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Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA
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Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA Li Yang a , Han Ouyang b , Kuangnan Fang b , Linglong Ye c, Jing Zhang b, * a
School of Humanities and Social Sciences, Anhui University of Science and Technology, China School of Economics, Xiamen University, China c School of Management, Fu Jen Catholic University, Taiwan b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 May 2014 Received in revised form 23 August 2014 Accepted 25 August 2014
This study measures the environmental efficiency of China based on environmental super-efficiency data envelopment analysis (SEDEA) model by using data of 30 provinces in China during the period of 2000– 2010. We find that environmental efficiencies across 30 provinces show regional disparities. Cities such as Beijing and Shanghai have experienced improvement in efficiency while Qinghai showed worse performance. More generally, East areas are more efficient in production while the west rank the last with central areas ranking in between during the period studied. Policies should be established to further promote production efficiency. ã 2014 Elsevier Ltd. All rights reserved.
Keywords: Environmental efficiency Data envelopment analysis Super-efficiency
1. Introduction China has experienced dramatic changes since the late 1970s. On one hand, living standards have been improved significantly while on the other, environmental pollution has become a serious issue and attracted increasing attention from both China and aboard. To show the seriousness of this issue to China, the 17th Party Congress adjusted China’s economic development strategy from “fast and good” to “good and fast” to further emphasize environmental protection. Environmental protection campaign in China can be traced back to as early as 1972 when the former premier Zhou Enlai sent a delegate to attend the Stockholm Conference on the Human Environment. The first national conference on environmental protection was then held in August 1973. Since then, China has gradually introduced a series of rules and regulations to protect the nation’s natural resources and environment. Though the enforcement of various environmental regulations and laws has contributed significantly to the protection of environment, government at various levels still have much room to improve in order to sustain development. One of the areas that deserve more attention is the evaluation of efficiency in terms of environmental protection. Existing research has largely focused on the recommendations of environmental policies; however, little has been done to empirically investigate the effectiveness of such policies. Therefore, to fill
* Corresponding author. fax: +86 5922186366. E-mail address: [email protected] (J. Zhang).
this gap, we wish to identify the effectiveness of environmental policies by measuring the environmental efficiency. Various methods have been developed to evaluate efficiency, one of which is called data envelopment analysis (DEA) model. It has gained its popularity as a methodology in evaluating bank performance (Charnes et al., 1990; Cook et al., 2000; Fukuyama and Weber, 2002; Drake and Hall, 2003; Barros et al., 2012), assessing universities research efficiency (Beasley, 1995), identifying excesses or deficits in production as well as examining buyer– supplier supply chain (Seiford and Zhu, 1998, 1999; Zhu 2003; Liang et al., 2006). Farrell (1957) first proposed a non-parametric method of computing the relative efficiency of a decision making unit (DMU) on the basis of a set of DMUs. Two decades later, Charnes et al. (1978) further proposed a line programming model to evaluate technical efficiency and technological progress. Afterwards, DEA was widely used in measuring energy and environment efficiency at a macro-economic level. Zhou et al. (2013) made use of non-radial DEA approach to measure environmental performance of OECD countries and they found that the environmental performance of OECD countries has been improved during 1995–1997. Freeman et al. (1997) and Hu and Wang (2006) used the DEA method to measure energy efficiency. Honma and Hu (2009) measured and compared regional energy efficiency during the period of 1993–2003 in Japan. Chien and Hu (2007) used DEA to analyze the effects of the use of renewable energy on the technical efficiency of 45 economies from 2001 to 2002. Zhou et al. (2008), however, did a careful review of 100 DEA applications in energy and environment policy and found out that most of the studies are measuring efficiency under assumption of
http://dx.doi.org/10.1016/j.ecolind.2014.08.040 1470-160X/ ã 2014 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040
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constant returns to scale (CRS) which often conflicts with the reality of production. In order to overcome this, Banker et al. (1984) proposed a DEA (BCC) model based on variable returns to scale (VRS), which better suits the reality. In recent years, many scholars pay more attention to the environment protection in China. Hu and Wang (2006) proposed the measure of total factor productivity and it was used to measure 29 administrative regions in China with CCR–DEA model during the period 1995–2002. Hu et al. (2005) used the CCR model to analyze the technical efficiency and productivity changes in 31 regions of China for the period 1997–2001. Chiu and Wu (2010) used the undesirable measure DEA model to calculate the impact of undesirable output and input on energy efficiency. The research sets up two models to analyze 27 provinces and cities in China during 2000–2003. Yang and Wang (2013) used the nonparametric DEA method to calculate the production frontier of environmental efficiency for provincial energy utilization. The results show that the environmental efficiency of energy utilization in Chinese provinces still has much room for improvement, and CO2 emissions still have potential to be reduced. Zhou et al. (2013) first proposed a new non-radial DEA approach by integrating the entropy weight and the SBM model to evaluate the environmental efficiency of the Chinese power industry at the provincial level during 2005–2010. Shi et al. (2010) measured total technology efficiency, pure technology efficiency and scale efficiency of 28 administrative regions in China using extensive DEA model. The existing literature, however, focuses on the measurement of environmental efficiency at industry level in China by using industrial investment or industrial output index without taking regional disparities into consideration. Regional resource endowments among different provinces are significant in China, and therefore, the ignorance of regional differences may lead to inaccurate and biased comparison across provinces. In order to gauge a clear picture of regional performance on environmental efficiency, we wish to empirically evaluate the technical efficiency of 30 provinces in China using provincial level data from 2000–2010. Two modeling approaches have been extensively used in this regard, parameter method and nonparameter method. For the former, it mainly includes the stochastic frontier analysis (SFA), which requires a given production function and the input–output efficiency values. The most common model for the latter is the traditional DEA model without requiring the estimation of a production function. But when multiple decisionmaking units are involved, the traditional DEA model present difficulty in ranking the decision-making units and thus make further analysis unavailable. In order to overcome this, Andersen and Petersen (1993) proposed a super-efficiency data envelopment analysis (SEDEA). Li et al. (2013) measured environmental efficiency of 30 provinces in China employing SEDEA model when undesirable outputs were taken into consideration. Therefore, this study adopts the super-efficiency data envelopment analysis (SEDEA) to measure environmental efficiency. We use energy consumption, labor, depreciation of fixed capital and sulfur dioxide emissions as inputs indicators and GDP as output indicators to calculate the environmental efficiency of 30 provinces (municipalities and autonomous regions) in mainland China during 2000–2010. Our results show that, in line with our expectation, environmental efficiency across provinces does show a regional disparity which is consistent with the level of economic development. Cities such as Beijing and Shanghai have shown much improvement in efficiencies while Qinghai and Ningxia are doing worse than their counterparts. Provinces in the east regions generally outperform those of the central and the west. The remainder of the paper is organized as follows: Section 2 describes the research methodology including the base
model as well as super efficiency DEA model. Data is described in Section 3. We discuss the empirical results in Section 4 and conclude in Section 5.
2. Methodology 2.1. The CCR model DEA models can be divided into two categories, the first group includes radial models and the second is related with non-radial models. The radial models are often referred to as “CCR” because it was proposed by Charnes, Cooper, and Rhodes (CCR) while “BCC” developed by Banker et al. (1984) belongs to the Debreu–Farrell measure. The basic assumption of these models is constant returns to scale, but many industries are not in a CRS state. In DEA method, each evaluated unit is seen as an independent homogeneous decision making unit (DMU), and several these DMUs form an evaluated group. Based on observations of each DMU, we set the weights of inputs and outputs in each DMU as variables, employing input–output relative efficiency computing and analysis, and then we could decide the DEA relative efficiency of DMUs. Suppose we have a set of independent homogeneous decision making units, denoted by DMUj(j = 1,2,...,n) Each DMU uses m resource inputs X and s desirable outputs Y. Denoting the ith resource input of the kth DMU as xij(i = 1,2,...,m,k = 1,2,...,n) and the jth desirable output of the kth DMU as yjk = (j = 1,2,...,s, k = 1,2,...,n. Then the technical efficiency of the kth DMU can be calculated by the output-oriented CCR model (1): Max s:t:
M X
Z m Y ms
m¼1 M X
Z m Y ms
m¼1
N X vn xnk ¼ 1
N X vn xnk 0 n¼1
(1)
n¼1
Zm 0; vn 0; k = 1,2,...,K; m = 1,2,...,M; n=1,2,...,N The efficiency score of DMUs can be derived by the solution of model 1. Model 1 has the following dual model 2 which has the same objective value on optimality and usually be calculated in order to get efficiency score.
s:t:
n X X j lj u X k j¼1 n X
(2)
Y j lj Y k
j¼1
lj 0, j = 1,...,n.
