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Scenarios of global climate change mitigation through competing biomass management options

Scenarios of global climate change mitigation through competing biomass management options. Hannes Böttcher 1 , Petr Havlík 1 , Arturo Castillo Castillo 2 , Jeremy Woods 2 , Robert Matthews 3 , Jo House 4 , Michael Obersteiner 1

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Scenarios of global climate change mitigation through competing biomass management options

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  1. Scenarios of global climate change mitigation through competing biomass 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, Austria 2 Centre for Environmental Policy, Faculty of Natural Sciences, Imperial College London, South Kensington campus, London SW7 2AZ, United Kingdom 3 Forest Research, Alice Holt Lodge, Farnham, Surrey GU10 4LH, United Kingdom 4 Department of Earth Sciences, University of Bristol, Wills Memorial Building, Queen's Road, Clifton, Bristol BS8 1RJ, United Kingdom bottcher@iiasa.ac.at IIASA Forestry Program Laxenburg, Austria QUEST – AIMES Earth System Science Conference Edinburgh, May 10-13 2010

  2. 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 use • Direct biofuel benefits are linked to indirect land use impacts and adverse externalities regarding GHG emission balances, ecosystem services, and security of food and water • In particular, the implementation of biofuel targets might conflict with other mitigation options like avoided deforestation or enhancing forest carbon stocks

  3. Effective mitigation Obersteiner, Böttcher et al. accepted COSUST

  4. High hopes

  5. QUATERMASS Overview Atmospheric greenhouse gases Synthesis & Policy Analysis (Imperial College) Global-regional scale impacts & opportunities modelling (IIASA) Regional to local impacts & opportunities modelling (Forest Research and Aberdeen) Local impacts & opportunities modelling Ground-truthing / Case studies (Ecometrica) Feedback & Communication

  6. Model description: GLOBIOM Global Biomass Optimisation Model Coverage: global, 28 regions 3 land based sectors: Forestry: traditional forests for sawnwood, and pulp and paper production Agriculture: major agricultural crops Bioenergy: conventional crops and dedicated forest plantations Optimization Model (FASOM structure) Recursive dynamic spatial equilibrium model Maximization of the social welfare (Producer plus consumer surplus) Partial equilibrium model (land use sector only): endogenous prices Output Production Consumption Prices, trade flows, etc. Havlik et al. 2010 Energy Policy

  7. GLOBIOM: Global Biomass Optimisation Model Integrated land-use and bioenergy modelling World divided into 28 regions Havlik et al. 2010 Energy Policy

  8. Model description: Supply chains Unmanaged Forest Forest products: Sawnwood Woodpulp Wood Processing Managed Forest Energy products: Ethanol (1st gen.) Biodiesel (1st gen.) Ethanol (2nd gen) Methanol Heat Power Gas Fuel wood Short Rotation Tree Plantations Bioenergy Processing Cropland Crops: Barley Corn Cotton … Grassland Livestock Feeding Livestock: Animal Calories Other Natural Vegetation Havlik et al. 2010 Energy Policy

  9. Model description: EPIC Agriculture Crop related parameters:SimU  EPIC Major inputs: Weather Soil Topography Land management Major outputs: Yields Environmental variables 4 management systems: High input, Low input, Irrigated, Subsistence EPIC Evaporation and Transpiration Rain, Snow, Chemicals Subsurface Flow Surface Flow Below Root Zone

  10. Model description: EPIC - Yields Yields Emissions Carbon stock

  11. Model description: Forest plantations Productivity distribution Productivity [m3/ha] Area [Mha]

  12. GLC2000 MODIS FAO(2000) Mha Cropland 2383 1701 1530 Forest 4165 5121 3989 Grassland 1328 1224 3430 GLC 2000 Other natural vegetation 2734 2788 4064 Sum of above classes 10610 10835 13013 MODIS Uncertainty of land cover • Mapping errors • Classification errors • Validation of global land cover: www.geo-wiki.org • Associated land use allocation Bellarby et al. 2010, see poster

  13. Detailed bioenergy chains (not yet fully implemented) Castillo et al. 2010, see poster

  14. Policy scenarios • Baseline without any additional bioenergy NO bioshock • Bioenergy demand increased by 50% in 2030 compared to baseline 50 bioshock • REDD, decreasing deforestation emissions by50/90% in 2020/2030 compared to baseline NO bioshock RED • Combination of Bioenergy and REDD 50 bioshock RED • Two alternative modeling settings • without biofuel feedstock trade • with biofuel feedstock trade

  15. Land use change implications of bioenergy

  16. Impact of bioenerydemand on land use

  17. Land expansion localisation: cropland

  18. Impacts of REDD policies

  19. Deforestation from cropland expansion

  20. Expansion into other land Forest saved Reduced cropland expansion Effect of REDD policydifference between bioenergy and bioenergy+REDD scenario

  21. Importance of trade

  22. 30 20 World biofuel targets, no trade World biofuel targets, with trade EU biofuel targets, no trade EU biofuel targets, with trade 10 0 Deforestation due to biofuel expansion Mha, based on WEO 2020 targets, If not constrained (e.g. by REDD) important deforestation occurs

  23. 6 6 4 4 2 2 Africa Africa South Asia South Asia Pacific Asia Pacific Asia South America South America 0 0 Deforestation due to EU biofuel expansion In Mha, EU mandates in 2020 put pressure on deforestation elsewhere even without trade – iLUC! With trade Without trade

  24. 1.20 1.15 With trade, allowing deforestation With trade, preventing deforestation Without trade, allowing deforestation Without trade, preventing deforestation 1.10 1.05 1.00 World biofuel expansion and crop prices Crop price index, avoiding deforestation further increases the effect of biofuels on crop prices

  25. Conclusions (1) Biofuel expansion generates important indirect GHG emissions (iLUC) Trade lowers global deforestation pressure by iLUC Dimension of iLUC depends more on efficient sourcing of biofuels than on the global scale of production Policies (like REDD) aiming at (i)LUC effects will put pressure on crop prices How will management systems adapt?

  26. Conclusions (2) • Decreasing the human footprint on the atmosphere will necessitate active management of terrestrial C pools and GHG fluxes • Most options might appear as competitive mitigation measures from an economic point of view • But issues of governance remain most contentious as they induce competition for land and other ecosystem services

  27. Status of global forest certification Certified forest area relative to area of forest available for wood supply Kraxner et al., 2008 compiled from FAO 2005, 2001; CIESIN 2007, ATFS 2008; FSC 2008; PEFC 2008

  28. Thank you! bottcher@iiasa.ac.at

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