Comparative study on power efficiency of China's provincial steel industry and its influencing factors

Energy 175 (2019) 1009e1020

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Comparative study on power efficiency of China's provincial steel industry and its influencing factors Ya Wu a, b, c, JingRong Su a, Ke Li d, **, Chuanwang Sun e, * a

College of Economics, Jinan University, Guangzhou, 510632, PR China Institute of Resources, Environment and Sustainable Development Research, Jinan University, Guangzhou, 510632, PR China c China Research Center for Economic Development and Innovation Strategy of Jinan University, Guangzhou, 510632, PR China d Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of China), School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan, 410081, PR China e China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, Fujian, 361005, PR China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 October 2018 Received in revised form 21 March 2019 Accepted 25 March 2019 Available online 26 March 2019

In China, power consumption in the steel industry accounts for about 9% of the whole society's power consumption. There is a big gap between the power efficiency of China's steel industry and the world's advanced level, and the power efficiency varies greatly in all regions of China. By using the three-stage data envelope analysis model, this study analyzes the influence of external factors (including environmental regulation, industrial structure, and trade openness) on the power efficiency of the steel industry in 28 provinces of China. Overall, the power efficiency of the steel industry in the eastern, central, and western China present the high, middle, and low power efficiency, respectively. The results reveal that improving trade openness and optimizing industrial structure are conducive to improving the power efficiency, while improving the intensity of environmental regulation can cause the excessive substitution of power sources for other energy resources so as to decrease the power efficiency. Moreover, power efficiency in the eastern and western China are more affected by the external factors than it in the central China. The research conclusions are favorable to introduce different measures for various regions so as to reduce the gap in power efficiency of the steel industry. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Provincial steel industry Three-stage data envelope analysis Comparative study External factors Power efficiency

1. Introduction In China, the steel industry is an important fundamental industry sector to the national economy, and it is regarded as a significant symbol of the comprehensive national power [1,2]. In the last decade, China's steel industry has observed a rapid development: the average annual growth rate of steel production is up to 14% from 1998 to 2016 [3]. At present, both its production and consumption rank first in the world. For example, in 2016, China's production and consumption of crude steel accounted for 50% and 44% of the world's production and consumption, respectively [4]. The steel industry is a typical energy-intensive industry that consumes various forms of energy, including power energy [5e9].

* Corresponding author. School of Economics, Xiamen University, Xiamen, Fujian, 361005, PR China. ** Corresponding author. School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan, 410081, PR China. E-mail addresses: [email protected] (K. Li), [email protected] (C. Sun). https://doi.org/10.1016/j.energy.2019.03.144 0360-5442/© 2019 Elsevier Ltd. All rights reserved.

In its energy consumption structure, the proportion of power energy is about 20%e30%, that due to the technological differences [10]. Thus, its large-scale expansion has brought about a rapid increase in power consumption. From 2000 to 2015, the annual growth rate of power consumption in China's steel industry reached 11.4%, which was higher than the growth rates of power consumption in industry (10.1%) and the whole society (10.3%). Moreover, its annual power consumption is generally far larger than other power-intensive sectors. For example, in 2014, the power consumption of the steel industry was 1.1, 1.3, and 1.7 times of the consumptions for chemical, non-ferrous metals and building materials industry, respectively [3]. In recent decades, the power efficiency of China's steel industry has been continuously improved [11,12]. As shown in Fig. 1, in China, the power consumption per ton of crude steel was 705 kWh/ t in 2014. Compared with the level of 2006, it has been reduced power consumption of 20 kwh, and the power efficiency has increased by 2.8% in 2014. However, there is a big gap between the power efficiency of China's steel industry and the international

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750 730

Unit: kWh/ton

710

China 725

722

721

Japan

714

704 693

697

705

influence of multiple external factors including intensity of environmental regulation, optimization of industrial structure and trade openness on the power efficiency of the steel industry were analyzed.

690 670

659 646

650 630 610 590 2006

2008

2010

2012

2014

Fig. 1. Power consumption per ton of crude steel: China and Japan. Note：The data was calculated by the authors. Among them, the electricity consumption and crude steel output of China's steel industry come from the National Bureau of Statistics of China (http://www.stats.gov.cn/), and the relevant data of Japan's steel industry come from Statistics Japan (http://www.stat.go.jp/).

advanced level. For example, in 2014, the power consumptions per ton of crude steel in China and Japan were about 705 kWh/t and 659 kWh/t, respectively. The gap between the two is up to 46 kWh/ t. Considering this gap, there is still a lot of electricity-saving potential for the steel industry in China, in the future. In 2016, the Ministry of Industry and Information Technology of the People's Republic of China formulated its Adjustment and Upgrade Policy for the Steel industry [13]. It is stated that energy consumptions and pollutant emissions in the steel industry during the 13th Five-Year Plan (2016e2020) must decrease by no less than 10% and 15%, respectively. Power consumption of China's steel industry is huge, which accounts for about 10% of the total power consumption of the whole society at present [3]. However, most of the existing research focuses on the overall energy efficiency of the steel industry, and little research has paid attention to its power efficiency [14,15]. Thus, in order to realize the governmental target of energy saving and emission reduction, it is very important to study the power efficiency of China's steel industry in different regions. In order to promote energy saving and emission reduction in China's steel industry, our previous research has studied the impact of internal factors such as labor, capital, and technology on energy efficiency in the steel industry [16]. This paper focuses on the external factors affecting the power efficiency of the steel industry and their regional differences. The motivation of this study is to investigate the following questions: How do the power efficiencies of the steel industry differ in different regions? Do different regions exhibit significant regional disparities? Except for the factors input by the steel operators, what influences do external environmental factors bring to bear on the power efficiency of the steel industry? Studying the above problems can not only provide a scientific policy basis for energy conservation and emission reduction in the steel industry during the 13th Five-Year Plan, but also improves the international competitiveness of China's steel industry. In this paper, a three-stage data envelope analysis (DEA) model is used to analyze regional differences of power efficiency and its external influences in the steel industry. The contribution of this study lies as follows: (1) The differences of power efficiencies in the steel industry in 28 regions were evaluated1; (2) The mechanism of

1 This paper focuses on the power efficiency of the steel industry in mainland China. Therefore, the research subjects do not include Hong Kong, Macau and Taiwan. In addition, due to the lack of long-term and continuous statistical data, the research objects do not include Tibet, Chongqing and Hainan.

