The impact of industrial structure efficiency on provincial industrial energy efficiency in China

Journal of Cleaner Production 215 (2019) 952e962

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The impact of industrial structure efficiency on provincial industrial energy efficiency in China Siqin Xiong a, Xiaoming Ma a, Junping Ji a, b, * a b

School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, 518055, China Energy Analysis and Environmental Impacts Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 May 2018 Received in revised form 14 December 2018 Accepted 9 January 2019 Available online 14 January 2019

Regional disparity in terms of industrial energy efficiency is noticeable in China due to the unbalanced economic progress in past decades. Analyzing the provincial industrial energy efficiency and its influencing factors is of great significance to formulate differentiated policies. To date, the influence of many social-economic factors on the industrial energy efficiency have been examined but the impact of the inter-industry structure has almost been ignored. In this paper, the slacks-based measure model incorporating undesirable output is applied to evaluate the industrial energy efficiency both at the provincial level and the sectoral level in China for the period 2010 to 2016. Then, industrial structure efficiency is introduced, which reflects the inter-industry structure and takes the sectoral-level energy efficiency into consideration. And the impact of the industrial structure efficiency on provincial industrial energy efficiency is tested by Tobit regression model. The results show that huge discrepancies of provincial industrial energy efficiency exist and the industrial structure efficiency is confirmed to be a determinative factor to the provincial industrial energy efficiency, with the coefficient of 0.525. Policy recommendations are provided for adjusting the provincial inter-industry structure and improving the industrial energy efficiency. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Industrial structure efficiency Provincial industrial energy efficiency Influencing factors SBM model Tobit regression

1. Introduction With accelerating industrialization, China has witnessed tremendous economic development in the past decades. Nowadays, China is one of the world's most important manufacturers and industrial producers. However, the remarkable economic achievements have been accomplished by excessive energy consumption and environmental risks. China has been the largest energy consumer and carbon emissions emitter in the world and over twothirds of the total energy consumption is attributed to industrial activities (IER, 2015). In this context, the improvement of industrial energy efficiency is of great importance to building a low-energy consumption, low-emission and sustainable industrial system. In addition, the regional disparity in industrial energy efficiency is noticeable in China due to the unbalanced economic progress in past decades. For example, in 2016, the ratio of energy consumption to the gross industrial output value in Xinjiang was 1.30 ton of coal

* Corresponding author. School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, 518055, China. E-mail address: [email protected] (J. Ji). https://doi.org/10.1016/j.jclepro.2019.01.095 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

equivalent (tce) per 10,000 Yuan, which was roughly 20 times greater than that of Beijing, with the figure of 0.07 tce per 10,000 Yuan (NSBC, 2017). Meanwhile, plenty of researchers are interested in analyzing provincial industrial energy efficiency by means of various methods. For example, based on Data Envelopment Analysis (DEA), Wang and Wei (2014), Wu et al. (2014), Zhang et al. (2016a,b), Zhao et al. (2016), Chen and Jia (2017) have evaluated the provincial environmental efficiency, energy efficiency or carbon dioxide emission efficiency in China. Wang et al. (2012) have established the total factor energy efficiency framework to determine the discrepancy of energy efficiency in the industrial sector from 2005 to 2009. Li et al. (2016) have proposed the energypollution efficiency index and the energy-pollution productivity change index, to evaluate the regional industrial energy-pollution performance in China during the period 1995e2014. Bian et al. (2015) have proposed a two-stage slacks-based measure (SBM model) to evaluate the regional industrial efficiency and decomposed these efficiencies into production efficiency and abatement efficiency. A common finding revealed by these studies is that the regional industrial energy efficiency (or other relevant efficiency indicators) is divergent and generally hovers at a low level. To

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ensure a differentiated and targeted policy formulation on industrial energy efficiency improvement, analyzing the influencing factors of provincial industrial energy efficiency (PIEE) is a foundation task. Although a lot of effort has been put on exploring the influencing factors of provincial energy efficiency (Song et al., 2013a,b; Zhang et al., 2016a,b; Guo et al., 2017; Yang et al., 2018), little specific attention has been paid to the industry. Some previous studies concerning the influencing factors of PIEE are summarized in Table 1. Important factors have been discussed in previous studies. In general, the economic development level and technological progress are believed to have positive effects on PIEE whereas energy structure, industrial structure, and property structure are more likely to exert negative impacts. Environmental regulation, energy price, market openness, and industrial concentration are less mentioned factors or correspond to controversial conclusions in different studies. In terms of the economic structure or industrial structure, these abovementioned studies have focused on the share of industry output to total GDP or the proportion of one or some specific sectors to the total industry and they have concluded that the economic (industrial) structure exert an insignificant or negative impact on PIEE (Zhao et al., 2014a; Wang and Zhao, 2017; Zhang

953

et al., 2017a,b). Nevertheless, the industrial energy efficiency is more closely associated with the inter-industry structure rather than the role of industry in the entire economics. Industrial branches do not have the same energy efficiency. For example, Bosseboeuf (2015) has noted that primary metals manufacturing require 30 times more energy to produce one unit of value added than machinery manufacturing. The great discrepancy of energy efficiency among sub-industries has also been highlighted by other studies (Chang et al., 2013; Kang and Lee, 2016; Xie et al., 2016, 2017; Li and Lin, 2017; Wang et al., 2017a,b,c; Li et al., 2018). In the light of this, if the share of high-efficient sub-sectors in the total industry is increasing, this will improve, if other things being equal, the average industrial energy efficiency of a specific region. According to this logic, it is reasonable to reckon that the interindustrial structure is crucial to determine the PIEE. Some empirical studies have attempted to justify the importance of adjusting the inter-industry structure. For example, by various decomposition technique, Shao et al. (2014), Yan and Fang (2015), Liu and Wang (2015) and Kopidou et al. (2016) have attributed the variation in industrial energy efficiency to the economic structure adjustment, in addition to other factors, like change in economic scale, energy structure and resource intensity. However, these studies have almost studied at the national level, and the regional features have not been considered. Another

Table 1 Majors studies of exploring the influencing factors of PIEE. Publication Pan et al. (2013)

Industry a

Period

Heavy chemical industry 2000 e2006

Method

Influencing factor

DEA;Tobit regression (Tobit)

