IAM-IAV-ESM linkage in AIM project - Focusing spatial resolution issues -

Kiyoshi Takahashi1, Shinichiro Fujimori1, Naota Hanasaki1, Tomoko Hasegawa1, Yasuaki Hijioka1, Toshihiko Masui1, Chan Park2, Akemi Tanaka3, Qian Zhou1

1National Institute for Environmental Studies

2University of Seoul 3National Agriculture and Food Research Organization

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AIM project’s research publications based on IAM-IAV-ESM linkage • Published

• Hanasaki N., Fujimori S., Yamamoto T., Yoshikawa S., Masaki Y., Hijioka Y., Kainuma M., Kanamori Y., Masui T., Takahashi K., Kanae S. (2013) A global water scarcity assessment under Shared Socio-economic Pathways - Part 1: Water use. Hydrology and Earth System Sciences, 17 (7), 2375-2391

• Hanasaki N., Fujimori S., Yamamoto T., Yoshikawa S., Masaki Y., Hijioka Y., Kainuma M., Kanamori Y., Masui T., Takahashi K., Kanae S. (2013) A global water scarcity assessment under Shared Socio-economic Pathways - Part 2: Water availability and scarcity. Hydrology and Earth System Sciences, 17 (7), 2393-2413

• Hasegawa T., Fujimori S., Shin Y., Takahashi K., Masui T., Tanaka A. (2014) Climate change impact and adaptation assessment on food consumption utilizing a new scenario framework. Environmental Science and Technology, 48, 438-445

• Ishida H., Kobayashi S., Kanae S., Hasegawa T., Fujimori S., Shin Y., Takahashi K., Masui T., Tanaka A., Honda Y. (2014) Global-scale projection and its sensitivity analysis of the health burden attributable to childhood undernutrition under the latest scenario framework for climate change research. Environmental Research Letters, 9 (6), 064014-064022

• Hasegawa T., Fujimori S., Takahashi K., Masui T. (2015) Scenarios for the risk of hunger in the twenty-first century using Shared Socioeconomic Pathways. Environmental Research Letters, 10 (1), 014010-014017

• Hasegawa T., Fujimori S., Shin Y., Tanaka A., Takahashi K., Masui T. (2015) Consequence of Climate Mitigation on the Risk of Hunger. Environmental Science & Technology, 49 (12),

• Hasegawa T., Fujimori S., Takahashi K., Yokohata T., Masui T. (2016) Economic implications of climate change impacts on human health through undernourishment. Climatic Change, 1-14

• Hasegawa T., Park C., Fujimori S., Takahashi K., Hijioka Y., Masui T. (2016) Quantifying the economic impact of changes in energy demand for space heating and cooling systems under varying climatic scenarios. Palgrave Communications.

• Submitted / Under review • Hasegawa et al., Development and Application of a Global Land-use Allocation Model Linked to an

Integrated Assessment Model. • Fujimori et al., Downscaling Global Emissions and Its Implication Derived from Climate Model Experiments. • Zhou et al., Economic consequences of global climate change and mitigation on future hydropower.

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Our incentives for IAM-IAV-ESM coupling

• International research trend • The SSP-RCP scenario framework

• Increasing focus on the analyses of the sustainable development goals

• Domestic research project funding • MOEJ-S14 research project for integrated analyses of global

mitigation and local adaptation

• NIES research program for proposing integrated solution of multiple environmental problems toward sustainable society

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Recursive Dynamic

Economic model

AIM/CGE

Land allocation model

AIM/PLUM

Emissions downscaling

AIM/DS

GHG and

air pollutant emissions

Biomass

supply curve

Gridded emissions

Energy production

and consumption

6

8

10

12

14

16

0 100 200 300 400

Yiel

d (

tDM

/ha)

Area (Mha)

DICE type

optimization model

AIM/Dynamic

MAC curve GHG emissions

pathway

Biophysical

potential

Land use and

agriculture price Transport model

AIM/Transport

Energy and

carbon price

Transport

demand

Simplified climate

MAGICC

Global mean

temperature

Gridded land

use

Current AIM/CGE interaction for the integrated scenario analyses

Drawn by Fujimori

AIM

Gridded emissions

Climate model MIROC

Air quality model CMAQ

Water resource model H08

Hydro power

potential

Cooling water

potential

Ecosystem Biodiversity model

Biodiversity index

Conservation area Gridded Land use

Crop model

Crop production

potential

Flood model CaMa-Flood

Flooded area

and loss

Coastal model

Coastal

damage

Health model

Health

damage

AIM/CGE interaction with external IAV&ESM models

Drawn by Fujimori

CDD/HDD model

Cooling/Heati

ng demand

Typical challenges in linking IAM and IAV

• Spatial resolution gap • Aggregation / Disaggregation

• Limited number of scenario cases analyzed in IAV models • Impact response functions (sensitivity analyses)

• Choice of variables transferred between models • Avoidance of double-counting

• Formulation in the CGE model

• Difficulty in reflective interaction between IAM and IAV • Land-use model as a key module for most interactions

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Spatial aggregation / disaggregation

• Aggregation (IAV -> IAM ; 0.5°-> 17 regions) • Crops productivity

• Food-health analysis

• Cooling/Heating Degree Days • Cooling/Heating service and energy demand analysis

• Hydropower potential based on river runoff change

• Disaggregation (IAM -> IAV/ESM; 17 regions -> 0.5°) • Spatial GHGs/Aerosol emission scenarios

• Input to MIROC ESM

• Spatial land-use scenarios • Input to ecosystem-biodiversity model

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Hasegawa et al., 2015,ERL

Impacts of climate change on food-health and its economic implications

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Mitigation

Socio-economic conditions

Adaptation

Undernourishment/ Risk of hunger

Health impacts

Economic implications

Hasegawa et al., 2015, EST

Hasegawa et al., 2014

Climate change

Hasegawa et al., 2016

Ishida et al., 2014

Consequence of Climate Mitigation on the Risk of Hunger

MethodsSocio-

economic

conditions

SSP2

Climate conditions

(RCP2.6/8.5/

No change)

Crop model

AIM/CGE model

Climate

mitigation

policy

RCP2.6/BaUYield change

12050 with NoCC: 2950 kcal/cap/day 2050 with NoCC: 90 mil.

