Effects of environmental regulation on capacity utilization: Evidence from energy enterprises in China

Ecological Indicators 113 (2020) 106217

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Effects of environmental regulation on capacity utilization: Evidence from energy enterprises in China

T

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Weijian Du, Faming Wang, Mengjie Li

Synergy Innovation Center for Energy Economics of Shandong, College of Economics, Shandong Technology and Business University, Yantai, Shandong 264005, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords: Environmental regulation Energy enterprises Capacity utilization Excess capacity management

Overcapacity seriously hinders the sustainable development of China's economy. However, although there is widespread concern about policy regarding this issue, the discussion of the causes of overcapacity and of the methods used for its management in academic research remains theoretical, and evidence from empirical data is lacking. Therefore, using data on energy enterprises, we measure enterprise-level capacity utilization with the improved production function method and empirically study the effects of environmental regulation on energy enterprises’ capacity utilization using a Tobit regression model. We conclude that environmental regulation is conducive to improving the utilization ratio of energy enterprises and managing overcapacity in the energy sector. Moreover, the effect of environmental regulation is influenced by the regional institutional environment. With the increases in decentralization and bureaucratic corruption, the influence of environmental regulation on the overcapacity of energy enterprises has weakened. In addition, the medium effect model reveals that environmental regulation can be used to manage overcapacity because it increases production costs and stimulates R&D innovation, and the path of stimulating innovation is dominant in improving the capacity utilization of energy enterprises during the sample period. Finally, environmental regulation can reduce the gap between the enterprises' capacity utilization ratios in the energy industry, making the distribution of the capacity utilization rate of energy enterprises uniform and improving the efficiency of resource allocation in the energy industry.

1. Introduction Since China’s reform and opening up, the country’s economy has grown rapidly. However, the extensive development mode has not changed fundamentally, and the problem of environmental pollution has become increasingly severe (Dong et al., 2019; Feng et al., 2019). Because the environment is non-competitive and non-exclusive with regard to public resources (Chang et al., 2019), it is easily over-consumed, and enterprises do not count environmental costs in their production costs, resulting in both the blind expansion of the production scale and overcapacity. This phenomenon is particularly prominent in China’s energy sector (Du et al., 2020). With the cyclical fluctuation of the economy, overcapacity often occurs. Generally, when a country's domestic supply exceeds effective demand, overcapacity will occur. Moderate excess capacity may not hinder a country's economic growth. However, the excess capacity caused by government intervention will destroy the effective operation of the market mechanism and affect the sustainable development of the economy (Du and Li, 2019b). The phenomenon of overcapacity in China's economic operations is manifested not only as “cyclical

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overcapacity” in the economic crisis but also as “non-cyclical overcapacity” caused by government intervention in the normal operation of the economy (Shen and Chen, 2017). This phenomenon will seriously restrict the structural adjustment of the industry and the optimization of resource allocation among enterprises. Due to the non-competitive and non-exclusive nature of public resources, the ecological environment is easy to overconsume. Enterprises do not include the environmental cost in the production cost, which is an important reason for the blind expansion of the production scale and overcapacity. Environmental regulation aims to impose environmental constraints on enterprises, correct the prices of environmental factors, incorporate environmental costs into the production costs of enterprises, and let the market mechanism play a role to force enterprises to make appropriate production adjustments and resolve overcapacity. We then ask the following questions. Can environmental regulation promote the governance of excess capacity in China's energy industry to a certain extent? Will the governance effect of environmental regulation on excess capacity be affected by the regional institutional environment? In addition, what mechanisms of environmental regulation promote excess capacity governance in China? All these questions will be answered in

Corresponding author. E-mail address: [email protected] (M. Li).

https://doi.org/10.1016/j.ecolind.2020.106217 Received 18 July 2019; Received in revised form 1 February 2020; Accepted 12 February 2020 1470-160X/ © 2020 Elsevier Ltd. All rights reserved.

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Fig. 1. Literature review thread.

the production decision-making of enterprises, but in reality, it is difficult for researchers to know the actual production decision-making of enterprises. The core assumption of the definition and measurement of the capacity utilization index proposed in this study lies in the relationship between capital depreciation and capacity utilization and does not depend on the optimization objective or cost function of the enterprise, which avoids the problems contained in the above two methods to a certain extent. The remainder of this paper is structured as follows. The second section reviews the main literature about this study. The third section concerns the data. The fourth section addresses the setting of the measurement model. We construct an empirical analysis model of the effects on capacity management. The fifth section presents the results of the benchmark analysis, and the influence channels of ERI on overcapacity management are further examined. The sixth section examines environmental regulation and the dispersion of capacity utilization. We study the effects of ERI on the distribution of capacity utilization in the energy industry. The final section provides the conclusions and policy implications.

