A comprehensive assessment of European forest-based biomass harvest potentials, their future utilization and implications on international wood product markets and forest carbon dynamics requires the capability to model forest resource development as well as global markets for wood-based commodities with sufficient geographical and product detail and, most importantly, their interactions. To this aim, we apply a model framework fully integrating a European forest resource model and a global economic forest sector model. In a business-as-usual (BaU) scenario, European Union harvests increase seven percent by 2030 compared to past levels (485 million m3 on 2000-2012 average and 517 million m3 in 2030). The subsequent annual carbon stock change is a ten percent reduction by 2030 compared to 2000-2012 average (equal to 119.3 Tg C yr-1), corresponding to decreasing carbon-dioxide removal by the European forests. A second, high mobilization scenario (HM), characterized by the full utilization of the potential wood supply and a doubling of EU wood pellets consumption, was designed to explore potential impacts on forest carbon dynamics and international wood product markets under intensive exploitation of biomass resources. In the HM scenario, harvest increases by 55% (754 million m3 in 2030) compared to the BaU scenario. Fuelwood accounts for this increase in harvest levels as overall competition effects from increased wood pellets consumption outweighs synergies for material uses of wood, resulting in slightly reduced harvests of industrial roundwood. As expected, this increasing harvest level would significantly impair carbon-dioxide forest sequestration from the atmosphere in the medium term (-83% in 2030, compared to 2000-2012 average).
The forest-based sector plays an essential role within the European bioeconomy, as a source of renewable materials and energy, substituting for non-renewable materials and products and fossil-based energy (
Forest biomass is currently the most important source of renewable energy, accounting for around half of the EU’s total renewable energy consumption (
The importance of forests and their products/services has resulted in a number of studies assessing woody biomass potential - mainly focusing on energy uses - at global (
Several studies have assessed the impact of an increased use of wood for energy on the forest-based sector, mainly with a regional focus (
In this paper, our aim is to build up a modelling framework that includes a detailed analysis of European forest resources and their link with the forest carbon dynamic, taking into account their interactions with the international wood product markets. Such a comprehensive assessment of forest-based biomass potentials and their possible utilization requires a detailed appraisal of (i) the maximum sustainable woody biomass harvest potential for material and energy uses, as well as an analysis of the impacts on (ii) forest resources and forest carbon dynamics and on (iii) international wood-based product markets. This in turn requires the capability to model international markets for wood-based products as well as forest resource development and carbon dynamics, and, most importantly, their interaction, with sufficient detail.
To this end, this study elaborates the full interaction between a forest resource model and an economic forest-based sector model. Though the modelling set up has a European focus as regards forest resources, we fully consider market implications at global level. First, the future maximum potential supply of wood to 2030 derived with the forest resource model is used to constrain the use of roundwood for the production of wood-based products and pellets in the economic forest-based sector model. Then the economic model, after having reached a market equilibrium, provides the demand for wood raw material to the forest resource model, which uses this “actual harvest level” to model the evolution of forest resources and for the computation of next period’s harvest potential, including wood for energy.
The CBM (
In this study, we use CBM to estimate, at country level, maximum wood supply (MWS), forest carbon dynamic for the historical period (2000-2012), and for two scenarios up to 2030. We define the MWS as the amount of wood available under applicable silvicultural practices, without decreasing the growing stock level in the forest area available for wood supply (FAWS). The fraction of industrial roundwood (IRW) derived from this amount is provided to the economic forest-based sector model and acts as a constraint to the demands of wood for material use, as estimated by GFTM.
