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STUDY ON THE IMPACTS OF CLIMATE CHANGE
ON CHINA’S AGRICULTURE HUI LIU 1, XIUBIN LI 1 , GUENTHER FISCHER 2 and LAIXIANG SUN 2
1 Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
E-mail: [email protected]
2 International Institute for Applied System Analysis, A-2361 Laxenburg, Austria
Abstract. This paper measures the economic impacts of climate change on China’s agriculture
based on the Ricardian model. By using county-level cross-sectional data on agricultural net revenue,
climate, and other economic and geographical data for 1275 agriculture dominated counties, we find
that under most climate change scenarios both higher temperature and more precipitation would
have an overall positive impact on China’s agriculture. However, the impacts vary seasonally and
regionally. Autumn effect is the most positive, but spring effect is the most negative. Applying the
model to five climate scenarios in the year 2050 shows that the East, the Central part, the South, the
northern part of the Northeast, and the Plateau would benefit from climate change, but the Southwest,
the Northwest and the southern part of the Northeast may be negatively affected. In the North, most
scenarios show that they may benefit from climate change. In summary, all of China would benefit
from climate change in most scenarios.
1. Introduction
Since recognition of potential climate change, efforts have been underway to estimate the economic impacts of projected changes in climate on important sectors,
such as agriculture, forestry and ecosystem, coastal zones and fisheries, water resources, and energy development. Although several sectors have been studied, none
have received more attention than agriculture. Only a few, however, have looked
at the agricultural impacts of climate change in developing countries like China
where agriculture is a large component of GDP.
Geographically, China touches the tropical belt in the south and extends into
the cold temperate zone in the north. China is also a large agricultural country
where agriculture constituted 18.7% of GDP in 1997. China’s agriculture has to
feed more than one-fifth of the world’s population, and, historically, China has
been famine prone. As recently as the late 1950s and early 1960s a great famine
claimed about thirty million lives (Ashton et al., 1984, Cambridge History of China
1987). Since economic reform, there has been an unprecedented conversion of
arable land into non-agricultural uses following rapid economic development and
industrialization. This loss of agricultural land, together with the trend towards
Foundation item: Young Scientist Summer Program at the International Institute for Applied
System Analysis, YSSP 1999, Austria.
Climatic Change 65: 125–148, 2004.
© 2004 Kluwer Academic Publishers. Printed in the Netherlands.
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HUI LIU ET AL.
a much higher demand for agricultural products for the growing and wealthier
population, has resulted in a debate about the country’s long-term capacity to feed
itself. How China will avoid national chronic food insecurity in the future is an
issue, which is inevitably of significant global implication. Whether China can feed
itself in the future not only depends on agricultural land resources but also depends
on the impacts of climate change on its agriculture in the future.
Some studies on the impacts of climate change on China’s agriculture have
been done. However, these studies were either in a limited area or for the yield of
a single crop, such as rice, wheat or maize (Ying, 1995; Gao, 1993; Deng, 1993).
Recently, some scientists have used the AEZ model developed by the International
Institute of Applied System Analysis (IIASA) and FAO to assess the impact of
climate change on China’s agricultural land productivity (Tang et al., 2000). Other
scientist use FARM (Future Agriculture Resource Model) promulgated by Darwin
et al. to provide an estimate of economic impact of climate change on agriculture
for all of China plus South Korea (Darwin, 1999a). Although some scientists have
studied the economic impact of climate change on agriculture in China (Darwin,
1999a), it only estimated the total average impact that includes not only China but
also Korea. A study on the impacts of climate change on China’s entire agricultural
output in value and their regional diversity has not been done yet.
This paper provides the first regionally detailed estimates of the economic impact of climate change on agriculture in China. The analysis computed the impacts
of changes in temperature and precipitation on agricultural net revenue per Mu
(1 Hectare = 15 Mu) and the aggregate effects across Mu’s for different scenarios.
We utilized county-level agricultural, climate, social economic and edaphic data
for 1275 agriculture-dominated counties, for the period of 1985–1991, to examine
farmer-adapted response to climate variations across the country. Although we applied methodologies developed in the United States, careful attention was paid to
adapting these methods to China’s conditions. For example, the studies paid careful
attention to irrigation, farm labor, management, and technology development.
2. Methodology
We use the Ricardian approach to estimate the impacts of climate change on
China’s agriculture. The Ricardian approach examines how climate in different
places affects the net rent or value of farmland (Mendelsohn et al., 1994). This
approach is a cross-sectional empirical analysis designed to capture the effect of
‘natural experiments’ practiced by farmers across different climate zones or locations. In other words, the farming activities across a large country with sufficiently
varying climate can be used as samples for comparing farmers’ response to change
climate. The method uses the typical economic measure of farm performance: net
revenue or net farm income. By examining the economic performance of farms
across different climate and regressing farm performance on long-term climate, one
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
127
can empirically estimate long-term climate sensitivity. The Ricardian approach has
been used to estimate the impacts of climate change on agriculture in the United
States (Mendelsohn et al., 1994, 1996, 1999; Schlenker et al., 2002), India (Dinar
et al., 1998) and Brazil.
