Spatial-temporal disparities and influencing factors of total-factor green use efficiency of industrial land in China

Journal of Cleaner Production 207 (2019) 1047e1058

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Spatial-temporal disparities and influencing factors of total-factor green use efficiency of industrial land in China Hualin Xie a, b, *, Qianru Chen a, Fucai Lu a, Wei Wang a, Guanrong Yao a, Jiangli Yu c a

Institute of Ecological Civilization, Jiangxi University of Finance and Economics, Nanchang 330013, China Co-innovation Center of Institutional Construction for Jiangxi Eco-civilization, Jiangxi University of Finance and Economics, Nanchang 330013, China c College of Business Administration, Inha University, Incheon, 22212, South Korea b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 February 2018 Received in revised form 14 September 2018 Accepted 9 October 2018 Available online 10 October 2018

Achieving “green” use of industrial land is beneficial to save precious land resources, protect ecological environments and achieve the green development of China's economy. In this paper, we estimate the dynamic performance of the total-factor green use efficiency of industrial land (TGUEIL) in China by using a global non-radial directional distance function (GNDDF) and the non-radial Malmquist performance of industrial land green use (NMPILGU). The results show that the TGUEIL had a rising trend in most years during 2006e2015. The eastern region enjoys the greatest TGUEIL, and the gaps in the TGUEIL between regions are narrowing. Technical progress in the industrial sector contributes more to the NMPILGU than efficiency progress. To raise the TGUEIL, we should increase the internal expenditures in R&D activities, the number of effective inventions and R&D staffs in industrial enterprises. We should promote the use of clean energy in industrial production and increase efforts in energy-saving and emission reduction. We should prohibit local governments from selling land to cover the deficit. The government should pay more attention to industrial labor redundancy in eastern China and guide orderly redundant industrial labor force transfer from eastern regions to central and western regions. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Industrial land Green use efficiency Land management Malmquist productivity index Non-radial directional distance function (NDDF) China

1. Introduction China has achieved notable economic development progress in the past four decades, with a total GDP ranked second in the world. The industrial sector, as a pillar of the Chinese economy, produced as much as 21.52 trillion yuan RMB (3.455 trillion US dollars) in 2015; this amount accounts for approximately 32% of the country's total GDP. However, this gratifying achievement has been accompanied by an amazing waste of precious land resources and sharp increases in industrial pollutant emissions (e.g., carbon dioxide emissions). As Fig. 1 shows, the total area of industrial land had a continuous rising trend in the last decade. Specifically, in 2015, the total industrial land area had reached over 10.3 thousand km2, * Corresponding author. Institute of Ecological Civilization, Jiangxi University of Finance and Economics, Nanchang 330013, China. E-mail address: [email protected] (H. Xie). 1 The catalogue of environmental protection policies and regulations promulgated in 2010. 2010 Report on the development of China's environmental protection industry. 2011.6. 2 In economics, a production frontier is a graph that shows the different quantities of two goods that an economy (or agent) could efficiently produce with limited productive resources. The global production technology frontier refers to the state of technology at the production frontier. https://doi.org/10.1016/j.jclepro.2018.10.087 0959-6526/© 2018 Elsevier Ltd. All rights reserved.

which accounts for approximately 19.77% of the urban built-up land and surpasses many other types of urban land use (e.g., utility land and commercial land). The contradiction between increasing rigid land demand and decreasing rigid land supply becomes aggravated in the process of integrating urban-rural development (Liu et al., 2014). In fact, wasting precious industrial land resources and discharging astonishing amounts of industrial pollutants is very common in China, and the amount of carbon emissions generated by the industrial sectors in particular has shown a rapid increasing trend in recent years. The amounts of two other representatives of industrial pollutants (industrial wastewater and industrial SO2) have remained at relatively high levels, with values of approximately 19.08 billion tons and 14.13 million tons in 2015, respectively. In recent years, industrial pollution incidents have become frequent in many regions across China; they have caused huge economic losses and poseing a threat to social stability (Xie and Wang, 2015). At a series of international environmental conferences in recent years, the national leaders of China once again stressed the importance of green development and required the industrial sectors to save resources, protect the environment and achieve green production. The green use of industrial land not only refers to

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(2) What is the trend of the NMPILGU? Which decomposition indicator (EC, BPC and TGC) is the driver of the NMPILGU's growth? (3) What are the impacts of the influencing indicators (from the economic, social, R&D and other aspects) on the TGUEIL?

2. Literature review

Fig. 1. Trend of total area of industrial land in China.

the input-output efficiency of industrial resources but also reduces emissions of industrial pollutants as much as possible (Wang et al., 2017). In fact, many enterprises located in industrial parks produce and discharge industrial pollutants responsible for most of the pollution events in recent years in China. Therefore, achieving green use of industrial land is an important guarantee for the development of a green economy. To address the issue of inefficient use of industrial land, the central and local governments have issued many targeted policies. To raise the economic efficiency of industrial land, the Decisions to deepen the reform of strict land management was introduced in 2004. Recently, another two famous policies began, the Guidance on promoting the intensive use of land in 2014 and Guidelines for the implementation of industrial land policy in 2016. To cut industrial pollutant discharge, the State Council of China issued the China's National Climate Change Program in 2007; this program aimed to make people fully understand the importance and urgency of tackling climate change and encouraged people to adopt lowcarbon living and production styles. In 2009, Chinese Primer Wen Jiabao made an ambitious commitment to cut carbon emissions, especially to reduce the carbon intensity in industrial production activities (Xie et al., 2017). To encourage low-carbon living and consumption, the famous Interim measures for certification of lowcarbon products and Management method of energy saving and low carbon product certification were released to guide the public and producers to accept low-carbon living and low-carbon production concepts. However, whether the policies will be effective remains unknown. In addition, the Water pollution control action plan issued in 2015 and the Industrial green development plan (2016e2020) issued in 2016 are two representative policies that address the illegal emission of industrial wastewater and industrial sulfur dioxide. However, whether the policies will be effective remains unknown. Many of these policies are a one-size-fits-all policy from the national macro perspective, but the conditions of culture, society and economy in different regions are very different (Wang et al., 2016). Based on related studies (Chen, 2014; Li, 2012), the total-factor green use efficiency of industrial land (TGUEIL) is defined as the economic, social and ecological benefits produced from industrial land resources when all inputs, such as labor, capital and resources, and desirable outputs, as well as environmental pollution as undesirable outputs, are integrated into the productivity accounting framework system. Therefore, it is necessary to estimate the totalfactor green use efficiency of industrial land (TGUEIL) from the national and regional levels and propose effective countermeasures. We will address the following questions in this study. (1) What are the trends of the TGUEIL at the country and regional levels in China?

