forest biomass for energy in the eu: current trends ... › ... ›...

Forest biomass for energy in the EU: current trends, carbon balance and sustainable potential for BirdLife Europe, EEB, and Transport & Environment - FINAL REPORT - prepared by IINAS - International Institute for Sustainability Analysis and Strategy

EFI - European Forest Institute, and

JR - Joanneum Research Darmstadt, Madrid, Joensuu, Graz May 2014

IINAS, EFI, JR i Woody Bio EU

Content List of Tables ........................................................................................ iii

Acronyms ............................................................................................. iv

Executive Summary............................................................................... v

1 Introduction and Overview ............................................................. 1

1.1 Scope and Overview 1

1.2 Introduction 2

2 Sources and Potential of Woody Biomass........................................ 4

2.1 Woody Biomass Potentials with Low Biodiversity Risks 4

2.2 Methodology 6

2.3 Forest Biomass Mobilization Potentials 8

2.4 Results 10

3 GHG Balances of Woody Bioenergy ............................................... 19

3.1 Methodology for GHG Emission Calculation 21

3.2 Emission Factors 22

3.3 GHG Emission Factors for Using Forest Bioenergy 25

4 Scenarios for Woody Bioenergy in the EU ..................................... 26

4.1 The Reference (REF) Scenario 26

4.2 The GHG Reduction (GHG) Scenario 29

4.3 The Sustainability (SUS) Scenario 31

4.4 Summary of the Scenarios 33

5 Scenario Results ............................................................................ 33

5.1 Electricity Generation 34

5.2 Heat Production 35

5.3 Transport Fuels 36

5.4 Final Energy Demand 37

5.5 Primary Energy Supply 38

5.6 GHG Emissions from Bioenergy 41

5.7 Overall GHG Emissions from Energy Supply and Use 45

6 Conclusions and Policy Implications .............................................. 48

References .......................................................................................... 50

Short Study on “Forest biomass for energy in the EU: current trends, carbon balance and sustainable potential” prepared for BLE, EEB and T&E

IINAS, EFI, JR ii Woody Bio EU

List of Figures Figure 1 Forest Wood Mobilization Potential in this Study (left column) and Comparison with

Scenarios from Previous Studies (right column) ........................................................................ 9 Figure 2 Forest Biomass Potentials 2010-2030 from Final Harvest, Thinning and Pre-commercial

(PC) Thinning ................................................................................................................................. 12 Figure 3 Non-Forest Woody Bioenergy Potentials 2020 and 2030 ...................................................... 12 Figure 4 Effect of Removing Constraints for Residue Extraction from Protected Forests ................ 13 Figure 5 Effect of Increasing the Area of Strictly Protected Forests by 5% on Forest Biomass

Potentials in 2020 ......................................................................................................................... 14 Figure 6 Effect of Additional 5% Strict Forest Protection plus 5% retained Trees on Forest Biomass

Potentials ....................................................................................................................................... 15 Figure 7 Effect of Stricter Environmental Criteria on Availability of EU28 Forest Biomass in 2020

and 2030 ........................................................................................................................................ 16 Figure 8 (a) GHG Emission Factors excluding Supply Chain Emissions for One-time Biomass Use

for Austrian Forest Conditions and (b) Effective GHG Emission Factors for a Specific Supply Scenario (B2) for all EU28 ............................................................................................... 23

Figure 9 Renewable Electricity Generation in the EC REF scenario for the EU27 from 2010-2030 . 27 Figure 10 Heat Supply in the EC REF Scenario for the EU27 from 2010-2030 ...................................... 28 Figure 11 Electricity Generation in the EU27 from 2010-2030 ............................................................... 34 Figure 12 Final Energy Supply for Heat in the EU27 from 2010-2030 .................................................... 36 Figure 13 Final Energy Supply for Transport in the EU27 from 2010-2030 ........................................... 37 Figure 14 Final Energy Demand in the EU27 from 2010 to 2030 ............................................................ 38 Figure 15 Primary Energy Supply in the EU27 from 2010-2030 .............................................................. 39 Figure 16 Primary Woody Bioenergy in the EU27 from 2010-2030 by source ..................................... 39 Figure 17 Primary Woody Bioenergy in the EU27 from 2010-2030 per Sector .................................... 40 Figure 18 GHG Emissions from Woody Bioenergy 2010 - 2030 (20 Year Time Horizon) .................... 42 Figure 19 GHG Emissions from Woody Bioenergy 2010 - 2030 (100 Year Time Horizon) .................. 43 Figure 20 GHG Emissions from Woody Bioenergy 2010 - 2030 Depending on Time Horizons and

Forest Reference Cases ................................................................................................................ 44 Figure 21 Life-Cycle GHG Emissions from Energy Supply and Use in the EU27 from 2010-2030 with

GHG Emissions from Forest Bioenergy for 20 Year Time Horizon and Optimistic Forest Reference Case ............................................................................................................................. 45

Figure 22 Life-Cycle GHG Emissions from Energy Supply and Use in the EU27 from 2010-2030 with GHG Emissions from Forest Bioenergy for 20 Year Time Horizon and Pessimistic Forest Reference Case ............................................................................................................................. 46

Figure 23 Life-Cycle GHG Emissions from Energy Supply and Use in the EU27 from 2010-2030 with GHG Emissions from Forest Bioenergy for 100 Year Time Horizon ...................................... 47


IINAS, EFI, JR iii Woody Bio EU

List of Tables

Table 1 Key Facts on European Forests ..................................................................................................... 3 Table 2 Reference Potential for Biomass from EU28 Forests 2010-2030 .......................................... 11 Table 3 Potential Availability of Forest Biomass from Final Harvest, Thinnings and Pre-

commercial (PC) Thinning for Reference Mobilization, Additional Constraints, and Low Mobilization 2010- 2030 .............................................................................................................. 17

Table 4 Forest Biomass Potential in Energy Equivalents from Final Harvest, Thinning and Pre-commercial (PC) Thinning for Reference Mobilization, Additional Constraints, and Low Mobilization 2010-2030 ............................................................................................................... 18

Table 5 Summary of Reference Systems for various Biomass Types .................................................. 20 Table 6 Life-Cycle GHG Emission Factors for European Bioenergy Supply Chains in 2020 and

2030 ................................................................................................................................................ 21 Table 7 The effective greenhouse gas emission factors for a specific supply scenario (B2

Medium) by country ..................................................................................................................... 24 Table 8 Forest Bioenergy GHG Emission Factors for C Stock Changes ............................................... 25 Table 9 Scenario Description .................................................................................................................... 33 Table 10 Bioenergy Demand and Potentials in the EU27 from 2010-2030 ......................................... 40


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Acronyms AEBIOM European Biomass Association

BMU Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (German Federal Ministry for Environment, Nature Protection and Nuclear Safety)

EC European Commission

EEA European Environment Agency

EFI European Forest Institute

ETS EU Emissions Trading System

EU European Union

FAO Food and Agriculture Organization of the United Nations

GEF Global Environment Facility

GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH

IC Imperial College

IINAS International Institute for Sustainability Analysis and Strategy

IPCC Intergovernmental Panel on Climate Change

JRC Joint Research Centre

M Million

MS Member States

MtOE million tons of oil equivalent

RED Renewable Energy Directive (EU 28/2009)

t ton

tOE tons of oil equivalent

UNECE United Nations Economic Commission for Europe

UNEP United Nations Environment Programme

WWF World-Wide Fund for Nature


IINAS, EFI, JR v Woody Bio EU

Executive Summary

This study aims to clarify possibilities and implications of woody bioenergy supply for the natural environment and climate for the EU by 2020 and 2030. For this, the amount of forest-derived and woody biomass is estimated that could be sustainably supplied for energy uses without compromising material uses of wood. Particular attention is given to biodiversity and GHG emissions implications of woody bioenergy supply. The role of sustainable woody bioenergy in the future EU energy system was then analyzed for electricity, heat and transport fuels, taking into account the potentials for energy efficiency, and non-bioenergy renewables. Three scenarios were modeled to evaluate how sustainable woody bioenergy could be used by 2020 and 2030:

• The reference scenario (REF) is based on the EC 2013 PRIMES reference. Overall demand for material uses of wood will increase, and co-firing of imported pellets becomes relevant. In REF, bioenergy from EU forest will provide about 1700 PJ by 2030, and woody residues and SRC will contribute with 1300 PJ while about 750 PJ of wood pellets would be imported to the EU. Non-woody bioenergy would contribute about 600 PJ.

• Two contrasting scenarios - one for greenhouse-gas emission reduction (GHG), and one for ambitious sustainability (SUS) assume more stringent energy efficiency and higher renewable energy targets.

• The reduced GHG emissions scenario (GHG) considers C stock changes for forest bioenergy, and implements cascading use of woody material. With that, the use of EU forest products is reduced to 1100 PJ by 2030, and imports can be reduced by 80 %. Domestic woody bioenergy from residues, wastes and SRC would supply 3100 PJ by 2030, a doubling compared to the REF scenario. Non-woody bioenergy use would also increase to 1200 PJ, mainly from straw, and manure.

• The sustainable bioenergy scenario (SUS) assumes same demand as in the other scenarios but reduces forest bioenergy use to avoid associated risks, especially from imports. As in the GHG scenario, cascading use of woody material is massively increased. The use of EU forest bioenergy will be only about 350 PJ by 2030, and no woody bioenergy would be imported. The use


IINAS, EFI, JR vi Woody Bio EU

of woody residues, wastes and SRC would increase to 2700 PJ, and non-woody bioenergy would contribute about 3100 PJ.

The implementation of stringent energy efficiency measures in all scenarios would significantly reduce the final energy demands for heat and transport while electricity demand could remain almost constant. For electricity generation, the share of woody bioenergy will remain at 5% in the REF and GHG scenarios, while in the SUS scenario it will be less than 1 % by 2030. The amount of woody bioenergy used for heat would be about 8% (REF) and 9% (GHG+SUS) by 2030, but the source of the wood is very different in the scenarios. For transport, the contribution of woody bioenergy in the REF scenario would reach 2% by 2030, while in the GHG and SUS scenarios it will be 6% - 7%, respectively. The GHG scenario would further reduce feedstocks imports by 60% compared to the REF scenario by 2030, while the SUS scenario would phase-out imports completely. Both the GHG and SUS scenarios would instead use woody residues and straw for 2nd generation biofuels. The different role of woody bioenergy in the scenarios is depicted in the following figure for the respective EU energy demand sectors.

Source: IINAS calculations

The GHG emissions from bioenergy in the REF scenario would reach 59 to 116 Mt CO2eq by 2030, depending on the time horizon of the forest C balance, and the forest reference case assumed.


IINAS, EFI, JR vii Woody Bio EU

In contrast, bioenergy GHG emissions by 2030 would be -40 to 8 Mt CO2eq in the GHG scenario and -45 to -33 Mt CO2eq in the SUS scenario, respectively. This includes a reduction of GHG emissions from displaced electricity and construction materials due to cogeneration and cascading use of woody biomass in new buildings in the EU. The overall GHG balance must include emissions from fossil, nuclear and non-bioenergy renewables and was calculated using life-cycle data which also factor in fossil fuel imports accordingly, as shown in the following figure.

Source: IINAS calculations; GHG emissions from woody bioenergy are shown for the 20 year time horizon and

the pessimistic forest reference case (i.e. the worst-case)

This clearly indicates that biogenic GHG emissions from woody bioenergy are rather small, compared to the emissions from the remaining fossil fuels. The differences between the results for the 20-year time horizon and the ones for the 100 year time horizon are also quite small, showing that the discussion of the “carbon debt” associated with forest bioenergy becomes insignificant if sustainable and low-C options for forest bioenergy are used, and the total energy system is considered.


IINAS, EFI, JR viii Woody Bio EU

The scenario results also show that with regard to policy,

• sustainable forest biomass potentials in the EU will be reduced by up to 30 % by 2030 if stringent sustainability requirements are considered;

• sustainable forest biomass potentials still suffice to meet woody material demands if resource-efficient cascades are implemented, more paper recycled and post-consumer wood be re-used;

• reducing energy demand by implementing stringent energy efficiency targets is key;

• a sustainable scenario without bioenergy imports and using only about 25% of the EU forest bioenergy consumed in 2010 is possible as long as woody and agricultural residues are mobilized;

• cascading biomass use for energy, improving biogenic waste collection and recycling allow for significant net GHG reductions;

• if sustainable and low-C options for forest bioenergy are used, the “carbon debt” discussion is not relevant.

Current EU and Member State energy and climate policies do not stimulate these developments, though:

• Bioenergy, forest, and waste policies are fragmented and unaligned, and incentive schemes mainly address bioenergy without considering the full GHG emissions from bioenergy use.

• Bioenergy supply - especially from forests and for electricity/heat - is not subject to any coherent sustainability regulation. Only few Member States have started to develop respective policies, which might lead to imbalances within the EU if no framework regulation is implemented.

• Imports of woody bioenergy is - with very few exceptions - unregulated as well, but growing relevance of pellets for bioelectricity (co-firing) imply a respective need for EU-level action to avoid internal market distortions.

Last but not least, sustainable woody bioenergy supply also requires regulating biodiversity impacts for forests in a legally binding manner for both the EU, and imports from abroad.


IINAS, EFI, JR 1 Woody Bio EU

1 Introduction and Overview

1.1 Scope and Overview

The Brussels-based NGOs Birdlife Europe, European Environment Bureau and Transport & Environment commissioned the International Institute for Sustainability Analysis and Strategy (IINAS) in cooperation with the European Forest Institute (EFI) and Joanneum Research (JR) to carry out a brief study on sustainable woody bioenergy in the EU-27. The study aims to clarify implications of increasing forest bioenergy supply for the natural environment and climate until 2020, and to estimate the amount of forest-derived and woody biomass that could be sustainably supplied for energy uses within the EU to 2030 (quantitatively) and 2050 (qualitatively). Given this background the study: • classified woody biomass resources (Section 2.1) • identified woody bioenergy potentials in the EU which pose low biodiversity

risks (Section 3) • determined the greenhouse-gas emission balances of woody bioenergy for

several time horizons and reference assumptions (Section 4) • developed three scenarios for future woody bioenergy use in the EU for 2020

and 2030 (Section 5), and • determined the GHG balances of these scenarios (Section 6) as well as • implications for policy (Section 7). Due to limitations in scope and available budgets, the study had to simplify the modelling of the EU energy system: • Issues of renewable fluctuating power (e.g. storage, transmission, and

system effects) for electricity were not explicitly considered • No changes in the mix of non-bioenergy renewables (only minor adjustments

of total supply) and in the fossil fuel mix (e.g. to reduce GHG emissions) were made.

• No changes in the demand for food/feed and respective ex- and imports were considered, thus excluding possible changes in available land resources.

Cost changes and implied economic effects were also outside of the scope of the analysis, although some respective data is available upon request.



1.2 Introduction

Woody and especially forest biomass has a relevant role to play within the Renewable Energy Directive (RED) 2020 target of a 20 % renewable energy share, as well in the ongoing discussions about a 2030 energy and climate strategy, and the longer-term 2050 perspective of a resource-efficient and sustainable European energy system. From 1990-2010, total solid bioenergy production has more than doubled (Eurobserver 2012). In 2010, the EU used about 113 million tons of oil equivalent (MtOE) of primary biomass of which 9.5 MtOE were imported and 4.2 MtOE were exported (AEBIOM 2012). In 2010, about half of all woody biomass was used for energy purposes (AEBIOM 2012). 50% of total woody bioenergy is used in the residential sector and 25% each by the wood industry, and powerplants (UNECE-FAO 2012). According to the National Renewable Energy Action Plans (NREAPs), domestic supply of wood directly from forestry is expected to account for approx. 32 % of the total heat and power generated with biomass by 2020 (IC et al. 2012). Results of the EU Biomass Futures project show that projected EU woody demands are considerably lower than sustainable EU bioenergy potentials for 2020 and 2030 (IC et al. 2012). Still, mobilization of wood will not depend just on availability but on prices, and resource efficiency as well as possible biodiversity, climate and social impacts. Trade-offs between these factors need to be assessed. On the other hand, at present, there are various European policies under revision that will have significant effect on medium-term biomass mobilization such as the EU RED “iLUC” revision1, the sustainability criteria for solid and gaseous biomass2 and the future of the EU Emission Trading System (ETS). In 2010, the total area of forest in the EU27 area was over 157 million hectares (Mha) or almost 38% of land area (Forest Europe et al. 2011). Of this, 133 Mha was estimated to be available for wood supply. The following table describes the key facts of the European forests.

1 The EC proposal to revise the RED (EC 2012a), limiting the share of first generation biofuels from edible feedstocks in the transport sector to 5% and promoting advanced biofuels, and later proposals from the European Parliament and the Council found - as of late December 2013 - no majority.

2 The EC will publish a report on sustainability criteria for solid and gaseous biomass, applying the same approach as for biofuels and bioliquids under the EU RED, but no binding legislation is expected (Volpi 2014).



Table 1 Key Facts on European Forests

Unit North Central-

West Central-

East South-West

South-East

EU27

Forest area Mha 69.3 36.9 22.5 30.8 29.9 157.2

Forest as % of total land % 52.1 26.4 30.0 34.8 23.1 37.6

Forest per capita ha 2.16 0.14 0.26 0.26 0.25 0.32

Forest area available for wood supply

Mha 54.5 34.4 19.6 24.8 21.9 133.3

Growing stock per ha m3/ha 117 227 237 81 140 154

Net annual increment per ha# m3/ha 4.7 7.8 8.0 3.9 5.9 5.8

Fellings Mm3 180.5 172.4 93.2 29.3 16.9 469.3

Fellings as % of increment % 71.1 65.0 66.1 37.4 46.9 64.9

Roundwood removals from forest

Mm3 152.7 150.5 80.7 33.0 36.1 412.8

Forest undisturbed by man % 5.8 0.3 1.7 0.4 5.5 3.1

Semi-natural forest % 92.3 85.8 90.9 86.0 77.2 88.6

Plantations % 1.9 13.8 7.4 13.6 17.3 8.2

Share of forest dominated by introduced tree species

% 1.6 10.7 3.7 7.3 1.4 5.2

Share of forest area protected for biodiversity

% 6.6 10.4 3.5 23.3 5.5 10.6

Share of forest area protected for landscape

% 2.3 26.2 12.3 6.0 0.8 10.1

Share of forest area designated for the protection of soil, water and other ecosystem services

% 11.9 17.6 25.0 41.6 9.8 19.8

Share of forests in private ownership*

% 70.7 62.3 26.9 72.5 16.6* 59.6

Forest sector work force 1000 FTE

346 923 658 582 405 2560

Source: Forest Europe et al. (2011); FTE = full time equivalent employes



2 Sources and Potential of Woody Biomass

This study distinguishes between primary and secondary sources, as follows: • Primary biomass sources

• Woody biomass from forests (residues, thinnings, stemwood) • Woody biomass from landscape care, urban park management,

gardening • Short-rotation coppice on agricultural land

• Secondary biomass sources • Solid forest and wood industry by-products (sawmill residues, bark,

wood industry wastes) • Liquid forest industry by-products (black liquor)

A description of these categories is given in Annex 1.

