Provincial green economic efficiency of China: A non-separable input–output SBM approach

Applied Energy 171 (2016) 58–66

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Provincial green economic efficiency of China: A non-separable input–output SBM approach Xueping Tao a,b, Ping Wang c, Bangzhu Zhu c,⇑ a

School of Economics and Management, Wuyi University, Jiangmen, Guangdong 529020, China Jiangmen Economic Research Center, Jiangmen, Guangdong 529020, China c School of Management, Jinan University, Guangzhou, Guangdong 510632, China b

h i g h l i g h t s
We use a non-separable input/output SBM model to measure China’s provincial green economic efficiencies.
We decompose green economic inefficiency from each input and output to find improving directions.
The whole green economic efficiency is low in China, and there are large differences among regions.
The sources for green economic inefficiencies in different regions are different.
Different measures should be made out for different regions.

a r t i c l e

i n f o

Article history: Received 17 July 2015 Received in revised form 2 February 2016 Accepted 25 February 2016 Available online 22 March 2016 Keywords: Green economic efficiency Non-separable input/output SBM model Input and output inefficiency Energy saving and emissions reduction potential

a b s t r a c t Aiming at the undesirable output (CO2 emission) and non-separable inputs and outputs, we employ a non-separable input/output SBM model to measure China’s provincial green economic efficiency during 1995–2012. Empirical results indicate that (i) there are larger interregional differences in green economic efficiencies. The highest efficiency of 0.7339 is recorded at the southern coastal region, followed by those at the eastern coastal and northern coastal regions. The lowest efficiency only reaches 0.3049 at the northwestern region. (ii) Energy and CO2 emission are the key factors for green economic efficiencies. (iii) Different regions have different energy-saving and CO2 emission reduction potentials. The southern coastal region should at least save energy of 4.7 million tons of standard coal. The middle Yellow River, northern coastal and northeast regions should save energy as much as 62, 60, 51 million tons of standard coal. CO2 emission excess in the middle Yellow River region reaches 450 million tons in 2012, while CO2 emission excess in the southern coastal region is only 12 million tons. Finally, we propose some target policies to improve China’s regional green economic efficiencies. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Due to the largest energy consumption and CO2 emission, China has witnessed severe energy and environmental problems in the past decades. The Chinese government promised that CO2 emission would keep increasing until 2030 [1]. How to realize this target without causing economic dislocation is a dilemma for China. Improving resource efficiency should be an effective way for solving this dilemma. Therefore, it is meaningful to accurately measure China’s provincial green economic efficiencies, which are helpful for making out the targeted economic and environmental policies.

⇑ Corresponding author. Tel.: +86 15915761388. E-mail address: [email protected] (B. Zhu). http://dx.doi.org/10.1016/j.apenergy.2016.02.133 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

Data envelopment analysis (DEA) is an effective method for evaluating the relative efficiency of decision-making units (DMUs) on the conditions of multiple-inputs and multiple-outputs. DEA has been widely used in various fields [2,3]. DEA was initially adopted by Hu et al. [4] to investigate total-factor energy efficiency (TFEE) of regions in China. After that, a large number of researchers explored energy and environmental efficiencies with the DEA method [5–11]. However, the traditional DEA models are either input-oriented or output-oriented, without considering input and output slacks simultaneously, it. To make an inefficient DMU more efficient, the input-oriented DEA modes mainly concern how to reduce inputs; the output-oriented DEA models mainly concern how to expand outputs. In fact, inputs and outputs should be considered at the same time. The slack-based measurement (SBM) model proposed by Tone [12] can deal with input reduction and