The most important advantage of the Ricardian approach is its ability to capture
the adaptation that farmers make in response to local environmental conditions.
It captures the actual response rather than the controlled ones. In addition, it is
capable of capturing the farmers’ choices over crop mix instead of yield.
A valid criticism of the Ricardian approach is that it has historically assumed
the price to be equilibrium, and in case of significant climate change the crop price
could change for a prolonged period. Under such circumstances, the Ricardian
estimate would be either over- or underestimating the climate change impacts,
depending on how the prices change. The bias was calculated to be small in most
relevant examples of climate change (Mendelsohn et al., 1996). However, Darwin
(1999a) showed that in a global generated equilibrium analysis, changes in Ricardian rents systematically overestimate both benefits and losses and on average are
upwardly biased because inflated benefits are larger than exaggerated costs. The
bias can be quite large. Another valid criticism is that it is difficult to incorporate
carbon dioxide fertilization into the regression. Moreover, when using the Ricardian method, the selection of weights and failure to control for irrigation could
influence the estimate of climate change on agriculture (Schlenker et al., 2002).
2.1. THE RICARDIAN MODEL
This section summarizes the theoretical understandings of the Ricardian model by
Roy Mendelsohn et al. (Mendelsohn et al., 1996; Dinar et al., 1998).
If use i is the best use for the land Li given the environment E and factor prices
R, the observed market rent of the land will be equal to the annual net profit from
the production of crop i, therefore, land rent per hectare is equal to net revenue per
hectare (Dinar et al., 1998), i.e.:
pL = [Pi Qi − Ci (Qi , R, E)]/Li ,
(1)
where pL is land rent per hectare, Pi and Qi are respectively the price and
quantity of crop i, Ci ( ) is the function of all purchase inputs other than land.
R = [R1 . . . . . .Rj ] is the vector of factor prices, E is an exogenous environmental
input into the production of goods, e.g., temperature, precipitation, and soils, which
would be the same for different goods’ production.
The present value of the stream of current and future revenues gives land value:
∞
Vl =
pL e
0
−rt
dt =
0
∞
[Pi Qi − Ci (Qi , R, E)]e−rt /Li dt .
(2)
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HUI LIU ET AL.
We assume that the change of environment from EA to EB will leave market
prices of inputs (fertilizer, labor) and outputs unchanged. The change in annual
welfare from this environment change is given by:
W (EB − EA ) = PQB − Ci (Qi , R, EB ) − [PQA − Ci (Qi , R, EA )] .
(3)
Substituting Equation (1) into Equation (3) gives:
W (EB − EA ) = (pLB LB − pLA LA ) ,
(4)
where pLA and LA are respectively net revenue per hectare and planted land area at
EA and pLB and LB are at EB . The present value of this welfare change is thus:
∞
W (EB − EA )e−rt dt = (VLB LB − VLA LA ) ,
(5)
0
where VLB and VLA are respectively land value at EB and EA .
The Ricardian model takes the form of either (1) or (5) depending on whether
the dependent variable is annual net revenues or farm value. The value of the
change in the environmental variables is captured exactly by the change in land
values across different conditions. Cross-sectional observation, where normal climate and edaphic factors vary, can hence be utilized to estimate farmer-adapted
climate impacts on production and land value.
2.2. MODIFIED RICARDIAN MODEL IN CHINA
Due to imperfect land markets and lack of documentation of agricultural farm
values in China, in this paper, we estimate (1), using annual net revenues as the
independent variable.
According to the Ricardian model, theoretically, the net revenue should be:
Net revenue = [Pi Qi − Ci (Qi , R, E)]/Li ,
(6)
where Pi and Qi are respectively the price and quantity of crop i, Ci ( ) is the
function of all purchase inputs other than land, Li is the area planted for crop i.
However, in practice, due to the limitations of available data in China, it is better
to use net income as net revenue.
Net income = [(Pi Qi ) − (Pn Mn ) − (Wg ∗ La ∗ days)]/Li ,
(7)
where Pi and Qi are respectively the price and quantity of crop i, Pn and Mn
are respectively the price and quantity of the input material n, such as, fertilizers,
pesticides, seeds, electricity, etc., Wg, La, and days are respectively the labor cost,
numbers of agricultural labor, and the average number of days worked in the states
by farm workers.
Actually, there is no price for each crop and farm labor in the statistical data.
There is only agricultural output in China’s statistical yearbook. In China, the pure
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
129
agricultural output is defined as the total output of agriculture minus the whole
expenditure of material input except labor forces. Therefore, the net income can be
defined as:
Net income = (PAO − Wg ∗ La ∗ days)/Li ,
(8)
where PAO is the pure agriculture output.
A key characteristic of Chinese agricultural sectors is the large share of nonhired household labor in agriculture and the absence of the labor price. Ignoring
non-hired labor underestimates input costs, and hence overestimates net revenue.
