petr havlík & michael obersteiner + > 30 collaborators
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Global Perspectives on Agriculture and Forest Mitigation with Emphasis on Induced Land Use Change. Petr Havlík & Michael Obersteiner + > 30 collaborators International Institute for Applied Systems Analysis (IIASA), Austria International Livestock Research Institute (ILRI), Kenya - PowerPoint PPT PresentationTRANSCRIPT
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Petr Havlík & Michael Obersteiner+ >30 collaborators
International Institute for Applied Systems Analysis (IIASA), AustriaInternational Livestock Research Institute (ILRI), Kenya
University of Natural Resources and Applied Life Sciences, Vienna (BOKU), AustriaUniversity of Hamburg, Sustainability and Global Change (FNU), Germany
Soil Science and Conservation Research Institute, Bratislava, Slovakia
Global Perspectives on Agriculture and Forest Mitigation with Emphasis on
Induced Land Use Change
Forestry and Agriculture GHG Modeling Forum, September 27, 2011, West Virginia
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In low stabilization scenarios LULUCF becomes the single most important GHG emitter by mid-century
For efficient mitigation policy design global and comprehensive tools needed
Global - Design of globally consistent national baselines- Accounting for potential international leakage effects
Comprehensive- Capture co-benefits and leakages across sectors
linked through land
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Outline
I. GLOBIOM Presentation
II. LULUCF Assessments
III. For further discussion…
IV. Conclusions
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I. GLOBIOM Presentation
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Global Biosphere Management ModelBasic resolution: 28 regions
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Supply functionsimplicit:production system 1 (grass based) productivity 1 + constant cost 1
production system 2 (mixed) productivity 2 + constant cost 2
Demand functions explicit: linearized non-linear functions
Partial equilibrium model (endogenous prices)Agriculture: major agricultural crops and livestock products
Forestry: traditional forests for sawnwood, and pulp and paper production
Bioenergy: conventional crops and dedicated forest plantations
Recursively dynamic (10 year periods)Maximization of the social welfare (PS + CS)
eqqpp /1)ˆ/(*ˆ
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International trade
Spatial equilibrium modelTrade flows between individual regions (BACI database, CEPII)
Homogeneous goods assumption - Within a region imported and domestically produced goods are valued equally
no mutual trade - Differences in prices between regions are due to external trade costs
Trade costsTrade barriers (MacMap database, ITC/CEPII)
+ Transport cost (Hummels, 2001)+ Calibration
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Output: Production Q - land use (change) - water use- GHG, - other environment (nutrient cycle, biodiversity,…)
Consumption QPricesTrade flows
Main exogenous drivers: PopulationGDPTechnological changeBio-energy demand (POLES team)Diets (FAO, 2006)
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Wood Processing
Bioenergy Processing
Livestock Production
Unmanaged Forest
Managed Forest
Short RotationTree Plantations
Cropland
Grassland
Other Natural Vegetation
Energy products:Ethanol (1st gen.)Biodiesel (1st gen.)Ethanol (2nd gen)MethanolHeat …
Forest products:SawnwoodWoodpulp
Livestock:Cattle meat & milkSheep & Goat meat & milkPork meatPoultry meat & egg
Crops:BarleyCornCotton …
Supply chains
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LandSimulation Units (SimU) = HRU & PX30 & Country zone
Source: Skalský et al. (2008)
Country HRU*PX30
PX5
SimU delineation relatedstatistics on LC classes and
Cropland management systems
reference for geo-coded data on crop management;
input statistical data for LC/LU economic optimization;
LC&LUstat> 200 000 SimU
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EPIC
Rain, Snow, Chemicals
Subsurface FlowSurface
Flow
Below Root Zone
Evaporation and
Transpiration
• Weather• Hydrology• Erosion• Carbon sequestration• Crop growth• Crop rotations• Fertilization• Tillage• Irrigation• Drainage• Pesticide• Grazing• Manure
Processes
Major outputs:Crop yields, Environmental effects (e.g. soil carbon, )
20 crops (>75% of harvested area)4 management systems: High input, Low input, Irrigated, Subsistence
Cropland - EPIC
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Relative Difference in Means (2050/2100) in Wheat Yields[Data: Tyndall, Afi Scenario, simulation model: EPIC]
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Forests – G4MStep 1: Downscaling FAO country level information on
above ground carbon in forests (FRA 2005) to 30 min grid
Source: Kindermann et al. (2008)
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Forests – G4MStep 2: Forest growth functions estimated from yield tables
Major outputs:Mean annual incrementTree sizeSawn wood suitabilityHarvesting cost
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Livestock Production System ApproachLivestock
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Livestock Production System Parameters
