Deliverable 3.4: Biomass availability & supply analysis

Transcription

1 Biomass role in achieving the Climate Change & Renewables EU policy targets. Demand and Supply dynamics under the perspective of stakeholders. IEE SI Deliverable 3.4: Biomass availability & supply analysis Authors: IIASA: Hannes Böttcher, Stefan Frank, Petr Havlik Contributers: Alterra: Berien Elbersen IIASA: Sylvain Leduc ECN: Joost van Stralen March, 212

2 Content Content... 2 Preface Introduction Methodology Linkages to other WPs Model description Definitions of biomass feedstocks and conversion technologies Applied sustainability criteria Bioenergy demand and other background drivers Bioenergy demand Other drivers Assumptions on trade Scenario description Results Biofuel and bioenergy use Reference scenario base run Sustainability scenario base run Market conditions and trade Reference scenario base run Sustainability scenario base run Land use and land use change Reference scenario base run Sustainability scenario base run Sensitivity analysis Loss of biodiversity Reference scenario base run Sensitivity analysis GHG emissions Reference scenario base run Sustainability scenario base run Sensitivity analysis Discussion and conclusions References Appendix

3 Preface This publication is part of the BIOMASS FUTURES project (Biomass role in achieving the Climate Change & Renewables EU policy targets. Demand and Supply dynamics under the perspective of stakeholders - IEE SI , ) funded by the European Union s Intelligent Energy Programme. The sole responsibility for the content of this publication lies with authors. It does not necessarily reflect the opinion of the European Communities. The European Commission is not responsible for any use that may be made of the information contained therein. 3

4 1 Introduction The aim of the Work Package (WP) 3 of the Biomass Futures (BF) project overall was to provide a comprehensive strategic analysis of biomass supply options and their availability in response to different demands in a timeframe from This report introduces the approach and assumptions that are used to assess spatially explicit biomass supply and associated impacts of increased biomass use on biophysical and economic indicators (Task 3.5). We applied integrated bio-physical process based and economic modelling to analyse the cost effectiveness of biomass supply strategies and their environmental impacts on agricultural systems, bioenergy systems, and forest ecosystems in a geographic explicit way. This task aims to contrast the supply maps developed in Task 3.4 and described in Deliverable 3.3 Availability with more detailed environmental constraints given a certain demand for certain feedstocks. Applying the GLOBIOM model, a global recursive dynamic partial equilibrium model integrating the agricultural, bioenergy and forestry sectors allows for modelling competition between feedstocks. By doing so the technical potential estimated in Deliverable 3.3 is transformed into an economic potential. Both approaches applied sustainability constraints to biomass supply that were harmonised to make estimates comparable. Typical for this task compared to other tasks in WP 3 but also other WPs is an orientation towards impacts outside EU. Additionally, the model accounts for a wider scope of sustainability issues, addressing (direct and indirect) land use change, environmental variables (water, nitrogen, GHG emissions), and economic effects (e.g. food prices). GLOBIOM includes additional biodiversity constraints on highly biodiverse land outside the EU based on WCMC information {Kapos, 28 #11784}. Besides general parameters which limit land use change (no grassland conversion and deforestation in EU27 etc.), conversion of cropland and other natural vegetation to short rotation tree plantations is restricted. Through sensitivity analyses by changing assumptions on biofuel trade and other mitigation policies such as avoiding deforestation, GLOBIOM results are used to investigate competition between major land-based sectors (bioenergy, agriculture and forestry), potential leakage effects through land use and land use change as well as effects on food security and GHG emissions in order to give policy advice taking into account such global impacts. As described above, the work presented in this report builds on the findings from other tasks and WPs. The detailed information flows in Biomass Futures that are relevant for this task are presented in the methodology section. The section also describes in detail the involved model and basic data sets used for the analysis. Further, definitions and assumptions are introduced that are essential for the production of consistent results across the different WPs and for the interpretation of results of this particular task. The sustainability criteria applied here refer to work done under Biomass Futures WP 4 (Sustainability). The results and discussion section presents the output of the model and contrasts the findings with initial assumptions and findings in other studies that are summarised in the conclusions. Finally, an appendix presents an overview of parameters of feedstocks and technologies used. 4