Marketization index (þ) Gross domestic production (GDP) per capita (þ) Industrial Research and development (R&D) investment per capita (þ) Energy structure () Enterprise scale (þ) Nationalization degree () Market openness () Economic level (þ) Economic structureb () Technological progress c (þ); Energy price (þ) Technical progress Productive scale Management level Enterprise scale (þ) Stated-owned share () Energy price (þ) Technology gap Management efficiency Scale efficiency Economic level () Population density (þ) Industrial structured () Energy price (þ) Environmental regulation () Technological innovation (þ) Economic level (þ) Industrial structuree () New product output () R&D intensity (þ) Labor productivity (þ) Industry structureb () Enterprise scale (þ) Market openness () Nationalization degree ()

Zhao et al. (2014a) a Industry

1997 e2007

DEA;Tobit

Wang et al. (2014)

2005 e2010

Global DEA;Decomposition analysis

Lin and Long (2015) Chemical industry

2005 e2011

Stochastic frontier analysis

Feng and Wang (2017)

Industry

2000 e2014

Meta-frontier DEA;Decomposition analysis

Wang and Zhao (2017)

Non-ferrous metals industry

2006 e2011

DEA;Tobit;Truncated regression

Zhang et al. (2017a,b)

Industry

2005 e2013

Three-stage DEA

He et al. (2018)

Industry

2005 e2012

DEA;Rough set theory;Fuzzy artificial neural network

Industry

Notes: (þ): Positive effect; (): Negative effect; (): Insignificant effect. a The results at a national level are considered if analysis on both a national level and regional level are included. b The ratio of industrial added value to gross domestic production. c The ratio of import and export trade volume and FDI (foreign direct investment) to GDP. d The ratio of non-ferrous metals industrial value added to local GDP. e Gross output value of heavy industries to the gross output value of industries.

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branch of studies seeks to establish optimization model and perform scenario analysis to analyze the impact of industrial structure on energy intensity or energy consumption, such as Zhu et al. (2014) and Mi et al. (2015). Nonetheless, the major limitation of this method is the subjective parameter setting and preference patterns. Some other studies use the Theil index to represent the industrial structure and explore its impact on regional industrial energy efficiency (Zhang et al., 2017a,b; Pan et al., 2019). But this index is calculated on the basis of the total output and the employment numbers of each sector, while other elements, like capital and energy input are ignored. In this paper, we introduce the factor of industrial structure efficiency, which is the weighted average value of the energy efficiency of each sub-industry, weighted by its respect industrial output value proportion. Then, the regression model is employed to test the impact of industrial structure efficiency on PIEE. The rest of this paper is organized as follows. Section 2 presents the methodology and data of the efficiency-evaluation model and the regression model; Section 3 evaluates the industrial energy efficiency both at the regional-level and sectoral-level. Section 4 provides the regression results of the impact of industrial structure efficiency on PIEE. The last section concludes and gives some policy implications.

far, this SBM-UN model has been widely applied to the measurement of energy efficiency (Zhou et al., 2006; Song et al., 2013a,b; Zhang et al., 2016a,b; Guo et al., 2017). Because of the advantage of the SBM-UN model, this paper applies it to analyze the PIEE and sectoral energy efficiency. Suppose there are n DMUs and each unit has three factors: input

2. Methodology and data

yg0 ¼ Yg l  Sg

2.1. Models 2.1.1. SBM-UN model The conception of energy efficiency was proposed by Patterson (1996), referring to use less energy to produce at least equal number of services or useful outputs. Single factor energy efficiency and total factor energy efficiency are two common approaches to evaluate the energy or environment efficiency. The single factor energy efficiency, using single input and output, is easy to calculate and understand, however, the impact of capital, labor, and other non-energy inputs are ignored (Song et al., 2013a,b; Lin and Tan, 2016; Mardani et al., 2017). The total factor energy efficiency index is defined as a ratio of optimal to actual energy input under a multi-factor framework. Hu and Wang (2006) originally introduce the total-factor index to analyze regional energy efficiencies in China. Data Envelopment Analysis (DEA), proposed by Charnes et al. (1978) is a representative non-parametric total factor efficiency measurement approach. This model does not require the presumption of functional form and is suitable to measure the efficiency of decision-making units (DMUs) with multiple inputs and outputs (Wu et al., 2014). Traditional DEA-based models are on the assumption of seeking to produce more outputs relative to less inputs. However, undesirable outputs (e.g. carbon dioxide) are usually inevitable in the production process, and thus, the efficient production units should be recognized as less inputs relative to more desirable outputs and less undesirable outputs (Xiao et al., 2018). To deal with undesirable outputs, several variant models have been introduced, such as the hyperbolic method (Rolf Faere, 1989), directional distance function method (Chung et al., 1997), treatment of undesirable outputs as inputs (Reinhard et al., 2000), and slack-based measure model incorporating undesirable outputs (SBM-UN) (Tone, 2004). In addition, the original DEA model adjusts all input or output variables by the same proportion to reach the frontier and cannot simultaneously consider both slacks of inputs and outputs (Zhou et al., 2007). SBM model, which was proposed by Tone (2001), directly considers all input excess and output shortfall into an efficiency measurement. Tone (2004) incorporated undesirable outputs to the SBM model, making this non-radial model more suitable to evaluate energy and environmental efficiency. So

x2Rm , desirable outputs yg 2Rs1 and undesirable outputs yb 2Rs2 ; assume that: X ¼ ½x1 ; x2 ; …; xn 2Rmn , Y g ¼ ½yg1 ; yg2 ; …; ygn 2Rs1 n , Y b ¼ ½yb1 ; yb2 ; …; ybn 2Rs2 n , X > 0; Y g > 0; Y b > 0. The production

possibility set (P) is defined as: P ¼ {ðx; yg ; yb Þ
x  Xl; yg  Y g l，yb  Y b l; l  0}, where l2Rn is the intensity vector. Based on the assumption of a constant returns-to-scale, the SBMUN is modeled as:



.  P  1  ð1=mÞ m xi0 i¼1 Si P t ¼ min   Ps2 b . b  g s1 g 1 þ 1=ðs1 þ s2 Þ r¼1 Sr yr0 þ r¼1 Sr yr0

(1)

