Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis

Energy Policy 49 (2012) 164–172

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Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis F. Wu a, L.W. Fan b, P. Zhou a,n, D.Q. Zhou a a b

College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China Business School, Hohai University, 8 Focheng West Road, Nanjing 211100, China

H I G H L I G H T S c c c

China’s industrial energy efficiency is evaluated by DEA models with CO2 emissions. China’s industrial energy efficiency improved by 5.6% annually since 1997. Industrial energy efficiency improvement in China was mainly driven by technological improvement.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 5 December 2011 Accepted 16 May 2012 Available online 1 June 2012

Global awareness on energy security and climate change has created much interest in assessing economy-wide energy efficiency performance. A number of previous studies have contributed to evaluate energy efficiency performance using different analytical techniques among which data envelopment analysis (DEA) has recently received increasing attention. Most of DEA-related energy efficiency studies do not consider undesirable outputs such as CO2 emissions in their modeling framework, which may lead to biased energy efficiency values. Within a joint production framework of desirable and undesirable outputs, in this paper we construct both static and dynamic energy efficiency performance indexes for measuring industrial energy efficiency performance by using several environmental DEA models with CO2 emissions. The dynamic energy efficiency performance indexes have further been decomposed into two contributing components. We finally apply the indexes proposed to assess the industrial energy efficiency performance of different provinces in China over time. Our empirical study shows that the energy efficiency improvement in China’s industrial sector was mainly driven by technological improvement. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Energy efficiency CO2 emissions Data envelopment analysis

1. Introduction With the acceleration of industrialization and urbanization, China’s energy consumption has kept growing in the past years. In the ‘Eleventh Five-Year’ (2006–2010), China has decreased its unit GDP energy consumption by 19% through establishing the strict energy conservation targets for regional governments. Nevertheless, its total energy consumption still increased by about 51% during the period (NBSC, 2011a). The increase in fossil energy consumption will result in the increase in CO2 emissions, which is widely accepted as a main contributor to global warming. To safeguard its energy security and mitigate global climate change, China has to find ways to control its fossil energy use and CO2 emissions.

n

Corresponding author. Tel.: þ86 25 84893751; fax: þ86 25 84892751. E-mail address: [email protected] (P. Zhou).

0301-4215/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2012.05.035

Improving energy efficiency has been widely regarded as one of the most cost-effective ways to increase energy security, improve industrial competitiveness and mitigate climate change (Ang et al., 2010). In China, industrial sector is the largest energy end-user, which accounted for 71% of total final energy consumption in 2009 (NBSC, 2011b). Clearly, improving industrial energy efficiency plays a significant role for China to enhance energy security and promote low-carbon development. It would therefore be meaningful to measure and compare the industrial energy efficiency performance in China, which may provide empirical and condensed information for policy makers to assess the effectiveness of energy efficiency policies and measures. In literature, researchers have developed different indicators for measuring economy-wide energy efficiency performance at different levels. For instance, Ang (2006) propose an analytical framework for tracking economy-wide energy efficiency trends, which is built upon a well established energy policy analysis tool—index decomposition analysis (IDA). Discussion on different IDA methods can be found in Ang and Zhang (2000) and Ang

F. Wu et al. / Energy Policy 49 (2012) 164–172

(2004). The IDA-based analytical framework has been adopted by a number of countries including Canada, New Zealand and the United States for tracking their economy-wide energy efficiency trends (Ang et al., 2010). In addition to IDA-based energy efficiency indicators, many researchers have also employed data envelopment analysis (DEA) to assess the energy efficiency performance of a set of comparable entities from a production efficiency point of view. DEA, proposed by Charnes et al. (1978), is a well-established non-parameter methodology to efficiency evaluation. It uses mathematical programming models to compute the distance between each decision-making unit (DMU) and the frontier of best practice constructed by the DMUs, based on which the efficiency score of each DMU can be calculated. With its methodological advancements, DEA has also received increasing attention in energy and environmental studies, in which energy efficiency measurement has been identified as an important application area of DEA (Zhou et al., 2008). Earlier studies dealing with this topic include Boyd and Pang (2000) and Ramanathan (2000). Later, Hu and Wang (2006) develop a total-factor energy efficiency index and apply it to evaluate China’s regional energy efficiency. Wei et al. (2007) conducts an empirical analysis of energy efficiency in iron and steel sector in China by using Malmquist index. Honma and Hu (2008) employ the total-factor energy efficiency model to evaluate the regional energy efficiency in Japan. Zhang et al. (2011) use the DEA window analysis to investigate the dynamic trends in the total-factor energy efficiency of a sample of developing countries. In addition to evaluate regional energy efficiency, several researchers have also used DEA to assess industrial energy efficiency performance. For example, Mukherjee (2008a, 2008b) employ DEA to assess the manufacturing energy use efficiency in the United States and India. A common feature of the studies mentioned above is that they evaluate total-factor energy efficiency performance within a production framework without considering undesirable outputs. However, fossil energy use will inevitably produce undesirable outputs such as CO2 emissions (Guo et al., 2011). As discussed by Zhou and Ang (2008) and Mandal (2010), analyzing energy efficiency without considering undesirable outputs may lead to biased efficiency scores. Motivated by this issue, Zhou and Ang (2008) first incorporate undesirable outputs into energy efficiency evaluation and develop several DEA models for evaluating energy efficiency based on environmental DEA technologies. Since then, more and more studies conduct energy efficiency analysis within a joint production framework of both desirable and undesirable outputs. For instance, Mandal (2010) use DEA to evaluate the energy efficiency of Indian cement industry and show that neglecting undesirable outputs would result in biased energy efficiency scores. Shi et al. (2010) develop an extended DEA model by treating undesirable outputs as inputs to evaluate the industrial energy efficiency in China. Yeh et al. (2010) compare the total-factor energy efficiency in China mainland with that in Taiwan by using DEA with undesirable outputs based on data translation. Bian and Yang (2010) develop a novel Shannon-DEA approach to analyzing the energy and environmental efficiency simultaneously based on the environmental DEA. Sueyoshi and Goto (2010, 2011) propose a new DEA approach for unified efficiency measurement of fossil fuel electricity generation by considering CO2 emissions. Wang et al. (in press) use DEA window analysis to measure the radial and non-radial energy and environmental efficiency of different provinces in China by considering undesirable outputs. Previous studies have so far developed a number of DEA models with undesirable outputs to evaluate energy efficiency performance. Nevertheless, according to our knowledge, the study by Shi et al. (2010) seems to be the only one that contributes to