59

X. Tao et al. / Applied Energy 171 (2016) 58–66

output expansion at the same time, and does not stick to a proportionate change of input and output. Thus, the SBM model is more in line with reality, which has been used to measure the energy efficiencies of Brazil, Russia, India and China [13]. However, the SBM model cannot deal with undesirable outputs. Roughly speaking, there are two categories of DEA approaches to deal with undesirable outputs. One is directional distance function approach [14]. The other is the slack-base measure that involves undesirable outputs, named the SBM-undesirable model [15–17]. As a new nonradial and non-oriented DEA model, the SBM-undesirable model can conduct input and output slacks at the same time, and do not need strict proportional changes of inputs and outputs. Therefore, it can discriminate inputs and outputs efficiencies or inefficiencies. In recent years, more and more studies have used the SBM-undesirable model to investigate energy efficiency or environmental efficiency [18–20]. The existing findings can offer important evidences for outlining relevant economic and environmental policies. However, they can still be improved for the reasons as follows. Firstly, although some existing studies take the undesirable outputs into consideration, the non-separable characteristics of inputs and outputs are not considered further. For example, fossil fuel consumption will bring about CO2 inevitably. CO2 is a by-product of the production process; it is non-separable and cannot be handled simply as an undesirable output. Secondly, most previous literature do not explore economic inefficiency from every input and output, so they could not obtain how much input should be saved, and how much undesirable output should be reduced, which are very helpful for making reasonable policies. To solve the shortcomings, this study uses a non-separable input–output SBM approach to measure the provincial green economic efficiencies of China during 1995–2012. The contributions lie in the following aspects: (i) in order to measure China’s economic efficiency more accurately, we use a non-separable input– output SBM approach to investigate green economic efficiency with the following advantages. Firstly, the non-separable input–output SBM model can conduct slacks of input and output simultaneously. Secondly, the non-separable input–output SBM model does not require increase desirable output and reduce undesirable output by the same proportion. Thirdly, the non-separable input–output SBM model can fully consider the non-separated characteristic of inputs and outputs. Hence, compared with the traditional DEA models, the non-separable input–output SBM model can measure green economic efficiency more accurately. (ii) The intertemporal frontier approach [21] is used for calculating green economic efficiency. Through this approach, all the DUMs during the sample period have the same reference set. This approach can solve the problem that the number of DMUs is too small to construct an effective convexity frontier of any DEA model. This method can not only measure static efficiency of DMUs in a single phase, but also capture the continuous efficiency changes during the whole sample period. The Malmquist index method is a good method to explore productivity changes of DMUs in two adjacent periods, instead of continuous changes during the whole sample period. Moreover, the aim of this study is to capture the continuous trend of green economic efficiencies in the sample period, rather than to explore the dynamic efficiency changes of green economic efficiencies in the sample period. Consequently, the Malmquist index method is not applied in this study. (iii) For improving the green economic efficiency, energy saving and CO2 emissions reduction potential is calculated for each region. Finally, we propose some target policies to improve China’s regional green economic efficiencies. The remainder of this paper is organized as follows. In Section 2, the research methods used in this study will be explained in detail. Section 3 provides the discussion and results. Conclusions will be drawn and corresponding policy suggestions proposed in Section 4.

2. Methodology 2.1. SBM model For inputs x e Rm > 0 and outputs y e Rs > 0, the matrices X and Y are defined as X = [x1, . . . , xn] e Rmn > 0, Y = [y1, . . .yn] e Rsn > 0. The production possibility set P : P ¼ fðx; yÞjx P Xk; y 6 Yk; k P 0g. Thus, the SBM model [12] is defined as:

q ¼ min

1 1m

Pm

s i i¼1 xi0 sþ r r¼1 yr0

Ps 1

1þn

S:t: x0 ¼ Xk þ s ; s P 0;

k P 0; 

ð1Þ

y0 ¼ Yk  sþ ; sþ P 0

+

where s , s are the slacks of inputs and outputs, respectively. If and only if s = 0 and s+ = 0, that is, when q = 1, DMU is efficient. 2.2. Non-separable input/output SBM model Although the SBM model has many advantages, it cannot deal with undesirable outputs, let alone non-separable undesirable outputs. To consider the impacts of non-separable desirable outputs and undesirable outputs on efficiency, Tone and Tsutsui [22] defined that Y Sg 2 Rs11 n , Y NSg 2 Rs21 n ; Y NSb 2 Rs22 n are separable desirable outputs, non-separable desirable outputs, and nonseparable undesirable outputs, respectively. X S 2 Rm1 n and X NS 2 Rm2 n are separable and non-separable inputs, respectively. All of the inputs and outputs are positive. The new production possibility set PNS under the constant return to scale (CRS) assumption is defined by:

  PNS ¼ fðxS ; xNS ; ySg ; yNSg ; yNSb ÞxS P X S k; xNS P X NS k; ySg 6 Y Sg k; yNSg 6 Y NSg k; yNSb P Y NSb k; k P 0g

Therefore, the non-separable input/output SBM model is defined as:

P 1 sS Pm2 sNS m2 1 i i 1  m1 m i¼1 xS  m i¼1 xNS  m ð1  aÞ i0 i0   q ¼ min Ps22 sNSb Ps11 sSgr r þ þ ðs þ s Þð1  a Þ 1 þ 1s 21 22 r¼1 Sg r¼1 yNSb yr0

s:t:

xS0

¼ X k þ s ;a S

S

r0

xNS 0

NS

¼ X k þ sNS ;

Sg NSg Sg ySg 6 Y NSg k; 0 ¼ Y k  s ; ay0

ayNSb ¼ Y NSb k þ sNSb ; 0 s11  s21 s11 s21  X X X X Sg ySg ySg yNSg þ a yNSg r0 þ sr r0 ¼ r0 þ r0 ; r¼1

sSg r ySg r0

r¼1

6U

r¼1

ð2Þ

r¼1

ð8rÞ;

sS P 0; sSg P 0; sSg P 0; sNSb P 0; k P 0; 0 6 a 6 1 m ¼ m1 þ m2 ; s ¼ s11 þ s21 þ s22 where a is the reduction proportion of inputs and outputs; U is the extendable upper limit of separable desirable outputs. sS ; sNS ; sSg ; sNSb are slacks of separable inputs, non-separable inputs, separable desirable outputs, and non-separable undesirable outputs, respectively. 2.3. Decomposition of efficiency In order to find out effective ways for promoting green economic efficiency, the above efficiency (formula (2)) can be decomposed into a set of inefficiencies [22]:

