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Environmental Regulation, Information Disclosure, and Clean Production in Heavy-Polluting Enterprises: Evidence from China

Sustainability 2025, 17(21), 9586; https://doi.org/10.3390/su17219586

by Zuting Zheng^* and Meiqing Wu

Reviewer 1:

Claudete Gindri

Reviewer 2: Anonymous

Reviewer 3: Anonymous

Sustainability 2025, 17(21), 9586; https://doi.org/10.3390/su17219586

Submission received: 22 September 2025 / Revised: 20 October 2025 / Accepted: 24 October 2025 / Published: 28 October 2025

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Editor,

Thank you for the opportunity to review the manuscript entitled "Environmental Regulation, Environmental Information Disclosure, and Clean Production Performance of Heavy-Polluting Enterprises: Empirical Data from China." I believe the work addresses a highly relevant topic in the field of sustainability, using a novel approach that combines an evolutionary game model with empirical analysis of heavily polluting enterprises in China.
The manuscript presents relevant contributions, particularly in:
The incorporation of environmental information regulators into the tripartite governance framework.
The use of a dual approach that integrates evolutionary game theory and empirical regression.
The proposal of public policy implications applicable to the transition to a green economy.
However, in the spirit of constructively strengthening the manuscript, I suggest the authors consider the following recommendations:

1. Title and Abstract

Lines 2–4: The title is clear, but too long. I suggest condensing it for greater impact, for example: “Environmental Regulation, Information Disclosure, and Clean Production in Heavy-Polluting Enterprises: Evidence from China.”
Lines 9–27 (Abstract): Please write more fluently; there are long, dense sentences. Item (4) in the list of findings appears as "(4)" while the others use "1), 2), 3)". This breaks the consistency. The relationship between costs, fines, and eco-fees should be summarized more clearly (it is currently repeated).
Line 25: A contradiction is detected between the statement about “short-term suppression” of clean production and the empirical results, which show positive effects. Please harmonize both sections and avoid contradictions.

2. Introduction
Lines 32–41: China's national policies are introduced, but international references are lacking to contextualize the global problem. Please compare with experiences in the EU or the US.
Lines 42–77: The state of the art is well documented, but very descriptive. I suggest synthesizing and organizing it into subtopics (regulation, green finance, outreach, digital technology).
Line 73: Research gaps are mentioned, but the wording "critical gaps remain: (1)… (2)…" is missing. I suggest expanding with specific examples of what needs to be measured or integrated to strengthen the scientific justification.

3. Game Model Methodology

Lines 84–122: The description of the tripartite model is clear, but overly narrative. I recommend including schematics or pseudocode alongside the diagram in Figure 1 (the quality of this figure should be improved).
Lines 139–163: Some variables are poorly explained (e.g., φ appears, but is not clearly defined upon first mention).
In the note on lines 159–163, the text is confusing (“... inseparable”). I recommend simplifying it.
Lines 167–186: There is a lack of intuitive explanation of what each equation means before presenting it.
Review notation: several formulas have line breaks that make reading difficult.

4. Stability Analysis

Lines 188–223 and 225–258: The explanation is redundant. It could be summarized using comparative tables instead of repeating stability conditions in long paragraphs.
Line 233: Mixed English and Chinese text appears (“F(y)=0且dF(y)/dy<0”). Please correct it to academic English.

5. Simulations

Lines 373–396: The figures (5a–5d) are described, but a clearer economic interpretation is needed (what they mean for public policy).
Lines 412–486: There are repetitions (“the strategy tends to (0,1,0)”). This could be summarized with a table summarizing how each parameter affects the result.
In Fig. 6, more legible labels on the axes are missing.

6. Empirical Analysis

Lines 488–497: Good selection, but a justification should be given for choosing 570 firms instead of a larger panel.
The use of linear interpolation (line 495) requires a warning about potential bias.
Lines 503–511: Please check for temporal consistency: 2014–2023 is mentioned, but “t=2014, 2012…2023” also appears (typo).
Lines 534–535 (Table 5): The constructed index is interesting, but requires further bibliographic validation. I suggest citing previous studies that use similar indices.

7. Results

Lines 537–557 (Table 6): Clear results, but a comparative discussion with international studies is missing in the discussion section.
Lines 559–570 (Mediating Effect): Well-structured, although it could be reinforced with an additional test (e.g., Sobel test or Bootstrap) for greater robustness.

8. Conclusions and Discussion

Please create a section dedicated to discussions, where comparative references from other international contexts should be incorporated, which would increase the global relevance of the study.

Lines 574–597 (Conclusions): Some sentences repeat what has already been explained in the results. I recommend condensing them.
There is no clear distinction between theoretical and practical contributions.
Lines 599–634 (Policy Recommendations): Very useful, but could be sorted into short, medium, and long-term recommendations.

9. Style and Form

Several sections contain very long sentences; please divide them into shorter ones.
Please standardize the English: there are sentences with literal translations from Chinese that hinder fluency.

Author Response

Dear Reviewers

Many thanks for your careful reading and valuable comments on our manuscript. The comments helped us further revise and improve the paper. We have studied your comments carefully and made corrections accordingly. And in the process, we believe the paper has been significantly improved. The revised parts of the manuscript have been highlighted in yellow font. Our detailed responses to the reviewer’s comments are outlined as follows (“Q” denotes question and “A” denotes answer).

Response to Referee 1.

Q1. Lines 2–4: The title is clear, but too long. I suggest condensing it for greater impact, for example: “Environmental Regulation, Information Disclosure, and Clean Production in Heavy-Polluting Enterprises: Evidence from China.”

A1..Thank you for your valuable comments. We have simplified the title and will attach the revised content for your review.

Environmental Regulation, Information Disclosure, and Clean Production in Heavy-Polluting Enterprises: Evidence from China.

Q2. Lines 9–27 (Abstract): Please write more fluently; there are long, dense sentences. Item (4) in the list of findings appears as "(4)" while the others use "1), 2), 3)". This breaks the consistency. The relationship between costs, fines, and eco-fees should be summarized more clearly (it is currently repeated).

A2.We greatly appreciate the reviewers' detailed review of our abstract and their valuable suggestions regarding clarity and consistency. We have made corresponding revisions to the abstract based on these comments and have attached the revised abstract for your review.

The key to achieving sustainable economic development and mitigating climate change lies in effective green transition governance. This study, based on evolutionary game theory, constructs a game model involving three subjects: heavily polluting enterprises, the government, and environmental information disclosure regulatory authorities, aiming to analyze the clean production decision-making mechanism under multi-subject interaction. It empirically examines the relationship among the three by combining panel data of listed companies in heavily polluting industries on China's A-share market. The research findings indicate: (1) Excessively high environmental technology upgrade and information disclosure costs will hinder enterprises' clean production. (2) The intensity of regulation is influenced by the government's benefits and costs. (3) Effective environmental policies require multi-dimensional coordination. (4) Environmental regulations can effectively enhance enterprises' environmental performance, and by improving the transparency and quality of environmental information disclosure, significantly improve their environmental performance.

Q3. Line 25: A contradiction is detected between the statement about “short-term suppression” of clean production and the empirical results, which show positive effects. Please harmonize both sections and avoid contradictions.

A3.Thank you for your professional advice. We agree that this phrasing could lead to misunderstanding, and we have revised the manuscript accordingly to ensure consistency and clarity.We will attach the modified content for your review.

(4) Environmental regulations can effectively enhance enterprises' environmental performance, and by improving the transparency and quality of environmental information disclosure, significantly improve their environmental performance.

Q4. Lines 32–41: China's national policies are introduced, but international references are lacking to contextualize the global problem. Please compare with experiences in the EU or the US.

A4.We sincerely appreciate the valuable suggestions provided by the reviewers. We agree that placing China's policies in a global context for comparative analysis can more clearly highlight their characteristics and significance. To this end, we have made supplementary revisions in the original text (lines 28-43) and have attached the revised content for your review.

Against the backdrop of intensifying global climate change, the Chinese government has been continuously improving its environmental governance system. The 20th National Congress of the Communist Party of China incorporated "coordinating carbon reduction, pollution control, green transformation and economic growth" into the overall layout of ecological civilization construction. The promulgation of the "Regulations on Environmental Information Disclosure by Enterprises" by the Ministry of Ecology and Environment in 2022 marked the establishment of a comprehensive mandatory information disclosure system. The government work report of the Two Sessions pointed out that by 2025, China will "accelerate the construction of a green and low-carbon economy" and "form a green and low-carbon production mode". Meanwhile, the European Union has set a grand goal of achieving carbon neutrality by 2050 through the "European Green Deal", establishing a multi-level policy framework and emphasizing systematic green transformation. In the United States, beyond the federal level, a multi-level, market-driven emission reduction system has been formed, represented by the Regional Greenhouse Gas Initiative (RGGI) and the Chicago Climate Exchange.

Q5. Lines 42–77: The state of the art is well documented, but very descriptive. I suggest synthesizing and organizing it into subtopics (regulation, green finance, outreach, digital technology).

A5.Thank you very much for your valuable suggestions. The issue you pointed out regarding the overly descriptive "current technical status" section is very pertinent. We fully agree with your opinion and have thoroughly rewritten this section based on the suggestions. The current technological status (i.e., research related to clean production in heavily polluting enterprises) has been integrated and structurally sorted out in accordance with the three sub-themes of "regulation, green finance, and digital technology". To facilitate your review, we have attached this part of the content.

Existing research on promoting clean production in heavily polluting enterprises has accumulated rich experience around various external driving factors. Specifically, it can be sorted out from core dimensions such as regulation [1-2], green finance [3-4], and digital technology [5-6].