Many previous studies have argued that the dynamic change in the pattern and mode of industrial land use is responsible for the rapid growth in industrial pollutants and that achieving green use of industrial land is an important issue in the green development of an economy. Arvin et al. (2015) confirmed the significant contribution of industrial land use to the growth in CO2 emissions in G20 countries. Li et al. (2008) argued that urban land expansion, especially the expansion of industrial land, is the major source of carbon dioxide in Jiangsu province, China. Similarly, Huang et al. (2018) confirmed that the sharp increase in urban construction land, especially industrial land, is responsible for the growth in CO2 emissions in the early 21st century. In addition, Wang et al. (2018) suggested that the astonishing discharge of industrial pollutants is an important cause of frequent environmental pollution accidents. Li et al. (2018) argued that the poor regulation of industrial wastewater discharge had caused most rivers to be seriously polluted in China, and Tian and Lin (2017) pointed out that the rising emissions of industrial SO2 is the main culprit in acid rain and haze weather. Beames et al. (2014) raised the question of whether the economic valuation of land provides an accurate reflection of the true value of the resource. Currently, environmental protection is as important as economic development, and it is thus necessary to rationally allocate industrial land and adopt effective countermeasures to reduce industrial pollutants. Regarding the methods for computing the efficiency of industrial land use, the comprehensive evaluation index approaches are widely used because they can incorporate many input and output indicators to make reasonable evaluations of the use efficiency of industrial land resources. The directional distance function (DDF) is very popular because it can overcome some shortages of subjective judgment (e.g., the Analytic hierarchy process, AHP) and information compression (e.g., Principal component analysis, PCA) (Yao et al., 2016). At an earlier time, Shephard (1970) developed the Shephard distance function, which can expand (or contract) all the input and output indicators at the same time with the same rates. However, to maximize the benefit, we hope to minimize the amounts of bad outputs and maximize the amounts of good outputs in industrial production activities. Chambers et al. (1996) overcame this problem by setting direction vectors to distinguish the desirable and undesirable outputs. Zhou et al. (2007) extended this model by developing a non-radial approach that can make better adjustments to the input and output indicators. Specifically, the improved model can maximize the desirable outputs and minimize the undesirable outputs at different rates. However, previous studies have been limited in that they have generally assessed environmental efficiency by using cross-sectional rather than time-series data (Zhang and Choi, 2013a). Therefore, it is not possible to obtain insights into dynamic changes in efficiency performance. To this end, Wang et al. (2017) adopted a global nonradial directional distance function (GNDDF) approach that not only maximized the good outputs and minimized the bad outputs at different rates over the study period but also included all contemporaneous technologies. In addition, the Malmquist productivity index is a widely applied method to describe the dynamic changes and the main €re et al., 1992). The determinants of the efficiency of resource use (Fa

H. Xie et al. / Journal of Cleaner Production 207 (2019) 1047e1058

Malmquist productivity index can be decomposed into several parts, e.g., the efficiency change (EC) index and the technical change (TC) index, to determine the contributors to the promotion of resource use efficiency. Chung et al. (1997) extended this index by incorporating undesirable outputs in production activities and setting direction vectors to distinguish the desirable and undesirable outputs. Some empirical studies that applied this index to measure productivity change in industrial sectors include Xue et al. (2015), Lin and Fei (2015) and Munisamy and Arabi (2015). However, if the non-radial slack is not considered, the index could be biased because inputs and outputs might not expand or shrink at the same rate in practice. To this end, Zhang and Choi (2013b) introduced non-radial slack into the Malmquist index. To integrate non-radial slack with industrial land green use (Wang et al., 2017), we incorporate the non-radial Malmquist index (Zhang and Choi, 2013b) and the global non-radial directional distance function (Wang et al., 2017) into this paper's model. For this reason, we refer to the proposed index as non-radial Malmquist performance of industrial land green use (NMPILGU). The decomposition of the Malmquist index enables us to obtain better insights into changes in industrial land green use performance over time. Regarding the influencing factors of land use efficiency, Xie et al. (2016) argued that protecting the ecological environments of industrial areas is an important guarantee for improving the utilization efficiency of industrial land. He et al. (2016) suggested that the progress of production technology in forestry departments is an important source of the increase of forested land use efficiency in China. Fang et al. (2017) established an embodied carbon accounting framework based on energy to identify the input-output structure and embody carbon emission flows of an industrial park. Wang and Li (2014) found that strengthening the infrastructure construction of rural traffic information services is very helpful to improve the performance of efficiency in using cultivated land resources. Li et al. (2014) argued that attracting investment and improving the efficiency of utilization of production resources could be two good measures to raise the efficiency of urban land use in China. However, there are few studies of the influence factors of industrial land. The contributions of this study are divided into several sections. First, we estimate the total-factor green use efficiency of industrial land (TGUEIL) by considering three major industrial pollutants: industrial carbon dioxide, industrial waste water and industrial sulfur dioxide. Second, we apply the GNDDF approach to estimate the TGUEIL; therefore, we can compare the TGUEIL of different years because the global technology contains all the contemporaneous technology of each year. Third, we propose the non-radial Malmquist performance of industrial land green use (NMPILGU)

to measure the dynamic changes of the TGUEIL. To determine the major promoter of the NMPILGU's growth, we decompose the NMPILGU into three indices: the efficiency change (EC) index, the best-practice efficiency change (BPC) index and the technology gap change (TGC) index. Lastly, we select some representative indicators from the perspectives of industrial structure and economic development R&D activities to analyze how they affect the NMPILGU, and we propose some effective policy implications to raise the NMPILGU. 3. Methodology and materials 3.1. GNDDF In this paper, we apply the GNDDF approach to estimate the €re and TGUEIL at the national and regional levels. According to Fa Grosskopf (2005), the production function theory that each unit under evaluation has invested industrial inputs to produce desirable industrial outputs and undesirable industrial outputs should be satisfied. As Eq. (1) shows, T(x) denotes the possibility set for industrial production; x, y, and z represent the industrial inputs, desirable industrial outputs and undesirable industrial outputs, respectively. Our goal is to maximize the desirable industrial outputs (e.g., industrial GDP) and minimize the undesirable industrial outputs (e.g., industrial CO2, industrial SO2 and industrial wastewater). However, reducing the undesirable industrial outputs costs money and we can thus express T(x) as follows:

TðxÞ ¼ fðx; y; bÞjxcan produceðy; bÞ; x  X l; y  Y l; b ¼ Bl; l  0g (1) where l is a non-negative vector. Eq. (2) shows the NDDF (Nonradial Directional Distance Function) model.

n o ! D ðx; y; b; gÞ ¼ sup wT b : ððx; y; bÞ þ g  diagðbÞÞ2T

(2)

where b denotes the rates of dynamic change of desirable industrial outputs and undesirable industrial outputs. wT ¼ ðx; y; bÞT represents the matrix for assigning weights to variables. According to Chambers et al. (1996), g ¼ ðgx ; gy ; gb Þ is the direction vector to distinguish the desirable and undesirable outputs. b ¼ ðbx ; by ; bb Þ denotes the maximum adjustment ratio of each indicator. Therefore, we can maximize the desirable industrial outputs and minimize the industrial inputs and undesirable industrial outputs.