60

X. Tao et al. / Applied Energy 171 (2016) 58–66

Pm1 1

1m

q ¼ min

1þ

i¼1

Pm2 1

a1i  m

i¼1

a2i

s21 X P 22 b2r þ sr¼1 b3r r¼1 b1r þ

Ps11

r¼1 

1 sS i a1i ¼ m xSi0

a2i ¼ b1r ¼

ði ¼ 1; . . . ; m1 Þ

1 1 sNS i ð1  a Þ þ m m xNS i0 1 s

ði ¼ 1; . . . ; m2 Þ

ð3Þ



sSg r ySg r0

ðr ¼ 1; . . . ; s11 Þ

1 ð1  a Þ ðr ¼ 1; . . . ; s21 Þ s  1 1 sNSb r ðr ¼ 1; . . . ; s22 Þ b3r ¼ ð1  a Þ þ s s yNSb r0

b2r ¼

where a1i, a2i, b1r, b2r and b3r are the inefficiency proportions of separable inputs, non-separable inputs, separable desirable outputs, and non-separable desirable outputs, respectively. a⁄ is the reduction proportion of each input and output obtained by formula (2).     sS , sNS , sSg and sNSb are slacks of separable inputs, nonseparable inputs, separable desirable outputs and non-separable undesirable outputs obtained by formula (2), respectively. If the DMU is inefficient, it can be improved into an efficient one by the following projection:

xS0p ¼ xS0  sS



 NS NS xNS 0p ¼ a x0  s Sg Sg ySg 0p ¼ y0 þ s

yNSg 0p

¼a

 NSg y0





þs

ð4Þ NSg  

 NSb yNSb  sNSb 0p ¼ a y0

Sg NSg NSb where xS0p , xNS 0p , y0p , y0p , y0p are the projections of separable inputs, non-separable inputs, separable desirable outputs, non-separable desirable outputs and non-separable undesirable outputs, respectively. And the improved amount of each input and output can be obtained:

xS0e ¼ ss NS NS xNS 0e ¼ ð1  aÞx0 þ s Sg ySg 0e ¼ s

Shandong. The northeast region includes Liaoning, Jilin, and Heilongjiang. The eastern coastal region consists of Shanghai, Jiangsu, and Zhejiang. The southern coastal region is composed of Fujian, Guangdong, and Hainan. The middle Yellow River region is made up of Shanxi, Inner Mongolia, Henan, and Shaanxi. The middle Yangtze River region covers Anhui, Jiangxi, Hubei and Hunan. The southeast region includes Guangxi, Sichuan, Guizhou and Yunnan. And Gansu, Qinghai, Ningxia and Xinjiang Provinces lie in the northwest region. According to production processes, we treat the annual data of capital stock and labor force as two separable inputs, and energy consumption as non-separable input, CO2 emission as a nonseparable undesirable output. As CO2 emission is a by-product of energy consumption, energy consumption and CO2 emission are treated as non-separable variables. Gross domestic product (GDP) is a separable desirable output. The inputs and outputs are explained as follows: (1) Labor. The number of employees of each province at the end of a year is considered as labor input, and the data is collected from the China Statistic Yearbook (1995–2012). (2) Capital. As official data for provincial capital stock are not available in China, we estimate capital stock by the perpetual inventory method proposed by Zhang et al. [23], which are converted into the 1995 constant price. (3) Energy consumption. Energy consumption of each province is chosen as non-separable input. Primary energy (e.g. coal, oil, natural gas and hydropower) is mainly used, and converted into standard coal. The data are collected from the China’s Energy Statistical Yearbook (1995–2012). (4) GDP. GDP of each province is chosen as the indicator for separable desirable output. GDP data are collected from the China Statistic Yearbook (1995–2012) and converted into the 1995 constant price. (5) Undesirable output. CO2 emission of each province is chosen as non-separable undesirable output. CO2 emissions are mainly produced by fossil fuel consumption. According to fossil fuel consumption, we apply the methods suggested by the intergovernmental panel on climate change (IPCC) to calculate CO2 emissions of each province. The data of primary fossil fuel consumption are extracted from the China’s Energy Statistical Yearbook (1995–2012).

