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TitleEconomics of Climate Change in East Asia
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Table of Contents
                            Executive Summary
	Policy Responses
	The Project
	Organization of the Report
	Key Considerations for the Economics of Climate Change Adaptation
	Combining Analyses of Mitigation and Adaptation
Climate Change Impact on East Asia
	Key Messages
	Study Area
	Greenhouse Gas Emissions
	Historical Climate Change and Climate Variability
	Projected Climate Change
	Climate Change Impacts
	Appendix: Historical Climate and Climate Change Projections
The Economics of Adaptation in the Infrastructure Sector
	Key Messages
	Appendix: Socioeconomic Projections
The Economics of Adaptation in the Coastal Sector
	Key Messages
The Economics of Adaptation in the Agriculture Sector
	Key Messages
The Impacts of Climate Change on Poverty in East Asia
	Key Messages
	Methodology and Results
Greenhouse Gas Projections and the Costs of Sector-Specific Mitigation Options
	Key Messages
	Appendix 1: Technology Options in the Asia-Pacific Integrated Model Enduse Database
	Appendix 2: Explanation of Some Technologies in the Marginal Abatement Cost Curves
Integrated Assessment Modeling in East Asia
	Key Messages
Climate Policy in East Asia
	Key Messages
	Country Policies and Arrangements for Addressing Climate Change
	The Key Pillars of Climate Policy
	Future Developments in Climate Policy
	Policies for Innovation to Enhance Climate Policies
	Opportunities for Regional Cooperation
Document Text Contents
Page 1

Economics of
Climate Change
in East Asia
Michael Westphal

Gordon Hughes

Jörn Brömmelhörster


Page 2

Economics of
Climate Change
in East Asia
Michael I. Westphal

Gordon A. Hughes

Jörn Brömmelhörster


Page 108

Economics of Climate Change in East Asia88

when faced with increasing water shortages, farmers will adopt water-saving technologies.6 For livestock,
adaptation measures in Inner Mongolia Autonomous Region include using groundwater to irrigate pastures,
reducing the size of herds to increase the rate of off-take, and using fences to reduce overgrazing.7

The adaptation measures described above are farm-level responses to climate change. At the regional
or national scale, governments can help foster adaptation in agriculture by funding agricultural
research and development, providing climate and weather information, climate proofing infrastructure
and improving logistics (i.e., the larger supply chain such as customs facilities, wholesale markets,
warehouses, and cold storage), and measures to enhance access to international markets (e.g., risk
arbitrage, improved procurement, and better management of stocks).8

At a macro level, the main adaptation policy considered in this chapter is a consumer subsidy—a
direct payment to consumers to cover the cost of increased food prices in order to maintain the
level of consumption that would occur in the absence of climate change. Indirectly, this will promote
autonomous adaptation because the stimulus to demand will increase farm-gate prices and, thus,
encourage farmers to adopt measures that increase agricultural production.

Previous estimates of the costs of adaptation in agriculture

The United Nations Framework Convention on Climate Change (UNFCCC) estimated the global cost of
adaptation in the agriculture sector at $11 billion–$13 billion in 2030.9 The World Bank’s Economics of
Adaptation to Climate Change (EACC) study estimated the cost of adaptation in agriculture, fisheries,
and forestry for developing countries at $7 billion–$8 billion per year during 2010–2050.10 Investment in
irrigation systems is a major component of adaptation for agriculture: one study estimates the global cost at
$8 billion in 2030.11 The cumulative cost of developing new crop varieties may also be significant: the cost
of developing 200 new crop varieties better adapted to local environments is estimated to be $43 million.12


Crop yield model

The Environmental Policy Integrated Climate (EPIC)13 model was used to project global crop yields for
18 major crops.14 The EPIC model simulates the spatial and temporal dynamics of the major processes

6 Blanke, A. et al. 2007. Water Saving Technology and Saving Water in the PRC. Agricultural Water Management 87: 139–50.
7 Liu, S. and T. Wang. 2012. Climate Change and Local Adaptation Strategies in the Middle Inner Mongolia, Northern PRC.

