Biomass role in achieving the Climate Change & Renewables EU policy
targets. Demand and Supply dynamics under the perspective of
stakeholders . IEE 08 653 SI2. 529 241
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, 2012
Content
Content .................................................................................................................................................. 2
Preface .................................................................................................................................................. 3
1
Introduction ................................................................................................................................... 4
2
Methodology ................................................................................................................................. 5
2.1 Linkages to other WPs ........................................................................................................................ 5
2.2 Model description ............................................................................................................................... 5
2.3 Definitions of biomass feedstocks and conversion technologies ........................................................ 7
2.4 Applied sustainability criteria ............................................................................................................. 8
2.5 Bioenergy demand and other background drivers ........................................................................... 10
2.5.1
Bioenergy demand.............................................................................................................. 10
2.5.2
Other drivers....................................................................................................................... 11
2.5.3
Assumptions on trade ......................................................................................................... 12
2.6 Scenario description.......................................................................................................................... 12
3
Results ......................................................................................................................................... 15
3.1 Biofuel and bioenergy use ................................................................................................................ 15
3.1.1
Reference scenario base run .............................................................................................. 15
3.1.2
Sustainability scenario base run ......................................................................................... 15
3.2 Market conditions and trade ............................................................................................................ 16
3.2.1
Reference scenario base run .............................................................................................. 16
3.2.2
Sustainability scenario base run ......................................................................................... 17
3.3 Land use and land use change .......................................................................................................... 17
3.3.1
Reference scenario base run .............................................................................................. 17
3.3.2
Sustainability scenario base run ......................................................................................... 20
3.3.3
Sensitivity analysis .............................................................................................................. 21
3.4 Loss of biodiversity............................................................................................................................ 22
3.4.1
Reference scenario base run .............................................................................................. 22
3.4.2
Sensitivity analysis .............................................................................................................. 23
3.5 GHG emissions .................................................................................................................................. 23
3.5.1
Reference scenario base run .............................................................................................. 23
3.5.2
Sustainability scenario base run ......................................................................................... 25
3.5.3
Sensitivity analysis .............................................................................................................. 26
3.6 Discussion and conclusions ............................................................................................................... 26
4
References ................................................................................................................................... 29
Appendix ............................................................................................................................................. 31
2
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 08 653 SI2. 529 241, www.biomassfutures.eu ) 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
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 2010- 2030.
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, 2008 #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
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 (www.biomassfutures.eu).
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 50 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 20 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 0.5° x 0.5° 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 www.globiom.org.
5
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 N2O 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. 2011). 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 (2006)). Changes in these prescribed input data are the underlying driver
of the model dynamics. The base year for the model is the year 2000, the model horizon in Biomass
Futures is 2030. In relation to yield development, GLOBIOM typically assumes 0.5 % autonomous
6
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 model2 (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. 2008). Tariffs on ethanol and biodiesel in OECD countries are taken from
the Global Subsidies Initiative (Steenblik 2007). 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 (2001). Trade
calibration method proposed by Jansson and Heckelei (2009) 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 N2O 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
2008) 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 (www.biomassfutures.eu).
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 2010)). 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 2010 POLES (Prospective Outlook on Long-Term Energy Systems) baseline scenario. See
http://www.enerdata.net/enerdatauk/solutions/energy-models/poles-model.php for POLES documentation.
7
Table 1: Technologies represented in GLOBIOM.
Feedstock
Process
Product
Wood
Wood
Wood
Wood
Wood
Wood
Wood
Wood
Corn
Sugar cane
Wheat
Soybean
Palm oil
Rapeseed
Gasification
Gasification
Fermentation
Fermentation
Fermentation
Fermentation
Combustion
Combustion
CornToEthanol
SugcToEthanol
WheatToEthanol
SoyaToFame
PalmoilToFame
RapeToFame
Methanol
Heat
Ethanol
Heat
Electricity
Gas
Heat
Electricity
Ethanol
Ethanol
Ethanol
FAME
FAME
FAME
Energy value GJ/t(m3)
feedstock
3.375
0.75
2.175
2.08
1.15
1.65
5.0
2.5
8.3
1.7
7.8
6.0
5.6
15.2
Emission saving from fossil
fuel displacement kg CO2/MJ
-0.30
-0.10
-0.19
-0.23
-0.22
-0.62
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 2000 as base
year, which minimises the variability of base years in the data sets used because many data sets with
base year 2000 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
Table 2: Sustainability criteria integrated into GLOBIOM.
Sustainability criteria
RED, Article 17.2
The greenhouse gas emission saving from the use of biofuels and
bioliquids shall at least be 35%. With effect from 1 January 2017, the
greenhouse gas emission saving from the use of biofuels and bioliquids
shall be at least 50%;
RED, Article 17.3
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;
RED, Article 17.4
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 17.2. 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. (2011) 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 2000) and additional information on high biodiversity areas as
NATURA 2000, 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 (2009) and interpret the probability as the actual HNV farmland area in this grid
cell i.e. 30% probability translates into 30% 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. 2008) global terrestrial biodiversity areas are identified by
overlapping six priority schemes (Conservation International’s Hotspots, WWF Global 200 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 2000 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 (2011) reports
9
7.4% of global forests designated under conservation of biodiversity in 2000 while Hoekstra et al. (2007)
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 2.6.
