Assessment and Decomposition of Total Factor Energy Efficiency: An Evidence Based on Energy Shadow Price in China

Transcription

1 sustainability Article Assessment Decomposition Total Facr Energy Efficiency: An Evidence Based on Energy Shadow Price in China Peihao Lai 1, Minzhe Du 2, Bing Wang 2, * Ziyue Chen 3 1 Department mamatics, College Information Science Technology, Jinan University, Guangzhou , China; laipeihao@stu2013.jnu.edu.cn 2 Department Economics, School Economics, Jinan University, Guangzhou , China; dmz@stu2014.jnu.edu.cn 3 Institute Industrial Economics, Jinan University, Guangzhou , China; zychen@stu2014.jnu.edu.cn * Correspondence: twangb@jnu.edu.cn; Tel./Fax: Academic Edir: Marc A. Rosen Received: 25 January 2016; Accepted: 19 April 2016; Published: 26 April 2016 Abstract: By adopting an energy-input based directional distance function, we calculated four types energy (i.e., coal,, gas electricity) among 30 areas in China from Moreover, a macro-energy efficiency index in China was estimated divided in intra-provincial technical efficiency, allocation efficiency energy input structure inter-provincial energy allocation efficiency. It shows that tal energy efficiency has decreased in recent years, where intra-provincial energy technical efficiency drops markedly extensive mode energy consumption rises. However, energy structure allocation improves slowly. Meanwhile, lacking an integrated energy market leads loss energy efficiency. Furr improvement market allocation structure adjustment play a pivotal role in increase energy efficiency. Keywords: energy efficiency; ; efficiency decomposition; directional distance function 1. Introduction Since reform opening up, extensive economic growth mode high input high energy consumption has made contributions boom Chinese economy side effect energy environmental problems. With development urbanization industrialization, contradiction energy supply dem would be evident in long term due growth energy consumption. For example, energy consumption reached 42.6 trillion Ton stard Coal Equivalent (Tce) in China in Saving energy reducing consumption is necessary for sustainable development, owing non-renewability scarcity fossil fuels such as coal gas. Therefore, in China s 12th Five-Year Plan, re are some main targets proposed. The first target is reduce energy consumption in GDP by 16% compared that in 2010 by control energy consumption intensity tal energy. The second one is optimize energy structure by lowering reliance on fossil fuels. The third target is promote marketization energy by reforming energy pricing mechanism. To realize se aims, we need answer se three questions: (1) Is energy pricing reasonable in China? (2) Which level is tal macro-energy efficiency at in China? (3) Is promotion energy marketization optimization energy structure effective in China? Compared advanced level in world, tal energy efficiency in China is not high enough for low energy using efficiency. By evaluating tal facr energy efficiency different economies, China is found as economy lowest value in 17 APEC countries or areas [1]. Even though tal facr energy efficiency has greatly increased in China in recent years, narrowed gap that developed countries, China still has large quantities energy consumption, leading Sustainability 2016, 8, 408; doi: /su

2 Sustainability 2016, 8, potentiality energy-saving emission-reduction [2]. Increasing numbers scholars focus on energy inefficiency technique inefficiency for reason low energy efficiency in China [3]. Firstly, as for energy resources in China, proportion coal is o large while production clean energy is not enough, resulting in unreasonable energy input structure. This part loss energy efficiency can be remedied by upgrading energy structure, aiming at restricting carbon emission intensity [4,5]. The or reason is energy market disrtions resulted from government intervention. During economic transformation, lag energy marketization causes energy disrtion, which furr restrains improvement energy efficiency in China [6,7]. Therefore, some measures, such as energy marketization, reduction government intervention optimization energy allocation by market, contribute realization energy saving emission reduction. In this paper, a macro-energy efficiency index in China (H) is established for quantized analyses impact facrs, such as energy structure allocation on energy efficiency. The index is n decomposed in intra-provincial technical efficiency (ATE), allocation efficiency energy input structure efficiency (AAE) inter-provincial energy allocation efficiency (RE). The first index is efficiency loss caused by technical inefficiency, while or two are allocation inefficiency caused by market disrtions. The result research would fer oretical support for assessment energy structure adjustment promotion market reform energy s. The rest paper is organized as follows: Section 2 reviews tal facr energy efficiency. Section 3 introduces energy-input based directional distance function (DDF) estimation energy constructs macro-energy efficiency indices as well as corresponding deposition model. Section 4 is about data processing analysis. Section 5 is an empirical analysis a case. The last section is conclusion. 2. Literature Review To evaluate energy efficiency in China accurately, we are required select indices energy efficiency reasonably, which can mainly be divided in single facr energy efficiency tal facr energy efficiency. With feature easy comparison simple calculation, single facr energy efficiency is introduced mainly for analyzing relationship single energy input output. Early research mostly applied this method evaluate energy efficiency. For example, energy consumption Chinese industrial secr in 1990s was decomposed by an improved Laspeyres Index [8]. The research found that technical effect is leading facr changes in industrial energy consumption. This method is criticized by many researchers because in single facr energy efficiency, contribution labor capital output is neglected, n substitution effect among different production facrs is not taken in account. Therefore, Hu Wang [9] proposed concept tal facr energy efficiency originally, which brought production facrs such as labor capital in efficiency analysis. By considering substitution effect between energy or production facrs, real production process could be well simulated. Thus, this method became main evaluation method for energy efficiency. By window analysis DEA method, tal facr energy efficiency 23 developing countries from were tested before analyzing relation between tal facr energy efficiency incomes based on Tobit model [4]. Meanwhile, a parametric meta-frontier approach based on Shephard energy distance was introduced, dividing 30 provinces in China in three technical frontiers for analyzing efficiency differences due technology gap among areas [10]. Some researches [11 13] discussed multiple inputs outputs based on model multiple input single output. That means y combined undesirable outputs such as pollutant during production process in output system, which better simulated byproducts in actual production process such as carbon dioxide, sulfur dioxide or industrial wastes. For example, Fujii et al. [11] employed a DDF that can hle multiple inputs outputs compute change TFP. Then it was found that environmentally sensitive productivity (ESPs) China s iron steel industry had continuously improved, even in period when conventional economic productivity (CEP)

