The Impact of Environmental Protection Tax on Green Innovation of Heavily Polluting Enterprises in China: A Mediating Role Based on ESG Performance

Abstract

This article selects 2992 Chinese heavily polluting listed companies on the Shanghai and Shenzhen stock markets from 2014 to 2022 as research samples and conducts a natural experiment based on the implementation of the Environmental Protection Tax Law in 2018. The empirical study investigates the impact of the implementation of the Environmental Protection Tax Law on green innovation in heavily polluting enterprises using the difference-in-differences method. The research finds that the levy of environmental protection tax is beneficial for improving the level of corporate ESG performance, thereby enhancing the green innovation capability of heavily polluting enterprises. At the same time, the promotion of green innovation levels in heavily polluting enterprises by the Environmental Protection Tax Law mainly depends on strategic green innovation rather than substantive green innovation. Moreover, the impact of environmental protection tax on enterprises of different natures and scales varies significantly. Environmental protection taxes have notably enhanced green innovation levels more in state-owned enterprises than their non-state-owned counterparts. Similarly, large-scale enterprises have seen a more substantial increase in green innovation due to environmental protection taxes than smaller enterprises. In addition, corporate ESG performance plays a mediating role in the impact of environmental protection taxes on green innovation in heavily polluting enterprises. From the dual perspectives of environmental protection taxes and corporate ESG performance, this paper proposes ideas for the improvement of green innovation levels in heavily polluting enterprises. At the same time, it provides empirical evidence for the economic consequences of environmental protection taxes and corporate ESG performance and suggests that enterprises improve their green innovation system and enhance the quality of ESG information disclosure. The government is improving the system of environmental taxation and ESG information disclosure, enhancing public awareness of environmental protection, and exercising supervision over energy supply.

1. Introduction

Globally, environmental protection and sustainable development have emerged as critical priorities for both governments and businesses [1]. The rapid expansion of the global economy has intensified pressure on resources and the environment, rendering the traditional model of extensive economic growth unsustainable [2]. Recognizing that green development is essential for fostering sustainable economic progress, countries worldwide have identified green innovation as a key component of this development strategy.

In response to environmental pollution and resource depletion, numerous countries have adopted measures to encourage green innovation. For instance, European Union nations have implemented a range of environmental taxes, including carbon and pollution emission taxes, which have effectively supported the research and deployment of green technologies by businesses [3]. Likewise, the United States, Canada, and Australia have introduced similar economic incentives to motivate companies to reduce emissions and pursue green innovations. These international policies highlight the crucial role of environmental protection taxes in driving green development through economic incentives.

In this context, environmental protection taxes, as an economic incentive-based environmental regulation tool, internalize the environmental costs of enterprises by taxing those that do not meet emission standards. This effectively unleashes the subjective initiative of enterprises and fully leverages the intrinsic incentives of the market. For instance, Sweden’s carbon tax and Norway’s pollution emission tax have significantly enhanced enterprises’ environmental awareness and green innovation capabilities [4]. These international experiences indicate that environmental protection taxes not only help reduce pollution emissions but also promote economic structural adjustment and drive sustainable development [5].

Nevertheless, the substantial capital investment, lengthy project cycles, and high financial risks associated with green innovation pose significant challenges for enterprises worldwide [6]. These challenges often result in high costs for companies pursuing green innovation, simultaneously increasing risks for investors and raising the external financing costs for enterprises. Moreover, corporate management tends to shy away from long-term innovation projects when utilizing investor funds, exacerbating the issue. This situation creates a dual dilemma for green innovation as enterprises grapple with both financing constraints and agency costs [7]. Globally, many heavily polluting industries still exhibit relatively low levels of green innovation. Tackling the challenges associated with green innovation, advancing the green transformation of enterprises, and securing both economic and environmental benefits are essential for achieving sustainable development globally.

Some scholars have already researched the influencing factors of green innovation from perspectives such as enterprise property rights, external stakeholders, and institutional environments [8,9]. Sustainable development, as a long-term development concept, embodies the harmonious coexistence of enterprises with nature and society. Regarding green innovation in enterprises, it should not be limited to short-term performance but should be measured from the perspective of corporate sustainable development. Focusing on environmental, social, and governance (ESG) aspects, corporate ESG integrates these three key pillars—environmental responsibility, social responsibility, and corporate governance—making the execution of green development strategies for enterprises more practical and directive.

Therefore, in the context of the “dual carbon” goal and green development, it is crucial to thoroughly investigate the impact of environmental protection taxes on green innovation in heavily polluting enterprises and to uncover the mechanisms influencing this relationship [10]. Studying the mediating role of corporate ESG performance in this process is crucial for the implementation of current environmental policies, promoting the green transformation and development of enterprises, and ultimately achieving dual benefits for the economy and the environment.

In this context, this paper analyzes the impact of environmental protection tax policies on green innovation by examining data from heavily polluting listed companies in China. The reasons for selecting China are as follows: Firstly, China’s heavily polluting enterprises are undergoing a critical phase of transformation and upgrading, placing substantial pressure on the environment. Investigating the green innovation challenges faced by these enterprises is essential for mitigating environmental pollution and enhancing ecological conditions.

Secondly, the Chinese government has increasingly prioritized ecological civilization construction in recent years, implementing a series of policies to promote green development, with the environmental protection tax being a key component. Researching this issue can provide scientific evidence for the Chinese government to further improve its green tax policies. Moreover, China’s investment and development potential in the field of green innovation are substantial. Although the current level of green innovation is relatively low, Chinese enterprises have significant room for improvement through appropriate policy guidance and market incentive mechanisms. Studying green innovation in China can not only help achieve domestic sustainable development goals but also provide valuable insights for other developing countries.

