12. Impact and Adaptation Assessment on a
National Scale: Case Studies on Water in China
Kiyoshi Takahashi1, Songcai You2, Jiulin Sun2, Zehui Li2, Toshihiko Masui1,
Tsuneyuki Morita1, Yuzuru Matsuoka3, and Hideo Harasawa1
Summary. In order to assess vulnerability to climate change, not only the degreeof climate change impact but also the adaptive capacity of the region with regard
to the impact needs to be considered. To achieve such an assessment, a nationalscale impact assessment model is being developed. This paper introduces two casestudies related to water problems in China. One of them is a scenario for anassessment of future water withdrawal and consumption in China taking into
consideration alternative future socio-economic development patterns. This
indicates that the adoption of appropriate policies and efficient technologies canensure that future water consumption is maintained at the current level, eventhough water withdrawal will increase under all socio-economic scenarios, whichreflects the expected increase in industrial withdrawal. In the other case study, anevaluation is made of the adaptation policies in China for mitigating current flood
damage due to climate variability as well as the future increase in flood damage
due to climate change is evaluated. Investment in infrastructure for flood
prevention in accordance with the expected future climate change can also reduce
current flood damage and is found to be robust and low-regret adaptation strategy.

12.1 Introduction and Framework of AIM/Impact [Country]
As shown in Chap. 3 (see the chapter by Harasawa et al.), an AIM/Impact model
has been developed mainly for the purpose of assessing climate change impacts on
several sectors (water, agriculture, ecosystems, and human health) on a global
scale and it has been used to elucidate the regions that would be significantly
affected by anticipated climate change. However, in order to assess thevulnerability of a region to climate change, not only the degree of the impacts ofclimate change, but also the adaptive capacity of the region to cope with these
impacts needs to be considered. To analyze concrete adaptation measures for
mitigating climate change impacts, more detailed analysis is required on an
appropriate spatial scale corresponding to that of the stakeholders (e.g.
governments, local communities, individuals) with regard to the adaptation
measures. Moreover, the role of each country in reporting the results of impact
1

National Institute for Environmental Studies, Tsukuba 305-8506, Japan
Institute of Geographical Sciences and Natural Resources Research, P.O. Box 9717,
Beijing 100101, China
3
Kyoto University, Kyoto 606-8501, Japan
2

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Takahashi, K. et al.

assessment to international processes, such as the National Communication for the
UNFCCC, increases the importance of national scale assessments carried out by
each country itself. Within the AIM/Impact project, there are increasing requests
from collaborative research teams in the Asian countries to pay more attention toassessments on the national scale. Taking into account this situation, thedevelopment of a national-scale assessment model of impact and adaptation has
started, mainly based on the models that have been developed for AIM/Impacts
on the global scale. This has been named AIM/Impact [Country].
AIM/Impact [Country] is a package of impact models, tools, databases, andvisualization modules with a user interface to enable these elements to be operated
in an integrated way. AIM/Impacts [Country] is used to assess climate change
impacts on various sectors on a national scale. Fig. 1 shows the framework of
AIM/Impact [Country], which illustrates the elements of the package (data,
models, tools, parameters, etc.) and their relationships and dependencies. Impact
assessment models for several sectors form the central part of the package (theupper right rectangle bounded by a dotted line in Fig. 1). Tools and databases
included in the package support the development of national-scale input data for
the impact assessment models (the upper left rectangle bounded by a dotted line in
Fig. 1) and the spatial and numerical analysis of the output results of anassessment (the bottom rectangle bounded by a dotted line in Fig. 1). Although
ready-made standardized data and model parameters are provided with the models
and tools for countries with data limitations, these can be replaced by the users
according to the specific conditions of each country. A visualization tool withsimple functions to graphically display the spatial data and results is also included

GIS data scaled down for
national scale assessment

GIS tool for input data
development(Scenario Creator)

GIS tool for trimming away
ex-focused area
GIS tool for spatial interpolation

Global GIS data

Socioeconomic and GHG
emissions scenarios

Input GIS data for impact
assessment models

Model parameters

Penman-PET model
Thornthwaite-PET model
Potential crop productivity model
Surface runoff modelRiver discharge model
Water demand model
Malarial potential model
Holdridge vegetation classification
Koeppen vegetation classification
Vegetation shift potential model(2) Impact/Adaptation assessment(1) Development of input GIS data for model(3) Analysis of GIS data and outputs

GIS tool for
sub-national
aggregation

Interface tool for visualizing
data on IDRIDIInterface tool for
visualizing data on plain
spatial data viewer

GIS output data of
the impact
assessment model

PREF.ID NAT REG
PREF
392010100 JPN Hokkaido Hokkaido
392020100 JPN Tohoku Aom ori
392020400 JPN Tohoku Akita
392020200 JPN Tohoku Iwate
392020400 JPN Tohoku Akita
392020500 JPN Tohoku Yam agata
392020300 JPN Tohoku M iyagi
392040100 JPN Hokuriku Niigata
392020600 JPN Tohoku Fukushim a
392040300 JPN Hokuriku Ishikawa
392030200 JPN Kanto
Tochigi
392030300 JPN Kanto
Gum m a392050200 JPN Chubu
Nagano392040200 JPN Hokuriku Toyam a
392030100 JPN Kanto
Ibaraki