For these reasons, the cost of labor forces is removed from the function of net
income and will be considered as an independent variable in the Ricardian climate
regression.
From the above analysis, the net revenue in China is modified as pure agricultural output per Mu of agricultural land. For county K in year y, the net revenue
is:
K
K
Net revenueK
y = PAOy /Ly ,
(9)
where L is the total agricultural area including the cultivated land area, forest area,
grassland area, and water area of a county because the pure agricultural output in
China includes the output of agriculture, forestry, stock-raising and fishery.
3. Data Processing and Empirical Specification
Using the Ricardian technique, we estimate the value of climate in China’s agriculture. Agriculture is the most appealing application of the Ricardian technique
both because of the significant impact of climate on agricultural productivity and
because of the extensive county-level data on farm inputs and outputs.
3.1. DATA SOURCES AND DATA PROCESSING
For the most part, the data are actual county averages, from the IIASA database of
LUC project for the period of 1985–1991. Those data are the source for much of
the agricultural data used here, including pure agricultural output, area irrigated,
land use data, agricultural labor, etc. All of these are at county level in all of China
except Taiwan and Hong Kong.
3.1.1. The Dependent Variable: Net Revenue per Agricultural Mu
As discussed in the second section, the pure agricultural output per agricultural Mu
is used as the net revenue for each county, which can be calculated from formula
(9).
However, as inflation was very high in the late 1980s in China, the pure agricultural output of each county in different years needs to be modified by price index
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HUI LIU ET AL.
Table I
The agricultural net revenue in China (Yuan/Mu)
Year
Original net
revenue
Index of GOVA a
Modified net
revenue
1985
1987
1988
1989
1990
1991
88
113
138
149
172
188
1
0.9360–1.2947
1.2006–1.6456
1.2487–1.7113
1.5588–1.9671
1.5594–1.9614
88
97
96
97
103
109
a Sources: 1985–1990, IIASA database; 1991, calculated
from China Statistic Year Book 1992, State Statistical Bureau of the People’s Republic of China.
of gross output value of agriculture (GOVA). In addition, because the price index
of GOVA varied much from province to province, it needs to use a different index
in different provinces. The index in different years was calculated based on the
assumption that the index of 1985, the first year for analysis, is 1. Table I shows the
difference between the original agricultural net revenue and modified agricultural
net revenue in different years.
3.1.2. Explanatory Variables
Table II shows the explanatory variables and their processing.
3.2. CLIMATE SCENARIOS
Three general circulation models (GCMs), i.e., HadCM2, CGCM1, and ECHAM4,
were used to simulate China’s climate change under five climate change scenarios
for the periods of 2020s,1 2050s,1 and 2080s (Tang et al., 2000). Table III shows
some information of the GCMs, climate scenarios and simulated climatic factors
that would be used to simulate the impacts of climate change on China’s agriculture, which will be discussed in the fourth part of this article. Table IV presents the
average changes of temperature and precipitation in China for the various climate
scenarios. HadCM2 is based on 0.5% increase of atmospheric carbon dioxide level
per year and the other models are based on 1%. That is why the average increase
of temperature based on HadCM2 is lower than that based on the other models.
Some of the scenarios, such as HadCM2-gs and CGCM1-gs, include the cooling
effects of sulfate emissions (‘gs’) and others, such as HadCM2-gx, CGCM1-gg
and ECHAM4-gg do not (‘gg’, ‘gx’). Air temperature is expected to increase in
2020s, 2050s and 2080s represent the year of 2010 ∼ 2039, 2040 ∼ 2069 and the year of
2070 ∼ 2099 respectively.
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
131
Table II
Explanatory variables
Variables
Sources
Processing
Soil
Soil map of China,
IIASA database
Selecting the major type and
characteristic of each county
Slope
IIASA database
Weighted average
Elevation
IIASA database
Weighted average
Climate data
310 meteorological stations
in China
Interpolating county climate
data from station data
Distance to market
IIASA database
Distance to the provincial
capital city of each county
Social and
economic data
IIASA database
Directly extracted from
IIASA database
China under all of the five scenarios. Most of the scenarios (except CGCM1-gs,
and CGCM1-gg in 2080s) show that the total precipitation in China is expected to
increase in the future.
3.3. UNITS OF ANALYSIS
The units of observations for this analysis are 1275 counties in thirty provinces
and autonomous regions of China, which are not the whole counties of China.
Some counties are removed from our analysis because of changes in administrative
divisions and variation in county codes from year to year. So, first of all, it needs to
match the county code to the standard county code. During the match work, some
counties are removed from the analysis, which are: (1) the counties for which there
are no county code in the standard code system, (2) the counties missing crucial
data, such as, pure agricultural output, land use data, climate date or soil data etc.
In order to reduce the impacts of none-cropping factors, such as forestry, fishery,
and livestock raising, on the agricultural net revenue and emphasize the influence
of climate on the grains, the counties in which the arable land area is less than 25%
of the total macro-agriculture area (total area of arable land, forest land, grass land,
and water area) are also removed from our analysis.