2.5 Bioenergy demand and other background drivers
2.5.1 Bioenergy demand
For European bioenergy demand we take projections from the PRIMES Reference Scenario 2030 for the
years 2000 and 2010. Bioenergy demand for the year 2020 is taken from the NREAP targets for 2020.
For the year 2030 we extrapolate NREAPs relative to the development in the PRIMES Reference Scenario
between 2020 and 2030. 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. 2009).
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 2000 until 2030 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 2000 to 74% in 2030. European ethanol share is also rising to 13% of total ethanol
demand in 2030 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 2030
amounts to 0.29 EJ and 0.88 EJ respectively.
10
2.50
Consumption in EJ
2.00
Global Ethanol
1.50
Global Biodiesel
EU27 Ethanol
1.00
EU27 Biodiesel
0.50
0.00
2000
2010
2020
2030
Figure 2: Global and EU biofuel demand in the Reference scenario in EJ for biodiesel and ethanol.
2.5.2 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.
2009). 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 2010. 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 2007
(prior to the financial and economic crisis for comparison) and 2009 (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.
2000
Population [Million]
GDP per capita [1000 USD]
World
2010
2020
2030
6,098
6,889
7,657
8,293
EU27
482
501
516
522
World
7.34
8.99
11.49
14.11
EU27
21.05
22.84
27.61
32.40
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 (2006). We assume 0.5 % 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
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 0.5 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.
2.5.3 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 2020 and 2030 to Europe which have been implemented in the model i.e. Brazil is expected to supply
65% of total ethanol imports to EU in 2020.
Table 4: Restriction of exporting regions to Europe based on Fritsche (2011).
Exporting region
Energy wood biomass
Biodiesel
2030
Brazil
20%
20%
USA & Canada
65%
62%
6%
10%
Africa
Ethanol
2020
Russia
9%
8%
Brazil
65%
65%
Africa
5%
20%
other Latin America
15%
10%
other South East Asia
15%
5%
USA & Canada
20%
5%
Indonesia/Malaysia
40%
50%
5%
15%
35%
30%
Africa
other Latin America
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 2020 and 2030. 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 (www.biomassfutures.eu).
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
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.
2.
Base run: Biofuel trade Rest of the world (RoW)-Europe and deforestation outside Europe
allowed
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
Table 5: Scenario description.
Scenario runs
Reference (base)
Bioenergy demand
Sustainability criteria
Sustainability (base)
Trade
Bioenergy demand
Sustainability criteria
Sensitivity runs
Reference
(no deforestation)
Reference
(no biofuel trade)
Sustainability
(no deforestation)
14
European Union
PRIMES Reference scenario
2030, NREAPs in 2020
Rest of the world
POLES Reference scenario
 No conversion HNV in
EU
 No conversion of
grasslands
 No deforestation
 50% GHG mitigation
target for biofuels
 Allow biofuel trade
PRIMES Reference Scenario
2030, NREAPs in 2020
 No HNV conversion and
intensification in EU
 No conversion of
grasslands
 No deforestation
 70% GHG mitigation
target for biomass in
2020, 80% in 2030
 No deforestation in
industrialized countries
Sustainability criteria
(as Reference base)
Trade
Sustainability criteria
Trade
(as Reference base)
(as Reference base)
 No biofuel trade
Sustainability criteria

(as Sustainability base)
Trade
(as Sustainability base)
POLES Reference Scenario
 No deforestation in
industrialized countries
 WCMC maps restrict LUC
 No deforestation anywhere
(as Reference base)
(as Reference base)
 (as Reference base)
 No deforestation anywhere
(as Sustainability base)
3 Results
3.1 Biofuel and bioenergy use
3.1.1 Reference scenario base run
In the Reference scenario base run 70% 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 2030 is exclusively
processed from rapeseed. Due to the 50% 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
0.5 Mt for rapeseed in 2000 to 36.5 Mt and 19.6 Mt of corn in 2030. Wheat is only marginally used for
ethanol processing in the Reference scenario base run. The sharp biofuel processing demand increase
for rapeseed requires 10 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 2030 is Brazil (65%) as
well as Indonesia and Malaysia (50%) and Latin America (30%) for biodiesel. Over the simulation time
the share of 2nd generation biofuels (either produced from short rotation plantation, sawmill residues or
directly from energy wood biomass sourced from forests) increases substantially and by 2030 cellulosic
ethanol outperforms 1st generation biofuels and accounts for 56% of the total ethanol production.
Globally 2.5 EJ of 2nd generation biofuels are produced in 2030 even though European 2 nd generation
biofuel mandate is with 31 PJ marginal compared to 189 PJ from 1st generation (Table 6).
Table 6: Biofuel production in Reference scenario in PJ.
