Energy Policy. Industrial energy efficiency with CO 2 emissions in China: A nonparametric analysis. F. Wu a, L.W. Fan b, P. Zhou a,n, D.Q.

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1 Energy Polcy 49 (2012) Contents lsts avalable at ScVerse ScenceDrect Energy Polcy ournal homepage: Industral energy effcency wth CO 2 emssons n Chna: A nonparametrc analyss F. Wu a, L.W. Fan b, P. Zhou a,n, D.Q. Zhou a a College of Economcs and Management, Nanng Unversty of Aeronautcs and Astronautcs, 29 Yudao Street, Nanng , Chna b Busness School, Hoha Unversty, 8 Focheng West Road, Nanng , Chna H I G H L I G H T S c Chna s ndustral energy effcency s evaluated by DEA models wth CO 2 emssons. c Chna s ndustral energy effcency mproved by 5.6% annually snce c Industral energy effcency mprovement n Chna was manly drven by technologcal mprovement. artcle nfo Artcle hstory: Receved 5 December 2011 Accepted 16 May 2012 Avalable onlne 1 June 2012 Keywords: Energy effcency CO 2 emssons Data envelopment analyss abstract Global awareness on energy securty and clmate change has created much nterest n assessng economy-wde energy effcency performance. A number of prevous studes have contrbuted to evaluate energy effcency performance usng dfferent analytcal technques among whch data envelopment analyss (DEA) has recently receved ncreasng attenton. Most of DEA-related energy effcency studes do not consder undesrable outputs such as CO 2 emssons n ther modelng framework, whch may lead to based energy effcency values. Wthn a ont producton framework of desrable and undesrable outputs, n ths paper we construct both statc and dynamc energy effcency performance ndexes for measurng ndustral energy effcency performance by usng several envronmental DEA models wth CO 2 emssons. The dynamc energy effcency performance ndexes have further been decomposed nto two contrbutng components. We fnally apply the ndexes proposed to assess the ndustral energy effcency performance of dfferent provnces n Chna over tme. Our emprcal study shows that the energy effcency mprovement n Chna s ndustral sector was manly drven by technologcal mprovement. & 2012 Elsever Ltd. All rghts reserved. 1. Introducton Wth the acceleraton of ndustralzaton and urbanzaton, Chna s energy consumpton has kept growng n the past years. In the Eleventh Fve-Year ( ), Chna has decreased ts unt GDP energy consumpton by 19% through establshng the strct energy conservaton targets for regonal governments. Nevertheless, ts total energy consumpton stll ncreased by about 51% durng the perod (NBSC, 2011a). The ncrease n fossl energy consumpton wll result n the ncrease n CO 2 emssons, whch s wdely accepted as a man contrbutor to global warmng. To safeguard ts energy securty and mtgate global clmate change, Chna has to fnd ways to control ts fossl energy use and CO 2 emssons. n Correspondng author. Tel.: þ ; fax: þ E-mal address: cemzp@nuaa.edu.cn (P. Zhou). Improvng energy effcency has been wdely regarded as one of the most cost-effectve ways to ncrease energy securty, mprove ndustral compettveness and mtgate clmate change (Ang et al., 2010). In Chna, ndustral sector s the largest energy end-user, whch accounted for 71% of total fnal energy consumpton n 2009 (NBSC, 2011b). Clearly, mprovng ndustral energy effcency plays a sgnfcant role for Chna to enhance energy securty and promote low-carbon development. It would therefore be meanngful to measure and compare the ndustral energy effcency performance n Chna, whch may provde emprcal and condensed nformaton for polcy makers to assess the effectveness of energy effcency polces and measures. In lterature, researchers have developed dfferent ndcators for measurng economy-wde energy effcency performance at dfferent levels. For nstance, Ang (2006) propose an analytcal framework for trackng economy-wde energy effcency trends, whch s bult upon a well establshed energy polcy analyss tool ndex decomposton analyss (IDA). Dscusson on dfferent IDA methods can be found n Ang and Zhang (2000) and Ang /$ - see front matter & 2012 Elsever Ltd. All rghts reserved.

