Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: A novel integrated approach

Energy Policy 134 (2019) 110946

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Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: A novel integrated approach

T

Bangzhu Zhua,b,∗, Mengfan Zhanga, Yanhua Zhouc,∗∗, Ping Wanga,∗∗∗, Jichuan Shengb, Kaijian Hed, Yi-Ming Weie, Rui Xief a

School of Management, Jinan University, Guangzhou, 510632, China School of Business, Nanjing University of Information Science & Technology, Nanjing, 210044, China c School of Business Administration, Xinjiang University of Finance and Economics, Urumqi 830012, China d Hunan Engineering Research Center for Industrial Big Data and Intelligent Decision Making, Hunan University of Science and Technology, Xiangtan, 411201, China e Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China f College of Economics and Trade, Hunan University, Changsha, 410082, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Industrial structure rationalization Industrial structure advancement Green development efficiency Super-efficiency slacks-based measure Panel regression analysis

Industrial structure adjustment is one of the effective measures for achieving green development. Aiming at improving green development efficiency through industrial structure adjustment, this study proposes a novel integrated approach incorporating industrial structure adjustment measurement, super-efficiency slacks-based measure with undesirable outputs and panel regression models to explore the effect of industrial structure adjustment on green development efficiency. Taking the provincial data of China from 1999 to 2016 as an example, the empirical results show that during the study period, China's provincial industrial structure rationalizations and advancements have three trends, i.e. rising, U-shaped and inverted U-shaped. In the meantime, China's provincial green development efficiency also has three trends, i.e. rising, falling and U-shaped. Industrial structure rationalization and advancement both have a positive effect on green development efficiency. Compared with the industrial structure rationalization, the advancement has a greater effect on green development efficiency. Environmental protection, urbanization, energy conservation and emission reduction policy are conducive to improving green development efficiency. However, human capital and openness have a negative effect on green development efficiency.

1. Introduction The global climate change poses the increasing levels of threats to human sustainable development, which attracts more and more attention. As a developing country with the largest energy consumption and CO2 emission, China faces a severe challenge to her sustainable development. In China, the fifth plenum of the 18th central committee of Communist Party of China put forward the five development concepts: innovation, coordination, green, open and sharing. The 19th national congress of communist party of China further took the new development concept as its basic new impel strategy. Green development is not only an inevitable choice to break through the bottleneck of resources and environment, but also is the key to China's ecological civilization construction. The 13th five-year plan of national economic and social

development of China (2016–2020) further stated that green development could be achieved through industrial structure adjustment. Therefore, how to improve green development efficiency through industrial structure adjustment has become one of the hot issues in the field of energy, environment and economy. Industrial structure is usually defined as a composition of industries. Thus, in essence, industrial structure adjustment is defined as the changes to the composition of industries (Huang et al., 2013; Yu, 2017). Since 1978, China's industrial structure has changed greatly. The primary and tertiary industry have showed a scissor-style symmetrical trend, and the secondary industry has fluctuated in a narrow range. The tertiary industry has gradually replaced the dominant position of the secondary industry (Yin et al., 2019). Numerous methods have been used to measure industrial structure adjustment, among which single

∗

Corresponding author. School of Management, Jinan University, Guangzhou 510632, China. Corresponding author. School of Business Administration, Xinjiang University of Finance and Economics, Urumqi 830012, China ∗∗∗ Corresponding author. School of Management, Jinan University, Guangzhou 510632, China E-mail addresses: [email protected] (B. Zhu), [email protected] (Y. Zhou), [email protected] (P. Wang). ∗∗

https://doi.org/10.1016/j.enpol.2019.110946 Received 23 February 2019; Received in revised form 10 July 2019; Accepted 19 August 2019 0301-4215/ © 2019 Published by Elsevier Ltd.

