If you dont like my assumptions IIASA GGI Land use Scenarios - - PDF document

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If you dont like my assumptions IIASA GGI Land use Scenarios - - PDF document

Jul 13, 2023 168 likes •743 views

If you dont like my assumptions IIASA GGI Land use Scenarios Michael Obersteiner EMF22, Tsukuba, 11-13 th Dec06, The IIASA-GGI Model Framework Agriculture AEZ-BLS-MESSAGE Forestry DIMA-MESSAGE Future and

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If you don’t like my assumptions…

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IIASA GGI Land use Scenarios

Michael Obersteiner EMF22, Tsukuba, 11-13th Dec06,

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  • The IIASA-GGI Model Framework
  • Agriculture

– AEZ-BLS-MESSAGE

  • Forestry

– DIMA-MESSAGE

  • Future and Current Work
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Scenario Storyline

  • Economic development
  • Demographic change
  • Technological change
  • Policies

Population Projections Economic Projections DIMA

Forest Management Model

AEZ-BLS

Agricultural Modeling Framework

Downscaling Tools

Spatially explicit (and national) projections of economic and demographic growth

MESSAGE-MACRO

Systems Engineering / Macro- Economic Modeling Framework (all GHGs and all sectors) Regional population & economic projections

Endogenous Climate Model

National, regional & spatially explicit socio-economic drivers Spatially explicit socio- economic drivers Consistency of land-cover changes (spatially explicit maps of agricultural, urban, and forest land)

Potential and costs

  • f forest bioenergy

and sinks Carbon and biomass price

Feedbacks

Agricultural bioenergy potentials and costs Drivers for land-use related non-CO2 emissions

Feedbacks

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Multigas Stabilization Portfolios

670 ppmV-eq

5 10 15 20 25 30 35 40 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Annual GHG emissions, GtC eq.

A2r A2r-670

1 9 9 2 1 2 3 2 5 2 7 2 9 Energy conservation and efficiency improvement Switch to natural gas Fossil CCS Nuclear Biomass (incl. CCS) Other renewables Sinks CH4 N2O F-gases CO2 5 10 15 20 25 30 35 40 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Annual GHG emissions, GtC eq.

B2 B2-670

5 10 15 20 25 30 35 40 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Annual GHG emissions, GtC eq.

B1 B1-670 Source: Riahi et al. 2006

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Develop a flexible method for generating spatially detailed scenarios of land use, which:

  • are consistent with IIASA-GGI scenario context,
  • make best use of available global data sets,
  • respect quality and distribution of ecosystems and of

land resources,

  • reproduce base-year land use distribution
  • geographic explicit in order to link to climate models

Land Use Land Use Modelling Modelling

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Agriculture – AEZ/BLS

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1 1 2 2 3 3 4 4 5 5 6 6

Source: Tubiello and Fischer, 2006

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Spatial Spatial Distribution and Distribution and Intensity Intensity ( (percent percent) )

  • f
  • f Cultivated

Cultivated Land, Land, year year 2000 2000

0.00 4.17 8.33 12.50 16.67 20.83 25.00 29.17 33.33 37.50 41.67 45.83 50.00 54.17 58.33 62.50 66.67 70.83 75.00 79.17 83.33 87.50 91.67 95.83 100.00

Note: calibration of GLC2000 class weights starts from estimated reference weights and is based on an iterative scheme to match national / sub-national statistics of year 2000 (FAO AT2015/2030 adjusted cultivated land).

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Built-up land % dynamics 2010-2100, B2

Fischer et al. 2006

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Cropland % dynamics 2010-2100, B2

AEZ/BLS (Fischer et al. 2006)

Fischer et al. 2006

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0.00 6.25 12.50 18.75 25.00 31.25 37.50 43.75 50.00 56.25 62.50 68.75 75.00 81.25 87.50 93.75 100.00 0.00 6.25 12.50 18.75 25.00 31.25 37.50 43.75 50.00 56.25 62.50 68.75 75.00 81.25 87.50 93.75 100.00

2000 2080

Percent of cultivated land in grid cell, scenario A2, 2000 Percent of cultivated land in grid cell, scenario A2, 2080

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Food and Agriculture Development

  • 1. Cereal production,

GGI scenario A2r, 1990 to 2080

  • 2. Pork & poultry production,

GGI scenario A2r, 1990 to 2080

Source: LUC World food system simulations of GGI scenarios, IIASA (2005).