165

use DEA to evaluate China’s industrial energy efficiency. However, Shi et al. (2010) simply treat the undesirable outputs as inputs which may not reflect the real production activities well. The study by Førsund (2008) also shows that taking undesirable outputs as inputs will result in a conflict with the material balance equation. As such, in this paper we propose to use environmental DEA models to evaluate industrial energy efficiency performance in China. Our environmental DEA models used depart from the study by Shi et al. (2010) since the environmental DEA technology is used to model the joint production of desirable and undesirable outputs. According to Zhou et al. (2008), environmental DEA technology has been widely adopted in the context of energy and environmental studies. It seems that this paper is the first using it to study China’s industrial energy efficiency performance. The rest of this paper is organized as follows. Section 2 proposes the nonparametric DEA models with undesirable outputs for measuring industrial energy efficiency performance. In Section 3, we present an empirical application study on measuring China’s industrial energy efficiency performance of different provinces over time. Section 4 concludes this study.

2. Methodology 2.1. Environmental DEA technology In order to use DEA to measure China’s industrial energy efficiency performance with undesirable outputs, we need to first characterize the production technology. Since controlling the increase in CO2 emissions has become a focus in China, this paper consider only one undesirable output, i.e., CO2 emissions in modeling industrial production process. Assume that each DMU, i.e., the industrial sector of each province in China, employs capital stock (K), labor force (L) and energy (E) as inputs to produce industrial value added (Y) and CO2 emissions (C) as the single desirable output and undesirable output, respectively. The production technology can be defined as T ¼ ðK,L,E,Y,CÞ : ðK,L,EÞ can produce ðY,CÞ

ð1Þ

Since finite inputs can only produce finite outputs, T is often assumed to be a bounded set in production theory. In addition, the three inputs and the single desirable output are often assumed to be strongly or freely disposable. It implies that the desirable output can be freely reduced with the same amounts of inputs or the inputs can be free increased with the same amounts of outputs. Mathematically, the strong disposability of inputs and desirable outputs can be represented as (K0 ,L0 ,E0 ,Y,C)AT (or (K,L,E,Y0 ,C)AT) if (K,L,E,Y,C)AT and (K0 ,L0 ,E0 )Z(K,L,E) (or Y0 rY). It is known that Chinese central government has set the target of reducing unit GDP CO2 emissions for each province, which requires the concerted efforts from various energy end-users including industrial sectors. Since China industrial sectors heavily depend on fossil energy use, reducing CO2 emissions for most industrial sectors is likely not free in the future. As such, it is logic to assume that the desirable and undesirable outputs in Eq. (1) are weakly disposable. It means that the proportional reductions in industrial value added and CO2 emissions are possible. In addition, we need to impose the null-jointness assumption on T which implies that the only way to remove all the industrial CO2 emissions is to cease the production activities. The weak disposability and null-jointness assumptions, which were first pro¨ et al. (1989), can be mathematically described as posed by Fare (i) If (K,L,E,Y,C)AT and 0o y r1, then (K,L,E,yY,yC)AT. (ii) If (K,L,E,Y,C)AT and C¼0, then Y ¼0.

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So far the environmental production technology for modeling the joint production of desirable and undesirable outputs has been well defined. The next step is to characterize the environmental production technology within a nonparametric DEA framework. Suppose that there are i ¼ 1,2, . . ., I provinces and for province i the vector of inputs, desirable, and undesirable outputs of industrial sector is (Ki, Li, Ei, Yi, Ci). The environmental DEA technology exhibiting constant returns to scale can be formulated as follows: T ¼ ðK,L,E,Y,CÞ :

I X

li K i rK,

i¼1 I X

li Li r L,

i¼1 I X

li Ei rE,

ð2Þ

i¼1 I X

2.3. Dynamic energy efficiency index

li Y i Z Y,

i¼1 I X

Eq. (4) attempts to contract the amount of energy input as much as possible for a given level of non-energy inputs, desirable and undesirable outputs. If SEEIj ¼1 it means DMUj is technically efficient in energy use. If SEEIj o1, it implies that DMUj is technically inefficient in energy use. A larger SEEIj value means that DMUj has a better energy efficiency performance. We have so far established the first DEA model to measure China’ industrial energy efficiency performance on the basis of environmental DEA technology. Since for China simultaneously maintaining rapid economic growth, reducing energy consumption and controlling CO 2 emissions is compulsory for promoting its low-carbon development (Song et al., 2011), Eq. (4) seems to be appropriate as it simultaneous considers economic output, energy use and CO2 emissions in measuring industrial energy efficiency performance.

li C i ¼ C,

i¼1

li Z 0, i ¼ 1,2, . . ., I In Eq. (2), li ði ¼ 1,2,. . .,IÞ refer to the intensity levels at which the DMUs conduct production activities, which provides the weights for constructing the environmental DEA technology. 2.2. Static energy efficiency index To measure the industrial energy efficiency performance of DMUj (j ¼ 1,2,. . .,I), we first define a Shephard sub-vector input distance function for energy use (hereafter referred to as the Shephard energy distance function) as follows: DE ðK j ,Lj ,Ej ,Y j ,C j Þ ¼ supa : ðK j ,Lj ,Ej =a,Y j ,C j Þ A T

ð3Þ

Eq. (3) attempts to reduce the use of energy by DMUj as much as possible while keeping the resulting input–output combination within the production possibility set. The Shephard energy distance function DE(Kj, Lj, Ej, Yj, Cj) measures the degree to which energy use can be reduced. As such, its reciprocal may be taken as an energy efficiency index that can be used to compare the industrial energy efficiency performance of different provinces at the same time scale. Here we refer to the reciprocal of the Shephard energy distance function as a static energy efficiency index (SEEI). The recent study by Zhou et al. (2012) has offered a parametric frontier method for calculating SEEI. Different from Zhou et al. (2012), we here propose to use the following DEA model to derive the SEEI of DMUj: I X

