Environmental efficiency analysis of transportation system in China: A non-radial DEA approach

Energy Policy 58 (2013) 277–283

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Environmental efficiency analysis of transportation system in China: A non-radial DEA approach Young-Tae Chang a,n, Ning Zhang b, Denise Danao a, Nan Zhang a a b

Graduate School of Logistics, Inha University, 253 Yonghyun-dong, Nam-gu, Incheon 402-751, Republic of Korea Department of International Trade, Inha University, Incheon 402-751, Republic of Korea

H I G H L I G H T S







Propose a non-radial DEA model with the slacks-based measure. Analyze the environmental efficiency of China's transportation sector. China's transportation industry is environmentally very inefficient. Millions of TOE carbon emissions can be reduced in most of the provinces.

art ic l e i nf o

a b s t r a c t

Article history: Received 29 August 2011 Accepted 8 March 2013 Available online 1 April 2013

Many countries are worried about reducing energy consumption and environmental pollution while increasing the productivity and efficiency of their industries. This study intends to contribute to the literature by proposing a non-radial DEA model with the slacks-based measure (SBM) to analyze the environmental efficiency of China's transportation sector. The results show that most of the provinces in China do not have an eco-efficient transportation industry. The environmental efficiency levels in most of the provinces are lower than 50% of the ideal or target level. Therefore, China's transportation industry is environmentally very inefficient. China can reduce a great deal of carbon emissions in each province ranging from at least 1.6 million TOEs in Qinghai and at most 33 million TOEs in Guangdong and Shanghai. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Environmental efficiency DEA Transportation industry in China

1. Introduction World economic growth has greatly affected the global community. One of the most serious problems arising from this economic growth is environmental damage. Global warming is one of the most challenging issues facing the human race because it requires long-term, broad, and complicated processes. Many countries are very concerned about reducing energy consumption and environmental pollution while increasing the productivity and efficiency of their industries. China is particularly concerned with environmental efficiency of their industries. The average energy consumption per GDP in China is double the world average (Hu, 2007). China's total energy consumption was just half the United States' energy consumption ten years ago, but China overtook the United States to become the world's largest

n

Corresponding author. Tel.: þ82 32 8607801; fax: þ 82 32 8608223. E-mail address: [email protected] (Y.-T. Chang).

0301-4215/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2013.03.011

energy user in 2010 (Choi et al., 2012). China's energy consumption is overwhelmingly dominated by fossil fuels, which generate large quantities of undesirable gases including carbon dioxide (CO2). To address this issue, China's 12th Five Year Plan for 2011–2015 (State Council of the People's Republic of China (SCPRC), 2011) seeks to establish a “green, low-carbon development concept.” In the 12th Five Year Plan, China announced several new carbon and energy targets from 2010 levels, such as:


Increase the proportion of non-fossil fuels in energy generation to 11.4% by 2015;


Reduce energy consumption per unit of gross domestic product (GDP) by 16% from 2010 levels by 2015;


Reduce CO2 emissions per unit of GDP by 17% from 2010 levels by 2015. Recognizing the importance of reducing energy consumption and evaluating environmental efficiency of industries in China, several studies have attempted to address these issues, particularly in

