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A closer look at the economic-environmental disparities for regional development in China
European Journal of Operational Research 183 (2007) 882–894 www.elsevier.com/locate/ejor
O.R. Applications
A closer look at the economic-environmental disparities for regional development in China Wen-Min Lu
a,*
, Shih-Fang Lo
b
a
b
Department and Graduate Institute of Finance, National Defense University, No. 70, Sec. 2, Zhongyang North Road, Beitou, Taipei 112, Taiwan International Division, Chung-Hua Institution for Economic Research, Rm. 516, 75 Chang-Hsing St., Taipei 106, Taiwan Received 15 June 2005; accepted 9 October 2006 Available online 11 December 2006
Abstract Although China has harvested the fruits of its rapid economic growth over a period of several decades, it has encountered serious environmental problems, an important one being air pollution in the form of soot, dust, and sulfur dioxide. In considering the concept of ‘green-GDP’, this paper analyzes China’s regional development by examining its economic performance while taking into account various environmental factors. In addition to computing technical efficiency for 31 regions in China, a cross-efficiency measure is applied to differentiate the genuine DMUs. ‘Overall’ efficient regions and ‘false positive’ ones are recognized by a false positive index (FPI). It is found that the coastal regions perform on average better than the inland regions both economically and environmentally. For inefficient regions, the benchmark should be those regions with high cross-efficiency mean scores (e.g., Guangdong) rather than those with high self-appraisal scores (e.g., Shanghai). A cross-tabulation illustrating the difference between GDP-oriented performance and Pollution-oriented performance shows that the coastal regions make up the dominant proportion in terms of the benchmarks for economicenvironmental optimization. 2006 Elsevier B.V. All rights reserved. Keywords: Data envelopment analysis; Cross-efficiency measure; Environment; OR in developing countries
1. Introduction China has emerged as one of the most important economies globally ever since the Chinese government adopted its policy of economic reform and opened up special economic zones and coastal cities to foreign investment in the late 1970s. By receiving $45 billion in foreign direct investment (FDI) in 1998, China became the largest FDI host country among the developing Asian economies (United Nations, 1999). However, while China has experienced fruitful economic growth for more than a decade, this growth has also been accompanied by severe environmental problems. Three major air pollutants, namely, soot, dust, and sulfur dioxide, have possibly affected not only China itself, but also its neighboring countries (Ramanathan and Crutzen, *
Corresponding author. Tel.: +886 2 2898 6600x228342; fax: +886 2 2898 5927. E-mail address: [email protected] (W.-M. Lu).
0377-2217/$ - see front matter 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2006.10.027
W.-M. Lu, S.-F. Lo / European Journal of Operational Research 183 (2007) 882–894
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2001). In realizing the serious consequences of these air pollutants, the Chinese government has taken action to deal with these problems. While dust emissions have declined, sulfur dioxide and soot emissions have still been increasing in recent years (Liu, 2001). Old-fashioned and inefficient technology and highly-polluting engines and fuels have been deemed to be the factors responsible for these pollutants. Aside from these environmental problems, China is now also facing a serious economic imbalance. The high economic inequality can be mainly attributed to the growing inland–coastal disparity in China (Chang, 2002; Yang, 2002). For years, the national development policies have been dominated by the norms of ‘economy first, environment later’ or ‘the coastal regions first, the inland regions later’. For instance, per capita foreign direct investment in the western part of the inland area is only 8% of that in the eastern part (Hu, 2001). By compounding this issue with environmental protection, people in China, especially those in the areas with lower income, may welcome dirtier industries so as to increase their income following China’s entry into the World Trade Organization (WTO) in 2001. China is now at a cross-roads between ‘economic growth versus environmental protection’ and ‘coastal versus inland priority’. When calculating a region’s or a country’s economic growth, gross domestic product (GDP) is commonly used as a representative index. However, this economic term fails to reflect the influence of economic growth on both natural resources and the environment. Recently, the term ‘green-GDP,’ which accounts for both economic and environmental costs, has been widely promoted. Green-GDP is generally defined as an adjustment of traditional GDP after deducting the cost of environmental damage, and reflects ‘the quality of growth’. While the calculation of green-GDP is still difficult nowadays, we can still incorporate this concept into our quantitative analysis in the best way possible. In Lovell et al. (1995) study, 19 organization for economic co-operation and development (OECD) countries have been evaluated and ranked from the perspective of achieving a balance between being pro-economy and pro-environment. Two environmental factors (carbon and nitrogen emissions) are incorporated to correct the macroeconomic performance, and the ranking list changes tremendously as a result. Environmental indicators do have crucial effects on a country’s relative performance. Since China is facing the problems associated with a deteriorating environment in its pursuit of rapid economic growth, it is worth bringing the concept of green-GDP into a regional performance evaluation. In light of the geographical and economic diversity that exists across regions in China, this study examines the overall performance for different regions in China while taking into account both economic as well as environmental factors. The major kinds of emissions, such as soot, dust, and sulfur dioxide, are included as environmental proxies. We use a linear programming technique known as data envelopment analysis (DEA) to analyze the relative macroeconomic performances of regions in China. DEA, which was first developed by Charnes et al. (1978, CCR model), is a methodology for constructing a best practice frontier, which tightly envelops observed data on the inputs and outputs of decision-making units (DMUs). The relative performance of a DMU is evaluated in terms of its proximity to the best practice frontier. In our analysis, both desirable outputs (e.g., GDP) and undesirable outputs (e.g., emissions) are incorporated. These two types of outputs should be treated differently when evaluating the performance of regions. Using the classification invariance property (Seiford and Zhu, 2002), the standard DEA model can be implemented to improve performance by increasing desirable outputs and decreasing undesirable outputs. For an efficient DMU, the role it plays in terms of being benchmarked by other inefficient DMUs is also important. Various efforts have been devoted to developing methods without a priori information so as to identify a benchmark in DEA (Andersen and Petersen, 1993; Seiford and Zhu, 1999; Li and Reeves, 1999; Tone, 2002). However, a difficulty arises with those prior research studies in that an inefficient DMU may not be inherently similar to their benchmarked performances. It is possible that these benchmarks gained from the methods proposed by previous studies are unattainable goals for those inefficient DMUs. A simple traditional radical efficiency score derived from the CCR model alone is insufficient while differentiating between those DMUs exhibiting good ‘overall’ performance or those that do not. The cross-efficiency measure (CEM), first introduced by Sexton et al. (1986), is a powerful extension of DEA, which forms a cross-efficiency matrix of efficiency values given to each DMU. Aside from self-evaluation (derived from the CCR model), each DMU also has peer evaluations received from other DMUs in the sample under CEM. This technique can identify the ‘overall’ efficient and ‘false positive’ regions, as well as select appropriate targets for poorly performing regions to learn from as a benchmark.
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This paper is organized as follows: Following this section, Section 2 provides an overview of China’s regional economic disparities. Section 3 introduces the estimation methodology, the data used, and the analytical process. Section 4 presents the empirical results. Section 5 concludes the paper.
08
07
06 31 01
05
02 03 04 30
15
29 28 10
16
27
12
26
09
17 23
11
22 14 18 13
24
25
20
19
21
Coastal Areas R01 R02 R03 R06 R09 R10 R11 R13 R15 R19 R20 R21
Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Guangxi Hainan
Inland Areas R04 R05 R07 R08 R12 R14 R16 R17 R18 R22 R23 R24
Shanxi Inner Mongolia Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Chongqing Sichuan Guizhou
R25 R26 R27 R28 R29 R30 R31
Fig. 1. Coastal/inland regions of China.
Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang
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2. The coastal and inland regions in China From the perspective of China’s development and political factors, its provinces, autonomous regions, and municipalities are usually divided into coastal and inland areas as Fig. 1 shows. There are 12 regions in the coastal area and 19 regions in the inland area. The coastal area stretches from the province of Liaoning (R06) to Guangxi (R20), and includes Shandong (R15), Hebei (R03), Jiangsu (R10), Zhejiang (R11), Fujian (R13), Guangdong (R19), and Hainan (R21), and the municipalities of Beijing (R01), Tianjin (R02), and Shanghai (R09). The government opened up four special economic zones and fourteen coastal cities to foreign investment in the 1980s. These coastal regions have ever since then enjoyed considerable autonomy, special tax treatments, and preferential resource allocations (Litwack and Qian, 1998). Foreign capital, international technology, as well as managerial know-how are clustered there. The inland area consists of Heilongjiang (R08), Jilin (R07), Inner Mongolia (R05), Henan (R16), Shanxi (R04), Anhui (R12), Hubei (R17), Hunan (R18), Jiangxi (R14), Gansu (R28), Guizhou (R24), Ningxia (R30), Qinghai (R29), Shaanxi (R27), Tibet (R26), Yunnan (R25), Xinjiang (R31), Sichuan (R23), and the municipality of Chongqing (R22). The inland area covers most of the land area of China. However, foreign investment in this area is not as high as in the coastal regions in the east. Per capita FDI in the greater western area is only 8% of that in the coastal area (Hu, 2001). The existing equipment there and the quality of the labor force, relatively speaking, lag behind. There are numerous studies investigating the economic disparities in China. For instance, during the reform period the coastal provinces have performed better than the inland ones with respect to per capita production and consumption (Kanbur and Zhang, 1999; Yao and Zhang, 2001). The total factor productivity (TFP) of the coastal provinces is roughly twice as high as that of the non-coastal provinces (Fleisher and Chen, 1997). Yang (2002) proposed that general explanations for these disparities arise from the superior geographical factors that reduce transportation costs, as well as the government’s policies that exhibit a preference for the coastal areas.