The MWS, up to 2030, is estimated assuming that all the net increment of the merchantable living biomass (
To estimate the MWS, it is necessary to run the model sequentially in order to account for the multiple dependencies and interactions among all the factors included. Hence, in an iterative process, merchantable C stock change (
The extent of the forest area available for wood supply (FAWS) and the area not available for wood supply (FnAWS) originated from the Territorial Modelling Platform LUISA (
Among the different silvicultural treatments considered in CBM, we have assumed that clear-cuts are restricted to FAWSt because of their well-known major impact on forest functions (
To implement these assumptions, we have scaled and distributed the original NFI area used by CBM proportionally to the three categories identified by LUISA (
Thus, we could differentiate the maximum harvestable wood volume for each country between the amount potentially provided by the FAWSt and the amount potentially provided by the FAWSp. This amount includes both the merchantable biomass (
Finally, at the end of the modelling exercise, CBM is used to assess the C stock change of each country, using total harvest as the main input. Results are further disaggregated into FAWS and FnAWS and into C pools (
The Global Forest Trade Model (
GFTM covers ten final products, four intermediate products, and four primary products (
GFTM has been set up focusing on wood-based commodities that are traded in significant amounts. The reason for this choice is technical, in that the mathematical formulation of GFTM largely relies on matrixes and linear algebra, requiring the different wood-based products to be homogeneous in the sense of industrially processed and traded. At the moment fuelwood is excluded from the analysis for two reasons. First of all, fuelwood is traded across borders to a limited extent, in particular considering extra-EU trade. Secondly, good quality data regarding fuelwood are scarce. The poor quality of data for fuelwood - a heterogeneous commodity comprising not only roundwood but also tree tops and branches - is to a large extent the consequence of large quantities being harvested and used by non-industrial private forest owners themselves, without entering a formal market (
Input/output coefficients used derive from various sources:
Wood-based products price and GDP elasticities of demand derive from
CBM estimates the MWS under a constant standing carbon stock assumption until 2020 and until 2030. The volumes of coniferous and non-coniferous IRW based on this values - further divided in sawlogs and pulpwood based on FAOSTAT production data series - are ingested by GFTM as upper bounds for the provision of coniferous and non-coniferous sawlogs and pulpwood respectively, used for producing wood-based commodities and wood pellets in each country. As for the cost of roundwood supply, the (upward) slope of the cost curves of GFTM depends solely on the price elasticity of timber supply. For the starting period, the timber supply shift parameter is derived from actual data for sawlogs and pulpwood removals, and prices of sawlogs and pulpwood. The supply shifter is updated from period to period (
For countries not covered by CBM (all non-EU countries or global sub-regions), the potential timber supply is derived from data on growing stock and increments, compiled from various sources: the Global Forest Resources Assessment (http://www.fao.org/forest-resources-assessment/explore-data/flude/en/), the State of Europe’s Forests (http://www.foresteurope.org/docs/SoeF2015/OUTPUTTABLES.pdf), and the European Forest Data Centre (http://forest.jrc.ec.europa.eu/efdac/). In these latter cases, annual potential harvest levels are set equal to annual increment. Then, the same as for the input from CBM, these volumes are divided into sawlogs and pulpwood based on FAOSTAT production data series. GFTM derives the market equilibrium, and the growing stocks (coniferous and non-coniferous, respectively) are updated based on the resulting demand for primary products in non-CBM countries and global sub-regions.
Scenario analysis concerns the development of alternative visions of the future. A key objective is to extend thinking in terms of length of time (
Taking into account this premise, this study encompasses two scenarios: (i) a business-as-usual scenario (BaU), where no major deviations from current market developments and utilization of forest resources are foreseen; and (ii) a high mobilization scenario (HM), envisioning the full utilization of the potential wood supply due to an increasing use of wood resources for energy.
In the BaU scenario, IRW harvests - within the bounds of forests available for wood supply (FAWS) - as well as the supply and demand of all processed wood-based products are determined exclusively by market forces as modelled by GFTM. Production, trade and apparent consumption of all wood-based commodities, pellets included, and thereby also the ensuing demand for IRW, are thus derived by GFTM as solutions to the welfare-optimization problem under resource, technology and equilibrium constraints, without the addition of any further exogenous assumption. In this scenario, GFTM first provides the amount of IRW for 2015 and 2020 (estimated as the average of a 5 years span period), based on the MWS estimated by CBM from 2013 to 2020. Using this last value as input to set the IRW harvest demand for 2020 and adding the corresponding demand for FW, CBM estimates the MWS for 2021-2030. Based on this last value, GFTM assesses the demand for IRW for 2025 and 2030. The gap periods, from 2013 to 2019, 2021 to 2024 and 2026 to 2029, have been filled through a linear interpolation between the starting and ending points.