The possible contributions of this article are as follows: Firstly, compared to previous studies, this research employs a more refined difference-in-differences (DID) method to control for endogeneity issues, thereby more accurately identifying the causal effects of the environmental protection tax. For example, past studies primarily focused on the impact of environmental protection tax on the overall environmental performance of companies, with less emphasis on the specific dimension of green innovation. This study provides more detailed and profound insights by analyzing companies’ performance in green technology research and development, sustainable product development, and eco-friendly process improvements. Secondly, unlike previous studies, this research not only considers the direct economic pressure of the environmental protection tax but also explores the role of ESG performance as a mediating variable. By incorporating ESG performance into the analytical framework, this study reveals how the environmental protection tax indirectly promotes green innovation by enhancing companies’ environmental, social, and governance performance. This finding contrasts with the simple linear relationships found in previous studies, enriching the theoretical understanding.

2. Literature Review

In order to systematically understand the relationship between environmental protection tax, corporate ESG performance, and green innovation and to facilitate the research hypothesis proposed later, this paper conducts a literature review from the following three aspects.

2.1. The Impact of Environmental Taxes on Green Innovation

It is of significant importance to delve into the mechanism of how environmental protection taxes influence green innovation in enterprises, as it holds implications for the implementation of environmental policies in China and ultimately achieving the dual benefits of green sustainable development and high-quality economic growth. However, there is still no consensus in the existing literature, both domestically and internationally, regarding the specific relationship between environmental protection taxes and green innovation. The literature presents four main perspectives on this matter. Firstly, some scholars argue that environmental protection taxes have a certain inhibitory effect on green innovation in enterprises. Porter et al. suggest that companies may alleviate the cost pressure of environmental protection taxes through economies of scale. This not only fails to promote green innovation in enterprises but also has a negative impact on regional green innovation [11]. Chen et al. argue that environmental protection taxes increase business costs, leading to a “crowding-out effect” on research and development investment, thereby inhibiting business innovation [12,13,14]. Secondly, Wang et al. and Peng et al. argue that environmental protection taxes can compel heavily polluting enterprises to achieve green innovation. They suggest that moderate environmental taxes increase production costs for enterprises but incentivize them to develop green products and processes, thereby generating an innovation compensation effect [15,16,17]. Thirdly, there is a non-linear relationship between environmental protection taxes and green innovation in enterprises. Studies by Wei et al. indicate that the intensity of environmental protection taxes has different effects on green innovation in enterprises, generally exhibiting a “U”-shaped relationship [18,19]. Fourthly, the influence of environmental protection taxes on corporate green innovation remains unclear. Cai, W. et al. discovered that the newly implemented Environmental Protection Tax Law does not significantly affect companies’ investments in technological innovation [20,21].

2.2. Environmental Protection Tax and Corporate ESG Performance

Regarding the relationship between environmental protection tax and corporate ESG performance, there is limited research in the existing literature, but most conclusions indicate that environmental protection tax can improve corporate ESG performance. He et al. found that following the implementation of the Environmental Protection Tax Law, the ESG performance of heavily polluting enterprises improved significantly, and they examined the mechanisms underlying this effect. The results indicate that this positive effect is mainly achieved through increased investment in environmental protection by enterprises and enhancing their level of green innovation [22,23,24]. Dameng et al., on the other hand, argue that green technological innovation by enterprises plays an intermediary role in the promotion effect of environmental protection tax on the ESG performance of heavily polluting enterprises [25]. Tan-Mullins et al. identified and evaluated the impact of environmental regulations on the ESG performance of enterprises with varying ownership structures by employing a difference-in-differences (DID) model. It was found that following the revision and implementation of the Environmental Protection Tax Law, state-owned enterprises demonstrated superior environmental and social responsibility performance compared to private enterprises [26,27,28].

2.3. Corporate ESG Performance and Green Innovation

ESG performance is a new concept that has emerged in the field of sustainable development in recent years. Regarding research on ESG performance, the current literature primarily centers on the economic outcomes of ESG information disclosure and the analysis of ESG investments. Few studies examine the influence of corporate ESG performance on green innovation, and those that do typically suggest a positive relationship between the two. However, Zhang found through empirical research that media attention has a significant moderating effect on green innovation, which can strengthen the relevance between ESG information disclosure and green innovation. Moreover, this enhancing effect is more pronounced in state-owned enterprises [29]. Francesco et al. used a double difference model to explore the impact of market soft regulation caused by Sustainalytics ESG ratings on corporate green innovation. The research results indicate that institutional ESG ratings encourage “incremental deterioration” of corporate green innovation [30,31,32,33]. Rahman et al. used listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2020 as samples [34]. Empirical research findings indicate that suitable ESG performance significantly promotes corporate green technology innovation by strengthening internal oversight and enhancing corporate transparency. There are also scholars with different views. Long et al. found that ESG disclosure is related to the green innovation performance of heavily polluting enterprises in a “U”-shaped manner. The main influencing factors of this correlation include cost effect, resource effect, and governance effect [35].

3. Research Hypotheses and Research Design

3.1. Research Hypotheses

3.1.1. Environmental Protection Taxes and Green Innovation

Firstly, environmental regulatory tools such as taxes internalize the externalities of pollution for companies, increasing their environmental costs. This compels them to increase research and development (R&D) investment to improve pollution control processes and enhance pollution control capabilities. Fiscal expenditures, government subsidies, and other environmental regulatory tools can offset the R&D expenses incurred by companies in conducting green innovation activities, alleviating the high risks and uncertainties associated with green innovation. Therefore, it is evident that fiscal and tax-related environmental regulatory tools can enhance companies’ green innovation levels by increasing their R&D intensity [36,37]. Moreover, the implementation of environmental protection tax laws introduces an information disclosure mechanism that is subject to public scrutiny. This includes requirements for the government to regularly publish environmental reports and disclose environmental impact assessment reports for corporate construction projects. Such measures expand public awareness and oversight rights, thereby increasing pressure on companies to address environmental pollution issues. In addition, environmental protection taxes strengthen penalties for illegal pollution discharge by heavily polluting enterprises. Enforcement is also more stringent, with the possibility of hefty fines or even administrative detention for violators, increasing the costs of non-compliance for companies [38]. Finally, environmental protection taxes belong to market-based environmental institutions. From the perspective of institutional theory, environmental protection taxes increase the pressure on the legitimacy of companies. On the one hand, governments and relevant departments investigate the payment status of environmental taxes and the green production status of companies. Based on the investigation results, they determine whether to impose administrative penalties or provide government subsidies, tax incentives, and other resources. Conversely, external stakeholders like suppliers and investors evaluate the legality of environmental taxes to gauge the risks linked to the company and decide whether to maintain their partnership. The demand for environmental legitimacy will drive companies to pursue green innovation [39]. Based on the analysis above, this paper proposes the following hypothesis:

H1: 

Environmental protection taxes positively influence the green innovation performance of heavily polluting enterprises (Figure 1).