GIS data for sub-national
spatial aggregation

Fig. 1. Framework of AIM/Impact [Country] (see color plates)

VALUE
12
-10
-5
-5
2
3
-13
-2
8
-6
-7
15
17
12
-1

12. Impact and Adaptation Assessment on a National Scale199

in the package, and users can quickly view the spatial information withoutpurchasing expensive GIS software.
While the main focus of AIM/Impact has been the assessment of the direct
physical impact of climate change, AIM/Impact [Country] is designed to more
explicitly consider socioeconomic aspects of the problem and adaptation, besides
the physical impacts. Therefore, in this chapter, two recent case studies related towater problems in China are introduced, whose models are planned to be included
into AIM/Impacts [Country] in a more generalized form. Section 2 describes anassessment of future water withdrawal and consumption in China taking into
consideration alternative future socio-economic development patterns. Section 3
introduces an assessment of adaptation policies in China for mitigating current
flood damage due to climatic variability as well as future increased flood damage
due to climate change.

12.2 Projection of Water Demand through 2032
12.2.1 Water withdrawal and consumption under alternativesocioeconomic development scenarios
According to the dynamic changes related to human activities, such as the increasein the population, industrialization, irrigation development, shortages of water
resources and consequent environmental problems are becoming more obvious inmany regions of the world. If the rapid development scenario is taken, whichassumes a significantly higher rate of population increase and industrialization indeveloping countries, more severe and more frequent water shortages are expected
to occur. More extensive water shortages can be a factor limiting futuresocioeconomic development. There is even speculation that regional conflicts orwars may occur as a result of competition for limited water resources in the 21st
century.
While such a pessimistic view of the prospects for water resource problems isbeing emphasized, future water demand depends substantially on future socioeconomic conditions, such as population changes and levels of economic
development. Since future socioeconomic conditions are still uncertain, diverse
sets of future development scenarios that could be potentially be chosen need to beanalyzed. In order to assess the impact of climate change on water resources, theestimation of water demand taking into consideration socioeconomic conditions is
as important as the estimation of future water supply under altered climaticconditions.
In this study, an assessment model of water withdrawal and water consumption
on a national scale was developed and then used to estimate water withdrawal andconsumption in China under four alternative future development scenarios for theperiod from 1995 to 2032. In the model, historical trends in the irrigated area andthe population supplied with water as well as assumed future trends in relation tovarious socioeconomic factors, such as an increase in the population and economic

200

Takahashi, K. et al.

development, are taken into account to estimate future withdrawal andconsumption. In many studies for the projection of water demand made so far, theamount of water withdrawal has been taken as the index of water demand.
However, for the purpose of evaluating the quantitative balance between watersupply and demand in order to investigate any shortfall in water supply, water
consumption is a better index than water withdrawal. Here, water withdrawalmeans the gross amount of water taken from water resources for human activities,
on the other hand, water consumption means the net amount of water consumed
mainly through evaporation before the water is recycled to form part of theavailable water resources. The ratio of water consumption to water withdrawal iscalled the water consumption ratio, while the ratio of water returned to water
resources to water withdrawal is called the return flow rate. In the industrial anddomestic sectors, most of the withdrawal is discharged back to replenish water
resources (the water consumption ratio is low), thus the amount of water
consumed is relatively small even if water withdrawal increases rapidly in future.
On the other hand, in the agricultural sector, most withdrawal is consumed
through evaporation and transpiration (the water consumption ratio is high).

12.2.2 Water demand model
Figure 2 shows the estimated flow of water withdrawal and water consumption.
Water withdrawal and consumption in three sectors (industry, agriculture,
domestic) are estimated separately. For the base year (1995), sectoral water
withdrawal data compiled by the World Resource Institute (2001) is used. Firstly,
based on the scenario for socioeconomic factors (urban/rural population andeconomic growth) and technological factors (water use efficiency improvementsin each sector), future water withdrawal in each sector is estimated. Then,considering the scenario for the water consumption ratio, future water

IRG
WatPOPU
WatPOPR

IRG (GDP,population)
WatPOPU(year,urban population)
WatPOPR(year,rural population)
Withdrawal
in 1995

domestic
industry
agriculture

WatPOPU:
Urban population with water supply
WatPOPR:
Rural population with water supply
UrbanRat:
Urbanization ratio
IRG:
Irrigated area

Projection ofdriving factors

Regression

Historical trend ofdriving factors

WatPOPU
WatPOPR
UrbanRat

Future scenario ofdriving factors

GDP
IRG
POP

Projection ofwithdrawal

GDPs
POPs
UrbanRats

domestic
industry
agriculture

Projection of
consumption

domestic
industry
agriculture

Historical Data / Scenario

domestic (m3/water supplied person)

domestic
industry
agricultureindustry (m3/$GDP)
agriculture (m3/m2irrigated area)

Withdrawal per unit
driving factor in future

Withdrawal per unit
driving factor in 1995
Scenario assumption ofwater use efficiency

