Law Reinforcement, Production Pattern and Enterprise Environmental Performance: Evidence from Environmental Courts in China

Abstract

Law reinforcement agencies can be established to improve enterprise environmental performance, a determinant of sustainable growth, but their micropractical evaluation is unclear. This paper uses panel data (1998–2014) from the Chinese Industrial Enterprise Pollution Database and the Chinese Industrial Enterprise Database and adopts the multiperiod difference-in-differences (DID) method to investigate the impact of law reinforcement on enterprise environmental performance (measured by air pollutant emissions). Using the establishment of China’s city-level environmental courts (ECs) as an identification strategy, the study finds a strong positive effect of EC establishment on firms’ environmental performance and confirms the validity of law reinforcement. Furthermore, the effects are heterogeneous for firms with different characteristics (including scale, profit, ownership, pollution intensity and location). Notably, we find that firms improve their environmental performance by changing their production pattern and energy consumption structure. Additionally, ECs could influence firms’ environmental behaviors by enhancing judicial efficiency and generating a deterrence effect. These findings show the government could improve enterprises’ environmental performance through law reinforcement but should consider the heterogeneous effect on different firms.

1. Introduction

In the 21st century, promoting firms to change their production models and reduce air pollutant emissions is a crucial way to achieve the Sustainable Development Goals (SDGs). To facilitate their realization, the previous literature mainly focused on demonstrating the impact of different environmental regulations on enterprise environmental behaviors, such as firms’ air pollutant emissions (APEs) [1,2,3], wastewater [4,5,6], green innovation and investment [7,8,9]. However, little attention has been paid to setting up law enforcement agencies (LEAs) (e.g., courts, associations and other organizations) for existing regulations, although LEAs, as a third party, have been highly emphasized by many jurists to improve the enforcement of nearly all environmental regulations [10,11,12]. While these studies are valuable for evaluating these regulations’ environmental or economic effects, their “scant discussion” is of concern in efficiently improving corporate environmental performance.

This paper attempts to estimate whether the law reinforcement has a positive influence on firms’ environmental performance. As the main subject of economic development and environmental pollution, firms’ environmental performance mainly depends on the environmental regulations’ governance capacity [13,14], which further relies on the enforcement of impartial and robust law enforcement [15,16]. Nevertheless, traditional LEAs have always attracted criticism for their defects in dealing with environmental issues, such as inefficiency, low enforcement, unprofessionalism, etc. [13,17]. Therefore, the environmental court (EC) is designed as a representative LEA to strengthen the enforcement of environmental regulations, bringing greater legal authority and more specialized trials [11,18]. The first EC in the world was established in Australia in 1979, and its success in environmental law reinforcement has drawn attention from other countries. As early as 2016, more than 1200 ECs and environmental tribunals were established in 44 countries [19]. There are more than 200 ECs in China (see Section 2). The booming development of ECs induces us to explore its effect on firms’ pollution performance.

Firms’ environmental performance could be influenced by changing their production pattern, induced by the establishment of an EC, a representative LEA in pollution control. For illegal polluting firms, the enforceability of ECs could enhance the enforcement of regulations. Once the sued enterprises lose the case, they will face a stiff penalty, and their subsequent production behaviors must be in accordance with the judgment [10,20]. Furthermore, the centralized jurisdiction and specialized trial of ECs could enhance the judicial efficiency [21], and more illegal firms will be trialed. For potential lawbreaking firms, ECs could increase their costs of pollution. ECs’ establishment will increase the possibility of firms being prosecuted for their pollution by the decline in litigation costs, including time consumption, inconvenience and gathering information necessary to assess the loss incurred [22]. Meanwhile, some unique characteristics (such as legal authority, information disclosure, public supervision, etc.) of ECs could generate a deterrent effect on firms’ illegal environmental behaviors [23,24], forcing them to proactively change production patterns. Hence, the establishment of ECs may improve the implementation of environmental regulations and encourage firms to reduce pollution [13,25].

However, the link between ECs and firms’ environmental performance has not yet been explored. ECs have been around worldwide for many years, but most previous research mainly focused on the construction of the EC as an LEA [11,12,26,27,28], rarely evaluating the environmental effect of the EC. Recently, a few papers found that the establishment of an EC significantly promoted the environmental investment of firms [17,29]. However, as the impact of environmental investment on environmental pollution is influenced by a variety of factors such as institutional quality, marketization level and regional corruption [30], the increase in firms’ environmental investment does not necessarily mean a decrease in firms’ pollution emissions.

The considerable influence of ECs on regional environmental performance in China has been demonstrated in some studies [17,29,31,32]. However, considering the notable changes in the industrial structure in many regions in China, these findings cannot separate the impact of ECs on within-sector pollution reduction, let alone the impact of ECs on firms’ environmental performance [33]. Furthermore, because of the pollution haven effect, the stringency of environmental instruments could result in industrial relocation and environmental inequality [34,35]. Therefore, the effect of regional environmental regulations was estimated too roughly from the overall national perspective, and the practical environmental improvements of ECs need to be carefully evaluated.

To bridge this gap in the research and reveal the practical effect of ECs on firms, this paper estimates the impact of the EC pilot program on firms’ environmental performance by constructing a quasinatural experiment. Furthermore, data used in this paper come from two novel and comprehensive firm-level datasets: China’s Industrial Enterprise Pollution Database (CIEPD) and China’s Industrial Enterprise Database (CIED), as well as city-level survey data of ECs from 1998 to 2014. We apply the multiperiod difference-in-differences (DID) method to investigate the impact of the establishment of ECs on firms’ air pollutant emissions (APEs) and adopt the PSM-DID strategy and a series of robustness tests to examine the results. Additionally, we explore the different impacts of the establishment of ECs on heterogeneous firms and we explore how ECs influence firms’ environmental performance.

