Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries

Energy xxx (2014) 1e11

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Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries Bai-Chen Xie a, b, *, Li-Feng Shang a, Si-Bo Yang a, Bo-Wen Yi a a b

College of Management and Economics, Tianjin University, Tianjin 300072, China Center for Energy and Environmental Policy, Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 September 2013 Received in revised form 9 April 2014 Accepted 29 April 2014 Available online xxx

As most power is consumed domestically, the environmental efficiencies of electric power industries in different countries may serve as a benchmark to evaluate their emission reduction efforts. Taking the power generated and carbon emissions as desirable and undesirable outputs respectively, this study adopts the environmental Malmquist index, which is based on a SBM-DEA (slack based measure data envelopment analysis) model, to investigate the environmental TFP (total factor productivity) index of electric power industries in 26 OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries from 1996 to 2010. In addition, we employ the Tobit regression model to analyze the influence of related factors on TFP and its decompositions. The empirical results indicate that the dynamic environmental efficiency or TFP provides a good perspective for evaluating emission reduction efforts of electric power industries in different countries. Fuel structure change and technological progress are the main driving forces to promote dynamic environmental efficiency. Furthermore, the economic situation and energy price changes also affect dynamic environmental efficiency significantly. Therefore, taking the energy structure and affordability into consideration, environmental TFP may indicate climate change mitigation efforts to a large extent.
2014 Elsevier Ltd. All rights reserved.

Keywords: Efficiency change Electric power industry Dynamic environmental efficiency Technological progress Tobit regression

1. Introduction The reduction of greenhouse gas emissions from fossil fuel combustion and the achievement of harmonious development of the economy, society and environment have become worldwide goals [1]. An objective evaluation of national efforts and achievements is a prerequisite for evaluating climate change mitigation efforts. The economic development, resource endowments and industry structure differ to a large extent among countries, which increases the complexity. To make the conclusions convincing, an industry should be selected as a benchmark. Electric power systems are the biggest emitters of all industries. Despite the increasing production of hydropower, wind power and nuclear power, thermal power has remained the main source of electricity in the past

* Corresponding author. College of Management and Economics, Tianjin University, Tianjin 300072, China. Tel.: þ86 13312188917; fax: þ86 2227401021. E-mail addresses: [email protected], [email protected] (B.-C. Xie).

decades. The carbon emissions resulting from power generation as a proportion of all the emissions from fossil fuel combustion increased from 32.31% (7,278.88 Mt) in 1996 to 37.53% (11,361.44 Mt) in 2010 [2]. Compared with other industries, the electric power industry has a lower proportion of imports and exports and a smaller technology gap around the world. Therefore, research on electric power industries can provide a benchmark for objectively evaluating the climate change mitigation efforts of different countries. In recent years, DEA (data envelopment analysis) has gained popularity in energy and environmental policy research, in particular, for the efficiency evaluation of power systems [3]. Most researches have concentrated on the productivity of thermal power enterprises and have analyzed the influences of pure technical efficiency, scale efficiency and input congestion [4]. The studies combining energy consumption and environmental effects are often labeled as environmental efficiency studies. The study of Färe et al. [5] was one of the earliest studies that had carried out environmental performance analysis with pollutants considered. Due to

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2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Xie B-C, et al., Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries, Energy (2014), http:// dx.doi.org/10.1016/j.energy.2014.04.109

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B.-C. Xie et al. / Energy xxx (2014) 1e11

the subjectivity of indicators selection, Sarıca and Or [6] proposed that a stability test should be adopted for the efficiency evaluation results in order to identify the ways to improve the inputeoutput structure. Recent research has attempted to further classify input or output indicators, or has calculated the environmental and operational efficiency, respectively [7]. Rezaee et al. [8]combined a cooperative bargaining game model and a conventional DEA model to measure the performance of thermal power plants in Iran, their approach discriminates the DMUs (decision making units) more effectively regardless of the number of studied DMUs. The above studies take enterprises as DMUs by assuming that they share the same external conditions. However, this is not true in most cases. To evaluate the environmental efficiency of the electric power industries objectively, it is necessary to design an assessment scheme that takes into consideration the natural conditions, economic development, energy structure, and emission reduction efforts of various countries that are under review. For a given DMU (decision making unit), a performance comparison between two periods can illustrate its efforts and avoid the influence of differing conditions due to few changes in economic development and resource endowments. Assuming constant returns to scale, Färe et al. [9] proposed an input-oriented radial Malmquist index that combined the CCD (Caves, Christensen & Diewert) method and Farrell’s techniques to measure efficiency. This improves the applicability and credibility of the research results and can be suitably used for comparing the performance of power systems in distinct periods. And then, some proposals were put forward aimed at improving the productivity of different enterprises. Yaisawarng and Klein [10] took pollutant variables as outputs to study the impact of property and pollution control costs on efficiency. Recent studies have considered the influence of weather, price effects and other uncontrollable variables [11,12]. Furthermore, LMDI (logarithmic mean divisia index), and ANN (artificial neural network) [13] are also used to improve the evaluation results. Some studies have tried to define the overall energy efficiency of power systems in different countries and regions through calculating the average value of sample corporations. Hattori et al. [14] found that a country’s economic conditions had little effect on the generation sector but on the other hand the regulatory policy and model selection did affect the efficiency significantly. The study of Fallahi et al. [15] showed that productivity changes were more closely related to low efficiency rather than technology changes. However, these studies only took power generation and IVA (industrial value added) as outputs without considering the impact of carbon emissions and other undesirable outputs. There are several previous environmental efficiency researches that concentrated on studying the energy consumption of nations or regions. Färe et al. [16] studied the productivity growth, technical progress, and efficiency change in industrialized countries. Kim et al. [17] analyzed the technical potential for improving energy efficiency and CO2 intensity in different countries. Their study indicated that economic activity change had been the dominant contributor to the growth of CO2 emissions. Ramanathan [18], and Zhang et al. [19] compared the dynamic environmental efficiency in different countries with similar economic conditions. However, these studies were not satisfactory due to the impact of various industrial structures. Dynamic Environmental Efficiency analysis of electric power industries can not only represent the emission reduction efforts of different countries but can also largely remove the influences of external factors such as energy structure and economic development. TFP (total factor productivity), also known as Malmquist index in non-parametric perspective, is the commonly used methodology to measure it [20]. Hattori et al. [14] and Xie et al. [21] have achieved valuable results in studying the

