принадлежащих всем секторам, равна сумме обязательств (в данных по запасам и потокам)

S1 S11 S12K S124 S12O S128 S129 S13 S1M S2 S1 S11 S12K S124 S12O S128 S129 S13 S1M S2

F1 Монетарное золото и СПЗ F2 Наличная валюта и депозиты F3 Долговые ценные бумаги F4 Ссуды F51 Акционерный капитал F52 Аакции инвестиционных фондов F62 Срахование жизни F6O Стандартизированные гарантии F6N Пенсионные програмы F7 Производные финансовые

инструменты F8 Прочая задолженность,

коммерческие кредиты и авансы

АКТИВЫ ОБЯЗАТЕЛЬСТВА

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С помощью данных «от кого к кому» позиции и потоки ( транзакции, переоценки и др.) разбиваются по секторам-контрагентам

1. Концепт «От кого к кому»

Обязательства -> Активы

S11 S12 S13 S1M S2 Всего

S11 S12 S13 S1M S2 Всего

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В столбцах представлены обязательства сектора в разбивке по контрагентам. Строки в разбивке по их активам.

1. Концепт «От кого к кому»

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• Иными словами, данные «от кого к кому» позволяют одновременно идентифицировать кредиторов (=держателей) и должников (=эмитентов).

• Таким образом, они дают полную картину секторальных взаимосвязей для всей экономики, соответствующую макроэкономическим агрегатам.

• При этом идентифицируются только сектора-контрагенты являющиеся резидентами, т.е. нерезидентные контрагенты объединяются в один сектор [что имеет ряд недостатков в условиях глобализации].

1. Концепт «От кого к кому»

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Насколько полезно «от кого к кому» • Он придает аналитическую ценность отчетности, поскольку показывает

взаимосвязи между секторами (например, ДФУ кредитирует НФК)

• В 2009 году Международный валютный фонд (МВФ) и Совет по финансовой стабильности (СФС) выпустили доклад «Финансовый кризис и информационные пробелы» => с целью изучения информационных пробелов и выработки соответствующих предложений по усилению сбора данных (IMF and FSB, 2009).

Эта первоначальная Инициатива по устранению пробелов в данных (DGI-I), одобренная G-20, включала 20 рекомендаций, сосредоточенных на трех ключевых областях статистики: i) нарастание рисков в финансовом секторе; ii) связи международных финансовых сетей; iii) уязвимость к шокам.:

1. Концепт «От кого к кому»

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Роль секторов, предоставляющих финансирование, во внешнем финансировании НФК зоны евро По видам финансовых инструментов (левая панель) и как доля в общем объеме обязательств НФК зоны евро (правая панель)

(процентов; данные за год; 2013 - 2019 гг.)

Источники: ЕЦБ (ЕЭЗ) и расчеты ЕЦБ.

1. Концепт «От кого к кому» Пример использования в монетарном анализе: финансирование

нефинансовых корпораций

0%

2%

4%

6%

8%

10%

12%

14%

16%

ДФУ НФК Другие ДФП ИФ СКПФ ОМ Другие

2013 2014 2015 2016

2017 2018 2019

Presenter Notes
Presentation Notes
ЕЭЗ – Европейская экономическая зона; ИФ – инвестиционные фонды; ДФП - другие финансовые посредники; СКПФ – страховые компании и пенсионные фонды; ОМ – остальной мир;

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Перспектива сбора данных • Новое измерение: в финасовом (бухгалтерском) учете

институциональный сектор контрагента не указывается.

• С точки зрения составления данных это создает дополнительные трудности, но также открывает возможности для повышения качества

• Для того чтобы можно было определить «От кого к кому», исходные данные должны содержать информацию о секторе контрагента

1. Концепт «От кого к кому»

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Пример: операции с долгосрочными долговыми ценными бумагами S S11 S12K S121 S12T S124 S12O S128 S129 S13 S1M S2

S 66.2 -79.0 0.0 -79.0 -1.4 79.1 0.7 0.0 184.5 0.0 388.3 S11 1.5 2.9 -6.4 0.0 -6.4 0.0 -0.3 0.2 0.0 5.9 0.0 -0.8

S12K 394.2 72.6 -4.7 0.0 -4.7 0.0 3.6 -1.2 0.0 341.7 0.0 -17.7 S121 696.7 63.2 61.8 0.0 61.8 0.0 59.6 0.3 0.0 496.5 0.0 15.4 S12T -302.5 9.4 -66.5 0.0 -66.5 0.0 -56.0 -1.5 0.0 -154.8 0.0 -33.1 S124 365.5 19.7 24.9 0.0 24.9 0.0 32.9 1.5 0.0 -36.1 0.0 322.7 S12O 50.9 -2.5 0.0 0.0 0.0 0.0 35.5 0.1 0.0 -24.7 0.0 42.5 S128 19.8 13.0 -14.4 0.0 -14.4 0.0 -2.4 0.7 0.0 1.5 0.0 21.5 S129 69.2 1.2 5.7 0.0 5.7 0.0 -1.3 0.0 0.0 38.4 0.0 25.2

S13 -26.9 -0.8 -0.7 0.0 -0.7 0.0 -7.8 -0.5 0.0 -14.2 0.0 -2.9 S1M -91.7 -6.7 -65.5 0.0 -65.5 0.0 -6.0 -0.1 0.0 -11.3 0.0 -2.2

S2 -144.2 -33.2 -17.8 0.0 -17.8 -1.4 24.8 0.0 0.0 -116.6 0.0 0.0

S12K: ДФУ, включая Eurosystem; S12O: другие финансовые подсектора; S1M Домашние хозяйства и некоммерческие организации, обслуживающие домохозяйства

1. Концепт «От кого к кому»

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2. «От кого к кому»: основные источники данных

11

Данные о ссудах (F4) и депозитах (F2M) в значительной степени могут быть получены на основе данных банков «от кого к кому»

Активно продаваемые ценные бумаги, т.е. F3 акции, включенные в листинг, F511 акции, включенные в листинг, F52 акции инвестиционных фондов, могут быть получены из статистики владения ценными бумагами и статистики выпусков ценных бумаг

В настоящее время в большинстве стран ЕС отсутствуют данные по другим инструментам, включая: - F21 Наличная валюта - F6 Программы страхования, пенсионного обеспечения и стандартизированных гарантийных схем - F7 Производные финансовые инструменты - F8 Прочая дебиторская/кредиторская задолженность

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3. Составление «От кого к кому»: два случая

12

Случай 1: полная информация о двусторонних связях

Итоговые показатели представляют собой простую сумму компонентов

В основном это касается счетов зоны евро по депозитам и ссудам.

Обязательства -> Активы

S11 S12 S13 S1M S2 Всего

S11 S12 S13 S1M S2 Всего

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Случай 2: отсутствие полной информации о двусторонних связях

Иногда известны только итоговые значения для некоторых строк или столбцов

и/или итоговые значения не из того же источника, что и компоненты

и/или отсутствуют некоторые двусторонние связи

=> Необходима оценка

3. Составление «От кого к кому»: два случая

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Это касается счетов зоны евро, охватывающих (i) краткосрочные и долгосрочные долговые ценные бумаги, (ii) акции, внесенные в листинг, и (iii) акции инвестиционных фондов

В частности:

- Полные данные, совпадающие с итоговыми показателями, доступны только для сектора МФО

- Итоговые данные доступны для правительственных активов и обязательств

- По другим компонентам имеются подробные данные (из отчетности НЦБ, как правило, основанные на статистике владения ценными бумагами), но они не всегда совпадают с другими имеющимися итоговыми данными

3. Составление «От кого к кому»: два случая

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4. Балансирование «От кого к кому» на практике

Исходная точка: уже сбалансировано, но все еще возможно ошибиться!

СЛУЧАЙ 1: итоговые показатели это простая сумма внутренних компонентов

S S11 S12K S124 S12O S128 S129 S13 S1M S2 S 110.3 0.0 -20.5 -139.2 -4.7 5.4 78.3 53.2 -213.7 -131.1

S11 -20.6 13.8 0.0 -0.1 -9.6 0.2 0.0 -1.1 0.1 -24.0 0.0 S12K 20.6 116.0 0.0 -4.6 -70.4 -4.3 -1.3 55.2 48.9 -118.9 0.0 S124 -17.0 -0.9 0.0 0.9 -6.7 0.0 0.0 3.1 1.4 -14.8 0.0 S12O -117.4 6.2 0.0 -2.9 -76.9 1.8 6.5 1.2 1.3 -54.6 0.0 S128 1.7 0.5 0.0 0.0 -0.6 2.9 0.1 -1.2 0.8 -0.8 0.0 S129 1.4 0.3 0.0 0.0 4.5 0.0 0.0 -4.4 0.3 0.7 0.0

S13 41.2 17.8 0.0 0.1 2.2 0.2 0.1 21.7 0.4 -1.4 0.0 S1M 1.3 1.1 0.0 0.0 0.5 -0.1 0.0 -0.1 -0.1 0.0 0.0

S2 -42.2 -44.5 0.0 -14.0 17.8 -5.4 0.0 3.8 0.0 0.0 SX -131.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Ссуды: неправильная версия

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4. Балансирование «От кого к кому» на практике Особенно осторожно следует подходить к: - межфирменных ссуды - распределение по секторам из каждого источника данных (особенно в тех случаях, когда делаются оценки) - распределение инструментов: например, торговые кредиты против ссуд

Ссуды: улучшенная версия S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 145.8 0.0 -22.4 -125.9 -6.4 5.4 82.8 58.1 -152.6 -15.1 S11 16.2 -6.6 0.0 0.1 -23.4 0.3 0.1 -0.9 0.1 46.5 0.0

S12K 15.7 114.3 0.0 -4.5 -70.8 -3.6 -1.3 58.5 49.0 -125.9 0.0 S124 2.6 -0.6 0.0 1.0 -3.9 0.0 0.0 2.8 3.3 0.0 0.0 S12O -89.9 -2.0 0.0 -3.5 -24.3 0.3 6.5 0.9 3.2 -70.9 0.0 S128 4.0 2.8 0.0 -0.1 0.3 2.0 0.1 -1.1 1.8 -1.8 0.0 S129 1.7 0.3 0.0 0.0 4.5 0.0 0.0 -4.3 0.3 0.8 0.0

S13 40.4 17.9 0.0 0.1 0.4 0.1 0.1 22.9 0.3 -1.3 0.0 S1M 0.7 0.4 0.0 0.0 0.4 -0.1 0.0 0.0 0.1 0.0 0.0

S2 -6.6 19.1 0.0 -15.3 -9.1 -5.4 0.0 4.1 0.0 0.0 SX -15.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

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S S11 S12K S124 S12O S128 S129 S13 S1M S2

S 18.5 23.0 1.0 -59.1 -0.5 0.0 63.5 0.0 69.5 Обяз:116.0

S11 0.9 -0.8 -0.1 0.0 0.9 0.0 0.0 0.4 0.0 -1.0 1.3

S12K 54.9 6.8 16.3 0.0 1.4 0.0 0.0 35.7 0.0 30.4 -35.7

S124 37.8 8.2 8.7 0.0 2.5 -0.2 0.0 -0.9 0.0 19.4 0.0

S12O -81.0 1.1 -3.5 0.0 -87.8 0.2 0.0 5.7 0.0 14.3 -11.0

S128 20.0 0.1 2.8 0.0 6.3 -0.1 0.0 7.0 0.0 4.0 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.5 0.0 1.6 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.1 0.0 -0.8 0.1

S1M -7.3 -0.3 -4.7 0.0 -1.2 0.0 0.0 -2.5 0.0 1.5 0.0

S2 30.4 1.6 -0.9 1.0 8.9 0.0 0.0 19.7 0.0 0.0

SX Всего активов: 55.7 1.2 3.0 0.0 11.2 -0.4 0.0 0.1 0.0 0.0

Сумма внутренних компонентов: 101.0

4. Балансирование «От кого к кому» на практике

Исходная точка: компоненты и итоговые показатели из различных источников

ПРИМЕР 2: итоговые и внутренние показатели имеют разные источники

Долговые ценные бумаги

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4. Балансирование «От кого к кому» на практике

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.5 23.0 1.0 -59.1 -0.5 0.0 63.5 0.0 69.5 116.0

S11 0.9

S12K 54.9

S124 37.8

S12O -81.0

S128 20.0

S129 4.5

S13 -4.3

S1M -7.3

S2 30.4

SX 55.7

Первый шаг: балансировка суммарных активов и обязательств по секторам

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4. Балансирование «От кого к кому» на практике

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 0.0 0.0 0.0 -20.0 0.0 0.0 0.0 0.0 -15.0 -35.0

S11 0.0

S12K 0.0

S124 0.0

S12O 20.0

S128 0.0

S129 0.0

S13 0.0

S1M 0.0

S2 0.0

SX 20.0

Первый шаг: балансировка суммарных активов и обязательств по секторам – 3 корректировки

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4. Балансирование «От кого к кому» на практике Первый шаг: балансировка суммарных активов и обязательств по секторам – 3 корректировки

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.5 23.0 1.0 -79.1 -0.5 0.0 63.5 0.0 54.5 81.0

S11 0.9 -0.8 -0.1 0.0 0.9 0.0 0.0 0.4 0.0 -1.0 1.3

S12K 54.9 6.8 16.3 0.0 1.4 0.0 0.0 35.7 0.0 30.4 -35.7

S124 37.8 8.2 8.7 0.0 2.5 -0.2 0.0 -0.9 0.0 19.4 0.0

S12O -61.0 1.1 -3.5 0.0 -87.8 0.2 0.0 5.7 0.0 14.3 9.0

S128 20.0 0.1 2.8 0.0 6.3 -0.1 0.0 7.0 0.0 4.0 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.5 0.0 1.6 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.1 0.0 -0.8 0.1

S1M -7.3 -0.3 -4.7 0.0 -1.2 0.0 0.0 -2.5 0.0 1.5 0.0

S2 30.4 1.6 -0.9 1.0 8.9 0.0 0.0 19.7 0.0 0.0

SX 75.7 1.2 3.0 0.0 -8.8 -0.4 0.0 0.1 0.0 -15.0

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4. Балансирование «От кого к кому» на практике Первый шаг: балансировка суммарных активов и обязательств по секторам – 3 корректировки

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.5 23.0 1.0 -79.1 -0.5 0.0 63.5 0.0 54.5 81.0

S11 0.9 -0.8 -0.1 0.0 0.9 0.0 0.0 0.4 0.0 -1.0 1.3

S12K 54.9 6.8 16.3 0.0 1.4 0.0 0.0 35.7 0.0 30.4 -35.7

S124 37.8 8.2 8.7 0.0 2.5 -0.2 0.0 -0.9 0.0 19.4 0.0

S12O -61.0 1.1 -3.5 0.0 -87.8 0.2 0.0 5.7 0.0 14.3 9.0

S128 20.0 0.1 2.8 0.0 6.3 -0.1 0.0 7.0 0.0 4.0 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.5 0.0 1.6 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.1 0.0 -0.8 0.1

S1M -7.3 -0.3 -4.7 0.0 -1.2 0.0 0.0 -2.5 0.0 1.5 0.0

S2 30.4 1.6 -0.9 1.0 8.9 0.0 0.0 19.7 0.0 0.0

SX 75.7 1.2 3.0 0.0 -8.8 -0.4 0.0 0.1 0.0 -15.0

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S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 0.0 0.0 0.0 -20.0 0.0 0.0 0.0 0.0 -15.0 -35.0

S11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S12K 0.0 0.0 0.0 0.0 -15.0 0.0 0.0 0.0 0.0 -15.0 30.0

S124 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S12O 20.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.0

S128 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S129 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S1M 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

S2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

SX 20.0 0.0 0.0 0.0 -5.0 0.0 0.0 0.0 0.0 0.0

4. Балансирование «От кого к кому» на практике Второй этап: корректировка в рамках матрицы, где выявляются основные пробелы

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S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.5 23.0 1.0 -79.1 -0.5 0.0 63.5 0.0 54.5 81.0

S11 0.9 -0.8 -0.1 0.0 0.9 0.0 0.0 0.4 0.0 -1.0 1.3

S12K 54.9 6.8 16.3 0.0 -13.6 0.0 0.0 35.7 0.0 15.4 -5.7

S124 37.8 8.2 8.7 0.0 2.5 -0.2 0.0 -0.9 0.0 19.4 0.0

S12O -61.0 1.1 -3.5 0.0 -87.8 0.2 0.0 5.7 0.0 14.3 9.0

S128 20.0 0.1 2.8 0.0 6.3 -0.1 0.0 7.0 0.0 4.0 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.5 0.0 1.6 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.1 0.0 -0.8 0.1

S1M -7.3 -0.3 -4.7 0.0 -1.2 0.0 0.0 -2.5 0.0 1.5 0.0

S2 30.4 1.6 -0.9 1.0 8.9 0.0 0.0 19.7 0.0 0.0

SX 75.7 1.2 3.0 0.0 6.2 -0.4 0.0 0.1 0.0 0.0

4. Балансирование «От кого к кому» на практике Итоги второго этапа: остались лишь небольшие пробелы

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S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S -0.1 0.0 0.0 -0.7 0.0 0.0 0.0 0.0 -1.1 -1.9

S11 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.6 -1.3

S12K 0.0 0.0 0.0 0.0 -0.8 0.0 0.0 -4.1 0.0 -0.8 5.7

S124 1.4 0.3 0.3 0.0 0.2 0.0 0.0 0.8 0.0 -0.1 0.0

S12O 0.1 0.2 0.4 0.0 7.1 -0.4 0.0 0.8 0.0 1.0 -9.0

S128 0.7 0.5 0.6 0.0 -0.1 0.0 0.0 1.3 0.0 -1.6 0.0

S129 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 -0.1 0.0

S13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1

S1M 0.0 0.0 0.3 0.0 -0.1 0.0 0.0 -0.1 0.0 -0.1 0.0

S2 1.1 0.1 1.4 0.0 -0.9 0.0 0.0 0.5 0.0 0.0

SX 3.4 -1.2 -3.0 0.0 -6.2 0.4 0.0 -0.1 0.0 0.0

4. Балансирование «От кого к кому» на практике Третий этап: автоматическая корректировка для устранения оставшихся расхождений

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S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX

S 18.4 23.0 1.0 -79.7 -0.5 0.0 63.5 0.0 53.4 79.1

S11 0.9 -0.7 0.0 0.0 1.0 0.0 0.0 1.1 0.0 -0.4 0.0

S12K 54.9 6.8 16.3 0.0 -14.4 0.0 0.0 31.6 0.0 14.6 0.0

S124 39.2 8.5 9.0 0.0 2.7 -0.2 0.0 -0.1 0.0 19.3 0.0

S12O -60.9 1.3 -3.1 0.0 -80.7 -0.2 0.0 6.5 0.0 15.3 0.0

S128 20.8 0.5 3.4 0.0 6.2 -0.1 0.0 8.3 0.0 2.4 0.0

S129 4.5 0.6 0.6 0.0 0.2 0.0 0.0 1.6 0.0 1.5 0.0

S13 -4.3 0.1 0.8 0.0 -1.4 0.0 0.0 -3.0 0.0 -0.8 0.0

S1M -7.3 -0.3 -4.4 0.0 -1.3 0.0 0.0 -2.7 0.0 1.5 0.0

S2 31.4 1.7 0.5 1.0 8.0 0.0 0.0 20.2 0.0 0.0 0.0

SX 79.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

4. Балансирование «От кого к кому» на практике Результат третьего этапа: сбалансированная таблица «от кого к кому»

Замечание: некоторые алгоритмы могут быть использованы и для балансировки матриц – с определенной осторожностью

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4. Балансирование «От кого к кому» на практике ПРИМЕР 3: балансирую щая цена/другие изменения

Акции, включенные в листинг

Учитывать соотношение изменение цены/началь ная позиция Но учитывайте возможное неправильное распределение изменений объема

S S11 S12K S124 S12O S128 S129 S13 S1M S2 SX S 716.9 58.7 0.0 123.9 21.3 0.0 0.0 0.0 440.4 1,361.2

S11 181.4 176.2 1.2 0.0 2.7 1.3 0.0 0.0 0.0 0.0 0.0 S12K 18.0 7.2 2.4 0.0 3.3 0.9 0.0 0.0 0.0 4.2 0.0 S124 485.1 96.4 8.0 0.0 6.0 5.5 0.0 0.0 0.0 369.2 0.0 S12O 63.8 35.9 7.3 0.0 18.2 1.4 0.0 0.0 0.0 1.0 0.0 S128 19.4 9.9 0.5 0.0 1.8 0.8 0.0 0.0 0.0 6.3 0.0 S129 31.2 5.8 0.3 0.0 0.6 0.2 0.0 0.0 0.0 24.3 0.0

S13 41.1 34.1 2.4 0.0 1.2 0.4 0.0 0.0 0.0 3.0 0.0 S1M 131.1 74.4 9.1 0.0 4.9 10.4 0.0 0.0 0.0 32.3 0.0

S2 390.2 276.9 27.7 0.0 85.4 0.2 0.0 0.0 0.0 0.0 0.0 SX 1,361.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

ПОЗИЦИИ S S11 S12K S124 S12O S128 S129 S13 S1M S2 S 4,986.7 386.1 0.0 1,037.3 177.4 NA 0.2 NA 2,958.3

S11 1,514.0 1157.7 29.5 0.0 54.8 9.7 NA 0.0 NA 105.2 S12K 247.4 137.1 19.6 0.0 27.2 7.9 NA 0.0 NA 232.1 S124 3,195.6 764.7 72.0 5.0 68.8 37.8 NA 0.0 NA 2168.0 S12O 867.1 223.8 24.5 0.0 114.1 5.3 NA 7.8 NA 388.6 S128 182.9 101.6 4.6 0.0 14.6 20.1 NA 0.0 NA 54.5 S129 217.9 49.8 2.3 0.0 3.9 1.6 NA 0.2 NA 167.3

S13 295.9 240.4 25.0 0.0 9.4 3.3 NA 0.0 NA 16.0 S1M 891.2 570.8 53.1 0.0 84.2 31.0 NA 0.0 NA 174.6

S2 2,729.4 1830.5 132.4 0.0 548.3 23.1 NA 0.1 NA ПРОЦЕНТЫ S S11 S12K S124 S12O S128 S129 S13 S1M S2

S 14.4 15.2 0.0 11.9 12.0 NA 0.0 NA 14.9 S11 12.0 15.2 3.9 NA 4.9 13.5 NA -62.3 NA 0.0

S12K 7.3 5.3 12.0 NA 12.1 11.3 NA 18.8 NA 1.8 S124 15.2 12.6 11.0 0.0 8.7 14.6 NA 0.4 NA 17.0 S12O 7.4 16.0 29.7 NA 16.0 25.9 NA 0.0 NA 0.3 S128 10.6 9.8 12.0 NA 12.0 4.2 NA NA NA 11.6 S129 14.3 11.7 12.4 NA 14.2 15.0 NA 0.0 NA 14.5

S13 13.9 14.2 9.5 NA 12.5 12.3 NA 0.0 NA 18.9 S1M 14.7 13.0 17.1 NA 5.8 33.7 NA NA NA 18.5

S2 14.3 15.1 20.9 NA 15.6 1.0 NA 0.0 NA

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5. Основные особенности таблиц зоны евро / национальных таблиц «От кого к кому»

27

Типы данных Запасы, транзакции, прочие изменения

Резидентство держателя

Зона евро,и страны ЕС 27

Сектор держателя 11-12 секторов (центральные банки доступны только для некоторых инструментов)

Резидентство эмитента Зона евро / зона не-евро

Сектор эмитента 10-11 секторов для эмитентов зоны евро Для эмитентов, не входящих в зону евро, детализация по секторам отсутствует

Инструменты Ценные бумаги (кроме акций, не включенных в листинг), ссуды и депозиты

Длина серии От 2013Q4 до 2023Q1 (ценные бумаги) От 1999Q1 до 2023Q1 (ссуды и депозиты)

Своевременность T+120 (ценные бумаги - счета зоны евро) T+102: данные по странам T+ 94 (депозиты и ссуды - счета зоны евро)

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Ежеквартальный пресс-релиз по экономическим и финансовым изменениям в зоне евро по институциональным секторам - Полный выпуск - Приложение Таблица 2.2 (для домашних хозяйств) и Таблица 3.2 (для нефинансовых корпораций) http://www.ecb.europa.eu/press/pr/stats/ffi/html/index.en.html

6. Доступ к данным и визуализация

Данные «от кого к кому» приводят к значительному увеличению объема данных. Это требует от статистиков / институций разработки средств визуализации данных, которые помогут пользователям

http://www.ecb.europa.eu/press/pr/stats/ffi/html/index.en.html

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6. Доступ к данным и визуализация

29

Отчет по счетам зоны евро в SDW: http://sdw.ecb.europa.eu/reports.do?node=1000005335 ЗОНА ЕВРО Детализация «от кого к кому»

4.1.2 Краткосрочные долговые ценные бумаги в разрезе секторов-контрагентов (млрд. евро в текущих ценах)

1. Транзакции

Presenter Notes
Presentation Notes
SDW - Хранилище статистических данных ЕЦБ
http://sdw.ecb.europa.eu/reports.do?node=1000005335

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На какие вопросы могут ответить данные «от кого к кому»?