2.1 Woody Biomass Potentials with Low Biodiversity Risks

Bioenergy policies which result in high levels of mobilization may have adverse effects on biodiversity (e.g. Verkerk et al. 2011a). The loss and degradation of the forest types that are naturally most diverse as well as the low levels of decaying wood in managed forests are the most relevant threats to forest biodiversity (Hanski, Walsh 2004). 37 Mha of the European forest area is protected for conservation purposes by the Natura 2000 network (EC, 2009; Forest Europe, 2011). The legal constraints on forest management in these areas range from a total ban on management to no limitations for sustainable management. Protected areas play a critical role in conservation of biodiversity, maintaining genetic resources, protecting important ecosystem functions and helping to protect many fragile human communities and cultural landscapes (Dudley, Phillips 2006). Protected Areas of various levels cover about 11% of forest area in the EU27. According to Forest Europe (2011), protected forests are classified in

(i) Non active intervention (1%), (ii) Minimum intervention (3%) and (iii) Conservation through active management (7%).

In Northern Europe and in some Eastern European countries, restrictive protection with no or minimal intervention dominates, whereas in Central and



Southern European countries, active management in protected areas is emphasized (Forest Europe 2011). The uniform forest structure associated with commercial forest management is a cause for concern when considering sustainability. The retention of some trees beyond the normal harvest cycle has been used as an approach to counteract this. It involves leaving some live and dead trees and small areas of intact forest in situ at the time of harvest (Gustafsson et al. 2012). Deadwood in the form of both standing dead trees and down wood and debris, is an essential structural component for biodiversity in forest systems (Janowiak, Webster 2010) and it has been acknowledged as a measure of habitat quality (EEA 2011). Due to shorter cycles, deadwood volumes can range from 2 m3/ha to 10 m3/ha in managed forests while in natural forest the amount of deadwood may reach more than 200 m3/ha (EEA 2011). Forest Europe (2011) reports that the average volume of deadwood, both standing and lying, in European Forests ranges from approximately 8 m3/ha in Northern Europe to 15 m3/ha in South-East Europe. Although retention levels can range more than forty fold, a minimum amount of 5-10 % in terms of the area or wood volume retained has been suggested (Gustafsson et al. 2012). Stricter environmental criteria If more strict environmental criteria are applied, we can also evaluate how this might impact on forest biomass potentials. A lower mobilisation rate in comparison with the reference potential was examined which applied a stricter set of environmental constraints (see Annex Report). Some significant differences between this and the reference mobilisation included stricter constraints on residue and stump removal from unproductive poor soils, slopes, shallow soils and peatlands. For the low mobilisation, application of fertilizer to limit detrimental effects of removing logging residue on the soil was not permitted. Stump extraction was also not permitted. The main differences between the reference and low mobilisations include:

• soil productivity was not considered a constraining factor for crown biomass removal after early thinning in the reference mobilisation as it was assumed that fertiliser could be applied to replace lost nutrients

• soil productivity was not considered a constraining factor for residue removal after final felling in the reference mobilisation as it was assumed that fertiliser could be applied to replace lost nutrients



• A maximum of 67% of residue removal from thinning was allowed on poor soils for the reference mobilisation but residue extraction was not allowed for the low mobilisations.

• 67% of stumps after final felling were extracted on poor soils for reference mobilisation, 0% for low.

• The reference mobilisation allowed stump extraction from peatland areas however in practice this only occurred in Fennoscandia where frozen winters occur, as constraints on soil bearing capacity prevented this extraction elsewhere.

• 67% of logging residues from thinning could be extracted from slopes up to 35% for the reference mobilisation, 0% for low.

• 67% of stumps from final felling could be extracted on slopes up to 35% for high mobilisation, 0% for low.

• Stump extraction did not occur in the low mobilisation.

Hanski and Walsh conclude that neither the current level of deadwood or protected areas are enough to avoid adverse effects on biodiversity (extinction debt3) in Northern and Central Europe forests. In order to reverse that situation, the amount of decaying wood at stand level should be 50 m3/ha (or 20-30 m3/ha if this average is met in wider areas). However, since this threshold is not achievable in managed forests they have proposed increasing the network of protected areas of various forest types to at least 10 percent of total forest area.

2.2 Methodology

For this study, we build on recent forest biomass resource assessments done for the EUwood and EFSOS II studies (Mantau et al. 2010; UN-ECE/FAO 2011) which used the large-scale European Forest Information SCENario model (EFISCEN) (Sallnäs 1990; Schelhaas et al. 2007). These studies examined biomass resource potentials under various development scenarios to 2020 and 2030. We evaluate the biomass potentials which are in line with various sustainability criteria and

3 Extinction debt refers to the numbers of species that will disappear sooner or later under the current environmental conditions



focus in particular on the quantification of biomass potentials which still leave room for more ambitious protection of biodiversity. The realisable potential for forest biomass supply was estimated for the period 2010 to 2030 in three steps. First, the maximum theoretical availability of forest biomass in Europe was estimated using EFISCEN (see box). These projections were based on recent, detailed National Forest Inventory (NFI) data on species and forest structure and provided the theoretical biomass potentials from broadleaved and coniferous tree species separately in the following assortment categories: stemwood; logging residues (i.e. stem tops, branches and needles); stumps; early thinning (thinning in very young stands; also referred to as pre-commercial thinning). Second, multiple environmental and technical, constraints were defined that reduced the amount of biomass that can be extracted from forests. Third, the theoretical potential according to EFISCEN was combined with the constraints to assess the realisable biomass potential from European forests (Verkerk et al. 2011a).

To assess biomass in branches, coarse roots, fine roots and foliage, stemwood volumes were converted to stem biomass by using basic wood density (dry weight per green volume) and to whole-tree biomass using age- and species specific biomass allocation functions. During thinning and final felling logging residues are formed. These residues consist of stemwood harvest losses (e.g. stem tops), as well as branches and foliage that are separated from the harvested trees. In

EFISCEN is a large-scale forest scenario model that assesses the availability of wood, and projects forest resource development on regional to European scale (Nabuurs et al., 2007; Eggers et al., 2008). A detailed model description is given by Schelhaas et al. (2007). In EFISCEN, the state of the forest is described as an area distribution over age- and volume-classes in matrices, based on forest inventory data on the forest area available for wood supply. Transitions of area between matrix cells during simulation represent different natural processes and are influenced by management regimes and changes in forest area. Growth dynamics are simulated by shifting area proportions between matrix cells. In each 5-year time step, the area in each matrix cell moves up one age-class to simulate ageing. Part of the area of a cell also moves to a higher volume-class, thereby simulating volume increment. Growth dynamics are estimated by the model’s growth functions whose coefficients are based on inventory data or yield tables.



addition to these logging residues, stumps and coarse roots are formed. In EFISCEN, it is possible to define which share of the residues and stumps/coarse roots are removed from the forest during thinning and final felling. Residues and stumps/roots that are left in the forest will decay eventually. During harvest operations more stemwood is felled than is removed from the forest. The proportion of volume from thinning or final felling being removed from the forest was calculated at country level, distinguishing between coniferous and broadleaved species (UNECE/FAO 2000). The proportion that is not removed as logs represents stemwood harvest losses and could be extracted as part of the logging residues.

2.3 Forest Biomass Mobilization Potentials

In this study, we examine biomass potential in the context of EU 2020 policy objectives. The reference potential we employ is the maximum realizable potential under B2 emissions4. This realizable potential is obtained by applying various environmental and technical constraints to a theoretical potential which is based on the average volume of wood which could be harvested, taking into account annual growth increment, age structure, stocking level and harvest losses (Mantau et al. 2010). It assumes a strong focus on using more wood for bioenergy and that policy recommendations have been successfully translated into measures that lead to an increased mobilization of wood, including formation of more forest owner associations and cooperatives which develop improved access of wood to the markets. It is further assumed that increased mechanization is adopted across Europe with existing technologies being shared between countries through improved information exchange. To exploit this potential, biomass harvesting guidelines would not be restrictive. The negative environmental effects of intensified use of forest resources would be weighed against and considered less important than negative effects of continued reliance on fossil fuels. Fertiliser application is allowed to compensate for the loss of nutrients through forest

4 The mobilization potentials for 2020 and 2030 utilized the B2 socioeconomic scenario from the IPCC (Nakicenovic et al. 2000). The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social, and environmental sustainability. In the B2 reference future, production and consumption growth rates slow down over the outlook period, with the exception of sawn-wood consumption. This slowing down of consumption growth is most pronounced for paper products and wood pulp. This is consistent with a future world characterised by heightened environmental concern, where, e.g., a higher demand for bioenergy drives up the prices of inputs for the wood-based panels and pulp & paper industry, while at the same time the sawn-wood industry will mainly benefit from this development through a growing demand for energy efficient and renewable construction.



residue extraction. In this study we evaluate the effect of applying various additional sustainability constraints to the reference forest biomass potential.

Figure 1 Forest Wood Mobilization Potential in this Study (left column) and Comparison with Scenarios from Previous Studies (right column)

Source: EFI compilation

2.3.1 Constraints to Forest Biomass Potential

The theoretical forest biomass potentials estimated by EFISCEN are higher than what can realistically be supplied from the forest due to various environmental, social, technical, and economic constraints. The EUWood study identified quantifiable constraints and applied them to the theoretical potential (see Annex). The constraints considered in this study include site productivity, slope, soil surface texture, depth, compaction risk, bearing capacity, retained trees and protected forest. The constraints applied are described in further detail in the Annex. For each constraint, a raster layer was created in ArcMap, with a resolution of 1 km. Extraction rates were assigned to the constraints according to the tables

• Maximum realizable potential• Comparable with: • EU Wood High Mobilisation Scenario • EFSOS II Promoting Wood Energy Scenario• Biomass Futures Sustainability Scenario

(non-forest)

REFERENCE

• Effect of removing constraints on stump and residue extraction from protected forest

• Effect of increasing area of strictly protected forest

• Effect of increasing protected forest area plus retained trees.

REFERENCE with

Biodiversity Constraints

• Strictest environmental criteria applied • Low wood mobilisation, comparable with:• EU Wood Low Mobilisation scenario• EFSOS II Priority to Biodiversity Scenario• Includes additional 5% retained trees

LOW - Strictest Site Constraints



above. On a cell-by-cell basis, all relevant layers were combined and the minimum extraction rate was defined for each cell. This was done separately for thinning residues, final felling residues and stumps. The resulting raster layers were then combined with a forest map, also on a 1 km resolution. Using zonal statistics, with an EU28 country layer or an EFISCEN region layer as zones, the weighted average per zone was then calculated.

2.3.2 Assessment of Biodiversity Risks

To examine the effect of increasing the area of protected forest on biomass potentials we used the previous resource assessments of EUWood and EFSOS-II and carried out a more detailed examination and quantification of biodiversity impacts. The sustainability constraints that were used to calculate the forest biomass potentials with EFISCEN were adjusted in order to examine their effect. This provides more information on how increasing the area of protected forests impacts on the biomass potentials from European forests. Protected Areas: Where management in protected areas is allowed under conservation designations, it is implemented as 'close-to-nature' or similar low-impact management (EEA 2007), with no or very limited residue or stump extraction. However, in fire prone areas, leaving residues in the forest could increase the forest fire risk. This study assumed that residues could only be harvested in protected areas that have a high or very high fire risk. Retained trees: The effect of an increase of 5 % in retained trees was evaluated. Stricter environmental criteria: If more strict environmental criteria are applied, we can also evaluate how this might impact on forest biomass potentials. A low mobilization rate by comparison with the reference was examined which applied a stricter set of environmental constraints (see Annex).

2.4 Results

2.4.1 Potential of European Forests for Wood Supply

Biomass potentials from forests were calculated for EU28 countries for 2010-2030 (Table 2) – note that this includes potential uses from both industry and bioenergy. The largest contributor to available volumes is stemwood from thinnings and final harvest, while pre-commercial thinnings (using EUwood/EFSOS II assumptions) are rather low.



Table 2 Reference Potential for Biomass from EU28 Forests 2010-2030

Mm3 overbark in 2010 2020 2030 Austria 35.93 43.01 41.90 Belgium 5.22 5.97 5.80 Bulgaria 8.13 9.87 9.91 Croatia 7.21 8.34 8.16 Cyprus 0.04 0.04 0.04 Czech Republic 25.11 29.96 27.66 Denmark 3.97 4.59 4.87 Estonia 13.12 14.57 13.88 Finland 85.51 111.89 111.60 France 88.11 101.69 108.02 Germany 103.25 128.26 124.15 Greece 4.45 5.41 4.97 Hungary 10.81 12.90 12.64 Ireland 3.12 4.42 5.16 Italy 26.74 29.19 28.07 Latvia 18.39 20.23 24.65 Lithuania 10.54 12.26 13.40 Luxembourg 0.98 1.10 1.04 Malta 0 0 0 Netherlands 1.48 1.74 1.90 Poland 58.41 68.94 67.32 Portugal 10.80 12.36 13.78 Romania 32.54 36.73 36.13 Slovakia 11.38 12.52 12.93 Slovenia 8.43 9.41 9.10 Spain 24.79 30.52 29.80 Sweden 111.92 142.98 154.09 UK 15.45 17.28 17.60 Grand Total 725.86 876.20 888.57

Source: EFISCEN calculations; data in Mm3 overbark; note that no data was available for Malta. Croatian figures derived from EU averages, Cyprus figures derived from average Greek values



Figure 2 Forest Biomass Potentials 2010-2030 from Final Harvest, Thinning and Pre-commercial (PC) Thinning

Source: EFISCEN calculations – EFI compilation; REF = reference potential; Res = residues; PC = pre-commercial

Figure 3 Non-Forest Woody Bioenergy Potentials 2020 and 2030

Source: IC et al. (2012) - Sustainability Scenario of the Biomass Futures project

Bioenergy potentials from non-forest biomass (see Annex for definitions) were also derived from data compiled in the Biomass Futures project (IC et al. 2012). To allow for comparison with forest potentials, the data is presented in PJ. These

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assortments total 4057 PJ and 2231 PJ in 2020 and 2030, respectively. The drop in potential especially for SRC up to 2030 is due to carbon mitigation requirements becoming stricter in the sustainability scenario which considers GHG emissions from indirect land use change (IC et al. 2012).

2.4.2 Effects of More Protected Forest Areas

To evaluate the effect of the constraint on residue extraction from protected forest area, this constraint was removed from the model calculation, which results in an increase of total volume available from 876 Mm3 to 918 Mm3 in 2020 (Figure 4), and a respective increase from 888 to 931 Mm3 in 2030.

Figure 4 Effect of Removing Constraints for Residue Extraction from Protected Forests

Source: EFISCEN calculations – EFI compilation; REF = reference potential; PC = pre-commercial

The effect of an increase of 5% in strictly protected forests was also quantified (Figure 5). This resulted in a decrease of 42 Mm3 in available volume (from 876 to 834 Mm3) in 2020 and 43 Mm3 (from 888 to 845 Mm3) in 2030.

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Figure 5 Effect of Increasing the Area of Strictly Protected Forests by 5% on Forest Biomass Potentials in 2020


2.4.3 Effects of Higher Tree Retention

It is difficult to evaluate the effect of forest biomass mobilization on standing deadwood over the short time frame of this study as any policy objectives would take much longer than twenty years to make an impact. However it was possible to evaluate how an increase in retained trees would impact on the forest biomass potentials. Figure 6 shows that a 5% increase in retained trees in combination with a 5% increase in strictly protected forest applied to the reference potential would result in a decrease from 876 Mm3 to 792 Mm3 in available forest biomass by 2020 and from 888 Mm3 to 803 Mm3 in 2030.

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Figure 6 Effect of Additional 5% Strict Forest Protection plus 5% retained Trees on Forest Biomass Potentials

Source: EFISCEN calculations - EFI compilation; REF = reference potential; PC = pre-commercial

2.4.4 Effect of stricter environmental criteria

Some significant differences between the stricter set of environmental constraints and the reference mobilisation included stricter constraints on residue removal from unproductive poor soils and a maximum of 70% residue removal allowed on other soils. For the low mobilisation which had the strictest environmental constraints, application of fertilizer to limit detrimental effects of removing logging residue on the soil was not permitted. Stump extraction was also not permitted. These stricter environmental constraints had a significant effect on biomass availability (Figure 7). The low mobilisation would give potential volumes of 583 Mm3 or 33% less available biomass compared with the reference mobilisation in 2020.

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Figure 7 Effect of Stricter Environmental Criteria on Availability of EU28 Forest Biomass in 2020 and 2030


The reference mobilisation allowed limited residue extraction from forests located on peatland (see Annex Report Section 1). A 33% maximum extraction rate of stumps and residues was applied for thinning and final felling. Stump and residue extraction was not permitted from peatland forests for the Low mobilisations. In the EU Wood Methods report (Mantau et al. 2010), a sensitivity analysis was carried out which evaluated the effect of increased removal of residues and stumps from forests on peatlands (from 0%-33%). If the restrictions on residue extraction on peatlands were reduced for environmental reasons (i.e. allow more extraction of residues), it was found in many countries to be technically still difficult to extract biomass from these areas due to the low soil bearing capacity. Finland and Sweden were an exception to this, as harvesting on frozen soil is possible in these countries. This means that other constraints are often the main limiting factor and do not allow much more residues or stumps to be extracted.

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Table 3 Potential Availability of Forest Biomass from Final Harvest, Thinnings and Pre-commercial (PC) Thinning for Reference Mobilization, Additional Constraints, and Low Mobilization 2010- 2030

Volumes (Mm3 overbark) PC Thin

stemwood PC Thin residues

Thin stemwood

Thin residues

Thin stumps

Harvest stemwood

Harvest residues

Harvest stumps Total

REF 2010 9.4 2.1 223.8 16.5 0.0 388.1 76.5 9.4 725.8 REF 2020 11.0 4.7 218.7 55.7 35.3 402.7 84.0 64.1 876.2

REF 2020 without dedicated constraints on stump and residue removal in protected areas 11.0 5.7 218.7 65.7 41.5 402.7 98.1 75.2 918.7 REF 2020 + additional 5% strict forest protection 10.4 4.5 207.8 52.9 35.3 382.6 79.8 60.9 834.1 REF 2020 + add. 5% strict forest protection and 5% retention trees 9.9 4.2 196.9 50.1 35.3 362.4 75.6 57.7 792.1 REF 2030 10.4 4.4 223.6 57.9 36.3 404.9 85.4 65.6 888.6 REF 2030 without dedicated constraints on stump and residue removal in protected areas 10.4 5.3 223.6 68.2 42.5 404.9 99.5 76.8 931.2 REF 2030 + additional 5% strict forest protection 9.9 4.1 212.5 55.1 36.3 384.6 81.2 62.4 846.0 REF 2030 + add. 5% strict forest protection + 5% retention trees 9.4 3.9 201.3 52.2 36.3 364.4 76.9 59.1 803.3 LOW 2020 9.4 0.6 185.3 0.0 0.0 340.9 48.4 0.0 584.5 LOW 2030 8.9 0.5 189.2 0.0 0.0 342.7 48.8 0.0 590.1

Source: EFISCEN calculations – EFI compilation



Table 4 Forest Biomass Potential in Energy Equivalents from Final Harvest, Thinning and Pre-commercial (PC) Thinning for Reference Mobilization, Additional Constraints, and Low Mobilization 2010-2030

Energy (PJ) PC Thin stemwood

PC Thin residues

Thin stemwood

Thin residues Thin stumps Harvest

stemwood Harvest residues

Harvest stumps Total

REF 2010 81.8 18.3 1947.1 143.6 0.0 3376.5 665.6 81.8 6314

REF 2020 95.7 40.9 1902.7 484.6 307.1 3503.5 730.8 557.7 7623

REF 2020 without dedicated constraints on stump and residue removal in protected areas

95.7 49.6 1902.7 571.6 361.1 3503.5 853.5 654.2 7992

REF 2020 + additional 5% strict forest protection

90.5 39.2 1807.9 460.2 307.1 3328.6 694.3 529.8 7258

REF 2020 + add. 5% strict forest protection and 5% retention trees

86.1 36.5 1713.0 435.9 307.1 3152.9 657.7 502.0 6891

REF 2030 90.5 38.3 1945.3 503.7 315.8 3522.6 743.0 570.7 7730

REF 2030 without dedicated constraints on stump and residue removal in protected areas

90.5 46.1 1945.3 593.3 369.8 3522.6 865.7 668.2 8101

REF 2030 + additional 5% strict forest protection

86.1 35.7 1848.8 479.4 315.8 3346.0 706.4 542.9 7361

REF 2030 + add. 5% strict forest protection + 5% retention trees

81.8 33.9 1751.3 454.1 315.8 3170.3 669.0 514.2 6990

LOW 2020 + 5% retention trees 82 5 1612 0 0 2966 421 0 5086

LOW 2030 + 5% retention trees 77 4 1646 0 0 2981 425 0 5134

Source: EFISCEN calculations; and conversion into energy by IINAS; energy content expressed as lower heating value of air-dry wood



3 GHG Balances of Woody Bioenergy

In addition to the biodiversity risks, the direct and total greenhouse-gas (GHG) emissions from bioenergy were analyzed. The emissions from bioenergy systems can be separated into two components: a) Life-cycle emissions: These are the emissions occurring due to biomass

combustion and upstream processes (e.g. fossil fuel for harvesting, transport, processing) - see Section 3.1.1.

b) C stock change emissions: These are CO2 emissions from changes in the forest carbon stock, e.g. extraction of forest thinnings for bioenergy, and C absorption as the forest regrows. In the case of forest residues, a time series of emissions occurs if the residues were left to decay.

