Energy efficiency in China's industry sectors: A non-parametric production frontier approach analysis

Accepted Manuscript Energy efficiency in China's industry sectors: A non-parametric production frontier approach analysis Rui Wang PII:

S0959-6526(18)32266-2

DOI:

10.1016/j.jclepro.2018.07.277

Reference:

JCLP 13731

To appear in:

Journal of Cleaner Production

Received Date: 6 November 2017 Revised Date:

1 July 2018

Accepted Date: 27 July 2018

Please cite this article as: Wang R, Energy efficiency in China's industry sectors: A non-parametric production frontier approach analysis, Journal of Cleaner Production (2018), doi: 10.1016/ j.jclepro.2018.07.277. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Title Page Energy efficiency in China´s industry sectors: A non-parametric production frontier approach analysis ： Rui： Wang,： School： of： Economics： and：

Management,：Tongji：University,：Shanghai：200092,：China：

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The： only： author： and： corresponding： author

Complete： mailing： address： of： the： corresponding： author:： Tongji： A： Building,： School： of：

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Economics：and：Management,：Tongji：University,：Shanghai：200092,：China

： Rui：Wang：

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Email：address：of：the：corresponding：author:：[email protected]： Phone：number：of：the：corresponding：author:：18817680251：

Information:：Rui：Wang,：Female,：Tongcheng：City,：Anhui：Province,：January：1th,：1991,：PHD： Student,：Green：economy：and：sustainable：development：

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Conflict of interest: the authors declare that they have no conflict of interest

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Energy efficiency in China´s industry sector: A non-parametric production frontier approach analysis

Highlights:

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Energy productivity growth is decomposed into three factors by a non-parametric model. Technical progress is the most powerful contributor followed by efficiency change. Dynamic changes of technical progress and efficiency form a general TFP growth. Light industries generate a higher input substitution effect than do heavy ones. Overall efficiency presents a growing trend in volatility from 1998 to 2011.

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Graphical Abstract:

Abstract: There is increasing recognition of the importance of energy productivity in China due to the considerable growth in industrial energy consumption in the past several decades. Energy productivity improvements should be approached through changes in factors such as efficiency, technology use and factor substitution. Thus, the aim of this study is to decompose the energy productivity rate of change in a non-parametric framework. We combine data envelopment analysis (DEA) and the Malmquist index, using a data set of 35 two-digit industries in China from 1998 to 2011. The conclusions highlight three key points. First, the dynamic DEA reveals that 1

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technical progress is the primary source of energy productivity improvement. Technical efficiency plays a promoting role. The contribution from factor substitution is the least. Second, regarding change, from 1998 to 2003, the positive effect of technology progress was very prominent; however, technological efficiency played a small role and may have even impeded progress. From 2007 to 2010, technology progress had insufficient power; however, the role of efficiency improvement was obvious. The dynamic changes between technology progress and efficiency improvement result in a wide range of technological innovations and efficiency improvements in the entire industry. Third, factor substitution has a prominent positive contribution in light industrial sectors but hinders the improvement of energy productivity in heavy industrial sectors. The most efficient industry is telecommunications equipment, computer and other electronic equipment. This study concludes that China should raise the relative cost of energy consumption and optimize energy allocations and other production factors.

Keywords: Energy productivity; Decision mechanisms; Non-parametric production frontier

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approach; China´s industry sector

Nomenclature

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Asia-Pacific Economic Cooperation Chinese Yuan Per Ton of Standard Coal Equivalent, which takes the Price in 1990 as the Price of the Base Year Chinese Yuan Per Ton of Standard Coal Equivalent Constant Returns to Scale Data Envelopment Analysis Decision Making Unit Energy Change of Production Efficiency Change of Energy Productivity Between Two Phases Gross Domestic Product Input Substitution Effect of Energy Productivity Change Capital Stock Capital Per Unit of Energy Labour Labour Per Unit of Energy Logarithmic Mean Divisia Index n=1, 2,…, Number of Decision-making Units (N) The Perpetual Inventory Method Research and Development Stochastic Impacts by Regression on Population, Affluence, and Technology Frontier in Period t based on the Input Output Set (y, k, l) Change of Technology Frontier

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APEC CNY 1990/tce CNY/tce CRS DEA DMU E EC EPC GDP IC K k L l LMDI n PIM R&D STIRPAT

τt TC

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ACCEPTED MANUSCRIPT Total Factor Productivity Change of Total Factor Productivity Period Various Returns to Scale Gross Industrial Product Produced by Decision-making Units Energy Productivity Three-dimensional Input Output Space that Represents the Reference Technology

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TFP TFPC t VRS Y y (y,k,l)

1. Introduction

Since the industrial revolution, there is increasing focus on the depletion of natural resources and its constraints on economic growth and social development. Largely, the rapid development of

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the Chinese economy in recent decades has been achieved by a substantial increase in energy consumption. The average annual energy consumption of the industrial sector accounts for more

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than 70% of China’s total energy consumption. Owing to the rapid development of heavy industry, China has surpassed the United States, becoming the world's largest energy consumer. Liu et al. (2015) use the ‘apparent consumption’ method to re-estimate Chinese energy consumption from the perspective of domestic energy production, international trade and transnational supply. They find that China’s total energy consumption in 2000-2012 was approximately 10% higher than the value reported by China’s National Bureau of Statistics. This highlights the urgent need for energy savings in China. As pointed out by Ma et al. (2017a), productive energy efficiency work is an

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important factor in China’s sustainable urbanization. It is necessary to increase energy efficiency in the production process significantly to save energy and maintain sustainable economic growth. Essentially, there are four ways to improve energy efficiency (Lin and Du, 2014). First, the state should optimize the industrial structure and allocate energy from low-productivity industries

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to high-productivity industries. Second, countries should promote energy efficiency through technological progress. Third, the state must reduce energy waste and inefficiency caused by imperfect management. Fourth, countries can improve energy efficiency through factor

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substitution mechanisms, such as replacing energy with capital or labour factors, and changing the relative input ratio between the factors of production (Lin and Liu, 2017). In the current and foreseeable future, the gradual upgrading of the industrial structure will reduce the potential for improving energy efficiency through industrial structure adjustment; however, technological progress can grant ‘unrestricted’ improvements in energy efficiency. The most often used method in studying the impact of technological progress on energy efficiency considers energy efficiency as a dependent variable, using technical progress indicators as independent variables, and the econometric method for regression analysis (Huang et al., 2017). As the dependent and independent variables are calculated from the same set of panel data, this method is very likely to cause endogenous problems. Therefore, we use the nonparametric production frontier decomposition method, which can provide information on both total and single factor productivity

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ACCEPTED MANUSCRIPT without model misspecification to decompose China’s industrial sectors’ energy productivity change index into three of the above mechanisms. Furthermore, we analyse the static efficiency and efficient industrial sectors in the decomposition process, which prior studies do not.

