Whether External Public Welfare Can Reduce the Corporate Carbon Emissions——Empirical Evidence Based on the Administrative Public Interest Litigation System

Whether External Public Welfare Can Reduce the Corporate Carbon Emissions——Empirical Evidence Based on the Administrative Public Interest Litigation System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Whether External Public Welfare Can Reduce the Corporate Carbon Emissions——Empirical Evidence Based on the Administrative Public Interest Litigation System Xinghua Cui, Shu Zhang, Dui Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4612053/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The administrative public interest litigation system (APILS) is an important guarantee for environmental public interest protection and an important institutional innovation of external supervision, which has great significance for low-carbon development. This study takes the listed companies from 2000 to 2021 in China as the research samples, and examines the impact of APILS on corporate carbon emissions (CCE). The results show that: (1) The APILS can significantly promote the reduction of CCE. (2) This research conclusion exhibits multidimensional heterogeneity, which varies depending on the industry type, market competition level, city size, and resource attributes. (3) The mechanism test shows that the APILS can promote CCE reduction through three mechanisms: green innovation, public environmental claims and environmental administrative regulation. (4) Further expansive analyses finds that social trust can strengthen the inhibitory effect of the APILS on CCE. The conclusion of this study provides empirical evidence for exploring the role of external public welfare system supervision in promoting CCE reduction. external public welfare system supervision administrative public interest litigation corporate carbon emissions social trust Figures Figure 1 Figure 2 1. Introduction Since the goal of “carbon peak” and “carbon neutrality” was proposed in 2020, the Chinese government and enterprises have been engaged in a concerted effort to promote emission reduction (Shao et al., 2024 ). This has resulted in the achievement of remarkable outcomes, the consumption of coal per unit of GDP was significantly reduced, and the country has achieved a “double growth” in forest coverage and stockpile volume for 30 consecutive years. Nevertheless, the reduction of carbon emissions is not a solution that can be achieved overnight. In this process, some enterprises may choose to ignore environmental regulations in order to maximize profits, do not to implement emission reduction measures or do not to fulfil their emission reduction obligations in accordance with the regulations, thus causing damage to the environment (Zhang and Liu, 2023 ). At this juncture, the pilot APILS serves to guarantee the creation of an environment conducive to the protection of the public interest. The establishment of this system realizes the purpose of judicial correction of harm to environmental public welfare. It forces enterprises and governments to promote environmental protection by punishing offenders and forcing them to assume responsibility for environmental remediation. So, will the external institutional supervision reflected in the pilot APILS have an impact onCCE? What is the specific mechanism of action? Are there multidimensional differences in its effects? Can social credit reinforce this inhibitory effect? Previous studies have extensively examined the factors influencing carbon emissions. For example, globalization escalate (Sultana et al., 2023 ) and geopolitical risk (Ding et al., 2023 ) factors at the national level; environmental regulation (Lin and Zhang, 2023 ) and energy conservation (Chen et al., 2023 ; Zhang et al., 2023 ) factors at the regional level; supportive policy (Ai et al., 2023 ), energy governance (Che et al., 2023 ) and green bonds (Xu and Li, 2023 ) factors at the city level; and intelligence (Wei et al., 2023 ) factors at the industry level. However, there are relatively few literatures on carbon emission reduction at the enterprise level, mainly focusing on external factors such as environmental regulation (Wang, 2023 ) and political affiliation (Wang et al., 2023 ) as well as internal corporate factors such as corporate technological innovation (Zhao et al., 2021 ; Chen and Wang, 2023 ), digital transformation (Shang et al., 2023 ) and corporate management (Goud, 2022 ). Despite the fact that existing research has analyzed the influencing factors of carbon emissions from many aspects, however, there is currently no literature examining its impact on CCE reduction from the perspective of APILS. In fact, the pilot policy of APILS, as an important initiative of the external regulatory system and innovation of environmental management tools, has great significance in promoting CCE reduction. Furthermore, the strictly regulated and specialized environmental public interest justice has improved the judicial punitive power and credibility (Gao and Wen, 2021 ; Wang et al., 2023 ), enhanced the public’s awareness of environmental protection, served as an external supervisory mechanism and played an important role on CCE. It is regrettable that the factor of external public welfare system supervision has not yet attracted sufficient attention, which represents a key entry point of this study’s research. The possible innovations of this study are as follows: (1) Firstly, different from the existing literature on the impact of environmental regulations on CCE reduction, this study analyzes the impact of pilot policies of the APILS on CCE from the innovative perspective of external public welfare system. It not only expands the research on the actual effect of the APILS, but also enriches the research on the institutional influencing factors of CCE. (2) Secondly, considering the three subjects of enterprises, the public and local governments, this study constructs a theoretical framework on the impact of external public welfare system on CCE from three aspects: enterprise green innovation, public environmental claims and environmental administrative regulations, which helps to clarify the internal logical relationship between external public welfare system and claims. (3) Thirdly, this study further incorporates social credit into the analysis framework, innovatively analyzes its impact on the relationship between the APILS and CCE, and reveals the important role played by social credit level in promoting the CCE reduction effect of external public welfare system. 2. Policy background and research hypotheses 2.1 Policy background China’s long-standing environmental protection sanctions are primarily implemented through administrative means. However, the inherent limitations and weaknesses of administrative means often render the punitive effect ineffective, making it difficult to produce a substantial effect. Therefore, the exploration, establishment and improvement of the APILS represents a viable approach to addressing the aforementioned issues (Nie and Zhou, 2023 ). On 1 July 2015, the decision of the Standing Committee of the National People’s Congress on authorizing the Supreme People’s Procuratorate to carry out pilot work of public interest litigation in some regions was adopted. This decision determined that a two-year pilot work would be carried out in 73 cities in 13 provinces. In the pilot cities, the procuratorial organs have actively performed their public interest litigation functions, and have accumulated a substantial number of case samples (Zhuang and Luo, 2023 ) 2.2 Theoretical assumptions 2.2.1 The impact mechanism of the APILS on CCE (1) The APILS, green innovation and CCE reduction. APILS represents a significant judicial system for enhancing the level of environmental judicial protection, rectifying the perceived “regulatory enforcement bias” of local governments, and safeguarding the public interest of the community (Zhang et al., 2022 ). As a means of external supervision, it has raised higher demands for corporate green innovation. Firstly, the system enhances the local government’s environmental penalties and environmental governance through the horizontal judicial supervision of local procuratorial organs. The pressure of environmental regulation prompts enterprises to increase their R&D investment and improve the innovation efficiency of innovation resources, as the litigation risk and the expected cost of violation of the law faced by enterprises are increased (Dai et al., 2023 ). Secondly, the implementation and publicity of the APILS will enhance the public’s concern for environmental protection. This, in turn, will encourage enterprises to assume social responsibility and drive them to carry out green innovation (Yi et al., 2022 ). At this point, green technology, as a representative of innovation, is conducive to environmental protection and ecological preservation (Yu, 2007 ; Cao et al., 2023 ). It improves energy production efficiency, contributes to CCE reduction (Zhang et al., 2013 ; Gu et al., 2022 ), and ultimately achieves ecological environment improvement (Li and Gong, 2021 ). (2) The APILS, public environmental claims and CCE reduction. The single subject of litigation will constrict the scope for expression of social forces, while the expansion of the qualification of social organizations and even individuals as subjects of APILS can compensate for the inadequacy of such a single “nationalized” structure for sensitive public interest remedies (Qin, 2019 ). Consequently, the APILS, as the principal mechanism for public participation in environmental governance, plays a pivotal role in enhancing the extent of public participation, expanding the range of litigants, and preventing harm to public interests. The aforementioned system affords the public a more direct and profound sense of public interest, thereby conferring certain advantages upon them in terms of discovering clues, collecting evidence and technical appraisal. As a result, the growing public awareness of environmental protection will exert a certain “deterrent effect”, restraining the behavior of individuals and organizations within society (Carpentier and Suret, 2015 ). Ultimately, in order to maintain a positive relationship with the public, enterprises must actively engage in environmental governance and pursue green production (Danneels et al., 2018 ). This will facilitate the reduction of CCE (Li et al., 2022 ). (3) The APILS, environmental administrative regulation and CCE reduction. The potential for curbing corporate environmental pollution behavior is contingent upon the efficacy of local government enforcement of environmental policies. In the past, the central government utilized GDP as a criterion for the appraisal and promotion of officials at the local government level. Local governments lacked incentives for environmental governance, and local officials may have strategically enforced environmental regulations on camera based on self-interested considerations and political pressure trade-offs. This resulted in a lack of incentives for local governments to implement stringent environmental governance standards at the expense of economic growth (Wang and He, 2022). The APILS, which is led by the procuratorial authorities, has been an effective means of resolving these issues. Firstly, the embedding of an external oversight system allows the prosecution and administrative authorities to overcome the shortcomings of the internal oversight system. This enables them to have a more unique regional advantage in access to information, and to avoid the behavior of superior and subordinate levels working together to circumvent unfavorable outcomes in the environmental assessment process (Zhao, 2023). Secondly, the APILS can enhance the prioritization of environmental regulation within local administrative activities, facilitate the centralized mobilization of administrative resources, and encourage local governments to fulfil their responsibilities in a timely manner (Lu Chao, 2018). Nevertheless, environmental regulation plays a pivotal role in reducing CCE and enhancing CCR performance (Zhou et al., 2024 ). It exerts a considerable influence on CCE through some distinct mechanisms: technological innovation effects (Lin et al., 2022 ) and factor allocation effects (Zhang, 2022 ), etc. Therefore, the following hypotheses are proposed in this study: H1: The APILS has a significant contribution to CCE reduction. H2: The APILS can facilitate CCE reduction by enhancing the corporate green innovation, intensifying public environmental demands, and increasing environmental administrative regulation. 2.2.2 The moderating effect of social trust Social trust, as an informal institution, is an important part of the soft environment that constitutes the implementation of public policy (Yang and Niu, 2023 ). The social trust can stringent the norms and constraints on behavior, these norms and constraints guide people’s behavior, reduce a series of social problems such as government corruption (Depetris-Chauvin et al., 2020 ), and have a certain safeguard effect on the successful implementation of environmental regulation. In addition, higher levels of social trust, which indicate broader and more stable public support for the work of the government, can facilitate the effective implementation of policies. This, in turn, enables policies to better serve the public interest (Guo and Wang, 2021 ). Concurrently, the public’s sense of collective action and necessity for oversight will be reinforced by an increase in social trust, and the expansion of participation in policy-making may, to a certain extent, enhance the impact of policy implementation and enhance the quality of the system’s functioning. Conversely, in areas with a low level of social trust, the public is likely to perceive a lack of bureaucracy or policy fairness, and will not voluntarily participate in or comply with policies related to environmental regulation. This results in a greater prevalence of “free-riding” behavior (Yang and Niu, 2023 ). The public’s inclination towards non-cooperative behavior with the government can impede the effective implementation of public policy. And in pursuit of profit maximization, enterprises must assume corresponding social responsibility towards relevant stakeholders. The value norms endorsed by social trust mediate the conflict of interests between enterprises and stakeholders, facilitate the fulfilment of corporate social responsibility (Yang et al., 2024 ), prompt enterprises to reflect on the relationship between their own development and green environmental protection, adjust corporate strategic decisions, implement green innovation activities (Yang et al., 2021 ), and then promote enterprises to achieve CCE reduction. Therefore, this study presents the hypothesis: H3: Social trust can strengthen the inhibitory effect of APILS on CCE. 3. Research design 3.1 Model specification This study using the DID method and constructing an econometric regression model as follows: $${CCE}_{ijt}={\beta }_{0}+{\beta }_{1}{APILS}_{it}+\lambda {Controls}_{ijt}+{u}_{j}+{u}_{t}+{\epsilon }_{ijt }$$ 1 where the subscripts \(i\) , \(j\) , and \(t\) denote city, firm and year respectively. And the dependent variable \(CCE\) denotes the carbon emission intensity of companies. The independent variable \(APILS\) indicates whether a city is designated as an administrative public interest litigation pilot city in a particular year, with values of 1 for that year and subsequent years, and 0 otherwise. \(Controls\) is the set of control variables indicating the value of each control variable. \({u}_{j}\) and \({u}_{t}\) are firm fixed effects and year fixed effects, and \({\epsilon }_{ijt}\) denotes the random error term. 3.2. Variable selection 3.2.1. Dependent variable corporate carbon emission ( CCE ). This study uses the ratio of the total carbon emissions of listed companies (100 tons) to the number of employees in the company. Carbon emissions per capita can increase comparability between enterprises, and can attenuate the impact of scale effects when comparing the CCE of different sizes and industries. Following the relevant practices of Wang et al. ( 2022 ) and Cui et al. ( 2023 ), this study calculates a company’s carbon emissions based on the annual direct, indirect, or total carbon emissions disclosed in the company’s annual reports, social responsibility reports, and environmental reports. For companies that do not directly disclose their annual CCE, their CCE are obtained by converting the disclosed coal consumption and using the carbon emission calculation coefficient. 