The fuelwood component (FW) is not included in the economic model, mainly due to scarce availability of good-quality, reliable data (
At country level, fuelwood can be estimated as the sum of two quantities: (i) branches and tops removed when harvesting for IRW (
The first quantity
For the BaU scenario, we applied this factor to the amount of IRW determined by GFTM.
The second component
where for each country
where
The high mobilization scenario (HM) is designed to explore system boundaries, assessing potential market adjustments and forest resource implications and impacts under intensive exploitation of biomass resources. The HM differs from the BaU in two ways. First, there is no feedback from GFTM in terms of harvest demand, as we assume that the MWS is fully used for material and/or energy purposes. In addition, consumption of wood-pellets within the EU as a whole is set to increase gradually, reaching a level by 2030 twice as high as that of 2015. We assume an (approximately) even split of the increase in consumption on the three periods: 2015-2020, 2020-2025, and 2025-2030. This implies that the projected level of pellets consumption in 2020 among EU 28 countries is exogenously forced to be at least 26 million tons, 33 million tons by 2025, and 40 million tons by 2030. We deem this increase, though steep, as being within the realms of the possible. Expert assessments as to EU wood pellets consumption vary considerably, from conservative estimates of 20 to 22 million tons of EU pellets demand by 2025 (
According to CBM results (
The overall MWS estimated with CBM at EU level is similar (+4% for HM and +5% for BaU in 2030) to the values obtained with the EFISCEN model in the medium mobilization scenario (734 and 731 Mm3 yr-1 in 2020 and 2030, respectively) in
Furthermore, CBM distinguishes between even-aged and uneven-aged forests, while EFISCEN does not (
In the BaU scenario, total harvest at EU level is 516 Mm3 in 2020 and 518 Mm3 in 2030 according to modelling outcomes. Coniferous IRW make up 64% of the harvest, broadleaves IRW 16% and wood for energy the remaining 20% (
Total EU harvest in the HM scenario is about 46% higher than in the BaU scenario in 2030. The allocation of the harvest on IRW and FW differs between the two scenarios: in the BaU scenario, FW accounts for 20% of the harvest, while in the HM scenario this share increases to 46% (on average, 2015-2030). In fact, total IRW harvest in the EU is lower in the HM than in the BaU scenario by 2030 (
IRW harvest patterns reflect changes in the production of processed wood-based products. Hence, in the BaU, sawnwood production, after an initial small increase, is projected to decrease by 2030, as is, above all, graphic paper (newsprint and printing + writing paper) production, reflecting falling consumption levels. Fiberboard and particle board production and consumption levels will essentially remain stable. At the same time, packaging paper production is foreseen to increase (
As for the HM scenario, IRW consumption patterns on EU level results from the higher demand for wood pellets being synergetic with sawnwood production through higher demand for sawnwood by-products, while at the same time crowding out production of particle board, fiberboard and paper products through increased competition for feedstocks (mainly sawnwood by-products). The increased production of sawnwood further crowds out particle board and fiberboard production. Hence, sawnwood production (
The patterns for production and consumption of wood-based commodities at global level in the HM scenario are the same as for the EU, with the exception of packaging paper (
In the BaU scenario CBM estimates show a decreasing C stock change in FAWS, from 99.6 Tg C yr-1 in the historical period 2000-2012 (average of the period) to 85.1 Tg C yr-1 in 2030, corresponding to about -1.4% per year (
As expected, the larger C stock change (about 82% of the total C stock change between 2010 and 2030) is concentrated on the FAWS, covering about 91% of the total forest area considered by our study. Due to the decreasing C stock change estimated for this area, the overall C stock change estimated at EU level decreases from 119.3 Tg C yr-1 in 2000-2012 (average of the period) to 106.6 Tg C yr-1 in 2030. Overall, we estimate a C sink (
Unsurprisingly, we foresee a considerably lower C stock change on the FAWS in the HM scenario, with a C source equal to -1.2 Tg C yr-1 in 2030 (
The higher removals applied in the HM scenario (+45% in 2030 with respect to the BaU scenario and +55% with respect to the historical period), directly affect the amount of C on the DOM pool, where we estimate a positive C stock change (
The main outcomes provided by our modelling set up in the BaU scenario, summarized in
Besides total forest area (see