! D ðx; y; b; gÞ ¼ maxðwLD bLD þ wY bY þ wSO2 bSO2 þ wCO2 bCO2 þ wWA bWA Þ 8 N N N > X X X > > > l LD  ð1  b ÞLD ; l L  L ; ln Kn  K0 n n n n > 0 0 LD > > > n¼1 n¼1 n¼1 > > > N N X X > > > > ln En  E0 ; ln Yn  ð1 þ bY ÞY0 > > > > n¼1 n¼1 > > > N N X > > >X N > > > > ln WAn ¼ ð1  bWA ÞWA0 ; > > > > n¼1 > > > > bLD  0; bY  0; bSO2  0; bCO2  0; bWA  0; > > > N > X > > > n ¼ 1; 2; :::; N; t ¼ 1; 2; :::; T; l  0; ln ¼ 1; n > : n¼1

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(3)

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where the subscripts LD, Y, SO2, CO2 and WA represent the industrial land, gross industrial GDP, the amount of industrial SO2, the amount of industrial CO2, and the amount of industrial wastewater discharge, respectively. We select industrial land (LD), industrial labor (L), industrial capital (K), and industrial energy (E) as inputs. Because our goal in this paper is to estimate the total factor green use efficiency of industrial land, we set the weight vector as (1/3, 0, 0, 0, 1/3, 1/9, 1/9, 1/9) to exclude the diluting effects of other input indicators. The subscript 0 represents the province being evaluated. The symbols bLD , bSO2 , bCO2 , bWA and bY are the reduction ratios of input and output indicators. Therefore, we can express the TGUEIL as Eq. (4).

TGUEIL ¼ *

. i h  * * * * 3 1  0:5  bLD þ bSO2 þ bCO2 þ bWA *

1 þ bY *

*

*

(4)

*

where bLD , bSO2 , bCO2 , bWA and bY are the optimal adjustment ratios for industrial land, industrial sulfur dioxide, industrial carbon dioxide, industrial waste water and industrial GDP, respectively. If the TGUEIL value equals 1, this unit has achieved green use of industrial land compared with other ones. 3.2. NMPILGU According to Lin and Benjamin (2017), we propose a non-radial Malmquist performance of industrial land green use (NMPILGU) based on a Non-radial Malmquist productivity index and the contemporaneous, intertemporal and global production technologies. Therefore, we can measure the dynamic change of the TGUEIL and determine the major promoter of the NMPILGU's growth by decomposing the NMPILGU into an efficiency change index (EC), a best-practice index (BPC) and a technology gap change index (TGC). Therefore, to estimate the NMPILGU and its decomposition indicators during a period, we should compute 6 NDDFs that can be expressed as follows:

. i3S h  2 * * * * 3 1  0:5  bLD þ bSO2 þ bCO2 þ bWA 5 TGUEIL ð:Þ ¼ 4 * 1 þ bY d

(6) Then, according to Eq. (6), we can define the NMPILGU, which can illustrate the dynamic change and determine the main driver of the TGUEIL. If the NMPILGU is greater than (less than) one, the TGUEIL is showing an increasing (decreasing) trend. By decomposing the NMPILGU, we can determine if there is efficiency progress and technological progress: if the value of the EC index is > 1 (<1), the efficiency of industrial land green use shows an increasing (decreasing) trend, and if the value of the TC index is > 1 (<1), the technology of industrial land use shows an increasing (decreasing) trend. Lastly, if the value of the TGC index is > 1 (<1), the technology gap between the group and the whole sample shows a narrowing (expanding) trend. Eq. (7) shows the NMPILGU and the decomposition indicators.

" # TGUEILtþ1 TGUEILtþ1 G ð:Þ C ð:Þ NMPILGU ð:Þ ¼ ¼ TGUEILtG ð:Þ TGUEILtC ð:Þ # " TGUEILtþ1 ð:Þ=TGUEILtI ð:Þ I  t TGUEILtþ1 C ð:Þ=TGUEILC ð:Þ # " t TGUEILtþ1 G ð:Þ=TGUEILG ð:Þ ¼ EC  BPC  TGC  TGUEILtþ1 ð:Þ=TGUEILtI ð:Þ I d

(7)

3.3. Influencing factor analysis model To explore the impacts of social, economic, R&D activities and policy variables on TGUEIL, we select some typical influential indicators as follows:

!d s s s D ðx ; y ; b ; gÞ ¼ maxðwLD bLD þ wY bY þ wSO2 bSO2 þ wCO2 bCO2 þ wWA bWA Þ X X 8X s s   ln LDn  1  bsLD LD0 ; lsn Lsn  L0 ; lsn Kns  K0 ; > > > > con con > X s X  con s  > s s s > > l E  E ; l Y  1 þ b ; Y > 0 0 n n Y n n > > con > X >X con    > > lsn SO2sn ¼ 1  bsSO2 SO20 ; lsn CO2sn ¼ 1  bsCO2 CO20 ; > < con con   s:t: X > lsn WAsn ¼ 1  bsWA WA0 ; > > > con > > s s s s s > > > bLD  0; bY  0; bSO2  0; bWA  0; bCO2  0; > > > N X > > > > ln ¼ 1; : n ¼ 1; 2; :::; N; t ¼ 1; 2; :::; T; ln  0;

(5)

n¼1

where the superscript d represent the type of the NDDF; specif!C ically, there are contemporaneous NDDF ( D ðxs ; ys ; bs ; gs Þ), inter!I and global NDDF temporal NDDF ( D ðxs ; ys ; bs ; gs Þ) !G s s s s ( D ðx ; y ; b ; g Þ). Therefore, the six NDDFs can be solved as follows:

ln yit ¼ a0 þ a1 EXit þ a2 INVit þ a3 ln RDit þ a4 ln EIit þ a5 ln ESit þ a6 ln LSit þ a7 ln FGit þ εit