2. Literature review Researches on energy efficiency of the steel industry have garnered the attention of scholars both at home and abroad. Accordingly, methods that found in previous studies can be classified into four categories. The advantages, disadvantages and representative literature of different methods are summarized in Table 1. Worrell et al. [17] investigated the energy intensities of the steel industry of seven countries by employing IDA. They found that the transformation of industrial structure greatly influenced the energy intensities in France and Japan, while the energy efficiency significantly affected the energy intensities in Brazil, China, Germany and the United States. Sheinbaum et al. [18] studied the change of energy consumptions and CO2 emissions of the steel industry in Mexico by using the logarithmic mean divisia index (LMDI). They also found that the industrial structure and energy efficiency in 1970e2006 are the main factors for energy consumption decreasing. Karimu et al. [19] revealed that energy prices have a significant influence on energy intensity after studying the energy intensities of 14 industrial sectors in Sweden (the steel industry included) during 1990e2008 by using IDA. Similar studies by
lez and Martínez (2012) [20], Shang et al. [21,22] and so on. Gonza The energy consumption can be decomposed into energy intensity and structural effect in a simple form and using easilyacquired data when using IDA; however, the method exhibits two drawbacks: on the one hand, the changes in energy consumption cannot be fully decomposed by using the method (such as Laspeyres and Divisia IDA), leaving residual terms and restricting the explanatory ability of the model. On the other hand, even though the residual terms are removed by using LMDI, IDA fails to consider the influence of changes in the economic, and energy, structures on energy efficiency. (2) Some studies evaluate the influence of different technologies on energy efficiency of the steel industry by using technology and economic methods (TEM). As a mature method, the technology and economy method is a bottom-up method that is widely used because it can evaluate the cost-benefit characteristics of various energy measures and corresponding potential for energy conservation. Zhang et al. [23], He and Wang [24], Morfeldt et al. [25] and Johansson [26] separately evaluated the influences of different measures for energy conservation on the energy efficiencies of the steel industry in China and Sweden. Similar studies by Wu et al. [27], Kuramochi [28] and so on. Hasanbeigi et al. [29] studied the potential for energy conservation in China's steel industry between 2010 and 2030 by using a bottomup curve of energy-conservation supply. Using the same method, Brunke and Blesl [30] and Iii et al. [31] separately investigated the energy efficiencies of the steel industry in Germany and India. Although the technology and economy method can effectively evaluate the potential of various technological measures in improving energy efficiency and promoting production practice, the method is only applied to microscopic technologies. In other words, it cannot be employed to study the influences of microscopic and macroscopic factors, such as industrial structure and economic policies.

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Table 1 Comprehensive evaluation of energy efficiency research in steel industry. (1) By using index decomposition analysis (IDA), large studies analysis the changes in energy efficiency of the steel industry. Method

Advantage

Disadvantages

IDA Laspeyres/Divisia decomposition analysis

Simple form and easy access to data.

Residual terms exist in the decomposition analysis.

LMDI Bottom-up approach

Econometric model: regression model, super logarithmic cost function, and so on

DEA BCC CCR

Literature

Karimu et al. (2016) [19] Gonz
alez and Martínez (2012) [20] Shang et al. (2016a, 2016b) [21,22] The residual terms are removed. The form of decomposition is limited. Worrell et al.(1997) [17] Sheinbaum et al. (2010) [18] Effective evaluation of micro energy saving Lack of analysis of macro factors. Zhang et al.(2014) [23] technology. He and Wang (2017) [24] Morfeldt et al. (2015) [25] Multiple factors affecting energy efficiency Endogenous problems among the variables often leads to Flues et al.(2015) [32] can be analyzed. spurious regressions Boyd et al. (2008) [37] Lin and Wang (2014) [38] Xu and Lin (2016a,2016b, 2016c) [39e42] Do not need to provide a specific Without considering the environmental differences faced Wei et al. (2007) [43] production function. by DMU He et al. (2013) [45] Nielsen (2016) [46]

(3) By constructing econometric models, most literature analysis the influence factors of energy efficiency of the steel industry. Lin et al. [16] estimated the energy intensity of China's steel industry by using a multiple regression model and found that industrial concentration degree and labor productivity are important factors influencing energy intensity. Flues et al. [32] established a regression model for input factors, energy efficiency, and output of iron and steel while studying the influence of technology, market and policy factors on the energy efficiency of the steel industry in the EU. Moreover, they illustrated that high energy prices are conducive to improving the energy efficiency of the iron and steel sector to some extent. By using the trans log cost function method, Wang and Lin [33] studied the influences of substitution of factors and fuels on energy efficiency of the steel industry, and they revealed that technical progress is one of the crucial factors determining the energy efficiency of China's steel industry. Similar studies by Fisher-vanden et al. [34], Zhou and Yang [35], Xu and Lin [36] and so on. Apart from traditional regressive analysis, stochastic frontier analysis (SFA) is also used to analyze the energy efficiency. Boyd [37] estimated the energy efficiency of manufacturing enterprises in the USA, including the steel industry, by using SFA. After analysing the total-factor energy efficiency of China's steel industry, Lin and Wang [38] indicated that the energy efficiency of the industry rose from 2005 to 2011 and north-eastern China exhibited a high energy efficiency while the central and western regions of China showed low energy efficiency. By taking energy intensity, or efficiency, as research objects, Xu and Lin [39e41] and Xu et al. [42] investigated the relationship between the energy intensity (energy efficiency) in China's steel industry and CO2 emissions by using quartile regression analysis, panel data model, and vector autoregression model, respectively. The research showed that energy intensity of the steel industry in different regions is one of the main reasons behind the differences in carbon emissions in various regions. In general, the econometric model can be utilized to study the influences of various variables (including capital, energy, cost, industrial concentration, and labor productivity) on energy efficiency of the steel industry from a multi-factor perspective; however, it is necessary to exercise caution while using the method because endogenous problems among the variables often leads to the biased results, which directly influence the interpretation of the

