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MWW2024_S_Poland_Dygaszewicz

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UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

ModernStats World Workshop

21-22 October 2024, Geneva

TOOLS FOR AUTOMATING METADATA-DRIVEN PROCESSES

IN STATISTICS POLAND

Speaker: Janusz Dygaszewicz, Statistics Poland

Author(s):

Abstract

In order to improve the effectiveness of public statistics, work was carried out on the optimization, redesign and

standardization of statistics production processes in accordance with the locally developed Model of Production

Processes for Statistics (MPPS).

MPPS is the Polish implementation of GSBPM. The MPPS model in relation to GSBPM has been

supplemented, for example, with geospatial sub-processes, quality verification and assessment, and planning.

MPPS provides a holistic approach to statistical production that uses multiple interconnected IT systems to

provide comprehensive support for the execution of statistical production processes - from the needs

determination phase to the evaluation phase.

Vital elements of the new MPPS based architecture are data repositories for collect, process, analyse and

disseminate phases, which will store the current "states" of the processed data. This approach facilitates access

to statistical data for subsequent "states". Subsequent "states" of data result from the successive steps of their

processing in the successive phases of the statistical production process.

An important element of the IT solutions landscape is the metadata system. Metadata play a key role in

defining the flow of statistical production processes. Scheduling of statistical production is achieved by

preparation of appropriate metadata. Metadata describe inputs, outputs and processing details. Metadata

orchestrate production processes by defining the sequence of production activities. Status of execution of

production activities as well as relevant quality indicators is further reflected in metadata. Developed solutions

enable automation of business processes increasing manageability, efficiency and repeatability of statistical

production.

The basis of MPPS solutions were the ModernStats standards, in particular GSBPM and GSIM, the ideas of

which were incorporated into the implemented production workflow and information models of business and IT

systems.

Presentation

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Languages and translations

English

Estimation of the number of irregular foreigners in Poland using non-linear count regression models

Maciej Beręsewicz, Marcin Szymkowiak

Statistical Office in Poznań, Poland Poznań University of Economics and Business

08-09.05.2024

Outline

Introduction

The model

Data

Results

Discussion

Literature

Outline

Introduction

Basic information about the Centre for the Methodology of Population Studies

▶ dr hab. Marcin Szymkowiak is Deputy Director of the Statistical Office in Poznań, Poland.

▶ dr Maciej Beręsewicz is head of the Centre for the Methodology of Population Studies at Statistical Office in Poznań, Poland.

▶ This is a new unit responsible for ▶ estimation of population size, ▶ integration of administrative data, ▶ studying hard-to-reach populations, ▶ developing tools in open-source languages (such as R, Julia), ▶ and developing new statistical methodology.

Acknowledgements

This study is based on the working paper Estimation of the number of irregular foreigners in Poland using non-linear count regression models by Beręsewicz & Pawlukiewicz (2020) [arXiv:2008.09407]

Since this paper is being considerably revised, the model and the results may change.

Motivation

▶ Irregular (undocumented) migration is hard to measure as the underlying population is hard-to-reach.

▶ Several approaches have been proposed in the literature, which are based for in- stance on residual, single-source capture-recapture or multiple estimation system methods.

▶ Majority of methods assumes access and integration of data at the unit-level. ▶ The proposed approach requires access to aggregated data and is based on a func-

tional form and certain assumptions that can be verified using available data.

Outline

The model

▶ The original model is based on Prof. Li-Chun Zhang’s (University of Southamp- ton, University of Oslo, Statistics Netherlands) working paper entitled Developing methods for determining the number of unauthorized foreigners in Norway (2008).

▶ The author proposes a model that requires only three types of variables: 1. the number of apprehended irregular foreigners (denoted as m), 2. the number of foreigners who faced criminal charges (denoted as n), 3. the number of foreigners registered in the central population register (denoted as N).

Assumptions

▶ Let Mt be the size of the population of unauthorized resident at time t (e.g. end of the year) – the random variable.

▶ Let Nt be the be the size of the known reference (proxy) population at the same time t – the fixed, known covariate.

▶ The target parameter is the theoretical size of irregular residents, which is defined as the conditional expectation of Mt given Nt with respect to f(Mt|Nt) denoted by

ξt = E(Mt|Nt).

Assumptions

▶ As Zhang (2008) notes, the theoretical size is defined as the conditional expectation of the random variable, which makes it possible to get rid of the spurious variation as long as the reference population size is held fixed.

▶ The purpose of introducing Nt is two-fold: 1. it serves as an explanatory variable for the irregular size Mt, 2. it provides an interpretation of the irregular size Mt in analogy to Nt.

▶ In this way, the theoretical size is a stable measure of the target variable as variation in Mt is linked to that of Nt.

Assumptions

▶ Let mit be the observed number of irregular foreigners from country i (this may also indicate more detailed populations e.g. sex-age group for a given country).

▶ Let nit be the observed number of (legally staying) foreigners from country i. ▶ Let pit be the probability for an irregular resident to be observed in administrative

data (say Border Guards). ▶ Let

mit ∼ Poisson(λit)

▶ Let λit = µituit, where µi = E (Mitpit | nit,Nit) = E (Mit | Nit) · E (pit | Mit, nit,Nit)

Assumptions

▶ The final model consists of the following set of equations

ξit = E (Mit | Nit) = Nα it,

ωi = E (pit | Mit, nit,Nit) = E (pit | nit,Nit) =

( nit Nit

)β

uit ∼ Gamma(1, ϕ),

(1)

▶ From which we can derive the following relationship for µit

µi = Nα i

( ni Ni

)β

(2)

The target quantity

▶ We are interested in the target parameter describing the number of irregular residents. Given the above model, the target parameter is defined as

ξ = C∑

i=1 E (Mi|Ni) =

C∑ i=1

Nα i , (3)

▶ and its estimator is given by

ξ̂ = C∑

i=1 Nα̂

i , (4)

where α̂ is the estimator of α.

Estimation of the parameters and verification of assumptions

▶ The parameters are estimated using maximum likelihood (the loglik function, gra- dient and hessian are provided in the working paper).

▶ This model can be further extended to account for covariates. ▶ Assumptions of the model can be verified using the following linearized model

log

( mi Ni

) = (α− 1) logNi + β log

( ni Ni

) + ϵi, (5)

▶ We should expect a negative relationship with logNi and a positive one with log(ni/Ni).

Outline

Data

Definitions

▶ For administrative purposes, Polish authorities (Polish Border Guard, 2020) use the term illegal stay, which is defined as a stay which does not comply with the legal provisions describing the conditions that foreigners must meet in order to enter and stay in the Republic of Poland.

▶ If a foreigner is found to be staying in Poland illegally, an administrative procedure is initiated whereby the person is obliged to leave the country.

Data – Border guard data

Tab. 1: The number of irregular foreigners in Poland by place of apprehension and re-apprehension status in 2019

Half Same year Within country Airports Ukraine Russia Belarus Total I No 3,190 710 6,879 106 785 11,670 I Yes 29 1 0 0 0 30 II No 3,437 1,016 8,492 143 1,052 14,140 II Yes 70 0 0 0 0 70

Data – Police data

Tab. 2: The number of foreigners in police records by registration type and residence status (registered for temporary stay or permanent residence) in 2019

Half Registered Procedural Search Traffic Criminal Total I Yes 1,499 715 9,286 10 11,510 I No 4,046 6,522 2,477 16 13,061 II Yes 2,080 878 11,988 6 14,952 II No 4,644 5,979 2,867 11 13,501

Data - registered population

Tab. 3: The number of foreigners in the PESEL register by registration type at quarter ends in 2019

As at No address Temporary Permanent De-registered Expired Outside 31.03 81,202 242,318 56,476 16,158 124,368 332,256 30.06 107,545 249,154 57,656 16,246 157,476 383,283 30.09 134,483 246,990 59,228 16,340 196,209 441,705 31.12 160,868 252,245 60,440 16,386 225,690 496,374

Data – comparison

Tab. 4: The number of foreigners and countries by data source, sex and period before applying the condition for the model

Classification Number of foreigners Number of countries Source Sex 1st period 2st period 1st period 2st period PESEL Total 232,468 234,194 151 147

Women 137,424 137,880 145 140 Men 95,044 96,314 127 130

Border Guard Total 3,187 3,435 77 68 Women 762 776 40 39 Men 2,425 2,659 72 67

Police (all) Total 20,138 23,330 100 98 Women 3,017 3,079 58 57 Men 17,121 20,251 94 94

Data – data for the model

▶ In our study we used Polish data from two halves of 2019 for the foreign population aged 18+.

▶ In addition, we derived data broken down by sex and economic age group (18-59 and 60+ for women; 18-64 and 65+ for men).

▶ The PESEL register contained people from 151 and 147 countries in the first and second half of the year, respectively, police data – around 100, and Border Guard records – around 70.

▶ The model requires that the following conditions hold: mtij > 0, ntij > 0 and ntij/Ntij < 1, so we created a new dataset that meets these requirements.

▶ After applying this condition, we received a total of 73 countries (including category other), of which 50 were observed in both periods and 23 only in one (65 in the first and 58 in the second half of 2019).

Outline

Results

Assumptions

Fig. 1: The relationship between the log of the PESEL population and the log of the BG-to-PESEL counts at the end of first and third quarter of 2019

Assumptions

Fig. 2: The relationship between the log of police-to-PESEL counts and the log of BG-to-PESEL counts at the end of first and third quarter of 2019

Population size estimation results

Tab. 5: Quality of models used in the study and the estimated population ξ̂

Covariates for α LogLik AIC BIC ξ̂

At the end of 1st quarter 2019 No covariates -733.1 1,470.3 1,475.5 24,119.9

Ukraine -648.7 1,303.5 1,311.3 20,835.8 Sex -682.5 1,371.0 1,378.8 51,982.8

Ukraine & Sex -630.1 1,268.1 1,278.6 34,870.1 At the end of 3rd quarter of 2019

No covariate -822.2 1,648.3 1,653.4 23,582.6 Ukraine -735.7 1,477.5 1,485.1 21,139.0

Sex -742.2 1,490.3 1,497.9 65,011.0 Ukraine & Sex -689.8 1,387.6 1,397.8 49,080.1

Outline

Discussion

▶ In the paper we propose a different approach to estimating the hard-to-reach pop- ulation of irregular foreigners based on a flexible non-linear count regression model.

▶ The approach is an alternative to classic capture-recapture methods, which rely on one or multiple sources, and the interpretation of results is more intuitive because the irregular population is conditionally dependent on the regular population.

▶ The approach only requires administrative data and, as a result, the quality of our estimates depends on the availability of high-quality register-based statistics.

▶ Selection of data for the model should be strictly connected with the definition of the irregular population used in the study.

Outline

Literature

Literature (selected)

▶ Beręsewicz, M., Gudaszewski, G., and Szymkowiak, M. (2019). Estymacja liczby cudzoziemców w Polsce z wykorzystaniem metody capture-recapture. Wiadomości Statystyczne. The Polish Statistician, 64(10), 7-35.

▶ Beręsewicz, M., & Pawlukiewicz, K. (2020). Estimation of the number of irreg- ular foreigners in Poland using non-linear count regression models. arXiv preprint arXiv:2008.09407.

▶ Polish Border Guard (2020). Consequences of illegal stay ▶ Zhang, L.-C. (2008). Developing methods for determining the number of unau-

thorized foreigners in Norway. Statistics Norway (SSB), Division for Statistical Methods and Standards. www. ssb. no.(accessed July 28, 2008)

Thank you!

Introduction
The model
Data
Results
Discussion
Literature

Work-related population flows – measurement of commuting time, Statistics Poland, Poland

Read more about Work-related population flows – measurement of commuting time, Statistics Poland, Poland

Languages and translations

English

1Meeting of the Group of Experts on Quality of Employment,

UNECE, Palais des Nations, Geneva 14-16.05.2024

Agnieszka Zgierska – Statistics Poland, CSO, Department of Labour Market

[email protected]

Sylwia Filas-Przybył – Statistical Office in Poznan, Centre for Urban Statistics

[email protected]

Work-related population flows

– measurement of commuting time

mailto:[email protected]

2stat.gov.pl

Necessary information to get you started,

helpful in understanding the further presentation

3stat.gov.pl

Administrative division of Poland

a) Of which 66 gminas that

are also cities with powiat

status

16 voivodships,

• 314 powiats,

• 2477 gminas a)

In Poland, there is a three-tier

administrative (territorial) division.

The territory of country is divided

into voivodships, then

voivodships into powiats,

and powiats into gminas.

Territory of Poland: 322 714 km2 (311 895 km2 land area)

Population (as of 31.12.2022): 37.8 millions (national definition) Population density by gminas (in 2016)

4stat.gov.pl

What am I going to talk about

in the main presentation?

Work-related population flows

5stat.gov.pl

Place of residence Place of work (of the main job)

(1) Number of commuters,

Commuting matrix

between gminas of:

What am I going to talk about

in the main presentation?

(2) Accessibility matrix

(road distances and travel times between centroids of gminas)

Estimation of commuting time

Work-related population flows

6stat.gov.pl

7stat.gov.pl

(1) A study of work-related commuting flows

I. Data processing

• Identification of main job for employed persons

• Identification of the address of the workplace.

• Generation of the population of commuters according to the following

definition:

A commuter :

an employed person

• living in a gmina other than the one in which their

workplace is located and

• as an individual Taxpayer reported higher deductible

expenses owing to commuting costs

Study methodology

8stat.gov.pl

The population of commuters was identified using data from:

1. Registers

• Ministry of Finance – database containing information about payers of

personal tax (PIT-11, PIT-40) - 21,6 milion records (2016)

• Social Insurance Institution (ZUS) – Central Register of Taxpayers,

Central Register of Insured Persons - 16,4 milion records (2016)

2. The National Census of Population and Housing (2021)

II. Data sources

Study methodology (cont.)

(1) A study of work-related commuting flows

9stat.gov.pl

The methodology of the study of work-related population flows was developed on the basis of previous experience of statisticians from the office in Poznań (2006, 2011, 2016).

Previous editions of the commuting survey were based solely on data from administrative registers (data from the Social Insurance Institution and the Ministry of Finance).

The Census 2021 was carried out using a mixed method, i.e. using data collected from respondents and data from administrative sources

For 2021, the previous source of data for this topic (tax registers) has been enriched with additional registers and answers from Census individual questionnaire. The information provided from these sources enabled commuters to be characterised by gender and age and, in particular, made it possible to better identify their workplaces, which, in combination with their actual place of residence, determine the direction of work-related flows.

Commuting to work according to the results of the National Census of Population and Housing 2021

10stat.gov.pl

Results for the whole country

In 2021 there were

5182,6 thousand employees

commuting to work in Poland,

which represented 37.7 % of the

total number of employees.

2006 2011 2016 2021

Commuters in Poland

Commuters (left scale) Commuters as % of total employees (right scale)

11stat.gov.pl

• The study of work-related commuting was conducted for the group of employees1) who

indicated that their main place of work was located in Poland at an address other than

their place of residence.

· When a person’s employee status was determined on the basis of information

provided directly by respondents in the Census questionnaire, the place of work

was determined based on the respondent’s answer.

