Institutional Legitimacy and Voluntary Environmental Certification: Diffusion Mechanisms of Green Factory Standards in China

Institutional Legitimacy and Voluntary Environmental Certification: Diffusion Mechanisms of Green Factory Standards in China | 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 Article Institutional Legitimacy and Voluntary Environmental Certification: Diffusion Mechanisms of Green Factory Standards in China YANG WANG, SHAOJING ZHANG, HONGSHENG ZHANG, XINYI JIANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8053608/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract In the context of global sustainable development and the “dual carbon” goals, environmental protection has become a crucial issue that cannot be ignored in the economic development of various countries. In particular, the Pearl River Delta region, as an important base for China’s manufacturing industry, is facing huge pressures and challenges in accelerating transformation and promoting green development. This paper selects the data of A-share manufacturing listed companies in the Pearl River Delta region from 2014 to 2021 as the research sample and uses methods such as the multi-period difference-in-differences model to examine the non-binding impact of green factory certification on non-certified enterprises. Furthermore, through channels such as resource acquisition, market competition, and information disclosure, the study analyzes how green factory certification influences the environmental protection investment behavior of non-certified enterprises. The research finds that green factory certification has a significant positive effect on the environmental investment of non-certified companies. It also reveals an inverted U-shaped relationship between the number of green factories and the environmental investment of non-certified companies. Additionally, the article points out that in regions with weaker mandatory environmental regulations, the non-binding impact of voluntary environmental regulations is more significant. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Social science/Environmental studies The Pearl River Delta region Voluntary Environmental Regulation Green Factory Certification Non-binding Mechanism Investment in Environmental Protection Figures Figure 1 Figure 2 Ⅰ. Introduction Driven by the “dual carbon” goal, the construction of a green manufacturing system has become the core task of the green transformation of the manufacturing industry. As a non-mandatory policy tool, voluntary environmental regulation has been widely applied globally. For example, the “33/50” program and the “Energy Star” program in the United States (Borck and Coglianese 2009), the “Responsible Care” system launched by the chemical industry associations in the United States and Canada (Prakash 2000 ), and the ISO 14001 environmental management system certification standard issued by the International Organization for Standardization (ISO) all confirm their roles in green development. Against this backdrop, since the State Council released the Made in China 2025 in 2015 and listed the “Green Manufacturing Project” as one of its core implementation projects, China has gradually advanced the construction of the green manufacturing system (Chen et al 2025 ). In 2016, the Ministry of Industry and Information Technology (MIIT) issued the Notice on the Development of Green Manufacturing System , which laid the foundational framework for the construction of the green manufacturing system (Chen et al 2025 ). Since 2017, green factory certification has been implemented annually. As a typical representative of voluntary environmental regulation in China, it promotes the green transformation of industries through the non-binding mechanism that combines corporate self-declaration with policy incentives. Existing research on voluntary environmental regulation mostly focuses on certified enterprises themselves, exploring the direct impact of regulation on internal environmental protection behaviors and performance. Some research has been extended to the macro-effects of environmental regulation, but still takes mandatory supervision as the core analysis object, focusing on the implementation effects of binding policies such as administrative orders and sewage charges. Neither type of research has adequately recognized the unique value embedded in the “non-binding” nature of voluntary regulation—that is, their spillover effect on the behaviors of non-certified enterprises, which makes it difficult to fully reveal the regulatory impact within industrial clusters. As the core of manufacturing agglomeration and the engine of economic growth in China, the Pearl River Delta (PRD) region not only features a high concentration of industries such as electronics and machinery, but also has long faced the prominent contradiction of the coexistence of “high production capacity and high pollution”. This dual attribute of “high industrial density and high environmental pressure” makes it an ideal setting for testing the spillover effect of voluntary environmental regulation. On the one hand, the rapid information transmission