environmental efficiency of power systems in different regions within a country. But there is no similar research on international comparison. The OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries comprise the world’s major economies and emissions sources. They accounted for 76.80% of the global carbon emissions in 2010 (23,252.16 Mt) [2]. Their active participation is a prerequisite to mitigate climate change. As listed above, although there are studies on evaluating climate change mitigation efforts of nations, they are not convincing due to the differences in economic development and resource endowments. On the other hand, the evaluation of national effort is totally different from that of different regions within a country. This study takes the electric power industries of 26 OECD and BRIC countries as DMUs to analyze their dynamic environmental efficiencies and strives to provide a benchmark for evaluating emission reduction efforts of different countries. In order to make the results more reasonable and practical, we employ the SBM-DEA (slack-based measure DEA) model to build the Malmquist index and to ensure its discrimination. In terms of outputs, this study takes carbon emissions and power generation as undesirable and desirable ones respectively. The remainder of this paper is organized in the following manner. Section 2 focuses on building the model for the slack-based environmental Malmquist index. In Section 3, we use the approach described in Section 2 to study the performance of electric power industries in sample countries. Furthermore, Tobit regression is performed to provide a benchmark for studying their emission reduction efforts and the influence of relevant factors. Section 4 summarizes this study and gives proposals that are conducive to evaluating climate change mitigation efforts. 2. Methodology 2.1. Environmental SBM-DEA model The assumption of a conventional input-oriented radial DEA model is too specific to be realistic [22]. To avoid the concentration of evaluation results and to be consistent with CCR (Charnes, Cooper and Rhodes) and BCC (Banker, Charnes and Cooper) models, an SBM-DEA model was proposed [23]. Zhou et al. indicated that this technique can be used in environmental performance evaluation [24]. In the traditional DEA framework, the DMU with fewer inputs and more outputs will have higher efficiency, but this is not true in the case of thermal power industry where lower carbon emissions lead to better performance [25]. Several techniques were developed to deal with this issue. A simple technique is to treat undesirable outputs as inputs directly [26]; others have suggested solving the problem through linear transformation or a directional distance function [27]. For the electric power industry of country j, j ¼ 1,2,., N. it needs the inputs of installed capacity, labor1 and materials to produce

1 According to the statistics of ILO (International Labor Organization), the most detailed statistic is the category E. Electricity, gas and water supply in version 3 of ISIC (International Standard Industrial Classification), and the category D. Electricity, gas, steam and air conditioning supply in version 4 of ISIC. There is no obvious fluctuation for almost all the countries between the two versions. Therefore, we use the total number of employees engaged in the electricity, gas and water supply industries instead in this study, it can fulfill the research demand to a large extent. Since there are no data for some countries in a certain years, a linear interpolation method is used to estimate the labor input in those years. The data for India are collected from the Statistical Yearbook of India 1998, 2003, 2008, 2013. The data for China are collected from the China Statistics Yearbook (1997e2012). The data of United States in 2008, 2009, 2010 are collected from the website of the government.

Please cite this article in press as: Xie B-C, et al., Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries, Energy (2014), http:// dx.doi.org/10.1016/j.energy.2014.04.109

B.-C. Xie et al. / Energy xxx (2014) 1e11

3

Fig. 1. Distribution of installed capacity, power generation and CO2 emissions of electric power industries in 2010. Note: 1. The data of installed capacity are collected from the Energy Statistics Database of United Nations (2012). 2. The data of power generation and carbon emissions are collected from IEA (the International Energy Agency) database, including CO2 Emissions from Fuel Combustion (detailed estimates) (2012), Energy Statistics of OECD Countries (2012) and Energy Statistics of Non-OECD Countries (2012).

power. Carbon emissions are also included as an output in order to acknowledge the importance of sustainable development. The material inputs include fossil fuels such as coal, oil, natural gas and nuclear fuel.2 Let x0 ˛ R3 be the three dimensional input vector of the decision-making units j0, let y0 ˛ R and u0 ˛ R be the one dimensional desirable and undesirable outputs respectively. Assuming the number of DMUs is n, we define the input and output matrix as follows: X ¼ ½ x1 x2 . xn ˛R3n ; Y ¼ ½ y1 y2 . yn ˛Rn ; U ¼ ½ u1 u2 . un ˛Rn where þ u X > 0, Y > 0, U > 0. Taking l, s 0 , s0 , s0 as the decision variables, we establish the SBM-DEA model of the power industry to get the efficiency of DMU (x0, y0, u0) [28].

2 As well as fossil and nuclear fuels, the material inputs also include water and wind, but they are renewable ones and don’t directly produce undesirable outputs, so we exclude them from material inputs.

1 ðSBMÞ

min r ¼

s:t:

3 . . 1 X xn0  su u0 sn 0 0 3 n¼1  1 þ sþ 0 y0

8  > > > x0 ¼ X l þ s0 > > > < y0 ¼ Y l  sþ 0

(1)

> u0 ¼ U l þ su > 0 > > > > : s ; sþ ; su ; l  0 0 0 0

n u Assuming that xn0 ¼ 0; u0 ¼ 0, sn 0 =x0 and s0 =u0 of the objective function will not be active, when y0  0, they will be substituted by a small positive number to ensure sþ 0 =y0 can function as a penalty term. The fractional programming is difficult to solve and so we introduce a scalar variable t1. Let 1=t1 ¼ 1 þ sþ 0 =y0 , S ¼ t1 s ; Su ¼ t1 su ; Sþ ¼ t1 sþ ; Lj ¼ t1 lj ; formula (1) will be converted into the following linear program:

Please cite this article in press as: Xie B-C, et al., Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries, Energy (2014), http:// dx.doi.org/10.1016/j.energy.2014.04.109

4

B.-C. Xie et al. / Energy xxx (2014) 1e11

ðLPÞ

min r ¼ t1 

3 . . 1 X xn0  Su u0 Sn 0 0 3 n¼1

8  > y0 1 ¼ t 1 þ sþ > 0 > > > > > t x ¼ XL þ S > > 0 < 1 0 s:t: t1 y0 ¼ YL  Sþ 0 > > > u > > t u ¼ UL þ S 1 0 > 0 > > > þ : t ; S ; S ; Su ; L  0 1 0 0 0

(2)

To overlook the increased constraint t1 > 0 and keep it as a free variable, we turn formula (2) into its dual form.