· However, if a person’s employee status was determined on the basis of an

administrative register (with information about people’s economic activity),

information from this register was used as the person’s place of work 2). To identify

the target population, the gmina of residence was compared with the gmina of work

determined in the way described above.

• The population of employees for whom the gmina of residence was different from

the gmina of employment is defined as the population of commuters.

• The size and intensity of commuting showed significant spatial variation. Individual

inter-municipal flows are included in the matrix of work-related population flows.

· In the study, work-related population flows were analysed between gminas, which

were treated as the basic. Since the distinction between urban and rural areas is

important in the study of these flows, urban and rural parts were analysed

separately. ------

1) The population of employees was determined on the basis of the population by the national definition.

2) The 2021 Census was a mixed-mode census, which means it was based on data collected from respondents and data

from administrative registers

Data sources and methodology (2021 Census questionnaire + administrative registers)

12stat.gov.pl

Structure of the commuting matrix

The commuting matrix could be presented in the so-called

„long form”.

or „Classical form” of matrix

13stat.gov.pl

Map 1. Incoming commuters by gminas in 2021

Łódź

Warszawa Poznań

Map 2. Outgoing commuters

as % of total employees

by gminas in 2021

Łomianki

Gdańsk

14stat.gov.pl

Gminas by work-related population flows in 2021

(in thousands)

15stat.gov.pl

Map 1. The share of incoming commuters

to Warszawa1) in the number of employees

in the gmina of residence in 2021

390.4 thousand people living outside

Warsaw 1) commuting to work in the

capital of Poland.

By far the largest number of people coming to

work in Warsaw had their place residence in the

Mazowieckie Voivodeship (66.1%). From 370

gminas of this voivodship, 258.2 thousand people

came to Warsaw.

The gminas from which the largest percentage

of residing there employees came to work in

Warsaw are concentrated in several rings

surrounding the city.

Employees from outside the Mazowieckie

Voivodeship (132.2 thousand) accounted for

33.9% of the total number of people coming to

work in Warsaw. These were the inhabitants of

2752 gminas in the country, most of which were

located in the Wielkopolskie (319; 11.6%),

Lubelskie (243; 8.8%) and Małopolskie (230;

8.4%) voivodeships.The largest streams of

people coming to work in Warsaw were directed

from the Łódź Voivodeship (19.0 thousand), the

Slaskie Voivodeship and the Lublin Voivodeship

(15.4 thousand each).

1) Warszawa is the capital of Poland and the capital of the Mazowieckie Voivodeship.

16stat.gov.pl

Map 2. The share of outgoing commuters

from Warszawa in the number of employees

in the gmina of residence in 2021

56.0 thousand people left Warsaw to

work in other territorial units.

Departures from Warsaw to work in the

Mazowieckie Voivodeship in 2021 were mainly

addressed to municipalities neighbouring the

city.

Employees living in Warsaw and going to work

outside the Mazowieckie Voivodeship

accounted for 19.6% of the total number of

people leaving the city. They went to work in

1133 communes, most of which were located in

the following voivodeships: Łódź (117),

Lubelskie (112) and Wielkopolskie (105). The

place of work of the largest group of these

people was in Cracow. The next places in this

respect were occupied by the following cities:

Wrocław, Łódź and Poznań

17stat.gov.pl

Among persons coming to work in and

leaving Warsaw, the largest number of

people were aged 35–44.

People of this age accounted for 27% of

the total number of people coming to

work in Warsaw and 30% going to work

outside the city.

Women dominated among those coming

to work in Warsaw (50.6%), while men

dominated among those leaving the city.

Chart 1 The structure of incoming commuters to Warszawa

and outgoing commuters from Warszawa by age in 2021

18stat.gov.pl

The matrix of work-related population flows available at:

https://stat.gov.pl/download/gfx/portalinformacyjny/pl/defaultaktualnosci/6536/9/2/1/macierz

_przeplywow_ludnosci_zwiazanych_z_zatrudnieniem.xlsx

PL/EN version of matrix

https://stat.gov.pl/download/gfx/portalinformacyjny/pl/defaultaktualnosci/6536/9/2/1/macierz_przeplywow_ludnosci_zwiazanych_z_zatrudnieniem.xlsx

19stat.gov.pl

One should bear in mind that the Polish Census questionnaire`2021 did not contain questions about commuting time, frequency of commuting, means of transport used, remote work or any other kind of additional information.

However, the Statistical Office in Poznań prepared an accessibility matrix, (as experimental work) which contains information about estimated distance and travel time by car between selected gminas in Poland.

The purpose of the study was to estimate the selected characteristics of spatial accessibility using resources of OpenStreetMap and making them available in the form of an accessibility matrix to users interested in conducting advanced spatial analyses.

The information included in the matrix fills the gap in official statistics in this area. It also meets the needs of users interested in problems associated with work-related commuting.

20stat.gov.pl

Assumptions:

• Remote work is not taken into account

• Means of transport – passenger car

• Only the commute from home to work is taken into account

• Gminas are represented by their centroids

Data Source Provider:

• Census 2021 Commuting Matrix

• Time matrix derived from OpenStreetMap resources

The methodology described for the 2016 study,

available at: Statistics Poland / Experimental statistics / Functional areas. Territorial accessibility / Estimation of distance and travel time between selected communes in Poland in 2016

(2) An accessibility matrix. Estimation of distance and travel time

between selected gminas

https://stat.gov.pl/en/experimental-statistics/functional-areas-territorial-accessibility/estimation-of-distance-and-travel-time-between-selected-communes-in-poland-in-2016,10,1.html

In 2019, Statistics Poland (the Centre for Urban Statistics in the Statistical Office in Poznań)

published accessibility matrix containing distances in "straight line", the so-called

orthodromes, road distances and travel times between gmina centroids. The last measure is

given either in minutes and hundredths of a minute or in the format “minutes:seconds”.

(2) An accessibility matrix. Estimation of distance and travel time

between selected gminas

22stat.gov.pl

The matrix follows the format of the matrix of work-related commuting flows.

Important facts about the matrix:

• gminas of work and residence are represented by their centroids, i.e. geometric

centres;

• spatial and attribute data used to produce the matrix come from the National

2016 and 2017, downloaded from the resources of www.geofrabrik.de;

• the orthodromic distance (the shortest distance in km) between gmina centroids

was calculated using the distGeo() function from the geosphere package in R;

• road distance (in km) and travel time (in mins) was estimated using

the ors_matrix() function from the openrouteservice package.

Authors also have included PNG files with isochrones of accessibility for gmina capital

cities, i.e. choropleth maps showing areas with the same travel time to gmina capital cities,

at 15-minute intervals. The maximum travel time is 90 minutes.

The study, which has led to the creation of the accessibility matrix and choropleth

presenting isochrones of accessibility, is an experimental work whose innovative character

consists in:

• the use of a new data source, in the form of OpenStreetMap resources. It is a crowd-

source solution, based on the open data licence;

• the use of open source software for data processing: the openrouteservice R package

licensed under CC-BY 4.0 .

(2) An accessibility matrix. Estimation of distance and travel time

between selected gminas

https://poznan.stat.gov.pl/download/gfx/poznan/pl/defaultstronaopisowa/1445/1/1/przeplywy-2016.xlsx

http://www.geofrabrik.de/

https://giscience.github.io/openrouteservice-r/articles/openrouteservice.html

https://creativecommons.org/licenses/by/4.0/legalcode.pl

(1)+(2) The commuting matrix +The accessibility matrix. Results.

The commuting matrix The accessibility matrix

24stat.gov.pl

Average commuting time in 2021

(minutes)

Indicators characterizing commuting time – preliminary results

Median commuting time in 2021

(minutes)

Poland: 43 minutes Poland: 28 minutes

Isochrones TORUŃ ŁÓDŹ

POZNAŃ

Indicators characterizing commuting time – preliminary results

WARSZAWA

26stat.gov.pl

Agnieszka Zgierska,  [email protected]

Sylwia Filas–Przybył,  [email protected]

Thank you for your attention

mailto:[email protected]

Estimation of the number of irregular foreigners in Poland using non-linear count regression models (Poland)

Read more about Estimation of the number of irregular foreigners in Poland using non-linear count regression models (Poland)

Languages and translations

English

*Prepared by Maciej Beręsewicz (Statistical Office in Poznań, Poland; Poznań University of Economics and Business, Poland NOTE: The designations employed in this document do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

Economic Commission for Europe Conference of European Statisticians Group of Experts on Migration Statistics Geneva, Switzerland, 7−8 May 2024 Item 4 of the provisional agenda Measuring undocumented migration

Estimation of the number of irregular foreigners in Poland using non-linear count regression models

Note by Statistical Office in Poznań, Poland

Abstract

Population size estimation requires access to unit-level data in order to correctly apply capture-recapture methods. Unfortunately, for reasons of confidentiality access to such data may be limited. To overcome this issue we apply and extend the hierarchical Poisson-Gamma model proposed by L.-C. Zhang (2008), which initially was used to estimate the number of irregular foreigners in Norway. The model is an alternative to the current capture-recapture approach as it does not require linking multiple sources and is solely based on aggregated administrative data that include (1) the number of apprehended irregular foreigners, (2) the number of foreigners who faced criminal charges and (3) the number of foreigners registered in the central population register. The model explicitly assumes a relationship between the unauthorized and registered population, which is motivated by the interconnection between these two groups. This makes the estimation conditionally dependent on the size of regular population, provides interpretation with analogy to registered population and makes the estimated parameter more stable over time. In this paper, we modify the original idea to allow for covariates and flexible count distributions in order to estimate the number of irregular foreigners in Poland in 2019. We also propose a parametric bootstrap for estimating standard errors of estimates. Based on the extended model we conclude that in as of 31.03.2019 and 30.09.2019 around 15,000 and 20,000 foreigners and were residing in Poland without valid permits. This means that those apprehended by the Polish Border Guard account for around 15-20% of the total.

This work is based on Beręsewicz, M., & Pawlukiewicz, K. (2020). Estimation of the number of irregular foreigners in Poland using non-linear count regression models. arXiv preprint arXiv:2008.09407.

Working paper 3

Distr.: General 29 April 2024 English

Working paper 3

I. Introduction

1. The demand for reliable estimates of the number of foreigners residing in a given country on a permanent and temporary basis as well as those that are part of the working population is expressed at various levels, including the central government, as well regional and local authorities. Information about the demographic, social and economic characteristics of foreigners is particularly important for the implementation of population, migration and economic policies. Another important issue is the scale of unregistered / irregular immigration1, i.e. remaining outside the administrative systems. There is currently no reliable and direct data source that would provide reliable information in this respect.

2. Determining the number of foreigners, including unregistered immigrants, is an important methodological challenge for official statistics. First, administrative registers provide information about the de iure (registered) population, while statistics are interested in the de facto (registered and unregistered) population. Secondly, foreigners constitute a hard-to-reach population, i.e. one that cannot be easily estimated using traditional statistical methods. This is because there is no available (exhaustive) sampling frame/list and it is difficult to obtain information from individual units . While some characteristics of hard-to-reach populations can be determined by collecting survey data (for example, the selection of units for a sample can be done using the snowball method and its extension – Respondent Driven Sampling), the task of estimating the size of such a population poses a methodological challenge.

3. A number of appropriate statistical methods for estimating population sizes based on capture- recapture techniques have been proposed in the literature (for a recent review see Böhning, Bunge, and Heijden 2017). We can categorise these approaches into two groups: the first one includes those based on a single data source (cf. Van Der Heijden et al. 2003; Böhning and Heijden 2009) and the second – on at least two data sources (cf. Van der Heijden et al. 2012; Coumans et al. 2017). The effective use of these techniques in practice largely depends on the availability of statistical data and is restricted by the need to meet certain assumptions underlying the individual methods. Dual or triple system capture-recapture methods require access to unit-level data (e.g. in order to calculate recapture counts) and are based on certain assumptions, which it may be difficult to meet in practice.

4. However, in practice it is often only possible to obtain aggregated data owing to privacy and sensitivity restrictions. For instance, Statistics Poland does not have access to individual data from police or Border Guard records. In such situations one can apply the residual method (Passel 2007; Hanson 2006), single-source capture-recapture based on distributional assumptions about count data (cf. Böhning, Heijden, et al. 2019) or models developed for correcting under-reporting as proposed by Bailey et al. (2005; Oliveira, Loschi, and Assunção 2017; Stoner, Economou, and Drummond Marques da Silva 2019). The first and most common method applied in economics, for instance by the Pew Research Center (Pew Research Center 2019b), is the residual method, where the size of the unauthorized population is calculated as the difference between the total number of foreigners, non- citizens (e.g. from census data) and that of authorized non-citizens (e.g. from register data). Single source capture-recapture based are more restrictive and is biased in presence heterogeneity and contamination (dependence between captures). To overcome these issue zero-truncated one-inflated distribution was proposed by Godwin and Böhning (2017) and proved to be equivalent with zero-one

1 In the paper we interchangeably use three terms – unregistered, unauthorized, irregular – to denote the same group of foreigners who reside in a given country without a valid permit.

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truncated distributions (Böhning, Heijden, et al. 2019). The latter method involves joint modelling of the binary indicator or proportion of under-reporting and observed counts. It also requires a set of strong covariates for each equation and instrumental variables that are connected only with one of these processes.

5. In this paper we take a different approach, which was initially proposed by L.-C. Zhang (2008) in an unpublished working paper. The model is based solely on aggregated data, with the assumption of a non-linear relationship between the registered and unregistered population under a Gamma-Poisson mixed model. The method requires three data sources: 1) observed irregular population (e.g. apprehensions), 2) foreigners listed in police registers (e.g. criminal charges), and 3) known legal population (e.g. from the population register). In the original paper, L.-C. Zhang (2008) used the following datasets for Norway: (1) foreigners who did not have a valid permit for staying in the country, determined on the basis of expulsion requests at the Norwegian Directorate of Immigration (further divided into those who had applied for asylum and those who had not), (2) foreign citizens who faced criminal charges, and (3) foreign-born persons aged 18 and over, registered in the Central Population Register. The main limitation of this method is the fact that some countries (e.g. UK, USA) do not have a central population register.

6. In this study, we critically assess, reuse and extend this model by including demographic covariates and different distributions of counts to estimate the number of of irregular foreigners in Poland. The structure of the paper is as follows. Section 2 provides a description of the situation in Poland, basic definitions of concepts referred to in the paper and data sources used for the estimation. Section 3 describes assumptions of the approach proposed by L.-C. Zhang (2008), the model, its critique and extension, including bootstrap MSE estimation. Section 6 offers a verification of the assumptions given the available data and estimation results. The paper ends with conclusions and discussion. All codes and data used in the paper are available in the supplementary materials in the paper Beręsewicz, M., & Pawlukiewicz, K. (2020). Estimation of the number of irregular foreigners in Poland using non-linear count regression models. arXiv preprint arXiv:2008.09407.