ð5Þ

DEPIj(t,s) can be used to measure the change in energy efficiency performance of DMUj from period t to period s. DEPIj(t,s)41 (or DEPIj(t,s)o1) indicates that the energy performance has improved (or deteriorated). To calculate DEPI, we need to calculate four Shephard energy distance functions, i.e., DlE1 ðK lj2 ,Llj2 ,Elj2 ,Y lj2 ,C lj2 Þ, l1,l2A{s,t}. The reciprocal of DlE1 ðK lj2 ,Llj2 ,Elj2 ,Y lj2 ,C lj2 Þ can be calculated by solving the following DEA model: ½DlE1 ðK lj2 ,Llj2 ,Elj2 ,Y lj2 ,C lj2 Þ1 ¼ minb I X

li K li1 r K lj2

li Lli1 rLlj2

I X

li Eli1 r bElj2

ð6Þ

i¼1

li Ei r bEj

ð4Þ

I X

li Y li1 Z Y lj2

i¼1

li Y i Z Y j

i¼1 I X

DtE ðK sj ,Lsj ,Esj ,Y sj ,C sj Þ  DsE ðK sj ,Lsj ,Esj ,Y sj ,C sj Þ

i¼1

li Li rLj

i¼1 I X

#1=2

i¼1

i¼1 I X

DtE ðK tj ,Ltj ,Etj ,Y tj ,C tj Þ  DsE ðK tj ,Ltj ,Etj ,Y tj ,C tj Þ

I X

li K i r K j

i¼1 I X

" DEPIj ðt,sÞ ¼

s:t:

SEEIj ¼ 1=DE ðK j ,Lj ,Ej ,Y j ,C j Þ ¼ minb s:t:

The DEA model described in Eq. (4) is mainly used to conduct cross-section energy efficiency comparisons between different DMUs. For a specific DMU, it is meaningful to track the changes in energy efficiency performance over time. Following the ideas of the nonparametric Malmquist productivity index developed by ¨ et al. (1994) as well as the Malmquist carbon emission index Fare developed by Zhou et al. (2010), we propose a dynamic energy performance index (DEPI) for assessing the change in energy efficiency performance over time. Let t and s (tos) refer to two time periods. Assume that DtE ðK tj ,Ltj ,Etj ,Y tj ,C tj Þ and DsE ðK tj ,Ltj ,Etj ,Y tj ,C tj Þ are the Shephard energy distance functions of DMUj based on its inputs and outputs at period t for the environmental DEA technologies at t and s, respectively. Further assume that DtE ðK sj ,Lsj ,Esj ,Y sj ,C sj Þ and DsE ðK sj ,Lsj ,Esj ,Y sj ,C sj Þ are, respectively, the Shephard energy distance functions of DMUj based on its inputs and outputs at period s for the environmental DEA technologies at t and s. The DEPI is defined as follows:

I X

li C li1 ¼ C lj2

i¼1

li C i ¼ C j

i¼1

li Z0,i ¼ 1,2,. . .,I

li Z 0,i ¼ 1,2,. . .,I Like the Malmquist carbon emission performance index defined in Zhou et al. (2010), DEPIj(t,s) can also be decomposed

F. Wu et al. / Energy Policy 49 (2012) 164–172

167

Table 1 Data sources and processing. Variable

Data source

Data compilation

Industrial capital stock (K)

China Statistical Yearbook 1998–2009, China Statistical Yearbook of Fixed Assets 1998–2009

Industrial labor force (L) Industrial energy consumption (E)

China Statistical Yearbook 1998–2009 China Energy Statistical Yearbook 1997–1999, 2000–2002 and 2004–2009

Industrial value added (Y)

China Statistical Yearbook 1998, 60 Years of New China Statistical Data Compilation

Industrial CO2 emissions (C) –

China Energy Statistical Yearbook 1997–1999, 2000–2002 and 2004–2009 –

The data on perpetual inventory method is used to calculate the industrial capital stock: the formula is Kt ¼Kt  1(1 d) þIt,where Kt and Kt  1 are, respectively, the industrial capital stock in year t and year t  1, d represents the depreciation rate of capital stock that is assumed to be 10% in accordance with the suggestions by Zhang et al. (2004), and It refer to the industrial fixed capital formation in year t that has been replaced by industrial fixed capital investment due to data unavailability. The data are converted into 1997 constant prices by using the price indices for fixed capital investment. Measured by annual average number of persons employed by industrial enterprises above designated size. Involves the consumption of raw coal, cleaned coal, other washed coal, briquettes, coke, coke oven gas, other gas, crude oil, gasoline, kerosene, diesel oil, fuel oil, liquefied petroleum gas, refinery gas natural gas, other petroleum products, heat, electricity, and other energy. All are converted to the standard coal equivalent based on relative factors. Measured by the industrial value added indicator of industrial enterprises above designated size and deflated to 1997 constant prices by using producer price indices for manufactured goods. Follow IPCC guidelines to use the formula. CO2 ¼

n X

ðai bi ÞEi  Zi 

i¼1

–

To calculate CO2 emissions from terminal energy consumption. E is apparent fuel consumption, a is carbon emission factor, b is carbon stored factor and Z is fraction of carbon oxidized.

–

into two components as follows: EFFCHj ðt,sÞ ¼

DtE ðK tj ,Ltj ,Etj ,Y tj ,C tj Þ Dsc ðK sj ,Lsj ,Esj ,Y sj ,C sj Þ "

TECHCHj ðt,sÞ ¼

44 12

¼

SEEIsj SEEItj

#1=2 DsE ðK tj ,Ltj ,Etj ,Y tj ,C tj Þ  DsE ðK sj ,Lsj ,Esj ,Y sj ,C sj Þ DtE ðK tj ,Ltj ,Etj ,Y tj ,C tj Þ  DtE ðK sj ,Lsj ,Esj ,Y sj ,C sj Þ

Table 2 Descriptive statistics of inputs and outputs.

ð7Þ

ð8Þ

The first component, i.e., Eq. (7), is an efficiency change component, which measures the change in the static energy efficiency index of DMUj. The second component, i.e., Eq. (8), is a technological change component, which measures how much the environmental DEA technology shifts from period t to s.