To calculate the C-stock change emissions it is necessary to define a bioenergy system (what happens to the carbon stocks when biomass for energy is extracted) and a reference system (what happens to the carbon stocks when biomass is not used for energy). It is important to realise that the reference system and its associated reference emission series is counterfactual. It should represent the most likely situation in absence of the bioenergy system. The selection of reference system effects the net emissions and energy dramatically. Table 5 list the assumed reference uses of biomass in the analysis. In some cases where the choice of reference system is not clear, it is advisable to produce two net emission and energy series which represent the systems that produce the lowest and highest net emissions and energy. For example, for the analysis of pre-commercial thinning an optimistic and pessimistic model were created. The C stock change emissions were calculated for 20 and 100 year time horizons5 to show the sensitivity of the results. Furthermore, optimistic and pessimistic forest reference cases were used in the calculation for the same reason.

5 The time-horizon indicator is the sum of emissions over the specified number of years due to of an action today. By using the time varying nature of the changes in carbon stocks is captured. However, the time-horizon is an indicator of emissions and not the actual emissions in a given year from an action sometime previously.



Table 5 Summary of Reference Systems for various Biomass Types

Biomass Source Reference System

Forest residues Residues remain in the forest and decay naturally without catastrophic disturbance

Stumps Stumps remain in the forest and decay naturally without catastrophic disturbance

Pre-commercial thinning

Optimistic option: Thinnings remain in the forest and decay naturally without catastrophic disturbance. The forest grows in a similar manner with and without biomass use for energy. Pessimistic option: Pre-commercial thinning does not occur. The unthinned forest has higher C stocks than the thinned forest (i.e. parallel growth curves)

Commercial Thinning

Thinning occurs in the same manner as in the bioenergy system, but the biomass from thinning is used for a mixture of purposes:

% Sawnwood 0% % Panels 25% % Paper 22% % Energy 53%

Advanced Harvests

The forest is harvested, but later than the “optimal” time. In the bioenergy systems, the forest is harvested at the “optimal time”. The delay, as compared to the bioenergy system, allows for an increase in forest biomass, and biomass at final harvest. The same proportion extracted biomass is used directly for sawnwood, panels, paper and energy in both the bioenergy and reference systems

Source: Consortium assumptions; for imported woody bioenergy (pellets), the same assumptions were applied



3.1 Methodology for GHG Emission Calculation

3.1.1 Life-Cycle GHG Emissions

The life-cycle emissions were calculated with GEMIS6, assuming that these are constant for all regions of Europe, but different over time (2010-2030)7. The fossil-fuel emissions (from coal, oil, natural gas)8 and life-cycle GHG emissions from nuclear and non-biomass renewables are also calculated with GEMIS for 2020 and 2030 as EU averages. The life-cycle GHG emissions (e.g., from transportation, processing) per unit of energy produced from woody biomass are 2 to 10 times smaller than the emissions from the changes in forest carbon stocks over a short-time frame (20 years), depending on the type of biomass feedstock.

Table 6 Life-Cycle GHG Emission Factors for European Bioenergy Supply Chains in 2020 and 2030

CO2eq in g/MJoutput 2020 2030 pellets-EU forest-products 6.4 5.8 pellets-EU wood-industry 3.9 3.5 pellets-EU SRC 11.5 10.9 pellets-US-import 16.2 15.6 wood-logs EU 0.0 0.0 BtL-black-iquor-EU 0.3 0.3 BtL-forest-residue-EU 37.2 32.9 BtL-SRC-EU 49.4 44.9 EtOH2G-forest-residues-EU 7.6 7.5 EtOH2G-SRC-EU 18.4 18.0

Source: GEMIS version 4.8 (IINAS 2013); note that the emission data exclude CO2 emissions from forest C stock changes, and also assume no indirect land use changes (iLUC)

3.1.2 Emissions from C stock changes in Forests

The time-varying GHG emissions from C stock changes in forests were modeled to reflect the growth rate of forests, decay rates of residues depend on forest

6 GEMIS (Global Emissions Model for integrated Systems) is a public-domain (i.e. freely available) life-cycle model and database maintained by IINAS (see www.gemis.de).

7 This simplification is needed to reduce the data requirements. From earlier projects, bioenergy life-cycle data for most EU Member States is available, but shows comparatively minor differences.

8 The oil and gas comparators for 2030 can be varied also to reflect synthetic crude oil (“Tar Sands”) and shale gas (“fracking”). In the results presented here, the average oil and gas emissions were used which exclude these sources.


http://www.gemis.de/


type, climate (temperature and precipitation) and residue quality, and the time horizon (20 vs. 100 years) as well as the forest reference case (counterfactual situation without bioenergy extraction). A more detailed description of the modelling assumptions and data background is given in the separate Annex Report.

3.2 Emission Factors

There are two types of emission factors to consider; the emission factor from the consumption of an amount of biomass in a single year, and the effective emission factor of the continuous consumption of biomass. The latter is calculated by summing the emissions from a specific year to the year of interest and dividing it by the total biomass consumed over the same period, hence it is the time average emission factor. Figure 8a shows the emission factors excluding supply-chain emissions of the presented models for Austrian forests and conditions. For example, the emission factor for the use of residues decreases quickly with time. The effective emission factor, however, is dependent on the amount of biomass consumed in specific year. For example, if the amount of bioenergy from residues is increasing the effective emission factor will decrease less quickly than for the case of consumption in a single year. This occurs because the effective emission factor includes both biomass extracted for many years and extracted recently. Since more biomass is extracted recently, it has a greater impact on the effective emission factor. Figure 8b shows the effective emission factors of the various biomass sources for a specified biomass scenario. Of the five models, only biomass from the two residues the advanced harvest biomass have intensities that over time are below the intensities of fossil fuels (coal = 88 g CO2/MJ, oil = 73 g CO2/MJ, and natural gas = 51 g CO2/MJ). Typically the intensity should start somewhere near that of wood without regrowth (94 g CO2/MJ). The advanced harvest model starts with a lower intensity because there are wood products created. The commercial thinning model starts with a higher intensity because material products are forsaken to create energy. The amounts above and below the typical value are approximately the same.



Figure 8 (a) GHG Emission Factors excluding Supply Chain Emissions for One-time Biomass Use for Austrian Forest Conditions and (b) Effective GHG Emission Factors for a Specific Supply Scenario (B2) for all EU28

a.

b. Note: Joanneum (2014) own elaboration; stumps and stemwood are purposely not considered in the B2 medium

mobilization scenario. Their effective emission factors are not shown.

When the models are applied to a biomass supply scenario (Figure 8b), the emission factors are different than for the individual models because intensity of the scenario is calculated as the sum of emissions to a specific year divided by the sum of energy to the same year.



Table 7 The effective greenhouse gas emission factors for a specific supply scenario (B2 Medium) by country

Source: Joanneum Research calculations

Table 7 shows the effective emission factors by country for the different types of biomass. The general trend is that warmer countries have faster decay rates and hence lower emission factors from the use of residues. This was also suggested by Repo et al. (2011). There are slight variations in the effective emission factors of other biomass sources too. For example, countries with longer rotation lengths have a lower emission factors from the use of harvest stemwood than do countries with shorter rotation period. This is because the typical current harvest delay is assumed to be 1/3 of the rotation period.

CountryPre co mmT hin

Ste mwo o dPre co mmT hin

Bio ma ssT hin

Ste mwo o dT hinRe s

T hinStump

Ha rve stSte mwo o d

Ha rve stRe s

Ha rve stStump

AT 109 109 149 150 0 89 65 0BE 111 111 152 153 0 109 55 0HR 108 108 149 150 0 101 36 0CY 0 0 151 152 0 102 47 0CR 109 109 149 150 0 90 61 0DK 110 110 150 152 0 100 60 0ES 108 108 149 150 0 90 50 0FI 111 111 150 151 0 99 68 0FR 109 110 150 151 0 101 46 0DE 109 110 149 150 0 95 55 0GR 0 0 151 152 0 104 43 0HU 109 109 150 151 0 105 39 0IE 115 115 154 156 0 108 61 0IT 108 108 150 151 0 108 36 0LV 139 139 158 160 0 49 53 0LT 110 110 150 151 0 105 60 0LU 115 115 157 158 0 59 42 0MA 0 0 0 0 0 0 0 0NL 109 108 150 151 0 100 51 0PO 110 110 150 151 0 98 60 0PT 110 110 152 153 0 103 38 0RO 109 109 150 151 0 108 47 0SK 110 111 151 151 0 105 53 0SI 109 109 150 151 0 105 54 0ES 108 108 149 150 0 90 50 0SW 110 110 149 150 0 89 70 0UK 110 111 151 151 0 102 55 0



3.3 GHG Emission Factors for Using Forest Bioenergy

Based on the model calculations, the emission factors for woody bioenergy were determined for two key assumptions of the reference forest system for pre-commercial thinnings and residues from commercial thinnings:

• In the optimistic forest reference case it is assumed that this biomass would remain in the forest and decay, i.e. release CO2 over time.

• In the pessimistic forest reference case it is assumed that this biomass would be taken out of the forest but used as feedstock for pulp & paper, and other low-quality wood use, i.e. without immediate CO2 release.

Both cases were calculated for the 20 and 100 year time horizons. The respective emission factors are given in the following table.

Table 8 Forest Bioenergy GHG Emission Factors for C Stock Changes

Emission factor in g CO2/MJ for

Pre-comm. thinning

stemwood

Pre-comm. thinning residues

Thinning stemwood

Thinning residues

Harvest residues

20 a optimistic 3.6 3.6 116.0 3.0 3.0 20 a pessimistic 118.0 118.0 118.0 3.0 3.0 100 a (both cases) 0.2 0 0.1 0 0

Source: Joanneum Research calculation; note that emissions do not include supply chains or the emissions saved from the displaced fossil energy



4 Scenarios for Woody Bioenergy in the EU

In order to estimate the future use of sustainable bioenergy in Europe, three scenarios were modelled to determine how much bioenergy would be needed by 2020 and 20309 for the different end-uses (i.e. electricity, heat, and transport) as well as the respective primary energy use, and GHG emissions. The scenarios consist of the reference (REF), a “reduced GHG emissions” (GHG) and a “sustainable bioenergy” (SUS) case. The REF scenario is based on the most recent EC reference scenario (EC 2013), while for the other two scenarios, the EUwood study (Mantau et al. 2010; Verkerk et al. 2011b), results of the Biomass Futures project (IC et al. 2012), EFSOS II (UNECE, FAO 2011) as well as Teske et al. (2012) and GP, EREC, GWEC (2012) were considered (see details in Section 5 of the Annex report). In the GHG and SUS scenarios, additional cascading use of wood was applied as a simplified strategy10 to increase sustainable wood use in buildings. The respective additional sawmill residues as well as improved recycling of woody material were considered accordingly.

4.1 The Reference (REF) Scenario

The REF scenario was built using the 2013 PRIMES reference scenario (EC 2013) for electricity and transport fuel demand and supply as well as end-energy and primary energy supply mix. Due to a lack of access to the PRIMES data for heat, the respective demand and supply were modeled using data from the reference case of the EC 2050 roadmap (EC 2011). The assumed contributions of non-biomass renewables to the final energy demand is shown in the following figures.

9 Due to the lack of consistent projections for both the energy and agriculture/forest sector for 2050, only qualitative perspectives could be derived for this timeframe.

10 See e.g. Keegan et al. (2013); Sikkema et al. (2013).



Figure 9 Renewable Electricity Generation in the EC REF scenario for the EU27 from 2010-2030

Source: IINAS calculation based on EC (2013)

REF assumes that electricity from non-biogenic renewables increases from 2010 to 2030 by a factor of 2.3 while electricity from bioenergy increases by a factor of 1.7, with a rising share from woody bioenergy (see Section 5.1). In the REF heat sector, geothermal and solar energy will gain significant shares until 2030, while biomass will increase only marginally (see Section 5.2). The non-bioenergy heat supply in REF is shown in the following figure.



Figure 10 Heat Supply in the EC REF Scenario for the EU27 from 2010-2030

Source: IINAS calculation based on EC (2013). Note that electricity and district heat also come partially from

renewables.

In the transport sector, the EC 2013 REF scenario assumes that the RED target of 10% renewable transport fuels by 2020 is met (with double-counting), and that the renewable transport share is increased slightly to 11% by 2030 (excluding multipliers), but only with a slight increase of biofuels share, as the (renewable) electricity share in transport increases far more. The overall demand for biofuels in the REF scenario is assumed to nearly double by 2020 (compared to 2010), while remaining on this level by 2030. As regards wood resources, the REF scenario by 2030 requires approx. 3700 PJ of woody bioenergy, mainly for electricity and heat (less than 10% for biofuels). Of that, about 2400 PJ come from forests (30% of that from imports), and about 1300 PJ from woody residues, and SRC. In particular, different types of feedstocks with different share over the time were assumed to be used. This scenario assumes that for bioelectricity the feedstocks used are wood chips and wood pellets from EU forest residues and EU woody residues as well as imported wood pellets, all of them increasing their contribution up to 2030. For bioheat, wood logs from EU forest residues (more than halving their contribution up to 2030), wood pellets from woody residues (increasing their contribution) and domestic SRC (small contribution) and imported wood pellets (increasing their contribution). For biofuels for transport,



it is mainly considered BtL from EU black liquor and BtL from imported forest residues (by 2030). In addition, the domestic non-energy use of woody materials will be approx. 5100 PJ by 2030 (energy equivalent, see Section 5.2 in the Annex Report). Thus, the total woody biomass resource demand in the REF scenario by 2030 – expressed in energy terms11 - will be approx. 8800 PJ. The high mobilization potential of EU woody biomass from forests - without any sustainability constraints - is about 7700 PJ (see Table 5b) plus approx. 2500 EJ of woody residues and SRC, i.e. a total of about 10000 PJ of woody biomass in the EU by 2030 (excluding post-consumer wood). The EU domestic wood potential alone could theoretically supply the total EC REF demand for woody products by 2030 - but this would be significantly more costly than imported wood. Therefore, the REF scenario assumed that about 750 PJ of wood will be imported by 2030 which represents a drastic increase compared 2010 when woody bioenergy imports were in the order of 110 PJ.

4.2 The GHG Reduction (GHG) Scenario

In the GHG scenario, significantly improved energy efficiency measures were assumed based on recent studies12 to achieve a 30% reduction of final energy demand by 2030 compared to the REF scenario, and a 22% reduction compared to 2010. This leads to significantly reduced demands for electricity, heat and transport fuels in all sectors. These very ambitious targets to reduce final energy demand in all sectors are achievable, as they are based on detailed potential studies on the EU level13. Next, the amount of renewables was increased using the mix given in the EC reference case, and taking into account the non-biomass renewable supply data given in Teske et al. (2012) to achieve renewable shares in final energy demand (including renewable electricity and district heat) of 25% by 2020, and of 45% by 2030, respectively. Next, the use of woody bioenergy was changed, reflecting the aim to reduce the CO2 emissions associated with forest C stock changes (see Section 3).

11 Note that the non-energy wood demand expressed as an energy equivalent is based on the lower heating value of the wood, even if the wood is not used for energy.

12 See ISI (2012a+b); OEKO (2011); Teske et al. (2012); GP, EREC, GWEC (2012) 13 See studies given in footnote 12.



For this, the use of forest products for bioenergy from EU forests was reduced by 25% compared to the REF scenario by 2030, and the use of imported forest products was reduced by nearly 80%. To balance this, the use of woody residues (mainly from sawmills) and from SRC was increased drastically, as these feedstocks have lower CO2 emissions. In the electricity sector, the woody bioenergy contribution by 2030 is reduced by 12% compared to the REF scenario, and woody bioenergy from EU forests and imports are decreased by about 14% and 62%, respectively. In heat supply in 2030, EU forest products are reduced by 57% compared to REF, and imported woody biomass is completely phased-out. In parallel, use of EU woody residues and SRC increased accordingly. In the transport sector, 1G biofuels - both domestic and imported - were nearly replaced by 2030 through 2G biofuel from domestic lignocellulose (mainly straw and black liquor) and all woody product imports are phased-out. For bioelectricity the feedstocks used are wood chips and wood pellets from EU forest residues (decreasing their share in comparison with 2010) and EU woody residues (significantly increasing their contribution), wood chips from EU landscape care wood (with a minor contribution) as well as imported wood pellets (maintaining their contribution in levels comparable to those of 2010). For bioheat: wood logs from EU forest residues (with a relevant decreasing of their contribution), wood pellets from woody residues (significantly increasing their contribution) and domestic SRC (with a low contribution) and imported wood pellets (increasing their contribution). For biofuels for transport, it is mainly considered BtL from EU black liquor and BtL from forest residues, both of them significantly increasing up to 2030. A key additional assumption of the GHG scenario is to mobilize sustainable stemwood from EU forests for increased cascading use. For this, additional wood use in 2.5% of new residential buildings in the EU by 2020 (increasing to 5% by 2030) was assumed which leads to a substitution of concrete and steel as construction materials, and also increases the amount of sawmill residues. The additional stemwood demand from this increase in material use of wood represents 17 Mt of roundwood (about 34 Mm3) by 2020 which would increase to 67 Mt (about 134 Mm3) by 2030, and would displace some 8 Mt of concrete and 3 Mt of steel by 2020, and some 33 Mt of concrete and 13 Mt of steel by 2030, respectively.