II. The population of irregular foreigners in Poland

1. Basic definitions

7. The population of unauthorized immigrants is not only hard to reach but also hard to define. To start with, a foreign-born person can be classified using three characteristics L.-C. Zhang (2008):

i. entry status: legal or illegal,

ii. residence status: legal, quasi-legal, temporary or illegal,

iii. working status: legal, illegal or no-work.

8. The exact definition of these categories will vary across countries and over time, as a result of the dynamism and intricacies of immigration laws. In the paper we focus on the residence status.

9. In the EU context, the term irregular migrant refers to a third-country national present on the territory of a Schengen State who does not fulfil, or no longer fulfils, the conditions of entry as set out in the Regulation (EU) 2016/399 (Schengen Borders Code) or other conditions for entry, stay or residence in that EU Member State.

10. Pew Research Center (2019a), which provides estimates of the irregular population calculated by applying the residual method, uses the following definition in the EU context: “Unauthorized immigrants in this report are people living without a residency permit in their country of residence

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who are not citizens of any European Union or European Free Trade Association (EFTA) country. The unauthorized population also includes those born in EU-EFTA countries to unauthorized immigrant parents, since most European countries do not have birthright citizenship. Finally, the European unauthorized immigrant population estimate includes asylum seekers with a pending decision.”

11. According to (Eurostat 2019) in 2019, “627,900 non-EU citizens were found to be illegally present in the EU-27. This was up 9.7% compared with one year before (572,200), but down 69.9 % when compared with the record level of 2015, when that number present stood at 2,085,500”. EU Member States with the largest numbers of non-EU citizens found to be illegally present in 2019 included Germany (133,500), Greece (123,000), France (120,500) and Spain (62,900), which together accounted for 70.1% of all non-EU citizens found to be illegally present in the EU-27. The corresponding figure for Poland in 2019 was 26,625, compared to 26,547 in 2018. Note that these statistics are based on border guard reports that will be discussed in the next section.

12. For administrative purposes, Polish authorities (Polish Border Guard 2020) use the term illegal stay, which is defined as a stay which does not comply with the legal provisions describing the conditions that foreigners must meet in order to enter and stay in the Republic of Poland . More specifically, a person’s stay in the Republic of Poland is regarded as illegal when a foreigner:

i. does not hold a valid visa or another valid document entitling them to enter and stay in Poland,

ii. has not left the territory of Poland after their period of stay in the country has expired,

iii. has crossed or attempted to cross the border illegally,

iv. performs or has performed work illegally,

v. has undertaken business activity in breach of the regulations,

vi. does not hold sufficient means of subsistence for the duration of their intended stay in Poland,

vii. is a person identified in an alert issued in the SIS (Schengen Information System) or in the national database for the purposes of refusing entry

13. If a foreigner is found to be staying in Poland illegally, an administrative procedure is initiated whereby the person is obliged to leave the country.

14. The legality of a foreigner’s stay in Poland can be carried out by representatives of the following agencies:

i. officers of the Customs Service,

ii. officers of the Border Guard,

iii. police officers,

iv. authorised employees of the Office for Foreigners,

v. authorised employees of the Provincial Office.

15. Currently, there are two institutions that provide information about irregular foreigners – the Border Guard on a quarterly basis and the Office for Foreigners within the Ministry of the Interior and Administration on an annual basis. The latter provides information about the number of third country nationals ordered to leave. In this paper we focus on data obtained from the Polish Border Guard, described in the section below.

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2. Data Sources

(a) Polish Border Guard data

16. The Polish Border Guard (PBG) reports the number of irregular foreigners according to the actual place of apprehension, which includes: within the country, at airports, at the border with Ukraine, Belarus and Russia separately. In the case of airports or borders, the legal status of foreigners exiting Poland was verified, i.e. some of them were found to be irregular (e.g. exceeded their period of stay) and were ordered to leave (i.e. this number is reported by the Office for Foreigners). Since these people were already leaving Poland, no apprehension procedure was involved. Consequently, these cases should not be taken into account while estimating the size of the unauthorized population.

17. Reports prepared by PBG are compiled on a quarterly basis and are broken down by sex and age. The current reporting suffers from multiple counts of the same individuals, because PBG does not normally remove duplicates from their quarterly statistics. Fortunately, at our request, the data we received from PBG had been deduplicated by accounting for information about re-apprehensions. Currently, PBG can only specify two levels – first and second or more apprehensions within a given year. In our study we focus on persons apprehended only once within the country. Table 1 presents statistics for the first and second half of 2019. In the first part of 2019 over 11,000 foreigners were found to stay in Poland illegally, with about 3,200 apprehended within the country. These figures increased in the second half: to over 14,000 and 3,500, respectively. The increase can most likely be attributed to those foreigners whose stay permit issued in the first half of the year expired. Note that most illegal foreigners were stopped while leaving Poland at the border with Ukraine, which is the main source country of non-citizens in Poland. We also note that the number of re-apprehensions is very low and follows a zero-truncated one-inflated distribution, thus limiting the possibility of applying single-source capture-recapture.

Table 1. The number of irregular foreigners in Poland by place of apprehension and re-apprehension status in 2019

Half Same year Within country Airports Ukraine Russia Belarus Total I No 3,190 710 6,879 106 785 11,670 I Yes 29 1 0 0 0 30 II No 3,437 1,016 8,492 143 1,052 14,140 II Yes 70 0 0 0 0 70

(b) Police data

18. The second data source used in the study is the National Police Information System (Pol. Krajowy System Informacji Policji; KSIP) which is the main police database containing information about individuals suspected of indictable offenses, persons wanted by the police or attempting to hide their identity, and about lost or stolen property. We were given access to police records about registered individuals containing the following classifications: 1) procedural registrations, 2) criminal registrations, 3) searches for missing and wanted individuals and 4) traffic violations.

19. We obtained anonymised, unit-level data, containing the following variables: a pseudo-identifier (each person in the KSIP register has a unique identifier), the quarter in which the registration was made, sex, age calculated at 28 Jan 2020 (date of data compilation), whether or not the person has a personal id, citizenship, and residence status (unknown, permanent, temporary stay or unregistered). These data are presented in Table 2.

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20. According to the police records, 24,571 foreigners were registered in the first half of 2019 and 28,453 in the second half. The increase is mainly due to the higher number of individuals who committed traffic offences, which in turn may result from more intensive police activity during summer holidays and the Christmas period. In both periods,the dataset contains a similar share of foreigners registered for permanent residence or temporary stay and those unregistered but there are differences in the categories of police registrations. For instance, most procedural and search registrations involved unregistered foreigners, while most traffic violations concerned registered foreigners. In general, the number of foreigners in the police register is higher than that reported by the Border Guard.

Table 2. The number of foreigners in police records by registration type and residence status (registered for temporary stay or permanent residence) in 2019

Half Registered Procedural Search Traffic Criminal Total I Yes 1,499 715 9,286 10 11,510 I No 4,046 6,522 2,477 16 13,061 II Yes 2,080 878 11,988 6 14,952 II No 4,644 5,979 2,867 11 13,501

21. A foreigner, a citizen of another EU Member State, who stays in Poland for more than 3 months, is obliged to register. Other foreigners are required to register if their stay is longer than 30 days. Since 2018, each foreigner who stays longer than 30 days and has registered, has been automatically assigned a personal identification number (PESEL), but if registration is not possible (e.g. no permanent place of residence), such a person can still apply for a PESEL number. The PESEL register is maintained by the Ministry of Digital Affairs2.

22. Table 3 presents information about foreigners in the PESEL register (holding a PESEL id) broken down by registration type: no address in Poland, temporary stay, permanent residence, deregistered ‘to nowhere’, temporary stay expired3, and residence outside Poland. According to the PESEL register, the majority of registered foreigners did not reside in Poland or their period of temporary stay expired. Moreover, in most cases, their stay was temporary. This is mainly due to the requirements that have to be met in order to qualify for permanent residence. In our study we focus on foreigners that have come for a temporary stay or permanent residence.

2 Throughout the paper we interchangeably use three descriptive terms to refer to foreigners listed in the PESEL register – the PESEL population, the registered population or the regular population, meaning those who reside in Poland temporarily or permanently. 3 In such cases it is unclear whether such persons have left the country without deregistering or remain in the country without a valid residence permit, which means they should be included in the irregular population. Without linking the PESEL register with other sources it is not possible to assess the quality of this variable. Note that these figures are over 10 times as high as the number of illegal stays reported by the Border Guard and thus may contain a significant number of misclassified cases.

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Table 3. The number of foreigners in the PESEL register by registration type at quarter ends in 2019

23. In section 6.1 we provide exact information regarding the sub-population of foreigners analysed in our study.

III. Theoretical properties of the L.-C. Zhang (2008) model

1. Model assumptions

24. L.-C. Zhang (2008) proposed a model to estimate the number of foreigners at a given time (i.e. census night, register reference point), which relies on administrative data (cf. Gerritse 2016) but contrasts with most single-source capture-recapture studies, which use data from the whole year or a specific period to obtain counts for the models. L.-C. Zhang (2008) model is based on the assumption that there is a relationship between the unauthorized and registered population, which is justified below.

25. Let 𝑀𝑀𝑡𝑡 be the size of the population of unauthorized residents at the time point of interest 𝑡𝑡. Let 𝑁𝑁𝑡𝑡 be the size of the known reference (proxy) population at the same time 𝑡𝑡 (e.g. census night; end of the year). We use 𝑁𝑁𝑡𝑡 to denote the number of foreign-born persons over 18 who are registered, i.e. have a temporary or permanent residence permit.

26. 𝑀𝑀𝑡𝑡 should be regarded as a random variable and 𝑁𝑁𝑡𝑡 as a known covariate. Let 𝑓𝑓(𝑀𝑀𝑡𝑡|𝑁𝑁𝑡𝑡) denote the conditional probabilistic distribution of 𝑀𝑀𝑡𝑡 given 𝑁𝑁𝑡𝑡. The target parameter is the theoretical size of irregular residents, which is defined as the conditional expectation of 𝑀𝑀𝑡𝑡 given 𝑁𝑁𝑡𝑡 with respect to 𝑓𝑓(𝑀𝑀𝑡𝑡|𝑁𝑁𝑡𝑡), denoted by

𝜉𝜉𝑡𝑡 = 𝐸𝐸(𝑀𝑀𝑡𝑡|𝑁𝑁𝑡𝑡).

27. As L.-C. Zhang (2008) notes, the theoretical size is defined as the conditional expectation of a random variable, which makes it possible to get rid of the spurious variation as long as the reference population size is held fixed. The purpose of introducing 𝑁𝑁𝑡𝑡 is two-fold: (a) it serves as an explanatory variable of the irregular size 𝑀𝑀𝑡𝑡, and (b) it provides an interpretation of the irregular size 𝑀𝑀𝑡𝑡 in analogy to 𝑁𝑁𝑡𝑡. In this way, the theoretical size is a stable measure of the target variable as variation in 𝑀𝑀𝑡𝑡 is linked to that of 𝑁𝑁𝑡𝑡.

28. Moreover, since the chosen 𝑁𝑁𝑡𝑡 is not subject to seasonal variation, neither is the theoretical 𝜉𝜉𝑡𝑡. In contrast, 𝑀𝑀𝑡𝑡 defined in a more naturalistic manner can be expected to vary greatly in the course of one year, being perhaps the highest in the summer months, which is another kind of spurious variation.

29. There is also a sociological and economic justification for why 𝑀𝑀𝑡𝑡 depends on the regular population 𝑁𝑁𝑡𝑡. Because irregular foreigners do not have regular job opportunities and cannot claim social and health benefits, they need a network of contacts with registered residents, who are much better off socially and economically. It is hard to imagine a completely closed community of Ukrainian or Vietnamese irregular residents in Poland. The first one is the largest immigrant group in Poland as

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a result of recent migration flows and the second is one of the most stable in terms of size and is confined to a relatively small area (living mainly in Warsaw and in neighbouring communes). This explains the choice of the reference population – the registered population aged 18 and over.

2. Zhang (2008) model

30. For both the target and the reference populations, let 𝑖𝑖 = 1, . . . ,𝐶𝐶 be the index of the sub-population classified by the country of citizenship and origin, respectively. For simplicity, we drop the 𝑡𝑡 index denoting the reference time. L.-C. Zhang (2008) assumed that the observed number of irregular residents follows a Poisson distribution, with parameter 𝜆𝜆𝑖𝑖, denoted by

𝑚𝑚𝑖𝑖 ∼ Poisson(𝜆𝜆𝑖𝑖). (1)

31. The parameter 𝜆𝜆𝑖𝑖 should depend on two other quantities: (a) the total number of irregular residents from country 𝑖𝑖, denoted by 𝑀𝑀𝑖𝑖, and (b) the probability of being observed, i.e. the probability for an irregular resident to be included in Border Guard data, denoted by 𝑝𝑝𝑖𝑖, i.e. 𝜆𝜆𝑖𝑖 = 𝑀𝑀𝑖𝑖𝑝𝑝𝑖𝑖.

32. In addition, let 𝑢𝑢𝑖𝑖 = 𝑀𝑀𝑖𝑖𝑝𝑝𝑖𝑖/𝐸𝐸(𝑀𝑀𝑖𝑖𝑝𝑝𝑖𝑖|𝑛𝑛𝑖𝑖,𝑁𝑁𝑖𝑖), where 𝐸𝐸(𝑀𝑀𝑖𝑖𝑝𝑝𝑖𝑖|𝑛𝑛𝑖𝑖,𝑀𝑀𝑖𝑖) denotes the conditional expectation of 𝑀𝑀𝑖𝑖𝑝𝑝𝑖𝑖 given 𝑛𝑛𝑖𝑖 and 𝑁𝑁𝑖𝑖. The 𝑢𝑢𝑖𝑖 is a random effect that accounts for heterogeneous variation from one country to another. Together, we obtain

𝜆𝜆𝑖𝑖 = 𝜇𝜇𝑖𝑖𝑢𝑢𝑖𝑖,

where 𝜇𝜇𝑖𝑖 = 𝐸𝐸(𝑀𝑀𝑖𝑖𝑝𝑝𝑖𝑖|𝑛𝑛𝑖𝑖,𝑁𝑁𝑖𝑖) = 𝐸𝐸(𝑀𝑀𝑖𝑖|𝑁𝑁𝑖𝑖) ⋅ 𝐸𝐸(𝑝𝑝𝑖𝑖|𝑀𝑀𝑖𝑖, 𝑛𝑛𝑖𝑖,𝑁𝑁𝑖𝑖). The final model is specified by the following set of equations

𝜉𝜉𝑖𝑖 = 𝐸𝐸(𝑀𝑀𝑖𝑖|𝑁𝑁𝑖𝑖) = 𝑁𝑁𝑖𝑖𝛼𝛼 ,

𝜔𝜔𝑖𝑖 = 𝐸𝐸(𝑝𝑝𝑖𝑖|𝑀𝑀𝑖𝑖,𝑛𝑛𝑖𝑖,𝑁𝑁𝑖𝑖) = 𝐸𝐸(𝑝𝑝𝑖𝑖|𝑛𝑛𝑖𝑖,𝑁𝑁𝑖𝑖) = �𝑛𝑛𝑖𝑖 𝑁𝑁𝑖𝑖 � 𝛽𝛽

𝑢𝑢𝑖𝑖 ∼ Gamma(1,𝜙𝜙),

(2)

where Gamma(1,𝜙𝜙) denotes the gamma distribution with the expectation 𝐸𝐸(𝑢𝑢𝑖𝑖) = 1 and variance 𝑉𝑉(𝑢𝑢𝑖𝑖) = 1/𝜙𝜙. Zhang uses term hierarchical Gamma-Poisson random effect model to describe [eq- zhang-model] and derives log-likelihood function that may be found in Appendix 9 but we show in Appendix 9.1 that it is actually Negative Binomial distribution as a special case of Poisson-Gamma mixture. Furthermore, we show in Appendix 9.4 that the simplified approximation of term log𝛤𝛤(𝑥𝑥) used by L.-C. Zhang (2008) leads to biased estimates of 𝛼𝛼,𝛽𝛽 and 𝜉𝜉.

