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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. 126 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) 128 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 130 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. 132 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 134 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. 136 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. 138 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. 139 140 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- 144 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. 148 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. 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Meteorol. 16 (3), 5–8. (Received 18 February 2001; in revised form 1 October 2003)