Is Public Participation Weak Environmental Regulation? Experience from China’s Environmental Public Interest Litigation Pilots

Abstract

Previous studies have generally concluded that public participation lacks substantive constraints and has weak environmental regulation effects. Using China’s environmental public interest litigation (EPIL), implemented in 2015, as a quasi-natural experiment to verify the environmental effects of public participation under judicial norms, the difference-in-differences (DID) estimates in this paper show that industrial wastewater and industrial sulfur dioxide (SO₂) emissions in the treated cities declined by an average of 2.76 million tons and 2.51 kilotons per year, respectively, which ultimately improved the city’s environmental quality. The results of the mechanism also show that the EPIL was able to mobilize all three parties: the public, government and enterprises. In the context of the environment as an externality product, where the interests of all the parties are difficult to coordinate, the EPIL has the advantage of overcoming conflicts of interest. Our study provides a quantitative justification for the environmental impact assessment of public litigation and contributes empirical references to overcome the weak binding defect of public participatory environmental regulation.

1. Introduction

The role of public participation in the environmental regulation system is becoming increasingly significant, as it can mobilize environmental governance resources more efficiently and at lower costs [1]. The involvement of the public in environmental decision-making provides stakeholders with an opportunity to express their perspectives on environmental issues, thereby facilitating the development of more informed and robust environmental decisions [2,3,4]. Public participation is carried out through complaints, reports, and lawsuits [5]. One of the most direct and significant forms of public participation in environmental governance is the submission of environmental complaints and reports [1]. Nevertheless, numerous studies have demonstrated that traditional forms of public environmental oversight, including environmental complaints and reports, are of limited binding force and are not sufficiently effective in terms of environmental governance.

Access to information, participation in decision-making, and access to justice and administrative procedures are the three pillars of public participation identified in the United Nations Declaration on Environment and Development, which means that the quality of public participation in environmental impact assessment (EIA) is affected by a legal framework [6,7]. When government agencies fail to ensure that residents are compensated for pollution persecution, persecuted residents turn to the courts to counteract pollution by increasing the political clout of their existential claims. Social groups worldwide are calling on courts to uphold environmental justice and demand compensation for environmental damage [8]. Consequently, environmental public interest litigation (EPIL) is proposed [9]. EPIL is a “bottom-up” judicialization that mobilizes around legal rights, which has become a trend in most countries since the 1980s, with more than 1000 environmental courts established in countries such as India, the Philippines, Kenya, Brazil, and Chile [10].

This paper employs a quasi-natural experimental estimation of the EPIL pilots implemented in 74 cities in China in 2015 to assess the environmental governance effects of public interest litigation. Our study confirms the positive environmental impacts of the EPIL policy, enriches the study of public participatory environmental regulation, and provides empirical evidence for exploring the new model of public participation in environmental governance.

2. Literature Review

2.1. Studies on Public Participation

Public participation, as a way of realizing democracy, complements the formal, closed decision-making of government [11]. At present, public participation occurs by way of complaints, reports and lawsuits, mainly concerning air pollution, odors and factory discharges [12]. Public participation in most decision-making processes is characterized by the invitation to the public to express their views or opinions on public decisions without the corresponding opportunity to influence the decisions [13]. Dealing with public environmental complaints helps to understand public demands and improve eco-efficiency [14]. It is worth noting that when individuals are engaged in public events but lack significant influence on government decisions, it not only disempowers them but also has a detrimental impact on their well-being and erodes their trust in government [15]. Wang et al. (2023) argued that the absence of institutional safeguards and public capacity in China impedes public participation [16]. Tang et al. (2005) posited that the process for citizens to claim compensation for environmental pollution is protracted and that the process of resolving disputes is less efficacious than litigation without the involvement of administrative and political actors [17]. Zhuang and Wolf (2021) posited that citizens victimized by pollution typically submit complaints to local or central inspection agencies and rarely resort to administrative or litigation means to defend their rights and interests [18].

2.2. Studies on Environmental Public Interest Litigation

When the government fails to protect the rights of the public, the public will turn to the courts for help. A growing number of scholars have begun to focus on the “political judicialization” of environmental processes [14]. Political judicialization includes “top-down” judicialization, which refers to the establishment of appropriate courts and institutions, and “bottom-up” judicialization, which refers to the mobilization of the legally unprotected to demand the help of the courts [10]. Environmental public interest litigation (EPIL) is a form of political judicialization, and many scholars argue that NGOs should be involved in the environmental process to defend the public interest [19,20]. Public participation in environmental regulation not only increases environmental monitoring but also speeds up the enforcement process [21]. Zhao et al. (2024) argued that public interest litigation is introduced to protect disadvantaged groups who do not have the means to hire lawyers to seek justice for themselves or access to the courts [22]. Zhai (2024) explored the application of public interest litigation in marine environmental protection and argued that the initiation of civil public interest litigation based on public law sanctions increased the overall responsibility of the offender and the punishments and threats [23]. Zhang and Wang (2023) stated that the purpose of public interest litigation is to enable the vulnerable and aggrieved groups in society to defend their legal rights effectively and further spread awareness of human rights in civil society [24].

