Emulating process-based simulationof economic impacts of climate change
Sep. 4, 2020
AIM international workshop
Jun’ya Takakura
National Institute for Environmental Studies, Japan

Framework of economic impact “simulation”
Emission scenario
RCP

Socioeconomic scenario
SSP

Climatechange
RCP8.5
RCP6.0
RCP4.5
RCP2.62℃

GHG emissions constraint
Socioeconomic assumptions

GHG concentration

Climate model
ClimateSSP1: Suntainability
SSP2: Middle of the Road
SSP3: Reginal Rivalry
SSP4: Inequality
SSP5: Fossil-fueled Development

Gridded population and so on

Crop model
Water model

Waterresource・・・・・・
Health model

Crop yield

IAM（AIM/CGE）
GDP

Number ofDeath

Bio/physicalimpacts

Economic
impacts1 1

Included Sectors
SectorInstitute

Way ofvaluation

Paper

Agricultural
productivity

NIAES
NIES

CGE

Fujimori et al.
(2018)

Undernourishment

Univ. Tsukuba
NIES

CGE+VSL

Hasegawa et al.
(2016)

Heat-related excess
mortality

Univ. Tsukuba
NIES

VSL

Takahashi et al.
(2018)

Occupational-healthcost

Univ. Tsukuba
NIES

CGE

Takakura et al.
(2017)

Cooling/heating
demandNIES

CGEPark et al.
(2018)

Hydropower
generation capacity

NIES

CGE

Zhou et al.
(2018)

Thermal power
generation capacity

NIESCGE

Zhou et al.
(2018)

River flooding

Univ. Tokyo

Econometric

Kinoshita et al.
(2018)

Coastal inundation

Ibaraki Univ.

Econometric

Tamura et al.
(2019)

2

Results of Economic Impact Simulation

Takakura et al. (2019) Nature Climate Change

3

Expanding scenarios
We have explored the impact
under substantial number ofscenario-combinations.
However, massive space still
remains unexplored.

explored

unexplored

RCP8.5
RCP6.0
RCP4.5

Exploring this space inthe same way is notcomputationally practical.

RCP2.6Emulation may be an effective way to expand the scenario analysis.
4Simulation and Emulation
RCP
scenarios

SSP
scenarios

Globalclimate
modelsSocio-economic
indicators

Griddedclimatevariables

Scenarios
ModelsVariables

Bio/physicalimpactmodels

Griddedbio/physicalimpacts

Regional
bio/physicalimpacts

AIM/CGEmodel

Economic
impact(GDP loss)

Process-based impact simulation
Aggregatedregional climate
variables

Impactemulator

Economic
impact(GDP loss)

Statistical impact emulation(mimic the behavior of simulation)
5

Selection of input variables

What kind of input variables should we use?
• Global mean temperature- the simplest, but widely used way particularly in global-scale assessments• Regional mean temperature/precipitation- heterogeneous reginal climate conditions can be considered• Regional population, GDP, GDP/capita- exposure and vulnerability, not only hazards can be considered• Regional mean temperature/precipitation including non-target regions• Regional population, GDP, GDP/capita including non-target regions- typically, when we want to emulate the impacts in a certain region,
climate and socioeconomic condition of the focused region is used.
- some economic impacts can be affected by conditions of the other regions
through relative advantages (or disadvantages) in international trades
6

Selection of functional forms

What kind of functional form should we use?
Ordinary-Least-Squares-regression-based techniques.
OLS1: yˆ=
a0 +t

∑a x

i t ,iiOLS2:

yˆt =a0 + ∑ a1i xt ,i + ∑ a2i xt2,iii2ax+ax∑ 1i t ,i ∑ 2i t ,i + ∑ aij xt ,i xt , j

a0 +OLS2i: yˆt =iii≠ j

Artificial-Neural-Network-based techniques.
MLP:yˆt = f (xt , W )RNN:

yˆt = f (st −1 , xt , W )st = g (st −1 , xt , W )Cited from: https://doi.org/10.1007/978-1-4842-3685-7_3
7Sample of emulation and evaluation

気候変化な し条件とEconomicimpact (%GDP)比較したGDP損失 [%]Example of simulation result
SSP1

7. 5
5. 0

SSP2

SSP3

RCP8.5
RCP6.0
RCP4.5
RCP2.6

2. 5
0. 0

Economic impact (%GDP)

Example of emulation result

Evaluation procedure① decide the parameters of anemulator based on thesimulation results for eachsector and region.

② conduct an emulation basedon the parameters calculated
in ① by using the crossvalidation procedure.

③ Evaluate the similarity

between simulation andemulation result.
8

Correlation coefficient between
simulation result and emulation result

Evaluation results

OLS1
OLS2
OLS2i
MLP
RNN

Number of input variables

Takakura et al. (in prep.)

The more input variables and the more complex functional forms we use, thebetter the emulators reproduce the results of the simulations.
Some sectors’ economic impacts are relatively easy to emulate, but other sectors’
are difficult even by the complex techniques.
9Example (relatively easy to emulate)10

Example (relatively hard to emulate)11Summary and future plans
Summary• We developed various kinds of emulators and evaluated theirperformances systematically.
• Among the tested emulators, emulators composed of artificial neuralnetworks, which can incorporate nonlinearities and interactions ofvariables, performed better particularly when finer input variables wereavailable.
• Whereas simple functional forms were also effective to grasp generaltendencies, complex models are necessary if the focus is regional orsectoral heterogeneity.
Future plans• Increasing spatial resolution (AIM’s 17 regions -> national or grid level)• Emulation of bio/physical impacts as well as economic impacts• Connecting to other tool-chains (e.g., simple climate models)12ご清聴ありがとうございました

Asia-Pacific Integrated Model

http://www-iam.nies.go.jp/aim/index.html