Shadow Prices of SO 2 Abatements for Regions in China

農業與資源經濟台灣農業與資源經濟學會 5:2 (2008), 59-78 Shadow Prices of SO 2 Abatements for Regions in China Tsz-Yi Ke *, Jin-Li Hu **, Yang Li *** and Yung-Ho Chiu **** Abstract We use the linear programming approach to compute the overall macroeconomic performance and shadow prices of SO 2 emission abatements for thirty regions in China during the 996-2003 period. Our major findings are as follows: On average, the east area is the most efficient and the central area is the most inefficient during these eight years. The average efficiency of those regions with serious acid rain is higher than the others. Shadow price in the west area is the highest, but it is the lowest in the central area. Shadow prices of the regions with serious acid rain are significantly lower than others, implying that the Chinese government should improve the SO 2 emission abatement problem in these regions. Keywords: Shadow Price, SO 2 Abatements, Output Distance Function, Undesirable Output, China JEL Classification: Q53 * Corresponding author: Tsz-Yi Ke, Department of Economics, Aletheia University, 32, Chen-Li St., Tamsui, Taipei County, Taiwan 25. tye@mail.au.edu.tw. Tel: 886-2- 26222 Ext.5342. ** Institute of Business and anagement, National Chiao Tung University. *** Institute of Business and anagement, National University of Kaohsiung. **** Department of Economics and College of Commerce, Soochow University.

60 Agricultural and Resources Economics: Articles I. Introduction The Chinese government sought economy growth starting in the 980s. However, it did not tae environment factors into account when the industry policies were made. Encouraging the industrial development naturally could be accompanied by heavy pollution. One ind of heavy pollution is air pollution. Air pollution alone contributes to the premature death of more than a quarter of a million people each year (World Ban, 998). One of the main sources of air pollution and acid rain is sulfur dioxide (SO 2 ). China is the third largest acid rain belt in the world. During the last ten years, because SO 2 emissions have increased day by day, China is now only behind Europe and North America. The provinces with the most serious acid rain problem include: Sichuan, Guizhou, Guangdong, Guangxi, Hubei, Hunan, Jiangxi, Zhejiang, and Jiangsu (Science useums of China, 2006). The Chinese government has realized that both economic resources and the environment are important in resent years, and since 2006 the policy of energysaving and emission abatement was initiated and promoted by the State Council (Xin Hua Net). 2 In June 2007, the State Council published a General Wor Plan for Energy Conservation and Pollutant Discharge Reduction, and it requires all government departments to provide details how they will carry out the plan (CCTV). 3 Although air pollution abatement is beneficial to China, people may worry that a drastic reduction in air pollution will hamper economic growth. A country s macroeconomic policies generally have two objectives: creation of wealth and good living conditions for citizens. Gross domestic product (GDP) is commonly used in assessing an economy s wealth, but it does not constitute a measure of wealth as it does not deal with environmental issues adequately. Environmental degradation should be taen into account as a correction factor into our regular definition of economic growth (van Dieren, 995). Economic growth depends on industry, especially in a developing country, but growth in 2 3 Science useums of China, http://www.epu.com.cn/gb/index.html. Xin Hua Net, http://www.xinhuanet.com. CCTV, http://www.cctv.com.