2.3. A Summary

Based on the above analysis, the contributions of this paper to the literature to date are the following:

Firstly, existing research is mainly devoted to improving judicial norms for environmental public interest litigation [1,7], but the actual impact of public interest litigation on the environment remains unknown. We provide a quantitative assessment of the environmental effects of EPIL, a specific form of public participation-type environmental regulation. Compared to previous estimation methods based on proxy variables such as environmental complaint cases and report letters [12], the quasi-natural experimental design employed in this paper can effectively identify the intervention effects, thereby providing more reliable estimated results.

Secondly, this paper focuses on the role of EPIL in controlling emissions from a diverse range of urban industrial sources, including industrial SO₂, industrial wastewater, and industrial solid particulate matter (SPM). Furthermore, we also explore the impacts on urban air and water quality. Compared to previous studies on single-source emissions [25,26], our study provides a complete evidence chain of the environmental impacts of the EPIL pilots, thus enabling a more comprehensive EIA.

Thirdly, we provide a comprehensive exploration of the micro-mechanisms through which EPIL works on environmental governance. Distinct from the direct effects of administrative order-orientated and incentive-orientated regulations [27,28,29,30], the effectiveness of EPIL necessitates a convergence of public concern, corporate response, and government collaboration. The externalities of environmental governance often result in disparate economic interests being attributed to different groups [31,32]. Our identification of micro-mechanisms suggests that EPIL significantly raises public awareness of urban environmental issues, increases corporate investment in environmental governance, and facilitates environmental penalties by the government. Our findings provide empirical references for leveraging EPIL to coordinate the environmental governance relationship among the public, enterprises, and the government, thus expanding the effectiveness of environmental regulation.

3. Policy Background and Theoretical Hypotheses

3.1. Policy Background

Litigation in China is inadequate in protecting the rights and interests of victims due to the excessive burden of proof [33]. China has taken considerable time to address the inefficiency of public participation in environmental events and establish a legal framework to support the participation process. In 2005, the State Council of China issued the Decision on Strengthening Environmental Protection through the Implementation of the Scientific Outlook on Development, which proposed the promotion of environmental public interest litigation (EPIL). Nevertheless, this document remained a draft and did not become a legally binding statute. Furthermore, there were no corresponding legal provisions to safeguard the litigation system. In 2014, the Supreme People’s Court of China established the Environmental Resources Trial Court, but the number of cases handled by this environmental court is very limited. Eventually, in 2015, the Standing Committee of China’s National People’s Congress authorized the Supreme People’s Procuratorate to carry out the pilot project of public interest litigation in 13 provinces. The provincial people’s procuratorates then established the 74 cities that would participate in the pilot (Figure 1). The pilot project permits members of the public and non-governmental organizations to initiate legal proceedings against perpetrators of water, air, and noise pollution.

EPIL is a legal action that aims to protect the public interest when the public interest in the environment is harmed by the unlawful acts or omissions of natural persons, legal persons, or organizations. EPIL is a unique form of litigation that differs from traditional approaches, such as civil and administrative litigation. Several defining characteristics distinguish it. Firstly, it should be noted that the subject of EPIL is not always an individual with a direct interest in the case. Indeed, any citizen, enterprise, or social organization can push the person who infringes on public environmental interests into the defendant’s seat [34]. Secondly, EPIL is preventive, and the public does not always have to file a lawsuit after an act of environmental damage has occurred. The public may file a lawsuit when they believe that an act may cause harm to the public interest. Thirdly, it should be noted that EPIL can be directed to both civil and administrative subjects. It is not only general civil subjects, such as companies or individuals, that can potentially harm public environmental interests. Administrative authorities can also be involved. If the administrative organs fail to respond to environmentally damaging behavior, the public may consider filing a lawsuit with the People’s Court.

3.2. Theoretical Hypotheses

Industrial pollution represents a significant contributor to urban pollution [35,36]. In China’s urban areas, a significant proportion of the population is compelled to reside in proximity to sources of pollution, to consume water that has been contaminated, and to breathe air that is of poor quality [16]. EPIL may impact urban industrial pollution emissions through the following pathways.

Firstly, public concern is a key factor in urban industrial pollution abatement. The public are lay individuals who are interested in, informed about, and influence environmental programs or decisions [12,37]. In addition, residents of areas proximate to industrial activities are more likely to suffer from stress, depression, and insomnia than those residing in other regions [38,39]. The symptoms of depression or helplessness exhibited by these individuals can be attributed to their perception of the threat to their own health posed by industrial activities. The growing public concern for the environment has led to a demand for environmental legislation and policy support [40]. EPIL, in the form of plaintiffs’ suits against polluters and administrative agencies, serves to reach a consensus view on environmental conflicts [34,37]. Public campaigning to resolve environmental disputes can have a spillover effect, increasing the publicity and visibility of environmental issues even if compensation for environmental persecution is not necessarily resolved [5]. EPIL has facilitated the amplification of public voices, thereby increasing the government’s incentives to regulate industrial pollution emissions [41,42].

Hypothesis 1.

The establishment of EPIL has curbed urban industrial pollution emissions by raising public concern.

Secondly, the environmental penalties imposed by the government in the context of EPIL have contributed to reducing industrial pollution in cities. According to the theory of information asymmetry, it is challenging for the government to obtain comprehensive data on environmental pollution incidents, including the public’s environmental needs and the impacts of polluting enterprises’ discharge behavior. Furthermore, the government would probably collude with polluting enterprises to enhance local economic performance while disregarding urban industrial pollution [43,44]. In contrast, the public has greater access to environmental pollution information and is more likely to take legal action against polluting behaviors. EPIL, which involves plaintiffs filing lawsuits against polluters and administrative agencies [34], facilitates information sharing between the public and administrative agencies, advancing negotiations and consensus building for resolving environmental conflicts [37]. These actions can prompt government intervention and enhance collaboration between government and public stakeholders. The mechanism diagram for the theoretical analysis is shown in Figure 2.