This paper does innovative work and contributes to current literature on firms’ environmental performance and law reinforcement in three aspects. First, while several previous studies have estimated the effect of ECs on regional environmental quality from a macro perspective [17,32,36], the estimated policy effect in these studies ignored changes in the industrial structure and the environmental performance within sectors. In this paper, we explore the impact of law reinforcement on the environmental performance of firms by systematically assessing the influence of ECs on firms’ APEs. Our study shows that firms’ environmental performance within sectors has been improved. Second, previous studies about the impact of ECs on firms’ environmental performance mainly belong to the theoretical analysis [13,22,23,25]. This research constructs a quasinatural experiment to explore this research topic and adopts suitable empirical methods (such as multiperiod DID and PSM-DID) and the unavailable firm-level datasets (CIEPD), providing our research conclusions with more authenticity. Third, as a supplement to the existing literature and consistent with the current operating status of ECs, we further discuss the heterogeneous effects of ECs on different firms and regions. Furthermore, we find firms improve their environmental performance by changing their production pattern and energy consumption structure. It could pave the way for policymakers to formulate more effective measures to improve environmental quality.

The remainder of this paper is organized as follows: Section 2 provides institutional background and proposes theoretical analysis. Section 3 elaborates on the methodology and data. Section 4 reports the empirical results and corresponding findings, as well as the influencing channels. Section 5 concludes this paper with policy implications.

2. Background and Theoretical Analysis

2.1. Environmental Court in China

In the latter half of the 20th century in China, since labor-intensive industries had developed energetically for a long time, the enterprises production model remained in a state of low efficiency, high energy consumption and high pollution. It inevitably resulted in the frequent appearance of environmental pollution issues. The production mode of enterprises is in urgent need of transformation. However, whether upgrading the standard of pollutant discharge fee or adopting other environmental regulations in these years, enterprise production models are still not clean enough. According to the report of the WHO, air pollution is responsible for about 2 million deaths in China per year, and industry is one of the significant contributors to air pollution. Given that China’s case is globally relevant, finding an effective way to control enterprise pollution attracts extensive attention worldwide.

The reform and construction of ecological civilization must rely on the rule of law. China has formed a relatively complete judicial system, which has made certain achievements. However, due to the increasing intensity of firms’ pollutant emissions, China’s environmental problems are becoming increasingly apparent, which are entering a stage of frequent occurrence [29]. It poses a challenge to China’s traditional judicial system. Because of the characteristics (such as long-term, complexity, professionalism and collectivity) of environmental litigation cases, traditional courts in China were no longer effective in solving these cases. Moreover, due to the difficulties in evidence collection, legal determination and enforcement, traditional LEAs in China are inefficient and inconvenient for victims [36].

The dramatic increase in environmental cases and the inefficiency of traditional courts prompted China to establish the EC. Referring to the development experience of other countries (such as Australia, Sweden and the United States), ECs were gradually established in China. Previous studies regard the EC established in Qingzhen city in 2007 as China’s first EC [29,32]. However, through our one-by-one city surveys across China, we found that the earliest court in China is the Baotou Environmental Court, which was established in Baotou city in 1993. This EC is a breakthrough in China’s efforts to reduce emissions and reflects the positive attitude of China toward solving environmental problems. The ECs in Baotou and Qingzhen greatly contribute to the development of ECs in China. As shown in Figure 1, the number of ECs has increased significantly in China since 2007.

In August 2010, the Supreme People’s Court in China issued the notice “Opinions on Judicial Guarantee and Service”, which clearly stated the necessity of establishing ECs. It highlights the role of the EC in carrying out specialized trials of environmental cases and improving the judicial level of environmental protection. In July 2014, the Supreme People’s Court in China announced the establishment of the Environmental and Resources Tribunal, marking the formal establishment of ECs in China. Subsequently, ECs were quickly established in different cities (as shown in Figure 1). Until 2021, 207 cities in China have established ECs. The pilot scope has been expanded over half of China (as shown in Figure 2). The incremental spread of the EC pilot city in China provides an ideal opportunity for us to test the effect of ECs on enterprise emissions.

ECs have become important LEAs in solving environmental disputes in China. The characteristics of ECs in China include their cross-regionalism, specialization and effectiveness. The scope of accepting cases of many ECs has broken through the traditional restrictions of administrative areas, meaning that cross-regional environmental disputes could be solved more efficiently. To improve judges’ professional quality, some ECs in China have formed an Expert Advisory Committee on Environmental Protection Adjudication. Experts in this committee are professionals in environment-related fields and can provide independent advisory opinions to ECs. This committee is beneficial for improving the specialization of ECs. Meanwhile, to ensure the enforcement of the judgments, some ECs (such as the EC in Qingzhen) have adopted a call-back system. The system plays the supervisory role in the enforcement of the judgments.

2.2. Theoretical Analysis and Research Hypotheses

We introduce APEs as the measurement index to evaluate the cleanability of enterprise production. As the byproduct of production activities of industrial enterprises, APEs have obvious negative externalities. To control pollution (such as APEs) effectively, more and more countries and regions have designed and adopted many environmental regulations, which could define rights and transfer costs to polluting enterprises based on the Coase theorem [37]. However, the practical effect of these regulations is subject to various restrictions; for example, the actual effect of market-based instruments depends on specific program design and institutional context [38]. Therefore, this paper focus on the effective reinforcement of environmental laws.

The effective implementation of environmental laws relies on the enactment and strict enforcement of laws. Multifarious environmental laws have been legislated for a long time. Therefore, law enforcement has become the key and needs to be strengthened. However, if the effect of the LEAs is not strong enough and litigation costs cannot be passed on to polluting firms, the emission reduction by enterprises will be lower than expected [13,25]. As representative LEAs in the environmental field, ECs have some important advantages in affecting the pollution behaviors of firms. According to the Porter hypothesis, the environmental regulation will stimulate enterprise innovation, while innovation compensation can partially or completely offset the cost of pollution. Furthermore, some studies offer evidence of a statistically significant positive impact of specialized courts on the judicial efficiency and economic domain (Botero et al., 2003; Lichand and Soares, 2014; Dammann, 2017) [39,40,41]. If ECs could increase the expected cost of enterprises’ illegal emissions, they will motivate enterprises to change their production patterns and reduce emissions actively.