Tsz-Yi Ke, Jin-Li Hu, Yang Li and Yung-Ho Chiu Shadow Prices of SO 2 Abatements for Regions in China 6 industry causes pollution. Thus, this study calculates how much industries will pay for pollution abatement in a developing economy. Using the output distance function is a popular way to estimate the shadow price of pollution abatement. Swinton (998) provides estimates of the shadow price of SO 2 abatement for Illinois, innesota, and Wisconsin coal-burning electric plants. Kumar and Rao (2002) estimate firm specific marginal abatement costs or shadow prices of suspended participate matters (SP) for India s thermal power sector. Coggins and Swinton (996) calculate the shadow price of SO 2 abatement. Färe and Zieschang (99) develop a method for computing output shadow prices when total cost and input prices are exogenous, using the indirect output distance function. He (2005) constructs a CGE model in which SO 2 emission is directly lined to energy input consumption in production, finding that the new desulfur policy will only bring small economic growth loss, but this study does not estimate the opportunity cost of SO 2 emission abatement. Hu (2006) and Hu and Lee (2008) use data envelopment analysis (DEA) to compute the target emissions of each region for each year in China, but do not compute the shadow prices of pollution. This paper will follow the output distance function approach to estimate efficiency and shadow prices of SO 2 abatements for regions in China. The aim of this paper is to measure performance of China by taing into account undesirable externalities of economic growth using data over the period 996-2003. This study defines performance in light of an economy s ability to provide its people with more wealth and at the same time control environmental pollution. The output distance function is thus applied to examine the national performance and to compute shadow prices of SO 2 emission in China. Based on the economic theory of production, inputs (such as capital and labor) are transformed into output (such as gross domestic product, GDP) in the production process. Environmental disamenities should be added into the analysis as undesirable output. The SO 2 emissions will be regarded as an undesirable output in our model. There are four sections in this paper. After this introductory section, the next section provides an introduction to the output distance function and describes the data sources. Section 3 presents the empirical results. Section 4 concludes this paper.

62 Agricultural and Resources Economics: Articles II. ethod and Data Sources 2. Output Distance Function x = x,..., x N R+ to produce output vector y = ( y,..., y ) R+. According to Shephard (970), the output distance function can be defined as follows: A producer employs input vector ( ) N where ( x) Do y { θ } ( ) = inf θ: P( ) x, y x, () P presents the output sets of production technology, describing the sets of input vectors, x, that can produce the output vector, y - that is: ( ) = { } P x y: x can produce y. (2) Of particular interest for our purposes are the disposability properties of technology with respect to outputs, especially undesirable output. Specifically, we wish to allow for regulation which restricts the ability of producers to costlessly dispose of undesirable byproducts of the production process. To that end we allow for what we call wea disposability of outputs, i.e., if y P( x) and θ [ 0,], then θy P( x ), but we do not necessarily allow for strong (free) disposability, which requires that if y y P( x), then y P( x). Under wea disposability, in contrast to strong disposability, a reduction of a byproduct can only be achieved by simultaneously reducing some desirable output(s). This is consistent with regulations which require abatement or a cleanup of pollutants. Since abatement is also resource-consuming, there is an associated opportunity cost of foregone maretable output. Note that the output distance function is homogeneous of degree + in outputs, and that if outputs are wealy disposable, then y P( x) if and only if D o( x, y) (Färe and Primont, 995). Suppose that ( x) D ( x, y) and revenue function ( x,r) o P is convex. Hence, the output distance function R are dual (Färe and Primont, 995):

Tsz-Yi Ke, Jin-Li Hu, Yang Li and Yung-Ho Chiu Shadow Prices of SO 2 Abatements for Regions in China { } (, ) (, ) R xr = max ry: D o xy, (3) Do where = ( ) y { } (, ) R(, ) x y = sup ry : x r, (4) r r r,...,r denotes the output price vector, and r y is the inner product of the output prices and quantity vectors. Equation (3) states that the revenue function can be derived from the output distance function by maximization over outputs y, while equation (4) represents that the output distance is obtained from maximization with respect to output prices r. Suppose that both D ( x, y) and ( x,r) o R are differentiable. The revenue function can be represented by the following Lagrange problem: ( x,r) = max r y + ( ( x, y) ) R λ, (5) y where λ is the Lagrangian multiplier. According to the first-order condition * * * for equation (5), we have the output shadow prices vector r = ( r,..., r ) as follows: * r = λ y Do D o ( x, y) 63. (6) The shadow prices for the undesirable output can be interpreted as the measure of the marginal cost of reducing it. Equation (6) indicates that the ratio of the shadow prices of undesirable output j and desirable output is : * j * r r D = D o( x, y) / y j o( x, y) / y. (7) Suppose that the maret price of desirable output is observable and equals its shadow prices. We then could calculate the shadow prices of undesirable output by the following formula (Färe et al., 993). r * j * = r D D o( x, y) / y j o( x, y) / y. (8)