The two model frameworks - CBM-GFTM and GLOBIOM-G4M respectively - further differ as regards: (i) growing stock detail (FAWS and FnAWS are divided in coniferous and broadleaf forests in the current study); (ii) timber assortments detail (in the current study IRW and FW are divided in coniferous and broadleaf); (iii) wood-based product scope and detail (we consider more products, notably different paper grades, and in greater detail, distinguishing between coniferous and broadleaf sawnwood, respectively) and modelling assumptions regarding wood pellets; (iv) sourcing (ReceBio considers only EU imports); (v) feedstocks (ReceBio considers SRC Eucalyptus plantations and industrial by-products, but not roundwood, while we consider roundwood and by-products); and (vi) application (ReceBio considers large scale, industrial use only, not small-scale household use, while we consider both uses).
Finally, and not the least important, are differences as to scope, focus, strength, and weaknesses of the two modelling frameworks in question. GLOBIOM-G4M addresses overall land use aspects at global level. In doing so, detail and precision as regards the forest sector, in particular at EU level, is lower than in the present study. Though modelling wood-based commodity markets globally, we focus on EU forest-based biomass potentials, their possible utilization, and the impact on EU forest resources and forest carbon dynamics. Unlike GLOBIOM-G4M, we do not yet address agricultural land use.
The Reference Scenario 2016 projects a total harvest of 565 million m3 for 2030,
Based on these assumptions, the EU Reference Scenario 2016 estimated for forestland (excluding afforestation) a total amount of CO2 removals (reported with negative numbers, from an atmosphere perspective) equal to -242 Mt CO2 in 2030 (for EU 28, including Malta and Cyprus, which are not considered in our study). This value is about 38% lower than the C sink estimated by CBM for 2030, equal to -391 Mt CO2 in the BaU scenario. The difference is due both to the lower harvest rate considered in the present study, and to differences in model input data. Indeed, the estimates provided by GLOBIOM-G4M use as main input the forest net annual increment and FAWS reported by the State of Europe’s Forests (
The baseline for the ReceBio study was the 2013 EU Base Scenario and, on this base, apart from different policy scenarios, a baseline scenario assuming the continuation of current trends in the bioenergy sector was constructed. This is the most comparable scenario with the BaU developed in our study. Note, moreover, that the ReceBio Emission Reduction scenario (which could also be of interest for comparison with our HM scenario), where an increase in biomass is envisioned, has results almost identical to the Baseline scenario for 2030.
The 2030 total harvest estimated in ReceBio is 19% and 9% higher than the harvest in the BaU scenario of our study and the EU Reference Scenario 2016, respectively. This is mainly due to a considerably higher harvest reported already for 2010 in ReceBio, used to calibrate the future trend (
Both in our study and in the ReceBio study, the wood used for energy includes wood pellets, forest residues and a roundwood component used for energy production. In our study, wood pellets derive from IRW (coniferous as well as non-coniferous pulplogs) and from industrial by-products (sawdust and wood chips), while in ReceBio (imported) wood pellets are produced from industrial by-products, SRC and eucalyptus plantations, not from pulplogs. Further, in this study, forest residues that can be used for energy are defined OWCs,
In ReceBio, the competition in the EU between wood pellets production and material uses of wood is not addressed, as ReceBio considers imported wood pellets only, and only for large scale industrial applications. These differences in modelling assumptions could explain some of the difference in modelling outcomes as regards size of wood removals and allocation of removals and industrial by-products. Further differences are due to the amount of forest residues used for energy: for 2010 equal to 67 Mm3 in our study and 43 Mm3 in the ReceBio study. Finally, yet another plausible reason for higher increases in material uses of wood in the EU in ReceBio and the Reference Scenario could be the circumstance that GLOBIOM does not model paper products. This model framework thus, reasonably, fails to account for the impact on pulp production of the decline in graphic paper demand. However, as indicated earlier, there a numerous differences between the two model frameworks. On a general note, caution is always called for when comparing the output from different model framework, due to differences in set-up, assumptions, as well as focus and overall purpose.