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this journal (Lu, 2007; Watanabe and Tanaka, 2007; Hu 2007; Yeh et al., 2010; Dianshu et al., 2010; Wang et al., 2011). Some of these studies, however, have focused on environmental performance of overall industries (Watanabe and Tanaka, 2007; Yeh et al., 2010; Wang et al., 2011) on a national level or a regional level. Or some others have worked on electricity industry (Lu, 2007; Dianshu et al., 2010) or iron and steel industry (Hu, 2007). No studies have been conducted yet on environmental performance evaluation on China's transportation industry in the literature. According to the International Energy Agency (IEA) (2011) electricity generation produced almost half (41%) of the global CO2 emissions in 2008. Transport emitted 22% of the world CO2. The two sectors account for two-thirds of global CO2 emissions in 2008. Therefore, it is meaningful and contributes to the literature to analyze the environmental efficiency of transportation sector in the largest polluting country of the world. China is a huge country and it has diverse policies and industrial structures by region. Environmental performances differ by region (Yeh et al., 2010) and some provinces have more proactive environmental policies and positions (Hu, 2007). This requires us to look into the environmental efficiency on a regional level rather than a national level. For a similar reason, Watanabe and Tanaka (2007) and Yeh et al. (2010) also evaluated environmental efficiency in China either on a provincial level or on a regional level. In this context, the main objective of this paper is to analyze environmental efficiency in China's transportation sector by province and estimate the potential CO2 emission reduction. More specifically we estimate the total factor green performance index (GPI) incorporating CO2 emissions, potential carbon reductions (PCR), and carbon efficiency (CE) in China's provincial transportation sector. Of various environmental aspects, we focus on CO2 emissions due to growing concern of global climate change and the major contribution of CO2 to the climate change. The main methodology to analyze the environmental efficiency is a non-radial Data Envelopment Analysis (DEA). The transportation sector in China is defined to contain transport, storage and post according to China's Statistical Yearbooks and we follow this definition to collect the relevant data. DEA is a technique that has enjoyed great popularity in measuring environmental performance at the macro-economic level because it provides a synthetic environmental performance index when undesirable outputs are appropriately incorporated into the DEA framework. However, the majority of previous studies (Seiford and Zhu, 2002; Knox Lovell et al., 1995; Zhou et al., 2007; Färe et al., 1989) have suffered from two common limitations: first, they follow the traditional radial and input-oriented assumption which may cause difficulties in characterizing the real energy consumption process. This traditional radial and input-oriented assumption suggests that all input factors including the energy consumption should be reduced proportionally to be efficient, but this cannot explain the real situation of energy consumption process; and second, they neglect to estimate carbon efficiency and potential carbon reduction. Therefore, this study attempts to contribute to the literature by presenting a non-radial and no input/output-oriented DEA framework based on the slacks-based measure (SBM) to assess environmental efficiency in China's transportation sector. The remainder of the paper is structured as follows. Section 2 is a relevant literature review. Section 3 explains the methodology of this study and Section 4 presents the results of the analysis and discussions. Finally, Section 5 concludes the paper by suggesting further research areas.

2. Literature review Various approaches for measuring environmental efficiency have been attempted in the literature. We first briefly describe

overall approaches of measuring environmental efficiency, and then more focus on dominant approach, DEA and finally review how the environmental efficiency has been studied in transportation sector and in China. The direct approach for measuring the environmental performance was pioneered by Pittman (1983), who extended Caves et al. (1982)'s study by incorporating undesirable outputs such as pollutants into a multilateral productivity index. Their multilateral productivity index approach intended to capture various relevant input usage and producing outputs based on ratio figures. The problem with Pittman's approach is measuring the price of pollutants, though he suggests using the shadow price of complying with environmental regulations. It is still difficult to measure the shadow price of undesirable outputs (Zhou et al., 2007). Another line of approach, which has become dominant in this area, is using Data Envelopment Analysis (DEA). Other approaches for measuring productivity/efficiency are the Stochastic Frontier Analysis (Cullinane and Song, 2006; Cook and Seiford, 2009) and Free Disposal Hull Model (Cook and Seiford, 2009) but these approaches have not yet incorporated undesirable outputs into the models. The relevant literature review in this section is focused on the DEA approach, since other approaches are deemed to have been relatively less employed. Charnes et al. (1978) first proposed the original Constant Return to Scale Data Envelopment Analysis (CCR-DEA). It is a nonparametric approach and measures relative efficiency of decision-making units (DMUs) by comparing multiple inputs with a single output (Cooper et al., 2000). Later, Banker et al. (1984) extended it to the Variable Return to Scale DEA (BCC-DEA) model. The DEA is used to identify the best practice within the set of comparable decision-making units (DMUs) and form an efficient frontier. The CCR model is appropriately referred to as providing a radial projection. Specifically, each input is reduced by the same proportionality. The progress in DEA in the past thirty years is well reviewed by Cook and Seiford (2009). Traditional DEA assumes that all the outputs should be maximized for a given input level. However, when undesirable outputs are also generated as by-products of desirable outputs, this assumption is inappropriate. Numerous methods for modeling the undesirable outputs in the traditional DEA framework have been proposed. One is based on the translation of original data and the utilization of traditional DEA models (Seiford and Zhu, 2002; Knox Lovell et al., 1995). Another is to treat the undesirable output as input. Using the original data, but based on the concept of weak disposable reference technology, is another line of approach (Zhou et al., 2007; Färe et al., 1989). The weak disposability property implies that the undesirable outputs can be reduced only if the desirable outputs are reduced, holding the input level constant (Färe and Grosskopf, 2004). In contrast, the strong disposability assumes that the desirable output can be reduced without changing the undesirable output (Watanabe and Tanaka, 2007). More recent approaches in handling undesirable outputs for the DEA framework are the slacks-based measurement model (Cook and Seiford, 2009; Hu and Wang, 2006; Tone, 2001; Zhou et al., 2006; Lozano and Gutiérrez, 2011) and non-radial DEA models (Zhou et al., 2007). Tone (2001) first introduced the theory and methodology of a slacks-based measure (SBM). In contrast to the CCR and BCC measures, which are based on the proportional reduction (enlargement) of input (output), SBM deals directly with input excess and output shortfall of the DMU, called slacks. The SBM projects the DMU to the furthest point on the efficient frontier, in the sense that the objective function is to be minimized by finding the maximum slacks (Tone, 2001). Therefore, it is in principle a non-radial model. As Tone (2001) claimed, it is “units invariant and monotone decreasing with respect to input excess and output shortfall.” Moreover, he states that “the SBM is reference-set