ðDPÞmax x1 8 x1 þ vx0  my0 þ mu u0 ¼ 1 > > > > > > > > j ¼ 1; 2; .; n > vXj  mYj þ mu Uj  0 > > > > > < s:t: v  1 ½1=x > 3 > > > > > > > m  x1 ½1=y > > > > > > : mu  1=u

(3)

  max qp x0;q ; y0;q ; u0;q 8 j ¼ 1; 2; .; n vXj;p  mYj;p þ mu Uj;p  0 > > > > > > > > 1 > > > < v  3 ½1=x s:t: >   > > > m m m ½1=y  1  vx þ y  u > 0;q 0;q 0;q u > > > > > > : mu  1=u

(5)

Upon solving four distinct linear programs, we can calculate the environmental Malmquist index of each DMU i. Following the popularized practice, formula (6) illustrates how to combine the operation status of two time periods to obtain the TFP index.

2 





31



5

qt xi;tþ1 ;yi;tþ1 ;ui;tþ1  qtþ1 xi;tþ1 ;yi;tþ1 ;ui;tþ1

Mi ðt;t þ 1Þ ¼ 4

where [1/x], [1/y], and 1/u denote column vector ð1=x10 ; 1=x20 ; 1=x30 ÞT , 1/y0, and 1/u0, formula (3) can be transformed to a general linear program: 0

ðDP Þmax q0 ¼ 1  vx0 þ my0  mu u0 8 j ¼ 1; 2; .; n vXj  mYj þ mu Uj  0 > > > > > > > > 1 > < v  ½1=x 3 s:t: > > > > m  ð1  vx0 þ my0  mu u0 Þ½1=y > > > > > : mu  1=u

inputs and outputs at period t compared with the production technology at period t and t þ 1 respectively. Similarly, qt(x0,tþ1, y0,tþ1, u0,tþ1) and qtþ1(x0,tþ1, y0,tþ1, u0,tþ1) are defined. Let p,q be two different periods, p, q ˛ {t,t þ 1}, we can get the efficiency of DMU j0 in time period q relative to the frontier in period p from formula (5),

(4)

Dual variables v ˛ R3, m, mu ˛ R can be considered as the price of inputs and outputs respectively. For the DMU with q0 ¼ 1, it is SBM (slack based measure) effective and the production technology set is braced by all the effective ones lying on the frontier.





qt xi;t ;yi;t ;ui;t  qtþ1 xi;t ;yi;t ;ui;t



2

(6) With the same amount of outputs, (xi,tþ1, yi,tþ1, ui,tþ1) comes closer to the frontier than (xi,t, yi,t, ui,t). As a result, the performance has improved and we get Mi(t, t þ 1) > 1. Similarly, Mi(t, t þ 1) < 1 (or ¼1) indicates that the performance has deteriorated (or remained at the same level). Its further decomposition can be seen as formulas (7) and (8) show.



ECi ðt; t þ 1Þ ¼

qtþ1 xi;tþ1 ; yi;tþ1 ; ui;tþ1 

qt xi;t ; yi;t ; ui;t 2

TPi ðt; t þ 1Þ ¼ 4







qt xi;tþ1 ; yi;tþ1 ; ui;tþ1 

(7)



qtþ1 xi;tþ1 ; yi;tþ1 ; ui;tþ1





qt xi;t ; yi;t ; ui;t 

 31 2

qtþ1 xi;t ; yi;t ; ui;t

5 (8)

2.2. Environmental Malmquist index With the wide application of environmental DEA, the research focus has shifted to measurement of dynamic efficiency of a given DMU in different periods [29]. Färe et al. [30]extended the CCD approach to an input oriented-Malmquist index based on a radial distance function. In their study, the TFP approach was decomposed into EC (efficiency change) and TP (technological progress). Zhou et al. [31] proposed a bootstrapping method to perform statistical inferences on the MCPI (Malmquist CO2 emission performance index). Their empirical study of the world’s 18 top CO2 emission countries indicated that the introduction of undesirable outputs would change the evaluation results considerably. Based on the MCPI, this study uses an environmental SBM-DEA model to improve the discrimination of the evaluation results. Let t and t þ 1 denote two time periods. Because the efficiency indicates the operation gap between the studied one and the frontier, a larger q0(x0, y0, u0) means a smaller input reduction potential; similarly, a lower q0(x0, y0, u0) leads to a larger opportunity to reduce inputs. Assume that qt(x0,t, y0,t, u0,t) and qtþ1(x0,t, y0,t, u0,t) are the efficiencies of DMU j0 based on its

EC is also known as the “catch-up” effect; it measures the operation and emission change of a specific DMU with regard to country i’s production frontier at periods t and t þ 1. TP is also defined as the “frontier shift effect”, which reflects the average technological progress of all DMUs from period t to t þ 1. The production frontier might be a high dimensional curved polyhedron, which may fluctuate up and down; therefore we take the average TP to represent the overall frontier movement.

3. Empirical study 3.1. Development of the electric power industry in OECD and BRIC countries The OECD includes most of developed countries and BRIC represents the four largest emerging market countries in the world. Owing to data availability, we take 26 OECD and 4 BRIC countries as the sample. Their power structure and emission status can be said to represent the appeal of different kinds of countries.