2. Literature review

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With increasing differences between world energy supply and demand, the international community is now focusing on improving energy efficiency. According to definitions of energy efficiency, the index can be divided into an economic efficiency index and physical efficiency index. The former is generally used for macro-level research (Rafiq et al., 2016), while the latter for micro-level analysis (Park, 2017). Furthermore, economic energy efficiency indicators can be

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divided into two categories: single factor energy efficiency indicators (Stern, 2012) and total factor energy efficiency indicators (Li et al., 2017). The single factor energy efficiency indicator is

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usually measured by the ratio of economic output to energy input or the input-output ratio. The former is called energy productivity, which measures the amount of output produced per unit of energy consumption (Wang and Wei, 2016), and the latter is called energy intensity, which measures the energy consumption per unit of output (Kander et al., 2017). The two indicators are reciprocal to each other. It is easy to calculate and use the single factor energy efficiency index. Therefore, many researchers study the current situation of single factor energy efficiency (Karimu et al., 2017). Energy can only be an important factor in human economic activities with other

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inputs such as capital and labour. Thus, economists take the role of substitution effects into account and calculate total factor energy efficiency indicators in specific countries, regions and industries (Wang et al., 2017). In addition, Ma et al. (2017b) use the energy savings indicator to show the effectiveness of energy efficiency task which broaden our research horizon.

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According to the above theoretical analysis, we can conclude that the single factor energy efficiency index and the total factor energy efficiency index are suitable indicators to evaluate energy efficiency. However, single factor productivity is not the same as total factor productivity,

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the difference being that the former measures the input-output relationship between energy and GDP, while the latter examines the efficiency of energy and other factors of production. Therefore, Shi et al. (2013) use both energy productivity and DEA multi-input, multi-output methods to measure China’s energy efficiency in various regions between 1998 and 2010. In practical application, single factor energy productivity and total factor energy productivity have the following limitations. (1) The single factor energy productivity index This traditional method measures only the simple ratio between energy input and economic output and does not account for the contribution of labour and capital. Another common criticism is that it neglects the substitution among different factors of production; therefore, the index cannot scientifically reflect efficiency in energy use.

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ACCEPTED MANUSCRIPT (2) Total factor energy productivity indicator The total factor energy productivity indicator is not convenient and is not commonly used in practice. It cannot be widely used in the setting of energy-saving policy goals. China’s government work report of 2006 and the ‘11th Five-year Plan’, both adopt the single, instead of the total factor energy efficiency indicators. In addition, the total factor energy productivity (TFP) index cannot distinguish real energy efficiency from the efficiency of other factors. The way to conserve energy

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is to improve the efficiency of energy, rather than the generalized energy efficiency, which includes the efficiency of other factors.

In terms of factor influence analysis in energy and environmental economics, many studies employ extended and derivative versions of the STIRPAT method and the LMDI decomposition approach to study the influence mechanisms. For example, Ma et al. (2017c) utilize the Stochastic

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Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and ridge regression analysis to quantify the driving forces’ contributions to carbon emissions in Chinese

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public buildings. Moreover, Shao et al. (2016a) adopt the Generalized Divisia Index Method for comprehensive and accurate quantification of the factorial impacts on energy-related carbon emissions changes. Shao et al. (2016b) use an extended LMDI model to decompose energy-related industrial CO2 emissions into microeconomic and macroeconomic factors. Zhang et al. (2017) apply an extended LMDI model to decompose China’s energy-related and process-related industrial CO2 emission intensity into seven techno-economic drivers. Zhao et al. (2016) use the extended LMDI model, which includes three investment factors, to investigate the role of

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investment in the mitigation and decoupling of CO2 emissions with industrial growth. Zhao et al. (2017) use the Logarithmic Mean Divisia Index (LMDI) method to quantify the contribution of several factors to the decoupling of China’s overall economy from CO2 emissions. Ma et al. (2017d) assess China's National building energy savings by summarizing all driving factors, such

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as technological progress and user behaviour, based on an IPAT-LMDI model. Furthermore, Ma et al. (2017e) estimate the carbon mitigation and energy savings in existing Chinese civil buildings from 2001 to 2015 based on an improved method of Ma et al. (2017d). These studies decompose

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pollution emissions, pollution mitigation, energy savings, single factor productivity index, and the decoupling of economic growth from CO2 emissions, into reasonable driving factors. However, they do not consider the contribution of input substitution or total factor productivity information in the decomposition process. Lin and Du (2014) verify that factor substitution and total factor items also play important roles in energy conservation. To alleviate this deficiency, Wang et al. (2017) decompose China’s energy productivity change rate into factor substitution effects, TFP information and random factors, based on a parametric SFA method using inter-provincial panel data on China from 1995 to 2012. However, the exact specification of the SFA model used in the existing studies cannot be achieved in most situations. In addition, they neither analyse static efficiency nor identify efficient DMUs in the decomposition analysis. To bridge this gap in the existing literature, our research draws on the nonparametric labour productivity decomposition

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ACCEPTED MANUSCRIPT framework that many studies use. Methodologically, there are three related literature streams: Kumar and Russell (2002) decompose the growth of labour productivity into technological progress, efficiency change and capital accumulation. Lin and Liu (2003) decompose GDP per capita into capital per labour, technical efficiency and technological progress. Wang et al. (2007) decompose labour productivity growth based on current and sequential DEA methods. Similar to labour productivity, energy productivity can be decomposed using DEA methods to obtain

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information on both single factor productivity and total factor productivity.

The existing literature widely acknowledges the decomposition of energy and environmental-related indicators. However, there is a lack of research examining the contribution of technological progress, efficiency improvement, and input substitution to energy productivity by employing China’s industrial sector data. Therefore, this study attempts to decompose the

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change of energy productivity in China’s industrial sectors from 1998 to 2011 into technological innovation, efficiency improvement and inputs substitution, based on the nonparametric

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production frontier. Our study makes the following primary contributions: we analyse from the perspective of energy productivity decomposition, reflecting the three important mechanisms for scientifically improving the energy productivity of industrial sectors in China. We take the energy efficiency of 35 double-digit industrial sectors as the object of our study to draw additional valuable conclusions. In addition, we obtain the information provided by combining single factor productivity with total factor productivity. Lastly, we perform static efficiency analysis to

decomposition process.