Hypothesis 2.

The establishment of EPIL has curbed urban industrial pollution emissions by increasing the environmental penalties imposed by the government.

The preceding analysis allows the formulation of the following hypothesis:

Hypothesis 3.

The establishment of EPIL results in a reduction in urban industrial pollution emissions.

4. Research Design

4.1. Econometric Model

The difference-in-differences (DID) method is regarded as one of the most efficacious methodologies for policy evaluation [45]. We begin our analysis with the DID model to estimate the average treatment effects of the EPIL policy on urban industrial pollution. The analysis period encompasses the years from 2010 to 2020, including the five years preceding the introduction of the initial EPIL pilots and extending to the most recent data on urban industrial pollution. The basic model is as follows:

$${Pollution}_{it}={\beta }_0+{\beta }_1{EPIL}_{it}+\gamma X_{it}+{\mu }_{jt}+{ϵ}_i+{\epsilon }_{it}$$

(1)

where subscripts $i$, $j$ and $t$ denote the city, province, and year, respectively. ${Pollution}_{it}$ denotes the composite index of pollution emissions in city $i$ in year $t$. Cities classified as EPIL pilots in 2015 are included as the treatment group, and cities without EPIL pilots in the same period are set as the control group. ${EPIL}_{it}$ denotes a dummy variable for the EPIL pilots, and the coefficient ${\beta }_1$ estimates the average treatment effect of the EPIL pilots. $X_{it}$ denotes a series of control variables. As the EPIL policy was implemented by first identifying pilot provinces and then having provincial procuratorates identify pilot cities, we simultaneously introduce the province and year interaction fixed effects (${\mu }_{jt}$) and city fixed effects (${ϵ}_i$). Such a study design helps us to exclude the impact of all the unobservable factors at the regional level and individual characteristics at the city level. ${\epsilon }_{it}$ denotes a random disturbance term.

4.2. Variables and Definitions

4.2.1. Dependent Variable

Urban industrial pollution is mainly reflected in industrial wastewater, sulfur dioxide (SO₂), and solid particulate matter (SPM). Industrial wastewater is produced during industrial production and contains heavy metals, toxic chemicals, oils and fats, dyes, and other harmful and hazardous substances. SO₂ is one of the main pollutants produced during the combustion of fossil fuels such as coal and oil. When SO₂ is discharged into the atmosphere, sulfuric acid mist or sulfate aerosols are formed as precursors to environmental acidification. SPM, which contains a large amount of particulate matter, mainly originates from combustion processes in industrial production, such as burning coal and fuel oil. To consider the overall pollution emission of urban industry, this paper standardizes these three pollutants and sums them up with equal weights to obtain a comprehensive index of urban industrial pollution emission, which is expressed in terms of ${Pollution}_{it}$.

4.2.2. Independent Variables

China’s EPIL pilots started in 2015, covering 74 prefecture-level cities in 13 provinces. Specifically, ${EPIL}_{it}=1$ if a city is treated in year t; otherwise, ${EPIL}_{it}$ takes the value of 0. All 74 cities chosen as EPIL pilots are classified as the treatment group, and the remaining cities are classified as the control group.

4.2.3. Control Variables

Based on the summary of the results of previous studies, this paper selects the following control variables:

(1)

Urban GDP growth rate (GGR). An increase in the level of urban economic development may lead to an increase in the level of pollution control and may also lead to more polluting behavior [46,47].

(2)

Urban population density (Density). Urban population density affects the demand for industrial products and the spatial concentration of economic activities [48], which in turn affects industrial pollution emissions [49].

(3)

Foreign direct investment (FDI). The Chinese government tends to set more lenient environmental regulatory standards for foreign corporations, which is not conducive to reducing industrial pollution in cities [50,51].

(4)

Environmental practitioners (EP). Urban environmental practitioners reflect the level of human capital in the city to deal with solving pollution problems.

(5)

Percentage of secondary industry (Secondary). The secondary industry is the main source of pollution [52]. See

Table 1. Variable symbols and definitions.

Symbols	Definitions
DID variable
EPIL	=1 if a city adopts the environmental public interest litigation in year $t$ and afterwards; =0 otherwise
LCC	=1 if a city belongs to the low carbon cities in year $t$ and afterwards; =0 otherwise
ESER	=1 if a city belongs to the energy-saving and emission-reduction cities in year $t$ and afterwards; =0 otherwise
Urban industrial emissions variables
SO2	Industrial sulfur dioxide emissions (1000 tons)
Water	Industrial wastewater emissions (million tons)
SPM	Industrial solid particulate emissions (1000 tons)
Pollution	Urban industrial pollution emissions. A composite index based on the z-score is constructed by normalizing and equally weighted summing of urban industrial wastewater, sulfur dioxide, and solid particulate matter
Urban economic and social variables
GGR	GDP growth rate (%)
Density	Population density, measured as the natural logarithm of population per square kilometer
FDI	Natural logarithm of foreign direct investment (CNY 10,000)
EP	Natural logarithm of environmental practitioners (10,000 people)
Secondary	Share of secondary sector in GDP (%)
Concern	Public environmental concern, measured by web search index
Punishment	Number of environmental punishment cases (100 items)
Urban environmental variables
wind	Annual average wind speed (m/s)
Precipitation	Annual precipitation (mm/m2)
Daylight	Annual daylight hours (hour)
Humidity	Annual average humidity (%rh)
Temperature	Annual average temperature (°C)
AQI	Air quality index
WQI	Water quality index
Corporate variables
EI	Share of corporate environmental investment in total assets (%)
ROA	Return on assets
Scale	Enterprise scale, measured by the natural logarithm of operating revenues (10,000 yuan)
Tobin’s q	Enterprise market value, measured by Tobin’s q
AGR	Asset growth rate (%)
ALR	Asset–liability ratio