At the regulatory level, there are both direct constraints from command-and-control policies and indirect guidance from market-based mechanisms. For instance, Gao et al. (2024) found that there is a "political resource curse" effect among heavily polluting enterprises with political connections in China, and environmental regulation can alleviate the suppression of enterprise innovation by strengthening market competition and curbing excessive investment [7]. The empirical analysis by Xu et al. (2025) on heavily polluting enterprises in China's A-share market from 2012 to 2021 indicates that flawed environmental subsidy designs can suppress green innovation, but stricter environmental law enforcement can reverse this impact [8]; Wang et al. (2024) further confirmed that enhancing environmental regulatory standards can significantly boost enterprises' R&D investment and promote green transformation through technology absorption and emission reduction [9]. In terms of the application of market-oriented tools, the roles of the carbon market and green finance policies show differentiated characteristics: Gan et al. (2024) proposed that the carbon market, as a market-based environmental tool, can achieve a coordinated reduction of pollution and carbon emissions by optimizing the energy structure and deploying green technologies [10], and Zhang et al. (2024) found through the comparison of policy tools that Market-based carbon policies outperform command-based methods in tolerating data errors, suppressing fraud, controlling total emissions and enhancing social welfare [11]; However, green finance policies have an industry asymmetry impact. Xiao et al. (2024) adopted the method of differences among differences to reveal how green finance policies promote clean production by enhancing energy efficiency and reducing emission intensity [12]. Gong et al. (2024) pointed out that although such policies can promote the transformation of non-heavily polluting enterprises by enhancing their financing capabilities, due to the failure to address financing constraints, they inhibit the transformation of heavily polluting enterprises, resulting in an inter-industry spillover effect that is beneficial to non-heavily polluting enterprises but detrimental to heavily polluting ones [13]. In terms of digital technology empowerment, industrial intelligent technology has become a key driving force for clean production. Relevant research [14-17] indicates that under regulatory pressure, industrial intelligent technology can reduce the cost of clean production and optimize end-of-pipe treatment through real-time data feedback and dynamic process optimization. Xu et al. (2023) also confirmed that they could drive green transformation through technology spillover and resource optimization [18]. However, Zhang et al. (2025) observed the contradiction of "environmental benefit deficiency" in digital transformation - although it could significantly improve the financial performance of enterprises by enhancing the efficiency of resource allocation, it did not generate obvious environmental benefits [19].

Q6. Line 73: Research gaps are mentioned, but the wording "critical gaps remain: (1)… (2)…" is missing. I suggest expanding with specific examples of what needs to be measured or integrated to strengthen the scientific justification.

A6.We thank the reviewer for this insightful suggestion. We agree that clearly pointing out the existing shortcomings will help strengthen the argument. Therefore, we have revised the sentence and attached the modifications for your review.

While existing studies have focused on individual regulatory mechanisms, critical gaps remain, specifically in two aspects:(1)They fail to fully integrate and analyze the comprehensive pressures faced by enterprises. Enterprises often confront pressures from three parties—regulatory authorities, consumers, and investors—simultaneously, yet existing studies have not clearly elaborated on how enterprises are actually affected by such multiple pressures.(2)Current exploration into the interactions among multiple stakeholders (i.e., regulatory authorities, consumers, investors, etc.) remains insufficient. In particular, there is a lack of in-depth research on the core question of "under what conditions these interactions can move beyond the scope of mere supervision and transition to genuine cooperation."

Q7. Lines 84–122: The description of the tripartite model is clear, but overly narrative. I recommend including schematics or pseudocode alongside the diagram in Figure 1 (the quality of this figure should be improved).

A7.Thank you for your valuable advice. This time, our modification has made the relationship between the model and the graphics clearer, making the presentation more structured and intuitive. For your convenience in reviewing, we have attached the revised content.

This paper constructs a tripartite game model among enterprises, the government and environmental information disclosure regulatory authorities. Taking the production mode selection of heavily polluting enterprises as the entry point, it analyzes the dynamic game relationship among the three parties under multiple factors such as environmental synergy benefits, cooperative transaction costs, regulatory incentives and information disclosure. Under the framework of collaborative governance, enterprises, as the main producers, their choice of production mode directly affects the coordination of the upstream ecosystem. The environmental information disclosure regulatory department is responsible for supervising the timeliness and quality of enterprises' information disclosure. The government departments supervise and verify the production models of enterprises, encourage enterprises to choose production methods with low environmental damage and punish enterprises with serious pollution. The two departments build a dual regulatory network covering production behavior and information disclosure through information sharing and functional complementarity. The evolution mechanism of the three parties is shown in Figure 1 (a). Figure 1 (b) presents the process: After the government formulates regulatory strategies, enterprises make production (clean/traditional) and information disclosure (true/false) decisions. The environmental department checks the authenticity of the information. If it is true, the enterprise will receive government incentives; if it is false, it will be punished by the government. Then, the benefits are calculated and the strategies are updated to form a cycle.

Q8. Lines 139–163: Some variables are poorly explained (e.g., φ appears, but is not clearly defined upon first mention).

A8.We sincerely thank the reviewers for pointing out that the explanations of some variables were not clear enough, especially when the definition of φ was mentioned for the first time. We agree that the precise definition of all variables is crucial for the readability and scientific rigor of the manuscript. In response to this comment, we have thoroughly reviewed the variable descriptions throughout the entire manuscript. To ensure that the definitions of all variables are clear and consistent, we have significantly enhanced Table 1 and transformed it into a comprehensive variable definition. For your convenience in reviewing, we have attached Table 1.

Table 1: Parameter Explanation of the Game between the Government, Environmental Information Disclosure Regulatory Authorities and Enterprises

Parameters or variables	explain
()	Enterprises disclose environmental information according to regulations, upgrade technologies and equipment for environmentally friendly production, the regulatory departments supervise environmental information disclosure, the government checks whether enterprises are producing cleanly, and the government verifies whether the regulatory departments are managing according to the law.
()	The company failed to disclose environmental information according to regulations, the company's pollution production has been verified, and the fines paid for the verified pollution production as well as the environmental protection tax (pollution discharge fee).
()	The profits obtained by the regulatory authorities during legal supervision (such as government rating income) and the losses incurred by the regulatory authorities when not supervising legally (such as downgrade losses)
()	The social benefits that the government can obtain from environmentally friendly production by enterprises, and the social benefits lost by the government from pollution-producing enterprises.
()	The social benefits obtained by the government when the regulatory authorities conduct oversight in accordance with the law, and the social benefits lost by the government when the regulatory authorities fail to conduct oversight in accordance with the law.
	The social benefits gained from companies disclosing environmental information according to regulations.
	The company did not disclose the negative impact it has brought to itself according to regulations.
	The economic benefits produced by enterprises under normal circumstances.
	The company's environmentally friendly production has been verified and received subsidies.
	The efficiency of regulatory authorities in conducting supervision.

Q9. In the note on lines 159–163, the text is confusing (“... inseparable”). I recommend simplifying it.

A9.We sincerely thank the reviewer for the valuable feedback regarding the clarity of the note on lines 159–163. We agree that the original phrasing was confusing. As suggested, we have simplified the text to enhance clarity. The revised version now reads:

Note 1:Heavily polluting enterprise implementing clean production are required to disclose environmental information in accordance with regulations, while others are not.

Note 2: The government's verification of enterprises incorporates the verification by the environmental information regulatory authorities.

Q10. Lines 167–186: There is a lack of intuitive explanation of what each equation means before presenting it.

A10.Thank you to the reviewers for pointing out that the formulas in lines 167-186 lack intuitive explanations. We fully agree with this opinion and have supplemented brief textual explanations for each formula in the revised draft. For your convenience in reviewing, we have revised the content and attached it.

Based on the revenue matrix in Table 2, the expected revenue of clean production for heavily polluting enterprises is , as shown in Formula (1).

(1)

The expected return for heavily polluting enterprises choosing non-clean production is , as shown in Formula (2).

(2)

The average expected revenue of heavily polluting enterprises is , as shown in Formula (3).

(3)

The replication dynamic equation (the replication dynamic equation is actually a dynamic differential equation that describes the frequency of a particular strategy adopted by a species in a population) is as shown in Equation (4).

(4)

Q11. Review notation: several formulas have line breaks that make reading difficult.

A11.Thank you for pointing out the problem that some formulas in the text are difficult to read due to line breaks. We fully agree with your opinion and have conducted a comprehensive review and optimization of the formula layout throughout the text. Specifically, we readjusted the layout of the relevant formulas, eliminated inappropriate line breaks, and ensured that each formula remained visually coherent and clear. The revised parts have been highlighted in yellow in the revised draft. Please review them.

Q12. Lines 188–223 and 225–258: The explanation is redundant. It could be summarized using comparative tables instead of repeating stability conditions in long paragraphs.

A12.Thank you to the reviewers for pointing out the issue of redundant explanations in lines 188-223 and 225-258. We have thoroughly revised these two parts based on the suggestions: Firstly, we have integrated the stability conditions repeatedly described in the original text into a comparison table (located on page X of the revised draft), replacing the lengthy textual description with a structured presentation. Secondly, repetitive sentences were deleted and a brief analysis of the table content was supplemented to highlight its core connotation. For your convenience in reviewing, I will attach some of the modifications. All other relevant adjustments in the full text are highlighted in yellow in the revised draft. Please review them.

According to the stability theorem of the dynamic equation of replication, for a single game player to be in a stable state, two conditions must be met.(1) Copy the dynamic equation to 0. (2) Copy the first derivative of the dynamic equation to be less than 0.The stability analysis of the three game players is shown in Table 3.