Y.-T. Chang et al. / Energy Policy 58 (2013) 277–283

dependent, i.e., the measure is determined only by its reference-set and is not affected by statistics over the whole data set as in the traditional DEA models (p. 501).” Hu and Wang (2006) used the slacks concept and radial adjustment approaches for traditional CCR DEA models in measuring the energy efficiencies of 29 administrative regions in China from 1995 to 2002. However, their model is not exactly the SBMDEA introduced by Tone (2001) since it is based on radial assumption. This radial approach has some weaknesses. First, it does not give information regarding the efficiency of the specific inputs or outputs that are included in the process (HernándezSancho et al., 2011). Second, the radial approach is difficult for ranking the environmental performance of the efficient DMUs. Another problem is that the radial model adjusts all undesirable outputs and inputs by the same proportion to efficient targets (Zhou et al., 2006). To overcome these problems, Zhou et al. (2006) and HernándezSancho et al. (2011) used non-radial efficiency SBM-DEA models for the following reasons. First, using the slacks-based non-radial DEA model, Zhou et al. (2006) shows that it has higher discriminatory power compared to the slack-based measure in the traditional DEA model of Cooper et al. (2000). Second, in the non-radial approach, inputs and outputs are not impelled to improve uniformly (Lozano and Gutiérrez, 2011). Third, the efficiency indicator for each variable in the process can be identified in order to increase the efficiency of the DMU being studied. For instance, Hernández-Sancho et al. (2011) used a non-radial DEA to measure the energy efficiency of 177 wastewater treatment plants (WWTP) in Spain. The variables that cause differences in the energy efficiency and potential energy savings of the each plant were identified which cannot be determined using a radial approach. As for the transportation sector, Tongzon (2001) used DEA to measure the efficiency of four Australian and twelve international container ports. Merkert and Hensher (2011) evaluated the important determinants of efficiency for 58 passenger airlines. Lin and Hong (2006) also used the DEA to evaluate the operational performance of 20 major airports around the world. However, all this research in the transportation sector did not include any environmental factors in estimating efficiency. Regarding the environmental efficiency in China, Zhang et al. (2008) used DEA to measure the eco-efficiency of the industrial system in China. The value added amount of the industry was used as the desirable output, while pollutants such as Chemical Oxygen Demand (COD), nitrogen, and solid waste were considered as the undesirable outputs. Material and energy were not only inputs considered, but undesirable outputs were also treated as inputs of the model (Zhang et al., 2008). Shi et al. (2010) also used a DEA model in evaluating China's regional industrial energy efficiency by treating undesirable outputs as inputs. Treating undesirable outputs as inputs in eco-efficiency analysis implies the requirement of a simultaneous reduction of inputs and undesirable outputs to improve the eco-efficiency of a DMU. This method is too simple or inappropriate to reflect the actual production process since undesirable outputs such as CO2 are in fact produced as the by-product of usual production process rather than the input. The literature survey shows that most of studies of considering environmental factors in DEA framework have used the traditional DEA models by data transition method or treating the undesirable outputs as inputs or outputs with a radial approach. The only study using slacks-based measurement DEA with a non-radial approach in environmental efficiency area is conducted by Zhou et al. (2006) to measure CO2 emissions of 30 OECD countries. Our study intends to contribute to the literature by proposing a nonradial DEA model with the slacks-based measure (SBM) to analyze environmental efficiency of China's transportation sector.