Fig. 2. Overview of water demand model

Resultdomestic
industry
agriculture
Consumption ratio

12. Impact and Adaptation Assessment on a National Scale201

consumption is calculated. The basic driving force for changes in industrial water
withdrawal is economic growth in the model. It is assumed that industrial water
withdrawal will change in proportion to changes in the GDP. The amount ofirrigated area is taken as the driving force for any change in agricultural water
withdrawal. Agricultural water withdrawal is assumed to change in proportion to
any change in the irrigated area. The future irrigated area is estimated from a multi
regression with the logarithm of population and the logarithm of per capita GDP
as the explanatory variables. The population supplied with water is taken as thedriving force for domestic water withdrawal. The future population supplied withwater is estimated taking into consideration the historical trend of the ratio of the
population supplied with water and the future population scenario. In order to take
into account technology improvements, sectoral withdrawals based on the basic
driving forces are then multiplied by a factor for water use efficiency
improvements, which is assumed taking into consideration the form ofsocioeconomic development. The sectoral water withdrawals calculated in thisway are summed up to provide the total water withdrawal for the country. Someportion of the withdrawal is consumed mainly through evaporation, while theother portion is recycled to replenish the water resource. In our model, the ratio of
the consumed volume to the withdrawal (consumption ratio) is assumed to be 0.16,
0.8, and 0.3 for the industrial, agricultural and domestic sectors, respectively,
according to information from Shiklomanov (1998).

12.2.3 Scenarios
Water withdrawal and consumption in the three sectors under the alternative four
sets of assumptions regarding socioeconomic changes are estimated (Table 1).
These four scenarios of socioeconomic changes are identical to the scenarios
adopted in the global environment outlook 3 (GEO3) formulated by UNEP (2001).
The comparative level of population change, economic development, and wateruse efficiency improvements for the four scenarios are shown in Table 1. Amongthese, the scenarios for population change and economic development are deduced
from the scenario of GEO3.
The scenario for water use efficiency improvements was set by the authors bymaintaining consistency with the background sequence of the GEO3 scenarios. In
the PR scenario, it is assumed that currently available efficient water use
technologies will be introduced from the first half of the projected period (1995 –2015) with the support of environmental policies, and that innovative efficientwater use technologies will become available in the second half of the period of
the projection (2015 – 2032). In the GT scenario, water use efficiency
improvements in the first half will be at the same level as in the MF scenario.
However, efficient technologies will be available in the second half of the period.
In the MF scenario, technology improvements are slower to emerge than in the PR
scenario in the first half, and are slower to emerge than in the PR and the GT
scenarios in the second half of the period. In the FW scenario, water use
technology improvements are much slower to emerge than in the other scenarios

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Takahashi, K. et al.

for the whole period of the projections.
Table 1. Socioeconomic development scenarios analyzed in the study

Population growth rate
Economic growth rate
Water use efficiency
improvements

MarketForces(MF)
MediumHigh

Fortress
World
(FW)
High
Low

PolicyReforms(PR)
MediumHigh

Medium

Slow

Rapid

Great
Transition(GT)
MediumMediumMedium –Rapid

12.2.4 Analysis
Figure 3a - 3c shows the water consumption for the three sectors. A common trend
for all the scenarios is that industrial consumption will increase even taking into
consideration water use efficiency improvements. Reflecting the expected rapid
economic growth and industrialization, the growth of industrial consumption willbe the greatest in the MF scenario. In the PR scenario and the GT scenario, thegrowth of industrial consumption will be slightly lower than that in the MF
scenario, while it is rather low in the FW scenario due to slow economic growth.
As for agricultural consumption, this will not increase significantly even in the
FW scenario with a high increase in the population. It will decrease in the otherscenarios with a lower increase in the population and higher water use efficiency
improvements. These trends reflect the recent historical situation, which indicatesthat irrigation in China has reached saturation point. Domestic consumption will
increase only in the FW scenario due to its high increase in population and low
rate of efficiency improvements.
Figure 4a and 4b show the total water consumption and total withdrawal in
China, respectively. By comparing these two figures, it can be found that they
have a different order with regard to the scenarios. In viewing the results ofwithdrawal, the MF scenario involves the largest increase. This is because
industrial withdrawal will increase much more rapidly in the MF scenario than in
the other scenarios. On the other hand, the reason of the second highest total
withdrawal in the FW scenario is not an increase in industrial withdrawals, but anincrease in agricultural and domestic withdrawals reflecting rapid population
growth and slow efficiency improvements. However, when viewing the results ofconsumption, the FW scenario will show the greatest increase, and the MF
scenario does not show a significant increase. Moreover, water consumption will
decrease slightly in the PR scenario and the GT scenario. This difference of order
between consumption and withdrawal is caused by the high water consumption
ratio in the agricultural sector. This result indicates that it is very important todifferentiate water consumption and withdrawal when assessing water demand.