By increasing the probability that polluters will be sued, ECs could influence the environmental performance of enterprises. On the one hand, ECs centralize the dispersed jurisdiction of environmental cases and improve judicial efficiency by optimizing adjudicators and allocating resources more effectively [10,11,27]. Therefore, the litigation costs of environmental cases will decline and public confidence in LEAs will improve [42]. Additionally, air pollution (especially the emission of smoke) is easier to observe, making firms more likely to be sued and sanctioned for their pollution. On the other hand, ECs could provide a network-based information disclosure service, which is an important way for the public to obtain information about enterprise pollution and judicial decisions. Through the accountable intermediary, more available information is beneficial for enhancing the effectiveness of public scrutiny and the public enthusiasm for prosecution, which will also increase the likelihood of enterprises being sued for polluting [20].

The establishment of ECs will release the deterrence signal to potential lawbreakers, which is one of the important functions of tort laws. The most powerful deterrent of judicature lies in the certainty that the lawbreakers will be caught and punished, and nothing will be gained from the crime [15,18,23]. ECs adopt the “combining trial and enforcement” mode, which can strengthen the enforcement power for environmental cases [17]. Once environmental cases are adjudicated, ECs not only could punish polluters, but generate the ensuing deterrence effect on other enterprises [16]. Even in the preliminary stage of the establishment of the EC, although no cases have been judged, local enterprises’ excessive emissions will be risky. Hence, establishing ECs may form an invisible constraint on firms’ pollution behavior.

In addition, concern for the reduction of different air pollutants is also needed. Air pollutants discharged by industrial enterprises mainly include sulfur dioxide (SO₂), nitrogen oxide (NO_X), smoke and dust, which account for 79.6%, 40.9% and 65.6% of the total social emissions, respectively (China Statistical Yearbook on the Environment, 2020. See https://www.mee.gov.cn/hjzl/sthjzk/sthjtjnb/202202/t20220218_969391.shtml (accessed on 6 September 2022)). However, in the context of depending on LEAs to solve environmental problems, the smoke has some characteristics that need to be noted: (a) Different from SO₂ and NO_x, which need to be observed by professional instruments, smoke is more visible to nearby residents, and thus the victims are more likely to take legal action. (b) It is easier to locate the polluter after the smoke has been observed, which may solve the difficulties in evidence collection and legal determination, providing efficiency and convenience for victims [36]. Therefore, based on the resource-based theory, enterprises may choose to spend most of their resources on reducing smoke emissions to obtain greater environmental governance benefits. Based on the above analysis, we propose the following research hypotheses:

H1: 

The establishment of ECs could improve enterprise environmental performance by reducing APEs.

H2: 

Compared to other air pollutants, smoke emission by enterprises will be reduced more after the establishment of ECs.

3. Methodology and Data

3.1. Methodology

3.1.1. Multiperiod DID Method

The DID method is widely used and credible to evaluate the policy’s effectiveness [8]. It can control systemic differences and the differences between the treatment and control groups before and after the implementation of the policy [43]. As shown in Figure 2, ECs were gradually set up in 207 cities in China. Considering that the traditional DID method is only applicable when the policy shock occurs at a certain point, we should use a more suitable method to evaluate the effect of the establishment of ECs on firms’ APEs. Following Beck et al. [44], we evaluate the impact of ECs on enterprise emissions using a multiperiod DID estimation. The model is shown in Equation (1):

$$\ln a{p}_{it}=\beta \times cour{t}_{it}+{\mathsf{\Gamma }}^{\prime }{X}_{it}+{\alpha }_i+{\alpha }_t+{\epsilon }_{it}$$

(1)

where $i$ and $t$ represent the firm and year, respectively; $\ln a{p}_{it}$ denotes the logarithm of the APEs of the firm $i$ in year $t$; $cour{t}_{it}$ is the independent variable of this model, and if the located city of firm $i$ has established an EC in year $t$, it equals 1, otherwise it equals 0; ${\alpha }_i$ and ${\alpha }_t$ represent the firm and year fixed effects, respectively, controlling for time-invariant firm characteristics; ${\epsilon }_{it}$ is the error term. The coefficient $\beta$ is the focus of this model, indicating whether the establishment of an EC could effectively promote firms to reduce APEs. Since the nonrandom establishment of ECs in cities implies that the estimated $\beta$ may be biased, we add time-varying firm characteristics ($X_{it}$) into the specification to control for heterogeneous firm composition in the piloted vs. unpiloted cities.

3.1.2. Parallel Trend Test (PTT)

The validity of the DID estimation depends on the parallel trend assumption. It requires that changing trends of the treatment and control groups should be parallel before the establishment of the EC [17]. To test the parallel trend assumption, this paper adopts the methods of Beck et al. [44] to test the same trend between treatment and control groups by using the following model:

$$\ln a{p}_{it}={\displaystyle \sum _{t=-16}^{-1}{\tilde{\beta }}_t\times ec\cdot d\left(t\right)}+{\displaystyle \sum _{t=1}^{16}{\tilde{\beta }}_t\times ec\cdot d\left(t\right)+{\mathsf{\Gamma }}^{\prime }{X}_{it}}+{\alpha }_i+{\alpha }_t+{\epsilon }_{it}$$

(2)

where $\ln a{p}_{it}$ denotes the logarithm of APE of firms in the year $t$; $ec$ represents whether the city establishes the EC, and if the city sets up the EC, it equals 1, otherwise it equals 0; $d(t)$ is a year dummy variable corresponding to time $t$. Specifically, $t=0$ corresponds to the current period of the establishment of the EC. In this study, sixteen years before and after the establishment of ECs are used as observation points. ${\tilde{\beta }}_t$ represents the parameter to be estimated.