Environmental Earth Sciences 66: 1,449–58.
8 World Bank. 2010. Development and Climate Change. Chapter 3: Managing Land and Water to Feed Nine Billion People and

Protect Natural Systems. World Development Report 2010. Washington, DC.
9 McCarl, B. 2007. Adaptation Options for Agriculture, Forestry and Fisheries. United Nations Framework Convention on

Climate Change (UNFCCC) Secretariat Financial and Technical Support Division.
10 World Bank. 2010. (see Introduction, footnote 4).
11 Fischer, G. et al. 2007. Climate Change Impacts on Irrigation Water Requirements: Effects of Mitigation, 1990–2080.

Technological Forecasting and Social Change 74: 1,083–107.
12 Parry, M. et al. 2009. Assessing the Costs of Adaptation to Climate Change: A Review of the UNFCCC and Other Recent

Estimates. London: International Institute for Environment and Development and Grantham Institute for Climate Change.
13 Williams, J. R. 1995. The EPIC Model. In Computer Models of Watershed Hydrology. Edited by V. P. Singh. Highlands Ranch:

Water Resources Public, 909–1,000; Liu, J. et al. 2007. Modelling the Role of Irrigation in Winter Wheat Yield, Crop Water
Productivity, and Production in [the PRC]. Irrigation Science 26 (1): 21–33; Liu, J. et al. 2007. EPIC – Modelling Wheat Yield
and Crop Water Productivity with High Resolution on a Global Scale. Agricultural Systems 94 (2): 478–93; Izaurralde, R. C.
et al. 2003. Integrated Assessment of Hadley Center (HadCM2) Climate-Change Impacts on Agricultural Productivity and
Irrigation Water Supply in the Conterminous United States. Part II. Regional Agricultural Production in 2030 and 2095.
Agricultural and Forest Meteorology 117: 97–122.

14 For further description of the methodology used in this chapter see the respective background report: A. Mosnier et al.
Globally Consistent Climate Adaptation in Agriculture for the People’s Republic of China, Japan, Republic of Korea and
Mongolia (TA 7465 report).

Page 109

The Economics of Adaptation in the Agriculture Sector 89

of the soil–crop–atmosphere management systems. The processes simulated include leaf interception
of solar radiation; conversion to biomass; division of biomass into roots, above-ground biomass, and
economic yield; root growth; water use; and nutrient uptake. EPIC simulates crop growth on a daily
time step at a resolution of 30 arc-min, and crop yield is calculated by multiplying the potential daily
photosynthetic production of biomass by a factor that accounts for decreases due to stress caused by
shortages of radiation, water, and nutrients; by temperature extremes; and by inadequate soil aeration
in proportion to the extent of the most severe stress during that day. The input data for the EPIC
model include daily and monthly weather data (minimum temperature [Tmin], maximum temperature
[Tmax], and precipitation), solar radiation, wind speed, relative humidity, soil data, harvest area of
the crops, fertilizer application rates, digital elevation model data, and terrain slopes. EPIC has been
validated successfully for simulating crop yield and evapotranspiration in the PRC as well as globally.15

EPIC was used to simulate crop yields for 1961–1990 and three future periods: the 2030s, 2050s, and
2090s. Three management systems were considered: automatic nitrogen (nitrogen fertilizer is added),
automatic nitrogen and irrigation (simulates the addition of both nitrogen and water), and subsistence
(no fertilizer or irrigated water is added).

Climate scenarios

Three General Circulation Models (GCMs) were used in the EPIC simulation of future crop yields: MRI-
CGCM232A, UKMO-HADGEM1, and CNRM-CM3 (Chapter 1 Appendix). These three were chosen
because they represent the Global Wet, Global Dry and Global Medium scenarios, respectively, based
on the global average changes in the annual Climate Moisture Index. The index is an indicator of
aridity, a function of both annual precipitation and average annual potential evapotranspiration.16 It
should be noted, though, that a GCM which is “dry” or “wet” globally may not be so at the regional
level. Of the three GCMs, CNRM-CM3 is the driest for East Asia, whereas both MRI-CGCM232A and
UKMO-HADGEM1 are (relatively) wetter climate scenarios for the region.