3. Estimation methodology 3.1. Undesirable output in data envelopment analysis Data envelopment analysis (DEA) measures the relative efficiency of decision-making units (DMUs) with multiple performance factors that are grouped into outputs and inputs. Once the efficient frontier is determined, inefficient DMUs can improve their performance to reach the efficient frontier by either increasing their current output levels or decreasing their current input levels. In conducting efficiency analysis, it is often assumed that all outputs are ‘good’. However, such an assumption is not always justified, because outputs may be ‘bad’. For example, if inefficiency exists in production processes where final products are manufactured along with the production of waste and pollutants, then the respective outputs of waste and pollutants are undesirable (bad) and should be reduced in order to improve performance. There are indeed alternatives for dealing with undesirable outputs in the DEA framework. The first is to simply ignore the undesirable outputs. The second is either to treat the undesirable outputs in terms of a non-linear DEA model or to treat the undesirable outputs as outputs and adjust the distance measurement in order to restrict the expansion of the undesirable outputs (Fa¨re et al., 1989). The third is either to treat the undesirable outputs as inputs or to apply a monotone decreasing transformation (e.g.,1/yb, where yb represents the undesirable output proposed by Lovell et al., 1995). However, these methods do not truly reflect the real production process or lose an invariant to the data transformation. To overcome the shortcomings mentioned above, Seiford and Zhu (2002) have proposed an approach which deals with undesirable outputs in the DEA framework. This approach can truly reflect the real production process and is invariant to the data transformation within the DEA model. We therefore apply this method to treat the undesirable output factors in this study. The multiplier model (output-oriented model) is expressed as a solution to the following linear programming (LP) problem:
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W.-M. Lu, S.-F. Lo / European Journal of Operational Research 183 (2007) 882–894 m X 1 ¼ Min vi xio þ vo TEVRS o i¼1
s:t: m X
vi xij
p X r¼1
i¼1 p X
ur y brj
ur y bro þ
r¼1
vi P 0;
s X
s X
ur y grj þ vo P 0;
j ¼ 1; . . . ; n
ð1Þ
r¼pþ1
ur y gro ¼ 1;
r¼pþ1
ur P 0;
y brj ¼ y brj þ wr > 0;
vo is free:
Here, TEVRS (a region is called ‘efficient’ if and only if TEVRS ¼ 1) is the optimal value of the pure technical o o efficiency (PTE) of the target DMUo under the output-oriented VRS (variable returns to scale) model; xij is the amount of input i to DMUj; yrj is the amount of output n or to DMUj; vi is the weight given to input i; ur is the weight given to output r; wr can be achieved by maxj y brj þ 1; s is the number of outputs; m is the number of inputs; y grj and y brj denote the desirable (good) and undesirable (bad) outputs, respectively; p shows the number of undesirable outputs; and n is the number of DMUj. If the vo is dropped from the LP problem above, then the technology is said to exhibit constant returns to scale (CRS). TECRS is defined as the optimal value of techo nical efficiency of the target DMUo under the output-oriented CRS model (Charnes et al., 1978). The scale efficiency for DMUo is then obtained as: =TEVRS : SEo ¼ TECRS o o
ð2Þ
The above equation represents the proportion of inputs that can be further reduced after pure technical inefficiency is eliminated if scale adjustments are possible. The value of SEo is less than or equal to one. If DMUo has a value equal to one, then DMUo is operating at a constant returns to scale size. If SEo is less than one, then DMUo is scale inefficient and there is potential input saving through the adjustment of its operational scale. The scale inefficient DMUo should be either downsizing or expanding, depending on its current operating scale. 3.2. Cross-efficiency measure (CEM) After identifying the efficient DMUs, the role they play as the benchmark for other inefficient DMUs is also crucial. Andersen and Petersen (1993) present the procedure referred to as the super-efficiency CCR model for ranking efficient units. Seiford and Zhu (1999) develop a super-efficiency DEA model referring to the superefficiency BCC model. Tone (2002) proposes the super-efficiency model using the slacks-based measure of efficiency. However, the various super-efficient DEA models mentioned above may give ‘specialized’ DMUs an excessively high ranking or infeasible solution. Li and Reeves (1999) propose a multiple criteria approach to DEA that is referred to as MCDEA. This method cannot handle the possibility that the most efficient DMU may never appear in the reference set. In other words, those inefficient DMUs may lack a learning benchmark. To sum up, an inefficient DMU may not be inherently similar to its benchmarks derived from the proposed methods above. These benchmarks can possibly become inimitable or unattainable goals for the inefficient DMUs. To improve the discriminatory power in terms of identifying genuine (or real) benchmarks, Sexton et al. (1986) first introduce the concept of a cross-efficiency measure in DEA. The basic idea is to use DEA in a peer-appraisal instead of a self-appraisal, which is calculated by means of the CRS model. A peer-appraisal refers to the efficiency score of a DMU that is achieved when evaluated with the optimal weights (input and output weights obtained by means of the output-oriented CRS model) of other DMUs. Thus, for each DMU there are (n 1) cross-efficiency scores where n represents the total number of DMUs. By averaging the cross-efficiency scores of DMUk by using the weighting schemes of other DMUs, we can compute the mean cross-efficiency score of DMUk using the following formulation:
W.-M. Lu, S.-F. Lo / European Journal of Operational Research 183 (2007) 882–894
Pn Pp j¼1
CEMMean ¼ k
urj y b þ
Pm rk
r¼1
i¼1
Ps
g
r¼pþ1
vrj xrk
ðn 1Þ
887
urj y rk
;
j 6¼ k:
ð3Þ
CEMMean then becomes an index for an effective differentiation between good and poor performers. Thus, the k performers of the DMUs can be ranked based on their mean cross-efficiency scores. As indicated by Baker and Talluri (1997), a limitation of the CEM evaluated from the classic DEA model is that input/output weights (optimal weights) obtained from this formulation may not be unique. This condition occurs if multiple optimum solutions exist, because one scheme can be favorable to one DMU and not favorable to another, or vice versa. Doyle and Green (1994) propose aggressive and benevolent formulations to solve this ambiguity. Doyle and Green not only maximize the efficiency of the target DMU, but also take a second goal into account. This second goal, in the case of aggressive formulation, minimizes the efficiency of the composite DMU constructed from (n 1) DMUs. The outputs and inputs of a composite DMU are obtained by summing the corresponding outputs and inputs of all the other DMUs except the target DMU. The weights obtained from this formulation make the efficiency of the target DMU the best that it can be, and all the other DMUs are the worst. The CEM, Eq. (3), evaluated from these weights is more meaningful. The aggressive formulation is generally used when relative dominance among the DMUs is to be identified. The formulation is shown below: ! ! p n s n X X X X g b y rj þ Min ur ur y rj j6¼k
r¼1
s:t: m X
vi
n X
ur y brj
þ
r¼1 p X
j6¼k
! xij
¼ 1;
j6¼k
i¼1 p X
r¼pþ1
s X
ur y grj
m X
r¼pþ1
ur y brk þ
r¼1
s X
vi xij 6 0 8j 6¼ k;
i¼1
ur y grk hkk
r¼pþ1
v i ; ur P 0
ð4Þ
m X
vi xij ¼ 0;
i¼1
8 i and r;
P P P Pp n s n g b where DMUk is the target DMU, u u y y þ is the weighted output of composite r r rj r¼1 j6 ¼ k r¼pþ1 j6 ¼ k rj Pm Pn DMU, i¼1 vi j6¼k xij is the weighted input of composite DMU, and hkk is the efficiency of DMUk obtained from the output-oriented CRS model. The benevolent formulation uses the same set of constraints except that the efficiency of the composite DMU is maximized. As reported by Angulo-Meza and Lins (2002), these two formulations give very similar results, which is why only one of these formulations is used, and generally it is the aggressive formulation. 3.3. Identification of ‘false positive’ DMUs A DMU potentially becomes as ‘false positive’ while it is exhibiting a high efficiency score by heavily weighting a few favorable inputs and outputs. The self-appraisal and peer-appraisal are used in computing a false positive index (FPI) for each of the 31 regions. The FPI relates to the percentage increment in efficiency that a DMU achieves when moving from peer-appraisal to self-appraisal. This FPI is similar to the maverick index suggested by Doyle and Green (1994), and is calculated using Eq. (5). The higher the value of FPIk, the more ‘false positive’ the DMUk will be. FPI is defined as: FPIk ¼
hkk CEMMean k ; CEMMean k
ð5Þ
where hkk is the self-appraisal efficiency of DMUk, and CEMMean is the mean cross-efficiency score of DMUk. k
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3.4. Data selection From the China Statistical Yearbook, we establish a dataset for 31 regions in China (27 provinces and 4 municipalities) for the year 2001. There are two inputs, one desirable output, and three undesirable outputs in our analysis. The two inputs are the capital stock1 and the number of employed persons. The one desirable output is the GDP of a specific region. These are aggregated input and output proxies. The three undesirable outputs in terms of emissions are the volumes of sulfur dioxide emissions, industrial soot emissions, and industrial dust emissions. These are China’s three most serious emissions and their emission volumes are also reported in the China Statistical Yearbook. The values of monetary inputs and outputs, such as GDP and the capital stock, are in current prices. Summary statistics of these inputs and outputs for the all of the regions, for the regions in the coastal area, and for the regions in the inland area are shown in Tables 1–3, respectively. There are obvious economic disparities between the coastal and the inland areas from Tables 2 and 3: The mean capital stock of the coastal areas is more than two times as much as that of the inland areas, and the mean regional GDP of the coastal areas is around twice as much as that of the inland areas. The regional economic disparities can be attributed to a greater access to world markets, better infrastructure, a more highlyeducated labor force, and the government’s preferential policies on foreign investment for the coastal region (World Bank, 1997). However, one can observe that the levels of emissions are comparably close between these two areas implying that the inland regions produce higher degrees of emissions while gaining one dollar in GDP. 3.5. Analytical process Gross domestic product (GDP) is commonly used to measure a nation’s wealth (or overall output). The labor force and capital stock are two major inputs for GDP. While GDP (income) is desirable for a nation, emissions (pollution) accompanying the production process are undesirable. Under the global trend of environmental protection, it is widely promoted internationally that pollution should be taken into account in order to correct a nation’s GDP. This concept is referred to as ‘green-GDP’. Green-GDP is derived from the GDP after deducting the negative environmental and social impacts. In this study we treat pollutants as negative externalities that directly reduce output and the productivity of capital and labor. By consistently regarding the capital stock and labor in the model as inputs, the analytical process in this study proceeds by means of three steps: First, in following the idea of Green-GDP, we incorporate one desirable output (GDP) and three undesirable outputs (emissions) into the model, and we refer to it as the ‘Green-GDP’ model. Secondly, one desirable output (GDP) is solely considered in the model – namely, the ‘GDP-oriented’ model – to measure a region’s efficiency purely from the perspective of economic benefits. Thirdly, three undesirable emissions are incorporated into the model as outputs – namely, the ‘pollution-oriented’ model – to measure a region’s ability so as to minimize its environmental impact through the process for generating wealth. 4. Results and discussion 4.1. Efficiency analysis All DEA models (the Green-GDP, GDP-oriented, and Pollution-oriented) used in this study are run under the analytical option of output maximization (also known as output orientation), because the objective of the regional development is maximal desirable outputs (GDP) and minimum undesirable (pollution) outputs for a constant level of inputs. The technical efficiency (TE, Mean = 0.802) is decomposed into pure technical efficiency (PTE, Mean = 0.903) and scale efficiency (SE, Mean = 0.887), and the status of the returns to scale 1
The data for capital stock cannot be directly retrieved from the China Statistical Yearbook. In this study the regional capital stock is therefore calculated by summing the capital stock in the previous year (2000) and capital formation in the current year (2001) less depreciation in the current year. We get the initial capital stock (the previous year’s data for 2000) from the research of Li (2003). All the figures are based on 2001 prices.