3. Methods and data

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demonstrate each industrial sector’s static efficiency and identify the efficient sectors in the

3.1. Energy productivity decomposition analysis

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The change in energy productivity in industrial sectors can be decomposed into technological progress, efficiency improvement and input substitution, using data envelopment analysis. The intuitive meaning of technological progress is the extrapolation of the production frontier. The

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improvement of the technology frontier is usually derived from technological innovation and the introduction of advanced technology. This requires considerable R&D personnel and funds. The technological efficiency effect reflects the gap with the most advanced technology. In this study, it reflects the gap between the actual energy productivity of the industry and the energy productivity on the frontier. The combination of technological progress and efficiency change is the growth rate of total factor productivity. The substitution effect includes the substitution of capital for energy and the substitution of labour for energy. The higher capital stock per unit of energy is the result of capital increases in the development of industrial sectors. The higher labour force per unit of energy reflects the constant flow of labour from the agricultural sector to the industrial sector in recent decades. We assume that there are n = 1,L , N decision-making units. In period t , capital K ,

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ACCEPTED MANUSCRIPT labour L , and energy E are used as inputs to produce a single commodity Y . Based on the sample data set, we can use the following formula to construct the constant returns to scale production technology set: the reference technology can be simplified into a three-dimensional space ( y, k , l ) . y = Y / E = energy productivity, k = K / E = capital per unit of energy and l = L / E = labour per unit of energy. The energy productivity changes can be decomposed into the following form:

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t +1  y t +1 ( k t +1 , l t +1 ,τ t ) ymax (k t +1 , l t +1 ,τ t +1 )  ×  maxt ×  t t t t ymax ( k t , l t ,τ t +1 )   ymax ( k , l ,τ )

1/ 2

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D t +1 ( K t +1 , Lt +1 , E t +1 , Y t +1 )  D0t ( K t +1 , Lt +1 , E t +1 , Y t +1 ) D0t ( K t , Lt , E t , Y t )  Y t +1 E t EPC = t × t +1 = 0 t t t t t ×  t +1 t +1 t +1 t +1 t +1 t +1 t t t t  Y E D0 ( K , L , E , Y )  D0 ( K , L , E , Y ) D0 ( K , L , E , Y )  = TC × EC × IC

(1)

represents the

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EPC represents the change in energy productivity between two phases, τ

t

frontier in period t based on the input output set (k , l , y ) . In formula (1), the first two terms TC and EC on the right-hand side represent the improvement in energy productivity caused by

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the change in the technology frontier and production efficiency. TC × EC is the change in total factor productivity TFPC . The third term IC is the change in energy productivity caused by changes in capital and labour per unit of energy. IC can be called the input substitution effect of energy productivity change. Through the above nonparametric production frontier decomposition method, this study decomposes traditional energy productivity into three parts: technological progress, production efficiency changes and change of substitution. 3.2. Data sources

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Since 1998, the China Industrial Economics statistics yearbook has provided industrial data on all state-owned and non-state owned industrial enterprises above the designated scale. To ensure consistency in the original statistical data, this study constructs a panel data of 35 double digits sub industries from 1998 to 2011. The 35 sample industries include Coal Mining and

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Dressing, Petroleum and Natural Gas Extraction, Metal Ore Mining, Non-ferrous Mineral Mining, Non-metal Mining, Food Processing and Manufacturing, Food Manufacturing, Wine Beverages

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and Refined Tea Manufacturing, Tobacco Processing, Textile, Clothing and other Fiber Products Manufacturing, Leather Furs Feather and Related Products, Wood Processing and Bamboo Rattan Palm Straw Products, Furniture, Paper and Paper Products, Printing and Reproduction, Cultural Office Art Sports and Entertainment Machinery, Petroleum Processing and Coking, Chemical Raw Materials and Chemical Products Manufacturing, Pharmaceutical Manufacturing, Chemical Fibre, Rubber Products, Plastics, Non-metal Mineral Products, Ferrous Metal Smelting and Pressing, Non-ferrous Metal Smelting and Pressing, Metal Products, Common Equipment Manufacturing, Special Equipment Manufacturing, Transportation Equipment Manufacturing, Electric Equipment and Machinery, Telecommunications Equipment Computer and Other Electronic Equipment, Instrumentation and Culture Office Machinery Manufacturing, Electricity Steam Hot Water Production and Supply, Gas Production and Supply. We define these 35 industries as the 1~35 industrial sector in turn. 7

ACCEPTED MANUSCRIPT Decomposition of energy productivity requires the construction of an input-output panel database. This study uses the traditional three inputs (capital, labour and energy) and single output (gross industrial output value) variables. The selection of indicators and data is as follows. Capital stock: The existing literature generally uses the Perpetual Inventory Method (PIM) to estimate the capital stock K . This study follows the same approach. Based on the net value of fixed assets in 1998, the capital stock of each industry can be obtained by accumulating the

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change in original fixed assets per year.

Labour input: We use the average number of employees to indicate the labour input of each industry.

Energy input: We use the total energy consumption (tons of standard coal) to indicate the energy input.

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Output: This study takes the price in 1990 as the price of the base year and performs price adjustment to obtain the comparable industrial total output of each industry.

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The above basic data are collected from the China Statistical Yearbook, China Industrial Economics Statistical Yearbook, Yearbook of China's economic census and the China Energy Statistical Yearbook.

4. Results

4.1. Energy productivity changes during the study period

According to the actual industrial output value based on the price in 1990, the energy

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productivity of China’s industrial sector increases from 5,929.23 CNY/tce in 1998 to 23,182.69 CNY/tce in 2011—or 2.91 times. The average annual increase rate in energy productivity from 1998 to 2011 was 11.06%. This means that the average amount of energy per unit of GDP is reduced by 9.96% per annum. If we take the first year as the point of reference, the energy

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consumption per unit of GDP decreases by 40.81% in the fifth year. Furthermore, the energy intensity indicators between 1998 and 2011 decrease from 1.69 tons to 0.43 tons of standard coal per 10,000 RMB of industrial output value. If the gross industrial output value is calculated at the

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nominal price, energy productivity rises from 6,955.85 CNY/tce in 1998 to 34,258.46 CNY/tce of standard coal in 2011—an increase of 3.93 times. Therefore, even using different indicators, they all reflect a significant increase in the trend of energy productivity in China’s industrial sector. The average growth rates of energy productivity from 1998 to 2011 are 8.98%, 12.91%, 11.01%, 11.48%, 8.69%, 26.67%, 2.48%, 11.44%, 14.49%, 12.21% and 9.10%, respectively, showing strong momentum in the growth of energy productivity. From the perspective of industrial distribution, the five sectors with the fastest growth rates are Gas Production and Supply (22.14% per annum between 1998 and 2011), Chemical Fibre (15.80%), Furniture (15.37%), Pharmaceutical Manufacturing (15.36%) and Food Manufacturing (15.20%). The five sectors with the slowest growth rates are Textile (7.76%), Metal Products (7.44%), Petroleum Processing and Coking (5.52%), Non-ferrous Mineral Mining (5.46%) and Petroleum and Natural Gas Extraction

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ACCEPTED MANUSCRIPT (3.26%). Table 1 shows the growth rates for each of the 10 industries in each year.

