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Article

Environmental Justice and Corporate Green Innovation: The Role of Legitimacy Pressures

School of Economics, Southwestern University of Finance and Economics, Chengdu 611130, China
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Author to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5599; https://doi.org/10.3390/su16135599
Submission received: 24 May 2024 / Revised: 20 June 2024 / Accepted: 27 June 2024 / Published: 29 June 2024

Abstract

:
This study examines the impact of environmental judicial reinforcement on green technology innovation, constructing a progressive difference-in-differences model using firm- and city-level panel data from 2006 to 2019 and taking the successive establishment of environmental courts by the Intermediate People’s Court in Chinese prefectural-level cities as a quasi-natural experiment. We find that establishing environmental courts in China promotes green technology innovation. This finding still holds after a series of robustness tests such as selected fixed-effects Poisson model regression. The mechanism analysis suggests that environmental courts primarily promote increased green innovation output by heavily polluting firms by exerting more substantial pressure regarding environmental legitimacy. The heterogeneity analysis reveals that the positive impact of the establishment of environmental courts on green technology innovation is more pronounced in water pollution-intensive industries and areas with high public participation and media attention. Our findings provide new insights into how environmental justice affects firms’ green innovation and validate the Porter hypothesis. Also, they serves as a reference for constructing environmental courts in China and other policy jurisdictions that may be interested.

1. Introduction

Green innovation refers to the enhancement of products or processes to reduce environmental burdens or achieve sustainable development goals [1,2]. It is crucial for harmonizing economic growth with environmental protection [3]. This mutually beneficial potential has garnered worldwide attention, prompting policymakers to allocate increasing amounts of public funds to stimulate the creation and diffusion of clean and sustainable technologies [4]. For instance, in 2022, China’s Ministry of Science and Technology issued the Implementation Plan for Science and Technology to Support Carbon Peak and Carbon Neutrality (2022–2030). The plan emphasizes supporting a significant and sustained reduction in carbon dioxide emissions per unit of gross domestic product (GDP) and energy consumption per unit by building a low-carbon, zero-carbon, and carbon-negative technological innovation system. Green innovations, such as renewable energy technologies and cleaner process modifications, are essential technical routes and critical directions for reducing carbon emissions from industrial production processes. However, the high cost and risk associated with green technology make it challenging to achieve a direct short-term impact on financial performance [5]. Enterprises need further incentives to engage in green technology innovation. Therefore, well-designed environmental regulatory policies are essential to guide firms toward adopting environmentally friendly production methods and eventually developing or adopting additional green technologies.
The well-known Porter hypothesis suggests that stringent but well-designed environmental regulations can stimulate technological innovation and create “innovation offsets” that exceed the costs associated with these regulations [6]. Enterprises that apply green innovation in production processes can reduce dependence on traditional polluting production methods and effectively avoid environmental regulatory costs [7]. Several theoretical and empirical papers have strongly supported the Porter hypothesis [8,9]. As a regularized environmental regulation with a long-term nature, environmental justice enhancement has received extensive research attention [10,11]. Gao et al. (2024) argued that environmental justice measures, represented by China’s environmental courts, significantly impact the quality of corporate green innovation [12]. Increased judicial efficiency in environmental cases and the reduced likelihood of cooperation between local governments and polluting enterprises are essential mechanisms by which environmental courts influence green innovation. Zhou et al. (2023) and Tao et al. (2023) asserted that environmental courts force firms to innovate green technologies by raising public ecological concerns and increasing external monitoring pressure [11,13]. To further enhance research on environmental regulation and green innovation, this study uses the successive establishment of environmental courts by the Intermediate People’s Courts (IPCs) in Chinese prefecture-level cities as a quasi-natural experiment. We analyze the impact of environmental courts on green technology innovation by constructing a progressive difference-in-differences (DID) model using data from 280 prefectural-level cities in China and 485 listed firms in heavy pollution industries in Shanghai and Shenzhen A-shares from 2006 to 2019.
Four relevant findings emerge. (i) Establishing environmental protection courts can significantly enhance the efficiency of environmental pollution cases and the level of green technology innovation in pilot cities. It can also promote heavily polluting enterprises’ green innovation output. (ii) Environmental courts primarily induce green technological innovation in heavily polluting firms by exerting more substantial pressure for environmental legitimacy. Environmental tribunals increase the risk of external environmental litigation and the cost of violating the law faced by heavily polluting industries. Environmental courts also compel heavily polluting enterprises to increase environmental investments and gain environmental legitimacy through green technological innovation. (iii) In terms of industrial categories, the positive impact of environmental courts on corporate green innovation is more pronounced in water pollution-intensive industries. Compared with industries emitting air pollution, environmental pollution infringements in water pollution-intensive industries are more likely to be judicially forensic, and water-polluting firms face tremendous pressure for environmental legitimacy. (iv) In terms of regional characteristics, environmental courts’ promotional role in advancing green innovation among enterprises in heavily polluting industries is more significant in regions with higher public participation and media attention.
This study contributes to the current literature in three significant ways. First, it integrates the theory of environmental legitimacy, thereby enriching the study of the relationship between environmental regulation and green innovation. Specifically, it examines how the establishment of environmental courts influences green technological innovation in heavily polluting industries through the mediating roles of environmental litigation risk and environmental investment. Second, we use a unique data collection method. The study employs a novel data collection approach by manually gathering information on the appointment dates of presidents of environmental courts in various cities from 2006 to 2019. This meticulous effort enables the creation of a more precise policy point-in-time dummy variable, enhancing the accuracy of the policy effects analysis. Third, we employed contextual and dimensional analysis. The paper reveals the differential impacts of environmental courts on green technological innovation across various contexts. It explores these differences across several dimensions, including industry category and external attention, thereby comprehensively understanding how environmental regulation affects firms in different scenarios. These contributions significantly advance our understanding of the mechanisms through which environmental regulation can drive green innovation, particularly in heavily polluting industries. Integrating environmental legitimacy theory, precise policy timing analysis, and contextual differentiation offers new insights into the dynamic interplay between regulatory frameworks and corporate environmental strategies.
The remainder of the paper is organized as follows: Section 2 presents the research background and hypotheses. Section 3 details our data sources and empirical strategy. Section 4 provides benchmark regression results and robustness tests. Section 5 discusses the impact mechanism and heterogeneity analyses. Section 6 concludes the paper.

