An evaluation of greenhouse gas emission efficiency in China's industry based on SFA

Science of the Total Environment 690 (2019) 1190–1202

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

An evaluation of greenhouse gas emission efficiency in China's industry based on SFA Jingchao Sun, Tao Du ⁎, Wenqiang Sun, Hongming Na, Jianfei He, Ziyang Qiu, Yuxing Yuan, Yingnan Li SEP Key Laboratory of Eco-Industry, Northeastern University, Shenyang, Liaoning 110819, People's Republic of China School of Metallurgy, Northeastern University, Shenyang, Liaoning 110819, People's Republic of China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• China's GHG efficiency at industry level is assessed by stochastic frontier analysis. • At night, net electricity is better than small-scale on-site coal-fired power plant. • Technology advance is better to improve efficiency and mitigate GHG emissions. • Energy efficiency and GHG efficiency are dependent but loosely related indexes.

a r t i c l e

i n f o

Article history: Received 21 May 2019 Received in revised form 5 July 2019 Accepted 6 July 2019 Available online 8 July 2019 Editor: Huu Hao Ngo Keywords: GHG efficiency Stochastic frontier analysis Correlation China's emission trading scheme (ETS)

a b s t r a c t As one of the largest countries with sound industrial systems in the world, China is a major emitter of greenhouse gases. In order to achieve sustainable development, the analysis of greenhouse gas emissions from industry is of great significance. This research evaluates the greenhouse gas emission efficiency (GHG efficiency) at industry level in 26 sectors of China and analyzes the impacts of its determinants using stochastic frontier approach. Furthermore, the correlations for GHG efficiency with its determinants and other proxies are estimated by Kendall's rank analysis. Industry level data from Chinese processing and manufacturing industries spanning over the period 2000–2016 are used for this analysis. Results show that there has little potential to improve GHG efficiency, then technology progress is necessary. The GHG efficiency performance responds both to changes in the proportion of net electricity use and the ratio of electricity price to coal price; meanwhile the technology capacity also has a positive impact. Besides, three main proxies of GHG intensity, energy efficiency and energy intensity have their own significance respectively, but could not represent GHG efficiency. © 2019 Elsevier B.V. All rights reserved.

1. Introduction

⁎ Corresponding author at: SEP Key Laboratory of Eco-Industry, Northeastern University, Shenyang, Liaoning 110819, People's Republic of China. E-mail address: [email protected] (T. Du).

https://doi.org/10.1016/j.scitotenv.2019.07.093 0048-9697/© 2019 Elsevier B.V. All rights reserved.

As the world's largest emitter of greenhouse gases currently, China has been the main pusher and catalyst for Global Climate Governance according to Greenpeace (Greenpeace, 2018). Referring to the Greenpeace and Intergovernmental Panel on Climate Change, the total carbon dioxide emission of China in 2017 is 9232.6 million tons, accounting

J. Sun et al. / Science of the Total Environment 690 (2019) 1190–1202

for about a quarter of global carbon dioxide emission, which leads to the deterioration of living environment and severe air pollution (Greenpeace, 2018; Shen and Wang, 2013). The primary cause of this phenomenon is the side effects of rapid China's industrial development (Gao et al., 2018). To face a series of environmental impact challenges (Sun et al., 2019a, 2019b) such as global warming caused by greenhouse gases, China started Emission Trade Scheme(ETS) in 2013, and it has implemented seven pilot carbon markets in various zones, including Beijing, Chongqing, Guangzhou, Hebei, Shanghai, Shenzhen and Tianjin to achieve the promise that China reduces the carbon intensity per unit of GDP by 60–65% by 2030 in the Conference of Parties (Lin and Jia, 2019). Coal is the main fossil resources and energy in China, while it is also a greenhouse gas producer. The changes have taken place in the structure of energy consumption used in manufacturing and processing industries in recent years that are mainly in the proportion of net electricity in entire energy consumption. Of course, the prices of electricity and coal are essential factors affecting greenhouse gas efficiency as well. The global CO2 efficiency was evaluated among 170 countries from 1997 to 2007 using a stochastic cost frontier, which indicated that there are great differences in efficiency level among countries and China has the lowest level though CO2 efficiency also improved like other counties, and the author put forward some political advise at last (Herrala and Goel, 2012). Furthermore, it was estimated at province level as well. Jiang et al. used stochastic frontier model by means of translog production function to evaluate the technical energy efficiency on the basis of panel data on 29 Chinese provinces from 2003 to 2011 (Jiang et al., 2016). Then, the author used a spatial Durbin error model to judge the major determinants of energy efficiency. In order to achieve the sustainable development of industry, measuring the carbon emission efficiency from 2009 to 2015 in Anhui province in China with the method of data envelopment analysis Malmquist analysis. It shows that the trend of carbon efficiency is rising, and the major factor is technical progress (Qiu and Yu, 2017). On the city level, the purpose was to evaluate energy efficiency for 285 cities in China during 2008–2012 (Wang et al., 2017a). And the other aim was to reduce carbon emission and improve its energy efficiency with the slacks-based measure approach. The results showed most of the cities had relatively low energy efficiency. However, it also means great potentials to improve energy efficiencies. There are also many studies on industry level. Martínez et al. estimated the energy efficiency and analyzed the trends in energy consumption and CO2 emissions in Swedish manufacturing industries in the period 1993–2008 using production frontier model at two-digit level (Pardo Martínez and Silveira, 2012). The results showed that many factors like electricity consumption, investments, total factor of productivity, energy prices and energy and CO2 taxes have negative influences on CO2 consumption. Similarly, Weyman-Jones et al. focused on the non-residential electric energy efficiency in the Portuguese economy measured by the method of stochastic frontier analysis (SFA) (Weyman-Jones et al., 2016) and using the time-series sample of nonresidential electrical energy consumption from 1970 to 2014 to analyze the influencing factors. It showed that measuring energy efficiency improvement is very critical for energy policy and energy studies. Moreover, Lundgren et al. estimated energy efficiency of the 14 sectors in Swedish manufacturing using single-stage SFA and analyzed the influencing factors of energy efficiency such as European Union Emission Trading System (EU ETS), carbon intensity and so on with a firm level. Results show that carbon efficiency doesn't equal to energy efficiency, and the EU ETS has little effect on energy efficiency (Lundgren et al., 2016). The efficiency of carbon trading market was evaluated in China, and it had weak efficiency but still had the signs of restoring market efficiency as results (Zhao et al., 2017). Recently, Löschel et al. investigated the impact of EU ETS for the efficiency of economic performance on the scale of manufacturing firms in Germany (Löschel et al., 2019). Its results suggested that the EU ETS had a significant positive impact on the regulated firms. Na et al. made a detailed analysis on CO2 of the different process in a specific iron and steel manufacturing route with

