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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 , 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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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


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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 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


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Effective mitigation

Obersteiner, Böttcher et al. accepted COSUST


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High hopes


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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


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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


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GLOBIOM: Global Biomass Optimisation Model

Integrated land-use and bioenergy modelling

World divided into 28 regions

Havlik et al. 2010 Energy Policy


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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


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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


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Model description: EPIC - Yields

Yields

Emissions

Carbon stock


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Model description: Forest plantations

Productivity distribution

Productivity [m3/ha]

Area [Mha]


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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


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Detailed bioenergy chains (not yet fully implemented)

Castillo et al. 2010, see poster


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Policy scenarios

  • Baseline without any additional bioenergyNO bioshock

  • Bioenergy demand increased by 50% in 2030 compared to baseline50 bioshock

  • REDD, decreasing deforestation emissions by50/90% 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


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Land use change implications of bioenergy


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Impact of bioenerydemand on land use


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Land expansion localisation: cropland


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Impacts of REDD policies


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Deforestation from cropland expansion


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Expansion into other land

Forest saved

Reduced cropland expansion

Effect of REDD policydifference between bioenergy and bioenergy+REDD scenario


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Importance of trade


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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


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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


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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


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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?


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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


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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


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Thank you!

bottcher@iiasa.ac.at


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