12. Impact and Adaptation Assessment on a National Scale

400
Water consumption (km / year)

140
120

350
300

3

3

Water consumption (km / year)

160

100
80
60
40
20
0
1990

(a) Industry
2000

2010

2020

2030

250
200
150
100
50

(b) Agriculture

0
1990

2040

2000

2010

20

3

2020

2030

2040

Year

Year
Water consumption (km / year)203

MF scenario
FW scenario
PR scenario
GT scenario

18
16
14
12
10
8
6
4
2
0
1990

(c) Domestic
2000

2010

2020

2030

2040

Year

1400

500

1200

/ y e a r)

450

3

350

1000
800
600
400
200
0
1990

(a) Withdrawal
2000

2010

2020

2030

2040

W a te r c o n s u m p tio n (k m3

Water withdrawal (km / year)

Fig. 3a-3c. Water consumption in the three sectors (km3/year)

400
MF

300
250

FW
PR

200

GT

150
100
50

(b) Consumption

0
1990

2000

Year

Fig. 4a and 4b. Total water withdrawal and consumption

2010

2020

Y ear

2030

2040

204

Takahashi, K. et al.

12.2.5 Findings and future improvements
In this case study, water withdrawal and consumption in China under four
alternative future development scenarios were estimated for the period from 1995
to 2032. Although withdrawal will increase in all the scenarios, reflecting thesignificant increase in industrial withdrawal, water consumption can be kept at thecurrent level if appropriate policies are adopted to accelerate the introduction ofefficient technologies. Increasing industrial water consumption, which isunavoidable under rapid industrialization, can be accommodated throughefficiency improvements in the agricultural sector.
This study requires improvement with regard to some points. Firstly, in thisstudy, only the quantity of water withdrawn from water resources and consumed
through human activities has been analyzed. However, water quality has not been
considered in any form. Even if a sufficient quantity of water is available for
future human activities, the actual quality of the water may not be appropriate forsome uses. The aspect of water quality should be included in the assessment.
Secondly, the results of the assessment depend substantially on the assumptions
with regard to water use efficiency improvements as well as on the socioeconomic
scenarios for the driving forces. In this study, the assumptions regarding efficiency
improvements are set exogenously considering the historical trend. However,these assumptions need to be related to the availability of concrete technology
options for more efficient water use. Detailed databases on technology options
with information on costs and level of efficiency need to be developed to add
greater reality to the assessment.

12.3 Adaptation Policies in China for Mitigating Flood
Damage12.3.1 Importance of long-term adaptation policy
Adaptation to climate change impacts is considered to be an important andefficient strategy. Nevertheless, the efficiency of each adaptation strategy has notbeen sufficiently analyzed to make it possible to propose a detailed action plan,though adaptation options have been compiled for each sector. The limitations aremainly derived from the following features of climate change impact andadaptation studies: (a) the climate change impacts are still uncertain; (b) the
mechanisms of adaptation are too complex to be evaluated. In spite of these
difficulties, each country should actively seek to elucidate what can and should bedone in the early 21st century to mitigate the negative impacts of climate change,as there are long time lags in relation to the introduction of capital stock and newtechnologies in human activities in response to changing economic conditions.
China is expected to experience significant impacts on the hydrological cyclefrom climate change. However, it is very difficult for the Chinese government tointroduce a long term adaptation policy since China places the priority on short- to12. Impact and Adaptation Assessment on a National Scale

205

medium-term policies. In order for China to introduce long term policies, thebenefits of the integration of long term adaptation policies with short term policies
need to be evaluated. A macroeconomic model is introduced to evaluate theeffects of investment in flood mitigation infrastructure in China considering
climatic variability and climate change.

12.3.2 Model structure
There are some references to the multi-sector models such as by Duraiappah(1993) and Masui et al. (2000). In this study, the focus is a dynamic optimization
model with two economic sectors: the agricultural sector and the non-agricultural
sector. Two climate discount factors are considered in the model: damage fromclimatic variability and damage from climate change. The solution of this model ishighly normative, since the production and distribution of goods are determined so
as to maximize the temporary sum of the discounted utility derived from final
consumption. The set of model equations is presented below.

Objective function
The fundamental assumption is that policies should be designed to maximize thegeneralized level of consumption now and in the future. This approach rests on theview that more consumption is preferred over less, and in addition that incrementsof consumption become less valuable as consumption levels increase (Nordhaus1994). In technical terms, these assumptions are embodied by maximizing a social
welfare function that is the discounted sum of the utility of per capita consumption.
The objective function to be maximized is:

Max U =∑ (∏ C (t ) ) × (1 + ρ ) 
i

t

vi

−t

(1)

i

Where U is the flow of utility, Ci is the flow of consumption from sector i percapita at year t, and ρ is the pure rate of social time preference that allows for
distinguishing the relative emphasis on different generations, a value of 0.03 peryear is applied by Nordhaus (1994). Also set to 0.03. ν is the consumption share ofeach sector product, the sum of νi (i=1,2) is equal to 1. The planning horizon spans
105 years, from 1995 to 2100, with each time period in the model specification
representing a one-year interval.

Production function
Total intermediate input and the production factor are aggregated to obtain total
output using the Leontief production function. The total outputs of the agriculturaland non-agricultural sectors are expressed as follows: Maa(t ) Mna(t ) YGDPa(t ) 
Ya(t ) = min
,
,

n 2a
a 2ao  a 2a(2)

206

Takahashi, K. et al.