3.1.3. Propensity Score Matching–Difference-in-Differences (PSM-DID) Strategy

This paper’s research samples include industrial firms in different cities across China. The establishment of ECs may also be affected by heterogeneity (e.g., degree of pollution, level of economic development and environmental regulations) among cities, which will lead to “confounding bias” and “selection bias” [43]. Following the research of Heckman et al. [45] to test the reliability of our baseline results, this paper adopts the PSM approach to overcome the possible confounding and selection bias and further test our baseline results. The steps are set as follows:

Step 1: the probit model is used to estimate the estimated propensity score:

$$P_i=probit\left(cour{t}_i|X_i\right)=\mathsf{\Phi }\left(X_i\right)$$

(3)

where $P_i$ is the propensity score of Equation (3) and $cour{t}_i$ is a dummy variable. If the located city of firm $i$ has established the EC, it equals 1, otherwise it equals 0. $\mathsf{\Phi }(\cdot )$ is a normal conditional distribution function. $X_i$ is the set of characteristic variables of selected cities, including the logarithm of city GDP, the urbanization degree, the logarithm of the city FDI and the city colonial root at the end of the nineteenth century. The estimated value ${\widehat{P}}_i$ of $P_i$ can be obtained from Equation (3).

Step 2: we adopt one-by-one nearest neighbor match. For each city $i$ that set up an EC, we select a city $j(i)$ that does not set up an EC as a control group. The matching requirement is the propensity scores of these two cities are as similar as possible, so the $j(i)$ must satisfy the following condition:

$$j(i)=\underset{\mathrm{j}}{\mathit{arg}\min }|{\widehat{P}}_j-{\widehat{P}}_i|,\forall j$$

(4)

According to the above steps, the matching samples can be obtained. Then, we use the DID estimation to evaluate the effect of the EC on firms’ APEs again. The robustness of our conclusions can be verified by comparing the matched regression results with the baseline results.

3.2. Data

3.2.1. Variable Selection

The dependent variable, $\ln ap$, is the logarithm of industrial firms’ APEs. The independent variable is a dummy variable, $court$, which equals 1 for cities equipped with an EC, and otherwise equals 0. As the focus of this paper, the coefficient $\beta$ of $court$ represents whether the establishment of an EC could contribute to a firm’s emissions reductions. Due to the availability of data, we further explore the effect of the establishment of ECs on four major APEs: SO₂, NOx, dust and smoke. Meanwhile, the characteristics of cities and firms are also essential for environmental behaviors. Following Wang et al. [46] and Zhang et al. [17], this paper adds the control variables to control characteristics of firms and cities. All variable descriptions are shown in

Table 1. Variable description.

Category	Variable	Index
Dependent variable	$\ln ap$	Log of air pollutant emission of industrial enterprises
Core explanatory variable	$court$	Environmental court
Control variable	$growth$	Growth rate of industrial enterprises gross output value
	$K$	Fixed capital stock of industrial enterprises (Absolute amount/one billion)
	$profit$	Profit margin of industrial enterprises (Output value/ten thousand)
	$\mathit{arg}cro$	Log of location entropy (Degree of industrial agglomeration)
	$indust$	Proportion of added value of secondary and tertiary industries (Industrial upgrading)
	$infra$	Log of urban railway passenger volume (Infrastructure)
	$fdi$	Log of urban foreign direct investment

.

3.2.2. Data Source

The firm-level datasets used in this paper come from CIEPD and CIED (1998–2014). The CIEPD is maintained by the National Bureau of Statistics of China, containing the most comprehensive environmental data at the firm-level in China [47]. For instance, the CIEPD not only includes more than 85% of the annual major pollutant emissions (e.g., SO₂ and smoke) of industrial enterprises throughout China, but also contains essential information about enterprises (e.g., firm name, industry code and legal representative) and emission indicators of various pollutants. The CIED is another large dataset that is also maintained by the National Bureau of Statistics of China. It covers information of industrial firms with income higher than 20 million Chinese Yuan (CNY) [48] and contains substantial financial indicators (e.g., current assets, fixed assets, total industrial output value and sales revenue).

In addition, we manually collect data of ECs from the official website of all local People’s Courts in China and related news reports. We also collected basic trial data of air pollution liability cases, which were trialed by Intermediate People’s Courts located in all prefecture-level cities in China. The data includes the total number of air pollution liability dispute cases and the number of judgments (All of the data are manually collected from the China Judgements Online (http://wenshu.court.gov.cn (accessed on 24 September 2022))). The descriptive statistics of the data are presented in

Table 2. Descriptive statistics.

Variable	N	Mean	Std. Dev	Min	Max
$\ln ap$	406,527	10.52182	2.36711	0	21.50265
$court$	534,972	0.07282	0.25965	0	1
$growth$	534,972	0.29096	0.28814	−13.14930	6.92647
$K$	534,972	0.03538	0.29765	0	48.66104
$profit$	534,972	−0.00005	0.02985	−12.57400	0.71640
$\mathit{arg}cro$	534,972	−9.81529	3.30024	−34.02110	8.85743
$indust$	534,972	83.54955	23.20705	0	99.97000
$infra$	534,972	8.12486	2.73743	0	11.75502
$fdi$	534,972	9.11579	3.07823	0	13.31672

.

3.2.3. Data Processing

This paper follows the approach of Brandt et al. [49] and uses enterprise identification numbers in the CIEPD to match the CIED and obtains 534,972 samples. Meanwhile, this paper uses the industrial GDP conversion index as an alternative measure of inflation to delete the price factor of the nominal variables of enterprise samples. Furthermore, to improve the quality of data, following Feenstra et al. [50] and the General Accepted Accounting Principles, the filter criteria of our original samples are as follows: First, the key emission indicators (SO₂, NOx, dust and smoke) and important financial variables (such as total output value, total assets and fixed assets) must be existing. Second, we select the samples based on the following rules: (ⅰ) the sales revenue must be higher than the export value; (ⅱ) the gross output value and the fixed assets must be greater than zero; (ⅲ) the total assets must be higher than the fixed or liquid assets; (ⅳ) the established time must be valid.