ð5Þ

NSg yNSg þ sNSg 0e ¼ ða  1Þy0 NSb yNSb þ sNSb 0e ¼ ð1  aÞy0 Sg NSg NSb where xS0e , xNS 0e , y0e , y0e and y0e are the improved amount of separable inputs, non-separable inputs, separable desirable outputs, nonseparable desirable outputs and non-separable undesirable outputs, respectively.

3. Empirical analyses 3.1. Data For the lack of the availability of data, we choose 29 provinces in mainland China as the sample of study (excluding Tibet, Taiwan, Hong Kong and Macao; the data of Chongqing are subsumed under Sichuan). In terms of the characteristics of geographical location, economic development level, resource endowment, population, and industry structure of different regions in mind, we divide these provinces into eight economic regions instead of the traditional three regions (eastern, central and western) in making target and suitable regional economic and environmental policies. The northern coastal region constitutes Beijing, Tianjin, Hebei, and

A brief description of all inputs and outputs of 8 economic regions in China is given in Table 1. It can be found that there are complicated relationships between energy consumption, capital, labor, CO2 emissions and GDP. The southern coastal region utilizes 9.28%, 11.88% and 10.07% of national total energy, capital and labors, and produces 15.21% and 6.71% of national total GDP and CO2 emissions. In contrast, the middle Yellow River region utilizes more resources while producing less desirable output and more undesirable output than the southern coastal region. Thus, it is meaning to find an effective way to measure the green economic efficiency of provinces in all regions. The non-separable input/output SBM model is good for this job, which can deal with efficiency comparisons between DMUs that have multiple inputs and outputs very well, especially for non-separable inputs and outputs, including undesirable outputs. 3.2. Green economic efficiency results Based on the intertemporal frontier method proposed by Tulkens et al. [21], we put all inputs and outputs of DMUs during the sample period into a single global frontier, and then investigate China’s provincial green economic efficiencies. According to Figs. 1 and 2 and Table 2, the overall green economic efficiency of China is

61

X. Tao et al. / Applied Energy 171 (2016) 58–66 Table 1 Descriptive statistics of inputs and outputs of 8 economic regions in China. Regions

Northern coast Northeast Eastern coast Southern coast Middle Yellow River Middle Yangtze River Southwest Northwest

Energy

Capital

Labor

CO2

GDP

Mean (104 tce)

% of nation’s total

Mean (108 RMB)

% of nation’s total

Mean (104 person)

% of nation’s total

Mean (104 t)

% of nation’s total

Mean (108 RMB)

% of nation’s total

11882.28 9273.48 11198.26 7593.48 9793.72 7275.22 8010.96 3353.26

19.36 11.33 13.69 9.28 15.96 11.86 13.05 5.46

21696.99 12698.59 30312.54 17050.24 13196.29 11897.65 12160.54 3607.03

20.16 8.85 21.13 11.88 12.26 11.06 11.30 3.35

2506.19 1622.19 2657.02 2281.98 2506.18 3025.6 3386.92 654.47

14.74 7.16 11.72 10.07 14.74 17.80 19.92 3.85

27349.11 25456.85 24277.02 12864.52 30645.17 15773.01 14781.78 8397.76

19.01 13.27 12.65 6.71 21.30 10.96 10.27 5.84

7805.65 5327.35 11563.44 8572.13 4603.65 5006.04 4661.26 1084.72

18.47 9.45 20.52 15.21 10.89 11.85 11.03 2.57

Note: Calculation is based on data from the China Statistic Yearbook and the China Energy Statistical Yearbook from 1995 to 2013.

Fig. 1. Provincial economic efficiencies of China (1995–2012).

Fig. 2. Green economic efficiencies of China’s 8 economic regions.

low, with the mean of 0.467. It means that with the current production technology, the overall green economic efficiency can be improved by 53.3%. Moreover, there are great gaps among regions. The southern coastal region shows the highest green economic efficiency, with the mean of 0.7337, followed by the eastern and northern coastal regions. Green economic efficiencies are divergent in the high efficiency regions. In the southern coastal region, Guangdong shows the highest green economic efficiency. However, the efficiencies of Fujian and Hainan are far below that of

Guangdong. Especially in Fujian, green economic efficiencies declined more than 32%, from 0.9153 to 0.6174, from 1995 to 2000. The green economic efficiencies of Hainan fluctuated observably with a U-shape curve during the sample period, from above 0.6 before 2005, down to below 0.5 in the middle three years, and up to above 0.5 after 2010. In the northern and eastern coastal regions, the green economic efficiencies of Shanghai, Beijing and Tianjin started low but improved rapidly during the sample period. The green economic efficiencies of Hebei and Shandong are far below those of Beijing and Tianjin, with no improvement during the sample period. The green economic efficiencies of the northeast and middle Yangtze River are between 0.4 and 0.5 on average. And the green economic efficiencies of the southwest, middle Yellow River and northwest regions are all lower than 0.4 on averages. In the northwest region, green economic efficiency is merely 0.3049 on average. Convergence trend is shown in the low efficiency regions. So it is essential for us to further explore the reasons for differences in provincial green economic efficiencies.