and strong competitive interaction among enterprises within the industrial cluster provide natural conditions for the spillover of non-binding mechanisms. On the other hand, the need to balance environmental protection demands and economic development in the region also makes it more practically significant to explore the environmental protection investment behaviors of uncertified enterprises. At present, the number of green factory certified enterprises has been continuously increasing since the implementation of the policy, providing a typical scenario for exploring the regional effects of voluntary environmental regulation. Based on the above, this paper focuses on the non-binding mechanism of voluntary environmental regulation in the PRD region, taking green factory certification as the research subject. It further selects A-share manufacturing listed companies in the PRD region from 2014 to 2021 as the research sample. Drawing on theories of institutional change, externality, and information asymmetry, this study utilizes methods such as the multi-period difference-in-differences model to conduct an in-depth analysis of the impact of green factory certification on the environmental protection investment by non-certified enterprises in the PRD region. Studying the non-binding mechanism of voluntary environmental regulation, represented by green factory certification, in the PRD region helps to systematically reveal the intrinsic mechanisms, implementation effects, and potential issues of this regulatory model in regional development. This research not only rich the exploration of existing literature on the non-binding effects of environmental regulation but also further expands the research boundaries of environmental regulation theory, providing new perspectives and ideas for subsequent related studies. Moreover, it can offer empirical evidence for the government to optimize environmental policies and stimulate the endogenous motivation of enterprises for green transformation, contributing to the collaborative development of the regional economy and ecological environment. Ⅱ. Literature Review 2.1 The Effects and Driving Factors of Voluntary Environmental Regulations As a non-mandatory policy tool, the core value of voluntary environmental regulation lies in guiding enterprises to internalize environmental externalities through flexible mechanisms. Existing research has primarily focused on its effects and driving factors. In terms of policy impact, current findings have concentrated on validating the positive effects of voluntary regulation on corporate environmental behavior and values. Some studies have also addressed the “non-binding” nature of the policy—that is, promoting corporate action through signal transmission and incentives, rather than through mandatory requirements. For example, the ISO14001 certification improves the efficiency of enterprises’ waste management and reduces pollution emissions through the organizational learning (Franchetti 2011 , Mungai et al 2020), and drives green innovation through the demand for green reputation (Ren et al 2018 ). The government's energy-saving procurement policy, through the demand-side signal, encourages enterprises to actively optimize the energy structure to obtain policy dividends (Bu and Zhao 2022 ). In the economic value dimension, voluntary regulation can alleviate enterprises' financing constraints (Luo and Yu 2024 , Zheng et al 2024 ) and enhance the value of social responsibility information disclosure (Chijoke-Mgbam et al 2020 ). These effects all rely on the non-binding feature of enterprises can obtain benefits by voluntarily participating, providing a mechanism reference for the subsequent analysis of the role of green factory certification. At the driving factor level, the interaction between external pressures and internal characteristics determines the willingness of enterprises to respond to non-binding mechanisms. On the external side, government regulation compels enterprises to participate through differences in compliance costs (Khanna and Anton 2002 ), while public attention amplifies the attractiveness of non-binding regulations through reputation risks (Chen and Chen 2022). On the internal side, CEO openness will strengthen the acceptance of non-binding regulations by enterprises (Luo and Yu 2024 ), while the self-interest motivation of senior executives may weaken its effect (Cui and Jiang 2020 ). The maturity and profitability of enterprises determine their resource capabilities to participate in non-binding regulations (Zheng et al 2024 ). The organizational culture of enterprises also has a subtle influence on whether enterprises voluntarily carry out environmental governance (Howard-Grenville 2006 ). These studies provide variable references for analyzing why non-certified enterprises respond to the green factory certification signal, but have not yet extended to the group of non-certified enterprises. Table 1 summarizes the main research results of the above-mentioned related literature. Table 1 The Effects and Driving Factors of Voluntary Environmental Regulations No. Research perspective Authors Main content 1 Environmental performance Franchetti, 2011 ; Mungai et al., 2020; Bu & Zhao, 2022 ; Voluntary environmental regulation can reduce pollution emissions and improve the environmental performance of enterprises 2 Enterprise performance Chijoke-Mgbame et al., 2020; Voluntary environmental regulation can have a positive impact on enterprise performance, among which the green management and social responsibility information disclosure of enterprises play an important role. 3 Green innovation Luo & Yu, 2024 ; Ren et al., 2018 ; Voluntary environmental regulation can promote green technological innovation in enterprises, but the strength of mandatory laws will weaken its effects. 