279

3. Methodology Our aim is to develop a framework of measuring environmental efficiency of transportation sector in China based on CO2 emissions. More specifically we estimate the total factor green performance index (GPI) incorporating CO2 emissions, potential carbon reductions (PCR), and carbon efficiency (CE) in China's provincial transportation sector. We present our DEA frameworks based on the slacks-based measure (SBM) developed by Tone (2001). The SBM-DEA model in this study extends Tone (2001)'s model by adding the undesirable output into both the objective function and constraint function. This approach differs from others in that we directly handle the undesirable output as the output following actual production process whereas others often treat them as inputs as explained in the literature section. We assume that producing more outputs relative to less input resources is a criterion for efficiency. In the presence of undesirable outputs, technologies with more good (desirable) outputs and less bad (undesirable) outputs relative to less input resources should be recognized as efficient. Suppose that there are n regions and that each reason has three factors—inputs, good outputs, and carbon emissions. Each region uses m number of input factors to produce s1 number of desirable outputs and s2 number of undesirable outputs, respectively. If we denote inputs, good outputs, and carbon emissions as X, Y and C matrices, we can define the matrices X, Y and C, as X ¼ ½xij  ¼ ½x1 ,:::,xn ∈Rmn ,

Y ¼ ½yij  ¼

½y1 ,:::,yn ∈Rs1n ,

and

s2n

C ¼ ½cij  ¼ ½c1 ,:::,cn ∈R . The production possibility set (PPS) can be described as follows:  PðxÞ ¼ ðy,cÞjx produce ðy,cÞ, x≥Xλ, y≤Yλ, c≥Cλ, λ≥0 ð1Þ where λ is the non-negative intensity vector, indicating that the above definition corresponds to the constant returns to scale (CRS) situation. Using Tone (2001)'s SBM model and adding undesirable outputs into both the objective function and a separate constraint function, the undesirable outputs SBM-DEA model can be measured as follows: ρ0 n ¼ min

1 m s−i0 ∑ m i ¼ 1 xi0

1− S

s

1 þ ð1=s1 þ s2 Þð∑r 1¼ 1 ðsyr0 =yr0 Þ þ ∑r2¼ 1 ðscr0 =cr0 ÞÞ

S:T: x0 ¼ Xλ þ s−0 y0 ¼ Yλ−sy0 c0 ¼ Cλ þ s0 c s−o ≥0,sy0 ≥0,sc0 ≥0,λ≥0 s0y

ð2Þ

The vector denotes the shortage of good outputs, whereas vectors s0− and s0c correspond to excesses of inputs and CO2 outputs, respectively. The subscript 0 means a DMU whose efficiency is being estimated. The DMU is efficient in the presence of undesirable outputs if ρn ¼ 1, indicating that all the slacks variables are 0, (s− ¼ 0,sy ¼ 0,sc ¼ 0 ), but the Model (2) is not a linear function. Using the transformation suggested by Tone (2001) and also adding undesirable outputs into both the objective function and a separate constraint function, we can establish an equivalent linear programming for t, φ, Sc , and Sy as follows: S−

1 m r 0 n ¼ min t−m ∑i ¼ 1 xi0i0   Sy 1 Sc 1 ¼ tþ ∑sr1¼ 1 r0 þ ∑sr2¼ 1 r0 s1 þs2 yr0 C r0 S:T x0 t ¼ Xφ þ S−0

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y0 t ¼ Yφ−Sy0 c0 t ¼ Cφ þ Sc0 S−0 ≥0,Sy0 ≥0,Sc0 ≥0,φ≥0,t 40:

ð3Þ

We can solve the optimal solution of linear programming Model (3) and let the solution be (t n ,φn ,S−n , Syn ,Scn ), where ρn ¼ t n ,λn ¼ φn =t n , s−n ¼S−n =t n ,syn ¼ Syn =t n scn , ¼Scn =t n from Model (2). Model (3) can guarantee the solution of (t n ,φn ,S− ,Sc ,Sy ) with t n 40. A similar LP solution idea of solving undesirable SBM-DEA can be found in Zhou et al. (2006) and Lozano and Gutiérrez (2011). In our paper, total factor green performance index (GPI) can be measured by ρn because it includes the slacks variables of all the inputs and outputs from a total factor view. The potential carbon reduction (PCR) of each region is estimated by slack variable Sc0 as it is the excess of carbon emissions. The carbon efficiency (CE) of each province is estimated as follows: Carbon efficiency ¼ ðTarget carbon emission=Real carbon emissionÞ ¼

c0 t−Sc0 c0 t

ð4Þ This target carbon emission/Real carbon emission idea was introduced by Hu and Wang (2006) when they calculated energy efficiency, and was developed by Zhou and Ang (2008) incorporating undesirable outputs of energy efficiency analysis.

4. Results 4.1. Input and output indicators This study examined the environmental efficiency in the transportation industry of 30 provinces in mainland China, excluding Tibet, due to the absence of relevant energy data from that province. The transportation sector in China is defined to consist of transport, storage and post according to China's Statistical Yearbooks. So we follow this definition to collect the relevant data in China. Considering that this research focused on the regional transportation sector in China, three factors were selected as the inputs and two factors as the outputs. Based on the principles of economics, labor and capital are the two basic inputs. Therefore the number of employed labor (thousand persons) and the amount of fixed capital investment (billion yuans) were used as the non-energy input values and the volume of energy consumed in the transportation sector as the energy-input value. As there are no capital stocks statistics in China by industry, changes in investment can closely convey changes in capital stock assuming a constant depreciation rate as Shi et al. (2010) suggested. We used the amount of fixed capital investment to represent capital stock input as some authors did (e.g., Zhu, 1998; Ng and Chang, 2003; Lee, 2005; Shi et al., 2010; Bian and Yang, 2010). The limitation of this approach is that the fixed capital investment can be different from true capital input even though it is a frequently recommended proxy in case of data unavailability. In the case of output, a value-added amount based on the current prices in the transportation industry was selected as the desirable output and the volume of CO2 emissions (thousand tons) related to fuel used in the sector as the undesirable output. The desirable output of transportation sector used in this study is “Gross Domestic Product by transportation sector” because GDP is a value-added concept. The China Statistical Year book named the “Gross Domestic Product by transportation sector” as “value-added of transportation sector”, and we checked and found the values of the two items are the same. In fact, GDP has the same characteristic of revenue and so Watanabe and Tanaka (2007) also used the value-

added amount as the output in their study and we follow this approach. The data related to non-energy input, energy input and desirable output are available in the China Statistical Yearbook 2010 (National Bureau of Statistics of China (NBSC), 2010b) and China Energy Statistical Yearbook 2010 (National Bureau of Statistics of China (NBSC), 2010a). Unfortunately, there are no official statistics yet on provincial CO2 emissions in China. Therefore our research team estimated the CO2 emissions in the regional transportation sector of the year 2009 using a fuel-based carbon footprint model. Following the Intergovernmental Panel on Climate Change guidelines (IPCC, 2006) for National Greenhouse Gas Inventories, Volume 2, Equation 2.2 (p.12) for calculating CO2 data, we can estimate CO2 emissions from fossil fuels using the equation that follows: n