64 Agricultural and Resources Economics: Articles This study will employ equation (8) to calculate the shadow prices of SO 2. 2.2 Parametric Linear Programming In order to apply the shadow prices formula, we have to parameterize and calculate the parameters of the output distance function. An appropriate functional form to the output distance function would ideally be flexible, easy to calculate, and permit the imposition of homogeneity. The flexible translog functional form provides a second-order Taylor approximation to the unnown technology. It satisfies all the above criteria and has been used by many researchers (Färe et al., 993; Lovell et al., 995; Hailu and Veeman, 2000). ore important for our purposes, it does not impose strong disposability of outputs. The translog distance function with outputs and N inputs is specified as: ln D o N ( x, y) = α + β ln x + α y + β ( ln x )( ln x ) 0 ln n n m n= m= m 2 N n= N nn n = n n + 2 α ( ln y )( ln y ) + ( ln x )( ln y ) mm m= m = m m N n= γ nm n m. (9) m= This research employs the linear programming method suggested by Aigner and Chu (968) to estimate unnown parameters. This procedure relies on the minimization of the sum of deviations upon the values of the logarithmic values of the output distance from the frontier. In other words, we try to estimate the parameters of a deterministic translog output distance function by solving the following problem: subject to max (i) ln D (, y ) 0 K [ ln Do (, y ) ln] = x, (0) o x, =,, K ln D o x ( ) (ii) 0 ln y m, y, m =,, i, =,, K

Tsz-Yi Ke, Jin-Li Hu, Yang Li and Yung-Ho Chiu Shadow Prices of SO 2 Abatements for Regions in China 65 ln D o x (iii) ( ) 0 ln D ln y o x m, y (iv) ( ) 0 m= ln x n, y, m = i+,,, =,, K, n =,, N, =,, K (v) α m =, α m m = γ nm = 0, m =,,, n =,, N m = m= (vi) α = α, m m m m m =,,, m =,, β = β, n =,, N, n =,, N n n n n where =,, K indexes individual observations; m =,, indexes outputs; n =,, N indexes inputs; ln D o ( x, y) has an explicit functional form as in equation (9); and the first i outputs are desirable while the next ( i) outputs are undesirable. The objective function minimizes the sum of the deviations of individual observations from the frontier of technology. Since the distance function taes a value of less than or equal to one, the natural logarithm of Do ( x, y ) is less than or equal to zero, and the deviation from the frontier for observation, ln Do ( x, y ) ln, is less than or equal to zero, hence maing the max. The first set of constraints labeled (i) restricts the individual observations to be on or below the frontier of the technology. The constraints in (ii) ensure that the desirable outputs have non-negative shadow prices and those in (iii) ensure that the undesirable outputs have non-positive shadow prices. The constraints in (iv) remar that the output distance will not increase with an increase with any input. The constraints in (v) impose homogeneity of degree + in outputs, which also ensures that technology satisfies wea disposability of outputs. The final set of constraints in (vi) imposes symmetry. 2.3 Data Sources Capital and labor are two major inputs in production, and when measuring a