• Много ли правительство предоставило кредитов НФК в 2020 году?

• Выпустило ли правительство большое количество долговых ценных бумаг?

• Получили ли НФК значительный объем финансирования от небанковских финансовых институтов?

• Увеличили ли домохозяйства свои депозиты в 2020 г.?

7. Упражнение 1

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Операции с долгосрочными долговыми ценными бумагами 7. Упражнение 2

S11: НФК; S12K: Банковский сектор, включая Центральный банк; S124: Инвестиционные фонды; S12O: Другие финансовые подсектора; S128: Страховые корпорации; S129: Пенсионные фонды; S13 Органы госуправления; S1M Домашние хозяйства и некоммерческие организации, обслуживающие домохозяйства; S2: Остальной мир

S S11 S12K S124 S12O S128 S129 S13 S1M S2

S 64.0 -78.4 -1.2 84.3 0.7 0.0 186.1 0.0 363.0 S11 -10.2 3.0 -6.4 0.0 -1.5 0.2 0.0 1.1 0.0 -6.6

S12K 410.1 70.8 -4.1 0.0 32.5 -1.0 0.0 332.9 0.0 -21.0 S124 348.0 16.0 31.6 0.0 30.9 1.5 0.0 -47.2 0.0 315.1 S12O 32.6 2.7 1.3 0.0 33.8 -0.1 0.0 -42.1 0.0 36.9 S128 10.7 8.3 -24.8 0.0 -1.8 0.8 0.0 11.9 0.0 16.3 S129 70.6 1.2 5.6 0.0 -1.2 0.0 0.0 39.8 0.0 25.1

S13 -26.5 -1.0 -0.7 0.0 -7.1 -0.4 0.0 -15.3 0.0 -1.9 S1M -80.9 -3.1 -79.7 0.0 -1.1 -0.3 0.0 4.1 0.0 -0.8

S2 -135.9 -34.1 -1.3 -1.2 -0.3 0.0 0.0 -99.1 0.0 0.0

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Вопросы по таблице на предыдущем слайде: • Какие секторы были основными чистыми (нетто) покупателями

долговых ценных бумаг, и у каких секторов (зоны евро) они их покупали?

• И наоборот, какие секторы были чистыми продавцами долговых ценных бумаг в этот период?

• Продавали ли чистые продавцы чистым покупателям?

• Сколько было эмитировано (выпущено) за этот период резидентами зоны евро?

• Насколько велики были покупки долговых ценных бумаг зоны евро резидентами зоны евро?

7. Упражнение 2

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Ответы:

• Банки (включая центральные банки) приобрели большое количество ценных бумаг, выпущенных в основном правительством.

• Инвестиционные фонды (S124) приобрели большое количество долгосрочных долговых ценных бумаг, выпущенных в основном резидентами стран не входящих в зону евро (S2).

• И наоборот, инвесторы и домашние хозяйства (S1M), стран не входящих в зону евро, в течение этого периода были чистыми продавцами долговых ценных бумаг.

7. Упражнение 2

www.ecb.europa.eu © 34

Ответы:

• Нерезиденты продавали в основном государственные долговые бумаги, а домохозяйства – ценные бумаги, выпущенные банковским сектором.

• Однако мы не знаем, какие секторы с кем «совершали сделки».

• Общий объем чистых эмиссий долговых ценных бумаг резидентами зоны евро достиг 255 млрд. евро, а чистых покупок резидентами ценных бумаг, выпущенных резидентами зоны евро, - 391 млрд. евро

7. Упражнение 2

www.ecb.europa.eu © 35

  • Матрицы�«от кого к кому»
  • Резюме
  • 1. Концепт «От кого к кому»
  • 1. Концепт «От кого к кому»�
  • 1. Концепт «От кого к кому»�
  • 1. Концепт «От кого к кому»�
  • 1. Концепт «От кого к кому»
  • Slide Number 8
  • 1. Концепт «От кого к кому»�
  • 1. Концепт «От кого к кому»�
  • 2. «От кого к кому»: основные источники данных
  • 3. Составление «От кого к кому»: два случая�
  • 3. Составление «От кого к кому»: два случая
  • Slide Number 14
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике�
  • 4. Балансирование «От кого к кому» на практике�
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике
  • 4. Балансирование «От кого к кому» на практике
  • 5. Основные особенности таблиц зоны евро / национальных таблиц «От кого к кому»
  • 6. Доступ к данным и визуализация��
  • 6. Доступ к данным и визуализация��
  • 7. Упражнение 1
  • 7. Упражнение 2
  • 7. Упражнение 2
  • 7. Упражнение 2
  • 7. Упражнение 2
  • Slide Number 35

Overview of the AnigeD Project and Potentials of Dataset Synthetization for Official Statistics and Research, DESTATIS

georeferenced data, integrated data, confidentiality procedures, complex datasets, dataset synthetization

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

Overview of the AnigeD Project and Potentials of Data Synthesis for

Official Statistics and Research

Yannik Garcia Ritz, Safiyye Aydin, Jannek Mühlhan, Markus Zwick, (Federal Statistical Office, Germany)

[email protected]

[email protected]

Jannek. Mü[email protected]

[email protected]

Abstract

Statistical Disclosure Control for integrated and georeferenced data is a new challenge for statistical institutes. New digital

data in combination with traditional data offer many new analysis possibilities. Moreover, these complex datasets are

usually georeferenced in a very fine-grained way. Traditional confidentiality procedures reach their limits here. Destatis,

the German Federal Statistical Office, is working together with various universities on the further development of existing

procedures in order to ensure the protection of individuals even for complex data. The lecture will present the first results

of the project "Anonymization for integrated and georeferenced Data" (AnigeD) funded by the German Ministry of

Research.

As part of AnigeD, Destatis further deals with dataset synthetization of population as well as economic statistics to

examine probable potentials for data provision to the research community as answer to the growing scientific interest in

official data. Confidentiality issues lead to several measures to ensure confidentiality of respondent units. Consequently,

there is a diametral relationship between level of anonymity and analytic potential of provided datasets which may not

completely satisfy the needs of the scientific community. Research on data synthetization is currently suspecting synthetic

datasets to be a probable solution to this problem due to their artificial nature. With the following research an official

statistics dataset will be synthesized and evaluated regarding analytical utility as well as the level of confidentiality.

Furthermore, an evaluation regarding the ease of use of certain provision methods of synthetic datasets will be presented.

2

1 Introduction

Data-based information plays a central role in politics, business, science and public life. With digitization and the

exponential growth of stored data, as well as new analytical methods such as machine learning, the possibilities

for evidence-based decision making have expanded and evolved significantly.

The COVID-19 crisis highlighted that many valuable data sets exist in principle, but are often held in a

decentralized manner in different silos by different actors, whether in companies or public institutions. At the

same time, advances in big data, also referred to as non-traditional data, have shown that the greatest value comes

especially when different non-traditional data sources are combined with traditional data, such as surveys and

administrative data. Individual data sets are often only pieces of a puzzle, unable to paint a complete picture.

A key challenge in integrating disparate data sets from different data custodians is the protection of personal

privacy and trade secrets within organizations. This currently hinders both the wider use of data as a product and

the use of integrated data in policy advice and scientific research. Methods for anonymization and statistical

confidentiality face the challenge of finding a compromise. On the one hand, they need to protect the information

of the data subjects, while on the other hand, the chosen methods should still offer sufficient analysis and

information potential for the anonymized data. Anonymization and confidentiality of individual data go hand in

hand with information reduction.

In the past it has been shown that common anonymization strategies for individual data in economic statistics led

to de facto or absolutely anonymized data sets, which were severely limited for scientific analyses due to the

reduced or even distorted information potential. Anonymization and pseudonymization of data, which limits the

risk of detection to an acceptable level while preserving sufficient analytical potential, is therefore essential for

wider use and value creation.

The AnigeD competence cluster is part of the "Research Network Anonymization for Secure Data Use" of the

German Federal Ministry of Education and Research (BMBF) within the framework of the Federal Government's

IT security research program "Digital. Secure. Sovereign". It is funded by the European Union –

NextGenerationEU. The thematic focus, which is supported by various research strands, is the further and new

development of strategies for the protection of personal and company-related data when using complex integrated

data sets. Not only the integration of different data via direct identifiers or probabilities is relevant, but also the

integration and linking of data via regional information in the form of georeferencing.

The AnigeD competence cluster is divided into the following research areas

• Formalization of substantive criteria for the success of anonymization provided by the legal system.

• Anonymization through synthetic data

• Anonymization of georeferenced data

• Evaluation of anonymized data according to formal criteria.

• Open software tools for anonymization

3

The thematic focus, which is supported by various research strands, is the further and new development of

strategies for the protection of personal and company-related data when using complex integrated data sets. Not

only the integration of different data via direct identifiers or probabilities is relevant, but also the integration and

linking of data via regional information in the form of georeferencing.

The present paper presents insight from the research area of anonymization through data synthesis. Therefore, by

synthesizing parts of the Structure of Earnings Survey 2018, a non-georeferenced database is chosen to gain

further insights into the potentials of anonymization and provision of the data synthesis of official databases.

Previous research of Loske & Wolfanger (2019), Hafner & Lenz (2011) dealt with the synthesis of official data

structural files, while Templ (2017) synthesized simulated data of the SES 2014. In contrast to the mentioned

prior research approaches, the present work deals with the partial data synthesis of the on-site material of the

company and employee datasets of the Structure of Earnings Survey 2018.

In addition to increasing the anonymity of respondent units through data synthesis, the potentially provided

synthetic data needs to comply with high-quality requirements placed on official data (Zwick, 2016). Thus, the

generated partially synthetic data material is evaluated concerning the attained global and analytic utility. Finally,

this paper will provide a weighted evaluation of the data synthesis with respect to the anonymization and analysis

potentials.

2 Background

Official data products and statistics face a steady increase in demand by the scientific community and the public,

in general (Allin, 2021). Anonymising data in such a way that the remaining information does not allow any

conclusions to be drawn about individual data subjects (be they persons, households or companies), but still

contains sufficient information potential, is a core concern of every data producer, whether private or public. In

addition to various legal regulations (EU-DSGVO, BDSG, BStatG), the quality of the data products is of

particular importance. Methods for anonymization or statistical confidentiality have to resolve a conflict of

objectives. On the one hand, the information provided by the data subjects must be protected; on the other hand,

the procedures must be chosen in such a way that the anonymized data still have sufficient potential for analysis

or information. Anonymizing and guaranteeing the confidentiality of individual data generally involves a

reduction of information and thus a loss of information. The Federal Statistical Office already has extensive

experience in anonymizing large amounts of data (i.e., Ronning et al. (2005), Hundepool et al. (2012), Templ

(2017)). In general, provided data is anonymized to a greater or lesser extent. In case of application of less

anonymization measures the way of data access is made more difficult (Rothe, 2015).

The demand for greater availability and transparency of data, while maintaining confidentiality and data

protection, can only be met by innovative methods of data processing, preparation and delivery. The use of

classical anonymization methods reaches its limits with increasing complexity and number of data usage requests.

Synthetic data offer opportunities to optimize the aspects of anonymization because respective measures can be

integrated into the synthesis process (Drechsler & Haensch, 2023).

4

For on-site access, the full data material, except for direct identifiers, is provided to the scientific community.

However, scientific data users must use the data either physically (safe center usage) or virtually (remote

execution) at the providing institution. In contrast, off-site data can be used in the scientific institution of the

contractor (Rothe, 2015).

The methodology of synthetic data generation is based on the principles of multiple imputation (Rubin, 1993).

However, instead of only estimating values to replace missing values, false declarations etc., estimations are used

to replace some or all variables of the original dataset (Little, 1993). Thus, there is a methodological distinction

between the concepts of full (Rubin, 1993) and partial synthesis (Little, 1993). Only sensitive variables or

variables which increase the reidentification risks are synthesized as part of partial synthesis. Little (1993) argues

that focusing only on sensitive or reidentification risk increasing variables should prevent the analysis quality

from being reduced too much by the estimation character of the synthesis approach. The possible, integrable

anonymization measures can make an important contribution to balancing the protection of the respondents and

ensuring of the analysis potential (Drechsler & Haensch, 2023).

The synthetic data should reflect the structure and relationships of the original data as closely as possible.

Simultaneously, the level of anonymity should be increased in comparison to the original on-site material (Reiter,

2023). The Statistical Offices of the Federation and the Federal States are obliged to protect the respondent units

according to Section 16 (1), Federal Statistics Act. At the same time, the must comply with the scientific privilege

derived from Section 16 (6), Federal Statistics Act. Thus, the Federal Statistical Offices of the Federation and the

Federal States founded the Research Data Centers (RDCs) in 2001 to enable scientific access to official data

(Zühlke, Zwick, & Scharnhorst, 2003).

As part of the AnigeD project and competence cluster, one working package deals with the assessment of the

supply potential of synthetic on-site material. At the international level, there are already first applications by the

national statistical authorities of New Zealand, Canada, Scotland and the United States of America, among

others.1 The use of synthetic data to anonymize personal data has found wider application so far, as documented

in the literature mentioned above (Burnett-Isaacs et al., 2021).

The cluster builds on previous research that has addressed, among other things, the de facto anonymity of

economic statistics. In the case of economic statistics data, these methods are sometimes limited by oligopolitical

market structures, and there have been few applications for georeferenced data. Georeferenced data offer new

possibilities for merging heterogeneous data. According to § 10 section 3 BStatG, individual statistical data with

regional information can be integrated on a hectare level. § 10 section 3 BStatG, which allows for detailed

regional information, but here too only a few anonymization approaches have been developed for such integrated

data. In this respect, AnigeD is expected to provide new insights that will be of great interest, especially for the

commercial use of the data.

Concrete preliminary work has been done in the area of mobile phone signal data in recent years. Since 2017, the

Federal Statistical Office has been researching possible applications of mobile phone signal data in official

1 Burnett-Isaacs et al. (2021)

5

statistics (Hadam, Schmid, & Simm, 2020). Within this framework, several studies have been carried out on

different application purposes and quality aspects. This has resulted in several modular software packages for

geolocation, deduplication and aggregation of activities (see 'Mobile network data' of the ESSnet Big Data I and

II project).

In addition, the European Statistical System is working on the concrete technical implementation of privacy-

compliant processing of mobile network data and on process models for cooperation between private data

providers and official statistics. The implementation of such a process offers official statistics, and thus also

research, society and politics, the possibility of making long-term statements on longitudinal changes in

population distribution and mobility - e.g. long-term intra-German migration patterns, analysis of the effects of

new forms of work and the development of sustainable means of transport.

Within the framework of the research project "Anonymization of official statistics through synthetic data", three

lines of action are highlighted. The first line of action focuses on exploring the possible uses of synthetic data for

the RDCs of the Statistical Office of the Federation and the Federal States. In this context, methods for the (partly)

automated creation of synthetic datasets will be developed and tested. These synthetic datasets will be used in

various applications, such as data exploration, writing and testing of analysis programs, teaching, and

anonymization of particularly sensitive features and geocoordinates. It will also explore whether synthetic data

can expand the range of data recipients, such as data journalists.

The second storyline looks at the potential of high-quality synthetic datasets for the way public and private data

producers work to produce and publish aggregated results. Here we explore whether synthetic or semi-synthetic

data can be used directly in the production of results to resolve trade-offs between protecting confidentiality and

making statistical results widely and flexibly available.

The third strand will systematically compare different approaches to synthetic data production. In particular, the

extent to which the methods developed are also suitable for statistical analyses such as regression analyses will

be investigated. It will be investigated how statistical approaches can be used in the context of machine learning

and vice versa. In addition, existing approaches will be methodologically refined to address possible weaknesses,

e.g. in the use of deep learning methods from computer science.

So far, the RDCs do not provide synthetic data. Previous research regarding synthesis of official data dealt with

data structural files (Loske & Wolfanger, 2019; Hafner & Lenz, 2011) or with simulations of official data (Templ,

2017). Even if the extensive use of synthetic data for the direct production of results is not always possible for

quality reasons, there are scenarios where the use of synthetic data offers advantages. For all storylines, the

standardizability of synthetic data generation and the effort involved is crucial.

The project will also develop privacy record linkage methods that allow geocoordinated data to be stored as

Bloom filters in individual records and used for linkage or distance calculations. The security of these methods

for encoding geocoordinates will be investigated, especially with regard to the problems of statistical secrecy

caused by enriched datasets.

6

The plan is to align the environment term with typical applications or analysis models for the target data and then

balance the two (usually conflicting) goals: Maximizing the analysis potential and minimizing the risk of re-

identification.

The Chair of Statistics at the Department of Economics of the FU Berlin has developed advanced methods for

the analysis of anonymized georeferenced data in cooperation with the company INWT Statistics. The focus of

anonymization is to reduce the accuracy of the georeferenced data in order to make it difficult or impossible to

identify individual units in a dataset. Nevertheless, the dataset should remain usable for content related

evaluations. This subproject deals with the use of anonymized georeferenced data and the limitations of

anonymization. Statistical methods will be developed that both take into account the anonymization process and

enable typical evaluations of georeferenced data. These procedures will be demonstrated for different application

areas. At the same time, user-friendly open source software will be developed for these applications.

Statistical procedures will be developed that allow for smooth map representations that are not bound to a specific

area system, but are still compatible with the anonymized area values. The aim is to adapt the statistical evaluation

of georeferenced data to the anonymization procedure and to make the use of anonymized georeferenced data

sets more efficient. To this end, adapted statistical estimation procedures will be developed and supported by

open source software to facilitate their use by a wide range of users.

In order to make sound predictions about the capabilities of a potential attacker, a consistent formalization of the

material criteria specified by the legal system is required. To accomplish this legally and technically challenging

task, the DUV (German University of Administrative Sciences Speyer, german: Deutsche Universität für

Verwaltungswissenschaften Speyer) adopts a research approach that measures the extent to which the provision

of a data set increases the likelihood that an attacker will obtain new information about the data subject. This

approach is based on the recognition that any natural person is already exposed to some basic risk from data that

is generally accessible or available to a potential attacker, and that this risk remains even if the entity holding the

data refrains from publishing or sharing it.

Another question that the DUV addresses is how the publication or dissemination of the dataset affects the pre-

existing baseline risk. The DUV's approach is to examine existing proposals for measuring risk shift, taking into

account their compatibility with the legal system and practice. In particular, two approaches will be considered:

Differential Privacy (DP) and GDA Score. However, it is not enough to merely measure the shift of the basic

risk. In a third step, the DUV therefore plans to investigate in more detail the maximum extent to which the basic

risk can be shifted so that the data-holder can legitimately assume that it is only passing on anonymized data.

The software system Diffix will be used as a demonstrator for the processing, evaluation and analysis of the data

within the framework of the research tasks. It is used for the technical implementation of the anonymization

methods developed in the cluster. The aim is to make the best use of Diffix as a stand-alone application and as

part of other programming languages such as Python and R, or anonymization packages, to enable feasible

solutions.

Aims:

7

AnigeD aims to advance current anonymization methods and to identify and implement new solutions for new

problems. This should not only secure but also extend the current state of data access for science. The methods

developed and researched in the cluster will be made available not only to the project partners involved, but also

to data-holding companies. In this way, the developed and new methods can generate added value for the

companies on the one hand, and expand access to company data for science and official statistics on the other.

The main objective of AnigeD is to secure and expand access to complex data while protecting individual

characteristics, and to create greater legal certainty for practitioners. Given the exponential growth of data

volumes and the increasing complexity of data, especially in the context of georeferencing, current strategies for

protecting individual identifiers are reaching their limits. Therefore, a sub-goal of AnigeD is to secure and expand

the supply of (complex) data for science in the research data network of RDCs.

In addition, existing methods will be further developed in cooperation with companies from the data industry and

made available for commercial purposes. In this way, insights and applications developed for science through

public funding of data access will also be opened up for data-driven business mod-els. At the same time, data

from the companies will be made available for use in science and society, with appropriate protection of feature

carriers and trade secrets.

This paper reports first results from the research of the AnigeD project on the evaluation of the potentials of

synthetic data for the scientific community as well as for the providing RDCs of the Statistical Offices of the

Federation and the Federal States. Therefore, the company and the employee file of the Structure of Earnings

Survey (SES) 2018 serve as base for several synthesis approaches and the respective evaluations regarding

disclosure risks and utility of the generated synthetic data. The following section elaborates on the

conceptualization of the synthesis approach and the subsequent assessment of the disclosure risks and utility of

the synthetic data generated.

3 Conceptualization

Following the argumentation of Little (1993), the concept of partial synthesis is used to synthesize the on-site

material of the SES 2018. The SES 2018 comprises a company and an employee dataset which are both partially

synthesized as part of the present work. Various statistical techniques and machine learning approaches can be

used to conduct data synthesis (Drechsler & Haensch). Research findings of Grinsztajn, Oyallon, & Varoquaux

(2022) indicate that Classification And Regression Trees (CARTs) outperform conventional statistical techniques

and other machine learning approaches in many occasions. Thus, CARTS are predominantly used to synthesize

the two on-site datasets of the SES 2018.

Furthermore, data synthesis enables data providers to make use of different smoothing approaches as

anonymization measure for variables obtaining highly skewed distributions. The resulting reduction in estimation

accuracy leads to an increase in the level of anonymity (Nowok, Raab, Dibben, Snoke & van Lissa, 2022;

Drechsler & Reiter, 2008). Reiter (2005) identifies several reasons for providing multiple synthetic datasets per

original dataset. Drechsler (2009) suggests to provide at least as many synthetic datasets per original dataset as

8

the number of original datasets. In the present work, five synthetic datasets are generated based on the original

company and original employee dataset, each. Hence, the minimal criterion of m ≥ r 8 (Drechsler, 2009) is

complied with.

Following the partial data synthesis carried out, the generated partially synthetic datasets are checked concerning

their disclosure risks. Here k-anonymity (Sweeney, 2002) is used as one measure to quantify the number of

observations violating k=2 or k=3 anonymity (Templ, 2017) and the number of high-risk observations (Templ

2017). The mentioned key measures are calculated as ratios to the key measures of the respective off-site material

as denominator. For baseline evaluations and to enable comparisons, the same is done for the original on-site

material of the company and employee datasets of the SES 2018.