CO2 emission ¼ ∑ A*CCFi *HEi *COFi *ð44=12Þ

ð5Þ

i¼1

CO2 emissions are related to the amount of all carbonaceous fuel combusted (A), the carbon content factor (CCF), the heat equivalent (HE), and the carbon oxidation factor (COF) of the carbonaceous fuel. The number (44/12) represents the ratio of the molecular weight of CO2 (44) to the molecular weight of carbon (12). {CCFinHEinCOFin(44/ 12)} is referred to as the CO2 emission factor (CEF) of a fuel. It represents the amount of carbon emission factor by the type of carbonaceous fossil fuel. There are several different international standards for the CO2 emission factor, such as the factors suggested by the IPCC (2006) or by the UK Department for Environment, Food and Rural Affairs (DEFRA) (2008). It should be noted that different types of fuels are used in different areas, for instance coal in China is different from coal used in the US in terms of carbon content and other characteristics. Therefore, these differences should be reflected in calculating the CO2 emission factors. The domestic report from the Energy Research Institute (ERI) of the National Development and Reform Commission (NDRC) (2007) in China best represents these characteristics, and so it was used. National Development and Reform Commission (NDRC) (2007) reported the CO2 emission factors by major type of carbonaceous fuels in China, as shown in Table 1. Table 1 shows that China still uses coal, which was used long time ago in developed countries. In addition, it uses natural gases whereas the trend in western developed world uses Liquefied Propane Gases. Moreover, carbon content by the fuel type is generally high compared with other countries. This implies that using high content carbon fuels in China must lead to high carbon emission factor. Then the amount of consumption of each fuel by province in the transportation sector was collected from the China Statistics Year Book 2010. After determining the amount of each fuel in the transportation sector, the provincial CO2 emissions of the regional transportation sector in China was calculated according to the formula of the IPCC guidelines. Fig. 1 shows the results of CO2 emissions relating to fuel used in the regional transportation sector. Guangdong and Shandong provinces showed the highest CO2 emissions while the lowest CO2 emissions were in Qinghai province. Table 1 CO2 emission factor by major carbonaceous fuel in China. Source: NDRC, 2007. Fuels a

CCF HEa COF (%)

Coal

Petrol

Kerosene

Diesel

Fuel oil

Nature gas

27.28 192.14 92.3

18.9 448 98.0

19.6 447.5 98.6

20.17 433.3 98.2

21.09 401.9 98.5

15.32 0.384 99.0

a CCF and HE are expressed in units of tons carbon/trillion Joules, and trillion Joules/104 t(m3), respectively.

Y.-T. Chang et al. / Energy Policy 58 (2013) 277–283

After collecting the data on the input and output from the two Statistical Yearbooks of 2010 and calculating the CO2 emissions by the carbon emission factor formula, a data set encompassing 30 provinces was prepared for analysis. Table 2 shows the descriptive statistics of the data sampled. China's provincial transportation sector invested 67 billion yuans on average, employed 218 thousand people, consumed 8 million TOEs of energy, produced 61 billion yuans in value added and emitted 17 million TOEs of CO2. The table shows much larger differences in value added, energy inputs, and CO2 emissions across the provinces as can be seen from the standard deviation figures, and relatively smaller differences in fixed capital investment and labor employment. The detailed values for the input and output variables by province are presented in Appendix 1. A correlation matrix of inputs and outputs was calculated to see if there was a significant relationship between the input and output variables. The results are shown in Table 3. All the correlation coefficients in the table are above 0.700, which indicates that a significant high correlation exists between the input and the output variables.

Xinjiang Ningxia Qinghai Gansu Shaanxi Yunnan Guizhou Sichuan Chongqing Hainan Guangxi Guangdong Hunan Hubei Henan Shandong Jiangxi Fujian Anhui Zhejiang Jiangsu Shanghai Heilongjiang Jilin Liaoning Inner Mongolia Shanxi Hebei Tianjin Beijing