3.1.2. Environmental Protection Taxes and Corporate ESG Performance

From an environmental perspective, the promulgation of environmental protection tax laws serves as a wake-up call for enterprises, signaling that environmental protection is a significant issue that requires attention. Subjectively, it strengthens the environmental awareness of companies. At the same time, the increase in environmental taxes and the strengthening of law enforcement objectively increase the cost of pollution control for enterprises. This will strengthen the motivation for companies to increase research and development expenditures and engage in green innovation [40]. From a social responsibility standpoint, a company’s obligations should go beyond the traditional focus on profit alone. It highlights the significance of respecting human dignity and value throughout the production process, along with contributing positively to the environment and society. Environmental protection taxes aid companies in developing green processes, reducing pollution emissions, improving employees’ working environments, and aiming to produce green products and services that are highly regarded and praised by users. This continual pursuit of excellence and high quality enhances the company’s level of social responsibility. From the perspective of corporate governance, the “G” in ESG does not represent conventional corporate governance in the general sense, as it does not involve specific corporate governance decisions. Instead, it integrates environmental and social concerns into the corporate governance framework, necessitating that company management effectively handle environmental, social, and economic challenges. This approach fosters the development of an internal ESG governance structure within the company [41]. The promulgation of environmental protection tax laws can incentivize enterprises to prioritize sustainable development and establish ESG supervisory governance systems. Based on the above analysis, the following hypothesis is proposed in this paper:

H2: 

Environmental protection taxes have a positive impact on the ESG performance of heavily polluting enterprises (Figure 2).

3.1.3. Mediating Effects of Corporate ESG Performance

Environmental protection tax may influence a company’s green performance through its ESG performance. Firstly, environmental protection tax increases the operating costs for enterprises, forcing them to adopt environmental measures to reduce tax burdens, thereby prompting improvements in environmental management and enhancing the environmental dimension (E) of their ESG performance. Additionally, to better comply with environmental regulations and taxes, enterprises may strengthen employee training, improve working conditions, and enhance governance transparency and compliance. These improvements not only elevate the social (S) and governance (G) dimensions of the ESG performance but also boost the overall ESG score. A strong ESG performance can attract investors focused on sustainability, providing the enterprise with more funding support and enabling it to allocate more resources to research and innovation. At the same time, high ESG performance enhances the company’s brand image and market reputation, increasing its competitiveness and driving more innovative activities. To improve ESG performance, enterprises may introduce new management concepts and technological methods, such as adopting more environmentally friendly production technologies, developing new products, or improving existing products to reduce environmental impact, leading to a series of internal innovative changes. Therefore, environmental protection tax indirectly promotes an enterprise’s innovation capacity and performance by improving its ESG performance. Based on the above analysis, the following hypothesis is proposed in this paper:

H3: 

Corporate ESG performance plays a mediating role in the relationship between environmental protection taxes and corporate green innovation (Figure 3).

3.2. Sample Selection and Data Sources

This study analyzes data from A-share listed companies on the Shanghai and Shenzhen stock exchanges between 2014 and 2022 to explore how the implementation of China’s Environmental Protection Tax Law has influenced green innovation among heavily polluting enterprises. Specifically, reference is made to the research of Deng [27]. Based on the classification standards for heavily polluting industries outlined in the “Management Catalogue of Industry Classification for Environmental Protection Inspection of Listed Companies” by China’s Ministry of Ecology and Environment, the sample of listed companies is divided into 16 industries, including steel, building materials, mining, and metallurgy, identified as heavily polluting sectors, while companies in other industries are classified as non-heavily polluting industries. Given that companies in heavily polluting industries are significantly affected by the “Environmental Protection Tax Law”, they are considered the experimental group, while companies in other industries less affected by the law are regarded as the control group.

Subsequently, the sample data were filtered. In line with the methodology used in Sunccc’s research, the sample excludes financial and conglomerate enterprises, ST and ST* enterprises within the sample period, delisted companies, and enterprises with incomplete data on key variables and control variables [42]. Finally, a total of 12,096 observations were obtained, covering 2992 listed companies. Among them, there were 1082 companies in the experimental group and 1910 companies in the control group. This paper utilizes the Huazheng ESG data from the Wind database to obtain ESG rating data. Green patent data, including the total number of green patent applications and the total number of green patent grants, are obtained from the China Research Data Service Platform (CNRDS). Control variable data are obtained from the Guotai An database (CSMAR). Finally, Winsorization is applied to all major continuous variables at the 1% level.

3.3. Model Design and Variable Definitions

3.3.1. Variable Descriptions

(1)

Dependent Variable: This study considers corporate green innovation performance as the dependent variable. Following the findings of previous scholars, the total number of green patent applications (LN_total) is utilized as a metric to assess corporate green innovation. Although the measurement of green innovation can generally be approached from the input or output stages, measuring the input stage is challenging, and green patents require time from the application stage to final authorization. Therefore, this study opts to use the total number of patent applications as the measurement indicator. To assess corporate green innovation, the natural logarithm of the total green patent applications is employed, allowing for a deeper examination of the impact of environmental protection taxes on green innovation. The data come from the National Tai’an Database (CSMAR).