4. Results and Discussions

4.1. Preliminary Observation of Data

One of the important assumptions of DID is that the average means of the treatment and control groups should follow parallel trends before the policy implementation. However, this assumption would be challenged if pretreatment characteristics are unbalanced between the treatment and control groups [51]. Thus, this paper creatively compares treatment and control groups’ density function curves. Figure 3 shows that the density distributions of treatment and control groups are highly similar, indicating no selection bias in the samples. The result fulfills the primary assumption of DID.

To further avoid selection bias in distinguishing the treatment and control groups, this paper takes a preliminary observation of the data by conducting a balancing test [47]. The trends of the two groups before the appearance of ECs are shown in Figure 4. We draw the dot chart and fitting line of the mean value of APEs in both treatment and control groups. As shown in Figure 4, the treatment group has a near-uniform trend in the upward growth to the control group. The result indicates that the trend and balanced characteristics of treatment and control groups were similar before the EC was established. In conclusion, the results confirmed the rationality of this paper’s the selection of empirical data and groups.

4.2. Baseline Results

The baseline results of the multiperiod DID method are reported in

Table 3. Baseline results.

Explanatory Variables	(1) No Control Variables	(2) With Control Variables
$court$	−0.0599 *** (0.0223)	−0.0623 *** (0.0223)
$growth$		0.0748 *** (0.0059)
$K$		0.0736 ** (0.0282)
$profit$		−0.0070 (0.0078)
$\mathit{arg}cro$		0.0035 *** (0.0010)
$indust$		0.0007 ** (0.0003)
$infra$		−0.0138 *** (0.0032)
$fdi$		0.0158 *** (0.0034)
Time FE	Y	Y
Individual FE	Y	Y
$R^2$	0.0185	0.0196
$N$	406,526	406,526

Notes: Standard errors in parentheses. ** and *** indicate statistical significance at 5% and 1% respectively.

. The result in column (1) of Table 3 shows that the coefficient of $cour{t}_{it}$ is significantly negative, indicating that the environmental performance of industrial enterprises in China has improved dramatically after EC’s establishment. It also indicates law reinforcement could improve the cleanability of enterprises production.

Since ECs are established in cities across China, we control city-level characteristics. As for firm-level characteristics, we control variables of fixed capital stocks, the growth rate of gross output value and profit margin. The results show in column (2) of Table 3 further confirm that the appearance of an EC can significantly reduce enterprises’ APEs (measured at 6.23%). These results all passed the significance test at the 1% level. The reason may be that the EC could handle environmental disputes more efficiently, and its appearance could generate a deterrence effect to polluting firms. The findings confirm our H1 in Section 2. It could also verify the viewpoints of scholars and jurists (mentioned in Section 1) by providing empirical evidence.

4.3. Robustness Tests

4.3.1. The results of Parallel Trend Test

We build Equation (2) to conduct the parallel trend testing. This paper uses sixteen years before and after the establishment of the EC as observation points. The results of the parallel trend test are shown in Figure 5. We can see that, before the policy shock, all estimators of ${\tilde{\beta }}_T$ are significantly positive and they are not significantly different from 0. It shows a similar trend between the firms’ APEs in pilot cities and nonpilot cities before the establishment of ECs, while, after the establishment of an EC, the trend of estimators of ${\tilde{\beta }}_T$ shows a reversal. These results indicate that the parallel trend hypothesis requirement of the DID estimation is satisfied.

4.3.2. Placebo Test

Although the changing trend of the treatment and control groups are similar before the establishment of ECs (see Figure 5), some unobserved factors may have a crucial impact on firms’ APEs. To solve the omitted variable problem, we adopt the placebo test based on the research design of Gan and McCarl [52] to estimate the policy treatment effect. Suppose there is a restrain effect of ECs on firms’ APEs, then if the event did not happen, the effect of ECs cannot be observed theoretically and the coefficient of $court$ should no longer be negatively significant. We set the setup time of ECs at 1 and 5 years earlier than the actual years of establishing ECs, respectively, then estimate the policy treatment effect. The estimated results shown in

Table 4. Placebo test.

Variables	(1) Pre-One Period	(2) Pre-Five Period
$court$	−0.0030 (0.0184)	0.0226 (0.0149)
Control variables	N	Y
Time FE	Y	Y
Individual FE	Y	Y
$R^2$	0.0195	0.0195
$N$	406,526	406,526

Notes: Standard errors in parentheses.

reflect that the coefficient of $court$ is no longer significant and the dummy time points no longer have an effect on firms’ APEs. This finding indicates that our study passed the placebo test. Therefore, the counterfactual outcomes further verify the robustness of our baseline results.

4.3.3. Heterogeneous Effects on Different Air Pollutants

In this section, we explore the heterogeneous effects of ECs on four major components (SO₂, NO_X, dust and smoke) of APEs. Taking $S{O}_2$, $N{O}_x$, $dust$ and $smoke$ as the dependent variables of Equation (1), respectively, we can obtain the results of

Table 5. Effect of ECs on air pollutants.

Explanatory Variables	(1) ln (SO2)	(2) ln (NOx)	(3) ln (Dust)	(4) ln (Smoke)
$court$	−0.0302 (0.0250)	−0.0420 * (0.0221)	0.1471 * (0.0812)	−0.1293 *** (0.0287)
Control variables	Y	Y	Y	Y
Time FE	Y	Y	Y	Y
Individual FE	Y	Y	Y	Y
$R^2$	0.0060	0.0326	0.0269	0.0082
$N$	379,781	200,535	76,168	344,719

Notes: Standard errors in parentheses. * and *** indicate statistical significance at 10% and 1% respectively.

. Clearly, the coefficients of $\ln (N{O}_x)$ and $\ln (Smoke)$ are both negatively significant, indicating that the establishment of ECs promotes enterprises to reduce emissions of smoke (12.93%) and NOx (4.2%). The emission reduction of smoke is about triple that of NO_X, as well as the largest among the four components.

Significantly, our results are not in line with Fan and Zhao (2019) and Zhang et al. (2019). They found that ECs can help to curb SO₂ emissions, which is different from our result. Furthermore, Zhang et al. (2019) think that there has no significant relationship between ECs and smoke, but we find that ECs obviously promote enterprises to reduce the emissions of smoke. Moreover, this result is consistent with H2 in Section 2 in this paper.