2. Research Background and Hypotheses

2.1. Environmental Courts in China

Western nations began exploring the establishment of specialized environmental courts in the 1980s, while China established its first environmental court in 2007. Compared with developed countries such as Australia (established in 1980), the US (1990), New Zealand (1991), and Switzerland (1999), although starting late, China’s environmental courts have developed at a remarkably accelerated speed and scale [14]. By the end of 2019, 513 environmental resources tribunals were established in China, according to the report China’s Environmental Resources Trials (2019). According to the hierarchy of courts, 26 high courts (provincial level), 118 intermediate courts (municipal level), and 368 primary courts (county level) have established environmental tribunals. Figure 1 illustrates the distribution of IPC’s environmental resource tribunals at the prefecture city level in China.
Three notable features characterize China’s environmental court system. (i) Training and selecting judges with professional backgrounds specializing in environmental resource cases. Compared to traditional criminal, civil, and administrative cases, environmental protection cases require trial judges to have a higher level of professional expertise for investigation and evidence collection [13]. Using professionals to adjudicate environmental cases can improve the accuracy and efficiency of case rulings. (ii) Adopting a centralized trial model for criminal, civil, and administrative cases. Acts of environmental pollution and ecological damage can simultaneously affect people, property, and the ecosystem. The unified trial of environmental resource cases, also referred to as the “three trials in one” mechanism [15], supports uniformity in judges’ rulings and optimization of trial resources. (iii) Building a multiparty participation mechanism for environmental resource protection. People’s Courts at all levels are actively promoting the establishment of law enforcement and cooperation mechanisms with public security, procuratorial, and administrative departments overseeing environmental resources. For example, monitoring data, surveys, and test reports generated by environmental protection authorities in the process of regulatory enforcement can be used as evidence in environmental judicial proceedings. Environmental courts can leverage the strengths of both judicial and local government bodies through a collaborative action mechanism established via judicial and administrative means to enhance collective enforcement of environmental protection [10].
In terms of practical results, after 2012, when environmental courts were established on a large scale [14], environmental resource trials’ quality and efficiency significantly improved. According to the statistics from China’s Environmental Resource Trials report, from 2002 to 2019, courts nationwide concluded about 1.5 million criminal, civil, and administrative first-instance cases concerning environmental resources, including about 120,000 cases from 2002 to 2012, over 550,000 cases from 2012 to 2016, and about 760,000 cases from 2017 to 2019. China’s environmental resource cases exhibit a clear upward annual trend. China’s environmental courts are rapidly advancing the specialization and professionalization of environmental justice by appointing professional adjudicators, establishing specified environmental judicial procedures, and integrating the combined strength of environmental law enforcement departments. Environmental courts endeavor to better match the demand for expedient environmental litigation related to environmental pollution.

2.2. Literature Review and Theoretical Hypotheses

2.2.1. Environmental Regulation and Green Innovation

The relationship between environmental regulation and corporate green innovation has been a topic of long-standing academic interest [8]. The well-known Porter hypothesis posits that well-designed environmental regulations motivate firms to engage in green technological innovation. According to this hypothesis, the costs associated with environmental regulation can be offset by the “innovation compensation” that arises when firms apply innovations to their production processes [6].
The academic community has widely investigated and tested the Porter hypothesis. For example, using data from the industrial sectors of Organization for Economic Cooperation and Development countries, Wang et al. (2019) found that well-designed environmental regulatory policies positively impact green productivity growth; however, when environmental regulatory policies are too strict, the innovation offsets effect is lower than the compliance cost effect, which does not advance green productivity growth [9]. It is crucial to implement appropriate environmental regulation measures. Environmental regulation encompasses command, market, and voluntary approaches. Wang et al. (2022) argued that both command-based regulations, which include laws, regulations, and administrative directives, and market-based policies such as environmental taxes and carbon trading, can facilitate enterprises’ green technological innovation [16]. The Chinese government has primarily employed a combination of command-based and market-based environmental regulations to curb firms’ pollution behaviors [17].

2.2.2. Environmental Courts and Green Innovation

Environmental courts have garnered considerable attention as a typical form of command-based environmental regulation. Zhang et al. (2019) assessed the impact of environmental justice reforms on environmental governance within heavily polluting industries from 2004 to 2014, using the establishment of environmental courts in China as a quasi-natural experiment [10]. They argued that environmental courts serve as practical institutional tools for addressing China’s environmental challenges by significantly raising the costs of violating environmental laws for polluting enterprises, thereby encouraging more significant investments in environmental protection. Similarly, Zhao et al. (2022) examined a sample of prefecture-level cities in China and found that environmental courts were associated with reduced carbon emissions. They attributed this outcome to heightened environmental enforcement by local governments and an enhanced capacity for green innovation among firms [15].
Despite these findings, there is limited research explicitly exploring the direct relationship between environmental courts and firms’ green innovation. Only a few studies have touched upon the role of environmental courts in promoting corporate green innovation, focusing on aspects such as environmental justice efficiency and public awareness of environmental protection [11,12,13]. For instance, Zhou et al. (2023) used the number of concluded urban environmental cases as a mediating variable to illustrate how environmental courts can bolster urban green patent output through improved efficiency in environmental justice and increased public awareness of environmental rights [11].
Analyzing the level of green technology innovation across cities in 2006—before the establishment of China’s environmental protection courts—revealed minimal differences in the number of green patent applications between cities (Figure 2). By 2019, however, the number of green patent applications had increased nearly tenfold (Figure 3), with significantly higher levels of green innovation observed in cities with established environmental courts (Figure 1). This suggests a potential positive correlation between environmental courts and green innovation. Accordingly, we propose Hypothesis 1:
Hypothesis 1.
Environmental justice can promote corporate green innovation.