this study. This study systematically investigates the relationship between environmental regulation intensity (ERI) and overcapacity management using micro-data, and the micro mechanism and influence of the institutional environment of ERI on overcapacity management is revealed by considering the capacity utilization of energy enterprises, which has strong theoretical and practical significance. Compared with the existing literature, the contribution of this paper is reflected mainly in three aspects. First, this study takes environmental regulation as a constraint and analyzes the prevention of and solution to China's overcapacity, which expands the research on overcapacity governance. The existing literature explores mainly the governance of overcapacity from the perspective of industrial policies (Yuan et al., 2016). Although some studies discuss the relationship between ERI and excess capacity, it often receives indirect references, and few studies have directly examined the relationship between ERI and overcapacity (Zeng et al., 2017). Promoting the governance of overcapacity by means of environmental regulation can help restrict the continuous generation of overcapacity when stimulating growth goals and investment. Therefore, this study provides new ideas regarding the governance of overcapacity. Second, we decompose the effects of ERI on overcapacity management in the energy industry at the micro level, which expands the micro-perspective of research about environmental protection and capacity governance. The literature on the relationship between environmental protection and capacity governance focuses mainly on the regional, industrial or national levels (Jiang et al., 2017; Li et al., 2017; Tang et al., 2017). Existing research at the enterprise level is related only to measuring the productivity utilization of enterprises and neglects discussing the effects of ERI on the capacity utilization of enterprises and the impact mechanism. This study uses energy enterprises as the direct object, making this paper more intuitive and the implications for policy clearer, and this study reaches new conclusions about overcapacity in the energy industry that have not appeared in the existing literature. Third, this study constructs environmental regulation indicators based on the comprehensive index method and measures the capacity utilization index of energy enterprises based on the improved production function method, which increases the representativeness and accuracy of the core indicators. At present, the measurement of environmental regulation mainly adopts single indicators such as pollutant emission intensity (Cole and Elliott, 2003), pollution charges (Levinson, 1996; Shapiro and Walker, 2018), and environmental acts (Greenstone and Hanna, 2014). However, considering that environmental regulation involves both policy tools and policy implementation, it is difficult to measure it completely with a single indicator. This study uses the comprehensive index method to construct a comprehensive evaluation system to measure environmental regulation intensity in China. In addition, data envelopment analysis (Dong et al., 2015; Fare et al., 1989) and production function analysis (Berndt and Morrison, 1981; Han et al., 2011) are the main methods to measure capacity utilization based on micro data. However, the premise of using data envelopment analysis is that there is little difference in efficiency among enterprises and there may be a large estimation deviation for Chinese energy enterprises with large differences in efficiency. The traditional production function analysis requires strict assumptions on

2. Literature review The research on overcapacity actually answers three questions: what is it, why, and how can it be addressed? It constitutes the three main aspects of overcapacity research: the definition and measurement, causes and governance of overcapacity. Thus, this study will review the relevant literature according to Fig. 1. Overcapacity was first proposed by Copeland (1934) based on the definition of complete capacity. Subsequently, many scholars have discussed and extended the concept. Kamien and Schwartz (1972) defined overcapacity at a theoretical level as a situation in which the utilization of production equipment in a monopoly or incomplete competition is lower than average utilization in the case of the minimum average cost. At present, scholars’ definitions of overcapacity can be divided into macro and micro levels. At the macro level, overcapacity refers to the phenomenon that social and economic activities do not reach the normal output level, resulting in idle production factors (Han et al., 2011). At the micro level, overcapacity refers to the production capacity formed when the actual output of an enterprise is lower than the production capacity to a certain extent (Du and Li, 2019a; Zhang et al., 2017). Foreign scholars mainly explain the origin of overcapacity by considering the micro enterprise competition strategy or the economic cycle and volatility (Mathis and Koscianski, 1997; Pirard and Irland, 2007). In contrast, Chinese scholars have analyzed China's excess capacity from the perspective of economic development and government intervention. The “development phase”, based on the dynamic development of the economy, indicates that developing countries are likely to reach a consensus on future industry because of their “post advantage” and to possess overcapacity (Lin et al., 2010). Because the government intervention perspective implies that the “local governments” in a region are competing for the dual incentives of financial benefits and political promotion, investment subsidies will eventually lead to the problem of overcapacity (Sun et al., 2017; Wang, 2014; Zhou, 2004). Some studies try to study overcapacity from the perspective of the 2

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state-owned industrial enterprises above the designated size (main business income is above 5 million yuan). This database is the largest microcosmic database that can currently be obtained in China, and the statistics include the basic situation and financial data of the enterprise. In this paper, the data on the characteristic variables of the energy enterprise are all derived from the database. In addition, based on Yu (2015) and the general accounting standards, this study eliminates the sample’s statistical problems in the industrial enterprise data.

environment. Among these, the literature on the cause of overcapacity considers that enterprises do not consider environmental costs in their production costs, which is an important reason for the blind expansion of scale and overcapacity (Jiang et al., 2017; Li et al., 2017; Tang et al., 2017). The defects of the environmental protection system and fuzzy environmental property rights cause local governments to relax the environmental requirements for enterprises. The overinvestment in production capacity caused by the externalization of internal costs by enterprises has led to excess capacity in some industries (Jiang et al., 2012). The overcapacity management in the west shows a certain regularity. When economic growth is marked by overcapacity, labor market adjustment (Greenidge et al., 2016) and institutional reform (Efendic et al., 2011) are adopted. Financial reform (Agnello et al., 2015) is adopted when a fluctuation in the cycle causes a backlog of capacity. In contrast, there are three main perspectives on overcapacity management in China. First, scholars advocate for the optimal allocation of production factors, circulation and reorganization (Zhang et al., 2017). Second, scholars advise optimizing investments, particularly for infrastructure construction (Wu, 2016). Third, scholars stress the comprehensive multi-structure adjustment of industry, consumption, growth momentum and income distribution (Jia and Sun, 2016). With the deepening of the discussion, research on this issue tends to be more detailed. Research on the relationship between environmental governance and the resolution of overcapacity has gradually attracted the attention of some scholars. The literature on overcapacity management from an environmental perspective is divided into two main categories. Based on neoclassical economic theory, some scholars explore the “compliance cost effect”. They believe that environmental policy may reduce the marginal income of capital, restrict the capital flow of industry, reduce the input of production factors in related industries, and ultimately decrease excess capacity (Berman and Bui, 2001; Jorgenson and Wilcoxen, 1990). Based on the “Porter hypothesis” (Porter and Linde, 1995), other scholars analyze the “innovation compensation effect”. They consider that a reasonable design of environmental policy may stimulate technological innovation, enhance product competitiveness and productivity utilization, and ultimately alleviate overcapacity (Leiter et al., 2011; Sun et al., 2017; Zhang et al., 2018). The aforementioned literature presents important implications for understanding the causes of overcapacity and the problem of capacity management from the perspective of environmental constraints. The above literature is of great significance for further studying the degree of influence and mechanism of environmental regulation on overcapacity from a micro perspective. However, the literature on the relationship between environmental regulation and excess capacity governance still has some limitations in terms of research ideas, research perspectives, and index construction, which need to be further deepened and expanded.