5 2 Methodology 2.1 Linkages to other WPs This report introduces models and assumptions that are used to assess spatially explicit biomass supply and impacts of on biophysical and economic indicators associated with increased biomass use. To achieve this, the work builds on the findings from other Work Packages (WP) and tasks. Essentially it makes use of the following information: Results from the analysis of advanced sustainability constraints analysed in WP4 (Sustainability) a. GHG emissions per feedstock and technology b. Harmonisation of biomass categories Demand scenarios from WP7 (Scenarios and Policy) and information on characteristics of technologies from WP5 (Energy modelling) Comparison and harmonisation to the degree possible of conversion coefficients used within WP3 (Availability and Supply) and WP5 (Energy modelling) Comparison of assumptions and data on agriculture in CAPRI (CAPRI projection is used in Task 3.4 (Availability) A more detailed description of information flow between WPs and tasks in Biomass Futures can be found in Deliverable 3.5 (Summary of main outcomes for policy makers) available on the Biomass Futures website ( 2.2 Model description The Global Biosphere Management Model (GLOBIOM) 1 has been developed and is used at the International Institute for Applied Systems Analysis (IIASA). GLOBIOM is a global recursive dynamic bottom-up partial equilibrium model integrating the agricultural, bioenergy and forestry sectors with the aim to provide policy analysis on global issues concerning land use competition between the major land-based production sectors. It is global in the sense that it encompasses all world regions aggregated in a way that can be altered. For the purpose of Biomass Futures, GLOBIOM covers 5 regions. These regions are a disaggregated representation of an eleven-region GLOBIOM version adapted to enable linkage with the POLES and/or PRIMES model. The disaggregation of the EU into 27 individual countries has been performed recently for the Biomass Futures project; originally five European regions were defined. Partial denotes that the model does not include the whole range of economic sectors in a country or region but focuses on agricultural and forestry production as well as bioenergy production. These sectors are, however, modelled in a detailed way accounting for about 2 globally most important crops, a range of livestock production activities, forestry commodities as well as different energy transformation pathways. GLOBIOM disaggregates available land into several land cover/use classes that deliver raw materials for wood processing, bioenergy processing and livestock feeding. Figure 1 illustrates this structure of different land uses and commodities. Forest land is made up of two categories (unmanaged forest and managed forest); the other categories include cropland, short rotation tree plantations, grassland (managed grassland) and other natural vegetation (includes unused grassland). GLOBIOM is a bottom-up model with a detailed representation of land based activities and technologies. This information is provided at Simulation Unit level, which is the smallest spatial infrastructure of the model. Simulation Units are delineated from Homogenous response Units (HRU) which are pixels with the same geo-spatial characteristics such as altitude, slope and soil type (that do not change over time. due to climate change and/or management practices) and are used to form geographical clusters. On top of this layer country boundaries and a.5 x.5 grid layer is placed that contains more detailed information such as data on climate, land use/cover, etc. This information forms Simulation Units (SimU) 1 Documentation of the GLOBIOM model can be found at 5

6 that are the basic geographical unit for the analysis. For each SimU, different management systems are distinguished. For the bulk of global crop production four management systems are available in GLOBIOM. These are irrigated, high input rainfed, low input rainfed and subsistence management. The global agricultural and forest market equilibrium is computed by choosing land use and processing activities to maximize welfare (i.e. the sum of producer and consumer surplus) subject to resource, technological, and policy constraints. These constraints ensure that demand and supply for among others irrigation water and land are met but also impose exogenous demand constraints so as to reach, for instance, a certain biofuel target. Prices and international trade flows are endogenously determined for respective aggregated world regions. Imported and domestic goods are assumed to be identical (homogenous), but the modelling of trade does take into account transportation costs and tariffs. GLOBIOM includes accounting for greenhouse gas emissions and sinks from agricultural and forestry activities. This includes among others accounting for N 2 O emissions from fertiliser use whose intensity in turn depends on the management system. It is possible within the model to convert one land cover or use to another. The arrows on the left-hand side of Figure 1 indicate the initial land category and therefore show the way in which land cover/use can change (i.e. unmanaged forest can be converted into managed forest or cropland). The greenhouse gas consequences from land use change are derived from the carbon content of above- and belowground living biomass of the respective land cover classes. Figure 1. GLOBIOM land use and product structures (Havlík et al. 211). Note: The arrows on the left represent the direction where a given land use/cover type can expand given the current constraints in the model. The model is recursive dynamic in the sense that changes in land use made in one period alter the land availability in the different categories in the next period. Land use change is thus transmitted from one period to the next. As GLOBIOM is a partial equilibrium model, not all economic sectors are modelled explicitly. Instead, several parameters enter the model exogenously and are pre-determined including wood and food demand which in turn are derived from changes over time in gross domestic product (GDP), population (same projections as used in PRIMES) and food (calorie) consumption per capita (projections according to FAO (26)). Changes in these prescribed input data are the underlying driver of the model dynamics. The base year for the model is the year 2, the model horizon in Biomass Futures is 23. In relation to yield development, GLOBIOM typically assumes.5 % autonomous 6