3.3. Decomposition of green economic efficiency For the purpose of revealing the reasons for differences in green economic efficiencies, we have explored the efficiency of each input and output. Green economic efficiency is decomposed into several aspects by formula (3): energy inefficiency, capital inefficiency, labor inefficiency, GDP inefficiency and CO2 emissions inefficiency. The empirical results show that GDP inefficiencies of all provinces are 0. It indicates that GDP has already approached the

62

X. Tao et al. / Applied Energy 171 (2016) 58–66

Table 2 Provincial economic efficiencies of China (1995–2012). Economic regions

Province

1995

2000

2005

2006

2007

2008

2009

2010

2011

2012

Cumulative rate of growth%

Annual rate of growth %

Southern coastal

Fujian Guangdong Hainan

0.9153 1.0000 0.6279

0.6174 0.9179 0.6714

0.5793 0.8723 0.6705

0.6014 0.9228 0.5185

0.6339 1.0000 0.4554

0.6609 1.0000 0.4772

0.6665 0.9983 0.4943

0.6994 1.0000 0.5320

0.6959 1.0000 0.5330

0.7221 1.0000 0.5351

21.106 0.000 14.779

1.385 0.000 0.936

Eastern coastal

Shanghai Jiangsu Zhejiang

0.4867 0.5866 0.6592

0.5697 0.5522 0.5024

0.6775 0.5698 0.5229

0.7436 0.6007 0.5387

0.8725 0.6373 0.5607

0.9219 0.6766 0.5925

0.9326 0.7103 0.6129

1.0000 0.7303 0.6323

0.9301 0.7412 0.6561

1.0000 0.7734 0.6945

105.445 31.858 5.346

4.326 1.640 0.307

Northern coastal

Beijing Tianjin Hebei Shandong

0.3521 0.3477 0.4050 0.5464

0.4120 0.4260 0.3373 0.4640

0.4856 0.5477 0.3520 0.4478

0.5138 0.5828 0.3631 0.4597

0.5550 0.6168 0.3706 0.4797

0.5888 0.6397 0.3766 0.4964

0.6297 0.6816 0.3747 0.5172

0.6821 0.6810 0.3935 0.5338

0.8111 0.6357 0.3996 0.5318

0.8899 0.6750 0.4025 0.5472

152.723 94.152 0.617 0.149

5.605 3.980 0.036 0.009

Northeast

Liaoning Jilin Heilongjiang

0.3476 0.3477 0.3635

0.4039 0.3898 0.3976

0.4545 0.4251 0.4874

0.4584 0.4278 0.5076

0.4748 0.4370 0.5237

0.4918 0.4428 0.5333

0.5019 0.4511 0.5511

0.5131 0.4471 0.5672

0.5132 0.4478 0.5665

0.5100 0.4633 0.5881

46.726 33.256 61.800

2.281 1.703 2.871

Middle Yellow + River

Shanxi Inner Mongolia Henan Shaanxi

0.2568 0.3549 0.4438 0.2975

0.2631 0.3355 0.3752 0.3721

0.2899 0.3563 0.3580 0.3608

0.2936 0.3667 0.3620 0.3657

0.3065 0.3832 0.3677 0.3769

0.3045 0.3969 0.3705 0.3864

0.2947 0.3925 0.3706 0.3941

0.3013 0.3898 0.3759 0.3990

0.3097 0.3856 0.3844 0.4053

0.3055 0.3788 0.4009 0.4159

18.970 6.720 9.685 39.827

1.027 0.383 0.597 1.992

Middle Yangtze River

Anhui Jiangxi Hubei Hunan

0.3769 0.4837 0.3903 0.3743

0.3631 0.4420 0.3425 0.4961

0.4075 0.4403 0.3621 0.4260

0.4173 0.4353 0.3759 0.4368

0.4327 0.4377 0.3961 0.4534

0.4424 0.4560 0.4197 0.4706

0.4541 0.4771 0.4352 0.4804

0.4718 0.4880 0.4478 0.4917

0.4838 0.4937 0.4424 0.4949

0.4991 0.5218 0.4789 0.5222

32.424 7.879 22.696 39.490

1.666 0.447 1.210 1.977

Southwest

Guangxi Sichuan Guizhou Yunnan

0.6205 0.3423 0.2569 0.4029

0.5238 0.3845 0.2222 0.3599

0.4927 0.3852 0.2124 0.3141

0.4926 0.3947 0.2171 0.3173

0.4942 0.4127 0.2261 0.3314

0.5072 0.4154 0.2339 0.3358

0.5017 0.4329 0.2375 0.3359

0.4771 0.4668 0.2433 0.3326

0.4620 0.5160 0.2703 0.3367

0.4740 0.5591 0.2736 0.3403

23.606 63.340 6.520 15.542

1.571 2.928 0.372 0.989

Northwest

Gansu Qinghai Ningxia Xinjiang

0.3261 0.2782 0.2371 0.2992

0.2826 0.2484 0.2244 0.2861

0.2840 0.2470 0.1987 0.2892

0.2877 0.2479 0.2006 0.2940

0.2940 0.2560 0.2050 0.3035

0.2973 0.2640 0.2107 0.3123

0.3033 0.2702 0.2059 0.3123

0.3045 0.2846 0.2094 0.3111

0.3050 0.2792 0.2046 0.3049

0.3118 0.2739 0.2008 0.2919

4.373 1.555 15.297 2.425

0.263 0.092 0.972 0.144