2.2.3. Environmental Legitimacy and Green Innovation

Environmental legitimacy extends the concept of legitimacy and refers to “the generalized perception or assumption that a firm’s corporate environmental performance is desirable, proper, or appropriate” [18,19]. Companies gain legitimacy when environmental practices (i.e., low-emissions strategies, waste recycling management, and cleaner production) meet stakeholders’ (i.e., public, investors, and governments) expectations and regulatory requirements [20]. Firms that lack legitimacy are more likely to face legal, economic, or social sanctions (i.e., judicial proceedings, consumer product boycotts, shareholders’ withdrawal of funds, and reduced loans from banks) [21]. In contrast, firms with legitimacy are more likely to have access to external resources that can improve financial performance and enhance market value [22]. As a result, heavily polluting firms are incentivized to implement green initiatives when confronting government regulatory and nongovernment normative pressures [7]. Firms gain environmental legitimacy by taking more environmentally friendly actions. According to Berrone et al. (2017), green technological innovation is one of the most essential ways for firms to gain environmental legitimacy and publicize positive, sustainable actions among key stakeholders [23].
Establishing environmental courts as a formal judicial system has improved the efficiency of settling environmental pollution cases and increased the risk of environmental litigation against polluting firms. In particular, due to the potential for considerable pollutant discharge, heavily polluting firms are more likely to receive focused attention from local environmental jurisdictions than general enterprises. When firms face a stricter judicial environment, the expected costs of violating the law rise significantly, including fines and complete closures. China’s environmental courts have generated more substantial pressure for heavily polluting firms to establish environmental legitimacy. When firms face a stricter judicial environment, the expected costs of violating the law rise significantly, including fines and closures related to litigation. Considering environmental legitimacy and sustainability, rational firms will increase research and development (R&D) and environmental investments to advance green transitions [24]. In the long run, firms’ active green transformation can alleviate the increasing regulatory pressure on heavily polluting industries as well as improve firms’ core competitiveness.
In summary, environmental courts externally increase the risk of environmental litigation for heavily polluting enterprises, compelling enterprises to increase environmental investment to advance green transformation internally. Therefore, based on this theory of environmental legitimacy, this study proposes the following hypothesis:
Hypothesis 2.
Environmental courts compel heavily polluting firms to adopt green technologies by increasing pressure for environmental legitimacy.

3. Data and Methodology

3.1. Sample Selection and Data Sources

This study uses data from A-share listed companies in China’s heavy pollution industries at the prefecture city level from 2006 to 2019. We chose the 2006–2019 sample interval for two reasons. First, the first intermediate environmental court was established in November 2007, and the large-scale establishment of formal environmental courts occurred from 2012 onward [14]. We chose the year before the implementation of the environmental justice specialization as the starting year to assess the policy effects of environmental court establishment more precisely. Second, the Chinese economy suffered a temporary disruption after 2019 from the impact of the COVID-2019 pandemic, which would likely skew our results.
The choice of listed companies in heavily polluting industries as research subjects is justified for several reasons. First, the transition of heavily polluting sectors toward greener practices is crucial for China to achieve its goals of carbon peaking and neutrality. Second, these firms are closely monitored by local environmental justice departments due to their potential for significant pollutant emissions, and the establishment of environmental courts directly influences the decisions and operations of these companies [10]. To define heavy-polluting industries, following Zhou et al. (2021), we initially identified sectors based on the Classified Management List of Environmental Protection Verification Industry of Listed Companies issued by China’s Ministry of Environmental Protection in 2008 [25]. Subsequently, we selected 18 specific industries from the latest Industrial Classification for National Economic Activities, including thermal power, iron and steel, electrolytic aluminum, coal, metallurgy, chemical, petrochemical, paper, pharmaceutical, textile, tannery, and mining industries.
The data in this study were obtained from the financial data for listed firms in the China Stock Market & Accounting Research database, and the green patent data for listed firms in the Chinese Research Data Services (CNRDS) platform. We processed the raw data as follows. (a) We excluded ST and ST* firms in the sample period and removed insolvent samples. (b) We removed firms with significant changes in primary business during the sample period (i.e., when the firm had been engaged in nonmanufacturing activities). (c) We deleted samples with missing variable observations. (d) We winsorized all consecutive variables at the 1% level to control for the anomalous effects of extreme values. Finally, we yielded 485 firms and 5515 observations.

3.2. Empirical Models

To assess the impact of strengthened environmental justice on firms’ green technological innovation, we construct the following progressive two-way fixed effects (FE) DID model:
g p a t e n t i t = β 0 + β 1 c o u r t i t + κ j Z i t + μ i + ν t + ε i t
where the explanatory variable g p a t e n t i t denotes firm i’s green innovation level in year t . c o u r t i t is a dummy variable for the policy established by the environmental court. Z i t is the control variable, μ i and ν t represent firm and year FEs, respectively, and ε i t is the error term. The estimated coefficient β 1 is the policy effect, which indicates that the policy is effective if β 1 is significantly positive.

3.3. Variable Definitions

3.3.1. Explanatory Variables

The explanatory variable in the model (1) is the firms’ green innovation capabilities, measured following standard practice in the literature as the natural logarithm of the number of firms’ green patent applications plus one ( g p a t e n t ) [26,27]. According to Du et al. (2021), green patents directly reflect firms’ output in green technology innovation activities, which can be quantitatively assessed and classified into different technologies [28]. Moreover, patent applications capture current technological innovation activities more accurately compared to patents that have been granted, considering various uncertainties from application to acceptance (e.g., lengthy examination processes and bureaucratic hurdles) [13]. As part of robustness testing, we also include the number of green patents granted.