7 technological progress in crop improvement. In addition, the possibility to shift between management systems as well as the relocation of crops to more productive areas also provides for regional average yield changes. When it comes to bioenergy dynamics, projections from the POLES model 2 (for regions outside Europe) and the PRIMES model (for EU 27 countries) on regional biomass demand in heat and power (BIOINEL), direct biomass use i.e. for cooking (BIOINBIOD) and liquid transport fuel use (BFP1 and BFP2 or first and second generation biofuels, respectively) over the next two decades are implemented in GLOBIOM as target demands or minimum demand constraints. In order to improve representation of bilateral trade flows in GLOBIOM we have included both tariffs and transportation costs differentiated among partners and products. The tariffs come from the MAcMap database (Bouët et al. 28). Tariffs on ethanol and biodiesel in OECD countries are taken from the Global Subsidies Initiative (Steenblik 27). In the absence of exhaustive information on transportation costs we have computed them using the coefficients between freight rates and distance and the ratio weight over value of the good that have been estimated by Hummels (21). Trade calibration method proposed by Jansson and Heckelei (29) is applied to reconcile observed bilateral trade flows, regional net trade, prices and trading costs for the base year. Together with improvements of transportation costs this results in better estimates on the development of bilateral trade flows in the future especially for those products related to bioenergy. Land rental prices have been implemented to further disaggregate the cost structure in the model and land use change elasticities have been revised and adapted based on historic land use change data according to FAO. Moreover, to improve accuracy of GHG emissions nitrogen input coefficients from EPIC have been harmonized with IFA (International Fertilizer Industry Association) data on global fertilizer use per crop species. This results in improved soil N 2 O emissions which are calculated using the IPCC tier 1 approach using adjusted EPIC fertilizer data. Emissions related to fertilizer production have been introduced in the model. Emissions from fertilizer production are calculated using RFA (Gallagher 28) emission coefficients on emissions from fertilizer production and total fertilizer use based on harmonized EPIC data. The GLOBIOM model is described in some greater detail in a Policy Briefing document Modelling biomass supply options with GLOBIOM: A non-technical introduction available on the Biomass Futures webpage ( 2.3 Definitions of biomass feedstocks and conversion technologies The biomass categories covered by Biomass Futures are introduced in Deliverable 3.3 (based on harmonised BEE Method Handbook (BEE 21)). The supply models build on these categories. However, the models applied in WP3 and WP5 represent the biomass categories and technology chains with varying degree of detail. Table 1 lists parameters and level of detail for GLOBIOM. The integration of biomass supply from different feedstocks and demand for bioenergy requires also an appropriate representation of bioenergy technologies that transform biomass into energy. The description of technologies is also task of WP2 (Demand analysis) and WP5 (Energy modelling). To assess the energy potential of biomass feedstocks, account for competition between them and to model land use change, trade and leakage associated with bioenergy scenarios, however, technologies play a role also in the supply analysis. Within Biomass Futures they were harmonised to the degree possible to ensure consistency and comparability of model results across WPs. 2 Taken from the 21 POLES (Prospective Outlook on Long-Term Energy Systems) baseline scenario. See for POLES documentation. 7

8 Table 1: Technologies represented in GLOBIOM. Feedstock Process Product Energy value GJ/t(m3) feedstock Emission saving from fossil fuel displacement kg CO2/MJ Wood Gasification Methanol Wood Gasification Heat.75 Wood Fermentation Ethanol Wood Fermentation Heat 2.8 Wood Fermentation Electricity 1.15 Wood Fermentation Gas 1.65 Wood Combustion Heat 5. Wood Combustion Electricity 2.5 Corn CornToEthanol Ethanol Sugar cane SugcToEthanol Ethanol Wheat WheatToEthanol Ethanol Soybean SoyaToFame FAME Palm oil PalmoilToFame FAME Rapeseed RapeToFame FAME The base year of different datasets used for the analysis of availability and supply of biomass varies. The base year of the analysis is therefore an average of the base years used. Where available the most recent data are used. Currently the integrated land use model GLOBIOM uses the year 2 as base year, which minimises the variability of base years in the data sets used because many data sets with base year 2 exist. Also this is consistent with the energy model PRIMES and its biomass model (applied in Work Package 5). 2.4 Applied sustainability criteria In the framework of the Biomass Futures project a detailed analysis was provided on how sustainability criteria may constrain the biomass feedstock availability (see results of WP4 and Deliverable 3.3). In this report the focus is on the integration of sustainability criteria into the dynamic integrated land use model GLOBIOM that are consistent with analyses done in other tasks and WPs. The consideration of sustainability constraints in WP3 is implemented at two levels. Deliverable 3.3 assesses the effect of sustainability constraints on the potential supply of biomass for energy purposes at the level of basic environmental indicators. They address, depending on the type of biomass feedstock and targeted area criteria focusing on, e.g.: Risk for increased input use with adverse effects on environmental quality (e.g. nitrogen pollution, soil degradation, depletion of water resources, etc.) Risk for disturbance of soil structure (compaction) Nutrient depletion in case of too much removal The use of an integrated land use model allows the inclusion of additional criteria that can only be assessed in an integrated framework. These include economic criteria and criteria related to direct and indirect land use change effects (e.g. leakage). Table 2 presents sustainability criteria in the RED that are explicitly addressed by the integrated land use model. 8