1. Subject to

x0 ¼ Xl þ S

yb0 ¼ Yb l þ Sb S  0; Sg  0; Sb  0; l  0 where t is the energy efficiency value of this evaluated DMU, within the range from 0 to 1; the subscript 0 denotes the DMU currently being evaluated; m, s1 and s2 denote the number of inputs, desirg b able outputs, and undesirable outputs; S i , Sr and Sr correspond to the slacks in inputs, desirable outputs and undesirable outputs, respectively and this objective function t strictly decreases with S i , Sgr and Sbr ; xi0 , ygr0 and ybr0 are the actual input and output values of the evaluated DMU; l is intensity vector for each DMU's contribution to the efficient projection. The evaluated DMU is efficient if g b t ¼ 1 and S i ¼ Sr ¼ Sr ¼ 0, otherwise this DMU is weaklyefficient or inefficient. However, the above model is nonlinear, making it difficult to calculate. Based on the Charnes-Cooper transformation (Charnes, 1962), the nonlinear model can be transformed as follows:

r ¼ mint  ð1=mÞ

m X

. xi0 s i

(2)

i¼1

Subject to

1¼tþ

ðs1 þ s2 Þ

P

1

s1 g  g r¼1 sr yr0

þ

Ps2

b r¼1 sr

 b  yr0

x0 t ¼ XL þ s i ygo t ¼ Y g L  sg ybo t ¼ Y b L þ sb Assuming that the optimum solution of model (2) is ðr* ; L* ; t * ; s* ; sg* ; sb* Þ; then the optimal solution of the original

model (1) can be calculated by: r* ¼ s* ; l* ¼ L* =t * ; S* ¼ s* =t * ; Sg* ¼ sg* =t * ; Sb* ¼ sb* =t * .

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2.1.2. Tobit model If the value of the dependent variable is censored, the use of Ordinary Least Square estimation method would cause bias and inconsistency of parameters (Wang et al., 2017a,b,c; Wang and Zhao, 2017). Tobin (1958) proposed the Tobit regression model, which is based on the principle of maximum likelihood estimation, to deal with fragment dependent variables. In recent years, Tobit model has been frequently used by scholars to analyze discontinuous dependent variables, especially those from the DEA-based results (Feng and Wang, 2017; He et al., 2018; Xie et al., 2018). We employ the Tobit regression model in this paper to analyze the determinants of PIEE given that our efficiency results from SBM-UN model are limited in the interval (0, 1). The Tobit regression model is shown as follows:

yi ¼ bXi þ mi ; i ¼ 1; 2; …; n

(3)

8  < yi ; 0 < yi < 1; 0; yi  0; yi ¼ : 1; yi  1: where i stands for the ith DMU and n equals to the number of DUMs; y*i is a latent variable; Xi is a ðK  1Þ vector on behalf of the explanatory variables; b denotes the regression coefficient of each explanatory variable; mi is stochastic error and submits to the distribution of Nð0; s2 Þ.

2.2. Data 2.2.1. Provincial industrial energy efficiency Considering the accessibility of data, this paper evaluates the PIEE from 2010 to 2016 and Tibet, Taiwan, Hong Kong and Macao are not concluded. During this study period, the Chinese government has highly stressed the importance of the adjustment of industrial structure and many measures have been taken to transform towards a high-end, green and sustainable industrial system. Therefore, empirical studies about the impact of industrial structure efficiency could be instrumental for energyenvironmental management and improved policymaking. Also due to the accessibility of data, the industrial enterprises above designated scale have been taken as the proxy to reflect the overall level of industrial energy efficiency. This paper chooses the net value of fixed asset, labor force, energy consumption as input variables, provincial gross industrial output as the desirable variable and carbon dioxide as the undesirable output variable. The detailed indicators and data source are explained as follows. Net value of fixed asset: Based on the principles of economics, capital investment is the basic production input element. Regarding regional energy efficiency measurement, the capital investment is always donated by capital stock (Hu and Wang, 2006). However, the data of capital stock is not available in any statistical yearbook, and the common perpetual inventory method (Goldsmith, 1951) involves many unknown variables. As some existing literature did in their studies (Wang et al., 2013; Zhao et al., 2014a,b; Zhang et al., 2017a,b; Guo et al., 2018), this paper employs annual average balance of net fixed assets in the industrial enterprises as a reliable proxy for capital stock and the values are converted into constant price in 2010 by the fixed asset price index. Labor force: Labor force is expressed by the number of people employed in a specific industry of each province. The statistics are collected from provincial statistical yearbooks. Energy consumption: Energy consumption is closely linked to industrial activities and should also be regarded as an input element in the industrial production. Data on energy consumption

955

are obtained from the provincial energy balance table in China Statistical Yearbooks and are all converted into the standard coal equivalent. It is worth noting that the statistics of the China Energy Statistical Yearbook have been adjusted since the year of 2013 and the adjusted data in the edition of China Energy Statistical Yearbook 2014 are used in this paper. Gross industrial output:We use industrial output values as the desirable output indicator. Provincial gross industrial output values are obtained from provincial statistical yearbooks and are all converted into 2010 constant prices based on the ex-factory industrial product price index. Carbon dioxide emissions: Climate change caused by carbon emissions poses a severe challenge to social-economic development and becomes a worldwide concern. As a major contributor to carbon dioxide emissions, industry should receive much attention to emissions cutting. Carbon dioxide emissions are used as undesirable output in the energy efficiency evaluation model and are calculated by using the estimation method proposed in the IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2006). Table 2 shows the descriptive statistical characteristics of the variables, based on the regional average value during 2010e2016. Notably, for the same variable, the maximum is as much as 130 times greater than the minimum across different provinces, coupled with large standard deviation, The unbalanced status reminds that an in-depth analysis of the drivers of the PIEE differences is essential for the further implementation of economic and environmental policies. 2.2.2. Energy efficiency of sub-industry This paper employs the SBM-UN model to evaluate the energy efficiency of sub-industries and adopts the same variables as those in the calculation of PIEE. Data are collected from national and provincial Industrial Statistic Yearbooks. Table 3 displays the included sub-industries and their corresponding codes. In this paper, Ming Auxiliary Activities and Other Minerals Mining and Processing are excluded, because their proportions are extremely small and unstable. Manufacturing of rubber and Manufacturing of rubber and plastics have been incorporated as Manufacturing of rubber and plastics; Automobile manufacturing industry and Railway, shipping, aerospace and other transportation equipment manufacturing have been incorporated as Manufacturing of transportation and equipment, given that the corresponding classification has changed in the Industrial Statistical Yearbook during the research period (NSBC, 2014). 2.2.3. Tobit regression Together with industrial structure efficiency, several other factors may contribute to the variation in PIEE. The selected variables in the Tobit regression model are explicated as follows. (1) Industrial structure efficiency: A high industrial structure efficiency, which implies large proportions of efficient subindustries, would result in an efficient level of PIEE. The industrial structure efficiency is measured by taking the weighted average value of the energy efficiency of each subindustry, weighted by the respective output value proportion of each sub-industry, as shown below.