3.3.2. Core Explanatory Variables

In this study, we use the establishment of environmental courts by prefecture-level municipal IPCs as a quasi-natural experiment. We manually organized data on the establishment of environmental resource tribunals in prefectural-level cities in China up to 2019, following a systematic approach to ensure accuracy. First, we used the Baidu search engine to identify prefecture-level cities that had established environmental courts before 2019. Next, we manually collected the dates when each city’s People’s Congress first appointed the president of the environmental court. This process resulted in a list of 67 prefectural-level cities that have formally implemented the environmental court system, along with the respective years of establishment.
In model (1), if firm i is in a city with an established environmental court, then c o u r t i t takes the value of 1 in year t and subsequent years and 0 otherwise. The regressions are conducted using a progressive DID model with cities that have established environmental courts as the treatment group and other cities as the control group. Considering the lag in policy implementation, we set the second half of the year (after June 30) as the second year for cities that established environmental courts. For example, Guiyang City in Guizhou Province began setting up an environmental court in November 2007, but challenges arose, including few trial cases and unsound working mechanisms; therefore, we set 2008 as the year of Guiyang’s formal implementation of an environmental court system.

3.3.3. Control Variables

Numerous factors influence green technology innovation [28]. Referencing Zhang et al. (2019) and Tang et al. (2021), we use six indicators with potential effects on firms’ green innovation as control variables: firm size ( l n s i z e ), age ( l n a g e ), financial leverage ( l e v ), firm profitability ( r o a ), management incentive ( s h a r e ), and firm ownership ( s o e ) [10]. Table 1 presents the main variable definitions and descriptive statistics.

4. Empirical Results

4.1. Baseline Regression Results

Table 2 presents the estimation results of the baseline regression model (1), with Columns (1)–(4) controlling for both firm- and year-fixed effects. Specifically, Column (1) reports the estimation results for the environmental court dummy variable ( c o u r t ) on firms’ green innovation ( g p a t e n t ), while Columns (2)–(4) progressively introduce control variables. The estimated coefficient of c o u r t is 0.142 and is significant at the 1% level when control variables are not included. When control variables are included, the estimated coefficient decreases to 0.127 and remains significant at the 5% level. These results indicate that the establishment of intermediate environmental courts increases the number of green patent applications by firms in heavily polluting industries. In terms of economic significance, the number of green patent applications among heavily polluting firms in cities with environmental courts increased by about 13.5% ( e 0.127 1 ) after accounting for control variables and firm- and year-fixed effects.

4.2. Time Trend Tests and Dynamic Effects

The DID model relies on the assumption that the treatment and control groups exhibit parallel trends before the treatment. To ensure this assumption holds, we reference Beck et al. (2010) and adopt an event analysis approach to investigate the dynamic effects of the Chinese IPR model pilot city policy [29]. This method enables us to examine the policy’s impact over time, offering a clearer understanding of its effects on both the treatment and control groups.
g p a t e n t i t = β 0 + n = 5 5 θ n × c o u r t i t n + η j Z i t + μ i + ν t + ε i t
Based on the model (2), this study introduces 11 dummy variables for five years before and five years after policy implementation. c o u r t i t 0 denotes the policy dummy variable for the year the environmental courts were established, and c o u r t i t n and c o u r t i t + n denote the years before and after the environmental courts were implemented.
Figure 4 presents the results of the parallel trend test based on model (2). The regression coefficients of c o u r t 5 , c o u r t 4 , c o u r t 3 , c o u r t 2 , and c o u r t 1 are insignificant, indicating that firms in the treatment and control groups do not significantly differ in terms of green innovation before policy implementation. Therefore, the parallel trend hypothesis holds. Regarding dynamic effects, the coefficient of c o u r t 0 is insignificant, and the regression coefficients of c o u r t + 1 , c o u r t + 2 , c o u r t + 3 , c o u r t + 4 , and c o u r t + 5 are all significantly positive, presenting an upward trend. This indicates that the establishment of environmental courts improves firms’ green innovation and the effect is sustainable in the long term.

4.3. Robustness Tests

4.3.1. Alternative Dependent Variables

Referencing Lee and Nie (2023), we introduce two alternative variables to measure firms’ green technology innovation [30]: (a) the number of green patents applied for by the firm in a given year that are eventually granted ( g p a t e n t _ a ) and (b) the number of green patents granted to firms in that year ( g p a t e n t _ b ). To mitigate heteroskedasticity, we add 1 to the patent counts in both categories before taking the natural logarithm. Columns (1) and (2) of Table 3 indicate that the coefficients of c o u r t are significantly positive, demonstrating that environmental courts increase the number of green patents granted to firms.

4.3.2. Controlling Endogeneity

To address endogeneity due to sample selection bias, we perform robustness tests using propensity score matching DID (PSM-DID) models. First, we include firm characteristics such as size ( l n s i z e ), age ( l n a g e ), leverage ( l e v ), return on assets ( r o a ), management shareholding ( s h a r e ), and nature of ownership ( s o e ) as matching variables. Drawing on Boeckerman and Ilmakunnas (2009), we implement period-by-period matching followed by asymptotic DID estimation using the matched sample [31]. In Column (3) of Table 3, we present the PSM-DID regression results, where the coefficient of c o u r t continues to show significant positivity, consistent with the baseline regression findings. This consistency underscores the robustness of the positive impact of environmental courts on firms’ green innovation, even after accounting for sample selection bias.