maximum efficiency under present condition. Fig. 3 shows the boxplots of inputs and outputs inefficiency of 8 economic regions. There are slight differences in labor efficiency between the high efficiency regions and the low efficiency regions. However, there are great differences in capital and energy efficiency, especially CO2 emissions efficiency among regions. The medians of capital inefficiencies are between 0 (the eastern coastal region) and 0.13 (the northwest region). According to Fig. 3b, labor inefficiencies (median) in different regions range from 0.14 (the eastern coastal region) to 0.26 (the southwest region). Labor inefficiencies in the southern, eastern, northern coast and northeast are more divergent than in the remaining four regions. With regard to the energy and CO2 emissions inefficiency, Fig. 3c illustrates that the region with the lowest energy inefficiency is the southern coastal region, with a median of 0.04, and the highest is the northwest region, with a median of 0.25. Fig. 3d shows that CO2 emissions inefficiency (median) is 0.06 in the southern coastal region, but is as high as 0.41, 0.41, and 0.38 in the middle Yellow River, northwest and northeast regions. Fig. 4 shows the efficiency changes of each input and output. Generally, in most regions, there is a decreasing trend of capital efficiency and increasing trend of energy and CO2 emissions efficiency. Energy and CO2 emissions are the key factors for green economic efficiency. The green economic efficiency is generally higher in the regions with low inefficiency of energy consumption and CO2 emissions and lower in regions with high inefficiency of energy consumption and CO2 emissions. With the reduction in energy inefficiency and CO2 emissions inefficiency in the eastern and northern coastal regions, there is a substantial increase in the green economy efficiency of these regions. In the eastern coastal region, the energy inefficiency and CO2 emissions inefficiency decrease by 66% and 93% cumulatively, respectively. CO2 emissions inefficiencies in northeast, middle Yellow River, middle

Yangtze River, southwest and northwest regions are at an extremely high level, with slight progress during the sample period. With the implementation of Big Development Strategy for Western Region and Rise Strategy for Central Region, economic development is emphasized, but with little attention paid to the environment, which leads to too much CO2 emissions. 3.4. Energy saving and CO2 emission reduction potentials Form above, it can be found that inefficiencies of energy and CO2 emission are a big part of green economic inefficiencies. But what we mainly concern about is how much energy should be saved and how much CO2 emissions should be reduced in each region. Energy redundancy means the amount of energy that can be saved (energy-saving potential). Also, CO2 emissions excess means the amount of CO2 emissions that can be reduced (emission reduction potential). According to Figs. 5 and 6, in most regions, energy redundancies are rising during the sample period. In the southern coastal region, energy redundancy is 4.7 million tons on average. In the eastern coastal, energy redundancies fluctuate greatly. There is a rising trend before 2005, a declining trend after 2005. In the northern coastal region, the difference between provinces within the region is large. Beijing and Tianjin’s energy redundancies are much smaller than those of Shandong and Hebei. Starting in 2000, energy redundancies of the middle Yellow River region and Shandong and Hebei provinces have increased dramatically, and reached to 100 million tons in 2012, which means that a huge amount of energy is wasted in those provinces. CO2 emission is mostly produced by energy consumption, and it is treated as a non-separable undesirable output. Therefore, CO2 emission excesses features are similar to that of energy redundancies. The regions with low energy redundancy are usually low in CO2 emissions excesses. And the regions with high energy excesses

X. Tao et al. / Applied Energy 171 (2016) 58–66

Fig. 3. Boxplots of inputs and outputs inefficiency (on average).

Fig. 4. Decomposition of green economic efficiency.