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results such as the more molten iron is used, the more carbon dioxide is emitted in the steel industry (Na et al., 2017). Brännlund et al. analyzed carbon intensity performance and the effectiveness of the Swedish CO2 tax (Brännlund et al., 2014). It found that Firms' carbon intensity performance had reactions with the changes of the CO2 tax and fossil fuel price, but was more sensitive to the tax. As discussed in the previous studies, there are four main energy efficiency evaluation methods. In order to evaluate the efficiency in industry reasonable, the author illustrates these four methods, including stochastic frontier analysis, data envelopment analysis, exergy analysis and benchmark comparison (Li and Tao, 2017). In addition, it is found that the analysis of the relationship between overall energy efficiency and influencing factors is applicable to econometric models. They also made a review of energy efficiency estimation using these four different methods (Li and Tao, 2017). Yang et al. did similar research along with SFA and data environment analysis (DEA) as well, and comparing the differences between these two methods (Yang and Lei, 2016). The energy efficiency and carbon dioxide emission are evaluated by the method of input-output analysis (Lin and Sun, 2010). Cheng et al. estimated energy efficiency, carbon dioxide emission efficiency and analyzed its impacts using DEA (Cheng et al., 2018). As for stochastic frontier approach, which is adopted in this research, and the different forms of SFA are used to evaluate energy efficiency in the diverse field. These results show that SFA is a suitable method to estimate energy efficiency and energy inefficiency, which are so important for policymakers (Filippini and Charles Hunt, 2011; Filippini and Hunt, 2015; Filippini et al., 2014). The robustness of these two measures of Spearman and Kendall correlation were researched. The Kendall's rank is better than Spearman's rank correlation in the aspects of robust and efficient (Croux and Dehon, 2010). That energy and other input prices play in reducing the energy intensity of manufacturing industries in Canada were studied. The results indicate that both capital and labor are complementary to energy (Gamtessa, 2016). The main purpose of this research is to evaluate emission reduction potential to increase carbon efficiency for 35 Chinese industries covering the period 2000–2016 using SFA. Carbon intensity is a normal indicator but as a measure of carbon efficiency is non-applicable as it could not reflect real efficiency. In this paper, the estimated efficiency scores are to be interpreted as revealing not only the progress of the technology of energy saving and emission reduction, the changes of industrial structure and energy structure but also the potential to reduce carbon emission while the level of production remains unchanged; in other words, it is the emission reduction potential of industries. And the other purposes are to investigate thoroughly and analyze the influencing factors of carbon efficiency which conditional estimate carbon efficiency of industries, including CHN ETS, the energy structure and the ratio of the price of electricity and coal. The rest of this paper is organized as follows. Section 2 introduces the methodology and the applied model. Section 3 describes the data and its resource. Section 4 shows the results of the estimation and discussions. Section 5 makes a conclusion. 2. Materials and methods In this study, in order to estimate the capability of the maximum amount of output that can be produced from a given combination of inputs or the ability of reducing the minimum amount of inputs from a given output, the stochastic frontier function approach introduced by Aigner et al. and Meeusen et al. (Aigner et al., 1977; Meeusen and Broeck, 1977) is used. In contrast, the nonparametric efficiency analysis methods such as data envelopment analysis (DEA) decided by their data without any assumptions on the specific statistical distribution of the error terms, but as for stochastic frontier analysis approach, it has the strength of allowing for random shocks and measurement error, and another

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advantage of the SFA approach is that it is useful to analyze the factors of and investigate the determinants of producer performance (Cullinane et al., 2006). There are many SFA models using different assumptions, and the condition of time invariance is a strong assumption, especially in longer panel. With the development of the stochastic frontier production function, a stochastic frontier analysis model with time-varying inefficiency was built up by Coelli (Battese and Coelli, 1995; Coelli, 1995). It relaxes this assumption and the modified inefficiency stochastic frontier production function is a significant improvement over the former stochastic frontier functions which do not have the technical inefficiency effects. We use the industrial specific distance to the frontier as a measure of the level of energy inefficiency. Green et al. pointed out two models to address the issue of unobserved heterogeneity when using the panel data, the true fixed effects model, and the true random effects model (Greene, 2005). And the true random effects model could consider unobserved heterogeneity of the two stochastic terms that is for time-variant factors. Thus, in order to discuss comprehensive factors of the industrial level carbon efficiency in the estimations, the true random effects model is used. The usefulness and advantages of SFA in Bayesian statistics in over traditional SFA which just uses classical statistics are proved as well (Carvalho and Marques, 2016). The production process is affected by the compound errors, which consists of two different unknown random error terms. One of the two error components results in the deviation from the production frontier by statistical objectives themselves and the reasons of the phenomena may be mismanagement, policies or unsatisfactory inputs, they all can also be interpreted as the indicators of nonnegative technical inefficiency. The other error component is a random variable which is not controlled by themselves, such as weather, statistical error and the different performances of machinery and employees daily (Kumbhakar and Lovell, 2000). It is so crucial to estimate the inefficiency and evaluate the drivers of inefficiency for policymakers (Battese and Coelli, 1995; Kumbhakar and Lovell, 2000). These two ways to achieve the estimations of inefficiency and inefficiency effects are two stages (Kalirajan, 1981) and singlestage (Battese and Coelli, 1995). And the single-stage approach is used in the random effects SFA model in this paper in order to estimate the inefficiency and obtain the conditional determinants of it as it avoids the estimation inconsistency in two-stage approach (Battese and Coelli, 1995). Carbon intensity of industry as a relevant criterion is to analyze the performance of the climate policy (Brännlund et al., 2014). According to the Intergovernmental Panel on Climate Change (IPCC), there are many greenhouse gases as atmospheric polluters such as CO2, N2O, CH4 and especially CO2. Due to the limitation of diverse technology levels in different industries, the combustion rate is commonly incomplete combustion, the average oxidation rate of different fuels is used in this paper. We use the emission factors of IPCC to calculate pollutants emission from industry level in this paper. The function is as follows: X EGHG ¼ FQ i  FOij  EF ij  GWPij ð1Þ where EGHG is greenhouse gas emissions, its unit is carbon dioxide equivalent. FQi is the quantity of fuel i. FOij is oxidization ratio of greenhouse gas j in fueli. EFij is the emission factors of greenhouse gas j in fuel i . GWP is global warming potential of gas j in fuel i, whose data sources refer to data provided by IPCC. Greenhouse gas intensity is a crucial role in the research field of atmospheric environmental protection, and it is also an indispensable data basic for policymakers. In this paper, greenhouse gas intensity is as follows: Em cm ¼ GHG Ym

ð2Þ Em GHG

is greenhouse where cm is greenhouse gas intensity of industry m. gas emission of industry m, it is calculated by Eq. (1). Ym is gross industrial output of industry m.