4.3.3. Changing the Research Sample

We adjust the sample interval in two ways. First, the significant social and economic impact of the 2019 coronavirus disease outbreak in China has led to a rapid decline in the number of lawsuits filed in the country’s environmental resource courts [32]. However, to more comprehensively reflect the impact of environmental courts on firms’ green innovation, we extended our sample period and used data from 2006 to 2023 for the regression estimation. Second, some firms did not apply for green patents during the sample period. If such enterprises are located in areas where environmental courts are not yet established, this may affect our regression results. We exclude enterprises with zero green patent applications during the sample period to eliminate this potential interference. Columns (4) and (5) of Table 3 present the regression results using the new sample. After adjusting for the sample interval, the coefficient of c o u r t is significantly positive at the 10% and 1% levels, respectively. Environmental courts still have a significant positive impact on firms’ green innovation, validating our baseline results.

4.3.4. Using an Alternate Regression Model

Firstly, the dataset of green patent applications from the sample firms displays a left-truncated distribution, with a significant number of zero values for the dependent variable. To address potential bias in estimation, following the approach of Ouyang et al. (2022), we utilize the panel Tobit model [33], detailed in Column (6) of Table 3. Secondly, as recommended by Cohn et al. (2022), we apply the Poisson model suitable for count data [34], given the nature of green patent applications as a count variable. In Column (7) of Table 3, we present the results of the Poisson model, incorporating firms’ green patent applications as an explanatory variable. These regression findings confirm that environmental courts effectively promote firms’ green technology innovation, consistent with the outcomes of the baseline regression analysis.

5. Mechanism Verification and Heterogeneity Analysis

5.1. Mechanism Verification

5.1.1. The Effect of Litigation Risk

The theoretical hypotheses in this study argue that environmental courts exert stronger environmental legitimacy pressure on firms in heavy pollution industries, exposing these industries to more significant environmental litigation risk. To avoid potential litigation risks and illegal costs, rational firms will actively engage in green innovation activities to advance transformational development. We construct the following progressive DID model to examine the effect of environmental courts on environmental protection cases at the regional level:
Y c t = α 0 + α 1 C o u r t c t + θ j X c t + γ c + λ t + ω c t
where the explanatory variable Y c t is the scale of environmental litigation of city c in year t . The scale of urban environmental litigation is measured by referencing the total number of environmental pollution litigation cases ( L n L a w s u i t ) and the number of cases per capita ( P L a w s u i t ). C o u r t c t is a dummy variable representing the policy established by the environmental court. X c t is the control variable, γ c and λ t represent city and year fixed effects, respectively, and ω c t is the error term. We introduce control variables such as the level of urban economic development, population size, R&D capabilities, industrial structure, foreign direct investment, and government intervention in model (3) to control for the influence of other factors on the model [15].
Economic development ( L n P G D P ) is measured using the natural logarithm of urban GDP per capita, while the population density of each region represents population size ( D e n s i t y ). R&D capacity ( R D ) is calculated as the total share of scientific and technical personnel in the labor force. The industrial structure ( S e c i n d ) is gauged by the share of secondary industry output in the regional GDP, and foreign direct investment ( F D I ) is represented by its share in the regional GDP. Government intervention ( G o v ) is measured using the ratio of public fiscal expenditure to regional GDP. City-level data were sourced from the CNRDS database, the China City Statistical Yearbook, and www.pkulaw.com.
The results in Columns (1) and (2) of Table 4 indicate that environmental courts significantly increase the number of environmental pollution cases tried in a city and enhance the efficiency of environmental case settlements. These findings suggest that local firms face more significant environmental litigation risks after the establishment of environmental courts than firms in other cities.
Referencing Gompers et al. (2022), we use grouping regression for mechanism testing [35]. We grouped the cities based on the average value of the number of environmental pollution cases per capita in the region each year considering city size. Specifically, we define cities with higher-than-average environmental pollution cases per capita as high environmental litigation risk cities, and cities with equal to or lower than the average number of environmental pollution cases per capita as low environmental litigation risk cities.
Table 5 presents the results of our grouping of environmental pollution litigation cases. Column (1) shows that the coefficient of c o u r t is 0.130 and significant at the 5% level, indicating that environmental courts promote green innovation output in areas with higher environmental litigation risk. In contrast, Column (2) reports a coefficient of c o u r t of 0.118, which is statistically insignificant. The policy effect tests in Table 4 and the results in Table 5 suggest that intermediate environmental courts increase the litigation risk for heavily polluting firms. Consequently, firms in these high-risk cities engage in more green innovation activities to manage external pressures for environmental legitimacy over the long term.

5.1.2. The Effect of Environmental Investment

Rational firms will increase R&D and environmental investments when confronting increased pressure for environmental legitimacy [24], and firms can improve future competitiveness while also meeting environmental regulatory requirements through green technological innovation. First, referencing Zhang et al. (2019), we manually collected expenditures that were directly related to environmental protection from the “construction projects in progress” section in the annual reports of listed firms in heavy pollution industries [10]. We also summed the data regarding desulfurization, denitrification, sewage treatment, waste gas, dust removal, energy saving, and other projects that characterize the firms’ increased environmental investment. Second, we used the natural logarithm of the amount of the firms’ environmental investment plus one ( l n E I ) and the proportion of environmental investment in the firms’ total assets ( r a t i o E I ) to measure the firms’ environmental investment. Finally, assessing each firm’s total environmental protection investment in 2006–2019, we defined firms above the median environmental investment as high environmental investment firms, and those equal to or below the median as low environmental investment firms.
Columns (1) and (2) of Table 6 present the regression results on the effect of environmental courts on firms’ environmental investment. The coefficients of c o u r t are significantly positive, indicating that environmental courts positively influence firms’ environmental investment. Columns (3) and (4) show the results of the group regression based on the firms’ environmental investment levels. Column (3) reveals that the coefficient of c o u r t is 0.190 and significant at the 5% level, suggesting that environmental courts promote green innovation output among firms with high environmental investment. Conversely, the coefficient of c o u r t in Column (4) is 0.025 and statistically insignificant, indicating that the legitimacy pressure exerted by intermediate environmental courts on heavily polluting firms has led to increased environmental investments. Thus, these firms seek to gain environmental legitimacy by adopting green technology innovation strategies.