3.2. Capacity utilization index The accurate measurement of capacity utilization is key to understanding and solving the problem of overcapacity. Capacity utilization is the most direct and commonly used indicator to measure the degree of overcapacity. The capacity utilization is measured by the proportion of the actual output of enterprises or industries in the potential production capacity. The improvement of the capacity utilization of an enterprise or industry indicates the governance of excess capacity. Therefore, how to effectively measure capacity utilization is the core issue with overcapacity. The measurement of capacity utilization at the enterprise level is achieved mainly through data envelopment analysis (DEA) and the production function method. Fare et al. (1989) first applied the data envelopment method to measure capacity utilization. After building the production front, the production capacity of the enterprise or individual was measured by fixed capital. Dong et al. (2015) used this method to measure the capacity utilization of China's related industries and enterprises. Berndt and Morrison (1981) first used the production function method to measure capacity utilization. They estimated the potential optimal output (in theory) through the cost function hypothesis and defined the ratio of actual output to the potential optimal output as the capacity utilization ratio. Han et al. (2011) used this method to measure the capacity utilization rate of China's industrial sector. Based on the research method proposed in Sims (2016), this paper defines capacity utilization and then relaxes the basic hypothesis of the production function method to measure the capacity utilization of energy enterprises in China based on the hypothesis that there is a positive correlation between the rate of capital depreciation and the capacity utilization ratio. Next, we introduce the method used to estimate the capacity utilization of the energy sector2 at the enterprise level. Consistent with Ackerberg et al. (2015), we assume that the structured value-added form of the enterprise production function is Equation (1): β

Yit = min{αK itβ1 Lit 2 exp(ωit ), β3 Mit } exp(εit )

(1)

where Y denotes the total output value of the energy enterprise; K and L denote the capital input and labor input, respectively; M denotes investment in intermediate goods; ω denotes the total factor productivity of the energy enterprise; and ε is the random error term. According to Leontief’s first condition, the production function is transformed into a logarithmic form as in Equation (2), where the lowercase letters represent the corresponding logarithmic values.

3. Data 3.1. Data description

yit = α + β1 kit + β2 lit + ωit + εit

The relevant indicators of environmental regulation are derived from the China Environmental Yearbook and the China Environmental Statistics Yearbook, and the regional data and enterprise data are matched according to the code and year of the region. The enterpriselevel data used in the study are mainly from the China Industrial Enterprise Database of the National Bureau of Statistics from 2000 to 20071. They include all state-owned industrial enterprises and non-

(2)

Based on the analytical framework proposed in Greenwood et al. (1988), we make the following assumptions for capacity utilization: First, the capacity utilization ratio hit is the ratio of the actual capital invested Kit to the enterprise’s capital stock K it∗. Second, the utilization rate of capacity is hit , which is a monotonically increasing function of (footnote continued) the enterprises’ capacity utilization. 2 The energy sector includes China's four energy industries: coal mining and washing; oil and gas mining and petroleum processing; coking and nuclear fuel processing; and the production and supply of electricity, gas and water.

1 This sample interval is selected only for the years 2000–2007 because the data on investment, capital and wages in the Chinese industrial enterprise database after the year 2008 are missing, which leads to an inability to measure

3

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4. Econometric model

the enterprise’s capital depreciation rate δit ; that is, hit = g (δit ) . Third, the capital stock K it∗ of energy enterprises is determined by the utilization rate of the previous capacity hit - 1, the previous capital stock K it∗− 1 and the previous investment Iit − 1. Fourth, the intermediate input Mit is determined by the capital input kit , labor input lit and productivity ωit ; that is, mit = ft (kit , lit , ωit ) . Before determining the capacity utilization of an enterprise, we need to specify the form of g (δit ) . Considering the basic relationship between capacity utilization and the rate of capital depreciation3, we use the exponential function form proposed by Greenwood et al. (1988), as shown in Equation (3):

hit = g (δit ) =

1 ⎛ δit ⎞ ln η ⎝ δ¯ ⎠

4.1. Benchmark model To investigate the effects of ERI on capacity utilization, this paper constructs a benchmark model for the empirical analysis based on the energy utilization ratio of energy enterprises:

Hit = α 0 + α1 ERIit + α2 Xit + ηi + ηt + εit

where i represents the enterprise and t represents the year. Hit stands for capacity utilization and takes a value between 0 and 1; ERIit is the environmental regulation intensity. The method used to measure the capacity utilization was introduced in the second part of this article. Following Du and Li (2019a), this paper constructs a comprehensive evaluation system for environmental regulation that uses the comprehensive index method to measure the comprehensive performance of China's ERI. According to existing theory and empirical research (Feng and Yan, 2019; Zhang et al., 2016), the control variables include the following: ① The scale of the enterprise. The scale of the enterprise affects both its production efficiency and its position in the market, and it ultimately impacts the capacity utilization of the enterprise (Ruslan, 2016; Song and Zhao, 2015). In this paper, we use the number of employees in the enterprise to measure the scale of the enterprise. ② Financing constraints. Financing constraints may restrict the investment behavior of the enterprise and impede equipment renewal and product R&D in the enterprise, thereby affecting its capacity utilization (Manova, 2013). We use the ratio of the financial costs to total assets to measure financing constraints (Cai and Liu, 2009). ③ The age of the enterprise. Over time, the management experience of enterprises and their ability to address market risk are enhanced (Charoenrat and Harvie, 2014), business operations stabilize, and their production and investment decisions will eventually affect capacity utilization. The number of years of operation is calculated by the difference between the current year and the year in which the enterprise was established. ④ Enterprise ownership. The internal management structure used in different types of enterprises may have significant heterogeneity (Yi et al., 2017); however, it may also affect the ability of enterprises to obtain resources and policy support and may ultimately affect their capacity utilization (Wang et al., 2019). In addition, because some factors are not affected by economic indicators, to improve the accuracy of the estimation results, these factors fall under the error terms in the benchmark Equation (6), which includes three main aspects: control for the fixed effects of individual enterprises that do not change with the time factors; control for the time fixed effects that do not vary with individual factors; and control for the random error items.

(3)

where η is a positive coefficient and δ¯ is the upper limit of the depreciation rate. The capacity utilization rate calculated by this function form is in the range [0,1]. Combined with Equations (2) and (3), we obtain the output equation of the energy enterprises.

yit = α + β1 kit∗ +

(5)

β1 δ 1 δ ln ⎛ it ⎞ + β2 lit + ft−1 ⎛⎜kit∗ + ln ⎛ it ⎞, lit , mit ⎟⎞ + εit η ⎝ δ¯ ⎠ η δ¯ ⎠ ⎝ ⎝ ⎠ (4)

As shown in Eq. (4), we adopt the two-step estimation method, following Ackerberg et al. (2015). The first step is polynomial estimation using the nonparametric method. The second step combines the moment estimation conditions and the first-step estimation results, estimates the parameters and calculates the capacity utilization at the enterprise level by substituting Eq. (3). 3.3. Distribution characteristics of the capacity utilization of energy enterprises in China To evaluate the current overcapacity of energy enterprises, we analyze the distribution characteristics of the capacity utilization of China's energy enterprises in the sample interval based on the estimated capacity utilization. In terms of the overall trend, the average capacity utilization rate of energy enterprises is between 73% and 76% and shows a decreasing trend year by year. After distinguishing the types of enterprise ownership, we found that the capacity utilization rate of state-owned enterprises is the highest, followed by that of private enterprises, while the capacity utilization rate of foreign enterprises is the lowest. This conclusion is consistent with Lin et al. (1998) and Zheng et al. (2003), who hold that China's state-owned monopoly enterprises have the highest capacity utilization rate and that highly competitive foreign enterprises have the lowest capacity utilization rate, leading to the high capacity utilization of the state-owned enterprises. This phenomenon is more obvious in the energy industry, which has a high degree of monopoly. At the regional level, the capacity utilization of Western China is the highest in China, and the capacity utilization of Eastern China is the lowest. Over time, the capacity utilization of each region in China has increased, but that of the eastern region remains the lowest. According to the preliminary statistics, the capacity utilization of China's various regions has deviated from the economic development level; the capacity utilization of China's more developed areas is lower, and the capacity utilization of enterprises has obvious regional differences. Therefore, location factors should be considered when analyzing the capacity utilization problem (Fig. 2.).

4.2. Model of the influence mechanism To further verify the theoretical mechanism by which environmental regulation affects capacity utilization, we test the influence mechanism of environmental regulation, as it can affect overcapacity management, by extending the benchmark Equation (5) and introduce the cost and innovation factors and their interaction with ERI into the benchmark model:

Hit = α 0 + α1 ERIit + βMVit + γERIit ∗ MVit + α2 Xit + ηi + ηt + εit

(6)

where MVit represents the intermediary variables, including the following: ① Enterprise production costs. We calculate the production costs and their logarithmic function according to accounting standards. For a description of the specific method used, please refer to Liu and Wang (2016). ② Enterprise innovation. Many studies have shown that innovation and technological progress can solve the backward and polluting capacity and promote the sustainable development of enterprises to a certain extent (Sun et al., 2017; Wu et al., 2019).

3

Intuitively, the relationship between capacity utilization and the capital depreciation rate should hold: The rate of capital depreciation increases monotonically with capacity utilization; when the rate of capacity utilization is 0, the rate of capital depreciation is also 0; when the rate of capacity utilization is 1, the capital depreciation rate reaches its upper limit. 4

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Fig. 2. Capacity utilization of enterprises under different ownership types and locations.