(8)

where i and t (t ¼ 2006, …, 2015) represent the ith province and year t, respectively. The term εit denotes the random error term. The

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symbol y is the TGUEIL. These indicators are as follows. (1) R&D (research and development) activities. According to New Economic Growth Theory (Romer, 1986), R&D and innovations are important factors in promoting technological progress and economic growth. R&D activities reflect the potential of regional economic development (Zhou, 2014). Internal expenditures for R&D activities (EX) and R&D staff, which serve as important technical support for industrial economy development, reflect regional scientific and technology capabilities. The number of effective inventions (INV) reflects regional technological innovation capability and vitality. We select the internal expenditure of R&D activities (EX), the number of effective inventions (INV) and R&D staffs in industrial enterprises (RD) to represent the impact of R&D activities on the TGUEIL. As Khoshnevis and Teirlinck (2018) suggested, more investment in R&D activities would boost economic development. The number of inventions is an effective measure to test the effectiveness of R&D activities. Therefore, we can assume that EX, INV and RD are positively related to the TGUEIL. (2) Industrial structure. According to Petty-Clark's law, the industrial structure and the employment structure are interrelated, and a dynamic relationship between them would be produced with the development of an economy. The interactive relationship is reflected by the positive relationship between the two under certain conditions (Sun, 2015). We select the industrial labor structure (LS) to represent the condition of industrial structure, which refers to the share of the number of laborers of the industrial sectors in the total number of laborers. As suggested by Ma (2018), the problem of redundancy in industrial labor is very serious and has already led to a significant negative influence on the production technology of industrial sectors in some regions of China. This outcome may occur because too much labor supply has depressed labor prices; this depression has led industrial companies to be reluctant to invest in new technologies to boost productivity (Sun and An, 2018). Therefore, we can assume that LS is negatively related to the TGUEIL. (3) Energy utilization. Improving the energy efficiency of China's industrial sector is the key to achieve comprehensive and coordinated economic and social development (Peng, 2012). Energy structure is an important indicator and reflects energy utilization. The energy structure is closely related to economic growth. The production and consumption structure of energy determines the speed of economic growth (Wang, 2012). We select the energy consumption structure (ES), which refers to the share of the amount of clean energy consumption used by the industrial sectors in the total energy consumption in industrial production activities. Clean energy generates less pollution than that of fossil energy, and we can assume that the cleaner the energy used, the higher the TGUEIL value. Therefore, energy structure is positively related to the TGUEIL. In addition, the indicator of energy intensity (EI), which refers to the amount of energy used to produce a given amount of industrial GDP, is selected. It can be adopted as an indicator to measure national, regional or sectional energy efficiency, and it is widely used in the analysis, comparison and research of international energy efficiency (Tang and Yang, 2009). As Xie et al. (2017) suggested, an important content of the development of a green economy is to improve energy efficiency: we can consume less energy to produce more GDP. Therefore, we can assume that the less the energy intensity, the higher the TGUEIL; energy intensity is negatively related to the TGUEIL.

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(4) The condition of local finance. The expansion of local fiscal deficits caused by Chinese fiscal decentralization drives local governments to achieve fiscal revenue targets by proactive land financial means (Guo and Wang, 2014). To cope with the gap between fiscal revenues and expenditures and the fierce competition among regions, the expansion of industrial land scale has become an important way for local governments to increase financial revenue and achieve rapid economic development. On one hand, the increasing industrial land scale and the artificially low land prices (Xue, 2011) cause a lack of incentive for producers to save resources. On the other hand, most of the local governments focus only on short-term economic benefits without enough regulation of the environmental polluting behavior of industrial enterprises. Therefore, we select the fiscal gap of the local government (FG) to represent the condition of local finance. We can assume that FG is negatively related to the TGUEIL. R&D activities, industrial employment structure, energy utilization and conditions of local finance are largely influenced by the special national conditions in China and national macro-control. ①Due to the “(quasi-) public goods” attribute of R&D activities, strong positive externalities and the existence of market failure, spontaneous R&D input from enterprises can hardly reach the social-optimal level (Klette and Moen, 2012). The industrial R&D activities in China are largely influenced by government's R&D funding and government's&T incentive policies (Guo et al., 2016; Zheng, 2014). In particular, for environmental protection enterprises, with the strengthening of environmental regulations, environmental protection enterprises' interest in R&D input would gradually be increased (Zhang, 2018). ② The industrial employment structure is greatly influenced by demographic dividend and labor transfer in China. China is the most populous country in the world. The demographic dividend has created favorable conditions for China's industrial development. As the boundary between urban-rural dual structures in China gradually blur, a large number of rural labor force have been transferred to urban industrial sectors. With the emergence of labor redundancy, it profoundly affects industrial employment structure. ③ In terms of external factors, the industrial enterprise energy conservation is largely influenced by government factors and market factors. ④The local government fiscal deficit is mainly related to China's fiscal policy, regional economic differentiation and local government's financial burden (Ge, 2016). Due to the overall relatively low level of TGUEIL in China, its impacts on R&D activities and energy utilization are limited, and endogeneity can be ignored. The data on the industrial inputs and industrial GDP, industrial sulfur dioxide, industrial wastewater, labor structure, internal expenditure of R&D activities, the number of effective inventions and R&D staffs in industrial enterprises are collected from the China Statistical Yearbook 2007e2016. The data on industrial energy, energy structure and energy intensity are collected from the China Energy Statistics Yearbook 2007e2016. Because of data unavailability, we compute the data on industrial fixed capital using the perpetual inventory method according to Zhang and Wei (2015) and Wu (2009) and the data on the amount of CO2 emissions according to Zhang and Choi (2013b). In addition, we convert the industrial GDP and industrial fixed capital into 2006 constant prices with their deflators (See Table 1). At present, there are obvious gradients in the economic development level in the eastern, central, and western regions of China. With obvious “industrial gradients”, the industrial level in the eastern region is higher than that in the central region, and the industrial level in the central region is higher than that in the west (Fan and Shao, 2015). There are great differences among eastern,

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Fig. 3. TGUEIL trends in China and its three regions from 2006e2015.