Based on the theoretical framework of Aigner et al. and the ‘true’ random effects specification of the stochastic production frontier referring to Belotti et al. (Aigner et al., 1977; Belotti et al., 2012) is as follows: !   ytm ln t ¼ βm;0 þ βm;L ln Ltm þ βm;K lnK tm þ βm;E −1 lnEtm Em þ α m ln T þ βm;LK ln Ltm ln K tm þ βm;LE ln Ltm C tm þ βm;LT ln Ltm ln T þ βm;KE lnK tm ln Etm þ βm;KT lnK tm ln T þ βm;ET ln Etm ln T þ vtm þ utm ð3Þ where ytm denotes the sell total production value of industry m at year t, L and K represent labor and fixed capital, respectively. T is a time trend variable in our case according to Greene et al., E stands for the aggregate greenhouse gas emission for GHG efficiency or energy use for energy efficiency (Greene and Dan, 2004). In Eq. (3), labor, capital, time trend and energy use are inputs, and they are treated as independent variables. We take an assumption of the deterministic frontier to adopt the form of a trans-log production function. We use maximum likelihood estimation to implement the model. The residual two error terms in Eq. (3) are a statistical noise term, vtm, and a non-positive random variable depicting inefficiency utm. In the first part, the statistical noise term vtm is assumed to be normally distributed usually that vtm~N(0, σv m2). The second part, utm, which represents the underlying efficiency and is assumed to have truncated-normal distribution that utm~N(μtm, σtm). In the following Eq. (4), utm is denoted as conditional GHG inefficiency, and it is assumed to be a function of a combination of GHG inefficiency factors. The function of conditional technical inefficiency is as follows: t

utm ¼ γm;CETS CETStm þ γm;value valuem þ γm;ELE ELEtm þ γmEC EC tm þ γ mEC TC tm þ υtm

ð4Þ

where CETS denotes the China's National emission trading system, which is a dummy variable taking the value one if industry m joins this scheme. value is annual average price of carbon emissions transaction. ELE is the ratio of purchased electricity to total energy consumption. EC is the ratio of annual average electricity price to annual average coal price. TC is the technical capacity, which indicates the total value of out-put per capita. γ are the coefficients of different determinant variables respectively. υ is the random error term. Eq. (4) means that the energy efficiency and GHG efficiency control variables are displayed in the mean of the exponential distribution of utm. υtm is explained as a random error term. Estimated energy efficiency and carbon equivalent efficiency estimates can be translated to efficiency mt mt scores and carbon equivalent scores according to EIe, c = exp (−ue, c). There are three measures that are used to looking into the cross correlation between two different values. The Pearson correlation is one of the most often used in statistics, but Spearman's rank and Kendall's rank have more robustness and higher efficiency than Pearson's correlation (Atkinson et al., 2004). The Kendall's rank measure is more robust and more efficient than Spearman's rank correlation, making it the preferable estimation (Croux and Dehon, 2010). This research will estimate the correlation between different values using Kendall's rank measure. The model approach is based on the assumption of the different industries which have the same characteristic of energy input and sell total production value output. In this case, we can get the GHG efficiency as the index used for measuring technical progress of greenhouse gas emission reduction and its reduction potential. 3. Data description The data of this study consist of a panel data which covers 26 Chinese manufacturing sectors above designated size from 2000 to 2016, including Ferrous Metals, Articles, Beverage, Chemical Fiber, Chemical Raw Material and Chemical Products, Electronic Equipment, Electrical Machinery, Foods, Furniture, General Purpose Machinery, Leather, Measuring Instrument, Medicines, Metal Products, Non-metallic Mineral

J. Sun et al. / Science of the Total Environment 690 (2019) 1190–1202 Table 1 Descriptive statistics of variables in the stochastic production frontier functions. Mean values 2000–2016. Variable/unit

Minimum

Maximum

Mean

Standard deviation

Output/billion yuan Labor/person Capital stock/billion yuan Emissions/t CO2e Energy/kg standard coal (kgce)

5.8763 2.9237 5.0947 5.5471 4.1326

10.8906 6.8499 10.5680 12.5115 11.6204

8.8437 5.2130 8.1205 8.5573 7.2219

1.0727 0.8820 1.0916 1.6436 1.7317

Products, Paper, Special Purpose Machinery, Textile, Textile Wearing Apparel, Tobacco, Transport Equipment, Non-ferrous Metals, Printing, Agricultural Products, Petroleum, Wood. These data come from the Statistics of China (NBSC, 2019) and China Statistical Yearbook (NBSC, 2001–2017a). Because of the change of the statistical caliber in 2003 and 2012, more detailed classifications of sectors were collected. For Table 2 Descriptive statistics of variables in the inefficiency function Mean values 2000–2016 (standard deviation in parenthesis). NO. Sector 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Ferrous metals

Electricity proportion

0.0919 (0.0109) Articles 0.4786 (0.0639) Beverage 0.1571 (0.0384) Chemical fiber 0.2961 (0.1075) Chemical raw material and 0.1373 chemical products (0.2526) Electronic equipment 0.6517 (0.1414) Electrical machinery 0.5119 (0.0949) Foods 0.1763 (0.0173) Furniture 0.4279 (0.1263) General purpose 0.3601 machinery (0.0696) Leather 0.4510 (0.1306) Measuring instrument and 0.5759 machinery (0.1190) Medicines 0.2564 (0.0389) Metal products 0.5948 (0.1160) Non-metallic mineral 0.1185 products (0.0237) Paper 0.1713 (0.0109) Special purpose 0.3504 machinery (0.1161) Textile 0.3823 (0.0858) Textile wearing apparel 0.3956 (0.0896) Tobacco 0.3541 (0.1412) Transport equipment 0.3841 (0.1081) Non-ferrous metals 0.4237 (0.4877) Printing 0.5511 (0.1086) Agricultural products 0.2284 (0.0565) Petroleum 0.0092 (0.0016) Wood 0.3371 (0.1203)

