Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model

EIR-06043; No of Pages 13 Environmental Impact Assessment Review xxx (2016) xxx–xxx

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Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model Juan Wang a,⁎, Tao Zhao a, Xiaohu Zhang b a b

College of Management and Economics, Tianjin University, Tianjin 300072, People's Republic of China College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 210016, People's Republic of China

a r t i c l e

i n f o

Article history: Received 8 September 2015 Received in revised form 26 March 2016 Accepted 9 May 2016 Available online xxxx Keywords: Energy and environmental performance DEA model Industrial sector R&D investment Investment strategies

a b s t r a c t As an energy-intensive industry, the industrial sector consumes 70% of energy consumption and causes serious environmental pollution in China. Also, the government emphasized the promotion of R&D investment in the industrial sector in China's National Plan on Climate Change (2014–2020). It is meaningful and contributes to assessing energy and environmental performance, as well as R&D and industrial pollution control (IPC) investment strategies of China's industrial sector. A non-radial DEA model, as with natural and managerial disposability, was adopted to evaluate this from provincial and regional perspectives during the 2008–2012 period. Energy and environmental performance was evaluated by unified efficiency under natural disposability (UEN), unified efficiency under managerial disposability (UEM), and unified efficiency under natural and managerial disposability (UENM). The empirical results indicated that Shandong and Hainan were efficient under natural and managerial disposability, while other provinces had the potential to improve their energy and environmental performance. The number of provinces that was fit for investments of R&D and IPC increased from 2008 to 2010, then decreased in 2011 and 2012. In spite of this, many provincial industrial sectors should make efforts to reduce pollution by investment on technology. Tianjin, Heilongjiang, Jiangxi and Henan were especially the best investment objects because investments of R&D and IPC turned to be effective for them during the whole study period. Moreover, western China had the highest average UENM, followed by eastern China and central China. Eastern China and central China were rewarding to expand investments. Coal consumption was the main factor to negatively affect unified efficiency whereas the increase in economic development level was primarily responsible for the improvement of unified efficiency. According to the results, differentiated suggestions to further improve energy and environmental performance were proposed. © 2016 Elsevier Inc. All rights reserved.

1. Introduction The development of industrialization, urbanization and modernization in China has achieved great progress since the implementation of a reform and opening-up policy in 1978, which results in the increase of energy consumption demand. Energy utilization is a major source of greenhouse gas emissions and environmental problems, which are serious challenges given that extreme weather conditions are spreading rapidly across China (Wang et al., 2012a). Nowadays, China has become the biggest energy consumer with a 23% share of global energy consumption and the largest CO2 emitter with a 23.4% share of carbon emissions in the world (BP Statistical Review, 2015; IPCC, 2014). To realize a resource-saving and environmental-friendly society, the Chinese government committed to reduce energy intensity (energy consumption per unit of GDP) by 16% and CO2 emissions by 17% during the 12th FYP (Twelfth Five-year Plan) period (Zhang et al., 2014). However, in the first two ⁎ Corresponding author. E-mail address: [email protected] (J. Wang).

years of the 12th FYP (2011−2012), the national energy intensity just decreased by 2.02% and 3.62% respectively, and both were lower than the annual reduction target (3.7%) (Wang and Wei, 2014). In addition, the Chinese government promised to reach the peak of CO2 emissions no later than 2030 and implement the carbon intensity (carbon emissions per unit of GDP) reduction of 60–65% below the 2005 level by 2030 in the Intended Nationally Determined Contributions in 2015. China is a major supplier of most industrial products in the world. As an energy-intensive sector, the energy consumption of China's industrial sector increased by 134% from 1996 to 2010, despite the fact that the energy intensity of the industrial sector decreased by 46% (Ke et al., 2012). Moreover, the industrial sector consumes 70% of the total energy consumption in China and generates about 70% domestic CO2 emissions (Wang et al., 2012b; Liu et al., 2015). To reduce energy consumption and CO2 emissions of the industrial sector, downturn enterprises with high energy consumptions have been closed down. A series of programs such as the “Top-1000 Enterprises Energy Saving Program” and the “10 Key Energy Saving Projects Program” have been implemented (Xu et al., 2014). Although policies for energy conservation in recent decades have

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Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

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J. Wang et al. / Environmental Impact Assessment Review xxx (2016) xxx–xxx

achieved major improvements in their energy technology efficiency efforts, energy conservation and emission reduction of the industrial sector are still great challenges (Wu and Huo, 2014). Under this circumstance, this paper applied a non-radial DEA (Data Envelopment Analysis) model to evaluate the unified efficiency (UEN, UEM and UENM) of China's industrial sector from 2008 to 2012. Besides, the government called the community to focus on promoting major R&D of energy saving technology and equipment as well as the energy utilization technology of the industrial sector in China's National Plan on Climate Change (2014–2020) (NDRC, 2014). Also, considering that the IPC investment is growing in importance, research on R&D and IPC investments is urgently required. Therefore, R&D investments of the industrial sector and IPC investments were considered as input indicators in the non-radial DEA framework, and the types of damages to return (DTR) as well as R&D and IPC investment strategies were identified. Based on the results of unified efficiency, a truncated regression model was applied to study the influencing factors of unified efficiency. Additionally, China is a large country with a vast territory and exhibits significant diversities in regional development. To analyze regional development more directly and clearly, China can be divided into three regions including eastern China, central China and western China according to the National Western Development Strategy. Thus, the discussions of our study would be conducted from provincial and regional perspectives. Due to the data absence, Tibet, Taiwan, Hong Kong and Macau are not included. The results of this study aided in identifying effective investment strategies and proposing suggestions to improve the energy and environmental performance for China's industrial sector. The remainder of this paper is organized as follows: Section 2 presents a literature review. Section 3 introduces the non-radial DEA model and the data set. Sections 4 and 5 present and discuss the results based on the DEA model and truncated regression model, respectively. Section 6 summarizes the main conclusions and proposes recommendations based on the results. 2. Literature review Measuring energy and environmental efficiency has become an essential direction in research. Estimating methods can be subdivided into parametric and nonparametric approaches depending on the parameter estimation in the frontier production function. The most commonly used parameter estimation method is the Stochastic Frontier Analysis (SFA), and DEA is an effective non-parametric method (Zhou et al., 2008). See Zhou et al. (2008) for historical literature reviews on DEA analysis in energy and environmental studies; different DEA-based models have been developed, such as the DEA radial measure, DEA non-radial measure, slacks-based model, hyperbolic measure and directional distance function. Recently, a number of researchers conducted related studies about China based on the different DEA models mentioned above. For instance, Wang et al. (2013a); Wang et al. (2013c); Huang et al. (2014); Sueyoshi and Yuan (2015); Chen et al. (2015) and Zhang and Ye (2015) have studied the energy and environmental performance at the national and provincial levels. Additionally, DEA models were also adopted to study various industries of China, such as the transportation sector (Chang et al., 2013), fossil fuel power plants (Zhang and Choi, 2013), construction industry (Xue et al., 2014), cement manufacturing (Long et al., 2015), thermal power generation (Bi et al., 2014), electronic information industry (Zhang and Xie, 2015) and non-ferrous metals industry (Wang and Zhao, 2016). Studies on energy and environmental efficiency of China's industrial sector were mainly conducted at the sectoral level and provincial level. Li and Shi (2014) adopted an improved Super-SBM model to assess the environmental efficiency of different industrial sectors in China. Meng et al. (2013) proposed a non-radial DEA approach considering both a static and a dynamic environmental performance index for measuring the environmental efficiency of China's industrial sectors. Hu and Lee (2008)