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efficiency also has three trends of rising, falling and U-shaped. Industrial structure rationalization and advancement both have a positive effect on green development efficiency. Compared with the former, the latter has a greater effect on green development efficiency. Environmental protection, urbanization, and energy conservation and emission reduction policy are conducive to improving green development efficiency, however, human capital and openness have a negative effect on green development efficiency. The rest of the study is organized as follows. Section 2 explains the methodology, including the industrial structure adjustment measurement, super-efficiency SBM with undesirable outputs, and our proposed integrated approach for exploring the effect of industrial structure adjustment on green development efficiency. Section 3 presents the data. Section 4 reports the empirical analysis results. Section 5 concludes with some policy suggestions.

dimension indicator is the mainstream method used widely (Zhang, 2015; Li et al., 2017; Hung, 2018). During the recent years, multi-dimension indicators have been used to measure industrial structure adjustment (Sun et al., 2018). Green development, aiming at efficiency, harmony and sustainability, is a comprehensive development pattern taking into account both economic growth and environmental protection (Hong et al., 2018). The main approaches to the measurement for green development can be divided into two categories: comprehensive index system and green development efficiency. One main method builds a comprehensive index system and obtains a composite index to measure green development (Xiao et al., 2013; Yi, 2016; Beijing Normal University. et al., 2017). The other main method introduces a green development efficiency to measure green development, including nonparametric methods such as data envelopment analysis (DEA) (Sun et al., 2018; Zhou, 2018) and parametric methods such as stochastic frontier analysis (SFA) (Feng et al., 2017; Liu et al., 2018). As for the effect of industrial structure adjustment on green development efficiency, the existed studies focus on the unilateral impact of industrial structure adjustment on economic growth (Xing, 2017; Gan et al., 2011; Chen et al., 2017) or environmental protection (Mi et al., 2015; Chang, 2015; Han et al., 2016). Through the comprehensive analysis of the existing literature, two observations on the status quo of the current research can be found. Firstly although multi-dimension indicators have been introduced to measure the industrial structure adjustment, single dimension indicator is the popular method used widely to measure industrial structure adjustment, which can only capture the simple relationship of industries, lacking a full-scale measure of industrial structure adjustment. Secondly, there is no unified index system to measure the green development and green development efficiency. The mainstream methods used are SFA and DEA. However, neither SFA nor DEA can deal with the measurement of green development efficiency well. SFA can only deal with the problems of single-input and single-output, single-input and multi-output. It is not applicable to solve multi-input and multi-output problem. The conventional DEA cannot deal with undesired outputs. In the meantime, the links between inputs and outputs change at the fixed proportion, which violates the empirical data characteristics. Besides, DEA cannot compare the cases with the same efficiencies of 1. Finally, industrial structure adjustment can play an important role in economic growth and environmental protection. However, existing studies are limited to explore the unilateral effect of industrial structure adjustment on economic growth or environmental protection. They do not take an integrated approach to the industrial structure adjustment on green development. To fill this gap, this study explores the effect of industrial structure adjustment on green development efficiency in terms of impact direction, impact degree and impact mode. The main contributions are as follows: firstly, a novel integrated approach incorporating industrial structure adjustment measurement, super-efficiency slacks-based measure (SBM) with undesirable outputs and panel regression models has been constructed to explore the effect of industrial structure adjustment on green development efficiency. In the proposed approach, industrial structure rationalization and advancement are introduced into measuring industrial structure adjustment, so as to obtain more accurate results than single dimension indicator. Super-efficiency SBM with undesirable outputs is introduced into measuring green development efficiency, so as to solve the problems of synchronously satisfying the reality needs of unexpected outputs, input-output changes at different proportions, and comparisons among the cases with efficiencies of 1. In the meantime, we conduct an integrated study of industrial structure adjustment on economic growth and environmental protection. Secondly, taking China's provincial data as an example, the empirical results show that during the study period, China's provincial industrial structure rationalizations and advancements have three trends of rising, U-shaped and inverted U-shaped. China's provincial green development

2. Methodology 2.1. Industrial structure adjustment measurement Inspired by the previous studies (Zhang, 2015; Li et al., 2017; Hung, 2018; Sun et al., 2018), this study introduces industrial structure rationalization and advancement to measure industrial structure adjustment. Industrial structure rationalization means that production elements are rationally allocated according to specific demand structures so as to achieve industries coordinated development with constraints of productivity levels and resource endowments. The coupling degree between elements input and output structures is used to measure industrial structure rationalization, which can capture the degrees of industrial structure coordination and resources effective utilization (Gan et al., 2011; Kraftova et al., 2016; Yu, 2017). Industrial structure rationalization (C1) is defined as: n

⎤ ⎡ C1 = 1/ ⎢∑ (Yi / Y ) (Yi / Li )/(Y / L) − 1 ⎥ ⎦ ⎣ i=1

(1)

where, Yi is output of sector i, Li is input of sector i, n is the number of industrial sectors. The larger C1, the higher industrial structure rationalization. Industrial structure advancement means that industrial structure is pushed from a low level to a high level, driven by technology progress. Industrial structure advancement is a measure of industrial structure upgrading, highlighted by changes of proportions between industries (Han et al., 2016, 2017; Li et al., 2017). Industrial structure advancement (C2) is defined as:

C2 = Y3/ Y2

(2)

where, Y3 is the added value of tertiary industry, and Y2 is the added value of secondary industry. The larger C2, the higher industrial structure advancement. 2.2. Super-efficiency SBM with undesirable outputs As a new development mode, green development is characterized by the organic unity of “three low”, i.e. low consumption, low emissions, and low pollution, as well as “three high”, i.e. high efficiency, high benefit and high cycle. To deal with the problems of unexpected outputs and input-output changes at different proportions, SBM was proposed by Tone (2001). For comparisons among the cases with efficiencies of 1, super efficiency model was proposed by Andersen and Petersen (1993). In essence, measuring green development efficiency is a problem of multi-input and multi-output (expected outputs and undesirable outputs). Therefore, super-efficiency SBM with undesirable outputs is introduced into measuring green development efficiency in this study, defined as: 2

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B. Zhu, et al. s− 1 ∑m i m i = 1 xik g 1 ⎛ q1 sr + q2 1− ∑t = 1 ⎜∑r = 1 q1 + q2 yrk ⎝

Step 2. Green development efficiency measurement. An input-output index system is built for measuring the green development efficiency. Furthermore, a super-efficiency SBM model with undesirable outputs is introduced into measuring green development efficiency.

1+

GDE =

stb ⎞ ⎟ btk ⎠

n

s. t . ∑ j = 1, j ≠ k x ij λj + si− ≤ x ik n ∑ j = 1, j ≠ k yrj λj n ∑ j = 1, j ≠ k btj λj

s−,

s g,

Step 3. Impacts of industrial structure adjustment on green development efficiency. Taking the industrial structure rationalization and advancement as the explanatory variables, and green development efficiency as the explained variable, panel regression analysis is introduced into modeling the impact direction, impact degree and impact mode of industrial structure adjustment on green development efficiency. The model is defined as:

− srg ≥ yrk +

stb

≤ btk

sb

λ, ≥0 n ∑ j = 1, j ≠ k λj = 1 i = 1,2, ⋯, m ; r = 1,2, ⋯, q1; t = 1,2, ⋯, q2 ; j = 1,2, ⋯, n (j ≠ k )

(3)

where, inputs, expected outputs, and undesired outputs, shown by x ik , yrk and btk , respectively. m, q1 and q2 are the number of inputs, expected outputs, and undesired outputs. si− , srg and stb are slack variables of inputs, expected outputs, and undesired outputs, si− is excessive input, srg is insufficient output, stb is undesired output, and λj is a weight vector.

GDEit = αi + βi C1it + γi C2it + χi Controlit + μit

(4)

where, i is the province and t is the year. GDE is green development efficiency, C1 is industrial structure rationalization, and C2 is industrial structure advancement. Control is control variable, α is region unobservable effect, and μ is stochastic error.

2.3. The proposed integrated approach

3. Data

In this study, we propose a novel integrated approach incorporating industrial structure adjustment measurement, super-efficiency SBM and panel regression analysis for exploring the effect of industrial structure adjustment on green development efficiency. The proposed approach is mainly composed of three steps: industrial structure adjustment measurement, green development efficiency measurement and impacts of industrial structure adjustment on green development efficiency, as shown in Fig. 1.

3.1. Explanatory variables: industrial structure rationalization and advancement The data used in this study include: gross domestic product (GDP, Y), primary industry added value (Y1), secondary industry added value (Y2), tertiary industry added value (Y3), total employment population (L), total employment in primary industry (L1), and total employment in the secondary industry (L2), total employment in tertiary industry (L3). Y, Y1, Y2, and Y3 are all converted into the fixed price of 1999. Employment = (employment at beginning of the year + employment at end of the year)/2. All the data are obtained from China statistical

Step 1. Industrial structure adjustment measurement. Industrial structure rationalization and advancement are introduced into comprehensively measuring the industrial structure adjustments.