for the variable symbols and specific construction methods.

4.2.4. Other Variables

(1)

Daylight. Daylight is expressed in terms of the annual average urban daylight hours.

(2)

Humidity. Humidity is expressed in terms of the average annual urban humidity values.

(3)

Temperature. Temperature is expressed in terms of the annual average temperature value of the city.

(4)

AQI and WQI. AQI and WQI denote the air quality index and water quality index, respectively (calculation of the AQI is available on the website: https://www.cnemc.cn/jcgf/dqhj/201706/t20170606_647274.shtml (accessed on 29 September 2024); calculation of the WQI is available on the website: https://www.cnemc.cn/jcgf/shj/200801/t20080128_647287.shtml (accessed on 29 September 2024).

4.3. Data and Sample

The EPIL pilot data are sourced from the official websites of the Higher People’s Courts of various provinces in China. If not otherwise stated, the remaining urban and corporate dimension data come from the China Urban Statistical Yearbook (2010–2020), the EPS database, and the CSMAR database. Environmental data are from the China National Environmental Monitoring Centre (CNEMC) (the website of China National Environmental Monitoring Centre is https://www.cnemc.cn/, accessed on 29 September 2024). After eliminating some samples with severe missing data, 282 cities are obtained as sample data.

Table 2. Descriptive statistics.

Symbols	Obs	Mean	SD	Min	Max
DID variable
EPIL	3102	0.132	0.338	0	1
LCC	3102	0.330	0.470	0	1
ESER	3102	0.067	0.250	0	1
Urban industrial emissions variables
SO2	3102	39.179	47.529	0.002	572.747
Water	3102	61.349	77.704	0.070	965.010
SPM	3102	30.101	128.332	0.011	5168.812
Pollution	3102	0	0.689	−0.615	13.772
Urban economic and social variables
GGR	3102	8.671	4.727	−20.630	109
Density	3102	0.044	0.037	0.001	0.779
FDI	3102	9.936	1.936	1.099	14.941
EP	3102	8.681	0.795	3.970	11.726
Secondary	3102	46.431	10.950	10.680	89.750
Concern	2790	22.340	24.156	0	140.364
Punishment	1581	1.541	4.064	1	5048
Urban environmental variables
Wind	3102	2.143	0.468	1.006	4.091
Precipitation	3102	1069.305	562.384	49.457	2759.072
Daylight	3102	1978.269	521.209	752.434	3386.191
Humidity	3102	69.575	9.849	35.632	84.459
Temperature	3102	14.640	5.160	−1.210	25.912
AQI	3102	79.136	23.411	21.011	384
WQI	3102	12.547	7.216	0	53.260
Corporate variables
EI	25,938	0.084	0.144	0	12.433
ROA	25,938	0.050	0.153	−14.292	8.149
Scale	25,938	22.232	1.383	18.380	31.138
Tobin’s q	25,938	1.966	1.189	0.674	9.997
AGR	25,938	0.223	0.802	−0.972	47.927
ALR	25,938	0.434	0.233	0.007	8.009

reports the descriptive statistics for all the variables based on the basic dataset.

5. Empirical Analysis

5.1. Basic Regression Analysis

Columns (1)–(4) of

Table 3. Basic regression results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Pollution	Pollution	Pollution	Pollution	Water	SO2	SPM
EPIL	−0.169 **	−0.172 **	−0.169 ***	−0.172 ***	−16.548 ***	−15.063 ***	1.758
	(0.063)	(0.062)	(0.047)	(0.047)	(4.992)	(4.701)	(3.240)
Controls	NO	YES	NO	YES	YES	YES	YES
Province#Year FE	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES
N	3102	3102	3102	3102	3102	3102	3102
R2	0.704	0.705	0.704	0.705	0.845	0.814	0.279

Notes: The standard errors in parentheses for columns (1) and (2) are clustered at the province level, and those for columns (3) to (7) are clustered at the city level. ** p < 0.05, *** p < 0.01.

show the average impact of the EPIL policy on the city’s industrial pollution emissions. Columns (1) and (2) present regressions for clustered provinces, while columns (3) and (4) present regressions for clustered cities. Column (4) shows the results of the basic regression on Equation (1). Independent of the inclusion of control variables and clustering levels, the coefficients of all the equations are significantly negative, indicating a clear reduction in industrial pollution emissions in the EPIL pilot cities. Columns (5) to (7) cluster at the city level and estimate the impact of EPIL on emissions of the three pollutants separately. The results show that industrial wastewater and industrial SO₂ have decreased by 16.55 million tons (or an average reduction of 2.76 million tons per year) and 15.06 kilotons (or an average reduction of 2.51 kilotons per year), respectively.