calculated three industrial waste abatements for 30 regions in China, demonstrating that the eastern area contained most of the efficient regions with the lowest average target abatement ratios and solid waste reduction was China's most urgent task. Zhang et al. (2008) used the DEA model treating the undesirable outputs as classic inputs to conduct an eco-efficiency analysis for 30 provincial industrial sectors in China. Shi et al. (2010) and Wang et al. (2012b) measured the regional energy efficiency of China's industrial sector by CCR and BCC methods. The results showed that the eastern area had the highest average energy efficiency while western provinces had the largest amount of energy input excess. Wu et al. (2014) presented a new DEA approach considering both the fixed and variant sum desirable outputs to evaluate the regional environmental efficiencies of China's industrial sector. Zhao et al. (2014) provided the changes of energy efficiency at the sectoral and provincial levels to illustrate the drivers behind such various changes in China's industry by DEA model. Wang and Wei (2014) evaluated the energy and emission efficiencies of the industrial sector in 30 major Chinese cities based on a weakly disposable undesirable outputs model. They found that coastal area cities had the highest total factor efficiency while western area cities were the lowest. Related to studies on efficiency by a two-stage approach (DEA & Regression models), Zhou et al. (2013b) investigated the environmental efficiency of China's industrial sectors with an improved weighted SBM method. Besides, the Tobit regression method was employed to explore the factors that affected the environmental efficiency and found that power structure and innovation ability had a significant positive effect while regulation and punishment had a hysteresis effect. Fang et al. (2013) computed the pure technical efficiency and energy-saving target of Taiwan's service sectors by using the input-oriented DEA approach and the Tobit regression model was also used to explore the influencing factors of the energy-saving target. Li and Hu (2012) computed the ecological total-factor energy efficiency of 30 regions in China using the SBM method. Based on the results, the truncated regression model was applied to analyze the factors influencing energy efficiency. Zhou et al. (2013a) first proposed a new non-radial DEA approach to assess the regional environmental efficiency of China's power industry, and then the Tobit regression model was utilized to study the influencing factors of environmental efficiency. Pan et al. (2013) employed an extended CCR model to evaluate the energy efficiency of the regional industrial sector in China and analyze its determinants based on the Tobit regression method. Song et al. (2013) and Li et al. (2013) applied the DEA model to measure regional environmental efficiency in China, and then identified influential factors of the environmental efficiency by the Tobit regression model. Lee and Worthington (2014) constructed the technical efficiency of mainstream airlines based on the DEA model, and then the truncated regression model was applied to study the driving factors of technical efficiency. Wijesiri et al. (2015) employed a twostage double bootstrap DEA approach (DEA method and truncated regression model) to analyze the efficiency of microfinance institutions in Sri Lanka. Wang and Zhao (2016) focused on evaluating the energy–environmental performance of China's non-ferrous metals industry by a non-radial DEA model and identified the influencing factors of eco-efficiency using the truncated regression model. Acknowledging the contributions of previous research works summarized in Table 1, this study can fill the gaps. First, most of these works depended upon the hypotheses of weak and strong disposability on undesirable outputs.1 However, the weak and strong 1 In the process of production, given the certain inputs, more desirable outputs and less undesirable outputs are expected. Therefore, there are two perspectives for handling the undesirable outputs in the DEA framework. One is to treat undesirable outputs as inputs, assuming a strong disposability for undesirable outputs and reducing both of them as much as possible. The other is to model undesirable outputs as outputs, assuming a weak disposability for undesirable outputs (Yang and Pollitt, 2010). The weak disposability on undesirable outputs implies that an increase in an input vector increases an undesirable output with a desirable output vector increase because the weak disposability is assigned to undesirable outputs.

Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

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Table 1 Previous DEA study efforts of China's industrial sector. Authors

Inputs

Desirable outputs

Undesirable outputs

Efficiency measure

Zhou et al. (2013a) Coal, labor, investment of fixed assets

CO2, NOx, SO2

N & SBM

Hu and Lee (2008)

Waste gas, waste water, waste solid SB & Tobit regression model Waste gas, waste water, industrial SB & Tobit regression residue model R

Electric energy production Zhou et al. (2013b) Industrial average annual investment, labor, Industrial production energy value Li and Shi (2014) Energy, labor, capital GDP Labor, capital, solid wastes, waste water, waste gas Zhang et al. (2008) Materials, energy

Shi et al. (2010) Wang et al. (2012b) Meng et al. (2013) Pan et al. (2013) Wu et al. (2014) Zhao et al. (2014) Wang and Wei (2014)

Energy, fixed assets investment, labor Energy, labor, capital

GDP Industrial value added

Energy, labor Energy, labor, capital

Industrial value added Gross industry production Industrial value added Industrial value added

Investment in fixed assets, electricity Energy, labor, capital Energy, labor, capital

GDP Industrial added value Industrial added value

Disposability concept

Weak & strong

COD, nitrogen, SO2, soot, dust, waste solid Waste gas

R & SB

Strong

R & SB R & SB

Strong

Waste water, solid waste, CO2 Waste gas

N R & Tobit regression model SBM R&S R

Weak & strong Strong

NO2 SO2, CO2

Weak & strong

Note: B: undesirable outputs; R: radial; NR: non-radial; SB: slacks-based; H: hyperbolic; DDF: directional distance function.

disposability concepts merely focus on the undesirable outputs and do not specify the vectors of inputs (Sueyoshi and Goto, 2012c). Therefore, this study is based on natural disposability and managerial disposability. Second, previous studies hardly invested special treatment for input indexes. In this study, the non-radial DEA method separates the inputs into two groups based on natural disposability and managerial disposability. Furthermore, the energy and environmental performance of China's industrial sector are assessed by unified efficiency under these two types of disposability. Third, previous researches which considered R&D investment and IPC investment as input indexes are limited. In fact, this is important because government and enterprises are paying an increasing attention to the investments in R&D and pollution control. In response, this study adds R&D investment and IPC investment into the DEA framework and discusses their effects on reducing undesirable outputs. Finally, researchers studied the determinants of unified efficiency mainly based on the Tobit regression model, however, the truncated regression model was proved to be more appropriate (Simar and Wilson, 2007). And this study employs the truncated regression model in the second-stage study.

outputs. The former is considered to be a negative adaptation to the change in environmental regulations and belongs to the traditional efficiency enhancement approach. However, under managerial disposability, DMUs regard the change in regulations to be an opportunity to increase competitiveness by replacing old production equipment and manageability with innovative technology. 3.1. Unified efficiency under natural disposability Here, we introduce the following non-radial DEA model for evaluating the unified efficiency of the k-th DMU under natural disposability (Sueyoshi and Goto, 2012a):

Max

m s h X X X x x b Rxi di þ Rxr dr þ Rbf d f r¼1

i¼1

s:t:

n X x xij λ j þ di ¼ xik

f ¼1

ði ¼ 1; …; mÞ;

j¼1 n X

g

ðr ¼ 1; …; sÞ;

j¼1 n X

b

ð f ¼ 1; …; hÞ;

g rj λ j −dr ¼ g rk

ð1Þ

bfj λ j þ d f ¼ bfk

3. Methodology j¼1

Cooper et al. (1999) first proposed the Range-Adjusted Measure (RAM) model and Sueyoshi and Goto (2012b) developed it in environmental strategy. Since the RAM model can easily combine both energy and environmental performance for each Decision Making Unit (DMU) under a unified treatment, this non-radial method is considered to be more practical than traditional radial DEA models (Wang et al., 2013b). Goto et al. (2014); Sueyoshi and Goto (2014) and Wang et al. (2014) further improved the model to separate inputs into two parts with the concepts of natural disposability and managerial disposability, one stands for the investments and the other represents the production activity needs. The definition of natural disposability and managerial disposability is as follows: natural disposability indicates that a DMU decreases the vector of inputs to decrease the vector of undesirable outputs. Given the decreased vector of inputs, the DMU attempts to increase the vector of desirable outputs as much as possible. Managerial disposability indicates that a firm increases the vector of inputs to increase the vector of desirable outputs while simultaneously decreasing the vector of undesirable

n X λ j ¼ 1; j¼1 x

λ j ≥0 ð j ¼ 1; …; nÞ; di ≥0 ði ¼ 1; …; mÞ; g b dr ≥0 ðr ¼ 1; …; sÞ; and d f ≥0 ð f ¼ 1; …; hÞ