research conclusions. (4) Recently, large studies evaluate the energy efficiency of steel industry by using DEA model. By using Malmquist productivity based on the DEA method, Wei et al. [43] investigated the energy efficiency of the steel industry from 1994 to 2003 in China. They found that the energy efficiency of China's steel industry improved by 60% due to the technological progress realized between 1994 and 2003. Using the same method, Morfeldt and Silveira [44] studied the energy efficiency of the steel industry in Europe between 2000 and 2010. By using a traditional DEA method, He et al. [45] studied the energy efficiency of China’ steel industry between 2001 and 2008 after considering undesirable outputs: ignoring undesirable outputs causes biased efficiency and technology change and environmental regulation exerts a potential positive effect on technological change. Recently, Nielsen [46] compared the production efficiency of the iron and steel industries in China and Czech to reveal that the prevailing economic system is not the main factor determining the differences in energy efficiency of the steel industry in planned and market economies while the mode of support for technological progress and supervising industries is probably the primary reason. Similar studies by Yang et al. [47], Shen and Lin [48] and so on. The non-parametric DEA can be used to consider multi-input and multi-output problems. It is not necessary to estimate the model function or to process, and statistically test, the parameter dimensions, which is an advantage of the method. While the method also has a disadvantage, that is, it is assumed that the decision making unit (DMU) is homogenous, so it is unable to analyze the environment of the DMU and random error terms by using the DEA and identify their influence on the modeled values of efficiencies. Also, this method provides results that are relative to the population of DMUs analyzed. Hence, it cannot be used to compare the DMUs to a base-line or best-practice. It can be observed that the existing research enriches our knowledge about the energy efficiency of China's steel industry from the perspective of research methods and influence factors. However, there are some limitations: on the one hand, they mainly study the total energy efficiency in the steel industry while failing to investigate the power consumption (a specific energy efficiency). This probably causes energy-conservation policies to be designed with good intentions, but be less pertinent in practice. On the other

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hand, these studies concentrate on the influences of the technological improvement and factor allocation of the industry on energy efficiency but ignore the imbalances in China's regional economic development [49]. In order to address it, this study investigated the power efficiency of China's provincial steel industry by using a three-stage DEA model. The model shows advantages of the traditional DEA model and gets rid of the interference of external environmental factors and random errors. Therefore, it can more objectively reflect reality and improves the reliability of the evaluation result on the efficiency thereof.

3.2. The second-stage: Stochastic frontier analysis (SFA) In the second stage, the SFA is applied to eliminate the influence of external environment factors and random noise on the measured efficiency. Thus, the slack is first calculated. The input-oriented DEA mode is applied in the study, so the slack is the difference between the actual, and ideal, values of the input variables, as shown in formula (2):

Sni ¼ Xni
lXn  0; n ¼ 1; 2; …; N; i ¼ 1; 2; …; I

3. Research method The three-stage DEA is a new model used for evaluating efficiencies proposed by Fried et al. [50]. They suggested that the low efficiency of DMUs is influenced not only by managerial inefficiencies but also by two exogenous factors including environmental influences and random errors. The three-stage DEA model is used to eliminate the influence of external environment and random error so as to reflect the efficiencies of various DMUs accurately. The three-stage DEA is divided into the steps described below. 3.1. The first stage: traditional DEA model The first use of the DEA method was reported by Charnes et al. [51]. The method is widely used in studying the production efficiencies of multi-input and multi-output sectors. The DEA model is divided into input and output orientations and different orientations are selected according to different production streams. The common model, including a CCR model, which was initially proposed by Charnes, Cooper and Rhodes [51], and its improved BCC model, which was initially proposed by Banker, Charnes and Cooper [52], are established from the assumptions of constant and variable returns to scale respectively. By using CCR and BCC models, the technology efficiency measured by using a DEA can be further decomposed into scale and pure technology efficiencies so as to effectively evaluate the production efficiencies of separate DMUs. For the steel industry, the constraint target of energy conservation and emission reduction is to ultimately reduce energy consumption and pollutant emissions on the precondition of maintaining a certain output: because it is easier to control inputs than outputs, the study investigated the total-factor power efficiency by using an input-orientated BCC model. The iron and steel industries in various provinces are regarded as an independent DMU so that the input-oriented BCC model can be expressed as follows:


minq
ε eT S
þ eT Sþ 8 n X > > > Xj lj þ S
¼ qX0 > > > > j¼1 > > > n X > > < Yj lj
Sþ ¼ Y0 s:t: j¼1 > > > > lj  0; Sþ  0; S
 0 > > > n >X > > > lj ¼ 1 > :

and pure technical efficiency (PTE).

(1)

j¼1

where j ¼ 1; 2; …; n represents DMUj ; Xj ¼ ðX1j ; X2j ; …; Xij Þ; Yj ¼ ðY1j ; Y2j ; …; Ysj Þ represent the input of i and the output of s by the DMUj , respectively. By solving the linear programming of formula (1), technical efficiency (TE) can be obtained, and TE can be further decomposed into scale efficiency (SE)

(2)

Sni is the first stage of the i region of the steel industry using the slack of the n input variables; and lXn is the optimal mapping of output vector corresponding to Xni on the input efficiency subset. The slack variable Sni reflects the initial inefficiency, which is affected not only by management inefficiency, but also by external environment factors and stochastic noise. Therefore, this paper establishes the SFA model for the slack variable and the environment explanatory variables. In the SFA regression, the slack variable Sni was used as the explanatory variable, and the external environmental factors were used as explanatory variables to construct the following model:

  Sni ¼ f n Zi ; bn þ nni þ mni ; n ¼ 1; 2; …; N; i ¼ 1; 2; …; I

(3)

Here, f n ðZi ; b Þ is the feasible slack frontier, indicating the influence of external environment factors on slack variable; Zi are the external environment factor variables, bn are their coefficients; generally, f n ðZi ; bn Þ is set to a linear model, i.e., Zi  bn ; nni þ mni are mixed errors, where nni  Nð0; s2 Þ are the random errors, indicating the influence of random noise on the input slack; mni are the management inefficiency, indicating the influence of management factors on the input slack, and assume that mni  Nþ ð0; s2 Þ. If the coefficients of the external factors obtained by the SFA regression model are significant, the effect of external factors on the slack variable is present, and the original input variables can be adjusted by formula (4). n

" X Ani

!!

c ¼ Xni þ max f Zi ; b n

!# c
f Zi ; b n

þ ½maxðnni Þ
nni i

¼ 1; 2; …; I; n ¼ 1; 2; …; N (4) X Ani

Here, are the adjusted inputs, Xni are the pre adjustment c ÞÞ
f ðZ ; b c Þ are adjustments to external enviinputs. maxðf ðZi ; b n i n ronmental factors, maxðnni Þ
nni are adjustments to random errors. This paper adopts formula (5) to separate management inefficiency, and then use formula (6) calculation of random error Efnni jnni þ mni g.