�̂�𝑘 = �̂�𝑘

1 − �̂�𝑘 𝑙𝑜𝑔 (

1

�̂�𝑘 ) | 𝑓𝑘 = 1 (1)

�̂�𝑘 =

�̂�𝑘

1 − �̂�𝑘 − (

�̂�𝑘

1 − �̂�𝑘 )

2

𝑙𝑜𝑔 ( 1

�̂�𝑘 ) | 𝑓𝑘 = 2

(2)

�̂�𝑘 = �̂�𝑘

𝑓𝑘 − (1 − �̂�𝑘)

(3)

Observations are classified as high-risk observations if their estimated individual risk �̂�𝑘 is higher than 10 % and

larger than the median individual risk �̂�𝑘 + factor δ times the median absolute deviation of �̂�𝑘 (δ ≥ 2; Templ,

2017).

Moreover, the generated partially synthetic data is evaluated regarding the number of expected random matches

as well as the absolute number of true and false matches to the original data (Drechsler & Reiter, 2008).

• Expected Match Risk for a selection based on a random guess:

∑ (

1

𝑐𝑗 ) ∗ 𝐼𝑗

𝑗∈𝑇 (4)

• True Match Rate for true matches of targets 𝐾𝑗 among all matches identified within cj units exemplarily

examined:

∑ 𝐾𝑗𝑗∈𝑇

∑ (𝑐𝑗 = 1)𝑗∈𝑇 ⁄ (5)

• False Match Rate for the share of incorrectly assumed matches within cj units exemplarily examined:

1 − (

∑ 𝐾𝑗𝑗∈𝑇

∑ (𝑐𝑗 = 1)𝑗∈𝑇 ⁄ ) (6)

Considering the high-quality standards for official data, it is important to further evaluate the analytic potential

of the generated partially synthetic SES 2018 data, in addition to the disclosure risk assessment. Consequently,

the generated partially synthetic company and employee datasets are examined concerning their global and model

specific utility. Variable transformations according to Raghunathan, Lepkowski, Van Hoewyk & Solenberger

(2001) are used to ensure compliance with basic logical constraints on variable relationships. Furthermore,

descriptive statistics and distributions of the generated partially synthetic and the original data of both files of the

9

SES 2018 are compared to assess global utility. Finally, the propensity Mean-Squared Error (pMSE) is used as a

final measure for global utility to rate the similarity of the generated partially synthetic datasets and the original

database.

𝑝𝑀𝑆𝐸 =

1

𝑚 ∑ (

1

𝑁 ∑(𝑝�̂� − 𝑐)2

𝑁

𝑖

)

𝑚

𝑗

(7)

A model specific utility evaluation provides deeper insights to the usefulness of the partially synthetic company

and employee datasets of the SES 2018. Considering that many research questions in the scientific community

are worked with several models, underlines the importance of a model specific utility evaluation even further.

The confidence interval overlap is a measure to assess the model specific utility and serves as an indicator for the

accuracy of estimates obtained from models which are estimated on synthetic data (Karr, Kohnen, Oganian,

Reiter, & Sanil 2006). Hence, the present work estimates exemplary linear and logistic regression models for

partially synthetic company and employee data material, each. These exemplary regression models are used to

estimate the average confidence interval overlap over all coefficients just as the separate confidence interval

overlap.

𝐽 𝑘

= 1

2 ∗ [

𝑈𝑜𝑣𝑒𝑟,𝑘 − 𝐿𝑜𝑣𝑒𝑟,𝑘

𝑈𝑜𝑟𝑖𝑔,𝑘 − 𝐿𝑜𝑟𝑖𝑔,𝑘

+ 𝑈𝑜𝑣𝑒𝑟,𝑘 − 𝐿𝑜𝑣𝑒𝑟,𝑘

𝑈𝑠𝑦𝑛𝑡ℎ,𝑘 − 𝐿𝑠𝑦𝑛𝑡ℎ,𝑘

] (8)

4 Results

4.1 Disclosure Risk Evaluation

Spline smoothing has proven to be the best approach for the data synthesis of the company data of the SES 2018

regarding the cost-benefit ratio of disclosure risks and global/model specific utility. The evaluation of disclosure

risks is executed by comparing k-anonymity key measures as well as the number of high-risk observations

building up on Templ (2017), as already described in section 3. Contrary to first expectations increases in the

mentioned key figures for the generated synthetic data material are recorded. Nevertheless, it needs to be

underlined that the increase results through the data synthesis, so there is actually no increase in real high-risk

observations which implies that the pool of partially synthetic high-risk observations is larger compared to the

respective numbers in the original data material. It is believed that this is more likely to indicate increased security

in terms of confidentiality, since the risk of finding a truly high-risk observation should decrease.

The examination of the key measures of Drechsler & Reiter (2008) slightly support this assumption because they

reveal that a random disclosure only arises with a probability of less than 0.1 %. Furthermore, it turns out that,

there is no true match to be observed in the generated partially synthetic company data.

In contrast to the company data, the best cost-benefit-ratio regarding disclosure risks and global/model specific

data utility is achieved for the partially synthetic employee data if kernel density smoothing is applied to synthesis

highly skewed variables (e.g., income-related variables). The data synthesis model which uses spline smoothing

10

leads to a noteworthy underestimation of outliers for the variable gross monthly income. Analogous to the

disclosure risk evaluation of the company data, an adapted form of the approach of Templ (2017) is used in the

first step. Thereby, increases in the ratios of the respective key measures are observed as well. However, these

increases are less high compared to the increases observable for the partially synthetic company data.

In the second step, disclosure risks are again further evaluated by examining the expected match risk and the true

match rate (Drechsler & Reiter, 2008). In contrast to the synthetic company data, there is no risk expected for a

random match. Moreover, this is also observed for the true match rate indicating that all observations considered

to be matching to original observations are actually false matches.

4.2 Utility Evaluation

As described in section 3 the utility of the generated partially synthetic data material based on the company and

employee file of the SES 2018 is assessed both globally as well as specifically for exemplary regression models.

Variable transformations as described by Raghunathan, Lepkowski, Van Hoewyk & Solenberger (2001) ensure

that basic boundary values constraints are met. Thus, enabling to directly start with comparisons of the original

and respective synthetic data material of the SES 2018 for global utility assessment. It can be observed that the

basic descriptive statistical key measures (mean, median and standard deviation) are reflected well in the

generated synthetic company and employee material.

Additionally, data utility is assessed by examining the mean pMSE for the partially synthetic company and

employee material of the SES 2018. The mean pMSE of 0.1142 lies in the middle of the possible interval which

indicates a still existing potential for utility improvement.

An exemplary linear regression model is estimated alongside an exemplary logistic regression model to evaluate

the model specific utility of the generated partially synthetic company data. The exemplary linear regression

model estimates potential effects of the craft affiliation of a company, participation of the public sector in

company’s capital as well as the number of common working days per week on the company’s number of

employees (see Table 1). In the next step, the average confidence interval of the exemplary synthetic data-based

estimates is computed in relation to their counterparts of the original data over all m = 5 partially synthetic

datasets. The average confidence interval overlap equals 85 % over all estimates of the exemplary linear

regression. Observing a confidence interval overlap around 62 % for the explanatory variable “participation of

the public sector in the company’s capital” reveals that the overall mean confidence interval overlap of the

exemplary linear regression model is negatively impacted by the CI of the estimate.

11

An exemplary logistic model estimates effects of several explanatory effects on a previously created binary

variable that indicates whether wages are determined primarily on the basis of collective bargaining agreements

(see Table 2). The highest confidence interval overlap is estimated for the coefficients of the explanatory variables

“Craft affiliation” (94.81 %) and “Type of corporate entity = Operation of a multi-business enterprise” (93.53 %).

However, both mentioned explanatory variables are the only variables exceeding 90 % and approximating the

target value of 95 % confidence interval overlap.

Table 1: Comparison of coefficients and confidence intervals of an exemplary linear regression on variable “Number

of Employees” with both original and synthesized on-site company dataset of the SES 2018.

Original on-site material

(employee dataset)

Synthesized on-site material

(employee dataset)

CI

Overlap

Coefficient

(Std. error)

Coefficient

(Std. error)

Intercept -97.7958***

(36.4197)

-92.9597**

(36.4198)

0.9661

Craft affiliation 20.8993***

(2.8277)

19.6029***

(2.8277)

0.8830

Participation of

the public

sector in

company’s

capital

223.6840***

(11.2451)

207.0839***

(11.2451)

0.6234

Working days

per week

-17.4124***

(6.6388)

-15.5643 **

(6.6388)

0.9290

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

* p < 0.10, ** p < 0.05, *** p < 0.01

Table 2: Comparison of coefficients and confidence intervals of an exemplary logit regression on variable “collective bargaining”

with both original and synthesized on-site company dataset of the SES 2018.

Original on-site material

(employee dataset)

Synthesized on-site material

(employee dataset)

CI

Overlap

Coefficient

(Std. error)

Coefficient

(Std. error)

Intercept -0.6124***

(0.17298)

-0.8626***

(0.1730)

0.6311

Craft affiliation -0.2445***

(0.01304)

-0.2471***

(0.0130)

0.9481

Number of Employees 0.00003***

(0.0000)

0.00003***

(0.0000)

0.7657

Working Days per Week -0.1116***

(0.0336)

-0.0624*

(0.0336)

0.6266

Type of corporate entity = Operation

of a multi-business enterprise

1.5793***

(0.0349)

1.5705***

(0.0349)

0.9353

Type of corporate entity = Operation

of a multi-country enterprise

1.5240***

(0.0265)

1.5723***

(0.0265)

0.5344

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

* p < 0.10, ** p < 0.05, *** p < 0.01

12

Consequently, the overall mean confidence interval overlap for the exemplary logistic model, which is used to

further evaluate the model specific utility of the generated synthetic company data, equals 74 %. Therefore, there

is an even higher deviation for the confidence interval of explanatory variables in the exemplary logistic

regression model in comparison to the explanatory variables in the exemplary linear regression model.

All in all, confidence interval overlaps of 85 % and 74 % suggests a good similarity between the exemplary linear

and logistic regression based on the original and the generated partially synthetic data. Nevertheless, a further

increase through extended tuning of the synthesis models for the company data is expected to achieve confidence

interval overlaps close to 95 %.

In contrast to the partially synthetic company data, the partially synthetic employee data is estimated by making

use of kernel density smoothing. The thereby generated partially synthetic employee data is assessed by

comparing the descriptive statistics with the respective counterparts of the original data. This examination reveals

that the descriptive key measures as well as the distribution of the monthly gross income is similarly well met as

the key figures for the company data set. The same is true for the pMSE (0.1110) which is equally close to the

pMSE of the company dataset. Consequently, this suggests that further tuning of the data synthesis model could

also lead to a further increase in data utility in this case.

Table 3: Comparison of coefficients and confidence intervals of an exemplary linear regression on variable gross

hourly income with both original and synthesized on-site employee dataset of the SES 2018.

Original on-site material

(employee dataset)

Synthesized on-site material

(employee dataset)

CI Overlap

Coefficient

(Std. error)

Coefficient

(Std. error)

Intercept 589.1931***

(2.569)

606.0611***

(2.56875)

-0.6752

Education 1.550***

(0.0197)

1.54555***

(0.01968)

0.9394

Sex -3.3940***

(0.0249)

-3.18269***

(0.02488)

-1.1664

Year of Birth -0.0304***

(0.0012)

-0.0682***

(0.0012)

-6.9942

Year of Entry -0.2567***

(0.0015)

-0.2279***

(0.0015)

-3.7869

Restriction of

term of contract

-1.4092***

(0.0101)

1.4784***

(0.01008)

-0.7522

Private sector 0.0716

(0.0542)

0.2492***

(0.0542)

0.1643

Company size -0.0000***

(0.0000)

-0.0000***

(0.0000)

0.23399

Vocational

education

3.5243***

(0.0124)

3.3684***

(0.01235)

-0.2990

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

* p < 0.10, ** p < 0.05, *** p < 0.01

13

Moreover, an exemplary linear regression model is estimated on the generated variable gross hourly income (see

Table 3). Additionally, an exemplary logistic regression model is estimated on a binary variable indicating

whether the gross hourly income of an employee exceeds the minimal wage (see Table 4). Assessing the model-

specific utility reveals that there is no mean confidence overlap to be observed, in average, for both exemplary

regression models estimated for the partially synthetic and the original employee data.

5 Discussion

Limitations

The present research on synthesis potential of the SES 2018 does not check and ensure cress-file references

between the company and employee file. It is likely that respective logical constraints need to be considered in

future synthesis models. A similar train of thought is followed for the use case of panel data, since the present

work only examines only a single survey year. It cannot be ruled out completely that relations in longitudinal

context are not reflected accurately.

In the present research, it is dealt with a partial synthesis of the employee dataset and the company of the SES

2018. Consequently, the results are only valid for the examined datasets. It cannot be ruled out completely that

the findings for the partial data synthesis of the SES 2018 files cannot be generalized for other official surveys

such as microcensus, DRG, for example.

Table 4: Comparison of coefficients and confidence intervals of an exemplary logit regression on variable “Gross

Hourly Wages Above Minimal Wage” with both original and synthesized on-site employee dataset of the SES 2018.

Original on-site material

(employee dataset)

Synthesized on-site material

(employee dataset)

CI Overlap

Coefficient

(Std. error)

Coefficient

(Std. error)

Intercept 99.76548***

(0.9690)

84.8586***

(0.9690)

-2.9244

Sex -0.24396***

(0.01248)

-0.2399***

(0.01248)

0.9172

Year of Birth -0.04444***

(0.00049)

-0.0379***

(0.00049)

-2.3949

Education -0.0408***

(0.0075)

-0.1054***

(0.0075)

-1.1864

Vocational

Education

0.4637***

(0.00736)

0.5034***

(0.00736)

-0.3762

Restriction of

term of

contract

-1.93796***

(0.0090)

-1.6336***

(0.0090)

-7.6049

Weekly

working

hours

-0.16288***

(0.00086)

-0.1277***

(0.00086)

-9.3753

Private sector 0.2412***

(0.02997)

0.2035***

(0.02997)

0.6791

Company

size

-0.0000***

(0.0000)

-0.0000***

(0.0000)

0.5961

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

* p < 0.10, ** p < 0.05, *** p < 0.01

14

The examination of the partially synthesized SES 2018 datasets does only lead to suggestive conclusion that the

partial synthesis led to a decrease in risks of deanonymization looking at k-anonymity and number of high-risks

observations. It is theorized that an increase in the respective key measures after partial data synthesis reflects an

increase of the pool of high-risk observations containing values which do not necessarily match the original data.

Assessing the low expected match risks and true match rates of both partially synthesized company and employee

files provides, further support for this hypothesis. Nevertheless, it needs to be acknowledged that there is yet no

actual linkage between the estimated key measures yet.

Looking at the results it needs to be acknowledged that for both the employee and company file of the SES 2018,

the disclosure risks and model-specific utility-related key measures do not meet the expectations. Thus, the

current results do not provide evidence that the present generated partially synthetic data is ready for provision

to the scientific community.

In principal, the assessment of all key measures provided in this paper is only offering a personal appraisal of

partial synthetic data provision by official statistical offices. The present thesis is not to be considered as a legal

report on how to deal with the provision of synthesized data material but is only offering a personal appraisal of

potentials.

Research and Practical Implications

It is believed that existing cross-file references may not be accurately reflected in the partially synthesized SES

2018 data. This is to be investigated as part of further work on the third research area of the AnigeD project. If

there are limitations concerning cross-file references, respective constraints need to be integrated into the partial

synthesis models.

Additionally, future research should illuminate the utility and disclosure risk evaluations about panel data which

has not yet been covered by the presented work. Since the scientific community is often interested in longitudinal

research questions, it needs to be made sure that respective relations are reflected correctly, as well.

Furthermore, future research should check on the hypothesis that the increase in high-risk observations after

partial data synthesis actually reflects a larger pool of untruthful high-risk observations, indicating lower

disclosure risks. As part of this examination, it should also be exposed whether the key measures of Templ (2017)

in combination with the key measures of Drechsler & Reiter (2008) could be harmonized.

Since the present work deals only with the evaluation of potentials of data synthesis of the on-site material of

SES 2018, the generalizability of the presented findings for other official surveys should be investigated to

provide lawyers with the knowledge needed for their legal assessment on simplified data access of less

anonymized synthesized data to the scientific community.

In addition, future research should tie up to the present work. Work should be done to further improve the key

figures, which have not been satisfactory in some places to date so that publication of synthetic data can be

examined by the legal authorities in the future and implemented if necessary.

15

Current research results indicate potentials to increase confidentiality and keep the structure of original official

survey data by making use of partial data synthesis of key and target variables of the SES 2018. However, the

more precise examination of utility specific key measures (pMSE and confidence interval overlap) suggests that

the partial data synthesis models need to be tuned before a release of partially synthetic SES 2018 data can be

considered. Hyperparameter optimization seems to be a beneficial approach fur future data synthesis, enabling a

structured search for hyperparameters which are able to maximize the desired result of utility metrics (Bergstra

& Bengio, 2012).

The presented work is no legal report on the legal possibility of providing easier data access to less conservatively

anonymized official data. Even a positive evaluation for the use case of partially synthesized data of the SES

2018 does not allow to make use of this approach for other survey years of the SES or other statistics.

Consequently, a continuous legal monitoring needs to be implemented as soon as new research insights on the

potential of synthesized official data are available.

Conclusion

All in all, the, so far, the generated partially synthetic data does not allow be made publicly available because

they do not meet the expectations of the scientific community concerning the utility. Future research should focus

on examining how to further increase the utility of the present partially synthetic on-site material of SES 2018.

Only after that, a legal evaluation regarding the provision possibilities on simplified ways of access for the

generated partially synthetic data on the SES 2018 is possible. Future research should also deal with further

official statistics and panel data to further increase the knowledge on synthesis potentials for official on-site data.

16

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Anonymization for Integrated and

Georeferenced Data (AnigeD) Yannik Garcia Ritz & Jannek Mühlhan

UNECE Expert meeting on Statistical Data Confidentiality 2023

Agenda (1) Competency cluster AnigeD

(2) Background of Data Synthesis

(3) Synthesis Approach

(4) Evaluation Approach

(5) Evaluation Results

(6) Discussion

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destatis.deUNECE Expert Meeting on Statistical Confidentiality

» Federal Ministry of Education and Research

(BMBF) initiates nationwide “Anonymization for

Secure Data Use” research network

» Individual research projects and

collaborative projects (competency

clusters) are funded for three years

» financed by the European Union –

NextGenerationEU

Initial situation

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 3

Information

content

CostsData

protection Provision of data

and statistics

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destatis.deUNECE Expert Meeting on Statistical Confidentiality

» AnigeD: Anonymization for Integrated and Georeferenced Data

» Objective: securing and extending access to complex data while observing protection

requirements

» Total funding amount: EUR 4.37 million

» Funding period: 11/2022 - 11/2025

» Cluster coordination: Federal Statistical Office (Destatis), Wiesbaden

Competency cluster AnigeD - Anonymization for Integrated and Georeferenced Data

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 4

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Research partners

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 5

Associated clusters

AnoMed

ANONY- MED

AnoMoB

AnigeD

Project partners:

Destatis, FU Berlin, IAB, TH Köln, Speyer University

Associated partners:

DIW, EuroDaT, MPI-SWS,

SMA Development GmbH,

Telekom, Duisburg-Essen

University,

Other research projects:

AnGer, DARIA, GANGES

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destatis.deUNECE Expert Meeting on Statistical Confidentiality

» Evaluation of anonymization methods using legal criteria

» Potential of anonymization by synthetic data

» Anonymization of georeferenced data

» Testing of software tools for the efficient analysis and provision of anonymized, georeferenced

data

Research priorities

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 6

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» Comparison of procedures from statistics and informatics

» Systematic evaluation of criteria

» Analysis and methodological refinement of synthesis

procedures

» Evaluation of synthetic data acceptance by the scientific

community

Work package 3

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 7

Anonymization by means of synthetic data

Synthetic data in statistics and informatics -

Systematic comparison and methodological

refinement (SynDeStatIk)

Anonymization by means of synthetic data to

provide microdata for the scientific community

Machine learning on anonymization by means of

synthetic data

Basis for assessing synthetic data generation

approaches for statistical confidentiality

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» Aim: reducing disclosure risks in provided datasets with less restrictions than established

measures (suppression, top coding etc.)

» Provide less aggregated microdata to enable better analysis for research at less complex

ways of data access

» Builds on previous approaches on partial (e.g., Little, 1993) and full synthesis/imputation

(Rubin, 1993)

Context of Research on Anonymization Potentials of Data Synthesis

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 8

Growing interest in

microdata Confidentiality

considerations

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Basic Idea of Data Synthesis

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 9

» Aim: generating data which mimics original data regarding distributions, relations etc.

but with lower risks of reidentification

» Idea: apply approach of imputation to critical variables / all variables

9

Non-natural NA

False declaration

Synthesized Values

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destatis.deUNECE Expert Meeting on Statistical Confidentiality

Increasing confidentiality

» Mostly CART-based synthesis

» Increasing the default minbucket parameter

leads to tree pruning

» Smoothing for heavily skewed metric

variables

» Spline smoothing

» Kernel density smoothing

Synthesis Approach

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 10

Company Dataset Employee Dataset

m=5m=5 R package:

synthpop

Nowok , Raab & Dibben (2016)

» Minimal # of synthetic datasets to be provided: m (=5) ≥ r (=2); Drechsler (2009); Reiter (2008)

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Disclosure Risk Evaluation

» Risk Ratios (k-anonymity, high-risk observations)

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&#x1d442;&#x1d453;&#x1d453;−&#x1d446;&#x1d456;&#x1d461;&#x1d452; &#x1d437;&#x1d44e;&#x1d461;&#x1d44e; vs.