5.2. Robustness Tests

5.2.1. Parallel Trend Test

A prerequisite for the operation of the DID model is that the treatment group and the control group have the same trend before implementing the EPIL pilots. In order to identify the need for techniques to meet the common trend, we constructed the following model:

$${Pollution}_{it}={\beta }_0+{\beta }_l{\sum }_{-5}^5{EPIL}_{it}^l+\gamma X_{it}+{\mu }_{jt}+{ϵ}_i+{\epsilon }_{it}$$

(2)

This study examines the evolution of trends over the five years preceding and following the implementation of the EPIL pilots. The initial period preceding the introduction of the pilots serves as the baseline for comparison. Figure 3 presents the parallel trend test plots for the EPIL policy.

Figure 3 (left) illustrates the divergence between the treatment and control groups before and following the EPIL policy. It can be observed that there is no discernible difference between the industrial pollution emissions of the treatment and control groups prior to the pilot implementation. Conversely, the industrial pollution emissions of the treatment group following the pilot implementation exhibit a notable decline.

Figure 3 (right) illustrates the discrepancy in industrial wastewater, SO₂, and SPM emissions between the treatment and control groups before and after the EPIL policy. It is evident that there is no difference in industrial wastewater, SO₂, and SPM emissions between the two groups prior to the pilots’ initiation. However, after the pilots’ implementation, the treatment group exhibits a notable decline in industrial wastewater and SO₂ emissions.

Consequently, the parallel trend test outcomes align with those of the main regression analysis.

5.2.2. Exclusion of Other Policies

In 2010, China established low-carbon city (LCC) pilots. This was followed by the launch of energy-saving and emission reduction (ESER) model city pilots in 2012. Both policies have demonstrated a reduction in industrial pollution emissions to varying degrees [53]. In order to exclude the effects of these two policies, dummy variables representing the LCC pilots and the ESER model city pilots have been introduced into the model.

The results presented in

Table 4. Exclusion of contemporaneous policy effects.

	(1)	(2)	(3)
	Pollution	Pollution	Pollution
EPIL	−0.170 ***	−0.158 ***	−0.157 ***
	(0.046)	(0.048)	(0.047)
LCC	−0.084 *		−0.080 *
	(0.046)		(0.047)
ESER		−0.288 *	−0.286 *
		(0.153)	(0.153)
Controls	YES	YES	YES
Province#Year FE	YES	YES	YES
City FE	YES	YES	YES
N	3102	3102	3102
R2	0.705	0.708	0.709

Notes: The standard errors are in parentheses. * p < 0.1, *** p < 0.01.

demonstrate that both the LCC and ESER pilots inhibited the emission of urban industrial pollution. Furthermore, the coefficients of the EPIL policy remain significantly negative, which is in line with the regression results presented in the previous section. The results suggest that other policies did not influence the emission reduction effect of the EPIL pilots.

5.2.3. Exclusion of Spatial Interference

Numerous studies have demonstrated that increased regional environmental regulation would result in pollution transfer to neighboring regions. Due to the potential spatial spillover effect, the benchmark regression result may violate the stable unit treatment value assumption (SUTVA) [54]. Therefore, referring to existing studies [53], this paper constructs the following spatial lag of X (SLX) model to exclude spatial interference:

$${Pollution}_{it}={\beta }_0+\left({\beta }_1+{\beta }_2\sum _{j=1}^N{w}_{ij}\right){EPIL}_{it}+\gamma X_{it}+{\mu }_{jt}+{ϵ}_i+{\epsilon }_{it}$$

(3)

where $w_{ij}$ indicates the element of the spatial weight matrix, and the coefficient ${\beta }_2$ represents the spatial spillover effect of the EPIL policy. Since the extent of the spatial impact cannot be determined in an a priori way, we introduce the geographic adjacency matrix ($W_1$) and geographic square distance matrix ($W_2$) to estimate Equation (3). $W_1$ and $W_2$ are defined as follows:

$$\begin{array}{l}{w}_{1,ij}=\left\{\begin{array}{cc}1\hfill & \mathrm{City} i \mathrm{is} \mathrm{adjoining} \mathrm{to} \mathrm{city} j\hfill \\ 0\hfill & \mathrm{City} i \mathrm{is} \mathrm{not} \mathrm{adjoining} \mathrm{to} \mathrm{city} j\hfill \end{array}\right. \\ \\ w_{2,ij}=\left\{\begin{array}{cc}{\displaystyle \frac{1}{d_{ij}^2}}& i\ne j\\ 0& i=j\end{array}\right.\end{array}$$

(4)

where $d_{ij}$ indicates the spatial distance between cities $i$ and $j$.

Table 5. Exclusion of spatial interference.

Variables	(1) Pollution	(2) Pollution
EPIL	−0.166 ***	−0.173 ***
	(0.046)	(0.050)
${\mathit{W}}_1 \times$ EPIL	0.084
	(0.097)
${\mathit{W}}_2 \times$ EPIL		−0.034
		(0.917)
Controls	YES	YES
Province#Year FE	YES	YES
City FE	YES	YES
N	3102	3102
R2	0.705	0.705

Notes: The standard errors are in parentheses. *** p < 0.01.

reports the results based on the two spatial weight matrices, which shows that the EPIL policy still has a significant local effect on the pilot cities. Moreover, the coefficients of the two spatial lag terms are not statistically significant, indicating that the EPIL policy has not led to an increase in industrial emissions in neighboring regions or in a wider area.