3. Empirical study The models introduced in Section 2 have been employed to evaluate the industrial energy efficiency performance of different provinces in China in 1997–2008. In Section 3.1, we describe the data used. Sections 3.2 and 3.3 present the results of static and dynamic energy performance indexes for China’s industrial sectors. 3.1. Data The data on the five variables including K, L, E, Y and C for 28 provinces in China are collected for the current study. The three provinces such as Hainan, Ningxia and Tibet are not considered due to the unavailability or inconsistency of their data. Table 1 describes the data sources and compilation procedures. Table 2 shows the descriptive statistics of the input and output variables. 3.2. Static energy efficiency performance We first compute the SEEIs for 28 provinces using Eq. (4), which are displayed in Table 3. It can be seen from Table 3 that the average industrial static energy efficiency performance score in China during the sample period was 0.816. It implies that as a whole it is possible to reduce the industrial energy consumption

Variable

Unit

Min

Max

K L

100 million Yuan 10,000 workers 10,000 tons of coal equivalent 100 million Chinese Yuan 10,000 tons

522.51 12.37

26,401.73 14,93.38

44,41.27 231.17

40,07.92 220.61

304.35

14,724.48

35,94.51

26,66.39

E Y C

43.51 921.91

17,832.61 63,512

Mean

18,46.53 14,305.05

Std. dev.

24,81.2 10,996.54

by 18.4% by removing the energy inefficiency in different provinces. Table 3 also shows that the industrial static energy efficiency indexes varied across different regions. In the east region, Guangdong and Shanghai registered for the highest average industrial energy efficiency score ( ¼1.000) while Liaoning registered for lowest score ( ¼0.704). In the central region, the industrial static energy efficiency indexes ranged from 0.66 to 0.91. In the west region, Inner Mongolia had the highest static industrial energy efficiency performance index, while Qinghai and Guangxi were found to be the worst industrial static energy efficiency performers with scores below 0.6. Table 3 also shows that Guangdong and Shanghai were most efficient in terms of industrial static energy efficiency performance, which is not surprising and consistent with the conclusions drawn by some previous studies such as Shi et al. (2010) and Michio and Katsuya (2007). However, Inner Mongolia and Shanxi, which are usually considered as energy-inefficiency regions, performs relatively better in static industrial energy efficiency. A possible explanation is that CO2 emissions are included in the production framework so that the two provinces became closer to the frontier of best practice. It implies that the inclusion of undesirable outputs may provide different results of energy efficiency scores compared to the case that only desirable outputs are considered. Fig. 1 shows the regional industrial static energy efficiency performance in the sample period. It can be easily found that the energy efficiency had a down trend in central and west areas which may be caused by their relative smaller progress in

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F. Wu et al. / Energy Policy 49 (2012) 164–172

Table 3 Industrial static energy efficiency performance in different provinces in China from 1997 to 2008. Province

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Average

(E)Beijing (E)Tianjin (E)Hebei (E)Liaoning (E)Shanghai (E)Jiangsu (E)Zhejiang (E)Fujian (E)Shandong (E)Guangdong – (C)Shanxi (C)Jilin (C)Heilongjiang (C)Anhui (C)Jiangxi (C)Henan (C)Hubei (C)Hunan

0.702 0.918 0.908 0.753 1.000 0.997 0.993 1.000 0.966 1.000 – 1.000 0.866 1.000 1.000 1.000 0.898 0.772 0.740

0.642 0.903 0.815 0.724 1.000 0.939 0.981 0.823 0.851 1.000 – 1.000 0.895 0.975 0.720 0.750 0.837 0.719 0.661

0.667 0.954 0.848 0.731 1.000 0.971 0.992 0.855 0.866 1.000 – 0.838 1.000 0.930 0.718 0.762 0.801 0.687 0.688

1.000 0.854 0.995 0.614 1.000 0.891 0.880 0.966 1.000 1.000 – 0.792 0.835 0.657 0.496 0.983 0.986 0.534 1.000

0.641 0.866 0.785 0.670 1.000 1.000 0.964 0.841 0.772 1.000 – 0.776 0.991 1.000 0.714 0.788 0.823 0.680 0.559

0.630 0.913 0.787 0.721 1.000 0.986 0.962 0.868 0.888 1.000 – 0.784 1.000 0.885 0.592 0.749 0.813 0.688 0.605

0.808 0.933 0.918 0.655 1.000 0.961 0.985 0.902 0.989 1.000 – 1.000 1.000 0.922 0.924 0.940 0.913 0.774 0.735

1.000 0.923 0.840 0.847 1.000 0.920 0.957 0.967 0.992 1.000 – 1.000 0.921 0.923 0.900 0.841 0.777 0.768 0.647

1.000 1.000 0.745 0.746 1.000 0.930 0.922 0.689 0.910 1.000 – 1.000 0.888 1.000 0.847 0.777 0.762 0.640 0.594

1.000 1.000 0.689 0.727 1.000 0.957 1.000 0.736 0.932 1.000 – 1.000 0.804 0.911 0.793 0.737 0.725 0.692 0.623

1.000 1.000 0.605 0.622 1.000 0.895 1.000 0.828 0.916 1.000 – 1.000 0.704 0.771 0.752 0.620 0.743 0.642 0.591

0.855 1.000 0.641 0.638 1.000 0.869 1.000 0.842 0.899 1.000 – 0.724 0.780 0.862 0.845 0.610 0.703 0.601 0.552

0.829 0.939 0.798 0.704 1.000 0.943 0.970 0.860 0.915 1.000 – 0.910 0.890 0.903 0.775 0.796 0.815 0.683 0.666

(W)Inner Mongolia (W)Guangxi (W)Chongqing (W)Sichuan (W)Guizhou (W)Yunnan (W)Shaanxi (W)Gansu (W)Qinghai (W)Xinjiang