Finally, 1275 agriculture-dominated counties are chosen as the basic observations for this analysis.
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HUI LIU ET AL.
Table III
Three GCMs and climate scenarios
Name of
GCMs
HadCM2
CGCM1
ECHAM4
Source of the
models
Britain
Canada
Germany
Climate
scenarios
HadCM2-gs,
HadCM2-gx
CGCM1-gg,
CGCM1-gs
ECHAM4-gg
Simulating
periods
2020s, 2050s, 2080s
2020s, 2050s, 2080s
2020s, 2050s, 2080s
Simulating
factors
Temperature and
precipitation in winter,
spring, summer, and
autumn for each county
Temperature and
precipitation in winter,
spring, summer, and
autumn for each county
Temperature and
precipitation in winter,
spring, summer, and
autumn for each county
Table IV
Average changes in temperature and precipitation in China for the various
Scenarios
Climate scenarios
Temperature (◦ C)
2020s 2050s 2080s
Precipitation (%)
2020s 2050s 2080s
HadCM2-gs
HadCM2-gx
CGCM1-gg
CGCM1-gs
ECHAM4-gg
0.8
1.6
2.5
1.7
1.8
1.0
5.8
1.2
–2.8
8.2
1.3
2.5
4.7
3.2
3.1
2.2
3.8
7.9
5.8
4.4
0.2
10.4
4.7
–6.2
10.4
2.9
18.6
–2.5
–8.1
13.6
3.4. RICARDIAN REGRESSION
The data is pooled and county level net agricultural revenue per Mu are regressed
on climate, soil, and other controlled variables, such as agricultural labor, distance
to market, etc., to estimate the best-use value function across different counties.
There are 1275 pooled cross-sectional observations.
The independent variables include temperature and precipitation terms for
spring, summer, autumn, and winter, pH and OM% (organic materials) of soil,
slope, elevation, agricultural labor per 100 Mu agricultural land, and distance to
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
133
market. For each variable, linear and quadratic terms are included to capture its
nonlinear effects on agricultural net revenue.
On the other hand, we should separate the irrigated agriculture and rain-fed
agriculture for the cultivated land and horticultural land because parameters on
climate variables in counties that rely heavily on irrigation differ from parameters
on climate variables in counties where there is no irrigation (Darwin, 1999b). To
illustrate these problems, the agricultural net revenue should be described as:
NR = SCrf NR(C+H )rf + SCir NR(C+H )ir + SGF NRGF
NRi = f (T , P , X) ,
and
(10)
where NR is net revenue per Mu, SCrf is the share of rain-fed cultivated and
horticultural land, NR(C+H )rf is net revenue per Mu on rain-fed cultivated and
horticultural land, SCir is the share of irrigated cultivated and horticultural land,
NR(C+H )ir is net revenue per Mu on irrigated cultivated and horticultural land,
SGF is the share of grassland and forest land, NRGF is net revenue per Mu on
grassland and forest land, NRi represents any of the three net revenue categories,
T is temperature, P is precipitation, and X represents other variables. Parameters
on the climate variables are expected to differ for each net revenue category.
Table V shows the original regression model. Where DIS.M, LAB._P, ELEV
and SLR are respectively distance to market, number of labor for agriculture per
100 Mu, elevation, and slope; spr.Pir(C + H ), win.Trf(C + H ), aut.P(GF) are
respectively spring precipitation for the share of irrigated cultivated and horticultural land, winter temperature for the share of rain-fed cultivated and horticultural
land, and autumn precipitation for the share of grassland and forestland. Through
stepwise regression, some variables, such as organic materials, pH, and slope,
which are insignificant in the analysis, are removed. Table VI shows the trimmed
regression model from which it can be seen that the squared terms for most of
the climate variables are significant, implying that the observed relationship is
nonlinear. However, some of the squared terms are positive, implying that there is a
minimal production level of those terms and that either more or less of these terms
will increase net agricultural revenue according to the observation’s current condition. The negative quadratic coefficient implies that there is an optimal level of
these climate variables from which the value function decreases in both directions.
For the grassland and forest, precipitation is more significant than temperature in
winter and spring, but in summer temperature is more significant. For the cultivated
land and horticulture land, both temperature and precipitation are significant in all
seasons.
The remaining control variables behave largely as expected. Social economic
variables, such as labor and distance to market, play a role in determining the value
of a farm. Agricultural labor has a positive impact on net revenue as expected.
Although both the relationship between net revenue and distance to market and the
relationship between net revenue and elevation are U-shaped, the minimal production level of distance to market and elevation is 509 Km and 1742 M respectively
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HUI LIU ET AL.