Table 3 Analysis of the Stability of Game Players

Game subject	The conditions to be met
Heavily Polluting Enterprises	,
·Environmental Regulatory Authorities
Government

Heavily Polluting Enterprises

In order to analyze the equilibrium conditions for a government subject to achieve a stable strategy from the perspective of a single game subject, the following analysis is conducted. Let find the stability of the strategies chosen by heavily polluting enterprises. Since the positive and negative values of are uncertain, the following discussion is held:

When , suppose , the following was true:

When ', then , and thus all are in an evolutionary steady state, that is, the ratio does not change over time regardless of the initial ratio of "cleaner production" and "non-cleaner production" chosen by heavy polluting enterprises.

When , let , we can get two possible evolutionary stability points as .

When ,, is the evolutionary and stable strategy of heavy polluting enterprises.

When , is the evolutionary and stable strategy of heavy polluting enterprises.To present the above conclusion more intuitively, this paper draws the strategy evolution trend chart of heavily polluting enterprises as shown in Figure 2.

Figure 2 The evolutionary trend of the heavy pollution enterprises strateg

Q13. Line 233: Mixed English and Chinese text appears (“F(y)=0且dF(y)/dy<0”). Please correct it to academic English.

A13.Thank you to the reviewers for pointing out the non-standard issue of the mixed use of Chinese and English in line 233 (" F(y)=0 and dF(y)/dy<0 "). We fully agree with your opinion and have made corrections to it. Specifically, we have uniformly transformed similar mixed expressions in the original text into pure English academic expressions. Meanwhile, we have conducted a systematic review of the entire text to ensure that such issues do not occur again, in order to comply with international academic publishing norms. All relevant modifications are reflected in the revised draft

Q14. Lines 373–396: The figures (5a–5d) are described, but a clearer economic interpretation is needed (what they mean for public policy).

A14.We sincerely appreciate the reviewer's valuable comment regarding the need for a clearer economic and policy interpretation of Figures 5a-5d. We agree that enhancing this aspect will significantly strengthen the practical relevance of our findings. Accordingly, we have thoroughly revised the accompanying text for each figure. For your convenience to review, we have attached a revised version.

This section utilizes Matlab software to simulate and validate the four pure strategy equilibrium points analyzed earlier, as illustrated in Figure 5.

The pure strategy equilibrium point (0,0,0)corresponds to Scenario 1: When assuming ,，,,,,,,,=70,, the conditions , are satisfied. This results in a stable state of (0,0,0), indicating "non-compliant production, non-compliant regulation, and lax supervision." The 50 time-evolved outcomes of different initial strategies are shown in Figure 5(a). When , it suggests that environmental information disclosure regulators' expected benefits from oversight significantly outweigh their regulatory costs, leading them to rationally choose "non-compliant regulation." Conversely, when , it indicates that the combined cost of government-imposed environmental taxes and fines for enterprises falls below the cost of conducting dual inspections by the government itself, resulting in a preference for lax supervision. These new stable states reveal critical flaws in the current policy framework: excessively low penalties coupled with prohibitively high regulatory costs have trapped the system in a "low-level equilibrium" state.

The Pure Strategy Equilibrium point (0,0,1) Scenario 2: Given parameters s=40, ,,,,，, , 32,,,=70,,, the system satisfies the conditions: ,,, The stable equilibrium is (0,0,1), indicating a scenario of "non-clean production, non-compliant regulation, and strict supervision". Figure 5(b) displays 50 time-evolved results across different initial strategies. When , enterprises find that the combined subsidies from eco-friendly production and avoided fines remain below total costs, resulting in insufficient motivation for transformation. When , regulatory authorities 'expected benefits from oversight fail to cover costs, reducing their supervisory incentives. Finally, when , the government's strict supervision strategy becomes economically viable as the cost of enforcement outweighs potential revenue from penalties. Although the policy system reflected in this stability situation can encourage the government to strictly supervise, it fails to effectively guide the behavior of enterprises and regulatory departments to comply with the law, and the overall governance efficiency is still low.

The pure strategy equilibrium point (0,1,0) corresponds to Scenario 3: Given parameters ,,,,,, , ,,,=70,, the conditions ,, are satisfied. The stable equilibrium is (0,1,0), indicating "non-clean production with legal compliance and lenient regulation". Figure 5(c) displays 50 time-evolved results from different initial strategies. These findings demonstrate that a rational incentive mechanism can theoretically encourage environmental regulators to enforce compliance. When , enterprises 'total benefits from cleaner production remain below their costs, reducing transformation incentives. When , regulatory authorities' expected gains (penalty revenue) exceed monitoring costs, incentivizing "legal compliance". Conversely, when , strict government enforcement becomes more costly than penalty revenue, leading to "lenient regulation". This stabilization scenario enhances regulatory incentives by reducing compliance costs, yet fails to resolve the economic viability of cleaner production in enterprises, highlighting the limitations of policy tool homogeneity.

The pure strategy equilibrium point (0,0,1) corresponds to Scenario 4: Given parameters ,,,,,, ,,,,,, satisfies the condition: ，,. The resulting stable state is (0,1,1), indicating "non-cleaner production with strict regulatory enforcement". The 50 time-evolved results under different initial strategies are illustrated in Figure 5(d). When the sum of is less than c i + c e, it indicates that the total benefits from implementing cleaner production remain lower than the total costs, leaving enterprises with insufficient motivation for transformation. When , it suggests that the expected regulatory benefits (including penalty commissions and reputational gains) surpass the regulatory costs, incentivizing "law-based regulation". If , it demonstrates that the benefits of strict government supervision outweigh the costs, motivating authorities to enhance oversight. These equilibrium conditions reveal that optimizing regulatory costs enhances enforcement incentives for governments and regulators, yet fail to resolve the fundamental issue of economic viability in corporate cleaner production.

Q15. Lines 412–486: There are repetitions (“the strategy tends to (0,1,0)”). This could be summarized with a table summarizing how each parameter affects the result.

A15.We sincerely thank the reviewer for this insightful suggestion. We agree that summarizing the repetitive textual descriptions of how different parameters influence the evolutionary stable strategy (0,1,0) with a table would significantly enhance the clarity and conciseness of the manuscript.For your convenience to review, we have attached a revised version.

From the analysis of the evolution trajectory in Figure 6, it can be seen that the values of each key parameter in the system will significantly affect the evolution path and convergence speed of the strategies of the government, heavily polluting enterprises, and environmental information regulatory departments. The specific impacts are shown in Table 4.

Parameter		Convergence rate	Specific explanation
		As the parameter increased from 80 to 150, the probability of heavy polluting enterprises choosing non-clean production showed an upward trend.	The cost of cleaner production will hinder the evolution speed of cleaner production. The reason is that the increase of cost will compress the profit space of enterprises, and then hit the motivation of cleaner production.
	When the parameter is 12, the policy trajectory shows a linear convergence feature		The increase of environmental information disclosure violation penalty can restrain the tendency of enterprises to pollute, accelerate the steady state convergence process of the system, and promote enterprises to reach the Pareto optimal equilibrium of clean production in a shorter time.
	When the parameter is increased to 30, the evolution trajectory of the system gradually forms a closed ring structure and the amplitude expands with the increase of the parameter.
	When the parameter is increased to 60, the probability of heavy polluting enterprises choosing non-clean production shows a monotonically decreasing trend, and the evolution speed of their clean production strategies increases significantly.
	When the parameters are initial values, the strategy selection tends to be (0,1,0)		The expected return of regulatory authorities to regulate according to law will affect the evolution speed of the system, and the larger the value is, the more likely the government is to strictly regulate.
	When the parameter is increased to 70, the strategy evolution shows a slow convergence in a spiral shape and tends to approach the stable point after several oscillations.。
	When the parameter is increased to 100, the government tends to be more stringent.
	As the regulatory efficiency continuously increased from 0.4 to 0.8, the evolution speed of the system rose		In the process of system evolution to the steady state, the efficiency of supervision by regulators will affect the evolution speed of the system.
	When the parameter is increased to 80, the evolution rate of the system increases, but when the parameter is further increased to 100, the evolution rate of the system shows a downward trend.		It shows that when the pollution production fines and environmental protection taxes paid by heavy polluting enterprises are too high, it will hinder the production and operation of enterprises to some extent. Unreasonable high fines will lead to the phenomenon that enterprises will not clean up even if they are fined.
	When the parameter is increased from 60 to 100, the evolution rate of the system increases, but when the parameter is further increased to 150, the evolution of the system will no longer approach (0,1,0).		When the cost of strict government regulation is too high, it will cause difficulties in government supervision, which is not conducive to effective government supervision.

Note: In matlab software, "" cannot be input. Therefore, in the figure, let =A.

Q16. In Fig. 6, more legible labels on the axes are missing.

A16.Thank you for your suggestion. We realize that the axes in the figure were indeed missing, and we have made the necessary corrections. In Figure 6, the x, y, and z axes represent heavily polluting enterprises, environmental regulatory authorities, and the probability of government decision-making, respectively. Additionally, we have attached the revised figure for your review.

Q17. Lines 488–497: Good selection, but a justification should be given for choosing 570 firms instead of a larger panel.

A17.Thanks for your noble comments, this study finally determines the sample size of 570 enterprises because: (1) the sample represents the most comprehensive set of A-share listed companies in specific heavy pollution industries that can continuously and completely disclose data during the study period, ensuring A high-quality balanced panel data. (2) The sample size far exceeds the common methodological requirements and provides sufficient statistical power for our robust econometric analysis. And we will modify the content attached, convenient for your review.