The ReceBio study also investigated the effect of a reduction on the use of forest residues on the other feedstocks used for energy (
This study presents a comprehensive assessment of the maximum sustainable woody biomass harvest potential from 2020 to 2030. The modelling framework used explores the interactions between forest growth and harvest demand for material and energy uses, and analyses the impact on EU forest carbon dynamics and international wood-based product markets. One of the strengths of our modeling framework is the ability to account for interdependencies - competition as well as complementarities/synergies - between wood pellets and wood-based products, at EU member state level as well as in a global context. This is essential for assessing the impacts of different climate change mitigation policy options. Furthermore, GFTM covers a wide range of wood products, including paper, which is overlooked in similar modeling frameworks. Finally, we are able to provide consistent, coherent, and detailed results concerning wood harvest and carbon stock changes in the whole framework, considering the consequences of the level of harvests on forest resources.
In the BaU scenario, we foresee a 7% EU harvest increase with respect to the historical period, resulting from projected developments in wood products markets: decreasing sawnwood consumption and production, falling demand and ensuing production of graphic paper, and significantly increasing packaging paper consumption and production. There is also a significant increase in wood pellets consumption within the EU, mainly the result of increased net-imports, as EU internal production is very stable over the outlook. The consequence of this scenario on the EU forest C stock change is a 9% reduction in 2030 compared to 2015, confirming a decreasing growth capacity of the forests.
In the HM scenario, the harvest level in 2030 is 55% higher than in 2015. The resulting C stock change in EU forests is 83% lower in 2030 than in 2015. Here, the additional wood is entirely made up of fuelwood, as the harvest of industrial roundwood is actually slightly lower in this scenario. In addition to accelerating net-imports, the doubling of wood pellets consumption in EU from 2015 to 2030 triggers an increased use of pulpwood and sawmill and plywood by-products for wood pellets production, thus crowding out material uses.
The outcomes of the HM scenario imply dramatic consequences for carbon in EU forests, and possibly also on other environmental aspects not dealt with in this study. The modelling results indicate that pushing the EU harvest level to the maximum could significantly impair the capacity of EU forests to sequester and store carbon from the atmosphere. The overall displacement of material uses of woody biomass witnessed in the HM scenario also raises questions as to what would be the impact on climate change mitigation in terms of substituting fossil-fuel based materials as well as carbon storage in harvested wood products (HWP). Assessing overall mitigation effect of carbon dynamics in the entire forest-based sector requires accounting also for the substitution of wood-based energy for fossil-based energy as well as material substitution and carbon storage in HWP (
Finally, it needs to be stressed that any modelling effort would greatly benefit from improved data quality. In particular data related to fuelwood (removals as well as trade) would need to considerably improve in quality to allow for refined scenario analysis.
The work described in this paper has (though not constituting its official output) been carried out in the context of the JRC Biomass assessment study (https://biobs.jrc.ec.europa.eu/biomass-assessment-study-jrc).
The authors declare no conflict of interest. The opinions expressed herein are those of the authors and do not necessarily reflect the views of the European Commission. The scientific output does not imply a policy position of the European Commission.