Fig. 2. Geographical distribution of the three regions in China.

central and western regions in terms of industrial distribution, technological innovation and resource endowment, among other things. With advantages such as strong scientific and technological power, abundant high-level intellectual talent, convenient transportation and high openness, relatively mature high-tech industrial belts have been formed in eastern China. With abundant resources and labor forces, the central region has focused on labor-intensive and resource-intensive industries. Due to natural constraints and historic factors, industrial development in western China started fairly late. There are obvious differences among the provinces and cities of the western region in terms of geographical location, resource endowment and industrial base level, and the regional industrial economic development is thus unbalanced (Zhou, 2014). Regarding the gaps in industrial development level in different regions, as shown in Fig. 2, we divide the 30 provinces into three groups (i.e., the eastern, central and western regions) in this paper. Specifically, the eastern region enjoys more developed industrial sectors and higher industrial GDP than that in the other two regions. In addition, in the central region, there are also many cities (e.g., Wuhan and Taiyuan) known for their history of hundreds of years of industrial development. However, they have preferred to develop traditional industrial sectors that consume an astonishing amount of resources and produce large pollutant emissions, and environmental pollution incidents have been frequent in recent years. The western region suffers the least developed industrial development, which may due to the weak industrial base and imperfect transport facilities (Xie and Wang, 2015). We exclude Tibet and Taiwan in our sample due to incomplete data.

carbon emissions and a series of policies on prevention and control of industrial pollution (Zheng and Shi, 2017). According to the “Pollution Haven Hypothesis” (Levinson and Taylor, 2008), strict environmental regulation will aggravate the cost of enterprises and is not conductive to the promotion of competitiveness. The year 2010 was a remarkable year in China's progress in environment protection. In that year, a total of 85 environment policies and regulations were issued by the Ministry of Environmental Protection in China and the State Council and other departments issued 78 environmental policies and regulations in total.1 Most of them were aimed at the intensive use of industrial land, and industrial transformation and upgrading began to take effect. Several industrial enterprises had to spend much money to address industrial pollutants and update production equipment. Some enterprises were even forced to relocate due to their failure to meet environmental requirements. Industrial profits were greatly influenced by these factors. As a result, industrial profits declined and TGUEIL decreased. Fortunately, the TGUEIL substantially increased after 2012. This outcome is consistent with the conclusions of Zhang and Choi (2013a), who stated that compulsory policy from the government is beneficial in improving the resource use efficiency. According to the Porter Hypothesis (Porter and Linde, 1995), the increase in the price and cost of production factors resulting from strict environmental regulation can stimulate industrial technology innovation and would bring two kinds of innovation compensation: compensations for technological innovation and for product innovation. The reduced cost or increased profits that resulted from the above two compensations would be able to compensate for the increased costs that resulted from environmental regulation.

4. Empirical results 4.1. TGUEIL In this subsection, we present the results for TGUEIL computed by Eqs. (3) and (4). Fig. 3 shows the TGUEIL trends in China. As shown in Figs. 3 and 4, the average TGUEIL is only 0.489 for the whole of China and indicates that we can increase the TGUEIL by 51.1%. Therefore, the condition of green use of industrial land is not good in China and needs to be improved. In addition, TGUEIL exhibits an increasing trend over the entire study period. The value of the TGUEIL in 2006 was only 0.228 and rose to 0.753 in 2015. The TGUEIL improved in the study period except for a slight decrease in 2011, and the same thing occurred in the three regions. This may have been due to the implementation of the national plan to cut

Fig. 4. TGUEIL for China and its three regions.

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Table 2 NMPILGU and decompositions.

China East Central West

NMPILGU

EC

BPC

TGC

1.167 1.147 1.157 1.193

1.029 1.000 1.034 1.053

1.172 1.151 1.187 1.182

1.002 1.002 0.977 1.012

relatively extensive. Therefore, industrial layout and environmental protection are very important to raise the TGUEIL. 4.2. NMPILGU

Fig. 5. Trends in NMPILGU and its decomposition indices in China.

Moreover, in reality, the additional increased cost that resulted from strict environmental regulation can be offset by other means, for example, cost transfers to domestic consumers by raising prices. In this way, when the benefits produced in the above ways surpass the costs resulting from environmental regulation, industrial profits will be improved (Li et al., 2013), and, consequently, the TGUEIL is also improved. Regarding the three regions, the eastern region shows the highest TGUEIL, with an average value of 0.574, followed by the western region (0.486) and the central region (0.442). Fig. 3 shows that the TGUEIL of the western region was lower than that of the central region before 2010; however, the status has been reversed since 2010. Restricted by geographical conditions and ecological environments, the economic foundations in Western China are weak. The economic development of the western region has been at the expense of environmental resources for a long time (Liu and Li, 2013). With the profound changes in the domestic and international environments, the extensive development mode can no longer be continued. In recent years, the western region has accelerated the strategic adjustment of the economic structure by exerting comparative advantages. New and high technology industries, such as new materials, new energy and electronic information, are rapidly developing. The optimization and upgrading of industrial structure has achieved notable results. However, the structural efficiency in central China is low. There are many resource-intensive heavy industries with rough processing rather than deep processing in the central region, for example, nonferrous metals and machinery industry. With low resource utilization and serious environmental pollution, the economic growth pattern is

As shown in Fig. 5, for China as a whole, the NMPILGU values are greater than 1 in most years; this outcome suggests that NMPILGU shows an increasing trend in most years. The average value of NMPILGU is 1.167, which means that the TGUEIL is increasing by 16.7 percent annually. The value of NMPILGU was only 0.958 during the 2010e2011 period, which indicates that the TGUEIL declined by 4.2 percent, perhaps due to the reduced industrial profits that resulted from a series of environmental regulations during 2010e2011. In addition, the BPC exhibits a relatively high average value of 1.172, followed by EC, with the value of 1.029, and TGC displays a lower value of 1.002. In addition, Fig. 5 shows that the value of BPC was greater than EC for most of the years in the study period, especially after the 2010e2011 period; this result suggests that technological progress was the major promoter of the NMPILGU's growth. In addition, the TGC values were greater than 1 during the periods of 2006e2007, 2010e2011 and 2013e2014; this outcome means that the regional technology frontier of industrial production and the global frontier of industrial production narrowed in the three periods. For the three regions, the annual average values of the NMPILGU in the eastern, central, and western regions are larger than 1, with values of 1.147, 1.157, and 1.193, respectively (Table 2). This indicates that the TGUEILs in the three regions show increasing trends in the study period. Moreover, the central and western regions enjoy higher NMPILGUs than those of the eastern region. According to the result in section 4.1, the TGUEIL varies greatly in the three regions. This result indicates that the huge gaps in the performance of green use of industrial land show a narrowing trend. In other words, the gaps of the TGUEIL among regions are narrowing, which is conducive to achieve balanced industrial economic development in China. To comprehensively promote the regional coordinated development, following regional policies, such as “China Western Development”, “Central Rise Policy”, and “Northeast Area

Table 1 Descriptive statistics. Category

Variable

Unit

Min.