2 F. Wu et al. / Energy Polcy 49 (2012) (2004). The IDA-based analytcal framework has been adopted by a number of countres ncludng Canada, New Zealand and the Unted States for trackng ther economy-wde energy effcency trends (Ang et al., 2010). In addton to IDA-based energy effcency ndcators, many researchers have also employed data envelopment analyss (DEA) to assess the energy effcency performance of a set of comparable enttes from a producton effcency pont of vew. DEA, proposed by Charnes et al. (1978), s a well-establshed non-parameter methodology to effcency evaluaton. It uses mathematcal programmng models to compute the dstance between each decson-makng unt (DMU) and the fronter of best practce constructed by the DMUs, based on whch the effcency score of each DMU can be calculated. Wth ts methodologcal advancements, DEA has also receved ncreasng attenton n energy and envronmental studes, n whch energy effcency measurement has been dentfed as an mportant applcaton area of DEA (Zhou et al., 2008). Earler studes dealng wth ths topc nclude Boyd and Pang (2000) and Ramanathan (2000). Later, Hu and Wang (2006) develop a total-factor energy effcency ndex and apply t to evaluate Chna s regonal energy effcency. We et al. (2007) conducts an emprcal analyss of energy effcency n ron and steel sector n Chna by usng Malmqust ndex. Honma and Hu (2008) employ the total-factor energy effcency model to evaluate the regonal energy effcency n Japan. Zhang et al. (2011) use the DEA wndow analyss to nvestgate the dynamc trends n the total-factor energy effcency of a sample of developng countres. In addton to evaluate regonal energy effcency, several researchers have also used DEA to assess ndustral energy effcency performance. For example, Mukheree (2008a, 2008b) employ DEA to assess the manufacturng energy use effcency n the Unted States and Inda. A common feature of the studes mentoned above s that they evaluate total-factor energy effcency performance wthn a producton framework wthout consderng undesrable outputs. However, fossl energy use wll nevtably produce undesrable outputs such as CO 2 emssons (Guo et al., 2011). As dscussed by Zhou and Ang (2008) and Mandal (2010), analyzng energy effcency wthout consderng undesrable outputs may lead to based effcency scores. Motvated by ths ssue, Zhou and Ang (2008) frst ncorporate undesrable outputs nto energy effcency evaluaton and develop several DEA models for evaluatng energy effcency based on envronmental DEA technologes. Snce then, more and more studes conduct energy effcency analyss wthn a ont producton framework of both desrable and undesrable outputs. For nstance, Mandal (2010) use DEA to evaluate the energy effcency of Indan cement ndustry and show that neglectng undesrable outputs would result n based energy effcency scores. Sh et al. (2010) develop an extended DEA model by treatng undesrable outputs as nputs to evaluate the ndustral energy effcency n Chna. Yeh et al. (2010) compare the total-factor energy effcency n Chna manland wth that n Tawan by usng DEA wth undesrable outputs based on data translaton. Ban and Yang (2010) develop a novel Shannon-DEA approach to analyzng the energy and envronmental effcency smultaneously based on the envronmental DEA. Sueyosh and Goto (2010, 2011) propose a new DEA approach for unfed effcency measurement of fossl fuel electrcty generaton by consderng CO 2 emssons. Wang et al. (n press) use DEA wndow analyss to measure the radal and non-radal energy and envronmental effcency of dfferent provnces n Chna by consderng undesrable outputs. Prevous studes have so far developed a number of DEA models wth undesrable outputs to evaluate energy effcency performance. Nevertheless, accordng to our knowledge, the study by Sh et al. (2010) seems to be the only one that contrbutes to use DEA to evaluate Chna s ndustral energy effcency. However, Sh et al. (2010) smply treat the undesrable outputs as nputs whch may not reflect the real producton actvtes well. The study by Førsund (2008) also shows that takng undesrable outputs as nputs wll result n a conflct wth the materal