The additional roundwood demand represents an energy equivalent of approx. 310 PJ by 2020, and 1250 PJ by 2030, respectively. The sustainable low-mobilization potential for stemwood from thinnings and final harvest represents approx. 4500 PJ (see Table 5b). The wood demand for material use represents about 5000 PJ (see Section 5.2 in the Annex Report), of which about 1000 PJ are used for pulp & paper production, i.e. approx. 20% of industrial wood use. It is assumed that increased cascading use of wood for paper and packaging could achieve a 50% reduction of fresh fiber needs by 2030 and 20% of low-quality material wood use (for short-live building materials, and furniture) could be re-used so that an equivalent of 500 PJ of woody material previously used for fiber and some 500 PJ of low-quality wood uses can be mobilized by 2030 with cascading technologies in the European wood-using industries. Thus, a potential sustainable supply of 5500 PJ of domestic EU wood products would be able to meet the (reduced) material demand of 4000 PJ, leaving approx. 1500 PJ for bioenergy use. Furthermore, the additional wood for building materials will provide some 100 PJ (by 2020) and 380 PJ (by 2030) of sawmill residues which can be used for bioenergy. Finally, post-consumer wood is assumed to be increasingly recycled for energy which would provide some 1200 PJ by 2030. The available domestic woody bioenergy potential in the GHG scenario is thus about 1500 PJ from EU forests (approx. 170 Mm3), and about 3800 PJ from residues, wastes and SRC, i.e. a total of 5300 PJ. The woody bioenergy demand in the GHG scenario would reach about the same level (5300 PJ by 2030) and would be supplied by the domestic potentials plus a minor amount of imported wood pellets (around 160 PJ), i.e. the import demand for woody bioenergy could be reduced by nearly 80% compared to REF. In parallel, use of EU non-woody bioenergy would increase: about 630 PJ of straw for biogas and biofuels and 550 PJ of manure for biogas would be mobilized by 2030, a nearly 3-fold increase compared to the REF scenario.

4.3 The Sustainability (SUS) Scenario

In the SUS scenario, the same demand levels for electricity, heat and transport fuels as in the GHG scenario are assumed, but the use of woody bioenergy is changed not only to reduce the CO2 emissions associated with forest C stock changes (see Section 3), but also to reduce biodiversity risks associated with EU bioenergy and respected imports - both for wood and (biofuel) crops. For this, the use of forest products for bioenergy from EU forests was reduced by 74% compared to the REF scenario by 2030, and no imported forest products are used. To balance this, the use of woody residues (mainly from sawmills) and from



SRC was increased to about the same level as in the GHG scenario, and additional EU non-woody bioenergy from agricultural residues and wastes was assumed to be mobilized more than in the GHG scenario. In the electricity sector, forest bioenergy by 2030 is nearly phased out (94% reduction vs. REF) and use of woody residues and wastes more than halved. To compensate for this reduction, non-woody bioenergy use increases about 3.6-fold compared to REF. In heat supply in 2030, EU forest products are again nearly phased out. In parallel, use of EU woody residues and SRC increases accordingly. In the transport sector, 1G biofuels - both domestic and imported - are fully phased-out by 2030 through 2G biofuel from domestic lignocellulose (mainly straw and black liquor) and no biofuel imports are assumed. For bioelectricity the feedstocks used are wood chips and wood pellets from EU forest residues (dramatically decreasing the total amount consumed from 2010 to 2030), EU woody residues (almost a constant total amount consumed from 2010 to 2030), wood chips from EU landscape care (significantly increasing from 2010 to 2030) and a decreasing amount of wood imported wood pellets (0 % in 2030). For bioheat, wood logs from EU forest residues (with a dramatic reduction up to 2030), wood pellets from woody residues (increasing its share up to 2030) and domestic SRC (increasing its share in comparison to 2010) and imported wood pellets (0 in 2030). For biofuels for transport, it is considered an increasing contribution of BtL from EU black liquor and BtL from domestic forest residues. As in the GHG scenario, sustainable stemwood from EU forests is increased through cascading use. For this, additional wood use in 2.5% of new residential buildings in the EU by 2020 (increasing to 5% by 2030) was assumed which leads to a substitution of concrete and steel as construction materials, and also increases sawmill residues (same assumptions as in the GHG scenario). The SUS scenario also shifts post-consumer organic wastes: incineration is phased out, and biowastes used more efficiently in decentral cogeneration plants14. In summary, SUS reduces demand for EU forest products to 400 PJ (below 50 Mm3) which is 76% less than in REF, and uses about 3900 PJ of domestic woody

14 This requires a better ”back end“, i.e. improved selective collection of biomass wastes: the solid bio-waste components are to be collected separately and chipped, while the organic (green) components are also collected separately and used for biogas, and the digestate is then composted.



residues and wastes plus a small amount (140 PJ) of SRC. In parallel, some 1500 PJ of domestic straw plus 1550 PJ of manure are used for bioenergy.

4.4 Summary of the Scenarios

The qualitative description of the scenarios is given in the following table.

Table 9 Scenario Description

Reference Scenario (REF)

Climate Scenario (GHG)

Sustainability Scenario (SUS)

Storyline “Unrestricted” woody bioenergy use

Reduce GHG emissions, including those from bioenergy

Reduce GHG emissions, avoid biodiversity risks from imports and in domestic forests

Wood material demand

+ 4 % in 2020; + 10% in 2030

same as REF, but includes more EU construction wood (5% of new buildings by 2020 and 10% by 2030 use wood)

same as GHG, plus intensified cascading of woody materials for energy; wood bioenergy demand reduced by more non-woody bioenergy use

Technologies Co-firing of imported pellets, and 1st G biofuels (also imports)

Co-firing of (imported) pellets, no 1st G biofuels by 2030

No co-firing by 2030, no 1st G biofuels by 2030, more decentral bioenergy use

Imports market driven (increase)

Low biofuel imports, reduces wood imports by 50% by 2030

No imports of wood and biofuels by 2030

Source: IINAS compilation

5 Scenario Results

The results of the scenario calculations are summarized in the following figures. The respective tables are included in Section 5 of the Annex Report. The summary begins with the sectoral end-use demands for electricity (Section 5.1), heat (Section 5.2) and transport fuels (Section 5.3), and the respective supply from bioenergy, other renewables, and non-renewable energy carriers. From the total final energy demand and supply (Section 5.4), the primary energy demand is calculated (Section 5.5), as well as the respective GHG emissions from bioenergy (Section 5.6) and those from overall primary energy (Section 5.7).



5.1 Electricity Generation

Total electricity generation in 2010 was 3410 TWh, and would remain stable by 2020 and increase to 2650 TWh by 2030 in the REF scenario. In the GHG and SUS scenarios it could be reduced by efficiency measures to 3290 TWh by 2020 and to 3320 TWh by 2030, respectively, as illustrated in Figure 11. In 2020, the share of woody bioenergy will remain at 5 % in the REF and GHG scenarios, while in SUS it will be 4 %. By 2030, the woody bioenergy share in REF remains at 5% while in the GHG scenario it is reduced to 4.9% and in the SUS scenario, only 0.8% of the electricity would come from woody bioenergy, but with a growing contribution of non-woody bioenergy.

Figure 11 Electricity Generation in the EU27 from 2010-2030

Source: IINAS calculations; woody bioenergy includes forest and non-forest bioenergy; non-woody bioenergy

includes biogas and liquid biofuels

The most relevant difference between the REF, GHG and SUS scenarios is the origin of woody bioenergy used for electricity: In the REF scenario, imported woody bioenergy is increasingly used for electricity generation, rising from about 100 PJ to 650 PJ in 2020, and is then reduced to 430 PJ in 2030. In the GHG scenario, imported wood pellets increase to only 230 PJ by 2020, and then are reduced to some 160 PJ in 2030. In the SUS scenario, non-woody (domestic) bioenergy replaces a high share of the imported wood pellets, and woody bioenergy mainly is sourced from EU wood residues and wastes, not from forests. Also, SRC contribute to replacing pellet



imports, but on a rather low level (approx. 140 PJ). By 2030, woody bioenergy imports are completely phased-out.

5.2 Heat Production

The first difference between the REF and the GHG/SUS scenarios for heat are the demand level: while in REF heat demand in 2020 increases by more than 10% compared to 2010, and remains higher than in 2010 even in 2030 (9%), the GHG and SUS scenarios assume far stricter demand-side efficiency measures which lead, compared to 2010, to a very light increase of demand in 2020 (<1%) and a net reduction of 9% by 2030, respectively. The second difference is the more prominent use of non-bioenergy renewables for heat in the GHG and SUS scenarios: solar and geothermal heat increase from less than 150 PJ in 2010 to 1550 PJ by 2020 and 5000 PJ by 2030. The REF scenario assumes 3500 PJ by 2030. The other difference is again the source of wood for bioenergy: in the REF scenario, EU forest products supply the major share, while residues and wastes are about only 1/3 of total woody bioenergy. In the GHG and SUS scenarios, forest products are reduced through increased sourcing of residues and wastes (see Figure 12). In the SUS scenario, EU forest product use is reduced by 94% compared to 2010 and replaced by domestic woody pellets from residues, wastes and SRC. In all scenarios, direct wood heating relies on domestic sources, i.e. no imported pellets are used.



Figure 12 Final Energy Supply for Heat in the EU27 from 2010-2030

Source: IINAS calculations; here, forest products include all biomass extracted directly from forests (e.g.

roundwood, thinning and harvest residues); woody residues include secondary and tertiary residues and wastes from wood industry, and post-consumer wood; SRC is short-rotation coppices; non-bio RES = non-biogenic renewables

It should be noted that there is also “indirect” bioenergy included in the electricity and cogenerated heat segments of the final heating supply (for detailed data see Annex Report).

5.3 Transport Fuels

In transport, the final energy demand in the REF scenario will decrease from about 16 EJ in 2010 to 15 EJ by 2020 and remain there by 2030, as shown in Figure 13.

In the GHG and SUS scenarios, transport fuel demand can be reduced to 12 EJ by 2020 and 9 EJ by 2030, respectively. This is a consequence of the assumed massive increase in efficiency of road transport, and modal shifts, and not connected to biofuels.

In REF, the contribution of renewables (including electricity) will increase from 4.2 % in 2010 to 8.9 % by 2020 and 11% by 2030, not considering double-counting or multipliers for electricity.

In REF, the contribution of biofuels and woody bioenergy in 2020 will reach 7.3% and 0.4%, respectively, and in 2030 the shares will be 7.7% and 2.1%, respectively.



In the GHG and SUS scenarios, biofuels and woody bioenergy will contribute in 2020 with 7.5% and 1.4% - 1.7%, respectively, and in 2030 with 8.4% and 6.7% - 6.1%, respectively.

The bioethanol and biodiesel shares will be the same in all scenarios, but the role of advanced conversion and the origin of the feedstocks are different: In the REF scenario, 1G biofuels will still dominate in 2030, and imports will contribute about 30% of total biofuels. In the GHG and SUS scenarios, all 1G biofuels are phased-out by 2030 with the exception of a small share of sugarcane EtOH from Brazil in the GHG scenario. For biodiesel, the key resources will be black liquor and woody residues, while for bioethanol, domestic straw will become the dominant source.

Figure 13 Final Energy Supply for Transport in the EU27 from 2010-2030

Source: IINAS calculations; here, forest products include all biomass extracted directly from forests (e.g.

roundwood, thinning and harvest residues); woody residues include secondary and tertiary residues and wastes from wood industry, and post-consumer wood

5.4 Final Energy Demand

In the REF scenario, the final energy demand will decrease from about 51 EJ in 2010 to 48 EJ by 2020 and will reach 50 EJ again by 2030, with shares of all renewables (including electricity and cogenerated heat from renewables) increasing from 13% in 2010 to 20% by 2020 and 27% by 2030, respectively. In the GHG and SUS scenarios, final energy demand will be reduced due to the assumed massive investments in energy efficiency to 42.5 EJ in 2020 and 37 EJ in



2030 (see Figure 14). The total renewable share will increase to 25% by 2020, and to 48% by 2030, respectively. The woody bioenergy shares in the REF scenario will decrease slightly from 8% in 2010 to 7% by 2020, and will remain there by 2030. In the GHG and SUS scenarios, the shares will remain at the 2010 level (except in GHG scenario in 2030 where it reaches 10%).

Figure 14 Final Energy Demand in the EU27 from 2010 to 2030

Source: IINAS calculations; shores from electricity and cogenerated heat are included in the categories

5.5 Primary Energy Supply

Primary energy supply in the EU27 in 2010 was about 71 EJ, and will be reduced in the REF scenario to about 69 EJ by 2020 and 67 EJ by 2030, respectively.

In the GHG and SUS scenarios, the primary energy supply will be reduced to 64 EJ by 2020 and 45 EJ by 2030, respectively.

As depicted in Figure 15, primary energy supply in the GHG and SUS scenarios will be reduced by nearly 40 % by 2030, compared to 2010, while the REF scenario achieves only a 6% reduction.

Woody bioenergy contributed 7% in 2010, and could reach 7% (REF + SUS) to 8% (GHG) by 2020 and 4% (REF), 10% (GHG) and 7% (SUS) by 2030.



Figure 15 Primary Energy Supply in the EU27 from 2010-2030


The total amount of primary woody bioenergy varies between the scenarios, as reflected in Figure 16 and 17, but contributions of the various bioenergy sources, and their use for electricity, heat and transport fuels shows even more significant differences between the scenarios.

Figure 16 Primary Woody Bioenergy in the EU27 from 2010-2030 by source




Figure 17 Primary Woody Bioenergy in the EU27 from 2010-2030 per Sector


The use of woody biomass – including non-energy uses – and the respective potentials are shown in the following table.

Table 10 Bioenergy Demand and Potentials in the EU27 from 2010-2030

2020 2030 Biomass, energy equiv. [PJ] 2010 REF GHG SUS REF GHG SUS forest products, EU for non-energy 4000 4200 3750 3500 5100 4000 4000 forest products, EU for bioenergy 3204 3387 1554 1291 1682 1058 345 total forest products, EU 7204 7587 5304 4791 6782 5058 4345 share of potential (excl. imports) 91% 95% 97% 94% 89% 82% 71% woody residues/wastes EU, for energy 1384 2185 3119 3049 1276 2960 2539 share of potential 45% 78% 70% 68% 64% 82% 70% SRC in EU, for bioenergy 14 34 68 60 25 87 141 share of potential 3% 4% 4% 4% 10% 44% 72% straw to biogas + biofuels 8 57 93 421 217 633 1553 used share of straw potential 0% 3% 4% 20% 11% 32% 76% manure to biogas, for bioenergy 108 216 260 450 373 546 1567 used share of manure potential 6% 11% 13% 23% 19% 26% 75%




This table clearly illustrates that the GHG and SUS scenarios can reduce both the demand on EU forest products, and for imports. In parallel, the use of agricultural residues and wastes will increase significantly above the REF levels.

5.6 GHG Emissions from Bioenergy

The GHG emissions from all bioenergy systems were calculated using GEMIS life-cycle emission factors, as given in the Annex Report (Table 17 in Section 4.5). For bioenergy systems using forest biomass, also the CO2 emissions from forest C stock changes were included which depend on time horizon (20 or 100 years), and optimistic or pessimistic forest reference case (see Table 3 in Section 3.3). The overall balance further takes into account GHG emission savings from substi-tuting construction materials with wood (see Table 20 in Section 4.6 of the Annex report) which is part of the GHG and SUS scenarios. Note that these balances do not take into account the GHG emissions of fossil energy systems - this will be considered in the next section. The GHG and SUS scenarios do reduce the biogenic emissions compared to those of the REF scenario, both for 2020 and 2030. The most relevant reductions are for bio-electricity, and through the substitution of non-renewable construction materials with wood. In the SUS scenario, the substitution effect from woody construction materials alone is nearly as large as the total biogenic GHG emissions so that this scenario can nearly achieve full carbon neutrality, i.e. nearly zero net GHG emissions by 2030.



Figure 18 GHG Emissions from Woody Bioenergy 2010 - 2030 (20 Year Time Horizon)

a) pessimistic forest reference case

b) optimistic forest reference case

Source: IINAS calculations using GEMIS life-cycle emissions and forest C stock change emission factors from

Joanneum Research



The same pattern can be observed for the GHG emissions using a 100-year time horizon (see Figure 19).

Figure 19 GHG Emissions from Woody Bioenergy 2010 - 2030 (100 Year Time Horizon)


Joanneum Research; for the 100 year time horizon, the results are independent from the pessimistic or optimistic reference scenario

In Figure 20, the GHG emissions from bioenergy are again shown for the totals and the assumed time horizon and forest references cases.



Figure 20 GHG Emissions from Woody Bioenergy 2010 - 2030 Depending on Time Horizons and Forest Reference Cases


Joanneum Research; for the 100 year time horizon, the results are independent from the pessimistic or optimistic reference scenario

This clearly shows that the GHG and SUS scenarios do reduce the overall GHG emissions from bioenergy compared to the REF scenario, whatever the time horizon of the GHG accounting, and disregarding which forest reference case is chosen. For the optimistic forest reference and 20 year and 100 year time horizons, the GHG and SUS scenarios achieve more than full carbon neutrality, i.e. net GHG emission reductions by 2030.



5.7 Overall GHG Emissions from Energy Supply and Use

To complete the GHG emission balance, the emissions from the non-biogenic energy systems must be factored in. For this, the life-cycle GHG emissions for all other energy systems were also taken from the GEMIS model: the fossil and nuclear systems (see Annex Report Table 18 in Section 4.6) and the non-bio-renewable electricity systems (Annex Report Table 19 in Section 4.6) also contribute to the overall GHG emissions of the EU energy system. The overall GHG emission balance of the total EU energy system is shown in the following figures, again differentiating between the 20 and 100 year time horizons for the forest bioenergy systems, and the optimistic and pessimistic forest reference case.

Figure 21 Life-Cycle GHG Emissions from Energy Supply and Use in the EU27 from 2010-2030 with GHG Emissions from Forest Bioenergy for 20 Year Time Horizon and Optimistic Forest Reference Case

Source: IINAS calculations; data include upstream life-cycle GHG emissions for all energy, and GHG emissions

from forest bioenergy using a 20 year time horizon and optimistic forest reference case

The overall GHG emission balance clearly indicates that the biogenic GHG emissions are rather small, compared to the emissions from the remaining fossil fuels. Also the GHG emissions from non-bio-renewables are very small.



These results do not change if a pessimistic forest reference case is assumed for the forest bioenergy, as shown in the following figure.

Figure 22 Life-Cycle GHG Emissions from Energy Supply and Use in the EU27 from 2010-2030 with GHG Emissions from Forest Bioenergy for 20 Year Time Horizon and Pessimistic Forest Reference Case


from forest bioenergy using a 20 year time horizon and pessimistic forest reference case

The differences between the results for the 20-year time horizon and the ones for the 100 year time horizon (see following figure) are also quite small - this shows that the discussion of the “carbon debt” associated with forest bioenergy becomes insignificant if sustainable and low-C options for forest bioenergy are used.



Figure 23 Life-Cycle GHG Emissions from Energy Supply and Use in the EU27 from 2010-2030 with GHG Emissions from Forest Bioenergy for 100 Year Time Horizon


from forest bioenergy using a 100 year time horizon (results are independent from forest reference case)

For the 100 year time horizon, the net GHG emissions from woody bioenergy in the GHG and SUS scenarios are less than zero due to the substitution effect from cogeneration and use of woody construction material. The remaining fossil fuels dominate the GHG emission balance, with still high contributions from oil, while emissions from coal are reduced significantly, and natural gas is in between. It should be noted that for oil and gas, the GHG emission factors used here do not reflect potential future contributions from “unconventional” sources such as tar sands, or shale gas which have higher GHG emissions. Furthermore, the GHG balances include emissions from outside of the EU (“upstream” parts of imported energy life-cycles) so that the results cannot be compared directly to the EU GHG emission reporting which is based on a territorial concept.



6 Conclusions and Policy Implications

The EU target of supplying 20% of its energy from renewable sources by 2020 implies to increase the domestic use of renewables significantly, and discussions on the role of renewables for 2030 and beyond are taking place (EC 2014).

• Currently, woody biomass from forests and residues is the largest source of renewables in Europe, and is expected to be used even more by 2020.