Example of the sequence of model’s runs applied by CBM to estimate the MWS in each country. The merchantable C stock change estimated after the first run (upper panel), assuming no harvest from 2013 to 2030 (lower panel), was applied as harvest demand for the second run. The C stock change resulting from this second run was used to correct the previous harvest amount for the third run. The iteration continues until the change in C stock is negligible.
Industry module of the GFTM.
Relationships between the quantities used in the estimation of FW (generic example applicable to each country). The MWS estimated with CBM is the red dotted line (CBM - MWS). The relationship of this amount with historical FW and IRW (derived from FAOSTAT) has been used to estimate the projected (post-2015) FW from CBM - MWS and GFTM - IRW projections.
Historical (until 2012) and future harvest demand at EU level (in million m3 over bark), based on Business as Usual (BaU) and HM scenarios, further distinguished between the amount of wood-based commodities (IRW) and wood for energy (FW).
The 2030 primary wood supply in the BAU scenario. For each country the total amount of harvest (proportional to the radius of the circle) is broken down between industrial roundwood (IRW) and fuelwood (FW), further distinguished between broadleaves and conifers. The background of each country highlights the maximum wood supply estimated by CBM (in m3 103).
The 2030 primary wood supply in the HM scenario. For each country the total amount of harvest (proportional to the radius of the circle) is broken down between industrial roundwood (IRW) and fuelwood (FW), further distinguished between broadleaves and conifers. The background of each country highlights the maximum wood supply estimated by CBM (in m3 103).
C stock change (in Tg C yr-1) estimated by CBM for the FAWS, the FnAWS and the total forest area, further distinguished between different mobilization scenarios. The estimates for the historical period (2000-2012) are based on the historical harvest demand and assumptions reported by
Summary of the main results provided by our modeling framework under the BaU scenario for 2030.
MWS (in million m3 yr-1) estimated by CBM for 2020 (2018-2022 average) and 2030 (2028-2032 average), under Business as Usual (BaU) and High Mobilization (HM) scenarios.
Country | MWS (Mm3 yr-1) | ||
---|---|---|---|
2020 | 2030 | ||
BaU & HM | HM | BaU | |
Austria | 34.3 | 33.1 | 35.4 |
Belgium | 4.0 | 3.8 | 3.0 |
Bulgaria | 14.5 | 14.3 | 11.8 |
Croatia | 10.4 | 9.9 | 10.5 |
Czech Rep. | 25.8 | 25.1 | 25.5 |
Denmark | 5.3 | 5.0 | 5.1 |
Estonia | 14.4 | 13.7 | 11.0 |
Finland | 87.6 | 82.7 | 86.0 |
France | 67.6 | 62.3 | 65.7 |
Germany | 108.5 | 106.4 | 118.7 |
Greece | 12.3 | 12.1 | 12.8 |
Hungary | 9.0 | 8.4 | 8.7 |
Ireland | 7.9 | 7.2 | 7.2 |
Italy | 25.0 | 24.7 | 23.9 |
Latvia | 31.9 | 30.3 | 31.1 |
Lithuania | 13.1 | 12.8 | 13.1 |
Luxembourg | 0.3 | 0.3 | 0.3 |
Netherlands | 3.4 | 3.4 | 3.1 |
Poland | 40.1 | 38.1 | 38.4 |
Portugal | 17.4 | 17.5 | 16.6 |
Romania | 27.2 | 26.0 | 21.1 |
Slovakia | 10.2 | 10.1 | 11.4 |
Slovenia | 5.4 | 5.3 | 5.4 |
Spain | 61.3 | 59.5 | 54.9 |
Sweden | 134.5 | 125.8 | 129.1 |
UK | 23.7 | 20.7 | 20.7 |
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EU IRW harvests, net trade and consumption (million m3 o.b.) in the BaU scenario, Global IRW harvests in the BaU scenario, and differences between the HM and BaU scenarios (million m3 o.b.).