In Model (1), the inputs, desirable outputs and undesirable outputs for j-th DMU (j = 1, …,n) are respectively represented by Xj = (x1j, …, xmj), Gj(g1j, …, gsj) and Bj(b1j, …, bfj). dxi (i = 1, …,m). dgr (r = 1, …s) and dbf (f = 1, …,h) are all slack variables related to inputs, and desirable and undesirable outputs, respectively. +dxi means decreasing all inputs to improve the energy and environmental performance of j-th DMU when satisfying the demand of desirable and undesirable outputs. λj(j = 1, …,n) is the unknown variables which are often referred to “structural” or “intensity ” variables associated with each DMU and used for connecting the input and output vectors by a convex combination. R are the ranges determined by the upper and lower bounds of inputs, and desirable and undesirable outputs. These upper and lower

Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

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J. Wang et al. / Environmental Impact Assessment Review xxx (2016) xxx–xxx

model:

bounds are specified as follows:    −1 max xij j j ¼ 1; …; n − min xij j j ¼ 1; …; n  n o n o−1 −1 Rgr ¼ ðm þ s þ hÞ max grj jj ¼ 1; …; n − min grj jj ¼ 1; …; n and    −1 −1  max bfj j j ¼ 1; …; n − min bfj j j ¼ 1; …; n Rbf ¼ ðm þ s þ hÞ Rxi ¼ ðm þ s þ hÞ

−1 

m− mþ s h X X X X x− xþ g b Rxi di þ Rxq dq þ Rgr dr þ Rbf d f

Max

q¼1

i¼1

f ¼1

r¼1

n X x− − x− ij λ j þ di ¼ xik

s:t:

ði ¼ 1; …; m− Þ;

j¼1 n X

xþ

j¼1 n X

g

ðr ¼ 1; …; sÞ;

b

ð f ¼ 1; …; hÞ;

þ xþ qj λ j −dq ¼ xqk

Unified energy and environmental efficiency under natural disposability can be defined as:

UEN ¼ 1−

m

x ∑ Rxi di i¼1

þ

s

g ∑ Rgr dr r¼1

þ

g rj λ j −dr ¼ g rk

j¼1 n X

!

h

bfj λ j þ d f ¼ bfk

b ∑ Rbf d f f ¼1

ðq ¼ 1; …; mþ Þ;

j¼1 n X

λ j ¼ 1;

j¼1

3.2. Unified efficiency under managerial disposability The DEA model for evaluating the unified efficiency of k-th DMU under managerial disposability proposed by Sueyoshi and Goto (2012a) is as follows:

m s h X X X x x b Rxi di þ Rxr dr þ Rbf d f

s:t:

f ¼1

r¼1

i¼1

n X x xij λ j −di ¼ xik j¼1 n X

ði ¼ 1; …; mÞ;

In this model, inputs are separated into two groups. Parameters m− and m+ stand for the number of inputs under natural disposability and − managerial disposability. x+ ij and xij represent the inputs under natural disposability and managerial disposability, respectively. Slacks about the corresponding inputs under natural disposability and managerial dis− + posability are represented by dxi and dxq . And other variables are the same as Model (1). Compared with Model (1) and (2), the difference between constraints is that Model (3) combines the first constraints of − Model (1) and Model (2), and the inputs are separated into x+ ij and xij . The unified efficiency under natural and managerial disposability can be determined by: m−

g

g rj λ j −dr ¼ g rk

UENM ¼ 1− ∑

ðr ¼ 1; …; sÞ;

x−

λ j ≥0 ð j ¼ 1; …; nÞ; di ≥0 ði ¼ 1; …; m− Þ; xþ g dq ≥0 ðq ¼ 1; …; mþ Þ; dr ≥0 ðr ¼ 1; …; sÞ; g and dr ≥0 ð f ¼ 1; …; hÞ:

All slack variables are determined by the optimality of Model (1), which represent the levels of inefficiency.

Max

i¼1

x− Rxi di

þ

mþ



xþ ∑ Rxq dq q¼1

þ

s

g ∑ Rgr dr r¼1

þ

ð2Þ

j¼1 n X

b

bfj λ j þ d f ¼ bfk

ð3Þ

ð f ¼ 1; …; hÞ;

j¼1 n X

λ j ¼ 1;

h

!

b ∑ Rbf d f f ¼1

All slack variables are determined by the optimality of Model (3), which represent the values of inefficiency. 3.4. Model of investment strategies

j¼1 x

λ j ≥0 ð j ¼ 1; …; nÞ; di ≥0 ði ¼ 1; …; mÞ; g b dr ≥0 ðr ¼ 1; …; sÞ; and d f ≥0 ð f ¼ 1; …; hÞ

The variables and parameters in Model (2) are same as those in Model (1). The only difference is the first constraint. − dxi means increasing all inputs to improve energy and environmental performance when being in line with the demand of desirable and undesirable outputs. The unified efficiency under managerial disposability can be defined as:

m

x

s

g

h

!

i¼1

r¼1



b

UEM ¼ 1− ∑ Rxi di þ ∑ Rgr dr þ ∑ Rbf d f f ¼1

To study investment strategies for reducing undesirable outputs of DMUs, Sueyoshi and Goto (2014) proposed the following model which can describe the effect of investments with DC (Desirable Congestion): Max

m− mþ h X X X x− xþ b Rxi di þ Rxq dq þ Rbf d f q¼1

i¼1

s:t:

n X x− − x− ij λ j þ di ¼ xik j¼1 n X

xþ

þ xþ qj λ j −dq ¼ xqk

j¼1 n X

g rj λ j

j¼1 n X

f ¼1

ði ¼ 1; …; m− Þ; ðq ¼ 1; …; mþ Þ;

¼ g rk b

bfj λ j −d f ¼ bfk

ðr ¼ 1; …; sÞ;

ð4Þ

ðr ¼ 1; …; hÞ;

j¼1 n X

All slack variables are determined by the optimality of Model (2), which represent the values of inefficiency.

λ j ¼ 1;

j¼1

λ j ≥0 ð j ¼ 1; …; nÞ; di ≥0 ði ¼ 1; …; m− Þ; x b dq ≥0 ðq ¼ 1; …; mþ Þ; and d f ≥0 ð f ¼ 1; …; hÞ x

3.3. Unified efficiency under natural and managerial disposability To evaluate the unified efficiency of k-th DMU under natural and managerial disposability, Goto et al. (2014) proposed the following

The variables and parameters in Model (4) are the same as those in Model (3). The slacks of undesirable outputs change from +dbf to −dbf . The reason for such changes is that Model (4) considers undesirable

Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

J. Wang et al. / Environmental Impact Assessment Review xxx (2016) xxx–xxx

outputs as the additional products of desirable outputs. Therefore, they are similar with the types of desirable outputs in Model (3). The slacks of desirable outputs change from −dgr to zero because their dual variables need to be free to identify an occurrence of DC, or technology innovation. Model (4) has the following dual formulation: Min

m− X

mþ s h X X X vi x− zq xþ ur g rk − w f bfk þ σ ik − qk þ

m¼1

s:t:

q¼1

f ¼1

r¼1

m− mþ s h X X X X vi x− − zq xþ ur g rj − w f bfj þ σ ≥0 ð j ¼ 1; …; nÞ; ij qj þ i¼1

q¼1

r¼1

5

(e) IPC investment: stand for the amount of investments on administering industrial pollutants.

Desirable output: (f) Industrial value added: in this study, industrial value added of the industrial sector is calculated using the officially released annual average growth rate of added values.

f ¼1

vi ≥Rxi ði ¼ 1; …; m− Þ zq ≥Rxq ðq ¼ 1; …; mþ Þ ur : URS ðr ¼ 1; …; sÞ w f ≥Rbf ð f ¼ 1; …; hÞ σ : URS ð5Þ

In Model (5), vi(i = 1, …,m−), zq(q=1, … ,m+), ur (r = 1, …, s) and wf (f = 1, …,h) are all dual variables related to the first, second, third and fourth groups of constraints in Model (4) that need to be optimized, and σ is an unrestricted variable. The damages to return (DTR) represents the relationships between desirable and undesirable outputs and can be defined as (db/dg)/(b/g) (b and g stand for undesirable outputs and desirable outputs, respectively). DTR and investment strategies can be obtained by Model (4) and (5) according to the rules shown in Table 2. What should be emphasized is that negative DTR implies an occurrence of DC which indicates that technology innovation is effective for DMUs to reduce undesirable outputs, while zero DTR and positive DTR can't be recommended. 3.5. Data and variables Based on the production process of the industrial sector, data set in this study consists of five inputs, one desirable output and four undesirable outputs. These variables include the following production, environmental and financial indicators. Production inputs: (a) Energy input: energy is regarded as important resources of industrial production as well as a major source of industrial pollution. In this paper, energy consumption includes coal, coke, petroleum, natural gas, electricity and others which are all converted into the standard coal equivalent (million tons of coal equivalent). (b) Labor input: labor input (10 thousand employees) is measured by the number of employees of the industrial sector by the end of the year. (c) Capital input: monetary assets, receivables and fixed assets investments etc. are included during the entire industrial production process. So we apply total assets (0.1 billion RMB) to measure the amount of capital. Investment inputs: (d) R&D investment: as the measurement of technology innovation, we regard it as an investment input.