Where Xk = (x1k,x2k..., xmk), Yk = (y1k,y2k..., ysk,) and u is the total efficiency of kth DMU. In the above formula, l is the weight multiplier of each decision making unit. The desirable output Y of one DMU can be expressed as a linear combination of all k DMUj (j = 1,2,...,k), the compression ratio of the resource inputs X of that DMU is u, which is also known as efficiency measurement scores. CCR model is the most popular efficiency measurement model in DEA method based on CRS and take efficient score unity as the most efficient DMU and other DMU’ scores between 0 and 1 are inefficient. DEA method does not assume function relationship between inputs and outputs and does not require any pre-estimate weights assumption. This method avoids some subjective factors, directly using a weighted ratio between input and output to calculate the
Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040
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input–output efficiency of DMUs. DEA method, as an effective tool to measure the efficiency and productivity problems of similar DMUs, has been employed in many articles on environmental efficiency study. 2.2. Super efficiency DEA model There are two types of DMU in CCR model, efficient and inefficient DEA model. The traditional CCR model has poor discrimination when DMUs are simultaneously on the frontier of production which often lead to difficulty in further evaluation and comparison between these DMUs. In order to remedy the shortage of CCR model and evaluate DMUs’ efficiencies effectively and realistically, SEDEA, a kind of comprehensive efficiency measure, proposed by Andersen and Petersen (1993), can further distinguish efficiency among DMUs and then sort the relative efficiency of these DMUs. SEDEA has the similar function formula with CCR model used for determining the most productive scale size in the traditional DEA framework and it can be described by the Formula (3): min u s:t:
n X
‘China Statistical Yearbook’) of 30 provinces in China during the period of 2000–2010. Tibet and Hong Kong are not included due to the lack of data. Regarding the specification of the inputs and outputs, we employ the cost-based indicators input variables and incomebased indicators for the output variable in DEA model, respectively. Therefore, we use labor, capital and resources as measures for the former and GDP for the later which is consistent with Shi et al. (2010). The inputs and outputs variables are defined as follows: 1. Labor input: labor input generally refers to the amount of labor
2.
X j lj u X k
j¼1
j6¼k n X
Y j lj Y k
j¼1
j6¼k
lj 0, j = 1,...,n. The difference between model 1 and model 2 is that when we evaluate the relative efficiency of kth DMU, the desirable output Y of the DMU can be expressed as a linear combination of all k DMUj (j = 1,2,...,k) except the input and output of the kth DMU. In superefficiency DEA model, the inefficient DMU has the same efficiency score as CCR–DEA model, but for an efficient DMU, its inputs can make a pro-rata increase with its efficiency score unchanged, then the ratio of its increasing input is its super-efficiency value. For example, given the environmental efficiency score is 1.2 of one province, then when increase the inputs of this province by 20%, its environmental efficiency score keep above one, so it is still relative efficient in the whole samples. Super-efficiency value indicates the maximum range of change under the premise of the DMU remaining efficient.
3.
4.
5.
3. Data and variables The data used in this study is obtained from ‘China statistical databases of economic and social development’ (originally from
3
used in the actual production process and is measured by working hours of standard labor intensity in developed countries. However, due to the lack of provincial level data of such kind, the majority of research in China is using the number of employees as a proxy for labor input. Therefore, the number of employees at the end of each calendar year is selected as the labor input in this study. Capital investment: most studies suggest that capital stock which represents the entire enterprise capital resources could be used to reflect the total amount of invested capital of all types and thus is an ideal measure for capital investment. But it cannot reflect the annual change in capital investment. Some research considers the total investment in fixed assets as capital investment, which is not proper either because the total investment in fixed assets includes assets establishing, updating as well as the other activities that cannot represent the actual inputs. Therefore, to fully reflect the capital investment, we use the depreciation of fixed assets to indicate the transferred value from fixed asset to the new product during current production process, which is fixed capital investment in the current economic activity. Resource input: the concept of environmental efficiency shows that environment is treated as an economic investment to support economic development and thus resource consumption should be taken into account in environmental efficiency calculation. Energy consumption is then chosen as a proxy of resource consumption in this paper. Environmental input: there are many items included in environmental investment projects, such as the total amount of water, construction area, etc. But given the data availability, we choose domestic CO2 emission and SO2 emission as a substitute index for environmental impact. Output: given the fact that environmental efficiency represents the input–output ratio, we have chosen labor, capital, resources, and environment as the input as discussed above. Therefore, GDP of each province is chosen as the indicator for expected output. The data description is shown in Table 1.
Table 1 Descriptive statistics. Year
Statistics
Capital
Labor
Energy consumption
GDP
CO2
SO2
2000
Mean Max Min S.D. Mean Max Min S.D. Mean Max Min S.D.
512.434 1853.6 49.7 423.267 983.089 3622.78 102.17 849.478 1871.815 6159.34 199.8 1481.432
2193.877 5572 275.5 1480.11 2269.214 5662.4 267.6 1489.563 2565.221 6402 326 1705.155
4953.542 12513.21 480 2999.834 8709.152 25104.79 819.1 5588.573 12952.39 34323 1358.5 8107.894
3279.548 10741.25 263.68 2496.158 6631.918 22557.37 543.32 5343.684 14551.15 46013.06 1350.43 11118.84
10574.16 25000.5 955.54 6174.473 16988.69 56062.1 1724.67 11428.16 28639.42 82132.66 3348.39 18278.86
64.255 180 2 43.280 84.973 200.3 2.2 50.157 72.823 153.8 2.9 40.464
2005
2010
The data is obtained from ‘China statistical databases of economic and social development’. Capital and labor are expressed in units of million RMB Yuan and ten-thousand persons. Energy consumption and GDP are in units of ten-thousand tons of standard coal and a hundred-million RMB. The units of CO2 and SO2 are both of ten-thousand tons.
Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040
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Fig. 1. Average of environmental efficiencies for each individual province in China from 2000 to 2010.