Table V
Result of Ricardian Regression (enter method)
(Constant)
DIS.M
DIS.M2
OM (%)
OM2
PH
PH2
LAB._P
LAB2
SLR
SLR2
ELEV
ELEV2
win Pir(C + H )
spr. Pir(C + H )
sum.Pir(C + H )
aut.Pir(C + H )
win Prf(C + H )
spr. Prf(C + H )
sum.Prf(C + H )
aut.Prf(C + H )
win P2ir(C + H )
spr. P2ir(C + H )
sum.P2ir(C + H )
aut.P2ir(C + H )
win P2rf(C + H )
spr. P2rf(C + H )
sum.P2rf(C + H )
aut.P2rf(C + H )
win Tir(C + H )
spr. Tir(C + H )
sum.Tir(C + H )
aut.Tir(C + H )
win Trf(C + H )
spr. Trf(C + H )
sum.Trf(C + H )
aut.Trf(C + H )
B
Std. error
t
Sig.
92.955
–7.542E-02
6.584E-05
–1.290
0.110
–15.076
1.059
4.888
–9.093E-03
–1.978E-02
8.483E-06
–3.549E-02
8.860E-06
0.182
–0.871
–2.334E-02
1.136
1.436
–0.103
–0.288
0.712
3.009E-04
8.859E-04
–1.605E-04
–5.532E-03
–1.501E-02
–1.745E-04
4.709E-04
–5.845E-03
0.143
–2.443
0.196
2.235
–0.163
0.206
–0.945
1.018
113.833
0.029
0.000
2.779
0.274
15.643
1.131
0.984
0.026
0.016
0.000
0.020
0.000
0.688
0.353
0.109
0.359
0.848
0.438
0.165
0.406
0.003
0.001
0.000
0.002
0.009
0.002
0.000
0.003
0.796
2.305
1.020
1.909
0.782
2.127
0.891
1.874
0.817
–2.604
1.748
–0.464
0.401
–0.964
0.936
4.968
–0.344
–1.216
1.072
–1.771
1.620
0.264
–2.467
–0.214
3.160
1.693
–0.235
–1.748
1.755
0.094
1.286
–1.437
–2.854
–1.710
–0.107
1.474
–2.117
0.180
–1.060
0.192
1.171
–0.208
0.097
–1.061
0.543
0.414
0.009
0.081
0.643
0.689
0.335
0.349
0.000
0.731
0.224
0.284
0.077
0.106
0.792
0.014
0.831
0.002
0.091
0.814
0.081
0.080
0.925
0.199
0.151
0.004
0.088
0.915
0.141
0.035
0.857
0.289
0.848
0.242
0.835
0.923
0.289
0.587
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
135
Table V
(Continued)
win T2ir(C + H )
spr. T2ir(C + H )
sum.T2ir(C + H )
aut.T2ir(C + H )
win T2rf(C + H )
spr. T2rf(C + H )
sum.T2rf(C + H )
aut.T2rf(C + H )
win.T(GF)
spr.T(GF)
sum.T(GF)
aut.T(GF)
win.P(GF)
spr.P(GF)
sum.P(GF)
aut.P(GF)
win.T2(GF)
spr.T2(GF)
sum.T2(GF)
aut.T2(GF)
win.P2(GF)
spr.P2(GF)
sum.P2(GF)
aut.P2(GF)
B
Std. error
t
Sig.
1.643E-02
–2.439E-02
1.763E-03
2.203E-02
1.843E-03
–1.115E-02
3.508E-03
2.735E-03
0.165
1.127
1.033
–1.155
–0.958
0.230
1.425E-02
–0.376
7.140E-04
–9.999E-03
–5.173E-03
8.482E-03
2.505E-03
1.068E-04
7.814E-05
1.129E-03
0.004
0.013
0.002
0.010
0.002
0.014
0.002
0.013
0.637
1.747
0.770
1.499
0.486
0.239
0.124
0.269
0.002
0.008
0.002
0.008
0.002
0.000
0.000
0.001
3.858
–1.915
0.992
2.164
1.062
–0.817
1.903
0.208
0.259
0.645
1.342
–0.771
–1.972
0.960
0.115
–1.394
0.327
–1.231
–2.636
1.062
1.262
0.352
0.544
1.238
0.000
0.056
0.321
0.031
0.288
0.414
0.057
0.835
0.796
0.519
0.180
0.441
0.049
0.337
0.908
0.164
0.744
0.219
0.008
0.289
0.207
0.725
0.586
0.216
a Dependent variable: net revenue.
b Weighted by cropland.
c Number of observation: 1275.
R 2 = 0.645.
according to the trimmed model. For almost all agriculture dominated counties in
China, the distance to market is less than 509 Km and the elevation is lower than
1742 M, that is to say, farther to the market or higher elevation decrease the value.
The characteristics of soil and slope are not significant to the value of farm in
China.
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HUI LIU ET AL.