4.3.4. Results of PSM-DID Strategy

Only using DID estimation may cause specific biased estimation results if there are some nonnegligible differences between the pilot cities and nonpilot cities [43]. To alleviate the “confounding bias” and “selective bias”, this paper adopts the PSM-DID strategy and uses the one-by-one nearest neighbor matching and estimates the results based on Equation (1).

To identify the treatment effect of the PSM, the balancing property of the propensity score needs to be satisfied [53]. We check this with a balance test that compares the descriptive statistics of variables before and after the PSM (see

Table 6. Applicability test of PSM-DID method.

Variable	N		Means		Std. Dev	95%CI
	Treatment Group	Control Group	Treatment Group	Control Group
$\ln ap$ before PSM	84,307	322,219	10.2099	10.6030	0.0091	−0.4114	−0.3756
$\ln ap$ after PSM	84,307	199,206	10.2099	10.1781	0.0106	0.0111	0.0525

). It reveals that the samples of the control group screened by Equation (4) are significantly reduced by about one-third after the PSM. Furthermore, the means of $\ln ap$ between treatment and control groups are more similar after the PSM for a 95% confidence interval, which indicates that our matched result is desirable.

Finally, this paper uses the matched data to re-estimate Equation (1) to measure the influence of ECs on firms’ emissions more accurately. The estimation results are shown in

Table 7. PSM-DID results.

Variables	(1) $\mathbf{ln}\mathit{a}\mathit{p}$	(2) $\mathbf{ln}\mathit{a}\mathit{p}$
$court$	−0.1428 *** (0.0198)	−0.1121 *** (0.0197)
Control variables	N	Y
Time FE	Y	Y
Individual FE	Y	Y
$R^2$	0.0413	0.0447
$N$	258,882	258,882

Notes: Standard errors in parentheses. *** indicate statistical significance at 1%.

. Obviously, the coefficients of $court$ are both negative under 1% significance level whether control variables are added or not. Furthermore, the coefficients of $court$ in Table 7 are still significant and higher than the baseline results in Table 3, indicating that the PSM-DID method indeed reduced the selection bias. These results once again confirm that the establishment of ECs indeed reduces the APEs of firms.

4.4. Heterogeneity Test

In this section, we further study the impacts of the EC on the APEs of enterprises with different scale, profit, ownership type, pollution intensity and locations.

Firm scale. According to the total assets of firms, firms are divided into three categories: small, medium and large firms. As the results show in Panel A of

Table 8. Heterogeneous emission effects by firm’s size and profit.

Variables	Panel A: Scale of Enterprises			Panel B: Profit of Enterprises
	(1) Small	(2) Medium	(3) Large	(4) Low	(5) High
$court$	−0.0627 ** (0.0267)	0.0301 (0.0480)	−0.0805 (0.0808)	0.0156 (0.0302)	−0.0722 ** (0.0358)
Control variables	Y	Y	Y	Y	Y
Time FE	Y	Y	Y	Y	Y
Individual FE	Y	Y	Y	Y	Y
$R^2$	0.0170	0.0319	0.0232	0.0243	0.0210
$N$	281,293	98,056	27,177	203,800	201,097

Notes: Standard errors in parentheses. ** indicate statistical significance at 5%.

, the establishment of ECs significantly impacts the APEs of small enterprises (6.27%). The reason may be that small firms face more severe resource constraints compared to medium and large firms, and they do not have the ability to sufficiently withstand policy shocks and risks [54]. Thus, existing resources (e.g., fund, reputation, consumer confidence) of small firms must be used carefully. After the appearance of the EC, to avoid making erroneous decisions (such as being sued) that cause more serious complications than larger firms, small firms may have more motivations to reduce emissions. In other words, the appearance of ECs may generate more deterrent effect on small firms than others.

Firm profit. Considering that firms’ profits could influence their environmental behaviors [46], we estimate heterogeneous effects of ECs on enterprises’ APEs with various profits. Firms are divided into two categories, low- and high-profit firms. Results are displayed in column (5) of Panel B in Table 8, showing that the establishment of ECs significantly promotes high-profit enterprises to reduce their APEs by 7.22%. With the establishment of ECs, to avoid paying legal costs, firms must invest for technological innovation or equipment procurement [48]. Therefore, high-profit enterprises are able to purchase more pollution-abatement equipment or alter their production process to reduce the emissions [46].

Firm ownership. Ownership of firms is another factor that should be taken into consideration. Firms with different ownership usually vary in many aspects, such as organizational form and regulatory intensity [48,55]. In the data sample of this paper, firms are divided into private- and state-owned categories. As shown in Panel A in

Table 9. Heterogeneous emission effects by firm’s ownership and pollution intensity.

Variables	Panel A: Ownership of Enterprises		Panel B: Pollution Intensity of Enterprises
	(1) State-Owned	(2) Private-Owned	(3) Low	(4) High
$court$	−0.0550 (0.0530)	−0.0584 ** (0.0246)	−0.0236 (0.0468)	−0.0327 * (0.0195)
Control variables	Y	Y	Y	Y
Time FE	Y	Y	Y	Y
Individual FE	Y	Y	Y	Y
$R^2$	0.0295	0.0194	0.0517	0.0452
$N$	96,806	309,720	138,568	266,386

Notes: Standard errors in parentheses. * and ** indicate statistical significance at 10% and 5% respectively.

, the establishment of ECs could significantly promote private-owned enterprises to reduce APEs but has no impact on state-owned enterprises. Due to the close connection with local government, state-owned enterprises benefit more from local protection than private-owned enterprises. Administrative shelters relieve the competition pressure of state-owned firms [56]. By contrast, private-owned enterprises face intense pressure from market competition and financial constraints, and they must invest more to maintain their market position [46]. Being sued for pollution will harm social reputation of firms and intensify their financial constraints. Therefore, ECs may have a more substantial deterrent effect on private-owned enterprises and promote them to reduce emissions because local governments are likely to intervene in environmental justice.