Fig. 1. The proposed integrated approach for exploring the effect of industrial structure adjustment on green development efficiency. 3

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optimization of their own industrial structures and significantly enhance their own industrial efficiencies. Thus, all of them have achieved more rational and efficient allocation of resources. Furthermore, 9 provinces show a U-shaped trend: Inner Mongolia, Liaoning, Guizhou, Jilin, Gansu, Jiangxi, Xinjiang, Guangxi and Hunan. In the early stage of development, these provinces were weak in economic foundation and backward in science and technology. As a result, their resource allocation structure was distorted and industrial structure rationalization decreased. With economic development and technological progress, their industrial structures have been continuously adjusted, and their structures of supply and demand have become more balanced. Thus, the resource elements have been utilized more efficiently, and industrial structure rationalization has turned to improve. Moreover, a total of 4 provinces show an inverted U-shaped trend: Ningxia, Shanghai, Shaanxi, and Shanxi. In the early stage, various industries have maintained a stable development. Although most of them are resource-based provinces, the acceleration of industrialization process has caused disruptions to the stability and sustainability of industrial structure, so as to bring a serious waste of resources and environmental pollution. It results in the obstruction of the rational development of their industrial structures. Shanghai has achieved the highest level of industrial structure rationalization. In recent years, the speed of economic development has slowed down. Its industrial structure rationalization has shown a downward trend.

yearbooks and interprovincial statistical yearbooks from 2000 to 2017. 3.2. Explained variables: green development efficiency Inspired by Han et al. (2016) and Feng et al. (2017), this study has built an input-output system of green development efficiency with inputs of capital stock, labor and energy consumption, and outputs of GDP (expected output) and CO2 emission (undesirable output). Inspired by Zhang et al. (2004), the perpetual inventory method is used to deal with capital stock: Kj, t = (1 − δ ) Kj, t − 1 + I j, t , in which, K and I are capital stock and new social fixed assets investment. j and t are the province and year respectively. δ is the depreciation rate of fixed assets, selected as 9.6%. Labor is the total employment population (L). Energy 4 44 consumption is the total energy consumption. CO2 = ∑ j − 1 Ai × Si × 12 , in which Ai and Si (i = 1, 2, 3, 4) are energy consumptions and carbon emission coefficients of coal, oil, natural gas and non-fossil respectively, recommended by the Intergovernmental Panel on Climate Change (Mi et al., 2017, 2019). All the data are obtained from China statistical yearbooks and interprovincial statistical yearbooks from 2000 to 2017. 3.3. Control variables Besides the explanatory variables and explained variables, this study introduces the control variables to capture their effects on green development efficiency (Chen et al., 2017; Sun et al., 2018). The control variables include environmental protection (hj, defined by the proportion of environmental investment in GDP), government influence (lnzf, defined by the fiscal proportion of fiscal expenditure to GDP and expressed in logarithm), population capital (lnhr, defined by the number of ten thousand students with high school and expressed in logarithm), openness (dwkf, defined by the proportion of actual use of foreign capital to GDP), and policy for energy conservation and emission reduction (zc, defined by a dummy variable of 1 or 0. China launched her energy conservation and emission reduction policy in 2006. Therefore, before 2006, zc = 0; after 2006, zc = 1). All the data are obtained from the China statistical yearbooks and inter-provincial statistical yearbooks from 2000 to 2017, and have been converted into the constant price of 1999. For illustration purposes, all the variables are shown in Table 1.

4.2. Results of China's interprovincial industrial structure advancement China's interprovincial industrial structure advancements from 1999 to 2016 are calculated by formula (2), as shown in Fig. 3. It is found that there are also three trends of industrial structure advancements during the sample period. 13 provinces show an upward trend: Beijing, Guangdong, Gansu, Hebei, Heilongjiang, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, Yunnan, Zhejiang and Chongqing. These provinces have advantages in terms of economy, science and technology, and geographical location, so as to speed up the upgrading and optimization of their own industrial structures. Numerous traditional industries have been upgraded into new industries, mainly in the development of modern agriculture, advanced manufacturing and hightech industries. The advantageous industries have been transferred from the primary industry into the tertiary industry, therefore their industrial structures have been highly elevated. A total of 15 provinces show a U-shaped trend: Guangxi, Anhui, Henan, Hubei, Hainan, Jilin, Jiangxi, Qinghai, Sichuan, Shaanxi, Shanxi, Ningxia, Xinjiang, Hunan, and Fujian. In the early stage of our analysis, their industrial structure levels were relatively low, and their development is limited by the traditional manufacturing industry, which had delayed their high development. With the improvement of economic strength and technological innovation capability, their industrial structures have been continuously updated and optimized. The backward industries with high energy consumption, high pollution and low output value have been eliminated, and the high-tech industry has risen, and their industrial structure levels have begun to rise. Moreover, Guizhou and Inner Mongolia show an inverted U-shaped trend. Their early industrial