From Table 1 we can see that the mean GDP of 2010 is 4.4 times as many as that of 2000 which indicates that 30 administrative regions have gained economic growth during the period of 2000– 2010. Meanwhile, the amount of SO2 emission in 2010 is 1.13 times over the amount in 2000 and the emission of CO2 is 2.71 times than that of the 2000 which indicates that the emission of SO2 is increasing gradually and the emission of CO2 is increasing drastically. Fig. 1 indicates that the energy intensity is decreasing sharply during the time of 2000–2010 which means that the Chinese government has achieved great success in measurement of energy save. 4. Empirical analysis Based on the data discussed in Section 3 including inputs (labor, capital investment, energy and waste emissions) as well as output (GDP), we apply EMS (version 3.1) software to calculate the environmental efficiencies for 30 Chinese provinces. The results are shown in Table 2. 4.1. Efficiency analysis by province Table 2 shows the environmental efficiency overtime trends in 30 Chinese provinces from 2000 to 2010 measured by the proposed model in Section 2. From Table 2, we find that the percentage of environmental efficient regions in China is lower than 50% from 2000 to 2010, which means that the majority of Chinese provinces (municipalities and autonomous regions) are environmentally inefficient. The national average environmental efficiency value, however, is greater than one, showing an improvement in national environmental efficiencies. More cities have become more environmentally efficient. The results demonstrate that China has made great effort in building a resource-conserving and environment-friendly society and has achieved great success in terms of environment protection and efficient use of natural resources. We can divide the regions into different categories based on the performance measured by the scores (Table 2). Technologically, the maximum value measuring efficiency is one. The closer the value to one, the better the performance is for a region. If the index is greater than one, it implies that even if the input increases, the production is still efficient. The first group includes Beijing, Shanghai, Tianjin and Fujian province which have shown great improvement indicated by the measured efficiency. These regions have experienced efficiency fluctuations around 1.0 during the period of 2000–2010. Therefore the increases in inputs within a certain pro-rata range would improve economic
performance without undermining the quality of environment. The reason behind this improvement is more advanced technology as well as more restricted environmental restrictions. Taking Beijing as an example (Table 2), its environmental efficiency improved greatly after 2002, reaching the top in 2008 and then declined slightly. Similar trend occurs in Tianjin. In order to achieve the ‘Green Olympics’ and create a good urban environment, Beijing and Tianjin implemented a series of ‘reduction’ policy, environmental friendly scheme before and after the 2008 Olympic Games leading to substantially improved environmental efficiency. Shanghai on the other hand, launched the first round of environmental protection and a three-year construction action plan in 2000, treating and controlling water, atmosphere, solid waste, and forestation, which contribute to the improved environment in Shanghai. The second group includes the environmental inefficient provinces such as Qinghai, Gansu, Sichuan and the average score of environmental efficiency is between 0.6 and 0.8. Although the environmental efficiency of most provinces in China has been improving gradually in the context of rapid economic boost, these areas have shown an inefficient economic development. Increased use of input in these regions may have a deteriorated impact on local economic performance. The possible explanations for this pattern are the less advanced economic development. With old technology, better economic performance can only be achieved with the use of resources and at the cost of serious environmental destruction. Considering economic growth the primary goal, governments have not been strict with the enforcement of environmental regulations. Areas such as Anhui, Yunnan, Sichuan, Qinghai and Gansu have a low environmental efficiency values and this means that under the same conditions, these provinces would have much more environmental inputs to keep the same level outputs. The final group moves to the coastal area, such as Guangdong, Jiangsu and Zhejiang which are relatively environment efficient, except for a few years of less efficient performance. This group of provinces is more likely to locate in the coastal area with their economic development ranking the top in the country. On the one hand, they are famous for their manufacturing production and have utilized more advanced technology in the process of production. On the other hand, there still exist firms heavily relying on the production line destroying the environment. Therefore, we could observe an unstable pattern in this group in terms of environmental efficiency. Evaluating individual provinces, we can also observe a significant difference in environmental efficiency across provinces.
Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040
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5
Table 2 Environmental efficiencies of 30 provinces in China from 2000 to 2010. Provinces
Year 2000
Year 2001
Year 2002
Year 2003
Year 2004
Year 2005
Year 2006
Year 2007
Beijing Shanghai Tianjin Chongqing Fujian InnerMongolia Guangxi Guizhou Ningxia Guangdong Shanxi Zhejiang Hainan Jiangsu Henan Heilongjiang Hunan Xinjiang Jiangxi Hebei Shan'xi Jilin Anhui Liaoning Shandong Hubei Yunnan Sichuan Qinghai Gansu
0.9751 1.2407 0.9926 1.1654 1.1936 1.0070 1.9406 2.2589 1.6839 1.0365 1.1291 1.0462 0.9603 0.8847 0.8862 0.9806 0.8835 0.7797 0.7447 0.8939 0.8688 0.8478 0.7760 0.8441 0.7746 0.7024 0.7911 0.7481 0.5508 0.6714
0.9764 1.3599 0.9103 1.1542 1.1437 1.0095 1.5035 1.7466 2.0815 1.1037 1.1953 0.9735 0.9487 0.9731 0.9273 0.9145 0.8724 0.7547 0.8060 0.9357 0.8597 0.8492 0.8503 0.8631 0.7604 0.7237 0.8021 0.7667 0.7102 0.6835
0.9984 1.4480 0.9491 1.1525 1.0832 1.0077 2.2310 1.8609 1.4055 1.0834 1.1795 1.1406 0.9401 1.0203 0.9120 0.9000 0.8154 0.7965 0.7863 0.8257 0.8244 0.9213 0.8746 0.8691 0.750 0.7898 0.7742 0.7525 0.6855 0.7277
1.1553 1.5536 0.8747 1.1752 1.0316 1.3259 2.3713 1.6355 1.1269 1.0615 0.8816 0.9785 0.9221 0.9799 0.8881 0.8607 0.7859 0.8666 0.7887 0.7861 0.8306 0.8486 0.8166 0.750 0.7022 0.7037 0.7668 0.6954 0.6548 0.6119
1.0292 1.3481 0.9837 1.1974 1.1622 1.2086 2.1610 1.6827 0.9929 1.0433 1.0142 0.9231 0.9839 0.9616 1.0355 0.9952 0.9069 1.0279 0.8794 0.9775 0.8706 0.8838 0.8590 0.8258 0.8831 0.8633 0.6133 0.776 0.6776 0.6457
1.0779 1.1810 1.1423 1.1759 0.9901 1.2891 2.1296 1.6734 1.3766 1.0706 1.0227 1.0136 1.0037 0.9680 0.9422 0.9766 0.9147 0.9619 1.0205 0.9676 0.9078 0.9074 0.8641 0.8713 0.8033 0.7680 0.7358 0.7161 0.7221 0.7502
1.1242 1.3012 1.1258 1.2087 1.0049 1.3092 2.2818 1.4927 1.3294 1.0447 0.9543 1.0138 0.9944 0.9582 1.0287 0.9587 0.8803 0.9821 1.0336 0.9353 0.8894 0.8544 0.8703 0.8763 0.8456 0.7650 0.7529 0.7053 0.7697 0.6814
1.1454 1.1899 1.0395 1.2111 1.0568 1.2322 2.1683 1.7404 1.3756 0.9980 1.0102 0.9499 0.9960 0.9772 0.9185 0.9422 0.8674 1.0153 1.0090 0.9023 0.9494 0.9304 0.8293 0.9254 0.8680 0.8119 0.6995 0.6826 0.7409 0.6861
1.2138 1.0474 1.2993 1.1805 1.0103 1.2928 1.8734 1.3327 1.3900 0.9998 0.9941 0.9749 1.0110 0.9473 0.9956 0.8989 1.1450 0.9583 1.0080 0.8854 0.9572 0.8416 0.8254 0.8233 0.7549 0.7238 0.8440 0.7452 0.9656 0.6798
1.1887 1.0656 1.1267 1.0972 1.0607 1.4111 2.0104 1.5006 1.2755 0.9821 0.8931 0.9826 0.9467 0.9809 0.9666 0.9466 1.0993 0.9954 1.0097 0.9120 0.9857 0.8660 0.8689 0.8503 0.8542 0.7987 0.8712 0.7717 0.7470 0.7487
1.2213 0.9528 1.2168 1.0594 1.0904 1.3972 1.9024 1.6571 1.3679 0.9649 0.9139 0.9987 1.0583 0.9619 0.8585 0.9521 1.0757 1.0514 1.0394 0.9386 0.9897 0.8533 0.9458 0.8600 0.8170 0.8173 0.8490 0.8422 0.8288 0.8344
Average
1.0086
1.0053
1.0168
0.9810
1.0138
1.0315
1.0324
1.0290
1.0206
1.0271
1.0439
Number (ratio) of score > 1
Year 2008
Year 2009
Year 2010
10 (0.3333) 9 (0.3000) 11 (0.3667) 9 (0.3000) 11 (0.3667) 13 (0.4333) 13 (0.4333) 12 (0.4000) 12 (0.4000) 11 (0.3667) 12 (0.4000)
During this period, there is a relatively high and stable level of environmental efficiency in Beijing, Shanghai, Tianjin, Inner Mongolia, Fujian, etc. The environmental efficiency value has remained within a certain range but generally showed to be increasing overtime. This could be attributed to the strengthened environmental management and dedication by environmental administration departments. On the other hand, it also indicates that individual provinces have a great potential to further enforce regulations or law to achieve effective environmental management. More attention should be paid to the implementation of energy saving and emission reduction policies to strengthen economic developments and technology improvements as well as to increase environmental efficiency. These results are scientific and realistic and better accords with the macro-economic reality. Comparing to 11 or less Chinese provinces (municipalities and autonomous regions) reaching the environment efficiency in earlier years (2000– 2003), more areas achieve environment efficiency from 2005 to 2008 showing the progress made by governments at both national and local level. 4.2. Average efficiency analysis Using the environmental efficiency constructed for each individual province from 2000 to 2010 in Table 2, we are able to compute the average efficiency for each province between 2000 and 2010. This is shown in Fig. 1 where a more direct comparison among cities (municipalities and autonomous regions) can be conducted. It generally exhibits a quite stable pattern across provinces in terms of the average environmental efficiency
(around 1) in Fig. 1; however, a few exceptions are worth mentioning. As discussed, more economically advanced regions have shown on average a better performance such as Beijing, Fujian, Shanghai and Guangzhou meaning fewer environmental inputs are required to produce a relative high output, denoted by GDP. The values start to decline as we move to the west part of China with Qinghai and Gansu ranking the bottom on the list. A few provinces in the west, however, show remarkably better performance even than Beijing or Shanghai. The top one performer is Guangxi province with an average efficiency score around two followed by Guizhou (1.5) and Ningxia (1.3). The reason behind this pattern can be attributed to the fact that these regions have more advanced technology as national government established the “Go West” policy in 2000 with more investment devoting to these places. Despite the awkward economic performance, these provinces have caught up with their more advanced counterparts. On the other hand, regions with the average environmental efficiency value less than 0.8, such as Gansu (0.70), Qinghai (0.73), Sichuan (0.75), Yunnan (0.77), Hubei (0.77) are environmental inefficient. There inland regions have suffered from their inconvenient locations which result in less investment, old technology and thus inefficient production as well as a less developed economy. The scores indicates that these provinces are below the environmental production frontier and a lot more needs to be done to establish a way of environmental friendly production by reducing the pollution emissions. Policies suggestions for these regions include coordination between the inputs and economic development. In order to achieve long run economic development, short sighted objectives at the expense of environment protection should be abandoned.
Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040
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Fig. 2. Environmental efficiencies in three areas in China from 2000 to 2010.
4.3. Regional efficiency analysis In this part, we would like to discuss the regional disparities in environmental efficiency given the fact that China is a large country with a vast territory and abundant resources and it exhibits remarkable divergence in regional development in terms of GDP as well as the environmental efficiency. This way, the paper would be able to gauge a more direct and clear picture of regional development. According to the frequently used geographic division, this paper divides the 30 Chinese provinces into three main regions—the Eastern region, the Central region and the Western region, denoted by E, C and W, respectively. Further analysis of the regional differences is based on this regional classification. The east area is mainly coastal area including 11 provinces: Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Beijing, Tianjin, Shanghai and Hainan; The central area includes Jilin, Heilongjiang, Shanxi, Henan, Anhui, Jiangxi, Hunan, Hubei; The west region includes Inner Mongolia, Xinjiang, Gansu, Shan'xi, Ningxia, Sichuan, Chongqing, Guizhou, Yunnan, Guangxi, and Qinghai. In order to intuitively understand the environmental efficiency distribution in different regions from 2000 to 2010, we have constructed the average environmental efficiency by three regions in Fig. 2. For comparison reason, we also mark the average regional environmental efficiency over years. According to Fig. 2, it is obvious that there are significant differences among three economic regions during 2000–2010. The environmental efficiency remained at a relative higher level in the east area (around 1.1) and the central area (around 0.85) followed by the west area (around 0.8). And the average environmental efficiency in the eastern, central and western areas between 2000 and 2010 are 1.05, 0.85 and 0.8, respectively as given in Fig. 2. This means that increasing the inputs by 11.92% for eastern areas will stimulate economic development without deteriorating the environmental quality. Second, it is worth mentioning that the environmental efficiency gap grows wider between eastern areas and central areas over time. According to chronicle analysis, the environmental efficiency of GDP in the west area fluctuated between 0.75 and 0.90 which is below than that of the central area. Unlike the instable environmental efficiencies around 0.9 in the central area, the efficiency scores of eastern part maintained at a high level in the past 10 years and increased slightly at the rate of 0.5% each year from 2000 to 2010. As discussed, the impressive performance in the east areas can be attributed to their advanced economic performance. Given a solid industrial foundation, provinces in this area have accumulated advanced technology and human capital stock which help build the base for more efficient production.
Besides, the regions are considered as the stimulus of national development and account for a large percentage of national output and therefore, more effective policies have been in place to ensure the overall enforcement of environmental efficiency nationwide. Albeit from a medium economic development, the central area is lack of effective enforcement or favorable policies to ensure their efficient production and thus lag behind. West part of China is the least developed area not only in economic terms but also lacked behind in technology and human capital. The already discussed “Go West” campaign has granted favorable policies in term of tax and land to these regions. Consequently, more firms have been established and more productions are present. However, in the pursuit of advanced economic development by attracting more firms, environmental restrictions have been loosened and therefore, in general, its environmental efficiency is in line with the local economic development. In contrast to the few exceptions in the west area, the majority provinces in this region has backward industrial structure which leads to inferior environmental efficiency. In terms of the environmental inefficient provinces, most are located in inland areas, where relatively weak economic growth, outdated technology and obsolete production process are prevalent. Not surprisingly, in central region, only two out of eight provinces, Hunan (1.0757) and Jiangxi (1.0394) reach one in terms of the average environmental efficiencies. Generally speaking, environmental efficiency in the year 2010 is much higher than that of the year 2000. This is because of the implementation of environmental protection policies such as energy saving policies and emissions reduction policies, as well as economic development and technology improvement in recent years. Given the result shown in this study, we can see that environmental protection policies should be adjusted in accordance to the economic conditions in three areas. Besides, the implementation of environmental protection laws and regulations related to improved productivity and increasing GDP should be strengthened. Although there are differences among three areas, Table 2 shows that the national environmental efficiency of GDP increased at a stable rate, which is correlated with great improvements in national environmental technology development in recent years. 5. Conclusion This study measures environmental efficiency of 30 provinces in China during the period of 2000–2010 using the SEDEA model. The result shows that, during the period studied, environmental efficiencies of 30 provinces in China generally experience
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improvements with scores fluctuating between 0.6 and 2.2, however, there is a significant divergence among regions. On the one hand, the environmental efficiency in the developed eastern areas such as Beijing, Shanghai and Fujian is higher followed by central and eastern areas. One thing worth mentioning is that despite being the least developed regions by economic criteria in the country, a few provinces in the west region have been an exception in the performance of environmental efficiency. This is due to the imbalanced development within the region. Moreover, the average environmental efficiency of 30 provinces shows a downward trend first, followed by an increase in 2004 and 2005. This means that the environmental efficiency is most likely to be related with the energy conservation policy proposed during the “Eleventh Five-Year Plan” in China. It can be observed from the results that China’s rapid economic development during the first decade of 21st century has been accompanied by obvious regional disparities. The central and the western regions implemented a relatively liberal environmental policy to develop economy and to improve the competitiveness of related industries. As a result, the environmental efficiency of the central and western region is severely lower than the east. The higher sensitivity of the central and western areas may lead to a dilemma of economy improvement and environment protection. The environmental issues have become increasingly serious. In order to improve the ecological environment and better use natural resources, the eastern region has stepped up efforts to control pollution, gradually given up the extensive mode of production as “high input of resources, low use and high emissions” and become the leading actor in promoting environmental efficiency. Several suggestions can be proposed based on conclusions above. First, environmental policies should be tailored to areas with different levels of development. The focus of energy reservation should be the central and western areas, which have low environmental efficiency and high enhancement potential. We should positively develop the service industry, which has the focus on improving productiveness and meets residents' needs. Then the proportion of the tertiary industry in the national economy should be increased, industrial structure optimized and environmentfriendly growth achieved. Acknowledgments We thank the editor and reviewers for careful review and insightful comments. This study has been partly supported by National Social Science Foundation of China (13&ZD148, 13CTJ001), National Natural Science Foundation of China (71201139, 71303200), National Bureau of Statistics Funds of China (2012LD001, 2011LD00) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (12YJC790263).
