scenarios of global climate change mitigation through competing biomass management options hannes...
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Scenarios of global climate change Scenarios of global climate change
mitigation through competing mitigation through competing
biomass management optionsbiomass management options
Hannes Böttcher1, Petr Havlík1, Arturo Castillo Castillo2, Jeremy Woods2,Robert Matthews3, Jo House4, Michael Obersteiner1
1 International Institute for Applied Systems Analysis, Schlossplatz 1, A-2231 Laxenburg, Austria2 Centre for Environmental Policy, Faculty of Natural Sciences, Imperial College London, South Kensington campus, London SW7 2AZ, United Kingdom3 Forest Research, Alice Holt Lodge, Farnham, Surrey GU10 4LH, United Kingdom4 Department of Earth Sciences, University of Bristol, Wills Memorial Building, Queen's Road, Clifton, Bristol BS8 1RJ, United Kingdom
[email protected] Forestry Program
Laxenburg, Austria
QUEST – AIMES Earth System Science Conference Edinburgh, May 10-13 2010
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Background
Many countries have set up bioenergy policies to support and regulate the production and use of fuels from biomass feedstocks (e.g. US, EU, Brazil, China, India)But biofuels are hotly debated today because their overall impacts are uncertain and difficult to assess, being highly dependant on both the bioenergy fuel chain (choice of crop and technology), and on the existing land useDirect biofuel benefits are linked to indirect land use impacts and adverse externalities regarding GHG emission balances, ecosystem services, and security of food and waterIn particular, the implementation of biofuel targets might conflict with other mitigation options like avoided deforestation or enhancing forest carbon stocks
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Effective mitigation
Obersteiner, Böttcher et al. accepted COSUST
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High hopes
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QUATERMASS OverviewQUATERMASS Overview
Global-regional scale impacts & opportunities modelling(IIASA)
Regional to local impacts & opportunities modelling(Forest Research and Aberdeen)
Local impacts & opportunities modellingGround-truthing / Case studies (Ecometrica)
Synth
esis &
Policy
Analy
sis(Im
peria
l Colle
ge)
Feedback & Communication
Atmospheric greenhouse gasesAtmospheric greenhouse gases
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Model description: GLOBIOMModel description: GLOBIOM
Global Biomass Optimisation ModelCoverage: global, 28 regions3 land based sectors:Forestry: traditional forests for sawnwood, and pulp and paper productionAgriculture: major agricultural cropsBioenergy: conventional crops and dedicated forest plantations
Optimization Model (FASOM structure)Recursive dynamic spatial equilibrium modelMaximization of the social welfare (Producer plus consumer surplus)Partial equilibrium model (land use sector only): endogenous prices
OutputProductionConsumptionPrices, trade flows, etc.
Havlik et al. 2010 Energy Policy
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GLOBIOM: Global Biomass Optimisation Model
Integrated land-use and bioenergy modellingWorld divided into 28 regions
Havlik et al. 2010 Energy Policy
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Model Model description: Supply chainsdescription: Supply chains
Wood Processing
Bioenergy Processing
Livestock Feeding
Unmanaged Forest
Managed Forest
Short RotationTree Plantations
Cropland
Grassland
Other Natural Vegetation
Energy products:Ethanol (1st gen.)Biodiesel (1st gen.)Ethanol (2nd gen)MethanolHeatPowerGasFuel wood
Forest products:SawnwoodWoodpulp
Livestock:Animal Calories
Crops:BarleyCornCotton …
Havlik et al. 2010 Energy Policy
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Model description: EPIC AgricultureModel description: EPIC Agriculture
Crop related parameters: SimU EPIC
Major inputs:WeatherSoilTopographyLand management
Major outputs:YieldsEnvironmental variables
4 management systems:High input, Low input, Irrigated, Subsistence
EPIC
Rain, Snow, Chemicals
Subsurface FlowSurface
Flow
Below Root Zone
Evaporation and
Transpiration
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Model description: EPIC - YieldsModel description: EPIC - Yields
Yields Emissions
Carbon stock
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Productivity distribution
Model description: Forest plantationsModel description: Forest plantations
Area [Mha]
Pro
du
cti
vit
y [
m3/h
a]
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Uncertainty of land cover
Mapping errorsClassification errorsValidation of global land cover: www.geo-wiki.orgAssociated land use allocation