Region
Product
2000
2010
2020
2030
Global
Corn ethanol
Sugar cane ethanol
Wheat ethanol
Cellulosic ethanol
Total ethanol
Palm oil biodiesel
Rapeseed biodiesel
Soybean biodiesel
Sunflower biodiesel
Total biodiesel
125
261
0
0
387
0
8
15
3
25
453
259
107
101
920
0
165
40
29
234
1,325
418
31
798
2,573
78
734
223
0
1,034
1,464
627
30
2,473
4,594
109
751
273
0
1,134
EU27
Corn ethanol
Wheat ethanol
Cellulosic ethanol
Total ethanol
Rapeseed biodiesel
Sunflower biodiesel
Total biodiesel
0
0
0
0
8
3
11
0
107
0
107
162
29
191
178
27
31
236
591
0
591
162
27
31
220
555
0
555
3.1.2 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 70% in 2020 and 80% in 2030 emissions
saving target, total biofuel demand has to be imported from the rest of the world. Besides biofuels also
15
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. (409 PJ) and Brazil (382 PJ).
Table 7: Global first generation biofuel demand per feedstock in PJ in 2030 in the Reference and
Sustainability scenario.
REF
Corn ethanol
SUST
1,464
1,095
627
990
30
3
2,121
2,088
Palm oil biodiesel
109
464
Rapeseed biodiesel
751
329
Soybean biodiesel
273
374
Total biodiesel
1,134
1,167
Total biofuel
3,254
3,254
Sugar cane ethanol
Wheat ethanol
Total ethanol
In the Sustainability scenario in 2030 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 (40%)
followed by soybean (32%) and rapeseed (28%). Biggest biodiesel producers are South and South East
Asia (55 PJ) and China (191 PJ). Production of 2nd generation biofuels in Europe remains with 31 PJ at the
same level in 2030 as in the Reference scenario base run.
3.2 Market conditions and trade
3.2.1 Reference scenario base run
Until 2030, 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
2010 and 2030 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 2030, 36.5 Mt of rapeseed are processed to biodiesel in the EU. Even
though rapeseed supply constantly increases and almost triples from 2000 until 2030 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 2030). 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 2030. Corn imports, feed and human demand remains at constant
levels.
16
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 2030. Due to the replacement of soybean in the animal feed sector, soybean demand
and imports decrease (-24% in 2030 compared to 2000) over time. Overall European crop exports to the
rest of the world decrease until 2030 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 Mm3 to 363 Mm3. In addition,
191 Mm3 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 2030.
3.2.2 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 2030. 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 80% emission saving target in 2030. Therefore, imports of industrial plantation biomass decline
by 191 Mm3 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 2030 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 40% 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
3.3.1 Reference scenario base run
In the Reference scenario base run global cropland and grassland areas increase by 37 Mha and 47 Mha
respectively from 2000 to 2030 due to increasing demand for agricultural crop and livestock products.
This expansion mainly takes place on other natural vegetation (-48 Mha) or through deforestation (105 Mha). Cropland expansion is mainly located in Sub-Saharan Africa (+24.5 Mha) and South and South
17
East Asia +17.2 Mha) whereas grassland increases in Latin America (+30.1 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 2000 until 2030 1.4 Mha/yr of
forests are converted in Latin America, 1.3 Mha/yr in Sub-Saharan Africa and 0.7 Mha/yr in South and
South East Asia. Global average deforestation rate over 2000-2030 is about 0.09% per year which
represents a decline in deforestation rate compared to the 0.13% per year from 2000-2010 that was
reported in FAO (2011). 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).
300
250
Other Natural Vegetation
Mha
200
Grassland
Short Rotation Coppice
150
Unmanaged Forest
100
Managed Forest
Cropland
50
0
2000
2010
2020
2030
Figure 3: Development of land cover in EU until 2030 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 2030, 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. 2012). Such effects cannot be assessed with the applied version of GLOBIOM.
18
Until 2030, rapeseed areas more than double compared to 2000 amounting to a total of 10 Mha in 2030
(Figure 4). Soybean and wheat areas decline and other natural vegetation is converted into cropland
(2 Mha). Other crop areas remain at constant level.
70
60
in Mha
50
Wheat
Sunflower
40
Rapeseed
30
Potato
20
Corn
Barley
10
0
2000
2010
2020
2030
Figure 4: Crop areas in Europe in the Reference scenario in Mha.
The total use of nitrogen increases between 2010 and 2030 in the EU27 by 3.5% (Figure 5). The average
nitrogen use per hectare of cultivated crop (nitrogen intensity) increases between 2010 and 2020 and
then remains constant in 2030, due to productivity growth. However, in the rest of the world, we
observe a substantial increase by 19% of total nitrogen use between 2010 and 2030. 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 (-10%) and remains constant
Canada (+0.4%).
100
140
90
Mt
70
100
60
80
50
40
60
30
40
20
kg/ha/yr
120
80
20
10
0
0
2000
Total N use EU27
2010
Total N use RoW
2020
N intensity EU27
2030
N intensity RoW
Figure 5: Evolution of nitrogen use in EU27 and rest of the world in the Reference scenario base run.
19
3.3.2 Sustainability scenario base run
Comparing land cover between Reference and Sustainability scenario base run in 2030, 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.
10.0
8.0
6.0
Area difference in Mha
4.0
2.0
0.0
-2.0
MENA
SSA
Pacific
CIS
China
-4.0
-6.0
South
and
South
East
Asia
LA
Canada
USA
EU27
-8.0
-10.0
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 2030. 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
20
area to expand. On the other hand, also a switch to more grassland based production systems is
observed since feeding costs are lower also resulting in a higher grazing area.