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Ecological Indicators xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA Li Yang a , Han Ouyang b , Kuangnan Fang b , Linglong Ye c, Jing Zhang b, * a
School of Humanities and Social Sciences, Anhui University of Science and Technology, China School of Economics, Xiamen University, China c School of Management, Fu Jen Catholic University, Taiwan b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 1 May 2014 Received in revised form 23 August 2014 Accepted 25 August 2014
This study measures the environmental efficiency of China based on environmental super-efficiency data envelopment analysis (SEDEA) model by using data of 30 provinces in China during the period of 2000– 2010. We find that environmental efficiencies across 30 provinces show regional disparities. Cities such as Beijing and Shanghai have experienced improvement in efficiency while Qinghai showed worse performance. More generally, East areas are more efficient in production while the west rank the last with central areas ranking in between during the period studied. Policies should be established to further promote production efficiency. ã 2014 Elsevier Ltd. All rights reserved.
Keywords: Environmental efficiency Data envelopment analysis Super-efficiency
1. Introduction China has experienced dramatic changes since the late 1970s. On one hand, living standards have been improved significantly while on the other, environmental pollution has become a serious issue and attracted increasing attention from both China and aboard. To show the seriousness of this issue to China, the 17th Party Congress adjusted China’s economic development strategy from “fast and good” to “good and fast” to further emphasize environmental protection. Environmental protection campaign in China can be traced back to as early as 1972 when the former premier Zhou Enlai sent a delegate to attend the Stockholm Conference on the Human Environment. The first national conference on environmental protection was then held in August 1973. Since then, China has gradually introduced a series of rules and regulations to protect the nation’s natural resources and environment. Though the enforcement of various environmental regulations and laws has contributed significantly to the protection of environment, government at various levels still have much room to improve in order to sustain development. One of the areas that deserve more attention is the evaluation of efficiency in terms of environmental protection. Existing research has largely focused on the recommendations of environmental policies; however, little has been done to empirically investigate the effectiveness of such policies. Therefore, to fill
* Corresponding author. fax: +86 5922186366. E-mail address: [email protected] (J. Zhang).
this gap, we wish to identify the effectiveness of environmental policies by measuring the environmental efficiency. Various methods have been developed to evaluate efficiency, one of which is called data envelopment analysis (DEA) model. It has gained its popularity as a methodology in evaluating bank performance (Charnes et al., 1990; Cook et al., 2000; Fukuyama and Weber, 2002; Drake and Hall, 2003; Barros et al., 2012), assessing universities research efficiency (Beasley, 1995), identifying excesses or deficits in production as well as examining buyer– supplier supply chain (Seiford and Zhu, 1998, 1999; Zhu 2003; Liang et al., 2006). Farrell (1957) first proposed a non-parametric method of computing the relative efficiency of a decision making unit (DMU) on the basis of a set of DMUs. Two decades later, Charnes et al. (1978) further proposed a line programming model to evaluate technical efficiency and technological progress. Afterwards, DEA was widely used in measuring energy and environment efficiency at a macro-economic level. Zhou et al. (2013) made use of non-radial DEA approach to measure environmental performance of OECD countries and they found that the environmental performance of OECD countries has been improved during 1995–1997. Freeman et al. (1997) and Hu and Wang (2006) used the DEA method to measure energy efficiency. Honma and Hu (2009) measured and compared regional energy efficiency during the period of 1993–2003 in Japan. Chien and Hu (2007) used DEA to analyze the effects of the use of renewable energy on the technical efficiency of 45 economies from 2001 to 2002. Zhou et al. (2008), however, did a careful review of 100 DEA applications in energy and environment policy and found out that most of the studies are measuring efficiency under assumption of
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Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040
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constant returns to scale (CRS) which often conflicts with the reality of production. In order to overcome this, Banker et al. (1984) proposed a DEA (BCC) model based on variable returns to scale (VRS), which better suits the reality. In recent years, many scholars pay more attention to the environment protection in China. Hu and Wang (2006) proposed the measure of total factor productivity and it was used to measure 29 administrative regions in China with CCR–DEA model during the period 1995–2002. Hu et al. (2005) used the CCR model to analyze the technical efficiency and productivity changes in 31 regions of China for the period 1997–2001. Chiu and Wu (2010) used the undesirable measure DEA model to calculate the impact of undesirable output and input on energy efficiency. The research sets up two models to analyze 27 provinces and cities in China during 2000–2003. Yang and Wang (2013) used the nonparametric DEA method to calculate the production frontier of environmental efficiency for provincial energy utilization. The results show that the environmental efficiency of energy utilization in Chinese provinces still has much room for improvement, and CO2 emissions still have potential to be reduced. Zhou et al. (2013) first proposed a new non-radial DEA approach by integrating the entropy weight and the SBM model to evaluate the environmental efficiency of the Chinese power industry at the provincial level during 2005–2010. Shi et al. (2010) measured total technology efficiency, pure technology efficiency and scale efficiency of 28 administrative regions in China using extensive DEA model. The existing literature, however, focuses on the measurement of environmental efficiency at industry level in China by using industrial investment or industrial output index without taking regional disparities into consideration. Regional resource endowments among different provinces are significant in China, and therefore, the ignorance of regional differences may lead to inaccurate and biased comparison across provinces. In order to gauge a clear picture of regional performance on environmental efficiency, we wish to empirically evaluate the technical efficiency of 30 provinces in China using provincial level data from 2000–2010. Two modeling approaches have been extensively used in this regard, parameter method and nonparameter method. For the former, it mainly includes the stochastic frontier analysis (SFA), which requires a given production function and the input–output efficiency values. The most common model for the latter is the traditional DEA model without requiring the estimation of a production function. But when multiple decisionmaking units are involved, the traditional DEA model present difficulty in ranking the decision-making units and thus make further analysis unavailable. In order to overcome this, Andersen and Petersen (1993) proposed a super-efficiency data envelopment analysis (SEDEA). Li et al. (2013) measured environmental efficiency of 30 provinces in China employing SEDEA model when undesirable outputs were taken into consideration. Therefore, this study adopts the super-efficiency data envelopment analysis (SEDEA) to measure environmental efficiency. We use energy consumption, labor, depreciation of fixed capital and sulfur dioxide emissions as inputs indicators and GDP as output indicators to calculate the environmental efficiency of 30 provinces (municipalities and autonomous regions) in mainland China during 2000–2010. Our results show that, in line with our expectation, environmental efficiency across provinces does show a regional disparity which is consistent with the level of economic development. Cities such as Beijing and Shanghai have shown much improvement in efficiencies while Qinghai and Ningxia are doing worse than their counterparts. Provinces in the east regions generally outperform those of the central and the west. The remainder of the paper is organized as follows: Section 2 describes the research methodology including the base
model as well as super efficiency DEA model. Data is described in Section 3. We discuss the empirical results in Section 4 and conclude in Section 5.
2. Methodology 2.1. The CCR model DEA models can be divided into two categories, the first group includes radial models and the second is related with non-radial models. The radial models are often referred to as “CCR” because it was proposed by Charnes, Cooper, and Rhodes (CCR) while “BCC” developed by Banker et al. (1984) belongs to the Debreu–Farrell measure. The basic assumption of these models is constant returns to scale, but many industries are not in a CRS state. In DEA method, each evaluated unit is seen as an independent homogeneous decision making unit (DMU), and several these DMUs form an evaluated group. Based on observations of each DMU, we set the weights of inputs and outputs in each DMU as variables, employing input–output relative efficiency computing and analysis, and then we could decide the DEA relative efficiency of DMUs. Suppose we have a set of independent homogeneous decision making units, denoted by DMUj(j = 1,2,...,n) Each DMU uses m resource inputs X and s desirable outputs Y. Denoting the ith resource input of the kth DMU as xij(i = 1,2,...,m,k = 1,2,...,n) and the jth desirable output of the kth DMU as yjk = (j = 1,2,...,s, k = 1,2,...,n. Then the technical efficiency of the kth DMU can be calculated by the output-oriented CCR model (1): Max s:t:
M X
Z m Y ms
m¼1 M X
Z m Y ms
m¼1
N X vn xnk ¼ 1
N X vn xnk 0 n¼1
(1)
n¼1
Zm 0; vn 0; k = 1,2,...,K; m = 1,2,...,M; n=1,2,...,N The efficiency score of DMUs can be derived by the solution of model 1. Model 1 has the following dual model 2 which has the same objective value on optimality and usually be calculated in order to get efficiency score.
s:t:
n X X j lj u X k j¼1 n X
(2)
Y j lj Y k
j¼1
lj 0, j = 1,...,n.