Table 1 Annual Growth Rate of Energy Productivity in the Five Fastest and Slowest Industries (1998-2011). 1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

35

-12.83

38.63

18.44

10.01

19.26

57.82

-3.89

29.17

31.31

47.56

33.91

21

18.30

3.67

-4.44

4.46

36.22

71.96

22.20

14.62

15.75

5.39

7.42

14

-12.47

24.52

6.52

28.12

13.29

116.84

-18.27

17.27

23.94

20

-0.58

26.31

11.17

18.97

5.81

24.76

16.51

14.76

23.62

7

-3.57

24.07

13.74

14.35

28.43

24.65

0.14

13.80

20.41

Average

8.98

12.91

11.01

11.48

8.69

26.67

-2.48

11.44

14.49

10

6.96

8.66

5.46

4.61

0.89

16.43

0.01

2.35

13.54

27

-5.05

9.16

-1.43

-0.18

2.35

35.54

-11.06

11.12

20.71

18

20.80

19.59

5.00

1.92

0.47

-4.19

15.72

2.06

4

14.48

-2.51

-0.51

7.21

-9.81

26.80

-0.92

9.56

2

-11.93

-13.72

6.54

-17.31

37.56

43.10

80 60 40 20 0 1999 -20

2000

2001

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Growth rate of energy productivity(%)

100

2002

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Gas Production and Supply Pharmaceutical Manufacturing Textile Non-ferrous Mineral Mining

2003

2004

2005 Year

2006

Chemical Fibre Food Manufacturing Metal Products Petroleum and Natural Gas Extraction

Total

9.33

29.01

22.14

13.09

13.15

15.80

10.22

11.25

16.86

15.37

10.57

20.20

14.31

16.37

15.36

10.37

16.76

23.52

15.70

15.20

12.21

9.10

14.09

7.31

11.06

14.83

11.77

15.24

2.04

7.76

16.09

10.04

3.08

14.38

7.44

4.32

3.06

-6.60

10.33

3.29

5.52

8.26

9.21

20.28

-0.98

-4.11

5.46

-9.15

14.48

-6.89

7.65

3.26

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4.13

2011

3.14

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120

2.45

2010

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Sector No.

3.56

2007

2008

2009

2010

2011

Furniture Average Petroleum Processing and Coking

Fig. 1. Annual Growth Rate of Energy Productivity in the Five Fastest and Slowest Industries (1998-2011).

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4.2. Changes in the decompositions of energy productivity We use the nonparametric frontier decomposition method based on the constant return to

scale assumption with DEA and select the output angle to calculate the change in the frontier technology, technological efficiency and factor substitution, which affect the changes in energy productivity of various industries. According to Färe et al. (1994), the annual growth rates for each indicator are the Malmquist index, the technical progress index and the efficiency change index minus 1. We plot the contribution of each decomposition term to the change in energy productivity during the sample period in a percent-stacking graph, as shown in Figure 2. Table 2 shows the dynamic effects of the decomposition items in energy productivity across the entire industrial sector.

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Input Substitution Technological Efficiency Technological Progress

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35 30 25 20 15 10 5 0 -5 -10 -15

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year

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Fig. 2. Percentage accumulation chart of contribution of various decomposition items to energy productivity changes during the sample period

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Figure 2 shows that in general, the significant increase in energy productivity during the sample period is because the positive effects among technological progress, production efficiency and input substitution largely offset the negative ones (Except in 2005). The first engine that boosts energy productivity is technological progress. The average contribution of technological progress to energy productivity growth between 1998 and 2011 is 8.3 percentage points per year. The second contributor is production efficiency. The average contribution of efficiency

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improvement to energy productivity growth between 1998 and 2011 is 1.7 percentage points per annum. Input substitution has the least effect. The corresponding contribution of factor substitution is 0.87 percentage points per year. The factor substitution at the industry level has less impact on energy productivity. This may be due to the industrial sectors’ rigid demand for energy. Therefore, in general, the effect of total factor productivity is more prominent than input

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substitution in improving energy productivity. The average contribution of the total factor productivity increase to energy productivity growth between 1998 and 2011 is 10.10 percentage

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points annually. The result is consistent with that of Brandt et al. (2012), who use China’s Industrial Enterprises Database as the research sample and find that the total factor productivity of China’s industrial enterprises grew at an annual rate of between 11% and 16% during the period 2002-2007.

Table 2

Energy productivity change and decompositions in the entire industry (1998-2011) Energy

Total factor

Technical

Technical

Input

productivity

productivity

efficiency

progress

Substitution

change index

change index

change index

change index

change index

1998—1999

8.98

7.30

-1.00

8.30

1.57

1999—2000

12.91

12.40

-4.30

17.40

0.45

2000—2001

11.01

9.10

3.60

5.30

1.75

Year

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11.40

2.70

8.50

0.07

2002—2003

8.69

12.80

-1.30

14.30

-3.65

2003—2004

26.67

32.40

15.20

14.90

-4.33

2004—2005

-2.48

-4.20

-7.20

3.20

1.80

2005—2006

11.44

10.70

-0.30

11.00

0.67

2006—2007

14.49

12.80

9.30

3.20

1.49

2007—2008

12.21

0.70

2.60

-1.90

11.43

2008—2009

9.10

9.90

5.10

2009—2010

14.09

7.70

5.30

2010—2011

7.31

12.70

-5.40

Geometric Average

11.06

10.10

1.70

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2001—2002

4.60

-0.73

2.20

5.93

19.10

-4.78

8.30

0.87

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Note: Data in the table indicates the weighted average change rate of 35 double digits sub industries.