(2)

Independent Variable: The explanatory variable in this paper is the interaction term Treati*Postt, denoted as DID_it, which represents the interaction effect of environmental protection tax implementation. It indicates whether heavily polluting enterprises are subject to environmental protection taxes. Treat and Post are used as dummy variables. Treat categorizes the experimental group and the control group, where companies classified as heavily polluting enterprises affected significantly by environmental protection tax policies are assigned a value of 1, while other companies less affected by environmental protection tax policies are assigned a value of 0. Post distinguishes between pre- and post-policy implementation periods, with 2018 chosen as the policy shock time. Specifically, Treat takes a value of 0 from 2014 to 2017 and a value of 1 from 2018 to 2022.

(3)

Mediating Variable: The mediating variable in this paper is ESG performance (ESG). According to the Huazheng ESG rating in the WIND database and referencing the research of Chunqiang, ESG rating results ranging from C to AAA are assigned values from 1 to 9, respectively [43]. These scores are based on publicly available information, such as corporate social responsibility reports, and comprehensively evaluate corporate performance across three dimensions: environmental, social, and governance. A higher numerical value indicates better corporate ESG performance. ESG data come from the Wind database.

(4)

Control Variables: To avoid endogeneity issues caused by omitted variables, we have chosen as many control variables as possible. Drawing on the research of Lyu, the control variables in this paper include enterprise size (Size), cash holdings (Cash), profitability (ROA), growth ability (TobinQ), equity concentration (Top10), board size (Board), property rights nature (SOE), and duality (Dual) [44]. The specific definitions of each variable are shown in

Table 1. Variable definitions.

Variable Types	Variable Names	Variable Symbols	Variable Definitions
The dependent variable	Overall green innovation of the enterprise	LN_total	The natural logarithm of the total number of green patent applications plus 1
The independent variable	Heavily polluting enterprises	Treat	Heavily polluting enterprises are coded as 1, while non-heavily polluting enterprises are coded as 0
	The year of environmental protection tax implementation	Post	The dummy variable for the enactment of the Environmental Protection Tax Law: coded as 1 for the years 2017 and onwards and 0 for the years before 2017
	Interaction term	DID	Heavily polluting enterprises affected by the enactment of the Environmental Protection Tax Law are coded as 1; otherwise, they are coded as 0
The mediating variable	Enterprise ESG performance	ESG	The Huazheng ESG Rating Index
Control variables	Enterprise size	Size	The natural logarithm of total assets of the enterprise
	Cash holdings	Cash	The proportion of cash and cash equivalents to total assets
	Profitability	ROA	Net profit divided by total assets of the enterprise
	Growth capability	TobinQ	The market value of the enterprise divided by total assets
	Degree of equity concentration	Top10	The sum of the shareholding proportions of the top ten shareholders
	Board size	Board	The natural logarithm of the number of board members
	Property ownership nature	SOE	Dummy variable: 1 for state-owned enterprises and 0 for others
	Dual roles combined	Dual	Dummy variable: 1 if there is a chairman who also serves as CEO; otherwise, 0

. The control variable data come from the National Tai’an Database (CSMAR).

3.3.2. Research Method

(1)

Model Setting for Environmental Protection Tax and Corporate Green Innovation

The double difference (DID) model is one of the most commonly used methods in policy evaluation among non-experimental approaches, and it can be used to assess the micro-effects of macro-policies. Drawing on the research by Guang Qiang, this study takes the enactment of the Environmental Protection Tax Law in 2018 as a quasi-natural experiment to examine the impact of the environmental protection tax on green innovation, constructing the following initial model [45]:

$${L{N}_{total}}_{it}=a_0+a_1{DID}_{it}+a_2{X}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(1)

where LNtotal represents the level of green innovation of the enterprise; DID_it represents the interaction term of policy variables; X_it represents a set of control variables; λ_t denotes time-fixed effects; μ_i denotes industry-fixed effects; and ε_it denotes the error term. This article focuses on the effect value of a₁.

(2)

Model Specification for Testing the Mediating Effect of Corporate ESG Performance

To examine the mediating effect of corporate ESG performance in the mechanism through which environmental protection taxes influence corporate green innovation, this paper adopts the mediation effect test method proposed by Zhang [46]. Building upon this, Equations (1)–(3) are further developed. The specific formulations are as follows:

$${ESG}_{it}={\beta }_0+{\beta }_1{DID}_{it}+{\beta }_2{X}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

$${L{N}_{total}}_{it}={\eta }_0+{\eta }_1{DID}_{it}+{\eta }_2{ESG}_{it}+{\eta }_3{X}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(3)

4. Empirical Analysis and Results

4.1. Descriptive Statistics

According to the descriptive statistics analysis, the total sample size is 12,096 (

Table 2. Descriptive statistics of main variables.

Variable	Size	Mean	Std	Minimum	Maximum
LN_total	12,096	1.289	1.427	0.000	5.576
ESG	12,096	4.152	1.108	1.000	6.000
Top10	12,096	0.595	0.155	0.250	0.918
SOE	12,096	0.447	0.497	0.000	1.000
Lnsize	12,096	22.60	1.363	20.090	26.63
Cash	12,096	0.142	0.105	0.0100	0.540
ROA	12,096	0.038	0.054	−0.180	0.190
Board	12,096	8.682	1.682	5.000	15.00
TobinQ	12,096	2.000	1.303	0.850	8.600
Dual	12,096	1.759	0.428	1.000	2.000

). From the perspective of companies’ green innovation performance, the mean of total green innovation performance is 1.289, with a maximum value of 5.576, a minimum value of 0, and a standard deviation of 1.427. This, to some extent, indicates that the level of green innovation among A-share listed companies is relatively low. From the perspective of ESG performance, the mean value of ESG ratings is 4.152, with a maximum value of 6, a minimum value of 1, and a standard deviation of 1.108, indicating significant differences in ESG performance among different companies. The minimum value of the top ten shareholder ownership ratio (TOP10) is 0.250, and the maximum value is 0.918, indicating significant differences in shareholder control among companies. The mean value of state-owned enterprises (SOE) is 0.447, indicating that approximately half of the companies are state-owned enterprises. The mean values of Lnsize, Cash, ROA, Board, TobinQ, and Dual are 22.60, 0.142, 0.0384, 8.682, 2.000, and 1.759, respectively.