5.2.4. Exclusion of Self-Selection Bias

The propensity score matching (PSM) model was employed to address the endogeneity issue resulting from sample selection bias, with robustness tests conducted as a further safeguard. Once the new sample data have been obtained through 1:1 matching based on the nearest neighbor matching method, the coefficients of the EPIL policy are presented in columns (1) and (2) of

Table 6. Exclusion of self-selection bias.

	(1)	(2)	(3)	(4)
	Pollution	Pollution	Pollution	Pollution
EPIL	−0.169 ***	−0.122 **	−0.182 ***	−0.184 ***
	(0.047)	(0.050)	(0.050)	(0.050)
Controls	NO	YES	NO	YES
Province#Year FE	YES	YES	YES	YES
City FE	YES	YES	YES	YES
N	1406	1406	3036	3036
R2	0.890	0.891	0.755	0.755

Notes: The standard errors are in parentheses. ** p < 0.05, *** p < 0.01.

. The results demonstrate that the EPIL policy significantly suppressed the emissions of urban industrial pollution at the 1% level. Furthermore, the entropy balance method was employed for estimation purposes. This method has the advantage of not resulting in a large number of lost samples compared with the PSM-DID method, and the results are more accurate [55]. The regression results in columns (3) and (4) of Table 6 indicate that the EPIL policy reduced urban industrial pollution emissions.

5.2.5. Placebo Test

We conducted a placebo test to rule out interference from random factors. In particular, 500 pseudo-DID variables were generated for this study, and the process in Equation (1) was repeated 500 times. The specific results are presented in Figure 4. Most estimated coefficients are distributed around 0, and most p-values are greater than 0.05. The result indicates that the basic regression results in this study are not the result of chance, further supporting the robustness of the benchmark regression results.

Therefore, Hypothesis 3 is verified.

6. Mechanism and Evidence

6.1. Mechanism Test

As previously demonstrated, the EPIL policy effectively reduced industrial pollution emissions in the city. This section examines the specific mechanisms at the public and government levels.

The following model is developed to test the micro-mechanism:

$$M_{it}={\phi }_0+{\phi }_1{EPIL}_{it}+{\phi }_2{X}_{it}+{\mu }_{jt}+{ϵ}_i+{\epsilon }_{it}$$

(5)

Equation (5) estimates the impacts of the EPIL policy on the mechanism variable $M_{it}$, including the public’s concern about environmental pollution (Concern) and the government’s punishment for pollution incidents (Punishment). The following are the explanations of the indicators for the mechanism variables:

(1)

Public concern about environmental pollution (Concern) is expressed by the frequency of Baidu searches for the keyword “environmental pollution”, and the data are mainly collected manually.

(2)

The government’s punishment for pollution incidents (Punishment) is expressed by the number of urban pollution penalty cases imposed by the government per year divided by 100.

6.2. Additional Evidence

An additional piece of evidence comes from changes in corporate behavior. Environmental investments by corporates in the context of EPIL have contributed to reducing industrial pollution in cities. According to the theory of corporate reputation, the fact that a lawsuit is filed against an enterprise in the context of public judicial decisions and trial publicity will hurt the enterprise’s goodwill in the local area, deteriorate the public’s social image of the enterprise’s brand and products, and be detrimental to the enterprise’s ability to attract investment. Polluters and policymakers are frequently unaware of the rights of others, and they will not be willing to acknowledge their moral obligations and invest in environmental remedies to reduce emissions until the lawsuit occurs [24]. EPIL may require enterprises to pay high compensation costs, directly increasing their operating costs. Therefore, enterprises are more inclined to invest in environmental protection facilities and technologies in advance, thus reducing potential risks and economic losses. Furthermore, the literature surrounding the Porter hypothesis suggests that strict environmental regulatory policies can increase corporates’ environmental investments [56,57]. The theory of the compensating effect of innovation posits that appropriate environmental regulations can motivate corporations to innovate and become more efficient [58], encouraging further environmental, social, and governance commitments.

Therefore, corporate environmental investments are introduced into model (5) as additional evidence to test the effectiveness of EPIL in reducing industrial pollution emissions.

Corporate environmental investment (EI) is the proportion of each listed corporation’s environmental investment projects (including desulphurization projects, sewage treatment, and dust removal) to its total assets. Furthermore, it is important to highlight that the study of micro-mechanisms at the corporate level expanded the sample by matching the data of listed corporates in each city to the original sample and adding corporates’ size (Scale), return on assets (ROA), Tobin’s Q (Tobin’s q), asset growth rate (AGR), and gearing ratio as control variables (ALR) to control of the factors that may affect industrial pollution at the corporate level. The interpretation of the indicators and descriptive statistics at the corporate level is presented in Table 1 and Table 2.

Columns (1) and (2) of

Table 7. Mechanism and evidence.