&#x1d446;&#x1d466;&#x1d45b;&#x1d461;ℎ&#x1d452;&#x1d461;&#x1d456;&#x1d450; &#x1d442;&#x1d45b;−&#x1d446;&#x1d456;&#x1d461;&#x1d452; &#x1d437;&#x1d44e;&#x1d461;&#x1d44e;

&#x1d442;&#x1d453;&#x1d453;−&#x1d446;&#x1d456;&#x1d461;&#x1d452; &#x1d437;&#x1d44e;&#x1d461;&#x1d44e; (Templ, Kowarik & Meindl, 2015)

» Drechsler & Reiter (2008): Expected Match Risk & True Match Rate

Utility Evaluation

» Ensuring logical constraints, comparing descriptive key measures, examining distributions of

analytic key variables and pMSE (global utility)

» Examining confidence interval overlaps of exemplary regression models

(model-specific utility)

Evaluation Approach

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 11

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Disclosure Risk Evaluation

Drechsler & Reiter (2008): Expected Match Risk & True Match Rate

• Expected Match Risk: data user randomly selects correct observation

σ&#x1d457;∈&#x1d447; ൗ1 &#x1d450;&#x1d457; ∗ &#x1d43c;&#x1d457;

• True Match Rate: share of truly matched targets w/ matches > 1 in D(m1-m5)

σ&#x1d457;∈&#x1d447; ൗ &#x1d43e;&#x1d457;

σ&#x1d457;∈&#x1d447;(&#x1d450;&#x1d457;=1)

Evaluation Approach

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Disclosure Risk Evaluation – Risk Ratios

» Against first assumption increase in ratios

» Potential interpretation:

» More unique (synthetic) observations

-> larger pool of risky observation

=> Lower risk of true matches

Evaluation Results

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 13

Original Synthesized, spline

smoothing

Synthesized, kernel

density smoothing

Company Dataset

Ratio k=2 violating Obs. (on-/off-site) 5,977.50 7,356.9 6,459.9

Ratio k=3 violating Obs. (on-/off-site) 1,397.00 4,884.65 4,532.25

Ratio High-Risk Obs. (on-/off-site) 89.32 120.60 91.76721

Employee Dataset

Ratio k=2 violating Obs. 1.51 2.39 2.43

Ratio k=3 violating Obs. 1.07 3.81 3.82

Ratio High-Risk Obs. (On-/Off-site) 4.06 1.60 1.53

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

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Disclosure Risk Evaluation – Expected Match Risk & True Match Rate

Evaluation Results

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 14

Synthesized

(Spline smoothing)

Synthesized

(Kernel density smoothing)

Company Dataset

Expect. Match Risk 0.0520 % 0.0502 %

True Match Rate 0.0000 % 0.0000 %

False Match Rate 100.0000 % 100.0000 %

Employee Dataset

Expect. Match Risk 0.0000 % 0.0000 %

True Match Rate 0.0000 % 0.0000 %

False Match Rate 100.0000 % 100.0000 %

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

» Minor expected risk for a random match for the

company data

» No expected match risk for a random match for

the company data

» No true matches for both data materials

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Utility Evaluation – Global Utility

• Ensuring logical constraints & comparing descriptive key measures

Evaluation Results

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 15

Original data

without Bavaria Synthetic data

without Bavaria [m1] Synthetic data

without Bavaria [m3] Synthetic data

without Bavaria [m5]

Federal state of selection

avg 7.6127 7.5935 7.5941 7.6024

median 7 7 7 7

SD 4.2972 4.2852 4.2863 4.3013

Total Number of Employees per Company

Avg 1338.7238 1377.4767 1350.4678 1336.3058

median 13 13 13 13

SD 12079.7587 12175.1597 11897.5250 11654.4577

Male workers in the company

Avg 59.3187 55.3524 55.5785 55.3697

median 5 5 5 5

SD 391.2943 290.0992 296.3359 272.0953

Female workers in the company

avg 43.4172 40.8809 41.7379 40.6903

median 4 4 4 4

SD 201.6976 165.3280 177.6063 164.0810

Common number of working days per week

Avg 5.0691 5.0691 5.0669 5.0675

median 5 5 5 5

SD 0.3340 0.3338 0.3314 0.3327

Total Number of Employees per Operational Unit

Avg 102.7359 96.2333 97.3165 96.0599

median 12 11 11 11

SD 534.7484 399.1344 413.5876 386.1275

Industry Code

Avg 38.6545 39.4663 39.4978 39.4355

median 14 14 14 14

SD 41.3318 41.6283 41.6528 41.6224

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

Original data without Bavaria

Synthetic data without Bavaria [m1]

Synthetic data without Bavaria [m3]

Synthetic data without Bavaria [m5]

Sex

Avg 1.4697 1.4696 1.4703 1.4697

median 1 1 1 1

SD 0.4991 0.4991 0. 4991 0.4991

Year of Birth

Avg 1973.6222 1973.6273 1973.6244 1973.6249

median 1972 1972 1972 1972

SD 13.0492 13.0436 13.0453 13.04500

Year of entry into the company

Avg 2005.0247 2005.2054 2005.2030 2005.2035

median 2010 2010 2010 2010

SD 12.6139 12.4221 12.4232 12.4226

Gross monthly income

Avg 2989.3089 2988.5670 2987.0762 2985.8921

median 2686 2685 2685 2688

SD 2490.2024 2518.3297 2453.5373 2371.0687

Total earnings for overtime hours

Avg 19.9182 19.4604 19.4583 19.5073

median 0 0 0 0

SD 126.1532 122.3120 122.0371 122.5444

Shift- and night shift credits, weekend and holiday extra charges,

Avg 29.5375 29.8927 29.7160 30.0267

median 0 0 0 0

SD 128.1537 127.4987 126.1038 127.4425

Statutory deductions due to income tax and solidarity surcharge

Avg 532.9553 518.4816 518.3125 517.7757

median 329 327 328 327

SD 875.9017 745.5711 766.2905 709.1242

Statutory deductions due to social insurance

avg 477.2533 484.0925 483.5502 483.7454

median 446 443 443 443

SD 309.9909 391.1970 352.7239 357.0693

Gross yearly income

avg 37903.7149 35862.8038 35844.9144 35830.7048

median 33367 32220 32220 32256

SD 36898.1805 30219.9569 29442.44793 28452.8244

Net monthly income

avg 1985.79994 1985.9929 1985.2135 1984.3709

median 1821 1812 1813 1814

SD 1494.4055 1545.2950 1511.5743 1467.8617

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

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Utility Evaluation – Distributional Examination of Key Variables

• Ensuring logical constraints & comparing descriptive key measures

• Distributions of analytic key variables

Evaluation Results

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 16

Firmendaten Angestelltendaten

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Utility Evaluation – Global Utility

• Ensuring logical constraints & comparing descriptive key measures

• Distributions of analytic key variables

• Propensity Mean-Squared Error (pMSE)

&#x1d45d;&#x1d440;&#x1d446;&#x1d438; = 1

&#x1d45a; σ&#x1d457; &#x1d45a; 1

&#x1d441; σ&#x1d456; &#x1d441; ෝ&#x1d45d;&#x1d456; − &#x1d450; 2

• pMSE interval per definition [0; 0.25]

Evaluation Results

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 17

Company Dataset Employee Dataset

Spline Smoothing Kernel Density Smoothing Spline Smoothing Kernel Density Smoothing

pMSE 0.1142 0.1142 0.1102 0.1110

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States.

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Utility Evaluation – Model-Specific Utility (Company Material)

• Examining confidence interval overlaps of exemplary regression models

Evaluation Results

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 18

mean Confidence Interval Overlap: 0.85 mean Confidence Interval Overlap: 0.74

Firmendaten

Coefficents for fit to

„Number of Employees/Operational Unit“

Coefficients for fit to „Company Loans are Negiotated Based

on Collective Bargaining“

Craft affiliation

Participation of

Public Sector

Common

Number of

Working Days

per Week

Craft affiliation

Employees per

Company Participation of

Public Sector Muti-unit

company Multi-country

company

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Utility Evaluation – Model-Specific Utility (Employee Material)

• Examining confidence interval overlaps of exemplary regression models

Evaluation Results

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 19

Original on-site material (employee dataset)

Synthesized on-site material (employee

dataset)

CI Overlap Coefficient

(Std. error) Coefficient (Std. error)

Intercept 589.1931*** (2.569)

606.0611*** (2.56875)

-0.6752

Education 1.550*** (0.0197)

1.54555*** (0.01968)

0.9394

Sex -3.3940*** (0.0249)

-3.18269*** (0.02488)

-1.1664

Year of Birth -0.0304*** (0.0012)

-0.0682*** (0.0012)

-6.9942

Year of Entry -0.2567*** (0.0015)

-0.2279*** (0.0015)

-3.7869

Restriction of term of contract

-1.4092*** (0.0101)

1.4784*** (0.01008)

-0.7522

Private sector 0.0716 (0.0542)

0.2492*** (0.0542)

0.1643

Company size -0.0000*** (0.0000)

-0.0000*** (0.0000)

0.23399

Vocational education

3.5243*** (0.0124)

3.3684*** (0.01235)

-0.2990

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States. * p < 0.10, ** p < 0.05, *** p < 0.01

Original on-site material (employee dataset)

Synthesized on-site material (employee dataset)

CI Overlap Coefficient

(Std. error) Coefficient (Std. error)

Intercept 99.76548*** (0.9690)

84.8586*** (0.9690)

-2.9244

Sex -0.24396*** (0.01248)

-0.2399*** (0.01248)

0.9172

Year of Birth -0.04444*** (0.00049)

-0.0379*** (0.00049)

-2.3949

Education -0.0408*** (0.0075)

-0.1054*** (0.0075)

-1.1864

Vocational Education

0.4637*** (0.00736)

0.5034*** (0.00736)

-0.3762

Restriction of term of contract

-1.93796*** (0.0090)

-1.6336*** (0.0090)

-7.6049

Weekly working hours

-0.16288*** (0.00086)

-0.1277*** (0.00086)

-9.3753

Private sector

0.2412*** (0.02997)

0.2035*** (0.02997)

0.6791

Company size

-0.0000*** (0.0000)

-0.0000*** (0.0000)

0.5961

Source: SES 2018. RDCs of the Statistical Offices of the Federation and the Federal States. * p < 0.10, ** p < 0.05, *** p < 0.01

Angestelltendaten

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Limitations

» Only partial synthesis is tested

» Results are not generizable for other surveys

» Assessment of Risk-Utility-Ratio yet not satisfying

» Research results cannot serve as legal report

Discussion

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 20

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Research Implications

» Replicate studies with a full synthesis approach

» Evaluate utility and risk for longitudinal data (of other surveys)

» Examine further surveys regarding potentials of synthetic data

» Applying hyperparameter tuning to optimize cost-utility-ratio

Discussion

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 21

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Practical Implications

» If future research is able to improve generated synthetic data regarding the Risk-Utility-Ratio:

» Lawyers need to evaluate possibilities to provide synthetic on-site material via off-site

access

» Synthetic data need to be provided as separate product at first without opportunities fo project-

specific processing until insights are gained

Discussion

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 22

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Discussion

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 23

Conclusion

» Attempt to synthetize and evaluate official German on-site material

» New insights on synthesis of further official surveys

» Encouraging results regarding global utility and disclosure risks

» Improvable results concerning utility

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Conclusion

» Two Options:

1. release partially synthetic data tailored to specific research questions of the data users

2. release fully synthetic datasets if follow-up research is able to provide evidence for an

improved model-specific

Discussion

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 24

Contact Statistisches Bundesamt

Postal address

65180 Wiesbaden

[email protected]

[email protected]

Functional mailbox

[email protected]

www.destatis.de/contact

mailto:[email protected]
mailto:[email protected]

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Drechsler, J. (2009). Generating multiply imputed synthetic datasets: theory and implementation. (Doctoral dissertation,

Otto-Friedrich-Universität Bamberg, Fakultät Sozial-und Wirtschaftswissenschaften). Bamberg.

Drechsler, J., & Reiter, J. P. (2008). Accounting for Intruder Uncertainty Due to Sampling When Estimating Identification

Disclosure Risks in Partially Synthetic Data. In J. Domingo-Ferrer, & Y. Saygin (Ed.), Privacy in Statistical Databases. 5262,

pp. 227-238. Berlin: Springer. doi:10.1007/978-3-540-87471-3_19

Hafner, H.-P., & Lenz, R. (2011). Some aspects concerning analytical validity and disclosure risk of CART generated

synthetic data. Joint UNECE/Eurostat work session on statistical data confidentiality, (pp. 1-10). Tarragona, Spain.

Loske, J., & Wolfanger, T. (2019). Entwicklung Synthetischer Datenstrukturfiles. Statistische Woche, (p. 113). Trier.

Karr, A. F., Kohnen, C. N., Oganian, A., Reiter, J. P., & Sanil, A. P. (2006). A Framework for Evaluating the Utility of Data

Altered to Protect Confidentiality. The American Statistician, 60(3), pp. 224-232. doi:10.1198/000313006X124640

Sources

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 26

[email protected]

destatis.deUNECE Expert Meeting on Statistical Confidentiality

Karr, A. F., Kohnen, C. N., Oganian, A., Reiter, J. P., & Sanil, A. P. (2006). A Framework for Evaluating the Utility of Data

Altered to Protect Confidentiality. The American Statistician, 60(3), pp. 224-232. doi:10.1198/000313006X124640

Little, R. J. (1993). Statistical analysis of masked data. Journal of Official Statistics, 9(2), pp. 407–426.

Nowok, B., Raab, G. M., & Dibben, C. (2016, October). synthpop: Bespoke Creation of Synthetic Data in R. Journal of

Statistical Software, 74(11), pp. 1-26. doi:10.18637/jss.v074.i11

Order of the First Senate of 15, 1 BvR 209/83 -, paras. 1-214 (BVerfG December 1983).

Reiter, J. P. (2008). Selecting the number of imputed datasets when using multiple imputation for missing data and

disclosure limitation. Statistics & Probability Letters, 78, pp. 15-20.

Rothe, D. (2015). Statistische Geheimhaltung - der Schutz vertraulicher Daten in der amtlichen Statistik - Teil 1:

Rechtliche und methodische Grundlagen. Bayern in Zahlen, pp. 294-303.

Rubin, D. B. (1993). Discussion: Statistical disclosure limitation. Journal of Official Statistics, 9(2),

pp. 462-468.

Sources

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 27

[email protected]

destatis.deUNECE Expert Meeting on Statistical Confidentiality

Rubin, D. B. (1993). Discussion: Statistical disclosure limitation. Journal of Official Statistics, 9(2), pp. 462-468.

Templ, M. (2017). Statistical Disclosure Control for Microdata - Methods and Applications in R (1. ed.). Basel:

Springer Cham. doi:10.1007/978-3-319-50272-4

Templ, M., Kowarik, A., & Meindl, B. (2015, October). Statistical Disclosure Control for Micro-Data Using the R Package

sdcMicro. Journal of Statistical Software, 67(4), pp. 1-36. doi:10.18637/jss.v067.i04

Woo, M.-J., Reiter, J. P., Oganian, A., & Karr, A. F. (2009). Global measures of data utility for microdata. Journal of

Privacy and Confidentiality, 1(1), pp. 111-124.

Zühlke, S., Zwick, M., Scharnhorst, S., & Wende, T. (2005). The research data centres of the Federal Statistical

Office and the statistical offices of the Länder. FDZ-Arbeitspapiere, 3, pp. 1-11.

Sources

29.09.2023Statistisches Bundesamt (Destatis) - IFEB 28

  • Slide 1: Anonymization for Integrated and Georeferenced Data (AnigeD)
  • Slide 2: Agenda
  • Slide 3: Initial situation
  • Slide 4: Competency cluster AnigeD - Anonymization for Integrated and Georeferenced Data
  • Slide 5: Research partners
  • Slide 6: Research priorities
  • Slide 7: Work package 3
  • Slide 8: Context of Research on Anonymization Potentials of Data Synthesis
  • Slide 9: Basic Idea of Data Synthesis
  • Slide 10: Synthesis Approach
  • Slide 11: Evaluation Approach
  • Slide 12: Evaluation Approach
  • Slide 13: Evaluation Results
  • Slide 14: Evaluation Results
  • Slide 15: Evaluation Results
  • Slide 16: Evaluation Results
  • Slide 17: Evaluation Results
  • Slide 18: Evaluation Results
  • Slide 19: Evaluation Results
  • Slide 20: Discussion
  • Slide 21: Discussion
  • Slide 22: Discussion
  • Slide 23: Discussion
  • Slide 24: Discussion
  • Slide 25: Contact
  • Slide 26: Sources
  • Slide 27: Sources
  • Slide 28: Sources

An overview of data protection strategies for individual-level geocoded data, Institute for Employment Research, Germany

individual data, georeferenced data, confidentiality concerns, privacy protection, utility, limited access

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

An overview of data protection strategies for individual-level geocoded data Maike Steffen, Konstantin Körner, Jörg Drechsler

Institute for Employment Research (IAB)

[email protected]

Abstract In response to a growing need for small-scale geographic information in various research areas, data-collecting institutions are increasingly georeferencing individual-level data. However, due to confidentiality concerns, external researchers typically have very limited access to these data if at all, resulting in a substantial loss of informational value. A growing body of literature on data protection strategies for geocoded data attempts to find solutions for the tradeoff between privacy protection and utility preservation of the individual-level data. The purpose of this paper is to systematically collect and review the literature in the field and to offer a classification of existing methods. Various strategies for estimating the utility and the remaining risk of disclosure for the protected data are also discussed.

1 Introduction

Geocoded data have become increasingly relevant in various research areas since they offer insights that can only be acquired considering spatial context. The granular information enables researchers to include fine geographic patterns and spatial variation of individual characteristics in their analyses. The detailed geographical information facilitates studying such diverse topics as neighborhood effects, mobility patterns, or the spread of diseases to name only a few of the possible applications. Moreover, the geo-coordinates are not subject to changes over time as it is the case with administrative borders, which often hampers longitudinal analyses. Finally, the availability of detailed geographical information allows to easily merge information from various data sources. However, access to detailed geocoding information is currently limited as it is well known that detailed geo- graphical information is highly identifying (De Montjoye et al., 2013). To still enable access to this valuable source of information, various strategies have been proposed in the literature to protect confidentiality while still maintaining the utility of the collected information. This paper aims to give an overview of the various approaches. We also provide an overview of metrics that have been used to assess the disclosure risk and the utility of the protected data. The remainder of the paper is organized as follows. In Section 2, we review the three most popular approaches for protecting geocoded data: aggregation, geographic masking, and data synthesis. In Section 3, we discuss various tools which are used to assess the risk and utility of the protected data. Section 4 concludes the article.

2 Data Protection Strategies

Two general strategies are commonly applied to reduce the risk of disclosure when disseminating data to the public: information reduction and perturbation. Information reduction limits the amount of detail that is available in the data. This can range form discretizing continuous variables (e.g., reporting age in five-year intervals) over coarsening categorical variables (e.g., reporting only the first two digits of a hierarchical classification code such as the NACE code) to removing entire variables. Perturbation approaches try to preserve the level of detail contained in the original data. They reduce the risk of disclosure by slightly altering the microdata on the record level. Examples include noise infusion, top-coding, or swapping. Both strategies are also used when disseminating detailed geo-information. Aggregation as a form of information reduction is probably the most widely adopted strategy to reduce the risk of reidentification. We will review different aggregation strategies in more detail in Section 2.1. The early influential paper by Armstrong et al. (1999) lists two alternative strategies to aggregation that rely on perturbation: affine transformations and geographic masking. Affine transformations are methods that displace, rescale, or rotate the entire vector of original locations. Since they are completely deterministic, these methods are relatively easy to reverse engineer. They also lead to a substantial loss of information since the transformation of the original locations are data independent and thus spatial clustering effects found in the original data can be destroyed. Furthermore, external geographical information can no longer be linked to the transformed data in a reasonable way (Zandbergen, 2014). For these reasons, these methods have never been widely adopted and we will only review geographic masking in more detail in Section 2.2. In recent years, synthetic data approaches have emerged as another perturbation strategy. With synthetic data, original values are replaced with synthetic values drawn from a model fitted to the original data. We will review synthetic data approaches for disseminating detailed geo-information in Section 2.3.

2.1 Aggregation

As discussed earlier, aggregation is the most widely adopted strategy to reduce risks from reidentification. Aggregation does not alter the information, that is, the number of observations per aggregated unit remains

2

accurate and the location of individuals may be coarsened but will not be replaced by fake locations. However, it does lead to a loss of information and thereby reduces the range of applications the data can be used for. Broadly, there are two general aggregation strategies: aggregation within pre-defined areas, such as grid cells or administrative areas, and more spatially flexible microaggregation, which ensures that each aggregation cell contains a predefined number of records. The use of aggregation within pre-defined areas is by far the most commonly adopted approach, and guidelines to assign observations to standardized grid cells have been developed (e.g., INSPIRE, 2014). Using standardized formats comes with the advantage that additional spatial information such as climate, health, or economic data can be easily linked using these grid cells (Klumpe et al., 2020). At the same time, it is a rather inflexible strategy. If the uniformly sized grid cells are sufficiently small, they allow detailed analyses, but may not protect confidentiality adequately in sparsely populated cells. If they are large enough to protect confidentiality even in rural areas, there is a high information loss in urban areas. To address this issue, grid cell sizes can be adapted to the population density (e.g., Lagonigro et al., 2017). This approach, however, renders the linking of external grid cell data more difficult. Some researchers (e.g., Groß et al., 2017, 2020) have proposed to improve the utility of the aggregated data by applying a smoothing function based on kernel density estimators, which randomly reassigns the individuals to point locations within the aggregation cell. This strategy can, for example, be beneficial if the goal is to compute distance measures or for plotting the data on a map. Microaggregation techniques allow to flexibly adapt the size of the aggregation area to the desired level of protection (Domingo-Ferrer and Torra, 2005; Castro et al., 2022). Research on microaggregation in the context of geographic data mainly focuses on anonymizing digital trace data (see, e.g., Domingo-Ferrer and Trujillo- Rasua, 2012; Rebollo-Monedero et al., 2011), but the approach has also been adopted to achieve strong privacy guarantees for geocoded data based on the concept of differential privacy (Soria-Cormas and Drechsler, 2013). While microaggregation can protect privacy consistently, it creates irregular polygons that are somewhat difficult to interpret and cannot easily be linked to external geographic data.

2.2 Geographic Masking

Geographic masking relies on randomly displacing the original location to protect confidentiality. A variety of methods have been developed in this field. The simplest form of geographic masking assigns new locations by drawing a circle with fixed radius around the original location and randomly picking a new location on that circle (Zandbergen, 2014). With such a fixed displacement distance, the risk of re-engineering the original locations from the masked data can be relatively high (Zandbergen, 2014), hence random perturbation within a predefined maximum distance from the original location is more commonly used (see Armstrong et al., 1999; Kwan et al., 2004; Zandbergen, 2014; Hampton et al., 2010). This increases the level of protection as the actual displacement distance is unknown to the end user even if the masking approach is disclosed. Various strategies how to randomly draw the displacement distance have been proposed in the literature. One strategy is to use a uniform distribution within the radius of a circle centered on the original value (Armstrong et al., 1999; Zimmerman and Pavlik, 2008). Since this allows for the masked location to be very close or even equal to the original location, an alternative method called donut masking that provides higher confidentiality protection has been suggested (Hampton et al., 2010; Allshouse et al., 2010; Kounadi and Leitner, 2015). This masking method requires a minimum displacement distance additionally to the maximum displacement distance, forming a donut shape around the original location. An alternative approach to increase the displacement distance is N-Rand masking (Wightman et al., 2011), which also uses perturbation within a circle but draws &#x1d441; potential displacement locations. The location that is furthest away from the original location is then selected as the final displacement location. Instead of displacing the original locations within a circle with fixed radius and using a uniform distribution, some authors have suggested drawing the distance and direction of displacement from a bivariate Gaussian probability distribution (Cassa et al., 2006, 2008; Zimmerman and Pavlik, 2008). Compared to drawing from a uniform distribution, using a Gaussian distribution renders a displacement close to the original location more likely and therefore has little effect on spatial clusters (Cassa et al., 2006). Of course, a negative consequence is

3

an increased risk of disclosure as most of the masked locations will be close to the original location. A variant of this method therefore uses a bimodal Gaussian distribution to approximate donut masking (Zandbergen, 2014). Note that, although unlikely, extremely high displacement distances can drawn from a normal distribution for a small fraction of the locations (Armstrong et al., 1999). If population density in the data varies substantially, perturbation with fixed maximum distance (or fixed variance for the bivariate Gaussian approach) may lead to an unnecessarily large alteration of spatial information in highly populated areas where shorter displacement distances may suffice, and to privacy risks where population density is low and locations should be displaced more. This can be addressed by taking population density into account, such that the radius of the displacement area is larger in less densely populated areas (Kwan et al., 2004; Cassa et al., 2006; Hampton et al., 2010; Lu et al., 2012; Zurbarán et al., 2018). This results in masked data that are more similar to the original data in urban areas while offering a higher level of confidentiality protection in rural areas. With the bivariate Gaussian approach, the variance of the distribution can be set to be inversely proportional to the square of the population density (Cassa et al., 2006). However, as illustrated in Allshouse et al. (2010), using externally provided population density data on an administrative area level as a benchmark, as done for example in Cassa et al. (2006); Hampton et al. (2010), may not sufficiently protect confidentiality in areas with high population distribution heterogeneity. As a remedy, the authors suggest tripling the displacement distance in areas with heterogeneous population distribution. Kounadi and Leitner (2016) argue that, when information is available at the point level, the actual distance to the &#x1d458;th nearest neighbor should be used to determine displacement distance rather than using external population density data at the administrative-area level. In recent years, some authors proposed masking techniques that displace the original locations taking the actual position of the surrounding locations into account, such as Voronoi masking or location swapping (Seidl et al., 2015; Zhang et al., 2017). Voronoi Masking, developed by Seidl et al. (2015), is based on Voronoi polygons (Voronoi, 1908), which are shapes built around each single location with boundaries marking the half of the distance to the next location in any direction. A Voronoi polygon surrounding a point location contains all locations that are closer to this location than they are to any neighboring point locations in the data. In the masking process, each original location is moved to the closest point along the boundaries of its polygon, placing it in the middle between two actual locations. Seidl et al. (2019) find that this decreases map users’ beliefs in being able to re-identify households. The locations are, on average, moved less in areas with higher density of the original points. At the same time, a group of at least two locations that are remote but close to each other will likely be displaced less than would be the case using random perturbation methods, and multiple locations may be relocated to the same masked location. Since many masking approaches do not account for geographic characteristics or whether units exist at the masked location, they may generate unrealistic locations, such as within water bodies or parks. Zhang et al. (2017) propose a location swapping approach to address these concerns. This method draws a circle or donut around the original location with varying distances based on population density. Then, the original location is swapped with another location with similar geographic characteristics within the specified area. They find that location swapping yields higher values of &#x1d458;-anonymity (defined in Section 3.1) than random perturbation using the same displacement area. However, we note that when applying random perturbation techniques with a maximum displacement distance, and especially in scarcely populated areas, the actual level of &#x1d458; achieved can be lower than the level implied by commonly applied techniques to measure &#x1d458; and, thus, we generally do not recommend using this measure to assess the level of protection (we will discuss this problem in more detail in Section 3.1). To address the problem with distance based perturbation techniques, Kounadi and Leitner (2016) propose adaptive areal elimination masking that guarantees a minimum &#x1d458;-anonymity for every location. This method merges predefined shapes, e.g., administrative areas, until the number of locations per polygon is &#x1d458; or higher. The locations are then aggregated or randomly perturbed within each polygon. While this guarantees to achieve the desired level of &#x1d458;-anonymity, most polygons will contain (substantially) more than &#x1d458; units and therefore spatial patterns can be altered excessively.

