Integrated Assessment PHOENIX - Land-use Modeling and Global Warming - - PowerPoint PPT Presentation

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Integrated Assessment PHOENIX - Land-use Modeling and Global Warming - - PowerPoint PPT Presentation

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Energy Modeling Forum (EMF) 22: Climate Policy Scenarios for Stabilization and In Transition December 12-14, 2006, Tsukuba Integrated Assessment PHOENIX - Land-use Modeling and Global Warming Impacts on Agriculture - Keigo Akimoto, Shunsuke

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Integrated Assessment PHOENIX

  • Land-use Modeling and Global Warming

Impacts on Agriculture -

Keigo Akimoto, Shunsuke Mori and Toshimasa Tomoda

Systems Analysis Group Research Institute of Innovative Technology for the Earth (RITE) Energy Modeling Forum (EMF) 22: Climate Policy Scenarios for Stabilization and In Transition December 12-14, 2006, Tsukuba

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RITE’s Study for Climate Change Assessment

  • Post-Kyoto frameworks
  • Assessments of Regional and

sectoral frameworks, e.g. APP

  • Transition scenarios
  • DNE21+ Model: -Y2030/2050

77 world regions; detailed bottom-up energy modeling

  • DEARS Model: -Y2050

18 world regions; 18 non-energy sectors (GTAP base); bottom-up energy modeling

Others

  • DNE21-ITC model

4 world regions

  • Country model for Japan

focusing particularly on CCS

PHOENIX

  • Addressing the Article 2
  • Stabilization scenarios

Integrated assessment

  • DNE21 Model: -Y2200

10 world regions

  • Non-CO2 GHG Models
  • Climate change model

(MAGICC+GCM results)

  • Global warming impact models
  • Water resources
  • Agriculture (GAEZ base)
  • Human health
  • Biodiversity (Biome base)
  • Sea level rise
  • Land use models
  • GLUE Model: bioenergy pot.
  • Forestation pot. estim. model
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Land-use Model – GLUE (1/2)

18 18 world divided regions world divided regions Biomass flows considered in GLUE

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Land-use Model – GLUE (2/2)

Estimation procedures for bioenergy potentials in GLUE

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Estimated Bioenergy Supply Potentials by Region

500 1000 1500 2000 2500 3000 3500 4000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 YEAR Bioenergy supply potential of dry biomass residues (Mtoe/year)

Other world Australia & New Zealand Turkey & Middle East ASEAN & Korea India China Japan South African Central African North Africa Former Soviet Union Eastern Europe Western Europe Rest of South America Brazil Mexico & Central America Canada USA

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Estimated Potentials of Bioenergy Supply in 2050

100 200 300 400 500 600

USA Canada Mexico & Central America Brazil Rest of South America Western Europe Eastern Europe Former Soviet Union North Africa Central African South African Japan China India ASEAN & Korea Turkey & Middle East Australia & New Zealand Other world

Region Bioenergy supply potential of dry biomass residues in Y2050 (Mtoe/year) Bagasse Sugarcane harvestingresidues Cereal harvesting residues Timber scrap Paper scrap Sawmill residues Black liquor Fuelwood harvesting residues Industrial roundwood harvesting residues

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Estimations of Carbon Sequestration Potential by Afforestation/Rehabilitation

0 - 100 100 - 200 200 - 300 300 - 400 400 - 500 500 - 600 600 - 700 700 - 800 800 - 900 900 -1000 1000 -1100 1100 -1200 1200 -1300 1300 -1400 1400 -1500 1500 -1600 1600 -1700 1700 -1800 1800 -1900 1900 -2000 2000 -2100 2100 -2200 2200 -2300 2300 -2400 2400 -2500 2500 -2600 2600 -2700 2700 -2800 2800 -2900 2900 -3000 500-1000 400-500 300-400 150-300 50-150 25-50 12.5-25 5-12.5 2.5-5 0-2.5

面積 (百万ha)

年間降水量 (mm/yr) 単位面積当りのバイオマス資源 (ton/ha)

450-480 420-450 390-420 360-390 330-360 300-330 270-300 240-270 210-240 180-210 150-180 120-150 90-120 60-90 30-60 0-30

Precipitation (mm/yr) Area (Mha) Phytomass stocks (ton/ha)

100 200 300 400 500 600 500 1000 1500 2000 2500 3000 Annual precipitation (mm/yr) Averaged stocks of phytomass (ton/ha)

  • The area having the stock under the averaged

stock for each precipitation level is assumed to achieve the increase in the stock up to the averaged one by afforestation/rehabilitation.

  • Land use, soil types, slope, temperature

conditions are also considered for the estimation.