2   3 ∅ l sε l ε EðmjεÞ ¼ s  4
 þ 5 s f lsε s s

Here, s* ¼ ms y , s ¼

(5)

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s2m þ s2y , l ¼ sm =sn .

Efnni jnni þ mni g ¼ sni
f ðZi ; bn Þ
Efmni jnni þ mni g

(6)

In formula (4), the adjusted input variables have eliminated the influence of external environmental factors and random noise. In essence, this step is to adjust the input vectors of all DMUs under an ordinary or common operating environment in order to measure the efficiency, thus, the efficiency values measured in the third stage are purely reflected the management level of DMUs.

Y. Wu et al. / Energy 175 (2019) 1009e1020

3.3. The third stage: adjusted DEA model The input variables X Ani adjusted by the second stage are used to replace the original input variables Xni , and the output variables are still the original output variables, then the DEA model is used to analyze the power efficiency value again. Compared with the result of the first stage, the power efficiency obtained in the third stage overcomes the influence of the external environmental factors and random interference and can more objectively reflect actual power consumptive efficiency. 4. Variable selection and data sources 4.1. Input-output variable The output variables in the steel industry include desired and undesired output variables. The total industrial output value is regarded as the desirable output with the unit of 100 million Yuan at 1992 price level, and CO2 emissions of the steel industry as the undesirable output. CO2 emissions from the steel industry were first estimated by multiplying the quantity of each fuel by its emission factor and summing across all fuels, which is also recommended by the IPCC [53]. Afterwards, the reciprocal CO2 emissions were calculated by applying the method of Lovell and Pastor [54] so as to satisfy the theory and logic, that the larger the demands of the undesired output, the lower the efficiency. The input variables of the steel industry include power and other energies inputs, labor, and capital stock. Among them, the power and other energy sources are measured by 104 ton of coal equivalent or 104 tce, and the labor is represented as the annual average number of employees in the steel industry. Capital stock is calculated by using the perpetual inventory method. The accuracy of the calculated capital stock depends on the proper selection of the base period and calculation formula. The longer the interval between the base period and the research sampled period, the less significant the influence of the estimation error on the capital stock of the samples. Considering the availability of data, 1983 is taken as the base year, then we calculated the capital stock in 1983 (converted into constant 1992 prices) which is regarded as the capital stock within the base period. A fixed depreciation rate is applied to formula used in previous research, however, there is a long timespan in our sample, so a fixed depreciation rate may unsuitable. According to Wang and Fan [55], this study adopted the follow formula to obtain the capital stock:

Kt ¼ Kt
1 þ ðIt
Dt Þ=Pt

(7)

Here, Kt and Kt
1 are capital stocks for year t and t-1, respectively; It is nominal investment for the year t; Dt is nominal depreciation for the year t, and nominal depreciation amount equals accumulated depreciation amount of that year, minus accumulated depreciation amount of last year; Pt is the price index for fixed asset investment in the year t. According to formula (7), we can get the capital stock of the provincial steel industry (converted into constant 1992 prices). 4.2. External environmental variables While conducting SFA modelling, the explanatory variable is selected based on an important principle, namely, an external environmental factor variable has an impact on the efficiency value of DMUs, but it does not hold on the contrary. Considering the developmental characteristics of the steel industry in different

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regions of China, the environmental regulation intensity of government, optimization of industrial structure, and trade openness are chosen as external environmental variables. In general, the environmental regulation of governments is implemented across the whole of society. Compared with agriculture and the service industry, the environmental regulation more significantly influences industry, namely, the stricter the intensity of environmental regulation, the more the industrial corporates invest in pollution abatement, so as to respond to such regulation. Moreover, the dispersive production decision-makers in the steel industry determine the inputs of various energy sources, including electrical power, according to the intensity of environmental regulation, however, their decisions can barely influence the intensity of the macroscopic environmental regulations. Therefore, the ratio of industrial emission reduction investment to industrial added value is considered as the representative variable used for measuring the intensity of environmental regulation. The same strategy was also adopted by Zhang et al. [56]. Expanding trade openness is conducive to the promotion of energy efficiency [57,58]. This is because improving trade openness is conducive to the introduction of FDI, while FDI shows a significant spatial spill-over effect and thereby decreases the energy intensity in neighboring areas and relevant industries. Thus, the proportion of the total import-export volume of various regions in GDP is used to measure the trade openness of a region. According to the Petty-Clark theorem, the industrial development is a dynamic transfer process transferring from primary to secondary industries, and thence to tertiary industries [59]. According to the scale of global economic development, and the actual conditions that see China in a rapid development stage of industrialization and urbanization, the optimization and upgrade of industrial structure actually mean the decrease in the proportion of secondary industries in the overall industrial structure. Namely, the proportion of the secondary industries declines, implying that China's industrial structure tends to be optimized. Therefore, the optimization of the industrial structure is represented by using the proportion of the added value of secondary industries to overall GDP.

4.3. Data sources This study takes the steel industry of 28 provincial-level administrative districts in China during the period 2005 to 2014 as the research object. Industrial added value data for the steel industry comes from China Industry Statistics Yearbook and China Steel industry Yearbook. The deflator of GDP in each province comes from China Statistical Yearbook. The data pertaining to power and other energy sources are taken from older statistical yearbooks from different provinces and missing data are estimated by using average values of data in the years before and after. The investment in the steel industry comes from Data Collection of China Steel industry in Fifty Years and China Steel industry Yearbook. The accumulated depreciation data comes from China Industry Statistics Yearbook, and the price index of fixed asset investment comes from China Statistical Yearbook. Labor data comes from China Industry Economy Statistical Yearbook and China Industry Statistics Yearbook. The indicators that reflect the intensity of environmental regulation, trade openness and industrial structure are calculated according to China Environment Yearbook and China Statistical Yearbook. The input and output variables of China's steel industry in 2005e2014 are shown in Appendix A.