Industrial structure efficiency ¼

R X

tr  ðoutputr =outputtotal Þ

r¼1

(4) where tr is the efficiency value of the rth sub-industry calculated by using SBM-UN model (1); R is the number of sub-industries and

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Table 2 Descriptive statistics of variables of industrial energy efficiency.

Unit Average Maximum Minimum Standard deviation

Net value of fixed asset

Labor force

Energy consumption

Gross industrial output

Carbon emissions

100 million Yuan 10,491 31,775 924 7613

10,000 persons 320 1471 12 342

tce 8716 23,906 967 5832

100 million Yuan 31,882 129,964 1653 32,315

10,000 tons 22,823 65,507 2005 16,374

Table 3 The code of each sub-industry. Code Sub-industry

Code Sub-industry

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13

S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31

S14 S15 S16 S17 S18

Coal mining and washing Oil and natural gas mining Ferrous metal mining Non-ferrous metal mining Non-metal mining Agricultural products processing Food manufacturing Beverage manufacturing Tobacco manufacturing Textile industry Textile clothes, shoes, hats manufacturing Leather, fur, feather manufacturing Wood processing, and wood, bamboo, cane, palm, and straw manufacturing Furniture manufacturing Papermaking and paper products Press and intermediary replication Cultural, educational and sports goods manufacturing Oil processing, coking and nuclear fuels processing

S32 S33 S34 S35 S36 S37

equals to 37 in this paper; outputr is the output value of the rth subindustry and outputtotal is the total gross industrial output value of each province. (2) Economic development level: A powerful economy ensures adequate resources to implement energy-saving techniques and support environment management (Li et al., 2013; Song et al., 2013a,b). The economic development level is widely recognized as a determinative factor to the provincial energy efficiency and it is represented by regional GDP per capita in this paper. (3) Energy consumption structure: Because different fuels emit different amounts of emissions in relation to the energy they produce, the energy structure may affect energy efficiency (Feng et al., 2017). This paper adopts the proportion of coal consumption as the energy structure since the emission factor of coal is much larger than that of other fossil fuels (IPCC, 2006; Ren et al., 2012). Different from previous studies, the indirect coal consumption from the electricity is also considered given that the electricity structure in China is coal-dominated. The formulation of indirect coal consumption is as follow:

electricity;coalðtceÞ Ei

PC ¼

thermal;cðtceÞ c¼1 Ei Ethermal i

 uthermal electricity;coalðtceÞ

electricityðtceÞ

 Ei

(5)

where Ei is the indirect coal consumption from inC P thermal;kðtceÞ dustrial electricity usage of the ith province; Ei repk¼1power generation resents the coal consumption during the thermal process of the ith province; C is the number of coal types, which

Manufacturing of chemical materials and products Manufacturing of medicines Manufacturing of chemical fiber Manufacturing of rubber and plastics Manufacturing of non-metal products Smelting and rolling process of nonferrous metal Smelting and rolling process of Ferrous metal Manufacturing of metal products Manufacturing of ordinary machinery Manufacturing of special equipment Manufacturing of transportation and equipment Manufacturing of electric machines Manufacturing of communication device, computers and other electronic equipment Manufacturing of instruments, cultural and official mechanics Crafts and other manufacturing Recycling of waste resources and Metal products, machinery and equipment repair Production and supply of electricity, power Gas production and supply Water production and supply

include raw coal, cleaned coal, other washed coal, briquettes, coke, coke oven gas, blast furnace gas, converter gas, other gas, and other coking products; Ethermal represents the thermal power generation i electricityðtceÞ of the ith province; Ei represents the industrial electricity consumption of the ith province; uthermal is the proportion of the thermal power consumption to total electricity consumption; here, we calculated uthermal at a national level for simplified data processing. (4) Technological progress: Technological improvement can not only improve energy use efficiency by using less energyintensive equipment and devices, but also help drive productivity via optimizing the producing process. Considering that the advancement of technologies needs constant investments for research and development (R&D) (Lin and Tan, 2017), we use the proportion of investment in R&D to the total industrial output value to characterize the degree of technological progress. (5) Industrial concentration: Industrial concentration can be defined as the degree at which a small number of firms account for large percentage of the total market (Times, 2018). The viewpoints of the impact of industrial concentration on PIEE vary among prior studies. Some argue that large enterprises not only have enough resources to purchase advanced equipment and employ productive labor, but also they are under more social responsibilities to promote the management of energy efficiency, resulting in a higher industrial efficiency (Lin and Long, 2015; Wang et al., 2017a,b,c). Some others claim that higher industrial concentration does not suit the market competition and enterprises with a higher degree of industry concentration are easily to attain cheap resources, and consequently, their

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motivation of efficiency improvement would be undermined (Li and Shi, 2014; Chen and Gong, 2017). As a control variable in our regression model, we use the average enterprise scale as the industrial concentration indicator, to examine the impact of industrial concentration on PIEE in China. The values are acquired by the gross output value of all industrial enterprises divided by the number of industrial enterprises, with the unit of 100 million Yuan per enterprise. (6) Property structure: In China, state ownership is a main form of property ownership of the industry and the state ownership is generally regarded as a negative factor of energy efficiency. In state-owned enterprises, capital is intensive and the payment for managers is guaranteed, henceforth, the motivation to improve energy efficiency is weak (Lin and Long, 2015; Wang et al., 2017a,b,c). The property structure is measured as the ratio of the gross industrial output value of state-owned industrial enterprises to the total gross industrial outputs of each province. (7) Market openness: Foreign capital entering China brings advanced technical and managerial knowledge; hence, a high level of market openness is expected to boost industrial energy efficiency. The level of market openness is presented by the proportion of gross industrial output value in foreigninvested enterprises plus Hong Kong, Macao, and Taiwan merchants, to all gross industrial output value. The statistics of above-mentioned variables are sourced from the China Statistical Yearbook, China Energy Statistical Yearbook, and provincial Industrial Statistical Yearbooks. All the monetary output values are converted into the constant price in 2010.