In the Sustainability scenario base run total deforestation decreases by 5.6 Mha until 2030. This results
from an increasing demand for energy wood from forests and the exclusion of conversion of high
biodiversity forests. As short rotation plantations often do not reach the mitigation target, the EU is
processing energy wood from existing forests, i.e. forestry residues and fellings into heat, electricity and
2nd generation biofuels. As a consequence, demand for energy wood increases and 20 Mha of unused
forests enter production at the expense of short rotation plantations which decrease by 4.9 Mha.
Deforestation is lower in South and South East Asia due to protection of highly biodiverse primary
forests. Interestingly, in Sub-Saharan Africa deforestation actually increases in the Sustainability
scenario base run by 4.1 Mha mainly driven by grassland expansion.
Overall, intensity for agricultural inputs is reduced in the Sustainability scenario base run (Figure 7).
Compared to the Reference scenario base run in 2030, nitrogen intensity decreases globally by 7% and
phosphorus by 4%. This is directly related to the composition of the crop shares and the management
systems. At global scale nitrogen intensity decreases for corn (-7%), potatoes (-29%), rapeseed (-35%) or
sugar cane (-17%) while it increases for soybean (+15%) or rice (+32%). This results from an
extensification effect through the shift to less intensive systems or less productive areas.
105%
100%
Water
95%
Nitrogen
Phosphorus
90%
85%
World
EU27
RoW
Figure 7: Relative use of agricultural inputs in Sustainability scenario compared to Reference scenario
in 2030. Reference is at 100%.
In Europe, nitrogen intense rapeseed areas decline and are replaced by wheat and barley that do not
require that much fertilizer inputs. Moreover, for some crops an extensification effect due to a switch to
less intense management systems can be observed i.e. for corn and sunflower (-3% of total nitrogen per
ha).
3.3.3 Sensitivity analysis
Figure 8 presents total cropland areas per region in 2030 compared to the Reference scenario base run.
When excluding deforestation, it can be observed that cropland expansion in Sub-Saharan Africa (max. 8 Mha), South and South East Asia (max. -9 Mha) and Latin America (max. -2 Mha) is substantially
restricted. This results from avoiding deforestation as cropland usually either expands directly into
primary forests (mainly in Sub-Saharan Africa) or indirectly through grassland conversion which then
21
expands into primary forests. Moreover, cropland increases mainly in the U.S. (2 Mha) and some
marginal expansion in other regions to compensate for the avoided cropland expansion in tropical
countries. Effects on cropland variation in the no biofuel trade scenario are marginal. Cropland variation
is mainly related to corn area variation in the Sustainability scenario (Sub-Saharan Africa -3 Mha, South
and South East Asia -8 Mha, Latin America -5 Mha). Decreasing corn production is therefore being
compensated by increasing corn imports from the U.S and China to those regions.
4
2
0
Cropland difference in Mha
MENA
SSA
Pacific
CIS
-2
-4
China
South
and
South
East Asia
LA
Canada
USA
EU27
-6
-8
-10
-12
Reference no biofuel trade
Reference no deforestation
Sustainability
Sustainability no deforestation
Figure 8: Cropland variation compared to the Reference scenario per region in 2030. MENA: Middle
East, North Africa; SSA: Sub-Saharan Africa; CIS: Commonwealth of Independent States; LA: Latin
Amercia.
3.4 Loss of biodiversity
3.4.1 Reference scenario base run
In the Reference scenario base run, where no sustainability criteria are imposed outside Europe,
increasing demand for food, bioenergy and wood products drives mayor land use changes around the
world. Our results indicate, that in the Reference scenario base run 35.7 Mha of high biodiversity areas
would disappear completely until 2030 (Table 8). This represents a loss of 7% of the total identified high
biodiversity areas in 2000. Mayor source of loss of highly biodiverse areas is deforestation caused by
cropland (-3.8 Mha) and grassland (-15.4 Mha) expansion into primary forests which can be observed in
Sub-Saharan Africa and Latin America. Almost one fifth of the total deforestation (105 Mha) until 2030
takes place in highly biodiverse primary forests contributing to an overall loss of 7% of total highly
biodiverse primary forest area.
Cropland expansion is also responsible for the conversion of grasslands and other natural vegetation.
High biodiversity natural vegetation declines by 8% until 2030. In addition, increasing biomass demand
causes the establishment of short rotation plantation on previously high biodiversity areas such as other
natural vegetation, grasslands and cropland.
22
Table 8: Loss of high biodiversity areas in Reference scenario base run due to land use change until
2030 in Mha. MENA: Middle East, North Africa; SSA: Sub-Saharan Africa; CIS: Commonwealth of
Independent States; LA: Latin America.