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2.3 Synthetic Data

An alternative to the information reduction and masking methods discussed in the previous sections is to replace the true observations with draws from a statistical model, i.e., to generate synthetic data. Such datasets aim to preserve distributional properties and the spatial structure of the original data. Since these patterns are preserved at a much smaller spatial level compared to other anonymization techniques, authors such as Quick et al. (2018); Lawson et al. (2012), and Bradley et al. (2017) argue that synthetic data is able to reduce the risk of ecological fallacies (i.e., misleading inferences from the protected data, see Freedman, 1999). Two general approaches are distinguished in the literature: fully and partially synthetic data. With fully synthetic data (Rubin, 1993), all records in the released data are synthetic. Since synthesizing all variables in a dataset can be challenging for large scale surveys, Little (1993) suggested synthesizing only those variables that are either sensitive or that could be used for re-identification. See Drechsler (2011); Drechsler and Haensch (2023) for a detailed overview on the topic. The approach has also been adopted in recent years for protecting data containing detailed geographical information. Two general strategies can be distinguished in the literature. Several papers do not synthesize the geographical information. Instead, they specifically account for the spatial structure of the data when synthesizing other variables in the dataset to improve the utility of the synthetic data. While these papers focus on protecting sensitive information in the data, i.e., reducing the risk of attribute disclosure, other approaches directly synthesize the geographical information, hence reducing the risk of reidentification. We will separately review the two strategies in the remainder of this section.

2.3.1 Synthesizing non-geographic variables while preserving the spatial information. Sakshaug and Raghu- nathan (2010) is one of the early papers that specifically adjust common synthesis strategies to preserve the detailed spatial information. The authors propose using mixed effects modeling strategies. Mixed effects synthe- sis models are a natural way to preserve the geographical clustering effect. These models are especially popular in the literature on small area estimation. The authors later (2014) extended their approach by incorporating area level covariates in the model, which allows to generate synthetic data even for small areas not included in the original sample. Zhou et al. (2010) offer a more rigorous treatment of the spatial information problem by modeling all variables as spatial processes and applying spatial smoothing when modeling the variables. They show that their method introduces bias for non-linear regression models and propose a strategy for choosing the smoothing function to keep this bias small. Yet another synthesis strategy is described in Quick et al. (2018), which uses a differential smoothing synthesizer for locations of home sale in San Francisco. Their approach is a two-step process. First, they model the log-transformed home sale prices using an unrestricted hierarchical model. Second, they identify spatial outliers based on the distances to their nearest neighbors, then fit a restricted hierarchical model to provide additional smoothing for higher protection. In a related approach, Quick and Waller (2018) also use a hierarchical Bayesian model that preserves spatial, temporal, and between age-groups dependencies. They synthesize county-level heart disease deaths to complete public use data, which would be suppressed at units with cases lower than 10. More recently, Koebe et al. (2023) suggest publishing two different versions of georeferenced data. The first version includes the original location, but all other attributes are synthesized using a Gaussian copula model. The second version omits the geographic identifier, but leaves the other attributes at their original values.

2.3.2 Synthesizing the geographical information. The first successful implementation of geographical synthesis was discussed in Machanavajjhala et al. (2008). The authors propose a strategy for synthesizing the place of living for all individuals working in the U.S. The synthesizer is used to generate the underlying data for an application called OnTheMap provided by the U.S. Census Bureau. This application graphically visualizes commuting patterns on a detailed geographical level. The authors used a Dirichlet/Multinomial model for synthesis and adjusted the Dirichlet priors such that they were able to prove that their synthesizer guaranteed some formal level of privacy called Y−&#x1d6ff;-probabilistic differential privacy (see Machanavajjhala et al. (2008) for details). However, the multinomial model used in this paper offers low utility if the population sizes or event rates are very heterogenious. To address this limitation, Quick (2021) suggests relying on Poisson models–popular

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in the disease mapping literature–for differentially private data synthesis. He later extended the approach by incorporating public knowledge to further improve the utility of the synthesizer (Quick, 2022). Another synthesis strategy proposed by Wang and Reiter (2012) is to treat the detailed geocoding information as a continuous variable and use CART models to sequentially synthesize the longitude and latitude of the geocodes. This approach was later compared in Drechsler and Hu (2021) with two other synthesis strategies for the geocodes: using a Dirichlet Process of Mixtures of Products of Multinomials (Si and Reiter, 2013; Hu et al., 2018, DPMPM) and CART models treating the geocoding information as categorical variables. The authors find that the categorical CART models offer the highest utility, but also the highest risk of disclosure. When trying to increase the level of protection, they find it to be more effective to synthesize additional variables instead of aggregating the geocoding information to a higher grid level. Burgette and Reiter (2013) generate a partially synthetic dataset in which they synthesize the location of US census tract identifiers using a Bayesian multinomial model with a group of Dirichlet processes priors and a multiple shrinkage prior distribution. This framework is chosen because it shrinks the parameters toward a small number of learned locations, which increases the utility of the data. Paiva et al. (2014) use areal level spatial models (often called disease mapping models in the literature) to synthesize the geographical information. Although they start with exact geographies, their methods require defining fine grids over the spatial domain, then using the conditional autoregressive (CAR) model of Besag et al. (1991) to model the distribution of grid-counts. When synthesizing exact geographies, they recommend first to synthesize grid cells for each individual, and second to randomly assign each individual a location within the grid cells. The approach is computationally intensive and can be challenging to apply if the number of categorical variables or the number of levels within the variables is large. The authors also note that their partially synthetic data do not preserve the spatial pattern because the independent draws from the underlying Poisson model can imply that close geographic units in the original data might be far apart in the synthetic data. This caveat is considered by Quick et al. (2015) who extend the spatial modeling process of geo-coordinates using marked point process models, which simultaneously model the location and the variables (Liang et al., 2008; Taddy and Kottas, 2012). Specifically, the authors propose to model the data in three steps: (i) specify multinomial models for the categorical variables in the data, (ii) use a log-Gaussian Cox process to model the geographical location within each cell specified by cross classifying all categorical variables, and (iii) specify a normal regression for continuous variables given the categorical variables and location. The authors point out that estimating this model can be computationally intractable and suggest several steps and simplifying assumptions to reduce the computational burden.

3 Risk and Utility Assessment

Data dissemination always faces two conflicting goals: minimizing the risk of disclosure and maintaining the usefulness of the data. Therefore, it is crucial to always evaluate data protection strategies for both of these dimensions. In this section we review strategies that have been proposed in the literature to measure the utility and the level of protection for geocoded data that underwent some form of disclosure protection.

3.1 Risk Evaluation

The most commonly applied measure for evaluating the disclosure risk of masked geodata is spatial &#x1d458;-anonymity. It is related to the classical definition as proposed by Sweeney (2002), which states that &#x1d458;-anonymity is achieved if a record is indstinguishable from &#x1d458; − 1 other records in the dataset based on a set of prespecified variables (e.g. age, sex, education). Specifically, spatial &#x1d458;-anonymity is reached if a location is indistinguishable from at least &#x1d458; − 1 other locations. However, in practice it is interpreted in many different ways (Cassa et al., 2006; Allshouse et al., 2010; Hampton et al., 2010; Kounadi and Leitner, 2016; Zhang et al., 2017; Hasanzadeh et al., 2020).

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There are two main definitions of &#x1d458;-anonymity for masked geodata. First, some researchers define spatial &#x1d458;-anonymity as the number of locations around the original point within a circle with radius equal to the displacement distance (Hampton et al., 2010; Allshouse et al., 2010). The second definition is to measure &#x1d458;-anonymity as the number of locations around the masked location that are within a circle with radius equal to the displacement distance (Lu et al., 2012; Zhang et al., 2017; Hasanzadeh et al., 2020). Note, however, that both approaches can overestimate the level of &#x1d458; , when random perturbation within a circle or donut is applied. This can be amplified if the maximum displacement distance depends on the population density (Allshouse et al., 2010) or is determined by the distance to the &#x1d458; &#x1d461;ℎ nearest neighbor. To illustrate, imagine one household located in an area with few observations or low population density which borders an urban area. If the displacement radius for this household is chosen to reach a certain level of &#x1d458;-anonymity, its maximum displacement distance will be relatively large reaching the outer areas of the urban area. A location in the urban area, on the contrary, has many neighbors in close proximity and will thus, taking &#x1d458;-anonymity as the objective, be displaced within a smaller area that does not include all possible displacements of the rural location. In this example, the rural location may be the only one that can be displaced far into the rural area. As a consequence an ill-intentioned user of the released data can be confident that a masked record in certain rural areas can only stem from one of the few observations in the rural area. Thus, neither counting the cases within a circle around the original point nor counting the cases within a circle around the masked point provides adequate information how well these points are protected. Kounadi and Leitner (2016) empirically demonstrate that to achieve the desired level of &#x1d458;-anonymity for close to 100% of the locations, the maximum distance of displacement needs to be substantially larger than the distance to the &#x1d458; &#x1d461;ℎ nearest neighbor. Beyond the (often flawed) risk assessment based on spatial &#x1d458;-anonymity, strategies for measuring the remaining risk of disclosure are surprisingly limited. Some authors discuss general aspects that impact the risk of disclosure. For example, Cassa et al., 2008 point out that risks of reidentification increase when multiple protected versions of the same georeferenced dataset are published. The original locations can then be approximated by averaging of the masked locations (assuming the same records can be uniquely identified in the different datasets). The more versions of the data are published, the higher the accuracy of this approximation. As Zimmerman and Pavlik (2008) point out, the risk is particularly high when the locations are labelled or details on the masking approach are disclosed such as the maximum displacement radius. A classical risk assessment strategy that has been used in some applications is to mount a record linkage attack. With these types of attacks, the intruder is assumed to possess some information about the units contained in the database (e.g., age, marital status, and employment status) and uses this information to identify units in the database. Risk measures based on record linkage attacks typically try to estimate how likely it is that such an attack will lead to a correct identification in the protected dataset. In the context of geocoded data, it is typically assumed that one of the attributes that is known to the attacker is the (approximately) exact location of the target record. Simulated record linkage attacks have for example been used in Drechsler and Hu (2021) (and implicitly in Koebe et al., 2023) to assess how well the different synthesis strategies protect the geographical information. Drechsler and Hu (2021) use risk measures originally proposed in Reiter and Mitra (2009) to specifically estimate reidentification risks for partially synthetic data. With this approach it is assumed that the attackers possess some background knowledge for a set of target records they wish to identify in the data. Based on this knowledge, they estimate the probability of a match for each unit in the released file. A match is declared for the record that has the highest average matching probability across the synthetic datasets. The risk is evaluated by means of these matches using two different measures. The first one calculates the expected number of correctly declared matches, i.e., the expected match risk. The second one calculates the number of correct unique matches, i.e., the true match rate. Another strategy to evaluate the level of protection specifically for partially synthetic data approaches was used in Quick et al. (2018). The authors focus on spatial outliers in the original data. For those records, they generate a large number of synthetic values by repeatedly drawing from the synthesis model. They then look at histograms of the generated values. If the spatial synthesis model is overfitting, the draws from the model will be centered around the true value with limited variability potentially indicating an unacceptable risk of disclosure. Using a related idea, Quick et al. (2015) and Quick and Waller (2018) compare synthesized values with the

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true, confidential values. In light of privacy protection, the objective is here to obtain different values. Given that they propose releasing two versions of the same dataset (see Section 2.3), Koebe et al. (2023) measure the risk of correctly re-identifying the sensitive small-area identifiers (zip codes) in the unprotected data without geoinformation using information from the synthetic data. They train random forest models on the dataset in which the geolocations have been protected. The trained model is then run on the original data to predict the locations. The fraction of successful predictions denotes the risk measure.

3.2 Utility Evaluation

While offering a sufficient level of protection should always be the primary goal of any disclosure limitation strategy, it is crucial to also measure its impacts on utility. In the geocoding context, the utility is typically assessed by measuring to what extent the spatial structure of the data is maintained. The list of metrics that is used for this purpose in the literature is almost as large as the disclosure avoidance literature itself. Here, we only focus on the utility assessment based on spatial pattern retention. A more general discussion on utility evaluations can be found for example in Domingo-Ferrer et al. (2012). In the following, we will classify the various approaches into four broad categories: (1) point locations and density measures; (2) cluster analysis; (3) spatial autocorrelation; and (4) land use assessment.

3.2.1 Point Locations and Density Measures. Utility evaluations often start by graphically comparing the population densities of the confidential data and the protected data. A simple approach is to visually compare the locations on a map (e.g., Kwan et al., 2004). However, unless the original data is non-confidential, this approach can only be used internally, as the plots of the original data might spill sensitive information otherwise. A more versatile approach is to estimate the population density using kernel density estimation (Shi et al., 2009; Gatrell et al., 1996). The kernel density estimator creates a smooth density surface which allows to graphically compare the densities of the original and masked data on a heatmap (e.g., Kwan et al., 2004; Zandbergen, 2014). The heatmaps can be used to either visualize the density levels for each dataset separately or to directly display the discrepancies between the two densities. Beyond visualizing the population densities (e.g., Gatrell et al., 1996) the approach can also be used to measure spatial discrepancies in any other variable contained in the data. For example, Seidl et al. (2015) show differences in total warm water consumption among others.

3.2.2 Clustering. Another common approach to evaluate the utility of the protected dataset is to assess whether the data show similar clustering behavior as the original data. A descriptive statistic that is often used to describe clustering in a point pattern is Ripley’s &#x1d43e; function (see, e.g., Kwan et al., 2004; Zhang et al., 2017; Quick et al., 2015; Seidl et al., 2015; Drechsler and Hu, 2021). It is defined as expected number of points within a predefined radius around the location of interest normalized by the average point density across the entire geographical area covered in the data (Ripley, 1976; Kwan et al., 2004). It assesses to which extent a point pattern deviates from spatial homogeneity (Drechsler and Hu, 2021). Based on the &#x1d43e; function, the more easily interpretable &#x1d43f; function can be computed. It takes values close to zero for homogeneously distributed data, while positive values indicate heterogeneity or clustering. Closely related, the cross-&#x1d43e; function and its analog for the &#x1d43f; statistic assess the clustering of one point pattern relative to another point pattern, for example the underlying population distribution (Kwan et al., 2004). As an alternative measure, Zhang et al. (2017) apply an average nearest-neighbor analysis to quantify how well the spatial pattern of the original data is preserved. Specifically, they compute a nearest-neighbor index that consists of the average distances from each unit to its nearest neighbor (measured in, e.g., Euclidean or Manhattan distance). An index value similar to that of the original data indicates comparable clustering intensity. In a related approach, Lu et al. (2012) apply a nearest-neighbor index that compares the average distance to the nearest neighbor with the expected distance assuming a uniform distribution of the locations. Values below one indicate clustering. Seidl et al. (2015) use a nearest-neighbor hierarchical clustering analysis to compare the number of clusters on the first level (clusters of individual data points) in the data (see also Levine, 2006; Kounadi and Leitner, 2015). They also compare standard deviational ellipses between the original and the protected data. These ellipses cover the area that is within, say, one or two standard deviations from the center of

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the cluster (Kounadi and Leitner, 2015). They facilitate understanding the two-dimensional clustering behavior. Another measure to assess clustering and to identify hotspots is the Gi* statistic proposed by Getis and Ord (1992); Ord and Getis (1995). The Gi* statistic can be used to test the null hypothesis of spatial independence. Rejecting the null hypothesis indicates clustering (Getis and Ord, 1992). Kounadi and Leitner (2015) develop an indicator that combines nearest-neighbor hierarchical clustering and the Gi* statistic. In health research, SatScan (Kulldorff, 1997) is a popular software tool for disease mapping. It can be used to identify spacial and temporal clustering in the data (Kulldorff et al., 2005). Several authors (Olson et al., 2006; Cassa et al., 2006; Hampton et al., 2010) use the software to compare the sensitivity and specificity of the underlying cluster detection approach run on the original and protected data. Finally, some researchers use the original and masked dots to identify a data-dependent geographical area. The utility of the protected data is assessed by measuring the overlap of this area between the two datasets. For example, Hasanzadeh et al. (2017) propose an approach that compares the similarity of individuals’ frequently visited points. Specifically, they extend the residential points to home areas, where the edges mark locations that are visited frequently. Large overlaps of the home areas of the protected and the confidential data indicate high similarity of individuals’ neighborhoods in both datasets.

3.2.3 Spatial Autocorrelation. While clustering analysis focuses on identifying the number and size of clusters in the data, spatial autocorrelation more generally assesses the spatial dependence in a point pattern. Both approaches are closely related. A prevalent measure for spatial autocorrelation is Moran’s I (e.g., Ord and Getis, 1995; Lu et al., 2012; Seidl et al., 2015). It tests whether the null hypothesis that the spatial autocorrelation is zero can be rejected. If this is the case, spatial autocorrelation can be assumed. Another common measure to compare spatial autocorrelation between datasets is the empirical semivariogram. (Matheron, 1963; Quick et al., 2018; Seidl et al., 2015)). It visualizes the homogeneity of non-geographic variables as a function of the distance between the locations. An output graph that increases and then flattens with further distance indicates positive spatial autocorrelation.

3.2.4 Land use. Another widely used approach to measure the utility of masked geodata is to compare the geography of the masked point-coordinates with their original counterparts. Quick and Waller (2018) and Zhang et al. (2017) consider, for instance, land cover categories or the proximity to roads. Regarding land cover rates, they compare whether the point-locations are in the same raster of either urban or rural areas. In an optimal scenario, the protected data would have the same share of points in urban areas as the original. Analogously, this applies to the proximity to roads, where the authors measure the closest distance of each point to the next road. The distances are compared using cumulative distribution functions (cdfs). The closer the two cdfs from the original and the protected data, the higher the utility of the protected data. Related works (e.g., Hasanzadeh et al., 2020) also evaluate other geographic characteristics such as the greenness of the surroundings.

4 Conclusion

Broad access to detailed geo-information can enhance the understanding of our society in numerous ways. Thus, it is not surprising that many data disseminating agencies are currently discussing how to provide access to these data for external researchers without compromising the confidentiality of the units contained in the data. Optimizing the trade-off between offering high utility granular information and sufficient data protection has been the subject of various methods for disclosure protection. In this paper, we have reviewed the literature on protection strategies for georeferenced microdata. Its main strands can be divided into coarsening the geo- information, masking it by altering, perturbing, or swapping the original locations, and disseminating synthetic data instead of the original data. We also discussed the different methods that are used to evaluate the risk and utility of the protected data. When assessing the risk of disclosure, we found that many papers rely on different notions of &#x1d458;-anonymity. We discussed a key concern with these notions, namely that for many of the distance based masking techniques, disclosure risks are underestimated based on this procedures as the obtained value

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of &#x1d458; tends to be much larger than the true number of indistinguishable records. We therefore strongly advice against using spatial &#x1d458;-anonymity in this context. Regarding the utility evaluation, we conclude that there are many useful approaches discussed in the literature and that it would be an interesting avenue for future research to consolidate the plethora of different measures.

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Paiva, T., A. Chakraborty, J. Reiter, and A. Gelfand (2014). Imputation of confidential data sets with spatial locations using disease mapping models. Statistics in medicine 33(11), 1928–1945.

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Quick, H., S. H. Holan, C. K. Wikle, and J. P. Reiter (2015). Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography. Spatial Statistics 14, 439–451.

Quick, H. and L. A. Waller (2018). Using spatiotemporal models to generate synthetic data for public use. Spatial and Spatio-Temporal Epidemiology 27, 37–45.

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13

  • 1. Introduction
  • 2. Data Protection Strategies
    • 2.1. Aggregation
    • 2.2. Geographic Masking
    • 2.3. Synthetic Data
  • 3. Risk and Utility Assessment
    • 3.1. Risk Evaluation
    • 3.2. Utility Evaluation
  • 4. Conclusion
  • References

AN OVERVIEW OF DATA PROTECTION STRATEGIES FOR INDIVIDUAL-LEVEL GEOCODED DATA UNECE Expert meeting on Statistical Data Confidentiality

Wiesbaden, 26-28 September 2023

Maike Steffen

Konstantin Körner

Jörg Drechsler

// PageSteffen, Körner, Drechsler

BACKGROUND

• More and more geo-referenced data are being collected

– Important for various research areas (e.g., to assess neighborhood effects, mobility patterns)

– Highly identifying, availability for research is limited

– IAB project on geo-referenced data → how to anonymize these data?

• Three main strategies for confidentiality protection

– Aggregation

– Geographic Masking

– Synthetic data

Data Protection Strategies for Geocoded Data 2

// PageSteffen, Körner, Drechsler

AGGREGATION

• Aggregation within pre-defined areas

– Administrative areas

– (Standardized) Grid cells

• Flexible aggregation

– Population-adjusted grid cells

– Microaggregation

Data Protection Strategies for Geocoded Data 3

External data can easily be linked Loss of spatial information

Choice of aggregation level can bias results

More efficient trade-off between confidentiality protection and utility

Cannot easily be linkend to

external data

Harder to interprete

// PageSteffen, Körner, Drechsler

GEOGRAPHIC MASKING

• Deterministic masking approaches

• Random perturbation

– Original locations are randomly displaced

– Different methods to draw maximum or minimum displacement distance

– Possibility to adapt for population density

Data Protection Strategies for Geocoded Data 4

Widely used, straightforward method

Point-locations as output

No guaranteed level of privacy protection, especially in rural areas or areas with heterogenous population density

Displacement within a circle

Donut masking Gaussian masking Bimodal gaussian masking

// PageSteffen, Körner, Drechsler

GEOGRAPHIC MASKING

• Deterministic masking approaches

• Random perturbation

– Original locations are randomly displaced

– Different methods to draw maximum or minimum displacement distance

– Possibility to adapt for population density

Data Protection Strategies for Geocoded Data 4

Widely used, straightforward method

Point-locations as output

No guaranteed level of privacy protection, especially in rural areas or areas with heterogenous population density

// PageSteffen, Körner, Drechsler

GEOGRAPHIC MASKING SOMETIMES OFFERS LITTLE PROTECTION

Data Protection Strategies for Geocoded Data 5

outlier

// PageSteffen, Körner, Drechsler

GEOGRAPHIC MASKING SOMETIMES OFFERS LITTLE PROTECTION

Data Protection Strategies for Geocoded Data 5

outlier

// PageSteffen, Körner, Drechsler

GEOGRAPHIC MASKING SOMETIMES OFFERS LITTLE PROTECTION

Data Protection Strategies for Geocoded Data 5

outlier

// PageSteffen, Körner, Drechsler

GEOGRAPHIC MASKING SOMETIMES OFFERS LITTLE PROTECTION

Data Protection Strategies for Geocoded Data 5

outlier

// PageSteffen, Körner, Drechsler

GEOGRAPHIC MASKING

• Location swapping (Zhang et al., 2017)

– Original location is swapped with another location within a circle or donut

• Adaptive Areal Masking (Kounadi & Leitner, 2016)

– random perturbation within pre-defined areas with at least &#x1d458; location points

– Guarantees a certain level of anonymity

– High alteration of locations

Data Protection Strategies for Geocoded Data 6

// PageSteffen, Körner, Drechsler

SYNTHETIC DATA

Synthesizing of non-geographic variables

• Account for spatial structure to synthesize non-geographic variables

• Data release

– Detail level of geographic information

– Separate release of 2 data sets (Koebe et al.