Scenario | Commodity | Harvests | Net-Imports | Consumption | Global harvests | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2015 | 2020 | 2030 | 2015 | 2020 | 2030 | 2015 | 2020 | 2030 | 2015 | 2020 | 2030 | ||
BaU | Conif. sawlogs | 220 | 221 | 212 | 11 | 12 | 13 | 232 | 233 | 225 | 903 | 915 | 936 |
Non-conif. sawlogs | 32 | 31 | 32 | 9 | 9 | 9 | 41 | 40 | 41 | 505 | 513 | 534 | |
Conif. pulpwood | 118 | 116 | 125 | -2 | -2 | -3 | 116 | 114 | 122 | 405 | 410 | 445 | |
Non-conif. pulpwood | 47 | 47 | 49 | 12 | 13 | 12 | 60 | 59 | 61 | 381 | 389 | 419 | |
Total IRW | 418 | 414 | 418 | 31 | 32 | 31 | 448 | 446 | 448 | 2194 | 2228 | 2334 | |
Differencesbetween HM and BaU scenarios | Conif. sawlogs | - | -0.5 | 5.4 | - | 0.5 | 7.6 | - | 0.0 | 13.0 | - | 4.1 | 21.8 |
Non-conif. sawlogs | - | 1.0 | 0.3 | - | 0.1 | 0.7 | - | 1.1 | 1.0 | - | 6.3 | 7.9 | |
Conif. pulpwood | - | -3.4 | -25.4 | - | -0.4 | -2.4 | - | -3.8 | -27.8 | - | 2.8 | -31.9 | |
Non-conif. pulpwood | - | -0.6 | -7.7 | - | -0.1 | -0.5 | - | -0.7 | -8.2 | - | 4.0 | -20.3 | |
Total IRW | - | -3.5 | -27.3 | - | 0.1 | 5.4 | - | -3.4 | -21.9 | - | 17.2 | -22.6 |
EU production, net trade and consumption of wood-based commodities in the BaU scenario, and differences between the HM and BaU scenarios (sawnwood, plywood, particle board, and fiberboard: million m3; newsprint, printing + writing paper, packaging, household + sanitary and wood pellets: million metric tons).
Scenario | Commodity | Production | Net-Imports | Consumption | ||||||
---|---|---|---|---|---|---|---|---|---|---|
2015 | 2020 | 2030 | 2015 | 2020 | 2030 | 2015 | 2020 | 2030 | ||
BaU | Conif. sawnwood | 98.8 | 100.5 | 96.8 | -9.4 | -10.3 | -9.1 | 89.5 | 90.2 | 87.7 |
Non-conif. sawnwood | 11.5 | 11.2 | 11.6 | 0.1 | -0.1 | -0.3 | 11.6 | 11.1 | 11.3 | |
Plywood | 4 | 3.9 | 3.9 | 3.1 | 2.9 | 2.9 | 7 | 6.8 | 6.7 | |
Particle board | 35 | 34.6 | 35.3 | -2.8 | -2.8 | -2.8 | 32.2 | 31.8 | 32.5 | |
Fiberboard | 14.9 | 14.8 | 14.9 | -2.3 | -2.3 | -2.5 | 12.7 | 12.4 | 12.4 | |
Newsprint | 7.1 | 6.6 | 5.7 | 0.6 | 0.7 | 0.6 | 7.7 | 7.2 | 6.3 | |
Printing + writing | 29.7 | 28 | 26.4 | -4.4 | -4.4 | -5.4 | 25.3 | 23.5 | 21 | |
Packaging | 45 | 44.8 | 49.9 | -7.7 | -8.3 | -9 | 37.3 | 36.5 | 40.9 | |
Household + sanitary | 4.8 | 4.4 | 4.8 | 0.1 | 0.1 | 0.1 | 4.8 | 4.4 | 4.9 | |
Wood pellets | 13.3 | 13.2 | 13.6 | 6.3 | 7.3 | 8.9 | 19.6 | 20.5 | 22.5 | |
Differencesbetween HMand BaUscenarios | Conif. sawnwood | - | 1.1 | 10.7 | - | -0.3 | -5.6 | - | 0.8 | 5.1 |
Non-conif. sawnwood | - | 0.6 | 1.5 | - | 0 | -1.1 | - | 0.6 | 0.4 | |
Plywood | - | 0.1 | -0.2 | - | 0 | -0.4 | - | 0.1 | -0.6 | |