4.2. Empirical Analysis

4.2.1. Correlation Analysis

The Pearson correlation coefficients of the main variables are shown in

Table 3. Pearson correlation coefficients of main variables.

	LN_Total	DID	ESG	Top10	SOE	Lnsize
LN_total	1
DID	0.106 ***	1
ESG	0.210 ***	0.059 ***	1
Top10	0.055 ***	0.033 ***	0.128 ***	1
SOE	0.104 ***	0.019 **	0.085 ***	−0.025 ***	1
Lnsize	0.475 ***	0.117 ***	0.239 ***	0.186 ***	0.346 ***	1
Cash	−0.061 ***	−0.077 ***	0.096 ***	0.086 ***	−0.021 **	−0.186 ***
ROA	0.0140	0.080 ***	0.195 ***	0.243 ***	−0.146 ***	−0.030 ***
Board	0.094 ***	0.034 ***	0.033 ***	0.018 **	0.257 ***	0.257 ***
TobinQ	−0.193 ***	−0.142 ***	−0.154 ***	−0.135 ***	−0.142 ***	−0.452 ***
Dual	0.026 ***	0.023 **	0.0140	−0.037 ***	0.303 ***	0.148 ***
	Cash	ROA	Board	TobinQ	Dual
Cash	1
ROA	0.248 ***	1
Board	−0.054 ***	−0.031 ***	1
TobinQ	0.198 ***	0.108 ***	−0.129 ***	1
Dual	−0.037 ***	−0.071 ***	0.166 ***	−0.066 ***	1

Note: Pearson correlation coefficients, *** indicates p < 0.01, ** indicates p < 0.05.

. The correlation coefficient between environmental protection tax and corporate green innovation reaches 0.106, indicating a significant impact of environmental protection tax on corporate green innovation. The correlation coefficient between environmental protection tax and corporate ESG performance is 0.059, suggesting a significant correlation between environmental protection tax and corporate ESG performance at a high level. Control variables are generally significantly correlated with corporate green innovation, indicating that the selected variables in the study are reasonable.

4.2.2. Basic Regression Analysis

Table 4. Environmental protection tax, corporate ESG performance, and green innovation.

	(1)	(2)	(3)
Variables	LN_Total	ESG	LN_Total
DID	0.0960 ***	0.0593 *	0.0936 ***
	(0.0291)	(0.0308)	(0.0290)
ESG			0.0409 ***
			(0.00959)
Top10	0.186	0.102	0.182
	(0.120)	(0.128)	(0.120)
SOE	−0.118 *	0.204 ***	−0.126 **
	(0.0633)	(0.0672)	(0.0633)
Lnsize	0.414 ***	0.204 ***	0.406 ***
	(0.0199)	(0.0211)	(0.0200)
Cash	−0.186 *	0.437 ***	−0.204 *
	(0.112)	(0.119)	(0.112)
ROA	−0.0618	0.670 ***	−0.0893
	(0.181)	(0.192)	(0.181)
Board	−0.00796	−0.0367 ***	−0.00645
	(0.00913)	(0.00969)	(0.00913)
TobinQ	0.00957	−0.0107	0.0100
	(0.00938)	(0.00996)	(0.00937)
Dual	−0.0559 **	−0.0208	−0.0550 **
	(0.0275)	(0.0292)	(0.0274)
Year Effect	Control	Control	Control
Firm Effect	Control	Control	Control
Constant	−7.961 ***	−0.344	−7.947 ***
	(0.449)	(0.476)	(0.448)
Observations	11,984	11,984	11,984
R-squared	0.814	0.651	0.814

*** indicates p < 0.01, ** indicates p < 0.05, * indicates p < 0.1.

presents the results of the multiple regression analysis on the impact of the Environmental Protection Tax Law on green innovation in heavily polluting enterprises. The coefficient of the DID variable in column (1) is 0.096, which is significant at the 1% level, indicating a positive impact of implementing the environmental protection tax on the overall green innovation performance of enterprises, thus confirming H1.

4.2.3. Analysis of the Mediating Effect of Corporate ESG Performance

According to the mediation analysis model, it is found that based on the regression results in column (1) of Table 4, the imposition of environmental protection taxes positively enhances the level of corporate green innovation; as seen from column (2), the coefficient of the interaction term DID is positive and significantly positively correlated at the 10% level of significance, indicating that the imposition of environmental protection taxes promotes corporate ESG performance, thus confirming H2. Subsequently, the regression equation was further examined by adding the explanatory variable DID and the mediating variable ESG. From the regression results in column (3) of Table 4, it can be observed that the impact of corporate ESG performance on overall green innovation performance remains significant, and the effect of environmental protection taxes on corporate green innovation is also significantly positive. The coefficient of the environmental protection tax on green innovation in column (3) is smaller than the direct path coefficient of the environmental protection tax on green innovation in column (1). This demonstrates that corporate ESG performance plays a partial mediating role in the mechanism through which environmental protection taxes affect corporate green innovation. Thus, H3 is validated.

4.3. Robustness Check

4.3.1. Parallel Trend Test

The implementation of an environmental protection tax may exhibit selectivity. For example, the government might choose to first implement the tax in regions or industries with severe environmental issues, where companies might already have poor green performance. This selective implementation characteristic can result in the relationship between the environmental protection tax and companies’ green performance not being purely causal, thereby leading to endogeneity issues. This study employs a double difference model to test the research hypotheses. To ensure the robustness of the research results, a parallel trend test is conducted. Following the approach of Burchinal, the year of policy implementation in 2018 is taken as the base year. Create dummy variables Pre_4 to represent 4 years before the policy, Pre_3 for 3 years before the policy, Pre_2 for 2 years before the policy, Pre_1 for 1 year before the policy, Post_1 for 1 year after the policy, Post_2 for 2 years after, and Post_3 for 3 years after the policy [47].