2023)

Data Protection Strategies for Geocoded Data 7

Synthesizing of geographic information

• Aggregated data (Quick, 2021; 2022; Paiva et al., 2014)

• Exact geographic coordninates (Wang & Reiter,

2012; Drechsler and Hu, 2021)

• Fully synthetic data (e.g., Quick et al., 2015)

RISK AND UTILITY ASSESSMENT

// PageSteffen, Körner, Drechsler

RISK ASSESSMENT

• K-anonymity

– Definition: a record must be indistinguishable from at least &#x1d458; − 1 other records

– Spatial k-anonymity for masking methods: measure the number of locations within a radius equal to the displacement distance

(1) number of locations around the original point

(2) number of locations around the masked location

– Problems with this measurement

• Alternatives

– Record linkage attacks (Drechsler and Hu, 2021; Quick et al. 2015)

– Assessment of overfitting regarding spatial outliers (Quick et al. 2018)

Data Protection Strategies for Geocoded Data 9

// PageSteffen, Körner, Drechsler

SPATIAL K-ANONYMITY EXAMPLE

Data Protection Strategies for Geocoded Data 10

outlier

// PageSteffen, Körner, Drechsler

SPATIAL K-ANONYMITY EXAMPLE

Data Protection Strategies for Geocoded Data 10

outlier

// PageSteffen, Körner, Drechsler

UTILITY ASSESSMENT

Comparison of original and anonymized data

1. Point locations and density measures

– Distances between original and masked locations

– Heatmaps using Kernel density estimation

2. Clustering

3. Spatial autocorrelation

4. Applied results

Data Protection Strategies for Geocoded Data 11

// PageSteffen, Körner, Drechsler

CONCLUSION

• Three main strands of confidentiality protecting strategies

• Some common masking techniques do not provide adequate confidentiality protection

• Common risk measures should be carefully evaluated

Data Protection Strategies for Geocoded Data 12

Aggregation

Fixed areas

Flexible aggregation

Geographic Masking

Deterministic methods

Random noise

Record swapping

Other

Synthetic Data

Synthesizing of non- geographic variables

Synthesizing of geographic variables

// PageSteffen, Körner, Drechsler

KEY REFERENCES

Drechsler, J. and J. Hu (2021). Synthesizing Geocodes to Facilitate Access to Detailed Geographical Information in Large-Scale Administrative Data. Journal of Survey Statistics and Methodology 9(3), 523–548.

Koebe, T., A. Arias-Salazar, and T. Schmid (2023). Releasing survey microdata with exact cluster locations and additional privacy safeguards. Humanities and Social Sciences Communications 10(1), 1–13.

Kounadi, O. and M. Leitner (2016). Adaptive areal elimination (aae): A transparent way of disclosing protected spatial datasets. Computers, Environment and Urban Systems 57, 59–67.

Paiva, T., A. Chakraborty, J. Reiter, and A. Gelfand (2014). Imputation of confidential data sets with spatial locations using disease mapping models. Statistics in medicine 33(11), 1928–1945.

Quick, H. (2021). Generating poisson-distributed differentially private synthetic data. Journal of the Royal Statistical Society Series A: Statistics in Society 184(3), 1093–1108.

Quick, H. (2022). Improving the utility of poisson-distributed, differentially private synthetic data via prior predictive truncation with an application to cdc wonder. Journal of Survey Statistics and Methodology 10(3), 596–617.

Quick, H., S. H. Holan, C. K. Wikle, and J. P. Reiter (2015). Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography. Spatial Statistics 14, 439–451.

Quick, H., S. H. Holan, and C. K. Wikle (2018). Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing. Journal of the Royal Statistical Society Series A: Statistics in Society 181(3), 649–661.

Quick, H. and L. A. Waller (2018). Using spatiotemporal models to generate synthetic data for public use. Spatial and Spatio-Temporal Epidemiology 27, 37–45.

Sakshaug, J. W. and T. E. Raghunathan (2010). Synthetic data for small area estimation. In J. Domingo-Ferrer and E. Magkos (Eds.), Privacy in Statistical Databases, Berlin, Heidelberg, pp. 162–173. Springer Berlin Heidelberg.

Sakshaug, J. W. and T. E. Raghunathan (2014). Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the national health interview survey. Journal of Applied Statistics 41(10), 2103–2122.

Wang, H. and J. P. Reiter (2012). Multiple imputation for sharing precise geographies in public use data. The annals of applied statistics 6(1), 229.

Zhang, S., Freundschuh, S. M., Lenzer, K., & Zandbergen, P. A. (2017). The location swapping method for geomasking. Cartography and Geographic Information Science, 44(1), 22-34.

Zhou, Y., F. Dominici, and T. A. Louis (2010). A smoothing approach for masking spatial data. The Annals of Applied Statistics 4(3), 1451–1475. DOI: 10.1214/09-AOAS325.

Data Protection Strategies for Geocoded Data 14

CONTACT

Maike Steffen

[email protected]

Remote Access for Scientific Use Files – a New Pathway for German Official Statistics Microdata Access, DESTATIS Germany

remote access, data access path, microdata, microdata for scientific purposes

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

Remote Access for Scientific Use Files – a New Pathway for German Official

Statistics Microdata Access

Hanna Brenzel ( Research Data Centre of the Federal Statistical Office)

Katharina Cramer (Research Data Centre of the Statistical Offices of the Federal States)

Volker Güttgemanns (Research Data Centre of the Statistical Offices of the Federal States)

Marcel Mathes (Research Data Centre of the Statistical Offices of the Federal States)

[email protected]

Abstract

The fundamental goal of the Research Data Centre of the Federal Statistical Office and the Research Data Centre of the

Statistical Offices of the Federal States (RDC) is not only to provide access to official statistics microdata, but also to

continuously improve and adapt the access to the changing needs of empirical science. In order to meet the broad range of

needs of the empirically working scientific community, the RDC have offered different access paths since their founding, through which differently anonymised data products are made available. Now, the RDC come up with a new remote access

prototype system including a new data product. All access paths differ both in terms of the anonymity degree of the

provided microdata as well as in the access way of data provision. At first, existing and firmly established data access

paths are outlined and their contractual and legal conditions explained. Subsequently, the newly installed remote access

prototype and its features and requirements are presented. Provided that the ongoing evaluation phase turns out positive,

this data access option will define one more way of data access operated regularly in its full version from 2024 onwards.

The analysis potential of the data provided therein will classify between the scientific-use files transmitted to the scientific

institutions and the data provided for on-site analysis at the RDC safe centres. This paper highlights various challenges,

such as data protection requirements and legal framework conditions, which must be considered.

2

1 Introduction

With the establishment of the Research Data Centre (RDC) of the Federal Statistical Office in the fall of 2001

and with the RDC of the statistical offices of the Länder in April 2002, an important cornerstone and a central intersection was created between the scientific community and official statistics as data and information service

provider.

Together, the RDCs offer the empirically working scientific community a coordinated range of data and services for the scientific use of high-quality microdata from official statistics.

Over time, however, expectations of the RDCs have evolved fundamentally, and stakeholders in politics and

scientific communities have been pushing for substantial improvements in data access and data usage capabilities for some time.

Remote access represents an up to date and modern way of accessing data and is accordingly demanded by data

users. The statistical offices of other European countries (e.g., the Netherlands, France or Finland) can be

mentioned as reference benchmarks. They have created the legal and technical prerequisites to make their data available to researchers via remote access some time ago. Last but not least, a remote access system is currently

being set up at European level by Eurostat.

On one hand, the establishment of a remote access system - with the investment in a connectable infrastructure - will advance the continuous development of the RDC. By catering towards the needs of the scientific

community, the status of the RDC as a modern data provider will be consolidated. On the other hand, the

currently complex and inefficient system of data access can be streamlined to a uniform and manageable system without limiting the flexibility of the users.

2 Status Quo

The RDC of the Federal Statistical Office, together with the RDC of the statistical offices of the Länder, offer

access to more than 3,000 different data products for over 90 statistics for scientific use via different ways of

access. They differ both in terms of the anonymity of the accessible data and in the type of data provision. Generally, the existing ways of data access can be divided into two categories, as figure 1 illustrates. In the case

of the so-called "on-site access", the data remains in the secure areas of the statistical offices of the Federation

and the Federal States. Since the RDCs can closely control the access to the data and provide output only after

confidentiality check, the data are only weakly anonymized. With the "off-site access," on the other hand, users can work with the individual data at their own institutes. Since the output are not checked by the data centers,

the individual data has to be more anonymized.

The category “off-site” includes the so-called Public Use Files (PUF), Campus Files (CF) and Scientific Use Files (SUF). “On-site” includes PC workplaces at the RDC, so called “safe centers” and remote execution (see

the homepage of the RDC, https://www.forschungsdatenzentrum.de/en/access).

Safe centers exist in all locations of both RDC. These can be used by researchers to analyse microdata inside

the safe premises of the statistical offices. As the individual data are already protected by the regulation of data

access and the equipment of the PC workstation, formally anonymous microdata can be provided at the safe

centers. Thus, a nationwide infrastructure in Germany is available for these data. The safe centers are equipped with common statistical programs (Stata, R as well as partly SPSS and SAS) and

are completely isolated from the outside. A separate PC workstation with internet connection is available for e-

mail communication and internet searches. In contrast to the safe centers, remote execution does not provide direct access to the microdata. Instead, data

structure files are made available that resemble the original material with regard to structure and variable

values, but do not permit any analyses in terms of content and do not hold any risk of exposing confidential

information. Using these data sets, program codes can be prepared by the users using the statistical programs SPSS, SAS, Stata or R. These program codes are applied by staff of the statistical offices to analyse the original

data. The data users receive the results of those analyses after the relevant confidentiality checks.

SUFs are standardized datasets created by the RDC for popular statistics. SUFs offer lower potential for

analyses than on-site ways of access, but are designed to be suitable for a large proportion of scientific research

projects. Due to the de facto anonymization of microdata, they may be used outside the protected premises of official statistics according to Sect. 16 para. 6 nos. 1 BStatG. Due to legal restrictions, SUF may only be used

3

by researchers who are employed by a research institution that is registered and located in Germany. The use of

SUF may only take place in Germany. Until recently, the SUF were sent by DVD to the respective scientific institution with which user contract was concluded. Since June 2023, recent modernization measures now allow

the SUF to be accessed directly via a download portal to the institution authorized to use the data.

In particular, on-site ways of access entail additional work for both data users and RDC staff. At the same time,

the share of data uses via these access paths steadily increases over time compared to off-site uses. The development of a remote access system therefore pursues the goal of ensuring the technical connectivity to a

modern and demand-oriented data provision for the scientific community. With this technology, the increased

expectations of the research community for an up-to-date and modern data provision can be fulfilled in the long term. In addition, the remote access system holds potential for future innovation by reducing or substituting

existing labor-intensive ways of access (reduction of on-site support, reduction of coordination of appointments

with users, reduction of coordination and support of remote execution, etc.). Consequently, the scarce resources of the RDC could be invested more efficiently, for example in supporting additional data usage or further

developing the data and service offers. At the same time, there is increased potential regarding data parsimony,

as it is expected that this system will reduce the number of intermediate results per project that require

confidentiality checks. Furthermore, the RDC aim to sustainably strengthen their leading role in the group of German RDC.

Figure 1: Ways of data access at the research data centres (RDC) of the statistical offices of the Federation and the Federal States

3 The Remote Access System

3.1 The technical structure

IT and data security play a crucial role in setting up the remote access system. The aim is to ensure that the

remote access system is implemented in compliance with the law while maintaining the required IT security

standards.

A virtual desktop infrastructure based on CITRIX was chosen as the IT-architecture. The system components

set up are located in the so-called IDMZ (Internet Demilitarized Zone), in which procedures are operated that

are to be accessible from the Internet. In the IDMZ, a distinction is made between three areas: Access Area

(Pex), Application Area (Pin1) and Data Area (Pin2). These three areas are separated from each other by

firewalls, which only allow approved communication between the neighboring areas within the application. A

so-called transport encryption secures the communication path between the server and the client.

Two-factor authentication and IP whitelisting are implemented as additional IT security measures for the Citrix

solution. Two-factor authentication means that, in addition to the user-specific work accounts protected by a

personal password, a uniquely generated token must be used for each log-in. IP whitelisting allows only

specific IP addresses to gain access to the remote access system. Prior to each authorized use, the IP address of

the respective facility is allowed or added to the whitelist. This ensures that unauthorized IP addresses do not

initially gain access to the system. This implements geoblocking as a technical measure as well as

strengthening protection against possible (automated) attack attempts.

In addition, app protection is used to, among other things, prevent the user from taking screenshots of the data.

Remote system access is controlled on a per user basis by an access management system, only authorized users

are granted access. Within the system, authorizations are limited to the extent required for data analysis. The

creation of user-specific working accounts, which are managed centrally and secured by the user and access

management, ensures that access is only possible to requested data. Each account is linked to a data folder in

which user-specific official microdata are stored by RDC staff.

In addition to the technical measures, a number of technical and contractual-organizational measures are

introduced to increase data protection. Before the data can be accessed, a user contract has to be concluded

between the scientific institution and the responsible statistical office. It is contractually stipulated that up-to-

4

date software, operating system and virus protection are used on the client side when accessing the virtual

desktop infrastructure. As well as, re-identification of individual cases is illicit. The RDC are legally bound to

check all statistical results for statistical confidentiality that were created within the context of scientific

projects based on provided microdata. This serves the protection of data according to section 16 (6) of the

Federal Statistics Law (BStatG). Should individual cases be part of the output then they have to be blocked

consistently across all results of a project. Data users who plan to re-identify individual cases are liable to

prosecution and are expelled from further data uses.

In order to ensure that the system is tied to a specific location, its use is contractually established and sanctions

are imposed in the event of violations. In addition, it is contractually stipulated that scientific institutions can be

excluded from using the remote access system or from the possibility of carrying out further research projects

via the RDC in the event of serious violations of the terms of use. In the event of a striking breach of contract,

the scientific institutions can also be sanctioned with a penalty payment of up to EUR 20,000.

3.2 Data material in the remote access system

Remote access to formally anonymized data is not feasible within the current legal framework. One possible

way of implementation is to offer remote access for de facto anonymized data with slight modifications, as this

would not require amendment of the law. In this case, the degree of data modification is of utmost relevance: If

the level of anonymization is too high, the data offered will not meet the needs of the scientific community; if

the level of data anonymization is too low, confidentiality can no longer be maintained. The degree of de facto

anonymization therefore largely determines the benefits and coverage of the demand of the scientific

community. In addition, the expected effects on the capacity of the RDC heavily depend on covering as many

of the science community's projects as possible via the remote access system and, in particular, on reducing the

costly uses of remote execution. However, this goal can only be achieved if significantly more data can be

provided via remote access than via the current dissemination path via off-site SUF.

Microdata are described as “de facto anonymous” if it is not possible to completely rule out de-anonymization

but assigning the information to the respective statistical unit “requires unreasonable effort in terms of time,

cost and manpower” (Section 16 (6) of the Federal Statistics Act). According to the Federal Statistics Act,

however, de facto anonymous data may only be used by scientific institutions and only to carry out scientific

projects.

When creating de facto anonymity, the aim is to virtually eliminate the probability of correctly assigning data to

respondents, while preserving the statistical information content as much as possible. Different anonymization

methods can be used for this purpose. Common methods are information reduction (e.g. aggregation, class

formation, censoring) and information modification (e.g. swapping). In order to determine de facto anonymity,

the effort and benefit of deanonymization must be evaluated.

Factual anonymity thus does not completely exclude the possibility of re-identification, but puts its risk in a

cost/benefit ratio. Costs for data users primarily include the consequences for actions in violation of the

contract. Re-identification is strictly prohibited and punishable by fine or imprisonment (Section 203 StGB). In

addition, consequences such as loss of reputation, loss of access to data of official statistics, etc., which threaten

in the event of de-anonymization of the data, must also be considered by scientific users. This is because the

users are obligated to maintain the anonymity of the data both by the formal obligation and the user agreement.

Factual anonymity therefore does not result solely from the remaining information content of the data, but is

composed of a triad: 1) modification of the data material, 2) technical/organizational measures, and 3)

contractual measures. Therefore, it also depends on the access condition, if a microdata set can be described as

Figure 2: Technical infrastructure of the remote access system

5

de facto anonymous. Of crucial importance here is what additional knowledge is available and where the data

access takes place. Depending on whether the microdata is used outside or inside the statistical offices, de facto

anonymity can be achieved with more (off-site SUF) or less (on-site SUF) severe losses of information.

The de facto anonymity of microdata from official statistics is thus not a fixed quantity, but can be mapped

along a continuum. In principle, it can be stated: The higher the technical and contractual measures, the fewer

anonymization measures need to be taken and the higher the analysis potential of the data.

No technical measures are used for the previous off-site SUFs. Factual anonymity must therefore only be

achieved from the two remaining measures: in addition to the contractual commitment and the commitment of

the users, de facto anonymity is achieved by strongly anonymizing the data material itself. For this purpose, a

statistics-specific anonymization concept is developed for each data material.

With the new remote SUF or on-site SUF, de facto anonymity can be achieved by significantly less

modification of the data. This is justified by the high level of technical measures and the associated possibility

to control the data access. In contrast to off-site SUF, the data is not passed on. It is solely possible to view the

data via a virtual desktop (VDI environment). A so-called "transport encryption" secures the communication

path between the server (sender) as well as the client (receiver). An exchange between the technical

infrastructure of the data users and the data on the server of the official statistics or a download of the official

data is thus technically impossible. Thus, unauthorized data linkage is impossible and the RDC has a high level

of use control via log files. With regard to the risk of de-anonymization, data access via remote access therefore

reduces many risks compared to the previous off-site SUFs.

3.3 The use of Remote Access

The remote access system, which is currently under construction, will be set up as a classic remote desktop

version. As in the past, scientific institutions that are entitled to use the system in accordance with Section 16

BStatG have to apply for data access. If the application is approved, the researchers are then able to access the

secure area within their scientific institution by using their own hardware. Within the secure area common

statistical software such as RStudio and Stata is available. The major advantage compared to remote execution

is that researchers can see the microdata and do not have to "blindly" program their syntaxes as before (see

Figure 3). By working directly with and being able to view the data, it should be possible to significantly

reduce the number of intermediate results previously generated via remote execution, thus minimizing a very

labor-intensive process step in the RDC. The goal should be that only final outputs are checked for

confidentiality by the RDC staff and will be released. This also supports the principle of data parsimony.

Figure 3: Remote Access at the RDC

Work on setting up such a system began in November 2021. The system is currently in the evaluation phase.

On one hand, the technical implementation of the system is being tested and its resilience checked using penetration tests. On the other hand, the user-friendliness and the attractiveness of the data material provided is

to be examined thoroughly. In a first step, only absolutely anonymous data material was made available via the

system for a selected group of people. In a second step, off-site SUFs will then be made available to power users who have already completed a valid user application with the RDC. The third step will then be to test the

redesigned on-site/remote SUF material. Since the system requires a redesign of all statistics-specific

anonymization concepts, a gradual integration of the existing data products in the RDC is planned. The start will be made with the most requested data product, the microcensus. In order to be able to evaluate the

operating grade of the system appropriately, DRG statistics will be offered as one of the first data products in

the remote access system in addition to the microcensus. If the evaluation of the system is positive, other data

products that are of high demand will follow.

6

4 BIBLIOGRAPHY

Brenzel, Hanna / Zwick, Markus. An information infrastructure has emerged in Germany – the Research Data

Centre of the Federal Statistical Office. German version published in WISTA | 6 | 2022, p. 54 et seq.

Homepage of the Research Data Centre of the Federal Statistical Office and the Federal States

https://www.forschungsdatenzentrum.de/en

https://www.forschungsdatenzentrum.de/en

Remote Access for Scientific Use Files – a New Pathway for German Official Statistics Microdata Access UNECE - Expert Meeting on Statistical Data Confidentiality

26-28 September 2023, Wiesbaden

Hanna Brenzel, Katharina Cramer, Volker Güttgemanns, Marcel Mathes, Hariolf Merkle

Agenda

(1) Motivation

(2) Status Quo

(3) The Remote Access System

(4) Outline

destatis.de

freepik

26.09.2023Federal Statistical Office (Destatis) 3

Remote access for SUF…

• offers convenient data access for scientists from their own scientific institution and thus enables up-to-date and efficient data analysis

• offers the scientific community the opportunity to save travel and waiting time

• contributes sustainably to the further development of the RDC and its range of services

• drives the digitization of procedures and processes

• promotes the awareness of confidentiality requirements by the scientific community and favors faster statistical confidentiality checks, provision and publication of results

Motivation

destatis.de

26.09.2023Federal Statistical Office (Destatis) 4

Status Quo

On-site use Off-site use

Way of access Remote execution Safe centres Off-site Scientific Use Files

Public Use Files/ Campus Files

Degree of data anonymisation

Formally anonymous

Formally anonymous

De facto anonymous

Absolutely anonymous

Entitled to use Research institution Research institution Research institution All

Data storage during use Statistical offices Statistical offices Research institution Any

Location of users during use

Any Location of the RDC Research institution Any

Anonymisation

Analysis potential

destatis.de

26.09.2023Federal Statistical Office (Destatis) 5

Where to go…

On-site use Under construction

Off-site use

Way of access Remote execution Safe centres Remote access Off-site Scientific Use Files

Public Use Files/ Campus Files

Degree of data anonymisation

Formally anonymous

Formally anonymous

De facto anonymous

De facto anonymous

Absolutely anonymous

Entitled to use Research institution

Research institution

Research institution

Research institution All

Data storage during use Statistical offices Statistical offices Statistical offices Research institution Any

Location of users during use

Any Location of the RDC

Research institution

Research institution Any

Anonymisation

Analysis potential

destatis.de

Technical and organizational measures Technical measures

» Transport encryption

» Two-factor authentication IP whitelisting

» Server-side monitoring/logging/backup

» Operation in high security environment

» Limited scope of use and user-specific working accounts

» BSI-compliant hardware and software administration

Remote Access System

26.09.2023Federal Statistical Office (Destatis) 6

Organizational/contractual measures

» Clause on up-to-date software on the hardware of the facilities

» Contractual ban on re-identification

» Exclusion of the institution in case of misconduct

» Clause on location-based access to data

» Access only for eligible institutions according to Section 16 (6) Federal Statistical Act

» Fines for breach of contract

Anonymisation measures

» Anonymisation concept and special anonymisation of vulnerable units

destatis.de

Data material (I)

“de facto anonymity”

» “requires unreasonable effort in terms of time, cost and manpower” (Section 16 (6) of the Federal Statistics Act)

» de facto anonymity is thus not a fixed quantity, but can be mapped along a continuum

» the higher the technical and contractual measures, the fewer anonymization measures need to be taken and the higher the analysis potential of the data

Remote Access System

26.09.2023Federal Statistical Office (Destatis) 7

De facto anonymity

Technical measure

Organizational /contractual

measure

Anonymisation measures

destatis.de

Data material (II)

» Significant difference to the current dissemination path via off-site SUF

» With the new remote SUF, de facto anonymity can be achieved by less modification of the data. This is justified by the high level of technical measures and the associated possibility to control the data access

» In contrast to off-site SUF, the data is not passed on. It is solely possible to view the data via a virtual desktop

» Unauthorized data linkage is impossible and the RDC has a high level of use control compared to off-site SUFs

Remote Access System

26.09.2023Federal Statistical Office (Destatis) 8

destatis.de

Source: German version published in WISTA | 6 | 2022, p. 54 et seq.