• For 2030, the role of woody biomass - and bioenergy in general - was analysed with a special focus on potential environmental consequences. Evaluating respective constraints such as biodiversity and GHG emissions showed that these would impact on EU forest biomass potentials.

• Extending protected forests area in the EU and restricting biomass extraction from existing forests would reduce forest potentials by 5% for 2020 and 2030. Applying strict environmental criteria will reduce biomass potential by 30 % compared to the reference potential.

• Considering time-dependent carbon balances of forest bioenergy leads to excluding high-quality roundwood from energy options to reduce GHG emissions in the timeframe of this study.

• On the other hand, the EU potentials for secondary and tertiary wood residues and wastes are high and could be mobilized through cascading use policies without negative impacts on biodiversity, and with high net GHG emissions reductions15.

• Bioenergy is currently also imported to the EU, and imports are expected to increase due to rising demand and cost advantages in the REF scenario. The GHG scenario could reduce imports by 50%, while the SUS scenario would allow to phase-out imports not only of woody bioenergy but also of biofuels and their feedstocks.

Fundamental to sustainable bioenergy use is to reduce demand by implementing stringent energy efficiency targets by 2020 and 2030, respectively. Furthermore, more environmentally-compatible non-biomass renewables such as geothermal, solar and wind should be considered, as these options have high domestic potentials and comparatively low overall cost. Under these assumptions, the SUS scenario by 2030 uses only about 25% of the forest bioenergy consumed in 2010, completely avoids imports of woody

15 This study also analyzed non-woody bioenergy residues and waste options such as straw and manure. For the straw potentials, soil and carbon conservation was assumed as well based on IC et al. (2012).



bioenergy and biofuels, and shifts towards domestic bioenergy residues and wastes, mainly from wood industries and post-consumer wood, and agricultural residues (straw, manure). In parallel, a 60% net GHG emission reduction from the energy system (including those from C stock changes in forests) could be achieved in the SUS scenario by 2030, compared to 2010, while the REF scenario would achieve only close to 20% reduction, respectively. A prerequisite for the GHG and SUS scenarios is to successfully introduce cascading biomass use for energy, improving biogenic waste collection and recycling, and to establish binding sustainability requirements for woody and gaseous bioenergy, in parallel to tightening the existing requirements for biofuels. The sustainable forest biomass potential will suffice to meet woody material demands if resource-efficient cascades are implemented, more paper recycled and post-consumer wood be re-used. Additional stemwood for construction material for 5% of new residential buildings in the EU by 2030 would then be available and would lead to significant GHG emission savings from substituting conventional building materials. Current EU and Member State energy and climate policies do not stimulate these developments, though: Bioenergy, forest, and waste policies are fragmented and unaligned, and incentive schemes mainly address bioenergy without considering the full GHG emissions from bioenergy use. Bioenergy supply - especially from forests and for electricity/heat - is not subject to any coherent sustainability regulation. Only a few Member States such as Denmark, the Netherlands and the UK have started to develop respective policies, which might lead to imbalances within the EU if no framework regulation is implemented. Imports of woody bioenergy is - with very few exceptions - unregulated as well, but growing relevance of pellets for bioelectricity (co-firing) imply a respective need for EU-level action to avoid internal market distortions. Last but not least, sustainable woody bioenergy supply also requires regulating biodiversity impacts for forests in a legally binding manner for both the EU, and imports from abroad.



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Forest biomass for energy in the EU: current trends, carbon balance and sustainable potential for BirdLife Europe, EEB, and Transport & Environment

- ANNEX to the FINAL REPORT - prepared by IINAS - International Institute for Sustainability Analysis and Strategy

EFI - European Forest Institute, and

JR - Joanneum Research Darmstadt, Madrid, Joensuu, Graz May 2014

IINAS, EFI, JR i Woody Bio EU: Annex

Content

List of Tables .......................................................................... ii

1 Biomass Sources ............................................................... 1

1.1 Biomass from Forests ................................................................. 1

1.2 Other Woody Biomass ............................................................... 2

1.3 Short Rotation Coppice on Agricultural Land .............................. 2

1.4 Secondary Forest Residues ......................................................... 2

2 Quantification of Biodiversity Constraints ......................... 5

3 Detailed Calculations of Biodiversity Risks ....................... 12

4 GHG Emission Calculations for Forest Bioenergy .............. 15

4.1 Description of bioenergy and reference systems ..................... 17

4.2 Details of models and parameters............................................ 20

4.4 Life-Cycle GHG Emissions of European Bioenergy Supply Chains ...................................................................................... 33

4.5 Total GHG Emission Balances ................................................... 35

5 Scenario Description ....................................................... 36

5.1 Background Data for the Wood Demand in the REF Scenario .. 36

5.2 The Reference (REF) Scenario................................................... 41

5.3 Bioenergy Demand and Supply Projections .............................. 43

6 Detailed Scenario Results ................................................ 45

Annex to the Short Study on “Forest biomass for energy in the EU: current trends, carbon balance and sustainable potential” prepared for BLE, EEB and T&E

IINAS, EFI, JR ii Woody Bio EU: Annex

List of Tables

Table 1 Maximum extraction rates for stemwood during early thinning due to environmental and technical constraints in mobilisation scenarios ............................................................................................ 5

Table 2 Maximum extraction rates for crown biomass during early thinnings due to environmental and technical constraints in mobilisation scenarios ............................................................................................ 6

Table 3 Maximum extraction rates for residues from final fellings due to environmental and technical constraints in mobilisation scenarios . 7

Table 4 Maximum extraction rates for residues from thinnings due to environmental and technical constraints in mobilisation scenarios . 8

Table 5 Maximum extraction rates for stumps from final fellings due to environmental and technical constraints in mobilisation scenarios .......................................................................................... 10

Table 6 Maximum extraction rates for stumps from thinnings due to environmental and technical constraints in mobilisation scenarios .......................................................................................... 11

Table 7 The theoretical (TP) and reference (RP) potentials of forest biomass in 2010 and for 2020 and 2030 from final harvest, thinnings and pre-commercial (PC) thinnings ........................................................ 12

Table 8 The effect of removing the constraint on residue extraction from protected forest ............................................................................... 12

Table 9 The effect on biomass potentials of increasing the area of strictly protected forest by 5% in 2020 ....................................................... 13

Table 10 The effect of additional 5% strict forest protection plus 5% retained trees on biomass potential by 2020 ................................................ 13

Table 11 Realizable forest biomass potentials under reference, medium and low mobilizations for EU28 in 2020 and 2030 ................................. 14

Table 12 Energy potentials from in 2020 and 2030 from other biomass sources (PJ) ...................................................................................... 14

Table 13 Summary of reference systems for various biomass types ............. 20

Table 14 Parameters for the estimation of decay-rate .................................. 21

Table 15 Auxiliary data for the emissions from forest residues ..................... 22

Table 16 Auxiliary data for stumps ................................................................. 22

Table 17 Parameters for the estimation of litter decay-rates ........................ 24

Table 18 Average mortality rates ................................................................... 24

Table 20 Life-Cycle GHG Emission Factors for European Bioenergy Supply Chains in 2020 and 2030 .................................................................. 34

Table 21 Life-Cycle GHG Emission Factors for European Non-Renewable Energy Sources in 2020 and 2030 .................................................... 35


IINAS, EFI, JR iii Woody Bio EU: Annex

Table 22 Life-Cycle GHG Emission Factors for European Non-Bio-Renewable Electricity Sources in 2020 and 2030 ............................................... 35

Table 23 Life-Cycle GHG Emission Factors for European Construction Materials in 2020 and 2030 ............................................................. 35

Table 24 Breakdown of the wood resource balance in 2010 ......................... 36

Table 25 Overview of EFSOS scenarios ........................................................... 38

Table 26 Components of wood supply in “Promoting wood energy” scenario, 2010-2030 ........................................................................................ 39

Table 27 Use of wood for energy in Promoting wood energy scenario, 2010-2030 ................................................................................................. 40

Table 28 Components of wood supply (without trade), by country group in “Promoting wood energy” scenario, 2010-2030 ............................. 40

Table 29 Consumption of solid biomass in the EU in 2011 by use ................. 41

Table 30 Demands in the Biomass Futures project for 2020 ......................... 43

Table 31 Overview of the final bioenergy demand per sector in the EU27 in different scenarios for 2020 ............................................................ 44

Table 32 Electricity Generation in the EU27 from 2010 to 2030 ................... 45

Table 33 Heat Production in the EU27 from 2010 to 2030 ............................ 45

Table 34 Transport Fuel Production in the EU27 from 2010 to 2030 ............ 46

Table 35 Final Energy Demand in the EU27 from 2010 to 2030 .................... 46

Table 36 Primary Energy Supply in the EU27 from 2010 to 2030 .................. 47

Table 37 Primary Bioenergy Supply and Use of Sustainable Potentials in the EU27 from 2010 to 2030 .................................................................. 47

Table 38 GHG Emissions from Bioenergy Supply and Use from 2010 to 2030 ................................................................................................. 49

Table 39 GHG Emissions from Energy Supply and Use in the EU27, 2010 to 2030 ................................................................................................. 50


IINAS, EFI, JR iv Woody Bio EU: Annex

List of Figures

Figure 1 Secondary forest residues ................................................................. 3

Figure 2 An example net biomass emission (a) and net biomass-for-energy time series (b) .................................................................................. 15

Figure 3 Biomass flows in the wood processing chain .................................. 26

Figure 4 Forest residues – annual net emissions (a.) and cumulative net emissions (b.) from the use of forest residues in Austria ................ 27

Figure 5 Net GHG Emissions from Stumps – annual (a) and cumulative (b) . 27

Figure 6 Pre-commercial thinnings – uncorrected annual net emissions (a.) and uncorrected annual net biomass for energy ............................ 28

Figure 7 Pre-commercial thinning – corrected annual net emissions (a.) and corrected cumulative net biomass for energy ................................. 29

Figure 8 Thinnings – uncorrected annual net emissions (a.) and uncorrected annual net biomass for energy (b.) .................................................. 29

Figure 9 Thinnings - corrected annual net emissions (a.) and corrected cumulative net emissions for forest bioenergy ............................... 30

Figure 10 Advanced harvest – uncorrected annual net emissions (a.) and uncorrected annual net biomass for energy ................................... 31

Figure 11 Advanced harvest – corrected annual net emissions (a.) and corrected cumulative net biomass for energy ................................. 31

Figure 11 Condensed wood flow chart from resources to end-use in the EU-27 in 2010 ............................................................................................. 42


IINAS, EFI, JR 1 Woody Bio EU: Annex

1 Biomass Sources

1.1 Biomass from Forests

Forests undisturbed by man are rare in Europe (less than 5%), while semi-natural forests – i.e. influenced by human interventions but to a certain extent maintaining the natural characteristics - make up almost 90%. Plantation forests - a group in particular comprising forests made up by exotic tree species – is thus on the whole relatively rare but nevertheless significant in a few European countries. Forest biomass for bioenergy can come from three different compartments. This includes:

• Stemwood, i.e. wood from the stem of a tree as distinct from branches, roots or stumps.

• Harvest residues i.e. stem tops, branches, and foliage. During thinnings and final fellings, logging residues are formed. These residues consist of stemwood harvest losses (e.g. stem tops) as well as branches and foliage that are separated from harvested stemwood.

• Stumps and coarse roots which are - in addition to logging residues - also potentially available as biomass.

In EFISCEN, to assess biomass in branches, coarse roots, fine roots and foliage, stemwood volumes are converted to stem biomass by using basic wood density (dry weight per green volume) and expanded to whole-tree biomass using age- and species specific biomass allocation functions. The biomass can come from the following harvest activities:

• Early thinning, i.e. thinning in very young forest stands which formerly was considered to be pre-commercial; also referred to as energy wood thinning. Stem dimensions are usually smaller than the typical pulp wood assortments. All the biomass extracted during an early thinning is commonly used for bioenergy.

• Thinning, usually producing assortments of different quality. This can include stemwood for both material and energy use as well as harvest residues.

• Final Harvest, i.e. removal of the remaining tree canopy. This usually produces stemwood predominantly for material use, but lower quality assortments can be used for bioenergy. Harvest residues and stumps and coarse roots may be extracted as well.



1.2 Other Woody Biomass

Wood from trees outside the forest is another important source of primary woody biomass. It becomes available during maintenance operations that are performed in order to keep the trees in the desired state. Hence, this biomass source differs from forests. In forestry, wood is regarded as a product whereas the wood from trees outside the forest is most often considered and/or treated as waste. The material is in many European countries referred to as landscape care wood. For this reason primary woody biomass from trees outside forests was in the EUwood study called “landscape care wood” (LCW). All fresh wood (e.g. roundwood, chips and branches) that is harvested from other sources than forests was included under this category. It does not refer to post-consumer wood or industrial wood residues. Landscape care wood comprises woody plants or plant components, which accumulate within landscape care activities. It refers to woody residues from landscape care such as: • Maintenance operations, tree cutting and pruning activities in agriculture and horticulture industry • Other landscape care or arboricultural activity in parks, cemeteries, etc. • Maintenance along roadsides, hedgerows and boundary ridges, rail- and waterways • Gardens

1.3 Short Rotation Coppice on Agricultural Land

Short-rotation coppices (SRC) are defined as plantations established and managed under short-rotation intensive culture practices. For energy purposes, SRC have rotations of 2 to 4 year with coppice management. Only plantations on agricultural land are considered as planted forests (plantations) are already accounted for under biomass from forests.

1.4 Secondary Forest Residues

The potential of secondary residues is interdependent on other forest production categories. Note that post-consumer wood was not considered in the potential analysis of this study, but data for the scenarios were taken from the BiomassFutures project results.



Figure 1 Secondary forest residues

Source: Mantau et al. (2010b)

1.4.1 Sawmill By-products

The segment of sawmill by-products comprises wood residues originating from sawnwood production. It includes wood chips, sawdust and particles, as well as sawmill rejects, slabs, edgings and trimmings. The assortments are suitable for material uses such as pulping, particleboard and fibreboard production as well as for energy use. Sawmill by-products exclude wood chips made either directly in the forest from roundwood or made from forest residues (i.e. already counted as pulpwood, round and split or wood chips and particles).

1.4.2 Other Industrial Wood Residues

This comprises wood residues accumulated during production of semi-finished wood products as well as during their processing (resawing, planing) and the production of manufactured wood products (construction, furniture). These residues include dust and shavings from planing, milling and drilling. Other assortments are trimmings, rejections, peeler cores, square cuttings. These residues can be modelled using input-output calculations and respective material recovery.



1.4.3 Liquid Forest Industry By-products (Black Liquor)

The pulp industry creates a huge amount of secondary residues in the form of black liquor. This by-product of pulp mills is almost completely used for energy production in the pulp and paper industry. Efficiency of pulp-making is low and thus a huge source of biomass for bioenergy is created. The amount as well as the composition of the liquid residues called “black liquor” depends highly on the specific pulping process as well as the tree species in each country (see chapter 5.4.4 in Mantau et al. 2010b). In many countries, chemical and semi-chemical pulp production represents the major energy producer and pulp mills are often the most important producer of electricity from biomass, today. Heat and power generated from these residues are mostly directly used to keep the pulping process running, notably for the recovery of the pulping chemicals.



2 Quantification of Biodiversity Constraints

The following Tables provide an overview of the assumptions made to quantify the constraints included in this study for the mobilisation scenarios for different types of biomass and different felling activities.

Table 1 Maximum extraction rates for stemwood during early thinning due to environmental and technical constraints in mobilisation scenarios

Type of constraint Current (2010) Reference mobilisation Low mobilisation –strictest site constraints

Site productivity Not a constraining factor Not a constraining factor Not a constraining factor

Soil and water protection: Slope

0% on slopes over 35%; not a constraining factor on slopes up to 35%



Soil and water protection: Soil depth

Not a constraining factor Not a constraining factor Not a constraining factor

Soil and water protection: Soil surface texture


Soil and water protection: Soil compaction risk


Biodiversity: protected forest areas

0%; not a constraining factor in areas with high or very high fire risk



Recovery rate 95% 95% 95%

Soil bearing capacity Not a constraining factor Not a constraining factor Not a constraining factor




Table 2 Maximum extraction rates for crown biomass during early thinnings due to environmental and technical constraints in mobilisation scenarios

Type of constraint Current (2010) Reference mobilisationmobilization

Low mobilisation –strictest site constraints

Site productivity 0% on poor soils (Acrisol, Podzoluvisol, Histosol, Podzol, Arenosol, Planosol, Xerosol); 70% on other soils

Not a constraining factor 0% on poor soils (Acrisol, Podzoluvisol, Histosol, Podzol, Arenosol, Planosol, Xerosol); 20% on other soils






0% on Rendzina, Lithosol and Ranker (very low soil depth)




35% on peatlands (Histosols)




0% on soils with very high compaction risk; 25% on soils with high compaction risk; not a constraining factor on other soils


0% on soils with very high and high compaction risk; not a constraining factor on other soils





Recovery rate 80% 80% 80%

Soil bearing capacity 0% on Histosols, Fluvisols, Gleysols and Andosols

0% on Histosols, Fluvisols, Gleysols and Andosols; not a constraining factor in Finland and Sweden

0% on Histosols, Fluvisols, Gleysols and Andosols




Table 3 Maximum extraction rates for residues from final fellings due to environmental and technical constraints in mobilisation scenarios

Type of constraint Current (2010) Reference mobilisation Low mobilisation –

strictest site constraints

Site productivity Not a constraining factor Not a constraining factor 35% extraction rate on poor soils (Acrisol, Podzoluvisol, Histosol, Podzol, Arenosol, Planosol, Xerosol); not a constraining factor on other soils


67%on slopes up to 35%; 0% on slopes over 35%, unless cable-crane systems are used

67% factor on slopes up to 35%; 0% on slopes over 35%, unless cable-crane systems are used







0% on peatlands (Histosols) 33% on peatlands (Histosols) 0% on peatlands (Histosols)




0% on soils with high or very high compaction risk; 50% on soils with medium compaction risk; not a constraining factor on other soils





Recovery rate 67% on slopes up to 35%; 0% on slopes over 35%, but 67% if cable-crane systems are used

70% on slopes up to 35%; 0% on slopes over 35%, but 67% if cable-crane systems are used


Cable cranes are applied in Austria, Italy, France, Germany, Czech Republic, Slovakia, Slovenia, Romania

Cable cranes are applied in Austria, Italy, France, Germany, Czech Republic, Slovakia, Slovenia, Romania, Bulgaria






Soil bearing capacity


0% on Histosols, Fluvisols, Gleysols and Andosols; not a constraining factor in Finland and Sweden



Table 4 Maximum extraction rates for residues from thinnings due to environmental and technical constraints in mobilisation scenarios



Site productivity 0% on poor soils (Acrisol, Podzoluvisol, Histosol, Podzol, Arenosol, Planosol, Xerosol); 33% on other soils

67% 0%


33% on slopes up to 35%; 0% on slopes over 35%, unless cable-crane systems are used


0%




0%


0% on peatlands (Histosols) 33% on peatlands (Histosols) 0%




0%




0%





Recovery rate 67% on slopes up to 35%; 0% on slopes over 35%, but 47% if cable-crane systems are used


0%


Cable cranes are applied in Austria, Italy, France, Germany, Czech Republic, Slovakia, Slovenia, Romania, Bulgaria

Soil bearing capacity


0% on Histosols, Fluvisols, Gleysols and Andosols ,not a constraint in Finland and Sweden

0%




Table 5 Maximum extraction rates for stumps from final fellings due to environmental and technical constraints in mobilisation scenarios