500 1000 1500 2000 2500 3000 3500 4000 4500 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 mln tons ROW+NES PAS SAS CPA MEA LAM AFR EEU+FSU PAO WEU NAM 0.0 10.0 20.0 30.0 40.0 50.0 60.0 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 mln tons protein ROW+NES PAS SAS CPA MEA LAM AFR EEU+FSU PAO WEU NAM

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Do you see the mitigation signal!?

Source: Tubiello and Fischer, 2006

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Wheat is more sensitive!

Source: Tubiello and Fischer, 2006

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Action is in 2nd half of 21st century

Source: Tubiello and Fischer, 2006

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LDC most vulnerable – biophysically!!

Source: Tubiello and Fischer, 2006

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LDCs would benefit most from Mitigation

Source: Tubiello and Fischer, 2006

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Additional Millions at hunger almost eliminated by mitigation!

Source: Tubiello and Fischer, 2006

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WHEAT MAIZE MILLET, SORGHUM

Undefined > 25 % 5 to 25 %

  • 5 to 5 %
  • 25 to -5 %

< -25 % Not suitable Water

ALL CEREALS

Source: Tubiello and Fischer, 2006

Changes in Rain-fed Cereal Potential

Reference climate vs climate of 2080s HadCM3-A2 Scenario

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Agriculture: Summary of Results Agriculture: Summary of Results

Additional food/feed required: cereal production to increase from 2.1 bln tons in year 2000 by 1.6 – 2.1 bln tons in 2080. Fertilizer use: from 83 mln tons nitrogen in 2000 increase by 100 – 150 mln tons N in 2080. Energy from crop residues: accounting for feed use, estimated bioenergy available from crop residues increases from 18.7 PJ in 2000 to 34.9 PJ in 2080, scenario A2r. Cultivated land in food production (hence not available for energy crops): from 1.5 bln ha in year 2000 to 1.8 bln ha in 2080, scenario A2r. Impacts of climate change on agriculture and food security are substantial (LDC); mitigation to 550 ppm atmospheric CO2 would avoid 75-100% of damage.

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DIMA: Fibre, Ligno-Cell Bioenergy and Sinks

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Scenario Storyline

  • Economic development
  • Demographic change
  • Technological change
  • Policies

Population Projections Economic Projections DIMA

Forest Management Model

AEZ-BLS

Agricultural Modeling Framework

Downscaling Tools

Spatially explicit (and national) projections of economic and demographic growth

MESSAGE-MACRO

Systems Engineering / Macro- Economic Modeling Framework (all GHGs and all sectors) Regional population & economic projections

Endogenous Climate Model

National, regional & spatially explicit socio-economic drivers Spatially explicit socio- economic drivers Consistency of land-cover changes (spatially explicit maps of agricultural, urban, and forest land)

Potential and costs

  • f forest bioenergy

and sinks Carbon and biomass price

Feedbacks

Agricultural bioenergy potentials and costs Drivers for land-use related non-CO2 emissions

Feedbacks

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Bioenergy and Sinks/GHG Modeling Framework

Multigas-MESSAGE Systems Engineering I A-Model Exogenous drivers for CH4 & N2O emissions: N-Fertilizer use, Rice production, Bovine Livestock Bottom-up mitigation technologies for non-CO2 emissions, Black carbon and

  • rganic carbon

emissions coefficients

Forest Sinks Potential, FSU

50 100 150 200 250 300 350 100 200 300 400 500 600 700 800 Rate of carbon sequestration MTC Increase in Prices 2100 2000 2050

Data Sources :Obersteiner & Rokityanskiy, FOR Data Sources: Fischer & Tubiello,LUC Data Sources:USEPA, EMF-21 Data Sources: Bond; Klimont & Kupiano, TAP

Agricultural residue potentials 1000 2000 3000 4000 5000 6000 7000 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 P J NAM WEU PAO FSU EEU AFR LAM MEA CPA SAS PAS

Data sources: Fischer & Tubiello, LUC Data Sources: Obersteiner & Rokityanskiy, FOR; Tubiello & Fischer, LUC

Biomass supply A2:WEU

2 4 6 8 10 12 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 2 1 Bioenergy potential (EJ)

  • Ag. residues

Biomass from forests 1$/GJ 6$/GJ 4$/GJ 5$/GJ 3$/GJ

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DIMA Model

  • Grid-based Decision Making

– Afforest, Maintain and/or Manage, Deforest – Exogenous prices – BLS cropland expansion – Wood products pool – Site Productivity – ….