From Figure 4, it can be observed that before the implementation of the “Environmental Protection Tax Law”, no significant differences were found between the experimental group and the control group, and the model passed the parallel trend test. With the implementation of the “Environmental Protection Tax Law”, the level of green innovation in these regions did not meet expectations in the period immediately after implementation. However, in the first, second, and third periods after implementation, significant growth trends in green innovation were observed among heavy-polluting enterprises in the experimental group areas. This indicates that environmental tax reform has played a positive incentive role in corporate green innovation, further confirming the robustness of the baseline regression results.

4.3.2. Replacement of Variable Indicators

To enhance the stability of the research findings, this paper adjusts the measurement method of the dependent variable, Enterprise Green Innovation Performance (LN_total), and conducts robustness tests on the research results. Specifically, the sum of granted utility model patents and granted invention patents is chosen as the measurement indicator for Enterprise Green Innovation Performance (LN_total), and regression analysis is conducted again. The results are shown in

Table 5. The robustness test results of replacing the dependent variable.

	(1)	(2)	(3)
Variables	LN_Total	ESG	LN_Total
DID	0.0625 *	0.0801 **	0.0606 *
	(0.0348)	(0.0372)	(0.0347)
ESG			0.0279 **
			(0.0113)
Top10	−0.00713	0.343 **	−0.0250
	(0.137)	(0.147)	(0.137)
SOE	−0.179 ***	0.195 ***	−0.182 ***
	(0.0619)	(0.0639)	(0.0618)
Lnsize	0.379 ***	0.317 ***	0.377 ***
	(0.0230)	(0.0248)	(0.0234)
Cash	−0.400 ***	0.613 ***	−0.403 ***
	(0.135)	(0.145)	(0.135)
ROA	−0.485 ***	0.655 ***	−0.503 ***
	(0.185)	(0.193)	(0.185)
Board	−0.0353 ***	−0.0217 *	−0.0336 ***
	(0.0112)	(0.0121)	(0.0112)
TobinQ	0.0297 ***	0.0105	0.0295 ***
	(0.0110)	(0.0120)	(0.0110)
Dual	−0.00318	0.102 ***	−0.00492
	(0.0312)	(0.0338)	(0.0311)
Year Effect	Control	Control	Control
Firm Effect	Control	Control	Control
Constant	−7.114 ***	−3.578 ***	−7.188 ***
	(0.517)	(0.558)	(0.521)
Observations	6127	7560	6116
R-squared	0.766	0.605	0.767

*** indicates p < 0.01, ** indicates p < 0.05, * indicates p < 0.1.

. Regarding the relationship between environmental protection tax and green innovation performance, the regression results in column (1) of Table 5 show that the coefficient of environmental protection tax is significantly positive at the 10% level. This is consistent with the significance of the relationship between environmental protection tax and Enterprise Green Innovation Performance in the baseline regression results, indicating that H1 is still supported. Regarding the relationship between environmental protection tax and enterprise ESG performance, the regression results in column (2) of Table 5 indicate a significant positive correlation between environmental protection tax and enterprise ESG performance at the 5% significance level, thus supporting H2. The regression results in column (3) of Table 5 also confirm the existence of the mediating effect of enterprise ESG performance, thus supporting H3.

4.3.3. Propensity Score Matching Scores

Given that key variables such as a firm’s innovation capabilities may also influence its innovation performance, this study employs propensity score matching to address the endogeneity issue arising from omitted variables. Specifically, the sample data are divided into two groups based on the interaction term between the explanatory variable, the Environmental Protection Tax Law, and heavily polluting enterprises. Subsequently, using firm size, cash holdings, profitability, growth ability, ownership concentration, board size, property rights nature, and dual roles as matching variables, we conducted a 1:1 sample matching using the nearest neighbor matching method. This resulted in 12,096 effectively matched sample data points. Finally, we performed regression analysis again on the matched sample data, and the balance test results of the propensity score matching are presented in

Table 6. Balance test table.

Variables	Treatment	Mean		Standard Deviation	Standard Deviation	T-Statistic
		Treatment Group	Control Group	/%	Reduction Amplitude/%
Top10	Before matching	0.6083	0.5927	10.1		3.64
	After matching	0.6084	0.6137	−3.4	66.3	−0.90
SOE	Before matching	0.4724	0.4432	5.9		2.12
	After matching	0.4727	0.5010	−5.7	2.9	−1.54
Lnsize	Before matching	23.024	22.537	34.7		12.99
	After matching	23.023	23.084	−4.4	87.3	−1.15
Cash	Before matching	0.1206	0.1455	−24.5		−8.54
	After matching	0.1207	0.1210	−0.3	98.6	−0.10
ROA	Before matching	0.0500	0.0368	24.2		8.87
	After matching	0.0497	0.0547	−9.2	61.9	−2.56
Board	Before matching	8.8363	8.6605	10.1		3.77
	After matching	8.8366	8.8393	−0.2	98.5	−0.04
TobinQ	Before matching	1.5055	2.0691	−49.4		−15.77
	After matching	1.5064	1.5726	−5.8	88.2	−2.10
Dual	Before matching	1.7850	1.7553	7.1		2.51
	After matching	1.7853	1.7799	1.3	81.9	0.36

. The results indicate that the absolute values of all standard deviations are less than 10%, suggesting that the matching results are reasonable. Additionally,

Table 7. PSM test results.