Total
Forests
Grassland
other natural land
World
35.7
19.2
6.8
9.7
MENA
2.5
0.3
1.2
1.0
SSA
13.6
9.0
2.1
2.5
Pacific
1.3
0.5
0.5
0.3
CIS
0.9
0.0
0.0
0.9
China
2.1
0.0
0.4
1.7
South and South East Asia
2.1
1.0
0.1
1.0
12.1
8.5
2.4
1.2
Canada
0.0
0.0
0.0
0.0
USA
0.7
0.0
0.2
0.5
LA
3.4.2 Sensitivity analysis
Due to the implementation of additional sustainability criteria outside Europe in the Sustainability
scenario, conversion of highly biodiverse areas does not occur in this scenario per definition. When
looking at the sensitivity runs, losses of highly biodiverse areas do not change significantly in the
Reference scenario no biofuel trade compared to the base run. However, in the no deforestation run,
results differ. Since deforestation is excluded in this sensitivity scenario, the conversion of highly
biodiverse forests in general is avoided. While deforestation of highly biodiverse forests is consequently
reduced to zero, conversion of highly biodiverse other natural vegetation increases by 3.1 Mha since
cropland and grassland expansion into forests is not allowed and therefore expansion takes place on
other natural vegetation. In total losses of highly biodiverse areas decrease by 58% to 15.1 Mha
compared to the base run. It can be concluded, that the successful implementation of a zero
deforestation target has positive impacts on conservation of highly biodiverse areas.
3.5 GHG emissions
3.5.1 Reference scenario base run
Total GHG emissions from agriculture and land use change remain constant in Europe since emission
savings from fossil fuel replacement can compensate for increasing emissions from livestock and crop
production (Figure 9). As a consequence, total emissions increase only marginally from 562 Mt in 2000
to 577 Mt in 2030.
23
700
Mt CO2 eq
600
500
Biofuels
400
Livestock sector
Crop sector
300
other LUC
200
Deforestation
100
Total GHG
0
2000
2010
2020
2030
-100
Figure 9: Development of annualized average GHG emissions in EU27 in the Reference scenario in Mt
CO2 eq.
Global total GHG emissions increase sharply by 48% from 2000 until 2030 amounting to a total of
8,078 Mt in the last period (Figure 10). The crop and livestock sector are responsible for the biggest
share in total emissions, emitting 52% and 36% of total emissions. Emissions from land use change
contribute 16% to total GHG emissions. However, emissions from land use change increase rapidly over
time and are the largest source of additional GHG emissions compared to the base year. Rising
emissions cannot be compensated by an increasing carbon sink due to establishment of short rotation
plantations and emission savings due to the replacement of fossil fuel by biofuels. The evolution of GHG
emissions is not only driven by biofuel expansion but also by other macroeconomic developments such
as population growth which trigger an increase in demand for agricultural products.
9,000
8,000
7,000
Biofuels
Mt CO2 eq
6,000
Livestock sector
5,000
Crop sector
4,000
other LUC
3,000
Deforestation
2,000
Total GHG
1,000
0
-1,000
2000
2010
2020
2030
Figure 10: Development of annualized average GHG emissions in the Reference scenario in the rest of
the world in Mt CO2 eq.
24
3.5.2 Sustainability scenario base run
When looking at GHG emissions in the Reference and Sustainability scenario base runs it can be
observed, that GHG emissions are smaller in the latter scenario. Emissions from land use change
decrease by 171 Mt in 2030 (Table 9) in the Sustainability scenario base run since more stringent
sustainability criteria are enforced. Main sources for emissions savings are decreasing emissions from
deforestation and soil N2O. Since emissions related to nitrogen use and production represent a
substantial share of emissions from crop production, these emissions decline due to the extensification
of cropland as described before. Overall GHG emissions can be reduced by 381 Mt CO2 eq in 2030
globally. In Europe, emissions actually increase in the Sustainability scenario compared to Reference
until 2030 due to the fact that emissions savings from fossil fuel replacement are allocated to the
countries actually producing biofuels.
Table 9: Evolution of annualized average GHG emissions in Mt CO 2 eq.
Reference
2000
EU27
2000
2020
2030
-2
0
0
0
0
Deforestation
0
0
0
0
0
0
Other LUC
0
3
4
0
4
4
Total LUC
0
1
4
0
4
4
Crop
286
315
316
286
299
301
Livestock
277
302
312
277
306
314
Total agriculture
563
618
628
563
605
615
-1
-59
-56
-1
-4
-2
Total
562
560
577
562
604
616
Afforestation
-23
-143
-115
-23
-126
-108
Deforestation
12
929
1,110
12
757
991
Other LUC
11
151
307
11
139
249
Total LUC
-1
938
1,303
-1
770
1,132
Crop
2,981
3,633
3,886
2,981
3,523
3,711
Livestock
1,943
2,406
2,624
1,943
2,382
2,590
Total agriculture
4,923
6,039
6,511
4,923
5,905
6,301
-26
-175
-312
-26
-218
-353
4,897
6,802
7,501
4,897
6,458
7,080
Afforestation
-23
-144
-114
-23
-126
-108
Deforestation
12
929
1,110
12
757
991
Other LUC
11
155
310
11
143
252
Total LUC
-1
939
1,306
-1
774
1,135
Crop
3,266
3,948
4,202
3,266
3,822
4,012
Livestock
2,220
2,709
2,937
2,220
2,688
2,904
Total agriculture
5,486
6,657
7,139
5,486
6,510
6,916
Total
Fossil Fuel Displacement
Total
25
2030
0
Fossil Fuel Displacement
Global
2020
Afforestation
Fossil Fuel Displacement
RoW
Sustainability
-27
-234
-368
-27
-222
-355
5,459
7,361
8,078
5,459
7,063
7,697
3.5.3 Sensitivity analysis
In the no deforestation scenarios global GHG emissions can be reduced by 19% (no deforestation
Reference) and 20% (no deforestation Sustainability) compared to the Reference scenario base run
(Figure 11). Emissions from land use change can be reduced from 1.306 Mt in the Reference scenario to
219 Mt in the Reference scenario no deforestation and 208 Mt in the Sustainability scenario no
deforestation respectively. In addition, also emissions from livestock and crop production decrease in
the two scenarios by 6% and 8% respectively. While in the Sustainability scenario emissions can only be
reduced by 5%, in the scenarios where deforestation is excluded, GHG emissions decrease much further.