GHG Technical intensity capacity

Energy intensity

7.2500 (2.5598) 0.9974 (0.4449) 0.8000 (0.3862) 2.2003 (1.6780) 3.9167 (1.7690) 0.1457 (0.0233) 0.2091 (0.0573) 0.9147 (0.4742) 0.2870 (0.1941) 0.5162 (0.2416) 0.2355 (0.0711) 0.2112 (0.0901) 0.6923 (0.3244) 0.7353 (0.2927) 5.9252 (2.9974) 2.7279 (0.8445) 0.4819 (0.2849) 1.0124 (0.2406) 0.2680 (0.0728) 0.2554 (0.2023) 0.3285 (0.1884) 2.6161 (0.9636) 0.4685 (0.2058) 0.6151 (0.4289) 12.6854 (2.7439) 0.8298 (0.4188)

8.5371 (0.5819) 3.0413 (0.3905) 3.5209 (0.2330) 5.6275 (0.4870) 6.5391 (0.3531) 4.2091 (0.3990) 4.2877 (0.7252) 5.4086 (0.3155) 1.3657 (0.4428) 6.8318 (0.8635) 2.3977 (0.7440) 2.4345 (0.3215) 4.6511 (0.5399) 3.6280 (0.6413) 7.5512 (0.6246) 5.2720 (0.9936) 3.6742 (0.5872) 5.6229 (0.2815) 4.1429 (0.2644) 1.8994 (0.3186) 4.9810 (0.2747) 6.8333 (0.7266) 1.9302 (0.3288) 6.9439 (1.8136) 7.5337 (0.3263) 4.4822 (0.2195)

0.7002 (0.1830) 0.2217 (0.0292) 0.1994 (0.1210) 0.1113 (0.0232) 0.8352 (0.1234) 0.8725 (0.1826) 0.7092 (0.1528) 0.2305 (0.0370) 0.0901 (0.0207) 0.5167 (0.1158) 0.2090 (0.0216) 0.1038 (0.0221) 0.2076 (0.0311) 0.3984 (0.0732) 0.6986 (0.1201) 0.2311 (0.0425) 0.3182 (0.0490) 0.7722 (0.1885) 0.3093 (0.0256) 0.0568 (0.0161) 0.7140 (0.1243) 0.4406 (0.1547) 0.0780 (0.0052) 0.8185 (0.1657) 0.3579 (0.0726) 0.1749 (0.0462)

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example, the transport equipment was divided into three sectors after 2012: automobile, railway-ship-aerospace and other transportation equipment manufacturing, which are merged in this study. Besides, the transaction prices of variables come from the China's Carbon Price Survey (Slater et al., 2018), the China Coal Resources Network (Sxcoal. com., 2019) and the National Power Price Regulatory Bulletin (Administration, 2017). The detailed descriptive statistics are shown in Table 1. As an output variable for stochastic production frontier model, the value of production measured in monetary values reflects 2000-year price denoted in RMB, the values of other variables are the same as it is. The annually average number of employees in industry indicates the use of labor from China Labor Statistics Yearbook (BPESC, 2001–2017). Capital stocks are calculated by the perpetual inventory method using gross investment data denoted in RMB with reference to China Statistical Yearbook (NBSC, 2001–2017a). The aggregated energy use is computed based on the electricity and fuel use is measured in kilogram standard coal (kgce) according to the Statistics of China (NBSC, 2019) and the China Energy Statistical Yearbook (NBSC, 2001–2017b). We calculate the CO2 equivalent emissions from the aggregate fuel use and the purchase of electricity referring to the National Power Price Regulatory Bulletin (Administration, 2017), the conversion coefficients refer to the Intergovernmental Panel on Climate Change (Shen and Wang, 2013) and China Industrial Statistical Yearbook (NBSC, 2001–2017c). The CO2 emissions are measured in t CO2 equivalent (t CO2e). The variables in the inefficiency function are given in Eq. (4), including ETS (CHN ETS dummy variable), electricity proportion (purchasing electricity/total energy consumption), the GHG intensity (emission/ sales), ratio of net electricity price to coal Price (value of a unit standard coal), technical capacity (sales/labor). Descriptive statistics for electricity proportion, GHG intensity, ratios of net electricity price to coal price and technical capacity are presented in Table 2. Fig. 1 illustrates the price ratio of net electricity to coal in Chinese industry from 2000 to 2016. The trend of the price ratio is stable during the first four years of the 2000s. In the followed ten years the price ratio fell. However, in 2015 it was back to 2006 level. Then in 2016 the ratio fell to approximately the minimum point that is at 2012 level. In Fig. 2, the greenhouse gas (GHG) intensity and energy intensity in all 26 sectors during 2000–2016 are presented, calculated as ten thousand tons CO2e/billion yuan output and ten million kgce/billion yuan output respectively. The general trend of GHG intensity and energy intensity is to fall, and the downward trend is more significant from 2000 to 2011, while the gentle trend after 2012 is shown. As mentioned before, GHG intensity is normal deemed as a proxy of greenhouse gas emissions. That the efficiency of greenhouse gas emission has negative correlation with its intensity is expected. Results of that will be shown in the next section. Notes: Ferrous Metals represents Manufacture and Processing of Ferrous Metals; Articles represents Manufacture of Articles for Culture, Education and Sport Activity; Beverage represents Manufacture of Beverage; Chemical Fiber represents Manufacture of Chemical Fiber; Chemical Raw Material and Chemical Products represents Manufacture of Chemical Raw Material and Chemical Products; Electronic Equipment represents Manufacture of Communication Equipment, Computer and Other Electronic Equipment; Electrical Machinery represents Manufacture of Electrical Machinery and Equipment; Foods represents Manufacture of Foods; Furniture represents Manufacture of Furniture; General Purpose Machinery represents Manufacture of General Purpose Machinery; Leather represents Manufacture of Leather, Fur, Feather and Its Products; Measuring Instrument and Machinery represents Manufacture of Measuring Instrument and Machinery for Cultural Activity and Office Work; Medicines represents Manufacture of Medicines; Metal Products represents Manufacture of Metal Products; Non-metallic Mineral Products represents Manufacture of Non-metallic Mineral Products; Paper represents Manufacture of Paper and Paper Products; Special Purpose Machinery represents Manufacture of Special Purpose Machinery; Textile represents Manufacture of Textile; Textile Wearing Apparel represents Manufacture of Textile Wearing Apparel, Footwear and Caps; Tobacco represents Manufacture of Tobacco; Transport Equipment represents Manufacture of Transport Equipment; Nonferrous Metals represents Manufacture and Processing of Non-ferrous Metals; Printing represents Printing, Reproduction of Recording Media; Agricultural Products represents Processing of Food from Agricultural Products; Petroleum represents Processing of Petroleum, Coking, Processing of Nucleus Fuel; Wood represents Processing of Timbers, Manufacture of Wood, Bamboo, Rattan, Palm and Straw Products.