4 External factors Chen & Chen, 2022; Khanna &Anton, 2002 ; Policy pressure plays a significant role in promoting enterprises’ participation in voluntary environmental regulation. 5 Internal factors Luo & Yu, 2024 ; Chui & Jiang, 2020; Zheng, 2024; Howard-Grenville, 2006 The awareness and decision-making of the management are critical in enterprises’ responses to voluntary environmental regulation. 2.2 International Green Certification Systems and China’s Green Factory Certification In international research, the valuable reference is the design logic of certification systems that is non-mandatory but implementable. For example, As early as 2000, Japan embraced the environmental protection concept of establishing a “circular economy society,” integrating its sustainable development policy into concrete actions(Xinhua News Agency 2000 ). Hitachi Group in Japan has the “Selected Environmentally Friendly Factories & Offices” certification. It guides its subordinate units to reduce environmental load through internal incentives rather than mandatory requirements (Yang and Liu 2017). This “incentive-oriented” non-binding design highly aligns with the logic of “voluntary application + policy support” of the Green Factory Certification. Although South Korea’s green certification system is based on a legal framework, its core still attracts enterprises to participate through the transmission of values such as new town development adaptability (Kim and Park 2014 ) and improvement of public welfare (Lee and Kim 2016 ), rather than administrative coercion. This further verifies the diffusion path of non-binding mechanisms. Domestic research has focused on the non-binding effects of green factory certification, and some of the research results have already touched on the potential for “diffusion”. For example, green factory certification alleviates the financial pressure of enterprises’ green transformation through policy subsidies (Cui and Shi 2025, Dai et al 2024). It also improves the quality of enterprises' environmental information disclosure through the “certification signal” (Wang et al 2024 ), promotes breakthroughs in innovation quality (Zhu et al 2023), and even helps enterprises break through international environmental barriers (Su et al 2025 , Yu et al 2024 , Zhang et al 2024 ). These studies have clarified the non-binding value of green factory certification for certified enterprises, but have not yet explored whether the signal of certified enterprises will spread to non-certified enterprises, that is, the “spillover and diffusion” effect of the non-binding mechanism. Table 2 summarizes the main research results of the above-mentioned related literature. Table 2 International Green Certification Systems and China’s Green Factory Certification No. Research perspective Authors Main content 1 South Korea’s “green certification system” Yang & Liu, 2017; Kim & Park, 2014 ; Lee & Kim, 2016 ; The green certification system in South Korea is applicable and practical in new city development and also promotes low-carbon cities and public welfare. 2 Japan’s “Circular Economy Society” Xinhua News Agency, 2000 ; Yang & Liu, 2017; Japan embraced the environmental protection concept of establishing a “circular economy society,” integrating its sustainable development policy into concrete actions. 3 China’s “green factory certification” Chui & Shi, 2025; Dai et al., 2024; Wang et al., 2024 ; Zhu et al., 2023; Zhang, 2024; Su, 2024; Yu et al., ( 2024 ) In China, green factory certification, as an important tool of voluntary environmental regulation, has promoted the construction of the green manufacturing system. 2.3 Research on the Adaptability of Regional Environmental Regulation and Voluntary Regulation in the Pearl River Delta The core conclusion of the research on regional environmental regulation is that different regions need to be matched with differentiated regulation models. Relevant studies in the Pearl River Delta (PRD) have clearly identified the limitations of traditional mandatory regulation, providing regional necessity support for the introduction of voluntary regulation. Existing regional studies have already recognized the inapplicability of mandatory regulations in the region. For example, Lei et al. ( 2021 ) pointed out that in the Pearl River Delta, command-based regulations are likely to lead to passive responses from enterprises, and fee-based regulations frequently fail. Only the flexible mechanism of “incentive