3.2.2. Independent variable Administrative public interest litigation system ( APILS ). This study treats the national public interest litigation pilot policy by manually collating a list of officially announced pilot cities and setting two dummy variables \(t{reat}_{i}\) and \({post}_{t}\) . If the city where firm j is located belongs to the APILS area in year t , the value of \({treat}_{i}\) take the value of 1, otherwise 0. If the year is in 2015 and later, the value of \({post}_{t}\) take the value 1, otherwise 0. Further with its interaction term \({treat}_{i}\) × \({post}_{t}\) as the core independent variable denotes the APILS treatment effect. 3.2.3 Control variables Rearing ratio ( \(Lev\) ): Corporate liabilities as a proportion of total assets. Profitability ( \(Roa\) ): Net profit of the enterprise as a percentage of total assets. cash ratio ( \(CAR\) ): Ratio of cash-based assets to current liabilities of the enterprise. Capital Intensity ( \(CI\) ): Sum of human capital intensity and physical capital intensity. Fixed Assets Ratio ( \(FAR\) ): Ratio of enterprise fixed assets to total enterprise assets. Enterprise size ( \(Size\) ): Expressed in terms of total enterprise assets and taking logarithmic form. enterprise growth ( \(Gro\) ): Growth rate of enterprise revenue. 3.3. Data sources This study selects A-share listed companies from 2000 to 2021 as the research sample. In accordance with the conventional data processing methods employed in existing studies, samples of companies that were ST, *ST, listed or delisted during the sample period were excluded from the analysis. The data on word frequency and environmental regulation efforts were obtained from local municipal government reports and manual collation. The data on public climate concern was derived from the Baidu search index. The data on the marketization index was sourced from the China Marketization Index Database. The remaining variables are sourced from the database of the CSMAR database. The descriptive statistics of the variables in this studyare shown in Table 1 . Table 1 Descriptive statistics of main variables Variables Mean SD Min Max N CCE 0.7041 1.1032 0.0271 7.7632 22606 \(APILS\) 0.2215 0.4153 0 1 22606 \(Lev\) 0.4547 0.2183 0.0570 1.0982 22606 \(Roa\) 0.0471 0.0842 -0.3626 0.2819 22606 CAR 0.8132 1.3494 0.0068 8.9020 22606 CI 3.1069 5.3093 0.3840 40.7048 22606 FAR 0.2246 0.1702 0.0019 0.7263 22606 Size 21.1232 1.5431 16.9965 25.3139 22606 \(Gro\) 0.4274 1.3025 -0.8503 9.8523 22606 4. Empirical result 4.1. Baseline regression results The baseline regression results of the impact of the APILS on CCE are shown in Table 2 . Among them, the results show that the regression coefficient of the APILS is significantly negative, which initially indicates that the public interest litigation policy significantly reduces the CCE. This verifies the theoretical hypothesis H1. In addition, as shown in column (4), gearing ratio ( \(Lev\) ), profitability ( \(Roa\) ), cash ratio ( CAR ), city size ( Size ), city GDP per capita ( \(Gro\) ), all five control variables significantly and positively increase the CCE, while capital intensity ( CI ) and fixed asset ratio ( FAR ) can significantly reduce CCE. Table 2 Baseline regression results Variables (1) (2) (3) (4) APILS 0.055*** 0.056*** 0.047*** 0.047*** (0.012) (0.012) (0.014) (0.014) Size 0.031*** 0.032*** 0.032*** (0.008) (0.009) (0.009) Lev -0.025** -0.022* -0.021* (0.010) (0.012) (0.012) ROA 0.005*** 0.005*** (0.001) (0.001) Dual 0.017* 0.018** (0.009) (0.009) Mfee -0.000*** (0.000) ListAge 0.018* (0.010) Liquid 0.002*** (0.001) Constant 0.599*** 0.374*** 0.345*** 0.307*** (0.001) (0.061) (0.069) (0.071) Firm FE Y Y Y Y Year FE Y Y Y Y Observations 31354 31188 22989 22606 R 2 0.745 0.747 0.746 0.746 Note: Standard errors are shown in parentheses; *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The tables below remain consistent. 4.2. Robustness test 4.2.1 Parallel trend test In order to ensure that the CCE of enterprises is reduced due to the implementation of the APILS, this article introduces the interaction term between the year dummy variable and the treatment group, and examines the dynamic characteristics of the CCE of enterprises by using the event study method, and the results are shown in Fig. 1 . It can be seen that pre-interaction terms are not significant, while post-interaction terms are significant. The coefficient \({\beta }_{1}\) is found to be insignificant until period 0, indicating that there is no discernible difference between the CCE of enterprises in the treatment group and the control group prior to the implementation of the APILS pilot. However, in the year of the pilot, the interaction terms are all significantly different from 0. 4.2.2 Placebo test Ideally, as an exogenous factor, policy would not be affected by unobservable factors, so \(\widehat{\beta }\) is the consensus estimate of the estimation coefficient \(\beta\) . In practice, however, the implementation of policies is influenced by a multitude of factors, including unobservable factors and unlisted control variables, which can lead to the misrepresentation of the estimated coefficients. In this paper, we use a spurious policy implementation year to generate an interaction term with the treatment group, and repeat the process of randomly generating the implementation year 500 times to observe whether the APILS reduces the CCE. The results are presented in Fig. 3. It can be assumed that the pilot cities remain unchanged and that the policy time is randomly assigned. The coefficients of the interaction terms are normally distributed with a mean of zero, indicating that the random factors in the model of this paper do not significantly affect the results. Consequently, the estimation results are robust. 4.2.3. PSM-DID This study adopts the propensity score matching method (PSM) to select the existing control variables as covariates and matches them year by year through the 1:1 nearest-neighbor matching method. This enables the identification of control firms that are most similar to the observable characteristics. Furthermore, this paper conducts regression analysis on the matched samples, the results of which are presented in Column (1) of Table 3 . It can be seen that the APILS can reduce the CCE. 4.2.4. Replacement of dependent variable This study draws on the research of Wu and Yan ( 2012 ) to replace the CCE with carbon per unit profit. This ratio reflects the carbon emissions per unit of profit during the operation period of the company. The regression results are shown in column (2) of Table 3 . The results show that the APILS can reduce the CCE significantly. 4.2.5 Exclusion of exceptional years In October 2018 and April 2019, the Standing Committee of the National People’s Congress (NPCSC) incorporated the public interest litigation inspection powers into the revised Organic Law of the People’s Procuratorate and the Procurator Law. In October 2019, the Supreme People’s Procuratorate issued a report on the status of public interest litigation work, summarizing the development situation and problems faced, and putting forward measures and suggestions for the next step. Therefore, this study considers 2019 as a special year and carries out the exclusion process, and regresses the processed data to obtain column (3) in Table 3 . It is obvious that the overall effect of the pilot of APILS on CCE remains robust. 4.2.6. Exclude the impact the impact of other policies In the process of implementing the APILS, it is also necessary to consider the potential impact of other policies in the category of environmental regulation on CCE. This study incorporates the pilot policy of urban double repair, the pilot policy of green finance, the pilot sponge city and the pilot low-carbon city as control variables in the model. The regression results are presented in column (4) of Table 3 . The test demonstrates a significant negative association between the APILS and the CCE, even after the exclusion of the four related policies. This finding suggests that the APILS continues to exert a notable influence on the reduction of CCE. Table 3 Robustness tests Variables (1) (2) (3) (4) PSM-DID Replacement of dependent variable Excluding exceptional years Excluding relevant policy effects APILS -0.0468** -0.0259*** -0.0622*** -0.0534*** (0.0188) (0.0063) (0.0197) (0.0193) Constant -2.981*** -2.0496*** -3.1104*** -2.3405*** (0.187) (0.0673) (0.1938) (0.2364) Control variable Y Y Y Y Firm FE Y Y Y Y Yesr FE Y Y Y Y N 33,186 30,527 30,478 19,914 \({R}^{2}\) 0.588 0.7037 0.5887 0.6516 4.3. Heterogeneity analysis In order to gain further insight, this study conducts a series of sub-sample regressions at the enterprise, industry, and regional levels. The objective is to examine the heterogeneous impact of the APILS. 4.4.1 Analysis of firm heterogeneity At the enterprise level, this study classifies the research sample into four categories according to the nature of the enterprise. These categories are “state-owned”, “private”, “foreign” and “others”. The classification is based on the nature of the enterprise, with the reform of private state-owned enterprises counted as “state-owned enterprises”. The different nature of enterprises, along with their own interests and strategies, results in varying outcomes. Among them, state-owned enterprises are more closely aligned with the government, exhibiting greater levels of cooperation and mutual assistance in terms of resources. Consequently, they are more susceptible to government policies and are more likely to alter their behaviors in accordance with the government’s guidance. In contrast, private enterprises exhibit a more diverse range of responses to policies, with their fundamental objective remaining the advancement of business development and profitability. This is evidenced by their focus on changes in business operations and profits. In conclusion, this paper identifies four categories of enterprises: state-owned, private, foreign and other. It then compares the performance of these subgroups under the public interest litigation pilot policy. In Table 4 , the regression coefficients of state-owned and private enterprises are significantly negative. The regression result of foreign enterprises is insignificant. In contrast, the results of other types of enterprises are significantly positive. The results indicate that the APILS has the most pronounced impact on CCE reduction in state-owned enterprises compared to other types of entities. The reason is that state-owned enterprises represent the state and the government to a certain extent, have greater social responsibility, and are more inclined to actively participate in policy pilots and fulfil their corporate social responsibility. Concurrently, state-owned enterprises are more prone to governmental monitoring and scrutiny, which can prompt them to respond more expeditiously to policy initiatives and adopt responsive measures. For private enterprises, the impact of the policy change on large enterprises is more pronounced. Of interest is the fact that large private enterprises will react more favorably to the pilot policy, and some small and medium-sized enterprises with a wait-and-see attitude or a weak resistance will also alter their strategies in line with the industry trend. Table 4 Results of firm heterogeneity Variables (1) (2) (3) (4) State-owned Private Foreign Others APILS -0.0749** -0.0647*** -0.0747 1.3398*** (0.0310) (0.0240) (0.0769) (0.2501) Constant -3.7261*** -2.9123*** 0.2775 -5.2322** (0.3324) (0.2502) (0.9780) (2.5163) Control variable Y Y Y Y Firm FE Y Y Y Y Yesr FE Y Y Y Y N 13,963 16,097 1,673 458 \({R}^{2}\) 0.6103 0.5866 0.7069 0.6128 4.4.2 Analysis of industry heterogeneity This study combines the nature of the industry with the three criteria of whether it is a high-tech enterprise, a heavy polluting enterprise, a manufacturing industry to categorize the enterprises into group regressions. Furthermore, the influence of market factors is considered in order to analyze the impact of heterogeneity resulting from differences in the degree of competition and the level of marketization within the industry. The regression results are shown in Table 5 and Table 6 respectively. Whether it is a high-tech enterprise. In the context of the continued growth in electricity demand, achieving carbon emission reduction is not simply a matter of returning to coal. Rather, it requires the development of a multi-energy complementary approach, which hinges on the technological innovation. In this study, the regression is grouped according to whether it is a high-tech enterprise or not. The results show that the APILS has a substantial role in promoting CEE reduction of high-tech enterprises. The reason for the above difference is that high-tech enterprises have a faster and more effective response to the implementation of the APILS policy due to their wider application of key carbon monitoring technologies, the proportion of new energy adoption, and the efficient and clean use of fossil energy. Whether it is a heavy polluting enterprise. Pollution is typically associated with high CCE. The energy consumed by enterprises during the production process results in the release of significant quantities of carbon dioxide. For instance, in key industries such as iron and steel, cement, and thermal power, the emissions of air pollutants are considerable, the cross-media compound pollution is severe, and the emission mechanism is intricate. In the context of the implementation of the APILS, the strengthening of the regulation of pollutant emission limits will have a direct impact on CCE reduction. This study compares the heterogeneity results of whether or not they are heavy polluters. We can see that the size of the coefficients differs considerably between the two groups, and the APILS has the most significant impact on CCE reduction for non-heavy polluters compared to heavy polluters. This may be attributed to the fact that heavy polluters face greater CCE reduction challenges than non-heavy polluters. That is, heavy polluters emit more carbon, but due to limitations such as technology and cost, it may be difficult for high polluters to reach their carbon reduction targets in the short term and adhere to them over an extended period. Whether it is a manufacturing enterprise. Although the manufacturing sector is a pillar of China’s economy, it is also a significant contributor to carbon emissions. Manufacturing processes, such as energy consumption and material transformation, typically result in high carbon emissions. Consequently, the manufacturing sector has implemented specific targeted policies with the objective of implementing more stringent emission reduction requirements. One such initiative is the Made in China 2025 initiative, which was introduced by the State in 2015. This initiative explicitly sets the goal of reducing carbon emissions per unit of value added by 40 per cent in 2025, in comparison to the 2015 level. In this study, a heterogeneity regression is conducted using whether a firm belongs to the manufacturing industry as the classification criterion. The results demonstrate that, relative to non-manufacturing firms, the effect of the APILS on the CCE is more significant. The reason is that the implementation of the APILS has prompted the manufacturing sector to undertake green development initiatives such as technological transformation and energy efficiency improvement, which significantly reduces CCE. Conversely, non-manufacturing industries, such as the service industry and the financial industry, produce less CCE, are less sensitive to green development-related policies and are less affected by them. Table 5 Results of industry heterogeneity regression (I) Variables (1) (2) (3) (4) (5) (6) High-tech Non-high-tech Heavy pollution Non-heavy pollution Manufacturing Non- manufacturing APILS -0.0497*** -0.0333 -0.0465* -0.0715*** -0.0439*** -0.0804 (0.0188) (0.0376) (0.0281) (0.0249) (0.0158) (0.0607) Constant -1.7236*** -5.5042*** -0.9933*** -4.4718*** -1.7954*** -8.2056*** (0.1952) (0.3762) (0.2966) (0.2533) (0.1657) (0.5920) Control variable Y Y Y Y Y Y Firm FE Y Y Y Y Y Y Yesr FE Y Y Y Y Y Y N 20,483 12,672 12,128 21,028 25,858 7,303 \({R}^{2}\) 0.5101 0.6336 0.5137 0.6290 0.5078 0.6168 (4) Degree of industry competition. The degree of industry competition is the key factor for enterprises to adjust their investment behavior. In this study, Lerner Industrial Index is used to measure the degree of competition in an industry, and the average value is taken as the measurement standard. The results of the heterogeneity regression are presented in columns (1) and (2) of Table 6 below. These findings indicate that APILS has a more pronounced impact on CCE reduction for firms with a higher degree of market competition. The reason is that many enterprises compete in the market, each holding a portion of the market share, and there is no instance in which enterprises monopolize the market alone. The implementation of the APILS will result in adjustments to the price and demand for products in the industry in line with market changes. These adjustments will affect the production of enterprises, prompting them to develop in the direction guided by the policy in order to seek greater benefits and better development space. (5) Market-oriented level. This study adopts the marketization index provided by Fan et al. ( 2021 ) to assess the degree and quality of marketization. In this study, the mean value of the marketization index obtained from the calculation is chosen as a measure, stipulating that a marketization index greater than or equal to the mean value is considered to be a high level of marketization, and vice versa is considered to be a low level of marketization. The regression results show that unlike firms with low marketization, APILS more significantly promotes CCE reduction with high marketization. This may be due to the fact that the latter are freer in terms of resource allocation and price formation, the behavior of market players is more variable, the sensitivity to respond to administrative public interest litigation policies is high, and enterprises can quickly adjust their production behaviors to meet the new requirements of the market. Table 6 Results of industry heterogeneity regression (II) Variables (1) (2) (3) (4) High level competition Low level competition High level marketization Low level marketization APILS -0.0937*** 0.0045 -0.0837*** 0.0371 (0.0240) (0.0310) (0.0264) (0.0311) Constant -2.1004*** -4.0343*** -3.6056*** -2.1577*** (0.2419) (0.3181) (0.3087) (0.2465) Control variable Y Y Y Y Firm FE Y Y Y Y Yesr FE Y Y Y Y N 16,666 16,097 18,957 14,020 \({R}^{2}\) 0.5484 0.6586 0.6506 0.5940 4.4.3 Analysis of regional heterogeneity Regional distribution. In this study, the regional differences indicator is selected as a classification criterion to explore the heterogeneity of results in terms of the spatial structure of CCE. The regression results in columns (1) to (3) of Table 7 demonstrate the heterogeneity of urban areas where firms are located. The coefficient is significantly negative only in the eastern region, indicating that the APILS exerts a more pronounced effect on the reduction of the CCE in the eastern region. The observed outcome may be attributed to a number of factors, including the higher concentration of firms in the eastern region, the pilot implementation of regulatory capture, more rigorous inspections in the east, and the enhanced responsiveness of firms in the east due to the more comprehensive infrastructure. Resource intensity. A significant proportion of industries are highly dependent on resources, with strong industrial linkages and obvious scale effects. Furthermore, the majority of industries are concentrated in regions with a more favorable resource base. In the context of the APILS, the place where the enterprise belongs to also affects the implementation effect of the policy. In this study, we distinguish between the size of resource intensity and delineate two groups of resource-based cities and non-resource-based cities. Regressions are carried out separately for each group, and the results are presented in column (4) and column (5) of Table 7 . The results indicate that the policy pilot effect in resource-based cities is highly significant. This implies that APILS has a more pronounced effect on CCE reduction in resource-based cities. This may be due to the strong effect of the original industrial clusters within the resource-based cities and the more stable development of green transformation of enterprises. Concurrently, in order to enhance their resilience to the effects of the pilot policy and improve their competitiveness, enterprises in resource cities are more competitive, and are able to promote green transformation and achieve CCE reduction more quickly. City size. The effects of cities of varying sizes on resource allocation, scientific and technological innovation, pollution reduction and emission reduction due to external effects such as agglomeration and congestion are distinct. Based on population size, the study divides cities into four levels, namely “super-large cities”, “large cities”, “medium cities”, and “small cities”. The regression results in Table 8 are obtained. The results indicate that the impact of the APILS is not significant for super-large cities, large cities and medium cities. However, it is significantly negative in small cities. This may be due to the fact that, with the same level of governance and willingness, the municipal governments of small cities are more inclined to adopt administrative orders and accountability to directly control the sources of infection (Shang et al., 2020). Table 7 Results of regional heterogeneity (I) Variables (1) (2) (3) (4) (5) Eastern Central Western Resource Non-resource APILS -0.0578** -0.0111 0.0044 -0.1609*** -0.0184 (0.0238) (0.0353) (0.0502) (0.0410) (0.0225) Constant -4.0931*** -1.9596*** -1.1200*** -2.5138*** -3.3105*** (0.2539) (0.3295) (0.4309) (0.3947) (0.2254) Control variable Y Y Y Y Y Firm FE Y Y Y Y Y Yesr FE Y Y Y Y Y N 22,269 5,931 4,988 4,384 24,181 \({R}^{2}\) 0.6149 0.5528 0.5010 0.5306 0.6014 Table 8 Results of regional heterogeneity (II) Variables (1) (2) (3) (4) Super-large Large Medium Small APILS -0.0321 -0.0337 0.0278 -0.3267*** (0.0483) (0.0226) (0.0553) (0.0822) Constant -3.3895*** -3.7850*** -0.1994 -5.2767*** (0.4722) (0.2385) (0.4039) (0.6812) Control variable Y Y Y Y Firm FE Y Y Y Y Yesr FE Y Y Y Y N 8,282 17,178 2,155 941 \({R}^{2}\) 0.6186 0.5679 0.5247 0.5606 4.3. Mechanism tests As for the mechanisms behind these effects, this study draws on the mechanism verification method proposed by Jiang (2022) and conducts mechanism analysis from three perspectives: corporate green innovation, public environmental demands, and environmental administrative regulation. This study constructs the econometric model as follows: $${M}_{ijt}={\beta }_{0}+{\beta }_{1}{DID}_{it}+{\beta }_{i}{Controls}_{ijt}+{u}_{j}+{u}_{t}+{\epsilon }_{ijt}$$ 2 Where M is each mechanism variable, including corporate green innovation, public environmental demands, and environmental administrative regulation. The rest of the variables have the same meaning as in model (1). Corporate green innovation ( CGI ). As the main body of green technology application, the green innovation capability of enterprises is crucial for reducing CCE. This study uses the logarithm of the total number of green patents held by a firm plus one to represent the CGI. The estimated coefficient of CGI is significantly positive, indicating that the APILS is able to promote the level of CGI. Furthermore, the utilization of CGI enables enterprises to enhance the efficiency of energy utilization and reduce the cost of resources, thereby achieving the objective of reducing CCE. Public environmental claims ( PEC ). In the 21st century, the internet has become an integral part of modern life. As a result, search engines have become one of the most important information portals on the internet. The search and browsing history of internet users can be used to identify their preferences for certain types of events and to analyze the trends in their attitudes and behaviors towards these events. Accordingly, this study employs the annual search index of “haze” and “environmental pollution” in each city to construct a measure of public environmental demands. In Table 9 , the coefficient of PEC is significantly positive. This suggests that the APILS has enhanced people’s concern for the public environment. Moreover, public environmental demands can effectively prompt local governments to prioritize environmental governance, act as an external watchdog on CCE (Wang et al.2024). Environmental administrative regulation ( EAR ). If local governments choose to seek benefits and avoid conflicts in the process of environmental governance, there will be a significant difference in motivation, strategy, and effect with formal environmental regulation (Zhao and Wang, 2020 ). The primary objective of enterprise development is to maximize profits. Consequently, there is no intrinsic motivation for carbon reduction. Therefore, enterprises should identify the essence of local governments’ strategies and adjust their attitudes and behaviors. This will enable them to take advantage of the opportunity presented by “formalized” local environmental regulations to reduce carbon reduction behaviors and pursue increased benefits (Zhou and Ma, 2023 ). Nevertheless, the APILS exerts greater pressure on local governments to implement environmental policies under the external horizontal monitoring mechanism. At the same time, the internal incentive of the promotion and assessment system motivates local governments to enforce environmental regulations and promote local environmental governance (Li and Zhang, 2020 ). Therefore, this study uses the word frequency sum of environmental regulation efforts in local and municipal government reports as a proxy variable for environmental administrative regulation. The larger the word frequency sum, the higher the local government’s concern and the greater the environmental administrative regulation efforts. From column (3) in Table 9 , it can be seen that the coefficient of APILS is positive and significantly affects local environmental administrative regulation. The APILS has been demonstrated to significantly enhance the efficacy of environmental administrative regulation. Consequently, enterprises will be subjected to more rigorous environmental regulation requirements, thereby reducing CCE. Through the above analyses, it can be found that the APILS can promote CCE reduction through three mechanisms, namely, improving the corporate green innovation, public environmental demands, and environmental administrative regulation. The theoretical hypothesis H 2 is verified. Table 9 Mechanism test results Variables (1) (2) (3) CGI PEC EAR APILS 2.6699** 13.8090*** 2.2279*** (1.0588) (0.8812) (0.3928) Constant -95.8468*** 83.5380*** 31.6352*** (14.4008) (9.4587) (3.6331) Control variable Y Y Y Firm FE Y Y Y Yesr FE Y Y Y N 15,427 27,267 8,897 \({R}^{2}\) 0.5510 0.8511 0.7610 5. Expanded Analysis: The Moderating Effects of Social Credit Social credit as an important part of social governance (Huang et al., 2023 ; Cao et al., 2022 ), can improve the enthusiasm of the public to participate in social affairs, and has implicit institutional constraints on the internal strategic decision-making of enterprises (Ding et al., 2019 ;Zhou et al., 2012 ). This also generates some questions that need to be answered urgently: does social credit have an impact on the relationship between APILS and CCE? Does the impact strengthen or weaken? To answer these questions, this article further constructs a model of the moderating effect of social trust on the relationship between the APILS and CCE as follows: $${\text{C}\text{C}\text{E} }_{ijt}={\beta }_{0}+{\beta }_{1}{APILS}_{it}+{\beta }_{2}{Trust}_{it}+{\beta }_{3}{\text{A}\text{P}\text{I}\text{L}\text{S} }_{it}\times {Trust}_{it}$$ $$+\lambda {Controls}_{ijt}+{u}_{j}+{u}_{t}+{\epsilon }_{ijt }$$ 3 Among them, Trust represents the regional trust index, which is measured by the regional trust index from the questionnaire survey data of China Entrepreneurship Survey System (CESS), with reference to the relevant research in Zhang and Ke ( 2002 ). The interaction term \({APILS}_{it}\times {Trust}_{it}\) is the moderating effect of social credit on the APILS and CCE. In Table 10 , the coefficients of the interaction term \(APILS\times Trust\) are all significantly negative, indicating that social trust can strengthen the inhibiting effect of APILS on CCE. The reason is that the invisible incentives and constraints of social trust can improve the constraints of the APILS on the behavior of CCE, presenting a synergistic and complementary effect (Xin, 2014 ). This ultimately results in the moderating effect of reinforcing the APILS to reduce the CCE. On the one hand, the higher the level of social trust in the region, the government responds to the public’s questions more quickly, reduces the friction with the public and the risk of information asymmetry, and promotes the implementation of regional environmental regulation (Yang and Niu, 2023 ). On the other hand, institutional trust is the dominant form of social trust, which serves as a key psychological support factor guaranteeing individuals to complete social activities such as ecological governance (Wu and Zang, 2017). When the public has a high level of trust in the policy, the greater the likelihood that they will engage in environmental behavior (Wynveen and Sutton, 2015 ) or perform the individual behaviors expected by the policy (Zhao et al., 2023 ). Therefore, for the moderating factor of social trust, the implementation of the APILS creates differences, which in turn affects the strength of its constraints on CCE. Table 10 Moderating effect results Variables (1) (2) (3) (4) APILS -0.0584*** -0.0646*** -0.0284** -0.0573*** (0.0186) (0.0200) (0.0133) (0.0187) Trust 0.1743*** 0.0931*** 0.0771*** (0.0258) (0.0055) (0.0265) APILS \(\times\) Trust -0.0335** -0.0732*** -0.0249* (0.0143) (0.0124) (0.0134) Constant -3.0902*** 0.7219*** -3.5978*** -3.0786*** (0.1843) (0.0062) (0.0923) (0.1843) Control variable Y N Y Y Firm FE Y Y N Y Yesr FE Y Y N Y N 33,196 35,128 33,551 33,190 \({R}^{2}\) 0.5873 0.5135 0.2121 0.5875 6. Conclusions and Recommendations 6.1 Conclusions of the study The research conclusion of this article are as follows: The pilot policy of APILS significantly reduces CCE. In the field of carbon emissions, the existing literature mostly explores the legal and institutional system construction from the overall perspective of environmental regulation (Ding et al., 2023 ). Some scholars have studied the role of carbon emissions trading (Zhang et al., 2023 ; Wang and Qian, 2024 ) and other specific pilot policies (Zhang Yao and Li, 2023 ; Jing and Wang, 2024 ) on the goal of “dual-carbon”, but they have not touched on the APILS. Therefore, this article provides a new entry point for analyzing the influencing factors of CCE reduction, enriches the theory of low carbon system. The heterogeneity analysis results show that in terms of the nature of enterprises, the APILS has the most significant effect on CCE reduction for state-owned enterprises. In terms of industry differences, APILS has a significant impact on high-tech industries, non-heavily polluting industries, manufacturing industries, industries with high levels of competition, and industries with high levels of marketization. At the regional level, CCE reduction in the eastern region, resource-based cities, and small cities is most significantly affected by APILS. The above results are similar to existing studies in the field of CCE reduction (Guo et al., 2022 ). Under the conditions of enterprises, industries and regions with different endowments and natures, the impact of APILS on CCE reduction is different. Therefore, it is necessary to tailor the situation to the local conditions and implement targeted policies to achieve the desired outcomes. The implementation of APILS pilot policies has been associated with a reduction in CCE. This has been attributed to the promotion of the corporate green innovation, public environmental demands, and environmental administrative regulation. However, there is a divergence of opinion among academics regarding the factors that contribute to CCE reduction, with differing variables being identified. For example, Yang et al. ( 2019 ) believed that the impact of energy structure adjustment, efficiency improvement and industrial structure adjustment on CCE reduction is not significant, which is somewhat different from the results of this paper. Ding et al. ( 2023 ) included transition finance as a mechanism variable in the model, while Yin et al. ( 2024 ) investigated the impact of the interaction of environmental regulation and green finance on CCE reduction. This study aims at examining the extent of the corporate green innovation, public environmental demands, and environmental administrative regulation to the role of CCE reduction. Furthermore, it aims to provide a framework for legal defense of rights and to inspire the formulation of policies that will facilitate the construction of a multilevel, multi-path positive development of the circular mechanism. Social trust can strengthen the inhibitory effect of APILS on CCE. Among the current progress of related research, most scholars studied its impact on the development situation of enterprises from the perspective of social trust (Li and Bao, 2023 ; Tian et al, 2023 ; Shen et al, 2024 ). A few analyses of its role on green development issues also focused on its impact on the level of green innovation (Ling and Sun, 2019 ; Yin, 2022 ), and green total factor productivity (Li and Liu, 2022 ; Zhong et al. 2024 ). The current literature does not address the role of social trust in the relationship between APILS and CCE reduction. This article addresses this gap in the literature by providing a new safeguard idea for the effective implementation of the APILS. 