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Table 1 Summary statistics of inputs and outputs in 2001 Mean Inputs Capital stock (100 million RMB) Number of Employed persons (10,000 persons) Desirable output Gross domestic product (100 million RMB) Undesirable outputs Volume of sulfur dioxide (tons) Volume of industrial soot (tons) Volume of industrial dust (tons)
Minimum
Maximum
Standard deviation
Valid N
15017.5 2019.7
1235.4 124.6
49077.9 5516.6
12091.5 1443.8
31 31
3444.0
137.7
10647.7
2762.6
31
484979.4 274866.5 266548.1
734.0 1101.0 2107.0
1408716.0 831995.0 763020.0
356230.7 221522.1 219507.8
31 31 31
Minimum
Maximum
Standard deviation
Valid N
24225.0 2206.5
3134.44 339.70
49078 4672
14320.0 1499.3
12 12
5302.0
545.96
10648
3260.5
12
598579.6 258022.2 290421.2
19283.00 9025.00 12667.00
1408716 552379 763020
451217.4 211199.2 250308.8
12 12 12
Minimum
Maximum
Standard deviation
Valid N
Table 2 Summary statistics of inputs and outputs in coastal regions Mean Inputs Capital stock (100 million RMB) Number of employed persons (10,000 persons) Desirable output Gross domestic product (100 million RMB) Undesirable outputs Volume of sulfur dioxide (tons) Volume of industrial soot (tons) Volume of industrial dust (tons)
Table 3 Summary statistics of inputs and outputs in inland regions Mean Inputs Capital stock (100 million RMB) Number of employed persons (10,000 persons)
9202.3 1901.7
1235.36 124.60
21047.6 5516.6
5113.1 1436.2
19 19
Desirable output Gross domestic product (100 million RMB)
2270.6
137.73
5640.1
1571.2
19
413231.8 285505.1 251470.4
734.00 1101.00 2107.00
940772.0 831995.0 705490.0
270264.8 232850.8 203463.4
19 19 19
Undesirable outputs Volume of sulfur dioxide (tons) Volume of industrial soot (tons) Volume of industrial dust (tons)
is presented in Table 4. The overall technical inefficiencies for the various regions are primarily due to the scale inefficiencies, and not the pure technical inefficiencies. We first consider the pure technical efficiency (PTE) in the case of the Green-GDP model. The mean pure technical efficiency score for all 31 regions is 0.903, indicating that these regions could increase their output levels by 9.7% with the same input level. Of the 31 regions, 9 regions are relatively efficient, namely, Beijing (R01), Heilongjiang (R08), Shanghai (R09), Jiangsu (R10), Fujian (R13), Hunan (R18), Guangdong (R19), Hainan (R21), and Tibet (R26). The other regions lack pure technical efficiency since their efficiency scores are less than one. The average scale efficiency (SE) is 0.887 in Table 4, which suggests that if these regions could restructure in order to achieve their optimal scale, then a further potential output improvement of 11.3% could be achieved. The t-test for scale efficiency is significantly less than one at the 5% level, suggesting that serious scale
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Table 4 Efficiency analysis for 31 regions using three performance models Code
Region
Performance model Green-GDP
R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 Mean
Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang
GDP-Oriented
Pollution-oriented
TE
PTE
SE
RTS
CEMMean
FPI (%)
PTE
PTE
0.853 0.974 0.888 0.548 0.704 0.567 0.803 0.884 1.000 0.837 0.836 0.790 0.956 0.782 0.779 0.744 0.796 1.000 1.000 0.893 0.789 0.463 0.842 0.735 0.825 1.000 0.534 0.645 0.848 0.745 0.794
1.000 0.989 0.891 0.555 0.863 0.815 0.931 1.000 1.000 1.000 0.955 0.876 1.000 0.882 0.886 0.783 0.854 1.000 1.000 0.902 1.000 0.895 0.853 0.749 0.951 1.000 0.687 0.872 0.989 0.886 0.919
0.853 0.985 0.996 0.988 0.816 0.696 0.863 0.884 1.000 0.837 0.875 0.902 0.956 0.886 0.879 0.950 0.932 1.000 1.000 0.991 0.789 0.517 0.986 0.981 0.868 1.000 0.778 0.739 0.858 0.840 0.864
DRS DRS IRS IRS DRS DRS DRS DRS CRS DRS DRS DRS DRS DRS DRS DRS DRS CRS CRS IRS DRS DRS DRS DRS DRS CRS DRS DRS DRS DRS DRS
0.545 0.635 0.797 0.515 0.648 0.374 0.767 0.845 0.618 0.676 0.730 0.660 0.898 0.703 0.748 0.633 0.755 0.835 0.975 0.715 0.705 0.429 0.697 0.540 0.666 0.862 0.486 0.547 0.739 0.649 0.666
56.50 53.37 11.43 6.37 8.58 51.68 4.69 4.64 61.94 23.80 14.47 19.79 6.49 11.28 4.13 17.52 5.51 19.73 2.54 24.90 11.91 7.85 20.75 36.01 23.96 16.01 9.87 17.87 14.79 14.68 19.22
0.840 0.972 0.891 0.555 0.677 0.725 0.786 0.871 1.000 0.955 0.831 0.756 0.947 0.761 0.886 0.755 0.788 1.000 1.000 0.902 0.719 0.430 0.845 0.702 0.767 1.000 0.514 0.589 0.738 0.646 0.775
0.949 0.972 0.337 0.360 0.812 0.569 0.865 0.867 0.979 0.650 0.776 0.753 0.893 0.817 0.367 0.461 0.659 0.584 0.782 0.530 0.990 0.869 0.332 0.674 0.875 1.000 0.647 0.854 0.984 0.881 0.883
0.802
0.903
0.887
0.794
0.741
Coastal or inland
C C C I I C I I C C C I C I C I I I C C C I I I I I I I I I I
Note: CEMMean is the abbreviation for the mean cross-efficiency measure. FPI is the abbreviation for the false positive index.