Based on the "Catalogue of Industry Classification Management for Environmental Protection Verification of Listed Companies" issued by the Ministry of Ecology and Environment, this study selected 570 heavily polluting listed companies from 2014 to 2023 as research objects by matching annual report information, covering 16 highly polluting industries such as thermal power, steel, and chemical engineering. This sample covers the most comprehensive group of A-share listed companies in specific heavily polluting industries, ensuring that these enterprises can continuously and completely disclose data during the research period, thereby obtaining high-quality balanced panel data. Moreover, the sample size far exceeds the requirements of conventional econometric methodology, providing sufficient statistical power guarantee for subsequent robust econometric analysis.

Q18. The use of linear interpolation (line 495) requires a warning about potential bias.

A18. Thank you to the reviewers for reminding us of the potential deviation of linear interpolation. We completely agree with you. In the revised draft, we have added a note in the "Sample Selection and Data Sources" section, clearly stating that when the proportion of missing data is low (less than 3% in this study) and the missing pattern can be regarded as completely random missing, the risk of bias introduced by linear interpolation is relatively small.For your convenience in reviewing, we have attached the modified content.

During the data organization stage, three operations are carried out: (1) Eliminate ST/*ST enterprises to avoid interference from abnormal transactions; (2) Delete the samples with missing key data, and fill in the missing variables with linear interpolation. (3) All continuous variables should be truncated by 1% to eliminate the influence of extreme values. This study adopted the linear interpolation method when dealing with a small amount of missing data. We have noticed that this method assumes a linear variation among data points, which may introduce deviations. However, given the low missing rate of this dataset and the gentle trend of the variables within a short time window, the impact of this bias on the overall analysis is limited.

Q19. Lines 503–511: Please check for temporal consistency: 2014–2023 is mentioned, but “t=2014, 2012…2023” also appears (typo).

A19.We sincerely thank the reviewer for carefully reading the manuscript and pointing out the inconsistencies. We deeply apologize for our oversight. The year '2012' was indeed a typographical error and has been corrected to '2014' in the relevant sentence of the revised manuscript to ensure the time range (2014-2023) is presented consistently. We have also attached the revised content for your review.

Among them, represents the stock code of the enterprise and represents the year;

Q20. Lines 534–535 (Table 5): The constructed index is interesting, but requires further bibliographic validation. I suggest citing previous studies that use similar indices.第534-535行（表5）：

A20.We sincerely thank the reviewers for their valuable suggestions. Your opinion that the index we constructed requires further literature verification is very pertinent and is crucial for enhancing the rigor and persuasiveness of our research.We have carefully followed your suggestions and made important supplements and revisions to the paper. For your convenience in reviewing, the modified content is attached.

Environmental Regulation (ER). Xue Lian et al. used the proportion of completed industrial pollution control investment in the secondary industry to represent the intensity of environmental regulation. This study draws on this approach and uses the proportion of completed industrial pollution control investment in the secondary industry to represent environmental regulation[32].

Environmental performance (EP) of heavily polluting enterprises in environmental cleaning. The examination of an enterprise's environmental performance based on relevant literature mainly includes three dimensions: environmental treatment situation, environmental management capacity and environmental governance performance. The indicator system is shown in Table 4. The measurement of clean environment performance (EP) of heavily polluting enterprises refers to the division of environmental performance dimensions by Ding et al. (2022) and Li et al. (2025), and combines the environmental information disclosure evaluation framework proposed by Wiseman (1982)[33-34]. This article conducts a comprehensive examination of an enterprise's clean environmental performance from three dimensions: environmental treatment situation (ET), environmental management capacity (EM), and environmental governance performance (EG))[35].

Table 6 Performance Index System for Clean Production of Heavily Polluting Enterprises

Variable	First-level indicator	Secondary indicators
Environmental performance of heavily polluting enterprises EP	Environmental treatment statusET	Handling of environmental petition cases
		The handling of environmental violations
		handling of sudden environmental accidents
	Environmental management capacity EM	Environmental education and training
		Environmental honors or awards
		"Three simultaneous" system
	Environmental governance performance EG	Implementation of cleaner production
		Solid waste utilization and disposal
		Exhaust gas emission reduction and treatment
		Wastewater emission reduction and treatment
		dust and soot control

The environmental information disclosure index of enterprises. This study, referring to relevant literature and combining the characteristics of environmental information disclosure and clean production of heavily polluting enterprises, constructed the environmental information disclosure index and clean production performance evaluation index system of heavily polluting enterprises as shown in Table 7 [36]. It mainly includes the disclosure of environment-related reports and the disclosure of environmental governance. The entropy method is utilized to assign weights to each indicator in the evaluation index system, and the environmental information disclosure index and clean production performance of heavily polluting enterprises are comprehensively calculated.

Table 7 Environmental Information Disclosure Index System for Heavily Polluting Enterprises

Variable	First-level indicator	Secondary indicators
Environmental Information Disclosure Index EIDI	Environmental regulatory certification ESC	Whether it is S09001 certified
		Whether it is certified by S014001
		Whether pollutant emissions meet the standards
	Environmental related reporting disclosure status ERR	Environmental protection information disclosure in the annual report
		Social responsibility report environmental protection information disclosure
		Disclosure of separate environmental reports
	Environmental governance disclosure EPG	Whether it has an environmental protection concept
		Whether to set environmental goals
		Whether the environmental protection management system is complete
		Whether the emergency response mechanism for environmental incidents is sound
		The development of special environmental protection actions

Q21. Lines 537–557 (Table 6): Clear results, but a comparative discussion with international studies is missing in the discussion section.

A21.Thank you very much for the valuable suggestions put forward by the reviewers. Based on the opinions, we have added a paragraph in the discussion section for comparative analysis with international research, specifically explaining how our research results echo or complement those of international research.

This conclusion is highly consistent with the discussions in the international academic community. For instance, the Porter Hypothesis suggests that well-designed environmental regulations can stimulate enterprise innovation, thereby partially or completely offsetting compliance costs and even enhancing enterprise competitiveness. This provides a classic theoretical support for the "innovation compensation" effect. In developed economies such as the European Union, the strict emissions Trading system (EU ETS) has been confirmed by a large number of studies to effectively promote enterprises (especially the manufacturing industry) to carry out green technological innovation to reduce carbon emissions. Compared with the traditional "command-and-control" type of regulation, market incentive tools often demonstrate higher cost-effectiveness in international practice because they provide enterprises with more flexible options to achieve emission reduction targets. Furthermore, research from Japan's manufacturing industry also indicates that environmental regulations can help enhance the energy utilization efficiency and technological innovation level of enterprises. International frameworks such as the ecological efficiency measurement guidelines promoted by the World Business Council for Sustainable Development (WBCSD) also emphasize the importance of integrating environmental performance with enterprise value creation.

Q22. Lines 559–570 (Mediating Effect): Well-structured, although it could be reinforced with an additional test (e.g., Sobel test or Bootstrap) for greater robustness.

A22.We sincerely thank the reviewer for the positive feedback on the structure of our mediating effect analysis and for the valuable suggestion to further enhance its robustness with an additional test, such as the Sobel test or Bootstrap method. We fully appreciate the intent behind this comment to strengthen the methodological rigor. In our manuscript, the current mediation analysis was conducted using established and statistically sound procedures. Given that our dataset contains very few missing values (which were handled with appropriate methods), and considering the need to maintain conciseness within the scope of the current article, we believe that the presented results already provide a valid and clear test of the mediating effect. Incorporating an additional test at this stage, while undoubtedly useful in certain contexts, might be methodologically redundant for the primary aim of establishing the presence of the mediation effect in this specific study.

Q23. Please create a section dedicated to discussions, where comparative references from other international contexts should be incorporated, which would increase the global relevance of the study.

A23.Thank you for your meticulous review and valuable suggestions on this article. We fully agree with your comments on supplementing research limitations and future directions, which will help enhance the integrity and academic value of the study. In response to your suggestions, we have added a "Discussion" section in Chapter Five of the text. For your convenience, we have attached the newly added discussion.

4.1 Discussions

This study explores the impact of environmental regulations and information disclosure on clean production in heavily polluting enterprises through evolutionary game theory and empirical analysis. The results echo existing research and international practices and highlight the particularity of the Chinese context.

In terms of the role of environmental regulations on the environmental performance of enterprises, the positive impact verified empirically conforms to the "Porter Hypothesis", which is consistent with the international experience of the European Union promoting green innovation in manufacturing through the carbon emissions trading system and Japan improving energy efficiency by relying on environmental regulations. However, China's "command-and-control" regulation can still effectively force enterprises to transform. Thanks to the strengthened environmental protection law enforcement in recent years; The mediating effect of environmental information disclosure shows the characteristics of "short-term inhibition - long-term promotion". Different from the direct positive correlation between regulation and disclosure under the mandatory disclosure system in Europe and the United States, the disclosure of heavily polluting enterprises in China is mainly voluntary. Under the short-term compliance pressure, some enterprises simplify the disclosure. However, high-quality disclosure promotes performance improvement by reducing information asymmetry and strengthening social supervision. It is still in line with the ecological efficiency framework of the World Business Council for Sustainable Development.

Evolutionary games show that the equilibrium point of the system points to "non-clean production by enterprises", with the core reason being that the total cost of clean production is higher than the benefits. This is consistent with the international governance "cost-benefit" logic, and the importance of improving regulatory efficiency, reasonable fine and subsidy design also contrasts with the practical experience of the US regional greenhouse gas Initiative in enhancing regulatory efficiency and the EU's carbon price stabilization mechanism.