1.000 0.639 0.683 0.708 0.752 1.000 0.811 0.703 0.667 1.000

1.000 0.691 0.570 0.666 0.751 1.000 0.722 0.651 0.627 1.000

1.000 0.575 0.557 0.640 0.701 1.000 0.823 0.636 0.533 0.846

1.000 0.612 0.388 0.680 0.611 1.000 0.808 0.568 0.501 1.000

1.000 0.516 0.589 0.637 0.684 1.000 0.677 0.701 0.696 1.000

1.000 0.511 0.540 0.667 0.662 1.000 0.769 0.773 0.641 0.718

1.000 0.556 0.725 0.759 0.920 1.000 0.956 0.980 0.731 0.745

1.000 0.628 0.793 0.732 0.770 0.619 0.873 0.800 0.586 0.913

1.000 0.585 0.581 0.695 0.816 0.673 0.712 0.699 0.564 0.763

1.000 0.586 0.973 0.656 1.000 0.713 0.790 0.670 0.512 0.731

1.000 0.536 0.602 0.667 1.000 0.615 0.797 0.618 0.460 0.653

1.000 0.521 0.542 0.643 0.862 0.602 0.806 0.637 0.423 0.654

1.000 0.580 0.629 0.679 0.794 0.852 0.795 0.703 0.578 0.835

Annual average

0.874

0.818

0.808

0.809

0.799

0.791

0.883

0.855

0.805

0.820

0.773

0.754

0.816

Note: E, C, and W in parentheses refer to the east, central, and west regions, respectively.

1.050 1.000

East

Central

West

SEEI Value

0.950 0.900 0.850 0.800 0.750 0.700 0.650 1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Fig. 1. The average SEEI scores of different regions over time.

promoting energy savings compared to the east area. Fig. 1 also shows that the west area gradually caught up with central area, but the gap between east area and west/central area had become larger. Furthermore, it has been found that the east region performed best while the west region performed least in industrial static energy efficiency, which is consistent with the conclusions drawn by Shi et al. (2010). The ranking of energy efficiency in the three regions is consistent with the status of economic development in China. In China, the east area is the most while the west area is the least developed region. In the east area the best technologies can be diffused more efficiently than the central and west areas. On the other hand, due to its relatively weak power in absorbing new technology and low degree of openingup, the west area had the worst performance in industrial energy efficiency. Since both this study and Shi et al. (2010) deal with China’s industrial energy efficiency performance, it is meaningful to carry out a simple comparison between the results given in the two

studies. It is found that our calculated average industrial static energy efficiency scores of the three areas in China are generally higher than those given in Shi et al. (2010), which could be mainly due to the differences in the modeling framework used. First, it comes from the difference in the choice of capital input. While this paper employs the industrial capital stock calculated from perpetual inventory method, Shi et al. (2010) uses industrial fixed assets as the capital input. Second, Shi et al. (2010) treat undesirable output as input and reduce it and energy input at the same rate. However, this study uses a tight constraint to model CO2 emissions which could make the DMUs much closer to the frontier of best practice. 3.3. Dynamic energy efficiency performance We also compute the DEPIs to assess the changes in industrial energy efficiency of the 28 provinces over time. Since the production frontier constructed by the observations from a

F. Wu et al. / Energy Policy 49 (2012) 164–172

previous year may not enclose all the observations from the second year, some mix-period linear programming models may ¨ et al. (2007) be infeasible. To overcome this issue, we follow Fare to use the three-year ‘‘windows’’ approach to construct the environmental DEA technology. That is to say, the environmental DEA technology in period t is constructed from the observations in period t, t  1 and t 2. Nevertheless, when use the environmental DEA technology for period t to assess the observations for period t þ1, there are still several infeasible linear programming models. Following Zhou et al. (2010), we set the efficiency scores to be unity for these infeasible linear programming models. Table 4 shows the DEPI of 28 provinces for all the consecutive two-year periods in 1997–2008. It can be seen from Table 4 that during the sample period the 28 provinces as a whole experienced a positive change (¼ 1.056), implying that the industrial energy efficiency was improved by 5.6% annually since 1997. However, four periods of time, i.e., 2000/2001, 2003/2004, 2004/2005 and 2007/2008, displayed a negative shift (below unity). The provincial average DEPI estimates during the sample period indicate that all the provinces except Shanxi and Jiangxi had an improvement in their industrial energy efficiency. Among them, Beijing and Fujian in the east region and Yunnan in the west region were found to have the highest annual average growth rate greater than 10%. Fig. 2 shows the average DEPI of different regions over time. It indicates that the central and west areas have the same trend and the three regions gradually converge to the same level. Next, we decompose DEPI into the effects of static energy efficiency index change (catching-up effect) and technological change (frontier shift effect) using Eqs. (7) and (8) in order to identify the driving factors of industrial energy efficiency performance change and quantify their impacts (Kim and Kim, in press). Table 5 shows the static energy efficiency performance change (EFFCH) components obtained. It is found that the 28 provinces as a whole had a drop in their SEEI scores over time. The results at

169

regional level reveal that Shanghai, Guangdong and Inner Mongolia did not experience changes in their technical efficiency ( ¼1.000) over time, which means that they were always on the production frontier. Among the 28 provinces, 19 provinces showed a decrease in annual efficiency score, which reveals that these provinces were not successful in catching up the frontier of best practice. Fig. 3 shows the average EFFCH values of different regions over time. It can be seen that in the sample period the scores of the three regions are quite close to one (the minimum 0.905 and the maximum 1.2) and most of them are below one. We may conclude that the technical efficiency did not experience significant change and the catching-up effect was not obvious. The efficiency change might not be the main contributor of Chinese regional industrial energy efficiency improvement. Table 6 shows the results of the technological change component for all the provinces. Of the 308 entries, only 83 registered for a negative shift in technology. That is to say, 73% of the entries registered for a positive shift in technology. Notably, Beijing, Jiangsu, Fujian and Yunnan had an improvement annually by over 10%. Besides, all the 28 provinces showed technological improvement during the sample period and only two periods of time, i.e., 2000/2001 and 2003/2004, saw a technological regress. Fig. 4 shows the average TECHCH scores of different regions over time. It indicates that the central and west areas have caught up with east area in magnitude in recent years, which might be an indication that they had experienced a significant technological improvement. Besides, among the 33 TECHCH scores, only six are below one and the average score is 1.065. We may conclude that the improvement in Chinese regional industrial energy efficiency performance is mainly attributable to technological improvement. To study the overall industrial energy efficiency changes of the 28 provinces from 1997 to 2008, we have also calculated the cumulative DEPI and its contributing components in 2008. Table 7 shows the results obtained with 1997 as the base year. It is found