balance equaton. As such, n ths paper we propose to use envronmental DEA models to evaluate ndustral energy effcency performance n Chna. Our envronmental DEA models used depart from the study by Sh et al. (2010) snce the envronmental DEA technology s used to model the ont producton of desrable and undesrable outputs. Accordng to Zhou et al. (2008), envronmental DEA technology has been wdely adopted n the context of energy and envronmental studes. It seems that ths paper s the frst usng t to study Chna s ndustral energy effcency performance. The rest of ths paper s organzed as follows. Secton 2 proposes the nonparametrc DEA models wth undesrable outputs for measurng ndustral energy effcency performance. In Secton 3, we present an emprcal applcaton study on measurng Chna s ndustral energy effcency performance of dfferent provnces over tme. Secton 4 concludes ths study. 2. Methodology 2.1. Envronmental DEA technology In order to use DEA to measure Chna s ndustral energy effcency performance wth undesrable outputs, we need to frst characterze the producton technology. Snce controllng the ncrease n CO 2 emssons has become a focus n Chna, ths paper consder only one undesrable output,.e., CO 2 emssons n modelng ndustral producton process. Assume that each DMU,.e., the ndustral sector of each provnce n Chna, employs captal stock (K), labor force (L) and energy (E) as nputs to produce ndustral value added (Y) and CO 2 emssons (C) as the sngle desrable output and undesrable output, respectvely. The producton technology can be defned as T ¼ðK,L,E,Y,CÞ : ðk,l,eþ can produce ðy,cþ ð1þ Snce fnte nputs can only produce fnte outputs, T s often assumed to be a bounded set n producton theory. In addton, the three nputs and the sngle desrable output are often assumed to be strongly or freely dsposable. It mples that the desrable output can be freely reduced wth the same amounts of nputs or the nputs can be free ncreased wth the same amounts of outputs. Mathematcally, the strong dsposablty of nputs and desrable outputs can be represented as (K 0,L 0,E 0,Y,C)AT (or (K,L,E,Y 0,C)AT) f(k,l,e,y,c)at and (K 0,L 0,E 0 )Z(K,L,E) (or Y 0 ry). It s known that Chnese central government has set the target of reducng unt GDP CO 2 emssons for each provnce, whch requres the concerted efforts from varous energy end-users ncludng ndustral sectors. Snce Chna ndustral sectors heavly depend on fossl energy use, reducng CO 2 emssons for most ndustral sectors s lkely not free n the future. As such, t s logc to assume that the desrable and undesrable outputs n Eq. (1) are weakly dsposable. It means that the proportonal reductons n ndustral value added and CO 2 emssons are possble. In addton, we need to mpose the null-ontness assumpton on T whch mples that the only way to remove all the ndustral CO 2 emssons s to cease the producton actvtes. The weak dsposablty and null-ontness assumptons, whch were frst proposed by Färe et al. (1989), can be mathematcally descrbed as () If (K,L,E,Y,C)AT and 0oyr1, then (K,L,E,yY,yC)AT. () If (K,L,E,Y,C)AT and C¼0, then Y¼0.

3 166 F. Wu et al. / Energy Polcy 49 (2012) So far the envronmental producton technology for modelng the ont producton of desrable and undesrable outputs has been well defned. The next step s to characterze the envronmental producton technology wthn a nonparametrc DEA framework. Suppose that there are,2,..., I provnces and for provnce the vector of nputs, desrable, and undesrable outputs of ndustral sector s (K, L, E, Y, C ). The envronmental DEA technology exhbtng constant returns to scale can be formulated as follows: T ¼ ðk,l,e,y,cþ : l Z0, l K rk, l L rl, l E re, l Y ZY, l C ¼ C,,2,..., I In Eq. (2), l ð,2,...,iþ refer to the ntensty levels at whch the DMUs conduct producton actvtes, whch provdes the weghts for constructng the envronmental DEA technology Statc energy effcency ndex To measure the ndustral energy effcency performance of DMU ( ¼ 1,2,...,I), we frst defne a Shephard sub-vector nput dstance functon for energy use (hereafter referred to as the Shephard energy dstance functon) as follows: D E ðk,l,e,y,c Þ¼supa : ðk,l,e =a,y,c ÞAT ð3þ Eq. (3) attempts to reduce the use of energy by DMU as much as possble whle keepng the resultng nput output combnaton wthn the producton possblty set. The Shephard energy dstance functon D E (K, L, E, Y, C ) measures the degree to whch energy use can be reduced. As such, ts recprocal may be taken as an energy effcency ndex that can be used to compare the ndustral energy effcency performance of dfferent provnces at the same tme scale. Here we refer to the recprocal of the Shephard energy dstance functon as a statc energy effcency ndex (SEEI). The recent study by Zhou et al. (2012) has offered a parametrc fronter method for calculatng SEEI. Dfferent from Zhou et al. (2012), we here propose to use the followng DEA model to derve the SEEI of DMU : SEEI ¼ 1=D E ðk,l,e,y,c Þ¼mnb s:t: XI l K rk l L rl l E rbe l Y ZY l C ¼ C l Z0,,2,...,I ð2þ ð4þ Eq. (4) attempts to contract the amount of energy nput as much as possble for a gven level of non-energy nputs, desrable and undesrable outputs. If SEEI ¼1 t means DMU s techncally effcent n energy use. If SEEI o1, t mples that DMU s techncally neffcent n energy use. A larger SEEI value means that DMU has a better energy effcency performance. We have so far establshed the frst DEA model to measure Chna ndustral energy effcency performance on the bass of envronmental DEA technology. Snce for Chna smultaneously mantanng rapd economc growth, reducng energy consumpton and controllng CO 2 emssons s compulsory for promotng ts low-carbon development (Song et al., 2011), Eq. (4) seems to be approprate as t smultaneous consders economc output, energy use and CO 2 emssons n measurng ndustral energy effcency performance Dynamc energy effcency ndex The DEA model descrbed n Eq. (4) s manly used to conduct cross-secton energy effcency comparsons between dfferent DMUs. For a specfc DMU, t s meanngful to track the changes n energy effcency performance over tme. Followng the deas of the nonparametrc Malmqust productvty ndex developed by Färe et al. (1994) as well as the Malmqust carbon emsson ndex developed by Zhou et al. (2010), we propose a dynamc energy performance ndex (DEPI) for assessng the change n energy effcency performance over tme. Let t and s (tos) refer to two tme perods. Assume that D t E ðkt,lt,et,yt,ct Þ and Ds E ðkt,lt,et,yt,ct Þ are the Shephard energy dstance functons of DMU based on ts nputs and outputs at perod t for the envronmental DEA technologes at t and s, respectvely. Further assume that D t E ðks,ls,es,ys,cs Þ and Ds E ðks,ls,es,ys,cs Þ are, respectvely, the Shephard energy dstance functons of DMU based on ts nputs and outputs at perod s for the envronmental DEA technologes at t and s. The DEPI s defned as follows: " DEPI ðt,sþ¼ Dt E ðkt,lt,et,yt,ct ÞDs E ðkt,lt,et,yt,ct Þ # 1=2 D t E ðks,ls,es,ys,cs ÞDs E ðks,ls,es,ys,cs Þ ð5þ DEPI (t,s) can be used to measure the change n energy effcency performance of DMU from perod t to perod s. DEPI (t,s)41 (or DEPI (t,s)o1) ndcates that the energy performance has mproved (or deterorated). To calculate DEPI, we need to calculate four Shephard energy dstance functons,.e., D l 1 E ðk l 2,L l 2,E l 2,Y l 2,C l 2 Þ, l 1,l 2 A{s,t}. The recprocal of D l 1 E ðk l 2,L l 2,E l 2,Y l 2,C l 2 Þ can be calculated by solvng the followng DEA model: ½D l 1 E ðk l 2,L l 2,E l 2,Y l 2,C l 2 ÞŠ 1 ¼ mnb s:t: XI l K l 1 rk l 2 l L l 1 rl l 2 l E l 1 rbe l 2 l Y l 1 ZY l 2 l C l 1 ¼ C l 2 l Z0,,2,...,I Lke the Malmqust carbon emsson performance ndex defned n Zhou et al. (2010), DEPI (t,s) can also be decomposed ð6þ