Max.

Mean

St. dev.

Input

Industrial land Industrial capital Industrial energy Industrial labor Industrial GDP Industrial CO2 Industrial SO2 Industrial wastewater Labor structure Energy structure Energy intensity Internal expenditure of R&D activities Number of effective inventions R&D staff in industrial enterprises Fiscal gap of the local government

km2 108yuan RMB 104 TCE 104 person 108 yuan RMB 104ton 104ton 108 ton % % 108 yuan/104 TCE 108 yuan RMB 104 104person 108 yuan RMB

7.02 84.81 158.08 4.71 119.68 1006.61 498.00 0.01 9.35 2.38 0.12 1.83 7.00 0.00 6.56

1364.34 21974.35 13542.42 1159.40 27426.26 155590.10 287191.00 167.48 163.21 77.85 3.94 1520.55 177047.00 57.12 2355.40

304.67 4710.12 3387.44 166.27 5436.54 42684.68 69988.53 56.76 36.94 17.63 0.92 206.35 7426.02 8.69 571.61

258.34 4339.89 2581.76 155.81 5348.53 30982.14 62216.32 37.26 15.19 14.29 0.60 274.50 17371.45 10.72

Desirable output Undesirable output

Influencing factor

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Revitalization Plan”, the central government has put forward three national development strategies for regions in recent years: “Coordinated Development for the Beijing-Tianjin-Hebei region”, “Yangtze River Economic Belt”, and “One Belt and One Road”. It is emphasized that we should pay more attention to realize green, high-quality and low-carbon development while achieving economic aggregate balance. In particular, in the process of accelerating economic development, a green and low-carbon circular development industry system, which is conducive to the protection of natural ecology, should be formed in the vast central and western regions (Du, 2017). In addition, the EC, BPC and TGC values in the three regions are greater than 1, except that the TGC value in the central region is only 0.977. This means that the efficiency progress and technological progress associated with industrial land green use in the three regions show rising trends. In addition, the eastern and western regions have managed to improve the industrial production technology to make their industrial production technology frontiers move toward the global industrial production technology frontier. However, the central region needs to work hard to improve the technology of industrial production. The central region should intensify scientific and technological innovation and develop new and high-technology industries. The investment in new and high technology should be encouraged to promote the formation of emerging industrial clusters, make full use of resource potential to update technologies and product structures and extend the resource mining and processing industry chain to increase product added-value. Note that the values of the BPC in the three regions are higher than the EC values; therefore, technological progress is the major promoter of the NMPILGU. The EC value in the western region is 1.053, following by the central region (1.034) and the eastern region (1.001). Similarly, the BPC value in the central region is 1.187, followed by the western (1.182) and eastern regions (1.151). This result shows that the regional gaps in the production efficiency and production technology are narrowing and indicates that the regional coordinated development strategy has taken effect. As shown in Table 3, among the provinces, Shaanxi exhibits the best NMPILGU (1.352), followed by Guangxi, Inner Mongolia and Xinjiang, with NMPILGU values of 1.231, 1.222 and 1.119, respectively. Heilongjiang suffers the lowest NMPILGU value, 1.084. This result means that the TGUEIL of each province shows a rising trend in the study period. Regarding EC, Jilin enjoys the highest EC value of 1.113, following by Shaanxi, Hunan and Inner Mongolia, with EC values of 1.107, 1.101 and 1.096, respectively. In addition, there are six provinces (Yunnan, Hebei, Shanghai, Henan, Shandong and Heilongjiang) that have EC values less than 1. This outcome means that the six provinces have experienced efficiency deterioration in industrial production. Three of them are in the eastern region (Hebei, Shandong and Shanghai) and two are in the central region (Henan and Heilongjiang); all of them are famous for their rapid industrial economy development. Yunnan province, which is in the western region of China, has achieved rapid development of the tourism industry with high economic output and relatively fewer industrial pollutants. In the process of industrial transformation and upgrading, several backward production enterprises with high energy consumption and high material consumption have closed. In addition to the direct negative influence on industrial GDP, a series of external costs that resulted from the reemployment for workers and debt disposal has affected the improvement of enterprise economic profit. Additionally, expenses in updating equipment and energy-saving reconstruction reduce enterprise profits; this outcome affects the improvement of enterprise efficiency in the short term. In contrast, Henan enjoys the highest BPC value of 1.269, followed by Heilongjiang, Xinjiang and Yunnan,

with values of 1.268, 1.252 and 1.25, respectively. This means that Henan, Heilongjiang, Xinjiang and Yunnan have achieved the fastest progress in industrial production technology, which indicates that the above provinces have performed well in introducing advanced technology, encouraging enterprise innovation and improving talent qualities. Lastly, Ningxia enjoys the highest TGC value of 1.11, followed by Yunnan, Xinjiang and Qinghai, with values of 1.06, 1.044 and 1.031, respectively. This means that the four provinces are the quickest to move toward the global production technology frontier2 among the 30 provinces in China. There are 13 provinces (Jiangsu, Shandong, Gansu, Jiangxi, Shanxi, Guizhou, Guangxi, Sichuan, Hubei, Anhui, Jilin, Heilongjiang and Chongqing) that have TGC values of less than 1. These 13 provinces are thus relatively the slowest in moving toward global production technology. Fig. 5 shows that the overall industrial production technologies are relatively low in the cities with relatively low BPC value; there is abundant room for technological improvement and fast industrial technology progress in these cities, which will enable them to quickly move to the global production technology frontier. Therefore, the technology gap between the group and the whole sample is narrowed, which results in relatively high TGC values. In contrast, the cities with relatively high BPC value have a certain industrial technology foundation. They are not far away from the global production technology frontier and their room for improvement is thus limited, which results in relatively low TGC values. Among them, the typical cities are Heilongjiang and Chongqin. Yunnan and Qinghai enjoy both high BPC and TGC values, indicating that industrial transformation and upgrading in the above two provinces have achieved initial results. To reach the global production technology frontier, other provinces should increase investment in R&D on green and high-efficiency industrial production technologies, increase industrial labor quality, encourage enterprise technology innovation and promote scientific achievement transformation.