Table VI
Result of Ricardian regression (stepwise method)
B
(Constant)
DIS.M
DIS.M2
LAB._P
ELEV
ELEV2
spr. Pir(C + H )
aut.Pir(C + H )
win Prf(C + H )
sum.Prf(C + H )
spr. P2ir(C + H )
sum.P2ir(C + H )
aut.P2ir(C + H )
win P2rf(C + H )
sum.P2rf(C + H )
aut.P2rf(C + H )
aut.Tir(C + H )
win T2ir(C + H )
spr. T2ir(C + H )
sum.T2ir(C + H )
aut.T2ir(C + H )
spr. T2rf(C + H )
sum.T2rf(C + H )
sum.T(GF)
win.P(GF)
spr.P(GF)
sum.T2(GF)
5.702
–8.050E-02
7.908E-05
4.548
–4.720E-02
1.355E-05
–0.834
1.035
1.023
–0.160
8.109E-04
–1.662E-04
–5.468E-03
–1.690E-02
4.609E-04
–2.270E-03
0.831
1.864E-02
–3.822E-02
1.911E-03
3.103E-02
–5.568E-03
1.802E-03
0.676
–0.258
0.232
–3.520E-03
Std. error
29.293
0.025
0.000
0.358
0.009
0.000
0.187
0.227
0.452
0.078
0.000
0.000
0.001
0.005
0.000
0.001
0.299
0.003
0.004
0.001
0.004
0.002
0.001
0.236
0.154
0.073
0.001
t
Sig.
0.195
–3.181
2.504
12.711
–4.982
3.280
–4.462
4.564
2.265
–2.045
1.887
–5.322
–3.762
–3.190
2.521
–2.277
2.780
6.446
–9.658
2.363
8.535
–3.633
2.362
2.859
–1.679
3.193
–4.294
0.846
0.002
0.012
0.000
0.000
0.001
0.000
0.000
0.024
0.041
0.059
0.000
0.000
0.001
0.012
0.023
0.006
0.000
0.000
0.018
0.000
0.000
0.018
0.004
0.093
0.001
0.000
a Dependent variable: net revenue.
b Weighted by cropland.
c Number of observation: 1275.
R 2 = 0.639.
4. Simulating the Impacts and Interpretation of Climate Coefficient
4.1. SIMULATING OF NET AGRICULTURAL REVENUE PER MU
Climate change data on the county level are used in simulating their impact
on China’s agriculture. First, the temperature and precipitation for each grid
(0.5◦ ×0.5◦ ) are calculated relying on GCMS. Then the grid data are translated into
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
137
average temperature and precipitation for each county in order to link the climate
data with agricultural data that are organized by county level.
The trimmed regression model is used to simulate the changes of net revenue
for each of 1275 counties for the analysis year. County-level changes in net revenue
per Mu are then aggregated to get a measure of the net impact for all of China as a
whole. The change of net revenue per Mu of the given scenario in year y is given
by:
CN =
y
1275
y
y
[Netrevnd (Td + TCS , Pd + PCS ) − Netrevnd (Td , Pd )]/1275 , (11)
d=1
where y is the year of 1985, 1987, 1988, 1989, 1990, 1991; (Td , Pd ) describes the
climate for county d; (Td + TCS , Pd + PCS ) describes the new climate under a
y
simulated climate scenario; Netrevnd (Td , Pd ) is predicted value of the net revenue
y
per Mu for county d in year y; Netrevnd (Td + TCS , Pd + PCS ) is the forecasted
value of net revenues under a climate scenario for county d in year y .
Yearly changes in the net revenue per Mu are correspondingly averaged over
the period (1985–1991) to yield an average net impact.
The change in net revenue per Mu is calculated for the new climate for each of
the four seasons. Table VII presents these impacts by season.
Overall, an increase in precipitation level is beneficial for increasing net revenue
per Mu of China’s agricultural land, whereas rise in temperature is whether harmful
or beneficial depending on different GCMs.
As shown in Table VII, there is significant seasonal variation in both temperature and precipitation effects. Temperature rise in all seasons except spring is
positive. Summer and autumn temperature effects are positive because warming
temperature during these two harvest seasons may facilitate the ripening process
and ensure optimal crop production. Positive winter temperature effect could be
the result of increasing the growing period by a warmer winter. However, a warmer
spring not only makes the winter crop grow too quickly, which leads to an increase
in the incidence of pests and insects that reduce the production, but also causes
high evaporation of soil water which would increase the damage to crops by spring
drought that happens frequently in China.
Increased precipitation in all of the four seasons is beneficial under most scenarios. More precipitation in winter can increase soil moisture for the winter crop. The
positive impact of spring precipitation under most scenarios is due to the increase
of rainfall, which in the spring can increase soil moisture and reduce spring drought
that is good for winter crop to turn green again and spring sowing. Autumn precipitation is good for planting winter crop. The negative effect of increased rainfall in
summer under some scenarios is expected for a summer-dominated rainfall climate
regime. It may lead to more flood disasters in China.
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HUI LIU ET AL.