5.2. Heterogeneous Effects

5.2.1. Heterogeneity of Industry Types

Litigants can establish evidence and facilitate the initiation of subsequent judicial proceedings for water pollution cases more easily than air pollution cases. To examine whether the policy effects of environmental courts differ across industries, we divide heavy pollution industries into water pollution-intensive and other polluting industries for group regression estimation. Referencing Cai et al. (2016), this study classifies the textile, paper, and paper products, chemical raw materials and chemical products manufacturing, and pharmaceutical manufacturing industries as water pollution-intensive industries [36]. Columns (1) and (2) of Table 7 present the estimation results for the samples of water pollution-intensive industries and other polluting industries, respectively. The results indicate that environmental courts promote green innovation among listed companies in water pollution-intensive industries but do not significantly impact green innovation in other polluting industries. This result demonstrates that environmental courts promote green innovation more for firms in industries for which it is easier to investigate and collect evidence on environmental pollution.

5.2.2. Heterogeneity of External Attention

High-intensity legal consequences or community environmental concerns can incentivize regions to expand green investments and push firms (particularly heavy polluters) to adopt and develop green technologies [37]. First, referencing Tao et al. (2023), we used the city-level Baidu haze search index to measure public environmental concerns [13]. We chose this index for two reasons. (a) Baidu is the largest search engine in China, with a market share of over 80%. (b) The occurrence of haze elicits high public environmental perception, and netizens’ concern about haze represents the public’s concern for the environment in general [38]. We group the sample according to the annual mean national haze search index value, classifying cities higher than the national mean as high public environmental concern cities and those equal to or lower than the national mean as low public environmental concern cities. Columns (3) and (4) of Table 7 present the grouping regression results, revealing that the effect of environmental courts on green innovation in heavily polluting firms is more significant in cities with high public environmental concerns.
Second, we collected the news reports regarding heavily polluting listed firms from the CNRDS database, quantifying the total number of annual adverse news reports. As the primary medium for disclosing and transmitting information, media reports help the public to expediently and clearly understand firms’ environmental protection information. Consequent increased media attention motivates firms to adopt more energy-saving and emission-reduction practices [39]. We grouped the sample based on the median number of annual news reports per firm. Firms surpassing the median are classified as high media attention firms, while those at or below the median are categorized as low media attention firms. The regression outcomes in Columns (5) and (6) of Table 7 reveal that the coefficient of c o u r t is 0.238 and statistically significant at the 1% level for firms with high media attention. Conversely, the coefficient of c o u r t is insignificant for firms with low media attention. When we synthesize these findings with those in Columns (3) and (4), it becomes evident that environmental courts notably stimulate green innovation among heavily polluting firms situated in regions characterized by robust public engagement and rigorous media scrutiny.

6. Conclusions and Policy Implications

Green technologies play a pivotal role in supporting China’s goals to achieve a carbon peak by 2030 and carbon neutrality by 2060. This study focuses on examining how local environmental justice frameworks influence heavily polluting firms, encouraging them to pursue green R&D initiatives. Specifically, utilizing data from Chinese heavily polluting firms spanning 2006 to 2019, we investigate the impact of environmental courts on green technology innovation. Employing a progressive DID model that leverages the establishment of environmental courts in the Intermediate People’s Court as a quasi-natural experiment, our analysis yields four key findings.
First, environmental courts can significantly boost heavily polluting firms’ green innovation output. This finding is validated by a series of robustness tests such as regressions using a FE Poisson model. Second, environmental courts have primarily contributed to heavily polluting firms’ green transformation by exerting more substantial environmental legitimacy pressure. Environmental courts increase the risk of environmental litigation by heavily polluting firms, compelling increased investment in environmental protection and advancing green technological innovation output. Third, regarding industry categories, environmental courts have a more significant role in promoting green innovation for firms in water pollution-intensive industries. The polluting behavior of firms in water pollution-intensive industries is more likely to produce legally legitimate evidence to execute successful judicial proceedings. Fourth, regarding regional characteristics, the role of environmental courts in promoting green innovation in heavily polluting firms is more significant in regions with higher public participation and media attention.

6.1. Policy Implications

Based on the above findings, we propose the following policy implications: First, policymakers should improve the environmental court system to increase the pressure for environmental legitimacy, guide firms to improve their technological processes, and ensure they actively fulfill their environmental protection obligations. For instance, in cases of environmental pollution, environmental courts should actively explore mechanisms for reviewing firms’ rectification efforts and evaluating the social impact before sentencing. This approach would encourage firms to enhance their technology and motivate the entire industrial chain, both upstream and downstream, to reduce hazardous waste and carbon emissions. Second, the government should broaden judicial channels for addressing environmental pollution, enabling public participation in environmental governance and media monitoring of firms’ polluting behaviors. This strategy is crucial for maximizing the effectiveness of environmental courts. For example, when environmental courts hear typical environmental pollution cases, they can invite the media to report on-site and broadcast the proceedings live on the Internet. This approach can boost public initiative and enthusiasm for participating in environmental resource protection and supervising firms’ pollution behavior.

6.2. Limitations and Future Directions

This study has the following limitations: First, due to data constraints, we can only indirectly measure the pressure on environmental legitimacy faced by firms through litigation risk and environmental investment. Future research should develop a comprehensive indicator framework to measure environmental legitimacy pressure from multiple dimensions directly. Second, the policy implications of establishing environmental tribunals are extensive, and this paper only examines their impact on firms’ green innovation. Future research could explore whether environmental justice affects firm productivity through green technological innovation. Although many studies have investigated the impact of environmental regulation on firms’ total productivity factor, the causal relationship between environmental justice and firms’ total productivity factor still needs to be explored in the literature. Finally, this paper focuses solely on the economic effects of environmental courts in China. The research sample and regional focus limit the generalizability of the findings. Future studies could utilize cross-country data samples for international comparative analyses to better understand the positive impact of environmental justice on firms’ green innovation.