 Mnn(t ) Man(t ) YGDPn(t ) Yn(t ) = min
,
,

a 2n
n 2no  n 2n(3)

Where, Ya(t) and Yn(t) are the total outputs of the agricultural and non-agricultural
sectors, respectively; Maa(t) and Man(t) are intermediate inputs from theagricultural sector to both the agricultural and non-agricultural sectors,
respectively; Mnn(t) and Mna(t) are intermediate inputs from the non-agricultural
sector to both the non-agricultural and agricultural sectors, respectively. YGDPa(t)and YGDPn(t) represent the productions of the agricultural and non-agricultural
sectors; a2a, n2a, n2n, and a2n are input coefficients and are equal to 0.16064,
0.24199, 0.59804, 0.05394 respectively; a2ao and n2no are production factors andare equal to 0.59737 and 0.34803, respectively. They are aggregated from six
sectors (SSB 1999).

Agricultural production is expressed as follows:

YGDPa(t ) = Aa(t ) × Ka (t ) β × La (t )γ × F (t ) λ(4)Where Aa(t) is the total factor of productivity; Ka(t) is the capital stock, La(t) is
the labor input, F(t) is the land input, β is the elasticity of the capital input, γ is theelasticity of labor, λ is the elasticity of land. Based on the historical data from1972 to 1995, β, λ and γ are determined to be 0.55, 0.20 and 0.25 in agriculture,
respectively, during the period 1991-1995 (Zhu and Liu 1998).
The non-agricultural sector production is expressed as a function of technology,
capital and labor.

YGDPn(t ) = An(t ) × Kn(t )1−α × Ln(t )α(5)Where α is the elasticity of output with respect to capital, An(t) is the total factor
of productivity, Kn(t) is the capital stock, Ln(t) is the labor input. The nonagricultural capital elasticity (α) is set to 0.3 in this study according to the latest
study by Jiang et al. (1998).
It is assumed that land is primarily used for agricultural activities, and that the
total area remains unchanged throughout the simulated period, which is the goal of
the government in guaranteeing the supply of food for its huge population. Since
the rapid development of the non-agricultural sector accounts for some land,
keeping the total area unchanged involves reclaiming some abandoned areas thatin general have contributed to a decline in land quality. According to a study (Gaoet al. 1998), land quality decreased 2.53% from 1985 to 1995 at mean annual rateof 0.25%. It could be expected that the occupation of cultivated land will approachzero when the population growth reaches zero and the urbanization process
basically stops. So it is assumed that land quality will decrease 0.25% annually
until 2030 and 0.15% per year from 2031 to 2050 and 0% after 2050.

12. Impact and Adaptation Assessment on a National Scale207

National accounts and capital constraints
The distribution relation of goods is of the standard input-output style. It statesthat the total sectoral output must be equal to the sum of consumption demand,investment, intermediate requirements, and adaptation investments, taking into
consideration negative climate change impacts. It is assumed that the nonagricultural sector is the primary sector that contributes to capital formation foritself as well as for the agricultural sector. Imports and exports are not considered
independently in goods distribution.

Ya(t ) = Ca (t ) + Ia(t ) + Iav(t ) + Iac(t ) + Maa(t ) + Man(t )Yn(t ) = Cn(t ) + In(t ) + Inv(t ) + Inc(t )+ Mnn(t ) + Mna(t ) + Ina(t )

(6)
(7)

Where, Ia(t), Iav(t) and Iac(t) are contributions of the agricultural sector toagricultural capital stock, investment for flood control, and extra investment for
projected flood damage from climate change. In(t), Inv(t) and Inc(t) arecontributions of the non-agricultural sector to non-agricultural capital stock,investment for flood control, and extra investment for projected flood damage
from climate change. Ca(t) and Cn(t) represent the consumption of agriculturaland non-agricultural goods, respectively; Ina is the contribution of the nonagricultural sector to the formation of agricultural capital stock.

Ka (t ) = (1 − δ ) Ka (t − 1) + Ia(t − 1) + Ina(t − 1) − Dak (t − 1)

(8)Kn(t ) = (1 − δ ) Kn(t − 1) + In(t − 1) − Dnk (t − 1)

(9)

INF (t ) = (1 − δ ) INF (t − 1) + Iav(t − 1) + Inv(t − 1)

(10)

INFc(t ) = (1 − δ ) INFc(t − 1) + Iac(t − 1) + Inc(t − 1)

(11)

Where: δ is the depreciation rate of the capital stock and flood mitigation
infrastructure; Ka and Kn are capital stocks of the agricultural and non-agricultural
sectors, respectively; INF, INFc are the infrastructure stock that is designed tomitigate flooding from climatic variability and projected climate change,
respectively, Dak and Dnk are the flooding damage to capital stocks of the
agricultural and non-agricultural sectors.
The respective capital stocks for the agricultural sector and for all sectors in1995 were 395.91 billion Yuan (Zhu and Liu 1998) and 5663.3 billion Yuan (Jiang
et al. 1998) at 1980 prices. Both of them are converted, based on the overall retailprice index (SSB 1996), to 1300.21 billion Yuan and 17298.74 billion Yuan at1995 prices. The stock of the infrastructure for flood mitigation was 26.045 billion
Yuan in 1995 at 1995 prices, too, calculated cumulatively from 1956. The
depreciation rate of the capital stock and infrastructure in China varies from year
to year, with a tendency towards a gradual increase. The depreciation rate for the
fixed assets of stated-owned enterprises increased from 4.1% in 1980 to 5.5% in208Takahashi, K. et al.