Table 3 The NMPILGU and its decomposition indices for each province. Province

NMPILGU

EC

BPC

TGC

Beijing(E) Tianjin(E) Hebei(E) Shanxi(C) Inner Mongolia(W) Liaoning(E) Jilin(C) Heilongjiang(C) Shanghai(E) Jiangsu(E) Zhejiang(E) Anhui(C) Fujian(E) Jiangxi(C) Shandong(E) Henan(C) Hubei(C) Hunan(C) Guangdong(E) Guangxi Hainan(E) Chongqing(W) Sichuan(W) Guizhou(W) Yunnan(W) Shaanxi(W) Gansu(W) Qinghai(W) Ningxia(W) Xinjiang(W)

1.193 1.155 1.152 1.163 1.222 1.201 1.179 1.084 1.117 1.119 1.133 1.156 1.125 1.132 1.095 1.178 1.150 1.215 1.144 1.231 1.189 1.162 1.189 1.127 1.127 1.352 1.118 1.201 1.171 1.219

1.000 1.031 0.984 1.031 1.096 1.042 1.113 0.922 0.982 1.000 1.002 1.050 1.000 1.038 0.960 0.981 1.035 1.101 1.000 1.076 1.000 1.061 1.063 1.021 0.992 1.107 1.011 1.074 1.085 1.000

1.193 1.124 1.181 1.151 1.119 1.131 1.104 1.268 1.176 1.120 1.134 1.141 1.120 1.234 1.145 1.269 1.165 1.160 1.144 1.187 1.189 1.233 1.183 1.203 1.250 1.189 1.113 1.207 1.068 1.252

1.000 1.000 1.002 0.982 1.012 1.013 0.961 0.932 1.000 0.998 1.000 0.966 1.014 0.991 0.997 1.006 0.968 1.011 1.000 0.980 1.000 0.929 0.971 0.980 1.060 1.013 0.996 1.031 1.110 1.044

H. Xie et al. / Journal of Cleaner Production 207 (2019) 1047e1058 Table 4 Results of the panel unit root tests. Variables

ln TGUEIL lnEX lnINV lnRD lnEI lnES lnLS lnFG

(1)

(2)

LLC

IPS

ADF-Fisher

PP-Fisher

Conclusion

18.478*** 26.108*** 8.724*** 13.047*** 15.255*** 10.765*** 5.688*** 9.132***

11.913*** 18.985*** 4.413*** 6.660*** 9.151*** 6.472*** 4.864*** 5.567***

10.682*** 14.648*** 5.269*** 7.391*** 9.293*** 7.073*** 4.164*** 5.927***

12.437*** 17.078*** 6.593*** 9.043*** 10.300*** 8.733*** 5.148*** 8.289***

Stationary Stationary Stationary Stationary Stationary Stationary Stationary Stationary

Note: *** denotes p < 0.001, ** denotes p < 0.05, * denotes p < 0.01.

4.3. Analysis of influencing factors Based on Eq. (8) and associated data, we select some representative indicators from the aspects of economic and social development, industrial scientific research activities and industrial structure to explore how they affect the TGUEIL. First, we apply some widely used approaches to test the stationarity of the data. Table 4 reports the estimated results, which indicate that the variables are stationary in the level form. Additionally, VIF value is widely adopted to test whether multi-collinearity exists among the dependent variables or not. If the VIF value is smaller than 10, it is considered that multi-collinearity is not existed among the dependent variables. In this study, the VIF values for variables are smaller than 3, indicating that multi-collinearity is not existed among the dependent variables. The regression analysis is based on an econometric model that imposes both individual-fixed effects and time-fixed effects models. Therefore, the regression could capture both the temporal and spatial heterogeneities of the TGUEIL and its influencing factors. Table 5 reports the estimation results. Table 5 shows that in the four models, the adjusted R-squared values are 0.7697, 0.8017, 0.8678 and 0.731, indicating that the results sufficiently explain the four models. The coefficient of the EX is significantly positive in all four models; this outcome means that an increase by 1% in the EX can lead to a 0.0093% increase in the TGUEIL for China; this indicates that it is essential to increase investment in scientific research. Similarly, INV is significantly positive in the four models. The coefficient of INV for China is 0.0273, which indicates that an increase by 1% in the INV can lead to a 0.0273% increase in the TGUEIL for China. Therefore, the government should further improve the innovation support policies and promote enterprise innovation efficiency. The coefficient of RD is significantly positive, with a value of 0.1416, which is greater than that of INV and EX. This may be due to the low transforming rate of

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scientific and technological achievements. Therefore, effective measures should be taken to promote the transformation of industrial scientific and technological achievements. Additionally, the construction of talent systems should be strengthened to improve the quality of human capital. Notably, the coefficients of the three regions are 0.0078, 0.006 and 0.0157 in terms of EX; the coefficients of the three regions are 0.0202, 0.0286 and 0.0347 in terms of INV; and the coefficients of the three regions are 0.2269, 0.1412 and 0.3198 in terms of RD. The coefficients in the western regions are the greatest of all the three indicators that represent R&D activities; this outcome indicates that increasing R&D activities are more effective in western regions than those in the other two regions. Therefore, in the implementation of the “China Western Development” strategy, scientific and technological support should be enhanced. The coefficient of EI is significantly negative in China and its three regions and the coefficient of ES shows the opposite results. The results indicate that decreases in EI and increases in ES can raise the TGUEIL. This is consistent with the study conclusions of Xie et al. (2017), who concluded that improving energy structure in China is very promising as non-fossil energy has more potential to reduce carbon emission comparing to fossil energy. Therefore, on one hand, we should actively promote the application of new energy-saving technologies and improve the energy utilization efficiency of various industries, and, on the other hand, we should improve the production and consumption structure of energy by promoting clean production technology and clean energy (e.g. nonfossil energy or renewable energy). The coefficient of LS is significantly negative in the second and third columns, indicating that an increase in industrial labor employment would lead to a decrease in the TGUEIL for China as well as the eastern region. In contrast, the coefficient is not significant in the other two regions. With the industrial transformation and upgrading in the eastern region, the traditional labor-intensive industries are gradually being replaced by the newly developing technology-intensive and capital-intensive industries. The demand for high-end labor force is increasing, and it is difficult for the original low-end labor force to adapt to the upgrading of the industrial structure, which results in redundant industrial labor in the eastern region. As eastern industries transfer to the central and western regions, effective measures should be taken to guide orderly industrial labor force transfer from eastern to central and western regions. The barriers that hinder the crossindustry and cross-region transfer of the labor force should be eliminated to ease industrial labor redundancy in the eastern region. The coefficient of FG is only significantly negative in the western region, with a value of 0.0192. This means that a 1 percent increase

Table 5 Regression results. Variable

China

East

Central

West

EX INV RD EI ES LS FG C Adjusted-R2 F-statistic Prob.