Undesirable outputs: We utilize the following indicators as undesirable outputs: (g) CO2 emissions; (h) SO2 emissions; (i) solid emissions; (J) wastewater emissions. Inputs and outputs of different models are expressed in Table 3. The variables are all related to the industrial sector of China's 30 provinces and the data set is obtained from the China Statistical Yearbooks (2009–2013) and China Energy Statistical Yearbooks (2009–2013). CO2 emissions come from our calculations based on the energy consumption and emission coefficients which are collected from the Intergovernmental Panel on Climate Change (IPCC, 2007). The DEA technique presumes the existence of a relationship among the inputs and outputs data set. First, Cooper et al. (2001) suggests that the number of DMUs should be at least triple the number of inputs and outputs considered. In this study the number of DMUs is 30, which is triple the selected ten factors for the model. Second, Table 4 shows the correlation analysis results. The correlation coefficients between input indexes and output indexes are positive, indicating that these input and output indexes hold an isotonic relationship. Additionally, the correlation coefficients between input indexes (output indexes) mean that they are not alternatives to each other and can be considered as inputs (outputs) in the DEA framework simultaneously. Following the rules above, the adopted DEA model in this study achieves high applicability. 4. Empirical results and analysis 4.1. Research questions and Hypotheses Given that various regulations for the reduction of undesirable outputs have been issued by the government and DMUs have different abilities to adapt to changes in regulations, natural disposability and managerial disposability are assumed in the method framework. This study aims to investigate the regional unified efficiency of China's industrial sector with different assumptions of disposability. Hypothesis 1. DMUs perform better when they decrease their production inputs and increase their investment inputs to raise desirable outputs and reduce undesirable outputs, implying that unified efficiency calculated by Model (3) is higher than those obtained by Models (1) and (2). Following China's National Plan on Climate Change (2014–2020) (NDRC, 2014), the government calls the community to focus on promoting major R&D of energy saving technology and equipment of the industrial sector. Meanwhile, the IPC investment is growing in importance currently. This study examines whether the R&D investment and IPC investment are effective for 30 regions to reduce their undesirable outputs. Hypothesis 2. R&D investment and IPC investment are effective for all provinces to reduce their undesirable outputs.

Table 2 Judgment rules of investment strategies. u⁎r

DTR

Investment strategy

u⁎r N 0 u⁎r = 0 u⁎r b 0 , zq − Rxq = 0 u⁎r b 0 , zq − Rxq N 0

Positive Zero Negative

Unnecessary Unnecessary A limited effect Effective

4.2. Provincial and regional UEN and UEM of industrial sector Table 5 shows provincial UEN and UEM of China's industrial sector from 2008 to 2012. We can find that only Hainan performed efficiently

Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

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J. Wang et al. / Environmental Impact Assessment Review xxx (2016) xxx–xxx

Table 3 Inputs and outputs of different models. Model (1)

Model (2)

Model (3)

Model (4)

Model (5)

Inputs

(a), (b), (c), (d), (e)

(a), (b), (c), (d), (e)

Desirable outputs Undesirable outputs

(f) (g), (h). (i), (j)

(f) (g), (h). (i), (j)

N: (a), (b), (c) M: (d), (e) (f) (g), (h). (i), (j)

N: (a), (b), (c) M: (d), (e) (f) (g), (h). (i), (j)

N: (a), (b), (c) M: (d), (e) (f) (g), (h). (i), (j)

under natural disposability during the whole study period. Hainan regards tourism and service as leading industries, and industrial value added of tertiary industry accounts for 56% in economic growth. Thus, energy consumption and pollution emissions of its industries are relatively low. It is followed by Tianjin and Beijing, whose average UEN were 0.9995 and 0.9976. Although Beijing and Tianjin have a comprehensive industrial base, their economy and technology are developing rapidly. Average UEN of five provinces including Shanghai, Jiangsu, Shandong, Heilongjiang and Xinjiang were all above 0.95. Among them, Heilongjiang and Xinjiang are from central China and western China respectively. Seven provinces with UEN below 0.9 are all from central and western China such as Shanxi etc. However, Liaoning in eastern China had the lowest UEN of 0.7828. This is because Liaoning has one of the largest industrial bases. However, it has not achieved success in promoting the modernization of economic restructuring. We also find that the UEN of Inner Mongolia, Jilin, Hubei and Shaanxi increased from 2008 to 2012, which indicated that they began to pay more attention to environmental protection. For three regions, eastern China had the highest average UEN of 0.9455 shown in Fig. 1. The central area had the lowest average UEN of 0.9146, because Shanxi, Inner Mongolia, Heilongjiang and Henan etc. in central China depend on the traditional energy structure. Fossil fuels always produce more CO2 emissions and other pollution. What should be emphasized was that there was an increased trend of UEN for central China and western China while eastern China decreased in 2012, demonstrating that the energy and environmental performance of central China and western China has been improving. Regarding UEM, there was no province that behaved efficiently from 2008 to 2012. Beijing, Hainan and Tianjin had the highest average efficiency scores of 0.9997, 0.9996 and 0.9994. Followed by Shanghai, the average UEM was 0.9614. Jilin, Guangxi and Yunnan had the lowest UEM which were all below 0.9. Provinces with better performance are all from eastern China, and provinces with worse performance are from central and western China. In addition, the average UEM of the other remaining provinces were between 0.90 and 0.95. The UEM of Liaoning increased from 0.8281 in 2008 to 0.9494 in 2012, which meant that increasing inputs was effective to enhancing the UEM. Similarly, Fig. 2 shows that eastern China had the highest average UEM with 0.9589 and central China performed worst, which was similar with the results obtained by Lin and Long (2015).

4.3. Provincial and regional UENM of industrial sector Table 6 illustrates the provincial UENM of China's industrial sector during the 2008–2012 period. Shandong and Hainan were efficient during the whole study period. A UENM of 1 indicated that DMUs got a win–win of economic development and environmental protection. Liaoning performed worst with the average score of 0.8705. Because Liaoning is one of the largest industrial bases, the development of economy and technology couldn't promote its environmental performance. The UENM of Anhui and Hunan were below 0.95. The remaining provinces had the UENM between 0.95 and 1. The results of UENM is different from UEN and UEM; western China had the highest average UENM, followed by eastern China and central China. The results implied that western China behaved best under the circumstance of combining economic benefits and environmental protection. Comparisons between UEN, UEM, and UENM are further analyzed in Fig. 3, and the empirical results do not support Hypothesis 1. The regional differences of energy control and technology innovation do exist in China. On average, the UENM was more balanced than UEN and UEM, which indicated that the unified efficiency difference among China's 30 regions was more significant from the perspective of single disposability. For most Chinese regions, UENM was much higher than UEN and UEM, displaying that these regions should pay attention to improvement in management and energy consumption control. However, there are also several regions, such as Jiangsu and Henan, whose UEN was the largest. Therefore, in order to increase unified efficiency, it would be more effective for these regions to focus on their management and technological innovation instead of just relying on energy consumption control. In the case of Hainan, the UEM was the largest, which indicated that it should pay more attention to energy consumption control. Fig. 4 shows the confrontation of average UENM about eastern China, central China, western China and overall China. The UENM of eastern China was lower than that of overall China in 2011 and 2012 while higher in 2008, 2009 and 2010. Eastern China performed worse under natural and managerial disposability than under natural disposability, and also under managerial disposability. Central China had higher UENM only in 2011, while western China had lower UENM just in 2010. The trend of UENM about western China increased from 2008 to 2012. However, the UENM of eastern China enhanced from 2008 to

Table 4 Correlation coefficients among inputs and outputs.