6.2 Policy recommendations It is necessary to clarify the actual situation and needs of enterprises in different industries and cities in order to avoid a “one-size-fits-all” implementation program. The heterogeneity test of this study has revealed that enterprises located in the central and western regions and resource-endowed cities do not have a significant response to the implementation of the policy. Consequently, it is necessary to increase the intensity of their support according to the local conditions and to provide them with more guidance and support. Moreover, the impact of the pilot on different types of industries and groups of enterprises varies considerably. For instance, non-high-tech, low-level marketization and low-degree of competition in the industry by the APILS is not significant, and the role of the opposite enterprises is obvious. In response to this phenomenon, it is necessary for the administration to make specific, detailed and differentiated policy arrangements, introduce development plans for different industries, and coordinate the work of various industries. The focus has been on the capacity of enterprises to innovate, the environmental demands placed on them by the public, and the intermediary effects of environmental administrative regulations on the internal and external environment of enterprises. It is recommended that enterprises be strongly supported in their efforts to engage in green innovation. This can be achieved by implementing incentives for innovation that are tailored to the specific circumstances of each enterprise, encouraging the adoption of environmental protection technology, and facilitating the advancement of green technology. Additionally, the production process can be improved and the level of pollution control can be enhanced through the implementation of these measures. Furthermore, the public environmental litigation system should be publicized and promoted, and an effective communication channel should be established to facilitate greater participation. It is also necessary to improve the public’s understanding of environmental aspects and their participation. Furthermore, local governments should strengthen their supervision of carbon emission behavior. This should include the promulgation of strict carbon emission limitation and supervision measures, as well as the establishment of effective supervision mechanisms. One such mechanism could be the formation of environmental supervision teams to visit enterprises, thus ensuring the effectiveness of environmental governance. The strengthening of social trust and the ensuring of the legality, fairness, transparency and standardization of credit information are both necessary for the construction of a harmonious society. In order to achieve this, the government must take measures to strengthen social trust and enhance public trust in government environmental policies. This can be achieved by strengthening the transparency of government information, disclosing environmental data and improving administrative efficiency. Furthermore, the government should establish a governance system with optimal functionality, administration in accordance with the law, transparency, and efficiency. This will help to prevent a “crisis of trust” and ensure that the government fulfils its duties. It will also help to cultivate governmental trust resources. This study presents a theoretical analysis and quantitative evaluation of the impact of the APILS on CCE. The findings provide a valuable reference point for the subsequent improvement and optimization of the APILS. However, due to the limitations of data availability, this study focuses on analyzing the effect of the APILS on the CCE of listed companies in China, and has not yet discussed the effect of the APILS on the CCE of small and medium-sized enterprises. Consequently, in the future, when data are available, we will further take small and medium-sized enterprises as the entry point and conduct a continuous tracking study on the impact of the APILS on CCE. Declarations Author Contribution Author Contributions: Conceptualization, X.C.; methodology, S.Z.; software, D.Z. ; validation, S.Z.. and X.C.; formal analysis, X.C.; investigation,D.Z. ; resources, X.C.; data curation, X.C.; writing—original draft preparation, X.C.; writing—review and editing, S.Z.; visualization, D.Z.; supervision, D.Z. and S.Z.; project administration, X.C.; funding acquisition, X.C. All authors reviewed the manuscript. Acknowledgement Declaration of interests☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Data Availability Sequence data that support the findings of this study have been deposited in the China Stock Market & Accounting Research Database. The link is : https://data.csmar.com/. References Ai H, Tan X, Zhou S, Liu W (2023) The impact of supportive policy for resource-exhausted cities on carbon emission: Evidence from China. Resour Policy 85:103951 Cao YY, Kong DM, Tao YY (2022) Evaluation on the effect of the pilot reform of the social credit system: from the perspective of corporate social responsibility. J Finance Econ 48(02):93–108 Cao H, Peng L, Yan Z, Xu J (2023) Does perfect regional innovation ecosystem curb carbon emissions? 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Econ Manage 37(3):40–48 Zhou JW, Gao X, Xie PP (2024) On the impact of environmental regulation on China’s green and low-carbon transformation and development under the constraint of Carbon Peaking and Carbon Neutrality goals. J Jiangsu Univ (Social Sci Edition) 26(2):55–67 Zhou ZS, He DX, Li Z (2012) Institutional environment and corporate social responsibility: Evidence from listed companies in China. China Soft Sci 10:59–68 Zhuang H, Luo YX (2023) The connection between environmental public interest litigation and ecological environmental damage compensation litigation. J Hubei Univ Police 36(4):5–14 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4612053","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317348253,"identity":"ae3b472e-2e94-47a6-abfe-a8fbcbeac30d","order_by":0,"name":"Xinghua Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie3RsWrDMBCA4QsGeVGSVaFgv8IZQekQ6KvIFDxlC4QOgVoE1CUPkE55hXTpHDjQpHTu0MFdMmVwtw4danXr4NhjIfoHD+I+JFkAodA/jMVaVwoFHwPtESDxi3iWjDgR1vfTZKKtakZlN0lEUUw2rpBIzk/2IAxmeDU0lJfW1fOhwQTi1YuA5fsZ4lB6oteHXfZkUAK3CwH22E4Ga7zzZCUOO/w033kpZtdiUFI7iTiSJyY9VZgbfCjTUwdhLNf++hwcNLugAsE7CI8I/E9ujo/Z5hUzw4v5jbLtJN1+PH75p7zd01HyBabjmJ7f6mU7+bPj73sw/1G9AEBU9RwMhUKhC+sH/mtWxIm229sAAAAASUVORK5CYII=","orcid":"","institution":"Jiangxi University of Finance and Economics","correspondingAuthor":true,"prefix":"","firstName":"Xinghua","middleName":"","lastName":"Cui","suffix":""},{"id":317348258,"identity":"0c65ea28-fd4e-49ce-be97-79398f8215ef","order_by":1,"name":"Shu Zhang","email":"","orcid":"","institution":"Jiangxi University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Zhang","suffix":""},{"id":317348259,"identity":"3d5a8c43-50b2-43ed-9ca1-01b76195e172","order_by":2,"name":"Dui Zheng","email":"","orcid":"","institution":"Jiangxi University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Dui","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2024-06-20 13:22:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4612053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4612053/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60130030,"identity":"d020077d-bc7e-49b1-aa4f-348198c06a00","added_by":"auto","created_at":"2024-07-12 06:55:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32224,"visible":true,"origin":"","legend":"\u003cp\u003eParallel trend test\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4612053/v1/0ce15737a3ccbfed77b1f4b0.png"},{"id":60130031,"identity":"b60b8afe-8a68-4547-a1b0-2cc983f420f7","added_by":"auto","created_at":"2024-07-12 06:55:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41861,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo test\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4612053/v1/6c44139d88fe2c02201b37d7.jpeg"},{"id":60345492,"identity":"dc06c5a5-db9e-4bac-a6ae-9d60c887496d","added_by":"auto","created_at":"2024-07-15 19:41:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1252800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4612053/v1/a5f83175-4735-4791-b478-4d71de0c2deb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whether External Public Welfare Can Reduce the Corporate Carbon Emissions——Empirical Evidence Based on the Administrative Public Interest Litigation System","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince the goal of \u0026ldquo;carbon peak\u0026rdquo; and \u0026ldquo;carbon neutrality\u0026rdquo; was proposed in 2020, the Chinese government and enterprises have been engaged in a concerted effort to promote emission reduction (Shao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This has resulted in the achievement of remarkable outcomes, the consumption of coal per unit of GDP was significantly reduced, and the country has achieved a \u0026ldquo;double growth\u0026rdquo; in forest coverage and stockpile volume for 30 consecutive years. Nevertheless, the reduction of carbon emissions is not a solution that can be achieved overnight. In this process, some enterprises may choose to ignore environmental regulations in order to maximize profits, do not to implement emission reduction measures or do not to fulfil their emission reduction obligations in accordance with the regulations, thus causing damage to the environment (Zhang and Liu, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At this juncture, the pilot APILS serves to guarantee the creation of an environment conducive to the protection of the public interest. The establishment of this system realizes the purpose of judicial correction of harm to environmental public welfare. It forces enterprises and governments to promote environmental protection by punishing offenders and forcing them to assume responsibility for environmental remediation. So, will the external institutional supervision reflected in the pilot APILS have an impact onCCE? What is the specific mechanism of action? Are there multidimensional differences in its effects? Can social credit reinforce this inhibitory effect?\u003c/p\u003e \u003cp\u003ePrevious studies have extensively examined the factors influencing carbon emissions. For example, globalization escalate (Sultana et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and geopolitical risk (Ding et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) factors at the national level; environmental regulation (Lin and Zhang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and energy conservation (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) factors at the regional level; supportive policy (Ai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), energy governance (Che et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and green bonds (Xu and Li, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) factors at the city level; and intelligence (Wei et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) factors at the industry level. However, there are relatively few literatures on carbon emission reduction at the enterprise level, mainly focusing on external factors such as environmental regulation (Wang, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and political affiliation (Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) as well as internal corporate factors such as corporate technological innovation (Zhao et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen and Wang, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), digital transformation (Shang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and corporate management (Goud, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the fact that existing research has analyzed the influencing factors of carbon emissions from many aspects, however, there is currently no literature examining its impact on CCE reduction from the perspective of APILS. In fact, the pilot policy of APILS, as an important initiative of the external regulatory system and innovation of environmental management tools, has great significance in promoting CCE reduction. Furthermore, the strictly regulated and specialized environmental public interest justice has improved the judicial punitive power and credibility (Gao and Wen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), enhanced the public\u0026rsquo;s awareness of environmental protection, served as an external supervisory mechanism and played an important role on CCE. It is regrettable that the factor of external public welfare system supervision has not yet attracted sufficient attention, which represents a key entry point of this study\u0026rsquo;s research.\u003c/p\u003e \u003cp\u003eThe possible innovations of this study are as follows: (1) Firstly, different from the existing literature on the impact of environmental regulations on CCE reduction, this study analyzes the impact of pilot policies of the APILS on CCE from the innovative perspective of external public welfare system. It not only expands the research on the actual effect of the APILS, but also enriches the research on the institutional influencing factors of CCE. (2) Secondly, considering the three subjects of enterprises, the public and local governments, this study constructs a theoretical framework on the impact of external public welfare system on CCE from three aspects: enterprise green innovation, public environmental claims and environmental administrative regulations, which helps to clarify the internal logical relationship between external public welfare system and claims. (3) Thirdly, this study further incorporates social credit into the analysis framework, innovatively analyzes its impact on the relationship between the APILS and CCE, and reveals the important role played by social credit level in promoting the CCE reduction effect of external public welfare system.