Q24. Lines 574–597 (Conclusions): Some sentences repeat what has already been explained in the results. I recommend condensing them.There is no clear distinction between theoretical and practical contributions.

A24.We appreciate the valuable feedback provided by the reviewers. We agree that the conclusion can be more concise and focused. We removed the repetitive statements that had already appeared in the results, making the conclusion more concise and influential. We have now clearly distinguished and outlined the theoretical and practical contributions of our work. This discussion has been integrated into the new section "4.4 Research Limitations and Future Research Directions" to provide a more logical narrative flow.

This study combines evolutionary game theory with econometrics to explore the impact of environmental regulations on clean production in heavily polluting enterprises. In terms of theoretical contributions, this study provides a formal framework, demonstrating that the stability of the tripartite regulatory system is highly sensitive to the relative cost-benefit structure of the relevant parties. Theoretically, it has been established that environmental information disclosure is an important intermediary mechanism for supervision to influence the environmental performance of enterprises. In terms of practical contributions, the research results provide policymakers with feasible insights. The effectiveness of environmental regulations depends on the severity of penalties and also on a carefully calibrated system that can both reduce compliance costs and ensure reliable law enforcement. It is emphasized that the government needs to formulate precise and targeted policies rather than adopt a one-size-fits-all approach.

Although this study provides valuable insights, there is still room for improvement. Firstly, although the current model incorporates the tripartite interaction among the government, enterprises and regulatory authorities, it fails to adequately depict the heterogeneity of the degree of limited rationality of each subject. Secondly, the empirical aspect is mainly based on data from heavily polluting industries. In the future, it can be expanded to the analysis of light-pollution industries.

All the modifications have been highlighted in the original manuscript. For your convenience in reviewing, we have attached the modified content.

Q25. Lines 599–634 (Policy Recommendations): Very useful, but could be sorted into short, medium, and long-term recommendations.

A25.We sincerely thank the reviewers for their constructive feedback on our manuscript. Opinions on organizational policy recommendations are particularly helpful. We have attached the revised policy recommendations for your review.

Based on the above conclusions and research findings, the following policy recommendations are proposed:

(1) The core task in the short term is to build a policy framework and incentive mechanisms for cleaner production, focusing on alleviating cost pressures on enterprises, especially small and medium-sized enterprises (SMEs). The government should establish a special subsidy fund for cleaner production, provide progressive tax reductions for SMEs that actively upgrade their environmental technologies, and reduce compliance costs by simplifying environmental information disclosure procedures and developing a unified digital reporting platform. Meanwhile, pilot programs for cleaner production audits should be launched in key industries such as metallurgy and chemical engineering, as well as in industrial parks, promoting a "subsidized audits and voluntary agreements" model. Enterprises that proactively disclose environmental data should be rewarded with credit rating bonuses or one-time incentives to initially establish a motivation mechanism for voluntary participation. Furthermore, the applicability of environmental taxes and fines across different industries should be dynamically assessed, implementing differentiated penalties for highly polluting enterprises to avoid excessive burdens on businesses caused by a "one-size-fits-all" approach.

(2) In the medium term, the focus should be on expanding the coverage of cleaner production and strengthening inter-departmental coordination. On one hand, cleaner production practices should be extended from the industrial sector to agriculture, construction, and services, establishing a comprehensive industrial evaluation indicator system and promoting innovation in green financial instruments. On the other hand, a "heterogeneous enterprise classification and governance mechanism" should be constructed: large enterprises should be mandatorily required to formulate mid-to-long-term environmental governance roadmaps, linking emission reduction targets with ESG ratings; for enterprises with high market valuation, the disclosure of environmental risks should be strengthened and linked to refinancing qualifications. Simultaneously, a regional collaborative mechanism for cleaner production should be established to unify standards and jointly promote technologies, achieving cross-regional joint prevention and control of pollution.

(3) The long-term goal is to foster endogenous motivation for green and low-carbon development through legislative improvement and societal co-governance. Supporting regulations should be revised to incorporate requirements for carbon emission reduction and resource recycling into the legal framework. A dynamic evaluation and remediation mechanism for corporate environmental credit should be established, rewarding enterprises that demonstrate continuous performance improvement. The development of a green consumer market should be promoted, and public participation in supervision should be encouraged by regularly disclosing corporate environmental data and government regulatory reports, forming a positive feedback loop of "disclosure-improvement-incentive." Cleaner production should be deeply integrated into the national "Dual Carbon" strategy, leading the industrialization of cleaner production technologies and equipment through scientific and technological innovation, and ultimately building a sustainable development pattern supported by the collaboration of government, enterprises, and society.

Q26. Several sections contain very long sentences; please divide them into shorter ones. Please standardize the English: there are sentences with literal translations from Chinese that hinder fluency.

A26.We sincerely thank the reviewers for their profound comments on the sentence structure and language fluency. We agree that improving the readability of manuscripts is of vital importance. We revised the language of the entire text and sent it to a professional institution for a complete revision of the language issues.

Again, we would like to thank you for reviewing our work. We have made a considerable effort to take into account your suggestions and comments. We hope you will be pleased with our response. In any case, we are open to consideration of any further comment on our answers.

Best regards,

16/10/2025

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Formatting Issues:

Structured Abstract is missing
no uniformity is citations, must be in numbering
References list is not formatted as per journal style,

Specific Comments on content of the paper:

1. Introduction must be finished with a research question on the relevant problem of this current study

2. There are too many equations in literature section without relevant literature on hypotheses development (missing), while results shows regression results so it is better to simplify the literature in following way: Add variable wise literature and relevant theory and develop hypothesis at end of each heading.

3. Table 4 some variables and their respective sample size vary, author must clarify these differences and how they rectify this missing values issue in the analysis.

4. Develop hypotheses for regression results

5. Also develop hypotheses for mediation analysis

6. First place discussion and justify the hypotheses in light of previous research. Followed by conclusions, policy implications, limitations and future directions

Author Response

Dear Reviewers

Many thanks for your careful reading and valuable comments on our manuscript. The comments helped us further revise and improve the paper. We have studied your comments carefully and made corrections accordingly. And in the process, we believe the paper has been significantly improved. The revised parts of the manuscript have been highlighted in red font. Our detailed responses to the reviewer’s comments are outlined as follows (“Q” denotes question and “A” denotes answer).

Response to Referee 2.

Q1.Structured Abstract is missing

A1.Thank you for pointing out the problem of the lack of structure in the abstract section. We fully agree with your opinion and have reorganized the Abstract into a Structured Abstract as required by the journal. It should clearly include sections such as Background, Objective, Methods, Results and Conclusion. We have attached the revised summary for your review.

Q2.no uniformity is citations, must be in numbering.References list is not formatted as per journal style.

A2.We sincerely thank the reviewer for pointing out the inconsistencies in the citation style and the formatting of the reference list. We deeply apologize for these oversights, which do not meet the required journal standards.In direct response to this comment, we have thoroughly revised all in-text citations and the reference list to ensure full compliance with the journal's style guide.

Q3. Introduction must be finished with a research question on the relevant problem of this current study

A3.We sincerely thank the reviewers for their important suggestions. We agree that ending the introduction with a clear research question can significantly enhance the focus and academic rigor of our manuscript. Based on the suggestions, we revised the conclusion section of the introduction to clearly state the research questions guiding this study. For your convenience in reviewing, we have attached the modified content.

Although existing research has covered the impact of single dimensions such as regulation, green finance, and digital technology on the clean production of heavily polluting enterprises, there are still two key deficiencies: (1) The comprehensive pressures faced by enterprises have not been fully integrated and analyzed - in actual operation, heavily polluting enterprises often simultaneously bear policy pressure from regulatory authorities, market pressure from consumers, and financial pressure from investors. However, existing research mostly focuses on a single source of pressure and fails to clearly explain the synergistic or conflicting impact of the superposition of multiple pressures on enterprises' clean production decisions. (2) There is insufficient exploration of the interaction among multiple stakeholders, especially a lack of in-depth discussion on the core issue of "under what conditions can these interactions go beyond the scope of simple supervision and transition to a collaborative model guided by regulatory authorities, participated by consumers, and supported by investors". Based on this, this study raises the core research question: From a multi-regulatory perspective, how do the multiple external pressures faced by heavily polluting enterprises interact with each other, and under what conditions can they drive stakeholders to shift from "supervision" to "collaborative cooperation", ultimately promoting clean production and green transformation of enterprises?

Q4.There are too many equations in literature section without relevant literature on hypotheses development (missing), while results shows regression results so it is better to simplify the literature in following way: Add variable wise literature and relevant theory and develop hypothesis at end of each heading.

A4.Thank you for your valuable suggestions. Although the current text is an introduction and not an independent literature chapter, we have adjusted it based on the logic you suggested: match the corresponding literature according to the three core variables of "regulatory pressure", "green finance pressure", and "digital technology empowerment". In the analysis, there are implicit supports such as "Porter's Hypothesis", "Internalization Theory of Environmental Cost", and "Stakeholder Theory", providing a theoretical basis for literature analysis. Meanwhile, by clarifying the research gaps such as "multiple pressure interactions" and "stakeholder collaboration conditions", it lays the foundation for the subsequent literature section to unfold in the structure of "variable - theory - hypothesis".

Q5. Table 4 some variables and their respective sample size vary, author must clarify these differences and how they rectify this missing values issue in the analysis.

A5.Thank you for your comment. Regarding the sample size differences in Table 4 and missing values, we clarify:

Sample size variations stem from missing values in variables b_size and g_ratio during data collection. We used multiple imputation (valid under missing at random, MAR) to handle missing data. This method generates plausible values for missing points, reduces bias, and preserves data integrity better than listwise deletion.