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5. Empirical analysis

with an average value of 0.96.

5.1. Power efficiency under original input-output data: DEA results from the first stage

5.2. Environmental factors influencing the power efficiency and adjustment: SFA results from the second stage

In the first stage, the power efficiencies of the steel industry of the 28 provincial-level administrative districts during 2005e2014 were analyzed by using the Deap 2.1 software. The detailed results are summarized in Appendix B. In order to better reflect the characteristics of power efficiency, Fig. 2 and Fig. 3 are collated according to these results. As shown in Fig. 2, the power efficiency of the steel industry in various regions of China in the first stage (2005e2014) showed a fluctuating, yet a upward trend. On the whole, the year 2010 is regarded as the watershed and power efficiency before 2010 showed a fluctuating downward trend. In the long-term, due to various reasons, the iron and steel producing enterprises built first without approval and were evaluated as illegal through environmental assessment in many different regions of China. These reasons include the fact that local government lacked industrial development plans and government performance appraisal merely focused on GDP. It meant that enterprises lacked fair competition and the fact that “bad money drives out good money” was remarkable, which exerted a negative effect on the power efficiency of the steel industry. After 2010, central government enhanced the energy conservation and emission reductions oversight regimes and shut down those old iron and steel enterprises with a total capacity of 9  107 tons during the 12th Five-Year Plan (2011e2015), which amounted to approximately 11% of China's gross capacity in 2010. The disordered expansion of industrial scale was significantly restricted, having a positive effect on improving the power efficiency of such industries. In general, the power efficiency of China's steel industry from 2005 to 2014 has improved. In terms of regions (as shown in Fig. 3), eastern China shows the highest power efficiency with a mean value of 0.91. The power consumption efficiencies of Tianjin, Shanghai, and Jiangsu provinces are always on the frontier, while the efficiencies of Beijing, Hebei, Zhejiang are also close to 1. The power efficiency in central regions has an insignificant difference to that in western parts, and their average efficiencies are 0.72 and 0.71 respectively. Moreover, there are small disparities in power efficiency among provinces in the central China, most power efficiencies ranging from 0.6 to 0.8. For western provinces, the efficiencies show a large range. For example, the power consumption efficiencies of Inner Mongolia, Guizhou, and Qinghai are lower than 0.5 in many years, while Xinjiang province exhibited a high power-consumption efficiency,

The slack variables of various input variables obtained in the first stage are separately considered as dependent variables while three external environmental factors (environmental regulation, industrial structure, and trade openness) are regarded as explanatory variables. Subsequently, stochastic frontier regression was conducted to analyze the influence of external factors on various slack variables. The result is summarized in Table 2. The LR tests of the one-sided error imply that SFA models are suitable, as the variation in managerial inefficiency plays an important role in the first-stage input slacks. These results suggest that the environmental factors do indeed exert a statistically significant influence on power efficiencies in China's steel industry. As shown in Table 2, among the four models with inputs of slack variables, most external factors have significance effects on slack variables of inputs at 5% or 10% level. This indicates that the variable selection of models is reasonable and external environmental factors have a significant effect on the input redundancy of the four variables. Environmental variables are obtained by conducting a regression analysis on various input slack variables, so increasing the number of environmental variables is conducive to decreasing the input of slack variables when the regression coefficient is negative, i.e., it is conducive to decreasing the waste of various input variables or reducing undesired outputs. The specific influences of various environmental variables are illustrated as follows: (1) Environmental regulation. The coefficients of slack variables of environmental regulations on power and other energy sources are 1208.8 and
428.7, respectively. These indicate that, the stricter the intensity of environmental regulation, the larger the gap between planned and actual input volumes of electrical power, while the smaller the gap between planned and actual input volumes of other energy sources. Namely, the stricter the intensity of environmental regulation, the more favorable it is to decreasing consumption of other energy types, including coking coal and gas, and increasing power consumption. This conforms to observed realities. Air pollution and CO2 emissions increase because of the use of much non-electrical energy including those raw materials (coking coal and gas) needed for producing iron and steel. Moreover, strict air environmental regulation reduces consumption of non-electrical energy while increasing

1.00 0.92

0.90

0.86

0.77

0.80 0.70

0.87

0.68

0.90

0.67

0.50

0.88

0.94

0.78 0.75

0.94

0.93

0.74

0.79

0.77

0.74

0.69 0.71

0.70

0.60

0.93 0.90

0.63

0.72

0.74

0.74 0.67

0.65

0.62

Eastern Mean

Median Mean

Western Mean

0.40 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Fig. 2. Regional power efficiency in the first stage from 2005 to 2014. Note: The data is from Appendix B.

Y. Wu et al. / Energy 175 (2019) 1009e1020

1.00

1.00 1.00 0.95

1.00 0.97 0.99

0.96

0.93

0.90 0.80

0.76

0.80

1015

0.80

0.81 0.78

0.81

0.80

0.78

0.74 0.69

0.69 0.61

0.69

0.68 0.64

0.69 0.59

0.60 0.49

0.40

0.36

0.20

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

0.00

Fig. 3. Average power efficiency of 28 provinces from 2005 to 2014 (in the first stage). Note: The data is from Appendix B.

Table 2 Stochastic frontier regression results from the second stage.