Energy IPC investment Labor Capital R&D investment Industrial value added CO2 Solid waste SO2 Waste water

Energy

IPC investment

Labor

Capital

R&D investment

Industrial value added

CO2

Solid waste

SO2

Waste water

1.0000 0.684⁎ 0.689⁎ 0.198⁎⁎ 0.574⁎ 0.780⁎⁎ 0.996⁎⁎ 0.679⁎⁎ 0.802⁎⁎ 0.694⁎⁎

1.000 0.581⁎ 0.392⁎ 0.435⁎ 0.635⁎⁎ 0.698⁎ 0.383⁎ 0.694⁎ 0.534⁎

1.000 0.365⁎ 0.751⁎ 0.860⁎ 0.704⁎ 0.135⁎⁎ 0.505⁎ 0.838⁎

1.000 0.162⁎⁎ 0.130⁎⁎ 0.198⁎⁎ 0.037⁎ 0.206⁎⁎ 0.302⁎

1.000 0.877⁎ 0.590⁎ 0.133⁎ 0.331⁎ 0.614⁎

1.000 0.792⁎ 0.282⁎ 0.571⁎ 0.763⁎

1.000 0.683⁎ 0.811⁎ 0.715⁎

1.000 0.659⁎ 0.202⁎⁎

1.000 0.576⁎

1.000

⁎ Present the significance at levels of 10% respectively. ⁎⁎ Present the significance at levels of 5% respectively.

Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

J. Wang et al. / Environmental Impact Assessment Review xxx (2016) xxx–xxx

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Table 5 Unified efficiency under natural disposability and under managerial disposability. UEN

UEM

Provinces

Year

Eastern China

2008

2009

2010

2011

2012

Average

Year 2008

2009

2010

2011

2012

Average

Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan

0.9995 0.9994 0.9296 0.7768 0.9589 0.9534 0.9468 0.9431 0.9447 0.9454 1.0000

1.0000 0.9990 0.8083 0.7591 0.9548 0.9549 0.9246 0.9471 0.9466 0.9491 1.0000

0.9887 1.0000 0.9137 0.8147 1.0000 1.0000 0.9177 0.9444 0.9458 0.9393 1.0000

1.0000 0.9998 0.9349 0.7892 1.0000 1.0000 0.9346 0.9426 1.0000 0.9456 1.0000

1.0000 0.9993 0.9307 0.7740 0.9587 0.9369 0.9237 0.9419 0.9411 0.9465 1.0000

0.9976 0.9995 0.9034 0.7828 0.9745 0.9690 0.9295 0.9438 0.9556 0.9452 1.0000

1.0000 1.0000 0.9472 0.8281 0.9489 0.9468 0.9435 0.9389 0.9391 0.9090 1.0000

1.0000 1.0000 0.9476 0.9388 1.0000 0.9443 0.9435 0.9485 0.9371 0.9452 1.0000

1.0000 0.9971 0.9489 0.9449 0.9192 0.9433 0.9357 0.9469 0.9493 0.9411 1.0000

0.9986 1.0000 0.9490 0.9481 1.0000 0.9472 0.9491 0.9487 0.9092 0.9493 0.9979

1.0000 0.9997 0.9416 0.9494 0.9390 0.9467 0.9492 0.9390 1.0000 0.9422 1.0000

0.9997 0.9994 0.9469 0.9219 0.9614 0.9457 0.9442 0.9444 0.9469 0.9374 0.9996

Central China Shanxi Inner Mongolia Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Guangxi

0.6839 0.7904 0.8635 1.0000 0.8529 0.9287 0.8484 0.8712 0.8775 0.7754

0.9336 0.8891 0.8868 0.9685 0.8397 0.9253 0.9316 0.9155 0.9310 0.9072

0.9015 0.9400 0.8950 0.9398 0.8849 0.9126 0.9431 0.8248 0.9344 0.9074

0.8499 0.9323 1.0000 1.0000 1.0000 0.9364 1.0000 0.9025 0.9291 0.9282

0.9256 0.9541 1.0000 1.0000 0.8776 0.9390 1.0000 1.0000 0.9317 0.9182

0.8589 0.9012 0.9291 0.9817 0.8910 0.9284 0.9446 0.9028 0.9207 0.8873

1.0000 0.9389 0.9282 0.9381 0.9059 0.8785 0.9326 0.9260 0.9024 0.9244

0.9072 0.9348 0.8458 0.9297 0.9075 0.8650 0.9389 1.0000 0.9355 0.9216

0.9019 0.8102 0.8267 0.9311 0.9249 0.8777 0.9163 0.9166 0.9361 0.9292

0.9376 0.9385 0.9362 0.9299 0.9393 0.9288 0.9387 0.9160 0.9262 0.7368

0.9305 0.9065 0.9377 0.9366 0.9158 0.9307 0.9380 0.9165 0.9109 0.9124

0.9354 0.9058 0.8949 0.9331 0.9187 0.8961 0.9329 0.9350 0.9222 0.8849

Western China Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.9363 0.8037 0.8477 0.8706 0.8189 0.9023 1.0000 0.9354 1.0000

0.9181 0.8279 0.8510 0.8672 0.8582 0.9236 0.9146 0.9331 0.9546

0.8697 0.8850 0.8270 0.9297 1.0000 0.9373 0.9310 0.9343 0.9534

0.8987 0.9065 0.9277 0.9293 1.0000 0.9267 1.0000 0.9289 0.9525

0.9395 0.9025 0.9173 0.9317 1.0000 0.9338 1.0000 0.9308 1.0000

0.9125 0.8651 0.8741 0.9057 0.9354 0.9247 0.9691 0.9325 0.9721

0.9203 0.9184 0.9289 0.8647 0.8771 0.8425 0.9273 0.9155 0.9002

0.9201 0.9075 0.9276 0.9273 0.9121 0.9284 0.9250 0.9263 0.9265

0.9245 0.9114 0.9290 0.8214 1.0000 1.0000 0.9273 0.9262 1.0000

0.9265 0.9166 1.0000 0.9290 1.0000 0.9227 0.9199 0.9274 1.0000

0.9179 0.9227 0.9292 0.9222 0.9034 0.9295 0.9144 0.9298 0.9184

0.9354 0.9219 0.9153 0.9429 0.8929 0.9385 0.9246 0.9228 0.9250

2010 and decreased in 2011. Central China and overall China increased from 2008 to 2011 and decreased in 2012. On the whole, under natural and managerial disposability, western China performed best, followed by eastern China, and central China behaved worst. The results indicated that combining industrial production with environmental protection can improve the energy and environmental performance of western China, and with technological innovation it would be better. To sum up, in terms of UEN, UEM and UENM, the gaps between different provinces were weakening gradually. This is mainly because of the implementation of energy-saving and emission-reduction policies. Since both this study and Wang et al. (2012b) deal with the energy and environmental performance of China's industrial sector, it is meaningful to carry out a comparison between the results given by the two studies. This study found that Beijing, Hainan and Tianjin performed well on UEN, UEM and UENM. On the contrary, Liaoning, Shanxi and

Sichuan had the lowest UEN; Jilin, Guangxi and Yunnan performed worst under managerial disposability; and Hunan and Anhui ranked in the bottom of the list of UENM. The results obtained by Wang et al. (2012b) indicated that Tianjin, Shanghai, Jiangsu, Shandong and Guangdong constituted the frontier of energy efficiency, whereas the efficiency scores of Shanxi, Qinghai and Ningxia were the lowest. Additionally, given that there are many efficient DMUs in the same year obtained by our study, especially for UEN and UENM, a comparison with studies applying a super-efficient DEA method should be of importance. Beijing, Shanghai and Guangdong were usually proved to be energy-efficiency regions, while Qinghai, Guizhou and Ningxia were evaluated as the most inefficient regions by Li et al. (2013) and Zhang et al. (2015). In spite of differences, these four studies indicated that more economically advanced regions had better performance and were mainly located in eastern China. Since Beijing, Tianjin, Shanghai and Guangdong etc. are

Fig. 1. Comparisons of average UEN between three regions.