281

4.2. Results of SBM-DEA and discussion As mentioned in Section 3, GPI in transportation sector can be evaluated by the ρn because it includes the slacks variables of all the inputs and outputs. Potential carbon reduction can be measured by slack variable Sc0 and the carbon efficiency of each province is estimated by Model (4). Table 4 shows the results of all these indicators. Only two provinces out of thirty, namely Hebei and Shandong, performed efficiently in the transportation sector in terms of both GPI and CE, as both scores of GPI and CE in the two provinces are 1. The GPI scores of the other 28 provinces ranged from 0.13 to 0.62, with Yunnan ranking last and Jiangsu ranking first among the inefficient provinces. In addition, Guangdong shows the highest potential for carbon reduction with a score of 33 million TOEs, and Qinghai the lowest PCR with a score of 1.6 million TOEs. The ranking is consistent between GPI and CE scores over the provinces. The results indicate that most of the provinces in China are not performing eco-efficiently in the transportation industry, as they use massive input resources in order to produce more outputs. The environmental efficiency levels in most of the provinces are lower than 50% of the ideal or target level, which consequently leads to the conclusion that China's transportation industry is environmentally very inefficient. Therefore, there is a great deal of possibility to reduce the carbon emission amounts in each province. We can see that China can reduce a great deal of carbon emissions ranging from at least 1.6 million TOEs in Qinghai to at most 33 million TOEs in Guangdong and Shanghai in all the inefficient provinces. Zhang et al. (2008) analyzed the eco-efficiency of the industrial system in China and showed that Tianjin, Shanghai, Guangdong, Beijing, Hainan, and Qinghai are eco-efficient, implying that provinces with higher economic activities show relatively high efficiency. However, their approach used the concept of eco-efficiency based on value-added amount per environment impact, and also solved the DEA model by treating the undesirable outputs as inputs. As already mentioned in Section 2, this type of approach is problematic as it does not capture the actual production process, since undesirable output is a by-product of energy usage, but is not an input during production activities. Moreover, the approach is radial, so it has the limitations mentioned in Section 2. In addition, conceptualizing the eco-efficiency based on value added per environmental impact presupposes that more economic output is better for a given level of undesirable output. On the contrary a

Table 3 Correlation matrix of input and output variables.

0

10000

20000 30000 Thousands Tons

40000

50000

Fig. 1. CO2 emissions in China's transportation sector by province, 2009.

Labor Capital Energy Value-added CO2 a

Labor

Capital

Energy

Value-added

CO2

1 0.797a 0.890a 0.814a 0.887a

1 0.836a 0.852a 0.851a

1 0.794a 0.993a

1 0.790a

1

shows significant correlation at 0.01 significance level (2-tailed)

Table 2 Descriptive statistics of input and output variables. Inputs and outputs

Variable

Unit

Mean

Max

Min

Std. dev.

Non-energy inputs

Capital Labor Energy Value-Added CO2 emissions

109 Yuan 103 persons 103 t 109 Yuan 103 t

67.1 211.7 7,813.8 61.3 16,846.0

159.6 535.1 22,646.2 174.2 48,059.2

9.0 30. 920.4 4.932 2069.4

34.8 122.2 5,544.1 44.131 11,699.9

Energy inputs Desirable output Undesirable

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Table 4 Results of SBM-DEA. Province

GPI

PCR (106 t)

Carbon efficiency

Hebei Shandong Jiangsu Henan Fujian Ningxia Tianjin Guizhou Anhui Inner Mongolia Jiangxi Gansu Zhejiang Hunan Guangdong Liaoning Jilin Shanxi Chongqing Heilongjiang Hubei Beijing Shaanxi Guangxi Xinjiang Shanghai Sichuan Hainan Qinghai Yunnan

1.00 1.00 0.62 0.52 0.50 0.50 0.50 0.49 0.46 0.45 0.44 0.42 0.42 0.38 0.37 0.34 0.32 0.30 0.29 0.28 0.28 0.27 0.27 0.25 0.23 0.22 0.21 0.20 0.18 0.13

0.00 0.00 11.32 7.45 6.72 1.67 3.77 4.76 4.60 15.98 3.72 3.74 12.99 8.80 32.93 22.35 6.55 10.74 6.25 6.84 18.06 11.99 11.87 10.61 7.46 32.56 16.99 4.94 1.60 12.43

1.00 1.00 0.54 0.51 0.51 0.39 0.54 0.44 0.49 0.31 0.50 0.35 0.39 0.43 0.31 0.25 0.33 0.32 0.35 0.38 0.25 0.31 0.25 0.25 0.21 0.16 0.23 0.15 0.23 0.12