Constant Trade openness Environmental regulation Industrial structure LR test of the one-sided error

Slack variable of power

Slack variable of other energies

Slack variable of labor

Slack variable of capital stock

60.1** (0.50)
86.4** (0.80) 1208.8** (1.00)
171.3** (0.90) 16.2**


199.3 (132.42)
334.7** (49.98)
428.7** (1.14) 406.2** (59.42) 23.2**


2.2** (0.48) 1.0 (1.10) 24.3** (3.03) 2.7 (2.03) 21.9**


45.2* (11.50)
138.3** (4.80)
4170.1** (1.00) 108.9** (5.12) 20.3**

Note: The values in brackets are standard deviation; ** and * indicant at 5% and 10%, respectively.

the utilization rate of clean energy sources (electricity included). Therefore, excessive substitution of electric power for coal probably decreases power efficiency. (2) Trade openness. In the similar SFA regression analysis separately taking power and other energy sources as dependent variables, the coefficients of trade openness are all negative. This indicates that, the higher the degree of foreign trade openness of a province, the lower the slack variables of power and other energy sources, and the smaller the gap between the target and the actual input volumes. Namely, increasing the degree of foreign trade openness is conducive to reducing energy inputs. (3) Industrial structure. The coefficients of slack variables of industrial structure optimization on power and other energy sources are
171.3 and 406.2, respectively and both are significant at the 5% level. This implies that optimization of industrial structure is conducive to improving the power efficiency, while decreasing the utilization efficiencies of other energies, in the steel industry. It is mainly caused by the optimal allocation effect of resources arising from adjustment of industrial structure and energy substitution effects in the steel industry. On the one hand, with the

adjustment of industrial structures in different regions, the power efficiency of the steel industry improves through optimal allocation of input resources [60]. On the other hand, electric power and non-electric energies including coal can be mutually substituted in the steel industry [61]. Adjusting the industrial structure can improve the power efficiency of steel industry, while at the same time the utilization of nonelectrical energy sources (including coal) probably increases. In fact, the electricity consumed by the industry is apparently reduced by high-efficiency generalization and utilization of electrical equipment; however, various non-power sources including coal are important raw materials used during steel production including coking and sintering, thus result in a low energy elasticity. Therefore, adjusting the industrial structure can not only improve the power efficiency and decrease the increase in power consumption, but can also increase the utilization of non-power sources including coal. Additionally, the influence of external environmental factors on labor and capital are analyzed. In SFA regression analysis, the trade openness and industrial structure have an insignificant influence on the labor force in the steel industry. Even though environmental

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1.00 0.92

0.91

0.94

0.92

0.93

0.88

0.90

0.84

0.83

0.84

0.83

0.80

0.78

0.76 0.73

0.72 0.68

0.70 0.68

0.78

0.71

0.74 0.71

0.62

0.60 0.61

0.69

0.68

0.67

0.65

0.50

0.78

0.75

0.61

0.59

Eastern Mean

Median Mean

Western Mean

0.40 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Fig. 4. Regional power efficiency in the third stage from 2005 to 2014. Note: The data is from Appendix C.

regulation reduces labor efficiency in the industry, this influence is slight, and therefore, this section only covers the analysis of the influence of external factors on capital stock. When capital stock is a dependent variable, all factors have significance effects at the 5% level. Specifically, environmental regulation and foreign trade openness present a negative correlation with the slack variables of capital stock, implying that these two factors improve the utilization efficiency of capital stock in the steel industry, optimization of

industrial structure increases the slack variable of capital input while reducing the capital input efficiency to the steel industry, which is indicative of unlimited capacity expansion of China's steel industry. Through the above analysis, different environmental variables exert different influences on power efficiency in the steel industry: improper evaluation of DMUs within the industry is probably likely as a result of the efficiency evaluation having involved the influence

1.00 1.00

1.00 0.88

0.96 0.93 0.83

0.80

0.89

0.86

0.78 0.74

0.82 0.77

0.76 0.77 0.72

0.76 0.72 0.68

0.73

0.60

0.81

0.78 0.74 0.63

0.62

0.69 0.53 0.44

0.45

0.40

0.20

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

0.00

Fig. 5. Average power efficiency of 28 provinces from 2005 to 2014 (in the third stage). Note: The data is from Appendix C.

Y. Wu et al. / Energy 175 (2019) 1009e1020

1017

Table 3 Testing the differences between the initial and adjusted efficiencies. Null hypothesis

Wilcoxon rank sum test

Paired-Samples T test

There is no difference of efficiencies in the first stage and the third stage

13684 (0.007)

2.945 (0.003)

Note: Values in parentheses are p-values.

T he third s tage

T he firs t s tage

Fig. 6. Comparison of power efficiency between the first and the third stage. Note: The data is from Appendix B and Appendix C.

of external environmental factors, namely, the DMUs, in a benign external environment, can show high power efficiency while those in a poor external environment probably exhibit undesirable efficiency. Thus, it is necessary to adjust primary input variables so as to calculate the true efficiency of the steel industry, in all regions, under the same external management environmental and random

Xinjiang Zhejiang Beijing Jiangsu Gansu Ningxia Qinghai Shaanxi Guizhou Sichuan Jiangxi Heilongjiang Shandong Shanghai Guangdong Guangxi Jilin Fujian Yunnan Hunan Tianjin -0.01 Hebei -0.04 Henan -0.04 Anhui -0.06 Hubei -0.08 Shanxi -0.08 Inner Mongolia -0.09 Liaoning -0.1

-0.05

0.15 0.12 0.09 0.07 0.07 0.06 0.06 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.02 0.02 0.02 0.01 0.01 0

0

0.05

0.1

0.15

Fig. 7. Provincial differences between the first and the third stages. Note: The difference value is equal to the first stage of power efficiency minus the third stage of power efficiency.

factor sets.

5.3. Power efficiency after adjusting the input value: DEA result of the third stage After adjusting the input variables by using formula (5) based on the results from the second stage, the power efficiency of the steel industry of the 28 provincial-level administrative districts in China from 2005 to 2014 was analyzed and the power efficiency, without the influences of external environmental factors and random noise, was acquired (as shown in Appendix C). In order to better reflect the characteristics of power efficiency in the third stage, Fig. 4 and Fig. 5 are collated according to these results. Compared to the first stage, the third stage presents that after eliminating the influence of external environmental factors, the power efficiencies of different regions all decrease. The average efficiencies of eastern, central, and western China change from 0.91, 0.72, 0.71 (in the first stage) to 0.88, 0.73, 0.67 (in the third stage), respectively. It illustrates that various external factors including environmental regulation, trade openness, and industrial structure exert positive effects on the power efficiency of iron and steel industries in eastern and western China. However, external environmental factors exert the least influence on the central China, this is because most provinces in central China depend more significantly on energy and resource consumption while external environmental factors have an insignificant effect on the increased efficiency of the steel industry therein. We run two tests to examine whether the efficiencies in the first stage and the third stage are statistically different, and the results are shown in Table 3. It can be seen that the null hypothesis that the efficiencies in the first stage and the third stage are the same is strongly rejected, which implies that it is necessary to adopt the three-stage DEA model to obtain the accurate efficiencies. (1) General differences between the first and the third stages

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Y. Wu et al. / Energy 175 (2019) 1009e1020

As shown in Fig. 6, comparison of the results obtained in the first and the third stages shows that the characteristic of power efficiency of the steel industry in various provinces of China does not fundamentally change, namely the eastern China shows the highest efficiency, while the western China shows the lowest one. This implies that the conclusion obtained in this study is robust.