the 1% level. To further verify this conclusion, control variables such as the scale of enterprises, financing constraints, the age of enterprises and enterprise ownership are introduced, and the results of models (2) and (3) show that the coefficient of ERI remains positive. The results indicate that environmental regulation policy is conducive to improving the capacity utilization of energy enterprises. The conclusions are consistent with the existing literature (Du and Li, 2019a; Li et al., 2017). They also found that moderate environmental regulation can increase capacity utilization and accelerate the exit of backward enterprises to achieve overcapacity governance. The results of the other control variables show that the investment trend and market influx of smaller enterprises are more likely to increase production capacity, which will inhibit the capacity efficiency of enterprises. To some extent, the financing constraints of enterprises will restrict investment in equipment and the technological innovation of energy enterprises, thereby restricting their capacity utilization. The coefficient of the age of enterprises is positive and significant, indicating that past management experience plays a certain role in promoting enterprise utilization capacity. In addition, the result is consistent with the abovementioned fact that state-owned enterprises have the highest productivity utilization rates, private enterprises rank second, and foreign enterprises have the lowest rates. The reason for this outcome is that some state-owned enterprises have significant monopolies, as a result of which the efficiency of the average capacity of state-owned enterprises is higher than that of private and foreign enterprises. Moreover, in view of the time lag between environmental regulation policy formulation and implementation, the effect of environmental regulation on capacity utilization may be revealed after a certain period of time; thus, we lag environmental regulation of one phase in the next regression, as shown in model (4). The regression results are consistent with those for the lag phase of ERI in terms of significance and value.

Consistent with Caldera (2010), we use the new product intensity of enterprises to measure enterprise innovation. In Eq. (6), we are most concerned about the saliency and symbol of the interaction coefficient. If the interaction coefficient between ERI and the intermediary variable is significant, this result shows that environmental regulation promotes the capacity utilization of energy enterprises by influencing production costs or innovation. However, if the interaction coefficient is significant and negative, environmental regulation can inhibit an increase in the capacity utilization of energy enterprises by affecting production costs or innovation. 5. Estimation results 5.1. Baseline regression This study used the Tobit model to estimate the basic regression. The Tobit model is an econometric model for the dependent variables of partial continuous distribution and partial discrete distribution. It is different from the discrete selection model and the general continuous variable selection model. Characteristically, the dependent variable is a limited variable. It studies mainly how the continuous variable changes under certain selection behaviors. Thus, this study uses the Tobit model to reduce the estimation deviation caused by the double-truncated characteristic of the capacity utilization variable, which takes a value between 0 and 1. The results are shown in Table 1. Model (1) regresses only capacity utilization and environmental regulation; the variable coefficient of ERI is 0.0082 and significant at Table 1 Baseline regression.

ERI

(1)

(2)

(3)

(4) Lag one phase

CU 0.0082*** (0.0012)

CU 0.0089*** (0.0012) −0.0070*** (0.0010) −0.0793*** (0.0131) 0.0001*** (0.0000)

CU 0.0081*** (0.0012) −0.0072*** (0.0010) −0.0780*** (0.0130) 0.0000*** (0.0000) 0.0032*** (0.0009) −0.0360*** (0.0055) 0.8087*** (0.0093) 0.0508 20,604

CU 0.0148*** (0.0025) −0.0061*** (0.0017) −0.0554*** (0.0190) 0.0001*** (0.0000) 0.0037*** (0.0005) −0.0569*** (0.0084) 0.7842*** (0.0129) 0.0868 7294

Scale Fc Age Cown Fown Constant Pseudo R2 Observation

0.7658*** (0.0071) 0.0163 20,692

0.8044*** (0.0092) 0.0397 20,604

5.2. The impact of the regional institutional environment The effective transmission and implementation of environmental regulation will directly affect the capacity utilization of energy enterprises, and the effectiveness of the implementation of a regional policy will inevitably be affected by the regional institutional environment. We take regional fiscal decentralization (Zhang, 2016) and local bureaucratic corruption (Liao et al., 2017) as measures of the regional institutional environment. These data are derived from the Chinese Statistical Yearbook and China Inspection Yearbook. Based on the benchmark regression, the interaction terms between ERI and fiscal decentralization and between ERI and local bureaucratic corruption are introduced. The empirical results are shown in Table 2. The results of Column (1) and (2) in Table 2 show that the coefficients of the interaction between ERI and fiscal decentralization are

Note: *, **, and *** denote significance at the levels of 10%, 5% and 1%, respectively. The standard errors appear in parentheses. 5

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Table 2 The impact of the regional institutional environment. Fiscal decentralization (1)CU

Table 3 Medium effect analytical model.

Local official corruption

(2) CU

(3)CU

ERI*FD

0.0115 (0.0029) −0.0014*** (0.0003)

***

0.0115 (0.0029) −0.0015** (0.0003)

ERI*LOC FD

−0.0027*** (0.0010)

Constant Controlled variable Pseudo R2 Observation

0.7728*** (0.0104) NO 0.0166 20,692

**

0.0056 (0.0022)

0.0060** (0.0022)

0.8339*** (0.0152) YES 0.0513 20,604

Capacity utilization (3) (4)

0.0946*** (0.0061)

0.0417*** (0.0017)

0.0065*** (0.0013) 0.0176*** (0.0016)

−0.0011*** (0.0001)

−0.0011*** (0.0001)

−0.0005** (0.0002) 0.7589*** (0.0094) NO 0.0129 20,692

−0.0006* (0.0003) 0.8008*** (0.0112) YES 0.0524 20,604

Production cost Innovation Controlled variable Constant

−0.0030*** (0.0011)

LOC

Innovation (2)

(4) CU ERI

ERI

***

Production cost (1)

Pseudo R2/R2 Observation

YES 3.2929*** (0.0363) 0.7450 20,583

YES 0.1374*** (0.0115) 0.3377 12,749

YES 0.8632*** (0.0105) 0.0807 20,583

0.0027*** (0.0007)

0.0155* (0.0087) YES 0.7945*** (0.0113) 0.0628 12,749

Note: The control variables are the same as those in Table 1.