\u003c/p\u003e"},{"header":"2. Policy background and research hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Policy background\u003c/h2\u003e \u003cp\u003eChina\u0026rsquo;s long-standing environmental protection sanctions are primarily implemented through administrative means. However, the inherent limitations and weaknesses of administrative means often render the punitive effect ineffective, making it difficult to produce a substantial effect. Therefore, the exploration, establishment and improvement of the APILS represents a viable approach to addressing the aforementioned issues (Nie and Zhou, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On 1 July 2015, the decision of the Standing Committee of the National People\u0026rsquo;s Congress on authorizing the Supreme People\u0026rsquo;s Procuratorate to carry out pilot work of public interest litigation in some regions was adopted. This decision determined that a two-year pilot work would be carried out in 73 cities in 13 provinces. In the pilot cities, the procuratorial organs have actively performed their public interest litigation functions, and have accumulated a substantial number of case samples (Zhuang and Luo, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Theoretical assumptions\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 The impact mechanism of the APILS on CCE\u003c/h2\u003e \u003cp\u003e(1) The APILS, green innovation and CCE reduction. APILS represents a significant judicial system for enhancing the level of environmental judicial protection, rectifying the perceived \u0026ldquo;regulatory enforcement bias\u0026rdquo; of local governments, and safeguarding the public interest of the community (Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a means of external supervision, it has raised higher demands for corporate green innovation. Firstly, the system enhances the local government\u0026rsquo;s environmental penalties and environmental governance through the horizontal judicial supervision of local procuratorial organs. The pressure of environmental regulation prompts enterprises to increase their R\u0026amp;D investment and improve the innovation efficiency of innovation resources, as the litigation risk and the expected cost of violation of the law faced by enterprises are increased (Dai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Secondly, the implementation and publicity of the APILS will enhance the public\u0026rsquo;s concern for environmental protection. This, in turn, will encourage enterprises to assume social responsibility and drive them to carry out green innovation (Yi et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At this point, green technology, as a representative of innovation, is conducive to environmental protection and ecological preservation (Yu, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It improves energy production efficiency, contributes to CCE reduction (Zhang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and ultimately achieves ecological environment improvement (Li and Gong, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e(2) The APILS, public environmental claims and CCE reduction. The single subject of litigation will constrict the scope for expression of social forces, while the expansion of the qualification of social organizations and even individuals as subjects of APILS can compensate for the inadequacy of such a single \u0026ldquo;nationalized\u0026rdquo; structure for sensitive public interest remedies (Qin, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, the APILS, as the principal mechanism for public participation in environmental governance, plays a pivotal role in enhancing the extent of public participation, expanding the range of litigants, and preventing harm to public interests. The aforementioned system affords the public a more direct and profound sense of public interest, thereby conferring certain advantages upon them in terms of discovering clues, collecting evidence and technical appraisal. As a result, the growing public awareness of environmental protection will exert a certain \u0026ldquo;deterrent effect\u0026rdquo;, restraining the behavior of individuals and organizations within society (Carpentier and Suret, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Ultimately, in order to maintain a positive relationship with the public, enterprises must actively engage in environmental governance and pursue green production (Danneels et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This will facilitate the reduction of CCE (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e(3) The APILS, environmental administrative regulation and CCE reduction. The potential for curbing corporate environmental pollution behavior is contingent upon the efficacy of local government enforcement of environmental policies. In the past, the central government utilized GDP as a criterion for the appraisal and promotion of officials at the local government level. Local governments lacked incentives for environmental governance, and local officials may have strategically enforced environmental regulations on camera based on self-interested considerations and political pressure trade-offs. This resulted in a lack of incentives for local governments to implement stringent environmental governance standards at the expense of economic growth (Wang and He, 2022). The APILS, which is led by the procuratorial authorities, has been an effective means of resolving these issues. Firstly, the embedding of an external oversight system allows the prosecution and administrative authorities to overcome the shortcomings of the internal oversight system. This enables them to have a more unique regional advantage in access to information, and to avoid the behavior of superior and subordinate levels working together to circumvent unfavorable outcomes in the environmental assessment process (Zhao, 2023). Secondly, the APILS can enhance the prioritization of environmental regulation within local administrative activities, facilitate the centralized mobilization of administrative resources, and encourage local governments to fulfil their responsibilities in a timely manner (Lu Chao, 2018). Nevertheless, environmental regulation plays a pivotal role in reducing CCE and enhancing CCR performance (Zhou et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It exerts a considerable influence on CCE through some distinct mechanisms: technological innovation effects (Lin et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and factor allocation effects (Zhang, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), etc.\u003c/p\u003e \u003cp\u003eTherefore, the following hypotheses are proposed in this study:\u003c/p\u003e \u003cp\u003eH1: The APILS has a significant contribution to CCE reduction.\u003c/p\u003e \u003cp\u003eH2: The APILS can facilitate CCE reduction by enhancing the corporate green innovation, intensifying public environmental demands, and increasing environmental administrative regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 The moderating effect of social trust\u003c/h2\u003e \u003cp\u003eSocial trust, as an informal institution, is an important part of the soft environment that constitutes the implementation of public policy (Yang and Niu, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The social trust can stringent the norms and constraints on behavior, these norms and constraints guide people\u0026rsquo;s behavior, reduce a series of social problems such as government corruption (Depetris-Chauvin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and have a certain safeguard effect on the successful implementation of environmental regulation. In addition, higher levels of social trust, which indicate broader and more stable public support for the work of the government, can facilitate the effective implementation of policies. This, in turn, enables policies to better serve the public interest (Guo and Wang, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Concurrently, the public\u0026rsquo;s sense of collective action and necessity for oversight will be reinforced by an increase in social trust, and the expansion of participation in policy-making may, to a certain extent, enhance the impact of policy implementation and enhance the quality of the system\u0026rsquo;s functioning. Conversely, in areas with a low level of social trust, the public is likely to perceive a lack of bureaucracy or policy fairness, and will not voluntarily participate in or comply with policies related to environmental regulation. This results in a greater prevalence of \u0026ldquo;free-riding\u0026rdquo; behavior (Yang and Niu, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The public\u0026rsquo;s inclination towards non-cooperative behavior with the government can impede the effective implementation of public policy. And in pursuit of profit maximization, enterprises must assume corresponding social responsibility towards relevant stakeholders. The value norms endorsed by social trust mediate the conflict of interests between enterprises and stakeholders, facilitate the fulfilment of corporate social responsibility (Yang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), prompt enterprises to reflect on the relationship between their own development and green environmental protection, adjust corporate strategic decisions, implement green innovation activities (Yang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and then promote enterprises to achieve CCE reduction.\u003c/p\u003e \u003cp\u003eTherefore, this study presents the hypothesis:\u003c/p\u003e \u003cp\u003eH3: Social trust can strengthen the inhibitory effect of APILS on CCE.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Research design","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Model specification\u003c/h2\u003e \u003cp\u003eThis study using the DID method and constructing an econometric regression model as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${CCE}_{ijt}={\\beta }_{0}+{\\beta }_{1}{APILS}_{it}+\\lambda {Controls}_{ijt}+{u}_{j}+{u}_{t}+{\\epsilon }_{ijt }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the subscripts \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e denote city, firm and year respectively. And the dependent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(CCE\\)\u003c/span\u003e\u003c/span\u003e denotes the carbon emission intensity of companies. The independent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(APILS\\)\u003c/span\u003e\u003c/span\u003e indicates whether a city is designated as an administrative public interest litigation pilot city in a particular year, with values of 1 for that year and subsequent years, and 0 otherwise. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Controls\\)\u003c/span\u003e\u003c/span\u003e is the set of control variables indicating the value of each control variable. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{j}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{t}\\)\u003c/span\u003e\u003c/span\u003e are firm fixed effects and year fixed effects, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{ijt}\\)\u003c/span\u003e\u003c/span\u003e denotes the random error term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Variable selection\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Dependent variable\u003c/h2\u003e \u003cp\u003ecorporate carbon emission (\u003cem\u003eCCE\u003c/em\u003e). This study uses the ratio of the total carbon emissions of listed companies (100 tons) to the number of employees in the company. Carbon emissions per capita can increase comparability between enterprises, and can attenuate the impact of scale effects when comparing the CCE of different sizes and industries. Following the relevant practices of Wang et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Cui et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this study calculates a company\u0026rsquo;s carbon emissions based on the annual direct, indirect, or total carbon emissions disclosed in the company\u0026rsquo;s annual reports, social responsibility reports, and environmental reports. For companies that do not directly disclose their annual CCE, their CCE are obtained by converting the disclosed coal consumption and using the carbon emission calculation coefficient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Independent variable\u003c/h2\u003e \u003cp\u003eAdministrative public interest litigation system (\u003cem\u003eAPILS\u003c/em\u003e). This study treats the national public interest litigation pilot policy by manually collating a list of officially announced pilot cities and setting two dummy variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t{reat}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({post}_{t}\\)\u003c/span\u003e\u003c/span\u003e. If the city where firm \u003cem\u003ej\u003c/em\u003e is located belongs to the APILS area in year \u003cem\u003et\u003c/em\u003e, the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({treat}_{i}\\)\u003c/span\u003e\u003c/span\u003e take the value of 1, otherwise 0. If the year is in 2015 and later, the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({post}_{t}\\)\u003c/span\u003e\u003c/span\u003e take the value 1, otherwise 0. Further with its interaction term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({treat}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u0026times;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({post}_{t}\\)\u003c/span\u003e\u003c/span\u003e as the core independent variable denotes the APILS treatment effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Control variables\u003c/h2\u003e \u003cp\u003eRearing ratio (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Lev\\)\u003c/span\u003e\u003c/span\u003e): Corporate liabilities as a proportion of total assets. Profitability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Roa\\)\u003c/span\u003e\u003c/span\u003e): Net profit of the enterprise as a percentage of total assets. cash ratio (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(CAR\\)\u003c/span\u003e\u003c/span\u003e): Ratio of cash-based assets to current liabilities of the enterprise. Capital Intensity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(CI\\)\u003c/span\u003e\u003c/span\u003e): Sum of human capital intensity and physical capital intensity. Fixed Assets Ratio (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(FAR\\)\u003c/span\u003e\u003c/span\u003e): Ratio of enterprise fixed assets to total enterprise assets. Enterprise size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Size\\)\u003c/span\u003e\u003c/span\u003e): Expressed in terms of total enterprise assets and taking logarithmic form. enterprise growth (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Gro\\)\u003c/span\u003e\u003c/span\u003e): Growth rate of enterprise revenue.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Data sources\u003c/h2\u003e \u003cp\u003eThis study selects A-share listed companies from 2000 to 2021 as the research sample. In accordance with the conventional data processing methods employed in existing studies, samples of companies that were ST, *ST, listed or delisted during the sample period were excluded from the analysis. The data on word frequency and environmental regulation efforts were obtained from local municipal government reports and manual collation. The data on public climate concern was derived from the Baidu search index. The data on the marketization index was sourced from the China Marketization Index Database. The remaining variables are sourced from the database of the CSMAR database. The descriptive statistics of the variables in this studyare shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of main variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCCE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(APILS\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Lev\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Roa\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCAR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.1069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.3093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.7048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFAR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.1232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.9965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.3139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Gro\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical result","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Baseline regression results\u003c/h2\u003e \u003cp\u003eThe baseline regression results of the impact of the APILS on CCE are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among them, the results show that the regression coefficient of the APILS is significantly negative, which initially indicates that the public interest litigation policy significantly reduces the CCE. This verifies the theoretical hypothesis H1. In addition, as shown in column (4), gearing ratio (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Lev\\)\u003c/span\u003e\u003c/span\u003e), profitability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Roa\\)\u003c/span\u003e\u003c/span\u003e), cash ratio (\u003cem\u003eCAR\u003c/em\u003e), city size (\u003cem\u003eSize\u003c/em\u003e), city GDP per capita (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Gro\\)\u003c/span\u003e\u003c/span\u003e), all five control variables significantly and positively increase the CCE, while capital intensity (\u003cem\u003eCI\u003c/em\u003e) and fixed asset ratio (\u003cem\u003eFAR\u003c/em\u003e) can significantly reduce CCE.