Type of constraint Current (2010) Reference mobilisationmobilization

Low mobilisation –strictest site constraints

Countries Finland, Sweden, UK All 0% Species Conifers All 0% Site productivity 33% on poor soils (Acrisol,

Podzoluvisol, Histosol, Podzol, Arenosol, Planosol, Xerosol); not a constraining factor on other soils

67% on poor soils (Acrisol, Podzoluvisol, Histosol, Podzol, Arenosol, Planosol, Xerosol); not a constraining factor on other soils

0%


0% on slopes over 20%; 33% - slope[%] * 0.33 on slopes up to 20%

0% on slopes over 35%; 67% - slope[%] * 0.67 on slopes up to 35%

0%


0% on peatlands (Histosols) 33% on peatlands (Histosols)

0%


0% on soils < 40 cm (including Rendzina, Lithosol and Ranker); 33% on soils >40 cm

0% on soils < 40 cm (including Rendzina, Lithosol and Ranker); 67% on soils >40 cm

0%




0%


0% 0% 0%

Recovery rate Not a constraining factor Not a constraining factor 0% Soil bearing capacity 0% on Histosols, Fluvisols,

Gleysols and Andosols 0% on Histosols, Fluvisols, Gleysols and Andosols; not a constraint in Finland and Sweden

0%




Table 6 Maximum extraction rates for stumps from thinnings due to environmental and technical constraints in mobilisation scenarios

Type of constraint Current (2010) Reference mobilisation Low mobilisation –strictest

site constraints

Countries 0% All 0%

Species

0% All 0%

Site productivity 0% 67% on poor soils (Acrisol, Podzoluvisol, Histosol, Podzol, Arenosol, Planosol, Xerosol); not a constraining factor on other soils

0%


0% 0% on slopes over 35%; 67% - slope[%] * 0.67 on slopes up to 35%

0%


0% 33% on peatlands (Histosols) 0%


0% 0% on soils < 40 cm (including Rendzina, Lithosol and Ranker); 67% on soils >40 cm

0%


0% 0% on soils with very high compaction risk; 33% on soils with high compaction risk; not a constraining factor on other soils

0%


0% 0% 0%

Recovery rate 0% Not a constraining factor 0%

Soil bearing capacity 0% 0% on Histosols, Fluvisols, Gleysols and Andosols; not a constraint in Finland and Sweden

0%




3 Detailed Calculations of Biodiversity Risks

Table 7 The theoretical (TP) and reference (RP) potentials of forest biomass in 2010 and for 2020 and 2030 from final harvest, thinnings and pre-commercial (PC) thinnings

PC Thin

Stemwood PC Thin Biomass

Thin Stemwood Thin Res

Thin Stump

Harvest Stemwood

Harvest Res

Harvest Stump Total

TP 2010 10 8 237 134 94 411 184 165 1243 TP 2020 12 9 224 123 89 413 186 167 1224 TP 2030 11 8 229 127 91 415 189 170 1241 RP 2010 9 2 224 16 0 388 77 9 726 RP 2020 11 5 219 56 35 403 84 64 876 RP 2030 10 4 224 58 36 405 85 66 889

Source: EFISCEN calculations - EFI compilation; data given in volumes Mm3 overbark

Table 8 The effect of removing the constraint on residue extraction from protected forest

PC Thin Stem-wood

PC Thin Bio-

mass

Thin Stem-wood

Thin Resid

Thin Stump

Harvest stem-wood

Harvest resid

Harvest stump Total

2010 9.4 2.1 223.8 16.5 0.0 388.1 76.5 9.4 725.8 2020 11.0 4.7 218.7 55.7 35.3 402.7 84.0 64.1 876.2

RP 2020 without dedicated constraints on stump and residue removal in protected areas 11.0 5.7 218.7 65.7 41.5 402.7 98.1 75.2 918.7 RP 2030 10.4 4.4 223.6 57.9 36.3 404.9 85.4 65.6 888.6 RP 2030 without dedicated constraints on stump and residue removal in protected areas 10.4 5.3 223.6 68.2 42.5 404.9 99.5 76.8 931.2




Table 9 The effect on biomass potentials of increasing the area of strictly protected forest by 5% in 2020

PC Thin Stemw

ood

PC Thin Biomas

s

Thin Stemw

ood Thin Res

Thin Stump

Harvest Stemw

ood Harvest

Res Harvest Stump Total

Ref 2010 9.4 2.1 223.8 16.5 0.0 388.1 76.5 9.4 725.8 Ref 2020 11.0 4.7 218.7 55.7 35.3 402.7 84.0 64.1 876.2 Ref 2020 with additional 5% strict forest protection 10.4 4.5 207.8 52.9 35.3 382.6 79.8 60.9 834.1 Ref 2030 10.4 4.4 223.6 57.9 36.3 404.9 85.4 65.6 888.6 Ref 2030 with additional 5% strict forest protection 9.9 4.1 212.5 55.1 36.3 384.6 81.2 62.4 846.0


Table 10 The effect of additional 5% strict forest protection plus 5% retained trees on biomass potential by 2020

PC Thin Stem-wood

PC Thin Bio-

mass

Thin Stem-wood

Thin resid

Thin Stump

Harvest stem-wood

Harvest resid

Harvest Stump Total

2010 9.4 2.1 223.8 16.5 0.0 388.1 76.5 9.4 725.8 Ref 2020 11.0 4.7 218.7 55.7 35.3 402.7 84.0 64.1 876.2 Ref 2020 with additional 5% strict forest protection and 5% retention trees 9.9 4.2 196.9 50.1 35.3 362.4 75.6 57.7 792.1 Ref 2030 10.4 4.4 223.6 57.9 36.3 404.9 85.4 65.6 888.6 Ref 2030 with additional 5% strict forest protection and 5% retention trees 9.4 3.9 201.3 52.2 36.3 364.4 76.9 59.1 803.3




Table 11 Realizable forest biomass potentials under reference, medium and low mobilizations for EU28 in 2020 and 2030

PC Thin

Stemwood PC Thin Biomass

Thin Stemwood

Thin Res

Thin Stump

Harvest Stemwood

Harvest Res

Harvest Stump Total

REF 2020 11.0 4.7 218.7 55.7 35.3 402.7 84.0 64.1 876.2 REF 2030 10.4 4.4 223.6 57.9 36.3 404.9 85.4 65.6 888.6 LOW 2020 9.9 0.6 195.1 0.0 0.0 358.9 50.9 0.0 615.3 LOW 2030 9.3 0.6 199.2 0.0 0.0 360.8 51.3 0.0 621.2

Source: EFISCEN calculations; data given in volumes Mm3 overbark

Table 12 Energy potentials from in 2020 and 2030 from other biomass sources (PJ)

Year

Prunings (fruit trees, vine-

yards, olives, citrus, nuts)

Landscape care wood

2020

Perennials: woody crops

Sawmill by-

products (excl. saw

dust) Saw-dust

Other industrial

wood residues

Black liquor

2020 423 461 1648 423 209 229 701 2030 370 461 196 474 234 272 366

Source: IC et al. 2012- Sustainability Scenario of the Biomass Futures project



4 GHG Emission Calculations for Forest Bioenergy

When a single decision is made in a given year, defined as y=0, to consume biomass for energy, it results in two time series. There is a continuous series of greenhouse gas emissions, the bioenergy emission series, due to both use of energy in the supply-chain and carbon stock changes. The decision also results in a biomass-for-energy time series; a sporadic series of events when energy is produced For example, in a forest, a decision is made to manage stands by thinning at 10 years of age. If the stand has a rotation of 62 years, then there is a bioenergy emission series that starts with the stand at age 10 and is repeated every 62 years (Figure 2). There is also an biomass-for-energy time series with energy created in year 0 when the stand is 10 years old and in year 52 when the stand is harvested at age 62 (if energy is part of the harvest use strategy).

Figure 2 An example net biomass emission (a) and net biomass-for-energy time series (b)

a. b. Source: Joanneum (2014) own elaboration

If the biomass is not used for energy, there is also a time series of emissions, which is defined here as the reference emission series. Of course there is also a reference biomass for energy series. Using the same example as above, let us assume that the stand is normally not thinned then the same stand would be harvested in year 52, when it is 62 years old. The amount of biomass for energy resulting from the final harvest (if energy is part of the harvest use strategy) will likely be different than in the bioenergy case as the amount of total biomass may be different. To have comparable systems, energy, probably of fossil origin, would need to be consumed as part of the reference energy series. The difference between the two emission time series is the net emission time series of the decision in year 0 to consume biomass for energy. There is also a net biomass for energy time series. It is important to realise that the reference system and its associated reference emission series is counterfactual. It should represent the



most likely situation in absence of the bioenergy system. The selection of reference system effects the net emissions and energy dramatically. In some cases where the choice of reference system is not clear, it is advisable to produce two net emission and energy series which represent the systems that produce the lowest and highest net emissions and energy. To estimate the net emissions from continuous consumption of biomass for energy, a convolutional model was adopted since this consumption is also a time series. The net emission and biomass-for-energy time series for the decision to consume biomass for energy in year ψ have the same shape as the series for y=0. However, they are scaled by the amount consumed and shifted in time so that they start in year ψ. The convolution model comes from filter theory. The consumption series is the signal, and the net emission and energy time series are filters. A correction needs to be made for the amount of biomass for energy in the future because the decision to switch now may already the amount of biomass for energy in the future, as explained above. Hence the amount of biomass for energy in the future must be reduced if the decision today creates more biomass in the future than the reference case or increased if the decision today produces less biomass in the future that the reference case. This is mathematically performed not by correcting the consumption series, but by calculating a correction filter that when applied to the net biomass-for-energy series makes it have the value 1 at time y=0, and 0 for all other times. The same filter is then applied to the net emission time series (convolution is associative and commutative – i.e. the order of the filters does not matter). As an alternative assessment approach, the 20-year and 100-year total net emissions and total energy from biomass are calculated using the uncorrected series. The total net emissions are divided by the total energy from biomass to calculate a 20-year and 100-year emission factor. This is typically the method used in the LCA community. This method does not represent actual emissions in a specific year but is indicates the impacts of a decision over the length of time selected (i.e. 20 or 100-years) and places this value at the time of the decision. For example, the 20-year value for an action in 2010 sums all emissions from 2010 to 2030 and places that value in 2010. For example, the 20-year value for an action in 2050 sums all emissions from 2050 to 2070 and places that value in 2050. As the emissions for bioenergy systems tend to decrease with time, the 20 and 100-year impacts will be less than the actual emissions and the cumulative sum of emissions in a specific year.



4.1 Description of bioenergy and reference systems

4.1.1 Emissions from the use of forest residues

Bioenergy system In the bioenergy system it is assumed that biomass used for bioenergy production is composed of branches, tops, and standing dead wood. The biomass is not “de-barked”. For this reason, the net calorific value used for the conversion of mass to energy must be specifically for forest residues and not for clean wood. Reference system In the reference system, it is assumed that the biomass is left on site where it decays following simple exponential decay.

4.1.2 Emissions from the use of stumps

For the description, please see the description for forest residues. The model is the same as is used for forest residues except that a correction is made to the decay rate to account for the stumps.

4.1.3 Emissions from pre-commercial thinnings

Bioenergy system In the bioenergy system it is assumed that the biomass that is used for bioenergy production comes from thinned forests.

1. The biomass is extracted at 10 years and 1/3 of the biomass is removed. It is assumed that the annual increment remains the same with and without thinning. Hence the thinned forest always has less biomass than the unthinned forest by the amount removed.

2. Dead wood on site is also remove at this time

3. All biomass from these operations (including branches and tops) is used for energy

4. The biomass is not “de-barked”. For this reason, the net calorific value used for the conversion of mass to energy will be a weighted mixture of forest residues and clean wood.

Reference system There are two options for the reference system for pre-commercial thinning. Either:

a) in the reference system, the forest is not thinned. The model for this option is described below. This option causes almost constant net emissions from the decision to apply pre-commercial thinning. This option is referred to



further as “the pessimistic pre-commercial thinning option” since it creates more emissions than the alternative (below); or

b) in the reference system, the forest is thinned and the material is left as residues. The residue model already discussed is applicable for this option This option is referred to further as “the optimistic pre-commercial thinning option” since it creates fewer emissions than the previous option (above).

Results from both models will be presented.

4.1.4 Emissions from commercial thinning - thinnings used for energy instead of products and energy

Bioenergy system In the bioenergy system, it is assumed that the forest is thinned exactly the same as in the reference system. However, all biomass is used for bioenergy. Reference system If the reference system, it is assumed that the forest is thinned exactly the same as in the bioenergy system. However, the biomass is used in a manner typical for thinnings.


A discussion point is the fate of residues from commercial thinning in the reference system. Just like pre-commercial thinnings, there are two options. Either

a) in the reference system, the residues are used as above. The model for this option is described below. This option causes increasing net emissions from the decision to use the residues; or

b) in the reference system, the thinning residues are left on site. The residue model already discussed is applicable for this option.

Model Since the forest is managed the same in both systems only the wood product chains must be modelled. They are modelled using the same system as the previous example.



4.1.5 Emissions from the harvesting of forests - advanced harvests

Bioenergy system In the bioenergy system it is assumed that the biomass that is used for bioenergy production comes from a shortening of the rotation time.

1. The biomass is extracted at the “optimal” time when the mean annual increment is a maximum.

2. The biomass including dead wood is used proportioned to the various production chains in the normal manner.

Shortening the rotation length causes an increase in biomass for harvest since one may be switching from a system where for example 1.5 units are harvest every 120 years to one where 1.2 units are harvested every 90 years. Reference system In the reference system, it is assumed that forest is harvested, but later than the “optimal” time. In Austria for example, beech forests are often harvested at about 120 year of age. The optimal time is about 90 years, however. The delayed harvest and age class structure resulting from increased consumption during the World War II causes the Austrian forests to have increasing biomass. Model The details of the model are the same as for pre-commercial and commercial thinning with the exception of the differences in harvesting.



Table 13 Summary of reference systems for various biomass types

Biomass Source Reference System

Forest residues Residues remain in the forest and decay naturally without catastrophic disturbance

Stumps Stumps remain in the forest and decay naturally without catastrophic disturbance

Pre-commercial thinning

Optimistic option: Thinnings remain in the forest and decay naturally without catastrophic disturbance. The forest grows in a similar manner with and without biomass use for energy. Pessimistic option: Pre-commercial thinning does not occur. The unthinned forest has consistently more biomass than does the thinned forest (i.e. parallel growth curves)

Commercial Thinning

Thinning occurs in the same manner as in the bioenergy system, but the biomass from thinning is used for a mixture of purposes:


Advanced Harvests

The forest is harvested, but later than the “optimal” time. In the bioenergy systems, the forest is harvested at the “optimal time”. The delay, as compared to the bioenergy system, allows for an increase in forest biomass, and biomass at final harvest. The same proportion extracted biomass is used directly for sawnwood, panels, paper and energy in both the bioenergy and reference systems

Source: Consortium assumptions

4.2 Details of models and parameters

4.2.1 Emissions from the use of forest residues

The emissions from the use of residues are given by

𝐵𝐵𝐵𝐵𝐵𝐵_𝐸𝐸𝐸𝐸𝐵𝐵𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸(𝑡𝑡) = 𝛿𝛿(𝑡𝑡)𝐵𝐵𝑜𝑜 �4412 ∗ 0.5 + 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆�



Where the first term is the emission from burning the biomass, Bo1 including the emissions from the supply chain, Supp. The 0.5 is the carbon fraction of dry biomass and the 44/12 is used to convert mass C into mass CO2. If the residues are not used, then the emissions in the first year are given by

𝑅𝑅𝑅𝑅𝑅𝑅_𝐸𝐸𝐸𝐸𝐵𝐵𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸1 = [𝐵𝐵𝑜𝑜𝑅𝑅−𝑘𝑘] ∗4412 ∗ 0.5

Emissions in year 2 are given by: 𝑅𝑅𝑅𝑅𝑅𝑅_𝐸𝐸𝐸𝐸𝐵𝐵𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸2 = 𝑅𝑅𝑅𝑅𝑅𝑅_𝐸𝐸𝐸𝐸𝐵𝐵𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸𝐸𝐸1(𝑅𝑅−𝑘𝑘 − 1)

Emissions in all other years are 𝑅𝑅𝑅𝑅𝑅𝑅_𝐸𝐸𝐸𝐸𝐵𝐵𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸𝑗𝑗 = 𝑅𝑅𝑅𝑅𝑅𝑅_𝐸𝐸𝐸𝐸𝐵𝐵𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸𝐸𝐸𝑗𝑗−1𝑅𝑅−𝑘𝑘

The decay constant has been shown to be a function of temperature and rainfall. Brovkin et al (2012) use the following relationship

𝑘𝑘𝑤𝑤𝑜𝑜𝑜𝑜𝑜𝑜 = 𝑘𝑘𝑤𝑤𝑜𝑜𝑜𝑜𝑜𝑜10𝑄𝑄10(𝑇𝑇−1010 )

And have studied a global compilation of reports to attain values for kwood10 and Q10. (see Table 12).

Table 14 Parameters for the estimation of decay-rate

Biome kwood10 Q10

Temp. needleleaved evergreen

0.041 1.97

Temp. broadleaved 0.104 1.37

Boreal needleleaved 0.041 1.97

Boreal broadleaved 0.104 1.37

Source: Joanneum (2014) own elaboration

For the purpose of this study, I will assume that temperate forests are predominantly broadleaved and boreal forests are predominantly needleleaved. Auxiliary data are provided in Table 15.

1 δ(t) is the dirac function. It equals 1 when t = 0, and = 0 when t ≠ 0



Table 15 Auxiliary data for the emissions from forest residues

Boreal Temperate

Average temperature (deg C) Country dependent

Country dependent

Annual rainfall (mm) Not required Not required

Net calorific value (MJ/kg) 18.5 – 20.7

19.0 – 20.0 SRC: 17.3 – 19.7

Source: for boreal EUBIONET (2003), ORNL (2011); for temperate Gravalos et al. 2010, McKendry (2002), OMAFRA (2001)

4.2.2 Emissions from the use of stumps

The model is the same as is used for forest residues except that a correction is made to the decay rate to account for the stumps. Generally, it is thought that stumps decay more slowly that do branches and residues. However, there is very little published data to support this claim. Repo et al modelled stumps to decay more slowly than branches (kstumps ≈ 0.5 kbranches). However, Shorohova et al measured stumps to decay more slowly and more quickly than branches (0.8 kbranches ≤ kstumps ≤ 1.5 kbranches). Both studies were for boreal forests. Since the biomass has more wood and less bark. net calorific values more typical of wood than residues will be used. Finally, stumps require more energy to extract that does collection of residues. Lindholm et al (2011) found that stumps required approximately 1.5 g CO2 / MJ more emissions than loose residues. Auxiliary data are shown in Table 16.

Table 16 Auxiliary data for stumps

Boreal Temperate

kstumps / kbranches 0.5 – 1.0 0.5 – 1.0

Net calorific value (MJ/kg) 18.6 – 21.1 18.6 – 20.7

Source: Joanneum (2014) own compilation



4.2.3 Emissions from pre-commercial thinning of forests

Above-ground live biomass (AGB)

To model the biomass growth of the forest I will use a “logistic” curve (Zweitering et al 1990).