Firm pollution intensity. Environmental behaviors of industrial enterprises with high pollution density will attract more attention. With the establishment of ECs, the pollution behaviors of these enterprises may be more constrained because they are more likely to be sued for their excessive emissions. Meanwhile, with the establishment of ECs, it may send a stronger deterrent signal to other polluters and improve the effect of emissions reductions [25]. As expected, we can see from the Panel B of Table 9, the establishment of ECs can promote industrial enterprises with high pollution intensity to reduce their APEs (3.27%).

Region marketization degree. Regions with a high degree of marketization face fierce competition and strong public supervision. As shown in

Table 10. The effects of ECs on firm in different regions.

Variables	Degree of Marketization
	(1) Low	(2) High
$court$	0.0099 (0.0379)	−0.1064 *** (0.0299)
Control variables	Y	Y
Time FE	Y	Y
Individual FE	Y	Y
$R^2$	0.0172	0.0269
$N$	217,831	187,946

Notes: Standard errors in parentheses. *** indicate statistical significance at 1%.

, the establishment of ECs significantly promotes enterprises in regions with a high degree of marketization to reduce their APEs (10.64%). The reason may be that, on the one hand, reputation is one of the competitive advantages of firms in a place with a high degree of marketization, but it is clear that the reputation of firms will be damaged if they get sued for pollution issues [24]. On the other hand, the role of ECs in reducing industrial emissions will be further strengthened in regions with stronger public supervision [57].

4.5. Mechanism Analysis

A few papers about ECs concentrate on firms’ reactions, but their concern only focuses on firms’ capital flow [17,36]. This study not only pays attention to the effect of the establishment of ECs on firms’ environmental performance but also focuses on firms’ subsequent behaviors. Specifically, this paper adopts the mediating effect of four variables, including the number of equipment purchased, production structure, energy consumption structure and labor productivity of firms. The results are shown in

Table 11. Mechanism analysis of ECs: enterprise behaviors.

	(1) Log (Purchasing Equipment)	(2) Log (Vertical Integration Index)	(3) Log (Secondary Energy Ratio)	(4) Log (Labor Productivity)
$court$	0.0769 *** (0.0162)	−0.1216 *** (0.0089)	0.0663 ** (0.0265)	0.1081 *** (0.0085)
Control variables	Y	Y	Y	Y
Time FE	Y	Y	Y	Y
Individual FE	Y	Y	Y	Y
$R^2$	0.0576	0.0073	0.0153	0.0321
$N$	859,705	1,333,497	909,576	2,316,697

Notes: Standard errors in parentheses. ** and *** indicate statistical significance at 5% and 1% respectively.

.

In the short term, firms may purchase more equipment and outsource some polluting production processes to reduce APEs rapidly. As shown in column (1) and (2) of Table 11, after the EC’s establishment, firms purchased more equipment and their vertical integration index decreased. A possible explanation for this result is that with the increasing possibility of being prosecuted for excessive pollution caused by production activities, enterprises have to take measures to reduce emissions as soon as possible. Therefore, enterprises may purchase more treatment equipment to upgrade their production equipment and purchase machines with lower emissions to reduce APEs to meet policy requirements [8]. Meanwhile, enterprises may also outsource their pollution-intensive production links, which can also reduce the overall production cost of enterprises and transfer their own emissions [58].

In the long term, due to the continuous running of ECs, firms may upgrade their production mode to achieve sustainable development. We adopt the ratio of electricity to coal and oil as an indicator to measure whether enterprises change their energy consumption structure. From the results in column (3) in Table 11, we can see, with the establishment of ECs, the secondary energy ratio of firms’ energy consumption increases, which may because of the cleanability of secondary energy. Reduction of primary energy consumption will reduce the pollution caused by the production process of enterprises [59] and alleviate the pressure of being sued. Meanwhile, as the results show in column (4) in Table 11, after the EC’s establishment, firms’ labor productivity increased. The results could verify the Porter hypothesis, which deems that environmental regulation will stimulate enterprise innovation and improve labor productivity while innovation compensation can partially or completely offset the cost of pollution [60].

5. Discussion

We have explored firms’ subsequent behaviors after the establishment of ECs, but according to previous research, the mechanisms of ECs are also worth researching. As previously mentioned, ECs may influence firms’ environmental behaviors by enhancing judicial efficiency and generating the deterrent. Compared with other LEAs, ECs have a more professional trial and centralized jurisdiction, which could improve trial efficiency in environmental field. The improvement of judicial efficiency will increase quantities of environmental disputes handled by ECs. Since the pollution-control effect of ECs is closely related to the efficiency of handling cases [21,29], pollutant emissions will be effectively reduced after the improvement of judicial efficiency. As an LEA, ECs will publish case information on its official website, which may release deterrence signals to potential lawbreakers. Thus, generating deterrent effects might be another crucial path affecting the impact of ECs on firms’ emissions.

Therefore, we further examine the mechanism of ECs from these two perspectives. The method proposed by Baron and Kenny [61] is an effective strategy for testing mediation hypotheses. According to this method, we introduce the mediating variable $channel$ to investigate the indirect effect of ECs on enterprises’ APEs. After introducing the mediating variable, if the coefficient of $court\times channel$ is significant, and comparing with baseline results, the absolute value of coefficient of $court$ is lower or insignificant, a mediating effect exists. Following the stepwise method of Tang et al. [8], we build Equation (5) and (6), respectively, as follows:

$$\ln a{p}_{it}={\beta }_1\times cour{t}_{it}+{\beta }_2\times JE+{\beta }_3\times cour{t}_{it}\times JE+{\alpha }_i+{\alpha }_t+{\epsilon }_{it}{}_{}$$

(5)

$$\ln a{p}_{it}={\beta }_1\times cour{t}_{it}+{\beta }_2\times DE+{\beta }_3\times cour{t}_{it}\times D{E}_{}+{\alpha }_i+{\alpha }_t+{\epsilon }_{it}{}_{}$$