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline regression results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.025**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.022*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMfee\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eListAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eLiquid\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.599***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.374***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.345***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.307***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.071)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors are shown in parentheses; *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The tables below remain consistent.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Robustness test\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Parallel trend test\u003c/h2\u003e \u003cp\u003eIn order to ensure that the CCE of enterprises is reduced due to the implementation of the APILS, this article introduces the interaction term between the year dummy variable and the treatment group, and examines the dynamic characteristics of the CCE of enterprises by using the event study method, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It can be seen that pre-interaction terms are not significant, while post-interaction terms are significant. The coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e is found to be insignificant until period 0, indicating that there is no discernible difference between the CCE of enterprises in the treatment group and the control group prior to the implementation of the APILS pilot. However, in the year of the pilot, the interaction terms are all significantly different from 0.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Placebo test\u003c/h2\u003e \u003cp\u003eIdeally, as an exogenous factor, policy would not be affected by unobservable factors, so \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{\\beta }\\)\u003c/span\u003e\u003c/span\u003e is the consensus estimate of the estimation coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e. In practice, however, the implementation of policies is influenced by a multitude of factors, including unobservable factors and unlisted control variables, which can lead to the misrepresentation of the estimated coefficients. In this paper, we use a spurious policy implementation year to generate an interaction term with the treatment group, and repeat the process of randomly generating the implementation year 500 times to observe whether the APILS reduces the CCE. The results are presented in Fig.\u0026nbsp;3. It can be assumed that the pilot cities remain unchanged and that the policy time is randomly assigned. The coefficients of the interaction terms are normally distributed with a mean of zero, indicating that the random factors in the model of this paper do not significantly affect the results. Consequently, the estimation results are robust.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3. PSM-DID\u003c/h2\u003e \u003cp\u003eThis study adopts the propensity score matching method (PSM) to select the existing control variables as covariates and matches them year by year through the 1:1 nearest-neighbor matching method. This enables the identification of control firms that are most similar to the observable characteristics. Furthermore, this paper conducts regression analysis on the matched samples, the results of which are presented in Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It can be seen that the APILS can reduce the CCE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4. Replacement of dependent variable\u003c/h2\u003e \u003cp\u003eThis study draws on the research of Wu and Yan (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) to replace the CCE with carbon per unit profit. This ratio reflects the carbon emissions per unit of profit during the operation period of the company. The regression results are shown in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results show that the APILS can reduce the CCE significantly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.5 Exclusion of exceptional years\u003c/h2\u003e \u003cp\u003eIn October 2018 and April 2019, the Standing Committee of the National People\u0026rsquo;s Congress (NPCSC) incorporated the public interest litigation inspection powers into the revised Organic Law of the People\u0026rsquo;s Procuratorate and the Procurator Law. In October 2019, the Supreme People\u0026rsquo;s Procuratorate issued a report on the status of public interest litigation work, summarizing the development situation and problems faced, and putting forward measures and suggestions for the next step. Therefore, this study considers 2019 as a special year and carries out the exclusion process, and regresses the processed data to obtain column (3) in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It is obvious that the overall effect of the pilot of APILS on CCE remains robust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e4.2.6. Exclude the impact the impact of other policies\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn the process of implementing the APILS, it is also necessary to consider the potential impact of other policies in the category of environmental regulation on CCE. This study incorporates the pilot policy of urban double repair, the pilot policy of green finance, the pilot sponge city and the pilot low-carbon city as control variables in the model. The regression results are presented in column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The test demonstrates a significant negative association between the APILS and the CCE, even after the exclusion of the four related policies. This finding suggests that the APILS continues to exert a notable influence on the reduction of CCE.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSM-DID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReplacement of dependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcluding exceptional years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcluding relevant policy effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0468**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0259***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0622***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0534***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0193)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.981***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.0496***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.1104***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.3405***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.2364)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYesr FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19,914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Heterogeneity analysis\u003c/h2\u003e \u003cp\u003eIn order to gain further insight, this study conducts a series of sub-sample regressions at the enterprise, industry, and regional levels. The objective is to examine the heterogeneous impact of the APILS.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Analysis of firm heterogeneity\u003c/h2\u003e \u003cp\u003eAt the enterprise level, this study classifies the research sample into four categories according to the nature of the enterprise. These categories are \u0026ldquo;state-owned\u0026rdquo;, \u0026ldquo;private\u0026rdquo;, \u0026ldquo;foreign\u0026rdquo; and \u0026ldquo;others\u0026rdquo;. The classification is based on the nature of the enterprise, with the reform of private state-owned enterprises counted as \u0026ldquo;state-owned enterprises\u0026rdquo;. The different nature of enterprises, along with their own interests and strategies, results in varying outcomes. Among them, state-owned enterprises are more closely aligned with the government, exhibiting greater levels of cooperation and mutual assistance in terms of resources. Consequently, they are more susceptible to government policies and are more likely to alter their behaviors in accordance with the government\u0026rsquo;s guidance. In contrast, private enterprises exhibit a more diverse range of responses to policies, with their fundamental objective remaining the advancement of business development and profitability. This is evidenced by their focus on changes in business operations and profits. In conclusion, this paper identifies four categories of enterprises: state-owned, private, foreign and other. It then compares the performance of these subgroups under the public interest litigation pilot policy.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the regression coefficients of state-owned and private enterprises are significantly negative. The regression result of foreign enterprises is insignificant. In contrast, the results of other types of enterprises are significantly positive. The results indicate that the APILS has the most pronounced impact on CCE reduction in state-owned enterprises compared to other types of entities. The reason is that state-owned enterprises represent the state and the government to a certain extent, have greater social responsibility, and are more inclined to actively participate in policy pilots and fulfil their corporate social responsibility. Concurrently, state-owned enterprises are more prone to governmental monitoring and scrutiny, which can prompt them to respond more expeditiously to policy initiatives and adopt responsive measures. For private enterprises, the impact of the policy change on large enterprises is more pronounced. Of interest is the fact that large private enterprises will react more favorably to the pilot policy, and some small and medium-sized enterprises with a wait-and-see attitude or a weak resistance will also alter their strategies in line with the industry trend.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of firm heterogeneity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eState-owned\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForeign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0749**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0647***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3398***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.2501)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.7261***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.9123***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.2322**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.3324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.9780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.5163)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYesr FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Analysis of industry heterogeneity\u003c/h2\u003e \u003cp\u003eThis study combines the nature of the industry with the three criteria of whether it is a high-tech enterprise, a heavy polluting enterprise, a manufacturing industry to categorize the enterprises into group regressions. Furthermore, the influence of market factors is considered in order to analyze the impact of heterogeneity resulting from differences in the degree of competition and the level of marketization within the industry. The regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e respectively.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhether it is a high-tech enterprise. In the context of the continued growth in electricity demand, achieving carbon emission reduction is not simply a matter of returning to coal. Rather, it requires the development of a multi-energy complementary approach, which hinges on the technological innovation. In this study, the regression is grouped according to whether it is a high-tech enterprise or not. The results show that the APILS has a substantial role in promoting CEE reduction of high-tech enterprises. The reason for the above difference is that high-tech enterprises have a faster and more effective response to the implementation of the APILS policy due to their wider application of key carbon monitoring technologies, the proportion of new energy adoption, and the efficient and clean use of fossil energy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhether it is a heavy polluting enterprise. Pollution is typically associated with high CCE. The energy consumed by enterprises during the production process results in the release of significant quantities of carbon dioxide. For instance, in key industries such as iron and steel, cement, and thermal power, the emissions of air pollutants are considerable, the cross-media compound pollution is severe, and the emission mechanism is intricate. In the context of the implementation of the APILS, the strengthening of the regulation of pollutant emission limits will have a direct impact on CCE reduction. This study compares the heterogeneity results of whether or not they are heavy polluters. We can see that the size of the coefficients differs considerably between the two groups, and the APILS has the most significant impact on CCE reduction for non-heavy polluters compared to heavy polluters. This may be attributed to the fact that heavy polluters face greater CCE reduction challenges than non-heavy polluters. That is, heavy polluters emit more carbon, but due to limitations such as technology and cost, it may be difficult for high polluters to reach their carbon reduction targets in the short term and adhere to them over an extended period.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhether it is a manufacturing enterprise. Although the manufacturing sector is a pillar of China\u0026rsquo;s economy, it is also a significant contributor to carbon emissions. Manufacturing processes, such as energy consumption and material transformation, typically result in high carbon emissions. Consequently, the manufacturing sector has implemented specific targeted policies with the objective of implementing more stringent emission reduction requirements. One such initiative is the Made in China 2025 initiative, which was introduced by the State in 2015. This initiative explicitly sets the goal of reducing carbon emissions per unit of value added by 40 per cent in 2025, in comparison to the 2015 level. In this study, a heterogeneity regression is conducted using whether a firm belongs to the manufacturing industry as the classification criterion. The results demonstrate that, relative to non-manufacturing firms, the effect of the APILS on the CCE is more significant. The reason is that the implementation of the APILS has prompted the manufacturing sector to undertake green development initiatives such as technological transformation and energy efficiency improvement, which significantly reduces CCE. Conversely, non-manufacturing industries, such as the service industry and the financial industry, produce less CCE, are less sensitive to green development-related policies and are less affected by them.