Climate Change Impacts on Agriculture:
Regional Economic Adaptation through 2050
Ron Sands
USDA Economic Research Service
On behalf of the AgMIP global economic modeling team
20th AIM International Workshop
Tsukuba, Japan
23-24 January 2015
The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

Overview• AgMIP Global Economic Modeling Team– Agricultural Model Intercomparison and Improvement Project– Models/Institutions– Model Types• Global Analysis During 2012-2013– Nine participating models– Reference scenario to 2050 (SSP2 “middle of the road”)– Economic responses to biophysical shocks (RCP 8.5)

• Global and Regional Analysis During 2014
– Five participating models– Scenarios• SSP1 and RCP 4.5• SSP2 and RCP 6.0• SSP3 and RCP 8.5

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

Key characteristics of participating economic models
Institution

TypeEconomy
coverage

Agr. sectors*

Regions**

Base year

Agr. Policies

Bioenergy

NIES, JapanCGE

Full
economy8/189 / 17

2005

Implicitly
assumedunchanged

Endogenous
1st and 2ndgeneration

ENVISAGE FAO/WorldCGE

Fulleconomy

10 / 5

11 / 9***Price wedges(based onGTAP)

None
explicitly
represented

FARM

USDA, USACGE

Fulleconomy

12 / 85 / 8***

2004 & 2007

Price wedges(based onGTAP)

Little forelectricity
and heating

GTEM

ABARE,
Australia

CGE

Full
economy7/7

5 / 8***

2004

Implicitly
assumedunchanged

Endogenous
1stgeneration

MAGNET

LEI-WUR,
The Netherlands

CGE

Fulleconomy

10 / 929 / 16

2004 & 2007

Price wedges(adjustedfrom GTAP);
milk quotas

Biofueltargets w/
endogenous
allocation

GCAM

PNNL, USAPE

Agriculture,
Energy18 / 07 / 9***

2005

Implicitly
assumedunchanged

GLOBIOMIIASA,
AustriaPE

Agriculture,
forestry,
Bioenergy

31 / 6

10 / 20

2000

IMPACTIFPRI, USA

PE

Agriculture

32 / 14101 / 14

MAgPIEPIK,
GermanyPE

Agriculture

21 / 00 / 10

Model
AIM

Bank/Purdue

Globalnumeraire
US CPI

Agric. supply

Price index
high-inc.
manuf’ed
exports
Price Index ofEuropean
Service
SectorAverage priceof capital
goods

Nested CES

Nested CES

Finaldemand
LES utility

Trade
Non-spatial;
Armington
gross-trade

LES utility(withdynamicshifters)
LES utility

Armington
spatialequilibrium

NestedLeontief andCES

CDE utility

Armington
spatialequilibrium

World GDP
Deflator

Nested CES

Armington
spatialequilibrium

Endogenous
1st and 2ndgeneration

n.a.

Leontief

Implicitly
assumedunchanged

Exogenous
demandn.a.

Leontief

CDE private
demand and
CobbDouglasutility
Demandelasticities
adjusted overtime
Demandelasticities
adjusted overtime

2000

Price wedges(based on
PSE/CSE)

n.a.

1995

Implicitly
assumedunchanged

Exogenous
demand forfeedstockcrops
Exogenous
Bioenergy
demand* Figures indicate the number of raw and processed agricultural products represented, respectively.
** Figures indicate the number of individual countries and multi-country aggregates represented, respectively.
*** Regional breakout specific for this application.

n.a.

Nested CES

SupplyDemandelasticities
elasticities
adjusted over adjusted overtime
time
Leontief
exogenous

Armington
spatialequilibrium

HeckscherOhlin nonspatial, nettrade
EnkeSamuelsonTakayamaJudge spatial
equilibrium
HeckscherOhlin nonspatial, nettrade
Based onhistoricalselfsufficiency
rates

The climate modeling chain: Frombiophysical to socioeconomic∆ Productivity

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

4AgMIP Agricultural Productivity GrowthLand-augmenting agricultural productivity index (2005 = 1)
2.5

2.0

1.5
2005
2030
1.0

2050

0.5

0.0
wheat

rice

coarse grains

oil seeds

sugar crops

fruits andvegetables

plant-based other crops
fibers

Source: IMPACT model maintained by the International Food Policy Research Institute. IMPACT values are based on
expert opinion about potential biological yield gains for crops in individual countries based on historical yield gains andexpectations about future private and public sector research and extension efforts.

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

Economic Responses to a Decline in Agricultural Productivity
Due to Climate Change (SSP2 and RCP 8.5)
80%
60%
40%
20%

20%

11%
0%
-20%

-17%

-2%-3%

Production

Consumption

-11%

-40%
-60%
Productivity

YieldAreaPrice

The black diamond is the average (mean) percent change with climate change compared to no climate change inyear 2050; the height of a column is the range across climate models, crop models, and economic models. Results
are a world average across major field crops: wheat, rice, coarse grains, and oil seeds.
Source: Nelson et al. (2014) Proceedings of the National Academy of Sciences, Vol. 111(9): 3274-3279.