2005: 2680 kcal/cap/day 2005: 830 mil.

Mean food calorie intake Global population at risk of hunger

-40

-35

-30

-25

-20

-15

-10

-5

0

Mitigation BaU

World

Ch

ange

in c

alo

rie

inta

ke

[kca

l/d

ay/c

ap]

RCP2.6 RCP8.5

0

2

4

6

8

10

12

14

16

Mitigation BaU

World

Ch

ange

in p

op

ula

tio

n a

t ri

sk o

f h

un

ger

[mill

ion

]

Landcompetition

Mitigationcost effects

Change incrop yields(median)

RCP2.6 RCP8.5

Hasegawa et al., 2015, EST

Economic implications of health impacts through undernourishment: SSP3-RCP8.5 in 2100

• Direct impacts (changes in labor force & healthcare costs): -0.1–0.0% of Global GDP

• Indirect impacts (value of lives lost): -0.4-0.0% of Global GDP; -4.0% at most in regional levels

Rest of Africa

North Africa

Middle East

Former Soviet Union

Rest of Latin America

Brazil

Rest of Asia

Southeast Asia

India

China

World

Direct impacts

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Rest of Africa

North Africa

Middle East

Former Soviet Union

Rest of Latin America

Brazil

Rest of Asia

Southeast Asia

India

China

World

Low

Middle

High

Proportion of GDP [%]

Proportion of GDP [%]

a) World b) By regionIndirect impacts

Hasegawa et al., 2016, Climatic Change

Economic impact of changes in energy demand for space heating and cooling systems under varying climatic scenarios

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Country-wise data

•Population

•GDP

AIM/CGE model

Gridded data

•Temperature

•Population

Changes in

heating and

cooling demand

Hasegawa et al., 2016, Palgrave Communications

Economic consequences of global climate change and mitigation on future hydropower

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EEC shocks

EEC: Economical exploitable capacity

Zhao et al. Submitted.

Typical challenges in linking IAM and IAV

• Spatial resolution gap • Aggregation / Disaggregation

• Limited number of scenario cases analyzed in IAV models • Impact response functions (sensitivity analyses)

• Choice of variables transferred between models • Avoidance of double-counting

• Formulation in the CGE model

• Difficulty in reflective interaction between IAM and IAV • Land-use model as a key module for most interactions

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• The impact response function is a database of country-averaged data from sensitivity analyses calculated by a process-based model.

• We developed an impact response function for productivities of maize, wheat, paddy-rice and other 11 crops with two explanatory variables, change in annual mean temperature (ΔT) and change in annual mean precipitation (ΔP), using the M-GAEZ model.

Base period: 1961-1990

percentage of productivity [%] against base period

Development of impact response functions

Tanaka et al. (2014) . Climate in Biosphere (in Japanese)

Incentives for IAM-IAV-ESM coupling

• International • The SSP-RCP framework

• Increasing focus on the analyses of the sustainable development goals

• Domestic research project funding • MOEJ-S14 research project for integrated analyses of global

mitigation and local adaptation

• NIES research program for proposing integrated solution of multiple environmental problems toward sustainable society

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0

10

20

30

40

50

60

70

80

197

01

97

51

98

01

98

51

99

01

99

52

00

02

00

52

01

02

01

52

02

02

02

52

03

02

03

52

04

02

04

52

05

02

05

52

06

02

06

52

07

02

07

52

08

02

08

52

09

02

09

52

10

0

10

00

20

05

US

$/c

apit

a

MER

SSP1

SSP2

SSP3

SSP4

SSP5

_GDP per capita_WLD

Global LCS scenario・ Climate impact scenario

Global SD scenario

From LCS scenario to SD scenario

Development of novel IAM at global scale

Model application

Scenario development

Feedback

Mitigatio

n ch

allenges

Adaptation challenges

Narrative storylines of socio-economic develoment

Quantification by region

Spatial information of socio-economic development

Emission pathway Mitigation cost

Sector impact Impact cost Adaptation cost

Emission pathway Mitigation cost

Sector impact Impact cost Adaptation cost Food production

Loss of Biodiversity

Bioenergy

Expansion of crop land

Analyses of tradeoff and synergy

(1) Development of novel global IAM and quantitative SD scenarios using the IAM (2) Downscaling of the quantified socio-economic scenarios

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NIES Research Program: Development of global SD scenario

Typical challenges in linking IAM and IAV

• Spatial resolution gap • Aggregation / Disaggregation

• Limited number of scenario cases analyzed in IAV models • Impact response functions (sensitivity analyses)

• Choice of variables transferred between models • Avoidance of double-counting

• Formulation in the CGE model

• Difficulty in reflective interaction between IAM and IAV • Land-use model as a key module for most interactions

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Summary of the talk

• Most of the recent efforts for linking IAM-IAV in the AIM project have been the consideration of climate change impacts on specific sectors in AIM/CGE.

• Based on the development of tools for spatially downscaling the CGE results (e.g. AIM/PLUM for land-use scenario), we are going to extend the integration with IAV & ESM models further.

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