As seen from Table 2, overall, technological frontier growth plays a large part in promoting

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energy productivity, while the efficiency gap among industries hinders energy productivity growth. In the change of total factor productivity, the contribution of technological progress to the improvement in energy productivity is positive in 12 years of the period. However, the role of efficiency change during the sample period is limited, and it plays a negative role in six years of period. This is consistent with some previous studies. For example, Wang et al. (2017) also find that positive technical progress is the main source of energy productivity change in China between

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1995 and 2012; however, the change rate of technical efficiency is negative. Specifically, the rapid growth in total factor productivity between 1998 and 2003 is mainly caused by rapid technological progress; however, production efficiency significantly hinders the improvement in energy productivity in most years. The pace of technological progress slows between 2007 and 2010. It even appears to decline in 2008. At these times, the rapid growth in total factor productivity

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mainly comes from the improvement in technical efficiency. Ultimately, in 2011, technological progress becomes the leading factor in total factor productivity again. The two components of total

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factor productivity, namely, technological progress and efficiency change, exhibit a reverse relationship in seven years of the period. From the periods with positive relationship (six) and reverse relationship (seven), technological progress effects and technical efficiency effects show a reciprocal relationship. This indicates a balance between the technological frontier and technical efficiency change. On the one hand, technological progress and efficiency change promote generalized technological progress, converging to the effective equilibrium state in complementary and alternative ways. On the other hand, the dynamic evolution of the technological frontier and efficiency do not favour one or both in the same direction, but improve TFP across the industry through extensive technological innovation and efficiency improvement. In summary, the sustained growth of China’s industrial sector mainly depends on technological improvements. When technological improvement slows or even stagnates, we can improve technical efficiency to replace and complement technological innovation. The effect of 11

ACCEPTED MANUSCRIPT technological efficiency on generalized technological progress usually occurs after technological innovation. This allows maintaining the technological progress of the entire industrial sector. The period 2004-2005 is special. Since the end of the twentieth century, China’s industrial energy productivity has been steadily improving. However, during the period 2004-2005, energy productivity declined by 2.48%. Furthermore, production efficiency decreased by 7.20%, technological progress increased by 3.20% and input substitution increased by 1.80%.

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This special phenomenon may be due to the rapid progress of urbanization and the large-scale housing and transportation needs resulting from improvement in the consumption structure during the ‘10th Five-year Plan’ (2001-2005) period. These promoted the rapid expansion of heavy industry in China, although the state continued to shut down more than 30,000 high-energy, high-polluting small enterprises. During this period, the industrial structure shows a significant

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heavy-duty characteristic. The proportion of heavy industry increased from 63% to 70% and the energy consumption per unit of industrial output value for heavy industry was very large. In

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addition, the enforcement of the environmental protection policy in the ‘10th Five-year Plan’ period decreased significantly and pollutant emission reduction targets were not fully achieved. For example, compared with the end of the ‘9th Five-year Plan’ period, the quantity of sulfur dioxide emitted by the industrial sector during the ‘10th Five-year Plan’ period increased by more than 30 percent. There were no breakthroughs in deeper environmental problems during this period. All these reasons led to a sharp surge in energy consumption. Mark Levine, founder of the China Energy Research Group at the Berkeley Lawrence National Laboratory in California, notes

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that during 2002-2005, China’s economic growth generated more energy demand than any country at any time. Simultaneously, the momentum of China’s energy productivity decreased for the first time. This does not meet the basic requirements of new-type industrialization. However, with the country’s ever-growing emphasis on energy saving, industrial energy productivity regained strong

Table 3

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growth momentum after 2006.

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Energy productivity change and decompositions of each industry (1998-2011) Energy

Energy

Total factor

Technical

Technical

Input

productivity

productivity

Annual

productivity

efficiency

progress

Substitution

in 1998

in 2011

average

annual

annual

change

annual

CNY

CNY

growth rate

change

change

annual

change

1990/tce

1990/tce

index

index

index

index

1

1174.03

4777.45

12.41

7.60

-0.10

7.70

4.47

2

2058.03

3123.03

3.54

5.00

-6.00

11.70

-1.39

3

2918.16

12124.23

12.60

10.90

2.90

7.80

1.53

4

6799.06

13565.75

5.93

7.10

-0.40

7.50

-1.10

5

5053.27

18691.43

11.52

8.30

2.00

6.20

2.97

6

12870.42

79202.30

16.35

7.20

0.30

7.00

8.53

7

10105.04

63602.87

16.57

8.90

1.80

7.00

7.04

Sector No.

12

14372.72

70272.86

14.14

14.00

2.70

11.00

0.12

9

34456.06

114362.52

10.51

13.50

1.70

11.60

-2.63

10

13750.17

36320.10

8.43

6.30

-0.20

6.50

2.00

11

51471.88

148705.09

9.24

4.60

-1.80

6.50

4.44

12

52175.72

186826.29

11.22

4.50

-1.90

6.50

6.43

13

13315.94

64695.96

14.08

9.40

2.70

6.50

4.28

14

30688.61

196882.68

16.75

5.60

-0.80

6.50

10.56

15

5487.67

23992.63

13.08

14.40

4.30

9.70

-1.15

16

26895.96

93387.80

10.93

10.20

2.70

7.30

0.66

17

39696.37

109667.85

8.84

3.40

-2.90

6.50

5.26

18

1720.35

3459.86

6.00

11.60

-0.10

11.70

-5.02

19

2959.58

11433.59

11.92

17.30

5.40

11.30

-4.59

20

18753.72

120154.81

16.74

10.10

2.80

6.03

21

5726.11

38566.14

17.23

15.70

4.00

11.30

1.32

22

11385.15

36172.28

10.11

7.60

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7.00

0.00

7.60

2.34

23

22989.90

62181.38

24

2095.10

25

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8

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ACCEPTED MANUSCRIPT

5.60

-0.80

6.50

2.88

9469.79

13.40

13.90

5.70

7.80

-0.44

1595.88

4637.11

9.30

17.90

5.60

11.70

-7.30

26

3936.73

11240.37

9.14

15.70

4.00

11.30

-5.67

27

19202.85

48814.77

8.09

4.90

-1.50

6.40

3.04

28

17201.26

93417.58

15.14

9.60

3.30

6.10

5.06

29

21593.19

116717.04

15.10

8.50

2.10

6.20

6.08

30

27605.32

170575.97

16.39

14.50

6.50

7.50

1.65

31

58101.37

211473.98

11.37

7.00

0.50

6.50

4.08

32

133668.07

442920.84

10.50

8.70

0.00

8.70

1.66

33

51125.54

258473.33

14.46

8.00

1.40

6.50

5.98

34

1401.75

6048.98

12.96

18.60

6.20

11.70

-4.76

35

1518.81

20449.74

24.19

21.80

9.10

11.70

1.97

Average

5929.23

23182.68

11.06

10.10

1.70

8.30

0.87

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8.65

Note: (1) Unit of energy productivity in the table is CNY 1990/tce; (2) Average annual growth rates in the

table are the geometric mean rates of variables throughout the period.