instead of punitive” is more suitable for the regional needs. Fang et al. ( 2023 ) further found that the industrial structure in the core area of the PRD is highly advanced. Strong regulatory constraints may inhibit innovation, so it is necessary to focus on flexible tools such as green certification. These studies directly provide a regional rationality basis for this paper to select green factory certification in the PRD as the research object, and clarify the premise that voluntary regulation is more suitable for the PRD. Table 3 summarizes the main research results of the above-mentioned related literature. Based on the above research, it is evident that most existing studies take “certified enterprises” as the core and explore the impact of voluntary regulation on themselves. However, it has not addressed whether “non-certified enterprises” will respond to the signals of non-binding mechanisms—especially the lack of research on how non-certified enterprises in industrial clusters respond to certification signals through information transmission and competitive interaction. This leaves the spillover effect of non-binding regulations insufficiently explained. Furthermore, the current implementation status, challenges, and potential advantages of voluntary environmental regulation in the PRD have not been fully explored. Additionally, it remains unverified whether these regulations can be adapted to the regional heterogeneity within the PRD. Based on this, this study will take green factory certification as the representative of non-binding mechanisms and focus on non-certified enterprises in the PRD. Through a multi-period difference-in-differences model, the study will empirically test the spillover effect of certification signals on the environmental investment of uncertified enterprises, so as to improve the regional diffusion theory of voluntary environmental regulation. Table 3 The Dynamic Impact of Environmental Regulation in the Pearl River Delta on Green Development No. Research perspective Authors Main content 1 classification and effect Lei et al., 2021 ; The limitations of traditional mandatory regulations provide a theoretical basis for the introduction of voluntary mechanisms. 2 regional heterogeneity Fang et al., 2023 ; The regional heterogeneity within the Pearl River Delta requires that environmental regulation policies have dynamic adaptability. III. Theoretical Foundations and Research Hypothesis 3.1 Theoretical Foundations In the context of the accelerated advancement of the global “dual carbon” goals and the continuous intensification of domestic ecological and environmental governance, corporate green transformation has evolved from a passive response to external pressure into an inevitable strategy for achieving sustainable development. From the perspective of institutional change theory, green factory certification is a government-led formal institutional innovation. It not only clarifies corporate environmental compliance standards through regulatory provisions, but also uses its “certification-incentive-supervision” closed-loop design. This design breaks the traditional perception that “environmental investment = cost burden.” It encourages enterprises to shift from passively meeting environmental bottom lines to proactively embedding green concepts throughout the entire production, R&D, and supply chain management processes. According to the externality theory, this certification enhances the returns of environmental protection investment through positive incentives such as tax relief and green credit. At the same time, relying on restrictive measures such as public supervision and environmental credit evaluation, it makes the costs of violations explicit. These dual mechanisms address the market failure where “the negative environmental externalities are borne by society” and promote the “internalization of external costs” to improve environmental performance. Based on information asymmetry theory, certification serves as an authoritative third-party evaluation system recognized by the state. By requiring companies to disclose environmental goals and technological pathways, and by quantifying “environmental efforts” through standardized indicators such as energy consumption per unit product and pollutant emission intensity, it assigns a “green credibility label” to businesses. This effectively reduces the information barriers between companies, investors, consumers, and regulatory bodies, providing crucial endorsement for obtaining external resources and trust. 