that an increase in ERI will increase the production costs of enterprises. The results show that the regression coefficient of ERI in model (3) is 0.0065, which is significantly lower than that of the benchmark model, 0.0081. In addition, the coefficient of production costs is significant and positive. The results show that increasing production costs is an important mechanism by which environmental regulation can achieve energy industry overcapacity management. The regression coefficient of ERI in model (2) is positive and significant, which indicates that environmental regulation can improve enterprises’ R&D innovation. In model (4), the coefficient of ERI is 0.0027, which is lower than the estimated value of 0.0081 in the benchmark model, and the coefficient of the R&D innovation variable is positive. We conclude that stimulating R&D innovation is an important mechanism by which environmental regulation affects the excess capacity management of energy enterprises. From the medium effect model analysis, we conclude that ERI may improve the capacity utilization of energy enterprises by increasing the costs of enterprises and encouraging enterprises’ R&D innovation. The conclusions support the “compliance cost effect” (Berman and Bui, 2001; Jorgenson and Wilcoxen, 1990) and “innovation compensation effect” of environmental regulation (Leiter et al., 2011; Sun et al., 2017; Zhang et al., 2018), indicating that environmental regulation can increase production costs and encourage enterprises to increase their R&D investment to improve their capacity utilization. In addition, the comparisons of the estimation coefficient and the benchmark model coefficient of ERI in models (3) and (4) show that the coefficient of ERI in model (4) decreases to a greater extent than that in model (3). This result reveals that environmental regulation can enhance overcapacity management in the energy industry mainly by stimulating R&D innovation and then by improving the capacity utilization of energy enterprises.

Note: *, **, and *** denote significance at the levels of 10%, 5% and 1%, respectively. The standard errors appear in parentheses.

significantly negative. Thus, with an increase in fiscal decentralization, the influence of environmental regulation on the capacity utilization of energy enterprises is reduced. The conclusions are consistent with the existing literature (Jiang et al., 2019; You et al., 2019). A possible explanation for these conclusions is that local governments can obtain financial autonomy as a result of fiscal decentralization and may independently implement public policies that are beneficial to their own interests. Environmental regulation is a process that local governments can control; therefore, it is a tool to obtain competitive liquidity resources, which inhibits the effectiveness of implementing environmental regulation policy. The results in Column (3) and (4) of Table 2 show that the coefficients of the interaction between ERI and local bureaucratic corruption are significantly negative, indicating that the increase in local bureaucratic corruption may inhibit the effectiveness of environmental regulation on the capacity utilization of energy enterprises. The conclusions are consistent with the existing literature (Oliva, 2015; Sheng et al., 2019). A possible explanation for this result is that local governments are motivated by private interests, as a result of which a conspiracy between governments and enterprises can easily form and then induce corruption, leading to environmental speculation. 5.3. Analysis of the influence mechanism According to the benchmarking analysis, environmental regulation can achieve the dual goals of environmental protection and overcapacity management by improving the capacity utilization ratio of China's energy enterprises. Thus, we seek to determine which environmental regulation mechanism affects the management of excess capacity. This study will help us further understand the internal relationship between environmental regulation and capacity management. Based on the theoretical analysis, we use the Baron and Kenny (1986) medium effect model for analysis. In recent years, the mediation effect model has been widely used in the empirical study of impact mechanism analysis (Pan et al., 2019; Shi and Li, 2018). Through the mediation effect model, we can further investigate the path of environmental regulation to affect the capacity utilization of energy enterprises and explore the significance and dominance of these paths. This study takes the production cost variable and the R&D innovation variable of energy enterprises as the intermediary variables to verify the related influence mechanism. The regression results are shown in Table 3. Table 3 reports the test results of the effects of ERI on capacity utilization. The coefficient of ERI in model (1) is positive, which shows

6. Environmental regulation and the distribution of capacity In recent years, scholars have paid increasing attention to the intraindustry distribution features caused by dynamic changes in enterprises, including the cost mark-up rate distribution (Atkin et al., 2015; Mao and Xu, 2016) and the enterprise scale distribution (Gaffeo et al., 2003; Sun and Wang, 2014). Environmental regulation imposes new constraints on the production and management decisions of enterprises, leading to the adjustment of enterprises' behavior. However, the impact of environmental regulation on energy enterprises with different capacity utilizations is asymmetric, which affects the distribution of capacity utilization (Pang et al., 2019; Tang et al., 2017). In general, the dispersion of capacity utilization is closely related to the allocation efficiency of resources in the market (Lu and Yu, 2015). Opp et al. (2014) also show that low capacity dispersion can make the industry or enterprise achieve optimal resource allocation efficiency. Therefore, studying the impact of environmental regulation on the 6

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Regional capital intensity. This variable is measured by the ratio of the fixed assets of energy enterprises and the number of employees of the energy enterprises in the region, and the logarithmic process is adopted. In this paper, we use this index to control for the influence of the regional capital ratio on the dispersion of capacity utilization. ③ The scale of employment in the region. This variable is measured by the number of employees of the energy enterprises in the region to control for the impact of the overall scale of the energy industry on the dispersion of capacity utilization. ④ The degree of regional export density. This variable is measured by using the ratio of the export scale to the sales scale of energy enterprises in the region, and it controls for the influence of external market demand on the dispersion of the capacity utilization of the energy industry. The variable for the characteristics of the explained variables takes a value between 0 and 1; therefore, the panel Tobit model is used in the econometric regression analysis. Table 4 reports the estimated results of the effects of ERI on the dispersion of capacity utilization in the energy industry. Model (1) examines only the effects of ERI on the distribution of capacity utilization as measured by the Theil index and does not introduce the control variables. The results show that the coefficient of ERI is negative and significant at the 10% level, indicating that ERI reduces the dispersion of capacity utilization; that is, environmental regulation makes the distribution of energy enterprises more even. Models (2) and (3) report the regression results as measured by the Gini index and the coefficient of variation, respectively. The coefficient of environmental regulation remains significant and negative, indicating that environmental regulation significantly reduces the dispersion of capacity utilization in the energy industry. Models (4)–(6) further introduce the control variables at the regional level. The results show that the symbol and the significance of environmental regulation are unchanged. Once again, the results prove that environmental regulation can reduce the gaps in capacity utilization among the energy enterprises in a region.