inefficiencies occur in these 31 regions in terms of the Green-GDP model. Four regions (Shanghai (R01), Hunan (R18), Guangdong (R19), and Tibet (R26)) operate at their appropriate scale level. With regard to returns to scale (RTS), three regions (Hebei (R03), Shanxi (R04), and Guangxi (R20)) experience increasing returns to scale. Most of the regions, which account for 24 of them, experience decreasing returns to scale, which means that these regions could improve their performance if a further potential desirable output improvement or undesirable output deduction could be achieved. By dividing the regions into two groups (the coastal and inland areas), the results show that the coastal regions on average are more efficient than the inland regions. Using a Mann–Whitney test (Brockett and Golany, 1996), we can reject the null hypothesis that there is no significant difference in pure technical efficiency (PTE) for the coastal and inland areas at the 5% level of significance. This result reveals that regions located in the coastal areas have more of a competitive advantage both economically and environmentally. (see Table 5). 4.2. Identifying benchmarks The technical efficiency (TE) in the case of the Green-GDP model is now considered in order to further analyze the best regions on the frontier. Among the 31 regions, 4 regions are relatively efficient with TE being
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Table 5 Non-parametric statistical analysis for coastal/inland areas Area
Number of regions
Performance model Green-GDP
Coastal Inland
12 19
GDP-only
Pollution-only
Mean
Test (P-value)
Mean
Test (P-value)
Mean
Test (P-value)
0.953 0.871
0.014
0.889 0.734
0.005
0.733 0.746
0.935
equal to one. They are Shanghai (R09), Hunan (R18), Guangdong (R19), and Tibet (R26). These efficiency scores are self-appraisal efficiencies obtained from the CRS model. Eq. (5) is utilized to obtain the optimal DEA weights for each region, and a CEM is evaluated on the order 31 · 31 matrix. The self-appraisal and CEMMean scores are shown in Table 4. Guangdong (R19) has the highest mean score of 0.975, indicating that it is rated as the region with the best overall practices by the other regions. On the other hand, although Fujian (R13) is shown to be inefficient based on the self-appraisal, it achieves a remarkable CEMMean score of 0.898. It is ranked the second highest in the entire set and has better overall practices than Shanghai (R09), Hunan (R18), and Tibet (R26), which are considered to be efficient in the CRS model. The richest region Shanghai (R09), which has a self-appraisal score of one, is a strong ‘false positive’ case with a low CEMMean with a score of only 0.618, and is ranked only twenty-fourth in the entire set. This result is analyzed further in the next step when FPI is calculated. The regions with good overall performance (with CEMMean scores exceeding 0.800), such as Guangdong (R19), Fujian (R13), Tibet (R26), Heilongjiang (R08), and Hunan (R18), in that order, should be referred to as ‘overall efficient’ performers to the central/local government officers as well as to researchers in the field of regional development. It is important to note that a region with a self-appraisal score of one is not always included as one of the best overall performers. The false positive index computed in Table 4 clearly indicates that Shanghai (R09) achieves a 61.94% difference (the highest degree of false positive effect) in efficiency when shifting from peer-appraisal to self-appraisal. Thus, it can be considered to be a strong ‘false positive’ region. The main reason for this managerial implication is that this efficient region uses a set of weights that are much different from those applied by the other regions. Therefore, this efficient region with a high degree of self-appraisal is hard to achieve. On the other hand, Guangdong (R19) achieves a 2.54% increase in efficiency when shifting from peer-appraisal to self-appraisal. This can be considered to be a weak ‘false positive’ region. The second best genuine efficient region is Fujian (R13), which is also a coastal region like Guangdong. This suggests that these efficient regions are easy to imitate, because the weights used are quite similar to those applied by the other regions. The genuine efficient region, which has allocated its inputsn outputs to optimization, can therefore be found. Furthermore, we can use a ‘reference comparison’ to help an inefficient region improve its performance by modifying our objectives. For example, if Shandong (the inefficient region) in our case would like to take Guangdong (the efficient region) as a learning benchmark, the reference comparison is as depicted in Fig. 2. We first take the input/output variables of Shandong as the basis of comparison and give them a value of one (or 100%). Fig. 2 indicates that Guangdong uses 88% less capital stock and 85% less labor than Shandong to generate 113% more GDP, 66% less sulfur dioxide, 35% less soot, and 56% less dust. Therefore, in light of the above ratio of outputs to inputs, Guangdong is a more productive region compared to Shandong. A reference comparison plot can therefore set performance targets for the inefficient regions. To sum up, the cross-efficiency measure helps inefficient DMUs set attainable goals, and furthermore the reference comparison suggests directions for improvement while avoiding recommending a benchmark with extremely high input/output allocations. 4.3. Cross analysis for GDP/Pollution-oriented models To examine the performance of China’s regions more closely, we further assess the performances in the GDP-oriented and Pollution-oriented models across these 31 regions as shown in Table 4. The mean PTE for the GDP-oriented model is 0.794 and the mean PTE for the Pollution-oriented model is 0.741, indicating
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100%
150%
Capital Stock
88%
Dust 50%
Labor 85%
0%
56%
35%
Soot
113% GDP
66%
Sulfur Dioxide Shandong
Guangdong
Fig. 2. Reference comparison.
that these regions on average do not operate at optimal levels either in the pro-economic or in the pro-environmental models. The results of the PTE scores derived from the GDP-oriented and Pollution-oriented models give us a twodimensional view of the economic-environmental performance of each region. The regions are expected to perform well in both models. The correlation coefficient between the GDP-oriented-PTE and Pollutionoriented-PTE is 0.0396, suggesting that there is a weak tendency for a region with a relatively high GDPoriented-PTE to be positively correlated with a high Pollution-oriented-PTE. A cross-tabulation is presented in Fig. 3 to further illustrate the difference between GDP-oriented-PTE and Pollution-oriented-PTE. In Fig. 3, the GDP-oriented-PTE and Pollution-oriented-PTE give rise to a two-bytwo matrix to classify the regions. The regions fall into four quadrants: I, II, III, and IV. The two side lines dividing these four quadrants are positioned by the rule to subjectively distribute the regions in each quadrant equally. We therefore have 8, 10, 7, and 6 regions in Zones I, II, III, and IV, respectively. A good performer exhibits high efficiency in both GDP-oriented-PTE and Pollution-oriented-PTE. The regions in each group are summarized as follows: Zone I: These regions enjoy high efficiency in both the GDP-oriented-PTE and Pollution-oriented-PTE dimensions. Eight regions are included here: R01, R02, R08, R09, R11, R13, R19, and R26. These regions appear to be good role models, and can be treated as benchmarks for others. Six of these eight regions are in the coastal areas. Zone II: These regions experience a higher level of Pollution-oriented-PTE, but a lower GDP-orientedPTE. Ten regions are included: R05, R07, R14, R21, R22, R25, R28, R29, R30, and R31. It is suggested that the regions in this zone should place more emphasis on economic activities to increase GDP. Only one region (R21) in this zone is located in the coastal area. Zone III: These regions have a high GDP-oriented-PTE, but low Pollution-oriented-PTE. Six regions are included here: R03, R10, R15, R18, R20, and R23. These regions should place more emphasis on activities to decrease pollution. Four of the six are coastal regions. Zone IV: These regions exhibit inferior performance both in the GDP-oriented-PTE and Pollution-oriented-PTE dimensions. Seven regions, i.e. R04, R06, R12, R16, R17, R24, and R27, are classified here. These
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1.1
0.9
R01 R31 R25 R07
R30
R22
R28
Pollution-PTE
R24
R19
R11
R12 R17
R27
0.6
R13
R08
R14
R05
0.8 0.7
R26 R02 R09
R21R29
1.0
R10 R18
R06 R20
0.5
R16
0.4
R04 R23
R15 R03
0.3 0.2 0.4
0.5
0.6
0.7 0.8 GDP-PTE
0.9
1.0
1.1
Fig. 3. GDP-oriented/pollution-oriented matrix.
regions should place more emphasis on activities that generate GDP as well as decrease pollution. Only one (R06) of the seven is in the coastal region. To sum up, the coastal regions perform better in the GDP-oriented model (10 of 12) with six regions also performing well environmentally, and four regions not performing well environmentally. The coastal regions make up the dominant proportion in Zone I, which are benchmarks of economic-environmental optimization.
5. Concluding remarks A region’s macroeconomic performance should be evaluated from the perspective of its ability to maximize its wealth as well as minimize its environmental impact. Although China has experienced rapid economic growth for decades, this has led to serious environmental problems, such as those characterized by air pollution in the form of soot, dust, and sulfur dioxide. By taking the concept of green-GDP, this paper analyzes the regional development of China by examining economic performance and considering various environmental factors. In addition to computing the technical efficiency for 31 regions in China, a cross-efficiency measure is applied to differentiate the genuine DMUs. The ‘overall’ efficient’ regions and ‘false positive’ ones are recognized by means of a false positive index (FPI). Moreover, a two-dimensional figure derived from the GDP-oriented and Pollution-oriented models gives us a closer view of the economic-environmental performances for each region. To our knowledge, this study appears to be at the forefront in terms of incorporating the green-GDP concept into regional development in China. It first presents evidence in China that experiences from the most efficient regions (e.g., Shanghai) are not all necessarily benchmarked by other inferior regions. The major empirical results are summarized as follows. First, the coastal regions on average perform better than the inland regions in terms of the technical efficiency derived from the Green-GDP model. Second, a DMU with a self-appraisal score of one is not necessarily the best overall performer. For inefficient regions, the benchmark should be those regions from which others can learn, such as Guangdong province, rather than superstar cities, such as Shanghai. Therefore, simply comparing the self-appraisal efficiency scores derived from the traditional CCR model cannot induce this important insight for policy-makers of regional planning and development. Third, a cross-tabulation illustrating the difference between GDP-oriented performance and Pollution-oriented performance is presented. It shows that the coastal regions account for the dominant proportion in terms of serving as the benchmarks for economic-environmental optimization. The results of this paper reveal two important managerial findings. The first is that the regional development disparities in China not only arise because of economic imbalances, but they still exist after taking into consideration the environmental deductions. Aside from the well-acknowledged economic differences, China’s
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sustainable development is now facing the gap of environmental protection technology and know-how between the coastal and inland areas. The other finding is that the benchmark regions for those poor performers are those regions that can be imitated as they control their resource allocation as opposed to those regions with unique conditions in terms of economic, geographical, or political advantages. This study aims to direct attention towards the balance between economic growth and environmental protection in China. The issues related to China’s regional development also merit further study. The effects of a region’s industrial structure, environmental policies, and the local government’s attitudes toward development priority, etc. can be considered to expand the scope of the results. We will leave these highly intriguing topics to future studies. Acknowledgement Financial support from the National Science Council (NSC 95-2416-H-170-004), Taiwan, R.O.C., is gratefully acknowledged. References Andersen, P., Petersen, N.C., 1993. A procedure for ranking efficient units in data envelopment analysis. Management Science 39 (10), 1261–1264. Angulo-Meza, L., Lins, M.P.E., 2002. Review of methods for increasing discrimination in data envelopment analysis. Annals of Operations Research 116, 225–242. Baker, R.C., Talluri, S., 1997. A closer look at the use of data envelopment analysis for technology selection. Computers and Industrial Engineering 32 (10), 101–108. Brockett, P.L., Golany, B., 1996. Using rank statistics for determining programmatic efficiency differences in data envelopment analysis. Management Science 42, 466–472. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6), 429–444. Chang, G.H., 2002. The cause and cure of China’s widening income disparity. China Economic Review 13, 335–340. Doyle, J., Green, R., 1994. Efficiency and cross-efficiency in DEA: derivation, meanings and uses. Journal of the Operational Research Society 45 (5), 567–578. Fa¨re, R., Grosskopf, S., Lovell, C.A.K., Pasurka, C., 1989. Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Review of Economics and Statistics 71, 90–98. Fleisher, B., Chen, J., 1997. The coast-noncoast income gap, productivity, and regional economic policy in China. Journal of Comparative Economics 25, 220–236. Hu, A.K., 2001. The future growth of China is decided by tfp (in Chinese) (downloadable from website http://jzfl.nease.net/hag3.htm). Kanbur, R., Zhang, X., 1999. Which regional inequality? The evolution of rural–urban and inland–coastal inequality in China from 1983 to 1995. Journal of Comparative Economics 27, 686–701. Li, K.W., 2003. China’s capital and productivity measurement using financial resources. (Economic Growth Center, Yale University. Downloadable from website http://www.econ.yale.edu/~egcenter/research.htm). Li, X.B., Reeves, G.R., 1999. A multiple criteria approach to data envelopment analysis. European Journal of Operational Research 115, 507–517. Litwack, J.M., Qian, Y., 1998. Balanced or unbalanced development: special economic zones as catalysts for transition. Journal of Comparative Economics 26, 117–141. Liu, S., 2001. Environmental Economics: Theory and Practice (in Chinese). Economic Science Publishers, Beijing. Lovell, C.A.K., Pastor, J.T., Turner, J.A., 1995. Measuring macroeconomic performance in the OECD: a comparison of European and non-European countries. European Journal of Operational Research 87, 507–518. Ramanathan, V., Crutzen, P. J., 2001. Concept Paper on Asian Brown Clouds, Center for Clouds, Chemistry and Climate, UNEP. Seiford, L.M., Zhu, J., 1999. Infeasibility of super-efficiency data envelopment analysis. INFOR 37 (2), 174–187. Seiford, L.M., Zhu, J., 2002. Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research 142 (1), 16–20. Sexton, T., Silkman, R., Hogan, A., 1986. Data envelopment analysis: critique and extensions. In: Silkman, R. (Ed.), Measuring Efficiency: An Assessment of Data Envelopment Analysis. Jossey-Bass, San Francisco. Tone, K., 2002. A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research 143, 32–41. Yang, D.T., 2002. What has caused regional inequality in China?. China Economic Review 13 331–334. Yao, S., Zhang, Z., 2001. On regional inequality and diverging clubs: a case study of contemporary China. Journal of Comparative Economics 29, 466–484. United Nations, 1999. World Investment Report (downloadable from website http://r0.unctad.org/wir/index.htm). World Bank, 1997. China’s challenges: ensuring growth with equity (downloadable from website http://www.worldbank.org).