33. The model has the following assumptions. First, country random variation refers only to observed apprehensions i.e. 𝑚𝑚𝑖𝑖 as 𝜇𝜇𝑖𝑖 is scaled by 𝑢𝑢𝑖𝑖. Second, the non-linear relationship between the regular population size 𝑁𝑁𝑖𝑖 and 𝑀𝑀𝑖𝑖 is imposed by the power function with 𝛼𝛼 being the same for all the countries. Finally, there is a similar relationship, defined by the 𝛽𝛽 parameter, which exists between police “catch rate” and the probability of being observed in Border Guard data.

34. From equations (2) we derive the following relationship

𝜇𝜇𝑖𝑖 = 𝑁𝑁𝑖𝑖𝛼𝛼 � 𝑛𝑛𝑖𝑖 𝑁𝑁𝑖𝑖 � 𝛽𝛽

that can be used to verify the model assumptions. After dividing both sides by 𝑁𝑁𝑖𝑖 and applying the log transformation we get

log � 𝜇𝜇𝑖𝑖 𝑁𝑁𝑖𝑖 � = (𝛼𝛼 − 1)log𝑁𝑁𝑖𝑖 + 𝛽𝛽log �

𝑛𝑛𝑖𝑖 𝑁𝑁𝑖𝑖 �.

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35. Then, we can plug in 𝑚𝑚𝑖𝑖 and model log(𝑚𝑚𝑖𝑖/𝑁𝑁𝑖𝑖) into the linearized model

log �𝑚𝑚𝑖𝑖 𝑁𝑁𝑖𝑖 � = (𝛼𝛼 − 1)log𝑁𝑁𝑖𝑖 + 𝛽𝛽log �𝑛𝑛𝑖𝑖

𝑁𝑁𝑖𝑖 � + 𝜖𝜖𝑖𝑖 , (4)

from which we should expect a negative relationship with log𝑁𝑁𝑖𝑖 and a positive one with log(𝑛𝑛𝑖𝑖/𝑁𝑁𝑖𝑖).

3. The target parameters

36. We are interested in the target parameter describing the population size of irregular residents. Given the above model, the target parameter is defined as

𝜉𝜉 = �𝐸𝐸 𝐶𝐶

𝑖𝑖=1

(𝑀𝑀𝑖𝑖|𝑁𝑁𝑖𝑖) = �𝑁𝑁𝑖𝑖𝛼𝛼 𝐶𝐶

𝑖𝑖=1

and its estimator is given by

𝜉𝜉 = �𝑁𝑁𝑖𝑖𝛼𝛼� 𝐶𝐶

𝑖𝑖=1

where 𝛼𝛼� is the estimator of 𝛼𝛼.

4. Extensions

37. A natural way of extending the above model is to include additional covariates, such as country, sex, age or place of residence to account for variability in 𝛼𝛼 or/and 𝛽𝛽 and including different distributions for the observed counts 𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡.

38. Let 𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡 be observed counts for period 𝑖𝑖 = 1, . . ,𝑇𝑇 , country 𝑖𝑖 = 1, . . . ,𝐶𝐶 and domain 𝑗𝑗 = 1, . . . , 𝐽𝐽 defined as an interaction between, e.g. sex and age. We assume that the observed counts are generated from the count distribution given [eq-count-general]

𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡 ∼ Count(𝛈𝛈),

where Count denotes a suitable count distribution, such as Poisson, Geometric or Negative Binomial (NB2), and 𝛈𝛈 is a vector of parameters for a given distribution – for Poisson: 𝛈𝛈 = �𝜇𝜇𝑡𝑡𝑖𝑖𝑡𝑡� or for NB2: 𝛈𝛈 = �𝜇𝜇𝑡𝑡𝑖𝑖𝑡𝑡,𝜙𝜙�, where 𝜇𝜇𝑡𝑡𝑖𝑖𝑡𝑡 is defined as in [eq-mu].

39. Note that, by definition, 𝜇𝜇𝑡𝑡𝑖𝑖𝑡𝑡 > 0 is positive as we only observe apprehended foreigners from a given country and belonging to a given domain. This resembles the situation in single-source capture- recapture studies based on re-apprehensions. Thus assuming [eq-poisson-start] will lead to underestimation estimates of the population total as shown in a limited simulation study in Appendix 9.4. This results suggest that the original model proposed by L.-C. Zhang (2008) may lead to biased estimates of 𝜉𝜉. Furthermore, zero-inflation may be presence. For instance, the study by Böhning, Heijden, et al. (2019) shows equivalence between zero-truncated one-inflated and zero-one truncated count distributions. Having that in mind, our extension we also consider distributions that may be zero-truncated as the one below

𝑓𝑓+�𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡,𝛈𝛈� = 𝑓𝑓�𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡,𝛈𝛈� 1 − 𝑝𝑝(0,𝛈𝛈),

or zero-one truncated as given by

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𝑓𝑓++�𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡,𝛈𝛈� = 𝑓𝑓�𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡,𝛈𝛈�

1 − 𝑓𝑓(0,𝛈𝛈) − 𝑓𝑓(1,𝛈𝛈),

where 𝑓𝑓+(. ),𝑓𝑓++(. ) denote truncated count densities, 𝑓𝑓(0,𝛈𝛈),𝑓𝑓(1,𝛈𝛈) represent 0 and 1 densities and 𝛈𝛈 is a vector of parameters for a given count distribution.

40. In our study we consider the following distributions for 𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡: Poisson (PO), zero-truncated Poisson (ztPO), Negative-Binomial (NB2) and zero-truncated Negative Binomial (ztNB2), where 𝜇𝜇 is the mean and 𝜙𝜙 is the dispersion parameter. Log-likelihood functions for the models considered in the paper are given in Appendix 9.

41. Furthermore, we extend equation (1) by including covariates

𝜇𝜇𝑡𝑡𝑖𝑖𝑡𝑡 = 𝑁𝑁𝑡𝑡𝑖𝑖𝑡𝑡𝐗𝐗 𝑇𝑇𝛂𝛂 �𝑛𝑛𝑡𝑡𝑖𝑖𝑡𝑡

𝑁𝑁𝑡𝑡𝑖𝑖𝑡𝑡 � 𝐙𝐙𝑇𝑇𝛃𝛃

, (3)

where 𝐗𝐗 and 𝐙𝐙 may be the same and 𝛈𝛈 is estimated using the maximum likelihood method. Note that 𝐗𝐗 and 𝐙𝐙 can refer to the domains defined by 𝑗𝑗, as the model uses only two covariates – population size 𝑁𝑁𝑡𝑡𝑖𝑖𝑡𝑡 and police records to population size 𝑛𝑛𝑡𝑡𝑖𝑖𝑡𝑡/𝑁𝑁𝑡𝑡𝑖𝑖𝑡𝑡. This is an interesting alternative to, for instance, the model proposed by Stoner, Economou, and Drummond Marques da Silva (2019), which requires strong covariates for under-reporting and observed counts.

42. Under the model (3) estimator for the target parameter given by [xi-estimator] changes to

𝜉𝜉 = �𝑁𝑁𝑖𝑖𝐗𝐗 𝑇𝑇𝛂𝛂�

𝐶𝐶

𝑖𝑖=1

5. Estimating uncertainty

(d) Zhang (2008) proposal

43. In the original paper, Zhang did not calculate variance for the target parameter 𝜉𝜉 but proposed a confidence interval for 𝜉𝜉 by plugging in the confidence interval for 𝛼𝛼. Thus, the CI for 𝜉𝜉 is given by

��𝑁𝑁𝑖𝑖 𝛼𝛼𝑙𝑙

𝐶𝐶

𝑖𝑖=1

,�𝑁𝑁𝑖𝑖 𝛼𝛼𝑢𝑢

𝐶𝐶

𝑖𝑖=1

�,

where 𝛼𝛼𝑙𝑙 ,𝛼𝛼𝑢𝑢 are the lower bound and upper bound of the CI interval for 𝛼𝛼. If we use additional covariates to explain variability in 𝜉𝜉 we can plug in lower and upper bounds for all parameters as shown below

��𝑁𝑁𝑖𝑖 𝐗𝐗𝑇𝑇𝛂𝛂𝑙𝑙

𝐶𝐶

𝑖𝑖=1

,�𝑁𝑁𝑖𝑖 𝐗𝐗𝑇𝑇𝛂𝛂𝑢𝑢

𝐶𝐶

𝑖𝑖=1

�,

where 𝛂𝛂𝑙𝑙 and 𝛂𝛂𝑢𝑢 are vectors with lower and upper bounds. (e) Parametric bootstrap

44. We use an alternative approach, based on parametric bootstrapping, to estimate the mean square error, which exploits an idea similar to that proposed by González-Manteiga et al. (2008) for small area estimation. It consisting of the following steps:

a. given 𝑛𝑛𝑖𝑖,𝑚𝑚𝑖𝑖 and 𝑁𝑁𝑖𝑖, calculate 𝛈𝛈� using the maximum likelihood function,

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b. given 𝛈𝛈�, generate 𝛈𝛈�∗ from a multivariate normal distribution MVN �𝛈𝛈�, Cov� (𝛈𝛈�)�, where Cov� denotes the covariance of 𝛈𝛈�. For instance, for the NB2 model we use

� 𝛂𝛂∗ 𝛃𝛃∗ 𝜙𝜙∗ � ∼ MVN��

𝛂𝛂� 𝛃𝛃� 𝜙𝜙� � , �

𝑉𝑉(𝛂𝛂�) Cov�𝛂𝛂�,𝛃𝛃�� Cov�𝛂𝛂�,𝜙𝜙�� Cov�𝛃𝛃�,𝛂𝛂�� 𝑉𝑉�𝛃𝛃�� Cov�𝛃𝛃�,𝜙𝜙�� Cov�𝜙𝜙�,𝛂𝛂�� Cov�𝜙𝜙�,𝛃𝛃�� 𝑉𝑉�𝜙𝜙��

��,

c. calculate 𝜉𝜉∗ = ∑ 𝑁𝑁𝑖𝑖𝛂𝛂 ∗𝐶𝐶

𝑖𝑖=1 ,

d. generate 𝑚𝑚𝑖𝑖 ∗ from the assumed distribution using 𝜇𝜇∗ = 𝑁𝑁𝐗𝐗𝑇𝑇𝛂𝛂∗ �𝑛𝑛

𝑁𝑁 � 𝐙𝐙𝑇𝑇𝛃𝛃∗

, e. fit the model to �𝑚𝑚𝑖𝑖

∗,𝑛𝑛𝑡𝑡𝑖𝑖𝑡𝑡,𝑁𝑁𝑡𝑡𝑖𝑖𝑡𝑡� and estimate 𝛈𝛈∗, f. estimate 𝜉𝜉∗ = ∑ 𝑁𝑁𝑖𝑖𝛂𝛂�

∗𝐶𝐶 𝑖𝑖=1 ,

g. repeat steps 2–6 𝐵𝐵 times and calculate the bootstrap MSE estimator

mse = 1 𝐵𝐵 ��𝜉𝜉∗ − 𝜉𝜉∗�

2 𝐵𝐵

𝑏𝑏=1

and Relative MSE estimator

rmse = √mse 𝜉𝜉‾∗

45. Based on bootstrapped 𝜉𝜉∗ from point 4 we calculate the confidence interval for 𝜉𝜉 using the 95% percentile method and a method recently introduced by Liu, Gelman, and Zheng (2015) called the shortest probability interval (SPIN). The latter method is recommended for asymmetric distributions, bounded variables (e.g. positive); intervals constructed using SPIN have better coverage.

IV. Results

A. Data for the model

46. In our study we used Polish data from two halves of 2019 for the foreign population aged 18+. The PESEL register reflected the state at 31 March and 30 September. Then, we prepared data for the first and second half of the year using police and Border Guard data. L.-C. Zhang (2008) used a similar approach involving population data as at 01 Jan 2006, police data about foreigners charged with criminal offences in 2005 and the number of unauthorized foreigners between May 2005 and April 2006. In addition, we derived data broken down by sex and economic age group (18-59 and 60+ for women; 18-64 and 65+ for men). Table 4 presents information about the number of foreigners and countries of origin present in the PESEL, police and Border Guard registers. The PESEL register contained 151 and 147 countries in the first and second half of the year, respectively, police data – around 100, and Border Guard records – around 70. The two latter sources contain a considerably greater percentage of men, in contrast to the PESEL register, where women account for around 60% of all foreigners.

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Table 4. The number of foreigners and countries by data source, sex and period before applying the condition for the model

Classification Number of foreigners Number of countries Source Sex 1𝑠𝑠𝑡𝑡 period 2𝑠𝑠𝑡𝑡 period 1𝑠𝑠𝑡𝑡 period 2𝑠𝑠𝑡𝑡 period PESEL Total 232,468 234,194 151 147 Women 137,424 137,880 145 140 Men 95,044 96,314 127 130 Border Guard Total 3,187 3,435 77 68 Women 762 776 40 39 Men 2,425 2,659 72 67 Police (all) Total 20,138 23,330 100 98 Women 3,017 3,079 58 57 Men 17,121 20,251 94 94

47. The model requires that the following conditions hold: 𝑚𝑚𝑡𝑡𝑖𝑖𝑡𝑡 > 0, 𝑛𝑛𝑡𝑡𝑖𝑖𝑡𝑡 > 0 and 𝑛𝑛𝑡𝑡𝑖𝑖𝑡𝑡/𝑁𝑁𝑡𝑡𝑖𝑖𝑡𝑡 < 1, so we created a new dataset that meets these requirements. Countries that do not satisfy these conditions were grouped to create a pseudo-country denoted as other4. After applying this condition, we received a total of 73 countries (including category other), of which 50 were observed in both periods and 23 only in one (65 in the first and 58 in the second half of 2019). The full list of countries is given in Appendix.

B. Verification of assumptions

48. To verify the model assumptions we investigate the relationships resulting from equation [eq-lin] and compare the log of the PESEL population with the log of Border Guard (BG) counts to the PESEL population (top) and the log of police counts to the PESEL population by country of origin (bottom) and sex in both halves of 2019. Figure 1 presents these relationships with a linear model defined in [eq-lin], which was calculated for the whole dataset, while figure 2 includes separate fits for each sex. The shapes are defined by the interaction of sex and age (working age and post- working age).