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Estimated Carbon Sequestration Potential by Afforestation/Rehabilitation in 1990

The global potentials of carbon sequestration: 170 The global potentials of carbon sequestration: 170 GtC GtC

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5000 10000 15000 20000 2000 2010 2020 2030 2040 2050 Year CO2 emissions & reductions (MtC/yr) Energy Saving Fuel Switching among Fossil Fuels Nuclear Power Hydro Wind Photovoltaics Bioenergy Forestation CO2 Seq - Oil Well (EOR) CO2 Seq - Depleted Gas Well CO2 Seq - Deep Saline Aquifer CO2 Seq - Coalbed (ECBM) Net Emission Net emission in Reference Case Net emission for the stabilization at 550 ppmv 5000 10000 15000 20000 2000 2010 2020 2030 2040 2050 Year CO2 emissions & reductions (MtC/yr) Energy Saving Fuel Switching among Fossil Fuels Nuclear Power Hydro Wind Photovoltaics Bioenergy Forestation CO2 Seq - Oil Well (EOR) CO2 Seq - Depleted Gas Well CO2 Seq - Deep Saline Aquifer CO2 Seq - Coalbed (ECBM) CO2 Seq - Ocean Net Emission Net emission in Reference Case Net emission for the stabilization at 550 ppmv

Without ET

Annex I: 60% reduction in 2050

Cost-effective Options for Emission Reductions at 550 ppmv by Using DNE21+ Model

With ET

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PHOENIX Project

♦ PHOENIX: Pathways toward Harmony Of Environment,

Natural resources and Industry compleX

♦ Integrated assessment of global warming impacts,

adaptations and mitigations

♦ Addressing the ultimate target of Article 2 of UNFCCC

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Assessment Procedure in PHOENIX

Reference emission pathways Tolerable emission pathways

(Long-term target)

  • Eval. of climate change
  • Eval. of mitigation measures
  • Eval. of impacts
  • Eval. of adapt. measures

Comprehensive Assess.

Type II events*; prevented to occur

(Precaution approach)

Expert judgment (Finally, based on world wide agreement)

Emission to be suppressed until catastrophic events do not occur regardless of mitigation costs

Type I events Using a high CS value Using a medium CS value

Emission to be suppressed considering mitigation costs, vulnerable regions etc.

(No climate policy) * Type II: abrupt and catastrophic events (THC, WAIS etc.)

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The Clim ate Model

  • I ntegration of SCM and the results of AOGCM -

CO Emission

2

SOx Emission Methane Emission N O Emission

2

Halocarbons (27 types) Emissions Atmospheric Concentration CO2

Oceans

Land Surface Radiative Forcing of CO2 Methane Concentration N O Concentration

2

Halocarbons (27 types) Concentration Radiative Forcing

  • f Methane

Radiative Forcing of N O

2

Radiative Forcing

  • f Halocarbons

(27 types) Radiative Forcing of SOx Radiative Forcing of H O

2

Radiative Forcing of Ozone Global & Annual Mean Temperature Rise Sea Level Change

Total Radiative Forcing

Monthly average temperature by grid

. . . . .

Monthly average precipitation by grid

AOGCM Results (grid data)

SCM: MAGICC base ECHAM4, MIROC etc.

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1 2 3 4 5 6 2000 2020 2040 2060 2080 2100 2120 2140 2160 2180 2200 Year Global mean temperature change from the pre-industrial level (degC) SRES B2-base WGI S650 (Non-CO2 GHG: B2) WGI S550 (Non-CO2 GHG: B2) WGI S450 (Non-CO2 GHG: B2)

Global mean temperature change Atmospheric CO2 concentration

Climate sensitivity: 2.5 ºC

300 500 700 900 1100 2000 2020 2040 2060 2080 2100 2120 2140 2160 2180 2200 Year Atmospheric CO2 concentration (ppmv) SRES B2-base WGI S650 WGI S550 WGI S450

CO2 Concentration & Tem perature Change

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Annual Mean Tem perature Change in 2 1 5 0

Global mean temperature change +4.2 ºC from pre-industrial levels Global mean temperature change +2.7 ºC from pre-industrial levels

SRES B2-base Reference IPCC WGI S550 GCM results: ECHAM4

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Annual Mean Precipitation Change in 2 1 5 0

SRES B2-base Reference IPCC WGI S550 GCM results: ECHAM4

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Overview of Crop Potential Model

♦ The model is based on the GAEZ (Global Agro-ecological

Zones) framework developed by IIASA/FAO.

♦ Crop production potentials are estimated by matching

between climate, soil condition etc. and characteristics of crops.

♦ AEZ has a detailed database of crop characteristics. ♦ AEZ provides the Leaf area index (LAI) and harvest index

depending on the agriculture input levels.