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3. Efficiency results 3.1. Provincial industrial energy efficiency This article uses DEA Solver Pro software to compute the PIEE over 2010 to 2016 and the results are shown in Table 4. According to the efficiency figures, the industrial energy efficiency values of Beijing, Tianjin, Jiangsu, and Guangdong always equal to 1, which implies that the four provinces have been retained on the industrial efficiency frontier and can be deemed as the benchmark for other provinces. Besides, Inner Mongolia has been efficient since 2013, due to the significant growth in industrial output value and the inconsiderable increase of inputs in 2013. Over 20% growth in the comparable industrial output value was observed in 2013, even if the ex-factory industrial product price index was just 0.97. In addition, Shanghai, Jiangxi, and Shandong perform well with average efficiency scores above 0.8. However, there are still nine provinces, nearly one-third of the total number of provinces in China, do not perform well during the study period, whose average energy efficiencies do not exceed 0.5. The results indicate that the overall industrial energy efficiency in China remains at a low level. The last efficient province is Shanxi during the seven years, with the efficiency values ranging from 0.249 to 0.381. What is worse, the industrial energy efficiency of Shanxi gets lower and lower since 2013. Except for Inner Mongolia, our results are almost in concert with previous studies, in which Beijing, Shanghai, Guangdong and Jiangsu are usually proved to be energy-efficiency regions, because these regions are either economically well-developed or geographically well-placed and have edges on economic pattern, technology level, and foreign capital utilization (Song et al.,

Table 4 Industrial energy efficiency of each province from 2010 to 2016 in China.

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shanxi Gansu Qinghai Ningxia Xinjiang Average Minimum Efficient units

2010

2011

2012

2013

2014

2015

2016

Average

Rank

1.000 1.000 0.547 0.340 0.523 0.602 0.613 0.461 1.000 1.000 0.880 0.596 0.772 0.711 0.816 0.549 0.532 0.605 1.000 0.498 0.653 0.525 0.556 0.310 0.359 0.514 0.374 0.312 0.314 0.395 0.612 0.310 5

1.000 1.000 0.600 0.371 0.525 0.683 0.674 0.477 0.982 1.000 0.817 0.701 0.748 0.801 0.832 0.646 0.575 0.697 1.000 0.571 0.590 0.579 0.620 0.367 0.384 0.493 0.432 0.344 0.290 0.396 0.640 0.290 4

1.000 1.000 0.578 0.381 0.527 0.657 0.661 0.467 0.825 1.000 0.820 0.699 0.700 0.816 0.733 0.642 0.603 0.641 1.000 0.597 0.604 0.516 0.564 0.333 0.375 0.511 0.404 0.344 0.326 0.350 0.622 0.326 4

1.000 1.000 0.543 0.324 1.000 0.667 0.673 0.439 0.784 1.000 0.628 0.641 0.747 0.809 1.000 0.623 0.639 0.712 1.000 0.609 0.468 0.534 0.485 0.356 0.347 0.479 0.397 0.314 0.336 0.348 0.630 0.314 6

1.000 1.000 0.491 0.277 1.000 0.610 0.610 0.396 0.827 1.000 0.516 0.657 0.758 1.000 0.898 0.584 0.645 0.696 1.000 0.644 0.502 0.519 0.476 0.363 0.318 0.407 0.361 0.300 0.317 0.322 0.616 0.277 6

1.000 1.000 0.534 0.262 1.000 0.468 0.704 0.372 0.949 1.000 0.505 0.680 0.858 0.800 0.930 0.678 0.720 0.729 1.000 0.714 0.439 0.638 0.512 0.390 0.324 0.430 0.347 0.306 0.319 0.293 0.630 0.262 5

1.000 1.000 0.531 0.249 1.000 0.341 0.707 0.349 1.000 1.000 0.775 0.771 1.000 0.790 0.931 0.679 0.766 0.765 1.000 0.803 0.399 0.710 0.528 0.422 0.324 0.391 0.320 0.339 0.325 0.269 0.650 0.249 7

1.000 1.000 0.546 0.315 0.796 0.575 0.663 0.423 0.910 1.000 0.706 0.678 0.798 0.818 0.877 0.629 0.640 0.692 1.000 0.634 0.522 0.574 0.534 0.363 0.347 0.461 0.376 0.323 0.318 0.339 0.629 0.315 4

1 1 19 30 9 17 13 23 5 1 10 12 8 7 6 16 14 11 1 15 21 18 20 25 26 22 24 28 29 27

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2013a,b). Although Shandong is less developed in China, as Hou et al. (2018) reported, Shandong has taken the lead in realizing a green transformation, contributing to its fairly good performance and this conclusion has been supported by Chen et al. (2016) and He et al. (2018). Shanxi, Qinghai and Ningxia have always been evaluated as the most inefficient regions due to the poorly developed infrastructure and lower environmental protection awareness of industrial employees (Zhao et al., 2016; Chen et al., 2016). With respect to Shanxi, its poor performance can mainly be attributed to its fuel-related industrial activities, by capitalizing on the rich coal reserve. As for Inner Mongolia, the seemingly controversial performance could occur because of the different research period or the different output indicator. As above said, the considerable increase in industrial output value in 2013 contributed to its incredibly high efficiency. In general, the average PIEE in each year is barely above 0.600 and there is no obvious increasing trend of the overall industrial efficiency of 30 provinces during 2010e2016. The above analysis suggests that the provincial industrial efficiencies in China are not satisfactory and regional industries are characterized as unbalanced development. 3.2. Energy efficiency of sub-industries Then we employ the SBM-UN model to calculate the energy efficiency of each sub-industry from 2010 to 2016 and the sectoral energy efficiency is presented in Table 5.

Tobacco manufacturing (S9) achieves the DEA-efficient during the research period, followed by Cultural, educational and sports goods manufacturing (S17) and Manufacturing of communication device, computers and other electronic equipment (S31). These sub-industries are all high value-added and low energy requirements and the efficiency results are consistent with previous studies (Li and Shi, 2014; Xie et al., 2017). However, only ten subindustries perform relatively well that their average efficiency values are more than 0.500. The rests are considered to have a large potential to improve the sectoral energy efficiency. The efficiency scores of Coal mining and washing (S1), Oil and natural gas mining (S2), Manufacturing of non-metal products (S23), and Water production and supply (S37) are always less than 0.2 in the seven years. Besides, the results reveal that the overall industrial energy efficiency is low, with the average sectoral energy efficiency below 0.500 and the disparities of energy efficiency across sub-industries are evident. It is noticeable that the efficiency of Manufacturing of transportation and equipment (S29) was exceptionally low in 2012. After examining the calculation process and checking the data source, we attribute this phenomenon to the disagreement of statistical caliber, in which Manufacturing of transportation and equipment (S29) was divided into Automobile manufacturing industry and Railway, shipping, aerospace and other transportation equipment manufacturing after 2012 (NSBC, 2013). But due to the data accessibility in 2010 and 2011, this paper still regards them as a corporate sector. Here, we used the average value in 2011 and in

Table 5 The energy efficiency of each sub-industry from 2010 to 2016 in China.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 Average Minimum Efficient units