66 Agricultural and Resources Economics: Articles nation s overall output, gross domestic product (GDP) is commonly used. For example, Färe et al. (994) analyze the productivity growth of OECD countries, by considering capital and labor as inputs and GDP as an output. Chang and Luh (2000) adopt similar inputs and outputs to analyze the productivity growth of ten Asian economies. There are two inputs and two outputs from 996 to 2003 in this study. Capital stoc and labor force are the two inputs. Real GDP is the desirable output and SO 2 emission is the undesirable output. The data of regional labor employment are established from the China Statistical Yearboo, and the data of GDP output in each region are collected as stated previously. Real capital stocs in 996 prices are constructed based on Li s method (Li, 2003; Hu et al., 2005; Hu, 2006; Hu and Lee, 2008). onetary inputs and outputs such as GDP and capital stoc are deflated to 996 values. Table presents that summary statistics of inputs and outputs. Inputs Capital Stoc (00 million Number of Employed Labor (0,000 Outputs Gross Domestic Product (00 Volume of Sulfur Dioxide Emissions Table Summary statistics of inputs and outputs by year 996 997 998 999 2000 200 2002 2003 ean 8028.0 8626.37 9332.57 9990.25 0633.34 287.23 96.42 2725.5 Std. Deviation 7054.00 7584.25 7955.06 8325.36 878.54 97.9 954.02 0044.47 ean 2094.59 222.22 24.2 2083.3 299.30 2087.02 225.99 262.2 Std. Deviation 54.44 562.38 5.96 557.86 562.84 582.87 574.98 595.0 ean 2286.4 2362.26 2349.05 2329.2 2382.4 244.07 2477.84 2586.88 Std. Deviation 759.43 85.52 820.93 84.3 884.66 940.83 984.59 202.83 ean 454524.77 454209.77 53007.07 486698.33 528694.57 5045.33 503993.53 59787.33 Std. Deviation 34829.9 348926.06 420839.03 375724.06 49936.2 399799.52 393258.32 446442.53 Note: () The monetary values are in 996 prices. (2) Data source: China Statistical Yearboo, 996-2003.

Tsz-Yi Ke, Jin-Li Hu, Yang Li and Yung-Ho Chiu Shadow Prices of SO 2 Abatements for Regions in China 67 III. Empirical Results This study employs the mathematical programming software LINGO 8.0 to compute the parameters of the translog output distance function. Table 2 shows the values of estimated parameters. These parameter estimates are used to calculate the technical efficiencies and the shadow prices of SO 2 reduction for each region in each year. Note that we are assuming that the price of real GDP is one. Table 2 Parameter estimates αoutput αoutput, output βintput βintput,intput γ intput, output α.902 α 0.260 β -2.526 β 0.84 γ 0.0006 α2-0.902 α2-0.260 β2-0.5852 β2 0.0445 γ2-0.0006 Constant α2-0.260 β2 0.0445 γ 2-0.0607 α0.6742 α22 0.260 β22-0.0842 γ 22 0.0607 Note: These parameters all satisfy restrictions (v) and (vi), (v) α m =, α m m = γ nm = 0, m =,,, n =,, N = m m = m= (vi) α = α, m =,,, m =,, m m m m β = β, n =,, N, n =,, N n n n n Table 3 lists the macroeconomic efficiency scores with SO 2 emissions considered and the shadow prices of SO 2. The thirty regions are categorized into three areas. The three areas are the east area (abbreviated as E ), the central area (abbreviated as C ), and the west area (abbreviated as W ) 4. The most efficient regions during the 996-2003 period are respectively: 4 According to the Grand Western Development Program, Inner ongolia and Guangxi are included in west area.

