land-use modeling and global warming impacts on …...integrated assessment phoenix - land-use...

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 TransitionDecember 12-14, 2006, Tsukuba

RITE’s Study for Climate Change Assessment

- Post-Kyoto frameworks- Assessments of Regional and sectoral frameworks, e.g. APP

- Transition scenarios

- DNE21+ Model: -Y2030/205077 world regions; detailed bottom-up energy modeling

- DEARS Model: -Y205018 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: -Y220010 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

Land-use Model – GLUE (1/2)

18 18 world divided regionsworld divided regions

Biomass flows considered in GLUE

Land-use Model – GLUE (2/2)Estimation procedures for bioenergy potentials in GLUE

Estimated Bioenergy Supply Potentialsby Region

0

500

1000

1500

2000

2500

3000

3500

400020

05

2010

2015

2020

2025

2030

2035

2040

2045

2050

YEAR

Bio

ener

gy s

uppl

y po

tent

ial o

f dry

biom

ass

resi

dues

(Mto

e/ye

ar)

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

Estimated Potentials of Bioenergy Supply in 2050

0

100

200

300

400

500

600 U

SA

Can

ada

Mex

ico

& C

entra

l Am

eric

a

Bra

zil

Res

t of S

outh

Am

eric

a

Wes

tern

Eur

ope

Eas

tern

Eur

ope

For

mer

Sov

iet U

nion

Nor

th A

frica

Cen

tral A

frica

n

Sou

th A

frica

n

Jap

an

Chi

na

Indi

a

AS

EAN

& K

orea

Tur

key

& M

iddl

e E

ast

Aus

tralia

& N

ew Z

eala

nd

Oth

er w

orld

Region

Bio

ener

gy s

uppl

y po

tent

ial o

f dry

bio

mas

sre

sidu

es in

Y20

50 (M

toe/

year

)

Bagasse

SugarcaneharvestingresiduesCereal harvestingresiduesTimber scrap

Paper scrap

Sawmill residues

Black liquor

Fuelwood harvestingresiduesIndustrial roundwoodharvesting residues

Estimations of Carbon Sequestration Potential by Afforestation/Rehabilitation

0 - 1

00

100

- 200

200

- 300

300

- 400

400

- 500

500

- 600

600

- 700

700

- 800

800

- 900

900

-100

0

1000

-110

0

1100

-120

0

1200

-130

0

1300

-140

0

1400

-150

0

1500

-160

0

1600

-170

0

1700

-180

0

1800

-190

0

1900

-200

0

2000

-210

0

2100

-220

0

2200

-230

0

2300

-240

0

2400

-250

0

2500

-260

0

2600

-270

0

2700

-280

0

2800

-290

0

2900

-300

0

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

420-450390-420360-390330-360300-330270-300240-270210-240180-210150-180120-15090-12060-9030-600-30

Precipitation (mm/yr)

Area (Mha)

Phyt

omas

s st

ocks

(ton

/ha)

0

100

200

300

400

500

600

0 500 1000 1500 2000 2500 3000

Annual precipitation (mm/yr)

Aver

aged

sto

cks

of p

hyto

mas

s (to

n/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.

Estimated Carbon Sequestration Potential by Afforestation/Rehabilitation in 1990

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

0

5000

10000

15000

20000

2000 2010 2020 2030 2040 2050Year

CO

2 em

issi

ons

& re

duct

ions

(MtC

/yr) Energy Saving

Fuel Switching among Fossil FuelsNuclear PowerHydroWindPhotovoltaicsBioenergyForestationCO2 Seq - Oil Well (EOR)CO2 Seq - Depleted Gas WellCO2 Seq - Deep Saline AquiferCO2 Seq - Coalbed (ECBM)Net Emission

Net emission in Reference Case

Net emission for the stabilization at 550 ppmv

0

5000

10000

15000

20000

2000 2010 2020 2030 2040 2050Year

CO

2 em

issi

ons

& re

duct

ions

(MtC

/yr) Energy Saving

Fuel Switching among Fossil FuelsNuclear PowerHydroWindPhotovoltaicsBioenergyForestationCO2 Seq - Oil Well (EOR)CO2 Seq - Depleted Gas WellCO2 Seq - Deep Saline AquiferCO2 Seq - Coalbed (ECBM)CO2 Seq - OceanNet Emission

Net emission in Reference Case

Net emission for the stabilization at 550 ppmv

Without ETAnnex I: 60%

reduction in 2050

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

With ET

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

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.)

The Climate Model- Integration of SCM and the results of AOGCM -

CO Emission2

SOx Emission

MethaneEmission

N O Emission2

Halocarbons(27 types)Emissions

Atmospheric Concentration

CO2

Oceans LandSurface

RadiativeForcing of CO2

MethaneConcentration

N OConcentration

2

Halocarbons (27 types)

Concentration

Radiative Forcingof Methane

RadiativeForcing of N O2

Radiative Forcingof Halocarbons

(27 types)

RadiativeForcing of SOx

RadiativeForcing of H O2

RadiativeForcing of Ozone

Global & Annual MeanTemperature

Rise

Sea LevelChange

Total RadiativeForcing

Monthly averagetemperature

by grid. . . . .