3 Sustainability 2016, 8, declined in 1990s. Moreover, Li et al. [12] brought byproducts (carbon dioxide sulfur dioxide) in production process, establishing an environmental tal facr energy efficiency index. According SBM model, energy efficiency is commonly lower than that regardless bad output, which means energy efficiency in China is probable be over-estimated. Moreover, Li et al. [13] introduced a meta-frontier framework improved DDF measure meta-frontier energy efficiency carbon dioxide emissions. The research indicated that main reason for low energy efficiency in China is managerial failure in east west areas, technological differences in middle part. Recently, more more scholars [14 17] pay attention impact energy input structure tal facr energy efficiency, resulting in incapability estimating energy allocation efficiency, which was first analyzed by Ouyang Sun [14] as well as Sheng [15,16]. As we know, underestimation energy leads excessive energy consumption during production process. For example, Ouyang et al. [14] pointed out that facr s capital, labor energy are disrted in China due government regulations. Moreover, energy s is relatively low compared capital, while is relatively high compared labor. Meanwhile, energy type would affect energy allocation significantly [16,17]. Kumar et al. [16] estimated Morishima elasticity substitution between different fossil fuels renewable resources, found that types energy would make different contributions tal facr energy efficiency. All se facrs would have furr influence on tal facr energy efficiency. Energy reflects marginal cost production or marginal output, which is when production process gets best allocation. The estimation can fer oretical support for policy decision. In recent years, is widely used in area estimation pollutant [18 21]. For example, Kaneko et al. [18] used a non-parametric DDF approach calculate s sulfur dioxide, n estimated regional pollution abatement cost under financial allocation strategy in China. Moreover, Ishinabe et al. [19] evaluated greenhouse gas (GHG) emissions for 1024 international companies. Meanwhile, Molinos-Senante et al. [19] analyzed carbon dioxide emission efficiency s sewage plant based on a parametric quadratic DDF got conclusion that mechanism carbon dioxide contributes emission reduction better than comm--control regulation. Among se studies, methods estimate differ from one anor, depending on respective needs. For example, Sheng et al. [15] estimated energy s by non-parametric approach. With DEA evaluate energy DDF, result showed that both national regional energy s are higher than market. Furr research (Sheng et al [16]) found that situation tal facr energy inefficiency in China has improved during 15 years. The similar method has been adopted by Kaneko et al. [18] Li et al. [13]. Although non-parametric DEA has advantage envelope characteristics, it shows limitation excluding statistical noise. Based on schastic frontier analysis (SFA), Ouyang et al. [14] analyzed facr allocative efficiency China s industrial secr, estimated energy savings potential from perspective allocative inefficiency. Comparing DEA, SFA has limitation envelopment, but it is convenient do parametric test. Anor method estimate is parametric linear programming (PLP). It combines envelope feature DEA parameters nature SFA, which is widely used estimate DDF [17,19]. For instance, Kumar et al. [17] applied quadratic function approximate distance function, estimated Morishima elasticity substitution between different fossil fuels renewable resources. In this paper, we are extending work Ouyang Sun [14], Sheng et al. [16] Kumar et al. [17] include energy structure. Firstly, an energy-input DDF is established evaluate tal facr energy efficiency. Next,we break down in four sources energy. Similar Kumar et al. [17], by adopting PLP, we set up a macro-energy index in China divide energy