(6)

In Equation (5), we introduce $JE$ as the proxy of judicial efficiency, which is measured as $\log (1+ratio)$, while $ratio$ is the ratio of the number of judgments to the total number of cases in air pollution liability disputes in a given city in year $t$. $cour{t}_{it}\times JE$ denotes the interaction term between $cour{t}_{it}$ and $JE$, which captures the average differential change of firms’ APEs in EC pilot cities. Because judgments could serve an important function in deterring crimes (Walters and Westerhuis, 2013), we choose the number of judgments as the measurement of courts’ deterrence effect. Therefore, in Equation (6), $DE$ represents the proxy of deterrent effect, measured as $\log (1+total)$, while $total$ is defined as the number of judgments of air pollution liability dispute cases in a given city in year $t$. $cour{t}_{it}\times DE$ denotes the interaction term between the $cour{t}_{it}$ and $DE$, which captures the average differential change in APEs of firms located in the given city.

We use Equation (5) to assess the impacts of the judicial efficiency of ECs on enterprises’ APEs. The results in Panel A of

Table 12. Mechanism analysis of ECs: judicial efficiency and deterrent effect.

Variables	Panel A: Judicial Efficiency		Panel B: Deterrent Effect
	(1) $\mathbf{ln}\mathit{a}{\mathit{p}}_{}$	(2) $\mathbf{ln}\mathit{a}{\mathit{p}}_{}$	(3) $\mathbf{ln}\mathit{a}{\mathit{p}}_{}$	(4) $\mathbf{ln}\mathit{a}{\mathit{p}}_{}$
$court$	−0.0579 *** (0.0222)	−0.0601 *** (0.0222)	−0.0580 *** (0.0222)	−0.0601 *** (0.0222)
$JE$	−0.0404 (0.0496)	−0.0319 (0.0715)
$court\times JE$	−0.3023 ** (0.1568)	−0.3080 * (0.1567)
$DE$			−0.0506 (0.0714)	−0.0423 (0.0714)
$court\times DE$			−0.2920 * (0.1567)	−0.2975 * (0.1567)
Control variables	N	Y	N	Y
Time FE	Y	Y	Y	Y
Individual FE	Y	Y	Y	Y
$R^2$	0.0185	0.0197	0.0185	0.0197
$N$	406,527	406,527	406,527	406,527

Notes: Standard errors in parentheses. *, ** and *** indicate statistical significance at 10%, 5% and 1% respectively.

illustrate that, with or without control variables, the coefficients of $court$ are significant. Compared with baseline results of Table 3, the absolute value of coefficients in Panel A of Table 12 are all lower. Meanwhile, the coefficients of $court\times JE$ in Table 12 are all significantly negative, indicating that ECs could promote firms to reduce APEs by improving the judicial efficiency. This result is in line with our analysis in Section 2. Moreover, previous studies discovered the pollution control effect of ECs is closely related to the efficiency of handling cases [21,29], and this paper confirms this view by providing empirical evidence.

Based on Equation (6), we obtain results in Panel B of Table 12. We can find that, with or without control variables, the coefficients of $court$ are always significant, and the absolute value of them are lower than that in Table 3. Meanwhile, the coefficients of $court\times DE$ are significantly negative, indicating that generating a deterrence effect is indeed one of the influencing channels for ECs to reduce firms’ APEs. This conclusion is in line with our analysis in Section 2 and confirms the previous viewpoints [16,25].

6. Conclusions and Policy Implications

The enterprise is not only the main subject of economic development and pollutant emissions, but its environmental performance plays an essential role in achieving the SDGs. The law reinforcement is a crucial way to influence firms’ production patterns and thus control firms’ pollutant emissions. Taking the establishment of ECs as a quasinatural experiment, this paper employs multiperiod DID estimation and uses enterprise data from 1988 to 2014 to examine the causal effects of the law reinforcement on the environmental performance of enterprises. Moreover, PSM-DID and a series of robustness tests are conducted to ensure the stability of our results.

The empirical results show that the establishment of ECs improves enterprise environmental performance. Specifically, this policy significantly reduces enterprise APEs by approximately 6.23%. Moreover, enterprises reduce emissions of smoke (12.93%) and NOx (4.2%) after the policy. The empirical results could verify our research hypotheses proposed in Section 2. More specifically, EC establishment generates the restraining effect on small, private-owned, high-profit firms and firms with high pollution intensity, as well as firms in regions with a high degree of marketization in China. Furthermore, we find that ECs impact firms’ APEs by enhancing the judicial efficiency and generating the deterrence effect. After the establishment of ECs, firms improve environmental performance by purchasing more equipment, changing their production patterns and energy consumption structure and improving labor productivity.

Based upon the findings above, some policy recommendations are suggested. First, the positive impacts of ECs on reducing firms’ APEs imply that policymakers should emphasize the role of strengthening law enforcement in environmental governance. It also provides a new perspective for researching the construction of LEAs in developing countries that are exploring the possibility of setting up ECs as the specialized LEAs to improve environmental quality. Moreover, it is recommended that governments establish some specialized institutions such as ECs to effectively improve enterprises’ environmental performance. Second, the influencing mechanisms of ECs should be appreciated and some measures should be taken to enhance the effect of ECs. To improve the judicial effectiveness of ECs, more efforts should be made to enhance adjudicators’ specialty literacy and optimize the EC’s existing resources. Meanwhile, to create more of a deterrence effect on potential lawbreakers, it is feasible to measure the judicial independence of the EC and strengthen the construction of their official information platforms, which could make the trial information on pollution publicly available and disseminate it to wider audiences. Third, results show that the heterogeneity of firm characteristics and regions affects the policy effect. Therefore, policymakers should consider regional and firms’ heterogeneities and develop more flexible, targeted, and dynamic ECs to gain better implementation effect. As our empirical results show, ECs could not impact firms located in regions with a low degree of marketization in China. Therefore, it is considerable for governments to establish independence and heterogeneous ECs or other LEAs in different regions.