dispersion of capacity utilization will not only contribute to the analysis of capacity utilization from the perspective of resource allocation efficiency but also have important practical significance for the improvement of environmental regulation. We have already deconstructed the problem of environmental regulation and overcapacity at the enterprise scale. Here, we shift the perspective of this research to examine the relationship between ERI and the distribution of capacity utilization in the energy industry and to further explore the efficiency of the resource allocation caused by environmental regulation. To measure the distribution of industry characteristics, the Gini index was widely used in the early literature. However, because it relies on a series of strong assumptions, such as the mean independence, sample size independence and symmetry, the Gini index measurement process can easily lead to errors (Cowell, 2011). Therefore, other entropy methods have been used to supplement and improve the Gini index, and the most widely used method is the Theil index. Following Lu and Yu (2015), we build the Theil index4 to measure the distribution of capacity utilization in the energy sector of various regions, as shown in Equation (7):

Theil jt =

1 njt

njt

∑ i=1

PHijt ⎞ PHijt log ⎛⎜ ¯ jt ¯ ⎟ PH ⎝ PHjt ⎠

(7)

where i represents the enterprise, j represents the region, and t represents the year. njt represents the number of energy enterprises in region j in period t ; PHijt indicates the capacity utilization of energy ¯ jt indicates the average capacity utilizaenterprise i in period t ; and PH tion of the energy industry in region j in period t . In addition, for stability, the Gini index and the coefficient of variation (Mao and Xu, 2016) are used as alternative measures of the distribution of industrial capacity utilization. The specific form of the coefficient of variation is shown in Eq. (8):

CVjt =

PHsdjt ¯ jt PH

(8)

7. Conclusions and policy implications

where PHsdjt represents the standard deviation of the capacity utilization of energy enterprises in region j . To investigate the effects that ERI has on the distribution of capacity utilization, we use the capacity utilization dispersion index of the energy sector in different regions as the explained variables; the empirical model is shown in Eq. (9):

H _disper jt = β0 + β1 ERI jt + β2 Xjt + ηj + ηt + εjt

Adopting environmental regulation as a policy to improve the capacity utilization of enterprises can help restrict the continuous generation of overcapacity by stimulating growth goals and investment. Some studies attempt to study the problem of overcapacity from the perspective of the environment and find that environmental regulation can achieve capacity governance to a certain extent (Jorgenson and Wilcoxen, 1990; Leiter et al., 2011). By matching data on environmental regulation and China's energy enterprises, this study explores the effects of environmental regulation on the overcapacity of China's energy industry against the backdrop of supply-side reform from capacity utilization. We find that environmental regulation is conducive to improving the capacity utilization ratio of energy enterprises. Based on the empirical evidence at the micro level in China, this study supports the effectiveness of environmental regulation as a means of overcapacity governance. In addition, with increases in fiscal decentralization and local bureaucratic corruption, the effect of environmental regulation on the governance of excess capacity in China's energy enterprises will be weakened. The effect analysis of environmental regulation includes mainly the “compliance cost effect” (Candau and Dienesch, 2017) and “innovation compensation effect” (Porter and Linde, 1995). Based on the above theory, this study further explores the significance and dominance of the impact mechanism of environmental regulation on capacity utilization. We find that environmental regulation improves the capacity utilization rate of energy enterprises by increasing costs and stimulating R&D innovation, and the path of stimulating innovation is dominant in the sample period. The conclusion of this study supports the existence of the “compliance cost effect” and “innovation compensation effect.” Finally, environmental regulations can not only improve the capacity utilization of enterprises but also enhance the allocation efficiency

(9)

where j represents the region, t represents the year, and H _disper jt represents the dispersion of capacity utilization based on the Theil index, the Gini index and the coefficient of variation. ERI jt represents environmental regulation intensity, which is the core explanatory variable. ηi and ηt represent individual fixed effects and time fixed effects, respectively. εit is the random error term. Xjt is the control variable for region j5. Following previous studies (Dong et al., 2015; Lu and Yu, 2015), the control variables are as follows: ① The degree of market concentration. This variable is measured by the Herfindahl-Hirschman Index (HHI), and this index is positively related to regional concentration; that is, the higher the HHI value is, the greater the concentration of regional energy enterprises. We use this index to control for the degree of competition in the energy market in different regions and its influence on the decentralization of capacity utilization. ② 4 The Theil index is proportional to the industrial dispersion of the capacity utilization ratio. The greater the Theil index is, the higher the industrial dispersion of the capacity utilization ratio; that is, the distribution of capacity utilization is more uneven in the energy industry. 5 Note that the measurement of the control variables at the regional level is calculated based on the sample of energy enterprises used in this study; thus, the area-level data include all the characteristics of the energy industry in each region. To a certain extent, this method avoids the estimation deviation caused by using the overall sample measure of each region.