𝐵𝐵(𝑡𝑡) =𝐵𝐵𝑜𝑜𝐵𝐵𝑚𝑚𝑚𝑚

𝐵𝐵𝑜𝑜 + (𝐵𝐵𝑚𝑚𝑚𝑚 − 𝐵𝐵𝑜𝑜)𝑅𝑅−𝑐𝑐𝑐𝑐

Where Bo = biomass at t=0, Bmx = maximum biomass and c is a constant that scales the time axis. If we assume that Bo = 0.01 Bmx, we can simplify the equation to

𝐵𝐵(𝑡𝑡) =0.01𝐵𝐵𝑚𝑚𝑚𝑚

0.01 + 0.99𝑅𝑅−𝑐𝑐𝑐𝑐

In this situation the maximum of the mean annual increment occurs when ct = 6.26. Therefore

𝑐𝑐 =6.26

𝑇𝑇𝑟𝑟𝑜𝑜𝑐𝑐𝑟𝑟𝑐𝑐𝑟𝑟𝑜𝑜𝑟𝑟

This is the time at which the biomass would be harvested. At this time Bharvest = 0.84 Bmx

So the biomass equation can be rewritten in terms of the harvest biomass as:

𝐵𝐵(𝑡𝑡) =0.01𝐵𝐵ℎ𝑟𝑟𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎𝑐𝑐

0.84 ∗ (0.01 + 0.99𝑅𝑅−𝑐𝑐𝑐𝑐)

Below-ground live biomass (BGB)

I will assume a constant root-to-shot ratio, R. Li et al (2003) suggest that fine-root biomass is a proportion of total root biomass using the equation

𝐹𝐹𝑅𝑅𝑅𝑅 = 0.072 + 0.354𝑅𝑅−0.060𝑅𝑅

However, this equation is not very conducive to the model formulation that I am using (normalized to harvest biomass), so I will use a simpler formula:

𝐹𝐹𝑅𝑅 = 𝑘𝑘𝑅𝑅 And calculate an average value from the Li equation for the range of root biomass expected.

Above-ground dead biomass

Litter Every year the forest produces litter which decays following simple exponential decay. Litter is typically about 4% of above ground biomass. The decay rate of



litter is temperature and biome following an equation derived by Brovkin et al. (2012). In this paper they suggest

𝑘𝑘𝑙𝑙𝑟𝑟𝑐𝑐𝑐𝑐𝑎𝑎𝑟𝑟 = 𝑘𝑘𝑙𝑙𝑟𝑟𝑐𝑐𝑐𝑐𝑎𝑎𝑟𝑟10𝑄𝑄10(𝑇𝑇−1010 )

and have studied a global compilation of reports to attain values for klitter10 and Q10. They suggest:

Table 17 Parameters for the estimation of litter decay-rates

Biome Klitter10 Q10

Temp. needleleaved evergreen

0.70 1.97

Temp. broadleaved 0.95 1.37

Boreal needleleaved 0.76 1.97

Boreal broadleaved 0.94 1.37

Source: Brovkin et al. (2012)

Dead wood In addition the forest produces dead wood due to mortality. Typical mortality rates are shown in Table 18. It is assumed that 50% of the dead wood is harvested for bioenergy when the stems are harvested.

Table 18 Average mortality rates

Biome Average mortality rate (fraction of standing biomass per year )

Evergreen forests 0.0116

Deciduous forests 0.0117

Source: IPCC. (2003)

Below-ground dead biomass Below-ground dead biomass comes from two sources: decaying roots post-harvest and fine root litter. The latter is a bit of a problem to model. The IPCC default method has soil organic carbon on a per hectare basis depending on soil type, forest type and management. Coarse roots post-harvest It is assumed that all dead roots decay following Brovkin’s relationship for wood.



Fine roots Brunner et al (2013) have recently published root turnover rates for European forests. They found that fine root turnover = 1.11 mean fine root biomass. This value is used for an estimate of fine root turnover for both temperate and boreal forests. For tropical forest a relationship based on that derived by Finér et al. (2011) is used. They found that

𝐿𝐿𝐸𝐸(𝐹𝐹𝑅𝑅𝐹𝐹) = 0.515 ∗ 𝑙𝑙𝐸𝐸(𝐹𝐹𝑅𝑅) + 2.51 Where FRP= fine-root production and FRB = fine-root biomass. I will simplify this equation to

𝐹𝐹𝑅𝑅𝐹𝐹 ≈ 𝐹𝐹𝑅𝑅𝐵𝐵𝑅𝑅0.515 And the fine root turnover

𝐹𝐹𝑅𝑅𝑇𝑇 = 𝐹𝐹𝑅𝑅𝐹𝐹 − 𝐹𝐹𝑅𝑅𝐵𝐵 = 𝐹𝐹𝑅𝑅𝐵𝐵 ∗ (𝑅𝑅0.515 − 1) = 0.673 Fine-roots are assumed to decay following Brovkin’s relationship for wood. Initial biomass in dead biomass pools One must estimate the initial biomass in the dead biomass pools. To do so, it is assumed that the forest is in dynamic equilibrium. This means that the initial biomass in each of the dead biomass pools is the same as in the year of harvest.

Harvested wood products

At final felling a typical mix of biomass to various wood products is assumed. The table below shows the end distribution. A higher amount goes directly to the sawmill, however a large portion of the amount entering the mill is used for energy


The biomass is cascaded through the various processing streams. The values above represent the final end product proportions. The biomass follows typical processing paths. Fresh biomass goes to the saw mill, panel mill, pulp mill and directly for energy. The biomass in each of the mills is used to create a product and the residues are cascaded to subsequent mills and energy. In addition, the product pools decay, each with a specific decay rate and the discarded material is recycled (sent back to the various mills) or used for energy.



Biomass can only be recycled or down-cycled (i.e. paper discards cannot be used in the sawmill).

Figure 3 Biomass flows in the wood processing chain

Source: Joanneum (2014) own elaboration.

Emissions from the processing of the wood products are included in the analysis as are emissions from the substitution of wood-based material products by non-wood based material products (cement for sawnwood and panels, plastics for paper).

4.3 Results

4.3.1 Emissions from forest residues

Figure 4 a & b show the annual and cumulative emissions from the use of forest residues in Austria. There is a large emission upon combustion and this is followed by a string of negative emissions as the residues decay, had they been left in the forest in the reference case. After 16 years the cumulative emissions without



supply-chain emissions are 1.06 kg CO2 / kg – approximately equivalent emissions to natural gas.

Figure 4 Forest residues – annual net emissions (a.) and cumulative net emissions (b.) from the use of forest residues in Austria

a. b. Note: Values shown are typical for Austria. The 20-year and 100-year impacts are 0.94 and 0.07 kg CO2/kg biomass respectively


4.3.2 Emissions from stumps

The annual and cumulative emissions from the use of stumps in Austria are shown in Figure 5 a & b respectively. The form is similar to that of residues but with slower decay. After 22 years the cumulative emissions without supply-chain emissions are 1.06 kg CO2/ kg – approximately equivalent emissions to natural gas.

Figure 5 Net GHG Emissions from Stumps – annual (a) and cumulative (b)

a b Note: Values shown are typical for Austria assuming a 0.75 decrease in k. The 20-year and 100-year impacts are 1.1 and 0.16 kg CO2/kg biomass respectively




4.3.3 Emissions from pre-commercial thinnings (optimistic option)

The results from the use of pre-commercial thinnings under the optimistic option are the same as Figure 3 (above) since the assumed reference system is that the thinned material would be left as residues.

4.3.4 Emissions from pre-commercial thinnings (pessimistic option)

Figure 6 shows the uncorrected emissions and biomass for energy from the pessimistic pre-commercial thinning model. There is a large emission with the initial use of energy and a negative emission and biomass use when the final harvest occurs, due to less biomass available for use in the bioenergy system as in the reference system.

Figure 6 Pre-commercial thinnings – uncorrected annual net emissions (a.) and uncorrected annual net biomass for energy

a. b. Note: Values shown are typical for Austria. The pre-commercial thinning produces biomass for energy at year 0

and year 90 (the rotation length). There is a negative net biomass in year 80 because there is less biomass at the final felling in the bioenergy system than in the reference system.


Figure 7 a & b show the annual and cumulative emissions from the pre-commercial thinning model after correction. The loss of biomass for energy at final harvest in the bioenergy system must be compensated by a small amount of pre-commercial thinning. As a result there is a second emission at final felling. Since the forest does not grow faster after thinning there is no sequestration or negative emissions after thinning as the cumulative emissions do not decrease over time. In fact they increase because with less above ground biomass, there is less dead wood, litter and soil organic matter as compared to the reference case.



Figure 7 Pre-commercial thinning – corrected annual net emissions (a.) and corrected cumulative net biomass for energy

a. b. Source: Joanneum (2014) own elaboration; note: values shown are typical for Austria

4.3.5 Emissions from commercial thinning - thinnings used for energy instead of products and energy

The uncorrected biomass for energy and net emissions are shown in Figure 8 a & b. The thinning produces biomass for energy at year 0 and year 90 (the rotation length). However, the net biomass is not 1 kg biomass / kg extracted because some of the biomass would have been used for energy in the reference system. There is negative net biomass for many years after the thinning takes place because the cascaded biomass in the reference system also provides biomass for energy.

Figure 8 Thinnings – uncorrected annual net emissions (a.) and uncorrected annual net biomass for energy (b.)

a. b. Note: Values shown are typical for Austria. The thinning produces biomass for energy at year 0 and year 90 (the rotation length). However, the net biomass is not 1 kg biomass / kg extracted because some of the biomass would have been used for energy in the reference system. There is negative net biomass for many years after the thinning takes place because the cascaded biomass in the reference system also provides biomass for energy. Source: Joanneum (2014) own elaboration



Figure 9 Thinnings - corrected annual net emissions (a.) and corrected cumulative net emissions for forest bioenergy

a. b. Source: Joanneum (2014) own elaboration; note: values shown are typical for Austria

There is a large negative emission at the thinning event. The emissions are significantly larger than the default emissions (1.83 kg CO2/kg biomass) because there is a large emission that results from the required substitution of paper products in the bioenergy system. The negative net biomass for energy means that a small amount of biomass through thinning must be applied consistently to compensate for the energy that would be produced through the wood product chain in the reference system. This means that cumulative emissions begin at 2.6 kg CO2/kg and increases to about 3.4 kg CO2/kg. As the rotation period approaches, the bioenergy system enters dynamic equilibrium.

4.3.6 Emissions from the harvesting of forests - advanced harvests

The shortening the rotation causes biomass to be harvested at year 0 and year 90 (the optimal rotation length). However, there is the loss of a harvest at year 120 (the delayed rotation length). There is a large emission (or negative emission) associated with all harvesting events. There is also a continuous, smaller, stream of emissions after each harvest event as the wood products resulting from the harvest are used, recycled and discarded (Figure 10 a & b).



Figure 10 Advanced harvest – uncorrected annual net emissions (a.) and uncorrected annual net biomass for energy

a. b. Note: Values shown are typical for Austria. The shortening the rotation causes biomass to be harvested at year 0 and year 90 (the optimal rotation length). However, there is the loss of a harvest at year 120 (the delayed rotation length). There is a large emission (or negative emission) associated with all harvesting events. There is also a continuous, smaller, stream of emissions after each harvest event as the wood products resulting from the harvest are used, recycled and discarded. Source: Joanneum (2014) own elaboration

Figure 11 a & b show the corrected net annual and net cumulative emissions from advancing the harvest. The corrected net annual emissions start at 0.86 kg CO2 / kg biomass for energy. This value is less that the default value for combustion of biomass for energy (1.83 kg CO2 / kg) because there are emissions saved due to the substitution of products made from non-woody materials by the wood products. However, over time, these products are discarded, downcycled and inevitably used for energy causing an emission. The cumulative emissions increase as the forest moves to a new dynamic equilibrium. However, once reached the cumulative emissions decrease because of the increase in wood products being generated from the advanced harvest.

Figure 11 Advanced harvest – corrected annual net emissions (a.) and corrected cumulative net biomass for energy

a. b. Source: Joanneum (2014) own elaboration Note: Values shown are typical for Austria.



There are actually two types of emission factors to consider; the emission factor from the consumption of an amount of biomass in a single year, and the effective emission factor of the continuous consumption of biomass. The latter is calculated by summing the emissions from a specific year to the year of interest and dividing it by the total biomass consumed over the same period, hence it is the time average emission factor. Figure 6a shows the emission factors excluding supply-chain emissions of the presented models for Austrian forests and conditions. For example, the emission factor for the use of residues decreases quickly with time. The effective emission factor, however, is dependent on the amount of biomass consumed in specific year. For example, if the amount of bioenergy from residues is increasing the effective emission factor will decrease less quickly than for the case of consumption in a single year. This occurs because the effective emission factor includes both biomass extracted for many years and extracted recently. Since more biomass is extracted recently, it has a greater impact on the effective emission factor. Figure 6b shows the effective emission factors of the various biomass sources for a specified biomass scenario. Of the five models, only biomass from the two residues the advanced harvest biomass have intensities that over time are below the intensities of fossil fuels (coal = 88 g CO2/MJ, oil = 73 g CO2/MJ, and natural gas = 51 g CO2/MJ). Typically the intensity should start somewhere near that of wood without regrowth (94 g CO2/MJ). The advanced harvest model starts with a lower intensity because there are wood products created. The commercial thinning model starts with a higher intensity because material products are forsaken to create energy. The amounts above and below the typical value are approximately the same. When the models are applied to a biomass supply scenario, the emission factors are different than for the individual models because intensity of the scenario is calculated as the sum of emissions to a specific year divided by the sum of energy to the same year. When considering effective emission factors by country for different types of biomass, the general trend is that warmer countries have faster decay rates and hence lower emission factors from the use of residues. This was also suggested by Repo et al (2011). There are slight variations in the effective emission factors of other biomass sources too. For example, countries with longer rotation lengths have a lower emission factors from the use of harvest stemwood than do countries with shorter rotation period. This is because the typical current harvest delay is assumed to be 1/3 of the rotation period.



4.4 Life-Cycle GHG Emissions of European Bioenergy Supply Chains

In addition to the CO2 emissions from forest C stock changes which were described above, the bioenergy supply chains for electricity, heat and transport fuels imply also GHG emissions from the production of the feedstocks, the various transport steps, and the conversion to the end-uses. The respective GHG emission factors for all bioenergy life-cycles (see Table 17) were taken from the GEMIS database (IINAS 2013), making use of the data compiled in the BiomassFutures project (IC et al. 2012). These emission factors exclude the CO2 emissions from forest C stock changes, as those depend on the time horizon, and assumptions on the forest reference cases (see Section 3.3 of the final report). Furthermore, the emission factors do not account for possible GHG emissions from indirect land use changes (iLUC), as only those potentials for e.g. SRC or biogas from grasslands were included in this study which do not imply displacement of previous use for food or feed.



Table 19 Life-Cycle GHG Emission Factors for European Bioenergy Supply Chains in 2020 and 2030

CO2eq in g/MJoutput 2020 2030 pellets-EU forest-products 6.4 5.8 pellets-EU wood-industry 3.9 3.5 pellets-EU SRC 11.5 10.9 pellets-US-import 16.2 15.6 wood-logs EU 0.0 0.0 biogas-maize 23.2 22.4 biogas-grass cuttings 9.5 9.1 biogas-manure 3.5 3.2 biogas-org.wastes 3.7 3.2 biomethane-maize 26.9 25.8 biomethane-grass cuttings 13.2 12.6 biomethane-manure 7.3 6.6 biomethane-org.wastes 7.4 7.3 AME-EU 7.1 6.9 rapeseed-oil-EU 33.4 30.6 RME-EU 38.8 35.6 PME-EU 26.5 24.3 SME-EU 18.2 18.0 BtL-black-iquor-EU 0.3 0.3 BtL-forest-residue-EU 37.2 32.9 BtL-SRC-EU 49.4 44.9 EtOH-wheat-EU 47.5 42.1 EtOH-sugarbeet-EU 44.8 42.4 EtOH-sugarcane-EU 27.1 24.7 EtOH2G-straw-EU 9.0 8.4 EtOH2G-forest-residues-EU 7.6 7.5 EtOH2G-SRC-EU 18.4 18.0

Source: GEMIS version 4.8 (IINAS 2013); note that the emission data exclude CO2 emissions from forest C stock changes, and also assume no indirect land use changes (iLUC)



4.5 Total GHG Emission Balances

To calculate the overall GHG balances of bioenergy and the other energy carriers in the scenarios (see Section 5.6 of the final report), the following contributions were considered: • Life-cycle GHG emissions for all bioenergy systems (see Table 21) • For bioenergy from forests also CO2 emissions from forest C stock changes,

depending on time horizon (20 or 100 years), and optimistic or pessimistic forest reference case (these data are given in Table 3 of the final report)

• Life-cycle GHG emissions for all other energy systems (see Table 21 and Table 22)

• GHG emission savings from substituting construction materials with wood (see Table 23).

Table 20 Life-Cycle GHG Emission Factors for European Non-Renewable Energy Sources in 2020 and 2030

CO2eq in g/MJinput 2020 2030 coal 109 108 oil 87 87 gas 66 66 nuclear 6 6

Source: GEMIS version 4.8 (IINAS 2013)

Table 21 Life-Cycle GHG Emission Factors for European Non-Bio-Renewable Electricity Sources in 2020 and 2030

CO2eq in g/kWhel 2020 2030 hydro 11 10 wind 3 3 solar-PV 26 23


Table 22 Life-Cycle GHG Emission Factors for European Construction Materials in 2020 and 2030

CO2eq in t/tmaterial 2020 2030 concrete 0.16 0.16 steel (mix) 1.49 1.50




5 Scenario Description

5.1 Background Data for the Wood Demand in the REF Scenario

5.1.1 EUwood Project and related work

The EUwood project aimed to examine the potential supply and demand of forest resources in Europe by 2020 and 2030 both for materials and energy. The theoretical biomass potential from European forests in 2010 was 1,277 Mm3. About 52% of the total potential is in stems, while logging residues represent 26%, and stumps 21%, respectively. Other biomass, i.e. stem and crown biomass from early thinnings, represent only 1% of the total potential. The “realistic” potential from European forests estimated within the EUwood project is 747 M m3 per year (overbark) in 2010 and could range from 625 to 898 million m3 per year by 2030. This potential represents the total potential that could be supplied by forests in the EU, regardless whether it is used for material or for energy use. It was not assessed whether the potential could become economically available. In the year 2010 the wood consumption in solid wood equivalents (swe) for all material uses was about 458 Mm³. It is assumed to increase by 15.4% to 529 Mm³ by 2020 and by 17.2 % to 620 Mm3 solid wood equivalents by 2030, as depicted in the following table.