Table VII
Changes in net revenue per Mu (1990 Rs) by seasonal temperature and precipitation
(Yuan/Mu)
Climate scenarios
E4-gg
H2-gs
H2-gx
CG-gs
CG-gg
Temperature
effects
2.961
–55.835
5.375
53.958
6.459
0.917
–17.309
2.398
29.936
15.942
1.574
–45.982
4.792
60.663
21.048
8.255
–155.128
4.267
41.128
–101.479
16.287
–166.999
6.651
65.794
–78.261
Precipitation
effects
4.366
1.754
–0.100
1.118
7.137
3.466
6.666
0.855
3.147
14.114
2.358
–1.026
1.311
2.849
5.491
3.465
4.419
0.996
3.802
12.618
3.623
–2.514
0.434
2.631
4.174
7.326
–54.081
5.275
55.076
13.596
4.363
–10.643
3.253
33.083
30.056
3.932
–47.008
6.103
63.511
26.539
11.720
–150.709
5.626
44.929
–88.798
19.909
–169.513
7.085
68.425
–74.092
Winter
Spring
Summer
Autumn
Total effects of temperature
Winter
Spring
Summer
Autumn
Total effects of precipitation
Effects of
temperature
and
precipitation
Total
Winter
Spring
Summer
Autumn
Average net revenue per Mu (in 1990Rs) = 102.8 Yuan.
4.2. SPATIAL VARIATION OF SEASONAL IMPACTS
The broad effects given above suggest that Chinese aggregate agriculture is not
at risk due to climate change. However, it does not protect local areas, as China
has so many different climate types. So, the regional variations in temperature and
precipitation impacts in each season are now discussed. Figures 1–8 exhibit the
regional distribution of net revenue changes per Mu in different seasons under the
scenario of H2-gs.
In winter, all parts of north China have a negative impact from increased temperature, but the southern parts of China benefit from warming winter (Figure 1).
This could be because in the most of northern China, the warmer winter is not good
for winter crops (like wheat). It may cause crop disease and cause wheat to pitch
its root to deep soil or reduce soil moisture. The warmer winter in the southern part
may be beneficial for the farmers to produce more fruits and vegetables. Spring
temperature effects are negative to almost all of China (Figure 2). The most negative are for the South, the North China Plain and the Yangtze River Delta. This may
be because in the north part of China, a warmer spring may cause wheat growing
too quickly and intensify spring drought. For the south, since it is warm enough,
the temperature increases in spring may be too high for crops growing there. The
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
Figure 1. Impact of winter temperature in China in the 2050s.
Figure 2. Impact of spring temperature in China in the 2050s.
139
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HUI LIU ET AL.
Figure 3. Impact of summer temperature in China in the 2050s.
Figure 4. Impact of autumn temperature in China in the 2050s.
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
Figure 5. Impact of winter precipitation in China in the 2050s.
Figure 6. Impact of spring precipitation in China in the 2050s.
141
142
HUI LIU ET AL.
Figure 7. Impact of summer precipitation in China in the 2050s.
Figure 8. Impact of autumn precipitation in China in the 2050s.
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
143
distribution of summer temperature effects is mostly neutral. But the most positive
areas are concentrated in the North China Plain and the Yangtze River Delta, and
the most negative areas are in the southern parts of China (Figure 3). This could be
because the regional disparity of summer temperature is very little in China. The
distribution of autumn temperature effects is uniformly beneficial (Figure 4), since
a warmer harvest season is expected to facilitate expeditious crop harvesting. The
most beneficial areas are the Yangtze River Delta and the North China Plain.
The increase in winter precipitation has negative effects in the Southwest, the
North China Plain and the southern part of the Northeast, whereas, the South, the
Southeast, and the middle reaches of the Yangtze River would benefit from the
increase in winter precipitation (Figure 5). The negative effects of spring precipitation increasing are concentrated greatly in the Southwest, the Loess Plateau, and
the middle and lower reaches of the Yangtze River. However, it would be beneficial for the North China Plain, the northern part of the Northeast, and Xinjiang
autonomous region (Figure 6). The increase of summer precipitation has negative
effects in the south part of China, especially in the middle and the lower reaches of
the Yangtze River, the South and the Southwest. But the North China Plain would
be benefited (Figure 7). Increase of autumn precipitation has different impact in
different part of China. It is negative in most part of the South, the Southwest, and
the middle reaches of the Yangtze River, but positive in the southern parts of the
North China Plain, the Northwest, and the Yangtze River Delta (Figure 8).
4.3. REGIONAL DISTRIBUTION OF ANNUAL TOTAL IMPACTS
The county-level changes in temperature and precipitation based on the runs of
three GCMs and five scenarios mentioned in Section 3 are applied for each county
to simulate their impacts on China’s agriculture.
Table VIII shows the regional distribution of the aggregate effects of climate
change on China’s agricultural net revenue for different scenarios in the year of
2050s.
It can be seen from Table VIII that different climate scenarios will have different
impacts on China’s agriculture. Most scenarios except CG-gs and CG-gg would
have an overall positive effect on China’s agriculture. That is because under most
scenarios, temperature and precipitation would increase simultaneously, which is
especially beneficial for arid and semi-arid agriculture in the temperate-zone of
China. But under the scenario of CG-gs, when temperature increases, precipitation
would decrease, which will lead to more evaporation and the shortage of water
resource that is harmful for China’s agriculture in most areas. Under the scenario
of CG-gg, the increase of temperature is much higher than that of other scenarios
(see Table IV), which causes a very large negative effect in spring and dominates
the total effects in a year.