	(1)	(2)	(3)	(4)	(5)	(6)
	Concern	Pollution	Punishment	Pollution	EI	Pollution
EPIL	4.676 ***	−0.138 ***	0.182 **	−0.330 **	0.016 **	−0.192
	(1.450)	(0.043)	(0.085)	(0.158)	(0.016)	(0.101)
Concern		−0.006 ***
		(0.001)
EI						−0.013
						(0.101)
Punishment				−0.004
				(0.005)
Controls	YES	YES	YES	YES	YES	YES
Province#Year FE	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES
N	2742	2742	1257	1526	25,700	25,700
R2	0.939	0.703	0.635	0.760	0.393	0.938

Notes: The standard errors are in parentheses. ** p < 0.05, *** p < 0.01.

demonstrate that the EPIL policy increased public concern about environmental pollution, which in turn reduced the emission of urban industrial pollution. Columns (3) and (4) of Table 7 indicate that the EPIL policy has resulted in an 18% increase in the number of cases of environmental penalties imposed by the government, which has subsequently led to a reduction in urban industrial pollution. All the evidence indicates that the EPIL policy increased public environmental concerns and government environmental penalties, ultimately reducing emissions from urban industrial pollution. Columns (5) and (6) of Table 7 indicate that the EPIL policy enhanced the environmental protection investment of corporates. However, the effect of environmental protection investment on urban industrial pollution was not significant. This may be due to the fact that the samples we took are samples of listed corporates, but listed corporates do not represent all of the corporates in the locality, and the possibility of its emissions is lower compared to non-listed corporates due to the information disclosure mechanism of listed corporates. Consequently, the absence of data for non-listed corporates results in an underestimation of the impact of the project on urban industrial pollution.

Therefore, Hypotheses 1 and 2 are verified. In addition, EPIL necessitates a convergence of public concern, corporate response, and government collaboration.

7. Further Studies

7.1. Heterogeneous Effects

7.1.1. Heterogeneity between Western and Non-Western Cities

There are considerable differences in the levels of industrial pollution emissions across China. Polluting corporations tend to locate their plants in the western region to reduce costs [59,60]. However, corporates frequently lack the requisite advanced environmental protection technology and equipment and cannot treat pollutants effectively. In contrast, the proportion of heavily polluting corporates in regions outside western China is relatively lower [35]. Consequently, this paper divided the cities into western cities and non-western cities. The regression results in columns (1) and (2) of

Table 8. Heterogeneity effects (1).

	(1) Western Cities	(2) Non-Western Cities	(3) Resource-Based Cities	(4) Non-Resource-Based Cities
EPIL	−0.206 **	−0.155 ***	−0.029	−0.218 ***
	(0.086)	(0.054)	(0.088)	(0.056)
Controls	YES	YES	YES	YES
Province#Year FE	YES	YES	YES	YES
City FE	YES	YES	YES	YES
N	880	2156	572	2387
R2	0.811	0.683	0.469	0.841

Notes: The standard errors are in parentheses. ** p < 0.05, *** p < 0.01.

indicate that the EPIL policy significantly reduced industrial pollution emissions in the two regions, although the mitigation of industrial pollution was more prominent in the western region. The western region’s weaker sense of rights defense and more serious industrial pollution mean that the EPIL policy has a greater impact on the region’s governance logic, leading to more effective policy outcomes regarding industrial emission reduction.

7.1.2. Heterogeneity between Resource-Based and Non-Resource-Based Cities

Resource-based cities are more dependent on resource extraction and processing industries. Resource-based cities are often susceptible to the “resource curse” phenomenon because their GDP growth is contingent upon the continued operation of highly polluting corporations [61]. Consequently, Chinese cities were divided into two categories: resource-based cities and non-resource-based cities. The regression results in columns (3) and (4) of Table 8 indicate that the EPIL policy did not significantly affect industrial pollution in resource-based cities but reduced pollution emissions in non-resource-based cities. This may be attributed to the fact that the pollution problems in resource-based cities are more entrenched than in non-resource-based cities. Implementing the EPIL pilots in resource-based cities may face business resistance and government pressure.

7.1.3. Heterogeneity between Developed and Underdeveloped Cities

The level of public participation in environmental events is closely related to cities’ economic development level [13,62]. Underdeveloped cities tend to prioritize economic growth over environmental protection. Local government officials in these cities may facilitate industrial waste discharge [43,44]. Nevertheless, developed cities that experience the process of urbanization and economic growth can give rise to green technological innovations and structural changes in the economy, with the potential to shift from an energy-intensive to a technology-intensive economy. Consequently, the median city GDP per capita was employed as the demarcation criterion, with those above the median categorized as developed cities and those below the median categorized as underdeveloped cities. The regression results are presented in columns (1) and (2) of

Table 9. Heterogeneity effects (2).

	(1) Developed Cities	(2) Underdeveloped Cities	(3) Large-Scale Cities	(4) Small-Scale Cities
EPIL	−0.249 ***	−0.0439	−0.172 **	−0.103 *
	(0.081)	(0.038)	(0.083)	(0.058)
Controls	YES	YES	YES	YES
Province#Year FE	YES	YES	YES	YES
City FE	YES	YES	YES	YES
N	1460	1509	1498	1517
R2	0.878	0.590	0.750	0.753

Notes: The standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

and show that the EPIL policy reduced industrial pollution in developed cities while not reducing industrial pollution in underdeveloped cities. Citizens in developed regions have higher expectations of environmental quality and are more willing to defend their environmental rights and interests [13]. In contrast, citizens in underdeveloped regions live in environments that have long suffered from industrial pollution, and their apathy toward the pollution has made it challenging for them to utilize litigation tools effectively.