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Fig. 1. The Price Ratio of Net Electricity to Coal in Chinese processing and manufacturing industry 2000–2016.

In Fig. 3, the energy consumption in all 26 sectors during 2000–2016 are presented. The general trend of total energy consumption is to rise, and the downward trend is more significant in recent year, while the gentle trend after 2013 is shown. As mentioned before, coal is normal deemed as the main energy during the whole time in China. Crude oil, coke and electricity are also in the important positions and showed in descending order by the amount of energy. The annual mean transaction value of ETS is adopted from 2013 to 2016. It is hard to see the influence of ETS pilots to GHG efficiency from the original data. We will discuss specific correlation through detail calculation in next section. 4. Results and discussions In this section, the results of GHG efficiency from estimating stochastic production frontier conditioned on various factors as mentioned above are presented and analyzed. There are two parts to explain the results. In the first part, the estimated time-variant GHG efficiency score and the influencing factors' correlation with it will be shown in Section 4.1. Second, comparing these proxies and the results of GHG efficiency, GHG intensity, energy efficiency and energy intensity will be displayed in Section 4.2.

Fig. 3. Energy consumption in Chinese processing and manufacturing industry 2000–2016.

and the gross sales output growth rate slowed as the changes of economic environment. The week negative sign for value which means that the week correlation between value and GHG inefficiency. ELE has negative effects with GHG inefficiency, and EC has opposite effects. In general, adding proportion of electric energy in whole energy and adjusting the radio of electricity to coal price would reduce GHG inefficiency. The impact of technical capacity has the similar correlation with GHG inefficiency totally like ELE, the increasing of GHG efficiency along with the progressing of technical capacity as well. The detail analysis for each sector is presented later. The last two columns show the variance parameters of σu and σv that are statistically significant. The descriptive statistics for the whole underlying GHG efficiency of all sectors in China from the econometric estimation in Table 4, showing that the average value efficiency is estimated to be about 89.55%, the median efficiency score is 92.80% and there are dispersions near the mean efficiency score. It presents the average GHG efficiency scores for every industry in three periods of the estimation, in the whole period and its minimum

4.1. GHG efficiency scores and the correlation with its determinants The estimation results for stochastic production frontier model, Eqs. (3) and (4), are given in Table 3, presenting the estimated coefficient of and the impacts of the determinants of GHG inefficiency in Chinese manufacturing and processing industries that cover 26 sectors. The positive sign for ETS which means that ETS may has no use to promote GHG efficiency rising, but the growth of energy use and greenhouse gas emission slowed down and declined during ETS implementation,

Fig. 2. GHG intensity and energy intensity in Chinese processing and manufacturing industry 2000–2016.

Table 3 Estimation results for the GHG efficiency. Parameters

Coefficients

Standard error

P-value

Ln E_GHG Ln L Ln K T Ln E_GHG Ln E_GHG Ln L Ln L Ln K Ln K TT Ln E_GHG Ln L Ln E_GHG Ln K Ln L Ln K T Ln E_GHG T Ln L T Ln K

−0.5856 1.2590 −0.4507 0.1551 0.0586 0.0660 0.3229 −0.0018 0.0826 −0.2387 −0.3488 0.0164 0.0272 −0.0419

0.0752 0.9479 0.8819 0.0164 0.0091 0.0085 0.0048 0.0006 – 0.0095 – 0.0026 0.0034 0.0034

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 – 0.000 – 0.000 0.000 0.000

Impacts of inefficiency determinants ETS 5.1595 value −0.1324 ELE −3.8929 EC 3.6203 capacity −1.6864

1.1788 0.0375 0.9554 0.8077 0.9114

0.000 0.000 0.000 0.000 0.064

Variance parameters E(σu) σV

0.0073

0.000

0.1562 0.0888

J. Sun et al. / Science of the Total Environment 690 (2019) 1190–1202 Table 4 Descriptive statistics of overall GHG efficiency scores. Min

Max

Mean

Median

St. dev.

0.4991

0.9958

0.8955

0.928

0.0931

and maximum in Table 5. From the perspective of time dimension, the overall trend of GHG efficiency in China's manufacturing industry is upward, but more obvious fluctuations are appeared for some Individual manufacturing industries in the period of 2012–2016. Meanwhile, the GHG efficiency scores in different industries vary remarkably from Table 5. Most industries are at high levels of GHG efficiency in this study. The industry with the highest GHG efficiency is the electronic equipment, whose GHG efficiency has achieved more than 90% in the whole period. Other industries such as the articles, the electrical machinery, the measuring instrument and machinery, the textile and so on also have higher levels of GHG efficiency relatively, whose mean GHG efficiency has also reached 90%. The relatively lower levels of GHG efficiency are estimated in the petroleum, the wood and the nonmetallic mineral products, but their mean GHG efficiency has got more than 84% similarly. Nevertheless, the ferrous metals sector has the lowest GHG efficiency, that gets 83.74% as well. All sectors have less underlying GHG efficiency in general description. For the logarithmic form of variables in the stochastic production frontier model, the estimated coefficients can be interpreted as elasticities Table 5 Descriptive statistics of Sector's GHG efficiency scores. Sector

2000–2005 2006–2011 2012–2016 Min (Mean) (Mean) (Mean)

Max

Mean

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0.7587 0.9152 0.7757 0.7854 0.8074