Q6. Develop hypotheses for regression results

A6.Thank you, reviewers, for your suggestions regarding the need to clarify research hypotheses. Based on the research model and theoretical framework, we have formally proposed the research hypotheses to be tested in this paper in the "Theoretical Hypotheses" section of the manuscript. For your convenience in reviewing, we have attached this part of the content.

Environmental regulations are a key measure to promote clean production in heavily polluting enterprises and a crucial approach to achieving their green transformation. The impact of environmental regulations on the clean environment performance of heavily polluting enterprises. The specific analysis is as follows: Strict command-and-control environmental regulations directly force enterprises to increase investment in environmental protection facilities and upgrade pollution control technologies by raising the environmental governance costs and compliance pressure of enterprises, thereby enhancing performance in the dimensions of environmental treatment (ET) and environmental governance performance (EG). Meanwhile, market-incentivized environmental regulations guide enterprises to internalize environmental costs through economic means, encouraging them to optimize production processes through green technological innovation, reduce pollutant emissions, enhance resource utilization efficiency, and thereby improve environmental management capabilities (EM) and overall environmental performance. Chen X (2024) et al. found that environmental regulations can significantly improve the environmental performance of heavily polluting enterprises[28]. Researchers Zhang W et al. (2022) analyzed the data of Chinese listed companies from 2008 to 2018. The results showed that government environmental regulations could significantly improve the environmental performance (CEP) of enterprises[29]. Based on this, this paper proposes Hypothesis 1: H1: Environmental regulations have a significant positive impact on enterprises' clean environmental performance.

Q7.Also develop hypotheses for mediation analysis

A7.Thank you to the reviewers for their suggestions regarding the need to develop hypotheses for the analysis of the mediating effect. Based on the theoretical framework and empirical research design, we have clearly defined the research hypothesis of the mediating effect in this paper. For your convenience in reviewing, we have attached the modified content.

In the analysis of the transmission mechanism based on environmental information disclosure, environmental regulations act on enterprises' clean environmental performance through mandatory disclosure requirements: As a command-and-control type of environmental regulation, the mandatory environmental information disclosure system implemented by the government first places enterprises' environmental behaviors under government supervision and public oversight by setting clear disclosure obligations, thus creating external pressure. This pressure prompts enterprises to proactively adjust their internal strategies in order to maintain their legitimacy and reputation, thereby transforming external regulatory pressure into the driving force for internal resource reconstruction. Furthermore, high-quality environmental information disclosure behavior itself will convey a positive "green" signal to the market, which helps enterprises win the favor of green investors, alleviate financing constraints, enhance brand image, and ultimately form a virtuous cycle through market incentive mechanisms. Promote the overall improvement of enterprises' clean environmental performance in dimensions such as environmental treatment (ET), environmental management capacity (EM), and environmental governance performance (EG). Qing L. (2022) et al. 's research confirmed that environmental information disclosure has a significant positive impact on the environmental performance of Chinese enterprises [30]. The research results of researchers such as Ren S show that although mandatory environmental information disclosure can improve the environmental performance of enterprises by increasing environmental management activities and costs, it will have a long-term positive impact on the economic performance of enterprises. Researchers Ye Y (2023) et al. analyzed the data of A-share listed companies in China. The results showed that this environmental regulation policy could significantly improve the environmental performance of enterprises [31].

Based on this, this paper proposes Hypothesis H2: The intensity of environmental regulations further positively affects the clean environmental performance of significant enterprises by influencing environmental information disclosure.

Q8. First place discussion and justify the hypotheses in light of previous research. Followed by conclusions, policy implications, limitations and future directions.

A8.We sincerely thank the reviewer for this critical suggestion regarding the structure and content of the discussion and conclusion sections. We agree that a more logical and in-depth presentation significantly enhances the scholarly value and impact of the manuscript.In direct response to this comment, we have thoroughly reorganized and enriched the final sections of the paper. The specific revisions are outlined below.

4.Conclusions and Recommendations

4.1 Discussions

4.2 Conclusions

This study constructs A three-party game model among heavily polluting enterprises, the government and environmental information disclosure regulatory authorities based on evolutionary game theory. Combined with the panel data of 570 listed companies in heavily polluting industries in China's A-share market from 2014 to 2023, it systematically explores the decision-making mechanism and influencing factors of enterprises' clean production under multi-subject interaction, and draws the core conclusion:

(1) At the evolutionary game level, the strategic choices of all three parties are dominated by the cost-benefit relationship. The willingness of heavily polluting enterprises to carry out clean production depends on the balance between the costs of upgrading environmental protection technologies, information disclosure, subsidies and fines.

(2) When the cost of clean production for enterprises is too high and the incentives and constraints are insufficient, enterprises tend to adopt non-clean production. Environmental information disclosure regulatory authorities will only choose to regulate in accordance with the law and strictly when the regulatory benefits exceed the regulatory costs, and the government will only choose to regulate strictly when the verification costs are lower than the fines and confiscations. The core bottleneck for heavily polluting enterprises in choosing strategies is that the total cost of clean production is higher than the comprehensive benefits.

(3) At the empirical level, environmental regulations have a significant positive impact on enterprises' clean environmental performance, verifying Hypothesis H1 and indicating that reasonable regulations can force enterprises to innovate in a green way by increasing the marginal cost of pollution emissions. Environmental information disclosure plays a mediating role in the relationship between the two. Although environmental regulations have a short-term inhibitory effect on EIDI due to the compliance pressure on enterprises, high-quality EIDI can significantly improve the environmental performance of enterprises by reducing information asymmetry and strengthening social supervision. Meanwhile, enterprise scale and total asset turnover rate have a positive impact on environmental performance, while Tobin's Q value has a negative impact. It reflects the temporary squeeze on the market valuation of enterprises caused by short-term environmental protection investment.

4.3 Recommendations

Based on the above conclusions and research findings, the following policy recommendations are proposed:

4.4 Research Limitations and Future Research Directions

Best regards,

16/10/2025

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Reviewing report

Manuscript entitled “Environmental Regulation, Environmental Information Disclosure and Clean Production Performance of Heavy Polluting Enterprises: Empirical Data from China “.

Please combine lines 32-77 as one paragraph.
Please, in the introduction explain the traditional role of government regulation (e.g., command-and-control, market-based mechanisms like environmental taxes/fees) as the primary external pressure forcing firms to clean up. Acknowledge the debate on its effectiveness (e.g., does it impose crippling costs, or does it stimulate innovation)
Introduce EID as a market-based or public-participation regulatory tool. Explain that disclosure creates transparency, subjects firms to public and stakeholder oversight (investors, consumers, media), and provides an incentive for better performance. Highlight that disclosure quality in China is often low or voluntary for many firms, making its effectiveness an empirical question.
Where are the references for your equations ?
Look to the numbering of the equations 1-3.
Please make a space between the word “Fig. ” and the number after “5”, 6,, etc.. Please revise the whole captions.
Under each table please put a foot note about the meaning for each abbreviation. For example table 4, need a footnote to add the meaning of EP, ER, etc.

All tables not just this.

Inside table 7, I’m confused from the numbers between brackets and numbers not between brackets.
Please make a space between the number and the word here “4.Conclusions and Discussions”. Please revise the whole manuscript.
In the regression table please add the R² value.
Please add subtitle about limitations and future research
You need to make a descriptive table about Indicators for corporate environmental performance.

For help see this “Environmental information disclosure and corporate performance: Evidence from Chinese listed companies” Don’t reference it. Just see my vision.

Another table for Variable definitions, Descriptive analysis.

How the conclusion part before discussion?
Please improve your conclusion part by more details.

GOOD LUCK

Author Response

Dear Reviewers

Response to Referee 3.

Q1.Please combine lines 32-77 as one paragraph.

A1.Thank you for your valuable comments. We have merged lines 32-77. The specific changes are highlighted in yellow in the text.

Q2.Please, in the introduction explain the traditional role of government regulation (e.g., command-and-control, market-based mechanisms like environmental taxes/fees) as the primary external pressure forcing firms to clean up. Acknowledge the debate on its effectiveness (e.g., does it impose crippling costs, or does it stimulate innovation)

A2.Thank you very much for your valuable suggestions. Your suggestion that the traditional role of government regulation and the debate on its effectiveness should be clearly defined in the introduction is very pertinent and provides a key direction for us to refine the theoretical background of the paper.

We have made important supplements and revisions to the introduction based on your suggestions. The core of the revision lies in: systematically expounding the traditional role of government regulation as the primary external pressure forcing enterprises to carry out clean production, clearly distinguishing the logic of the two core policy tools of "command and control" and "market mechanism", and objectively presenting the main disputes in the academic circle regarding its effectiveness. For your convenience in reviewing, we have attached the modified content.

From the perspective of traditional functions, government supervision has always been the primary external pressure compelling enterprises to carry out clean production. Specifically, it encompasses two core mechanisms: The first is the command-and-control type of environmental regulation, which directly restricts enterprises' pollutant discharge behavior by formulating clear pollution discharge standards, production technology requirements and other hard rules. If enterprises fail to meet the standards, they will face penalties such as fines and production suspension. The second type is market-incentivized environmental regulations such as environmental taxes and pollution discharge fees. By internalizing environmental costs, they guide enterprises to proactively reduce pollution emissions to lower costs. However, there has always been controversy in the academic circle over the effectiveness of government supervision: One view holds that strict supervision would impose a heavy cost burden on enterprises, such as the large amount of capital investment required for purchasing pollution control equipment and adjusting production processes, squeezing research and development and production funds, and significantly affecting the profit margins of heavily polluting enterprises due to environmental protection investment, leading to increased operational pressure. The other side of the view puts forward the "Porter Hypothesis", which holds that reasonable regulatory pressure can force enterprises to innovate. For instance, by developing cleaner production technologies and optimizing energy utilization methods, they can form new competitive advantages while meeting environmental protection requirements, ultimately achieving a "win-win situation for environmental protection and benefits".