4. Results and discussions 4.1. Results of China's interprovincial industrial structure rationalization China's interprovincial industrial structure rationalizations from 1999 to 2016 are calculated by formula (1), as shown in Fig. 2. It can be found that there are three trends of industrial structure rationalizations during the period. More specifically, 17 provinces show an upward trend: Anhui, Beijing, Chongqing, Fujian, Guangdong, Henan, Hubei, Hebei, Hainan, Heilongjiang, Jiangsu, Qinghai, Sichuan, Shandong, Zhejiang and Tianjin, Yunnan. During the process of industrial structure adjustment, these provinces have taken advantage of economy, geographical location and policy, so as to fully accelerate the update and Table 1 Variables and definitions. Variables

Symbol

Definition

Measured methods

Explained Variables Explanatory Variables

GDE C1 C2 hj lnzf lnhr dwkf zc

Green development efficiency Industrial structure rationalization Industrial structure advancement Environmental protection Government influence Population capital Degree of openness Energy conservation and emission reduction policy

Formula (3) Formula (1) Formula (2) Environmental investment as a share of GDP Ratio of fiscal expenditure to GDP in logarithm Number of students enrolled in high school or above per 10,000 people in logarithm Actual utilization of foreign capital accounts for GDP 0 before 2006, 1 after 2006

Control Variables

4

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Fig. 2. China's industrial structure rationalizations from 1999 to 2016.

trend. Their bases of green development efficiency were relatively large. In recent years, the economic benefit-oriented development mode has brought about the weakening advantages of ecological environment and the declining of green development efficiency. Moreover, Chongqing, Qinghai and Guangxi show a U-shaped trend. In the early part of the sample period, it was plagued by the thinking of “GDP-only”. These provinces pursued the rapid economic development at the high cost of resources and the environment, which had caused the green development to hit a bottleneck. In recent years, these provinces have actively responded to the national central and western support programs of China, eliminated the backward production capacity, and introduced the high-tech industries. In order to ensure the healthy development of economy, they have strengthened the environmental governance, thus their green development efficiencies have begun to rise.

structure adjustments followed the typical pattern of economic and social developments, the process of industrial structure advancement was stable. In recent years, the process of industrial structure industrialization has been accelerated. Both of them have paid more attention to shifting their industries to the secondary industry instead of the tertiary industry, and their industrial structure advancement levels have declined.

4.3. Results of China's interprovincial green development efficiency China's interprovincial green development efficiencies from 1999 to 2016 are calculated by formula (3), as shown in Fig. 4. It can be found that there are also three trends of green development efficiencies from 1999 to 2016. A total of 25 provinces show an upward trend: Anhui, Beijing, Guangdong, Gansu, Guizhou, Henan, Hubei, Hebei, Heilongjiang, Hunan, Jilin, Jiangsu, Liaoning, Inner Mongolia, Sichuan, Shandong, Shanghai, Shaanxi, Tianjin, Shanxi, Xinjiang, Yunnan, Zhejiang, Fujian and Jiangxi. The main reason for this observation is that different regions can rely on different advantages such as natural environment, ecological resources and economic basis to promote healthy economic development or ensure the harmonious development of ecological environment, so as to achieve the gradual improvement of green development efficiency. Hainan and Ningxia show a downward

4.4. The effects of industrial structure adjustment on green development efficiency Firstly, the stationarity of data is checked to avoid the pseudo regression, as shown in Table 2. It can be found that all variables are stationary at the significant level of 5%. In order to test for the multicollinearity of data, variance inflation 5

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Fig. 2. (continued)

significantly improve green development efficiency. Industrial structure rationalization refers to the degree of industries coordination and aggregation, characterized by rational allocation of resource elements and dynamic balance of resource elements. Green development emphasizes the coexistence of economic growth and environmental protection. During the study period, resource elements such as labor, capital, and energy have been transferred from agriculture to manufacturing and services. The balance between supply and demand has changed, from uncoordinated to coordinated, and the irrational allocation of resource elements under the distorted structure has also been adjusted. Industries have achieved coordinated and stable developments, and productivity has been effectively improved. Efficient utilization of resource elements has significantly reduced resources waste and ecological destruction. It promotes the healthy development of economy. Overall, industrial structure rationalization has promoted green development efficiency during the study period. Model 3 is the regression equations of industrial structure advancement to green development efficiency without control variables. At this time, industrial structure is positively correlated to green development efficiency at the significant level of 1%. Model 4 includes the control variables. Industrial structure advancement is still positively correlated to green development efficiency at the significant level of 1%, indicating that industrial structure advancement can effectively improve green development efficiency. Industrial structure