4 Sustainability 2016, 8, inefficiency in three parts, energy technical inefficiency in each area, energy efficiency loss in each area due irrational input structure allocation inefficiency due energy misallocation nationwide. 3. Methodology 3.1. Energy Efficiency Directional Distance Function Firstly, we assume that in each province re are J types energy input, e pe 1, e 2,, e J q P R`J, N types or input, x px 1, x 2, x N q P RǸ, M kinds output, y py 1, y 2, y M q P R`M. Therefore, energy technology is represented by production possibility set T. T tpx, e, yq : px, eq can produce yu (1) where T, as a set containing all possible input output vecrs, is constructed for describing technical efficiency production. According production ory, T is assumed as a closed finite set. Besides, both inputs outputs should have property strong disposability. That means, if px 1, e 1 q ě px, eq y 1 ď y, n px 1, e 1, y 1 q P T. Aiming at reming neglect definition measurement energy efficiency, Zhou et al. [22] defined energy-input based Shephard Distance Function. D I px, e, yq sub tα px, e{α, yq P Tu (2) According production ory, energy-input based Shephard Distance Function has two features. One is D I px, e, yq ď 1. The or is that D I px, e, yq is a homogeneous linear function energy input. Meanwhile, energy-input based directional distance function reflects maximal cuttable ratio energy input when technique or facr inputs (i.e., labor, capital, etc.) remain [23]. Therefore, e{d I px, e, yq, as energy input when maximal energy efficiency occurs in area, is optimal energy input oretically. Assuming that g e is direction vecr g e 0, energy-input based directional distance function is Ñ DI px, e, y; g e q maxtβ : px, e βg e, yq P Tu (3) Corresponding homogeneity energy-input based Shephard Distance Function, feature energy-input based directional distance function is Ñ DI px, e βg e, y; g e q Ñ D I px, e, yq β (4) Let g e e, relation between directional distance function energy-input based Shephard Distance Function could be calculated. Ñ DI px, e, y; eq suptβ : D I px, e βe, yq ď 1u sup tβ : p1 βqd I px, e, yq ď 1u suptβ : β ď D I px, e, yq 1 D I px, e, yq u (5) Directional input distance function should satisfy following constraints as well. If e 1 ě e, n Ñ D I px, e 1, yq ě Ñ D I px, e, yq (6) If Ñ D I px, e, y; g e q ď 1, α ě 1, n Ñ D I px, αe, yq ď 1 (7)

5 Sustainability 2016, 8, Energy Shadow Evaluation Shadow energy reflects both scarcity marginal use value energy. Moreover, scarcity energy is pivotal for working out reasonable energy, regulating supply dem relation energy market promoting enterprises raise energy using efficiency. As a result, decomposition energy efficiency is analyzed by in this paper. Dual relation between maximized prit function energy-input based directional distance function should be considered for calculating energy input. We assume that J types energy inputs are p pp 1, p 2,, p J q P R`J, M types or input facrs are q pq 1, q 2,, q M q P R`M, N types outputs are w pw 1, w 2,, w N q P RǸ. Then prit function is πpq, p, wq max x,e,y twy qx pe : e P Tu (8) The prit function means that producer would earn maximal prit when output is given all facrs except energy remain. Because Ñ D I px, e, y; g e q ě 1 be equivalent e P T, prit function could be converted as πpq, p, wq max x,e,y twy qx pe : Ñ D I px, e, y; g e q ě 1u (9) It is feasible reduce energy input along direction g. converted as Thus prit, function could be πpq, p, wq ě pwy qx peq ` p ˆ Ñ D I px, e, y; g e q ˆ g e (10) The surplus receipt is value conserved energy. After transposition, Ñ DI px, e, y; g e q ď πpq, p, wq pwy qx peq pg e (11) Therefore, defined directional distance function is " * Ñ πpq, p, wq pwy qx peq DI px, e, y; g e q min p pg e (12) Envelope orem is applied in Equation (12). Then model is B Ñ D I px, e, y; g e q Be B Ñ D I px, e, y; g e q By p pg e ě 0 (13) w pg e ď 0 (14) Therefore, if jth output is w i, ith type energy could be calculated. p i B Ñ DI px, e, y; g e q{bpe i q w j B Ñ D I px, e, y; g e q{bpy j q (15) 3.3. Parametric Forms Directional Input Distance Function Translog function is commonly introduced for parameterizing Shephard Distance Function but not directional distance function because form translog function cannot be limited satisfy transfer properties. Moreover, quadratic function is second-order approximate an unknown distance function, which could perfectly satisfy properties directional distance function [24]. Therefore, energy efficiency is estimated by quadratic directional distance function.