♦ Consideration of the productivity increase (LAI and harvest

index) of agriculture depending on economic levels

♦ Maximizing the production potentials considering the

changes in implantation crops and month, which can evaluate the adaptation effects for global warming

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Estim ation Procedure of Production Estim ation Procedure of Production Potentials of Crops Potentials of Crops

Elevation Potential Evapotranspiration Monthly average Temperature From Climate Model Monthly average precipitation Monthly average wind speed Soils Terrain slopes Max temperature Min temperature Crop yields potentials Crop characteristics

Historical monthly Max/Min temperature Historical monthly averagecloud cover

Actual Evapotranspiration

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SRES B2-base Reference IPCC WGI S550

Change in Production Potential

  • f W heat in 2 1 5 0
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Optim al I m plantation Month of W heat

e.g., The optimal implantation month shifts from April-May in 1990 to January- February in 2150 Year 2150: IPCC WGI S550 Year 1990 Year 2150: SRES B2-base Reference

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IPCC WGI S550 SRES B2-base Reference

Change in Production Potential

  • f Rice in 2 1 5 0
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Optim al I m plantation Month of Rice

Year 2150: IPCC WGI S550 Year 1990 Year 2150: SRES B2-base Reference e.g., The optimal implantation month shifts from April in 1990 to March in 2150

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Change in Production Potential

  • f W heat from 1 9 9 0
  • 60%
  • 50%
  • 40%
  • 30%
  • 20%
  • 10%

0% 10% 20% 30% 40% 50%

2050 2150

Change in production potential/per-capita production potential of wheat from 1990 levels

Reference WGI S650 WGI S550 WGI S450

Change in production potentials of wheat Change in production potentials of wheat Change in per-capita production potentials of wheat Change in per-capita production potentials of wheat

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Change in Production Potential

  • f Rice from 1 9 9 0
  • 40%
  • 30%
  • 20%
  • 10%

0% 10% 20% 30% 40% 50% 60% 70%

2050 2150

Change in production potential/per-capita production potential of rice from 1990 levels

Reference WGI S650 WGI S550 WGI S450

Change in production potentials of rice Change in production potentials of rice Change in per-capita production potentials of rice Change in per-capita production potentials of rice

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Effects of I ncrease in Crop Productivity

  • 30%
  • 20%
  • 10%

0% 10% 20% 30% 40% 50% 60% 70% Change in production potential of wheat from 1990 levels

2050 2150

No consideration

  • f productivity increase

Under consideration

  • f productivity increase
  • 30%
  • 20%
  • 10%

0% 10% 20% 30% 40% 50% 60% 70% Change in production potential of wheat from 1990 levels

Reference WGI S650 WGI S550 WGI S450

2050 2150

No consideration

  • f productivity increase

Under consideration

  • f productivity increase

Wheat Rice

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Alternative Socio-Economic Scenarios for Sensitivity Analysis

Temperature Change

1 2 3 4 5 6 7 2000 2025 2050 2075 2100 2125 2150 Year Global mean temperature change from the pre-industrial level (°C) SRES A1FI-base SRES B2-base WGI S650 (Non-CO2 GHG: B2) WGI S550 (Non-CO2 GHG: B2) WGI S450 (Non-CO2 GHG: B2)

2000 4000 6000 8000 10000 12000 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120 2130 2140 2150 Year World population (million people)

SRES-A1 SRES-B2

OECD/IEA Statistics (World Bank) DNE21 - Scenario (IPCC SRES) 200000 400000 600000 800000 1000000 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120 2130 2140 2150 Year Gross world products (billion US

90$)

SRES-A1 SRES-B2 OECD/IEA Statistics (World Bank) DNE21 - Scenario (IPCC SRES)

Population Assumptions World GDP

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Sensitivity to Socio-Economic Conditions

  • 50%
  • 40%
  • 30%
  • 20%
  • 10%

0% 10% 20% 30% 40%

2050 2150

Change in production potential/per-capita production potential of wheat from 1990 levels

SRES B2-base Reference SRES A1FI-base Reference

Change in production potentials of wheat Change in per-capita production potentials of wheat

  • Wheat -

However, the potential production in A1FI will be decrease in 2150, due to large temperature rise. Although large temperature rise is estimated in A1FI, the decrease in the per-capita potential productivity is smaller than in B2, due to a smaller population assumption in A1FI. Higher economic growth and improvements of agriculture productivity are assumed in A1FI than in B2, and therefore, the potential production is larger than in B2 instead of larger temperature rise.

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Final Rem arks Final Rem arks

♦ PHOENIX is conducting consistent assessments for different

levels of stabilization scenarios.

♦ Bioenergy and forestation potentials are evaluated. ♦ Global warming impacts on potential productions of crops are

also evaluated.

♦ However, Socio-economic conditions would be more influential

  • n crop production potentials than stabilization levels.

♦ Harder linkages among sea level rise, water resources,

agriculture, bioenergy supply potentials, forestation potentials, socio-economic estimates etc. are needed.

♦ The linkage between DEARS model (using GTAP database) and

global warming impacts on agriculture is also an important future work.