Please cite this article in press as: Xie B-C, et al., Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries, Energy (2014), http:// dx.doi.org/10.1016/j.energy.2014.04.109

B.-C. Xie et al. / Energy xxx (2014) 1e11 Table 1 Inputs and outputs of electric industry for 26 OECD and BRIC countries. Variables Inputs

Desirable output Undesirable output

Labor Installed capacity Fuel and nuclear input Power generation Carbon emissions

Unit

Source

Million workers GW M toe TWh Mt

ILO (2013) UNSD (2012) IEA (2012) IEA (2012) IEA (2012)

To make the study more specific, we divide these countries into four categories, namely OECD AO (OECD Asia & Oceania), OECD EU (OECD Europe), OECD AM (OECD America) and BRIC countries according to their economic development and geographical location. Fig. 1 shows the fundamental information of installed capacity, power generation and carbon emissions for these countries in 2010. As can be seen from the figure, the shares of these 26 OECD and BRIC countries account for more than 80% of the world in all the terms. Also it could be noted that the distributions of installed capacity and power generation are very similar. For the BRIC countries, the ratio of their emissions relative to their power generation is significantly higher than is the case for the other three regions. On the other hand, the OECD EU has a significantly lower emission share compared with its power generation proportion. The power demand mainly depends on economic development and energy structure. Different power generation forms vary considerably. Thermal and nuclear power have the advantage of stability but their fuels are nonrenewable and they are concomitant

5

with increased emissions and related environmental hazards [21]. Hydro, wind, solar power and other clean energy will not cause emissions but they need huge investment and are greatly affected by resource endowments. Therefore, the efficiency evaluation should balance the development of different generation forms. Due to the statistic scope adjustment of ISIC (International Standard Industrial Classification), 1996 is the earliest time with available data encompassing a complete dataset for all the countries. On the other hand, 2010 is the latest time for all the data can be collected. The specific definitions of inputs and outputs and their data sources are shown in Table 1. For the missing data of Mexico, Canada and other countries, we use a liner interpolation method to estimate them. 3.2. Environmental Malmquist index analysis Based on the environmental SBM-DEA model, as shown in formula (6), we obtain the dynamic environmental efficiency of the listed countries from 1996/1997 to 2009/2010. For some mixperiod Linear Programming problems that are infeasible, the DEA efficiencies are obtained according to the actual situations and Färe et al. (2007) approach [32]. The dynamic results have considered the differences in economic development and carbon emissions among countries and periods. As shown in Table 2, the average environmental Malmquist index fluctuate around 1 over the entire period. Although none of the countries has achieved rising dynamic efficiency throughout the period, most of their average values are greater than 1. Its

Table 2 The Malmquist index of electric power industries for 26 OECD and BRIC countries from 1996/1997 to 2009/2010 period.

OECD AO

OECD EU

OECD AM

BRIC

Mean Std. dev.

Countries

96/97

97/98

98/99

99/00

00/01

01/02

02/03

03/04

04/05

05/06

06/07

07/08

08/09

09/10

AUS JAP KOR Mean Std. dev AUT BEL CZE DEN FIN FRA GER HUN IRE ITA NLD NOR POL PRT SVK ESP SWE TUR UK Mean Std. dev CAN CHI MEX US Mean Std. dev BRA RUS IND CHN Mean Std. dev

0.9397 0.9973 0.8434 0.9268 0.0777 1.0127 1.1768 1.0189 0.7555 1.0163 0.9650 0.9798 1.0191 1.0951 1.0167 1.0748 1.1451 0.9985 0.9637 0.9358 1.1106 1.1620 1.0924 1.0324 1.0301 0.0961 1.0093 1.0769 1.0695 0.9854 1.0353 0.0450 1.0456 0.9425 1.0197 0.9675 0.9939 0.0472 1.0156 0.0875

1.1693 0.9811 0.8602 1.0035 0.1558 1.0291 1.0620 1.0164 0.9084 0.9206 0.9317 1.0357 1.3715 1.1363 1.0219 0.6856 1.0221 1.0087 1.1207 1.0427 0.9952 1.1642 1.0456 1.1139 1.0333 0.1332 0.9150 0.8286 1.0037 1.0938 0.9603 0.1141 1.0007 0.9766 1.0206 0.9806 0.9946 0.0203 1.0154 0.1216

1.1179 1.0066 1.0704 1.0650 0.0558 1.0817 1.0762 0.8798 0.9506 0.9301 1.1848 0.9976 0.8112 1.1352 1.0347 0.8502 0.9695 0.9092 1.0207 1.0948 1.0541 0.9596 0.9248 0.9373 0.9896 0.0991 1.0975 0.9668 1.0369 1.0816 1.0457 0.0585 0.9942 1.0248 1.0426 0.9742 1.0090 0.0306 1.0072 0.0867

1.0387 1.0417 1.1949 1.0918 0.0893 1.0269 0.8543 1.1447 1.0154 1.0045 1.2373 1.0130 0.8919 1.0739 0.9152 1.0247 1.0171 1.0356 1.0288 1.0650 1.0089 0.8839 0.9586 0.9804 1.0095 0.0898 1.2401 1.0086 1.1026 1.0263 1.0944 0.1053 0.9562 1.0131 1.0008 1.0405 1.0026 0.0351 1.0281 0.0904

1.0489 0.9533 1.0907 1.0310 0.0705 1.0220 0.8482 0.9895 0.9412 1.1024 1.0961 1.0239 1.0304 1.1730 1.0044 1.0876 0.8831 0.9968 1.0232 0.9952 1.0199 1.2841 0.8731 1.0073 1.0211 0.1025 0.8993 0.9897 0.8505 0.8625 0.9005 0.0630 0.8435 1.0175 0.9864 1.0317 0.9698 0.0863 0.9992 0.0996

1.1407 1.0056 1.1028 1.0830 0.0697 0.9313 1.0693 0.9157 0.9923 1.0300 1.2899 0.9062 0.9767 0.7858 1.0154 1.0169 1.0766 0.9798 0.9549 1.0505 1.0024 0.8305 0.9283 1.0700 0.9907 0.1068 1.0006 1.0251 0.9082 0.9890 0.9807 0.0506 0.9795 0.9943 0.9866 1.0356 0.9990 0.0251 0.9997 0.0926

0.9901 0.9840 0.9018 0.9586 0.0493 0.8727 1.1655 1.0998 1.1797 1.0422 1.0009 1.0780 0.9545 0.9487 1.0150 1.0279 0.8261 1.0681 1.0057 0.9063 0.9051 0.8675 0.9712 1.0261 0.9979 0.0975 0.8999 1.1050 0.8831 0.9990 0.9718 0.1025 1.0184 1.0258 1.0596 1.0865 1.0476 0.0316 0.9971 0.0886