0.0093*** (2.2164) 0.0273** (2.1090) 0.1416*** (2.9808) 0.0559** (2.2579) 0.0025*** (2.9956) 0.0018** (2.1783) 0.0209 (1.5232) 0.2179 (1.1529) 0.7697 18.3825 0.0000

0.0078*** (4.0729) 0.0202** (2.0075) 0.2269*** (3.3127) 0.1006** (2.3585) 0.0026* (1.8382) 0.0039** (2.1228) 0.0179 (0.6076) 1.0170*** (3.5083) 0.8017 12.2784 0.0000

0.0060* (1.8594) 0.0286** (2.3621) 0.1412* (1.8169) 0.0521*** (3.0525) 0.0028** (2.2043) 0.0002 (0.0865) 0.0843 (1.4104) 0.3163 (0.6404) 0.8678 15.0392 0.0000

0.0157*** (3.3164) 0.0347* (1.7668) 0.3198*** (3.4404) 0.1888*** (3.0561) 0.0001** (2.0552) 0.0008 (0.6310) 0.0192* (1.9426) 0.4659 (1.1496) 0.7310 8.2511 0.0000

Note: *** denotes p < 0.001, ** denotes p < 0.05, * denotes p < 0.01.

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in the fiscal gap of the local government would cause a 0.0192 percent decrease in the TGUEIL. At the national level, the coefficient is 0.0209. After the reform of the tax distribution system, expanding industrial land scale has become an important way for local governments to increase financial revenue to cope with the gap between fiscal revenue and expenditure and the fierce competition among regions (Xie et al., 2018b). The booming increase of industrial land leads to extensive land use and unilateral pursuit of industrial GDP, which results in serious industrial pollution. Both have negative effects on the promotion of industrial land green-use efficiency. Therefore, it is unwise for the local government officials to rely on selling land to increase revenues. In summary, the coefficients vary greatly among regions. The eastern region should pay more attention to the issue of labor surplus in the industrial sectors. The western region should not rely on land financial revenue to make up fiscal gaps. All three regions need to promote clean energy consumption and invest more in industrial R&D activities and they should reduce energy consumption intensity by improving industrial production technology and upgrading production equipment. 5. Conclusions and recommendations Industrial land use is a major driver of the rapid growth in industrial pollutants. Therefore, achieving green use of industrial land is beneficial to achieve “green” economic development. In this paper, we estimate the TGUEIL and its dynamic changes using the GNDDF and non-radial Malmquist index approaches at the national and regional levels in China, and we further estimate the impacts of the influencing indicators (from the economic, social, R&D and other aspects) on the TGUEIL. The results are as follows. (1) The TGUEIL has a rising trend in the study period except for the year 2011. The eastern region enjoys the highest TGUEIL, and the western region has surpassed the eastern region in the TGUEIL since 2010. (2) The NMPILGU shows values greater than 1 in most years. The EC and BPC values are greater than 1 for all three regions. The BPC is greater than EC for China and its three regions. Therefore, the technological progress of the industrial sector is the major promoter of the NMPILGU. The TGCs of the eastern and western regions are greater than 1, and the TGC of the central region is less than 1. Therefore, the central region should pay more attention to improve the technological progress of the industrial sector to reach the industrial production technology frontier. Among provinces, each province shows a rising trend in NMPILGU in the study period. Jilin, Henan and Ningxia are the best performers in EC, BPC and TGC respectively. (3) The regression model results show that the coefficients of influencing factors vary greatly among regions. Internal expenditure of R&D activities, the number of effective inventions, R&D staffs in industrial enterprises and energy structures are significantly positive with the TGUEIL. R&D activities are more effective in western regions than those in the other two regions. Energy intensity is significantly negative with TGUEIL, and the use of clean energy is conductive to improve TGUEIL. LS is significantly negative with TGUEIL in China as a whole and in the eastern region, which indicates that labor redundancy is serious in the eastern region. FG is significantly negative with TGUEIL in the western region, and it is thus unwise to rely on land fiscal revenue to remedy the financial deficit. Based on the empirical results, we make the following policy

suggestions. (1) Train more qualified workers for the industrial sector and guide orderly industrial labor force transfer to central and western regions. The problem of labor surplus poses a threat not only to social stability but also to technological progress in the industrial sector. In the future, industrial development, there will be an extreme demand for workers with higher skills. Therefore, it is imperative to deepen the reform of the educational system by supporting professional education institutions and expanding the enrollment scale of vocational schools to provide more high-technology workers to meet the requirements of industrial development. In the process of industrial transfer, we should eliminate the barriers that hinder the cross-industry and cross-region transfer of the labor force and create favorable conditions for labor force transfer. (2) Prohibit local governments from selling land to cover deficits. The central government needs to enact policies to prohibit local government officials from lowering the standard of entry for industrial enterprises to increase land sales revenue. The central government should establish independent regulators in various regions to supervise the behavior of local government officials. In addition, the central government needs to regulate the land transfer market. According to Xie et al. (2016), the actual industrial land prices in most cities were significantly lower than the shadow prices. Therefore, the pricing power of industrial land should be transferred from local governments to land markets by giving full play to the role of market mechanisms. Finally, the local people's congresses and the public should supervise the total amount and usage of land transaction fees. The central government can encourage the public to give positive feedback on the behavior of local government officials. (3) Enhance technology innovation in the industrial sector. The empirical results have confirmed the significantly positive effect of R&D activities on the NMPILGU. Therefore, it is necessary to upgrade traditional industries by increasing the investment in science and technology innovation and to give priority to new industrial enterprises with low energy consumption, zero pollution and high efficiency. The government should establish a cooperative system for innovating industrial production technology that includes enterprises, research institutions and universities. An effective partnership among them can lead them to joint development of new technologies and improve the market competitiveness of industrial sectors. In addition, it is necessary to establish a fair and effective reward and punishment mechanism to encourage enterprises, scientific research institutions and other institutions to actively focus on the development of new technologies. Finally, government should create favorable conditions to encourage the transformation of scientific achievements (Xie et al., 2018a). (4) Optimize the energy consumption structure and improve energy use efficiency. Coal accounted for 64% of the energy consumption of industrial energy in China in 2015. Coal consumption in China rose rapidly, which is different from the decreased coal consumption in European and American countries. The share of China's coal consumption in global coal consumption rose from 30% in 2000 to 51% in 2015; this outcome accounts for almost half of global coal

H. Xie et al. / Journal of Cleaner Production 207 (2019) 1047e1058

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