Table 23 Breakdown of the wood resource balance in 2010

Potential in Mm3swe Demand in Mm3

swe Stemwood C, ME 362 196 Sawmill industry

Stemwood NC, ME 182 11 Veneer/plywood industry Forest residues, ME 118 143 Pulp industry

Bark, ME 55 92 Panel industry Landscape cw (USE) ME 59 15 Other material uses

Short rotation coppices* 21 Producer of solid wood fuels Sawmill by products 87 86 Forest sector internal use

Other industrial residues 30 83 Biomass power plants Black liquor 60 23 Households (pellets)

Solid wood fuels 21 155 Households (other) Post consumer wood 52 0 Liquid biofuels

Total 1026 825 Total

Source: Mantau (2013); C: Coniferous; NC: Non-Coniferous; ME: Medium mobilization scenario; * The potential of SRC was not quantified within the project



About 100 to 200 Mm³ of additional wood could be needed, depending on the scenarios and the qualitative assumptions on new product developments. The share of material uses in total wood consumption is expected to decrease from 55.5% in 2010 to 46.5% in 2020 and 43.5% in 2030. It is worth noting that a relevant part of the material uses is used directly to energy. According to Mantau (2012) the wood resources from trees represent 577 Mm3 in 2010 (544 Mm3 from forestry and 33 Mm3 from wood outside of forests). From this total, 260 Mm3 were used for wood production and 108 Mm3 for pulp and paper. The remaining 209 Mm3 were used for energy. Wood consumption for energy generation is expected to grow from 346 Mm³ in 2010 (3.1 EJ) to 573 Mm³ (5 EJ) by 2020 and might reach 752 Mm³ by 2030 (6.6 EJ). The results of the EUwood analysis show that for the medium mobilisation scenario and for high bioenergy demand growth the expected demand is likely to exceed the sustainable wood potential by 2020. Even if all measures for increased wood mobilization were implemented, wood demand from industry and meeting the renewable energy targets can hardly be satisfied from domestic sources by 2020. EUwood has shown that with a high mobilization scenario it is difficult but not impossible to supply, on a sustainable basis, enough wood to satisfy the needs of the industry and to meet the targets for renewable energy in 2020. There is definitely not enough wood to satisfy the combined needs from forest-based industries and wood energy producers from domestic EU-27 sources by 2030.

5.1.2 EFSOS II (UNECE, FAO 2011)

The EFSOS II study focused on a reference scenario and four policy storylines between 2010 and 2030. The reference scenario assumes no major change in policies so consumption of forest products and wood energy will grow steadily, and trends outside the forest sector are considered according to the IPCC B2 scenario. The EFSOS-II policy scenarios provide insights of “what if”:

- Maximizing biomass carbon: maintaining the level of harvest it explores the amount of carbon that could be stored in forests. In the short term, the best strategy is to combine forest management with the aim of accumulating carbon in the forests (longer rotations and a greater share of thinning) with a steady flow of wood for products and energy while in the long term regular harvesting should be promoted because the sequestration capacity limit of forest would have been reached.



- Priority to biodiversity: considers biological diversity a priority and assumes, for example, that forest residues and stumps are not harvested for bioenergy. The supply in this scenario is 12 % lower than in the reference scenario so reduced consumption of products and energy and/or increased imports and/or intensified use of other sources would be needed.

Promoting wood energy: investigates the amount of wood needed to meet the RED targets. This scenario implies that supply should increase by nearly 50 %in 20 years. SRC should be established on the agricultural land. The EFSOS-II study resumes: “Europe is, and will remain in all scenarios, a net exporter of wood and forest products: significant net exports of products outweigh relatively minor net imports of wood, even in the Promoting wood energy scenario”. On the other hand, “Projections show a steady rise in prices of forest products and wood over the whole period, driven by expanding global demand and increasing scarcity in several regions” (UNECE, FAO 2011).

In these scenarios2, wood for material use would increase from 534 Mm3 in 2010 to 585 Mm3 by 2030 (see Table 25).

Table 24 Overview of EFSOS scenarios

Source: UNECE, FAO (2011)

2 The additional “Fostering innovation and competitiveness” scenarios provide only a qualitative view, i.e. no data given.



The EFSOS-II study resumes: “Europe is, and will remain in all scenarios, a net exporter of wood and forest products: significant net exports of products outweigh relatively minor net imports of wood, even in the Promoting wood energy scenario”. On the other hand, “Projections show a steady rise in prices of forest products and wood over the whole period, driven by expanding global demand and increasing scarcity in several regions” (UNECE, FAO 2011).

Table 25 Components of wood supply in “Promoting wood energy” scenario, 2010-2030

Source: UNECE-FAO (2011)



Table 26 Use of wood for energy in Promoting wood energy scenario, 2010-2030


Table 27 Components of wood supply (without trade), by country group in “Promoting wood energy” scenario, 2010-2030




5.2 The Reference (REF) Scenario

5.2.1 Wood Material Demand

Given the variety of study results presented above, the following basic assumptions for the REF scenario are recommended with regard to materials from woody biomass:

• Wood demand for materials will increase from 531 Mm3 in 2010 to 550 Mm3 by 2020 and to 585 Mm3 by 2030, based on the EFSOS-II study. This represents energy equivalents of about 4,800 PJ (2020) and 5,100 PJ (2030), respectively. The demand growth from 2010 to 2020 will be approx. 4%, rising to 10% until 2030.

• It is assumed most of the increment will be for short-life products (< 10 years) so that the energy content could be recovered from post-consumer wood, packaging materials etc. if adequate waste collection is implemented.

• Demand growth will be for rather low-quality wood (for pulp & paper/packaging, MDF etc.).

5.2.2 Background Data on Solid Bioenergy Consumption

Table 22 depicts the consumption of solid biomass in the EU in 2011 by use.

Table 28 Consumption of solid biomass in the EU in 2011 by use

Solid Bioenergy Use Consumption Unit

for heat consumption in 64.9 MtOE

- residential and industrial sector 58.0 MtOE

- processing sector 6.9 MtOE

- heat plants 2.7 MtOE

- CHP plants 4.2 MtOE

for electricity produced from 72.8 TWh

- electricity-only plants 30.6 TWh

- CHP plants 42.2 TWh

Primary energy production 78.8 MtOE

Source: EurObserv’ER (2012)



The overall flows of wood from sources to end uses in the EU-27 in the year 2010 are shown in the following figure.

Figure 12 Condensed wood flow chart from resources to end-use in the EU-27 in 2010

Source: Mantau (2012)



5.3 Bioenergy Demand and Supply Projections

According to the NREAPs, bioenergy will contribute approximately 12 % of the final energy demand by 2020, representing an increase about 60 % in comparison with 2010 (Uslu, van Stralen 2012). Several research studies with different specific targets have been conducted aiming at assessing the bioenergy potential supply and demand within the EU taking particular consideration of forest biomass i.e. Biomass Futures (IC et al. 2012), EUwood (Mantau et al. 2010) or EEA (2006, 2013) assessments.

Biomass Futures Project

The purpose of this IIE project was to examine the role that biomass could play into meeting the RED targets. It concluded that meeting NREAPs will be very challenging (IC et al. 2012; van Stralen et al. 2013) despite the surplus of biomass available at European level. The demands according to the Biomass Futures scenarios assumptions are shown in Table 23. An additional “reference” scenario resulted in a demand of 618 TWh bioenergy with a demand of 357 TWh from wood directly from forestry and 261 TWh of wood byproduct from industry. The relevant differences in demand between the RED and RED+ scenario come from different assumptions on issues related to sustainability such as GHG balances and land requirements (Uslu et al. 2012).

Table 29 Demands in the Biomass Futures project for 2020

Feedstock Unit High biomass

scenario Sustainability

scenario Primary forestry residues TWh 334 110

Sawmill by-products TWh 90 90 Landscape care wood TWh 100 96 SRC TWh 498 180 Final energy from wood TWh 1022 476 Feedstock LHV TWh 1795 858 MtOE 154 74 Feedstock volume Mm³swe 646 309

Source: own calculation based on data from Biomass Futures project (IC et al. 2012)

In the reference scenario, despite the significant roundwood and additional harvestable roundwood potential in 2020 and 2030, these resources are not utilized due to their higher prices (> 400 €/tOE; 83.5 €/m3

swe) in comparison to alternatives, i.e. wood pellets imports.



In the sustainability scenario, due to more stringent environmental constraint there are potentials neither from roundwood production nor from additional harvest roundwood. Primary forestry potentials are reduced dramatically due to the sustainability criteria but this is compensated by larger utilization of perennial energy crops and more use of expensive biomass coming from wood processing such as sawmill by-products and higher imports. The high biomass scenario doesn´t foresee neither roundwood nor additional harvests of solid biomass but expects high utilization of residues. In any case, for all scenarios and timeframes, EU27 bioenergy potentials are higher than current supply and far less than the demand, as illustrated in Table 24. However, the biomass Futures project concluded that around 15 % of the total primary biomass would be imported and this share would be higher in the “high biomass scenario” (Uslu et al. 2012).

Table 30 Overview of the final bioenergy demand per sector in the EU27 in different scenarios for 2020

Final demand (all) biomass (PJ) 2010 NREAP Reference Sustainability High Biomass

Electricity 440 835 796 677 867 Heat 3081 3768 3182 3186 3517 Biofuels 544 1244 1163 547 1163 total 4065 5847 5141 4410 5547

Source: van Stralen et al. (2013)



6 Detailed Scenario Results

Table 31 Electricity Generation in the EU27 from 2010 to 2030

2020 2030

Electricity generation, TWhel 2010 REF GHG SUS REF GHG SUS

total generation 3.410 3.414 3.290 3.290 3.650 3.320 3.320 - fossil & nuclear 2.722 2.185 1.848 1.848 2.030 958 958 - RES 688 1.229 1.442 1.442 1.620 2.362 2.362 of that: non-bio renewables 528 1.008 1.192 1.192 1.377 2.112 2.062 of that: bioenergy 160 221 250 250 243 300 300 of that: non-woody bioenergy -11 50 75 115 60 139 275 of that: woody bioenergy 171 171 175 135 183 161 25 share of fossil & nuclear 79,8% 64,0% 56,2% 56,2% 55,6% 28,9% 28,9% share of RES 20,2% 36,0% 43,8% 43,8% 44,4% 71,1% 71,1% share of bioenergy 4,7% 6,5% 7,6% 7,6% 6,6% 9,0% 9,0%

share of woody bioenergy 5,0% 5,0% 5,3% 4,1% 5,0% 4,9% 0,8%


Table 32 Heat Production in the EU27 from 2010 to 2030

2020 2030

Heat Production, PJ 2010 REF GHG SUS REF GHG SUS

fossil, direct 16.657 16.657 15.389 15.389 15.800 11.046 11.046 district heat incl. cogen 2.336 2.336 2.219 2.219 2.330 1.981 1.981 electricity incl. heatpumps 1.330 1.330 1.197 1.197 1.721 1.291 1.291 RES (excluding cogen heat) 2.892 6.034 4.550 4.550 5.530 6.885 6.885 - non-bio renewables 127 1.550 1.550 1.550 3.500 5.000 5.000 - bioenergy 2.765 4.484 3.000 3.000 2.030 1.885 1.885 of that: forest products EU 2.383 2.847 968 975 1.269 353 71 of that: woody residues EU 368 1.603 1.965 1.965 736 1.461 1.673 of that: SRC EU 14 34 68 60 25 71 141 of that: forest products imported 0 0 0 0 0 0 0 heat final energy demand 23.215 26.357 23.355 23.355 25.381 21.202 21.202 share of RES 12,5% 22,9% 19,5% 19,5% 21,8% 32,5% 32,5% share of RES incl. cogen heat + el. 17,9% 28,6% 26,7% 26,5% 28,8% 42,8% 41,8% share of bioenergy 11,9% 17,0% 12,8% 12,8% 8,0% 8,9% 8,9%

share of woody bioenergy 11,9% 17,0% 12,8% 12,8% 8,0% 8,9% 8,9%




Table 33 Transport Fuel Production in the EU27 from 2010 to 2030

2020 2030

Transport Fuels, PJ 2010 REF GHG SUS REF GHG SUS fossil, direct 14.564 13.178 10.322 10.322 12.467 6.722 6.722 electricity 520 666 699 699 1.121 1.271 1.271 biofuels 546 1.096 888 888 1.140 730 730 - annual crops EU 524 939 648 613 690 0 0 - non-woody residues/wastes EU 4 16 13 16 21 27 27 - straw EU 0 16 24 37 110 164 182 - crops imported 19 102 73 49 72 18 0 - forest products EU 0 8 64 77 0 192 137 - woody residues EU 0 16 64 97 83 328 383 - SRC EU 0 0 0 0 0 0 0 - forest products imported 0 0 0 0 165 0 0 transport final energy demand 15.630 14.940 11.909 11.909 14.729 8.723 8.723

RE share including electricity 4,2% 8,9% 10,0% 10,0% 11,1% 18,7% 18,7% Bioenergy share 3,5% 7,3% 7,5% 7,5% 7,7% 8,4% 8,4% Woody bioenergy share incl. el. 0,2% 0,4% 1,4% 1,7% 2,1% 6,7% 6,1%

Source: IINAS calculations; note than no double-counting for biofuels nor multipliers for electricity were applied

Table 34 Final Energy Demand in the EU27 from 2010 to 2030 2020 2030 Final Energy Supply 2010 REF GHG SUS REF GHG SUS fossil, direct 31.220 29.835 25.711 25.711 28.267 17.768 17.768 non-renewable electricity 9.799 7.865 6.653 6.653 7.307 3.449 3.449 renewable electricity 2.478 4.425 5.191 5.191 5.832 8.503 8.503 of that: bioenergy 576 795 900 900 873 1.080 1.080 of that: woody bioenergy 615 615 630 486 657 581 90 fossil cogen heat 1.343 1.302 1.061 1.106 1.311 712 912 renewable cogen heat 992 1.034 1.158 1.113 1.019 1.269 1.068 of that: woody bioenergy 615 593 635 508 432 627 200 non-bio renewables 127 1.550 1.550 1.550 3.500 5.000 5.000 non-woody bioenergy 546 1.073 759 715 892 210 210 woody bioenergy 2.765 4.507 3.129 3.173 2.278 2.405 2.405 total 49.271 51.591 45.212 45.212 50.407 39.316 39.316

RE share (incl. el + cogen heat): 14% 24% 26% 26% 27% 44% 44% bio share (incl. el + cogen heat): 9% 14% 12% 12% 9% 11% 10% woody share (incl. el + cogen heat): 8% 11% 10% 9% 7% 9% 7%




Table 35 Primary Energy Supply in the EU27 from 2010 to 2030 2020 2030

2010 REF GHG SUS REF GHG SUS Primary energy, in PJ 70.676 69.337 64.600 64.625 67.110 45.130 45.204

- fossil & nuclear 63.004 57.827 51.350 51.244 53.854 26.438 26.323 of that: coal 11.722 9.878 8.295 8.248 7.268 3.590 3.537 of that: oil 25.834 22.952 20.061 20.014 21.643 11.702 11.656 of that: natural gas 18.497 16.896 15.057 15.046 16.518 7.992 7.982 of that: nuclear 9.905 8.101 7.936 7.936 8.424 3.155 3.148 - RES 7.672 11.597 13.251 13.381 13.341 18.692 18.882 of that: non-bio renewables 2.029 4.469 5.841 5.841 8.719 12.603 12.423 of that: bioenergy & waste 6.644 7.128 7.410 7.540 4.622 6.089 6.458 of that: non-bio waste 500 383 383 383 364 364 364 of that: non-woody bio 1.434 1.961 2.055 2.501 1.686 1.457 3.069 of that: woody bioenergy 4.711 4.784 4.971 4.656 2.572 4.268 3.025 share of RE 11% 17% 21% 21% 20% 41% 42% share of bioenergy 9% 10% 11% 11% 6% 13% 13% share of woody bioenergy 7% 7% 8% 7% 4% 9% 7%


Table 36 Primary Bioenergy Supply and Use of Sustainable Potentials in the EU27 from 2010 to 2030

2020 2030

in PJ 2010 REF GHG SUS REF GHG SUS

total woody bioenergy 4.711 6.253 4.971 4.656 3.718 4.268 3.025 - forest products, EU 3.204 3.387 1.554 1.291 1.682 1.058 345 - woody residues/wastes, EU 1.384 2.185 3.119 3.049 1.276 2.960 2.539 - SRC, EU 14 34 68 60 25 87 141 - forest products, imported 108 647 231 256 734 163 0 share of potential (REF/SUS) 2010 REF GHG SUS REF GHG SUS

- forest products, EU 52% 100% 77% 64% 49% 51% 17% - woody residues/wastes, EU 56% 78% 68% 67% 64% 74% 64% - SRC, EU 2% 4% 4% 4% 10% 44% 72%



2020 2030

in PJ 2010 REF GHG SUS REF GHG SUS

total non-woody bioenergy 116 273 352 861 590 1.179 3.069 straw for biofuels 8 57 68 96 217 319 350 straw for biogas 0 0 25 315 0 314 1153 residues for biogas el-cogen 108 216 260 450 373 546 1567 share of potential (REF/SUS) 2010 REF GHG SUS REF GHG SUS

straw for biofuels 0% 3% 3% 5% 11% 16% 18% straw for biogas el-cogen 0% 0% 1% 15% 0% 16% 58% residues for biogas el-cogen 6% 11% 13% 23% 18% 26% 75%




Table 37 GHG Emissions from Bioenergy Supply and Use from 2010 to 2030

GHG emissions woody bioenergy, Mt CO2eq/a 2020 2030

20 year horizon, pessimistic REF 2010 REF GHG SUS REF GHG SUS

from electricity (incl. CHP) 78.7 104.3 61.7 47.5 78.5 47.1 11.8 from heat 2.8 4.5 3.0 3.0 2.0 1.9 1.9

from biofuels 22.1 42.1 35.7 34.8 35.4 22.2 16.2

from materials substitution -6.3 -6.3 -63.0 -63.0

total from bioenergy 103.5 150.9 94.1 79.0 116.0 8.2 -33.2


20 year horizon, optimistic REF 2010 REF GHG SUS REF GHG SUS


from biofuels 22.1 42.1 35.3 34.3 35.4 20.9 15.3


total from bioenergy 95.7 132.0 85.2 71.3 102.9 0.7 -34.3


100 year horizon (independent from REF) 2010 REF GHG SUS REF GHG SUS


from biofuels 22.1 41.6 31.1 29.3 35.4 8.4 6.3


total from bioenergy 41.7 72.0 43.6 45.7 59.2 -40.2 -44.7

total GHG Emission Balances for Bioenergy 2020 2030

2010 REF GHG SUS REF GHG SUS

20 year horizon, pessimistic REF 103.5 150.9 94.1 79.0 116.0 8.2 -33.2 20 year horizon, optimistic REF 95.7 132.0 85.2 71.3 102.9 0.7 -34.3 100 year horizon (independent from REF) 41.7 72.0 43.6 45.7 59.2 -40.2 -44.7




Table 38 GHG Emissions from Energy Supply and Use in the EU27, 2010 to 2030

2020 2030

Overall GHG emissions, in Mt CO2eq/a 2010 REF GHG SUS REF GHG SUS

coal 1.213 1.075 863 861 783 387 381 oil 2.226 2.007 1.841 1.837 1.878 1.015 1.011 natural gas 1.200 1.107 916 914 1.093 529 528 nuclear 62 50 52 52 52 20 20 renewables excluding bioenergy 1 12 12 12 18 18 18 bioenergy excluding woody 28 49 33 37 38 6 11 woody bioenergy (20 a, pessimistic REF) 104 151 94 79 116 8 -33 woody bioenergy (20 a, optimistic REF) 96 132 85 71 103 1 -34 woody bioenergy (100 a, indep. from REF) 42 72 44 46 59 -40 -45 total (20 year horizon, pessimistic REF) 4.834 4.451 3.811 3.792 3.978 1.983 1.936 total (20 year horizon, optimistic REF) 4.826 4.432 3.802 3.784 3.965 1.975 1.935 total (100 year horizon, indep. from REF) 4.772 4.372 3.760 3.759 3.921 1.934 1.924

GHG reduction vs. 2010 (20 a, pess. REF) -8% -21% -22% -18% -59% -60%

GHG reduction vs. 2010 (20 a, opt. REF) -8% -21% -22% -18% -59% -60%

GHG reduction vs. 2010 (100 a) -10% -22% -22% -19% -60% -60%

Source: IINAS calculations; data include upstream life-cycle GHG emissions for all energy, and GHG emissions from forest bioenergy using different time horizons and reference systems


forest biomass for energy in the eu: current trends ... › ... ›...

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