0.9211 1.0380 1.0189 0.9927 0.0627 1.1726 0.9995 1.0152 0.8535 0.9612 0.9778 1.0409 0.9312 0.9688 1.0880 1.0055 1.0853 1.0239 0.8155 0.9740 0.9702 1.3543 1.1845 0.9360 1.0188 0.1223 1.0231 1.0635 0.9750 1.0215 1.0208 0.0362 1.0244 1.0337 0.9762 0.9515 0.9964 0.0392 1.0135 0.0998

1.0527 0.9763 1.1400 1.0563 0.0819 1.0306 0.9712 0.9743 0.9114 0.9771 1.0792 1.0243 1.0936 0.9564 0.9354 0.9583 1.1200 1.0075 1.0460 1.0334 0.9930 1.0330 1.0588 0.9754 1.0094 0.0555 1.0775 0.9118 1.0981 1.0156 1.0258 0.0837 1.0056 0.9154 0.9982 0.9700 0.9723 0.0409 1.0113 0.0610

1.0378 0.9921 0.9534 0.9944 0.0422 0.9575 0.9054 1.0289 1.1170 1.0569 1.0345 0.8953 0.9949 1.0597 1.0071 0.8559 0.8918 1.1217 0.9837 1.0052 0.9856 0.8978 1.1159 0.9577 0.9933 0.0806 0.8466 1.0063 0.9875 0.9961 0.9591 0.0754 1.0153 1.1599 0.9775 0.9474 1.0250 0.0941 0.9931 0.0772

0.9111 1.0544 0.9987 0.9881 0.0722 1.0012 1.1629 1.1412 0.9398 0.9668 1.0010 0.9707 1.1525 0.8686 1.0414 1.0610 0.9871 0.9488 0.8880 0.9941 0.9743 1.2123 1.2515 0.9474 1.0269 0.1084 1.0864 0.9150 1.0879 1.0021 1.0228 0.0823 1.0140 1.0184 0.9781 1.0234 1.0085 0.0206 1.0200 0.0925

0.9961 0.9630 0.9668 0.9753 0.0181 1.0758 0.7576 0.7593 0.9782 0.8486 0.9588 0.8718 1.0176 1.0616 1.0217 1.0506 1.1051 0.8730 1.0148 1.0810 1.1005 1.0597 0.8389 0.9425 0.9693 0.1131 0.9310 1.1004 0.6835 1.0443 0.9398 0.1848 0.9900 1.0434 1.0829 0.9546 1.0177 0.0568 0.9724 0.1107

0.8283 0.8925 1.0007 0.9072 0.0871 1.0201 1.2050 0.8773 0.9718 0.8961 0.7960 0.8737 0.8560 0.9468 0.9096 1.0270 0.8538 0.8912 1.0455 0.9050 0.9121 0.8847 0.9604 0.9057 0.9336 0.0916 0.7792 0.8183 1.3174 0.9654 0.9701 0.2450 0.9978 0.9097 0.8723 1.1190 0.9747 0.1096 0.9413 0.1167

0.8958 1.0311 1.1368 1.0213 0.1208 0.9789 0.9886 0.8653 1.0501 1.2368 1.1071 1.0024 1.0344 0.9269 1.0103 1.0998 0.9535 1.0884 1.1618 0.9977 1.0475 1.0409 1.0288 0.9337 1.0291 0.0865 0.7917 0.9500 0.9493 1.0459 0.9342 0.1053 1.0311 1.1344 0.9070 0.9641 1.0091 0.0977 1.0130 0.0939

Frequency 7 6 8

10 7 7 3 7 9 7 7 7 11 10 7 7 9 7 7 8 7 5

11 10 7 7 9 7 7 8 7 5

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fluctuation trend is exactly synchronous with the world fuel market fluctuations and economic development in developed countries [33]. The biggest one appears in period 1999/2000 together with the sharpest oil price increase and the second rapidest GDP (gross domestic production) growth over the studied period. With the rebound of the global economy and an increasing focus on lowcarbon economic development after 2000, the world paid more attention to environmental efficiency and TFP did not increase until 2003/2004 due to policy hysteresis. The smallest average Malmquist index appears in period 2008/2009 together with the global economic recession and deepest decrease of energy prices over the entire period. As can be seen, the production of oil-fired and gasfired power plants would decrease when the prices of oil and natural gas increased [34]. To meet the demand, the production of coal-fired power plants expanded and carbon emissions would increase, which resulted in smaller TFP. When the fuel prices increased, a reversal trend appeared.

The standard deviation of overall efficiencies remains below 0.1, except for the periods 1997/1998, 2007/2008 and 2008/2009. The dynamic efficiency gaps are not large because most countries are clearly aware of the damages brought by climate change. They either consciously employ advanced generation technologies to substitute their older ones or directly rely on clean energy to meet the increased power demand. However, due to differences in resource endowments, the ability to respond to fuel price fluctuations varies greatly among countries. In 1998, the sharp increase of oil price resulted in an increase of coal-fired power generation and the biggest environmental efficiency gap among countries over the time period under consideration. According to the detailed data of installed capacity, gas-fired equipment accounted for 50e60% of new fossil power capacity installations after 2000. As the prices of oil and natural gas rose, the proportion of coal-fired units rebounded to 50% after 2005 [35]. As a result, the efficiency gap widened in periods 2007/2008 and 2008/2009 when fuel prices fluctuated dramatically.

Fig. 2. Geometric mean and std. dev. of Malmquist index for 26 OECD and BRIC countries from 1996/1997 to 2009/2010 period.

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Fig. 3. The average efficiency changes of 26 OECD and BRIC countries from 1996/1997 to 2009/2010 period.