Hence, preventing deforestation is an important climate mitigation option since it reduces much more
emissions than the sustainability criteria implemented in the Sustainability scenario. However, even
though total emissions decline compared to the Reference scenario, even in the no deforestation
Sustainability scenarios, net GHG emission increase by 18% compared to 2000 due to overall changes on
the demand side.
105%
100%
95%
90%
85%
80%
75%
70%
2000
2010
2020
2030
Reference no biofuel trade
Reference no deforestation
Sustainability
Sustainability no deforestation
Figure 11: Evolution of total GHG emissions relative to Reference scenario.
3.6 Discussion and conclusions
This study aimed at an assessment of impacts of bioenergy scenarios that largely occur outside EU. The
model we applied allows for a consideration of sustainability issues, accounting for (direct and indirect)
land use change, environmental variables (water, nitrogen, GHG emissions), and economic effects (e.g.
food prices).
Even though biofuels offer the potential to reduce fossil fuel based energy production and net emissions
(Farrell et al. 2006; Edwards et al. 2008), increasing biofuel demand can result in higher GHG emissions
through land use change (Fargione et al. 2008; Searchinger et al. 2008). Furthermore, biofuel production
can also lead to biodiversity losses through direct or indirect displacement of natural habitat and other
ecologically valuable land (Eggers et al. 2009; Hellmann and Verburg 2010). In order to ensure GHG
emissions savings, prevent biodiversity loss, and avoid other negative impacts on the environment,
sustainability criteria guiding biofuel production have been included in the RED. Already numerous
studies have analysed effects of biofuels on land use change and GHG emissions at global scale (Al-Riffai
2010; Britz and Hertel 2011). Here we assess such effects for the concrete Biomass Futures scenarios.
26
Satisfying European bioenergy targets in 2030 requires a substantial increase in biomass produced
inside Europe and additional imports from the rest of the world. In the Reference scenario base run
which provides an outlook on how bioenergy markets could develop towards 2030, 70% of the
European ethanol and 66% of the biodiesel demand can be met by EU refined biofuels. Increasing
demand for biofuel results in increasing crop prices. Prices for important European biofuel feedstocks
experience sharp increases (rapeseed +54%, corn +15% until 2030). In the forestry sector 191 Mm3 of
biomass imports are required to meet European demand for heat and electricity.
Moreover, our results indicate that in the Reference scenario 105 Mha may be deforested globally until
2030, one fifth of it highly biodiverse primary forests. Deforestation mainly takes place in Latin America
and Sub-Saharan Africa. In the Sustainability scenario base run, where strict sustainability criteria are
enforced, deforestation can be reduced by 5.6 Mha and conversion of highly biodiverse areas is avoided.
Especially in Latin America and South and South East Asia the imposed sustainability criteria avoid
deforestation and limit conversion of highly biodiverse areas. However, in Sub-Saharan Africa a slight
increase in deforestation can be observed. Since increasing biofuel exports to Europe trigger
competition between cropland for biofuel production and grasslands for animal feeding, this causes
additional grasslands to expand into forests as cropland for biofuel production expands into highly
productive grasslands.
With the implementation of more stringent sustainability criteria in the Sustainability scenario no first
generation biofuels can be produced in the EU due to non-compliance with the high GHG mitigation
criteria. Hence, biofuels have to be imported from the rest of the world in order to satisfy European
biofuel demand. Consequently, a shift in biofuel feedstock occurs and feedstocks such as sugar cane and
palm oil gain importance in the biofuel feedstock mix. Moreover, also in the forest sector a shift from
short rotation tree plantation biomass to energy wood biomass sourced from forests can be observed,
when sustainability criteria are applied to the whole biomass sector.
In the Sustainability scenario, global emissions can be reduced by 5% through avoiding deforestation
and more extensive cropland management. However, over the whole period GHG emissions increase in
both scenarios due to overall developments on the demand side driven by population growth,
increasing calorie requirements and biofuel expansion. Nitrogen inputs decrease globally by 7% and
phosphorus by 4% in the Sustainability scenario. This is caused by the reallocation of biofuel production
in the rest of the world and the related the change in biofuel feedstock mix. Since crops with high
fertilizer requirements previously produced in the EU such as rapeseed and corn are being replaced with
less nitrogen intensive biofuel feedstocks like sugar cane and palm oil in the rest of the world in the
Sustainability scenario.
In the sensitivity runs large GHG mitigation potential can be observed when no deforestation is allowed.