26.09.2023Federal Statistical Office (Destatis) 9

Remote Access at the RDC

destatis.de

26.09.2023Federal Statistical Office (Destatis) 10

» Phase 2: Evaluation of the system

» multi-stage application and function tests for various user groups

» penetration, accessibility, load and performance tests

» feasibility and usability of SecureBootSticks

» System dimensioning for the desired number of usage accesses

» Expansion of the offered data materials

Outline

Contact Research Data Centre of the Federal Statistical Office

Phone: +49 611 / 75-2420 E-Mail: [email protected]

www.forschungsdatenzentrum.de

  • Slide 1: Remote Access for Scientific Use Files – a New Pathway for German Official Statistics Microdata Access
  • Slide 2: Agenda
  • Slide 3: Motivation
  • Slide 4: Status Quo
  • Slide 5: Where to go…
  • Slide 6: Remote Access System
  • Slide 7: Remote Access System
  • Slide 8: Remote Access System
  • Slide 9: Remote Access at the RDC
  • Slide 10: Outline
  • Slide 11: Contact

An overview of data protection strategies for individual-level geocoded data

data protection strategies, geocoded individual data, georeferencing individual data, confidentiality, access

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

An overview of data protection strategies for individual-level geocoded data Maike Steffen, Konstantin Körner, Jörg Drechsler

Institute for Employment Research (IAB)

[email protected]

Abstract In response to a growing need for small-scale geographic information in various research areas, data-collecting institutions are increasingly georeferencing individual-level data. However, due to confidentiality concerns, external researchers typically have very limited access to these data if at all, resulting in a substantial loss of informational value. A growing body of literature on data protection strategies for geocoded data attempts to find solutions for the tradeoff between privacy protection and utility preservation of the individual-level data. The purpose of this paper is to systematically collect and review the literature in the field and to offer a classification of existing methods. Various strategies for estimating the utility and the remaining risk of disclosure for the protected data are also discussed.

1 Introduction

Geocoded data have become increasingly relevant in various research areas since they offer insights that can only be acquired considering spatial context. The granular information enables researchers to include fine geographic patterns and spatial variation of individual characteristics in their analyses. The detailed geographical information facilitates studying such diverse topics as neighborhood effects, mobility patterns, or the spread of diseases to name only a few of the possible applications. Moreover, the geo-coordinates are not subject to changes over time as it is the case with administrative borders, which often hampers longitudinal analyses. Finally, the availability of detailed geographical information allows to easily merge information from various data sources. However, access to detailed geocoding information is currently limited as it is well known that detailed geo- graphical information is highly identifying (De Montjoye et al., 2013). To still enable access to this valuable source of information, various strategies have been proposed in the literature to protect confidentiality while still maintaining the utility of the collected information. This paper aims to give an overview of the various approaches. We also provide an overview of metrics that have been used to assess the disclosure risk and the utility of the protected data. The remainder of the paper is organized as follows. In Section 2, we review the three most popular approaches for protecting geocoded data: aggregation, geographic masking, and data synthesis. In Section 3, we discuss various tools which are used to assess the risk and utility of the protected data. Section 4 concludes the article.

2 Data Protection Strategies

Two general strategies are commonly applied to reduce the risk of disclosure when disseminating data to the public: information reduction and perturbation. Information reduction limits the amount of detail that is available in the data. This can range form discretizing continuous variables (e.g., reporting age in five-year intervals) over coarsening categorical variables (e.g., reporting only the first two digits of a hierarchical classification code such as the NACE code) to removing entire variables. Perturbation approaches try to preserve the level of detail contained in the original data. They reduce the risk of disclosure by slightly altering the microdata on the record level. Examples include noise infusion, top-coding, or swapping. Both strategies are also used when disseminating detailed geo-information. Aggregation as a form of information reduction is probably the most widely adopted strategy to reduce the risk of reidentification. We will review different aggregation strategies in more detail in Section 2.1. The early influential paper by Armstrong et al. (1999) lists two alternative strategies to aggregation that rely on perturbation: affine transformations and geographic masking. Affine transformations are methods that displace, rescale, or rotate the entire vector of original locations. Since they are completely deterministic, these methods are relatively easy to reverse engineer. They also lead to a substantial loss of information since the transformation of the original locations are data independent and thus spatial clustering effects found in the original data can be destroyed. Furthermore, external geographical information can no longer be linked to the transformed data in a reasonable way (Zandbergen, 2014). For these reasons, these methods have never been widely adopted and we will only review geographic masking in more detail in Section 2.2. In recent years, synthetic data approaches have emerged as another perturbation strategy. With synthetic data, original values are replaced with synthetic values drawn from a model fitted to the original data. We will review synthetic data approaches for disseminating detailed geo-information in Section 2.3.

2.1 Aggregation

As discussed earlier, aggregation is the most widely adopted strategy to reduce risks from reidentification. Aggregation does not alter the information, that is, the number of observations per aggregated unit remains

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accurate and the location of individuals may be coarsened but will not be replaced by fake locations. However, it does lead to a loss of information and thereby reduces the range of applications the data can be used for. Broadly, there are two general aggregation strategies: aggregation within pre-defined areas, such as grid cells or administrative areas, and more spatially flexible microaggregation, which ensures that each aggregation cell contains a predefined number of records. The use of aggregation within pre-defined areas is by far the most commonly adopted approach, and guidelines to assign observations to standardized grid cells have been developed (e.g., INSPIRE, 2014). Using standardized formats comes with the advantage that additional spatial information such as climate, health, or economic data can be easily linked using these grid cells (Klumpe et al., 2020). At the same time, it is a rather inflexible strategy. If the uniformly sized grid cells are sufficiently small, they allow detailed analyses, but may not protect confidentiality adequately in sparsely populated cells. If they are large enough to protect confidentiality even in rural areas, there is a high information loss in urban areas. To address this issue, grid cell sizes can be adapted to the population density (e.g., Lagonigro et al., 2017). This approach, however, renders the linking of external grid cell data more difficult. Some researchers (e.g., Groß et al., 2017, 2020) have proposed to improve the utility of the aggregated data by applying a smoothing function based on kernel density estimators, which randomly reassigns the individuals to point locations within the aggregation cell. This strategy can, for example, be beneficial if the goal is to compute distance measures or for plotting the data on a map. Microaggregation techniques allow to flexibly adapt the size of the aggregation area to the desired level of protection (Domingo-Ferrer and Torra, 2005; Castro et al., 2022). Research on microaggregation in the context of geographic data mainly focuses on anonymizing digital trace data (see, e.g., Domingo-Ferrer and Trujillo- Rasua, 2012; Rebollo-Monedero et al., 2011), but the approach has also been adopted to achieve strong privacy guarantees for geocoded data based on the concept of differential privacy (Soria-Cormas and Drechsler, 2013). While microaggregation can protect privacy consistently, it creates irregular polygons that are somewhat difficult to interpret and cannot easily be linked to external geographic data.

2.2 Geographic Masking

Geographic masking relies on randomly displacing the original location to protect confidentiality. A variety of methods have been developed in this field. The simplest form of geographic masking assigns new locations by drawing a circle with fixed radius around the original location and randomly picking a new location on that circle (Zandbergen, 2014). With such a fixed displacement distance, the risk of re-engineering the original locations from the masked data can be relatively high (Zandbergen, 2014), hence random perturbation within a predefined maximum distance from the original location is more commonly used (see Armstrong et al., 1999; Kwan et al., 2004; Zandbergen, 2014; Hampton et al., 2010). This increases the level of protection as the actual displacement distance is unknown to the end user even if the masking approach is disclosed. Various strategies how to randomly draw the displacement distance have been proposed in the literature. One strategy is to use a uniform distribution within the radius of a circle centered on the original value (Armstrong et al., 1999; Zimmerman and Pavlik, 2008). Since this allows for the masked location to be very close or even equal to the original location, an alternative method called donut masking that provides higher confidentiality protection has been suggested (Hampton et al., 2010; Allshouse et al., 2010; Kounadi and Leitner, 2015). This masking method requires a minimum displacement distance additionally to the maximum displacement distance, forming a donut shape around the original location. An alternative approach to increase the displacement distance is N-Rand masking (Wightman et al., 2011), which also uses perturbation within a circle but draws &#x1d441; potential displacement locations. The location that is furthest away from the original location is then selected as the final displacement location. Instead of displacing the original locations within a circle with fixed radius and using a uniform distribution, some authors have suggested drawing the distance and direction of displacement from a bivariate Gaussian probability distribution (Cassa et al., 2006, 2008; Zimmerman and Pavlik, 2008). Compared to drawing from a uniform distribution, using a Gaussian distribution renders a displacement close to the original location more likely and therefore has little effect on spatial clusters (Cassa et al., 2006). Of course, a negative consequence is

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an increased risk of disclosure as most of the masked locations will be close to the original location. A variant of this method therefore uses a bimodal Gaussian distribution to approximate donut masking (Zandbergen, 2014). Note that, although unlikely, extremely high displacement distances can drawn from a normal distribution for a small fraction of the locations (Armstrong et al., 1999). If population density in the data varies substantially, perturbation with fixed maximum distance (or fixed variance for the bivariate Gaussian approach) may lead to an unnecessarily large alteration of spatial information in highly populated areas where shorter displacement distances may suffice, and to privacy risks where population density is low and locations should be displaced more. This can be addressed by taking population density into account, such that the radius of the displacement area is larger in less densely populated areas (Kwan et al., 2004; Cassa et al., 2006; Hampton et al., 2010; Lu et al., 2012; Zurbarán et al., 2018). This results in masked data that are more similar to the original data in urban areas while offering a higher level of confidentiality protection in rural areas. With the bivariate Gaussian approach, the variance of the distribution can be set to be inversely proportional to the square of the population density (Cassa et al., 2006). However, as illustrated in Allshouse et al. (2010), using externally provided population density data on an administrative area level as a benchmark, as done for example in Cassa et al. (2006); Hampton et al. (2010), may not sufficiently protect confidentiality in areas with high population distribution heterogeneity. As a remedy, the authors suggest tripling the displacement distance in areas with heterogeneous population distribution. Kounadi and Leitner (2016) argue that, when information is available at the point level, the actual distance to the &#x1d458;th nearest neighbor should be used to determine displacement distance rather than using external population density data at the administrative-area level. In recent years, some authors proposed masking techniques that displace the original locations taking the actual position of the surrounding locations into account, such as Voronoi masking or location swapping (Seidl et al., 2015; Zhang et al., 2017). Voronoi Masking, developed by Seidl et al. (2015), is based on Voronoi polygons (Voronoi, 1908), which are shapes built around each single location with boundaries marking the half of the distance to the next location in any direction. A Voronoi polygon surrounding a point location contains all locations that are closer to this location than they are to any neighboring point locations in the data. In the masking process, each original location is moved to the closest point along the boundaries of its polygon, placing it in the middle between two actual locations. Seidl et al. (2019) find that this decreases map users’ beliefs in being able to re-identify households. The locations are, on average, moved less in areas with higher density of the original points. At the same time, a group of at least two locations that are remote but close to each other will likely be displaced less than would be the case using random perturbation methods, and multiple locations may be relocated to the same masked location. Since many masking approaches do not account for geographic characteristics or whether units exist at the masked location, they may generate unrealistic locations, such as within water bodies or parks. Zhang et al. (2017) propose a location swapping approach to address these concerns. This method draws a circle or donut around the original location with varying distances based on population density. Then, the original location is swapped with another location with similar geographic characteristics within the specified area. They find that location swapping yields higher values of &#x1d458;-anonymity (defined in Section 3.1) than random perturbation using the same displacement area. However, we note that when applying random perturbation techniques with a maximum displacement distance, and especially in scarcely populated areas, the actual level of &#x1d458; achieved can be lower than the level implied by commonly applied techniques to measure &#x1d458; and, thus, we generally do not recommend using this measure to assess the level of protection (we will discuss this problem in more detail in Section 3.1). To address the problem with distance based perturbation techniques, Kounadi and Leitner (2016) propose adaptive areal elimination masking that guarantees a minimum &#x1d458;-anonymity for every location. This method merges predefined shapes, e.g., administrative areas, until the number of locations per polygon is &#x1d458; or higher. The locations are then aggregated or randomly perturbed within each polygon. While this guarantees to achieve the desired level of &#x1d458;-anonymity, most polygons will contain (substantially) more than &#x1d458; units and therefore spatial patterns can be altered excessively.

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2.3 Synthetic Data

An alternative to the information reduction and masking methods discussed in the previous sections is to replace the true observations with draws from a statistical model, i.e., to generate synthetic data. Such datasets aim to preserve distributional properties and the spatial structure of the original data. Since these patterns are preserved at a much smaller spatial level compared to other anonymization techniques, authors such as Quick et al. (2018); Lawson et al. (2012), and Bradley et al. (2017) argue that synthetic data is able to reduce the risk of ecological fallacies (i.e., misleading inferences from the protected data, see Freedman, 1999). Two general approaches are distinguished in the literature: fully and partially synthetic data. With fully synthetic data (Rubin, 1993), all records in the released data are synthetic. Since synthesizing all variables in a dataset can be challenging for large scale surveys, Little (1993) suggested synthesizing only those variables that are either sensitive or that could be used for re-identification. See Drechsler (2011); Drechsler and Haensch (2023) for a detailed overview on the topic. The approach has also been adopted in recent years for protecting data containing detailed geographical information. Two general strategies can be distinguished in the literature. Several papers do not synthesize the geographical information. Instead, they specifically account for the spatial structure of the data when synthesizing other variables in the dataset to improve the utility of the synthetic data. While these papers focus on protecting sensitive information in the data, i.e., reducing the risk of attribute disclosure, other approaches directly synthesize the geographical information, hence reducing the risk of reidentification. We will separately review the two strategies in the remainder of this section.

2.3.1 Synthesizing non-geographic variables while preserving the spatial information. Sakshaug and Raghu- nathan (2010) is one of the early papers that specifically adjust common synthesis strategies to preserve the detailed spatial information. The authors propose using mixed effects modeling strategies. Mixed effects synthe- sis models are a natural way to preserve the geographical clustering effect. These models are especially popular in the literature on small area estimation. The authors later (2014) extended their approach by incorporating area level covariates in the model, which allows to generate synthetic data even for small areas not included in the original sample. Zhou et al. (2010) offer a more rigorous treatment of the spatial information problem by modeling all variables as spatial processes and applying spatial smoothing when modeling the variables. They show that their method introduces bias for non-linear regression models and propose a strategy for choosing the smoothing function to keep this bias small. Yet another synthesis strategy is described in Quick et al. (2018), which uses a differential smoothing synthesizer for locations of home sale in San Francisco. Their approach is a two-step process. First, they model the log-transformed home sale prices using an unrestricted hierarchical model. Second, they identify spatial outliers based on the distances to their nearest neighbors, then fit a restricted hierarchical model to provide additional smoothing for higher protection. In a related approach, Quick and Waller (2018) also use a hierarchical Bayesian model that preserves spatial, temporal, and between age-groups dependencies. They synthesize county-level heart disease deaths to complete public use data, which would be suppressed at units with cases lower than 10. More recently, Koebe et al. (2023) suggest publishing two different versions of georeferenced data. The first version includes the original location, but all other attributes are synthesized using a Gaussian copula model. The second version omits the geographic identifier, but leaves the other attributes at their original values.

2.3.2 Synthesizing the geographical information. The first successful implementation of geographical synthesis was discussed in Machanavajjhala et al. (2008). The authors propose a strategy for synthesizing the place of living for all individuals working in the U.S. The synthesizer is used to generate the underlying data for an application called OnTheMap provided by the U.S. Census Bureau. This application graphically visualizes commuting patterns on a detailed geographical level. The authors used a Dirichlet/Multinomial model for synthesis and adjusted the Dirichlet priors such that they were able to prove that their synthesizer guaranteed some formal level of privacy called Y−&#x1d6ff;-probabilistic differential privacy (see Machanavajjhala et al. (2008) for details). However, the multinomial model used in this paper offers low utility if the population sizes or event rates are very heterogenious. To address this limitation, Quick (2021) suggests relying on Poisson models–popular

5

in the disease mapping literature–for differentially private data synthesis. He later extended the approach by incorporating public knowledge to further improve the utility of the synthesizer (Quick, 2022). Another synthesis strategy proposed by Wang and Reiter (2012) is to treat the detailed geocoding information as a continuous variable and use CART models to sequentially synthesize the longitude and latitude of the geocodes. This approach was later compared in Drechsler and Hu (2021) with two other synthesis strategies for the geocodes: using a Dirichlet Process of Mixtures of Products of Multinomials (Si and Reiter, 2013; Hu et al., 2018, DPMPM) and CART models treating the geocoding information as categorical variables. The authors find that the categorical CART models offer the highest utility, but also the highest risk of disclosure. When trying to increase the level of protection, they find it to be more effective to synthesize additional variables instead of aggregating the geocoding information to a higher grid level. Burgette and Reiter (2013) generate a partially synthetic dataset in which they synthesize the location of US census tract identifiers using a Bayesian multinomial model with a group of Dirichlet processes priors and a multiple shrinkage prior distribution. This framework is chosen because it shrinks the parameters toward a small number of learned locations, which increases the utility of the data. Paiva et al. (2014) use areal level spatial models (often called disease mapping models in the literature) to synthesize the geographical information. Although they start with exact geographies, their methods require defining fine grids over the spatial domain, then using the conditional autoregressive (CAR) model of Besag et al. (1991) to model the distribution of grid-counts. When synthesizing exact geographies, they recommend first to synthesize grid cells for each individual, and second to randomly assign each individual a location within the grid cells. The approach is computationally intensive and can be challenging to apply if the number of categorical variables or the number of levels within the variables is large. The authors also note that their partially synthetic data do not preserve the spatial pattern because the independent draws from the underlying Poisson model can imply that close geographic units in the original data might be far apart in the synthetic data. This caveat is considered by Quick et al. (2015) who extend the spatial modeling process of geo-coordinates using marked point process models, which simultaneously model the location and the variables (Liang et al., 2008; Taddy and Kottas, 2012). Specifically, the authors propose to model the data in three steps: (i) specify multinomial models for the categorical variables in the data, (ii) use a log-Gaussian Cox process to model the geographical location within each cell specified by cross classifying all categorical variables, and (iii) specify a normal regression for continuous variables given the categorical variables and location. The authors point out that estimating this model can be computationally intractable and suggest several steps and simplifying assumptions to reduce the computational burden.

3 Risk and Utility Assessment

Data dissemination always faces two conflicting goals: minimizing the risk of disclosure and maintaining the usefulness of the data. Therefore, it is crucial to always evaluate data protection strategies for both of these dimensions. In this section we review strategies that have been proposed in the literature to measure the utility and the level of protection for geocoded data that underwent some form of disclosure protection.

3.1 Risk Evaluation

The most commonly applied measure for evaluating the disclosure risk of masked geodata is spatial &#x1d458;-anonymity. It is related to the classical definition as proposed by Sweeney (2002), which states that &#x1d458;-anonymity is achieved if a record is indstinguishable from &#x1d458; − 1 other records in the dataset based on a set of prespecified variables (e.g. age, sex, education). Specifically, spatial &#x1d458;-anonymity is reached if a location is indistinguishable from at least &#x1d458; − 1 other locations. However, in practice it is interpreted in many different ways (Cassa et al., 2006; Allshouse et al., 2010; Hampton et al., 2010; Kounadi and Leitner, 2016; Zhang et al., 2017; Hasanzadeh et al., 2020).

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There are two main definitions of &#x1d458;-anonymity for masked geodata. First, some researchers define spatial &#x1d458;-anonymity as the number of locations around the original point within a circle with radius equal to the displacement distance (Hampton et al., 2010; Allshouse et al., 2010). The second definition is to measure &#x1d458;-anonymity as the number of locations around the masked location that are within a circle with radius equal to the displacement distance (Lu et al., 2012; Zhang et al., 2017; Hasanzadeh et al., 2020). Note, however, that both approaches can overestimate the level of &#x1d458; , when random perturbation within a circle or donut is applied. This can be amplified if the maximum displacement distance depends on the population density (Allshouse et al., 2010) or is determined by the distance to the &#x1d458; &#x1d461;ℎ nearest neighbor. To illustrate, imagine one household located in an area with few observations or low population density which borders an urban area. If the displacement radius for this household is chosen to reach a certain level of &#x1d458;-anonymity, its maximum displacement distance will be relatively large reaching the outer areas of the urban area. A location in the urban area, on the contrary, has many neighbors in close proximity and will thus, taking &#x1d458;-anonymity as the objective, be displaced within a smaller area that does not include all possible displacements of the rural location. In this example, the rural location may be the only one that can be displaced far into the rural area. As a consequence an ill-intentioned user of the released data can be confident that a masked record in certain rural areas can only stem from one of the few observations in the rural area. Thus, neither counting the cases within a circle around the original point nor counting the cases within a circle around the masked point provides adequate information how well these points are protected. Kounadi and Leitner (2016) empirically demonstrate that to achieve the desired level of &#x1d458;-anonymity for close to 100% of the locations, the maximum distance of displacement needs to be substantially larger than the distance to the &#x1d458; &#x1d461;ℎ nearest neighbor. Beyond the (often flawed) risk assessment based on spatial &#x1d458;-anonymity, strategies for measuring the remaining risk of disclosure are surprisingly limited. Some authors discuss general aspects that impact the risk of disclosure. For example, Cassa et al., 2008 point out that risks of reidentification increase when multiple protected versions of the same georeferenced dataset are published. The original locations can then be approximated by averaging of the masked locations (assuming the same records can be uniquely identified in the different datasets). The more versions of the data are published, the higher the accuracy of this approximation. As Zimmerman and Pavlik (2008) point out, the risk is particularly high when the locations are labelled or details on the masking approach are disclosed such as the maximum displacement radius. A classical risk assessment strategy that has been used in some applications is to mount a record linkage attack. With these types of attacks, the intruder is assumed to possess some information about the units contained in the database (e.g., age, marital status, and employment status) and uses this information to identify units in the database. Risk measures based on record linkage attacks typically try to estimate how likely it is that such an attack will lead to a correct identification in the protected dataset. In the context of geocoded data, it is typically assumed that one of the attributes that is known to the attacker is the (approximately) exact location of the target record. Simulated record linkage attacks have for example been used in Drechsler and Hu (2021) (and implicitly in Koebe et al., 2023) to assess how well the different synthesis strategies protect the geographical information. Drechsler and Hu (2021) use risk measures originally proposed in Reiter and Mitra (2009) to specifically estimate reidentification risks for partially synthetic data. With this approach it is assumed that the attackers possess some background knowledge for a set of target records they wish to identify in the data. Based on this knowledge, they estimate the probability of a match for each unit in the released file. A match is declared for the record that has the highest average matching probability across the synthetic datasets. The risk is evaluated by means of these matches using two different measures. The first one calculates the expected number of correctly declared matches, i.e., the expected match risk. The second one calculates the number of correct unique matches, i.e., the true match rate. Another strategy to evaluate the level of protection specifically for partially synthetic data approaches was used in Quick et al. (2018). The authors focus on spatial outliers in the original data. For those records, they generate a large number of synthetic values by repeatedly drawing from the synthesis model. They then look at histograms of the generated values. If the spatial synthesis model is overfitting, the draws from the model will be centered around the true value with limited variability potentially indicating an unacceptable risk of disclosure. Using a related idea, Quick et al. (2015) and Quick and Waller (2018) compare synthesized values with the

7

true, confidential values. In light of privacy protection, the objective is here to obtain different values. Given that they propose releasing two versions of the same dataset (see Section 2.3), Koebe et al. (2023) measure the risk of correctly re-identifying the sensitive small-area identifiers (zip codes) in the unprotected data without geoinformation using information from the synthetic data. They train random forest models on the dataset in which the geolocations have been protected. The trained model is then run on the original data to predict the locations. The fraction of successful predictions denotes the risk measure.