In general, none of the four regions has had the largest or smallest dynamic efficiency or standard deviation over the entire period. The OECD AO, OECD EU, OECD AM and BRIC countries have the highest average TFP 6, 3, 2 and 3 times respectively. These results match to our expectations to a certain extent. As all the OECD AO countries are developed countries; OECD EU members are active participants in mitigating climate change and are also the major driving force for drafting new agreements in the post-Kyoto Protocol era, their develop mode should be encouraged in the future. Despite their economic disparity, the evaluation results for the BRIC countries are relatively good during the corresponding period owing to their rapid economic growth, sharp increase of outputs and large emission reduction potentials. Each country has dynamic efficiency larger than 1 over the period but their frequencies differ substantially. The TFP of Italy is greater than 1 in eleven periods and that of Austria and Netherlands exceeds 1 ten times. Even Denmark and United Kingdom with fewer periods of dynamic efficiency greater than 1 succeed three and five times respectively. Following the idea of Färe et al. [16]and Zhou et al., [31] for international efficiency comparison, Fig. 2 shows the Geometric mean TFP estimates and the standard deviations of each country, these results can also reflect the cumulative Malmquist index to a certain extent. During the studied period, the geometric mean indexes of 16 countries exceed 1. Among them, France has the highest dynamic efficiency, with an average annual increase of 3.96%, followed by Sweden with an average increase of 3.31%. Over the period, their proportions of nuclear power and hydropower remained stable while wind, biomass and other generation forms developed rapidly.

As a result, the carbon emissions reduced. At the same time, their labor inputs decreased slightly. When we combine these factors, it can be seen that the dynamic efficiencies of France and Sweden improved significantly. Their successes indicate that the development of nuclear power can reduce carbon emissions effectively and then lead to improvements in TFP. With respect to the standard deviation analysis, Sweden has the largest and Japan has the smallest one. As the largest developed country, USA did not show its expected advantage in the earlier periods. However, its average TFP ranks 8th over the period because of better performance in the later periods perhaps coming from shale gas exploitation. When it comes to the largest developing country, China ranks 12th due to its accelerating power demand and coal dominated fuel structure. 3.3. Technical efficiency change and technological progress analysis According to formulas (7) and (8), the Malmquist index can be decomposed into EC and TP. Fig. 3 shows the average EC of various regions. The value over unity implies that the corresponding DMU is approaching the production frontier. During the period, none of the region’s EC is continuously either rising or descending. The differences between regions are mainly reflected by the fluctuation spreads. In most periods, the average EC always fluctuate around 1, indicating that the majority of power systems have made great efforts to catch up with the most advanced technology and management. Before the period 1999/2000, almost all the regions present similar EC trends while the average EC of BRIC countries presents a reversal trend with the other three regions after the

Fig. 4. The average technological progresses of OECD and BRIC countries from 1996/1997 to 2009/2010 period.

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period 2000/2001. The BRIC countries are still in a period of rapid development and so suffer little from economic downturns such as those happens in the periods 2000e2002 and 2007e2009. They attach too much emphasis on increasing the supply of electricity while giving inadequate attention to optimizing their power structures. A combination of these factors leads to lower ECs compared with that of OECD countries when the world is in economic prosperity. The period of 2001/2002 witnesses the only one period with all mean ECs larger than 1. Surprisingly, the BRIC Countries have the biggest number of 9 times of average ECs greater than unity over the period. The ECs of OECD AM and OECD EU wave quite smoothly while that of OECD AO presents sharpest fluctuation. Generally speaking, although the EC is influenced by external conditions, the gap between regions is becoming narrower. Similarly to the EC, Fig. 4 shows that the TPs of OECD AO and BRIC countries fluctuate acutely and present reversal trends in the later periods. The TPs of OECD EU and OECD AM countries vary slightly. Interestingly, almost all of the extreme points emerge one year after the economic turning point. The frontier moved forward fastest in period 1999/2000, and it is only in this period that all TPs are larger than 1. By contrast, in period 2001/ 2002, the TPs of all areas are less than 1, which indicates a setback of the frontier. To be sure, each economic recession is concomitant with an improvement of TP, two or three years later. External emergencies may result in lower management efficiency in the short term but may also result in the introduction of new technologies. According to Färe et al. [16], TP tells us what happens to the frontier at the input level, but not whether that country actually push the frontier forward. The innovators are those countries that contribute to the shift in the frontier between period t and t þ 1. The first part of TP in the bracket could provide us with some suggestions about it. Table 3 indicates countries shifting the frontier in periods from 1996/1997 to 2009/2010. As it can be seen, the results differ a lot from that of TP and Malmquist index. Norway contributes to the production frontier movement eight times under constant returns to scale and the SBM framework. Other countries that push the production frontier upward include France, Korea, Belgium, Canada, Brazil and Sweden. Among the innovator countries, Norway, Canada and Brazil are renewable energy dominated countries, while nuclear power amounts to a large share in France, Korea and Belgium. With the technological breakthrough in shale gas exploitation, some coalfired power plants were substituted by gas-fired ones with less carbon emissions in US, and then it contributed to the production frontier movement in period 2009/2010. However, there are no obvious common characteristics for the countries with higher Malmquist Indexes and TPs. To meet the demand of the sustainable development, other countries may learn more from innovators,

while the develop mode of countries with higher Malmquist Indexes and TPs should also be encouraged for reasons involving resource endowments. Fig. 5 analyzes the geometric average EC and TP of each country during the studied period. Among all the countries, Sweden has the highest geometric average EC of 1.0197. This indicates that although the performance of Sweden is not the best in the early periods, its distance from the production frontier becomes narrower in the later periods. Its carbon emissions have decreased as a result of reduction in fossil fuel use, and an increase in the proportion of nuclear power and management promotion. It should be noted that Sweden ranks 11th in TP and it contributes to the frontier shift five times over the period, which suggest that there is still room for technological progress and dynamic efficiency improvement. Consistent with the previous analysis, France has the highest TP and ranks 9th in geometric mean EC, thus it has the highest dynamic efficiency. Similar to France, Korea not only has bigger TPs, but they also play important roles in pushing the frontier forward. Both EC and TP of United States fluctuate around 1. Although it has made some efforts to improve its energy structure, the improvements have little effect in the earlier periods compared with other countries, nor does it play a leadership role. Only in the later periods, the advantage of innovation appears. The performances of China and India are exactly opposite to that of Belgium. Although their TPs are larger than 1, due to imperfect ECs, their dynamic efficiencies have not improved significantly. This shows that although developing countries can improve their technologies rapidly in a short period, their management skills and abilities to optimize their resource structures have not caught up, which may restrict their efficiency improvements. Considering the differences in energy structures and resource endowments among countries, it is difficult to take a unified approach to achieve the low-carbon goal. An analysis of Figs. 3e5 and Tables 2 and 3 reveals that the main driving force of dynamic environmental efficiency is TP, EC has a relatively small impact. This indicates that we should rely on technology breakthroughs such as renewable energy, shale gas and nuclear power to mitigate climate change in the long term; at the same time such measures as optimizing fuel structures and enhancing management should also be