Excluding deforestation for the Reference and the Sustainability scenario in the sensitivity runs results in
emission savings of 19% and 20% respectively. This exceeds by far the emission saving potential due to
implementation of sustainability criteria. Hence, global land use change policies targeting all drivers of
deforestation (not only the biofuel sector) such as REDD help preventing indirect land use change effects
and avoiding GHG emissions from direct and indirect land use change. As shown in the sensitivity runs
implementing such policies effectively results in higher GHG emissions savings and conservation if of
highly biodiverse areas.
The Renewable Energy Directive (RED) addresses some of these impacts, sets sustainability criteria and
excludes sources, feedstocks and pathways that violate these. Despite the model’s ability to cover a
wide range of processes, challenges remain when it comes to an appropriate implementation of these
criteria. While the inclusion of RED sustainability criteria for domestic production was achieved by
producing a stencil to exclude feedstocks and pathways that violate criteria, a similar application to
imported feedstocks and biofuels is difficult. This is due to the fact that biomass cannot be tracked from
field to final product in the model. Therefore, a set of sensitivity runs was performed that allowed or
banned global deforestation and opened or constrained global trade of biofuels into EU.
27
Some general observations that can be made from a comparison of Biomass Future scenarios and
sensitivity runs are twofold. European biofuel mandates do have an effect on global land use that can be
assessed with an economic land use model as we applied it. These land use effects cannot be mitigated
by applying sustainability criteria to biofuel production and imports only. However, when globally
effective land use policies e.g. targeting emissions from deforestation and biodiversity loss in general
are successful, no indirect effects of increased bioenergy use on biodiversity and GHG emissions are
occurring.
This finding is somehow trivial but important as it stresses that globally an application of sustainability
criteria on biofuel products only is not effective. Findings from Biomass Futures’ WP3 presented in
Deliverable 3.3 identify significant amounts of biomass that are technically available in Europe to satisfy
bioenergy targets even when applying sustainability criteria. Also Frank et al. (submitted) identified
large feedstock potentials for bioethanol production in North America, Brazil and Asia complying with
RED sustainability criteria. Global “sustainable” production could amount to more than 10 times of the
2020 EU biofuel demand without violating RED sustainability criteria. However, the challenge is to avoid
a leakage of biomass production to sectors not covered by the criteria, e.g. timber, food and feed
sectors or simply the biomass production for energy consumption outside EU (Frank et al. submitted).
An added value of this study lies not in the quantification of potentials of biomass for EU bienergy
supply (see for an overview Deliverable 3.1 Review of biomass assessments available on the Biomass
Futures website www.biomassfutures.eu). We applied sustainability criteria to constrain the potential to
more realistic numbers. However, also these numbers are still uncertain and depend on the many
assumptions made related to accessibility, availability and costs (see Deliverable 3.3). We acknowledge
that total amounts regarding biomass potentials and impacts need to be regarded in the light of these
assumptions. This is especially true for those made on the demand for biomass for bioenergy (but also
other purposes) outside EU. It is likely that the behaviour of the rest of the world matters more than
decisions of agents inside EU. Therefore the value added by this assessment can instead be seen more in
the contribution to a rational debate about sustainable biomass supply for EU.
28
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Bouët, A., Y. Decreux, et al. (2008). Assessing applied protection across the world. Review of
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Edwards, R., S. Szekeres, et al. (2008). Biofuels in the european context: facts and uncertainties. Petten,
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30
Appendix
Table 10: General parameters.
GJ
GJ
MWh
tce
toe
bbl
m3 wood
t biomass
MWh
tce
1
3.60
29.31
41.87
5.694
0.278
1
8.148
11.64
1.583
toe
0.034
0.122
1
1.424
0.194
bbl
0.024
0.086
0.703
1
0.137
4.65
2.325
0.176
0.632
5.155
7.299
1
Table 11: Sources of parameters used in GLOBIOM.