6 Sustainability 2016, 8, Ñ D t Ipx t i, et i, yt i ; g eq α ` Nř n 1 ` 1 Nř 2 ` Nř β n x t nk ` Mř n 1 n 1 Mř n 1 m 1 m 1 α m y t nk ` Nř β nn 1 xnk t xt nk ` 1 Mř 2 δ nm x t nk yt mk ` Nř Jř γ j e t jk j 1 m 1 m 1 n 1 j 1 Mř α mm 1y t mk yt mk ` 1 Jř 2 Jř η nj xnk t et jk ` Mř m 1 j 1 j 1 j 1 Jř µ mj y t mk et jk Jř γ jj 1e t jk et jk (16) where α α 0 ` Kř k 1 D Sk S k ` Tř Dt t Time t. t 1 Parameter linear program was used for estimation quadratic directional distance function in this paper according Yuan [24] Wang et al. [25]. S. t. Tÿ max Kÿ t 1 k 1 (1) Energy-input based directional distance function (2) Mononicity in inputs j ÑD t Ipxk t, et k, yt k, g eq 0 (17) Ñ D t Ipx t k, et k, yt k ; g eq ď 1 (18) B Ñ D t Ipx t k, et k, yt k ; g eq Bx i ě 0 (19) (3) Mononicity in outputs B Ñ D t Ipx t k, et k, yt k ; g eq Be i ě 0 (20) B Ñ D t Ipx t k, et k, yt k ; g eq By i ď 0 (21) (4) Homogeneous linear restriction Jř γ j 1; j 1 Mř m 1 α mm 1 Jř γ jj 1 j 1 1 Mř m 1 δ nm 1 M ř Jř µ mj, m 1, M j 1 m 1 µ mj, j 1, J Jř η nj, n 1, N j 1 (22) (5) Symmetry quadratic form β nn 1 β n 1 n, n n 1 ; α mm 1 α m 1 m, m m 1 ; γ jj 1 γ j 1 j, j j 1 ; (23)

7 Sustainability 2016, 8, Therefore, i-th type energy is Energy efficiency corresponded area is p i BÑ D t Ipxk t, et k, yt k ; g eq{bpe i q B Ñ D t w (24) Ipxk t, et k, yt k ; g eq{bpyq Fpx, e, yq 3.4. Establishment Decomposition Overall Energy Efficiency 1 D I px, e, yq 1 β (25) Referring research Li Ng [26], a national index was constructed called national energy efficiency. To build index, first step is construct a virtual area, where input ř output area equals national average. That is say, x I x i {I x 0 {I i 1 ř e I e i {I e 0 ř {I are national input, while y I y i {I y 0 {I is national output. When T is i 1 i 1 convex, national energy efficiency equals energy efficiency virtual area. Therefore, national energy efficiency is: Hpx 0, e 0, y 0 q 1 β. According Li Ng [26], first facr equals sum minimal energy input all areas, R TE ř I Fpx i, e i, y i q ˆ pp ˆ e i q, divided by tal real energy i 1 input, R 0 p ˆ e 0. The facr equals weighted average technical efficiency index each area as well. ATE RTE R 0 Iř Fpx i, e i, y i q ˆ p ˆ e i p ˆ e 0 i 1 Iÿ τ i Fpx i, e i, y i q (26) where p is s minimal energy input in China. The second facr equals minimal real energy input each area, R AE I ř i 1 Fpx i, e i, y i q ˆ p i ˆ e i, divided by sum minimal energy input all areas. Therefore, misallocation available energy due, which is called allocation efficiency energy input structure, would lead efficiency loss energy structure. AAE RAE R TE Iř tr i i 1 i 1 R TE (27) where tr i min tp i ˆ e i : px i, e i, y i q P Tu. The third facr is efficiency loss due energy misallocation among areas. For example, when area A is under condition increasing returns scale, while area B is under condition decreasing returns scale, energy flows from area A B would lead raise tal output. By eliminating effect first two facrs from tal energy efficiency, impact misallocation is RE Hpx, e, y q ˆ pp ˆ e 0 q R AE (28) where RE is inter-provincial energy allocation efficiency index. Based on definitions above, national energy efficiency could be decomposed as Hpx 0, e 0, y 0 q ATE ˆ AAE ˆ RE (29)

8 Sustainability 2016, 8, Sustainability 2016, 8, Data 4. Data After dropping data Hong Kong, Macau, Taiwan Tibet, data 30 areas from After are selected dropping make data an input output Hong Kong, panel Macau, for Taiwan empirical study. Tibet, All data data 30 come areas from fromchina 1998 Statistical 2012 are Yearbook selected[27] make China an input output Energy Statistical panel for Yearbook empirical [28]. study. All data come from China Statistical The variables Yearbook include [27] China inputs Energy (labor, Statistical capital sck Yearbook energy [28]. consumption), an output (gross The regional variables product). includein previous inputs (labor, studies, capital labor sck force was energy widely consumption), used as index an output labor input. (gross However, regional product). human capital In previous is a better studies, index labor on force measuring was widely contribution used as index labor during labor production input. However, [29]. Thus, human capital index in isthis a better paper index is on number measuring employees contribution weighted labor by average during education production attainment [29]. Thus, different index in areas thisat paper end is number year. As for employees capital weighted sck, we by introduce average education permanent attainment invenry different method areas estimate end capital year. input. As for According capital sck, weresearch introduce [30], investment permanent invenry fixed capital method is selected estimate as investment capital input. index According year. By research constructing [30], investment index fixed capital investment is selected in as fixed assets investment from index , year. real By constructing investment s areas indexare calculated. investmentthe model fixedfor assets capital from sck 1952 accounting 2010, is real investment s areas are calculated. The model for capital sck accounting is Kt It (1 ) Kt 1 (30) where K is sck capital in period K t I ` p1 δqk t 1 (30) t, is depreciation rate, I is amount t investment where K t is in period sck t. capital in period t, δ is depreciation rate, I t is amount investment in period Data t. merely energy consumption each area come from China Energy Statistical Yearbook [28]. Data After categorizing merely energy consumption data in four each main area types, comeincluding from China coal, Energy, gas Statistical electricity Yearbook (i.e., [28]. hydropower After categorizing nuclear datapower) in four based mainon types, energy including balance coal, sheet, gas each area, electricity measurement (i.e., hydropower unit is converted nuclear power) 1000 based Tce by on heat energy quantity. balance Specifically, sheet each data area, Hainan measurement in 2002 unit data is converted Ningxia from Tce by 2002 heat are quantity. estimated Specifically, based on or data data Hainan in area Gross regional data Ningxia product from each 2000 area is 2002 selected are estimated as output basedin onthis or paper. datato eliminate area. Gross effect regional product facr, each we use area gross is selected regional as product output deflar. in thisall paper. nominal To eliminate variables are effect deflated facr, real variables we use gross by using regional a product index deflar. for year All nominal variables are deflated real variables by using a index for year In Figure 1 we will show tal energy consumption in China China from from , 2012, while while sort sort out out descriptive analysis analysis tal tal input input output output in Table in Table , , , , , , , t Coal Oil Natural gas Electic power Figure1. Total energy consumption in China.