0.9757 0.9830 0.9676 0.9718 0.9787

0.8374 0.9308 0.8634 0.8662 0.8905

0.9555 0.9519 0.9014 0.9450 0.9523

0.7902 0.9240 0.9231 0.8684 0.9159

0.5863 0.8635 0.7008 0.6590 0.7310

0.9420

0.9876

0.9731

0.9055 0.9958 0.9672

0.8693

0.9815

0.9501

0.8025 0.9901 0.9327

0.7767 0.8638 0.7600

0.9165 0.9427 0.9646

0.9189 0.9399 0.9225

0.7576 0.9627 0.8679 0.8251 0.9762 0.9140 0.6582 0.9779 0.8800

0.9387 0.9023

0.9409 0.9673

0.9287 0.9357

0.8785 0.9856 0.9365 0.8490 0.9796 0.9350

0.8348 0.8567 0.7177

0.9143 0.9725 0.9134

0.9217 0.9551 0.9466

0.7750 0.9733 0.8884 0.6955 0.9905 0.9265 0.6936 0.9762 0.8541

0.8982 0.6721

0.9320 0.9370

0.8530 0.9453

0.7831 0.9470 0.8968 0.5965 0.9813 0.8459

0.9386 0.9234

0.9726 0.9337

0.8971 0.9347

0.7825 0.9849 0.9384 0.8732 0.9800 0.9304

0.9035 0.7643

0.8946 0.9513

0.9501 0.9621

0.8304 0.9738 0.9141 0.6573 0.9907 0.8885

0.7246

0.9725

0.9130

0.4991 0.9845 0.8675

0.8592 0.8060

0.9412 0.9599

0.9682 0.9395

0.8318 0.9848 0.9202 0.7342 0.9855 0.8996

0.8622 0.7155

0.9204 0.8900

0.7218 0.9597

0.5154 0.9466 0.8414 0.6521 0.9797 0.8489

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that have plausible signs from the economic perspective in Table 3. The positive sign for time means that output per unit of GHG (CO2e) increasing overtime in most sectors. Regarding the efficiency determinant equation, the results of estimations reveal that the impacts of CHN ETS and transaction value have a neutral effect on GHG efficiency in 2013–2016 for most sectors, the reasons for this phenomenon may be that the period of ETS from starting is not for long enough, and the coverage is toonarrow for the entire China's industries, such as Zhao et al. found that the efficiency of carbon trading market had weak efficiency in China (Zhao et al., 2017). The factors of electricity proportion and capacity have positive effects on GHG efficiency. However, there is a negative effect of the impact of Electricity to Coal for vast majority sectors in China. Again, there are more detailed analysis results of statistics as shown in Table 6. Coal is the primary fossil fuel consumed by China's industry, and electricity to coal is a significant index on the performance of GHG efficiency. The high correlation between GHG efficiency and the impacts of electricity proportion and electricity to coal are revealed. The positive signs for electricity proportion in most sectors, adding the proportion of electric energy in whole energy would increase GHG efficiencys. For example, the correlation of electricity to coal of petroleum is 0 because the main fossil fuel of petroleum is fossil oil having no correlation with coal. Table 6 Descriptive statistics of the cross-correlation between Sector's GHG efficiency and the impacts of its determinants. Sector

Electricity Electricity proportion to Coal

ETS

Transaction value

Technical capacity

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0.3529⁎⁎ 0.5588⁎⁎ 0.6912⁎⁎⁎ 0.5000⁎⁎ 0.6118⁎⁎⁎

−0.3088⁎ −0.3677⁎⁎ −0.6618⁎⁎⁎ −0.5000⁎⁎ −0.6765⁎⁎⁎

−0.4281⁎⁎ −0.2616 0.2616 −0.2140 −0.0238

−0.3603⁎ −0.2702 0.2927 −0.1576 0.0225

0.6324⁎⁎⁎ 0.1471 0.5588⁎⁎⁎ 0.4412⁎⁎ 0.8823⁎⁎⁎

0.5588⁎⁎⁎

−0.6471⁎⁎⁎ −0.0476

0.0000

0.5000⁎⁎⁎

0.5294⁎⁎⁎

−0.5882⁎⁎⁎ −0.0951

−0.0676

0.8529⁎⁎⁎

0.4265⁎⁎ 0.5588⁎⁎⁎ 0.3971⁎⁎

−0.7353⁎⁎⁎ 0.1189 −0.7059⁎⁎⁎ 0.0951 −0.6471⁎⁎⁎ −0.0951

0.1576 0.1351 −0.0450

0.6618⁎⁎⁎ 0.5588⁎⁎⁎ 0.8382⁎⁎⁎

0.0294 0.3668⁎⁎

−0.1177 −0.2854 −0.4853⁎⁎⁎ −0.1903

−0.2252 −0.1351

−0.0294 0.4706⁎⁎⁎

0.3971⁎⁎ 0.6176⁎⁎⁎ 0.6324⁎⁎⁎

−0.5441⁎⁎⁎ 0.1427 −0.7059⁎⁎⁎ 0.7059 −0.7059⁎⁎⁎ 0.2616

0.1802 0.1351 0.3153

0.5⁎⁎⁎ 0.7647⁎⁎⁎ 0.7206⁎⁎⁎

−0.2206 0.6176⁎⁎⁎

0.1471 −0.5946⁎⁎⁎ −0.5179⁎⁎ −0.7794⁎⁎⁎ 0.1903 0.2252

0.5735⁎⁎⁎ 0.8382⁎⁎⁎

0.5147⁎⁎⁎ 0.1765

−0.4265⁎⁎ −0.1912

−0.2252 −0.0225

0.2059 −0.0147

0.2941⁎ 0.5882⁎⁎⁎

−0.3382⁎ 0.3805⁎ −0.7941⁎⁎⁎ 0.2378

0.4279⁎⁎ 0.2477

−0.25 0.8235⁎⁎⁎

0.4706⁎⁎⁎

−0.5147⁎⁎⁎ −0.1189

−0.0676

0.6912⁎⁎⁎

0.4706⁎⁎⁎ 0.6176⁎⁎⁎

−0.75⁎⁎⁎ 0.3805⁎ −0.6471⁎⁎⁎ 0.0000

0.3828⁎ 0.0676

0.2794 0.8529⁎⁎⁎

−0.3092 −0.0714

−0.3529⁎⁎ 0.0000 −0.5470⁎⁎⁎ −0.4729⁎⁎ 0.5735⁎⁎⁎ −0.6324⁎⁎⁎ 0.3805⁎ 0.3828⁎

⁎ Statistical significance at 10%. ⁎⁎ Statistical significance at 5%. ⁎⁎⁎ Statistical significance at 1%.