68 Agricultural and Resources Economics: Articles Table 3 996-2003 efficiency scores and shadow prices for regions in China No. Province Area Efficiency Shadow price 996 997 998 999 Efficiency Shadow price Efficiency Shadow price Efficiency Shadow price Beijing E 0.708 0.524 0.700 0.526 0.704 0.53 0.679 0.539 2 Tianjin E 0.784 0.55 0.780 0.54 0.775 0.57 0.742 0.524 3 Hebei E.000 0.504.000 0.504 0.984 0.504 0.90 0.504 4 Shanxi C 0.849 0.504 0.835 0.504 0.906 0.503 0.790 0.503 5 Inner ongolia W 0.926 0.503 0.888 0.504 0.883 0.504 0.83 0.504 6 Liaoning E 0.69 0.59 0.66 0.520 0.645 0.523 0.642 0.524 7 Jilin C 0.743 0.5 0.72 0.52 0.734 0.5 0.73 0.52 8 Heilongjiang C 0.886 0.53 0.848 0.55 0.764 0.56 0.726 0.58 9 Shanghai E 0.944 0.520 0.957 0.523.000 0.525 0.976 0.533 0 Jiangsu E 0.893 0.50 0.882 0.5 0.97 0.50 0.846 0.54 Zhejiang E 0.805 0.55 0.805 0.56 0.828 0.52 0.797 0.53 2 Anhui C 0.629 0.50 0.624 0.50 0.600 0.50 0.558 0.5 3 Fujian E.000 0.57 0.998 0.523 0.93 0.52 0.873 0.522 4 Jiangxi C 0.656 0.509 0.620 0.52 0.623 0.5 0.593 0.52 5 Shandong E 0.983 0.505 0.958 0.506 0.978 0.505 0.890 0.507 6 Henan C 0.6 0.50 0.628 0.508 0.620 0.508 0.558 0.50 7 Hubei C 0.758 0.50 0.765 0.5 0.775 0.509 0.737 0.50 8 Hunan C 0.803 0.505 0.765 0.506 0.768 0.505 0.727 0.506 9 Guangdong E 0.962 0.53 0.94 0.55 0.96 0.52 0.935 0.53 20 Guangxi W 0.992 0.504 0.93 0.504 0.853 0.503 0.745 0.504 2 Hainan E 0.845 0.537 0.787 0.553 0.745 0.547 0.699 0.546 22 Sichuan W 0.587 0.508 0.599 0.508 0.597 0.508 0.609 0.507 23 Guizhou W 0.868 0.502 0.827 0.502 0.878 0.502 0.744 0.503 24 Yunnan W 0.723 0.507 0.687 0.508 0.660 0.508 0.596 0.509 25 Tibet W 0.57 0.678 0.667.028 0.6 0.86 0.79.009 26 Shaanxi W 0.64 0.505 0.607 0.506 0.579 0.506 0.570 0.506 27 Gansu W 0.698 0.505 0.667 0.505 0.639 0.506 0.567 0.508 28 Qinghai W 0.78 0.529 0.727 0.522 0.686 0.530 0.636 0.534 29 Ningxia W.000 0.503 0.934 0.503 0.88 0.503 0.80 0.504 30 Xinjiang W 0.796 0.5 0.772 0.52 0.737 0.5 0.698 0.52 Area Average E 0.868 0.56 0.857 0.59 0.86 0.59 0.86 0.52 C 0.742 0.509 0.726 0.50 0.724 0.509 0.675 0.5 W 0.775 0.523 0.755 0.555 0.687 0.540 0.668 0.555