Q3.Introduce EID as a market-based or public-participation regulatory tool. Explain that disclosure creates transparency, subjects firms to public and stakeholder oversight (investors, consumers, media), and provides an incentive for better performance. Highlight that disclosure quality in China is often low or voluntary for many firms, making its effectiveness an empirical question.

A3.We sincerely thank the reviewers for their insightful suggestions on the introduction of the environmental information disclosure (EID) system. We completely agree. We have added relevant content in the introduction: EID is clearly positioned as a regulatory tool that combines marketization and public participation. We will elaborate on the mechanism by which it enhances the transparency of enterprises' environmental behaviors and enables them to be supervised by the public, investors, consumers, media and other stakeholders, as well as the incentive effect of such supervision on enterprises' improvement of environmental performance. At the same time, it is particularly emphasized that the actual situation of the Chinese market - the quality of EID of most heavily polluting enterprises is relatively low, and the disclosure behavior is mostly voluntary. This points out that the actual promoting effect of EID on the clean production of enterprises still needs to be empirically verified, echoing your concern about the effectiveness of EID. For your convenience in reviewing, we have attached the modified content.

In the innovation of regulatory tools, the environmental information disclosure system (EID), as an important tool combining marketization and public participation, has gradually drawn attention to its mechanism of action and practical predicaments. The core value of EID lies in enhancing the transparency of enterprises' environmental behaviors by requiring them to disclose information such as environmental pollutant discharge data, pollution control measures, and environmental protection investments. On the one hand, transparent information enables enterprises to be under multiple supervision from the public and stakeholders - investors can assess enterprise risks and adjust investment decisions based on environmental information. Consumers tend to choose products with better environmental performance, creating a market pressure. The media can strengthen social supervision by exposing environmental violation information. On the other hand, this multi-dimensional supervision can provide enterprises with incentives to improve their environmental performance and encourage them to proactively optimize their clean production processes. However, in terms of the Chinese market, the implementation effect of EID is still limited by the actual conditions: The quality of information disclosure in most heavily polluting enterprises is generally low, with problems such as inaccurate data and incomplete content. Moreover, the disclosure behavior is mostly voluntary in nature and lacks mandatory constraints, which leads to the actual promoting effect of EID on the clean production of enterprises not being fully exerted. Its effectiveness still needs more empirical research for verification.

Q4.Where are the references for your equations ?Look to the numbering of the equations 1-3.

A4.Thank you to the reviewers for reminding us to supplement the references for the equations (Equations 1-3) in the text. We fully agree with you that rigorous academic writing should clearly indicate the source of theoretical methods.

The core theories on which the game payoff matrix and the replication dynamic equation are based in this paper mainly come from the classic literature and textbooks in the field of evolutionary game theory. We have supplemented the following key references for these equations in the revised draft:

Friedman D. Evolutionary games in economics [J]. Econometrica, 1991, 59(3): 637-666.
Friedman D. On economic applications of evolutionary game theory[J]. Journal of Evolutionary Economics, 1998, 8(1): 15-43.

Q5.Please make a space between the word “Fig. ” and the number after “5”, 6,, etc.. Please revise the whole captions.

A5.We sincerely thank the reviewer for this meticulous observation regarding the formatting details of the figure captions. We agree that consistent and proper spacing enhances the professionalism and readability of the manuscript.

As suggested, we have carefully revised all figure captions throughout the manuscript to ensure a space is inserted between the word “Fig.” and the following number. This revision has been applied uniformly to maintain formatting consistency.

We have conducted a thorough check to ensure this formatting standard is met in all relevant parts of the paper.

Q6.Under each table please put a foot note about the meaning for each abbreviation. For example table 4, need a footnote to add the meaning of EP, ER, etc.All tables not just this.

A6.We sincerely thank the reviewers for their detailed comments on the clarity of the abbreviations in our table. We fully agree that providing clear definitions for all abbreviations is crucial for ensuring the readability and academic rigor of the paper.

According to the suggestion, we have now added comprehensive footnotes below each table in the manuscript to explain the meaning of each abbreviation used. For your convenience in reviewing, we have attached the added footnotes.

Note: EP represents the environmental cleanliness performance of heavily polluting enterprises, ER represents the level of environmental regulation, b_size represents the enterprise scale, g_rate represents the asset-liability ratio, q_value represents the Tobin q value of the enterprise, t_asset represents the total asset turnover rate, s_ratio represents the shareholding ratio of the largest shareholder, and g_rate represents the growth rate of operating income.

Q7.Inside table 7, I’m confused from the numbers between brackets and numbers not between brackets.

A7.Thank you to the reviewers for pointing out the possible confusion caused by the expression of the figures in the table. We apologize for the unclear expression and have added clear annotations below Table 7 (Regression Results of mediating Effects) in the revised draft.

The specific clarification is as follows: The numbers outside the brackets represent the regression coefficients of the variable. The numbers in parentheses represent the t-statistics corresponding to the regression coefficients, which are used to test whether the coefficients are significantly different from zero. And we have added footnotes in the text: ***p<0.01, **p<0.05, *p<0.10.

Q8.Please make a space between the number and the word here

A8. We sincerely thank the reviewers for their meticulous review of the formatting details throughout the manuscript. We agree that maintaining a consistent space between numbers and subsequent text enhances the professionalism and readability of the paper. We have conducted a comprehensive check to ensure that all relevant parts of the manuscript comply with this formatting standard.

Q9.In the regression table please add the R2 value.

A9.Thank you very much for your professional suggestions. We fully agree with you that the R² value is a key indicator for evaluating the goodness of fit of a model. We have checked and updated all the regression analysis tables in the manuscript and have supplemented the values of R².

Q10.Please add subtitle about limitations and future research

A10.Thank you for your meticulous review and valuable suggestions on this article. We fully agree with your comments on supplementing research limitations and future directions, which will help enhance the integrity and academic value of the study. In response to your suggestions, we have added a "Research Limitations and Future Research Directions" section in Chapter Four of the text. For your convenience, we have attached the newly added Research Limitations and Future Research Directions

Q11.You need to make a descriptive table about Indicators for corporate environmental performance.Another table for Variable definitions, Descriptive analysis.

A11.We have, in accordance with your suggestion, added a dedicated variable definition and descriptive analysis table in the paper (i.e., Table 8 in the text) to present the basic information of the research data more clearly. For your convenience in reviewing, we have attached the form.

Table 8 Descriptive Statistics of Key Indicators

Variable	Sample size	Mean	Median	Standard deviation	Minimum value	Maximum value
EP	4794	0.0900	0.0800	0.0800	0	0.330
ER	4794	0.260	0.210	0.210	0	0.880
EIDI	4794	0.220	0.170	0.210	0	0.930
	4794	6594	2950	10182	257	64794
	4794	0.410	0.400	0.190	0.0500	0.860
	4794	2.010	1.570	1.310	0.810	8.330
	4794	0.650	0.570	0.410	0.0900	2.470
	4794	34.33	32.27	14.88	9.080	74.57
	4794	0.130	0.0700	0.390	-0.650	2.160

Q12.How the conclusion part before discussion?

A13.We sincerely thank the reviewers for the important feedback they provided on the structure of the discussion and conclusion sections. We apologize for the negligence of placing the conclusion before the discussion, which disrupted the logical flow of the manuscript. In response to this comment directly, we reorganized the last few chapters of the manuscript to adhere to the standard academic structure. For your convenience in reviewing, we have attached the modified content.

4.1 Discussions

Q14.Please improve your conclusion part by more details.

A14.Thank you for your valuable suggestions. I have supplemented and improved the conclusion section based on your suggestions. The main improvements include a more detailed summary of research results, clarification of theoretical contributions, elaboration of policy implications, indication of research limitations, and proposal of future research directions.For your convenience in reviewing, we have attached the modified content.

4.2 Conclusions

4.3 Recommendations

Based on the above conclusions and research findings, the following policy recommendations are proposed:

4.4 Research Limitations and Future Research Directions

Best regards,

16/10/2025

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors made all the requested corrections.
Congratulations!

Author Response

Thank you very much for your email and your positive decision on our manuscript.

Reviewer 3 Report

Comments and Suggestions for Authors

Reviewing report

Manuscript entitled “Environmental Regulation, Information Disclosure, and Clean in Heavy-Polluting Enterprises: Evidence from China “.

Again, please combine lines 27-80 as 1 paragraph.
Please remove the dot after the title “Environmental Regulation, Information Disclosure, and Clean 2 Production in Heavy-Polluting Enterprises: Evidence from 3 China”
Again, please write the text books references for the equations.
Again, please look to the fogures captions and use “Figure number.” Or “Fig. number.”

Please remember the dot

Line 218, there is a space here “Note 1:Heavily pollu”
In the tables captions please put a dot after the number. For example : “Table 5.”
Please inside the table 9, please put the meaning of the numbers beween brackets and the numbers which are not between brackets. For example: 0.015** (2.47) Please put the name in a separate row inside the table in front of and in the same line with its number.
Inside table 9 its R2 not R-squared
Please combine lines 821-838 as 1 paragraph.
Please combine the discussions and conclusions under one subtitle called “Conclusion.”