O.R. Applications
A closer look at the economic-environmental disparities for regional development in China Wen-Min Lu
a,*
, Shih-Fang Lo
b
a
b
Department and Graduate Institute of Finance, National Defense University, No. 70, Sec. 2, Zhongyang North Road, Beitou, Taipei 112, Taiwan International Division, Chung-Hua Institution for Economic Research, Rm. 516, 75 Chang-Hsing St., Taipei 106, Taiwan Received 15 June 2005; accepted 9 October 2006 Available online 11 December 2006
Abstract Although China has harvested the fruits of its rapid economic growth over a period of several decades, it has encountered serious environmental problems, an important one being air pollution in the form of soot, dust, and sulfur dioxide. In considering the concept of ‘green-GDP’, this paper analyzes China’s regional development by examining its economic performance while taking into account various environmental factors. In addition to computing technical efficiency for 31 regions in China, a cross-efficiency measure is applied to differentiate the genuine DMUs. ‘Overall’ efficient regions and ‘false positive’ ones are recognized by a false positive index (FPI). It is found that the coastal regions perform on average better than the inland regions both economically and environmentally. For inefficient regions, the benchmark should be those regions with high cross-efficiency mean scores (e.g., Guangdong) rather than those with high self-appraisal scores (e.g., Shanghai). A cross-tabulation illustrating the difference between GDP-oriented performance and Pollution-oriented performance shows that the coastal regions make up the dominant proportion in terms of the benchmarks for economicenvironmental optimization. 2006 Elsevier B.V. All rights reserved. Keywords: Data envelopment analysis; Cross-efficiency measure; Environment; OR in developing countries
1. Introduction China has emerged as one of the most important economies globally ever since the Chinese government adopted its policy of economic reform and opened up special economic zones and coastal cities to foreign investment in the late 1970s. By receiving $45 billion in foreign direct investment (FDI) in 1998, China became the largest FDI host country among the developing Asian economies (United Nations, 1999). However, while China has experienced fruitful economic growth for more than a decade, this growth has also been accompanied by severe environmental problems. Three major air pollutants, namely, soot, dust, and sulfur dioxide, have possibly affected not only China itself, but also its neighboring countries (Ramanathan and Crutzen, *
Corresponding author. Tel.: +886 2 2898 6600x228342; fax: +886 2 2898 5927. E-mail address: [email protected] (W.-M. Lu).
0377-2217/$ - see front matter 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2006.10.027
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2001). In realizing the serious consequences of these air pollutants, the Chinese government has taken action to deal with these problems. While dust emissions have declined, sulfur dioxide and soot emissions have still been increasing in recent years (Liu, 2001). Old-fashioned and inefficient technology and highly-polluting engines and fuels have been deemed to be the factors responsible for these pollutants. Aside from these environmental problems, China is now also facing a serious economic imbalance. The high economic inequality can be mainly attributed to the growing inland–coastal disparity in China (Chang, 2002; Yang, 2002). For years, the national development policies have been dominated by the norms of ‘economy first, environment later’ or ‘the coastal regions first, the inland regions later’. For instance, per capita foreign direct investment in the western part of the inland area is only 8% of that in the eastern part (Hu, 2001). By compounding this issue with environmental protection, people in China, especially those in the areas with lower income, may welcome dirtier industries so as to increase their income following China’s entry into the World Trade Organization (WTO) in 2001. China is now at a cross-roads between ‘economic growth versus environmental protection’ and ‘coastal versus inland priority’. When calculating a region’s or a country’s economic growth, gross domestic product (GDP) is commonly used as a representative index. However, this economic term fails to reflect the influence of economic growth on both natural resources and the environment. Recently, the term ‘green-GDP,’ which accounts for both economic and environmental costs, has been widely promoted. Green-GDP is generally defined as an adjustment of traditional GDP after deducting the cost of environmental damage, and reflects ‘the quality of growth’. While the calculation of green-GDP is still difficult nowadays, we can still incorporate this concept into our quantitative analysis in the best way possible. In Lovell et al. (1995) study, 19 organization for economic co-operation and development (OECD) countries have been evaluated and ranked from the perspective of achieving a balance between being pro-economy and pro-environment. Two environmental factors (carbon and nitrogen emissions) are incorporated to correct the macroeconomic performance, and the ranking list changes tremendously as a result. Environmental indicators do have crucial effects on a country’s relative performance. Since China is facing the problems associated with a deteriorating environment in its pursuit of rapid economic growth, it is worth bringing the concept of green-GDP into a regional performance evaluation. In light of the geographical and economic diversity that exists across regions in China, this study examines the overall performance for different regions in China while taking into account both economic as well as environmental factors. The major kinds of emissions, such as soot, dust, and sulfur dioxide, are included as environmental proxies. We use a linear programming technique known as data envelopment analysis (DEA) to analyze the relative macroeconomic performances of regions in China. DEA, which was first developed by Charnes et al. (1978, CCR model), is a methodology for constructing a best practice frontier, which tightly envelops observed data on the inputs and outputs of decision-making units (DMUs). The relative performance of a DMU is evaluated in terms of its proximity to the best practice frontier. In our analysis, both desirable outputs (e.g., GDP) and undesirable outputs (e.g., emissions) are incorporated. These two types of outputs should be treated differently when evaluating the performance of regions. Using the classification invariance property (Seiford and Zhu, 2002), the standard DEA model can be implemented to improve performance by increasing desirable outputs and decreasing undesirable outputs. For an efficient DMU, the role it plays in terms of being benchmarked by other inefficient DMUs is also important. Various efforts have been devoted to developing methods without a priori information so as to identify a benchmark in DEA (Andersen and Petersen, 1993; Seiford and Zhu, 1999; Li and Reeves, 1999; Tone, 2002). However, a difficulty arises with those prior research studies in that an inefficient DMU may not be inherently similar to their benchmarked performances. It is possible that these benchmarks gained from the methods proposed by previous studies are unattainable goals for those inefficient DMUs. A simple traditional radical efficiency score derived from the CCR model alone is insufficient while differentiating between those DMUs exhibiting good ‘overall’ performance or those that do not. The cross-efficiency measure (CEM), first introduced by Sexton et al. (1986), is a powerful extension of DEA, which forms a cross-efficiency matrix of efficiency values given to each DMU. Aside from self-evaluation (derived from the CCR model), each DMU also has peer evaluations received from other DMUs in the sample under CEM. This technique can identify the ‘overall’ efficient and ‘false positive’ regions, as well as select appropriate targets for poorly performing regions to learn from as a benchmark.
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This paper is organized as follows: Following this section, Section 2 provides an overview of China’s regional economic disparities. Section 3 introduces the estimation methodology, the data used, and the analytical process. Section 4 presents the empirical results. Section 5 concludes the paper.
08
07
06 31 01
05
02 03 04 30
15
29 28 10
16
27
12
26
09
17 23
11
22 14 18 13
24
25
20
19
21
Coastal Areas R01 R02 R03 R06 R09 R10 R11 R13 R15 R19 R20 R21
Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Guangxi Hainan
Inland Areas R04 R05 R07 R08 R12 R14 R16 R17 R18 R22 R23 R24
Shanxi Inner Mongolia Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Chongqing Sichuan Guizhou
R25 R26 R27 R28 R29 R30 R31
Fig. 1. Coastal/inland regions of China.
Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang
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2. The coastal and inland regions in China From the perspective of China’s development and political factors, its provinces, autonomous regions, and municipalities are usually divided into coastal and inland areas as Fig. 1 shows. There are 12 regions in the coastal area and 19 regions in the inland area. The coastal area stretches from the province of Liaoning (R06) to Guangxi (R20), and includes Shandong (R15), Hebei (R03), Jiangsu (R10), Zhejiang (R11), Fujian (R13), Guangdong (R19), and Hainan (R21), and the municipalities of Beijing (R01), Tianjin (R02), and Shanghai (R09). The government opened up four special economic zones and fourteen coastal cities to foreign investment in the 1980s. These coastal regions have ever since then enjoyed considerable autonomy, special tax treatments, and preferential resource allocations (Litwack and Qian, 1998). Foreign capital, international technology, as well as managerial know-how are clustered there. The inland area consists of Heilongjiang (R08), Jilin (R07), Inner Mongolia (R05), Henan (R16), Shanxi (R04), Anhui (R12), Hubei (R17), Hunan (R18), Jiangxi (R14), Gansu (R28), Guizhou (R24), Ningxia (R30), Qinghai (R29), Shaanxi (R27), Tibet (R26), Yunnan (R25), Xinjiang (R31), Sichuan (R23), and the municipality of Chongqing (R22). The inland area covers most of the land area of China. However, foreign investment in this area is not as high as in the coastal regions in the east. Per capita FDI in the greater western area is only 8% of that in the coastal area (Hu, 2001). The existing equipment there and the quality of the labor force, relatively speaking, lag behind. There are numerous studies investigating the economic disparities in China. For instance, during the reform period the coastal provinces have performed better than the inland ones with respect to per capita production and consumption (Kanbur and Zhang, 1999; Yao and Zhang, 2001). The total factor productivity (TFP) of the coastal provinces is roughly twice as high as that of the non-coastal provinces (Fleisher and Chen, 1997). Yang (2002) proposed that general explanations for these disparities arise from the superior geographical factors that reduce transportation costs, as well as the government’s policies that exhibit a preference for the coastal areas.
3. Estimation methodology 3.1. Undesirable output in data envelopment analysis Data envelopment analysis (DEA) measures the relative efficiency of decision-making units (DMUs) with multiple performance factors that are grouped into outputs and inputs. Once the efficient frontier is determined, inefficient DMUs can improve their performance to reach the efficient frontier by either increasing their current output levels or decreasing their current input levels. In conducting efficiency analysis, it is often assumed that all outputs are ‘good’. However, such an assumption is not always justified, because outputs may be ‘bad’. For example, if inefficiency exists in production processes where final products are manufactured along with the production of waste and pollutants, then the respective outputs of waste and pollutants are undesirable (bad) and should be reduced in order to improve performance. There are indeed alternatives for dealing with undesirable outputs in the DEA framework. The first is to simply ignore the undesirable outputs. The second is either to treat the undesirable outputs in terms of a non-linear DEA model or to treat the undesirable outputs as outputs and adjust the distance measurement in order to restrict the expansion of the undesirable outputs (Fa¨re et al., 1989). The third is either to treat the undesirable outputs as inputs or to apply a monotone decreasing transformation (e.g.,1/yb, where yb represents the undesirable output proposed by Lovell et al., 1995). However, these methods do not truly reflect the real production process or lose an invariant to the data transformation. To overcome the shortcomings mentioned above, Seiford and Zhu (2002) have proposed an approach which deals with undesirable outputs in the DEA framework. This approach can truly reflect the real production process and is invariant to the data transformation within the DEA model. We therefore apply this method to treat the undesirable output factors in this study. The multiplier model (output-oriented model) is expressed as a solution to the following linear programming (LP) problem:
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s:t: m X
vi xij
p X r¼1
i¼1 p X
ur y brj
ur y bro þ
r¼1
vi P 0;
s X
s X
ur y grj þ vo P 0;
j ¼ 1; . . . ; n
ð1Þ
r¼pþ1
ur y gro ¼ 1;
r¼pþ1
ur P 0;
y brj ¼ y brj þ wr > 0;
vo is free:
Here, TEVRS (a region is called ‘efficient’ if and only if TEVRS ¼ 1) is the optimal value of the pure technical o o efficiency (PTE) of the target DMUo under the output-oriented VRS (variable returns to scale) model; xij is the amount of input i to DMUj; yrj is the amount of output n or to DMUj; vi is the weight given to input i; ur is the weight given to output r; wr can be achieved by maxj y brj þ 1; s is the number of outputs; m is the number of inputs; y grj and y brj denote the desirable (good) and undesirable (bad) outputs, respectively; p shows the number of undesirable outputs; and n is the number of DMUj. If the vo is dropped from the LP problem above, then the technology is said to exhibit constant returns to scale (CRS). TECRS is defined as the optimal value of techo nical efficiency of the target DMUo under the output-oriented CRS model (Charnes et al., 1978). The scale efficiency for DMUo is then obtained as: =TEVRS : SEo ¼ TECRS o o
ð2Þ
The above equation represents the proportion of inputs that can be further reduced after pure technical inefficiency is eliminated if scale adjustments are possible. The value of SEo is less than or equal to one. If DMUo has a value equal to one, then DMUo is operating at a constant returns to scale size. If SEo is less than one, then DMUo is scale inefficient and there is potential input saving through the adjustment of its operational scale. The scale inefficient DMUo should be either downsizing or expanding, depending on its current operating scale. 3.2. Cross-efficiency measure (CEM) After identifying the efficient DMUs, the role they play as the benchmark for other inefficient DMUs is also crucial. Andersen and Petersen (1993) present the procedure referred to as the super-efficiency CCR model for ranking efficient units. Seiford and Zhu (1999) develop a super-efficiency DEA model referring to the superefficiency BCC model. Tone (2002) proposes the super-efficiency model using the slacks-based measure of efficiency. However, the various super-efficient DEA models mentioned above may give ‘specialized’ DMUs an excessively high ranking or infeasible solution. Li and Reeves (1999) propose a multiple criteria approach to DEA that is referred to as MCDEA. This method cannot handle the possibility that the most efficient DMU may never appear in the reference set. In other words, those inefficient DMUs may lack a learning benchmark. To sum up, an inefficient DMU may not be inherently similar to its benchmarks derived from the proposed methods above. These benchmarks can possibly become inimitable or unattainable goals for the inefficient DMUs. To improve the discriminatory power in terms of identifying genuine (or real) benchmarks, Sexton et al. (1986) first introduce the concept of a cross-efficiency measure in DEA. The basic idea is to use DEA in a peer-appraisal instead of a self-appraisal, which is calculated by means of the CRS model. A peer-appraisal refers to the efficiency score of a DMU that is achieved when evaluated with the optimal weights (input and output weights obtained by means of the output-oriented CRS model) of other DMUs. Thus, for each DMU there are (n 1) cross-efficiency scores where n represents the total number of DMUs. By averaging the cross-efficiency scores of DMUk by using the weighting schemes of other DMUs, we can compute the mean cross-efficiency score of DMUk using the following formulation:
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Pn Pp j¼1
CEMMean ¼ k
urj y b þ
Pm rk
r¼1
i¼1
Ps
g
r¼pþ1
vrj xrk
ðn 1Þ
887
urj y rk
;
j 6¼ k:
ð3Þ
CEMMean then becomes an index for an effective differentiation between good and poor performers. Thus, the k performers of the DMUs can be ranked based on their mean cross-efficiency scores. As indicated by Baker and Talluri (1997), a limitation of the CEM evaluated from the classic DEA model is that input/output weights (optimal weights) obtained from this formulation may not be unique. This condition occurs if multiple optimum solutions exist, because one scheme can be favorable to one DMU and not favorable to another, or vice versa. Doyle and Green (1994) propose aggressive and benevolent formulations to solve this ambiguity. Doyle and Green not only maximize the efficiency of the target DMU, but also take a second goal into account. This second goal, in the case of aggressive formulation, minimizes the efficiency of the composite DMU constructed from (n 1) DMUs. The outputs and inputs of a composite DMU are obtained by summing the corresponding outputs and inputs of all the other DMUs except the target DMU. The weights obtained from this formulation make the efficiency of the target DMU the best that it can be, and all the other DMUs are the worst. The CEM, Eq. (3), evaluated from these weights is more meaningful. The aggressive formulation is generally used when relative dominance among the DMUs is to be identified. The formulation is shown below: ! ! p n s n X X X X g b y rj þ Min ur ur y rj j6¼k
r¼1
s:t: m X
vi
n X
ur y brj
þ
r¼1 p X
j6¼k
! xij
¼ 1;
j6¼k
i¼1 p X
r¼pþ1
s X
ur y grj
m X
r¼pþ1
ur y brk þ
r¼1
s X
vi xij 6 0 8j 6¼ k;
i¼1
ur y grk hkk
r¼pþ1
v i ; ur P 0
ð4Þ
m X
vi xij ¼ 0;
i¼1
8 i and r;