Particle board | - | -1.5 | -8.7 | - | 0.2 | 0.7 | - | -1.3 | -8 | |
Fiberboard | - | -0.6 | -3.3 | - | 0.1 | 0.4 | - | -0.5 | -2.9 | |
Newsprint | - | -0.5 | -1.8 | - | -0.1 | 0 | - | -0.6 | -1.8 | |
Printing + writing | - | -1.1 | -4.8 | - | -0.3 | -0.5 | - | -1.4 | -5.2 | |
Packaging | - | 0 | -6 | - | 0.1 | -0.5 | - | 0 | -6.5 | |
Household + sanitary | - | -0.3 | -1.8 | - | 0 | 0 | - | -0.3 | -1.8 | |
Wood pellets | - | 2.3 | 6.6 | - | 3.2 | 11 | - | 5.5 | 17.6 |
Global production of wood-based commodities in the BaU scenario, and difference between the HM and BaU scenarios (sawnwood, plywood, particle board, and fiberboard: million m3; newsprint, printing + writing paper, packaging, household + sanitary and wood pellets: million metric tons).
Commodity | BaU Production | Difference between the HM and BaU scenarios | ||||
---|---|---|---|---|---|---|
2015 | 2020 | 2030 | 2015 | 2020 | 2030 | |
Conif. sawnwood | 326.3 | 331.8 | 341.2 | - | 3.2 | 16.4 |
Non-conif. sawnwood | 139.6 | 143.0 | 153.9 | - | 4.8 | 7.9 |
Plywood | 123.9 | 129.5 | 135.0 | - | 0.2 | 2.6 |
Particle board | 102.5 | 103.3 | 108.0 | - | -3.1 | -20.1 |
Fiberboard | 97.5 | 100.4 | 108.7 | - | -0.8 | -10.7 |
Newsprint | 29.0 | 27.4 | 24.9 | - | -1.8 | -6.5 |
Printing + writing | 101.8 | 98.1 | 93.9 | - | -4.0 | -14.5 |
Packaging | 258.6 | 264.3 | 286.1 | - | 6.9 | 9.5 |
Household + sanitary | 31.9 | 31.8 | 34.2 | - | 0.1 | -2.7 |
Wood pellets | 25.5 | 25.9 | 27.2 | - | 4.4 | 14.8 |
Comparison between the main drivers considered in the three modelling frameworks. (AR): afforestation and reforestation; (SRF): short rotation forests.
Factor | Year | CBM - GFTM BaU | EU Ref. Scenario 2016 | ReceBio Baseline scenario |
---|---|---|---|---|
Forest area in 2010 (Mha) | - | 140 (FAWS) + 13 (FnAWS), assumed as constant until 2030 | 148, defined as area under Forest Management | 154, including 105 defined as “area of used forests” |
AR and SRC from 2010 to 2030 (Mha) | - | No, assumed as negligible in the short period | 5 AR + SRCs | 9 AR + 2.5 SRC |
Total Harvest(Mm3 o.b.) | 2010 | 498 | 492 | 556 |
2030 | 517 (+4%) | 565 (+14%) | 616 (+11%) | |
Harvest Share | 2010 | 82% IRW / 18% FW | 78% IRW / 22% FW | 75% IRW / 25% FW |
2030 | 80% IRW / 20% FW | 72% IRW / 28% FW | 77% IRW / 23% FW |
Tab. S1 - Total forest area, forest area available (FAWS) and not available (FnAWS) for wood supply (in kha) in EU countries, as derived from the LUISA platform for 2010.
Tab. S2 - Total wood harvested for the historical period and for the two scenarios and percentage difference between 2030 and the average historical period 2000 - 2012 (m3 103).