Future projections of daily precipitation and temperature are unreliable, and thus a variation of the
“delta method” was used to produce future daily values of precipitation and temperature from the
Princeton daily historical climate dataset (Chapter 1 Appendix). For temperature, the projected change
(“delta”) in mean monthly temperature between the future period (2030s, 2050s, or 2090s) and the
1961–1990 baseline period for each GCM is added to the historical daily temperature value (Tmin,
Tmax). The method was similar for precipitation, except that the number of “wet” days between the
historical and future periods was preserved.

Other physical variables needed for EPIC include solar radiation, wind speed and relative humidity. These
can either be inputted or calibrated directly by the model; given the lack of reliable future projections
of wind speed and relative humidity, EPIC was used to calibrate these values for both the historical and
future time periods. Due to the computation requirements of simulating potential crop yields for 18 crops
for the entire globe in order to provide inputs into the global trade and economic model (GLOBIOM), the
EPIC runs include carbon fertilization (Box 1). For the historical period 1961–1990, the carbon dioxide
(CO2) concentration varies from 316 parts per million (ppm) to 352 ppm, while the future concentrations
were assumed to be 444 ppm for the 2030s, 522 ppm for the 2050s, and 754 for the 2090s.17

15 Liu, J. et al. 2007. Modelling the Role of Irrigation in Winter Wheat Yield, Crop Water Productivity, and Production in [the
PRC]. Irrigation Science 26 (1): 21–33; Liu, J. et al. 2009. Global Consumptive Water Use for Crop Production: The Importance
of Green Water and Virtual Water. Water Resources Research 45 (5): W05428; Liu, J. and H. Yang. 2010. Spatially Explicit
Assessment of Global Consumptive Water Uses in Cropland: Green and Blue Water. Journal of Hydrology 384 (3-4): 187–97.

16 Strzepek, K. 2012. Indicators to Understanding the Impact of Climate Variability and Change to Flood Risk (TA 7465 report).
Manila; and Strzepek, K. 2012. A Basin Scale Indicator Approach to Understanding the Risk of Climate Variability and
Strzepek, K. 2012. Change to Water Resources Development and Management (TA 7465 report). Manila.

17 See carbon cycle models and GCMs documentation of the IPCC Fourth Assessment Report.
co2.html. The BERN CO2 concentration was used for all GCMs for the sake of consistency. This is the concentration used in
11 out of 17 GCMs employed for this study.

Page 215


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Page 216

Economics of Climate Change in East Asia

This regional study includes the People’s Republic of China, Japan, the Republic of Korea,
and Mongolia and examines how strategies for adapting to climate change up to 2050
can be combined with measures to reduce greenhouse gas emissions in East Asia. Besides
discussing climate model results for costs of adaptation in infrastructure, coastal protection,
and agriculture, the study estimates costs for sector-specific mitigation options and the total
abatement potential for 2020 and 2030. Long-term strategies for addressing the impacts of
climate change in East Asia are explored with a focus on the linkages between adaptation
and mitigation taking account uncertainty about key climate variables. Finally, it discusses
opportunities for enhancing the effectiveness of some critical climate change policies such as
regional carbon markets.

About the Asian Development Bank

ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing
member countries reduce poverty and improve the quality of life of their people. Despite the
region’s many successes, it remains home to two-thirds of the world’s poor: 1.7 billion people
who live on less than $2 a day, with 828 million struggling on less than $1.25 a day. ADB is
committed to reducing poverty through inclusive economic growth, environmentally sustainable
growth, and regional integration.

Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main
instruments for helping its developing member countries are policy dialogue, loans, equity
investments, guarantees, grants, and technical assistance.

Asian Development Bank
6 ADB Avenue, Mandaluyong City
1550 Metro Manila, Philippines

Printed on recycled paper Printed in the Philippines

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