P P P Pp n s n g b where DMUk is the target DMU, u u y y þ is the weighted output of composite r r rj r¼1 j6 ¼ k r¼pþ1 j6 ¼ k rj Pm Pn DMU, i¼1 vi j6¼k xij is the weighted input of composite DMU, and hkk is the efficiency of DMUk obtained from the output-oriented CRS model. The benevolent formulation uses the same set of constraints except that the efficiency of the composite DMU is maximized. As reported by Angulo-Meza and Lins (2002), these two formulations give very similar results, which is why only one of these formulations is used, and generally it is the aggressive formulation. 3.3. Identification of ‘false positive’ DMUs A DMU potentially becomes as ‘false positive’ while it is exhibiting a high efficiency score by heavily weighting a few favorable inputs and outputs. The self-appraisal and peer-appraisal are used in computing a false positive index (FPI) for each of the 31 regions. The FPI relates to the percentage increment in efficiency that a DMU achieves when moving from peer-appraisal to self-appraisal. This FPI is similar to the maverick index suggested by Doyle and Green (1994), and is calculated using Eq. (5). The higher the value of FPIk, the more ‘false positive’ the DMUk will be. FPI is defined as: FPIk ¼
hkk CEMMean k ; CEMMean k
ð5Þ
where hkk is the self-appraisal efficiency of DMUk, and CEMMean is the mean cross-efficiency score of DMUk. k
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3.4. Data selection From the China Statistical Yearbook, we establish a dataset for 31 regions in China (27 provinces and 4 municipalities) for the year 2001. There are two inputs, one desirable output, and three undesirable outputs in our analysis. The two inputs are the capital stock1 and the number of employed persons. The one desirable output is the GDP of a specific region. These are aggregated input and output proxies. The three undesirable outputs in terms of emissions are the volumes of sulfur dioxide emissions, industrial soot emissions, and industrial dust emissions. These are China’s three most serious emissions and their emission volumes are also reported in the China Statistical Yearbook. The values of monetary inputs and outputs, such as GDP and the capital stock, are in current prices. Summary statistics of these inputs and outputs for the all of the regions, for the regions in the coastal area, and for the regions in the inland area are shown in Tables 1–3, respectively. There are obvious economic disparities between the coastal and the inland areas from Tables 2 and 3: The mean capital stock of the coastal areas is more than two times as much as that of the inland areas, and the mean regional GDP of the coastal areas is around twice as much as that of the inland areas. The regional economic disparities can be attributed to a greater access to world markets, better infrastructure, a more highlyeducated labor force, and the government’s preferential policies on foreign investment for the coastal region (World Bank, 1997). However, one can observe that the levels of emissions are comparably close between these two areas implying that the inland regions produce higher degrees of emissions while gaining one dollar in GDP. 3.5. Analytical process Gross domestic product (GDP) is commonly used to measure a nation’s wealth (or overall output). The labor force and capital stock are two major inputs for GDP. While GDP (income) is desirable for a nation, emissions (pollution) accompanying the production process are undesirable. Under the global trend of environmental protection, it is widely promoted internationally that pollution should be taken into account in order to correct a nation’s GDP. This concept is referred to as ‘green-GDP’. Green-GDP is derived from the GDP after deducting the negative environmental and social impacts. In this study we treat pollutants as negative externalities that directly reduce output and the productivity of capital and labor. By consistently regarding the capital stock and labor in the model as inputs, the analytical process in this study proceeds by means of three steps: First, in following the idea of Green-GDP, we incorporate one desirable output (GDP) and three undesirable outputs (emissions) into the model, and we refer to it as the ‘Green-GDP’ model. Secondly, one desirable output (GDP) is solely considered in the model – namely, the ‘GDP-oriented’ model – to measure a region’s efficiency purely from the perspective of economic benefits. Thirdly, three undesirable emissions are incorporated into the model as outputs – namely, the ‘pollution-oriented’ model – to measure a region’s ability so as to minimize its environmental impact through the process for generating wealth. 4. Results and discussion 4.1. Efficiency analysis All DEA models (the Green-GDP, GDP-oriented, and Pollution-oriented) used in this study are run under the analytical option of output maximization (also known as output orientation), because the objective of the regional development is maximal desirable outputs (GDP) and minimum undesirable (pollution) outputs for a constant level of inputs. The technical efficiency (TE, Mean = 0.802) is decomposed into pure technical efficiency (PTE, Mean = 0.903) and scale efficiency (SE, Mean = 0.887), and the status of the returns to scale 1
The data for capital stock cannot be directly retrieved from the China Statistical Yearbook. In this study the regional capital stock is therefore calculated by summing the capital stock in the previous year (2000) and capital formation in the current year (2001) less depreciation in the current year. We get the initial capital stock (the previous year’s data for 2000) from the research of Li (2003). All the figures are based on 2001 prices.
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Table 1 Summary statistics of inputs and outputs in 2001 Mean Inputs Capital stock (100 million RMB) Number of Employed persons (10,000 persons) Desirable output Gross domestic product (100 million RMB) Undesirable outputs Volume of sulfur dioxide (tons) Volume of industrial soot (tons) Volume of industrial dust (tons)
Minimum
Maximum
Standard deviation
Valid N
15017.5 2019.7
1235.4 124.6
49077.9 5516.6
12091.5 1443.8
31 31
3444.0
137.7
10647.7
2762.6
31
484979.4 274866.5 266548.1
734.0 1101.0 2107.0
1408716.0 831995.0 763020.0
356230.7 221522.1 219507.8
31 31 31
Minimum
Maximum
Standard deviation
Valid N
24225.0 2206.5
3134.44 339.70
49078 4672
14320.0 1499.3
12 12
5302.0
545.96
10648
3260.5
12
598579.6 258022.2 290421.2
19283.00 9025.00 12667.00
1408716 552379 763020
451217.4 211199.2 250308.8
12 12 12
Minimum
Maximum
Standard deviation
Valid N
Table 2 Summary statistics of inputs and outputs in coastal regions Mean Inputs Capital stock (100 million RMB) Number of employed persons (10,000 persons) Desirable output Gross domestic product (100 million RMB) Undesirable outputs Volume of sulfur dioxide (tons) Volume of industrial soot (tons) Volume of industrial dust (tons)
Table 3 Summary statistics of inputs and outputs in inland regions Mean Inputs Capital stock (100 million RMB) Number of employed persons (10,000 persons)
9202.3 1901.7
1235.36 124.60
21047.6 5516.6
5113.1 1436.2
19 19
Desirable output Gross domestic product (100 million RMB)
2270.6
137.73
5640.1
1571.2
19
413231.8 285505.1 251470.4
734.00 1101.00 2107.00
940772.0 831995.0 705490.0
270264.8 232850.8 203463.4
19 19 19
Undesirable outputs Volume of sulfur dioxide (tons) Volume of industrial soot (tons) Volume of industrial dust (tons)
is presented in Table 4. The overall technical inefficiencies for the various regions are primarily due to the scale inefficiencies, and not the pure technical inefficiencies. We first consider the pure technical efficiency (PTE) in the case of the Green-GDP model. The mean pure technical efficiency score for all 31 regions is 0.903, indicating that these regions could increase their output levels by 9.7% with the same input level. Of the 31 regions, 9 regions are relatively efficient, namely, Beijing (R01), Heilongjiang (R08), Shanghai (R09), Jiangsu (R10), Fujian (R13), Hunan (R18), Guangdong (R19), Hainan (R21), and Tibet (R26). The other regions lack pure technical efficiency since their efficiency scores are less than one. The average scale efficiency (SE) is 0.887 in Table 4, which suggests that if these regions could restructure in order to achieve their optimal scale, then a further potential output improvement of 11.3% could be achieved. The t-test for scale efficiency is significantly less than one at the 5% level, suggesting that serious scale
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Table 4 Efficiency analysis for 31 regions using three performance models Code
Region
Performance model Green-GDP
R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 Mean
Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang
GDP-Oriented
Pollution-oriented
TE
PTE
SE
RTS
CEMMean
FPI (%)
PTE
PTE
0.853 0.974 0.888 0.548 0.704 0.567 0.803 0.884 1.000 0.837 0.836 0.790 0.956 0.782 0.779 0.744 0.796 1.000 1.000 0.893 0.789 0.463 0.842 0.735 0.825 1.000 0.534 0.645 0.848 0.745 0.794
1.000 0.989 0.891 0.555 0.863 0.815 0.931 1.000 1.000 1.000 0.955 0.876 1.000 0.882 0.886 0.783 0.854 1.000 1.000 0.902 1.000 0.895 0.853 0.749 0.951 1.000 0.687 0.872 0.989 0.886 0.919
0.853 0.985 0.996 0.988 0.816 0.696 0.863 0.884 1.000 0.837 0.875 0.902 0.956 0.886 0.879 0.950 0.932 1.000 1.000 0.991 0.789 0.517 0.986 0.981 0.868 1.000 0.778 0.739 0.858 0.840 0.864
DRS DRS IRS IRS DRS DRS DRS DRS CRS DRS DRS DRS DRS DRS DRS DRS DRS CRS CRS IRS DRS DRS DRS DRS DRS CRS DRS DRS DRS DRS DRS
0.545 0.635 0.797 0.515 0.648 0.374 0.767 0.845 0.618 0.676 0.730 0.660 0.898 0.703 0.748 0.633 0.755 0.835 0.975 0.715 0.705 0.429 0.697 0.540 0.666 0.862 0.486 0.547 0.739 0.649 0.666
56.50 53.37 11.43 6.37 8.58 51.68 4.69 4.64 61.94 23.80 14.47 19.79 6.49 11.28 4.13 17.52 5.51 19.73 2.54 24.90 11.91 7.85 20.75 36.01 23.96 16.01 9.87 17.87 14.79 14.68 19.22
0.840 0.972 0.891 0.555 0.677 0.725 0.786 0.871 1.000 0.955 0.831 0.756 0.947 0.761 0.886 0.755 0.788 1.000 1.000 0.902 0.719 0.430 0.845 0.702 0.767 1.000 0.514 0.589 0.738 0.646 0.775
0.949 0.972 0.337 0.360 0.812 0.569 0.865 0.867 0.979 0.650 0.776 0.753 0.893 0.817 0.367 0.461 0.659 0.584 0.782 0.530 0.990 0.869 0.332 0.674 0.875 1.000 0.647 0.854 0.984 0.881 0.883
0.802
0.903
0.887
0.794
0.741
Coastal or inland
C C C I I C I I C C C I C I C I I I C C C I I I I I I I I I I
Note: CEMMean is the abbreviation for the mean cross-efficiency measure. FPI is the abbreviation for the false positive index.