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Table 4 Environmental regulation and capacity utilization dispersion in the energy industry.

ERI Controlled variable Constant Pseudo R2 Observation

(1) Theil

(2) Gini

(3) CV

(4) Theil

(5) Gini

(6) CV

−0.0421* (0.0242) NO 0.4809*** (0.1101) 0.1438 226

−0.0253* (0.0137) NO 0.3957*** (0.0628) 0.4512 226

−0.0649** (0.0256) NO 0.7700*** (0.1153) 0.1655 226

−0.0349** (0.0151) YES 0.4798*** (0.0291) 0.1824 225

−0.0210* (0.0121) YES 0.2785*** (0.0131) 0.5950 225

−0.0537** (0.0261) YES 0.4786*** (0.0327) 0.2162 225

process of the initial implementation of the environmental regulation, considering the rising cost of enterprises, the government should increase the investment in and subsidies for technology and provide more financial support to encourage enterprises to carry out technology R&D in order to strengthen the “innovation compensation effect” of environmental regulation on energy enterprises and effectively address the overcapacity in the energy industry. This study also has some potential limitations and prospects that need further investigation in future research. First, overcapacity is a grand and complex topic that is influenced by many factors such as the economic cycle, market competition and government intervention. This paper conducts a preliminary exploration from the perspective of environmental regulation and reaches some valuable conclusions. However, due to data limitations, analysis methods and other reasons, this paper does not examine the impact of the economic cycle, market competition and other factors on the relationship between environmental regulation and overcapacity in detail. With the improvement of data, the introduction of the economic cycle, market competition and other factors to further explore the policy effect of environmental regulation on excess capacity governance in different scenarios will be an important direction of future research. Second, this study analyzes mainly the static impact of environmental regulation on the capacity utilization of the remaining energy enterprises and does not involve the discussion of the impact of energy enterprises' entry and exit on capacity utilization, given the role of environmental regulation. Due to the neglect of the enterprise replacement effect caused by environmental regulation, the impact of environmental regulation on the excess capacity of the energy industry may be underestimated to some extent. Future research that studies the relationship between environmental regulation and capacity governance should introduce a dynamic perspective on enterprise entry and exit and further analyze how the dynamic evolution of enterprises affects the capacity utilization of incumbent enterprises and the distribution of capacity utilization under the role of environmental regulation. Third, differences in the degree of environmental regulation between different energy sectors may have different effects. In the present study, the energy industry is considered as a whole. The energy industry can identify the common characteristics among energy enterprises and improve the sample size. However, the differences between energy sub-industries cannot be ignored. Therefore, selecting a representative energy sub-industry to carry out research and improve the pertinence of research issues will be a further research direction of the present study.

of resources by affecting the distribution of capacity utilization in the industry (Opp et al., 2014; Pang et al., 2019). This study finds that environmental regulation can reduce the dispersion of capacity utilization, causing the distribution of the capacity utilization of energy enterprises to become more uniform and improving the efficiency of resource allocation in the energy industry. This study shed light on how the dual outcomes of environmental protection and capacity management can be achieved by considering supply-side reform. The main policy implications of this study are summarized as follows. First, government departments should improve the policy system of environmental regulation and strengthen the role of environmental constraints in overcapacity governance. Attention should be paid to the role of environmental constraints in the process of improving the capacity utilization of the energy industry. Government departments should appropriately improve the intensity of environmental regulation, use the increase in production costs to force China's energy enterprises to transform and upgrade the system, and promote the capacity utilization of the energy industry. Meanwhile, the enhancement of the intensity of environmental regulation must be controlled within the range that energy enterprises can bear, and a long-term mechanism of environmental regulation to resolve overcapacity needs to be established. If the cost of environmental regulation is too high, it may make some enterprises choose to evade the high cost of pollution by secretly discharging pollutants, which may lead to environmental quality deterioration and market disorder. In addition, for some energy enterprises with serious overcapacity, the government should formulate stricter environmental regulation standards to increase the market access threshold through environmental regulation policies, achieving overcapacity governance. Second, the central government should increase environmental supervision over local governments, strengthen the centralization of environmental management and eliminate the conditions that lead to corruption, providing a good institutional environment for the implementation of environmental policies and overcapacity governance. China's environmental management needs to further centralize its power, improve the vertical management system of environmental policy enforcement, reduce the discretionary space of local government enforcement, and expand the central government's expenditure scope in environmental protection affairs, so as to form a more matching pattern of financial power and administrative power in environmental management. In addition, anti-corruption measures are conducive to avoiding the collusion of local governments and polluting enterprises, limiting the incentive of private interests of local government officials, and reducing the space for rent-seeking. By optimizing the institutional environment, the policy effects of environmental regulation on overcapacity management can be strengthened. Finally, government departments should increase the investment in innovation among energy enterprises and encourage technological innovation. From the research in this paper, we can see that the role of technological innovation is indispensable in the process by which environmental regulation policies promote capacity utilization. China's energy industry is generally weak in technology development and independent innovation. High investment and pollution seriously hinder the transformation and upgrading of China's energy industry. In the

CRediT authorship contribution statement Weijian Du: Formal analysis, Writing - original draft. Faming Wang: Writing - review & editing, Funding acquisition. Mengjie Li: Conceptualization, Methodology, Software, Validation. Acknowledgements This research is sponsored by the National Natural Science Foundation of China (71803102, 71773028) and the Science Foundation of Ministry of Education of China (17YJC790027 & 8

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18YJC790086).

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