3.2 Research Hypothesis Based on the integration and deepening of the above three theories, this paper breaks through the limitations of a single perspective and constructs a theoretical analysis framework of four-dimensional linkage of “institutional promotion—resource acquisition—market competition—information disclosure”. It systematically explains the transmission path and internal logic of the green factory certification on the environmental protection investment behavior of non-certified enterprises. Based on this, the following four research hypotheses are proposed: H1: Green factory certification can significantly encourage non-certified companies to increase their investment in environmental protection. The benchmark effect in institutional change theory indicates that when a formal institution is promoted by the government and granted legitimacy, it will generate strong normative pressure and imitation pressure. On the one hand, as the pioneers of the system in the industry, the certified enterprises can obtain policy recognition and social reputation through certification, which will become a reference benchmark for non-certified enterprises. This positions them as reference benchmarks for non-certified firms, fostering among the latter a perception that failure to emulate these leaders may result in being perceived as non-compliant. On the other hand, when local governments implement environmental protection policies, they often take the standards of certified enterprises as an “implicit threshold” (e.g., giving priority to certified enterprises in government procurement and project approval). This further strengthens the imitation motivation of non-certified enterprises. Eventually, this prompts them to increase environmental protection investment to conform to the certification standards. H2: Green factory certification can help companies improve resource acquisition and enhance non-certified companies’ willingness to invest in environmental protection. Based on the “cost-benefit reconstruction” logic of the externality theory, the green factory certification is essentially a process of resource redistribution within the industry. To encourage enterprises to participate in the certification, the government usually tilts limited policy resources (such as special subsidies, tax exemptions, and low-interest loans) towards certified enterprises (Nie and Wang 2025 ). This directly changes the cost-benefit structure of non-certified enterprises: if they do not make environmental protection investments, non-certified enterprises not only cannot obtain the above-mentioned resources but also have to bear higher environmental compliance costs (such as fines and production restrictions). In addition, certified companies will also demonstrate stronger competitiveness in the supply chain, making suppliers more inclined to establish long-term and stable partnerships with them. This differentiated resource allocation will significantly enhance the environmental protection investment willingness of non-certified enterprises and encourage them to actively invest in green projects such as equipment upgrades and technological research and development. H3: The resource advantages of certified companies intensify competition pressure within the local market, forcing non-certified companies to increase environmental protection investment to maintain their market position. On the premise that resources such as customers, channels, and supply chain cooperation opportunities in the regional market are limited, certified enterprises will form an obvious competitive barrier by virtue of the resource advantages brought by the certification. At the same time, green-certified enterprises are increasingly favored by consumers for their products that meet environmental protection standards and occupy a larger share in the market.This trend has progressively reduced the market space available to non-certified enterprises, placing their survival and development under unprecedented challenges. To maintain competitiveness in the intense market competition, non-certified companies are compelled to increase their investment in environmental protection, enhancing their environmental technologies and production capabilities to avoid being eliminated from the market. H4: The information disclosure of certified companies reduces the environmental trial-and-error costs of non-certified companies and promotes their imitation and innovation. High-quality environmental information disclosure can enhance the reputation of enterprises (Deegan and Rankin 1996 ) and reduce information asymmetry (Leuz and Verrecchia 2000). The public knowledge spillover effect in information asymmetry theory suggests that the environmental information disclosed by certified enterprises during and after the certification process becomes a public knowledge resource within the industry. For non-certified enterprises, one of the biggest risks of environmental investment is the trial-and-error cost. The information disclosure by certified enterprises is equivalent to providing ready-made learning samples for non-certified enterprises. By accessing the environmental practices and information shared by certified enterprises, non-certified firms can reduce this risk, making it easier for them to adopt effective and proven environmental strategies without the need to experiment from scratch. This kind of low-cost learning will prompt non-certified enterprises to imitate and innovate in environmental protection investment more quickly, and accelerate their green transformation process. Figure 1 illustrates the proposed impact mechanism. Ⅳ. Empirical Research 4.1 Model Specification and Data Description 4.1.1 Sample Selection This paper takes the A-share