Tsz-Yi Ke, Jin-Li Hu, Yang Li and Yung-Ho Chiu Shadow Prices of SO 2 Abatements for Regions in China 69 Table 3 996-2003 efficiency scores and shadow prices for regions in China No. Province Area Efficiency Shadow price 2000 200 2002 2003 Efficiency Shadow price Efficiency Shadow price Efficiency Shadow price Beijing E 0.689 0.546 0.709 0.557 0.640 0.563 0.623 0.57 2 Tianjin E 0.793 0.56 0.78 0.520 0.780 0.52 0.8 0.59 3 Hebei E 0.87 0.505 0.847 0.506 0.8 0.506 0.844 0.506 4 Shanxi C 0.759 0.504 0.740 0.504 0.736 0.504 0.783 0.504 5 Inner ongolia W 0.780 0.505 0.759 0.505 0.773 0.505.000 0.503 6 Liaoning E 0.649 0.525 0.626 0.529 0.63 0.53 0.620 0.529 7 Jilin C 0.698 0.53 0.74 0.55 0.67 0.56 0.675 0.57 8 Heilongjiang C 0.742 0.58 0.738 0.59 0.79 0.52 0.756 0.56 9 Shanghai E 0.999 0.533 0.978 0.538 0.943 0.536 0.954 0.539 0 Jiangsu E 0.894 0.52 0.89 0.54 0.896 0.55 0.935 0.54 Zhejiang E 0.780 0.55 0.774 0.57 0.79 0.57 0.835 0.56 2 Anhui C 0.56 0.52 0.503 0.53 0.487 0.54 0.495 0.53 3 Fujian E 0.842 0.59 0.795 0.525 0.758 0.528 0.762 0.59 4 Jiangxi C 0.576 0.5 0.625 0.52 0.546 0.53 0.60 0.509 5 Shandong E 0.889 0.508 0.878 0.508 0.868 0.509 0.92 0.509 6 Henan C 0.548 0.50 0.548 0.50 0.546 0.50 0.565 0.50 7 Hubei C 0.73 0.5 0.78 0.52 0.683 0.53 0.670 0.52 8 Hunan C 0.722 0.506 0.688 0.507 0.658 0.508 0.646 0.507 9 Guangdong E 0.989 0.5 0.986 0.5 0.98 0.5.000 0.5 20 Guangxi W 0.783 0.503 0.707 0.504 0.675 0.505 0.708 0.504 2 Hainan E 0.74 0.583 0.628 0.559 0.608 0.554 0.579 0.556 22 Sichuan W 0.657 0.506 0.636 0.507 0.627 0.507 0.647 0.507 23 Guizhou W 0.673 0.503 0.607 0.504 0.575 0.504 0.549 0.504 24 Yunnan W 0.579 0.509 0.532 0.50 0.507 0.5 0.52 0.509 25 Tibet W 0.847.09 0.947.36.000.5 0.994.65 26 Shaanxi W 0.55 0.507 0.547 0.507 0.526 0.508 0.555 0.507 27 Gansu W 0.568 0.507 0.559 0.507 0.553 0.506 0.566 0.505 28 Qinghai W 0.67 0.533 0.60 0.530 0.58 0.535 0.583 0.57 29 Ningxia W 0.758 0.504 0.725 0.505 0.704 0.504 0.748 0.504 30 Xinjiang W 0.70 0.53 0.684 0.54 0.645 0.55 0.657 0.54 Area Average E 0.779 0.525 0.808 0.526 0.790 0.527 0.807 0.526 C 0.66 0.50 0.659 0.52 0.63 0.52 0.649 0.5 W 0.684 0.563 0.665 0.566 0.65 0.568 0.684 0.567

70 Agricultural and Resources Economics: Articles Hebei (E), Fujian (E), and Nigngxia (W) in 996, Hebei (E) in 997, Shanghai (E) in 998, Shanghai (E) in 999, Shanghai (E) in 2000, Shanghai (E) in 200, Tibet (W) in 2002, and Inner ongolia (C) and Guangdong (E) in 2003. Because the location of the east is better and economic development progressed earlier than others, the east s average efficiency score is the highest. The average efficiency in the central area is the lowest. Tibet (W) has the highest shadow prices of SO 2 emission abatement in these eight years: 0.68,.03, 0.86,.0,.,.4,.5, and.7 RB in 996 prices per ton, respectively. Tibet is a province that has not been developed and is not polluted. The cost of SO 2 emission abatement must be the highest. Therefore, the shadow prices of Tibet are higher than the other provinces in the study periods. On average, the shadow prices in the west area are the highest - that is, the opportunity cost of SO 2 emission is the heaviest in the west area. The average shadow price in the central area is the lowest. Table 4 shows the mean difference between the major acid rain regions (including Sichuan, Guizhou, Guangdong, Guangxi, Hubei, Hunan, Jiangxi, Zhejiang, and Jiangsu) and other areas. The average efficiency of those regions with serious acid rain is higher than the rest of the regions. The shadow prices of these regions are lower than the others. Because the opportunity costs of SO 2 emission abatement in acid rain regions are lower, the Chinese government should abate SO 2 emission in these major acid rain regions. The SO 2 emission abatement in these major acid rain regions is not only good for China but also benefit surrounded countries. From Figures and 2, we also find that the trend of regional efficiency scores decreases over time, but increases in 2003. The trend of shadow prices of SO 2 abatement increases over time, but decreases in 2003. An earlier start in abatement helps avoid increasing costs of SO 2 abatement over time. For example, Guangdong accomplished its emission abatement and energy saving goals in 2006, because it was one of the first regions to tae care of environmental issues (China Electric Power News). 5 5 China Electric Power News, http://www.cepn.sp.com.cn.