63

64

X. Tao et al. / Applied Energy 171 (2016) 58–66

energy redundancies and CO2 emissions excesses in Guangdong, Shanghai, Beijing and Tianjin are very small, below 20 million tons of standard coal and 50 million tons of CO2 respectively. Energy redundancies of several provinces, including Hebei, Shandong, Shanxi, Inner Mongolia and Henan, are very high. In these provinces, energy redundancies increase rapidly during the sample period. CO2 emissions excesses are increasing rapidly too, at an average annual rate of 5.6%, 8.6%, 4.6%, 13%, 5.8%. To 2012 CO2 emissions excesses of these provinces reached 170 million, 127 million, 141 million, 128 million and 99 million tons of CO2, respectively. In 2012, these provinces should reduce the amount of CO2 emission accounted for 25% of the country’s total CO2 emission. We should focus on these provinces to reduce CO2 emission. However, a special attention should be paid to Guangxi, Guizhou, Yunnan, Ningxia, Xinjiang and other provinces in the southwest and northwest regions. In these provinces, energy redundancies are low, but grows rapidly. CO2 emissions excesses also increase rapidly. If not strengthening energy conservation and emissions reduction policies, environment in these provinces will suffer serious damage, like the provinces in the middle Yellow River and northern coastal regions.

Fig. 5. Energy redundancy in each region.

4. Conclusions and policy implications This study has measured the provincial green economic efficiencies of China during 1995–2012 by the non-separable input/ output SBM model. To explore effective ways for improving green economic efficiency, we have investigated energy-saving and emission reduction potential of each province. The obtained results reveal that:

Fig. 6. CO2 emissions excess in each region.

usually are high in CO2 emissions excesses. CO2 emissions excess in the middle Yellow River region reaches 450 million tons in 2012, while in the southern coastal region it is only 12 million tons. But there are still some exceptions. Southwest is rich in clean energy (hydropower) and natural gas. Thus, the southwest region has relatively high energy excess, but relatively low CO2 emissions excess. To explore exactly the energy saving and CO2 emissions reduction potential in each province, from Figs. 7 and 8, we can find that

(i) The overall green economic efficiency is low in China, and there are great differences in different regions: the southern coastal region shows the highest green economic efficiency, followed by eastern and northern coastal regions. While the green economic efficiencies of the northern region, the middle Yangtze River and middle Yellow River basins, and the southwest and northwest regions are about 0.4 on average, there is still big room for further improvement. (ii) Each input and output efficiencies are different. There are slight differences in labor efficiency among different regions, but great differences in the efficiency of capital and energy, as well as CO2 emission among regions. In most regions, there is

Fig. 7. Energy redundancy in each province.

X. Tao et al. / Applied Energy 171 (2016) 58–66

65

Fig. 8. CO2 emissions excess in each province.

a decreasing trend of capital efficiency and increasing trend of energy and CO2 emissions efficiency. Energy and CO2 emissions are the key factors for green economic efficiency. There is a rapid progress of energy efficiency and CO2 emissions efficiency in the northern and eastern coastal regions, but a slight progress in the northeast, middle Yellow River, middle Yangtze River, southwest and northwest regions. (iii) Different regions have different energy-saving and CO2 emission reduction potentials. Energy redundancies and CO2 emissions excesses in Guangdong, Shanghai, Beijing and Tianjin are very small, but very high in Hebei, Shandong, Shanxi, Inner Mongolia and Henan. Energy redundancies and CO2 emissions excesses of the middle Yellow River and northern coastal regions have increased dramatically ever since 2000. In 2012, these provinces should reduce the amount of CO2 emission of an equivalent of 25% of the country’s total CO2 emission. We should focus on these provinces to reduce emission. Moreover, we cannot ignore the northwest region, where there is a rapid growth of energy redundancy and CO2 emissions excess. Based on the above conclusions, we can formulate the following policy recommendations: (i) Accelerate the industrial structural adjustment. Firstly, the industrial structure should be further adjusted. Heavy industry with high energy consumption and heavy pollution should be controlled in major regions, such as the middle Yellow River, northeast, and northern coastal regions. The third industry should be vigorously developed, such as transportation [24], financial services and information services. Modern agriculture also should be promoted in the regions suitable for natural farming. Secondly, it is very urgent to adjust the energy structure. We should reduce the demand for coal, and vigorously develop clean energy, such as nuclear, hydropower, wind, and solar, especially nuclear. (ii) Strengthen regional cooperation. Economically less developed regions provide a large amount of energy and materials to economically developed regions. But due to their backward technique, technology and equipment, the green economic efficiencies in those less developed regions are generally low. Economically developed provinces in the southern, eastern and northern coastal regions should take advantage of their advanced production technology, tech-