Author Contributions

Conceptualization, D.C.; Methodology, D.C.; Software, D.C.; Validation, D.C.; Formal analysis, D.C.; Resources, G.Z.; Data curation, D.C.; Writing—original draft, D.C.; Writing—review & editing, D.C.; Supervision, D.C.; Project administration, D.C.; Funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Fund of China, grant number [21XJY006].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset is available at https://doi.org/10.6084/m9.figshare.24707979.v1, accessed on 15 December 2023. The dataset contains Stata .do files and, where available, data sets to replicate the empirical analyses in the main paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The distribution of environmental resources trial courts in China (as of 2019). Note: We identified 67 IPCs that established formal environmental courts and recorded presidential appointments via manual search. See Section 3.3.2 for details regarding our specific data collection method.
Figure 1. The distribution of environmental resources trial courts in China (as of 2019). Note: We identified 67 IPCs that established formal environmental courts and recorded presidential appointments via manual search. See Section 3.3.2 for details regarding our specific data collection method.
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Figure 2. The distribution of green patent applications per 10,000 people by cities in China, 2006.
Figure 2. The distribution of green patent applications per 10,000 people by cities in China, 2006.
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Figure 3. The distribution of green patent applications per 10,000 people by cities in China, 2019.
Figure 3. The distribution of green patent applications per 10,000 people by cities in China, 2019.
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Figure 4. The dynamic effect of environmental courts on green innovation in heavily polluting firms.
Figure 4. The dynamic effect of environmental courts on green innovation in heavily polluting firms.
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Table 1. Variable definitions and summary statistics.
Table 1. Variable definitions and summary statistics.
VariablesDefinitionnMeanStd. Err[95% Conf. Interval]
c o u r t = 1   if   a   firm   is   located   in   a   city   that   established an   environmental   court ; = 0   otherwise 55150.1350.0050.1260.144
g p a t e n t The   natural   logarithm   of   the   number   of   firms green   patent   applications   plus   1 55150.5330.0110.5110.555
ln s i z e The   natural   logarithm   of   total   firm   assets 55153.7230.0163.6913.755
ln a g e The   natural   logarithm   of   the   firm s   age 55152.7360.0052.7262.746
l e v The   ratio   of   a   firm s   total   liabilities   to   total   assets 55150.4180.0030.4130.423
r o a The   ratio   of   a   firm s   net   profit   to   total   assets 55150.0460.0010.0450.048
s h a r e The   ratio   of   management   shareholding   to   the total   number   of   shares   in   a   firm 55150.0800.0020.0760.084
s o e = 1   if   a   firm   is   state - owned ; = 0   otherwise 55150.4440.0070.4300.457
Table 2. Baseline regression results.
Table 2. Baseline regression results.
VariablesGpatentGpatent Gpatent Gpatent
(1)(2)(3)(4)
c o u r t 0.142 ***0.132 **0.132 **0.127 **
(0.053)(0.051)(0.051)(0.051)
ln s i z e 0.180 ***0.179 ***0.183 ***
(0.041)(0.043)(0.044)
ln a g e −0.092−0.094−0.099
(0.151)(0.150)(0.148)
l e v 0.0180.028
(0.116)(0.114)
r o a −0.0080.038
(0.292)(0.290)
s h a r e 0.247 *
(0.138)
s o e 0.224 ***
(0.077)
Constant 0.513 ***0.0950.099−0.028
(0.007)(0.418)(0.420)(0.426)
Firm   FE Yes Yes Yes Yes
Year   FE Yes Yes Yes Yes
Observations 5515551555155515
R 2 0.5800.5850.5850.587
Note: Robust standard errors clustered at the city level are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
Table 3. Results of robustness tests.
Table 3. Results of robustness tests.
VariablesReplace VariablesPSM-DIDChange SamplesTobitPoisson
(1)(2)(3)(4)(5)(6)(7)
Gpatent_aGpatent_bGpatentGpatentGpatentGpatentGpatent_n
c o u r t 0.112 **0.081 *0.124 **0.099 *0.152 ***0.153 *0.361 ***
(0.046)(0.042)(0.051)(0.060)(0.055)(0.082)(0.119)
ln s i z e 0.128 ***0.106 ***0.182 ***0.276 ***0.182 ***0.406 ***0.523 ***
(0.040)(0.038)(0.044)(0.044)(0.047)(0.088)(0.108)
ln a g e −0.0620.098−0.144−0.243−0.113−0.301−2.242 ***
(0.136)(0.148)(0.150)(0.150)(0.153)(0.311)(0.631)
l e v 0.0500.1090.020−0.1130.033−0.242−1.107 **
(0.109)(0.118)(0.116)(0.130)(0.136)(0.281)(0.450)
r o a 0.0200.0010.0570.136−0.165−0.635−0.055
(0.247)(0.252)(0.290)(0.231)(0.328)(0.705)(0.924)
s h a r e 0.0940.1280.2040.446 ***0.211−0.192−3.111 ***
(0.121)(0.116)(0.140)(0.141)(0.144)(0.320)(0.957)
s o e 0.173 **0.1060.235 ***0.152 **0.260 ***0.391 **0.236