inefficiencies occur in these 31 regions in terms of the Green-GDP model. Four regions (Shanghai (R01), Hunan (R18), Guangdong (R19), and Tibet (R26)) operate at their appropriate scale level. With regard to returns to scale (RTS), three regions (Hebei (R03), Shanxi (R04), and Guangxi (R20)) experience increasing returns to scale. Most of the regions, which account for 24 of them, experience decreasing returns to scale, which means that these regions could improve their performance if a further potential desirable output improvement or undesirable output deduction could be achieved. By dividing the regions into two groups (the coastal and inland areas), the results show that the coastal regions on average are more efficient than the inland regions. Using a Mann–Whitney test (Brockett and Golany, 1996), we can reject the null hypothesis that there is no significant difference in pure technical efficiency (PTE) for the coastal and inland areas at the 5% level of significance. This result reveals that regions located in the coastal areas have more of a competitive advantage both economically and environmentally. (see Table 5). 4.2. Identifying benchmarks The technical efficiency (TE) in the case of the Green-GDP model is now considered in order to further analyze the best regions on the frontier. Among the 31 regions, 4 regions are relatively efficient with TE being
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Table 5 Non-parametric statistical analysis for coastal/inland areas Area
Number of regions
Performance model Green-GDP
Coastal Inland
12 19
GDP-only
Pollution-only
Mean
Test (P-value)
Mean
Test (P-value)
Mean
Test (P-value)
0.953 0.871
0.014
0.889 0.734
0.005
0.733 0.746
0.935
equal to one. They are Shanghai (R09), Hunan (R18), Guangdong (R19), and Tibet (R26). These efficiency scores are self-appraisal efficiencies obtained from the CRS model. Eq. (5) is utilized to obtain the optimal DEA weights for each region, and a CEM is evaluated on the order 31 · 31 matrix. The self-appraisal and CEMMean scores are shown in Table 4. Guangdong (R19) has the highest mean score of 0.975, indicating that it is rated as the region with the best overall practices by the other regions. On the other hand, although Fujian (R13) is shown to be inefficient based on the self-appraisal, it achieves a remarkable CEMMean score of 0.898. It is ranked the second highest in the entire set and has better overall practices than Shanghai (R09), Hunan (R18), and Tibet (R26), which are considered to be efficient in the CRS model. The richest region Shanghai (R09), which has a self-appraisal score of one, is a strong ‘false positive’ case with a low CEMMean with a score of only 0.618, and is ranked only twenty-fourth in the entire set. This result is analyzed further in the next step when FPI is calculated. The regions with good overall performance (with CEMMean scores exceeding 0.800), such as Guangdong (R19), Fujian (R13), Tibet (R26), Heilongjiang (R08), and Hunan (R18), in that order, should be referred to as ‘overall efficient’ performers to the central/local government officers as well as to researchers in the field of regional development. It is important to note that a region with a self-appraisal score of one is not always included as one of the best overall performers. The false positive index computed in Table 4 clearly indicates that Shanghai (R09) achieves a 61.94% difference (the highest degree of false positive effect) in efficiency when shifting from peer-appraisal to self-appraisal. Thus, it can be considered to be a strong ‘false positive’ region. The main reason for this managerial implication is that this efficient region uses a set of weights that are much different from those applied by the other regions. Therefore, this efficient region with a high degree of self-appraisal is hard to achieve. On the other hand, Guangdong (R19) achieves a 2.54% increase in efficiency when shifting from peer-appraisal to self-appraisal. This can be considered to be a weak ‘false positive’ region. The second best genuine efficient region is Fujian (R13), which is also a coastal region like Guangdong. This suggests that these efficient regions are easy to imitate, because the weights used are quite similar to those applied by the other regions. The genuine efficient region, which has allocated its inputsn outputs to optimization, can therefore be found. Furthermore, we can use a ‘reference comparison’ to help an inefficient region improve its performance by modifying our objectives. For example, if Shandong (the inefficient region) in our case would like to take Guangdong (the efficient region) as a learning benchmark, the reference comparison is as depicted in Fig. 2. We first take the input/output variables of Shandong as the basis of comparison and give them a value of one (or 100%). Fig. 2 indicates that Guangdong uses 88% less capital stock and 85% less labor than Shandong to generate 113% more GDP, 66% less sulfur dioxide, 35% less soot, and 56% less dust. Therefore, in light of the above ratio of outputs to inputs, Guangdong is a more productive region compared to Shandong. A reference comparison plot can therefore set performance targets for the inefficient regions. To sum up, the cross-efficiency measure helps inefficient DMUs set attainable goals, and furthermore the reference comparison suggests directions for improvement while avoiding recommending a benchmark with extremely high input/output allocations. 4.3. Cross analysis for GDP/Pollution-oriented models To examine the performance of China’s regions more closely, we further assess the performances in the GDP-oriented and Pollution-oriented models across these 31 regions as shown in Table 4. The mean PTE for the GDP-oriented model is 0.794 and the mean PTE for the Pollution-oriented model is 0.741, indicating
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100%
150%
Capital Stock
88%
Dust 50%
Labor 85%
0%
56%
35%
Soot
113% GDP
66%
Sulfur Dioxide Shandong
Guangdong
Fig. 2. Reference comparison.
that these regions on average do not operate at optimal levels either in the pro-economic or in the pro-environmental models. The results of the PTE scores derived from the GDP-oriented and Pollution-oriented models give us a twodimensional view of the economic-environmental performance of each region. The regions are expected to perform well in both models. The correlation coefficient between the GDP-oriented-PTE and Pollutionoriented-PTE is 0.0396, suggesting that there is a weak tendency for a region with a relatively high GDPoriented-PTE to be positively correlated with a high Pollution-oriented-PTE. A cross-tabulation is presented in Fig. 3 to further illustrate the difference between GDP-oriented-PTE and Pollution-oriented-PTE. In Fig. 3, the GDP-oriented-PTE and Pollution-oriented-PTE give rise to a two-bytwo matrix to classify the regions. The regions fall into four quadrants: I, II, III, and IV. The two side lines dividing these four quadrants are positioned by the rule to subjectively distribute the regions in each quadrant equally. We therefore have 8, 10, 7, and 6 regions in Zones I, II, III, and IV, respectively. A good performer exhibits high efficiency in both GDP-oriented-PTE and Pollution-oriented-PTE. The regions in each group are summarized as follows: Zone I: These regions enjoy high efficiency in both the GDP-oriented-PTE and Pollution-oriented-PTE dimensions. Eight regions are included here: R01, R02, R08, R09, R11, R13, R19, and R26. These regions appear to be good role models, and can be treated as benchmarks for others. Six of these eight regions are in the coastal areas. Zone II: These regions experience a higher level of Pollution-oriented-PTE, but a lower GDP-orientedPTE. Ten regions are included: R05, R07, R14, R21, R22, R25, R28, R29, R30, and R31. It is suggested that the regions in this zone should place more emphasis on economic activities to increase GDP. Only one region (R21) in this zone is located in the coastal area. Zone III: These regions have a high GDP-oriented-PTE, but low Pollution-oriented-PTE. Six regions are included here: R03, R10, R15, R18, R20, and R23. These regions should place more emphasis on activities to decrease pollution. Four of the six are coastal regions. Zone IV: These regions exhibit inferior performance both in the GDP-oriented-PTE and Pollution-oriented-PTE dimensions. Seven regions, i.e. R04, R06, R12, R16, R17, R24, and R27, are classified here. These
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893
1.1
0.9
R01 R31 R25 R07
R30
R22
R28
Pollution-PTE
R24
R19
R11
R12 R17
R27
0.6
R13
R08
R14
R05
0.8 0.7
R26 R02 R09
R21R29
1.0
R10 R18
R06 R20
0.5
R16
0.4
R04 R23
R15 R03
0.3 0.2 0.4
0.5
0.6
0.7 0.8 GDP-PTE
0.9
1.0
1.1
Fig. 3. GDP-oriented/pollution-oriented matrix.
regions should place more emphasis on activities that generate GDP as well as decrease pollution. Only one (R06) of the seven is in the coastal region. To sum up, the coastal regions perform better in the GDP-oriented model (10 of 12) with six regions also performing well environmentally, and four regions not performing well environmentally. The coastal regions make up the dominant proportion in Zone I, which are benchmarks of economic-environmental optimization.
5. Concluding remarks A region’s macroeconomic performance should be evaluated from the perspective of its ability to maximize its wealth as well as minimize its environmental impact. Although China has experienced rapid economic growth for decades, this has led to serious environmental problems, such as those characterized by air pollution in the form of soot, dust, and sulfur dioxide. By taking the concept of green-GDP, this paper analyzes the regional development of China by examining economic performance and considering various environmental factors. In addition to computing the technical efficiency for 31 regions in China, a cross-efficiency measure is applied to differentiate the genuine DMUs. The ‘overall’ efficient’ regions and ‘false positive’ ones are recognized by means of a false positive index (FPI). Moreover, a two-dimensional figure derived from the GDP-oriented and Pollution-oriented models gives us a closer view of the economic-environmental performances for each region. To our knowledge, this study appears to be at the forefront in terms of incorporating the green-GDP concept into regional development in China. It first presents evidence in China that experiences from the most efficient regions (e.g., Shanghai) are not all necessarily benchmarked by other inferior regions. The major empirical results are summarized as follows. First, the coastal regions on average perform better than the inland regions in terms of the technical efficiency derived from the Green-GDP model. Second, a DMU with a self-appraisal score of one is not necessarily the best overall performer. For inefficient regions, the benchmark should be those regions from which others can learn, such as Guangdong province, rather than superstar cities, such as Shanghai. Therefore, simply comparing the self-appraisal efficiency scores derived from the traditional CCR model cannot induce this important insight for policy-makers of regional planning and development. Third, a cross-tabulation illustrating the difference between GDP-oriented performance and Pollution-oriented performance is presented. It shows that the coastal regions account for the dominant proportion in terms of serving as the benchmarks for economic-environmental optimization. The results of this paper reveal two important managerial findings. The first is that the regional development disparities in China not only arise because of economic imbalances, but they still exist after taking into consideration the environmental deductions. Aside from the well-acknowledged economic differences, China’s
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