Table 4 The DEPI values of 28 provinces from 1997/1998 to 2007/2008. Province

1997/1998 1998/1999 1999/2000 2000/2001 2001/2002 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008 Average

(E)Beijing (E)Tianjin (E)Hebei (E)Liaoning (E)Shanghai (E)Jiangsu (E)Zhejiang (E)Fujian (E)Shandong (E)Guangdong

1.085 1.043 0.993 1.022 1.070 1.002 1.046 1.030 0.941 1.029

1.064 1.098 1.042 1.038 1.000 1.076 1.052 1.150 1.059 1.023

1.892 1.497 1.590 1.423 1.192 1.625 1.413 1.713 1.433 1.000

1.163 1.019 0.602 0.831 1.052 1.122 1.059 0.949 0.717 1.000

1.468 1.119 1.049 1.114 1.050 1.059 1.096 1.169 1.197 1.042

1.668 1.202 1.107 0.872 1.075 1.081 1.120 1.084 1.104 1.002

1.401 0.986 0.789 1.133 1.000 0.927 0.953 1.093 0.922 1.000

1.234 1.103 0.940 0.912 1.000 1.040 0.985 0.746 0.951 1.000

1.169 1.000 0.955 0.995 1.000 1.027 1.084 1.074 1.030 1.000

1.139 1.000 1.014 0.969 1.022 0.968 1.063 1.135 1.045 1.011

0.839 1.000 1.051 1.025 0.994 0.984 1.034 1.022 0.983 1.020

1.284 1.097 1.012 1.030 1.041 1.083 1.082 1.106 1.035 1.012

(C)Shanxi (C)Jilin (C)Heilongjiang (C)Anhui (C)Jiangxi (C)Henan (C)Hubei (C)Hunan

0.958 1.099 1.073 0.842 0.870 0.967 0.997 0.948

1.010 1.155 0.971 1.015 1.029 0.995 0.984 1.077

1.428 1.204 1.300 1.236 1.748 2.155 1.392 1.902

0.742 0.914 1.208 1.096 0.613 0.655 1.024 0.451

1.023 1.055 0.926 0.857 0.971 1.003 1.041 1.082

1.144 1.028 1.041 1.504 1.160 1.073 1.109 1.168

0.761 0.796 0.842 0.834 0.729 0.710 0.866 0.758

1.002 1.013 1.120 0.975 0.957 1.011 0.861 0.945

0.977 0.930 0.931 0.954 0.968 0.970 1.098 1.068

1.053 0.989 0.971 1.060 0.951 1.134 1.037 1.061

0.847 1.105 1.111 1.129 0.983 0.946 0.938 0.931

0.995 1.026 1.045 1.046 0.998 1.056 1.032 1.035

(W)Inner Mongolia (W)Guangxi (W)Chongqing (W)Sichuan (W)Guizhou (W)Yunnan (W)Shaanxi (W)Gansu (W)Qinghai (W)Xinjiang

1.000 1.145 0.885 0.981 1.190 1.017 0.932 0.970 0.992 1.089

1.000 0.860 0.986 0.998 0.940 1.000 1.183 1.014 0.873 0.832

1.336 1.804 1.492 1.845 1.485 1.419 1.731 1.564 1.764 1.619

0.768 0.655 1.155 0.762 0.850 1.000 0.672 0.954 1.059 1.000

1.033 1.016 0.931 1.062 0.972 1.394 1.118 1.108 0.972 0.620

1.275 1.011 1.356 1.096 1.296 1.423 1.217 1.172 1.093 1.007

1.101 0.920 0.970 0.828 0.705 0.682 0.770 0.664 0.685 1.070

1.000 0.964 0.753 0.983 1.114 1.354 0.845 0.902 0.998 0.903

1.000 1.026 1.700 0.948 1.141 1.276 1.122 0.986 0.929 0.992

1.077 1.043 0.675 1.071 1.058 0.986 1.100 1.068 1.016 1.035

1.000 0.970 0.901 0.963 0.916 0.973 1.007 1.022 0.918 0.993

1.054 1.038 1.073 1.049 1.061 1.139 1.063 1.039 1.027 1.014

Annual average

1.008

1.019

1.543

0.896

1.055

1.160

0.889

0.986

1.048

1.027

0.986

1.056

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F. Wu et al. / Energy Policy 49 (2012) 164–172

1.700

East

Central

West

1.600 1.500

DEPI Value

1.400 1.300 1.200 1.100 1.000 0.900 0.800 0.700 97-98

98-99

99-00

00-01

01-02

02-03

03-04

04-05

05-06

06-07

07-08

Fig. 2. The average DEPI of different regions over time.

Table 5 Static energy efficiency performance change component of DEPI from 1997/1998 to 2007/2008. Region

1997/1998 1998/1999 1999/2000 2000/2001 2001/2002 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008 Average

(E)Beijing (E)Tianjin (E)Hebei (E)Liaoning (E)Shanghai (E)Jiangsu (E)Zhejiang (E)Fujian (E)Shandong (E)Guangdong

0.916 0.983 0.897 0.961 1.000 0.942 0.987 0.823 0.880 1.000

1.038 1.056 1.041 1.011 1.000 1.034 1.011 1.039 1.017 1.000

1.499 0.895 1.173 0.840 1.000 0.917 0.887 1.130 1.155 1.000

0.641 1.015 0.789 1.091 1.000 1.122 1.096 0.870 0.772 1.000

0.983 1.054 1.002 1.075 1.000 0.986 0.998 1.031 1.151 1.000

1.283 1.022 1.166 0.909 1.000 0.975 1.024 1.039 1.113 1.000

1.238 0.989 0.915 1.293 1.000 0.958 0.971 1.072 1.003 1.000

1.000 1.084 0.888 0.881 1.000 1.010 0.964 0.713 0.917 1.000

1.000 1.000 0.925 0.974 1.000 1.029 1.085 1.067 1.024 1.000

1.000 1.000 0.878 0.856 1.000 0.935 1.000 1.125 0.983 1.000

0.855 1.000 1.059 1.025 1.000 0.971 1.000 1.017 0.982 1.000

1.041 1.009 0.976 0.992 1.000 0.989 1.002 0.993 0.999 1.000

(C)Shanxi (C)Jilin (C)Heilongjiang (C)Anhui (C)Jiangxi (C)Henan (C)Hubei (C)Hunan