manufacturing listed companies in the Pearl River Delta region from 2014 to 2021 as the research sample. To ensure the quality of the research sample and the reliability of the research results, strict screening and processing were carried out on the initial sample: First, enterprises marked as ST or PT were excluded due to their typically severe issues in operational and financial performance, which could distort the results and compromise the conclusion’s accuracy; enterprises with negative net assets were also excluded to avoid the influence of extreme financial conditions on the validity of the study. Since this study focuses on the impact of green factory certification on non-certified enterprises, listed companies that were the first (batch) of green factories in the industry dimension in the Pearl River Delta region were excluded. For enterprises that were not the first (batch) of green factories, the observation values in the year of their selection as a green factory and thereafter were excluded. Additionally, Enterprise samples with missing data on key variables were also excluded to ensure the integrity and continuity of the data. After the above screening steps, a final sample of 460 companies and 2,343 observations was obtained. The green factory data was manually collected and organized through the public list from the Ministry of Industry and Information Technology and the Tianyancha platform. The environmental investment data of the companies was obtained by reviewing the “Projects Under Construction” section in the notes of the annual reports of the listed companies. Relevant new capitalized investments related to environmental protection subjects were manually selected, and the data was then summed and standardized. Other financial data and company characteristics data were sourced from the CSMAR database. To eliminate the influence of extreme values on the research results and ensure the stability and validity of the data, the main continuous variables were winsorized at the 1% percentile at both the upper and lower ends. 4.1.2 Research Variables Dependent variable: Environmental protection investment (EnvInvest). Referring to existing relevant studies, the environmental investment was identified by manually reading the “Projects Under Construction” section in the annual reports and selecting newly capitalized investments related to environmental protection subjects such as dust removal, desulfurization, denitrification, wastewater, waste gas, and solid waste treatment, water and energy conservation, clean energy utilization, and waste heat and pressure recovery. The new capitalized investments for each subject were summed up and divided by the company’s total assets, and then the result was multiplied by 100 to serve as an indicator for measuring the company’s level of environmental investment. This indicator can intuitively and accurately reflect the company’s capital investment intensity in environmental protection, demonstrating the company’s actual investment and attention to environmental protection. Independent variable: Green factory certification (Green). In the current year and subsequent years when green factory enterprises emerge in the industry dimension in the Pearl River Delta region, Green is assigned a value of 1; otherwise, it is 0. This variable is used to precisely capture the time point and scope of the impact of green factory certification on non-certified enterprises. By comparing the changes in non-certified enterprises before and after certification, the mechanisms and effects of green factory certification are deeply revealed. Control variables: Capital structure (Lev), company size (Size), profitability (ROE), government attention (Pollution), degree of financialization (Fin), commercial credit financing (Cred), number of subsidiaries in different locations (SubDiff). The specific definitions of these variables are provided in Table 4 . Table 4 Major Variables Type Name Symbol Description Dependent variable Environmental protection investment EnvInvest The new capitalized investments for each subject were summed up and divided by the company’s total assets, and then the result was multiplied by 100. Independent variable Green factory certification Green In the current year and subsequent years when green factory enterprises emerge in the industry dimension, Green is assigned a value of 1; otherwise, it is 0. Control variables Capital structure Lev debt-to-asset ratio company size Size the natural logarithm of the total assets of the enterprise profitability ROE return on equity government attention Pollution If the enterprise belongs to a heavily polluting industry, the value is 1; otherwise, it is 0. degree of financialization Fin the proportion of financial assets in total assets commercial credit financing Cred the ratio of accounts payable, advance receipts, and notes payable to total assets number of subsidiaries in different locations SubDiff the natural logarithm of the number of subsidiaries in different locations 4.1.3 Model Specification To accurately examine the impact of green factory certification on the environmental protection investment