Where Xk = (x1k,x2k..., xmk), Yk = (y1k,y2k..., ysk,) and u is the total efficiency of kth DMU. In the above formula, l is the weight multiplier of each decision making unit. The desirable output Y of one DMU can be expressed as a linear combination of all k DMUj (j = 1,2,...,k), the compression ratio of the resource inputs X of that DMU is u, which is also known as efficiency measurement scores. CCR model is the most popular efficiency measurement model in DEA method based on CRS and take efficient score unity as the most efficient DMU and other DMU’ scores between 0 and 1 are inefficient. DEA method does not assume function relationship between inputs and outputs and does not require any pre-estimate weights assumption. This method avoids some subjective factors, directly using a weighted ratio between input and output to calculate the
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input–output efficiency of DMUs. DEA method, as an effective tool to measure the efficiency and productivity problems of similar DMUs, has been employed in many articles on environmental efficiency study. 2.2. Super efficiency DEA model There are two types of DMU in CCR model, efficient and inefficient DEA model. The traditional CCR model has poor discrimination when DMUs are simultaneously on the frontier of production which often lead to difficulty in further evaluation and comparison between these DMUs. In order to remedy the shortage of CCR model and evaluate DMUs’ efficiencies effectively and realistically, SEDEA, a kind of comprehensive efficiency measure, proposed by Andersen and Petersen (1993), can further distinguish efficiency among DMUs and then sort the relative efficiency of these DMUs. SEDEA has the similar function formula with CCR model used for determining the most productive scale size in the traditional DEA framework and it can be described by the Formula (3): min u s:t:
n X
‘China Statistical Yearbook’) of 30 provinces in China during the period of 2000–2010. Tibet and Hong Kong are not included due to the lack of data. Regarding the specification of the inputs and outputs, we employ the cost-based indicators input variables and incomebased indicators for the output variable in DEA model, respectively. Therefore, we use labor, capital and resources as measures for the former and GDP for the later which is consistent with Shi et al. (2010). The inputs and outputs variables are defined as follows: 1. Labor input: labor input generally refers to the amount of labor
2.
X j lj u X k
j¼1
j6¼k n X
Y j lj Y k
j¼1
j6¼k
lj 0, j = 1,...,n. The difference between model 1 and model 2 is that when we evaluate the relative efficiency of kth DMU, the desirable output Y of the DMU can be expressed as a linear combination of all k DMUj (j = 1,2,...,k) except the input and output of the kth DMU. In superefficiency DEA model, the inefficient DMU has the same efficiency score as CCR–DEA model, but for an efficient DMU, its inputs can make a pro-rata increase with its efficiency score unchanged, then the ratio of its increasing input is its super-efficiency value. For example, given the environmental efficiency score is 1.2 of one province, then when increase the inputs of this province by 20%, its environmental efficiency score keep above one, so it is still relative efficient in the whole samples. Super-efficiency value indicates the maximum range of change under the premise of the DMU remaining efficient.
3.
4.
5.
3. Data and variables The data used in this study is obtained from ‘China statistical databases of economic and social development’ (originally from
3
used in the actual production process and is measured by working hours of standard labor intensity in developed countries. However, due to the lack of provincial level data of such kind, the majority of research in China is using the number of employees as a proxy for labor input. Therefore, the number of employees at the end of each calendar year is selected as the labor input in this study. Capital investment: most studies suggest that capital stock which represents the entire enterprise capital resources could be used to reflect the total amount of invested capital of all types and thus is an ideal measure for capital investment. But it cannot reflect the annual change in capital investment. Some research considers the total investment in fixed assets as capital investment, which is not proper either because the total investment in fixed assets includes assets establishing, updating as well as the other activities that cannot represent the actual inputs. Therefore, to fully reflect the capital investment, we use the depreciation of fixed assets to indicate the transferred value from fixed asset to the new product during current production process, which is fixed capital investment in the current economic activity. Resource input: the concept of environmental efficiency shows that environment is treated as an economic investment to support economic development and thus resource consumption should be taken into account in environmental efficiency calculation. Energy consumption is then chosen as a proxy of resource consumption in this paper. Environmental input: there are many items included in environmental investment projects, such as the total amount of water, construction area, etc. But given the data availability, we choose domestic CO2 emission and SO2 emission as a substitute index for environmental impact. Output: given the fact that environmental efficiency represents the input–output ratio, we have chosen labor, capital, resources, and environment as the input as discussed above. Therefore, GDP of each province is chosen as the indicator for expected output. The data description is shown in Table 1.
Table 1 Descriptive statistics. Year
Statistics
Capital
Labor
Energy consumption
GDP
CO2
SO2
2000
Mean Max Min S.D. Mean Max Min S.D. Mean Max Min S.D.
512.434 1853.6 49.7 423.267 983.089 3622.78 102.17 849.478 1871.815 6159.34 199.8 1481.432
2193.877 5572 275.5 1480.11 2269.214 5662.4 267.6 1489.563 2565.221 6402 326 1705.155
4953.542 12513.21 480 2999.834 8709.152 25104.79 819.1 5588.573 12952.39 34323 1358.5 8107.894
3279.548 10741.25 263.68 2496.158 6631.918 22557.37 543.32 5343.684 14551.15 46013.06 1350.43 11118.84
10574.16 25000.5 955.54 6174.473 16988.69 56062.1 1724.67 11428.16 28639.42 82132.66 3348.39 18278.86
64.255 180 2 43.280 84.973 200.3 2.2 50.157 72.823 153.8 2.9 40.464
2005
2010
The data is obtained from ‘China statistical databases of economic and social development’. Capital and labor are expressed in units of million RMB Yuan and ten-thousand persons. Energy consumption and GDP are in units of ten-thousand tons of standard coal and a hundred-million RMB. The units of CO2 and SO2 are both of ten-thousand tons.
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Fig. 1. Average of environmental efficiencies for each individual province in China from 2000 to 2010.