	(1) LN_Total	(2) ESG
DID	0.463 ***	0.200 ***
	(0.0393)	(0.0307)
Controls	Control	Control
Year Effect Firm Effect	Control Control	Control Control
Constant	1.232 ***	4.128 ***
	(0.0138)	(0.0107)
Observations	12,096	12,096
R-squared	0.011	0.003

*** indicates p < 0.01.

presents the regression results after propensity score matching (PSM). It can be observed that in column (1), the DID coefficient is 0.463, which is significant at the 1% level, and in column (2), the DID coefficient is 0.200, which is also significant at the 1% level. These test results are consistent with the baseline regression results, indicating the robustness of the research findings.

4.4. Further Analysis

4.4.1. Heterogeneity Analysis Based on Types of Green Innovation

Drawing on Tong et al.’s (2014) classification of corporate innovation motivation, this article starts with the motivation of corporate green innovation and divides it into two types: one is the “high-quality” green innovation behavior aimed at promoting technological progress, gaining competitive advantages, and environmental performance, namely substantive green innovation. Strategic green innovation is a green innovation behavior that aims to seek other benefits by pursuing innovation “quantity” and “speed” to cater to government policies and systems. Substantive green innovation (LNPATENT) is measured by the number of invention patent applications, while strategic green innovation (LNPATENTUD) is measured by the number of utility model and design patent applications. The regression results are shown in

Table 8. Heterogeneity of green innovation types.

Variables	(1)
	Lnpatent	Lnpatentud
DID	0.0317	0.119 ***
	(0.0254)	(0.0259)
Top10	0.263 **	0.109
	(0.105)	(0.107)
SOE	−0.0978 *	−0.141 **
	(0.0554)	(0.0564)
Lnsize	0.324 ***	0.316 ***
	(0.0174)	(0.0177)
Cash	−0.150	−0.179 *
	(0.0978)	(0.0995)
ROA	−0.0340	−0.132
	(0.159)	(0.161)
Board	−0.000529	−0.0108
	(0.00799)	(0.00813)
TobinQ	0.0142 *	0.00483
	(0.00820)	(0.00835)
Dual	−0.0725 ***	0.00125
	(0.0240)	(0.0245)
Year Effect	Control	Control
Firm Effect	Control	Control
Constant	−6.404 ***	−6.132 ***
	(0.392)	(0.399)
Observations	11,984	11,984
R-squared	0.800	0.778

*** indicates p < 0.01, ** indicates p < 0.05, * indicates p < 0.1.

. In column (1), the coefficient between DID and LNPATENT is not significant, while the coefficient between DID and LNPATENTUD is 0.119, significant at the 1% level, indicating that the promotion of green innovation by environmental protection taxes is mainly achieved through enhancing strategic green innovation in enterprises. After analysis, it was found that after the implementation of the Environmental Protection Tax Law, enterprises facing legitimacy pressures often actively engage in corresponding green innovation activities to comply with government environmental incentives. At this time, the information asymmetry between the government and enterprises may lead to enterprises engaging in opportunistic behavior, where their purpose in conducting green innovation is not to improve production technology, reduce energy consumption, or develop green products, but rather to pursue “high-speed, high-volume” green innovation to disguise emissions and thereby reduce the burden of environmental taxes and qualify for government environmental incentives. Substantial green innovation is often achieved through invention patents, but the research and development investment for invention patents is significant and time-consuming. Enterprises typically engage in high-quality green innovation only when the environmental costs exceed the research and development costs. Therefore, within less than five years since the introduction of the Environmental Protection Tax Law, the improvement in enterprise green innovation levels mainly relies on strategic green innovation behavior.

4.4.2. Heterogeneity Analysis of Property Rights

Based on the differing ownership structures, the sample of listed companies is categorized into two groups: state-owned enterprises and non-state-owned enterprises. A group regression analysis was then performed on these categories. As indicated by the regression results in

Table 9. Heterogeneity by property rights.

Variables	LN_Total
	(1)	(2)
	State-Owned	Non-State-Owned
DID	0.172 ***	0.0169
	(0.0423)	(0.0405)
Top10	0.361 **	−0.144
	(0.180)	(0.176)
Lnsize	0.440 ***	0.422 ***
	(0.0306)	(0.0289)
Cash	−0.142	−0.215
	(0.187)	(0.142)
ROA	−0.484 *	0.130
	(0.283)	(0.243)
Board	0.00568	−0.0262 *
	(0.0120)	(0.0142)
TobinQ	0.0242	0.000673
	(0.0162)	(0.0119)
Dual	−0.0835 *	−0.0423
	(0.0457)	(0.0352)
Year Effect	Control	Control
Firm Effect	Control	Control
Constant	−8.846 ***	−7.810 ***
	(0.698)	(0.641)
Observations	5358	6580
R-squared	0.844	0.780

*** indicates p < 0.01, ** indicates p < 0.05, * indicates p < 0.1.

, the DID coefficient for the state-owned enterprise group exhibits a significantly positive trend, whereas the DID coefficient for the non-state-owned enterprise group does not achieve statistical significance. This indicates that the promulgation of the “Environmental Protection Tax Law” has played a positive role in promoting green innovation in state-owned heavily polluting enterprises, but the impact on green innovation in non-state-owned enterprises is not significant. Through analysis, it is found that, on the one hand, facing the implementation of the new environmental protection law, state-owned enterprises, as important tools for local governments to achieve performance goals, are more inclined to boost environmental protection investments and elevate their levels of green innovation. Additionally, due to the strong ties between the government and state-owned enterprises, these enterprises benefit from greater financial support and resource availability for green innovation.

4.4.3. Heterogeneity Analysis of Enterprise Size

To examine the variations in the impact of environmental protection taxes on corporate green innovation across different-sized enterprises, the sample was split into two groups using the median of the companies’ total assets as the dividing line (

Table 10. Heterogeneity analysis of firm size.