49. Both plots show the expected relationship, i.e. a negative correlation with the population size (less than -0.6) and a positive correlation with the proportion of police-to-PESEL counts for both quarters (over 0.7). This means that the relationship between the size of the unauthorized and registered population decreases as the registered population grows. However, there is an outlier in our population – Ukraine. Citizens of this country are the biggest immigrant group in Poland in all datasets (over 70% in the PESEL population, around 60% of BG apprehensions and close to 70% of all police registrations). Ukraine is an outlier for both sexes but not for the relationship seen within the police data. If Ukraine is excluded, the correlation with the PESEL population changes to around -0.7 while the correlation with the log of police-to-PESEL counts stays the same. In addition, the pseudo-country, denoted by UNK, is an outlier but only for males.

4 In the plots they are marked as UNK, i.e. unknown

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Figure 1. The relationship between the log of the PESEL population and the log of the BG-to-PESEL counts (top) and between the log of police-to-PESEL counts and the log of BG-to-PESEL counts (bottom) at the end of first and third quarter of 2019. Shapes represent domains cross-classified by sex and age, symbol size represents the square root of the PESEL population and solid lines are regression lines. Pearson correlation coefficient is denoted by 𝜌𝜌 in the top left corner.

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Figure 2. The relationship between the log of the PESEL population and the log of the BG-to-PESEL counts (top) and between the log of police-to-PESEL counts and the log of BG-to-PESEL counts (bottom) at the end of first and third quarter of 2019 by sex. Shapes represent domains cross-classified by sex and age, symbol size represents the square root of the PESEL population and solid lines are regression lines. Pearson correlation coefficient is denoted by 𝜌𝜌 in the top left corner.

50. Figure 2 presents the same relationship but separately for each sex. As can be seen, there are differences in this respect, particularly in the comparison with the log of the PESEL population, as evidenced by the shift in the regression lines. This means that the unauthorized population mainly consists of males, in contrast to the registered (PESEL) population, which is dominated by females. A similar pattern can be observed regarding the relationship with the police data, where Pearson’s correlation coefficient for both sexes is around 0.5-0.6, while without accounting for sex – around 0.7.

51. The above claims are also confirmed by results from fitting the linearized model given by (4). For both periods 𝛼𝛼 − 1 parameter associated with log�𝑁𝑁𝑡𝑡𝑖𝑖𝑡𝑡� was equal to -0.4109 and -0.4190 indicating that the relationship with regular population is stable overtime and 𝛽𝛽 for log�𝑛𝑛𝑡𝑡𝑖𝑖𝑡𝑡/𝑁𝑁𝑡𝑡𝑖𝑖𝑡𝑡� was equal to 0.5694 and 0.5841.

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C. Estimation results

52. Table 5 contains the main model performance measures, while additional details, including diagnostics, are presented in Appendix 10. Results are broken down by quarter end, distribution and covariates used in the modelling phase. We also report AIC and BIC. As expected, truncated distributions yield better lower values of information criteria and higher values of 𝜉𝜉.

Table 5. Quality of models used in the study and the estimated population ξ̂

Distribution Covariates for 𝛼𝛼 LogLik AIC BIC 𝜉𝜉 At the end of 1𝑠𝑠𝑡𝑡 quarter 2019

PO No covariates -733.1 1,470.3 1,475.5 24,119.9 Ukraine -648.7 1,303.5 1,311.3 20,835.8 Sex -682.5 1,371.0 1,378.8 51,982.8 Ukraine & Sex -630.1 1,268.1 1,278.6 34,870.1

NB2 No covariate -285.7 577.4 585.2 9,664.0 Ukraine -283.1 574.1 584.5 11,817.1 Sex -285.6 579.1 589.6 10,447.2 Ukraine & Sex -283.1 576.1 589.1 11,568.0

Truncated PO No covariate -721.2 1,446.4 1,451.6 24,799.2 Ukraine -636.2 1,278.4 1,286.3 21,476.9 Sex -657.4 1,320.8 1,328.6 64,142.1 Ukraine & Sex -608.9 1,225.8 1,236.2 42,769.8 Truncated NB2 No covariate -267.1 540.2 548.0 11,390.6 Ukraine -264.9 537.8 548.2 14,453.0 Sex -266.4 540.8 551.2 14,239.6 Ukraine & Sex -264.7 539.5 552.5 15,959.0

At the end of 3𝑟𝑟𝑟𝑟 quarter of 2019 PO No covariate -822.2 1,648.3 1,653.4 23,582.6

Ukraine -735.7 1,477.5 1,485.1 21,139.0 Sex -742.2 1,490.3 1,497.9 65,011.0 Ukraine & Sex -689.8 1,387.6 1,397.8 49,080.1

NB2 No covariate -278.8 563.6 571.2 11,421.8 Ukraine -276.5 561.1 571.3 14,568.7 Sex -276.6 561.2 571.4 19,128.5 Ukraine & Sex -275.5 561.0 573.7 20,258.7

Truncated PO No covariate -812.9 1,629.9 1,635.0 24,043.0 Ukraine -725.4 1,456.7 1,464.3 21,615.4 Sex -718.0 1,442.0 1,449.6 80,318.6 Ukraine & Sex -666.5 1,341.1 1,351.2 61,718.0 Truncated NB2 No covariate -258.4 522.8 530.4 14,377.1 Ukraine -256.5 521.1 531.3 19,388.6 Sex -253.8 515.6 525.8 45,008.1 Ukraine & Sex -253.3 516.6 529.3 48,387.7

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53. For both periods, the Poisson and truncated Poisson distributions perform poorly and the estimated irregular population is very large. Results from Table 5 indicate that at the end of the first quarter the truncated NB2 with no covariates or with one covariate for 𝛼𝛼, i.e. Ukraine, is the best model in terms of information criteria (BIC). For the end of the third quarter, the best models also assume the NB2 distribution but the ranking of covariates is different i.e. the model that accounts for sex is the best (BIC=529.3), while the model with Ukraine as a covariate is slightly worse (BIC=531.3). The main difference between these models is the degree of uncertainty, since in the first model the confidence interval is narrower than in the second. There is no justification for such an increase between two periods, given that the regular population grew from 232,500 to 234,200 and a big change in the irregular population is unlikely. This result is mainly due to high values of 𝛼𝛼�0, which for the truncated NB2 with sex as a covariate equals 0.875, with sex and Ukraine – 0.838 and the model with Ukraine – 0.673. Based on that we decided to focus on truncated NB2 models without covariates and that with Ukraine as the only covariate in 𝛼𝛼�.

54. Estimated 𝛼𝛼�0,𝛼𝛼�1, �̂�𝛽 and 𝜙𝜙� are reported in Table 6. In addition we provide the Sum of Squares (in thousands) denoted by SSQ. Diagnostics for the final model are presented in Appendix 10. For both quarters models with no covariates are characterised by higher 𝛼𝛼�0 and �̂�𝛽 and the SSQ over 5 times as high as that for the models with one covariate (Ukraine, 𝛼𝛼�1). As expected, the parameter for Ukraine is positive but is characterised by a high standard error as we have only 4 observations for this country.

Table 6. Estimated parameters for models with no covariates (no cov.) and with Ukraine as a covariate under the truncated NB2 distribution. Standard errors are reported in parenthesis.

As at Model 𝛼𝛼�0 𝛼𝛼�1 �̂�𝛽 𝜙𝜙� SSq 31.03 No cov. 0.685 (0.032) – 0.710 (0.067) 1.267 (0.320) 823.3 31.03 Ukraine 0.649 (0.034) 0.095 (0.05) 0.665 (0.067) 1.367 (0.350) 149.6 31.03 No cov. 0.712 (0.038) – 0.814 (0.081) 0.914 (0.246) 818.2 31.03 Ukraine 0.673 (0.041) 0.104 (0.06) 0.761 (0.081) 0.975 (0.263) 179.2

55. The research on irregular migration in Poland is limited. As far as we know, the only results about the unauthorized population in Poland can be found in (Pew Research Center 2019a). The analysis was carried out for the period 2014-2017, and the population was estimated to be lower than 100,000, regardless of whether or not waiting asylum seekers were included Pew Research Center (2019a). In their report, Pew Research Center (2019a) does not provide any point estimates or quantify the uncertainty behind this number. Thus, currently there is no other estimate that our results can be compared with5.

56. To provide some context, we compare our estimates with relevant statistics on migration to Poland reported by the Office for Foreigners for 2019. Table 7 contains three indicators that can be connected with illegal stays – negative decisions issued to applications for temporary and permanent stay and decisions about the compulsory return of an individual to their country of origin. A foreigner who has received a negative decision is obliged to leave Poland within 30 days from the date when the decision was issued. If a foreigner does not leave Poland within this period and is apprehended, they are ordered to return. There are multiple reasons why such an order can be issued,

5 Note that Eurostat’s data presented in the second section are based on Border Guard data and are not included here

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such as illegal stay or work or being considered persona non grata6. The order to return is issued by the commanding officer of the Border Guard unit or the commanding officer of the locally competent Border Guard unit and most of such orders are given to foreigners who exit Poland and were identified as staying illegally (29,072 obligations in Table 7 and 25,810 in Table 1).

57. The number of refusals concerning applications for a temporary and permanent stay is close to 36000 and is significantly higher than our estimates. This is mainly because of a variety of reasons for issuing a negative decision (e.g. not meeting requirements for a temporary stay or detention. The full list is given in Appendix 11.1). Our point estimate is lower than the total number of refusals and orders to return, which suggests that the size of the unauthorized population is plausible.

Table 7. Comparison of estimated ξ classified by Ukraine, age group and sex with data from Polish registers

Period Total Ukraine Working age Non-working age Males Females 𝜉𝜉

31.03.2019 14,453 9,378 13,586 867 6,492 7,961 30.09.2019 19,389 13,619 18,212 1,177 8,641 10,747

Refused applications for a temporary stay 2019 32,835 19,685 – – 21,623 11,212

Refused applications for a permanent stay 2019 3,096 434 – – 1,674 1,180

Decisions of return to the country of origin 2019 29,072 21,694 – – 20,774 8,298

58. Table 7 contains information about the number of irregular residents from Ukraine and by age and sex. The total in comparison to the regular population in Poland in 2019 (37.97 million) is close to 0.04% on 31 December 2019, and 0.05% on 30 September 2019 is small and plausible. (Pew Research Center 2019a) reports that the irregular population for most countries is lower than 1%.

59. The demographic structure is also probable except for sex. Ukrainians account for over 65% the irregular population, which is similar to the percentage of refusals or return decisions for Ukrainians. Most of them are people of working age, since their motivation for migrating to Poland is mainly economic. The main problem is the sex structure. Our estimates show that the majority are females, while all other data (apprehensions, refusals, return decisions, etc.) indicate the opposite. The main reason for this result is the structure of the PESEL register, in which the majority (about 60%) are women. However, if we compare our estimate to the regular foreign population of males and females, we get 6.8% and 5.8% respectively, which indicates that males are more likely to be irregular migrants.

60. Finally, Table 8 contains interval estimates for the size of the irregular population using three different measures: L.-C. Zhang (2008) plugin interval, the bootstrap calculated using SPIN and the percentile method. The method used by L.-C. Zhang (2008) yields a very wide interval ranging from 4,000 to 67,000 at 31 March and from 4,000 to 134,000 at 30 September. The SPIN and quantile method provides similar intervals for the first period suggesting that in the first period the irregular

6 The full list is provided in Appendix 11.1

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population ranged from 7,000 to 30,000. For the second period SPIN yields a shorter interval between 6,000 and 57,000. The estimated √𝑚𝑚𝑚𝑚𝑚𝑚� equals 8,259 and 15,904 respectively. The SPIN method is preferred as the bootstrapped 𝜉𝜉∗ are right-skewed, as shown in Figure 10.2.

Table 8. Estimated size of the irregular population in Poland in 2019 with 95% interval estimates based on three methods, MSE and RRMSE based on parametric bootstrap

Period Method Estimate Lower Upper 31.03.2019 Plug-in 14,453 4,696 67,404 SPIN 14,453 6,802 29,381 Percentile 14,453 7,616 30,651 √𝑚𝑚𝑚𝑚𝑚𝑚� 8,259 – –

𝑟𝑟𝑟𝑟𝑚𝑚𝑚𝑚𝑚𝑚� 45% – – 30.09.2019 Plug-in 19,389 4,836 133,792 SPIN 19,389 6,011 47,681 Percentile 19,389 9,275 56,555 √𝑚𝑚𝑚𝑚𝑚𝑚� 15,904 – –

𝑟𝑟𝑟𝑟𝑚𝑚𝑚𝑚𝑚𝑚� 64% – –

V. Discussion

61. In the paper we propose a different approach to estimate the hard-to-reach population of irregular foreigners based on a flexible non-linear count regression model. The approach is an alternative to classic capture-recapture methods based on one or multiple sources and the interpretation of results is more intuitive as the irregular population is conditionally dependent on the regular population. Extending the model for additional covariates and zero-truncated distributions makes it more general. That said, the proposed model has certain limitations.

62. The approach is based solely on the administrative data and, as a result, the quality of our estimates depends on the availability of high-quality register-based statistics. Beresewicz, Gudaszewski, and Szymkowiak (2019) provided estimates of the size of the de facto population of foreigners for 2015 and 2016. The paper includes information about the co-occurrence of regular foreigners in the PESEL and two external registers maintained by the National Insurance Institution (ZUS) and the Office for Foreigners. For instance, the PESEL register data for 2016 were linked with two other sources and around 7,000 (out of 47,000) foreigners were observed exclusively in the PESEL register. This figure, however, cannot be used as a measure of overcoverage because only three sources were used. In a more recent study, Statistics Poland (2020) published a detailed analysis of foreigners based on 9 registers linked by the PESEL id. Only about 1,500 out of 2.1 million foreigners were found to be listed exclusively in the PESEL register while over 980,000 were listed only in one of the other registers. This indicates that the PESEL register is not considerably affected by overcoverage.

63. Because not all countries have a population register (e.g. United States or Ireland), it is possible to use population surveys, such as the Current Population Survey conducted by the US Census Bureau, or a system of integrated registers with signs-of-life methodology as in L. Zhang and Dunne (2018).

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64. Selection of data for the model should be strictly connected with the definition of the irregular population used in the study. Currently, there is no information about how long apprehended foreigners have been staying in Poland, which means that this group can include a mix of persons who have been residing for a period longer than 3 months, have exceeded their temporary residence permit or have been staying without any permit. Therefore, there is a need for a close collaboration with the Border Guard.

65. However, the model assumes a relationship between the regular and irregular population and therefore the approach can be applied to different populations, such as illegal workers or the homeless population, given the existence of register-based proxy populations and auxiliary variables.

VI. Acknowledgements

This work is partially based on Katarzyna Pawlukiewicz Master’s thesis entitled Estimation of the number of irregular migrants in Poland using hierarchical Gamma-Poisson model defended on 19 September 2019 at Poznań University of Economics and Business in Poland.