4 F. Wu et al. / Energy Polcy 49 (2012) Table 1 Data sources and processng. Varable Data source Data complaton Industral captal stock (K) Industral labor force (L) Industral energy consumpton (E) Industral value added (Y) Chna Statstcal Yearbook , Chna Statstcal Yearbook of Fxed Assets Chna Statstcal Yearbook Chna Energy Statstcal Yearbook , and Chna Statstcal Yearbook 1998, 60 Years of New Chna Statstcal Data Complaton Industral CO 2 Chna Energy Statstcal Yearbook , emssons (C) and The data on perpetual nventory method s used to calculate the ndustral captal stock: the formula s K t ¼K t 1 (1 d)þi t,where K t and K t 1 are, respectvely, the ndustral captal stock n year t and year t 1, d represents the deprecaton rate of captal stock that s assumed to be 10% n accordance wth the suggestons by Zhang et al. (2004), and I t refer to the ndustral fxed captal formaton n year t that has been replaced by ndustral fxed captal nvestment due to data unavalablty. The data are converted nto 1997 constant prces by usng the prce ndces for fxed captal nvestment. Measured by annual average number of persons employed by ndustral enterprses above desgnated sze. Involves the consumpton of raw coal, cleaned coal, other washed coal, brquettes, coke, coke oven gas, other gas, crude ol, gasolne, kerosene, desel ol, fuel ol, lquefed petroleum gas, refnery gas natural gas, other petroleum products, heat, electrcty, and other energy. All are converted to the standard coal equvalent based on relatve factors. Measured by the ndustral value added ndcator of ndustral enterprses above desgnated sze and deflated to 1997 constant prces by usng producer prce ndces for manufactured goods. Follow IPCC gudelnes to use the formula. CO 2 ¼ Xn ða b ÞE Z To calculate CO 2 emssons from termnal energy consumpton. E s apparent fuel consumpton, a s carbon emsson factor, b s carbon stored factor and Z s fracton of carbon oxdzed. nto two components as follows: EFFCH ðt,sþ¼ Dt E ðkt,lt,et,yt,ct Þ D s c ðks,ls,es,ys,cs Þ ¼ SEEI s SEEI t ð7þ Table 2 Descrptve statstcs of nputs and outputs. Varable Unt Mn Max Mean Std. dev. " TECHCH ðt,sþ¼ Ds E ðkt,lt,et,yt,ct ÞDs E ðks,ls,es,ys,cs Þ # 1=2 D t E ðkt,lt,et,yt,ct ÞDt E ðks,ls,es,ys,cs Þ ð8þ The frst component,.e., Eq. (7), s an effcency change component, whch measures the change n the statc energy effcency ndex of DMU. The second component,.e., Eq. (8), s a technologcal change component, whch measures how much the envronmental DEA technology shfts from perod t to s. 3. Emprcal study The models ntroduced n Secton 2 have been employed to evaluate the ndustral energy effcency performance of dfferent provnces n Chna n In Secton 3.1, we descrbe the data used. Sectons 3.2 and 3.3 present the results of statc and dynamc energy performance ndexes for Chna s ndustral sectors Data The data on the fve varables ncludng K, L, E, Y and C for 28 provnces n Chna are collected for the current study. The three provnces such as Hanan, Nngxa and Tbet are not consdered due to the unavalablty or nconsstency of ther data. Table 1 descrbes the data sources and complaton procedures. Table 2 shows the descrptve statstcs of the nput and output varables Statc energy effcency performance We frst compute the SEEIs for 28 provnces usng Eq. (4), whch are dsplayed n Table 3. It can be seen from Table 3 that the average ndustral statc energy effcency performance score n Chna durng the sample perod was It mples that as a whole t s possble to reduce the ndustral energy consumpton K 100 mllon Yuan , , ,07.92 L 10,000 workers , E 10,000 tons of coal equvalent , , ,66.39 Y 100 mllon Chnese Yuan , , ,81.2 C 10,000 tons ,512 14, , by 18.4% by removng the energy neffcency n dfferent provnces. Table 3 also shows that the ndustral statc energy effcency ndexes vared across dfferent regons. In the east regon, Guangdong and Shangha regstered for the hghest average ndustral energy effcency score (¼1.000) whle Laonng regstered for lowest score (¼0.704). In the central regon, the ndustral statc energy effcency ndexes ranged from 0.66 to In the west regon, Inner Mongola had the hghest statc ndustral energy effcency performance ndex, whle Qngha and Guangx were found to be the worst ndustral statc energy effcency performers wth scores below 0.6. Table 3 also shows that Guangdong and Shangha were most effcent n terms of ndustral statc energy effcency performance, whch s not surprsng and consstent wth the conclusons drawn by some prevous studes such as Sh et al. (2010) and Mcho and Katsuya (2007). However, Inner Mongola and Shanx, whch are usually consdered as energy-neffcency regons, performs relatvely better n statc ndustral energy effcency. A possble explanaton s that CO 2 emssons are ncluded n the producton framework so that the two provnces became closer to the fronter of best practce. It mples that the ncluson of undesrable outputs may provde dfferent results of energy effcency scores compared to the case that only desrable outputs are consdered. Fg. 1 shows the regonal ndustral statc energy effcency performance n the sample perod. It can be easly found that the energy effcency had a down trend n central and west areas whch may be caused by ther relatve smaller progress n