Tsz-Yi Ke, Jin-Li Hu, Yang Li and Yung-Ho Chiu Shadow Prices of SO 2 Abatements for Regions in China 7 Table 4 996-2003 efficiency scores and shadow prices in acid rain regions Average of acid rain regions Average of the other regions Efficiency Shadow Price Efficiency Shadow Price 996 997 998 999 2000 200 2002 2003 0.84 0.792 0.800 0.748 0.756 0.737 0.75 0.732 0.508 0.509 0.508 0.509 0.509 0.50 0.50 0.509 0.794 0.78 0.743 0.725 0.694 0.707 0.689 0.75 0.520 0.538 0.53 0.540 0.547 0.548 0.550 0.549 Average 0.76 0.509 0.73 0.540 Efficiency 0.9 0.8 0.7 0.6 996 997 998 999 2000 200 2002 2003 Period Figure The trend of average efficiency

72 Agricultural and Resources Economics: Articles 0.56 Shadow price 0.54 0.52 0.5 996 997 998 999 2000 200 2002 2003 Period Figure 2 The trend of average shadow price We now use the non-parametric test to chec the correlation between the periods and regions. Because the numbers of periods and regions are both strictly greater than two, we use the Krusal-Wallis test. Table 5 shows that the shadow prices of SO 2 in those eight years do not have any significant change, but the efficiency has a significant change. Table 6 lists that the shadow price of SO 2 emission and efficiency in the three regions do present a significance difference. The efficiency has obviously decreasing during the study period. The operating of each province seems worse. The shadow price in years does not have evident difference, but the shadow price in the last three years has the highest value during the study period. According to this result, to solve the SO 2 emission problem will get harder and harder. The west area has higher shadow price then central and east area obviously. The most parts of province in the west area belong to an undeveloped district. To handle the pollution will be costly. The east area gets the most efficient score. The business activities in the cities along the coast have developed completely. They can get better performance.

Tsz-Yi Ke, Jin-Li Hu, Yang Li and Yung-Ho Chiu Shadow Prices of SO 2 Abatements for Regions in China 73 Table 5 Krusal-Wallis test in years Period Shadow Price Efficiency ean P value ean P value 996 0.57 0.800 997 0.530 0.785 998 0.524 0.760 999 0.53 0.732 0.785 2000 0.535 0.73 0.034** 200 0.537 0.76 2002 0.538 0.697 2003 0.537 0.720 Note: ** represents significance at the 5% level. Table 6 Krusal-Wallis test in areas Area Shadow Price Efficiency ean P value ean P value East area 0.522 0.823 Central area 0.5 < 0.00*** 0.683 < 0.00*** West area 0.555 0.699 Note: *** represents significance at the % level. Table 7 presents that the shadow price of SO 2 emission in the major acid rain area is lower than the other. We suggest that the local governments in the major acid rain area could pay more attention about the SO 2 emission problem at first. Because the opportunity cost of improving pollution problem in the major acid rain area is lower then the other. Table 7 ann-whitney test Shadow Price Efficiency Area ean P value ean P value Non acid rain area 0.540 0.73 < 0.00*** 0.56 Acid rain area 0.509 0.762 Note: *** represents significance at the % level.