1992 (SSB 1995). It is set at 5% in this study.

Damage function
It is quite difficult to establish the damage function between flood mitigation
infrastructure stock per capita and flood damage to the agricultural and nonagricultural sectors, respectively, since there exists only very limited reported
flood damage, and even that is in an aggregated form. There are two types of
direct flood damage: damage to land/crops in the agricultural sector, and damage
to capital stock in both sectors. The first type damages crop yields by inundation,
often leading to a loss of the harvest, or damages land as a result of erosion or
fluvial and alluvial sedimentation. In the latter case, floods not only destroy thesown crops, but also make future cultivation impossible. In this study, it isassumed that flood damage to land has no impact on future use.
Based on the total direct flood damage and the statistical data for the sown
crops covered and affected by flood (SSB 1981), the following damage functions
are established.

Dnkv(t ) = 10 nk 0 × [INF (t − t ' ) / P(t − t ' )](12)

Dakv(t ) = 10 ak 0 × [INF (t − t ' ) / P(t − t ' )]

ak 1

(13)

Dalv(t ) = 10 al 0 × [INF (t − t ' ) / P(t − t ' )]

al 1

(14)

nk 1

Where, Dnkv, Dakv, Dalv represent damage to capital stocks of the nonagricultural and agricultural sectors, and land, respectively. P is population. t’ isthe time lag during which investment takes effect and equals 5; nk0, nk1, ak0, ak1,
al0, al1 equal to 1.513, -0.918, 0.794, -0.771, 0.984, -0.355, respectively.
By assuming that equations (12)~(14) are still applicable in the future if
climate change occurs and that the marginal cost of reducing unit flood damage
from climate change is the same as that from climatic variability, the damage
functions of climate change can be derived from equations (12)~(14).

Dnkc(t ) = 10

nk 0

Dakc(t ) = 10

ak 0

Dalc(t ) = 10

al 0

 INFc(t − t ' )  Dc(t ) 1/ nk 1 ×+  nk 0   10   P(t − t ' )nk 1

 INFc(t − t ' )  Dc(t ) 1/ ak 1 ×+  ak 0   10   P(t − t ' )ak 1

 INFc(t − t ' )  Dc(t ) 1/ al1 ×+  al 0   10   P(t − t ' )

(15)

(16)

al 1

(17)

12. Impact and Adaptation Assessment on a National Scale

209

Dnk (t ) = ( Dnkv(t − 1) + Dnkc(t − 1)) × YGDPn(t − 1)

(18)

Dak (t ) = ( Dakv(t − 1) + Dakc(t − 1)) × YGDPa(t − 1)

(19)

F (t ) = F 0 × [1 − Q(t )] × [1 − Dalv(t ) − Dalc(t )](20)

F is the effective land input considering flood damage and the reduction of land
quality. The equation for climate change damage, Dc(t), is expressed as

Dc(t ) = Dref T (t ) 2 / 6.25

(21)

Where T(t) is the temperature increase in year t. Damage caused by flooding underclimate change with a 2.5oC increase in temperature is assumed to be Dref, thequadratic term of the temperature reflects the assumption that the damage isquadratic, along with the temperature increase (Nordhaus 1994). Based on a studyof physical impacts on surface runoff, and the intensity and frequency of floods
under projected climate change (paper in preparation), it is assumed that the flood
damage under a future climate in the year 2100 will double, and the damage fromclimate change is the same as the current maximum damage of 3.9% in 1994.

Growth rate of technology and the total productivity factor
The growth rate of technology during the period 1991-1995 was 2.5% for the
agricultural sector (Zhu and Liu 1998) and 3.1% for all sectors, estimated basedon a study (Jiang et al. 1998). The figure 3.1% is taken as the approximate growthrate of technology in the non-agricultural sector due to the approximately 20%
share of the total GDP held by the agricultural sector. So the initial values for thetechnology growth rate for the agricultural and the non-agricultural sectors are
2.5% and 3.1%, respectively. Due to a lower technological level and a lower
contribution of technology growth to economic growth (about 30%) (Ministry ofAgriculture 1996; Jiang et al. 1998), it is expected that technology growth willcontinue to speed up within the next several decades, and then level off andafterwards decline annually. So it is assumed that the rate of change in thetechnology growth rate is to be 1%, 0% and -1%, annually for the periods 19962030, 2031-2050 and 2051-2100, respectively. The growth rates of technology forthe agricultural and the non-agricultural sectors at year t are estimated as follows(Nordhaus 1994):

GTa (t ) = GTa (t − 1) × [1 + ϕa(t )](22)

GTn(t ) = GTn(t − 1) × [1 + ϕn(t )](23)

where φa(t) and φn(t) are the rates of change in technology growth for the
agricultural and the non-agricultural sectors. GTa and GTn are the growth rates oftechnology for both sectors. Then the total productivity factors are calculated as,

210

Takahashi, K. et al.