1.000 1.033 0.975 0.720 0.750 0.932 0.932 0.894

0.838 1.118 0.953 0.998 1.016 0.958 0.955 1.041

0.945 0.835 0.707 0.690 1.291 1.230 0.778 1.454

0.979 1.188 1.522 1.441 0.801 0.835 1.274 0.559

1.010 1.009 0.885 0.829 0.950 0.987 1.012 1.081

1.276 1.000 1.042 1.561 1.255 1.123 1.124 1.216

1.000 0.920 1.001 0.973 0.895 0.851 0.993 0.881

1.000 0.965 1.084 0.942 0.924 0.980 0.833 0.918

1.000 0.905 0.911 0.936 0.948 0.952 1.080 1.049

1.000 0.876 0.846 0.948 0.841 1.025 0.929 0.948

0.724 1.109 1.119 1.124 0.984 0.946 0.936 0.934

0.979 0.996 1.004 1.015 0.969 0.984 0.986 0.998

(W)Inner Mongolia (W)Guangxi (W)Chongqing (W)Sichuan (W)Guizhou (W)Yunnan (W)Shaanxi (W)Gansu (W)Qinghai (W)Xinjiang

1.000 1.082 0.834 0.941 0.998 1.000 0.891 0.926 0.940 1.000

1.000 0.831 0.977 0.961 0.934 1.000 1.139 0.977 0.850 0.846

1.000 1.065 0.696 1.063 0.872 1.000 0.983 0.893 0.940 1.183

1.000 0.844 1.520 0.936 1.119 1.000 0.837 1.236 1.389 1.000

1.000 0.990 0.917 1.048 0.967 1.000 1.137 1.102 0.921 0.718

1.000 1.089 1.342 1.137 1.390 1.000 1.242 1.268 1.141 1.037

1.000 1.128 1.094 0.965 0.837 0.618 0.914 0.816 0.801 1.226

1.000 0.932 0.732 0.950 1.060 1.088 0.815 0.873 0.963 0.836

1.000 1.002 1.676 0.943 1.226 1.059 1.109 0.959 0.908 0.958

1.000 0.915 0.619 1.017 1.000 0.864 1.008 0.922 0.899 0.893

1.000 0.972 0.899 0.965 0.862 0.978 1.011 1.031 0.920 1.002

1.000 0.986 1.028 0.993 1.024 0.964 1.008 1.001 0.970 0.972

Annual average

0.937

0.987

1.004

1.031

0.994

1.135

0.977

0.941

1.026

0.940

0.979

0.996

1.250

East

Central

West

1.200

EFFCH Value

1.150 1.100 1.050 1.000 0.950 0.900 0.850 97-98

98-99

99-00

00-01

01-02

02-03

03-04

04-05

05-06

Fig. 3. The average EFFCH values of different regions over time.

06-07

07-08

F. Wu et al. / Energy Policy 49 (2012) 164–172

171

Table 6 Environment DEA technological change component of DEPI from 1997/1998 to 2007/2008. Province

1997/1998 1998/1999 1999/2000 2000/2001 2001/2002 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008 Average

(E)Beijing (E)Tianjin (E)Hebei (E)Liaoning (E)Shanghai (E)Jiangsu (E)Zhejiang (E)Fujian (E)Shandong (E)Guangdong

1.185 1.061 1.107 1.064 1.070 1.064 1.060 1.252 1.068 1.029

1.024 1.040 1.000 1.026 1.000 1.040 1.040 1.107 1.041 1.023

1.262 1.673 1.355 1.693 1.192 1.771 1.594 1.515 1.240 1.000

1.814 1.005 0.763 0.762 1.052 1.000 0.966 1.090 0.929 1.000

1.494 1.062 1.047 1.036 1.050 1.075 1.097 1.134 1.040 1.042

1.300 1.176 0.949 0.960 1.075 1.109 1.094 1.043 0.992 1.002

1.132 0.997 0.862 0.876 1.000 0.968 0.982 1.019 0.919 1.000

1.234 1.018 1.059 1.035 1.000 1.029 1.022 1.047 1.037 1.000

1.169 1.000 1.033 1.021 1.000 0.998 0.999 1.007 1.005 1.000

1.139 1.000 1.156 1.131 1.022 1.035 1.063 1.009 1.064 1.011

0.981 1.000 0.992 1.000 0.994 1.014 1.034 1.005 1.001 1.020

1.249 1.094 1.029 1.055 1.041 1.100 1.087 1.112 1.031 1.012

(C)Shanxi (C)Jilin (C)Heilongjiang (C)Anhui (C)Jiangxi (C)Henan (C)Hubei (C)Hunan

0.958 1.063 1.100 1.169 1.160 1.038 1.070 1.060

1.205 1.033 1.018 1.017 1.013 1.039 1.031 1.035

1.511 1.443 1.839 1.792 1.354 1.752 1.790 1.309

0.758 0.770 0.794 0.761 0.765 0.783 0.804 0.806

1.013 1.046 1.046 1.034 1.022 1.016 1.029 1.001

0.896 1.028 0.999 0.963 0.925 0.955 0.986 0.961

0.761 0.864 0.841 0.857 0.815 0.834 0.873 0.860

1.002 1.050 1.033 1.035 1.036 1.032 1.034 1.029

0.977 1.028 1.023 1.019 1.020 1.019 1.017 1.019

1.053 1.129 1.147 1.118 1.130 1.106 1.117 1.118

1.169 0.996 0.993 1.004 0.999 0.999 1.003 0.997

1.028 1.041 1.076 1.070 1.022 1.052 1.068 1.018

(W)Inner Mongolia (W)Guangxi (W)Chongqing (W)Sichuan (W)Guizhou (W)Yunnan (W)Shaanxi (W)Gansu (W)Qinghai (W)Xinjiang