Variables	LN_Total
	(1)	(2)
	Large Enterprises	Small Businesses
DID	0.167 ***	0.0145
	(0.0425)	(0.0418)
Top10	0.615 ***	−0.111
	(0.181)	(0.194)
SOE	−0.138	−0.114
	(0.100)	(0.0855)
Cash	−0.494 **	−0.194
	(0.203)	(0.132)
ROA	0.0721	0.0702
	(0.313)	(0.220)
Board	0.0114	−0.0143
	(0.0125)	(0.0143)
TobinQ	−0.0483 **	−0.0308 ***
	(0.0218)	(0.0107)
Dual	−0.126 ***	0.0887 **
	(0.0433)	(0.0363)
Year Effect	Control	Control
Firm Effect	Control	Control
Constant	1.717 ***	0.921 ***
	(0.179)	(0.176)
Observations	6261	5466
R-squared	0.827	0.735

*** indicates p < 0.01, ** indicates p < 0.05.

). Large enterprises were above the median, while small enterprises were below the median. The regression analysis revealed a significant positive trend for large enterprises at a 1% green innovation level, while small businesses showed a positive coefficient for green innovation activities but no significant difference. This suggests that the implementation of the Environmental Protection Tax Law effectively boosts green innovation in large enterprises. The reason might be that small businesses face financial constraints and limited capabilities, which hinder their engagement in green technological innovation.

5. Conclusions and Recommendations

5.1. Research Conclusions

Green development is the necessary path to building a “Beautiful China”. In 2018, China promulgated the Environmental Protection Tax Law. As one of the most significant environmental institutional reforms in recent years, environmental tax reform has the potential to effectively enhance the level of corporate green innovation, which is worth exploring. This study employs a difference-in-differences model and utilizes empirical evidence from panel data of Shanghai and Shenzhen A-share listed companies from 2014 to 2022 to investigate the impact of environmental taxes on corporate green transformation and its mechanisms.

The study findings are as follows: (1) The promulgation of the Environmental Protection Tax Law has injected new impetus into the green innovation of heavily polluting enterprises, effectively enhancing their overall level of green innovation. (2) The introduction of the mediation effect model reveals the positive impact of the implementation of the Environmental Protection Tax Law on the improvement of corporate ESG performance, thereby promoting the enhancement of corporate green innovation levels. At the same time, it also validates the mediating role of corporate ESG in environmental protection taxes and green innovation. (3) Among different types of patents, the incentive effect of the Environmental Protection Tax Law on green strategic innovation is stronger than that on green substantive innovation. Although the Environmental Protection Tax Law has stimulated green innovation activities among heavily polluting enterprises, in the short term, there is a greater increase in the number of utility models and design patents, promoting strategic green innovation by enterprises. (4) There are differences in the effects of the Environmental Protection Tax Law among enterprises with different property rights. Compared to non-state-owned enterprises, the policy’s effect on enhancing the level of green innovation in enterprises is more prominent in non-state-owned enterprises. (5) The impact of the Environmental Protection Tax Law varies depending on the size of the enterprise. The imposition of environmental taxes has a greater impact on large enterprises, which “forces” them to engage in green innovation activities, while the effect on small enterprises is not significant.

5.2. Research Recommendations

Policy Enhancement and Execution: Considering the substantial effect of the Environmental Protection Tax Law in promoting green innovation within heavily polluting enterprises, it is advisable for the government to further improve related tax policies and bolster incentives for environmental protection taxes. This could be accomplished through strategies like tax reductions and financial subsidies aimed at further motivating enterprises, especially small- and medium-sized businesses and traditional industries, to invest in green innovation.

Promoting Green Innovation through Policy Support: Research indicates that the Environmental Protection Tax Law has a more pronounced incentive effect on corporate green strategic innovation compared to green substantive innovation. Therefore, it is advised that policymakers focus on incentivizing enterprises to engage in green strategic planning and innovation when formulating related policies to foster the development and application of green technologies and products with long-term impacts.

Enhancing ESG Performance Management: Recognizing the pivotal mediating role of ESG performance between environmental protection taxes and green innovation, it is recommended that companies strengthen their ESG performance management by embedding environmental, social, and governance (ESG) considerations into their core strategies and day-to-day operations. This approach not only elevates the level of green innovation but also enhances the overall competitiveness and market performance of the enterprises.

Addressing Differences Among Enterprise Types: The research shows that the impact of the Environmental Protection Tax Law varies among enterprises with different ownership structures. Non-state-owned enterprises demonstrate more pronounced performance in green innovation. Therefore, it is recommended that policies take into account the needs of enterprises with different ownership types and develop targeted support measures to ensure fairness and effectiveness.

Supporting Green Transition for Large Enterprises: Large enterprises exhibit a strong propensity for green innovation activities under the Environmental Protection Tax Law. It is suggested that the government and relevant departments provide more support for large enterprises in their green transition, such as green financial products and innovation technology support, to further drive their investment in green technologies and products.

5.3. Shortcomings and Prospects

Firstly, the environmental protection tax has been in effect only since 2018, and during the empirical analysis, there was insufficient data directly related to the environmental protection tax. Therefore, proxies such as pollutant discharge fees were used in the study. Moreover, many heavily polluting enterprises have longer innovation cycles, and the effects of the environmental protection tax policy may not yet be fully realized. Additionally, there may be gaps in the green patent data collected from most companies, which could lead to slight deviations in the test results. Therefore, future research should focus on obtaining more comprehensive data to improve the accuracy and effectiveness of the analysis.

Secondly, this study empirically examined the impact of the environmental protection tax on corporate green innovation and further explored the role of corporate ESG performance as a mediating factor. However, due to the limited perspective of this study, other influencing mechanisms that could significantly impact the effect of the environmental protection tax on corporate green innovation may not have been considered and thus remain unexplored. For example, regional differences in policy implementation, variations in internal management mechanisms of enterprises, and industry-specific green technology demands could all influence the effectiveness of the policy. Therefore, future research should expand its perspective to explore additional possible influencing mechanisms to provide a more comprehensive and complete explanation of the impact of environmental protection taxes on corporate green innovation. Addressing these limitations in further research is essential.