Fig. 2. Comparisons of average UEM between three regions.

Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

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J. Wang et al. / Environmental Impact Assessment Review xxx (2016) xxx–xxx

Table 6 Unified efficiency under natural and managerial disposability. UENM Provinces

Year

Eastern China

2008

2009

2010

2011

2012

Average

Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan

1.0000 1.0000 0.9700 0.8173 1.0000 0.9935 0.9961 0.9929 1.0000 0.9957 1.0000

1.0000 1.0000 0.9898 0.8699 0.9977 0.9934 0.9968 0.9963 1.0000 0.9985 1.0000

1.0000 0.9992 0.9785 1.0000 0.9975 0.9915 0.9960 0.9922 1.0000 1.0000 1.0000

0.9998 0.9999 0.9831 0.8338 0.9938 0.9977 0.9863 0.9944 1.0000 0.9899 1.0000

1.0000 0.9989 0.9760 0.8316 0.9980 0.9982 0.9916 0.9901 1.0000 0.9915 1.0000

0.99996 0.9996 0.9795 0.8705 0.9974 0.9949 0.9934 0.9932 1.0000 0.9951 1.0000

Central China Shanxi Inner Mongolia Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Guangxi

1.0000 1.0000 0.9462 1.0000 0.8955 0.9895 0.9888 0.9324 0.9275 0.8524

1.0000 1.0000 0.9881 0.9878 0.9034 0.9210 1.0000 1.0000 0.9293 0.9662

1.0000 0.9946 1.0000 0.9894 0.9386 0.9227 0.9971 0.969 0.9422 0.9731

1.0000 1.0000 0.9981 0.9959 1.0000 0.9868 0.9910 0.9660 0.9340 0.9782

0.9975 1.0000 0.9981 0.9981 0.9391 0.9910 0.9992 0.9193 0.9503 0.9928

0.9995 0.9989 0.9861 0.9942 0.9353 0.9622 0.9952 0.9573 0.9367 0.9525

Western China Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.9961 0.8935 1.0000 0.9333 0.9411 0.9742 1.0000 1.0000 0.9990

0.9244 0.9958 1.0000 1.0000 1.0000 0.9970 0.9966 0.9913 1.0000

0.9407 1.0000 0.9212 1.0000 1.0000 0.9998 0.9966 0.9954 1.0000

0.9297 1.0000 1.0000 0.9966 1.0000 1.0000 0.9958 0.9957 1.0000

0.9868 1.0000 1.0000 0.9991 1.0000 1.0000 0.9974 0.9855 1.0000

0.9555 0.9779 0.9842 0.9858 0.9882 0.9942 0.9973 0.9936 0.9998

all economically and socially well-developed regions of China, their economic patterns, natural resource endowments and energy utilization structures have distinct advantages compared with other regions. Additionally, this study showed that eastern China ranked at the top in terms of UEN and UEM, whereas western China had the highest UENM. Central China performed worst in any case. Wang et al. (2012a, 2012b), Li

Fig. 3. Comparisons of average UEN, UEM and UENM in different provinces.

Fig. 4. Comparisons of UENM between three regions and overall China (2008–2012).

et al. (2013) and Zhang et al. (2015) pointed out that the energy efficiency of eastern China remained at a relatively higher level, followed by central China, and western China had the largest room to improve. The comparison results displayed that both energy conservation and technology innovation could improve the energy and environmental performance of western China. The discrepancies among these studies are mainly due to the differences in the modeling framework and the study period. For example, the natural disposability and managerial disposability are added into the DEA model in this study. 4.4. The types of DTR and investment strategies about industry sectors To further analyze the impacts of R&D and IPC investments for China's industrial sector to reduce undesirable outputs, the regional types of DTR and investment strategies of R&D and IPC from 2008 to 2012 are reported in Table 7. P stands for positive DTR which indicates that R&D and IPC investments are unnecessary for improving energy and environmental performance. N represents negative DTR, which means that R&D and IPC investments are necessary. In terms of N, investment strategies of R&D and IPC also could be distinguished by E or L. E stands for R&D investment or that IPC investment is effective, and L means that R&D investment or IPC investment is effective but limited. From Table 7, in 2008, fifteen provinces had positive DTR and investment would not be recommended. The remaining fifteen provinces had negative DTR, and could be divided into three groups: investments on R&D and IPC were all effective to reduce undesirable outputs for Tianjin, Shandong and Hainan; in seven provinces including Beijing, Jiangsu, Fujian, Guangdong, Jiangxi, Yunnan and Qinghai, R&D investments were effective while IPC investments were effective but limited; and the effects of R&D and IPC investments for Zhejiang, Heilongjiang, Henan, Shaanxi and Xinjiang were all limited. In 2009, the number of provinces had negative DTR increased to eighteen. For Tianjin, Fujian, Shandong, Guangdong and Jiangxi, R&D and IPC investments were all effective. R&D investment was effective and IPC investment was limited for Beijing, Shanghai, Jiangsu, Zhejiang and Shaanxi. For Heilongjiang, Henan and Xinjiang, R&D investment was limited and IPC investment was effective. Regarding five provinces including Hainan, Inner Mongolia, Anhui, Qinghai and Ningxia, R&D and IPC investments were all limited. In 2010, twenty provinces had negative DTR. The details are clustered in Fig. 5. In 2011, just eleven provinces had negative DTR and were grouped into three categories: R&D and IPC investments were all effective for Shaanxi; R&D investment was effective and IPC investment was limited for Beijing, Shanghai, Fujian and Sichuan; and R&D and IPC investments for Tianjin, Inner Mongolia, Jilin, Heilongjiang, Jiangxi and Henan were all limited. In 2012, ten provinces had N DTR and can be clustered into four sets: R&D and IPC were effective for Jiangsu; R&D investment was effective and IPC investment was limited for Tianjin, Inner Mongolia, Jilin, Anhui and Sichuan; R&D investment was limited and IPC investment was effective for Shandong; and in terms of Heilongjiang, Jiangxi and Henan, R&D and IPC investments were all limited.

Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

J. Wang et al. / Environmental Impact Assessment Review xxx (2016) xxx–xxx

9

Table 7 The types of damages to return and investment strategies. 2008 Eastern China Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan

DTR N N P P P N N N N N N

Central China Inner Mongolia Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Guangxi

P P P N P N N P P P

Western China Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

P P P N N P N P N

2009 R&D E E

IPCI L E

E L E E E E

L L L E L E

L

L

E L

L L

E L

L L

E

L

L

L

DTR N N P P N N N N N N N

N P P N N N N P P P

P P P P N P N N N

2010 R&D E E

IPCI L E

E E E E E E L

L L L E E E L

L

L

L L E L

E L E E

E

L

L L L

L L E

2011

DTR N N N P N P P N N N N

R&D E E E

IPCI L E L

E E E E E E L

L L L E E L L

N P N N N N N P P N

L

E

E L E E E

E L L E E

E

E

P P P P N P N P N

E

E

L

L

E

L

DTR N N P P N P P N P P P

N P N N P N N P P P

P N P P N P P P P

2012 R&D E L

IPCI L L

E

L

E

L

L

L

L L

L L

L L

L L

E

L

E

E

DTR P N P P P N P P N P P

N P N N N N N P P P

P N P P P P P P P

R&D

IPCI

E

L

E

E

L

E

E

L

E L E L L

L L L L L

E

L

Note: P stands for positive damages to return, and E, L represent effective and limited investments respectively. The blank means R&D and IPC investments are not recommended.

Fig. 5. Investment strategies of different provinces in 2010. Note: UN: investments are unnecessary; EE: R&D investments and IPC investments are all effective; EL: R&D investments are effective and IPC investments are limited; LE: R&D investments are limited and IPC investments are effective; LL: R&D investments and IPC investments are all limited.