Table 3 Countries shifting the frontier from the period 1996/1997 to 2009/2010. Period

Countries

1996e1997 1997e1998 1999e2000 2000e2001 2001e2002 2002e2003 2003e2004 2004e2005 2005e2006 2006e2007 2007e2008 2008e2009 2009e2010

BEL BEL BEL KOR CAN BEL BEL BRA

BRA BRA CAN

HUN CAN FRA

NOR NOR KOR

FRA FRA BRA CAN

KOR

NOR

CAN FRA

BEL FRA KOR BRA

BRA NOR BEL FRA

SWE SWE MEX

NOR

KOR KOR

NOR NOR

SWE SWE

CAN

FRA

NOR

SWE

KOR

USA

Fig. 5. The geometric average efficiency changes and technology progresses of 26 OECD and BRIC countries from 1996/1997 to 2009/2010 period.

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encouraged. The above analysis further confirms that energy structure adjustments, undesirable output reductions and desirable output growth are equally important in improving TP. 3.4. A cross-country regression analysis Exploring the influence of exogenous factors related to emission reduction efforts should be attached with the same importance as the evaluation of dynamic environmental efficiency. As the efficiency results are left censored, a lot of researches such as Zhou et al. [31], Kumar [36] prefer Tobit regression, which is specialized in dealing with censored data. The study of Hoff [37] showed that OLS (Ordinary Least Square method) may actually in many cases replace Tobit as a sufficient tool in the second stage regression. Simar and Wilson [38] employed the double bootstrap procedure to improve statistical efficiency in the second stage. Our study aims to ascertain that external factors may influence reduction efforts and their decompositions. After data processing for the index and related factors, we will also use Tobit regression in the second stage for its popularity and reliability. According to Kumar [36], Taniguchi and Kaneko [39], the following factors may affect efficiency: CD (customer density), the RIH (ratio of industrial sales to household sales), PD (population density), GPC (GDP per capita). Nevertheless, there are still a number of other factors that may have an impact on efficiency, such as: UR (the urbanization rate), SEC (the proportion of second industry), GG (GDP growth), R&D (the proportion of Research and development investment in GDP); ECPG (energy consumption per unit of GDP), ECPC (electricity consumption per capita) and THE_r (the thermal power proportion).3 Some of the listed factors are excluded through correlation analysis and stepwise regression. The final reserved indexes include THM_r, PD, GPC, ECPG, SEC, GG, and R&D and ECPC which can comprehensively reflect the situation of national economy, industrial structure and the extent to which technology improvement and fuel structure changes. Let COUNTRYi be a dummy variable that takes a value of 1 to signify the specific country in regression,4 the regression model of TFP will be written as formula (9).

M nu ¼ CONS þ r1 THM r þ r2 PD þ r3 GDPPC þ r4 ECPG þ r5 SECP þ r6 GG þ r7 R&D þ r8 ECPC þ

29 X

bi Countryi þ ε

i¼1

(9) We can get similar formulas for EC and TP. In accordance with the general process for dealing with panel data [40], we first perform a Hausman test. The results show that a fixed effect model is appropriate for all cases. To get a better analysis on the influence of the stated variables, all the results are arranged in Table 4. Regression of the environmental Malmquist index shows that GPC, THM_r, SEC and R&D are statistically significant and the impact of the GPC is negative while the impacts of the others are positive. Developed countries may have favorable external conditions for emission reduction and climate change mitigation, but they cannot achieve continuous efficiency improvements due to complications arising from life style changes. Thermal power dominated countries

3 The data of UR, SEC, GG, R&D and ECPG, ECPC are collected from the World Development Indicators (2012) of the World Bank and the United Nations Statistics Division respectively. To make the efficiency evaluation supportable, all the economic data have been converted into constant prices of 2005. In addition, the root data of THM_r come from the Energy Statistics of OECD Countries (2012) and the IEA Energy Statistics of Non-OECD Countries (2012). 4 When the USA is studied, COUNTRYi (i ¼ 1,.,29) are all equal to 0 as it is taken as the base country for comparisons.

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Table 4 Detriments of environmental Malmquist and its decompositions. Variables

CONS THM_r PD GPC ECPG SEC GG R&D ECPC AUS AUT BEL BRA CAN CHI CHN CZE DEN FIN FRA GER HUN IND IRE ITA JAP KOR MEX NLD NOR POL PRT RUS SVK ESP SWE R2

M

EC

TP

Coefficient

P-value

Coefficient

P-value

Coefficient

P-value

0.1777* 0.5573* 1.5280 0.1164** 0.3945 0.3420* 0.0822 0.4063* 0.5440 0.2175 0.2328 1.1155 0.1396 0.1208 0.1301 0.0848 0.0518 0.0518 0.1635 0.6715* 0.3812 0.1742 0.2991 0.2991 0.3717 0.7181 1.1714 0.1219 1.1326 0.4272 0.0147 0.1518 0.1815 0.3415 0.1661 0.2470 0.6807

0.0970 0.0560 0.3850 0.0430 0.6400 0.0850 0.2200 0.0610 0.1560 0.1840 0.4330 0.3220 0.6290 0.6020 0.5820 0.6240 0.8280 0.8860 0.3270 0.0690 0.6030 0.6090 0.5210 0.1320 0.5400 0.5280 0.4760 0.5850 0.4810 0.2360 0.9690 0.6530 0.3910 0.3600 0.5060 0.2990