GLC 2000
MODIS
GLC2000 MODIS FAO(2000)Cropland 2383 1701 1530Forest 4165 5121 3989Grassland 1328 1224 3430Other natural vegetation 2734 2788 4064Sum of above classes 10610 10835 13013
Mha
Bellarby et al. 2010, see poster
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Detailed bioenergy chains (not yet fully implemented)
Castillo et al. 2010, see poster
Feedstock
Process Current land use Energy generation
Chains
Sweet sorghum
1 Convntl. Ethanol “1st G” 2 Advanced Ethanol “2nd G”
1 Degraded pasture2 Existing cropland3 Marginal/abandoned4 Grassland
1 Residue boiler CHP 2 Residue boiler + grid electricity3 Diesel genset
24
Wheat 1 Convntl. Ethanol “1st G” 2 Advanced Ethanol “2nd G”
1 Degraded pasture2 Set-aside3 Grassland4 Existing cropland
1 NG boiler + ST2 NG + grid electricity3 CCGT4 Straw boiler + ST5 Biogas CHP
40
Palm oil 1 Convntl. Biodiesel “1st G”
1 Existing cropland 2 Degraded pasture3 Forest4 Grassland (Imperata)
1 Oil boiler + ST2 Oil CHP3 Residue boiler + ST 12
Soy 1 Convntl. Biodiesel “1st G”
1 Grassland2 Existing cropland3 Set-aside4 Forest
1 NG boiler + ST2 NG + grid electricity3 CCGT4 Straw boiler + ST
16
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Policy scenariosPolicy scenarios
Baseline without any additional bioenergy NO bioshockBioenergy demand increased by 50% in 2030 compared to baseline 50 bioshockREDD, decreasing deforestation emissions by 50/90% in 2020/2030 compared to baselineNO bioshock REDCombination of Bioenergy and REDD 50 bioshock REDTwo alternative modeling settings
without biofuel feedstock trade with biofuel feedstock trade
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Land use change implications of bioenergy
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Impact of bioenery demand on land useImpact of bioenery demand on land use
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Land expansion localisation: croplandLand expansion localisation: cropland
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Impacts of REDD policies
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Deforestation from cropland expansionDeforestation from cropland expansion
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Effect of REDD policyEffect of REDD policydifference between bioenergy and bioenergy+REDD difference between bioenergy and bioenergy+REDD
scenarioscenario
Forest saved
Expansion into other
land
Reduced cropland expansion
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Importance of trade
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Mha, based on WEO 2020 targets, If not constrained (e.g. by REDD) important deforestation occurs
30
20
10
0
World biofuel targets, no trade
World biofuel targets, with trade
EU biofuel targets, no trade
EU biofuel targets, with trade
Deforestation due to biofuel expansion
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In Mha, EU mandates in 2020 put pressure on deforestation elsewhere even without trade – iLUC!
6
4
2
0South
America Pacific
AsiaAfrica South
Asia
6
4
2
0South
America Pacific
AsiaAfrica South
Asia
Deforestation due to EU biofuel expansion
With tradeWithout trade
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Crop price index, avoiding deforestation further increases the effect of biofuels on crop prices
With trade, allowing deforestation
With trade, preventing deforestation
Without trade, allowing deforestation
Without trade, preventing deforestation
1.10
1.05
1.00
1.15
1.20
World biofuel expansion and crop prices
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Conclusions (1)
Biofuel expansion generates important indirect GHG emissions (iLUC) Trade lowers global deforestation pressure by iLUCDimension of iLUC depends more on efficient sourcing of biofuels than on the global scale of productionPolicies (like REDD) aiming at (i)LUC effects will put pressure on crop pricesHow will management systems adapt?
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Conclusions (2)
Decreasing the human footprint on the atmosphere will necessitate active management of terrestrial C pools and GHG fluxesMost options might appear as competitive mitigation measures from an economic point of viewBut issues of governance remain most contentious as they induce competition for land and other ecosystem services
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Status of global forest certificationStatus of global forest certification
compiled from FAO 2005, 2001; CIESIN 2007, ATFS 2008; FSC 2008; PEFC 2008
Kraxner et al., 2008
Certified forest area relative to area of forest available for wood supply
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Thank [email protected]