PARAMETER
Land characteristics
Soil Classes
Slope Classes
Altitude Classses
Country Boundaries
Aridity Index
Temperature threshold
Protected area
Land cover
Agriculture
Area
Cropland area (1000 ha)
SOURCE
ISRIC
SRTM 90m Digital Elevation Data
(http://srtm.csi.cgiar.org)
ICRAF, Zomer at al. (2009)
European Centre for Medium Range Weather
Forecasting (ECMWF)
FORAF
Global Land Cover (GLC 2000 )- Institute for
Environment and Sustainability
EPIC crop area (1000 ha)
Cash crop area (1000 ha)
Irrigated area (1000 ha)
Global Land Cover (GLC 2000 )- Institute for
Environment and Sustainability
IFPRI- You and Wood (2006)
IFPRI- You, Wood, Wood-Sichra (2007)
FAO
Yield
EPIC crop yield (T/ha)
Cash crop yield (T/ha)
Average regional yield (T/ha)
BOKU, Erwin Schmid
IFPRI- You, Wood, Wood-Sichra (2007)
FAO
Input use
Quantity of nitrogen (FTN) (kg/ha)
Quantity of phosphorous (FTP)(kg/ha)
Quantity of water (1000 m3/ha)
Fertilizer application rates
Fertilizer application rates
Costs for 4 irrigation systems
Production
Crop production (1000 T)
YEAR
2000
2000
2000
2000
average
1998-2002
2000
average
1998-2002
BOKU, Erwin Schmid
BOKU, Erwin Schmid
BOKU, Erwin Schmid
IFA (1992)
FAOSTAT
Sauer et al. (2008)
FAO
Livestock production
FAO
Prices
Crops (USD/T)
FAO
average
1998-2002
average
1998-2002
average
1998-2002
PARAMETER
Fertilizer price (USD/kg)
Forestry
Maximum share of saw logs in the mean
annual increment (m3/ha/year)
Harvestable wood for pulp production
(m3/ha/year)
Mean annual increment (m3/ha/year)
Biomass and Wood production (m3 or
1000 T)
Harvesting costs
Short rotation plantation
Suitable area (1000 ha)
Maximum Annual Increment (m3 per ha)
Potential NPP
Potentials for biomass plantations
Sapling cost for manual planting
Labour requirements for plantation
establishment
Average wages
Unit cost of harvesting equipment and
labour
Slope factor
Ratio of mean PPP adjustment
GHG emissions
N2O emissions from application of
synthetic fertilizers (kg CO2/ha)
Fertilizer application rates
CO2 savings/emission coefficients
Above and below-ground living biomass
in forests[tCO2eq per ha]
Above and below ground living biomass
in grassland and other natural land
[tCO2eq per ha]
Total Non-Carbon Emissions (Million
Metric CO2-Equivalent)
Crop Carbon Dioxide Emissions (Tons CO2
/ hectare)
GHG sequestration in SRP (tCO2/ha)
International Trade
MacMap database
BACI (based on COMTRADE)
International freight costs
Process
Conversion coefficients for sawn wood
Conversion coefficients for wood pulp
Conversion coefficients and costs for
energy
Conversion coefficients and costs for
Ethanol
Conversion coefficients and costs for
Biodiesel
Production costs for sawn wood and
wood pulp
Population
Population per country (1000 hab)
32
SOURCE
USDA (
http://www.ers.usda.gov/Data/FertilizerUse/)
YEAR
average
2001-2005
Kinderman et al. (2006)
Kinderman et al. (2006)
Kinderman et al. (2008) based on the Global Forest
Resources Assessment (FAO, 2006a)
FAO
Kinderman et al. (2006)
Havlik et al. (2010)
Zomer at al. (2008)
Alig et al., 2000; Chiba and Nagata, 1987; FAO,
2006b; Mitchell, 2000; Stanturf et al., 2002; Uri et al.,
2002; Wadsworth, 1997; Webb et al., 1984
Cramer et al. (1999
Zomer at al. (2008
(Carpentieri et al., 1993; Herzogbaum GmbH, 2008).
Jurvélius (1997),
2000
2010
ILO (2007).
FPP, 1999; Jiroušek et al., 2007; Stokes et al., 1986;
Wang et al., 2004
Hartsough et al., 2001
Heston et al., 2006
IPCC Guidelines, 1996
IFA, 1992
CONCAWE/JRC/EUCAR (2007) , Renewable Fuels
Agency (2008)
Kindermann et al. (2008)
Ruesch and Gibbs (2008)
(http://cdiac.ornl.gov/epubs/ndp/global_carbon/car
bon_documentation.html)
EPA, 2006
EPA, 2006
Chiba and Nagata, 1987;
Bouet et al., 2005
Gaulier and Zignago, 2009
Hummels et al., 2001
4DSM model - Rametsteiner et al. (2007)
4DSM model - Rametsteiner et al. (2007)
Biomass Technology Group, 2005; Hamelinck and
Faaij, 2001; Leduc et al., 2008; Sørensen, 2005
Hermann and Patel (2008)
Haas et al. (2007)
internal IIASA database and RISI database (
http://www.risiinfo.com)
JRC Sevilla, POLES, Russ et al. (2007) updated
average
PARAMETER
SOURCE
Estimated total population per region
every ten years between 2000 and
2100 (1000 hab)
0.5 degree grid
Population density
Demand
Initial food demand for crops (1000 T)
GGI Scenario Database (2007)- Grubler et al.
Initial feed demand for crops (1000 T)
FBS data - FAO
Crop requirement per animal calories
(T/1 000 000 kcal)
Crop energy equivalent (kcal/T)
Relative change in consumption for meat,
anim, veg, milk (kcal/capita)
Own price elasticity
Supply Utilisation Accounts, FAOSTAT
GDP projections
SUA data for crops (1000 tones)
FBS data
Bioenergy projections
Biomass and Woodconsumption (m3/ha
or 1000 T/ha)
33
YEAR
1999-2001
GGI Scenario Database (2007)- Grubler et al.
CIESIN (2005).
FBS data - FAO
FBS data - FAO
FAO(2006) World agriculture: towards 2030/2050
(Tables: 2.1, 2.7, 2.8)
"International Evidence on Food Consumption
Patterns", James Seale, Jr., Anita Regmi, and Jason A.
Bernstein, Technical Bulletin No. (TB1904) 70 pp,
October 2003
GGI Scenario Database (2007)
FAO
FAO
JRC Sevilla, POLES, Russ et al. (2007) updated
FAO
average
1998-2002
average
1998-2002
average
1998-2002