0.0463* 0.4088 0.3202 0.0264 0.0091 0.5508** 0.0313 0.9322*** 0.7646* 0.0366 0.1131 0.0477 0.2296 0.2896 0.0317 0.5916 0.2520 0.4582 0.3264* 0.2146 0.4688 0.0379 0.2898 0.3219 0.1072 0.7045 0.8657 0.0192 0.4522 0.5470 0.1020 0.0105 0.0428 0.1238 0.0751 0.0059 0.6626

0.0830 0.2090 0.8680 0.6770 0.9220 0.0120 0.6710 0.0000 0.0970 0.8410 0.7280 0.9690 0.4710 0.2680 0.9040 0.2280 0.5580 0.2510 0.0760 0.5970 0.5610 0.9200 0.8320 0.1510 0.8720 0.5720 0.6310 0.9400 0.7980 0.1910 0.8090 0.9770 0.8560 0.7620 0.7850 0.9820

0.3167 0.5752* 1.9421 0.0619 0.0135 0.2133 0.2014*** 0.0679 0.5551 0.3403* 0.4411 1.2056 0.0275 0.1398 0.1008 0.1275 0.2469 0.1916 0.2520 0.4938 0.7248 0.2200 0.9481 0.1074 0.4816 1.3544 1.8797 0.2306 1.5109 0.7365* 0.0646 0.1480 0.2257 0.4383 0.1645 0.4385 0.5911

0.1250 0.0650 0.3030 0.3090 0.8810 0.3050 0.0050 0.7660 0.1130 0.0580 0.1440 0.3180 0.9130 0.5950 0.6520 0.7840 0.5530 0.6210 0.1620 0.1950 0.3560 0.5350 0.4790 0.6150 0.4600 0.2670 0.2860 0.3210 0.3810 0.0840 0.8760 0.6790 0.2770 0.2540 0.5220 0.0850

Note: ***, ** and * denote that the variables are statistically significant at the 1%, 5% and 10% levels, respectively. M, EC and TP stand for environmental Malmquist index, efficiency change and technological progress respectively.

may have more chances to gain higher dynamic efficiencies because of larger scopes to reduce carbon emissions, but they can hardly push the production frontier forward. On the other hand, although a larger share of the second industry means more reduction potential, imbalanced industry structure will also harm TFP and the human living environment if improperly used. Considering its decompositions, SEC and R&D, have significant positive influences on EC while ECPG has negative influence. When it comes to technological progress, THM_r and GG play positive roles. Higher proportions of thermal power lead to more advanced technology requirement while rapid economic growth brings more investment. Somewhat unexpectedly, the results indicate that R&D investment leads to advances of EC while it has no obvious effect on TP. Constant R&D ratio may lead to improvements in management, but it may also result in stagnation of ideas and lack of innovation. Perhaps the increasing investment will bring more chances to achieve technological progress. The influences of SEC on dynamic efficiency and TP are positive. One of the possible explanations may be that most countries with a high proportion of second industry are developing countries. It is easier for them to advance technologies than for developed countries. In addition, most of these countries lie in lower latitude areas, which lead to less energy consumption for living. Furthermore, even though COUNTRYi affects the environmental efficiency, their influences on emission reduction efforts are not statistically

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B.-C. Xie et al. / Energy xxx (2014) 1e11

significant as a whole. This is possibly because the results have been explained by other variables, such as THM_r and R&D, and the countries with passive attitudes towards climate change mitigation have carried out their efforts even before the studied period. 4. Conclusions Taking power generation, carbon emissions as outputs, this study employs the slack based dynamic environmental Malmquist index and its decompositions to measure the dynamic environmental efficiencies of power systems in 26 OECD and BRIC countries from 1996 to 2010, which provides a reasonable perspective for objectively evaluating the national emission performance. This process was followed by Tobit regressions to explore the link between environmental performance and related factors. Based on the analyses, we reach the following conclusions. (1) Fuel structure change and TP are the main driving forces to improve dynamic environmental efficiency. The results of the Malmquist index can overcome the impact of the existing economic situation and resource endowments and reflect fuel structure changes as well as policy reforms. Efficiency change plays a less important role than technological progress in improving environmental TFP. For developing countries with rapid economic growth, the optimization of power structure and absorption of clean energy are the most critical issues, whereas for developed countries with steady growth, more efforts on developing new technology will help them achieve higher environmental TFP. (2) The economic development and changes in energy prices significantly affect the environmental TFP of electric power industries. Almost all the countries have taken efforts to strive for sustainable development. The countries with higher TFP are not those shifting production frontiers upward. This uptrend was interrupted by economic recession when oil and gas prices fluctuated sharply. Furthermore, the influence of fuel price fluctuations on TFP varies greatly for different kinds of countries. As it can be seen, although the sharp increase in oil and gas prices results in adverse effects on TFP as a whole, it provides an opportunity for the technological progress of coal-fired power and clean energy technologies. (3) Regression against external factors shows that environmental TFP of electric power industries and its decompositions are greatly affected by economic indicators. Reducing the proportion of thermal power and increasing R&D investment are effective ways to improve TFP. Balancing the rapid growth of power supply coming from the second industry and its environmental impact is a complicated problem. Larger share of second industry may harm sustainable development while a proper industry structure will benefit the environmental efficiency as a whole. GPC plays a negative role in improving the environmental TFP. Furthermore, SEC and R&D have positive influences on EC; GG and THM_r play positive roles in improving TP. Our sample in the present study does not span a large set of countries and then it is hard to reflect various challenges faced by underdeveloped countries. Due to the lag of the ILO and IEA database, our study did not examine the environmental efficiency after 2010. All these issues will be addressed in future research. Acknowledgments The authors would like to thank anonymous reviewers for their constructive suggestions on the earlier draft of this paper. We are

also grateful to the financial support from the National Natural Science Foundation of China under grants Nos.71003072, 71373172 and 71133005, and the support from National Development and Reform Commission under grant No. 2012023. In addition, we would sincerely like to thank Professor Ying Fan for her comments in details. And the authors deeply acknowledge the weekly seminar at CEEP in CAS (Chinese Academy of Sciences), from where the earlier draft of the paper got improved.

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Please cite this article in press as: Xie B-C, et al., Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries, Energy (2014), http:// dx.doi.org/10.1016/j.energy.2014.04.109