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Spatial Spatial Distribution Distribution of

  • f Forests

Forests, , year year 2000 2000

0.00 4.17 8.33 12.50 16.67 20.83 25.00 29.17 33.33 37.50 41.67 45.83 50.00 54.17 58.33 62.50 66.67 70.83 75.00 79.17 83.33 87.50 91.67 95.83 100.00

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Four Global Land Cover Sets

The global level of agreement among the four datasets using complete IGBP classification

McCallum et al. 2006

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NPP Map

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Management Intensity: Human Activity Map

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Cumulative biomass production (EJ/grid) for bioenergy between 2000 and 2100 at the energy price supplied by MESSAGE based on the revised IPCC SRES A2r scenario (country investment risk excluded).

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Biomass supply

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Financials of Biomass Supply Financials of Biomass Supply

Bioenergy Expenditures

100 200 300 400 500 600 700 800 2000 2020 2050 2100 billion $ (1990) A2 Baseline A2 Stabilization

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Bioenergy: B2 Baseline 2050

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Bioenergy: B2 480ppmv 2050

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Sink Development

M

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Avoided Deforestation Scenario Cutting 50% by 2025

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  • Compensated Reduction

6 $/tC/5years

  • Carbon Tax

12 $/tC

  • 9 $/tC if slash-burn
  • 25 $/tC if forest products
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Geography Avoided Deforestation

MtC/Grid 21

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Global Supply Schedule of Avoided Deforestation to 2025

10 20 30 40 50 60 70 80 90 100 0.2 0.4 0.6 0.8 1 1.2 1.4

Avoided Deforestation [Gt/Year] Carbon Price [$/tC]

Burn Product

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Scenario Results

  • Forest conversion (virgin => management)

– <~800 Mha A2rM, 20% of forest area

  • Rural Development (mainly tropical belt)

– Bioenergy market

  • 780 billion/a in A2rM (24% due to climate policy)

– Carbon Credit Transfers

  • 245 billion/a in A2rM
  • Biodiversity co-benefits through avoided

deforestation

Computation: IIASA GGI/INSEA DIMA model

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Conclusion from IIASA GGI scenarios

  • Bioenergy, Sinks and Mitigation are conducive to

Rural Development and improved Land Management

  • Agriculture

– Mitigation benefits

  • Geography matters
  • Small aggregate but large LDC benefit
  • Sinks and Ligno-cellulosic Bioenergy

– Large Potentials and Key Mitigation technology – Operated on Marginal Lands and Existing Forest

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Current Work: Economies of Scale in Biofuel Production

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Biomass supply: Costs for Baden- Württemberg

Average cost (EUR/t, Baden-Würrtemberg) 10 20 30 40 50 60 70 Extensive Robusta Poplar Poplar coppice (unfert.) Poplar coppice (fert.) Miscanthus (low fert.) Miscanthus (high fert.)

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Methanol from Poplar: 10% Car Fleet, 8,3% Arable Land, 25ha Plantation / 100ha

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Actual Sugar Cane Yields Actual Sugar Cane Yields

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Sugar Cane Potential Sugar Cane Potential

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Cost of Ethanol Production Cost of Ethanol Production

~ 0.26 €/l

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Productivity 200 ton / ha Production 2.1 billion tons Planted Area 10.5 million ha African Arable Land Share 5.3 % Equivalent Ethanol Productivity 15,000 L / ha Ethanol Production 78.8 billion liters 1.4 EJ Africa's Fuel Consumption 1.37 EJ Share of world ethanol Consumption 156 % Share of world fuel Consumption 1.85 %

Africa Self Sufficient ! Africa Self Sufficient !

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Actual Yields vs Potential

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GEPIC Outputs: global map of wheat yield

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crop water productivity (Yield/ET) of wheat

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Ireland UK Denmark Sweden Netherlands Belgium Germany France Rwanda Zimbabwe Namibia Zambia Egypt R2 = 0.8788 0.5 1 1.5 2 2.5 3 2000 4000 6000 8000 10000 Yield (kg ha-1) Crop Water Productivity (kg m -3) Asia Europe North America South America Africa Oceania Ireland UK Denmark Sweden Eritrea Chad Sudan Mali R2 = 0.7859 0.5 1 1.5 2 2.5 3 2000 4000 6000 8000 10000 12000 14000 Yield (kg ha-1) Crop Water Productivity (kg m

  • 3)

Asia Europe North America South America Africa Oceania

Potential yield and yield gap at a global scale

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Future

  • Daily Climate for EPIC and DIMA
  • DIMA

– Attack the 8-9GtC/year of “Disturbances” – Avoided Deforestation

  • Competition over land with FASOM
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