9 Table 2: Sustainability criteria integrated into GLOBIOM. Sustainability criteria RED, Article 17.2 RED, Article 17.3 RED, Article 17.4 The greenhouse gas emission saving from the use of biofuels and bioliquids shall at least be 35%. With effect from 1 January 217, the greenhouse gas emission saving from the use of biofuels and bioliquids shall be at least 5%; Biofuels and bioliquids shall not be made from raw material obtained from land with high biodiversity value namely primary forests and other wooded land, areas designated or highly biodiverse grassland; Biofuels and bioliquids shall not be made from raw material obtained from land with high carbon stock namely wetlands and continuously forested areas; To address Article 17.2 we implemented the GHG mitigation target indirectly into GLOBIOM by using NUTS2 GHG mitigation potentials for selected bioenergy processing pathways calculated in Deliverable 3.3 (Availability). The calculation of the mitigation potential per NUTS2 region and feedstock is based on GEMIS data and land based emissions (for the methodology and data used see Deliverable 3.3). NUTS2 mitigation potentials were used to exclude certain biofuel processing paths in the model that do not comply with the minimum GHG emission saving target specified in RED Article If not a single NUTS2 region in member state does reach the mitigation target for certain pathway i.e. corn ethanol, we exclude the pathway in GLOBIOM for that particular country. By doing so we create a stencil that we put over all the processing technologies at the EU country level in the model. To address Article 17.3 we use high nature value (HNV) farmland areas to identify agricultural production on highly biodiverse areas in Europe (Article 17.3). Paracchini et al. (211) define HNV farmland as agricultural land having: a high share of semi natural vegetation; a mosaic of low intensity agriculture and natural and structural elements; a population of rare species or a high proportion of European or World populations. By using information on land cover (CLC 2) and additional information on high biodiversity areas as NATURA 2, important bird areas, prime butterfly areas and national biodiversity datasets they produce a HNV farmland distribution map that gives the probability to find HNV farmland in a certain area. To determine European HNV farmland in our datasets we follow the approach suggested by Hellmann and Verburg (29) and interpret the probability as the actual HNV farmland area in this grid cell i.e. 3% probability translates into 3% HNV area in a grid cell. To identify highly biodiverse areas outside Europe data from UNEP-WCMC has been used. In the Carbon and Biodiversity Report (Kapos et al. 28) global terrestrial biodiversity areas are identified by overlapping six priority schemes (Conservation International s Hotspots, WWF Global 2 terrestrial and freshwater eco-regions, Birdlife International Endemic Bird Areas, WWF/IUCN Centres of Plant Diversity and Amphibian Diversity Areas). Here we assume that areas where four or more priority schemes overlap are highly biodiverse and therefore excluded from conversion. In the base year 2 7.8% of global forests, 5.2% of natural vegetation and 8.2% of grasslands are considered highly biodiverse according to our definition. While highly biodiverse primary forests and other natural vegetation are mainly located in Latin America, Sub Saharan Africa and Asia, a major share of highly biodiverse grasslands appears in Europe due to HNV farmland classification. Designated highly biodiverse forest and grassland area are within the ranges specified in other sources. FAO (211) reports 9

10 7.4% of global forests designated under conservation of biodiversity in 2 while Hoekstra et al. (27) find 4.6% of temperate and 11.9% of tropical grasslands, savannahs and shrub lands under conservation. To address Article 17.4, we assume that deforestation is not allowed in EU countries (for any reason, including also biofuel production) through strict laws on land use change. Since this cannot be assumed for countries outside EU we use a different approach to address Article 17.4 with respect to imports to EU. The GLOBIOM model is here used to perform a series of sensitivity runs to assess the impact of trade and quantify the emissions from land use change associated with imports to EU countries, rather than theoretically excluding crops and pathways that violate the sustainability criteria, which is technically not feasible. Details about the implementation of these sensitivity runs are presented in Section Bioenergy demand and other background drivers Bioenergy demand For European bioenergy demand we take projections from the PRIMES Reference Scenario 23 for the years 2 and 21. Bioenergy demand for the year 22 is taken from the NREAP targets for 22. For the year 23 we extrapolate NREAPs relative to the development in the PRIMES Reference Scenario between 22 and 23. European bioenergy mandates implemented in GLOBIOM include first and second generation biofuels as well as biomass for heat and electricity production. Technologies represented in GLOBIOM include ethanol made from sugarcane, corn and wheat, and biodiesel made from rapeseed, palm oil and soybeans. Biomass for second generation biofuels is used either from existing forests, wood processing residues or from short rotation tree plantations. Bioenergy demand for the rest of the world is directly taken from the POLES Reference scenario (Russ et al. 29). Bioenergy demand from POLES is in GLOBIOM imposed as an exogenous constraint. Bioenergy demand is at the regional resolution delivered by POLES, and four types of bioenergy are differentiated BFP1 (first generation biofuels), BFP2 (second generation biofuels), BIOINEL (heat and electricity generation), and BIOINBIOD (direct biomass use). From 2 until 23 global biofuel demand rises to 2.1 EJ and 1.1 EJ respectively for ethanol and biodiesel (Figure 2). Especially European biodiesel demand is driven by an ambitious European biofuel mandate. As a consequence, the share of European biodiesel demand in total global biodiesel demand increases from 42% in 2 to 74% in 23. European ethanol share is also rising to 13% of total ethanol demand in 23 but remains rather modest since other countries such as Brazil and the U.S. continue as well to expand their ethanol production. Total European bioethanol and biodiesel demand in 23 amounts to.29 EJ and.88 EJ respectively. 1

11 Consumption in EJ 2,5 2, 1,5 1, Global Ethanol Global Biodiesel EU27 Ethanol EU27 Biodiesel,5, Figure 2: Global and EU biofuel demand in the Reference scenario in EJ for biodiesel and ethanol Other drivers As GLOBIOM is a partial equilibrium model, several parameters enter the projections as exogenous drivers. Wood and food demand is driven by gross domestic product (GDP) and population changes. In addition, food demand must meet minimum per capita calorie intake criteria, which are differentiated with respect to the source between crop and livestock calories. Demand is calculated for the different world regions on the basis of projections of regional per capita calorie consumption by source (vegetal, meat, milk and eggs). The regional population and GDP development is taken from POLES (Russ et al. 29). All scenarios applied in this report build on the same macro projections of GDP and population. European data on GDP and population reflect the recent economic downturn, followed by sustained economic growth resuming after 21. This data is entering GLOBIOM that uses these projections to translate them into demand for timber and agricultural commodities. The GDP and population data dataset was also consistently used in the PRIMES biomass model that provided bioenergy projections to GLOBIOM. The data for population and GDP development in EU countries for both, the base year 27 (prior to the financial and economic crisis for comparison) and 29 (used for this study) are displayed in Table 3. Population change is driving food and wood demand. Wood demand is further adjusted according to GDP change. Table 3: Development of total population and GDP per capita Population [Million] World 6,98 6,889 7,657 8,293 EU GDP per capita [1 USD] World EU Other important global drivers of the results and crucial underlying assumptions in GLOBIOM are calorie consumption and yield growth. Calorie consumption per capita was derived from projections according to FAO (26). We assume.5 % autonomous yield increase per year due to technical progress. Autonomous yield increase is only one of three components of the yield change in GLOBIOM. The other 11