We thank Ministry of Digital Affairs, Polish Border Guards and Polish Police for compiling the summaries according to our requirements.

For appendix please refer to Beręsewicz, M., & Pawlukiewicz, K. (2020). Estimation of the number of irregular foreigners in Poland using non-linear count regression models. arXiv preprint arXiv:2008.09407.

VII. References

Bailey, TC, Marilia Sa Carvalho, TM Lapa, WV Souza, and MJ Brewer. 2005. “Modeling of Under- Detection of Cases in Disease Surveillance.” Annals of Epidemiology 15 (5): 335–43.

Beresewicz, M, G Gudaszewski, and M Szymkowiak. 2019. “Estymacja Liczby Cudzoziemców w Polsce z Wykorzystaniem Metody Capture-Recapture.” Wiadomości Statystyczne 64 (10).

Böhning, Dankmar, John Bunge, and P. G. M. van der Heijden, eds. 2017. Capture-Recapture Methods for the Social and Medical Sciences. Boca Raton, Florida: CRC Press.

Böhning, Dankmar, and Peter G. M. van der Heijden. 2009. “A covariate adjustment for zero-truncated approaches to estimating the size of hidden and elusive populations.” Annals of Applied Statistics 3 (2): 595– 610. https://doi.org/10.1214/08-AOAS214.

Böhning, Dankmar, Peter GM van der Heijden, et al. 2019. “The Identity of the Zero-Truncated, One- Inflated Likelihood and the Zero-One-Truncated Likelihood for General Count Densities with an Application to Drink-Driving in Britain.” The Annals of Applied Statistics 13 (2): 1198–1211.

Cameron, A Colin, and Pravin K Trivedi. 1986. “Econometric Models Based on Count Data. Comparisons and Applications of Some Estimators and Tests.” Journal of Applied Econometrics 1 (1): 29–53.

———. 2013. Regression Analysis of Count Data. Vol. 53. Cambridge university press.

Coumans, AM, MJLF Cruyff, Peter GM Van der Heijden, JRLM Wolf, and HJSIR Schmeets. 2017. “Estimating Homelessness in the Netherlands Using a Capture-Recapture Approach.” Social Indicators Research 130 (1): 189–212.

https://doi.org/10.1214/08-AOAS214

Working paper 3

Eurostat. 2019. “Enforcement of Immigration Legislation Statistics.” https://ec.europa.eu/eurostat/statistics- explained/index.php/Enforcement_of_immigration_legislation_statistics.

Gerritse, Susanna Charlotte. 2016. “An Application of Population Size Estimation to Official Statistics: Sensitivity of Model Assumptions and the Effect of Implied Coverage.” PhD thesis, Utrecht University.

Godwin, Ryan T, and Dankmar Böhning. 2017. “Estimation of the Population Size by Using the One- Inflated Positive Poisson Model.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 66 (2): 425–48.

González-Manteiga, Wenceslao, Maria J Lombardı́a, Isabel Molina, Domingo Morales, and Laureano Santamarıá. 2008. “Bootstrap Mean Squared Error of a Small-Area EBLUP.” Journal of Statistical Computation and Simulation 78 (5): 443–62.

Greenwood, Major, and G Udny Yule. 1920. “An Inquiry into the Nature of Frequency Distributions Representative of Multiple Happenings with Particular Reference to the Occurrence of Multiple Attacks of Disease or of Repeated Accidents.” Journal of the Royal Statistical Society 83 (2): 255–79.

Hanson, Gordon H. 2006. “Illegal Migration from Mexico to the United States.” Journal of Economic Literature 44 (4): 869–924.

Henningsen, Arne, and Ott Toomet. 2011. “maxLik: A Package for Maximum Likelihood Estimation in R.” Computational Statistics 26 (3): 443–58. https://doi.org/10.1007/s00180-010-0217-1.

Liu, Ying, Andrew Gelman, and Tian Zheng. 2015. “Simulation-Efficient Shortest Probability Intervals.” Statistics and Computing 25 (4): 809–19.

Oliveira, Guilherme Lopes de, Rosangela Helena Loschi, and Renato Martins Assunção. 2017. “A Random- Censoring Poisson Model for Underreported Data.” Statistics in Medicine 36 (30): 4873–92.

Passel, Jeffrey. 2007. “Unauthorized Migrants in the United States: Estimates, Methods, and Characteristics.”

Pew Research Center. 2019a. “Europe’s Unauthorized Immigrant Population Peaks in 2016, Then Levels Off.” https://www.pewresearch.org/global/2019/11/13/europes-unauthorized-immigrant-population- peaks-in-2016-then-levels-off/.

———. 2019b. “Europe’s Unauthorized Immigrant Population Peaks in 2016, Then Levels Off – Methodology.” https://www.pewresearch.org/global/2019/11/13/eu-unauthorized-immigrants- methodology/.

Polish Border Guard. 2020. “Consequences of Illegal Stay.” https://www.strazgraniczna.pl/pl/cudzoziemcy/konsekwencje-nielegalne/8445,consequences-of- illegal-stay.htmll.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Statistics Poland. 2020. “The Foreign Population in Poland During the COVID-19 Pandemic.” https://stat.gov.pl/en/experimental-statistics/human-capital/the-foreign-population-in-poland- during-the-covid-19-pandemic,10,1.html.

https://ec.europa.eu/eurostat/statistics-explained/index.php/Enforcement_of_immigration_legislation_statistics

https://doi.org/10.1007/s00180-010-0217-1

https://www.pewresearch.org/global/2019/11/13/europes-unauthorized-immigrant-population-peaks-in-2016-then-levels-off/

https://www.pewresearch.org/global/2019/11/13/eu-unauthorized-immigrants-methodology/

https://www.strazgraniczna.pl/pl/cudzoziemcy/konsekwencje-nielegalne/8445,consequences-of-illegal-stay.htmll

https://www.r-project.org/

https://stat.gov.pl/en/experimental-statistics/human-capital/the-foreign-population-in-poland-during-the-covid-19-pandemic,10,1.html

Working paper 3

Stoner, Oliver, Theo Economou, and Gabriela Drummond Marques da Silva. 2019. “A Hierarchical Framework for Correcting Under-Reporting in Count Data.” Journal of the American Statistical Association 114 (528): 1481–92.

Van Der Heijden, Peter Gm, Rami Bustami, Maarten JLF Cruyff, Godfried Engbersen, and Hans C Van Houwelingen. 2003. “Point and Interval Estimation of the Population Size Using the Truncated Poisson Regression Model.” Statistical Modelling 3 (4): 305–22.

Van der Heijden, Peter GM, Joe Whittaker, Maarten Cruyff, Bart Bakker, Rik Van der Vliet, et al. 2012. “People Born in the Middle East but Residing in the Netherlands: Invariant Population Size Estimates and the Role of Active and Passive Covariates.” The Annals of Applied Statistics 6 (3): 831–52.

Zhang, LC, and J Dunne. 2018. “Trimmed Dual System Estimation. W: D. Böhning, PGM van Der Heijden, j. Bunge (Red.).” Capture-Recapture Methods for the Social and Medical Sciences, 237–57.

Zhang, Li-Chun. 2008. “Developing Methods for Determining the Number of Unauthorized Foreigners in Norway.” Statistics Norway (SSB), Division for Statistical Methods and Standards. Www. Ssb. No.(accessed July 28, 2008).

I. Introduction
II. The population of irregular foreigners in Poland

1. Basic definitions
2. Data Sources

(a) Polish Border Guard data
(b) Police data
(c) The registered (legal) population

III. Theoretical properties of the L.-C. Zhang (2008) model

1. Model assumptions
2. Zhang (2008) model
3. The target parameters
4. Extensions
5. Estimating uncertainty

(d) Zhang (2008) proposal
(e) Parametric bootstrap

IV. Results

A. Data for the model
B. Verification of assumptions
C. Estimation results

V. Discussion
VI. Acknowledgements
VII. References

DC2024_S1_Poland_Peszat_A.pdf

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UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS Expert Meeting on Statistical Data Collection and Sources 22-24 May 2024, Geneva, Switzerland

19 April 2024

Tapping into web data for European statistics – challenges and experiences of the ESSnet Web Intelligence Network

Klaudia Peszat and Dominika Nowak (Statistics Poland, Poland) [email protected] Abstract The experiences of many National Statistical Offices have provided evidence of the relevant role of web data in producing new and augmenting existing statistics. However, the integration of web data with official statistics is a demanding process and the quality of the output very much depends on the quality of the source. Thus, transforming the information available on the web into statistical data requires significant methodological work. Within the ESSnet Web Intelligence Network project the partnership of 17 organizations explore the potential of web-scraped data for the production of European statistics in several domains, such as online job advertisements, online-based enterprise characteristics, real estate market data, construction activities, online prices for household appliances, tourism, and business registers enhancement. The current experience demonstrates the challenges related to data acquisition and processing steps, which cover landscaping of web data sources relevant for the topic of interest, the stability of web sources, technical aspects of web scraping, dealing with deduplication, annotation of data sets, ensuring the quality of classification models, etc. Additional issue is the automation of data collection processes in the Web Intelligence Hub – the platform for central web scraping, developed by Eurostat. This paper discusses the selected issues related to the use of web data sources in official statistics in the context of developing a universal tool enabling the acquisition, processing and analysis of web data at the European level, i.e. WIH.

Tapping into web data for European statistics – challenges and experiences of the ESSnet Web Intelligence Network

Driving forces of informal employment: An empirical study based on Polish enterprise data, Gdansk University of Technology

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Motivation and background Aim of this study Data Results Conclusions

Driving forces of informal employment: An empirical study based on Polish enterprise data

Dagmara Nikulin

Gdansk University of Technology Faculty of Management and Economics

Meeting of the Group of Experts on Quality of Employment Geneva, 14-16 May, 2024

The research has been conducted within the project financed by the National Science Centre, Poland (Narodowe Centrum Nauki – NCN) – decision number 2018/02/x/hs4/00441.

Motivation and background Aim of this study Data Results Conclusions

Motivation

the informal sector together with the broadly understood shadow economy is of interest to both the scientific community and government institutions

research areas related to the shadow economy are considered extremely important and require further exploration

a dichotomy between the formal and informal sectors, which contributes to the marginalization of hybrid phenomena occurring on the borderline of the informal zone

informal employment existing in registered enterprises

Motivation and background Aim of this study Data Results Conclusions

Driving forces of informal employment: literature review

severity of taxes: in countries with higher level of taxes the prevalence of shadow economy and informal employment should be larger. Empirical evidence inconclusive in this matter (Nur-Tegin, 2008; Joulfaian, 2009; Bernasconi, Corazzini and Seri, 2014)

social and moral determinants; non-economic social factors are becoming more and more relevant in explaining the inclination to be engaged in shadow economy (Pickhardt and Prinz, 2014); plenty of research investigates the relationship between tax morale and tendency to evade taxes (Alm, Martinez-Vazque & Torgler, 2006; Alm & Torgler, 2006; Torgler, 2005; Torgler Schneider, 2009)

institutional factors like the quality of institutions (Torgler & Schneider, 2007; Hanousek & Palda 2004, Barone & Mocetti, 2011)

Motivation and background Aim of this study Data Results Conclusions

Aim of this study

Our aim is to investigate the main drivers of informal employment in Poland.

Motivation and background Aim of this study Data Results Conclusions

Survey design

survey conducted among polish small and medium (SMEs) enterprises

representative sample of 952 Polish entrepreneurs

survey carried out between November and December 2018

CATI method (computer-assisted telephone interview)

respondents: owners or highest level managers of Polish private enterprises

quota sampling regarding the specific number of companies according to the size (less than 9 employees, 10-49, and 49-250 employees)

within each group stratified random sampling scheme with two stratas: NUTS 2 units and four main sectors (manufacturing, construction, retail and services)

tools for surveys on sensitive topics applied

Motivation and background Aim of this study Data Results Conclusions

Outcome variable

In particular, the question on the informal employment activities has been formulated as follows: “Due to high non-wage labour costs, some entrepreneurs use various mechanisms to minimize these burdens. Bearing in mind the companies operating in your industry, please asses what proportion of employees are employed informally?”

Our dependent variable is recoded into binary one, where 0 means that respondent indicates no extent of informal employees in firms in their industry and 1 if there is any extent of informal employees

Motivation and background Aim of this study Data Results Conclusions

Descriptive statistics (1)

Motivation and background Aim of this study Data Results Conclusions

Descriptive statistics (2)

Motivation and background Aim of this study Data Results Conclusions

Model

Motivation and background Aim of this study Data Results Conclusions

Results

Motivation and background Aim of this study Data Results Conclusions

Robustness check

Motivation and background Aim of this study Data Results Conclusions

Conclusions

the main aim of this paper is to find possible determinants of using informal workers

tax morality and obstacles related to setting up a business as significant factors influencing the probability of using informal workers

we do not find any clear relationship between the tax severity and the inclination to using informal workers among polish enterprises

Motivation and background Aim of this study Data Results Conclusions

Thank you for your attention.

Contact: [email protected]

Motivation and background
Aim of this study
Data
Results
Conclusions

ICE-CMM Poland - Report, by Mr. Piotr Kasza, ICE-CMM Poland

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Annual Activity Report

19th Session of the Group of Experts on Coal Mine Methane and Just Transition

Geneva, March 18-19, 2024

Piotr Kasza on behalf of ICE-CMM Poland

Agenda

✓ Activities and achievements of ICE CMM Poland in 2023

✓ Work plan for 2024

32nd School of Underground Mining Methane from coal mines in Poland - current state and consequences of introducing the proposed EU regulation in this regard

Krakow, Poland, April 28, 2023

• Session organized and sponsored by ICE-CMM Poland

• 16 presentations including 7 ICE-CMM members

• participation of numerous experts and representatives of NGOs

• Discussion panel on methane emissions from coal mines

The Center’s involvement in activities related to energy transformation

18th Session of the Group of Experts on CMM & JT Geneva, Switzerland, March 20-21, 2023

• Annual Activity Report on ICE-CMM Poland

• JSW and GIG’s report on the use of methane from coal mines in Poland

• Projects implemented by JSW SA in cooperation with ICE-CMM Poland members

The Center’s involvement in activities related to energy transformation

✓ World Mining Congress (WMC 2023), Brisbane, Autralia, June 26-29, 2023: presentation of the REM, MASTERMINE projects, presentation on simulation of air flow and methane emissions in goafs

✓ Krynica Forum 2023, September 13-15, 2023: debate on Polish roads towards climate neutrality

The Center’s involvement in activities related to energy transformation

✓ Distributed Energy Congress, Krakow, September 25-26, 2023: session prepared by ICE-CMM

• 6 papers presented.

The Center’s involvement in activities related to energy transformation

✓ Organization of the session as part of International Science and Technology Conference Natural Mining Hazards, Wisła, November 14-16, 2023

• Session organized and sponsored by ICE-CMM Poland

• 7 presentations and discussion

✓ Preparation and promotion of the Annual report on methane emissions and its use in the Polish hard coal mining industry for 2022 (published on the website: www.cmm-energy.eu )

http://www.cmm-energy.eu/

✓ Meeting with members of the Hard Coal Section Agreement of Trade Unions KADRA, March 2023: presentation on methane emissions from Polish coal mines.