Table 3 reports the average annual growth rate of energy productivity changes and the

respective decomposition items in 35 industrial sub-sectors. From the perspective of industrial distribution, the five industries with the highest contribution of technological progress on energy productivity growth from 1998 to 2011 are Gas Production and Supply by 11.70% (contribution rate of 51.39%), Electricity, Steam, Hot Water Production and Supply by 11.70% (89.02%), Petroleum Processing and Coking by 11.70% (177.87%), Petroleum and Natural Gas Extraction by 11.70% (271.70%), ferrous metal smelting and rolling processing industry by 11.70 percentage points (116.97%). The five industries with the lowest contribution of technological progress on 13

ACCEPTED MANUSCRIPT energy productivity growth are Electric Equipment and Machinery by 6.50% (contribution rate of 58.66%), Metal Products by 6.40% (80.64%), Special Equipment Manufacturing by 6.20% (43.11%), Non-metal Mining by 6.20% (55.51%) and Common Equipment Manufacturing by 6.10% (42.19%). We see that even the contributions of the five lowest industries are high, and the highest contribution is as much as 271.70%. This also means that the other two decomposition items significantly hinder the energy productivity of the industry that contributes the highest.

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The five industries with the highest contribution of efficiency improvement to energy productivity from 1998 to 2011 are Gas Production and Supply by 9.10% (contribution rate of 39.97%), Transportation Equipment Manufacturing by 6.50% (41.54%), Electricity, Steam, Hot Water Production and Supply by 6.20% (47.17%), Non-metal Mining by 5.70% (43.66%), Ferrous Metal Smelting and Pressing by 5.60% (55.99%). However, the five industries that contributed the

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least to increase in energy productivity led to a decline in energy productivity. They are Petroleum and Natural Gas Extraction by -6.00% (contribution rate of -139.33%), Instrumentation and

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Culture, Office Machinery Manufacturing by -2.90% (-32.74%), Leather, Furs, Feather and Related Products by -1.90% (-17.23%), Clothing and other Fibre Products Manufacturing by -1.80% (-19.70%) and Metal Products by -1.50% (-18.90%). Overall, the technological efficiency gap among industries poses a serious challenge to the growth of energy productivity. The industries not only need to innovate technology and improve machinery and equipment but also perfect the market economic system, strengthen competition, make full use of the capital market and learn from successful international and domestic experiences to improve production

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efficiency. We can improve energy productivity by shortening the distance between actual production points and the frontier.

The five industries with the highest contribution of factor substitution to energy productivity from 1998 to 2011 are Furniture Manufacturing by 10.56% (contribution rate of 64.95%), Food

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Processing and Manufacturing by 8.53% (53.90%), Food Manufacturing by 7.04% (44.45%), Leather, Furs, Feather and Related Products by 6.43% (58.28%), and Special Equipment Manufacturing by 6.08% (42.29%). The five industries that contributed the least to energy

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productivity growth are Ferrous Metal Smelting and Pressing by -7.30% (contribution rate of -72.96%), Non-ferrous Metal Smelting and Pressing by -5.67% (-58.92%), Petroleum Processing and Coking by -5.02% (-76.35%), Electricity, Steam, Hot Water Production and Supply by -4.76% (-36.19%) and Chemical Raw Materials and Chemical Products Manufacturing by -4.59% (-37.85%). These results show that factor substitution played a prominent role in light industries such as Furniture Manufacturing, Food Manufacturing and Leather Products. This is consistent with the characteristics of light industries, which can easily use labour to replace energy consumption to improve energy productivity. In contrast, heavy industries do not have much substitution potential. 4.3. Static efficiency analysis The above analysis is a dynamic analysis. Using the method proposed by Färe et al. (2001),

14

ACCEPTED MANUSCRIPT we use static analysis to depict the relative distance of each industry from the production frontier. We can then identify innovative industries whose technological advances are highlighted or those significantly driving the technological boundaries of the entire industrial sector outward. Table 4 reports the results of the static analysis, showing that the Telecommunications Equipment, Computer and Other Electronic Equipment industry promotes the technology frontier between 1998 and 2011. It always ranks first. In addition, the Tobacco Processing industry is located on the

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frontier in 2011. The production efficiency of the Tobacco Processing industry is also high in other years and ranked second in most years. Notably, the production efficiency of the Transportation Equipment Manufacturing industry grew rapidly from 0.36 in 1998 to 0.81 in 2011. The Chemical Fibre, Chemical Raw Materials and Chemical Products Manufacturing, Gas Production and Supply, Ferrous Metal Smelting and Pressing, Pharmaceutical Manufacturing, Electricity Steam

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Hot Water Production and Supply, Non-ferrous Metal Smelting and Pressing industries also have rapid production efficiency growth rates. The efficiencies of these industries grew by more than

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0.20. During the research period, the Culture, Office Machinery Manufacturing, Leather, Furs, Feather and Related Products, and the Clothing and other Fibre Products Manufacturing industries show a significant downward trend in efficiency. The efficiencies of these industries fall by more than 0.14. At the end of the study period, industries with efficiencies below 0.30 include Coal Mining and Dressing, Petroleum and Natural Gas Extraction, Metal Ore Mining, Non-ferrous Mineral Mining and Non-metal Mining industry. These industries all belong to energy-intensive heavy chemical industries. The efficiency performance highlights the drawbacks of high energy

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consumption. In general, the static efficiency in China’s industry presents a growing trend in volatility.

Table 4

Static efficiency of energy productivity decomposition of each industry (1998-2011)

1

0.07

2

0.16

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

0.06

0.06

0.06

0.06

0.05

0.07

0.06

0.05

0.06

0.06

0.06

0.07

0.07

0.14

0.20

0.18

0.16

0.15

0.13

0.13

0.11

0.11

0.09

0.10

0.09

0.07

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1998

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Sector No.