2010

2011

2012

2013

2014

2015

2016

Average

Rank

0.180 0.187 0.278 0.277 0.231 0.504 0.341 0.311 1.000 0.271 0.585 0.674 0.304 0.657 0.213 0.366 0.581 1.000 0.247 0.338 0.322 0.292 0.176 0.291 0.357 0.352 0.444 0.499 0.623 0.775 0.798 0.706 0.320 1.000 0.229 0.359 0.074 0.437 0.074 3

0.194 0.169 0.306 0.290 0.318 0.649 0.401 0.354 1.000 0.327 0.651 1.000 0.470 0.745 0.235 0.407 0.552 0.489 0.267 0.386 0.326 0.374 0.219 0.290 0.357 0.440 0.479 0.556 0.634 0.862 1.000 0.853 0.491 1.000 0.248 0.348 0.065 0.480 0.065 4

0.153 0.139 0.277 0.243 0.228 0.462 0.309 0.288 1.000 0.251 0.525 0.627 0.364 0.602 0.195 0.339 1.000 0.399 0.220 0.317 0.275 0.290 0.175 0.242 0.295 0.341 0.383 0.468 0.215 0.637 0.757 0.578 0.172 1.000 0.204 0.249 0.053 0.386 0.053 3

0.142 0.139 0.271 0.241 0.226 0.447 0.301 0.277 1.000 0.256 0.515 0.526 0.366 0.612 0.187 0.380 1.000 0.396 0.219 0.312 0.271 0.285 0.178 0.233 0.303 0.328 0.393 0.456 0.515 0.641 0.764 0.585 0.174 0.311 0.192 0.241 0.052 0.371 0.052 2

0.147 0.139 0.280 0.239 0.224 0.440 0.302 0.284 1.000 0.259 0.510 0.531 0.358 0.478 0.198 0.377 1.000 0.408 0.228 0.314 0.288 0.289 0.184 0.248 0.319 0.308 0.388 0.434 0.525 0.651 0.749 0.594 0.175 0.328 0.194 0.270 0.053 0.371 0.053 2

0.146 0.148 0.297 0.261 0.232 0.441 0.307 0.293 1.000 0.280 0.514 0.566 0.381 0.472 0.206 0.390 1.000 0.430 0.237 0.316 0.314 0.304 0.194 0.266 0.338 0.349 0.385 0.447 0.532 0.658 1.000 0.600 0.192 0.335 0.185 0.276 0.051 0.388 0.051 3

0.116 0.082 0.210 0.277 0.211 0.523 0.366 0.342 1.000 0.301 0.585 1.000 0.468 0.551 0.221 0.409 1.000 0.356 0.239 0.355 0.257 0.316 0.215 0.222 0.324 0.354 0.393 0.478 0.557 0.639 0.674 0.635 0.235 0.492 0.181 0.285 0.065 0.404 0.065 3

0.154 0.143 0.274 0.261 0.239 0.495 0.332 0.307 1.000 0.278 0.555 0.703 0.387 0.588 0.208 0.381 0.876 0.497 0.237 0.334 0.293 0.307 0.192 0.256 0.328 0.353 0.409 0.477 0.514 0.695 0.820 0.650 0.251 0.638 0.205 0.290 0.059 0.405 0.059 1

35 36 26 27 30 12 19 22 1 25 9 4 15 8 32 16 2 11 31 18 23 21 34 28 20 17 14 13 10 5 3 6 29 7 33 24 37

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Table 6 The classification of sub-industries. Group

Sub-industry

High efficiency group Medium efficiency group Low efficiency group

S9, S11, S12, S14, S17, S29, S30, S31, S32, S34 S6, S7, S8, S13, S16, S18, S20, S22, S25, S26, S27, S28 S1, S2, S3, S4, S5, S10, S15, S19, S21, S23, S24, S33, S35, S36, S37

2013 as the assumed efficiency value in 2012 in the following analysis. The efficiency score of Cultural, educational and sports goods manufacturing (S17) jumped to 1 in 2012 and remained at the efficient frontier from 2012 to 2015. According to the original data, we find that both inputs and outputs of S17 remarkably increased in 2013, and the desirable output was even more than twice as that in 2012. The reason might be the change in statistical caliber, although no official statement has been found; or it might be a sudden boost under the implementation of industrial structure adjustment. Quite on the contrary, the efficiency value of Oil processing, coking and nuclear fuels processing (S18) deviated from the frontier in 2011 and hovered between 0.4 and 0.5 in the following years. Since no obvious deviation are observed concerning inputs and outputs for S18, this change might due to the movement of the frontier, resulting from the improvements of other units, such as the change of S17. To better understand the characteristics of sub-industries, this paper categorizes the 37 sub-industries into the high, medium and low energy efficiency groups according to their efficiency scores. From Table 6, the high-efficiency industry group includes ten subindustries, whose average efficiency exceed 0.500. The medium groups cover sub-industries with efficiency values ranging from 0.300 to 0.500, The remaining fifteen sub-industries are in the level of the low-efficiency group. Viewed from the categories, the industries which are classified as high efficiency sub-industries are mainly composed of high technology industries (i.e., Manufacture of electrical machines (S30), Manufacture of Communication, Computer, Other Electronic Equipment (S31)) and clean industries (i.e., Manufacture of Tobacco (S9); Cultural, educational and sports goods manufacturing (S17)). On the contrary, all the mining-related industries and supply industries belong to the medium or low efficiency group. This category can provide a basic foundation for the implement of industrial structure adjustment. 4. The impact of industrial structure efficiency 4.1. Correlation analysis The industrial structure efficiency is calculated by Equation (3).

Fig. 1 shows the relationship between industrial structure efficiency and PIEE. For efficient provinces, whose PIEE are over 0.800, the corresponding industrial structure efficiencies are more than, or at least approximately 0.400. For moderately efficient provinces, whose PIEE are ranging from 0.500 to 0.800, the industrial structure efficiencies are between 0.350 and 0.450. The obtrusive value of Inner Mongolia is presumably due to the PIEE variation in 2013, or because of the influence of other environmental variables. The remaining provinces with poor efficiency have lower industrial structure efficiencies, mostly blow 0.350. This preliminary analysis implies a positive correlation between industrial structure efficiency and PIEE.

4.2. Tobit regression By using the Stata software, the Tobit regression results are obtained, as shown in Table 7. The results reveal that the industrial structure efficiency, economic development level and the degree of industrial concentration have significantly positive effects on PIEE whereas the energy consumption structure, property right structure, market openness and technological progress are found to be insignificant to affect the PIEE.