9 Sustainability 2016, 8, Table 1. Descriptive analysis tal input output. Count Mean SD Min Max Coal (Tce) , Oil (Tce) Natural gas (Tce) Electic power (Tce) Labor (10,000 persons) Capital (100 million yuan) , , , GNP (100 million yuan) , Note: (1) Electric power only consists hydropower nuclear power, excluding electricity generated from coal gas. (2) Some regions did have little natural gas or electric power consumption in 1990s. For example, Neimenggu had little hydropower nuclear power from , Zhejiang had no natural gas consumption in Results Discussions 5.1. Estimated Results ANALYSIS In Table 2, we stardize process input output data 30 areas in China from , n estimate parameters directional distance function by linear programming model. Table 2. Parameter estimation quadratic directional distance function. Parameter Estimated Value Parameter Estimated Value Parameter Estimated Value α γ η α γ η α γ η β γ η β γ η β γ η β γ δ β γ δ γ γ µ γ γ µ γ η µ γ η µ Results Analysis Shadow Prices Shadow s facrs reflect marginal output energy; in or words, scarcity energy. When energy is higher than market, firms would choose more energy input for higher prit during production. Therefore, is key work out reasonable energy, regulate supply dem relation energy market promote enterprises raise energy using efficiency. On perspective national condition, coal, gas electricity kept stable from The s three types energy decreased slightly annually before 2007, while had a modest rise since n. As shown in Figure 2, coal fluctuated dramatically, though maintained at a level Meanwhile, s gas electricity fluctuated around , respectively.