Livestock
Input parameters
Cut&Carry
Grains
Stover
Grazing
Occasional
Bovines
Sheep & Goat
Pigs
Poultry
Output parameters
Bovine Milk & Meat
Shoat Milk & Meat
Pig Meat
Poultry Meat & Eggs
CH4Manure
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Herrero, Havlik et al (PNAS forthcoming)
Non-CO2 intensity of milk production
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II. LULUCF Assessments
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Model cluster approach
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G4M - Spatially explicit results
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Recent applied projects (Highlights)DG Climate Action: EU LULUCF Reference Level for Forest
Management accounting Baseline runs for the construction of country specific
Reference Levels Accounting of emissions from FM will compare development
of emissions from forestry against RL- Reviewed by UNFCCC
DG Climate Action: EU Roadmap for moving to a low-carbon economy in 2050
- Contribution to the impact assessment
DECC (UK, Depatment of energy and climate change), DEA (Dannish Energy Agency)
– Global Forestry Emissions Projections and Abatement Costs– Feeding MACCs for forestry activities into GLOCAF model
World Bank: Congo BasinWWF Living Forest ReportPackard Foundation: USA climate policies international leakage
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DO NOTHING scenario – Projected forest area
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DO NOTHING scenario – Projected tropical deforestation
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TARGET
Zero Net Deforestationand Forest
Degradation by 2020(ZNDD)
REDD policy scenario
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Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus
Alternative futures scenarios
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Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus
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Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus
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Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus
Kapos et al. (2008)
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Total land cover change (2010-2050)
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Agricultural commodity prices compared to DO NOTHING
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Agricultural input usecompared to DO NOTHING
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A Roadmap for moving to a competitive low carbon economy in 2050
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GLOBAL – GHG emissions from agriculture and gross deforestation
Global baseline - globally no additional climate action is undertaken up to 2050. The EU implements the climate and energy package but nothing additional is undertaken.
Global Action - global action that leads to a reduction of global emissions of 50% by 2050 compared to 1990
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EU27 – Alternative LULUCF emission pathways
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Packard: International leakage effects of US biofuels policies
Total LUC Fertilizer use Livestock Sub-TOTAL Fossil fuel
replacement TOTAL
BioenScen_50 -32 1769 1478 3182 -266 2916BioenScen_75 -30 1792 1446 3178 -396 2781BioenScen_100 -24 1830 1440 3223 -531 2692BioenScen_125 8 1889 1432 3337 -668 2669BioenScen_150 12 1956 1432 3412 -810 2602
US cumulative GHG emissions from agriculture and LUC over 2000-2050 [MtCO2eq]
preliminary results
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Total LUC Fertilizer use Livestock Sub-TOTAL Fossil fuel
replacement TOTAL
BioenScen_50 11556 16785 20278 48619 -1329 47290BioenScen_75 11666 16832 20315 48814 -1476 47338BioenScen_100 11745 16903 20322 48970 -1628 47342BioenScen_125 11859 16973 20336 49167 -1781 47386BioenScen_150 11985 17077 20360 49422 -1941 47482
Packard: International leakage effects of US biofuels policies
World cumulative GHG emissions from agriculture and LUC over 2000-2050 [MtCO2eq]
preliminary results
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III. For further discussion…
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W. Africa 1966 – pastoral system 2004 – crop-livestock system
Courtesy of B. Gerard
What is the potential contribution of LPS changeto food security, land sparing and GHG reduction?
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Total abatement calorie cost (TACC) curves for different policy options by 2030
Herrero, Havlik et al (PNAS forthcoming)
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IV. CONCLUSIONS
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Bottom-up modeling of global agriculture and forestry sectors becoming feasible through integration of economic and bio-physical models
REDD still appears as the low hanging fruit
Sustainable intensification can provide benefits in terms of food security, reduced LUC, and GHG emissions
Not only intensification but also reduction of yield volatility can act as land sparing measure in view of extreme weather events
Large uncertainties in very basic datasets need to be properly handled…