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Scenarios of global climate change mitigation through competing biomass management options Hannes Böttcher 1, Petr Havlík 1, Arturo Castillo Castillo 2,

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Presentation on theme: "Scenarios of global climate change mitigation through competing biomass management options Hannes Böttcher 1, Petr Havlík 1, Arturo Castillo Castillo 2,"— Presentation transcript:

1 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 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 IIASA Forestry Program Laxenburg, Austria QUEST – AIMES Earth System Science Conference Edinburgh, May

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 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) Synthesis & Policy Analysis (Imperial College) Feedback & Communication Atmospheric greenhouse gases

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 Energy Policy

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

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

10 Model description: EPIC - Yields YieldsEmissions Carbon stock

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

12 Uncertainty of land cover Mapping errors Classification errors Validation of global land cover: Associated land use allocation GLC 2000 MODIS GLC2000MODISFAO(2000) Cropland Forest Grassland Other natural vegetation Sum of above classes Mha Bellarby et al. 2010, see poster

13 Detailed bioenergy chains (not yet fully implemented) Castillo et al. 2010, see poster FeedstockProcessCurrent land useEnergy generationChains Sweet sorghum 1 Convntl. Ethanol 1 st G 2 Advanced Ethanol 2 nd G 1 Degraded pasture 2 Existing cropland 3 Marginal/abandoned 4 Grassland 1 Residue boiler CHP 2 Residue boiler + grid electricity 3 Diesel genset 24 Wheat1 Convntl. Ethanol 1 st G 2 Advanced Ethanol 2 nd G 1 Degraded pasture 2 Set-aside 3 Grassland 4 Existing cropland 1 NG boiler + ST 2 NG + grid electricity 3 CCGT 4 Straw boiler + ST 5 Biogas CHP 40 Palm oil1 Convntl. Biodiesel 1 st G 1 Existing cropland 2 Degraded pasture 3 Forest 4 Grassland (Imperata) 1 Oil boiler + ST 2 Oil CHP 3 Residue boiler + ST 12 Soy1 Convntl. Biodiesel 1 st G 1 Grassland 2 Existing cropland 3 Set-aside 4 Forest 1 NG boiler + ST 2 NG + grid electricity 3 CCGT 4 Straw boiler + ST 16

14 Policy scenarios Baseline without any additional bioenergyNO bioshock Bioenergy demand increased by 50% in 2030 compared to baseline50 bioshock REDD, decreasing deforestation emissions by 50/90% in 2020/2030 compared to baselineNO bioshock RED Combination of Bioenergy and REDD50 bioshock RED Two alternative modeling settings without biofuel feedstock trade with biofuel feedstock trade

15 Land use change implications of bioenergy

16 Impact of bioenery demand on land use

17 Land expansion localisation: cropland

18 Impacts of REDD policies

19 Deforestation from cropland expansion

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

21 Importance of trade

22 Mha, based on WEO 2020 targets, If not constrained (e.g. by REDD) important deforestation occurs 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

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

24 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 World biofuel expansion and 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 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

28 Thank you!

29 Additional slides

30 The perfect assessment Space: Including indirect land use effects by budgeting all land categories to achieve global consistency of local action. Time: Integrate benefits of measures over time and allow for the probability of innovative new technologies to occur. Sector: Sector interaction needs to be considered in terms of direct provisioning services such as timber, bioenergy, food and more indirect such as biodiversity, water, cultural heritage. In addition, accounting for market feedback effects such as price increases of agricultural commodities due to bioenergy policy shocks need to be considered. Technology: The full chain of GHG emissions from cradle to grave and production systems need to be assessed with respect to polyproduction. Interaction with the rest of the technosphere and social sphere need to be considered within integrated assessments. Obersteiner, Böttcher et al. accepted COSUST

31 Livestock Production System Approach (14 systems) Model presentation: Livestock (ILRI)

32 Baseline description (1) Variable Source General Population (billion) POLES GDP (USD per Capita) POLES Vegetable calories (kcal per capita) FAO Animal calories (kcal per capita) FAO Bioenergy Biofuels 1st GEN (1000 ktoe final energy) POLES Biomass electricity (+Heat) (1000 ktoe primary energy) POLES Direct Biomass Use (1000ktoe primary energy) POLES Baseline is consistent with POLES energy projection Base year 2000 (determined by land cover information)

33 Baseline description (2) Variable2000Source Source Wood (logs) demand (1000 m3) Demand for sawn wood, pulp wood, other IR FAO GLOBIOM Traditional use (1000 m3) Fuel wood use FAO FAO + GLOBIOM VariableValueSource Protected landWorld Database on Protected Areasareas excludedWDPA Forestry Current rotation lengthto be applied in G4M, Carbine? Rotation maximizing timber supplynot applied Rotation maximizing carbon storagenot applied Deforestation Deforestation ratebased on past dataFAO, national data Deforestation reductionnot applied Degradation ratenot included AfforestationAfforestation ratebased on past dataFAO, national data Biodiversity not constrained

34 34 Current status from GIS database (circa 2000) Projected road network proposed by the African Development Bank (Buys, 2006) Mean accessibility in the region will be reduced from 40 to 23 hours Current road network Planned road network Infrastructure scenarios Further developments

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