10 Sustainability 2016, 8, Sustainability 2016, 8, Figure 2. Variation trend energy in China. Figure 2. Variation trend energy in China. Remarkably, Remarkably, increase increase sharply, sharply, from from during during 15 years. 15 years. It means It means that that marginal marginal output output soared soared rapidly. rapidly. Over Over same same period, period, crude crude s s soared soared from from $20 $20 per per barrel barrel in in $90 $90 per per barrel. barrel. Aiming Aiming at at increase increase on on products products in in China, China, we we refer refer explanation explanation Zheng Zheng et et al. al. [31] [31] conclude conclude reasons reasons as as followed. followed. For For one one thing, thing, cost cost processing processing would would raise raise due due higher higher emission emission stard stard (Chinese (Chinese emission emission stard stard at Phase at Phase II II Phase Phase VI) VI) higher higher requirement requirement for for quality. quality. Moreover, Moreover, improving improving quality quality would would increase increase marginal marginal output output also also.. Furrmore, Furrmore, as as kept kept climbing, climbing, not not only only have have small small vehicles vehicles low low emissions emissions become become popular popular consumers, consumers, but but y y have have also been also been supported supported by national by national policy like policy tax like allowance tax allowance (half purchase (half purchase tax for household tax for household car lower than car lower 1.6 L). than On such 1.6 L). occasion, On such unit occasion, output unit would output grow, would accordingly grow, accordingly would increase. would increase. As As is is shown shown in in Table Table 3, 3, four four types types energy energy differs differs from from each each or. or. On On perspective perspective coal, coal, areas areas high high s s coal coal (over (over Yuan/Tce) Yuan/Tce) are are Sichuan Sichuan (0.822), (0.822), Beijing Beijing (0.564), (0.564), Xinjiang Xinjiang (0.516), (0.516), Chongqing Chongqing (0.509) (0.509) Guangdong Guangdong (0.504). (0.504). For For example, example, coal coal in in Sichuan Sichuan rose rose from from Yuan/Tce Yuan/Tce 18,020 18,020 Yuan/Tce, Yuan/Tce, similar similar Hunan, Hunan, Beijing Beijing Guangdong. Guangdong. That That means means marginal marginal output output coal coal increases increases significantly. significantly. In In contrast, contrast, areas areas low low coal coal (less (less than than 3000 Yuan/Tce) 3000 Yuan/Tce) are Hebei are Hebei (0.184), (0.184), Shanxi Shanxi (0.192), (0.192), Neimenggu Neimenggu (0.229), (0.229), Shong Shong (0.261) (0.261) Jiangsu Jiangsu (0.293). (0.293). Out Out se areas, se areas, Hebei Hebei is lowest, is lowest, whose whose value reached value reached 560 Yuan/Tce, 560 Yuan/Tce, far lower far than lower national than average national (3880 average Yuan/Tce). (3880 Besides, Yuan/Tce). Besides, s coal in s Heilongjiang, coal in Hebei Heilongjiang, Neimenggu Hebei had Neimenggu greatest reduction, had greatest which means reduction, marginal which means output coal marginal declined output sharply coal in declined recent years. sharply in recent years. As As for for,, areas areas high high (over (over Yuan/Tce) Yuan/Tce) include include Hunan Hunan (0.351), (0.351), Henan Henan (0.309), (0.309), Guangxi Guangxi (0.309), (0.309), Yunnan Yunnan (0.307) (0.307) Hebei Hebei (0.305). (0.305). Namely, Namely, Hunan Hunan rose rose from from 880 Yuan/Tce 880 Yuan/Tce 8450 Yuan/Tce, 8450 Yuan/Tce, similar similar that Hubei, that Henan, Hubei, Jiangsu, Henan, Shanxi, Jiangsu, Neimenggu, Shanxi, Neimenggu, Sichuan Sichuan Chongqing, which Chongqing, means which that means marginal that output marginal increased output remarkably. increased On remarkably. contrary, On areas contrary, low areas s low (less than s 1500 Yuan/Tce) (less than are 1500 Guangdong Yuan/Tce) (0.054), are Guangdong Shanghai (0.054), (0.097), Shanghai Beijing (0.142) (0.097), Beijing Jiangsu (0.142) (0.150). Specifically, Jiangsu (0.150). Specifically, Guangdong, at only Guangdong, 540 Yuan/Tce, at only is 540 lowest, Yuan/Tce, far lower is than lowest, national far lower average than (2280 national Yuan/Tce). average Areas (2280 Yuan/Tce). growth rates Areas that are far growth from rates national that average are far include from Guangdong, national Shong, average include Jiangsu, Guangdong, Zhejiang, Liaoning. Shong, Jiangsu, Zhejiang, Liaoning.

11 Sustainability 2016, 8, Table 3. Shadow each area (Unit: 10,000 Yuan/Tce). Coal Oil Natural Gas Electric Power Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Eastern Central Western Total Note: The data are divided in three periods. Due space limitation, we did not give all data in body part, but s in each year can be found in Appendix.

12 Sustainability 2016, 8, On perspective gas, areas high gas (over 1500 Yuan/Tce) are Hebei (0.183), Jiangsu (0.168), Henan (0.156), Shong (0.153) Hunan (0.150). Specifically, in Heibei is always higher than national average, increasing significantly in recent years, which is similar Shanxi, Neimenggu, Zhejiang, Shong Henan. On contrary, areas low gas s (less than 800 Yuan/Tce) include Xinjiang (0.045), Hainan (0.056), Sichuan (0.066), Qinghai (0.067), Beijing (0.079) Heilongjiang (0.078). For example, Xinjiang decreased from a low value recently. When it comes electricity (i.e., nuclear power hydropower), areas high electricity (over 2000 Yuan/Tce) include Shanghai (0.227) Beijing (0.218). Besides, or areas such as Tianjin (0.198), Neimenggu (0.186), Hainan (0.180) Xinjiang (0.191) have high electricity output. In contrast, areas low electricity (less than Yuan/Tce) include Sichuan (0.060), Henan (0.061), Hunan (0.064), Anhui (0.089), Hubei (0.094) Guangxi (0.092). Based on previous studies, we categorize energy in four types, finding that s same type energy differ in different areas, which also shows marginal output energy in different areas varies. For example, when coal reached 18,020 Yuan/Tce in Sichuan, coal in Hebei is only 550 Yuan/Tce. In a perfectly competitive market, same type energy should be same. This situation indicates existence trade barrier, which means energy facr is not complete circulation in different regions. Besides, in areas rich energy resources, energy marginal output is low in most areas. For example, in Shanxi Neimenggu, major coal producing provinces, coal s are lower than national average. This result is similar conclusion that richer energy resource is, lower energy is [32], due lack an integrated energy market. This facr has seriously hindered scale economy regional industries has resulted in loss tal facr energy efficiency. Meanwhile, in Guangdong, Shanghai Beijing, areas large amount importation, processing consumption, marginal output is lower than national average. Obviously, re are not rich resources in se areas. However, since most state-owned large petrochemical project located in se areas due policy support, efficiency is o low save resources. Therefore, compared lack energy resources, misallocation facr market resulting from administrative interference leads inefficiency energy more seriously [33] Results Analysis Energy Efficiency According average data value past years, areas high energy efficiency are Qinghai (0.972), Ningxia (0.970), Guangxi (0.942), Gansu (0.952), Sichuan (0.952) Hainan (0.951). Energy efficiencies all se areas are higher than On contrary, areas low energy efficiency are Liaoning (0.724), Yunnan (0.783), Jiangsu (0.806), Neimenggu (0.814) Guizhou (0.845). Energy efficiencies all se areas are lower than All Energy efficiency each province national energy efficiency decomposition is disposed in Table 4 Figure 3. The sorted result is consistent results from or research [12,34]. Besides, tal facr energy efficiency Jiangsu, Guizhou, Shong Beijing shows an increasing tendency in fluctuation, reaching an advanced level country, especially Jiangsu, whose value increased from during 15 years. In contrast, re is a significant decline energy efficiency in Liaoning, Yunnan, Xinjiang Zhejiang. For example, tal facr energy efficiency Xinjiang is far lower than national average due decreased fluctuation from 1998.