Table 7 The regression results by Tobit model. Variable

Coefficient

P-value

Std. Error

Industrial structure efficiency Economic development Energy consumption structure Technological progress Industrial concentration Property right structure Market openness Constant Log likelihood LR (chi2)

0.516*** 0.123*** 0.0367 0.398 0.0139* 0.012 0.000 0.0316 105.579*** 123.31***

0.001 0.000 0.916 0.192 0.086 0.832 0.960 0.890

0.163 0.020 0.209 0.306 0.008 0.056 0.000 0.197

Note: The superscripts ***, **, and * present the statistical significance at the 1%, 5%, and 10% levels respectively.

Fig. 1. The relationship between industrial structure efficiency and PIEE.

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4.2.1. Industrial structure efficiency The positive impact of industrial structure efficiency on PIEE is verified. When the industrial structure efficiency goes up by 1%, PIEE will rise by 51.6%. The role of industrial structure efficiency for PIEE is mainly embodied in two aspects: first, the industrial structure is optimized by promoting the development of low energy consumption and technology-intensive sub-industries whereas restraining excessive expansion of energy-intensive subindustries. Second, the energy efficiency of each sub-sector is enhanced by using advanced technologies and equipment. As analyzed before, the overall sectoral energy efficiency has not seen notable increase during the seven years. Hence, it might be more practical to adjust the provincial industrial structure to achieve higher industrial structure efficiency and PIEE in the future. 4.2.2. Economic development level Economic development level is another important factor contributing to the PIEE and its coefficient is 0.123. This result is in line with economic theory and conclusions drawn by previous studies (Zhang et al., 2016a,b; Wang and Zhao, 2017). It is easy to understand that the provinces with high per capita GDP are more likely to access advanced production technology. In addition, these provinces can have more investment in energy conservation technology and process optimization, and are more concern about environmental protection, thereby improving industrial energy efficiency. 4.2.3. Energy consumption structure It seems conflicting with prior studies that the effect of energy consumption structure is not significant (Pan et al., 2013; Xu and Lin, 2017), but this result can be explained by three reasons. First, unlike other studies, the coal consumption considered in this paper includes indirect coal consumption from electricity. For some electric sub-industries (e.g. S30 and S31), the higher proportion of electricity consumption might imply more industrial productions; considering high unit prices of electric products, a considerable amount of revenue could be consequently generated. Second, compared with other types of energy, coal reserve is abundant and the exploitation cost is cheap in China, leading to the consequence that the economic contribution of coal consumption masks the negative impact on the environment (Guo et al., 2018). Third, when the industrial structure efficiency is considered, the efficiency results partly cover the effect of the energy structure, leading to the multicollinearity. 4.2.4. Technological progress It is expected that the increase of the R&D investment can optimize the production process and improve the production performance to gain more revenue. However, this paper exhibits an insignificant negative effect of the R&D investment proportion. We reason that the overall effect of the R&D investment on energy efficiency has not yet reflected, due to the lag-effect characteristics of the R&D investment. Besides, another possible reason, referred from Li and Shi (2014), is the diversification of R&D investment; we cannot peel off the parts related to energy efficiency improvement. 4.2.5. Industrial concentration The degree of industrial concentration has a significantly positive effect on PIEE at the 10% significant level. A 1% increase in the degree of industrial concentration will lead to a 1.39% rise of the PIEE. The large industrial enterprises are more likely to access cheaper resources and achieve the economies of scale. The possible negative effect of industrial concentration caused by weakcompetition, as we mentioned before, may not apply in current

China, given that many enterprises want a piece of the massive industrial market. 4.2.6. Property structure The effect of property structure is insignificant in our analysis. Since 2013, the Chinese government committed to the comprehensive deepening of state ownership reform, with a view to strengthening the competitiveness of state-owned enterprises (SCIO, 2013). Since that, more specific strategies have been issued and pilot programs have been launched. For example, China Unicom's, a state-owned telecommunication company, has made efforts to reform its ownership by bringing in strategic investors and the significant increase of earnings have demonstrated the improved performance of state-owned enterprises (CFI, 2018). The decent performance of state-owned enterprises can also be explained in another view. As reported by Kostka and Hobbs (2012) and Ma and Yu (2017), state-owned enterprises which are under heavier political burden, are more likely incentivized to meet energy-saving targets, instead of primarily seeking to maximise profit as other private enterprises do. Therefore, it is reasonable to believe that the propriety structure has no significant influence on PIEE in recent years. 4.2.7. Market openness The impact of FDI on improving PIEE is insignificant, which does not conform to our theoretical analysis. Taking note of explanations made by Cheng et al. (2018), we reason that FDI in China is principally put in low-tech processing and assembly, or other inputintensive industries, causing large amounts of energy consumption along with massive carbon emissions. On the other hand, driven by the desire of innovation, Chinese industrial enterprises are moving toward high-quality and technological intensive development. Some domestic firms have already been the world's forefront level in recent years, like Huawei telecommunication company and China South Locomotive and Rolling Stock Corporation. As the leading role of foreign companies is not as obvious as decades ago, we have the reason to believe the positive effect of market openness on PIEE is dwindling. 4.3. Robustness test To confirm the conclusions, we conduct a robustness test by removing insignificant variables out of the regression model (Model A). Table 8 shows that the Log-likelihood value, coefficients and significance levels of the remain variables almost unchanged compared with previous results, testifying the insignificant effects of energy structure, technological progress, property right structure and market openness on PIEE. To verify the role of industrial structure efficiency, we exclude the industrial structure efficiency in the Tobit model (Model B). The result indicates that the Log-

Table 8 The regression results of robust tests.
Coefficient (Model A)

Coefficient

(Model B) Industrial structure efficiency Economic development Energy consumption structure Technological progress Industrial concentration Property right structure Market openness Constant Log likelihood LR (chi2)