12 two components are management system change (intensification) and shift of the production to more or less yielding zones (re-allocation). It was found that the.5 value enables best to reproduce recent total yield changes according to an analysis of FAOSTAT data. Disaggregated data which would enable to define the autonomous yield growth in a less arbitrary and more differentiated way (by region and crop) is not available Assumptions on trade In order to better represent potential impacts of bioenergy imports to Europe in the rest of the world, we restricted imports of biofuels and biomass for bioenergy purposes (energy wood and biomass from short rotation plantations). Trade flows of these products are limited to relative shares per region based on expert knowledge. This is necessary to accommodate potential constraints on trade flows that cannot be explicitly taken into account by the model such as infrastructure and capacities of infrastructure e.g. harbours, trade barriers etc. Table 4 presents the relative shares of exporting regions in 22 and 23 to Europe which have been implemented in the model i.e. Brazil is expected to supply 65% of total ethanol imports to EU in 22. Table 4: Restriction of exporting regions to Europe based on Fritsche (211). Exporting region Energy wood biomass Brazil 2% 2% USA & Canada 65% 62% Africa 6% 1% Russia 9% 8% Ethanol Brazil 65% 65% Africa 5% 2% other Latin America 15% 1% other South East Asia 15% 5% Biodiesel USA & Canada 2% 5% Indonesia/Malaysia 4% 5% Africa 5% 15% other Latin America 35% 3% 2.6 Scenario description Definitions, assumptions and background drivers discussed above are combined in two alternative scenarios Reference and Sustainability scenario. These scenarios have been commonly elaborated in WP7 (Scenarios and policies) to assess the impacts of biofuels and related policies implementing different sets of sustainability criteria on bioenergy demand. The Reference scenario represents the current legal framework related to the bioenergy sector in EU countries including NREAP targets and RED sustainability criteria. This scenario reflects present developments and provides an outlook on how bioenergy markets could develop towards 22 and 23. Important driving forces are macro-economic developments (population, GDP growth) as well as projected bioenergy demand (Primes Reference Scenario, NREAPs, POLES). The Sustainability scenario applies more stringent and/or more comprehensive sustainability criteria than those that have been introduced by the RED at present. The aim of the scenario is to analyse effects of a more constrained biomass supply in Europe and the rest of the world. The scenarios are described in detail in a Biomass Futures Policy Briefing document Introducing the Biomass Futures scenarios available on the webpage ( The structure and nature of the GLOBIOM model requires specific interpretation and implementation of the general assumptions. An application of the RED sustainability criteria to imports requires tracing 12

13 back the geographical origin of biomass. The specific history of a single ton of biomass including the emission backpack would decide whether this ton meets or violates the criteria. Tracing individual parcels of biomass geographically in GLOBIOM is impossible. The model is well suited to estimate, e.g. the additional emissions from land use change in a specific region triggered by additional biomass supply. However, at the border between regions it does not distinguish which share of the production has actually caused the concrete emissions and whether this share is exported or actually consumed domestically. In addition, in GLOBIOM markets are all related and substitution between products and uses make direct and indirect effects of land allocation decision impossible to disentangle. Consequently, the model does not distinguish between agricultural production for food and biofuels at the grid cell level. Therefore sustainability criteria can t be imposed on the biofuel sector only but are implemented on the whole agricultural sector since seperation between the different sectors at grid cell level is not possible. This makes it impossible to attribute emissions of the production to the final process without averaging over the entire production amount. However, direct and indirect effects of land use change in the sense of the RED are simultaneously accounted for and reflected in the results. A simple assumption would be that biofuels from outside EU are highly efficient and meet the mitigation requirements of the RED. In fact, this is likely for parts of the sugarcane ethanol from Brazil. Also imported biofuels might have been produced from crops grown on degraded lands causing rather low indirect land use change. Therefore, in addition to the two common Biomass Futures scenarios we consider three different sensitivity runs where we change assumptions on biofuel trade to the EU and we allow or prohibit any deforestation outside Europe. 1. Base run: Biofuel trade Rest of the world (RoW)-Europe and deforestation outside Europe allowed 2. No deforestation run: Biofuel trade RoW-Europe allowed but deforestation prohibited outside Europe 3. No trade run: Biofuel trade Rest of the world (RoW)-Europe prohibited but deforestation allowed outside Europe Effects of European bioenergy mandates and sustainability criteria on the agricultural production, environment and GHG emissions in and outside Europe might also interact with competitive environmental or trade policies. The rationale behind these model runs is therefore to show on the one hand effects of international biofuel trade but also effects of parallel implementation of other policies such as Reduced Emissions from Deforestation and Forest Degradation (REDD). The implications of biofuel trade on the one side and impacts of other global land use policy developments such as the efforts invested into REDD cannot be ignored in a comprehensive assessment of bioenergy impacts. The no biofuel trade scenario basically describes a situation where the EU produces the total biofuel demand domestically whereas the no deforestation scenario mimics the successful implementation of a REDD policy framework. An overview of the assumptions made in the two scenarios combined with three variant runs is presented in Table 5. 13