factor (VIF) test is carried out, as shown in Table 3. When VIF < 10, there is no strong multicollinearity. When VIF≥10, there is a strong multicollinearity. It can be found that all the VIFs of variables are much less than 10, so there is no strong multicollinearity among the independent variables. In this study, the Breusch and Pagan method is used to test the heteroscedasticity, as shown in Table 4. The model estimations are reported in Table 5. It can be found that all p-values are greater than 0.05, so as to accept the null hypothesis of same variances. In order to test the endogeneity, the Davidson-Mackinson method is used to test whether C1 and C2 are endogenous variables. The p-values are 0.3930 and 0.8569 respectively, which are greater than the cutoff value of 0.05. Thus, the null hypothesis is accepted. Through the above tests, the obtained equations can be used to explore the effects of industrial structure adjustment on green development efficiency, as shown in Table 5. Model 1 is the regression equations of industrial structure rationalization to green development efficiency without any control variables. Industrial structure rationalization is found to be positively correlated to green development efficiency at the significance level of 1%. Model 2 adds the control variables on the basis of model 1. The coefficient of industrial structure rationalization is 0.007345, which is also positively correlated to green development efficiency at the significant level of 1%. It can be found that industrial structure rationalization can 6

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Fig. 3. China's industrial structure advancements from 1999 to 2016.

indicating that energy conservation and emission reduction policy has played a role in promoting green development efficiency. The impact of government influence is not significant at the level of 10%, which meant that government expenditure has no significant impact on green development efficiency.

advancement reflects the service orientation of industrial structure. It is mainly manifested in two aspects. One is the replacement of old leading industries by new leading industries, and the other is the replacement of traditional backward technologies by advanced technologies. The two replacements not only promote economic development but also guarantee ecological health by energy conservation and emission reduction. It indicates that industrial structure advancement does exert a positive effect on green development efficiency. As for control variables, environmental protection is positively correlated to green development efficiency at the significance level of 5%, indicating that environmental protection has a significant positive impact on green development efficiency. Population capital is significantly negative at the level of 1%, indicating that regional population capital has an inhibitory effect on the promotion of green development efficiency. Because of the technology lock-in effect, the external technology diffusion and internal technology innovation caused by human capital may induce the rebound effect, which in fact reflects the conflict between human capital and energy conservation and emission reduction. Thus, it limits the role of human capital in improving the efficiency of green development. Degree of openness is significantly negative at the level of 1%, which to a certain extent has verified the pollution paradise hypothesis of foreign investment in China. Openness has a slightly negative effect on green development efficiency. Energy conservation and emission reduction policy is significantly positive,

4.5. Robustness tests As is stated above, we propose a novel integrated approach incorporating industrial structure adjustment measurement, super-efficiency SBM with undesirable outputs and panel regression analysis to explore the effect of industrial structure adjustment on green development efficiency. Taking the provincial data of China from 1999 to 2016 as an example, the empirical results show that industrial structure adjustment can improve green development efficiency. Industrial structure rationalization (C1) can improve green development efficiency, industrial structure advancement (C2) can also significantly improve green development efficiency, and industrial structure advancement has a greater effect than rationalization on green development efficiency. In order to verify the accuracy of these conclusions, it is necessary to analyze the robustness of the conclusions. The robustness test is carried out by regression analysis with adjusting explanatory variables and increasing control variables. In this study, two methods of adjusting explanatory variables and 7

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Fig. 3. (continued)

5. Conclusions and policy implications

increasing control variables are used to re-estimate the regression equations, as shown in Table 6. Model 1 uses the industrial structure rationalization (C1) and advancement (C2) as explanatory variables to conduct the regressions. The coefficients of C1 and C2 are 0.012137 and 0.14138 respectively, both of which are significant at the level of 1%. On the basis of Model 1, Model 2 adds control variables to perform the regressions again. The coefficient of C1 is positive, but is not significant at the level of 10%. The coefficient of C2 is also positive and significant at the level of 1%. The results of control variables are consistent with those of Table 5. Therefore, it can be found that industrial structure advancement is more effective than industrial structure rationalization on enhancing green development efficiency. Models 3 and 4 add urbanization (czh), industrialization (gyh) as control variables. Model 3 shows that industrial structure rationalization is significantly positive at the level of 5%, and the coefficients of control variables are consistent with those of Table 5. Model 4 shows that industrial structure advancement is significantly positive at the level of 1%, and the coefficients of control variables are consistent with those of Table 5. Within these two models, urbanization is significantly positive at the level of 5%, while industrialization is not significant at the level of 5%. In summary, the robustness tests show that the results obtained are robust and reliable.