0.525*** 0.120*** e e 0.014* e e 0.146 104.68*** 131.40***

e 0.127*** 0.080 7.014 0.013 0.014 0.000 0.286 99.86*** 155.97***

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likelihood declines from 105.579 to 99.86, confirming the important influence of industrial structure efficiency. 5. Conclusion and policy implications This study evaluates the industrial energy efficiency of provinces in China with SBM-UN model during the period from 2010 to 2016. Furthermore, we introduce the industrial structure efficiency into Tobit regression to analyze its effect on PIEE. The main conclusions and policy implications based on this study are as follows. First, the efficiency evaluation results manifest the prominent distinction of PIEE. Beijing, Tianjin, Jiangsu and Guangdong show the best energy efficiency performance during the research period whereas Shanxi, Ningxia and Qinghai are far from the frontier with the low efficiency values of approximately 0.300. Second, disparities in energy efficiency are also huge across subindustries and the overall energy efficiency at the sectoral level is low. About a quarter of sub-industries have poor efficiency, with the average values below 0.500. S9 is the most efficient subindustry while S37 is the lowest efficient one whose average score is just 0.059. Third, as we expected, the results of Tobit regression have demonstrated that industrial structure efficiency is a key factor for explaining the difference of PIEE, with the coefficient of 0.525. Besides, economic development level and industrial concentration have significantly positive impacts on PIEE, while energy consumption structure, technological progress, property structure and market openness pose insignificant impacts on PIEE. Policy implications for improving industrial energy efficiency can be given based on our results. Above all, the adjustment of industrial structure merits more attention. It is suggested that provinces put their priorities on the development of high-efficient sub-industries, on the grounds of their comparative advantages and natural endowment. For efficient and developed provinces where the industrial structure is basically optimized, energy-intensive industrial enterprises on a large-scale should be strictly restricted while green manufacturing and clean-energy industries should be actively promoted. Economic subsidies, tax abatement, green credit and other economic policies should be further implemented to support the construction and development of high-tech, high added value with low energy consumption sub-industries, like S30, S31, S32. For low efficient provinces, a gradual structure transformation is required. Medium-efficient sub-industries, such as S6, S7, S13 are considered as key transferred industries in the near future. Excess capacity of low-efficient sub-industries should be eliminated and small-sized enterprises with low-quality productions should be phased out. For low-efficient regions, crossregional merger and reorganization of enterprises can be adopted and some coordinated development systems can be established, like the Beijing-Tianjin-Hebei region. Concerning regions with abundant resources, like Shanxi, the government should encourage enterprises to improve the clean production level, optimize management and build dynamic monitoring, forecasting analysis and warning mechanism of energy consumption and emissions. With regard to suggestions associated with other variables, we suggest that the degree of industrial concentration can be partly enhanced through merging and reorganizing, especially for industrial enterprises with high energy consumption and little alternative in products, such as iron and steel, metallurgy and cement industries. Moreover, it should be emphasized that the mining of coal is environmentally harmful and the clean electricity generation technology has not been widely adopted in China. Hence, it is absolutely necessary to utilize clean and renewable energy to substitute the consumption of coal, even if the energy structure is tested to be insignificant to the PIEE. Next, this paper

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encourages governments and companies to establish specific funds or increase the allocation proportion of R&D funds into energyintensive processes. Furthermore, the state-owned enterprise reform should continue and speed up, so as to optimize resources allocation and improve the operation efficiency. Finally, when the local governments attempt to attract foreign investments, the targeted companies should be selective to advocate advanced technology and avoid the plunder of cheap resources. The contribution of this paper mainly includes four aspects. First of all, our study is the first ever that examines the effect of industrial structure among sub-industries on the PIEE, which gives a new insight into understanding the role of inter-industrial structure on PIEE. Second, although studies evaluating the energy efficiency are abundant, papers specifying the provincial industrial energy efficiency are limited, let alone the analysis of influencing factors. This paper calculates the PIEE and investigates the influencing factors and helps to provide a basic foundation for energy efficiency improving policy-making. Third, previous literatures focus on industrial energy efficiency either at the provincial level or at the sector level, while this paper makes the attempt to build their relationship. Last, regarding the energy structure, the indirect coal consumption from electricity usage is taken into consideration in this paper. Seeing that industrial activities are almost electricityintensive and the electricity structure is coal-dominated in China, this consideration is necessary but often ignored by previous related studies. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.01.095. References Bian, Y., Liang, N., Xu, H., 2015. Efficiency evaluation of Chinese regional industrial systems with undesirable factors using a two-stage slacks-based measure approach. J. Clean. Prod. 87, 348e356. Bosseboeuf, D., 2015. Energy Efficiency Trends and Policies in Industry. European Union. CFI, 2018. China Unicom (Hong Kong) Limited 2017 Annual Report 20 May, 2018. Chang, D., Kuo, L.R., Chen, Y., 2013. Industrial changes in corporate sustainability performance e an empirical overview using data envelopment analysis. J. Clean. Prod. 56, 147e155. Charnes, A.A.C.W., 1962. Programming with linear fractional functionals. Nav. Res. Logist. Q. 9, 181e186. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring efficiency of decisionmaking units. Eur. J. Oper. Res. 2, 429e444. Chen, L., Wang, Y., Wang, L., 2016. Congestion measurement under different policy objectives: an analysis of Chinese industry. J. Clean. Prod. 112, 2943e2952. Chen, L., Jia, G., 2017. Environmental efficiency analysis of China's regional industry: a data envelopment analysis (DEA) based approach. J. Clean. Prod. 142, 846e853. Chen, X., Gong, Z., 2017. DEA efficiency of energy consumption in China's manufacturing sectors with environmental regulation policy constraints. Sustain. Basel 9, 210. Cheng, Z., Li, L., Liu, J., 2018. Industrial structure, technical progress and carbon intensity in China's provinces. Renew. Sustain. Energy Rev. 81, 2935e2946. Chung, Y.H., Fare, R., Grosskopf, S., 1997. Productivity and undesirable outputs: a directional distance function approach. J. Environ. Manag. 51, 229e240. Feng, C., Wang, M., Liu, G., Huang, J., 2017. Green development performance and its influencing factors: a global perspective. J. Clean. Prod. 144, 323e333. Feng, C., Wang, M., 2017. Analysis of energy efficiency and energy savings potential in China's provincial industrial sectors. J. Clean. Prod. 164, 1531e1541. Goldsmith, R.W., 1951. A perpetual inventory of national wealth, NBER chapters. In: Studies in Income and Wealth. National Bureau of Economic Research, Inc., pp. 5e73 Guo, P., Qi, X., Zhou, X., Li, W., 2018. Total-factor energy efficiency of coal consumption: an empirical analysis of China's energy intensive industries. J. Clean. Prod. 172, 2618e2624. Guo, X., Zhu, Q., Lv, L., Chu, J., Wu, J., 2017. Efficiency evaluation of regional energy saving and emission reduction in China: a modified slacks-based measure approach. J. Clean. Prod. 140, 1313e1321. He, Y., Liao, N., Zhou, Y., 2018. Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN. Energy 142, 79e89.

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