3

0.17

0.20

0.18

0.20

0.18

0.17

0.19

0.13

0.14

0.15

0.16

0.22

0.23

0.24

4

0.21

0.22

0.20

0.21

0.22

0.22

0.27

0.23

0.19

0.19

0.18

0.20

0.19

0.20

5

0.22

0.21

0.15

0.16

0.16

0.18

0.41

0.29

0.24

0.28

0.26

0.30

0.34

0.29

6

0.39

0.39

0.39

0.44

0.47

0.46

0.47

0.43

0.40

0.39

0.37

0.39

0.39

0.41

7

0.30

0.30

0.30

0.33

0.33

0.33

0.34

0.32

0.32

0.34

0.35

0.36

0.37

0.38

8

0.30

0.28

0.25

0.24

0.25

0.25

0.25

0.29

0.32

0.36

0.39

0.41

0.41

0.43

9

0.80

0.75

0.65

0.60

0.62

0.62

0.61

0.71

0.75

0.86

0.96

0.96

1.00

1.00

10

0.32

0.32

0.32

0.34

0.36

0.32

0.36

0.34

0.32

0.35

0.35

0.37

0.40

0.31

11

0.69

0.69

0.65

0.69

0.68

0.63

0.68

0.60

0.56

0.59

0.58

0.62

0.67

0.55

12

0.75

0.73

0.68

0.74

0.77

0.74

0.75

0.66

0.64

0.68

0.66

0.68

0.75

0.59

13

0.31

0.34

0.31

0.32

0.33

0.32

0.46

0.35

0.36

0.41

0.41

0.46

0.49

0.43

15

14

0.49

0.48

0.44

0.48

0.47

0.43

0.59

0.44

0.40

0.41

0.46

0.48

0.53

0.44

15

0.26

0.25

0.22

0.22

0.23

0.24

0.29

0.29

0.30

0.35

0.38

0.39

0.42

0.44

16

0.26

0.25

0.22

0.22

0.25

0.23

0.31

0.22

0.23

0.26

0.29

0.30

0.32

0.36

17

0.64

0.62

0.56

0.59

0.54

0.52

0.54

0.48

0.47

0.48

0.47

0.48

0.54

0.44

18

0.40

0.44

0.51

0.53

0.53

0.50

0.50

0.50

0.47

0.48

0.48

0.46

0.46

0.39

19

0.29

0.29

0.28

0.30

0.33

0.34

0.38

0.40

0.43

0.48

0.49

0.55

0.57

0.58

20

0.50

0.47

0.45

0.47

0.45

0.41

0.39

0.49

0.50

0.57

0.62

0.65

0.67

0.71

21

0.51

0.56

0.56

0.49

0.55

0.62

0.57

0.68

0.73

22

0.41

0.38

0.35

0.35

0.36

0.33

0.37

0.32

0.31

23

0.42

0.40

0.37

0.38

0.38

0.37

0.40

0.33

0.33

24

0.19

0.20

0.18

0.21

0.21

0.21

0.26

0.20

0.22

25

0.25

0.24

0.24

0.26

0.29

0.31

0.36

0.41

0.44

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ACCEPTED MANUSCRIPT

26

0.33

0.33

0.32

0.30

0.33

0.36

0.39

0.42

0.48

0.49

27

0.47

0.46

0.44

0.46

0.49

0.51

0.58

0.46

0.45

28

0.33

0.33

0.32

0.36

0.39

0.42

0.54

0.45

0.45

29

0.36

0.36

0.35

0.38

0.42

0.35

0.43

0.38

30

0.36

0.37

0.35

0.40

0.47

0.49

0.50

31

0.53

0.56

0.57

0.62

0.64

0.67

32

1.00

1.00

1.00

1.00

1.00

33

0.50

0.52

0.54

0.58

34

0.17

0.16

0.15

35

0.13

0.12

0.13

Average

0.39

0.38

0.37

0.83

0.88

0.88

0.86

0.32

0.34

0.35

0.37

0.41

0.38

0.38

0.41

0.45

0.37

0.26

0.30

0.32

0.35

0.38

0.51

0.55

0.56

0.55

0.52

0.50

0.57

0.57

0.54

0.48

0.44

0.43

0.47

0.39

0.50

0.47

0.47

0.49

0.51

0.38

0.42

0.41

0.42

0.47

0.48

0.51

0.57

0.66

0.70

0.77

0.82

0.81

0.74

0.68

0.66

0.71

0.69

0.68

0.72

0.57

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

0.57

0.56

0.64

0.61

0.61

0.66

0.65

0.61

0.66

0.59

0.13

0.13

0.13

0.23

0.26

0.27

0.31

0.35

0.33

0.37

0.37

0.13

0.14

0.13

0.17

0.20

0.24

0.27

0.35

0.38

0.43

0.40

0.43

0.41

0.41

0.45

0.46

0.47

0.50

0.47

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0.84

0.39

0.39

Source: Data are calculated using MAXDEA software.

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5. Discussion

As described in Section 3.1, the energy productivity decomposition model is an effective method to explain the periodic fluctuation of China's industry sector’s energy productivity change,

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and the significant sectoral differences. Although our research has provided meaningful results, we should point out some drawbacks. First, no statistical properties of the frontier estimators can be obtained because our estimation relies on a mathematical programming technique. Compared with the parameter stochastic frontier regression method, the nonparametric method can’t be used to perform the usual statistical inferences. Second, because the boundary of the production function measured by DEA is deterministic, another disadvantage is that the estimation results may be contaminated by random shocks and measurement errors. Third, the potentially different production environments can’t be considered because the static efficiency term can’t be further specified as a function of environmental variables beyond the control of sectors based on one-step estimation. Last but no least, the decomposition results may be impacted by extreme sample values. The outliers should be removed from the sample if exist to improve the data quality. The overall decomposition results indicate that since 1998, the sustainability of China’s 16

ACCEPTED MANUSCRIPT economic growth mode has become increasingly prominent. After 1998, China gradually joined the global production network, and strengthened domestic and international competition. China also increased its inputs in R&D activities, which contributed to the improvement of technical level and production efficiency of its industrial sector. In future economic development, with the Chinese government’s increasing emphasis on energy-savings, the influence of technological innovation and efficiency improvements will become more prominent. The static efficiency

production processes in traditional heavy chemical industries.

6. Conclusions 6.1. Main findings

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performance reflects the urgent need to renovate machinery and equipment, and upgrade the

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The driving force of energy productivity has been a research hotspot in the field of energy economics. Considering that a government’s decision-making process for an energy policy

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requires assessing the change in energy productivity, we sought to combine data envelopment analysis and the Malmquist index to analyse the energy productivity change in China’s industry sectors and its driving force over the period 1998–2011. These underlying driving forces include the rates of change in technical progress, technical efficiency and the substitution between energy and other input factors. We also identified the key industries contributing to the static efficiency level, which is an original contribution of this study. The nonparametric method can improve accounting accuracy in comparison to the SFA parametric production frontier decomposition

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method. The key empirical results are as follows.