5 168 F. Wu et al. / Energy Polcy 49 (2012) Table 3 Industral statc energy effcency performance n dfferent provnces n Chna from 1997 to Provnce Average (E)Beng (E)Tann (E)Hebe (E)Laonng (E)Shangha (E)Jangsu (E)Zheang (E)Fuan (E)Shandong (E)Guangdong (C)Shanx (C)Jln (C)Helongang (C)Anhu (C)Jangx (C)Henan (C)Hube (C)Hunan (W)Inner Mongola (W)Guangx (W)Chongqng (W)Schuan (W)Guzhou (W)Yunnan (W)Shaanx (W)Gansu (W)Qngha (W)Xnang Annual average Note: E, C, and W n parentheses refer to the east, central, and west regons, respectvely East Central West SEEI Value Fg. 1. The average SEEI scores of dfferent regons over tme. promotng energy savngs compared to the east area. Fg. 1 also shows that the west area gradually caught up wth central area, but the gap between east area and west/central area had become larger. Furthermore, t has been found that the east regon performed best whle the west regon performed least n ndustral statc energy effcency, whch s consstent wth the conclusons drawn by Sh et al. (2010). The rankng of energy effcency n the three regons s consstent wth the status of economc development n Chna. In Chna, the east area s the most whle the west area s the least developed regon. In the east area the best technologes can be dffused more effcently than the central and west areas. On the other hand, due to ts relatvely weak power n absorbng new technology and low degree of openngup, the west area had the worst performance n ndustral energy effcency. Snce both ths study and Sh et al. (2010) deal wth Chna s ndustral energy effcency performance, t s meanngful to carry out a smple comparson between the results gven n the two studes. It s found that our calculated average ndustral statc energy effcency scores of the three areas n Chna are generally hgher than those gven n Sh et al. (2010), whch could be manly due to the dfferences n the modelng framework used. Frst, t comes from the dfference n the choce of captal nput. Whle ths paper employs the ndustral captal stock calculated from perpetual nventory method, Sh et al. (2010) uses ndustral fxed assets as the captal nput. Second, Sh et al. (2010) treat undesrable output as nput and reduce t and energy nput at the same rate. However, ths study uses a tght constrant to model CO 2 emssons whch could make the DMUs much closer to the fronter of best practce Dynamc energy effcency performance We also compute the DEPIs to assess the changes n ndustral energy effcency of the 28 provnces over tme. Snce the producton fronter constructed by the observatons from a

6 F. Wu et al. / Energy Polcy 49 (2012) prevous year may not enclose all the observatons from the second year, some mx-perod lnear programmng models may be nfeasble. To overcome ths ssue, we follow Färe et al. (2007) to use the three-year wndows approach to construct the envronmental DEA technology. That s to say, the envronmental DEA technology n perod t s constructed from the observatons n perod t, t 1 and t 2. Nevertheless, when use the envronmental DEA technology for perod t to assess the observatons for perod tþ1, there are stll several nfeasble lnear programmng models. Followng Zhou et al. (2010), we set the effcency scores to be unty for these nfeasble lnear programmng models. Table 4 shows the DEPI of 28 provnces for all the consecutve two-year perods n It can be seen from Table 4 that durng the sample perod the 28 provnces as a whole experenced a postve change (¼1.056), mplyng that the ndustral energy effcency was mproved by 5.6% annually snce However, four perods