GOOD LUCK

Author Response

Dear Reviewers

Response to Referee 3.

Q1.Again, please combine lines 27-80 as 1 paragraph.

A1.Thank you for your valuable comments. We have merged lines 32-77. The specific changes are highlighted in yellow in the text.

Q2.Please remove the dot after the title “Environmental Regulation, Information Disclosure, and Clean 2 Production in Heavy-Polluting Enterprises: Evidence from 3 China”

A2.We thank the reviewer for this careful observation. The dot after the article title has been removed as suggested. The title now reads: “Environmental Regulation, Information Disclosure, and Clean Production in Heavy-Polluting Enterprises: Evidence from China ”.

Q3.Again, please write the text books references for the equations.

A3. We are grateful to the reviewers for reminding us to provide textbook references for the econometric equations used in this study. In response, we added appropriate citations in the article. Specific modifications will be highlighted in the text.

Q4.Again, please look to the fogures captions and use “Figure number.” Or “Fig. number.”

A4. We are grateful for the valuable comments from the reviewers, which aim to enhance the consistency of our manuscript. According to the suggestions, we have thoroughly checked and revised all the image titles to ensure consistency. For example, Figure 1. Etc. We believe this change has enhanced the uniformity and professionalism of the manuscript. And the modified content has been highlighted in yellow in the text.

Q5.Line 218, there is a space here “Note 1:Heavily pollu”

A5. Thank you, reviewers, for your careful observation. The missing space after the colon was added in line 218. The correct way to write this passage now is: "Note 1: Severe pollution..." In addition, we took this opportunity to carefully review the entire manuscript to ensure the consistency of punctuation spacing. For your convenience in reviewing, we have marked the modified content in yellow in the text.

Q6.In the tables captions please put a dot after the number. For example : “Table 5.”

A6. We thank the reviewers for their accurate feedback on the title format. We carefully reviewed and revised all the table titles in the entire manuscript to ensure that a dot was placed after the table numbers as suggested. For your convenience in reviewing, we have highlighted the modified content in the text.

Q7.Please inside the table 9, please put the meaning of the numbers beween brackets and the numbers which are not between brackets. For example: 0.015** (2.47) Please put the name in a separate row inside the table in front of and in the same line with its number.

A7. We are extremely grateful to the reviewers for their excellent suggestions on improving the clarity of Table 9. We restructured the table. Strictly following the advice, we restructured the table by explicitly marking numbers for each variable with separate rows. The values outside the parentheses are now clearly identified as "coefficients", while the values within the parentheses are identified as "t-statistics" in their respective dedicated rows. For your convenience in reviewing, we have attached the modified content.

Table 9. Benchmark regression results.

variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
ER	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients
	0.015**	0.019***	0.020***	0.018***	0.017***	0.018***	0.017***
	T-statistic	T-statistic	T-statistic	T-statistic	T-statistic	T-statistic	T-statistic
	(2.47)	(3.44)	(3.53)	(3.18)	(2.99)	(3.16)	(3.06)
		Coefficients	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients
		0.000***	0.000***	0.000***	0.000***	0.000***	0.000***
		T-statistic	T-statistic	T-statistic	T-statistic	T-statistic	T-statistic
		(21.75)	(19.81)	(19.22)	(18.26)	(16.80)	(16.55)
			Coefficients	Coefficients	Coefficients	Coefficients	Coefficients
			0.028***	0.016**	0.011*	0.011*	0.011*
			T-statistic	T-statistic	T-statistic	T-statistic	T-statistic
			(4.67)	(2.57)	(1.72)	(1.71)	(1.79)
				Coefficients	Coefficients	Coefficients	Coefficients
				-0.005***	-0.005***	-0.005***	-0.005***
				T-statistic	T-statistic	T-statistic	T-statistic
				(-4.94)	(-5.56)	(-5.50)	(-5.48)
					Coefficients	Coefficients	Coefficients
					0.023***	0.022***	0.022***
					T-statistic	T-statistic	T-statistic
					(8.43)	(8.17)	(7.90)
						Coefficients	Coefficients
						0.000**	0.000**
						T-statistic	T-statistic
						(2.35)	(2.34)
							Coefficients
							-0.005*
							T-statistic
							(-1.78)
Constant	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients	Coefficients
	0.075***	0.060***	0.049***	0.063***	0.051***	0.045***	0.046***
	T-statistic	T-statistic	T-statistic	T-statistic	T-statistic	T-statistic	T-statistic
	(20.31)	(16.52)	(11.39)	(12.04)	(9.60)	(7.54)	(7.69)
R²	0.0164	0.1053	0.1090	0.1135	0.1265	0.1275	0.1280
Observations	4,860	4,859	4,859	4,793	4,793	4,793	4,791

***p<0.01, **p<0.05, *p<0.10

Q8.Inside table 9 its R2 not R-squared

A8. We thank the reviewer for this suggestion regarding the notation clarity. We have revised Table 9 to use the standard statistical symbol "R²" instead of the full term "R-squared" for conciseness, and have ensured consistency across all tables and figures in the manuscript.

Q9.Please combine lines 821-838 as 1 paragraph.

A9. Thank you for your valuable comments. We have merged lines 821-838. The specific changes are highlighted in yellow in the text.

Q10.Please combine the discussions and conclusions under one subtitle called “Conclusion.”

A10. We are grateful for the reviewers' suggestions. As suggested, we will merge the discussion section and the conclusion section into a unified section named "Conclusion". For your convenience in reviewing, we will attach the revised "4.1 Conclusion".

4.1Conclusions

In the research on the role of environmental regulations in the environmental performance of enterprises, the positive impacts verified by the empirical results are in line with the "Porter Hypothesis", and this conclusion also echoes international experience - for instance, the European Union promotes green innovation in manufacturing through a carbon emissions trading system, and Japan relies on environmental regulations to enhance energy efficiency. All of them reflect the positive promoting effect of environmental regulations on the environmental performance of enterprises. It is worth noting that the "command and control" style of supervision adopted by China can also effectively force enterprises to transform. The realization of this effect is attributed to the continuous strengthening of environmental protection law enforcement in recent years.

In terms of the mechanism of environmental information disclosure, it shows a mediating effect feature of "short-term inhibition - long-term promotion", which is different from the model of "direct positive correlation between regulation and disclosure" under the mandatory disclosure system in Europe and the United States. In China, the environmental information disclosure of heavily polluting enterprises is mainly voluntary. In the short term, affected by compliance pressure, some enterprises will simplify the content of information disclosure. However, in the long term, high-quality environmental information disclosure can contribute to the improvement of enterprises' environmental performance by reducing information asymmetry and strengthening social supervision. This mechanism is also in line with the ecological efficiency framework of the World Business Council for Sustainable Development.

From the perspective of evolutionary game theory, the equilibrium point of this system tends to be "enterprise non-clean production". The core problem lies in that the total cost of clean production is higher than the benefits it brings, which is consistent with the "cost-benefit" logic in international governance. This also highlights the importance of enhancing regulatory efficiency and designing reasonable fine and subsidy mechanisms. This is in sharp contrast to the practical experience of the US Regional Greenhouse Gas Initiative in improving regulatory efficiency and the EU in stabilizing carbon prices, further confirming the necessity of optimizing regulatory and incentive mechanisms.

Based on the above background, this paper constructs A three-party game model among heavily polluting enterprises, the government and environmental information disclosure regulatory authorities on the basis of evolutionary game theory, and combines the panel data of 570 listed companies in heavily polluting industries in China's A-share market from 2014 to 2022. Systematically explore the decision-making mechanism and influencing factors of enterprise clean production in multi-agent interaction scenarios, and ultimately draw the following core conclusions:

First, at the evolutionary game level, the strategic choices of the three parties are all dominated by the cost-benefit relationship. Among them, the willingness of heavily polluting enterprises to implement clean production depends on the balance among the costs of environmental protection technology upgrades, information disclosure, subsidies and fines - the relative level of cost and benefit directly determines whether the enterprise is inclined to carry out clean production.

Secondly, when the cost of clean production for enterprises is too high and the relevant incentive and restraint mechanisms are insufficient, enterprises will be more inclined to choose non-clean production strategies. From the perspective of regulatory authorities, environmental information disclosure regulatory departments will only choose to strictly supervise in accordance with the law when the regulatory benefits exceed the regulatory costs. The government will only take strict regulatory measures when the verification cost is lower than the fines and confiscation of gains. Overall, the core bottleneck for heavily polluting enterprises in their strategic choices has always been that the total cost of clean production is higher than its comprehensive benefits.

Thirdly, at the empirical level, environmental regulations have a significant positive impact on enterprises' clean environmental performance. This result validates Hypothesis H1, indicating that reasonable environmental regulations can enhance environmental performance by increasing the marginal cost of pollution emissions, compelling enterprises to increase their investment in green innovation. Meanwhile, environmental information disclosure plays a mediating role in the relationship between environmental regulations and corporate environmental performance: although environmental laws and regulations may have an inhibitory effect on corporate environmental performance due to compliance pressure in the short term, in the long run, high-quality environmental information disclosure can significantly promote the improvement of corporate environmental performance by reducing information asymmetry and strengthening social supervision. Furthermore, the empirical results also show that the scale of enterprises and the total asset turnover rate have a positive impact on environmental performance, while the Tobin Q value has a negative impact. This phenomenon reflects that short-term environmental protection investment will temporarily squeeze the market valuation of enterprises.

Best regards,

19/10/2025

Author Response File: Author Response.pdf

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