On the other hand, different regions have different reactions to climate changes.
The overall regional impacts of temperature and precipitation under different sce-
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HUI LIU ET AL.
Table VIII
Possible impacts due to different climate scenarios in 2050s (billion Yuan)
Region
Changes of net revenue
E4-gg
CG-gg
CG-gs
H2-gs
H2-gx
◦
◦
◦
◦
(3.1 C, 10.4%) (4.7 C, 4.7%) (3.2 C, –6.2%) (1.3 C, 0.2%) (2.5 ◦ C, 10.4%)
North
8.820
(18.16)
–57.307
(–117.91)
–57.611
(–118.62)
19.122
(39.37)
18.133
(37.33)
Northeast
–1.213
(–8.61)
–4.298
(–30.50)
–3.635
(–25.9)
–0.340
(–2.41)
–1.075
(–7.63)
East
4.859
(19.25)
–8.333
(–33.01)
–9.520
(–37.71)
13.306
(52.71)
9.192
(36.41)
Central
1.190
(13.89)
–3.289
(–38.36)
–4.653
(–54.27)
1.548
(18.05)
1.883
(21.97)
South
1.8761
(15.76)
0.633
(5.31)
1.316
(11.05)
2.087
(17.53)
2.406
(20.20)
Southwest
0.275
(1.81)
–7.386
(–48.54)
–8.915
(–58.58)
0.416
(2.73)
–0.628
(–4.13)
Northwest
–0.271
(–4.27)
–8.561
(–134.98)
–6.559
(–103.36)
0.678
(10.68)
0.794
(12.51)
0.007
(6.97)
–0.018
(–17.36)
–0.011
(–10.77)
0.004
(4.29)
0.0138
(13.49)
15.543
(11.95)
–88.559
(–68.09)
–89.587
(–68.88)
36.821
(28.31)
30.718
(23.62)
Plateau
Total
a Total net revenue in 1990 = 130.05 billion Yuan.
b Numbers in parentheses are % change in net revenue.
narios are portrayed in Figures 9–13. From a comparison of Figures 9–13, it can be
seen that the Southeast and the northern part of the Northeast always benefit from
climate change. But the southern part of the Northeast, the Southwest (excluding
the Sichuan basin) and most parts of the Northwest would have negative effects.
For the North, the Central, the East, the Plateau and the Sichuan basin there are different results from different scenarios. Most scenarios indicate that climate change
would be harmful for the southern part of the South, such as the Hainan province,
but beneficial for the North, the East, the Central and the Sichuan basin.
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
Figure 9. Changes of Net Revenue under the scenario of ECHAM4-gg in 2050s.
Figure 10. Changes of Net Revenue under the scenario of CGCM1-gg in 2050s.
145
146
HUI LIU ET AL.
Figure 11. Changes of Net Revenue under the scenario of CGCM1-gs in 2050s.
Figure 12. Changes of Net Revenue under the scenario of HadCM2-gs in 2050s.
STUDY ON THE IMPACTS OF CLIMATE CHANGE ON CHINA’S AGRICULTURE
147
Figure 13. Changes of Net Revenue under the scenario of HadCM2-gx in 2050s.
5. Conclusions and Discussions
The Ricardian approach is a feasible alternative method to provide an assessment of
the economic impacts of climate change on agriculture. But it needs to be modified
in China according to the database and characteristics of the country’s agriculture.
The Ricardian approach is not offered as a replacement for other methods,
such as the production function approach, but instead as a complement for crosschecking each other. Besides, when using the Ricardian approach in China, we face
the difficulties of how to measure input price, wage, and marginal contributions of
family labor and animal work. These prices are crucial for the calculation of net
revenues. Therefore, other alternatives are necessary for cross checking each other.
Findings from the study indicate that the agricultural impacts of climate change
in China are uncertain. The total average impact may be positive or negative depending on the climate scenarios. But most scenarios show that climate change
will have an overall positive impact on China’s agriculture. Impacts also vary
both quantitatively and qualitatively by region and season. They are positive in
the middle and the east regions of China in most scenarios, but negative in the west
regions (including the Southwest and the Northwest) in some scenarios. As to the
seasonal impacts, the spring effect is the most negative, whereas the autumn effect
is the most positive.
As any new technique, there are still some problems, which need to be studied
further.
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HUI LIU ET AL.
The effects of CO2 fertilization should be included, for some studies indicate
that this may produce a significant increase in yield. This effect would likely be
also offset somewhat by damages due to other fossil fuel emission (e.g., sulfates).
This paper only examined the existing farm and did not explore the possibility
that climate may affect whether land is farmed or not. As for analyzing the overall
impact of climate change on China’s agriculture, it also needs to analyze climate
effects on the fraction of land farmed.
This analysis does not adequately capture potential damages that might be
caused by an increase in flooding and other extreme events.
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(Received 18 February 2001; in revised form 1 October 2003)