Please cite this article as: Wang, J., et al., Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model, Environ Impact Asses Rev (2016), http://dx.doi.org/10.1016/j.eiar.2016.05.002

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J. Wang et al. / Environmental Impact Assessment Review xxx (2016) xxx–xxx

During the whole study period, Tianjin, Heilongjiang, Jiangxi and Henan had negative DTR. Although Tianjin is a developed municipality, as one of the biggest industrial bases, it should also expand the amount of R&D and IPC investments, which could be proved by Table 5 and Table 6. Broadly speaking, from 2008 to 2010, the number of provinces fitted for investment increased from fifteen to twenty. Then, the number was decreased gradually from twenty in 2010 to ten in 2012. For three regions, from 2008 to 2010, provinces suitable for investment in eastern China were the largest with percentages of 53.3%, 50% and 50% respectively. However, in 2011 and 2012, the provinces in central China were the best investment objects, and the proportions were 45.5% and 60%, respectively. The results do not support Hypothesis 2, indicating that there are differences in investment demand across provinces and regions. 4.5. Generalization of results This study empirically evaluated the unified efficiency of China's industrial sector from provincial and regional perspectives under different assumptions of disposability, which can be used in other countries and other industries at the sectoral level. In addition, the effects of R&D and IPC investments on reducing undesirable outputs were also confirmed. However, due to the absence of the data set for other economic industries (e.g., agricultural sector, transportation sector and commercial & service sector) and industrial sub-sectors (e.g., chemical industry, non-ferrous metals industry and non-metallic mineral products industry), the R&D and IPC investments which are considered as the input indexes under managerial disposability in Models (3), (4) and (5) can be replaced by capital investments and investments in the fixed assets. Thus, the research method and design in this study can be extended to other fields. 5. Influencing factors of unified efficiency Utilizing DEA non-radial Models (1), (2) and (3), we can obtain the results of unified efficiency, and analyze disparities of unified efficiency across different provinces. Yet, the determinants of unified efficiency and how they operate are still unknown. Therefore, a further study with a regression model was conducted. 5.1. Selection of variables in the regression model “The Tenth-Five Plan” and “The Eleventh-Five Plan” issued the hot topic of whether industrial economy transformation could enhance energy efficiency (Li and Shi, 2014). For this purpose, this study employed the enterprise scale and capital–labor structure as the factors impacting the unified efficiency. Seeing that the unified efficiency scores had significant differences in different provinces, economic development level was important for this issue. From the perspective of energy consumption, we analyzed the impacts of industrial energy mix and energy price on unified efficiency. Besides, technology improvement and innovation could be presented by the number of R&D researchers. Finally,

the current industrial economy is in the stage of strengthening market competition, therefore, the market competition would also be an influencing factor. The data is from the China Statistical Yearbooks (2009–2013) and China Energy Statistical Yearbooks (2009–2013). Table 8 shows the corresponding symbols and units of variable index. 5.2. Regression model The combination study between the DEA model and the Tobit regression model reported by Tobin in 1958 has been criticized by Simar and Wilson (2007). In their study, Simar and Wilson reported that independent variables are correlated with the error term, as input and output variables are correlated with independent variables using Monte Carlo experiments. Moreover, they stated that DEA efficiency estimates are serially correlated and consequently endanger inconsistent and biased estimates in the regression stage. Therefore, they proposed a single bootstrap algorithm and a double bootstrap algorithm, and the latter one was proved to be better. The double bootstrap truncated regression is as follows: θi ¼ α þ zi β þ εi ;

i ¼ 1; …n

ð7Þ

where α is a constant term, β is a vector of parameters for influencing factor i expected to affect unified efficiency, and εi is the statistical noise. 5.3. Hypotheses Based on previous studies, this study makes the following hypotheses. Hypothesis 3. The enterprise scale will affect unified efficiency positively and the capital–labor structure will affect it negatively. Li and Shi (2014) stated that the industrial scale of China has not yet been the optimal level, and increasing the outputs of industries to a designated scale will improve the energy and environmental efficiency. Additionally, the capital–labor ratio in China was increasing continuously and the industry has suffered a more heavy-oriented trend, which would restrain the improvement of industrial energy efficiency. Hypothesis 4. The economic development has a positive effect on unified efficiency. With the continuous improvement of China's economic level and enlargement of economic scale, the residents will pay more attention to their social surroundings (Pan et al., 2013; Li et al., 2013; Song et al., 2013). Hypothesis 5. The ratios of coal and oil to total industrial energy consumption have negative impacts on unified efficiency, while the ratio of natural gas has a positive effect. Being emission-intensive sources of energy, coal and oil will result in more pollution emissions, while natural gas is a type of clean energy which will produce less pollution emissions. Hypothesis 6. Energy price has a positive effect on unified efficiency. The different prices of energy determine energy consumption in the

Table 8 Influencing factors and related symbols. Factors

Variables

Symbols

Definition and unit

Industrial structure

Enterprise scale Capital–labor structure Economic development level Coal Oil Natural gas The purchasing price index of raw fuel R&D researchers The number of enterprises

ES CL EDL CS OS NS Ln(EP) Ln(RDR) Ln(MC)

Ratio of gross output value to enterprise numbers (0.1 billion/per unit) Ratio of net value of fixed assets to employee numbers (10 thousand/per capital) GDP per capital (0.1 billion/the thousand people) Ratio of coal consumption to total industrial energy consumption (%) Ratio of oil consumption to total industrial energy consumption (%) Ratio of natural gas consumption to total industrial energy consumption (%) The purchasing price index of raw fuel R&D researchers of industrial sector (people) The number of industrial enterprises (per unit)

Economic development level Energy structure

Energy price Technology innovation Market competition

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industrial sector directly and affect carbon emissions indirectly (Yuan et al., 2010). According to the economic theory, the relationship between price and demand is negative, therefore, an increase in energy price will promote the improvement of energy efficiency. Hypothesis 7. The number of R&D researchers has a positive effect on unified efficiency. Generally, R&D researchers could bring innovation to the enterprises and innovation can result in technical progress (Pan et al., 2013; Li et al., 2013; Zhou et al., 2013a, 2013b). Hypothesis 8. Market competition has a positive effect on unified efficiency. Enterprises must make efforts to improve the level of management and technology to gain advantages in the market competition (Pan et al., 2013). 5.4. Empirical results and discussions The results from the truncated regression model are illustrated in Table 9. The estimation results show that ES was not significant, which indicated that the industry scale of China has currently achieved the optimal level. Furthermore, the ratio of oil consumption and market competition was also not significant. The capital–labor ratio was significant and impacted unified efficiency negatively. In the past decades, the capital–labor ratio in China was increasing which implied that the industrial enterprises paid more attention to the benefits and ignored the improvement of unified efficiency. The economic development level affected the unified efficiency positively and significantly. With economic prosperity, the local government could pay more attention to the energy utilization efficiency and high-quality staff team. Coal consumption was negatively related with unified efficiency at the 1% significance level and natural gas consumption has positively affected unified efficiency at the 10% significance level. This indicated that increasing the proportion of emission-intensive energy such as coal would reduce the unified efficiency while raising the proportion of clean energy such as natural gas could enhance the unified efficiency. The effect of energy price on unified efficiency was negative and significant, which meant that the growth of energy price would restrict the development of industrial enterprises. Generally, the number of R&D researchers should be positively related with energy efficiency; however, the estimated coefficient was negative. One possible explanation is the diversification of expenditures on technological investment and that the government attaches great importance to the investment in the industrial sector, but it will take time for these efforts on energy efficiency improvement to gradually take effect. 6. Conclusions and policy implications 6.1. Conclusions A balance between industrial pollution abatement and economic growth has become a major policy issue for attaining a sustainable Table 9 Truncated regression results of UENM. UENM

Coef.

z

p N |z|

ES CL EDL CS OS NS Ln(EP) Ln(RDR) Ln(MC) _cons

0.00408 −0.000275⁎ 0.0443⁎⁎ −0.05345⁎⁎⁎

1.02 −1.76 1.99 −6.25 0.55 1.87 −2.53 −2.65 1.47 3.44

0.310 0.078 0.047 0.000 0.583 0.061 0.011 0.008 0.142 0.001

0.02072 0.01296⁎ −0.01925⁎⁎ −0.05331⁎⁎⁎ 0.00041 1.28309⁎⁎⁎

⁎ Present the significance at levels of 10% respectively. ⁎⁎ Present the significance at levels of 5% respectively. ⁎⁎⁎ Present the significance at levels of 1% respectively.