Aa(t ) = Aa 0 × exp[GTa (t )](24)

An(t ) = An0 × exp[GTn(t )](25)

The values of the total productivity factors in the initial year, Aa0 and An0, can becalculated, based on equations (4) and (5).

Population and labor
Labor is assumed to be proportional to population. According to a report (Li et al.
2000), demographers at the China Renmin University put forward three
population scenarios for the next 100 years (High, Medium and Low). The
projected population in 2100 is 1.5 billion, 1.0 billion, and 0.8 billion under High,
Medium and Low scenarios, respectively. The common assumption for the three
scenarios is that population increases from 1.21 billion in 1995 to near 1.4 billion
by 2010, 1.6 billion by 2030 and then levels off after 2030 until 2035. The
population growth rate in 1995 is 1.0605%, and the decrease in the rate of
population growth from 1996 to 2030 is estimated to be 1.6%.
In 1995, 52.2% of the labor force was employed in the agricultural sector and47.8 % in the non-agricultural sector (SSB 1996). As the share of the agricultural
sector declines along with economic development, labor migrates to the cities andenters the formal or informal labor market. In this research, a simplified migration
scenario is assumed in which 70% and 80% of labor will be employed in the nonagricultural sector by 2050 and 2100 respectively.

12.3.3 Scenarios of adaptation and climate change
Four scenarios combining climate change and investment in the flood mitigation
infrastructure were assumed for simulation, based on the assumption that policy
makers will optimize investment in flood prevention infrastructure to reduce the cost ofdamage caused by floods from the current climatic variability, whether climate change
occurs or not (Table 2).

1. CnAn: policymakers do not arrange adaptation investment for the projectedclimate change and climate change does not occur.
2. CyAn: policymakers believe climate change will not occur and thus noadaptation investment is implemented to mitigate flood damage from climatechange, but unfortunately, climate change occurs.
3. CyAy: policymakers believe climate change will occur and thus adaptation
investment is arranged to mitigate flood damage from climate change andclimate change occurs.
4. CnAy: policymakers believe that climate change will occur and adaptation
investment is arranged to mitigate flood damage from climate change, theamount of investment is assumed to be the same as that in CyAy, but climatechange does not occur.

12. Impact and Adaptation Assessment on a National Scale

211

Table 2. Scenarios considering policy options and the probability of climate change
occurring
InvestClimate change
Yes
No
CyAy
CnAy
CyAn
CnAn

Yes
No

Policy options

12.3.4 Analysis
The model first is run on the following assumptions: population growth follows
the medium scenario (1.0 billion in 2100); the non-agricultural sector employs
70% of the labor force by 2050 and 80% by 2100; the marginal adaptation costs ofadapting to climate change are assumed to be the same as those for current
climatic variability. Taking CnAn as a base scenario, flood damage to cultivatedland (Fig. 5) from climatic variability and climate change is expected to increase
to 1.13% in 2100 even if investment takes climate change into consideration andclimate changes do occur (scenario CyAy); the highest level of damage is about
1.58% around 2050. The damage gradually increases to 3.11% by the end of the
century when there is no investment in flood prevention infrastructure to combat
projected climate change (scenario CyAn). When climate change does not occur
while adaptive investment is being implemented (scenario CnAy), flood damage islower than the base scenario (scenario CnAn) as adaptation investment adds extra
adaptive capacity, thus mitigating the damage from climatic variability. Damage tothe agricultural capital stock and non-agricultural capital stock (Figs. 6 and 7)
under these four scenarios shows a similar pattern to that of agricultural land.
3.5

CnAn
CyAn
CyAy
CnAy

3

%

2.5
2
1.5
1
0.5
0
2000

2020

2040

2060
Year

Fig. 5. Flood damage to cultivated land

2080

2100

212

Takahashi, K. et al.

Investments ignoring climate change will cause damage starting around 2020
when climate change occurs.
Utility per capita decreased by 0.83% when no adaptation investment is
implemented while climate change occurs, but the reduction can be reduced to
0.1% if adaptation investment is implemented (Fig. 8).
Changes in utility per capita were accumulated to analyse the effects of longterm adaptation policy and short term policy. Two criteria (“maximax” and“maximin”) for decision making under uncertainty were applied in the study toselect the best option in response to uncertain climate change. The maximax
criterion is based on the assumption of an optimistic decision maker. The best
outcomes expected under each decision alternative considering uncertainty arecompared, and the alternative that represents the best of the best outcomes isselected. The maximin criterion is based on the assumption of a conservative(pessimistic) decision maker. Worst outcomes expected under each decision
alternative are compared, and the alternative that represents the best of the worst
outcomes is selected.
Table 3. Decision making based on changes in utility
Climate change
YesNo-563.2-17.6-4158.7
0.0
-22.8
-1.8
-21.4
0.0

Invest1995-2100
1995-2020

Yes
No
Yes
No

Best option
Maximax
Maximin!
!
!