3.2 Utility Evaluation

While offering a sufficient level of protection should always be the primary goal of any disclosure limitation strategy, it is crucial to also measure its impacts on utility. In the geocoding context, the utility is typically assessed by measuring to what extent the spatial structure of the data is maintained. The list of metrics that is used for this purpose in the literature is almost as large as the disclosure avoidance literature itself. Here, we only focus on the utility assessment based on spatial pattern retention. A more general discussion on utility evaluations can be found for example in Domingo-Ferrer et al. (2012). In the following, we will classify the various approaches into four broad categories: (1) point locations and density measures; (2) cluster analysis; (3) spatial autocorrelation; and (4) land use assessment.

3.2.1 Point Locations and Density Measures. Utility evaluations often start by graphically comparing the population densities of the confidential data and the protected data. A simple approach is to visually compare the locations on a map (e.g., Kwan et al., 2004). However, unless the original data is non-confidential, this approach can only be used internally, as the plots of the original data might spill sensitive information otherwise. A more versatile approach is to estimate the population density using kernel density estimation (Shi et al., 2009; Gatrell et al., 1996). The kernel density estimator creates a smooth density surface which allows to graphically compare the densities of the original and masked data on a heatmap (e.g., Kwan et al., 2004; Zandbergen, 2014). The heatmaps can be used to either visualize the density levels for each dataset separately or to directly display the discrepancies between the two densities. Beyond visualizing the population densities (e.g., Gatrell et al., 1996) the approach can also be used to measure spatial discrepancies in any other variable contained in the data. For example, Seidl et al. (2015) show differences in total warm water consumption among others.

3.2.2 Clustering. Another common approach to evaluate the utility of the protected dataset is to assess whether the data show similar clustering behavior as the original data. A descriptive statistic that is often used to describe clustering in a point pattern is Ripley’s &#x1d43e; function (see, e.g., Kwan et al., 2004; Zhang et al., 2017; Quick et al., 2015; Seidl et al., 2015; Drechsler and Hu, 2021). It is defined as expected number of points within a predefined radius around the location of interest normalized by the average point density across the entire geographical area covered in the data (Ripley, 1976; Kwan et al., 2004). It assesses to which extent a point pattern deviates from spatial homogeneity (Drechsler and Hu, 2021). Based on the &#x1d43e; function, the more easily interpretable &#x1d43f; function can be computed. It takes values close to zero for homogeneously distributed data, while positive values indicate heterogeneity or clustering. Closely related, the cross-&#x1d43e; function and its analog for the &#x1d43f; statistic assess the clustering of one point pattern relative to another point pattern, for example the underlying population distribution (Kwan et al., 2004). As an alternative measure, Zhang et al. (2017) apply an average nearest-neighbor analysis to quantify how well the spatial pattern of the original data is preserved. Specifically, they compute a nearest-neighbor index that consists of the average distances from each unit to its nearest neighbor (measured in, e.g., Euclidean or Manhattan distance). An index value similar to that of the original data indicates comparable clustering intensity. In a related approach, Lu et al. (2012) apply a nearest-neighbor index that compares the average distance to the nearest neighbor with the expected distance assuming a uniform distribution of the locations. Values below one indicate clustering. Seidl et al. (2015) use a nearest-neighbor hierarchical clustering analysis to compare the number of clusters on the first level (clusters of individual data points) in the data (see also Levine, 2006; Kounadi and Leitner, 2015). They also compare standard deviational ellipses between the original and the protected data. These ellipses cover the area that is within, say, one or two standard deviations from the center of

8

the cluster (Kounadi and Leitner, 2015). They facilitate understanding the two-dimensional clustering behavior. Another measure to assess clustering and to identify hotspots is the Gi* statistic proposed by Getis and Ord (1992); Ord and Getis (1995). The Gi* statistic can be used to test the null hypothesis of spatial independence. Rejecting the null hypothesis indicates clustering (Getis and Ord, 1992). Kounadi and Leitner (2015) develop an indicator that combines nearest-neighbor hierarchical clustering and the Gi* statistic. In health research, SatScan (Kulldorff, 1997) is a popular software tool for disease mapping. It can be used to identify spacial and temporal clustering in the data (Kulldorff et al., 2005). Several authors (Olson et al., 2006; Cassa et al., 2006; Hampton et al., 2010) use the software to compare the sensitivity and specificity of the underlying cluster detection approach run on the original and protected data. Finally, some researchers use the original and masked dots to identify a data-dependent geographical area. The utility of the protected data is assessed by measuring the overlap of this area between the two datasets. For example, Hasanzadeh et al. (2017) propose an approach that compares the similarity of individuals’ frequently visited points. Specifically, they extend the residential points to home areas, where the edges mark locations that are visited frequently. Large overlaps of the home areas of the protected and the confidential data indicate high similarity of individuals’ neighborhoods in both datasets.

3.2.3 Spatial Autocorrelation. While clustering analysis focuses on identifying the number and size of clusters in the data, spatial autocorrelation more generally assesses the spatial dependence in a point pattern. Both approaches are closely related. A prevalent measure for spatial autocorrelation is Moran’s I (e.g., Ord and Getis, 1995; Lu et al., 2012; Seidl et al., 2015). It tests whether the null hypothesis that the spatial autocorrelation is zero can be rejected. If this is the case, spatial autocorrelation can be assumed. Another common measure to compare spatial autocorrelation between datasets is the empirical semivariogram. (Matheron, 1963; Quick et al., 2018; Seidl et al., 2015)). It visualizes the homogeneity of non-geographic variables as a function of the distance between the locations. An output graph that increases and then flattens with further distance indicates positive spatial autocorrelation.

3.2.4 Land use. Another widely used approach to measure the utility of masked geodata is to compare the geography of the masked point-coordinates with their original counterparts. Quick and Waller (2018) and Zhang et al. (2017) consider, for instance, land cover categories or the proximity to roads. Regarding land cover rates, they compare whether the point-locations are in the same raster of either urban or rural areas. In an optimal scenario, the protected data would have the same share of points in urban areas as the original. Analogously, this applies to the proximity to roads, where the authors measure the closest distance of each point to the next road. The distances are compared using cumulative distribution functions (cdfs). The closer the two cdfs from the original and the protected data, the higher the utility of the protected data. Related works (e.g., Hasanzadeh et al., 2020) also evaluate other geographic characteristics such as the greenness of the surroundings.

4 Conclusion

Broad access to detailed geo-information can enhance the understanding of our society in numerous ways. Thus, it is not surprising that many data disseminating agencies are currently discussing how to provide access to these data for external researchers without compromising the confidentiality of the units contained in the data. Optimizing the trade-off between offering high utility granular information and sufficient data protection has been the subject of various methods for disclosure protection. In this paper, we have reviewed the literature on protection strategies for georeferenced microdata. Its main strands can be divided into coarsening the geo- information, masking it by altering, perturbing, or swapping the original locations, and disseminating synthetic data instead of the original data. We also discussed the different methods that are used to evaluate the risk and utility of the protected data. When assessing the risk of disclosure, we found that many papers rely on different notions of &#x1d458;-anonymity. We discussed a key concern with these notions, namely that for many of the distance based masking techniques, disclosure risks are underestimated based on this procedures as the obtained value

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of &#x1d458; tends to be much larger than the true number of indistinguishable records. We therefore strongly advice against using spatial &#x1d458;-anonymity in this context. Regarding the utility evaluation, we conclude that there are many useful approaches discussed in the literature and that it would be an interesting avenue for future research to consolidate the plethora of different measures.

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  • 1. Introduction
  • 2. Data Protection Strategies
    • 2.1. Aggregation
    • 2.2. Geographic Masking
    • 2.3. Synthetic Data
  • 3. Risk and Utility Assessment
    • 3.1. Risk Evaluation
    • 3.2. Utility Evaluation
  • 4. Conclusion
  • References

Remote Access for Scientific Use Files – a New Pathway for German Official Statistics Microdata Access

remote access, microdata for scientific purposes, access path

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

Remote Access for Scientific Use Files – a New Pathway for German Official

Statistics Microdata Access

Hanna Brenzel ( Research Data Centre of the Federal Statistical Office)

Katharina Cramer (Research Data Centre of the Statistical Offices of the Federal States)

Volker Güttgemanns (Research Data Centre of the Statistical Offices of the Federal States)

Marcel Mathes (Research Data Centre of the Statistical Offices of the Federal States)

[email protected]

Abstract

The fundamental goal of the Research Data Centre of the Federal Statistical Office and the Research Data Centre of the

Statistical Offices of the Federal States (RDC) is not only to provide access to official statistics microdata, but also to

continuously improve and adapt the access to the changing needs of empirical science. In order to meet the broad range of

needs of the empirically working scientific community, the RDC have offered different access paths since their founding, through which differently anonymised data products are made available. Now, the RDC come up with a new remote access

prototype system including a new data product. All access paths differ both in terms of the anonymity degree of the

provided microdata as well as in the access way of data provision. At first, existing and firmly established data access

paths are outlined and their contractual and legal conditions explained. Subsequently, the newly installed remote access

prototype and its features and requirements are presented. Provided that the ongoing evaluation phase turns out positive,

this data access option will define one more way of data access operated regularly in its full version from 2024 onwards.

The analysis potential of the data provided therein will classify between the scientific-use files transmitted to the scientific

institutions and the data provided for on-site analysis at the RDC safe centres. This paper highlights various challenges,

such as data protection requirements and legal framework conditions, which must be considered.

2

1 Introduction

With the establishment of the Research Data Centre (RDC) of the Federal Statistical Office in the fall of 2001

and with the RDC of the statistical offices of the Länder in April 2002, an important cornerstone and a central intersection was created between the scientific community and official statistics as data and information service

provider.

Together, the RDCs offer the empirically working scientific community a coordinated range of data and services for the scientific use of high-quality microdata from official statistics.

Over time, however, expectations of the RDCs have evolved fundamentally, and stakeholders in politics and

scientific communities have been pushing for substantial improvements in data access and data usage capabilities for some time.

Remote access represents an up to date and modern way of accessing data and is accordingly demanded by data

users. The statistical offices of other European countries (e.g., the Netherlands, France or Finland) can be

mentioned as reference benchmarks. They have created the legal and technical prerequisites to make their data available to researchers via remote access some time ago. Last but not least, a remote access system is currently

being set up at European level by Eurostat.

On one hand, the establishment of a remote access system - with the investment in a connectable infrastructure - will advance the continuous development of the RDC. By catering towards the needs of the scientific

community, the status of the RDC as a modern data provider will be consolidated. On the other hand, the

currently complex and inefficient system of data access can be streamlined to a uniform and manageable system without limiting the flexibility of the users.

2 Status Quo

The RDC of the Federal Statistical Office, together with the RDC of the statistical offices of the Länder, offer

access to more than 3,000 different data products for over 90 statistics for scientific use via different ways of

access. They differ both in terms of the anonymity of the accessible data and in the type of data provision. Generally, the existing ways of data access can be divided into two categories, as figure 1 illustrates. In the case

of the so-called "on-site access", the data remains in the secure areas of the statistical offices of the Federation

and the Federal States. Since the RDCs can closely control the access to the data and provide output only after

confidentiality check, the data are only weakly anonymized. With the "off-site access," on the other hand, users can work with the individual data at their own institutes. Since the output are not checked by the data centers,

the individual data has to be more anonymized.

The category “off-site” includes the so-called Public Use Files (PUF), Campus Files (CF) and Scientific Use Files (SUF). “On-site” includes PC workplaces at the RDC, so called “safe centers” and remote execution (see

the homepage of the RDC, https://www.forschungsdatenzentrum.de/en/access).

Safe centers exist in all locations of both RDC. These can be used by researchers to analyse microdata inside

the safe premises of the statistical offices. As the individual data are already protected by the regulation of data

access and the equipment of the PC workstation, formally anonymous microdata can be provided at the safe

centers. Thus, a nationwide infrastructure in Germany is available for these data. The safe centers are equipped with common statistical programs (Stata, R as well as partly SPSS and SAS) and

are completely isolated from the outside. A separate PC workstation with internet connection is available for e-

mail communication and internet searches. In contrast to the safe centers, remote execution does not provide direct access to the microdata. Instead, data

structure files are made available that resemble the original material with regard to structure and variable

values, but do not permit any analyses in terms of content and do not hold any risk of exposing confidential

information. Using these data sets, program codes can be prepared by the users using the statistical programs SPSS, SAS, Stata or R. These program codes are applied by staff of the statistical offices to analyse the original

data. The data users receive the results of those analyses after the relevant confidentiality checks.

SUFs are standardized datasets created by the RDC for popular statistics. SUFs offer lower potential for

analyses than on-site ways of access, but are designed to be suitable for a large proportion of scientific research

projects. Due to the de facto anonymization of microdata, they may be used outside the protected premises of official statistics according to Sect. 16 para. 6 nos. 1 BStatG. Due to legal restrictions, SUF may only be used

3

by researchers who are employed by a research institution that is registered and located in Germany. The use of

SUF may only take place in Germany. Until recently, the SUF were sent by DVD to the respective scientific institution with which user contract was concluded. Since June 2023, recent modernization measures now allow

the SUF to be accessed directly via a download portal to the institution authorized to use the data.

In particular, on-site ways of access entail additional work for both data users and RDC staff. At the same time,

the share of data uses via these access paths steadily increases over time compared to off-site uses. The development of a remote access system therefore pursues the goal of ensuring the technical connectivity to a

modern and demand-oriented data provision for the scientific community. With this technology, the increased

expectations of the research community for an up-to-date and modern data provision can be fulfilled in the long term. In addition, the remote access system holds potential for future innovation by reducing or substituting

existing labor-intensive ways of access (reduction of on-site support, reduction of coordination of appointments

with users, reduction of coordination and support of remote execution, etc.). Consequently, the scarce resources of the RDC could be invested more efficiently, for example in supporting additional data usage or further

developing the data and service offers. At the same time, there is increased potential regarding data parsimony,

as it is expected that this system will reduce the number of intermediate results per project that require

confidentiality checks. Furthermore, the RDC aim to sustainably strengthen their leading role in the group of German RDC.

Figure 1: Ways of data access at the research data centres (RDC) of the statistical offices of the Federation and the Federal States

3 The Remote Access System

3.1 The technical structure

IT and data security play a crucial role in setting up the remote access system. The aim is to ensure that the

remote access system is implemented in compliance with the law while maintaining the required IT security

standards.

A virtual desktop infrastructure based on CITRIX was chosen as the IT-architecture. The system components

set up are located in the so-called IDMZ (Internet Demilitarized Zone), in which procedures are operated that

are to be accessible from the Internet. In the IDMZ, a distinction is made between three areas: Access Area

(Pex), Application Area (Pin1) and Data Area (Pin2). These three areas are separated from each other by

firewalls, which only allow approved communication between the neighboring areas within the application. A

so-called transport encryption secures the communication path between the server and the client.

Two-factor authentication and IP whitelisting are implemented as additional IT security measures for the Citrix

solution. Two-factor authentication means that, in addition to the user-specific work accounts protected by a

personal password, a uniquely generated token must be used for each log-in. IP whitelisting allows only

specific IP addresses to gain access to the remote access system. Prior to each authorized use, the IP address of

the respective facility is allowed or added to the whitelist. This ensures that unauthorized IP addresses do not

initially gain access to the system. This implements geoblocking as a technical measure as well as

strengthening protection against possible (automated) attack attempts.

In addition, app protection is used to, among other things, prevent the user from taking screenshots of the data.

Remote system access is controlled on a per user basis by an access management system, only authorized users

are granted access. Within the system, authorizations are limited to the extent required for data analysis. The

creation of user-specific working accounts, which are managed centrally and secured by the user and access

management, ensures that access is only possible to requested data. Each account is linked to a data folder in

which user-specific official microdata are stored by RDC staff.

In addition to the technical measures, a number of technical and contractual-organizational measures are

introduced to increase data protection. Before the data can be accessed, a user contract has to be concluded

between the scientific institution and the responsible statistical office. It is contractually stipulated that up-to-

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date software, operating system and virus protection are used on the client side when accessing the virtual

desktop infrastructure. As well as, re-identification of individual cases is illicit. The RDC are legally bound to

check all statistical results for statistical confidentiality that were created within the context of scientific

projects based on provided microdata. This serves the protection of data according to section 16 (6) of the

Federal Statistics Law (BStatG). Should individual cases be part of the output then they have to be blocked

consistently across all results of a project. Data users who plan to re-identify individual cases are liable to

prosecution and are expelled from further data uses.

In order to ensure that the system is tied to a specific location, its use is contractually established and sanctions

are imposed in the event of violations. In addition, it is contractually stipulated that scientific institutions can be

excluded from using the remote access system or from the possibility of carrying out further research projects

via the RDC in the event of serious violations of the terms of use. In the event of a striking breach of contract,

the scientific institutions can also be sanctioned with a penalty payment of up to EUR 20,000.

3.2 Data material in the remote access system

Remote access to formally anonymized data is not feasible within the current legal framework. One possible

way of implementation is to offer remote access for de facto anonymized data with slight modifications, as this

would not require amendment of the law. In this case, the degree of data modification is of utmost relevance: If

the level of anonymization is too high, the data offered will not meet the needs of the scientific community; if

the level of data anonymization is too low, confidentiality can no longer be maintained. The degree of de facto

anonymization therefore largely determines the benefits and coverage of the demand of the scientific

community. In addition, the expected effects on the capacity of the RDC heavily depend on covering as many

of the science community's projects as possible via the remote access system and, in particular, on reducing the

costly uses of remote execution. However, this goal can only be achieved if significantly more data can be

provided via remote access than via the current dissemination path via off-site SUF.

Microdata are described as “de facto anonymous” if it is not possible to completely rule out de-anonymization

but assigning the information to the respective statistical unit “requires unreasonable effort in terms of time,

cost and manpower” (Section 16 (6) of the Federal Statistics Act). According to the Federal Statistics Act,

however, de facto anonymous data may only be used by scientific institutions and only to carry out scientific

projects.

When creating de facto anonymity, the aim is to virtually eliminate the probability of correctly assigning data to

respondents, while preserving the statistical information content as much as possible. Different anonymization

methods can be used for this purpose. Common methods are information reduction (e.g. aggregation, class

formation, censoring) and information modification (e.g. swapping). In order to determine de facto anonymity,

the effort and benefit of deanonymization must be evaluated.

Factual anonymity thus does not completely exclude the possibility of re-identification, but puts its risk in a

cost/benefit ratio. Costs for data users primarily include the consequences for actions in violation of the

contract. Re-identification is strictly prohibited and punishable by fine or imprisonment (Section 203 StGB). In

addition, consequences such as loss of reputation, loss of access to data of official statistics, etc., which threaten

in the event of de-anonymization of the data, must also be considered by scientific users. This is because the

users are obligated to maintain the anonymity of the data both by the formal obligation and the user agreement.

Factual anonymity therefore does not result solely from the remaining information content of the data, but is

composed of a triad: 1) modification of the data material, 2) technical/organizational measures, and 3)

contractual measures. Therefore, it also depends on the access condition, if a microdata set can be described as

Figure 2: Technical infrastructure of the remote access system

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de facto anonymous. Of crucial importance here is what additional knowledge is available and where the data

access takes place. Depending on whether the microdata is used outside or inside the statistical offices, de facto

anonymity can be achieved with more (off-site SUF) or less (on-site SUF) severe losses of information.

The de facto anonymity of microdata from official statistics is thus not a fixed quantity, but can be mapped

along a continuum. In principle, it can be stated: The higher the technical and contractual measures, the fewer

anonymization measures need to be taken and the higher the analysis potential of the data.

No technical measures are used for the previous off-site SUFs. Factual anonymity must therefore only be

achieved from the two remaining measures: in addition to the contractual commitment and the commitment of

the users, de facto anonymity is achieved by strongly anonymizing the data material itself. For this purpose, a

statistics-specific anonymization concept is developed for each data material.

With the new remote SUF or on-site SUF, de facto anonymity can be achieved by significantly less

modification of the data. This is justified by the high level of technical measures and the associated possibility

to control the data access. In contrast to off-site SUF, the data is not passed on. It is solely possible to view the

data via a virtual desktop (VDI environment). A so-called "transport encryption" secures the communication

path between the server (sender) as well as the client (receiver). An exchange between the technical

infrastructure of the data users and the data on the server of the official statistics or a download of the official

data is thus technically impossible. Thus, unauthorized data linkage is impossible and the RDC has a high level

of use control via log files. With regard to the risk of de-anonymization, data access via remote access therefore

reduces many risks compared to the previous off-site SUFs.

3.3 The use of Remote Access

The remote access system, which is currently under construction, will be set up as a classic remote desktop

version. As in the past, scientific institutions that are entitled to use the system in accordance with Section 16

BStatG have to apply for data access. If the application is approved, the researchers are then able to access the

secure area within their scientific institution by using their own hardware. Within the secure area common

statistical software such as RStudio and Stata is available. The major advantage compared to remote execution

is that researchers can see the microdata and do not have to "blindly" program their syntaxes as before (see

Figure 3). By working directly with and being able to view the data, it should be possible to significantly

reduce the number of intermediate results previously generated via remote execution, thus minimizing a very

labor-intensive process step in the RDC. The goal should be that only final outputs are checked for

confidentiality by the RDC staff and will be released. This also supports the principle of data parsimony.

Figure 3: Remote Access at the RDC

Work on setting up such a system began in November 2021. The system is currently in the evaluation phase.

On one hand, the technical implementation of the system is being tested and its resilience checked using penetration tests. On the other hand, the user-friendliness and the attractiveness of the data material provided is

to be examined thoroughly. In a first step, only absolutely anonymous data material was made available via the

system for a selected group of people. In a second step, off-site SUFs will then be made available to power users who have already completed a valid user application with the RDC. The third step will then be to test the

redesigned on-site/remote SUF material. Since the system requires a redesign of all statistics-specific

anonymization concepts, a gradual integration of the existing data products in the RDC is planned. The start will be made with the most requested data product, the microcensus. In order to be able to evaluate the

operating grade of the system appropriately, DRG statistics will be offered as one of the first data products in

the remote access system in addition to the microcensus. If the evaluation of the system is positive, other data

products that are of high demand will follow.

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4 BIBLIOGRAPHY

Brenzel, Hanna / Zwick, Markus. An information infrastructure has emerged in Germany – the Research Data

Centre of the Federal Statistical Office. German version published in WISTA | 6 | 2022, p. 54 et seq.

Homepage of the Research Data Centre of the Federal Statistical Office and the Federal States

https://www.forschungsdatenzentrum.de/en

https://www.forschungsdatenzentrum.de/en
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