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of industry heterogeneity regression (I)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-tech\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-high-tech\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeavy pollution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-heavy pollution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-\u003c/p\u003e \u003cp\u003emanufacturing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0497***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0465*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0715***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0439***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0376)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0607)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.7236***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.5042***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.9933***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.4718***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.7954***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-8.2056***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.3762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2966)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.2533)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.1657)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.5920)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYesr FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25,858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(4) Degree of industry competition. The degree of industry competition is the key factor for enterprises to adjust their investment behavior. In this study, Lerner Industrial Index is used to measure the degree of competition in an industry, and the average value is taken as the measurement standard. The results of the heterogeneity regression are presented in columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below. These findings indicate that APILS has a more pronounced impact on CCE reduction for firms with a higher degree of market competition. The reason is that many enterprises compete in the market, each holding a portion of the market share, and there is no instance in which enterprises monopolize the market alone. The implementation of the APILS will result in adjustments to the price and demand for products in the industry in line with market changes. These adjustments will affect the production of enterprises, prompting them to develop in the direction guided by the policy in order to seek greater benefits and better development space.\u003c/p\u003e \u003cp\u003e(5) Market-oriented level. This study adopts the marketization index provided by Fan et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to assess the degree and quality of marketization. In this study, the mean value of the marketization index obtained from the calculation is chosen as a measure, stipulating that a marketization index greater than or equal to the mean value is considered to be a high level of marketization, and vice versa is considered to be a low level of marketization. The regression results show that unlike firms with low marketization, APILS more significantly promotes CCE reduction with high marketization. This may be due to the fact that the latter are freer in terms of resource allocation and price formation, the behavior of market players is more variable, the sensitivity to respond to administrative public interest litigation policies is high, and enterprises can quickly adjust their production behaviors to meet the new requirements of the market.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of industry heterogeneity regression (II)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh level competition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow level competition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh level marketization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow level marketization\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0937***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0837***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0311)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.1004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.0343***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.6056***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.1577***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.2419)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.3181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.3087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.2465)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYesr FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Analysis of regional heterogeneity\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRegional distribution. In this study, the regional differences indicator is selected as a classification criterion to explore the heterogeneity of results in terms of the spatial structure of CCE. The regression results in columns (1) to (3) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrate the heterogeneity of urban areas where firms are located. The coefficient is significantly negative only in the eastern region, indicating that the APILS exerts a more pronounced effect on the reduction of the CCE in the eastern region. The observed outcome may be attributed to a number of factors, including the higher concentration of firms in the eastern region, the pilot implementation of regulatory capture, more rigorous inspections in the east, and the enhanced responsiveness of firms in the east due to the more comprehensive infrastructure.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eResource intensity. A significant proportion of industries are highly dependent on resources, with strong industrial linkages and obvious scale effects. Furthermore, the majority of industries are concentrated in regions with a more favorable resource base. In the context of the APILS, the place where the enterprise belongs to also affects the implementation effect of the policy. In this study, we distinguish between the size of resource intensity and delineate two groups of resource-based cities and non-resource-based cities. Regressions are carried out separately for each group, and the results are presented in column (4) and column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The results indicate that the policy pilot effect in resource-based cities is highly significant. This implies that APILS has a more pronounced effect on CCE reduction in resource-based cities. This may be due to the strong effect of the original industrial clusters within the resource-based cities and the more stable development of green transformation of enterprises. Concurrently, in order to enhance their resilience to the effects of the pilot policy and improve their competitiveness, enterprises in resource cities are more competitive, and are able to promote green transformation and achieve CCE reduction more quickly.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCity size. The effects of cities of varying sizes on resource allocation, scientific and technological innovation, pollution reduction and emission reduction due to external effects such as agglomeration and congestion are distinct. Based on population size, the study divides cities into four levels, namely \u0026ldquo;super-large cities\u0026rdquo;, \u0026ldquo;large cities\u0026rdquo;, \u0026ldquo;medium cities\u0026rdquo;, and \u0026ldquo;small cities\u0026rdquo;. The regression results in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e are obtained. The results indicate that the impact of the APILS is not significant for super-large cities, large cities and medium cities. However, it is significantly negative in small cities. This may be due to the fact that, with the same level of governance and willingness, the municipal governments of small cities are more inclined to adopt administrative orders and accountability to directly control the sources of infection (Shang et al., 2020).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of regional heterogeneity (I)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-resource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0578**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1609***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0225)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.0931***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.9596***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.1200***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.5138***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.3105***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.2539)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.3295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.4309)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.3947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.2254)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYesr FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24,181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of regional heterogeneity (II)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuper-large\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3267***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0822)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.3895***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.7850***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.2767***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.4722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.4039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.6812)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYesr FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Mechanism tests\u003c/h2\u003e \u003cp\u003eAs for the mechanisms behind these effects, this study draws on the mechanism verification method proposed by Jiang (2022) and conducts mechanism analysis from three perspectives: corporate green innovation, public environmental demands, and environmental administrative regulation. This study constructs the econometric model as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${M}_{ijt}={\\beta }_{0}+{\\beta }_{1}{DID}_{it}+{\\beta }_{i}{Controls}_{ijt}+{u}_{j}+{u}_{t}+{\\epsilon }_{ijt}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eM\u003c/em\u003e is each mechanism variable, including corporate green innovation, public environmental demands, and environmental administrative regulation. The rest of the variables have the same meaning as in model (1).\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCorporate green innovation (\u003cem\u003eCGI\u003c/em\u003e). As the main body of green technology application, the green innovation capability of enterprises is crucial for reducing CCE. This study uses the logarithm of the total number of green patents held by a firm plus one to represent the CGI. The estimated coefficient of CGI is significantly positive, indicating that the APILS is able to promote the level of CGI. Furthermore, the utilization of CGI enables enterprises to enhance the efficiency of energy utilization and reduce the cost of resources, thereby achieving the objective of reducing CCE.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePublic environmental claims (\u003cem\u003ePEC\u003c/em\u003e). In the 21st century, the internet has become an integral part of modern life. As a result, search engines have become one of the most important information portals on the internet. The search and browsing history of internet users can be used to identify their preferences for certain types of events and to analyze the trends in their attitudes and behaviors towards these events. Accordingly, this study employs the annual search index of \u0026ldquo;haze\u0026rdquo; and \u0026ldquo;environmental pollution\u0026rdquo; in each city to construct a measure of public environmental demands. In Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the coefficient of \u003cem\u003ePEC\u003c/em\u003e is significantly positive. This suggests that the APILS has enhanced people\u0026rsquo;s concern for the public environment. Moreover, public environmental demands can effectively prompt local governments to prioritize environmental governance, act as an external watchdog on CCE (Wang et al.2024).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEnvironmental administrative regulation (\u003cem\u003eEAR\u003c/em\u003e). If local governments choose to seek benefits and avoid conflicts in the process of environmental governance, there will be a significant difference in motivation, strategy, and effect with formal environmental regulation (Zhao and Wang, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The primary objective of enterprise development is to maximize profits. Consequently, there is no intrinsic motivation for carbon reduction. Therefore, enterprises should identify the essence of local governments\u0026rsquo; strategies and adjust their attitudes and behaviors. This will enable them to take advantage of the opportunity presented by \u0026ldquo;formalized\u0026rdquo; local environmental regulations to reduce carbon reduction behaviors and pursue increased benefits (Zhou and Ma, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, the APILS exerts greater pressure on local governments to implement environmental policies under the external horizontal monitoring mechanism. At the same time, the internal incentive of the promotion and assessment system motivates local governments to enforce environmental regulations and promote local environmental governance (Li and Zhang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, this study uses the word frequency sum of environmental regulation efforts in local and municipal government reports as a proxy variable for environmental administrative regulation. The larger the word frequency sum, the higher the local government\u0026rsquo;s concern and the greater the environmental administrative regulation efforts. From column (3) in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, it can be seen that the coefficient of APILS is positive and significantly affects local environmental administrative regulation. The APILS has been demonstrated to significantly enhance the efficacy of environmental administrative regulation. Consequently, enterprises will be subjected to more rigorous environmental regulation requirements, thereby reducing CCE.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThrough the above analyses, it can be found that the APILS can promote CCE reduction through three mechanisms, namely, improving the corporate green innovation, public environmental demands, and environmental administrative regulation. The theoretical hypothesis H\u003csub\u003e2\u003c/sub\u003e is verified.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePEC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEAR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6699**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2279***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.0588)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.8812)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.3928)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-95.8468***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.5380***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.6352***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(14.4008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(9.4587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.6331)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYesr FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27,267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Expanded Analysis: The Moderating Effects of Social Credit","content":"\u003cp\u003eSocial credit as an important part of social governance (Huang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), can improve the enthusiasm of the public to participate in social affairs, and has implicit institutional constraints on the internal strategic decision-making of enterprises (Ding et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e;Zhou et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This also generates some questions that need to be answered urgently: does social credit have an impact on the relationship between APILS and CCE? Does the impact strengthen or weaken? To answer these questions, this article further constructs a model of the moderating effect of social trust on the relationship between the APILS and CCE as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${\\text{C}\\text{C}\\text{E} }_{ijt}={\\beta }_{0}+{\\beta }_{1}{APILS}_{it}+{\\beta }_{2}{Trust}_{it}+{\\beta }_{3}{\\text{A}\\text{P}\\text{I}\\text{L}\\text{S} }_{it}\\times {Trust}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$+\\lambda {Controls}_{ijt}+{u}_{j}+{u}_{t}+{\\epsilon }_{ijt }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAmong them, \u003cem\u003eTrust\u003c/em\u003e represents the regional trust index, which is measured by the regional trust index from the questionnaire survey data of China Entrepreneurship Survey System (CESS), with reference to the relevant research in Zhang and Ke (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The interaction term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({APILS}_{it}\\times {Trust}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the moderating effect of social credit on the APILS and CCE.