7.1.4. Heterogeneity between Large-Scale and Small-Scale Cities

The size of a city is a significant factor influencing industrial pollution. Large-scale cities tend to have more intensive industrial activities and are more difficult to regulate. This allows some companies or individuals to evade environmental regulations. Furthermore, the available studies indicate a positive correlation between pollution levels and urban size. Urban expansion leads to increased residential activity, which further exacerbates pollution problems [9,63]. Therefore, the median city population was used as a boundary to categorize cities as either large scale or small scale. The regression results in columns (3) and (4) of Table 9 demonstrate that the EPIL policy has achieved more favorable emission reductions in large-scale cities compared to small-scale cities. The superior pollution reduction outcomes observed in the EPIL pilots in large-scale cities can be attributed to a more robust social monitoring force.

7.2. Impacts on Urban Environmental Quality

In order to further validate the changes in the urban environment following the EPIL pilots, the water quality index (WQI) and air quality index (AQI) were used as explanatory variables. A reduction in the WQI and AQI values indicates an improvement in water quality and air quality. Furthermore, the following control variables were incorporated into the model: urban average annual wind speed (m/s), annual precipitation (mm/m²), annual light duration (hour), annual average humidity (%rh), and annual average temperature (degrees Celsius).

7.2.1. Impacts on Water Quality

Industrial wastewater discharge contains a considerable quantity of heavy metals, organic compounds, and chemical substances that can impair water quality. The impact of the EPIL policy on the water quality improvement was tested, and the results are presented in columns (1) and (2) of

Table 10. Results of the impacts on urban environmental quality.

	(1)	(2)	(3)	(4)	(5)
	WQI	WQI	AQI	AQI	AQI
EPIL	−1.664 **	−1.365 **	−3.348 **	−2.470	−3.358 **
	(0.676)	(0.683)	(1.646)	(1.576)	(1.639)
Water		0.018 ***
		(0.004)
SO2				0.058 ***
				(0.019)
SPM					0.005
					(0.003)
Controls	YES	YES	YES	YES	YES
Province#Year FE	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES
N	3036	3036	3036	3036	3036
R2	0.799	0.804	0.819	0.821	0.820

Notes: The standard errors are in parentheses. ** p < 0.05, *** p < 0.01.

. The results show a negative relationship between the EPIL policy and the city WQI index.

7.2.2. Impacts on Air Quality

The emission of SO₂ and SPM has a detrimental impact on urban air quality. Several studies have demonstrated that particulate matter, including PM_2.5 and PM₁₀, represents a significant threat to human health, resulting in increased mortality and morbidity from cardiovascular and respiratory diseases [64,65]. The regression results in columns (3) to (5) of Table 10 indicate that the EPIL policy enhanced urban air quality through industrial pollution abatement.

In summary, the EPIL policy has improved the water and air quality in the pilot cities.

8. Conclusions and Policy Implications

There are many ways for the public to participate in environmental monitoring, but they are all weak to varying degrees. In the absence of a corresponding judicial system, the executive branch’s opening of environmental monitoring channels is mostly inefficient. China’s EPIL pilots have enriched the public participation channel in environmental supervision and reversed the weak position of public participation in the past. This paper takes China’s EPIL pilots as an example and discusses the importance of judicial intervention for public participation.

The principal findings are as follows. First, the EPIL policy resulted in an average annual reduction of 2.76 million tons of industrial wastewater and 2.51 kilotons of industrial SO₂ emissions in the treated city. Second, the EPIL policy reduced urban industrial pollution emissions by increasing public environmental concerns, corporate environmental investments, and government environmental penalties. Third, the EPIL policy has achieved better industrial emission reductions in western, non-resource-based, developed, and large-scale cities. Furthermore, the EPIL policy ultimately improved the water and air quality in the pilot cities.

Based on the preceding analysis, the following policy recommendations are proposed:

Firstly, the results of this study indicate that EPIL has led to a reduction in industrial wastewater and industrial sulfur dioxide emissions in the city. However, the effect on industrial solid particulate emissions is not significant, suggesting the need for additional environmental regulation. The government can set reasonable emission standards and limits according to the characteristics of solid particulate matter and environmental impacts to ensure that the wastes discharged by enterprises comply with the requirements of environmental protection.

Secondly, it is imperative to extend the channels for public participation in environmental monitoring, reverse the weak position of public participation in environmental monitoring, and harness concern for environmental issues to strengthen legislation and optimize the mode of environmental justice. In the meantime, it is necessary to enhance public awareness of the judicial process and its potential to protect their interests. The current EPIL in China’s resource-based and less-developed cities has not yet achieved the desired effect of industrial emission reduction, likely due to a lack of awareness of the importance of protecting rights at the local level. Therefore, the future priority is to vigorously publicize the judicial process and the rights and interests it safeguards, improving the degree of public participation.

Thirdly, government agencies are obliged to facilitate the transparency and accessibility of environmental information by using mobile technology. This enables the real-time monitoring of environmental procedures, thereby ensuring that the public can obtain timely updates on environmental protection dynamics and provide timely feedback to administrative agencies.

Lastly, the government should establish a balanced environmental regulation portfolio, encourage corporations to disclose environmental, social, and governance information, promote green innovation, develop green and clean technologies, and reduce industrial pollution emissions at the source.