✓ Workshop "Mining in the 21st century and what else" in Ostrava, May 2023: paper on the EP "methane" regulation and its impact on mining in Poland.

✓ Participation of members of the ICE-CMM Presidium in meetings on the reduction of methane emissions at the European Parliament, the Ministry of State Assets, and the Chancellery of the President of the Republic of Poland

The Center’s involvement in activities related to energy transformation

✓ Translation and publication of the Polish version of the AMM best practice guidance

• report on necessary changes in the methodology for estimating methane resources in closed mines prepared by PGI-NRI

• results of this report as an appendix to the translation of the AMM Best Practice Guidance

The Center’s involvement in activities related to energy transformation - postponed task -

Work plan of ICE-CMM Poland for 2024

33rd School of Underground Mining Methane from hard coal mines in Poland - effective reduction of emissions as part of climate protection

Krakow, Poland, February26, 2024

19th Session of the Group of Experts on CMM & JT Geneva, Switzerland, March 18-19, 2024

• Annual Activity Report on ICE-CMM Poland

Organization and sponsorship of the session as part of 31st International Science and Technology Conference Natural Mining Hazards

Work plan of ICE-CMM Poland for 2024

An overview report on the evaluation of the resource potential and possibilities of extracting methane from coal beds in abandoned hard coal deposits in Poland

Participation in events promoting the reduction of methane emission

12th School of Mining Aerology Kocierz, Poland, 12-14 June, 2024

Scientific seminar in Vietnam Challenges and strategies for reducing methane emissions in coal mining. International experiences and perspectives for Vietnam

Thank you for your attention

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Building trust culture in the office – examples of ethics-driven proactive internal communication at Statistics Poland. Anna Borowska and Olga Świerkot-Strużewska (Statistics Poland)

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Building trust culture in the office – examples of ethics-driven proactive internal communication

at Statistics Poland

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS

The Workshop on Ethics in Modern Statistical Organisations

Anna Borowska Olga Świerkot-Strużewska

26 - 28 March 2024, Geneva, Switzerland

Purpose of the presentation

• To apprehend ethics from the international angle, to embed it in the international statistical context

• To present fundamental rules and values for ethics, both in the layer of data ethics but also in the layer of culture organisation, especially in the area of internal communication

• To present examples of staff surveys in various areas, not from the point of view of their results but from the point of view their response rate, awarness and engagememt of our employees on their role in this process

Ethics. Contextualised. • Definitions

Source: Britannica

users

Is ethics a new concept of the international debate? Certainly not.

• UN Fundamental Principles of Official Statistics • New Advisory Board to be created

• The European Statistics Code of Practice (quality lens) • OECD Recommendation on Good Statistical Practice • ISI Declaration on Professional Ethics • IAOS Krakow Group

https://unstats.un.org/unsd/dnss/gp/fp-english.pdf

https://ec.europa.eu/eurostat/web/quality/european-quality-standards/european-statistics-code-of-practice#:%7E:text=The%20European%20Statistics%20Code%20of%20Practice%20%28CoP%29%20is,standards%20for%20developing%2C%20producing%20and%20disseminating%20European%20statistics.

https://www.oecd.org/statistics/good-practice-toolkit/

https://www.isi-web.org/isi-declaration-professional-ethics-0

https://iaos-isi.org/wp-content/uploads/2022/11/Rationale-for-Krakow-Group_IAOS2022-Special-Event.pdf

https://unstats.un.org/UNSDWebsite/statcom/session_55/side-events/UN55SC-MondayHLF-26Feb2024.pdf

https://w3.unece.org/fpos30/

• Proliferating Data Ecosystems • Skyrocketing number of Actors • Insatiable demand for insight • New roles, new skills & competencies, resources

Times of redefinition – statistics in a changing environment

Growing importance of ethics in the international context

• Trust debate „Our value/worth conversation is more about 'why' do we do what we do. What does it add to our society? Whereas the trust conversation is about 'how' we do what we do” Giles Sullivan

• Value debate

• User-centric approach

https://unece.org/VOS

Ethics – derivatives and dimensions

• „Data ethics - a key enabler of the social acceptability” • Social acceptability - a strategic communication topic • ESS Strategic Communication Expert Group

• „Social acceptability: roadmap for communicating the ethical approach taken to data collection, processing, storage, dissemination and retention”

•Why do we need to keep providing evidence of our trustworthiness?

https://unece.org/statistics/events/CES2023

Ethics underlying official statistics/ e.g. 1 New data sources

• FPOS and Big Data - Mapping the United Nations Fundamental Principles of Official Statistics against new and big data sources

• Europe’s Ethical Guidelines [7] related to the use of Big Data in European statistics.

• A reference point in considering the 6 challenges of an ethical use of new and big data sources

• The guidelines draw the attention of NSOs to possible issues of professional ethics that can appear with the use of big data in the production of official statistics and examine at three main stages of the statistical production process – acquisition, processing and dissemination – questions of an ethical nature concerning the cornerstone values of official statistics

https://content.iospress.com/articles/statistical-journal-of-the-iaos/sji210789

"Data for statistical purposes may be drawn from all types of sources, be they statistical surveys or administrative records. Statistical agencies are to choose the source with regard to quality, timeliness, costs and the burden on respondents." • Social media data

• an area to explore for the official statistics. • In several countries research is conducted to use social media in order to measure

the level of well-being of societies • Social media are a vulnerable source of data when it comes to manipulation. While disseminating

statistics based on social media, it is important to accompany them with proper metadata and to describe them in an understandable way.

• EXAMPLE of juxtaposing official statistics to fake news • existence of fake accounts which manipulate the truth, so called “bots”. They may affect the factual

image, hindering the quality of social media data to be used for statistical purposes. • On this background, the ethical reference provided by the UNFPOS seems to be one of the most

effective remedies, as they oppose official statistics to raw social media data, thereby valuing more highly the production of reliable, comparable and high-quality data – those which meet international standards .

UN Principle 5 – through an ethical lens

Ethics underlying official statistics/ e.g. 2 Data stewardship

Data stewardship – from the UNECE TF Report „Data Stewardship and the Role of National Statistical Offices in the New Data Ecosystem” is ensuring the ethical and responsible creation, collection, management, use, and reuse of data. It is expressed through long- term, inter-generational curation of data assets so that they benefit the full community of data users and are used for public good.

https://unece.org/sites/default/files/2024-01/Data_stewardship_Publ_PrePrint_2.pdf

• „An emerging definition of data stewardship outlines it as a collection of practices that ensure data and statistics from across national systems are accessible, useable, safe, and trusted. The priorities of a data steward are context-dependent and cannot be universally defined. However, data stewards are responsible for data across the value chain, from production, analysis, and use.

• These functions aim to: • increase collaboration and interoperability across national data systems; • promote data sharing and build trust; • build strong data management and capacity development; • safeguard data quality and inclusivity; • improve data privacy, security, and ownership”.

• Source: Fitting into the new data-driven reality: results from the global consultation on data stewardship and the role of National Statistical Offices

13.03.2024 11

Ethics underlying official statistics/ e.g. 2 Data stewardship/ global perspective

https://unstats.un.org/UNSDWebsite/statcom/session_55/documents/BG-5j-Fitting_into_the_new_data-driven_reality-E.pdf

• “[…] The timelessness and relevance of the FPOS make them fit to the new data-driven reality. However, the proliferation of new data sources, new stakeholders and new concepts, such as data stewardship is a good opportunity to reinterpret the Principles, to set directions of their new possible explanations, which seem to be unavoidable in spite of their pertinence and universal character”

Back to the basics: The concept of data stewardship and its linkages with the Fundamental Principles of Official Statistics (FPOS), COL/PL

13.03.2024 12

https://unece.org/sites/default/files/2022-06/ECE_CES_2022_29-2208645E_0.pdf

Being internationally vocal about ethics

• Official Statistics as a cornerstone of democracy · Statistical data essential for:

· evidence-based decision-making, · political accountability

· Attributes of official statistics: · transparent, · scientifically robust and · politically independent

• UN Fundamental Principles of Official Statistics

• Robust legislation, common standards, international comparability

• Statistics  right to the truth.

Ethical

Ethics through internal lens Official statistics in numbers

5 309 employment in official

statistics as of 31.12.2023

737 employment in Statistics Poland as of

31.12.2023

4,12 % of disabled persons in Statistics Poland as

of 31.12.2023*

71 % of employed women in Statistics Poland

as of 31.12.2023

* according to the State Fund for Rehabilitation of Disabled People methodology

Our publications in 2023

291 publication titles

490 news releases

41 titles of

communications and announcements

135 357

376

831

1111 Female Male

No Yes

Higher Secondary

Gender

Membership in management staff

Education

Age Seniority

Under 26 years old

36-45 years old 26-35 years old Over 55 years old

46-55 years old Under a year

4-10 years 1-3 years Over 21 years

11-20 years

35 341

936

905

593

A survey among all Statistics Poland’s employees to determine: • organizational culture • work style of the employees • eagerness to change in behaviors • motivation • communication style. The survey collected 2 810 responses (~50% of all employees).

Staff survey conducted within Stat!Up project

Staff survey on hybrid work conducted by trade unions acting at Statistics Poland – central office

• The purpose of the survey was to check staff opinion about hybrid work

• The survey was conducted: 20th February -17th March 2023

• The survey collected 380 answers (~50% of all employees), including:

• 265 by electronic way • 115 by traditional way (paper).

• No statistical data about participants

Staff survey on social activity for the employees of Statistics Poland – central office

• The purpose of the survey was to gain staff opinions about social activities offered to them by Statistics Poland

• The survey was conducted: 19th October – 9th December 2022

• The survey collected 256 responses,

(32% of all employees), including:

- 206 responses by electronic way

- 50 responses by traditional way

4 6

2 2

1 2 1

5 5 6

below 25

25 – 35

35 – 50

50 – 60

below 25

25 – 35

35 – 50

50 – 60

Over 60

35 – 50

50 – 60

Over 60

below 1 year 1 – 5 5 – 15 Over 15

Female

Male

Staff survey on onboarding process at Statistics Poland – central office

The purpose of the survey was to monitor and evalute onboarding proces

The survey was sent to new employees and was conducted 3 months after their employment in our Office

The survey was sent to 50 persons and collected 25 responses (50% of all new employees)

2525

Participation in survey

answers lack of answers

Gender

femaile maile

Staff survey on exit interview at Statistics Poland – central office

• The purpose of the survey was to find out reasons of leaving our Office by employees

• The survey was focused on employees leaving us (due to different reasons)

• The survey was sent to 44 persons and collected 21 responses (48% of all new employees)

21 23

Participation in survey

answers lack of answers

Gender

femaile maile

The guidelines for compliance with the rules of the civil service and on the principles of the civil service code of ethics

Staff survey on “Diagnosis of the level of integrity culture in the civil service” as an example of fantastic failure within internal communication

• The purpose of the survey was to know the level of integrity culture in the civil service

• The survey was sent to all employees (674) and was conducted from 16th to 30th May 2023

• The survey collected only 61responses (9,6% of all employees)

Gender

femaile maile

613

Participation in survey

answers lack of answers

Level of position

managers employees

Staff survey on “Diagnosis of the level of integrity culture in the civil service” as an example of fantastic failure within internal communication

As a consequence of this fantastic failure, a letter was sent to managers and employees indicating that:

• only 61 persons, which means that only 9,6% of all employees from civil service corps took part in the survey;

• this result is unsatisfactory, having also in mind that the role of Statistics Poland is to conduct statistical surveys, thus our staff should be aware of the importance of response rates;

• we need deeper and deeper engagement of all staff in building our organisational culture, including also taking issues in our own hands to have influence on what is going on around us.

Summary and conclusions

• Ethics is a paramount concept, visible in many aspects of international statistical debate;

• Internally we do not have standardised policy of conducting staff surveys yet; • It is not easy to compare given examples and draw systemic conclusions but

to some extent it is possible; • It is clear that internal communication including part of getting staff opinions

is crucial and may bring benefits for all of us in:  building common trust, changing our organisational culture, knowing each other better, fitting better solutions in i.e. CSR, social activities, finding expectations of both sides: employees and employers

• Therefore: all the derivatives and examples of the practical handling of ethics are very much welcome to be adjusted/implemented at the internal level.

Thank You for listening! 

Anna Borowska Civil Service Director [email protected]

Olga Świerkot-Strużewska International Relations and Statistical Cooperation Director [email protected]

mailto:[email protected]

Building trust culture in the office – examples of ethics-driven proactive internal communication �at Statistics Poland
Purpose of the presentation
Ethics. Contextualised.
Is ethics a new concept of the international debate? Certainly not.
Times of redefinition – statistics in a changing environment
Growing importance of ethics in the international context
Ethics – derivatives and dimensions
Ethics underlying official statistics/ e.g. 1�New data sources
UN Principle 5 – through an ethical lens�
Ethics underlying official statistics/ e.g. 2�Data stewardship
Ethics underlying official statistics/ e.g. 2�Data stewardship/ global perspective
Slide Number 12
Being internationally vocal about ethics
Ethics through internal lens�Official statistics in numbers
Our publications in 2023
Slide Number 16
Staff survey on hybrid work conducted by trade unions acting at Statistics Poland – central office
Staff survey on social activity for the employees of Statistics Poland – central office
Staff survey on onboarding process �at Statistics Poland – central office
Staff survey on exit interview�at Statistics Poland – central office
The guidelines for compliance with the rules �of the civil service and on the principles �of the civil service code of ethics
Staff survey on “Diagnosis of the level of integrity culture in the civil service” as an example �of fantastic failure within internal communication
Staff survey on “Diagnosis of the level of integrity culture in the civil service” as an example of fantastic failure within internal communication
Summary and conclusions
Thank You for listening! 

Just Transition in Coal Regions of Poland, by Mr. Jan Bondaruk, Deputy Director for Environmental Engineering, Central Mining Institute in Poland - GIG

Read more about Just Transition in Coal Regions of Poland, by Mr. Jan Bondaruk, Deputy Director for Environmental Engineering, Central Mining Institute in Poland - GIG

Languages and translations

English

JUST TRANSITION PROCESS IN POLAND – STATUS

AND FUTURE CHALLENGES

Jan Bondaruk

BASIC AREAS OF GIG ACTIVITY

OCCUPATIONAL SAFETY IN THE INDUSTRY

MATERIAL ENGINEERING

CERTIFICATION AND ATTESTATION

MINING AND GEOENGINEERING

ENVIRONMENTAL ENGINNERING

TRAINING AND EDUCATION

CLEAN COAL TECHNOLOGIES

Hard coal

Lignite

TRANSITION PATHWAYS

2000/2002

10% share of mining in GDP

2016

9,7% share of mining in GDP

2021 3,3% share of mining in GDP

2049?