(0.076)(0.079)(0.076)(0.073)(0.085)(0.196)(0.259)
Constant 0.004−0.3940.1010.1910.081−0.4734.474 ***
(0.389)(0.422)(0.433)(0.444)(0.452)(0.873)(1.468)
Firm   FE Yes Yes Yes Yes Yes Yes Yes
Year   FE Yes Yes Yes Yes Yes Yes Yes
Observations 5515551554257455467155155515
R 2 0.5410.5320.5880.5910.576--
Note: Unless otherwise indicated, see Table 2.
Table 4. Results of the mechanism analysis of litigation risk effects.
Table 4. Results of the mechanism analysis of litigation risk effects.
VariablesEnvironmental Lawsuit
(1)(2)
L n L a w s u i t P L a w s u i t
C o u r t 0.382 ***0.100 **
(0.104)(0.047)
L n P G D P −0.763 ***−0.374 ***
(0.174)(0.103)
D e n s i t y 1.104 ***0.174 **
(0.250)(0.077)
R D 0.0980.039 **
(0.060)(0.019)
S e c i n d 0.009 *0.002
(0.005)(0.002)
F D I −0.036 **−0.010 ***
(0.017)(0.004)
G o v −0.019 ***−0.004 ***
(0.006)(0.001)
Constant 8.051 ***3.851 ***
(1.692)(1.009)
City   FE Yes Yes
Year   FE Yes Yes
Observations 39203920
R 2 0.7150.356
Note: Unless otherwise indicated, see Table 2.
Table 5. Results of the mechanism analysis of litigation risk effects.
Table 5. Results of the mechanism analysis of litigation risk effects.
VariablesHigh Environmental Litigation RiskLow Environmental Litigation Risk
(1)(2)
G p a t e n t G p a t e n t
c o u r t 0.130 **0.118
(0.057)(0.099)
ln s i z e 0.174 **0.200 ***
(0.075)(0.056)
ln a g e −0.2810.163
(0.188)(0.240)
l e v 0.091−0.061
(0.153)(0.171)
r o a 0.735 *−0.676
(0.376)(0.443)
s h a r e 0.2500.285
(0.189)(0.199)
s o e 0.0600.344 ***
(0.096)(0.097)
Constant 0.499−0.779
(0.539)(0.675)
Firm   FE Yes Yes
Year   FE Yes Yes
Observations 27362779
R 2 0.5820.596
Note: Unless otherwise indicated, see Table 2.
Table 6. Results of mechanism analysis of environmental investments.
Table 6. Results of mechanism analysis of environmental investments.
VariablesEnvironmental Investments H i g h L o w
(1)(2)(3)(4)
R a i o n E I L n E I G p a t e n t G p a t e n t
c o u r t 0.095 *0.214 *0.190 **0.025
(0.051)(0.110)(0.079)(0.060)
ln s i z e 0.0410.325 ***0.254 ***0.111 **
(0.038)(0.089)(0.062)(0.048)
ln a g e −0.025−0.032−0.1700.094
(0.162)(0.352)(0.233)(0.167)
l e v −0.150−0.622 **0.0180.030
(0.157)(0.301)(0.172)(0.126)
r o a 0.2211.520 **0.440−0.529
(0.250)(0.629)(0.399)(0.401)
s h a r e −0.242−0.0190.0670.094
(0.149)(0.229)(0.255)(0.143)
s o e −0.0200.1290.223 **0.114
(0.061)(0.186)(0.112)(0.070)
Constant 0.234−0.020−0.057−0.317
(0.416)(0.978)(0.663)(0.453)
Firm   FE Yes Yes Yes Yes
Year   FE Yes Yes Yes Yes
Observations 5515551529812534
R 2 0.3100.5440.5910.562
Note: Unless otherwise indicated, see Table 2.
Table 7. Results of heterogeneity analysis of industry type and external attention.
Table 7. Results of heterogeneity analysis of industry type and external attention.
VariablesIndustry TypesExternal Attention
Water PollutionOther PollutionHigh Public AttentionLow Public AttentionHigh Media ConcernHigh Media Concern
(1)(2)(3)(4)(5)(6)
c o u r t 0.152 **0.1070.121 **0.1150.238 ***0.006
(0.071)(0.087)(0.057)(0.132)(0.084)(0.065)
ln s i z e 0.168 ***0.208 ***0.188 ***0.170 ***0.0540.272 ***
(0.057)(0.067)(0.063)(0.062)(0.058)(0.058)
ln a g e 0.036−0.154−0.294 *0.337−0.3320.169
(0.190)(0.285)(0.164)(0.293)(0.288)(0.170)
l e v −0.0030.058−0.0280.0800.062−0.022
(0.133)(0.191)(0.146)(0.193)(0.179)(0.150)
r o a −0.2530.4160.601*−0.727−0.0030.325
(0.403)(0.391)(0.328)(0.494)(0.388)(0.443)
s h a r e 0.1800.2480.2210.341 *−0.1520.327 *
(0.183)(0.222)(0.193)(0.191)(0.324)(0.173)
s o e 0.169 **0.243*0.0960.289 ***0.288 ***0.203 *
(0.079)(0.129)(0.121)(0.093)(0.106)(0.107)
Constant −0.287−0.0210.552−1.1941.131−1.026 **
(0.545)(0.782)(0.511)(0.776)(0.826)(0.471)
Firm   FE Yes Yes Yes Yes Yes Yes
Year   FE Yes Yes Yes Yes Yes Yes
Observations 283226833085243025672856
R 2 0.5650.6030.6110.5590.6410.584
Note: Unless otherwise indicated, see Table 2.
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Zhang, G.; Chen, D. Environmental Justice and Corporate Green Innovation: The Role of Legitimacy Pressures. Sustainability 2024, 16, 5599. https://doi.org/10.3390/su16135599

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Zhang G, Chen D. Environmental Justice and Corporate Green Innovation: The Role of Legitimacy Pressures. Sustainability. 2024; 16(13):5599. https://doi.org/10.3390/su16135599

Chicago/Turabian Style

Zhang, Guoyi, and Dong Chen. 2024. "Environmental Justice and Corporate Green Innovation: The Role of Legitimacy Pressures" Sustainability 16, no. 13: 5599. https://doi.org/10.3390/su16135599

APA Style

Zhang, G., & Chen, D. (2024). Environmental Justice and Corporate Green Innovation: The Role of Legitimacy Pressures. Sustainability, 16(13), 5599. https://doi.org/10.3390/su16135599

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