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

AgMIP Scenarios
Radiative
forcing

SSP 1

SSP 2

SSP 3

RCP 8.5

AgMIP Phase 1

HadGEM (3.1)
IPSL (3.2)
MIROC (3.3)

RCP 6.0

HadGEM (2.1)
IPSL (2.2)
MIROC (2.3)

RCP 4.5

SSP 4

HadGEM (1.1)
IPSL (1.2)
MIROC (1.3)

RCP 2.6

No climatechange

Reference (1.0) Reference (2.0) Reference (3.0)
AgMIP Phase 1 AgMIP Phase 1

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

SSP 5

Socio-economic
challenges for mitigation

Shared Socio-economic Pathways (SSPs)
SSP3

SSP5

(mitigation challenges dominate)(high challenges)

Conventional
Development

Fragmentation
SSP2(intermediate challenges)

Middle of the Road
SSP1

(low challenges)

SustainabilitySSP4(adaptation challenges dominate)

Inequality

Socio-economic
challenges for adaptation
Source: O’Neill, B.C., E. Kriegler, K. Riahi, K. Ebi, S. Hallegatte, T.R. Carter, R. Mathur, D.P. van Vuuren.
February 2014. “A New Scenario Framework for Climate Change Research: The Concept of Shared
Socio-Economic Pathways,” Special Issue on “A Framework for the Development of New
Socioeconomic Scenarios for Climate Change Research,” Climatic Change 122(3): 387-400.

Regional aggregations
CodeUSA
CAN
BRA
OSA
EUR
FSUMENSSA
CHN
IND
SEA
OAS
ANZRegion nameUnited States of America
CanadaBrazil
Other South America, Central America & Caribbean
EuropeFormer Soviet Union
Middle-East North Africa
Sub-Saharan Africa
China
India
South-East Asia
Other Asia
Australia/New ZealandComments

Excl. Turkey
European and Asian
Incl. Turkey

Incl. Japan
Incl. Other Oceania

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

Figure 1. Percent change in economic indicators from 2005
through 2050 for the 5-commodity aggregate without
climate change, across three SSPs (reference scenarios)

At the world level, change in production equals change in consumption; but they differ atregional level.
YEXO reflects income-related capacity for agricultural research.
For SSP 3 relative to SSP 2, greater population and lower income are partially offsetting.

Figure 2. Percent change in economic indicators from 2005
through 2050 by crop without climate change, for SSP2
(reference scenario 2.0)

CGR = coarse grains
OSD = oil seeds
RIC = rice
SUG = sugar
WHT = wheatFigure 3. Percent change in USA economic indicators for the5-commodity aggregate with climate change, across three
climate modelsSmall decline in consumption across climate scenarios and climate models.

Figure 4. Percent change in economic indicators by crop for
SSP 2 and RCP 6.0 using the MIROC climate model (scenario2.3)

CGR = coarse grains
OSD = oil seeds
RIC = rice
SUG = sugar
WHT = wheat

In some parts of the world, yield for sugar crops
increases with climate change. In India, the yieldfor sugar crops decreases.

Figure 5. Percent change in imports and exports for SSP 2and RCP 6.0 with HadGEM (average of FARM, ENVISAGE,and MAGNET models; scenario 2.1)

CGR = coarse grains
OSD = oil seeds
RIC = rice
SUG = sugar
WHT = wheat

USA: increase in exports with climate change
SE and E Asia: increase in imports
Brazil: increase in exports

Outstanding Issues• How to apply output from crop process models toglobal economic models• Response of food consumption to increasing
per-capita income• Link to analysis at sub-national level

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

Further Reading• Special issue of Agricultural Economics on AgMIP global economicscenarios (January 2014)• Nelson, G.C., H. Valin, R.D. Sands, P. Havlik, H. Ahammad, D. Deryng,J. Elliott, S. Fujimori, T. Hasegawa, E. Heyhoe, P. Kyle, M. von Lampe,
H. Lotze-Campen, D. Mason d’Croz, H. van Meijl, D. van der
Mensbrugghe, C. Müller, A. Popp, R. Robertson, S. Robinson, E.
Schmid, C. Schmitz, A. Tabeau, and D. Willenbockel, 4 March 2014,“Climate change effects on agriculture: Economic responses tobiophysical shocks,” Proceedings of the National Academy ofSciences (special feature) 111(9): 3274-3279.

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

World Population Projections
1210SSP3
SSP2
SSP18billion
6people4202005

2015

2025

2035

2045

Source: OECD

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

2055

World Projections of Total GDP
250

200

SSP1
SSP2150
trilliondollars

SSP3
100

500
2005

2015

2025

2035

Source: OECD

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

20452055

World Projections of Average GDP Per Capita
25,000SSP1
20,000SSP2
15,000USdollars

SSP3
10,000

5,000

0
2005

2015

2025

2035

Source: OECD

The views expressed are those of the author(s) and should not be attributed to the Economic Research Service or USDA.

2045

2055