of tme,.e., 2000/2001, 2003/2004, 2004/2005 and 2007/2008, dsplayed a negatve shft (below unty). The provncal average DEPI estmates durng the sample perod ndcate that all the provnces except Shanx and Jangx had an mprovement n ther ndustral energy effcency. Among them, Beng and Fuan n the east regon and Yunnan n the west regon were found to have the hghest annual average growth rate greater than 10%. Fg. 2 shows the average DEPI of dfferent regons over tme. It ndcates that the central and west areas have the same trend and the three regons gradually converge to the same level. Next, we decompose DEPI nto the effects of statc energy effcency ndex change (catchng-up effect) and technologcal change (fronter shft effect) usng Eqs. (7) and (8) n order to dentfy the drvng factors of ndustral energy effcency performance change and quantfy ther mpacts (Km and Km, n press). Table 5 shows the statc energy effcency performance change (EFFCH) components obtaned. It s found that the 28 provnces as a whole had a drop n ther SEEI scores over tme. The results at regonal level reveal that Shangha, Guangdong and Inner Mongola dd not experence changes n ther techncal effcency (¼1.000) over tme, whch means that they were always on the producton fronter. Among the 28 provnces, 19 provnces showed a decrease n annual effcency score, whch reveals that these provnces were not successful n catchng up the fronter of best practce. Fg. 3 shows the average EFFCH values of dfferent regons over tme. It can be seen that n the sample perod the scores of the three regons are qute close to one (the mnmum and the maxmum 1.2) and most of them are below one. We may conclude that the techncal effcency dd not experence sgnfcant change and the catchng-up effect was not obvous. The effcency change mght not be the man contrbutor of Chnese regonal ndustral energy effcency mprovement. Table 6 shows the results of the technologcal change component for all the provnces. Of the 308 entres, only 83 regstered for a negatve shft n technology. That s to say, 73% of the entres regstered for a postve shft n technology. Notably, Beng, Jangsu, Fuan and Yunnan had an mprovement annually by over 10%. Besdes, all the 28 provnces showed technologcal mprovement durng the sample perod and only two perods of tme,.e., 2000/2001 and 2003/2004, saw a technologcal regress. Fg. 4 shows the average TECHCH scores of dfferent regons over tme. It ndcates that the central and west areas have caught up wth east area n magntude n recent years, whch mght be an ndcaton that they had experenced a sgnfcant technologcal mprovement. Besdes, among the 33 TECHCH scores, only sx are below one and the average score s We may conclude that the mprovement n Chnese regonal ndustral energy effcency performance s manly attrbutable to technologcal mprovement. To study the overall ndustral energy effcency changes of the 28 provnces from 1997 to 2008, we have also calculated the cumulatve DEPI and ts contrbutng components n Table 7 shows the results obtaned wth 1997 as the base year. It s found Table 4 The DEPI values of 28 provnces from 1997/1998 to 2007/2008. Provnce 1997/ / / / / / / / / / /2008 Average (E)Beng (E)Tann (E)Hebe (E)Laonng (E)Shangha (E)Jangsu (E)Zheang (E)Fuan (E)Shandong (E)Guangdong (C)Shanx (C)Jln (C)Helongang (C)Anhu (C)Jangx (C)Henan (C)Hube (C)Hunan (W)Inner Mongola (W)Guangx (W)Chongqng (W)Schuan (W)Guzhou (W)Yunnan (W)Shaanx (W)Gansu (W)Qngha (W)Xnang Annual average

7 170 F. Wu et al. / Energy Polcy 49 (2012) DEPI Value East Central West Fg. 2. The average DEPI of dfferent regons over tme. Table 5 Statc energy effcency performance change component of DEPI from 1997/1998 to 2007/2008. Regon 1997/ / / / / / / / / / /2008 Average (E)Beng (E)Tann (E)Hebe (E)Laonng (E)Shangha (E)Jangsu (E)Zheang (E)Fuan (E)Shandong (E)Guangdong (C)Shanx (C)Jln (C)Helongang (C)Anhu (C)Jangx (C)Henan (C)Hube (C)Hunan (W)Inner Mongola (W)Guangx (W)Chongqng (W)Schuan (W)Guzhou (W)Yunnan (W)Shaanx (W)Gansu (W)Qngha (W)Xnang Annual average East Central West EFFCH Value Fg. 3. The average EFFCH values of dfferent regons over tme.