!

The result (Table 3) shows the best option in the short term is not to adapt toclimate change. In the long term, the best option is to adapt to climate change, if
policy makers prefer risk aversion decisions (maximin criterion).

5

0.2

4

%

03
%

-0.2
-0.4

2

-0.6 1-0.8 0-1 2000
2000

CnAn
CyAn
CyCy
CnAy

CnAn
CyAn
CyAy
CnAy

2020

2040

2020

2040

2060

2080

2100

Year 2060

2080

2100

2080

2100

Year

Fig. 7. Damage to capital stock in the non-agricultural sector
5Fig. 8. Changes in the utility compared with the baseline (CnAn)

CnAn
CyAn
CyAy
CnAy

4
%

3
2
1
0
2000

2020

2040

2060
Year

Fig. 6. Damage to capital stock in the agricultural sector

12. Impact and Adaptation Assessment on a National Scale

213

12.3.5 Findings from the simulation
In this case study, a dynamic macroeconomic model dealing with investment
optimization considering climate variability and climate change is created and asimulation extended for 105 years from 1995 to 2100 in China. A feature of thismodel is that it considers two sectors, two discount factors from climatic
variability and climate change, as well as labor migration and capital flows
existing between the two sectors. The following results have been obtained byusing this model:
1. Investments that are optimized ignoring climate change will cause severe
damage starting about 2020 and reaching its peak by 2100 when climate change
occurs. Optimizing investment considering both climatic variability and climatechange can effectively mitigate flood damage from climate change.
2. Investment against projected climate change is the best option from a centurylong perspective in a situation of uncertainty in relation to climate change. In
the short term, the best option is not to invest.

12.4 Conclusion
The importance of impact assessment on a regional or national scale has been
increasing. In order to evaluate regional impacts and adaptation measures, inaddition to spatially detailed data, new assessment tools that consider diverse
alternative socioeconomic development paths and effective adaptation strategies
are expected to be developed. Moreover, there is also a need for tools to build
more facilities for local actors, who are better able to comprehend the significant
problems and the feasible solutions, to evaluate impact and adaptation strategies
by themselves. AIM/Impacts [Country] is expected to meet these needs.
In this report, the results of case studies on China were presented, wherecollaborative research with local organizations has continued for a long time. In
the next stage, while the AIM/Impact [Country] model continues to be developed
and refined, the area that is subject to national scale assessment will expand from
China to the South and Southeast Asian countries, where the most drastic
socioeconomic developmental transformation and serious climate change impacts
are expected.

References
Duraiappah AK (1993) Global warming and economic development, a holistic approach tointernational policy co-operation and co-ordination. Kluwer Academic Publishers.
Gao ZQ, Liu JY, Zhuang DF (1998) The dynamic changes of the gravity center of the
farmland area and the quality of the farmland ecological background in China. Journalof Natural Resources 13(1): 92-96 (in Chinese)

214

Takahashi, K. et al.

Jiang JL, Ruan GF, Ren L (1998) Analysis on effect of technology progress on economic
development in China. In: Estimation of technology progress contribution to economic
development: theory and practice. China Planning Press (in Chinese)
Kong SH (1999) Effects of transfer of surplus rural labor on advance of industrial structure.
China Economics Publishing House (in Chinese)
Li Y, Shen ZH, Guo N (2000) One birth or two births? Life Weekly 10: 31-44 (in Chinese)
Masui T, Morita T, Kyogoku J (2000) Analysis of recycling activities using multi-sectoral
economic model with material flow. European Journal of Operational Research 122:405-415
Ministry of Agriculture (1996) China agricultural development report-96. China
Agricultural Press (in Chinese)
Nordhaus WD (1994) Managing the global commons - the economics of climate change.
The MIT Press
Shi QQ and Qin BT (1998) Study on effect of technology progress on economic
development. In: Estimation of technology progress contribution to economic
development: theory and practice. China Planning Press (in Chinese)
Shiklomanov IA (1998) Assessment of water resources and water availability in the world,
Comprehensive assessment of the freshwater resources of the world, Stockholm
Environment Institute
SSB (1981) China Statistical Yearbook-1981. China Statistical Publishing House (inChinese)SSB (1995) China Statistical Yearbook-1995. China Statistical Publishing House (inChinese)SSB (1996) China Statistical Yearbook-1996. China Statistical Publishing House (inChinese)SSB (1999) China Statistical Yearbook-1998. China Statistical Publishing House (inChinese)UNEP (2002) GEO-3, Past, present and future perspectives. Earthscan
WRI (2001) World resources 2000-2001: People and ecosystems: The fraying web of life.
World Resource Institute
Zhu DJ (1999) Study on labor migration between rural and urban under market of threesectors. China Economics Publishing House (in Chinese)
Zhu XG, Liu YF (1998) Estimation of the contribution of technology growth to agriculturaldevelopment in China. In: Estimation of the contribution of technology progress toeconomic development: theory and practice. China Planning Press (in Chinese)