(1) Overall, China’s energy productivity has increased continuously with an annual average growth rate of 11.06% since 1998, except during the period 2004-2005. Technological progress, with an average annual growth rate of 8.30%, plays a significant role in the rate of change in

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energy productivity. It is the largest driving force for energy productivity growth. The overall contribution rate of technical efficiency due to the digestion of introduced technology is not high. Technical efficiency grew at an annual rate of only 1.70% and had a significant negative effect in

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six years during the period. As a complementary technological innovation method, the contribution of the catch-up effect to energy productivity has become increasingly prominent in recent years. The contribution of technology efficiency in 2009-2010 was much larger than in 1998-1999. However, there remains considerable room to promote technology efficiency. Generally, the rate of change in technical efficiency tended to decline as the rate of change in technical progress increased. The input substitution effect had the least influence on the rate of change in energy productivity. It grew at an annual rate of only 0.87%. This may be caused by the industrial sector’s rigid demand for energy. (2) At the sector level, the five sectors with the fastest energy productivity growth rates are Gas Production and Supply, Chemical Fibre, Furniture, Pharmaceutical Manufacturing and Food Manufacturing. The five sectors with the slowest growth rates are Textile, Metal Products,

17

ACCEPTED MANUSCRIPT Petroleum Processing and Coking, Non-ferrous Mineral Mining and Petroleum and Natural Gas Extraction. Factor replacement contributed prominently in furniture manufacturing, food manufacturing, leather manufacturing and other light industries. This is consistent with the characteristics of light industries. Light industries find it easier to use labour to replace energy consumption and improve energy productivity. In contrast, heavy industries use too much energy to replace labour inputs, which inhibits improvement in energy productivity.

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(3) The identification of the key industries contributing to static efficiency is extremely informative for devising sector-specific strategies and guidelines. In accordance with the sustainable assessment, the Telecommunications Equipment, Computer and Other Electronic Equipment sector appeared to be the most efficient sector, with unified efficiency of 1 from 1998 to 2011. The Tobacco Processing industry was located on the frontier in 2011. In contrast, the Coal

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Mining and Dressing sector seemed to be the worst. The key sectors selected (e.g., Coal Mining and Dressing, Petroleum and Natural Gas Extraction, Metal Ore Mining, Non-ferrous Mineral

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Mining and Non-metal Mining industries) need to improve static efficiency to promote the sustainable development of China’s industries. 6.2. Policy implications

Based on the above conclusions, we propose the following specific policy recommendations to promote the energy productivity growth of industrial sectors in China. First, there are marked sector differences in the development of China’s industries; thus, different policies should be applied in different sectors. Critical energy-intensive industries, such

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as Coal Mining and Dressing, and Petroleum and Natural Gas Extraction, should rapidly reduce efficiency loss to increase energy productivity. Meanwhile, industries with backward technologies and equipment should be gradually phased out. A feasible strategy is to restrain the expansion of energy-intensive industries, such as the steel, cement and coal industry. For industries with

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overcapacity, it is necessary to control low-level redundant construction while encouraging mergers and reorganization to avoid wasting energy. As for light, advanced and technology-intensive industries with low energy consumption and high output value, the Chinese

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government should formulate appropriate policies to encourage the further development of these sectors to improve overall energy productivity. Second, this study revealed that between 1998 and 2011, the change in technological progress

had a much larger contribution to energy productivity than the other two factors. The fact that more than three-quarter of the total energy productivity growth is attributable to technological progress highlights its important role in tackling energy conservation. The policy priorities for the improvement of energy productivity in Chinese industrial development should focus on technological innovation. This means that the change in R&D input may have great potential of increasing energy productivity. Therefore, national energy conservation plans should focus on strengthening financial, technical and management support for sectors to increase their investment in energy-saving R&D and technology imports. The government must create a superior R&D

18

ACCEPTED MANUSCRIPT environment and strengthen efforts in maintaining sustainable, effective progress of energy-saving technology. Third, as an alternative and complementary way of technological innovation, there is still considerable scope for the promotion of technology efficiency. Owing to the imperfect development of China’s financial market, sectors with strong market competition are liable to be constrained by liquidity and capital. The lack of funds inhibits the implementation of efficiency

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improvement activities such as the introduction of advanced equipment. Therefore, the capital market should continue market-oriented and competitive reform to provide necessary funds to sustain the improvement in technological efficiency. Meanwhile, it is necessary to improve technical efficiency through institutional reform and gain management experience from developed regions and countries to reduce the gap with the technological frontiers.

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Fourth, to improve the effect of input substitution, for capital-intensive industries, China should use more efficient and low energy consuming machines to reverse the close alignment of

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capital and energy. Labour-intensive industries must be promoted to encourage employment to replace energy input. For the factor price, China should consider raising the relative cost of energy consumption and optimizing energy allocations and other production factors. The relative and absolute price of energy in China is relatively lower than energy prices in other countries. Consequently, economic actors prefer using energy than other alternative production resources, negatively affecting the effect of input substitution. The relatively low energy tax in China contributes significantly to low energy prices. Effective policy instruments may include increasing

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energy and environmental tax rates, removing energy price ceilings and reducing inefficient energy subsidies to raise energy prices. 6.3. Research prospects

This study is a preliminary attempt to investigate the effect of input substitution on energy

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productivity in China´s industry sectors. The limitations of this study are as follows. First, we only focus on the substitution effect between energy and all other input factors, and do not distinguish the substitution effect between energy and labour from the substitution effect between energy and

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capital, as well as inter-fuel substitution (substitution between petroleum and electricity). Therefore, it is worthwhile to extend our study to incorporate inter-factor and inter-fuel substitution in a non-parametric decomposition model simultaneously. Second, we adopt industrial-level data from 1998 to 2011 owing to data unavailability and the quality of sufficient data at the disaggregate level. It would be better to conduct the decomposition using the newest data. However, the National Bureau of Statistics of China has not released the gross industrial output value since 2012. Therefore, extending the sample to 2016 would require us to make a rough estimation, resulting in the loss of accuracy. Third, the DEA model with CRS technology considered in this study is admittedly simple. Its simplicity allows us to obtain explicit analytical results. However, an interesting extension would be to study the energy productivity decomposition in more general VRS models. Furthermore, an input-oriented model may lead to

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ACCEPTED MANUSCRIPT interesting results, and this will be the subject of future research. Finally, we do not evaluate the influencing factors of input substitution and other decomposition items. Future studies can improve upon the above deficiencies to provide valuable conclusions.

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