function, the green performance, potential carbon reduction, and carbon efficiency of each province in China were calculated. Our models show that we can calculate exact amounts of input resources waste and excessive CO2 emissions as well as the shortage of good outputs. Unlike in existing DEA models, these shortages or excessive amounts known as slacks do not need to increase or decrease with the same equal proportion for all input factors or outputs. This approach is more realistic in explaining energy consumption system. Using the model, the results show that most of the provinces in China are not performing eco-efficiently in the transportation industry. The environmental efficiency levels in most of the provinces are lower than 50% of the ideal or target level. Therefore, we can conclude that China's transportation industry is environmentally inefficient. Our model shows that China can reduce million tons of carbon emissions in each province ranging from at least 1.6 million TOEs in Qinghai to at most 33 million TOEs in Guangdong and Shanghai. This is a good indication to Chinese government, who aspires to develop more eco-friendly industries and reduce energy consumptions. This study has some limitations. First of all, China's environmental efficiency was compared only among the Chinese provinces. The efficiency scores would likely have been much worse if the data included other advanced countries, for instance, OECD countries. Furthermore, the data collected was only one year cross-sectional. To capture a more dynamic nature of the industry in China, panel data should be collected in the future. All these remain avenues for future research.

Appendix true measurement of eco-efficiency should be minimizing the summation of excesses of inputs and undesirable outputs, and shortfalls of desirable outputs, as modeled in this study. Hu and Wang (2006) analyzed China's energy efficiency by province using DEA. Their approach focused on energy input reduction compared with a target frontier level. They did not capture any undesirable outputs. Moreover, in calculating the reduction level, they also used a radial approach. Some caveats should be taken in interpreting the results of this study. Thus far we have employed a technique in capturing both environmental efficiency and economic efficiency on China's transportation sector using the SBM-DEA methodology. It is noted that we should be aware that the results of DEA techniques show relative efficiency depending on the collected sample rather than absolute one. This stems from the fact that all efficiency estimations of decision-making units are affected by how we sample the DMUs since the efficient frontier line is drawn from the given sample. Therefore, including other countries' cases in the sample would have shown different results. Likewise, if we had sampled the data over the years rather than from one year, the results would have been different as well. The panel data over the years for multiple DMUs can show intertemporal changes in efficiency and technology development separately using Malmquist productivity index. All these should be our future studies.

5. Conclusions This study intended to contribute to the literature by proposing a non-radial DEA model with the slacks-based measure (SBM) to analyze the environmental efficiency of China's transportation sector. Modifying Tone (2001)'s SBM model by adding undesirable outputs into both the objective function and a separate constraint

See Appendix Table A1. Table A1 Input and output data by province. Province

Labor (103 persons)

Capital (109 Energy Yuan) (103 t)

Value-add (109 Yuan)

CO2 (103 Tons)

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 Shaanxi Gansu Qinghai Ningxia Xinjiang

501.0 123.0 255.0 213.0 162.0

66.3 48.4 102.6 73.6 78.6

7,641.1 3,815.7 6,402.2 7,289.8 10,980.4

55.7 47.1 149.2 52.3 77.3

17,268.0 8,233.7 14,150.5 15,707.9 23,314.8

318.0 153.0 261.0 356.0 307.0 239.0 147.0 162.0 156.0 332.0 288.0 318.0 218.0 535.0 175.0 42.0 135.0 238.0 91.0 143.0 192.0 103.0 33.0 30.0 109.0

75.8 42.4 65.2 88.3 102.0 100.9 46.0 88.5 38.2 103.3 58.4 76.7 102.8 159.6 60.2 18.6 64.3 125.0 39.7 57.1 60.0 15.5 12.4 9.0 34.0

13,963.6 4,349.5 5,253.3 18,373.5 11,447.9 9,855.5 4,107.7 6,467.1 3,417.9 22,258.3 7,375.8 11,926.6 7,096.7 22,646.2 6,551.7 2,580.1 4,243.2 10,049.1 3,807.3 6,508.9 7,040.8 2,625.1 920.4 1,130.6 4,287.9

79.1 34.2 43.4 63.5 142.3 88.8 46.8 75.1 39.5 174.2 82.4 64.3 70.5 159.6 37.9 8.9 34.8 52.1 40.0 17.9 42.3 21.4 4.9 11.5 20.9

29,847.0 9,790.6 10,956.1 38,579.4 24,814.2 21,409.6 9,040.3 13,851.6 7,463.5 47,910.2 15,259.5 24,161.0 15,488.4 48,059.2 14,202.3 5,778.8 9,553.5 21,928.1 8,552.0 14,129.6 15,885.6 5,763.9 2,069.4 2,763.0 9,447.0

Y.-T. Chang et al. / Energy Policy 58 (2013) 277–283

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