6. Conclusions Using the three-stage DEA model, which can address the influence of external environmental factors and random error on efficiency, this study evaluates the power efficiency of steel industry in 28 provinces of China. Some meaningful conclusions are listed below:

(2) Provincial differences between the first and the third stages In order to illustrate provincial differences of power efficiency between the first and the third stages, Fig. 7 has sorted the provincial differences from small to large. If the difference is positive, it shows that the power efficiency of the first stage is greater than that of the third stage; on the contrary, the power efficiency of the first stage is less than the third stage of power efficiency. Compared with the first stage, eliminating the influence of external environmental factors, the power efficiency of seven provinces increased in the third stage (as shown in Fig. 7). According to the order of difference, these provinces are Liaoning, Inner Mongolia, Shanxi, Hubei, Anhui, Henan, and Hebei provinces. The reasons for this phenomenon are as follows: on the one hand, most of these provinces are located in the inland area of China, and the driving factors of foreign trade on economic development are relatively small; on the other hand, all these provinces are resource driven, that is, the development of economy is heavily dependent on the consumption of resources. Thus, the external factors such as environmental regulation, trade openness and industrial structure have little effect on the power efficiency of the steel industry, and even are not conducive to the improvement of power efficiency. The power efficiency of most provinces has declined after eliminating the influence of external factors. Among them, the reduction of power efficiency in Xinjiang, Beijing, Zhejiang, Jiangsu and Gansu provinces ranked the top five. This shows that external factors have a good effect on improving the power efficiency of these provinces which characterized by rapid economic development and small dependence on resources. In general, the characteristics of provincial differences can be divided into three types: the first group includes the economically developed provinces in the Eastern China, such as Shanghai, Jiangsu and Tianjin et al., which always show high power efficiencies. The reason is that the factor input and resource allocation of the steel industry in these areas shows high efficiencies, and retain their have high power consumption efficiencies even after eliminating the influence of external environmental factors. The second group includes economically developing provinces, and most in central China such as Shanxi, Anhui, and Henan provinces et al., where economic developments is heavily dependent on resource consumption, and are insensitive to the influence of external factors. This indicates that the iron and steel industries in theses provinces exhibit strong rigidity in their consumption of energy. The third group refers to underdeveloped provinces in western China including Gansu, Ningxia, Guizhou provinces et al., where economic development is relatively less dependent on resource consumption, and are also greatly affected by the external factors. Additionally, in terms of power efficiency difference, the gap between the Central China and the Western China widens after eliminating the influence of external factors. It reveals that external environmental factors not only improve the power consumption efficiencies in most provinces but also reduce the gap therein in central and western provinces.

(a) The power efficiency of the iron and steel industries in different regions during the period 2005 to 2014 shows a fluctuating increase, and the eastern, central, and western China separately present the highest, median, and lowest efficiencies. (b) External environmental factors exert a significant influence on the power efficiency. Improving trade openness and optimizing industrial structure are conducive to improving the power efficiency, while improving the intensity of environmental regulation can cause the excessive substitution of power sources for other energy resources so as to decrease the power efficiency. (c) After controlling the external environmental factors, the power efficiency of the steel industry in the third stage is lower than that in the first stage, but the efficiencies in different regions decline at different levels. The influences of external environmental factors on eastern and western China are more significant than those on central China. Power efficiency improvement of the steel industry first depends on industrial operators’ optimal allocation of factor input (such as industrial capital, energy sources, and labors). However, according to the comparison of regional power efficiency in this study, there are limited effects depending only on the internal optimization of various input factors to reduce the gap in power efficiencies of the steel industry in different provinces. Therefore, it is necessary to implement differential external policies aiming at different regions to achieve promotion and coordination thereof. Acknowledgments This work is supported by the National Natural Science Foundation of China (Grand Nos. 71703060, 71673230, 71773028 and 71303199), the Youth Foundation of Social Science and Humanity, China Ministry of Education (Grand No. 17YJC790170), the Fundamental Research Funds for the Central Universities at Xiamen University (Grand No.20720191006), and the Education Department of Hunan province (Grand No. 17A142). Appendix A Descriptive statistics of relevant data of China's steel industry in 2005e2014.

Electricity (million tce) Other Energies (million tce) Labor (thousand) Capital Stock (Billion ￥) Industrial output value (Billion ￥) CO2 (thousand tons)

Mean value

Standard deviation

Maximum Minimum

48 470 3409 1052 624

12 83 452 234 77

65 568 4160 1375 716

30 322 2875 671 495

1534

278

1857

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Y. Wu et al. / Energy 175 (2019) 1009e1020

1019

Appendix B Power efficiency in the first stage from 2005 to 2014.

Province

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Mean value

Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Eastern Mean Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Median Mean Inner Mongolia Guangxi Sichuan Guizhou Yunnan Shanxi Gansu Qinghai Ningxia Xinjiang Western Mean

0.96 1.00 1.00 0.79 1.00 1.00 0.91 0.89 0.94 0.69 0.92 0.61 0.69 1.00 0.59 0.71 0.70 0.57 0.54 0.68 0.38 0.57 0.81 0.36 0.59 0.74 0.48 0.73 1.00 1.00 0.67

0.83 1.00 0.95 0.65 1.00 1.00 0.84 0.85 0.78 0.70 0.86 0.54 0.72 1.00 0.51 0.70 0.61 0.51 0.60 0.65 0.35 0.68 0.80 0.38 0.55 0.86 0.50 0.81 1.00 1.00 0.69

0.86 1.00 1.00 0.55 1.00 1.00 0.96 0.81 0.84 0.73 0.87 0.58 0.76 0.93 0.59 0.76 0.83 0.46 0.66 0.70 0.32 0.77 1.00 0.42 0.68 1.00 0.61 0.92 1.00 1.00 0.77