In this study, industrial structure adjustment is measured by rationalization and advancement, and green development efficiency is measured by super-efficiency SBM with undesirable outputs. Based on these measurements, a novel integrated approach incorporating industrial structure adjustment measurement, super-efficiency SBM with undesirable outputs and panel regression analysis is proposed for exploring the effect of industrial structure adjustment on green development efficiency. Taking 30 provinces in China from 1999 to 2016 as an example, the impact direction, degree and mode of industrial structure adjustment on green development efficiency are empirically investigated. The obtained findings are as follows. Firstly, China's provincial industrial structure rationalization and advancement show the trends of rising, U-shaped and inverted Ushaped during the study period. Meanwhile, China's provincial green development efficiency also show the trends of rising, falling and Ushaped. Secondly, industrial structure rationalization and advancement play a key role in promoting green development efficiency. Industrial structure rationalization and advancement both have a positive effect on green development efficiency. Compared with the former, the latter has a greater effect on green development efficiency. 8

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Fig. 4. China's green development efficiencies from 1999 to 2016. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

cultivation of strategic emerging industry and high-end service industry. In the end, those low-end industries with heavy pollution, high consumption and high emission will be replaced by the high-end industries with zero pollution, low consumption and zero emission and the green development efficiency will be largely improved. Secondly, China should prompt and accelerate the upgrade of the traditional industries and develop green emerging industries. More specifically, the government should strengthen the import and R&D of new technologies of resource conservation and environment protection and implement technical transformation in key process, projects, enterprises and industries, so as to improve resource production efficiency and control the emission of pollution and greenhouse gas. Furthermore, the development of green emerging industries is also quite essential. To improve the development of energy-saving industries, it is necessary to promote the wide application of the mature energy-saving technologies and launch projects like “Ten Major Energy Conservation projects” and “Subsidy of Energy-efficient Products Purchase”. Besides, China should promote comprehensive utilization of resources, strengthen the construction of renewable resource recovery system. Moreover, the government also need to consolidate the development of new energy including water, wind and nuclear. Then on the base of these, while maintaining stable economic growth, realizing the improvement of

Thirdly, environmental protection, urbanization, and energy conservation and emission reduction policy are all conducive to the improvement of green development efficiency. Human capital and openness have a negative effect on green development efficiency. Government expenditure has no significant effect on green development efficiency during the study period. Green development attempts to achieve the win-win between economic development and environmental protection. Based on these findings, several policy implications are proposed as follows: Firstly, China should accelerate industrial structure adjustment while realizing industrial structure rationalization and gradually moving to industrial structure advancement. The government should pay more attention to the coordination of factor endowments and industrial structure, promote the transition of production factors from the surplus departments to the inadequate ones, which can largely enhance resource allocation efficiency and resource utilization rate and avoid resource waste. Furthermore, on the premise of industrial structure rationalization, the local governments should prioritize the industrial structure advancement and focus more on self-innovation and technological progress. With industrial planning, industrial policies and other methods, the government should promote the informatization and technicalization of industry, create more opportunities for the

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Fig. 4. (continued) Table 2 Levin-Lin-Chiu (LLC) test results. Variables

GDE

C1

C2

hj

lnzf

lnhr

dwkf

zc

Results

-9.1762 (0.0000**)

-1.8551 (0.0318**)

-7.5403 (0.0000**)

-4.0213 (0.000**)

-3.0413 (0.0012**)

-13.4068 (0.0000**)

-3.1729 (0.0008**)

-1.6402 (0.050**)

Note: *, **, and ***are significant at the levels of 10%, 5%, and 1%, respectively. Table 3 VIF test results.

Table 4 Breusch and Pagan test results.