13 Sustainability 2016, 8, Table 4. Energy efficiency each province national energy efficiency decomposition Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Eastern Central Western A T E A A E R E H

14 Sustainability 2016, 8, Sustainability 2016, 8, Sustainability 2016, 8, Figure Figure Total facr energy efficiency in in China. China. Based Based on on on condition three three regions, regions, efficiency east east area area area have have have fluctuated greatly greatly greatly decline decline recent in in years. recent years. The efficiency The efficiency drops drops from from in 2008 in in in 2012, in in 2012, 2012, illustrating rise rise rise extensive extensive development development mode. mode. In In middle middle area, area, result result significant decline is is similar is result result Li et al Li Li [12]. et et al Li al believes [12]. Li that believes reason that forreason sharp for decline sharp is decline dropis is management drop drop efficiency management in efficiency middle in area. in On middle contrary, area. On energy contrary, efficiency energy performs efficiency well, performs even well, though well, even even support though policy support is not policy as good is is not as inas as good east as area in east area energy energy endowment endowment is is not as good is not not as as that as good good as in as middle that that in in area. middle According area. area. According Lin et al [23], Lin et result al [23], is due result is contribution is due contribution western development western western policy development on energy policy policy efficiency. on on energy efficiency. Figure 4. Variation trend national energy efficiency its decomposition. Figure 5. Variation trend national energy efficiency its decomposition. Figure 5. Variation trend national energy efficiency its decomposition. Figure Figure 4 shows 5 variation trend national efficiency its decomposition. As As we we see, see, macro-energy macro energy Figure 5 efficiency show index variation index (H) (H) presents trend an national an invert invert U efficiency [35,36], U [35,36], which which steadily its steadily decomposition. rises rises from from 1998 As 1998 we see, After macro energy reaching After reaching efficiency peak in index 2002 peak (H) in , presents 2003, energy an invert efficiency energy U [35,36], efficiency keeps which declining, keeps steadily declining, even rises lower even from than lower (i.e., than : After 0.761) (i.e., reaching in 2007: recent0.761) years. in peak in These recent 2002 results years. 2003, imply These that results energy extensive imply that extensive mode energy efficiency mode keeps energy declining, consumption even lower rises in consumption rises in recent decades tal energy utilization is not in an optimistic condition. than recent decades (i.e., 2007: 0.761) tal in energy recent utilization years. These is not results in an optimistic imply that condition. extensive mode energy By macro energy efficiency index (H) in intra provincial technical efficiency consumption By decomposing rises in macro-energy recent decades efficiency tal index energy (H) in utilization intra-provincial is not in technical an optimistic efficiency condition. (ATE), allocation (ATE), allocation efficiency energy input structure (AAE) inter provincial energy allocation By decomposing efficiency energy macro energy input structure efficiency (AAE) index (H) inter-provincial in intra provincial energy allocation technical efficiency efficiency (ATE), allocation efficiency energy input structure (AAE) inter provincial energy allocation