From Table 1 we can see that the mean GDP of 2010 is 4.4 times as many as that of 2000 which indicates that 30 administrative regions have gained economic growth during the period of 2000– 2010. Meanwhile, the amount of SO2 emission in 2010 is 1.13 times over the amount in 2000 and the emission of CO2 is 2.71 times than that of the 2000 which indicates that the emission of SO2 is increasing gradually and the emission of CO2 is increasing drastically. Fig. 1 indicates that the energy intensity is decreasing sharply during the time of 2000–2010 which means that the Chinese government has achieved great success in measurement of energy save. 4. Empirical analysis Based on the data discussed in Section 3 including inputs (labor, capital investment, energy and waste emissions) as well as output (GDP), we apply EMS (version 3.1) software to calculate the environmental efficiencies for 30 Chinese provinces. The results are shown in Table 2. 4.1. Efficiency analysis by province Table 2 shows the environmental efficiency overtime trends in 30 Chinese provinces from 2000 to 2010 measured by the proposed model in Section 2. From Table 2, we find that the percentage of environmental efficient regions in China is lower than 50% from 2000 to 2010, which means that the majority of Chinese provinces (municipalities and autonomous regions) are environmentally inefficient. The national average environmental efficiency value, however, is greater than one, showing an improvement in national environmental efficiencies. More cities have become more environmentally efficient. The results demonstrate that China has made great effort in building a resource-conserving and environment-friendly society and has achieved great success in terms of environment protection and efficient use of natural resources. We can divide the regions into different categories based on the performance measured by the scores (Table 2). Technologically, the maximum value measuring efficiency is one. The closer the value to one, the better the performance is for a region. If the index is greater than one, it implies that even if the input increases, the production is still efficient. The first group includes Beijing, Shanghai, Tianjin and Fujian province which have shown great improvement indicated by the measured efficiency. These regions have experienced efficiency fluctuations around 1.0 during the period of 2000–2010. Therefore the increases in inputs within a certain pro-rata range would improve economic
performance without undermining the quality of environment. The reason behind this improvement is more advanced technology as well as more restricted environmental restrictions. Taking Beijing as an example (Table 2), its environmental efficiency improved greatly after 2002, reaching the top in 2008 and then declined slightly. Similar trend occurs in Tianjin. In order to achieve the ‘Green Olympics’ and create a good urban environment, Beijing and Tianjin implemented a series of ‘reduction’ policy, environmental friendly scheme before and after the 2008 Olympic Games leading to substantially improved environmental efficiency. Shanghai on the other hand, launched the first round of environmental protection and a three-year construction action plan in 2000, treating and controlling water, atmosphere, solid waste, and forestation, which contribute to the improved environment in Shanghai. The second group includes the environmental inefficient provinces such as Qinghai, Gansu, Sichuan and the average score of environmental efficiency is between 0.6 and 0.8. Although the environmental efficiency of most provinces in China has been improving gradually in the context of rapid economic boost, these areas have shown an inefficient economic development. Increased use of input in these regions may have a deteriorated impact on local economic performance. The possible explanations for this pattern are the less advanced economic development. With old technology, better economic performance can only be achieved with the use of resources and at the cost of serious environmental destruction. Considering economic growth the primary goal, governments have not been strict with the enforcement of environmental regulations. Areas such as Anhui, Yunnan, Sichuan, Qinghai and Gansu have a low environmental efficiency values and this means that under the same conditions, these provinces would have much more environmental inputs to keep the same level outputs. The final group moves to the coastal area, such as Guangdong, Jiangsu and Zhejiang which are relatively environment efficient, except for a few years of less efficient performance. This group of provinces is more likely to locate in the coastal area with their economic development ranking the top in the country. On the one hand, they are famous for their manufacturing production and have utilized more advanced technology in the process of production. On the other hand, there still exist firms heavily relying on the production line destroying the environment. Therefore, we could observe an unstable pattern in this group in terms of environmental efficiency. Evaluating individual provinces, we can also observe a significant difference in environmental efficiency across provinces.
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Table 2 Environmental efficiencies of 30 provinces in China from 2000 to 2010. Provinces
Year 2000
Year 2001
Year 2002
Year 2003
Year 2004
Year 2005
Year 2006
Year 2007
Beijing Shanghai Tianjin Chongqing Fujian InnerMongolia Guangxi Guizhou Ningxia Guangdong Shanxi Zhejiang Hainan Jiangsu Henan Heilongjiang Hunan Xinjiang Jiangxi Hebei Shan'xi Jilin Anhui Liaoning Shandong Hubei Yunnan Sichuan Qinghai Gansu
0.9751 1.2407 0.9926 1.1654 1.1936 1.0070 1.9406 2.2589 1.6839 1.0365 1.1291 1.0462 0.9603 0.8847 0.8862 0.9806 0.8835 0.7797 0.7447 0.8939 0.8688 0.8478 0.7760 0.8441 0.7746 0.7024 0.7911 0.7481 0.5508 0.6714
0.9764 1.3599 0.9103 1.1542 1.1437 1.0095 1.5035 1.7466 2.0815 1.1037 1.1953 0.9735 0.9487 0.9731 0.9273 0.9145 0.8724 0.7547 0.8060 0.9357 0.8597 0.8492 0.8503 0.8631 0.7604 0.7237 0.8021 0.7667 0.7102 0.6835
0.9984 1.4480 0.9491 1.1525 1.0832 1.0077 2.2310 1.8609 1.4055 1.0834 1.1795 1.1406 0.9401 1.0203 0.9120 0.9000 0.8154 0.7965 0.7863 0.8257 0.8244 0.9213 0.8746 0.8691 0.750 0.7898 0.7742 0.7525 0.6855 0.7277
1.1553 1.5536 0.8747 1.1752 1.0316 1.3259 2.3713 1.6355 1.1269 1.0615 0.8816 0.9785 0.9221 0.9799 0.8881 0.8607 0.7859 0.8666 0.7887 0.7861 0.8306 0.8486 0.8166 0.750 0.7022 0.7037 0.7668 0.6954 0.6548 0.6119
1.0292 1.3481 0.9837 1.1974 1.1622 1.2086 2.1610 1.6827 0.9929 1.0433 1.0142 0.9231 0.9839 0.9616 1.0355 0.9952 0.9069 1.0279 0.8794 0.9775 0.8706 0.8838 0.8590 0.8258 0.8831 0.8633 0.6133 0.776 0.6776 0.6457
1.0779 1.1810 1.1423 1.1759 0.9901 1.2891 2.1296 1.6734 1.3766 1.0706 1.0227 1.0136 1.0037 0.9680 0.9422 0.9766 0.9147 0.9619 1.0205 0.9676 0.9078 0.9074 0.8641 0.8713 0.8033 0.7680 0.7358 0.7161 0.7221 0.7502
1.1242 1.3012 1.1258 1.2087 1.0049 1.3092 2.2818 1.4927 1.3294 1.0447 0.9543 1.0138 0.9944 0.9582 1.0287 0.9587 0.8803 0.9821 1.0336 0.9353 0.8894 0.8544 0.8703 0.8763 0.8456 0.7650 0.7529 0.7053 0.7697 0.6814
1.1454 1.1899 1.0395 1.2111 1.0568 1.2322 2.1683 1.7404 1.3756 0.9980 1.0102 0.9499 0.9960 0.9772 0.9185 0.9422 0.8674 1.0153 1.0090 0.9023 0.9494 0.9304 0.8293 0.9254 0.8680 0.8119 0.6995 0.6826 0.7409 0.6861
1.2138 1.0474 1.2993 1.1805 1.0103 1.2928 1.8734 1.3327 1.3900 0.9998 0.9941 0.9749 1.0110 0.9473 0.9956 0.8989 1.1450 0.9583 1.0080 0.8854 0.9572 0.8416 0.8254 0.8233 0.7549 0.7238 0.8440 0.7452 0.9656 0.6798
1.1887 1.0656 1.1267 1.0972 1.0607 1.4111 2.0104 1.5006 1.2755 0.9821 0.8931 0.9826 0.9467 0.9809 0.9666 0.9466 1.0993 0.9954 1.0097 0.9120 0.9857 0.8660 0.8689 0.8503 0.8542 0.7987 0.8712 0.7717 0.7470 0.7487
1.2213 0.9528 1.2168 1.0594 1.0904 1.3972 1.9024 1.6571 1.3679 0.9649 0.9139 0.9987 1.0583 0.9619 0.8585 0.9521 1.0757 1.0514 1.0394 0.9386 0.9897 0.8533 0.9458 0.8600 0.8170 0.8173 0.8490 0.8422 0.8288 0.8344
Average
1.0086
1.0053
1.0168
0.9810
1.0138
1.0315
1.0324
1.0290
1.0206
1.0271
1.0439
Number (ratio) of score > 1
Year 2008
Year 2009
Year 2010
10 (0.3333) 9 (0.3000) 11 (0.3667) 9 (0.3000) 11 (0.3667) 13 (0.4333) 13 (0.4333) 12 (0.4000) 12 (0.4000) 11 (0.3667) 12 (0.4000)
During this period, there is a relatively high and stable level of environmental efficiency in Beijing, Shanghai, Tianjin, Inner Mongolia, Fujian, etc. The environmental efficiency value has remained within a certain range but generally showed to be increasing overtime. This could be attributed to the strengthened environmental management and dedication by environmental administration departments. On the other hand, it also indicates that individual provinces have a great potential to further enforce regulations or law to achieve effective environmental management. More attention should be paid to the implementation of energy saving and emission reduction policies to strengthen economic developments and technology improvements as well as to increase environmental efficiency. These results are scientific and realistic and better accords with the macro-economic reality. Comparing to 11 or less Chinese provinces (municipalities and autonomous regions) reaching the environment efficiency in earlier years (2000– 2003), more areas achieve environment efficiency from 2005 to 2008 showing the progress made by governments at both national and local level. 4.2. Average efficiency analysis Using the environmental efficiency constructed for each individual province from 2000 to 2010 in Table 2, we are able to compute the average efficiency for each province between 2000 and 2010. This is shown in Fig. 1 where a more direct comparison among cities (municipalities and autonomous regions) can be conducted. It generally exhibits a quite stable pattern across provinces in terms of the average environmental efficiency
(around 1) in Fig. 1; however, a few exceptions are worth mentioning. As discussed, more economically advanced regions have shown on average a better performance such as Beijing, Fujian, Shanghai and Guangzhou meaning fewer environmental inputs are required to produce a relative high output, denoted by GDP. The values start to decline as we move to the west part of China with Qinghai and Gansu ranking the bottom on the list. A few provinces in the west, however, show remarkably better performance even than Beijing or Shanghai. The top one performer is Guangxi province with an average efficiency score around two followed by Guizhou (1.5) and Ningxia (1.3). The reason behind this pattern can be attributed to the fact that these regions have more advanced technology as national government established the “Go West” policy in 2000 with more investment devoting to these places. Despite the awkward economic performance, these provinces have caught up with their more advanced counterparts. On the other hand, regions with the average environmental efficiency value less than 0.8, such as Gansu (0.70), Qinghai (0.73), Sichuan (0.75), Yunnan (0.77), Hubei (0.77) are environmental inefficient. There inland regions have suffered from their inconvenient locations which result in less investment, old technology and thus inefficient production as well as a less developed economy. The scores indicates that these provinces are below the environmental production frontier and a lot more needs to be done to establish a way of environmental friendly production by reducing the pollution emissions. Policies suggestions for these regions include coordination between the inputs and economic development. In order to achieve long run economic development, short sighted objectives at the expense of environment protection should be abandoned.
Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040
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Fig. 2. Environmental efficiencies in three areas in China from 2000 to 2010.
4.3. Regional efficiency analysis In this part, we would like to discuss the regional disparities in environmental efficiency given the fact that China is a large country with a vast territory and abundant resources and it exhibits remarkable divergence in regional development in terms of GDP as well as the environmental efficiency. This way, the paper would be able to gauge a more direct and clear picture of regional development. According to the frequently used geographic division, this paper divides the 30 Chinese provinces into three main regions—the Eastern region, the Central region and the Western region, denoted by E, C and W, respectively. Further analysis of the regional differences is based on this regional classification. The east area is mainly coastal area including 11 provinces: Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Beijing, Tianjin, Shanghai and Hainan; The central area includes Jilin, Heilongjiang, Shanxi, Henan, Anhui, Jiangxi, Hunan, Hubei; The west region includes Inner Mongolia, Xinjiang, Gansu, Shan'xi, Ningxia, Sichuan, Chongqing, Guizhou, Yunnan, Guangxi, and Qinghai. In order to intuitively understand the environmental efficiency distribution in different regions from 2000 to 2010, we have constructed the average environmental efficiency by three regions in Fig. 2. For comparison reason, we also mark the average regional environmental efficiency over years. According to Fig. 2, it is obvious that there are significant differences among three economic regions during 2000–2010. The environmental efficiency remained at a relative higher level in the east area (around 1.1) and the central area (around 0.85) followed by the west area (around 0.8). And the average environmental efficiency in the eastern, central and western areas between 2000 and 2010 are 1.05, 0.85 and 0.8, respectively as given in Fig. 2. This means that increasing the inputs by 11.92% for eastern areas will stimulate economic development without deteriorating the environmental quality. Second, it is worth mentioning that the environmental efficiency gap grows wider between eastern areas and central areas over time. According to chronicle analysis, the environmental efficiency of GDP in the west area fluctuated between 0.75 and 0.90 which is below than that of the central area. Unlike the instable environmental efficiencies around 0.9 in the central area, the efficiency scores of eastern part maintained at a high level in the past 10 years and increased slightly at the rate of 0.5% each year from 2000 to 2010. As discussed, the impressive performance in the east areas can be attributed to their advanced economic performance. Given a solid industrial foundation, provinces in this area have accumulated advanced technology and human capital stock which help build the base for more efficient production.
Besides, the regions are considered as the stimulus of national development and account for a large percentage of national output and therefore, more effective policies have been in place to ensure the overall enforcement of environmental efficiency nationwide. Albeit from a medium economic development, the central area is lack of effective enforcement or favorable policies to ensure their efficient production and thus lag behind. West part of China is the least developed area not only in economic terms but also lacked behind in technology and human capital. The already discussed “Go West” campaign has granted favorable policies in term of tax and land to these regions. Consequently, more firms have been established and more productions are present. However, in the pursuit of advanced economic development by attracting more firms, environmental restrictions have been loosened and therefore, in general, its environmental efficiency is in line with the local economic development. In contrast to the few exceptions in the west area, the majority provinces in this region has backward industrial structure which leads to inferior environmental efficiency. In terms of the environmental inefficient provinces, most are located in inland areas, where relatively weak economic growth, outdated technology and obsolete production process are prevalent. Not surprisingly, in central region, only two out of eight provinces, Hunan (1.0757) and Jiangxi (1.0394) reach one in terms of the average environmental efficiencies. Generally speaking, environmental efficiency in the year 2010 is much higher than that of the year 2000. This is because of the implementation of environmental protection policies such as energy saving policies and emissions reduction policies, as well as economic development and technology improvement in recent years. Given the result shown in this study, we can see that environmental protection policies should be adjusted in accordance to the economic conditions in three areas. Besides, the implementation of environmental protection laws and regulations related to improved productivity and increasing GDP should be strengthened. Although there are differences among three areas, Table 2 shows that the national environmental efficiency of GDP increased at a stable rate, which is correlated with great improvements in national environmental technology development in recent years. 5. Conclusion This study measures environmental efficiency of 30 provinces in China during the period of 2000–2010 using the SEDEA model. The result shows that, during the period studied, environmental efficiencies of 30 provinces in China generally experience
Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040
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improvements with scores fluctuating between 0.6 and 2.2, however, there is a significant divergence among regions. On the one hand, the environmental efficiency in the developed eastern areas such as Beijing, Shanghai and Fujian is higher followed by central and eastern areas. One thing worth mentioning is that despite being the least developed regions by economic criteria in the country, a few provinces in the west region have been an exception in the performance of environmental efficiency. This is due to the imbalanced development within the region. Moreover, the average environmental efficiency of 30 provinces shows a downward trend first, followed by an increase in 2004 and 2005. This means that the environmental efficiency is most likely to be related with the energy conservation policy proposed during the “Eleventh Five-Year Plan” in China. It can be observed from the results that China’s rapid economic development during the first decade of 21st century has been accompanied by obvious regional disparities. The central and the western regions implemented a relatively liberal environmental policy to develop economy and to improve the competitiveness of related industries. As a result, the environmental efficiency of the central and western region is severely lower than the east. The higher sensitivity of the central and western areas may lead to a dilemma of economy improvement and environment protection. The environmental issues have become increasingly serious. In order to improve the ecological environment and better use natural resources, the eastern region has stepped up efforts to control pollution, gradually given up the extensive mode of production as “high input of resources, low use and high emissions” and become the leading actor in promoting environmental efficiency. Several suggestions can be proposed based on conclusions above. First, environmental policies should be tailored to areas with different levels of development. The focus of energy reservation should be the central and western areas, which have low environmental efficiency and high enhancement potential. We should positively develop the service industry, which has the focus on improving productiveness and meets residents' needs. Then the proportion of the tertiary industry in the national economy should be increased, industrial structure optimized and environmentfriendly growth achieved. Acknowledgments We thank the editor and reviewers for careful review and insightful comments. This study has been partly supported by National Social Science Foundation of China (13&ZD148, 13CTJ001), National Natural Science Foundation of China (71201139, 71303200), National Bureau of Statistics Funds of China (2012LD001, 2011LD00) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (12YJC790263).
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Please cite this article in press as: Yang, L., et al., Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indicat. (2014), http://dx.doi.org/10.1016/j.ecolind.2014.08.040