1.000 1.058 1.061 1.043 1.192 1.017 1.046 1.048 1.056 1.089

1.000 1.035 1.010 1.038 1.007 1.000 1.039 1.037 1.028 0.984

1.336 1.694 2.143 1.736 1.703 1.419 1.761 1.752 1.876 1.369

0.768 0.776 0.760 0.814 0.759 1.000 0.803 0.772 0.762 1.000

1.033 1.026 1.016 1.013 1.005 1.394 0.983 1.006 1.055 0.863

1.275 0.929 1.011 0.964 0.932 1.423 0.980 0.924 0.958 0.972

1.101 0.815 0.886 0.858 0.842 1.103 0.843 0.814 0.856 0.873

1.000 1.035 1.028 1.035 1.051 1.245 1.036 1.033 1.036 1.081

1.000 1.024 1.015 1.005 0.931 1.205 1.012 1.028 1.023 1.035

1.077 1.140 1.091 1.053 1.058 1.142 1.091 1.159 1.131 1.159

1.000 0.998 1.002 0.998 1.062 0.995 0.996 0.992 0.998 0.991

1.054 1.048 1.093 1.051 1.049 1.177 1.054 1.051 1.071 1.038

Annual average

1.078

1.033

1.567

0.887

1.060

1.028

0.909

1.047

1.023

1.095

1.008

1.067

1.700

East

Central

West

1.600

TECHCH Value

1.500 1.400 1.300 1.200 1.100 1.000 0.900 0.800 0.700 97-98

98-99

99-00

00-01

01-02

02-03

03-04

04-05

05-06

06-07

07-08

Fig. 4. The average TECHCH of different regions over time

that the 28 provinces as a whole showed an improvement in industrial dynamic energy efficiency performance by 82.3% from 1997 to 2008. The cumulative EFFCH is found to be below unity, which shows that Chinese industrial energy efficiency had experienced a negative change in technical efficiency from 1997 to 2008. With regards to the individual province, Beijing, Yunnan, Tianjin, Fujian and Zhejiang are the top five while Xinjiang, Hebei, Hunan, Shanxi and Jiangxi are the bottom five performs.

4. Conclusion This study introduces several nonparametric DEA models with CO2 emission to evaluate industrial energy efficiency based on the environmental DEA technology. Considering the fact that reducing CO2 emissions for most China’ industrial sectors is likely not free in the future, we assume that the CO2 emissions as an

undesirable output are weakly disposable in this paper. Then we propose several DEA models to evaluate both static and dynamic energy efficiency performance of industrial sectors in China’s 28 provinces from 1997 to 2008. The dynamic energy efficiency performance indexes have also been decomposed to its two contributing components to study what are driving the change in energy efficiency performance over time. The empirical study shows that the 28 provinces could reduce energy consumption by 18.4% annually through energy efficiency improvement. East area has the highest average energy efficiency score followed by central and west areas, which is consistent with the findings by previous studies. Dynamic energy efficiency analysis shows that China’s industrial energy efficiency improved by 5.6% annually since 1997. By decomposing the DEPI into its two contributing components, we find that static energy efficiency change had a negative impact while technological change had a positive impact on the change in dynamic energy efficiency

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F. Wu et al. / Energy Policy 49 (2012) 164–172

Table 7 Cumulative DEPI and its decomposition in 2008 (1997 ¼1). Region

DEPI

EFFCH

TECHCH

RANK

(E)Beijing (E)Tianjin (E)Hebei (E)Liaoning (E)Shanghai (E)Jiangsu (E)Zhejiang (E)Fujian (E)Shandong (E)Guangdong

12.001 2.558 0.869 1.242 1.539 2.123 2.262 2.482 1.255 1.133

1.218 1.089 0.706 0.846 1.000 0.871 1.007 0.841 0.930 1.000

9.851 2.349 1.231 1.468 1.539 2.437 2.247 2.949 1.349 1.133

1 3 25 17 9 6 5 4 16 20

(C)Shanxi (C)Jilin (C)Heilongjiang (C)Anhui (C)Jiangxi (C)Henan (C)Hubei (C)Hunan

0.797 1.242 1.492 1.385 0.682 1.090 1.289 0.835

0.724 0.901 0.862 0.845 0.610 0.783 0.779 0.747

1.101 1.378 1.730 1.639 1.118 1.392 1.655 1.118

27 18 10 13 28 22 14 26

(W)Inner Mongolia (W)Guangxi (W)Chongqing (W)Sichuan (W)Guizhou (W)Yunnan (W)Shaanxi (W)Gansu (W)Qinghai (W)Xinjiang

1.603 1.100 1.437 1.272 1.544 3.241 1.413 1.231 1.019 0.902

1.000 0.815 0.793 0.909 1.147 0.602 0.994 0.906 0.634 0.654

1.603 1.349 1.812 1.400 1.347 5.384 1.421 1.358 1.606 1.379

7 21 11 15 8 2 12 19 23 24

Mean

1.823

0.865

2.012

–

performance. It indicates that the energy efficiency improvement in China’s industrial sector is mainly driven by technological improvement. It should be pointed out that industrial energy efficiency performance is relevant to not only technology and technical efficiency but also some other factors. Further research may be carried out by exploring the influencing factors of industrial energy efficiency scores using statistical regression analysis. Given data availability, this study could also be expanded by using the data over a longer time period.

Acknowledgements The authors are grateful to the financial support provided by the National Natural Science Foundation of China (nos. 70903031 and 41071348), the Program for New Century Excellent Talents in University (no. NCET-10-0073), the Humanities and Social Science Foundation of the Ministry of Education (12YJCZH039), and the Jiangsu Qing Lan Project. References Ang, B.W., 2004. Decomposition analysis for policymaking in energy: which is the preferred method? Energy Policy 32, 1131–1139. Ang, B.W., 2006. Monitoring changes in economy-wide energy efficiency: from energy-GDP ratio to composite efficiency index. Energy Policy 34, 574–582. Ang, B.W., Mu, A.R., Zhou, P., 2010. Accounting frameworks for tracking energy efficiency trends. Energy Economics 32, 1209–1219.

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