15 Sustainability 2016, 8, (RE), we found that ATE has similar trends macro-energy efficiency nationwide, which presents an invert U as well. Moreover, value ATE decreases sharply recently, which is consistent result that technical inefficiency tends increase [3]. AAE drops from 96.8% 80.9% in ten years since 1998 presents a V. Not until 2008 did value start rise again go back This fluctuation indicates that improvement energy structure could raise energy efficiency effectively. Therefore, it is crucial for government adjust policy energy structure, which has already achieved some results. As for RE, its value is in 1998, which means that energy allocation efficiency could increase by if energy could flow freely among areas. Since 1998, energy allocation efficiency among areas increased in fluctuation, so that RE decreased from during 15 years accordingly. These results imply that marketization could break inter-provincial energy barrier. During past decades, liberalization in energy markets has developed remarkably. 6. Conclusions In this paper, an energy-input based directional distance function was established for measuring tal facr energy efficiency four types energy (i.e., coal,, gas electricity) among 30 areas in China from Moreover, a macro-energy efficiency index in China was estimated divided in intra-provincial technical efficiency, allocation efficiency energy input structure inter-provincial energy allocation efficiency. The main conclusions are as follows: By extending work prior research, we break down in four sources energy, find that s different kinds energy are quite different, same kind energy also has different s in different regions. The situation indicates existence energy trade barriers among regions different contribution several types energy economy. Among kinds energy mentioned, has climbed dramatically, which means efficiency consumption has greatly improved. The increasingly rigid vehicle emissions stards increasing tax allowance on smaller vehicles, may lead increase marginal output. In areas rich energy resources, energy marginal output is low in most areas due lack an integrated energy market, resulting in reduction tal facr energy efficiency. Besides, misallocation facr market owing administrative interference remarkably brings about inefficiency energy. Both structure energy input inter-provincial allocation energy impact on energy efficiency. Increasing values two indices in recent years imply effect China s adjusted energy policy. Inter-provincial energy barrier will be broken for marketization energy facr better negotiability energy among areas. Specifically, it is great necessity realize marketing disposition energy, energy structure adjustment energy efficiency improvement for achieving reduction goal carbon emission. Thus, we call for a reform on energy facr market, in order reduce impact administrative interference make market allocation effective. Besides, we suggest a need for greater environment regulation greater emission stards improve tal facr energy efficiency. There are some limitations in this paper. First all, energy categories are roughly defined, which can be better classified in future research. Besides, we do not discuss carbon dioxide or or byproducts, thus, may neglect influence bad outputs. Furrmore, lack a comparison between market s s needs be extended in future. Acknowledgments: The authors are grateful for financial support provided by GDUPS(2015), National Science Foundation China ( ), National Social Science Foundation China (14ZDB44), New Century Excellent Talents in University (NCET ), Guangdong Project Key Research Institute Humanities Social Sciences at Universities-IRESD (2012JDXM0009).

16 Sustainability 2016, 8, Sustainability 2016, 8, Author Contributions: Peihao Lai contributed data acquisition, methodological design drafting article; Minzhe Du provided statistical analysis data interpretation; Bing Wang made substantial contributions concept design article, helped revise manuscript approved its final Author Contributions: Peihao Lai contributed data acquisition, methodological design drafting publication; article; Minzhe Ziyue Du Chen provided assisted in statistical literature analysis review data conclusions. interpretation; Bing Wang made substantial contributions concept design article, helped revise manuscript approved its final Conflicts Interest: The authors declare no conflict interest. publication; Ziyue Chen assisted in literature review conclusions. Appendix Conflicts Interest: The authors declare no conflict interest. Appendix Appendix A1. Efficiency Decomposin Framework Appendix According A1. Efficiency Figure Decomposin A1, a typical Framework province could only reach point a instead production frontier province. Similarly, a country, as a whole, could only reach Point B instead Point D in Figure According A2. To reach Figure A1, national a typical optimal province Point could D, it is only vital reach improve point a instead technical efficiency production frontier province. Similarly, a country, TE as a whole, could only reach Point B instead Point D R in Figure province, A2. which To reach means that national ATEoptimal achieves Point D, it is optimal vital value improve Point technical A is replaced efficiency by 0 R Point B. province, Then, improving which means provincial that ATE allocation RTE R 0 efficiency achieves is great optimal importance value as Point well, A which is replaced means AE by Point R AAE B. Then, improving provincial allocation efficiency is great importance as well, which achieves optimal value Point B moves Point C. Finally, interprovincial TE R means AAE RAE achieves optimal value Point B moves Point C. Finally, interprovincial RTE H efficiency is increase, which means RE efficiency is increase, which means RE H reaches reaches optimal optimal value value Point Point C C moves moves R AE RAE Point D. D. Figure A1. Production frontier a province. Figure Figure A2. A2. Production Production frontier frontier a country. country. Appendix A2. Depreciation Rate Different Areas We introduced value depreciation rate rate according Wu s Wu s research [37], [37], where where value value depreciation rate rate differs differs from from areas areas as is as in is Table in Table A1. A1.

17 Sustainability 2016, 8, Table A1. Depreciation rate different areas. Province Beijing Tianjin Hebei Shanxi Neimenggu liaoning Jilin Heilongjiang Shanghai Jiangsu Depreciation rate (%) Province Zhejiang Anhui Fujian Jiangxi Shong Henan Hubei Hunan Guangdong Guangxi Depreciation rate (%) Province Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Depreciation rate (%)

18 Sustainability 2016, 8, Appendix A3. The Shadow Price Each Energy from are as Follow Table A2. The coal Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang eastern central western Total

19 Sustainability 2016, 8, Table A3. The Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang eastern middle western Total

20 Sustainability 2016, 8, Table A4. The natural gas Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang eastern middle western Total

21 Sustainability 2016, 8, Table A5. The electric power Beijing Tianjin Hebei Shanxi Neimenggu Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang eastern middle western Total

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