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society in China (Sueyoshi and Goto, 2014). To discuss the issue, this study added two input indicators including R&D investments of the industrial sector and IPC investments into the DEA model which are combined with natural disposability and managerial disposability. The model divided the inputs into two groups. Total energy consumption, labor and capital stock were inputs under natural disposability, and R&D investment and IPC investment were inputs under managerial disposability. Then, we applied this method to evaluate the energy and environmental performance and identify R&D and IPC investment strategies of China's industrial sector from 2008 to 2012. Energy and environmental performance was assessed by UEN, UEM and UENM. Based on the results, the determinants of energy and environmental performance were studied by the truncated regression model. To our best knowledge, no similar research has been discussed before. The main results this study has obtained are as follows: firstly, for UEN and UEM, Hainan, Beijing and Shanghai performed best while Liaoning performed worst. Eastern China had the highest UEN and UEM, followed by western China and central China. The UEN of Inner Mongolia, Jilin, Hubei and Shaanxi increased from 2008 to 2012, which indicated that these provinces began to pay more attention to environmental protection. Liaoning had higher UEM in 2012 than 2008, implying that its environmental performance was improved under managerial disposability. Secondly, Shandong and Hainan had the largest average UENM while Liaoning had the lowest UENM. Finally, from 2008 to 2010, provinces fitted for investment increased from 15 to 20, and then decreased to 10 in 2012. R&D and IPC investments of the industrial sector were valuable for Tianjin, Heilongjiang, Jiangxi and Henan to reduce undesirable outputs during the whole study period. For the three regions, the amount of provinces suitable for investment in eastern China was the largest in 2008, 2009 and 2012, respectively. However, in 2011 and 2012, central China had the best investment objects. The regression results showed that coal consumption was the main factor to affect unified efficiency negatively whereas the increase in economic development level was primarily responsible for the improvement of unified efficiency.

6.2. Policy implications The results not only enrich the existing literatures, but also deserve the particular attention of policy-makers. First of all, according to the types of DTR, provinces with negative DTR means that they can reduce the undesirable outputs by IPC investment and/or R&D investment. However, the effect of investment is limited for provinces in the two measures, implying that the investments don't have immediate results on the pollution reduction while they are useful in a long term horizon. Government should increase or decrease investments moderately based on the actual situation. For example, provinces in eastern China were the best investment objects from 2008 to 2010, which can be explained by the severe fog and haze that eastern China had in recent years which needed to be relieved urgently. Government should increase the R&D and IPC investments of eastern China by transferring funds from other areas or setting up special investment funds. Next, provinces in central China behaved worst in any case, which mainly resulted from the traditional energy structure and underdeveloped economy. For example, the energy structure of Shanxi, Inner Mongolia, and Henan was mostly composed of pollutant-intensive fossil fuels (coal and oil etc.). Therefore, renewable and low-carbon energy (e.g. wind, solar, hydro, nuclear, biogas, and natural gas) should be further developed. The associative policy was proposed at the China–US joint statement on climate change of APCE on November 12, 2014, which stated that the ratio of non-fossil energy in primary energy consumption would increase to 20% in 2030. Ultimately, with the development of internet technology, combining internet technology with large machines of the industry sector can release their production potential and improve production efficiency.

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This study has some limitations. First, due to data availability, the study period of empirical analysis extends from 2008 to 2012. Future research should consider a longer period based on the sub-industry level data set. Second, our research has not considered PM2.5 and PM10 as undesirable outputs. Future study will pay more attention to these environmental indicators which are byproducts of economic growth and influence the environment and health conditions of people not only in China but also in other surrounding nations (Sueyoshi and Yuan, 2015). Methodologically, this method just evaluated energy and environmental efficiency from a static perspective. Future research may study China's energy and environmental performance within a time series. For example, the Malmquist productivity index can be used to measure dynamic changes in energy and environmental performance. Another is to employ a bootstrap procedure to perform the statistical inference for energy and environmental performance and its decompositions to estimate whether the change in energy and environmental performance is significant in the time series (Zhang and Choi, 2013). Thus, better insights into the energy and environmental performance can be obtained over time. Acknowledgements This study is supported by the National Natural Science Foundation of China (71373172), the Ministry of Education of Humanities and Social Science Research Fund Plan (15YJA790091) and the Ministry of Education of Philosophy and Social Major Science Project (15JZD021). We also appreciate the anonymous reviewers for their valuable comments on an earlier draft of our paper. References Bi, G.B., Song, W., Zhou, P., Liang, L., 2014. Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacksbased DEA model. Energy Policy 66, 537–546. BP, 2015. BP statistical review of world energy market. Available at: http://www.bp.com/ content/dam/bp/pdf/energy-economics/statistical-review-2015/bp-statisticalreview-of-world-energy-2015-full-report.pdf. Chang, Y.T., Zhang, N., Danao, D., Zhang, N., 2013. Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58, 277–283. Chen, J., Song, M., Xu, L., 2015. Evaluation of environmental efficiency in China using data envelopment analysis. Ecol. Indic. 52, 577–583. Cooper, W.W., Park, K.S., Pastor, J.T., 1999. RAM: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Product Anal 11, 5–42. Cooper, W.W., Li, S.L., Seiford, L.M., Tone, K., Thrall, R.M., Zhu, J., 2001. Sensitivity and stability analysis in DEA: some recent developments. J. Prod. Anal. 15 (3), 217–246. Fang, C.Y., Hu, J.L., Lou, T.K., 2013. Environment-adjusted total-factor energy efficiency of Taiwan's service sectors. Energy Policy 63, 1160–1168. Goto, M., Otsuka, A., Sueyoshi, T., 2014. DEA (data envelopment analysis) assessment of operational and environmental efficiencies on Japanese regional industries. Energy 66, 535–549. Hu, J.L., Lee, Y.C., 2008. Efficient three industrial waste abatement for regions in China. Int. J. Sustain. Dev. World Ecol. 15 (2), 132–144. Huang, J., Yang, X., Cheng, G., Wang, S., 2014. A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China. J. Clean. Prod. 67 (6), 228–238. Intergovernmental Panel on Climate Change (IPCC), 2007. IPCC Fourth Assessment Report: Mitigation of Climate Change 2007. Cambridge University Press, Cambridge. Intergovernmental Panel on Climate Change (IPCC), 2014. Mitigation of Climate Change (2014) Working Group III Contribution to the Fifth Assessment Report of IPCC Fifth Assessment Report. Cambridge University Press, UK. Ke, J., Price, L., Ohshita, S., Fridley, D., Khanna, N.Z., Zhou, N., Levine, M., 2012. China's industrial energy consumption trends and impacts of the Top-1000 Enterprises EnergySaving Program and the Ten Key Energy-Saving Projects. Energy Policy 50, 562–569. Lee, B.L., Worthington, A.C., 2014. Technical efficiency of mainstream airlines and lowcost carriers: new evidence using bootstrap data envelopment analysis truncated regression. J. Air Transp. Manag. 38, 15–20. Li, L.B., Hu, J.L., 2012. Ecological total-factor energy efficiency of regions in China. Energy Policy 46, 216–224. Li, H., Shi, J., 2014. Energy efficiency analysis on Chinese industrial sectors: an improved Super-SBM model with undesirable outputs. J. Clean. Prod. 65, 97–107. Li, H., Fang, K., Yang, W., Wang, D., Hong, X., 2013. Regional environmental efficiency evaluation in China: analysis based on the Super-SBM model with undesirable outputs. Math. Comput. Model. 58, 1018–1031. Lin, B., Long, H., 2015. A stochastic frontier analysis of energy efficiency of China's chemical industry. J. Clean. Prod. 87, 235–244.

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