niques and equipment to develop clean energy technology. More importantly, they should strengthen the cooperation with other regions. (iii) Implement target policies for different regions. As Shandong and Hebei in the northern coastal region are important heavy industrial bases, with high energy consumption and heavy pollution characteristics, they should introduce advanced technology and equipment to reduce energy consumption and CO2 emission. At the same time, Hebei and Shandong are also important bases of agriculture; they can focus on the development of modern agriculture. Northeast old industrial base should carry on the industrial upgrading, strive to shift the way of production, carry on the system innovation. The middle Yellow River region is rich in coal resources. It makes a very important contribution to the country’s economic development as an important national energy base. But the mining and processing technology is relatively backward. Therefore, other developed regions should provide financial and technical support to the middle Yellow River region. The natural condition in the middle Yangtze River region is suitable for the development of agriculture, so the middle Yangtze River region can rely on modern information technology to drive modern agricultural production, processing and sales, and develop agricultural e-commerce. The southwest region is rich in energy too, especially hydropower. CO2 emissions in southwest region are not very high. The southwest region is also rich in forest resources, beautiful natural environment. The southwest region should develop tourism vigorously. There is vast land and abundant mineral resources, but relatively barren land in the northwest region. In the northwest region, there is small but rapid growth of the amount of energy consumption and CO2 emission. Backward technology and equipment lead to the low efficiency of the green economy. So it should upgrade manufacturing processing and introduce new advanced technology.

Acknowledgments We express our gratitude to the National Natural Science Foundation of China (71303174), Soft Science Foundation of Guangdong (2014A070703062), Social Science Foundation of Guangdong (GD14XYJ21), Science and Technology Bureau of Jiangmen city

66

X. Tao et al. / Applied Energy 171 (2016) 58–66

(JK[2014]145), Social Science Foundation of Jiangmen city (JM2014C37) and Science Foundation for Young Teachers of Wuyi University (2014zk02) for funding supports. References [1] Xinhuanet. UN climate chief hails China-U.S. announcement on climate change. . [2] Nikolaus E. Evaluating capital and operating cost efficiency of offshore wind farms: a DEA approach. Renew Sustain Energy Rev 2015;42:1034–46. [3] Flavius B. Ranking trade resistance variables using data envelopment analysis. Eur J Oper Res 2015;247(3):978–86. [4] Hu JL, Wang SC. Total-factor energy efficiency of regions in China. Energy Policy 2006;34(17):3206–17. [5] Cui Q, Li Y. An empirical study on the influencing factors of transportation carbon efficiency: evidences from fifteen countries. Appl Energy 2015;141:209–17. [6] Toshiyuki S, Mika G. DEA radial measurement for environmental assessment: a comparative study between Japanese chemical and pharmaceutical firms. Appl Energy 2014;115:502–13. [7] Wang QW, Zhao ZY, Zhou P, Zhou DQ. Energy efficiency and production technology heterogeneity in China: a meta-frontier DEA approach. Econ Model 2013;35:283–9. [8] Yang L, Wang KL. Regional differences of environmental efficiency of China’s energy utilization and environmental regulation cost based on provincial panel data and DEA method. Math Comput Model 2013;58(5–6):1074–83. [9] Cheng G, Zervopoulos PD. Estimating the technical efficiency of health care systems: a cross-country comparison using the directional distance function. Eur J Oper Res 2014;238(3):899–910. [10] Honma S, Hu JL. Industry-level total-factor energy efficiency in developed countries: a Japan-centered analysis. Appl Energy 2014;119(15):67–78. [11] Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A. Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach. Appl Energy 2011;88(11):3765–72.

[12] Tone K. A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 2001;130(3):498–509. [13] Song ML, Zhang LL, Liu W, Fisher R. Bootstrap-DEA analysis of BRICS’ energy efficiency based on small sample data. Appl Energy 2013;112:1049–55. [14] Chambers RG, Chung Y, Färe R. Benefit and distance functions. J Econ Theory 1996;70(2):407–19. [15] Fukuyama H, Weber WL. A slacks-based inefficiency measure for a two stage system with bad outputs. Omega 2010;38:239–410. [16] Wang ZH, Feng C. A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: an application of global data envelopment analysis. Appl Energy 2015;147(1):617–26. [17] Cooper WW, Seiford LM, Tone K. Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Germany Springer LLC Press; 2007. [18] Li LB, Hu JL. Ecological total-factor energy efficiency of regions in China. Energy Policy 2012;46:216–24. [19] Chang YT, Zhang N, Danao D, Zhang N. Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 2013;58:277–83. [20] Gómez-Calvet R, Conesa D, Gómez-Calvet AR, Tortosa-Ausina E. Energy efficiency in the European Union: what can be learned from the joint application of directional distance functions and slacks-based measures? Appl Energy 2014;132(1):137–54. [21] Tulkens H, Vanden Eeckaut P. Non-parametric efficiency, progress, and regress measure for panel data: methodological aspects. Eur J Oper Res 1995;80:474–99. [22] Tone K, Tsutsui M. An efficiency measure of goods and bads in DEA and its application to US electric utilities. In: Asia Pacific productivity conference Korea; 2006. [23] Zhang J, Wu GY, Zhang JP. The estimation of China’s provincial capital stock: 1952–2000. Econ Res J 2004;10:35–44. [24] Song ML, Wang SH, Fisher R. Transportation, iceberg costs and the adjustment of industrial structure in China. Transport Res Part D-Transp Environ 2014;32:278–86.