0.5735⁎⁎⁎ 0.4706⁎⁎⁎

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But for electricity to coal, it has negative effects on the contrary with electricity proportion that means adjusting the ratio of electricity to coal price to decline could improve GHG efficiency. The causes leading to this result may be the difference in generating efficiency between smaller-capacity generating set and larger-capacity generating set, which have higher power generation efficiency. In order to reduce energy costs, the manufacturing and processing industries in China prefer to build their own captive power plant to satisfy self-demand for electricity, but the captive power plant has lower power generation efficiency that means producing the same amount of electricity requires more energy and more greenhouses gas emissions, it is not recommended for GHG efficiency. Power grid, waste heat power generation, renewable energy power generation and on-site coal-fired power plants are the main sources of power energy for manufacturing and processing industries, moreover, waste heat power generation and new energy power generation have no excess greenhouse gas produced. In fact, according to the relevant investigation, it is more appropriate to choose to use net electricity when the power load is low at night. Furthermore, industry black carbon is the second strongest contribution to current global warming that mainly comes from fuel incomplete combustion in mini-plant. The volume of black carbon can be reduced by the above-mentioned methods (Ramanathan and Carmichael, 2008; Surabi et al., 2002). The impact of technical capacity has the similar effects with electricity proportion, which has the positive signs in most sectors, while neutral in some, indicating a progressing of technical capacity along with an increase of the GHG efficiency. For example, there is a strong positive correlation for GHG efficiency with the impacts of electricity proportion and technical capacity, but negative for electricity to coal in non-metallic mineral products. So, the industry of nonmetallic mineral products should be encouraged to adopt waste heat power generation and renewable energy power generation firstly, while the inadequate demand for energy should use purchasing electricity rather than commission small-scale coal-fired power stations, improve production technology to increase labor productivity for higher GHG efficiency, and in terms of price regulation, the price ratio of electricity to coal should be reduced. In general, there is less potential for GHG emission reduction from Table 5, illustrating the booming emission reduction technologies recently have not been widely applied in China's industries. From a common point of view, GHG efficiency is often assumed to be synonymous with GHG intensity. And energy efficiency is always assumed to take place of GHG efficiency to be studied. In the next section, we investigate the difference among these four proxies of GHG efficiency, GHG intensity, energy efficiency and energy intensity. The following analysis provides enough evidence to support if this assumption of these four proxies mentioned-above is valid.

4.2. The correlations between GHG efficiency and these indexes of GHG intensity, energy efficiency and energy intensity The estimation results of energy efficiency using the similar model, Eq. (3), are given in Table 7, that presents the estimated coefficient of energy efficiency. The last two columns show the variance parameters of σu and σv are statistically significant. The sign of λ indicates that energy inefficiency is demonstrated in this model. The descriptive statistics for the whole underlying GHG efficiency of all sectors in China from the econometric estimation are listed in Table 8, showing that the average value efficiency is estimated to be about 89.55%, the median efficiency score is 92.80% and there are dispersions near the mean efficiency score. The more detailed descriptions are given in Fig. A1 of appendix for GHG efficiency, GHG intensity, energy efficiency and energy intensity. In this section, the main purpose is that comparing the time-variant industry level GHG efficiency scores with calculated indexes of GHG intensity, energy efficiency and energy intensity by Kendall's correlation.

Table 7 Estimation results for the energy efficiency. Parameters

Coefficients

Standard error

P-value

Ln E_EN Ln L Ln K T Ln E_EN Ln E_EN Ln L Ln L Ln K Ln K TT Ln E_EN Ln L Ln E_EN Ln K Ln L Ln K T Ln E_EN T Ln L T Ln K

1.4847 −0.7438 2.899415 0.0211 −0.0506 −0.1164 −0.2648 −0.0019 −0.1239 −0.0675 0.3933 0.0347 −0.0191 −0.0046

0.0883 – – 0.0364 0.0099 0.0127 0.0082 0.0008 0.0205 – – 0.0018 0.0049 0.0068

0.000 – – 0.562 0.000 0.000 0.000 0.022 0.000 – – 0.000 0.000 0.500

Variance parameters σu σV λ

1.2099 0.1401 8.6342

0.5609 0.0155 0.5613

0.031 0.000 0.000

The correlations of that of 26 industry sectors are presented in Table 9, detailed see Fig. A2 of appendix. The negative correlations between GHG efficiency and GHG intensity are expected as the previous researches. Meanwhile the closer the score of the correlation is to −1, the more consistent the results are with previous studies. However, the average score of the correlations that are above statistical significance at 10% is 0.5853, which means medium correlation between them. So, the GHG efficiency cannot be the proxy that represents GHG intensity completely, but the GHG intensity could be one of the measurements of GHG efficiency. Furthermore, the positive correlations between GHG efficiency and energy efficiency are expected, but the correlation score of some sectors like Special Purpose Machinery is negative and some sectors have no relationship with GHG efficiency, indicating that energy efficiency cannot be used to stand for GHG efficiency despite having a strong correlation in a minority of sectors. There are many ways to reduce greenhouse gases, one of the most important and popular ways is to improve energy efficiency (Sun et al., 2018; Sun et al., 2017), the other one of that is to reduce the greenhouse gas emission directly by emission reduction technology like CO2 recovery and utilization technology (Wang et al., 2017b). But the energy Table 8 Descriptive statistics of energy efficiency. Sector

Min

Max

Mean

Median

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0.6292 0.6566 0.6671 0.6294 0.7563 0.6386 0.7019 0.7045 0.5857 0.3095 0.4598 0.7680 0.4623 0.7416 0.5431 0.3125 0.5932 0.8531 0.6831 0.4110 0.6488 0.6378 0.6215 0.6964 0.7734 0.7015

0.9605 0.9359 0.9321 0.9309 0.9360 0.9318 0.9518 0.9469 0.9340 0.9580 0.9325 0.9335 0.9414 0.9562 0.9506 0.9631 0.9449 0.9310 0.9360 0.9461 0.9591 0.9508 0.9488 0.9415 0.9295 0.9581

0.8168 0.8621 0.8429 0.8133 0.8704 0.8400 0.8464 0.8334 0.8244 0.7465 0.7931 0.8679 0.8005 0.8501 0.8438 0.7505 0.8406 0.8776 0.8488 0.7782 0.8130 0.8560 0.7993 0.8454 0.8684 0.8468

0.8324 0.8871 0.8719 0.9058 0.8915 0.8599 0.8707 0.8255 0.8838 0.8606 0.8835 0.8589 0.8664 0.8512 0.8734 0.8565 0.8610 0.8762 0.8788 0.8835 0.8558 0.8688 0.8265 0.8424 0.8720 0.8523

J. Sun et al. / Science of the Total Environment 690 (2019) 1190–1202 Table 9 Correlation between GHG efficiency and these indexes of GHG intensity, energy intensity and energy efficiency. Sector

GHG intensity

Energy intensity

Energy efficiency

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−0.5735⁎⁎ −0.1618 −0.6618⁎⁎⁎ −0.6177⁎⁎⁎ −0.6324⁎⁎⁎