1.00 1.00 1.00 0.67 1.00 1.00 0.91 0.75 0.88 0.76 0.90 0.64 0.69 0.98 0.72 0.78 0.80 0.67 0.69 0.75 0.39 0.79 0.85 0.48 0.72 1.00 0.59 0.67 1.00 1.00 0.75

1.00 1.00 1.00 0.66 1.00 1.00 0.84 0.66 0.80 0.82 0.88 0.57 0.66 1.00 0.68 0.71 0.76 0.73 0.56 0.71 0.44 0.68 1.00 0.48 0.72 0.81 0.64 0.59 1.00 1.00 0.74

1.00 1.00 1.00 0.63 1.00 1.00 1.00 0.65 0.93 0.78 0.90 0.55 0.52 0.57 0.65 0.81 0.69 0.64 0.61 0.63 0.32 0.63 0.82 0.50 0.73 0.55 0.62 0.41 0.57 1.00 0.62

1.00 1.00 1.00 0.69 1.00 1.00 1.00 0.77 1.00 0.83 0.93 0.67 0.69 0.65 0.83 0.84 0.74 0.82 0.67 0.74 0.36 0.87 1.00 0.52 0.75 0.82 0.88 0.42 0.54 1.00 0.72

1.00 1.00 1.00 0.79 1.00 1.00 1.00 0.77 1.00 0.82 0.94 0.66 0.86 0.62 0.83 0.86 0.86 0.86 0.68 0.78 0.38 1.00 1.00 0.56 0.78 0.79 0.85 0.45 0.57 1.00 0.74

1.00 1.00 1.00 0.75 1.00 1.00 1.00 0.70 1.00 0.91 0.94 0.68 0.90 0.67 0.78 0.92 0.86 0.83 0.69 0.79 0.35 1.00 1.00 0.60 0.76 0.78 1.00 0.45 0.61 0.87 0.74

1.00 1.00 1.00 0.76 1.00 1.00 1.00 0.79 0.86 0.93 0.93 0.62 0.95 0.56 0.77 0.97 0.90 0.69 0.70 0.77 0.28 1.00 1.00 0.57 0.64 0.78 0.73 0.41 0.54 0.71 0.67

0.97 1.00 0.99 0.69 1.00 1.00 0.95 0.76 0.90 0.80 0.91 0.61 0.74 0.80 0.69 0.81 0.78 0.68 0.64 0.72 0.36 0.80 0.93 0.49 0.69 0.81 0.69 0.59 0.78 0.96 0.71

Appendix C Power efficiency in the third stage from 2005 to 2014.

Province

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Mean value

Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Eastern Mean Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Median Mean Inner Mongolia Guangxi Sichuan Guizhou Yunnan Shanxi Gansu Qinghai Ningxia Xinjiang Western Mean

0.94 1.00 1.00 0.95 1.00 1.00 0.75 0.93 0.94 0.56 0.91 0.61 0.67 1.00 0.67 0.71 0.77 0.74 0.62 0.72 0.40 0.69 0.97 0.43 0.64 0.73 0.62 0.46 1.00 0.88 0.68

0.75 1.00 1.00 0.87 1.00 1.00 0.58 0.76 0.79 0.61 0.84 0.63 0.61 1.00 0.49 0.60 0.63 0.54 0.50 0.62 0.35 0.46 0.77 0.35 0.50 0.76 0.48 0.67 1.00 0.76 0.61

0.74 1.00 1.00 0.61 1.00 1.00 0.69 0.72 0.83 0.69 0.83 0.64 0.64 0.84 0.53 0.65 0.83 0.50 0.57 0.65 0.30 0.64 0.88 0.33 0.54 1.00 0.61 0.66 1.00 0.83 0.68

0.94 1.00 1.00 0.70 1.00 1.00 0.82 0.71 0.88 0.74 0.88 0.67 0.73 0.80 0.76 0.76 0.82 0.71 0.70 0.74 0.42 0.82 0.87 0.48 0.75 0.94 0.61 0.68 1.00 1.00 0.76

0.67 1.00 1.00 0.68 1.00 1.00 0.72 0.63 0.80 0.76 0.83 0.59 0.66 0.96 0.69 0.69 0.79 0.76 0.55 0.71 0.48 0.68 1.00 0.44 0.71 0.72 0.64 0.60 1.00 1.00 0.73

1.00 1.00 1.00 0.77 1.00 1.00 1.00 0.64 0.94 0.88 0.92 0.62 0.61 0.62 0.80 0.85 0.78 0.77 0.62 0.71 0.38 0.69 1.00 0.41 0.74 0.42 0.50 0.41 0.36 1.00 0.59

1.00 1.00 1.00 0.71 1.00 1.00 1.00 0.73 1.00 0.79 0.92 0.71 0.73 0.53 0.87 0.84 0.80 0.87 0.67 0.75 0.39 0.86 1.00 0.42 0.69 0.79 0.78 0.43 0.37 0.96 0.67

1.00 1.00 1.00 0.79 1.00 1.00 1.00 0.76 1.00 0.80 0.94 0.69 0.86 0.53 0.85 0.83 0.90 0.88 0.68 0.78 0.40 1.00 0.86 0.48 0.71 0.79 0.71 0.47 0.43 0.93 0.68

1.00 1.00 1.00 0.76 1.00 1.00 1.00 0.69 1.00 0.88 0.93 0.69 0.88 0.56 0.79 0.90 0.90 0.86 0.71 0.78 0.34 1.00 1.00 0.55 0.71 0.77 0.89 0.40 0.42 0.78 0.69

0.80 1.00 1.00 1.00 0.63 0.29 0.77 0.84 0.45 1.00 0.78 1.00 0.77 0.79 0.86 0.85 0.99 0.74 0.71 0.84 1.00 1.00 0.52 0.60 0.78 0.67 0.35 0.52 0.67 0.00 0.61

0.88 1.00 1.00 0.78 0.96 0.93 0.83 0.74 0.86 0.77 0.88 0.69 0.72 0.76 0.73 0.77 0.82 0.74 0.63 0.73 0.44 0.78 0.89 0.45 0.68 0.76 0.62 0.53 0.72 0.81 0.67

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