Variable

VIF

Variable

VIF

Model

C1 dwkf lnzf hj zc lnhr

1.62 1.42 1.32 1.26 1.25 1.13

lnzf dwkf zc C2 hj lnhr

1.32 1.25 1.25 1.23 1.18 1.11

Model Model Model Model

1 2 3 4

p-value

Result

0.2184 0.1088 0.8836 0.3959

Accept Accept Accept Accept

the the the the

null null null null

hypothesis, hypothesis, hypothesis, hypothesis,

the the the the

same same same same

variance variance. variance variance

Note: *, **, and *** are significant at the levels of 10%, 5%, and 1%, respectively.

technological innovation, achieving the reinforcement of energy conservation and environmental protection, which can reduce economic and social development negative effects on resources, energy consumption and ecological environment.

Thirdly, the local government should formulate customized strategies and corresponding policies and measures of industrial structure adjustment based on current industrial development and local comparative advantages. Throughout the process of green development, the 10

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Table 5 Impacts of industrial structure adjustment on green development efficiency. Variable

C1 C2 hj lnzf lnhr dwkf zc R2 Constant

Model Model 1

Model 2

0.461409*** (7.515317)

0.007345*** (3.527855)

0.680100 0.461409*** (55.76434)

0.033208*** (3.758858) 0.015608 (1.111611) -0.196985*** (-7.535653) -0.001426*** (-5.378446) 0.144720*** (13.46041) 0.828427 1.665741*** (5.007815)

Model 3

Model 4

0.192695*** (8.336614)

0.138161*** (7.520728) 0.033321*** (3.929834) -0.006900 (-0.497532) -0.150625*** (-5.779484) -0.001295*** (-5.085022) 0.154750*** (15.41948) 0.830952 1.293898 (7.642909)

0.687299 0.323805*** (14.04015)

Note: *, **, and *** are significant at the levels of 10%, 5%, and 1%, respectively. Table 6 Robustness test results. Variable

C1 C2 hj lnzf lnhr czh gyh dwkf zc R2 Constant

Model Model 1

Model 2

Model 3

0.012137*** (4.247417) 0.141380*** (5.491053)

0.000646 (0.287635) 0.135462*** (6.561770) 0.033356*** (3.929945) -0.006645 (-0.477776) -0.150394*** (-5.762644)

0.007308*** (3.628702)

0.697429 0.342466*** (14.82043)

-0.001299*** (-5.088523) 0.153948*** (14.76794) 0.830644 1.293231*** (7.631298)

0.026515** (3.100731) -0.022339 (-1.533082) -0.182284*** (-6.944851) 0.005076*** (6.750694) -0.000740** (-2.006125) -0.001287*** (-5.004888) 0.112627*** (9.615478) 0.843097 1.488898*** (9.091758)

Model 4

0.145833*** (7.766844) 0.025460** (3.110408) -0.041661** (-2.932361) -0.151808*** (-5.950233) 0.005023*** (6.981090) 0.000107 (0.286964) -0.001239*** (-5.037215) 0.118770*** (10.89417) 0.856255 1.174764*** (7.187156)

Note: *, **, and *** are significant at the levels of 10%, 5%, and 1%, respectively.

strategic concept of realizing the Chinese dream should be implemented and the new development concept should be carried out. The heterogeneity of green development in different provinces should be respected, so they should formulate targeted and focused policy measures to promote green development improvement in different regions. Meanwhile, those provinces with high green development efficiency should well exert the demonstration and leading effect, giving more supports for provinces with lower level of green development efficiency, in industrial transfer and environment protection, and finally achieve regional ecological all-win situations. Last but not the least, China should enforce the high level of public exposure and education of green development concept. The citizens should be encouraged to develop healthy consumption habit and lifestyles, such as broader use of clean energy and energy-saving products. In this way, people's awareness of energy conservation will be improved and a green society will be achieved in the near future.

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Acknowledgements Our heartfelt thanks should be given to the National Natural Science Foundation of China (71771105 and 71671013), National Philosophy and Social Science Foundation of China (16ZZD049), Guangdong Young Zhujiang Scholar (Yue Jiaoshi [2016]95), Guangdong Key Base of Humanities and Social Science—Enterprise Development Research, and Guangzhou key Base of Humanities and Social Science—Centre for Low Carbon Economic Research for funding supports. References Andersen, P., Petersen, N., 1993. A procedure for ranking efficient units in data envelopment analysis. Manag. Sci. 39 (10), 1261–1265. Beijing Normal University, et al., 2017. China Green Development Index Report 2016. Beijing normal university publishing group, Beijing.

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