−0.5735⁎⁎⁎ −0.2059 −0.6471⁎⁎⁎ −0.6177⁎⁎⁎ −0.6618⁎⁎⁎

0.4265⁎⁎ 0.4265⁎⁎ −0.2647 0.5882⁎⁎⁎ 0.5882⁎⁎⁎

−0.6324⁎⁎⁎ 0.3529⁎⁎ −0.2206 0.0147 0.8088⁎⁎⁎ 0.1912 −0.0147 −0.0588 0.4265⁎⁎ −0.3971⁎⁎ −0.0735 −0.0735 0.1912 −0.6177⁎⁎⁎ −0.6029⁎⁎⁎ −0.4265⁎⁎ −0.4559⁎⁎ −0.4706⁎⁎⁎ −0.0147 −0.1029 −0.1324 0.0441 −0.2647 −0.2647 0.3235⁎ −0.6618⁎⁎⁎ −0.6618⁎⁎⁎ 0.8088⁎⁎⁎ −0.7794⁎⁎⁎ −0.8088⁎⁎⁎ 0.0441 −0.6324⁎⁎⁎ −0.6471⁎⁎⁎ 0.7794⁎⁎⁎ −0.6471⁎⁎⁎ −0.6471⁎⁎⁎ 0.3971⁎⁎ −0.3235⁎ −0.2941⁎ 0.2941⁎ −0.5588⁎⁎⁎ −0.5882⁎⁎⁎ −0.1029 −0.07353 −0.6324⁎⁎⁎ −0.6177⁎⁎⁎ −0.6765⁎⁎⁎ −0.4706⁎⁎⁎ 0.0147 −0.3824⁎⁎ −0.5147⁎⁎⁎ −0.6471⁎⁎⁎ −0.6029⁎⁎⁎

−0.1618 −0.6471⁎⁎⁎ −0.6471⁎⁎⁎ 0.6618⁎⁎⁎ −0.4706⁎⁎⁎ −0.0441 −0.3971⁎⁎ −0.5147⁎⁎⁎ −0.6618⁎⁎⁎ −0.6177⁎⁎⁎

⁎ Statistical significance at 10%. ⁎⁎ Statistical significance at 5%. ⁎⁎⁎ Statistical significance at 1%.

efficiency cannot be suitable as the proxy to measure the greenhouse gas emission with the progress of emission reduction technology. Moreover, that the different fuel has different compositions is another essential reason, which is demonstrated by the correlation between GHG efficiency and energy intensity; in other words, the structures of energy use have a significant impact on GHG emission. The negative correlations between GHG efficiency and energy intensity are expected, the average score of the correlations that are above statistical significance at 10% is 0.6641, which means medium correlation. As for the other unbelievable correlations which are under statistical significance at 10% may have other possible reasons like insufficient statistical data or others, the exact reasons are not clear. To sum up, GHG efficiency scores show the emission reduction potential which means that there is room to improvement in energysaving and emission-reduction in Chinese industries, and the correlations between GHG efficiency and three indexes of GHG intensity, energy intensity and energy efficiency illustrate that there is medium and strong correlation in most sectors. However, they cannot represent each other due to the individual but different impacts, like the previous study considered that energy intensity can only provide indirect and delayed evidence of technological and engineering energy efficiency (Proskuryakova and Kovalev, 2015). The impacts of GHG efficiency determinants, including electricity proportion, electricity to coal, ETS, transaction value and technical capacity are analyzed above. The CHN ETS and its transaction value are not statistically significant in most sectors. The analysis suffers to some degree from the CHN ETS trading period (2013–2016) admittedly, which launched seven pilots in China from 2013, and now it is ready to turn to the whole country. Although we would like to get more accurate data from the monitoring system, they are not developed fully now. The volume of black carbon should be considered in further research because of the limitations of current technology. In any case, it is meaningful since the results discussed are so crucial for some rules of GHG efficiency, and they are useful for policy formulation.

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5. Conclusions One of the main purposes of this research is to evaluate the GHG efficiency in Chinese manufacturing and processing industries that cover 26 sectors during 2000–2016. GHG efficiency is defined as to explain that the room could be improved on the production frontier of industry that can also be interpreted as in the best practice technology. Furthermore, GHG efficiency is evaluated by stochastic production frontier analysis and estimated by applying Greene's ‘true’ random effect model. The results show that there is little space to improve GHG efficiency in fixed technology. This reflects that the new energy saving and emission reduction technology should make new breakthroughs and be applied in Chinses industry. In addition, the results of impacts of GHG efficiency determinants indicate three policy advises for GHG efficiency. First, using more grid electricity instead of small-scale on-site coal-fired power plant in most sectors in Chinese industry when it is impossible to generate electricity from waste heat or renewable energy sources. Then the lower the ratio of the electricity purchase price to coal purchase price appropriately, the higher the GHG efficiency is. Finally, enhancing the application of some advanced technologies in industry can improve the GHG efficiency. The impact of the China's Emission Trading System (CHN ETS) has a moderate effect on GHG efficiency, while the results are unbelievable in most sectors. Also, the carbon trading value has a moderate effect in minority industry sectors. The other purpose is to investigate the correlation between GHG efficiency and three indexes of GHG intensity, energy intensity and energy efficiency, which are common used in the sphere of learning and of public. For example, there is a widespread assumption in energy statistic and econometrics that energy intensity and energy efficiency are equivalent measures of energy performance of economies. GHG intensity, energy intensity and energy efficiency are dependent indexes for measure GHG and energy performance, but they cannot represent each other. For instance, the proxy of energy efficiency could not be used as a measure of GHG efficiency. The same is true on other proxies. Finally, there is still a lot to be done in order to achieve the goal that China reduces the carbon intensity per unit of GDP by 60–65% by 2030 and how to pursue climate and policy to stimulate industry to invest in energy saving and emission reduction technology and ultimately lower carbon emission. Now the implementation time and coverage of the China's Emission Trading System (CHN ETS) are insufficient, and its effect is not obvious for GHG efficiency. For a more comprehensive understanding of the GHG efficiency and effect of CHN ETS, it should be left for the future when more data is available to us.

Acknowledgements The authors are grateful to the financial support provided by National Key Research and Development Program of China (2017YFB0304000 & 2017YFB0304001). Appendix A The detailed descriptions of GHG efficiency, GHG intensity, energy efficiency and energy intensity are depicted in Fig. A1. The blue lines represent energy efficiency. The red lines represent energy intensity. The green lines represent GHG efficiency. The black lines represent GHG intensity. The detailed descriptions of the correlations between GHG efficiency and three determinants of ELE, EC and TC are depicted in Fig. A2. The red scatter points represent the correlation for GHG efficiency with the price ratios of electricity to coal (EC). The green scatter points represent the correlation for GHG efficiency with the proportion of net electricity in the total energy used (ELE). The black scatter points represent the correlation for GHG efficiency with technical capacity (TC).

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Fig. A1. the proxies of GHG efficiency, GHG intensity, Energy efficiency and Energy intensity in 26 sectors of China's industry, 2000–2016.

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Fig. A2. Correlations between GHG efficiency and three determinants of ELE, EC and TC.

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