14 Table 5: Scenario description. Scenario runs European Union Rest of the world Reference (base) Bioenergy demand PRIMES Reference scenario POLES Reference scenario 23, NREAPs in 22 Sustainability criteria No conversion HNV in EU No conversion of grasslands No deforestation 5% GHG mitigation target for biofuels Trade Allow biofuel trade Sustainability (base) Bioenergy demand PRIMES Reference Scenario 23, NREAPs in 22 Sustainability criteria No HNV conversion and intensification in EU No conversion of grasslands No deforestation 7% GHG mitigation target for biomass in 22, 8% in 23 Sensitivity runs Reference (no deforestation) Reference (no biofuel trade) No deforestation in industrialized countries POLES Reference Scenario No deforestation in industrialized countries WCMC maps restrict LUC Sustainability criteria (as Reference base) No deforestation anywhere Trade (as Reference base) (as Reference base) Sustainability criteria (as Reference base) (as Reference base) Trade No biofuel trade (as Reference base) Sustainability (no deforestation) Sustainability criteria (as Sustainability base) No deforestation anywhere Trade (as Sustainability base) (as Sustainability base) 14

15 3 Results 3.1 Biofuel and bioenergy use Reference scenario base run In the Reference scenario base run 7% of European ethanol and 66% of biodiesel demand is refined inside the EU while the remaining quantity is imported from the rest of the world. Corn is the mayor feedstock for ethanol production inside Europe while European biodiesel in 23 is exclusively processed from rapeseed. Due to the 5% emissions saving target specified in the RED, other biodiesel processing paths such as palm oil and soybean conversion are excluded inside the EU since they do not comply with the GHG mitigation criteria. The quantity of crops which is processed to biofuels rises from.5 Mt for rapeseed in 2 to 36.5 Mt and 19.6 Mt of corn in 23. Wheat is only marginally used for ethanol processing in the Reference scenario base run. The sharp biofuel processing demand increase for rapeseed requires 1 Mt of rapeseed imports to the EU in order to fulfil the European biofuel targets. In the rest of the world 52% of first generation biofuel is based on corn, 25% on sugar cane, 11% on soya, 8% on rapeseed and 4% on palm oil. Biggest ethanol exporter to Europe in 23 is Brazil (65%) as well as Indonesia and Malaysia (5%) and Latin America (3%) for biodiesel. Over the simulation time the share of 2 nd generation biofuels (either produced from short rotation plantation, sawmill residues or directly from energy wood biomass sourced from forests) increases substantially and by 23 cellulosic ethanol outperforms 1 st generation biofuels and accounts for 56% of the total ethanol production. Globally 2.5 EJ of 2 nd generation biofuels are produced in 23 even though European 2 nd generation biofuel mandate is with 31 PJ marginal compared to 189 PJ from 1 st generation (Table 6). Table 6: Biofuel production in Reference scenario in PJ. Region Product Global EU27 Corn ethanol Sugar cane ethanol Wheat ethanol Cellulosic ethanol Total ethanol Palm oil biodiesel Rapeseed biodiesel Soybean biodiesel Sunflower biodiesel Total biodiesel Corn ethanol Wheat ethanol Cellulosic ethanol Total ethanol Rapeseed biodiesel Sunflower biodiesel Total biodiesel , , , , ,473 4, , Sustainability scenario base run Imposing strict sustainability criteria on biofuel production prevents biofuels from being produced inside Europe. Since none of the conversion paths complies with the 7% in 22 and 8% in 23 emissions saving target, total biofuel demand has to be imported from the rest of the world. Besides biofuels also 15

16 biomass supply for bioenergy production has to comply with high mitigation criteria. As a consequence, the feedstock composition for biofuel and bioenergy production differs significantly between the two base run scenarios. Looking at ethanol production globally, it can be observed that ethanol processed from sugar cane increases (+363 PJ) at the expense of ethanol processed from corn (-369 PJ) and wheat (-27 PJ). Sugar cane ethanol constitutes almost half of total ethanol production (47%) (Table 7) and processing quantities from sugar cane rise from 368 Mt to 582 Mt while corn declines from 176 Mt to 132 Mt. Main ethanol producing countries are the U.S. (49 PJ) and Brazil (382 PJ). Table 7: Global first generation biofuel demand per feedstock in PJ in 23 in the Reference and Sustainability scenario. REF SUST Corn ethanol 1,464 1,95 Sugar cane ethanol Wheat ethanol 3 3 Total ethanol 2,121 2,88 Palm oil biodiesel Rapeseed biodiesel Soybean biodiesel Total biodiesel 1,134 1,167 Total biofuel 3,254 3,254 In the Sustainability scenario in 23 rapeseed loses importance as biodiesel feedstock globally since European biofuel feedstocks do not comply with high mitigation criteria. Hence, Europe has to import its total biofuel demand. Processing quantities for rapeseed are more than halved globally from 49 Mt to 22 Mt, while palm oil (+63 Mt) and soybean (17 Mt) processing quantities increase. Therefore, in the Sustainability scenario palm oil contributes the biggest share in global biodiesel production (4%) followed by soybean (32%) and rapeseed (28%). Biggest biodiesel producers are South and South East Asia (55 PJ) and China (191 PJ). Production of 2 nd generation biofuels in Europe remains with 31 PJ at the same level in 23 as in the Reference scenario base run. 3.2 Market conditions and trade Reference scenario base run Until 23, prices for most crops increase in the Reference scenario base run in Europe as well as in the rest of the world. Main driver is the additional feedstock demand for biofuel processing which cannot be compensated by increasing productivity through yield increase, reallocation to more productive regions or shift in management systems. In Europe, especially the main biofuel feedstock experience a substantial price increase and rapeseed and corn prices go up by 54% and 15% respectively between 21 and 23 due to increasing biofuel demand. While corn processing to biofuels accounts only for 24% of total demand, this share is for rapeseed with 77% a lot higher, thus explaining the big impact of biofuel demand on rape prices. In 23, 36.5 Mt of rapeseed are processed to biodiesel in the EU. Even though rapeseed supply constantly increases and almost triples from 2 until 23 it is not sufficiently large to satisfy increasing biofuel demand. As a consequence, Europe turns from a net exporting region of rapeseed to an importing region (13.3 Mt in 23). Biggest rapeseed exporter to Europe is Canada (37%) and South and South East Asia (43%). Increasing corn production is mainly driven by ethanol demand and increases by 39% until 23. Corn imports, feed and human demand remains at constant levels. 16