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the coefficients of the interaction term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(APILS\\times Trust\\)\u003c/span\u003e\u003c/span\u003e are all significantly negative, indicating that social trust can strengthen the inhibiting effect of APILS on CCE. The reason is that the invisible incentives and constraints of social trust can improve the constraints of the APILS on the behavior of CCE, presenting a synergistic and complementary effect (Xin, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This ultimately results in the moderating effect of reinforcing the APILS to reduce the CCE. On the one hand, the higher the level of social trust in the region, the government responds to the public\u0026rsquo;s questions more quickly, reduces the friction with the public and the risk of information asymmetry, and promotes the implementation of regional environmental regulation (Yang and Niu, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, institutional trust is the dominant form of social trust, which serves as a key psychological support factor guaranteeing individuals to complete social activities such as ecological governance (Wu and Zang, 2017). When the public has a high level of trust in the policy, the greater the likelihood that they will engage in environmental behavior (Wynveen and Sutton, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) or perform the individual behaviors expected by the policy (Zhao et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, for the moderating factor of social trust, the implementation of the APILS creates differences, which in turn affects the strength of its constraints on CCE.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModerating effect results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0584***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0646***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0284**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0573***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0187)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTrust\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1743***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0931***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0771***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0265)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAPILS\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eTrust\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0732***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0249*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0134)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.0902***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7219***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.5978***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.0786***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1843)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYesr FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33,551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33,190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6. Conclusions and Recommendations","content":"\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Conclusions of the study\u003c/h2\u003e \u003cp\u003eThe research conclusion of this article are as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe pilot policy of APILS significantly reduces CCE. In the field of carbon emissions, the existing literature mostly explores the legal and institutional system construction from the overall perspective of environmental regulation (Ding et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Some scholars have studied the role of carbon emissions trading (Zhang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang and Qian, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and other specific pilot policies (Zhang Yao and Li, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jing and Wang, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on the goal of \u0026ldquo;dual-carbon\u0026rdquo;, but they have not touched on the APILS. Therefore, this article provides a new entry point for analyzing the influencing factors of CCE reduction, enriches the theory of low carbon system.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe heterogeneity analysis results show that in terms of the nature of enterprises, the APILS has the most significant effect on CCE reduction for state-owned enterprises. In terms of industry differences, APILS has a significant impact on high-tech industries, non-heavily polluting industries, manufacturing industries, industries with high levels of competition, and industries with high levels of marketization. At the regional level, CCE reduction in the eastern region, resource-based cities, and small cities is most significantly affected by APILS. The above results are similar to existing studies in the field of CCE reduction (Guo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Under the conditions of enterprises, industries and regions with different endowments and natures, the impact of APILS on CCE reduction is different. Therefore, it is necessary to tailor the situation to the local conditions and implement targeted policies to achieve the desired outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe implementation of APILS pilot policies has been associated with a reduction in CCE. This has been attributed to the promotion of the corporate green innovation, public environmental demands, and environmental administrative regulation. However, there is a divergence of opinion among academics regarding the factors that contribute to CCE reduction, with differing variables being identified. For example, Yang et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) believed that the impact of energy structure adjustment, efficiency improvement and industrial structure adjustment on CCE reduction is not significant, which is somewhat different from the results of this paper. Ding et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) included transition finance as a mechanism variable in the model, while Yin et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the impact of the interaction of environmental regulation and green finance on CCE reduction. This study aims at examining the extent of the corporate green innovation, public environmental demands, and environmental administrative regulation to the role of CCE reduction. Furthermore, it aims to provide a framework for legal defense of rights and to inspire the formulation of policies that will facilitate the construction of a multilevel, multi-path positive development of the circular mechanism.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSocial trust can strengthen the inhibitory effect of APILS on CCE. Among the current progress of related research, most scholars studied its impact on the development situation of enterprises from the perspective of social trust (Li and Bao, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tian et al, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shen et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A few analyses of its role on green development issues also focused on its impact on the level of green innovation (Ling and Sun, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yin, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and green total factor productivity (Li and Liu, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhong et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The current literature does not address the role of social trust in the relationship between APILS and CCE reduction. This article addresses this gap in the literature by providing a new safeguard idea for the effective implementation of the APILS.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Policy recommendations\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIt is necessary to clarify the actual situation and needs of enterprises in different industries and cities in order to avoid a \u0026ldquo;one-size-fits-all\u0026rdquo; implementation program. The heterogeneity test of this study has revealed that enterprises located in the central and western regions and resource-endowed cities do not have a significant response to the implementation of the policy. Consequently, it is necessary to increase the intensity of their support according to the local conditions and to provide them with more guidance and support. Moreover, the impact of the pilot on different types of industries and groups of enterprises varies considerably. For instance, non-high-tech, low-level marketization and low-degree of competition in the industry by the APILS is not significant, and the role of the opposite enterprises is obvious. In response to this phenomenon, it is necessary for the administration to make specific, detailed and differentiated policy arrangements, introduce development plans for different industries, and coordinate the work of various industries.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe focus has been on the capacity of enterprises to innovate, the environmental demands placed on them by the public, and the intermediary effects of environmental administrative regulations on the internal and external environment of enterprises. It is recommended that enterprises be strongly supported in their efforts to engage in green innovation. This can be achieved by implementing incentives for innovation that are tailored to the specific circumstances of each enterprise, encouraging the adoption of environmental protection technology, and facilitating the advancement of green technology. Additionally, the production process can be improved and the level of pollution control can be enhanced through the implementation of these measures. Furthermore, the public environmental litigation system should be publicized and promoted, and an effective communication channel should be established to facilitate greater participation. It is also necessary to improve the public\u0026rsquo;s understanding of environmental aspects and their participation. Furthermore, local governments should strengthen their supervision of carbon emission behavior. This should include the promulgation of strict carbon emission limitation and supervision measures, as well as the establishment of effective supervision mechanisms. One such mechanism could be the formation of environmental supervision teams to visit enterprises, thus ensuring the effectiveness of environmental governance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe strengthening of social trust and the ensuring of the legality, fairness, transparency and standardization of credit information are both necessary for the construction of a harmonious society. In order to achieve this, the government must take measures to strengthen social trust and enhance public trust in government environmental policies. This can be achieved by strengthening the transparency of government information, disclosing environmental data and improving administrative efficiency. Furthermore, the government should establish a governance system with optimal functionality, administration in accordance with the law, transparency, and efficiency. This will help to prevent a \u0026ldquo;crisis of trust\u0026rdquo; and ensure that the government fulfils its duties. It will also help to cultivate governmental trust resources.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis study presents a theoretical analysis and quantitative evaluation of the impact of the APILS on CCE. The findings provide a valuable reference point for the subsequent improvement and optimization of the APILS. However, due to the limitations of data availability, this study focuses on analyzing the effect of the APILS on the CCE of listed companies in China, and has not yet discussed the effect of the APILS on the CCE of small and medium-sized enterprises. Consequently, in the future, when data are available, we will further take small and medium-sized enterprises as the entry point and conduct a continuous tracking study on the impact of the APILS on CCE.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Conceptualization, X.C.; methodology, S.Z.; software, D.Z. ; validation, S.Z.. and X.C.; formal analysis, X.C.; investigation,D.Z. ; resources, X.C.; data curation, X.C.; writing\u0026mdash;original draft preparation, X.C.; writing\u0026mdash;review and editing, S.Z.; visualization, D.Z.; supervision, D.Z. and S.Z.; project administration, X.C.; funding acquisition, X.C. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eDeclaration of interests☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSequence data that support the findings of this study have been deposited in the China Stock Market \u0026amp; Accounting Research Database. The link is : https://data.csmar.com/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAi H, Tan X, Zhou S, Liu W (2023) The impact of supportive policy for resource-exhausted cities on carbon emission: Evidence from China. 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J Hubei Univ Police 36(4):5\u0026ndash;14\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"external public welfare system supervision, administrative public interest litigation, corporate carbon emissions, social trust","lastPublishedDoi":"10.21203/rs.3.rs-4612053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4612053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe administrative public interest litigation system (APILS) is an important guarantee for environmental public interest protection and an important institutional innovation of external supervision, which has great significance for low-carbon development. This study takes the listed companies from 2000 to 2021 in China as the research samples, and examines the impact of APILS on corporate carbon emissions (CCE). The results show that: (1) The APILS can significantly promote the reduction of CCE. (2) This research conclusion exhibits multidimensional heterogeneity, which varies depending on the industry type, market competition level, city size, and resource attributes. (3) The mechanism test shows that the APILS can promote CCE reduction through three mechanisms: green innovation, public environmental claims and environmental administrative regulation. (4) Further expansive analyses finds that social trust can strengthen the inhibitory effect of the APILS on CCE. The conclusion of this study provides empirical evidence for exploring the role of external public welfare system supervision in promoting CCE reduction.\u003c/p\u003e","manuscriptTitle":"Whether External Public Welfare Can Reduce the Corporate Carbon Emissions——Empirical Evidence Based on the Administrative Public Interest Litigation System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 06:55:51","doi":"10.21203/rs.3.rs-4612053/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17b21227-ca40-4a6a-ad75-471786c7ce46","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-15T19:33:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-12 06:55:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4612053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4612053","identity":"rs-4612053","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}