8 F. Wu et al. / Energy Polcy 49 (2012) Table 6 Envronment DEA technologcal change component of DEPI from 1997/1998 to 2007/2008. Provnce 1997/ / / / / / / / / / /2008 Average (E)Beng (E)Tann (E)Hebe (E)Laonng (E)Shangha (E)Jangsu (E)Zheang (E)Fuan (E)Shandong (E)Guangdong (C)Shanx (C)Jln (C)Helongang (C)Anhu (C)Jangx (C)Henan (C)Hube (C)Hunan (W)Inner Mongola (W)Guangx (W)Chongqng (W)Schuan (W)Guzhou (W)Yunnan (W)Shaanx (W)Gansu (W)Qngha (W)Xnang Annual average TECHCH Value East Central West Fg. 4. The average TECHCH of dfferent regons over tme that the 28 provnces as a whole showed an mprovement n ndustral dynamc energy effcency performance by 82.3% from 1997 to The cumulatve EFFCH s found to be below unty, whch shows that Chnese ndustral energy effcency had experenced a negatve change n techncal effcency from 1997 to Wth regards to the ndvdual provnce, Beng, Yunnan, Tann, Fuan and Zheang are the top fve whle Xnang, Hebe, Hunan, Shanx and Jangx are the bottom fve performs. 4. Concluson Ths study ntroduces several nonparametrc DEA models wth CO 2 emsson to evaluate ndustral energy effcency based on the envronmental DEA technology. Consderng the fact that reducng CO 2 emssons for most Chna ndustral sectors s lkely not free n the future, we assume that the CO 2 emssons as an undesrable output are weakly dsposable n ths paper. Then we propose several DEA models to evaluate both statc and dynamc energy effcency performance of ndustral sectors n Chna s 28 provnces from 1997 to The dynamc energy effcency performance ndexes have also been decomposed to ts two contrbutng components to study what are drvng the change n energy effcency performance over tme. The emprcal study shows that the 28 provnces could reduce energy consumpton by 18.4% annually through energy effcency mprovement. East area has the hghest average energy effcency score followed by central and west areas, whch s consstent wth the fndngs by prevous studes. Dynamc energy effcency analyss shows that Chna s ndustral energy effcency mproved by 5.6% annually snce By decomposng the DEPI nto ts two contrbutng components, we fnd that statc energy effcency change had a negatve mpact whle technologcal change had a postve mpact on the change n dynamc energy effcency

9 172 F. Wu et al. / Energy Polcy 49 (2012) Table 7 Cumulatve DEPI and ts decomposton n 2008 (1997¼1). Regon DEPI EFFCH TECHCH RANK (E)Beng (E)Tann (E)Hebe (E)Laonng (E)Shangha (E)Jangsu (E)Zheang (E)Fuan (E)Shandong (E)Guangdong (C)Shanx (C)Jln (C)Helongang (C)Anhu (C)Jangx (C)Henan (C)Hube (C)Hunan (W)Inner Mongola (W)Guangx (W)Chongqng (W)Schuan (W)Guzhou (W)Yunnan (W)Shaanx (W)Gansu (W)Qngha (W)Xnang Mean performance. It ndcates that the energy effcency mprovement n Chna s ndustral sector s manly drven by technologcal mprovement. It should be ponted out that ndustral energy effcency performance s relevant to not only technology and techncal effcency but also some other factors. Further research may be carred out by explorng the nfluencng factors of ndustral energy effcency scores usng statstcal regresson analyss. Gven data avalablty, ths study could also be expanded by usng the data over a longer tme perod. 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