17 While prices increase for rapeseed and corn, soybean prices drop over time. Main reason is the substitution of soybean with by-products from biofuel processing (rapeseed cake and corn DDGS) which make cheap by-products for animal feeding available and put pressure on the soybean market. Since European farmer are not competitive at low prices, soybean production in the EU continuously decreases until 23. Due to the replacement of soybean in the animal feed sector, soybean demand and imports decrease (-24% in 23 compared to 2) over time. Overall European crop exports to the rest of the world decrease until 23 while at the same time imports are rising slightly. Since there is a shift towards the cultivation of biofuel crops (corn and rapeseed), wheat production expansion slows down and European wheat exports decrease substantially. In the European forest sector production of woody biomass for energy purposes more than doubles from 149 Mm 3 to 363 Mm 3. In addition, 191 Mm 3 of woody biomass from short rotation tree plantations has to be imported to Europe in order to fulfil the European bioenergy targets for heat and electricity production in Sustainability scenario base run The European net trade balance for agricultural products is more balanced in the Sustainability scenario base run compared to the Reference scenario base run as exports for agricultural products increase and imports at the same time decrease. Since biofuel production is reallocated to the rest of the world, European crop production can be either used for domestic human food or animal feed demand or be exported to the rest of the world. Namely barley (+9 Mt), rapeseed (+4 Mt) and wheat exports (+14 Mt) experience a substantial increase in 23. In the livestock sector, exports of poultry meat increase by 1 Mt. Rapeseed and corn imports can be reduced by 13 Mt and 2 Mt respectively due to non-existing processing demand. Soybean imports go up by 13 Mt since feed demand increases due to the missing cheap by-products from biofuel processing. Biofuels are exclusively imported from the rest of the world. In the wood sector some adjustments can be observed due to implementation of sustainability criteria and the exclusion of some bioenergy conversion paths. Mayor difference between the Reference and Sustainability scenario is the substitution of biomass from short rotation plantation with energy wood from forests or sawmill residues since short rotation plantations in most EU countries do not comply with the 8% emission saving target in 23. Therefore, imports of industrial plantation biomass decline by 191 Mm 3 mainly coming from Canada while imports of energy wood increase by 123 Mt. In addition supply of energy wood in EU increases from 363 Mt in the Reference scenario base run to 479 Mt in the Sustainability scenario base run. However, this increase comes at the expense of sawn and pulpwood production which decreases resulting in additional imports of 7 Mt of sawn and 18 Mt of pulpwood to EU. Prices for most products in Europe decline in the Sustainability scenario compared to the Reference scenario until 23 as pressure on the crop production is relieved due to abolishment of biofuel production inside the EU. When looking at biofuel feedstock prices, prices for corn decline by 8%, rapeseed by 4% and wheat by 14% but also other crop like barley (-19%), potatoes (-2%) or sunflower (-3%) benefit from the abolishment of first generation biofuel production in Europe. However, globally prices increase in the Sustainability scenario especially for palm oil (7%) and soybean (8%) but also for other food crops and woody biomass a price increase can be observed. For some crops this results from an increased demand due to the changed global biofuel feedstock mix. Moreover it is linked to the implementation of sustainability criteria globally which limit land use change in the rest of the world hence increasing production prices. 3.3 Land use and land use change Reference scenario base run In the Reference scenario base run global cropland and grassland areas increase by 37 Mha and 47 Mha respectively from 2 to 23 due to increasing demand for agricultural crop and livestock products. This expansion mainly takes place on other natural vegetation (-48 Mha) or through deforestation (- 15 Mha). Cropland expansion is mainly located in Sub-Saharan Africa (+24.5 Mha) and South and South 17