Mines Restructuring Company: 8 non- perspective mines or parts of mines

2000 40 operating coal- mines in Silesia region Carbon neutral

economy in Europe in 2050

2021 20 operating coal- mines in Silesia region 2016

23 operating coal- mines in Silesia region

Transformation of the sector induced by economic factors

Transition of the economy carried out taking into account climate goals

Silesia region - heart of Polish hard coal

mine sector

~78 % of hard coal balance deposits occur in Upper Silesian Coal

Basin

NECP PL AND ENERGY POLICY OF POLAND UNTIL 2040 POLAND’S NATIONAL ENERGY AND CLIMATE PLAN FOR YEARS 2021-2030 (NECP PL) along with attachments has been developed in fulfilment of the obligation set out in Regulation (EU) 2018/1999 of the European Parliament and of the Council of 11 December 2018 on the Governance of the Energy Union and Climate Action.

Integrated approach to the implementation of the five dimensions.

The energy transition will be based on three pillars

ENERGY POLICY OF POLAND UNTIL 2040 (PEP2040) sets the framework for the energy transition in Poland. It contains strategic decision regarding the selection of technologies used to establish a low-emission energy system. PEP2040 contributes to the implementation of the Paris Agreement concluded in December 2015 at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (COP21), taking into account the need to achieve the transition in a just and solidary manner.

TRANSITION SCHEDULE

https://energy.ec.europa.eu/topics/oil-gas-and-coal/eu-coal-regions/coal-regions- transition_en

2023 - the deepest hard coal mine operates at the level of 1290 m

1990 – 70 operating hard coal mines with the average depth = 510 m

More than 6 400 hectares of post-industrial and post-mining areas to

redevelopment in Silesia Region

€ 2.4 billion for Silesia and Western Małopolska

€ 415 million for Wielkopolska

€ 581.5 million for Lower Silesia

€ 369.5 million for Łódzkie

Territorial just transition plans (TJTPs)

JUST TRANSITION FUND IN POLAND

TERRITORIAL JUST TRANSITION PLAN OF THE SILESIAN VOIVODESHIP

Coal mining employment in 2022 in Silesia region (hard coal + cocking coal mines)

62 000 miners (76 000 in total Poland)

Reduction of employment

up to 2030 - 12 400 miners up to 2049 - 49 000 miners

Estimated decrease in the number of jobs in mining-related companies (value chain)

up to 2030 - 24 500 employees up to 2049 - 96 000 employees

The main objective of the TJTP is assumed to be: • Equitable and efficient transformation of mining

subregions towards a green, digital economy, ensuring a high quality of life for residents in a clean environment.

Operational objectives of 7 mining subregions embrace:

• Innovative and diversified economy

• Resource and energy efficient economy

• Strong entrepreneurship

• Balanced distribution of energy

• Repurpose of post-industrial areas for economic, environmental and social purposes

• Socially responsible transition management system

• Attractive and effective education

• Labour market support system and skills upgrading mechanism

• Comprehensive social support system to activate residents <25 000 hectares of post-industrial and post-

mining areas Identification of stakeholders and stronger partnerships

MINING WORKFORCES AND VALUE CHAIN

The socio-economic contribution of mining in terms of

employment can be measured on three levels:

• direct employment – the workforce employed by coal

enterprises themselves,

• indirect employment – those employed at companies

that produce goods or deliver services directly to coal

enterprises,

• induced employment – those employed to provide

goods and services to meet the consumption

demand of directly and indirectly employed workers

(Bacon and Kojima, 2011).

Employment structure in mining-dependent companies by NACE sections and dependence on coal mining contracts

Source: Mapping the indirect employment of hard coal mining: a case study of Upper Silesia, Poland, IBS Working Paper 07/2022, November 2022

SOCIAL AGREEMENT OF 28 MAY 2021=> MINE CLOSURE SCHEDULE

=> mine closure schedule

GOVERNMENT - TRADE UNIONS - MINING MUNICIPALITIES -

MINING COMPANIES

OBJECTIVES OF SOCIAL AGREEMENT

mechanism for financing coal mining companies in the transition process

indexation of salaries

rules for the construction and implementation of clean coal

installations

guarantee of employment

social protection package for employees from liquidated coal mines

• covering extraordinary costs • subsidies for capacity reduction costs

• inclusion of the salary costs of the companies' employees with the indexation mechanism of average monthly salaries from the previous year

• support for investments using available resources • industrial-scale (TRL8/9) investments:

• coal gasification plant (GCC+CCS) • production of low-carbon fuel, • hydrogen generation, • CO2 storage in the rock mass.

• employee realocation mechanism -> mainly to other mines • trainings and courses within the sector

• mining leave • severance pay

Relocations to other mines up to 2030

2 500 miners

Retirement up to 2030

1 800 miners

MINING WORKFORCES AND VALUE CHAIN TRANSITION

SOCIAL AGREEMENT

Mining-dependent workplaces

Mining workforces

MINING REGIONS IN TRANSITION

JTF/TJTPs

Direct Support

(miners and coal mine operators)

Indirect new workplaces

Indirect suport

Development of technology

Direct JTF mechanism

Develompent of regional economy

 Vocational education in just transition proces

 Support for starting a business: outplacement projects

 Social inclusion - strengthening the just transition process

 Supporting SMEs for transformation  Use of degraded areas to develop

the regional economy through business investment

Public intervention-> EU public support

regulations

STRATEGIC CHALLENGES

Reindustrialisation and revitalisation

Cooperation between the administration - industry -

science

Finance and new business models

Innovation and integration of knowledge

"Black to Green" sustainable transformation of the Silesia region

ADVANTAGES OF THE POST-MINING ASSETS

New business models and

collaboration schemes

Scenarios of the redevelopment process

NEW VALUE CHAIN & SUCCESS STORIES

Culture Zone - new image and functions

Katowice Coal Mine (1823–1999) 120 000 000 tons of coal

Katowice Coal Mine brownfield – 2001 demolition works

Szombierki Coal Mine - Bytom

The Golf Club Armada

R&D PROJECTS – STATUS AND PERSPECTIVES

REGIONAL OBSERVATORY OF THE TRANSFORMATION PROCESS

The aim of the project was to collect and disseminate knowledge:

- on the socio-economic processes taking place in the region,

- effective transformation activities and tools,

- innovative technologies supporting the process of diversification towards a

green digital economy,

- promoting framework directions for professional reorientation in the areas

of regional smart specializations by initiating cooperation of local partners

from areas undergoing socio-economic transformation and R&D with

business entities.

The aim of the project was to provide insight into perceptions of various aspects of the transformation process.

ROPT supports the implementation of the objectives of the regional transformation plan and the regional development strategy in the social and

economic dimension

LEVERAGING THE COMPETITIVE ADVANTAGES OF END-OF-LIFE UNDERGROUND COAL MINES TO MAXIMISE THE CREATION OF GREEN AND QUALITY JOBS

GreenJOBS focuses on repurposing end-of-life underground coal mines by deploying emerging renewable energy and circular economy technologies to promote sustainable local economic growth and maximise the number of

green, quality jobs. 2 business plans (Virtual Power Plant

and a Green Hydrogen Plant).

The project consortium:  UNIVERSIDAD DE OVIEDO, Spain  GLOWNY INSTYTUT GORNICTWA, Poland  FUNDACION ASTURIANA DE LA ENERGIA, Spain  DMT-GESELLSCHAFT FUR LEHRE UND BILDUNG MBH, Germany  MAGELLAN & BARENTS SL, Spain  WEGLOKOKS KRAJ SPOLKA AKCYJNA, Poland,  HULLERAS DEL NORTE SA, Spain,  PREMOGOVNIK VELENJE, Slovenia.

PILOT ACTIONS

No. Action 1 Virtual power plant

2 Green hydrogen plant

3 Eco-industrial park

4 Cultural heritage and sports/recreations

areas using green energy

5 Floating PV panels at flooded open-pit

coal mine

6 Pumped hydroelectric storage (PHS) at

former open-pit coal mines

7 Fisheries in flooded open-pit coal mines

8 Combined-cycle gas turbine (CCGT) power

plant powered by natural gas

9 Mine gas utilization for gas-powered CHP

power units

10 Small modular reactors (SMRs)

11 Biofuels combustion energy plant

12 Molten salt plant

13 Agrophotovoltaics (APV) at former open-

pit coal mine areas

No. Mikro-action

1 Ancillary services provided by batteries

2 Recovery of resources from coal mining

waste heaps

3 Usage of methane from degasification units

on closed coal mines

4 Circular mining technologies for pumped

water material recovery.

5 Forest restoration at former open-pit coal

mines

6 Large scale IT infrastructure - power plant

7 Geothermal energy

8 Gravitricity

9 Dense fluids

10 Underground hydropumping

EXTENSION OF THE POST-MINING LAND MANAGEMENT SYSTEM IN THE SILESIAN VOIVODESHIP

Supportive tool for management of transition proces.

new public e-service

database of post- mining areas

tool for the valorisation of post-

mining areas

digital repository of documents including plans, maps, photographs of post-mining areas

Make it easier for investors to get information about post-mining areas and help them assess their economic

attractiveness.

https://www.youtube.com/watch?v=0AjJbo560JE

TRANSITION PROCESS IN POLAND

 Along with the phase-out plan, the expected outcome of the transition process is to ensure the security of the national energy system combined with climate neutrality goals

 Silesia region, due to concentration of different type of challenges is percieved as the reference laboratory and source of good practices of the just transition process in Europe

 Post-mining period creates new models of collaboration between industry, researchers and administration

 Reskilling mining workforce and employees of mining-dependent enterprises (value chain) is a key challenge for the well-embedded just transition

 Just transition process is implemented through an extensive support program that includes, among others:

 Regeneration, decontamination and restoration of post-mining assets

 Raising and changing the qualifications of employees and jobseekers

 Investment in SMEs, including start-ups, leading to economic diversification and economic restructuring

 Business creation through business incubators and consulting services

 Research and innovation activities and supporting the transfer of advanced technologies

Dziękuję za uwagę

WE INVITE YOU TO COLLABORATE

Jan BONDARUK Deputy Director for Environmental Engineering Central Mining Institute – National Research Institute Plac Gwarków 1 40-166 Katowice Poland t: +48 32 259 24 66 f: +48 32 259 21 54 m: +48 512 293 850 [email protected] www.gig.eu

http://www.gig.eu/

Market Forecast Tables 2023

Read more about Market Forecast Tables 2023

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

Languages and translations

English

List of tables

List of Tables and Notes

Table 1 - Sawn Softwood

Table 2 - Sawn Hardwood (total)

Table 2a - Sawn Hardwood (temperate)

Table 2b - Sawn Hardwood (tropical)

Table 3 - Veneer Sheets

Table 4 - Plywood

Table 5 - Particle Board (excluding OSB)

Table 5a - Oriented Strand Board

Table 6 - Fibreboard

Table 6a - Hardboard

Table 6b - MDF/HDF

Table 6c - Other Fibreboard

Table 7 - Wood Pulp

Table 8 - Paper and Paperboard

Table 9 - Removals of wood in the rough

Table 9a - Removals of wood in the rough (softwood)

Table 9b - Removals of wood in the rough (hardwood)

Table 10 - Softwood sawlogs

Table 11 - Hardwood sawlogs

Table 11a - Hardwood logs (temperate)

Table 11b - Hardwood logs (tropical)

Table 12 - Pulpwood

Table 12a - Pulpwood (softwood)

Table 12b - Pulpwood (hardwood)

Table 12c - Wood Residues, Chips and Particles

Table 13 - Wood Pellets

Table 14 - Europe: Summary table of market forecasts for 2023 and 2024

Table 15 - North America: Summary table of market forecasts for 2023 and 2024

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

Notes: Data in italics are estimated by the secretariat. EECCA is Eastern Europe, Caucasus and Central Asia.

Data for the two latest years are forecasts.

In contrast to previous years, data are shown only for countries providing forecasts. Sub-regional totals are only for reporting countries.

In contrast to years prior to 2020, data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subregion.

Confidential data have not been included. Please inform secretariat in case you notice any confidential data which might have been included inadvertently.

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

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

Countries with nil, missing or confidential data for all years on a table are not shown.

Consumption figures are the sum of production and national imports minus national exports. Softwood = coniferous, hardwood = non-coniferous. United Kingdom production figures for OSB is secretariat estimate.

Uzbekistan – data extrapolated by the Secretariat based on national data for the first eight months 2023.

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

Softwood = coniferous, hardwood = non-coniferous

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

Countries with nil, missing or confidential data for all years on a table are not shown.

Table1


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

Table2


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

Table 2a


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

Table 2b

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

Table 3


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

Table 4


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

Table 5


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

Table 5a


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

Table 6


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

Table 6a


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

Table 6b


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

Table 6c


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

Table 7


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

Table 8


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

Table 9


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

Table 9a


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

Table 9b


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

Table 10


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

Table 11


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

Table 11a


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

Table 11b


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

Table12


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

Table 12a


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

Table 12b


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

Table 12c


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

Table 13


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

Table 14


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

Table 15


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

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

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

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

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

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

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

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

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

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

Imports Exports

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

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

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 1 SAWN SOFTWOOD SCIAGES CONIFERES

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

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

Apparent Consumption

Imports Exports

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

1000 m3

Apparent Consumption Country Consommation Apparente Production Imports - Importations

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

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

Exports - Exportations Pays

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 3 VENEER SHEETS FEUILLES DE PLACAGE

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 4 PLYWOOD CONTREPLAQUES

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6 FIBREBOARD PANNEAUX DE FIBRES

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6a HARDBOARD PANNEAUX DURS

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6b MDF/HDF

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6c OTHER FIBREBOARD AUTRES PANNEAUX DE FIBRES

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

Apparent Consumption

Imports Exports

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

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

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 7 WOOD PULP PATE DE BOIS

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

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 8 PAPER AND PAPERBOARD PAPIERS ET CARTONS

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

Apparent Consumption

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

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

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

used for energy purposes à des fins energétiques

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

Industrial wood - Bois industriels

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

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

Wood fuel c

Bois de chauffage c Pays

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

used for energy purposes à des fins energétiques

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

Industrial wood - Bois industriels

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

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

Wood fuel c

Bois de chauffage c Pays

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

used for energy purposes à des fins energétiques

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

Industrial wood - Bois industriels

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

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

Wood fuel c

Bois de chauffage c Pays

Imports Exports

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

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

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

TABLE 10 SOFTWOOD SAWLOGS GRUMES DE SCIAGES DES CONIFERES

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

Apparent Consumption a

Imports Exports

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

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

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

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

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

Apparent Consumption a

Imports Exports

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

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

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

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

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

Apparent Consumption a

Imports Exports

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

Country Commerce Net Production Imports - Importations Exports - Exportations Pays

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

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

Net Trade

Imports Exports

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

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

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

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

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

Apparent Consumption a

Imports Exports

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

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

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

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

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

Imports Exports

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

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

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

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

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

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

1000 m3

Apparent Consumption

Imports Exports

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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 13 WOOD PELLETS GRANULES DE BOIS

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

Apparent Consumption

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

TABLE 14

Europe: Summary table of market forecasts for 2023 and 2024

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

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

Consommation Apparente Production Imports - Importations Exports - Exportations

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

TABLE 15

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

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

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

Consommation Apparente Production Imports - Importations Exports - Exportations

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

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

Poland

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

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