Monthly averageprecipitation

by grid

AOGCM Results (grid data)

SCM: MAGICC base

ECHAM4, MIROC etc.

0

1

2

3

4

5

6

2000 2020 2040 2060 2080 2100 2120 2140 2160 2180 2200

Year

Glo

bal m

ean

tem

pera

ture

cha

nge

from

the

pre-

indu

stria

l leve

l (de

gC)

SRES B2-base

WGI S650 (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

Atm

osph

eric

CO

2 co

ncen

tratio

n (p

pmv) SRES B2-base

WGI S650

WGI S550

WGI S450

CO2 Concentration & Temperature Change

Annual Mean Temperature Change in 2150

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

Annual Mean Precipitation Change in 2150


IPCC WGI S550

GCM results: ECHAM4

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

Estimation Procedure of Production Estimation Procedure of Production Potentials of CropsPotentials of Crops

Elevation

PotentialEvapotranspirationMonthly average

Temperature

From Climate Model

Monthly averageprecipitation

Monthly averagewind speed

Soils

Terrain slopes

Max temperature

Min temperature

Crop yieldspotentials

Crop characteristics

Historical monthlyMax/Min temperature

Historical monthly averagecloud cover

ActualEvapotranspiration


IPCC WGI S550

Change in Production Potentialof Wheat in 2150

Optimal Implantation Month of Wheat

e.g.,The optimal implantation month shifts from April-May in 1990 to January-February in 2150

Year 2150: IPCC WGI S550Year 1990

Year 2150: SRES B2-base Reference

IPCC WGI S550


Change in Production Potential of Rice in 2150

Optimal Implantation Month of Rice

Year 2150: IPCC WGI S550Year 1990

Year 2150: SRES B2-base Referencee.g.,The optimal implantation month shifts from April in 1990 to March in 2150

Change in Production Potential of Wheat from 1990

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

2050 2150

Cha

nge

in p

rodu

ctio

n po

tent

ial/p

er-c

apita

pro

duct

ion

pote

ntia

l of

whe

at fr

om 1

990

leve

ls Reference

WGI S650

WGI S550

WGI S450

Change in productionpotentials of wheat


Change in per-capitaproduction potentials of wheat


Change in Production Potential of Rice from 1990

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

2050 2150

Cha

nge

in p

rodu

ctio

n po

tent

ial/p

er-c

apita

pro

duct

ion

pote

ntia

l of r

ice

from

199

0 le

vels

Reference

WGI S650

WGI S550

WGI S450

Change in productionpotentials of rice

Change in productionpotentials of rice

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

production potentials of rice

Effects of Increase in Crop Productivity

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

Cha

nge

in p

rodu

ctio

n po

tent

ial o

f whe

at fr

om 1

990

leve

ls

2050 2150

No considerationof productivity increase

Under considerationof productivity increase

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

Cha

nge

in p

rodu

ctio

n po

tent

ial o

f whe

at fr

om 1

990

leve

ls

Reference

WGI S650

WGI S550

WGI S450

2050 2150

No considerationof productivity increase

Under considerationof productivity increase

Wheat Rice

Alternative Socio-Economic Scenarios for Sensitivity Analysis

Temperature Change

0

1

2

3

4

5

6

7

2000 2025 2050 2075 2100 2125 2150

Year

Glo

bal m

ean

tem

pera

ture

cha

nge

from

the

pre-

indu

stria

l lev

el (°

C)

SRES A1FI-baseSRES B2-baseWGI S650 (Non-CO2 GHG: B2)WGI S550 (Non-CO2 GHG: B2)WGI S450 (Non-CO2 GHG: B2)

0

2000

4000

6000

8000

10000

12000

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120 2130 2140 2150Year

Wor

ld p

opul

atio

n (m

illion

peo

ple)

SRES-A1

SRES-B2

OECD/IEA Statistics(World Bank)

DNE21 - Scenario(IPCC SRES)

0

200000

400000

600000

800000

1000000

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120 2130 2140 2150Year

Gro

ss w

orld

pro

duct

s (b

illion

US9

0 $)

SRES-A1 SRES-B2

OECD/IEA Statistics(World Bank)

DNE21 - Scenario(IPCC SRES)

Population Assumptions

World GDP

Sensitivity to Socio-Economic Conditions

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

2050 2150

Cha

nge

in p

rodu

ctio

n po

tent

ial/p

er-c

apita

pro

duct

ion

pote

ntia

l of

whe

at fr

om 1

990

leve

ls

SRES B2-baseReference

SRES A1FI-baseReference



- 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.

Final RemarksFinal Remarks

♦ 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 on 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.

land-use modeling and global warming impacts on …...integrated assessment phoenix - land-use...

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