74 Agricultural and Resources Economics: Articles In recent years, many local governments in China have been monitoring SO 2 emission and enforced emission abatement. For examples, during the Tenth Five-year Plan period, the local government of Guizhou planed to build the thermal power plant to abate the SO 2 emission. 6 The Guangdong Yudean Group has a plan to expending more than 5.5 billion RB to improve the air quality in 2002, and then SO 2 emission has reduced and the efficient has rose in 2003. The local government of Yunnan had enhanced investment in pollution management in 2002, then increased the efficient and decreased the shadow price. 7 The local government of Beijing has been monitoring SO 2 emission since 2002. If any firm over-emits, local government departments fined them. 8 The environmental protection departments of Guangxi have inspected environmental illegal activities since 200. The outcome is, the SO 2 emission has reduced obviously, the efficient has increased and shadow prices have decreased. 9 Although the opportunity cost of SO 2 emission abatement loos not high, abated one ton of SO 2 emission cost about 0.5 RB in average 0. While the central and local governments eep focusing on environmental improvement, the efficient will be better and shadow prices decrease. IV. Concluding Remars This paper computes regional shadow prices of SO 2 abatements in China from 996 to 2003. Real capital stoc and labor force as the two inputs. Real GDP is a desirable output and SO 2 emission is an undesirable output. The output distance function is used to examine the macroeconomic performance and compute shadow prices of SO 2 emission in China. Our major findings are as follows: On average, the east area is the most efficient and the central area is the most inefficient during these eight years. 6 7 8 9 0 Xin Hua Net, http://sinhuanet.com. China News Net, http://www.chinanews.com. Beijing Technology Net, http://bb.ynet.com. Guangxi News, http://www.gxnews.com. Färe et al. (2005), the shadow price of a ton of SO 2 in America is about US$0.6 in 993 and 997.

Tsz-Yi Ke, Jin-Li Hu, Yang Li and Yung-Ho Chiu Shadow Prices of SO 2 Abatements for Regions in China 75 Shadow price in the west area is the highest, but it is the lowest in the central area. On average, those regions with serious acid rain have higher efficiency and a lower shadow price of SO 2 abatement than the others. An earlier start in abatement helps avoid increasing costs of SO 2 abatement over time. Acnowledgements We are grateful to the anonymous referees for their constructive and helpful comments. All errors are solely ours. The authors are indebted to Xiaoling Huang and seminar participants at the Conference on WTO, China, and the Asian Economies in Beijing for their helpful comments. Partial financial support from Taiwan s Science Council (NSC 93-245-H-009-002) is gratefully acnowledged. The usual disclaimer applies.

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78 Agricultural and Resources Economics: Articles * ** *** **** 本研究利用產出投入概念的線性規劃方式估計中國大陸三十個省分從 996 到 2003 年間的整體績效表現及二氧化硫 (SO 2 ) 減排的影子價格產出面包含衡量國家總體產出的國內生產毛額 (GDP) 及二氧化硫的排放量, 投入項目為資本及勞動, 並將所有省分區分為三大區 : 東部中部及西部區域以進行分析比較研究結果發現 : 觀察期間, 以平均而言, 東部的效率表現最佳, 而中部的效率表現最需改善 ; 酸雨較為嚴重的省分其效率表現高於整個國家的平均值關於影子價格, 亦即處理二氧化硫所需的邊際成本, 西部的平圴影子價格最高而中部的影子價格最低 ; 酸雨較為嚴重的省分其影子價格低於整個國家的平均值故, 中國大陸減少二氧化硫的排放確實是刻不容緩之事影子價格二氧化硫減排產出距離函數非意欲產出中國大陸 JEL :Q53 * ** *** ****