18 Mha East Asia Mha) whereas grassland increases in Latin America (+3.1 Mha) and Sub-Saharan Africa (+19.4 Mha). While in Latin America deforestation is mainly driven by grassland expansion, cropland expansion is the biggest driver in South and South East Asia and Sub-Saharan Africa. From 2 until Mha/yr of forests are converted in Latin America, 1.3 Mha/yr in Sub-Saharan Africa and.7 Mha/yr in South and South East Asia. Global average deforestation rate over 2-23 is about.9% per year which represents a decline in deforestation rate compared to the.13% per year from 2-21 that was reported in FAO (211). Global short rotation plantation area grows constantly over time by 69 Mha due to increasing demand for bioenergy. The global changes in land use described above are the result of the Reference scenario base run and the interplay of different drivers. As described in the Methods section these include the increase bioenergy and food demand, increase of population and development of GDP in the world regions and EU countries. From the Reference scenario results alone no attribution to single drivers such as biofuel expansion is possible. A more analytical assessment of environmental impacts of European biofuel expansion is presented in Frank et al (submitted) Other Natural Vegetation Grassland Short Rotation Coppice Unmanaged Forest Managed Forest Cropland Figure 3: Development of land cover in EU until 23 in the Reference scenario in Mha. In Europe, only minor land use changes can be observed due to restrictive legislation. As deforestation and grassland conversion are excluded forest and grassland land cover types remain constant over the simulation time. Main difference is the expansion of forest production through conversion of unmanaged into managed forests (Figure 3). Until 23, 42 Mha of forests enter production in Europe to satisfy increasing wood biomass demand mainly driven by bioenergy mandates. Technically for Europe this does not necessarily imply a conversion of native old growth forests into plantations. Most forests in EU are managed already in the sense that they are accessible and potentially available for wood supply. But wood demand around the base year is relatively low so that not the entire increment in European forests is harvested. With the increase in bioenergy and also total wood demand, more forests are harvested without affecting sustainability from a natural resource point of view. However, higher harvest rates might affect stand level biodiversity and have also an impact on the forest carbon sink (Böttcher et al. 212). Such effects cannot be assessed with the applied version of GLOBIOM. 18

19 Mt kg/ha/yr in Mha Until 23, rapeseed areas more than double compared to 2 amounting to a total of 1 Mha in 23 (Figure 4). Soybean and wheat areas decline and other natural vegetation is converted into cropland (2 Mha). Other crop areas remain at constant level Wheat Sunflower Rapeseed Potato Corn Barley Figure 4: Crop areas in Europe in the Reference scenario in Mha. The total use of nitrogen increases between 21 and 23 in the EU27 by 3.5% (Figure 5). The average nitrogen use per hectare of cultivated crop (nitrogen intensity) increases between 21 and 22 and then remains constant in 23, due to productivity growth. However, in the rest of the world, we observe a substantial increase by 19% of total nitrogen use between 21 and 23. This reflects very different situations with strong nitrogen consumption increase in MENA (+47%) and in Sub-Saharan Africa (+45%) for instance while the consumption decreases in the CIS (-1%) and remains constant Canada (+.4%) Total N use EU27 Total N use RoW N intensity EU27 N intensity RoW Figure 5: Evolution of nitrogen use in EU27 and rest of the world in the Reference scenario base run. 19

20 Area difference in Mha Sustainability scenario base run Comparing land cover between Reference and Sustainability scenario base run in 23, it can be observed that global cropland area is by 2.3 Mha larger in the Sustainability scenario base run (Figure 6). This results from the reallocation of biofuel production into the rest of the world which favours other biofuel feedstocks than those being produced in Europe in the Reference scenario. As biofuel production from rapeseed and corn declines, also production of by-products which can be used as animal feed decreases. Therefore, the substitution effect of by-products partially disappears and other crops for livestock feeding have to be produced. Moreover, cropland expansion into highly biodiverse areas but with sometimes high productivity is not allowed. Therefore, crops are being pushed into less productive areas with smaller yields which also forces additional cropland into production. Global soybean area increases by 9.3 Mha due to rising livestock feed and processing demand for biofuel production in the Sustainability scenario. At the same time sugar cane (+2.5 Mha) and palm oil (+3.6 Mha) areas increase while rapeseed (-8.8 Mha) and corn areas decrease due smaller processing demand for biofuel production. Since productivity of other crops is affected indirectly through reallocation of crop areas i.e. globally wheat yields decrease by 3% bringing additional 5.1 Mha into production; in total 2. Mha of additional cropland is needed in the Sustainability scenario. Additional cropland is mainly located in Latin America with a 3.1 Mha expansion due to soybean, rapeseed and sugar cane area increase. 1, 8, 6, 4, 2,, -2, -4, -6, -8, -1, MENA SSA Pacific CIS China South and South East Asia LA Canada USA EU27 Cropland Grassland Short Rotation Coppice Other Natural Vegetation Forests Figure 6: Land cover change by region in Sustainability scenario base run compared to Reference scenario base run in 23. MENA: Middle East, North Africa; SSA: Sub-Saharan Africa; CIS: Commonwealth of Independent States; LA: Latin Amercia. Globally grassland declines by 5.4 Mha in the Sustainability scenario compared to the Reference scenario. Especially in South and South East Asia a substantial decrease in grassland can be observed. Main reason is the restriction of grassland expansion into highly biodiverse primary forests due to sustainability constraint in the Sustainability scenario which limits expansion of grassland areas. As a result, a switch to less grassland based livestock production systems can be observed. However, this is not sufficient to keep production at the small level and hence production decreases slightly in the Sustainability scenario. Interestingly in Sub-Saharan Africa the opposite trend can be observed. More grassland expands into primary forests in the Sustainability scenario driving additional deforestation. This is driven by a combination of two factors. On the one hand, overall grassland productivity decreases due to competition with cropland (which supplies additional biofuel exports to Europe) forcing grassland 2