of non-certified enterprises in the Pearl River Delta region, the following multi-period difference-in-differences model is set: In this model, i represents the enterprise, j represents the industry, t represents the year, and k represents the city. EnvInvest indicates the environmental protection investment level of the enterprise; Green jt is the core explanatory variable, representing whether there are green factory enterprises in the j industry in the t year; X it represents a series of control variables, including the previously mentioned capital structure, enterprise scale, and other various factors that affect the environmental protection investment of the enterprise; µ i represents the company fixed effect, used to control individual differences in company characteristics, such as management level, corporate culture, and other factors that do not change over time; ν jt represents the industry-year fixed effect, controlling for common trends across industries over time and industry-specific annual shocks, such as technological advances and policy changes within the industry; η kt represents the city-year fixed effect; ε it is the random error term. By constructing this model, the interference of other factors can be effectively controlled, and the impact of green factory certification on the environmental protection investment of non-certified enterprises can be accurately identified. 4.2 Descriptive Statistical Analysis Table 5 presents the descriptive statistics of the main variables. The mean of environmental investment (EnvInvest) is 0.0829, the median is 0, and the standard deviation is 0.6985. This indicates that environmental protection investment among companies in the Pearl River Delta region is at a relatively low level. The mean of green factory certification (Green) is 0.4942, suggesting that 49.42% of the companies in the sample have been impacted by the certification policy. At the same time, the values of the control variables fall within a reasonable range and are generally consistent with existing studies. Table 5 Descriptive statistical results VarName Obs Mean SD Min Median Max EnvInvest 2343 0.0829 0.6985 0.0000 0.0000 13.9953 Green 2343 0.4942 0.5001 0.0000 0.0000 1.0000 Size 2343 21.8856 1.1441 19.1168 21.7107 25.5054 Lev 2343 0.3859 0.1791 0.0532 0.3804 0.8838 ROE 2343 0.0702 0.1408 -0.6432 0.0808 0.3899 Pollution 2343 0.1669 0.3729 0.0000 0.0000 1.0000 Fin 2343 0.0921 0.1097 0.0000 0.0487 0.5090 Cred 2343 0.1620 0.1069 0.0096 0.1372 0.4827 SubDiff 2343 1.8767 1.0229 0.0000 1.7918 6.6107 4.3 Benchmark Regression Analysis To examine whether green factory certification has a positive impact on corporate environmental protection investment, this paper uses a stepwise regression method for analysis. The regression results are presented in Table 6 . Column (1) does not include control variables but only accounts for fixed effects of enterprises and years. The findings indicate that the impact of green factory certification on enterprises’ environmental protection investment is significantly positive. In column (2), city-year fixed effects and industry-year fixed effects are added based on column (1). In column (3), control variables are further included. The coefficient of the core explanatory variable, Green, remains significantly positive, validating hypothesis 1. At the economic significance level, taking the results of column (3) as an example, the Green coefficient of 0.1768 means that the green factory certification has increased the environmental protection investment level of non-certified enterprises in the same industry by approximately 17.68%. At the same time, considering the mean of EnvInvest from Table 5 , which is 0.0829, this policy shock effect is equivalent to doubling the average environmental investment, highlighting the non-binding impact of green factory certification. Table 6 Benchmark regression results (1) (2) (3) Green 0.1816 * (0.0994) 0.1640 ** (0.0764) 0.1768 ** (0.0792) Control Firm Year City-year Industry-year No Yes Yes No No No Yes No Yes Yes Yes Yes No Yes Yes N R-squared 2274 0.0069 2240 0.0029 2240 0.0153 Notes: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. The values in parentheses are robust standard errors. The same is below. 4.4 Robustness Tests 4.4.1 Parallel Trend Test This paper conducts a robustness analysis using methods such as parallel trend tests, Heckman tests, alternative the measurement of the dependent variable, and other testing methods. Assume that without the green factory certification policy, the environmental investment levels of certified and non-certified enterprises would change along similar trends and there would be no systematic differences. To test whether the parallel trend assumption holds, the following model is set: This paper takes the year before the implementation of the green factory certification policy as the base period ( Green − 1 ). In Fig. 2, aft0 represents the current period of policy implementation, and this figure is within the confidence interval at the 90% level. In the model setting, θ t is the fixed effect of the year, and the definitions of other variables are consistent with Eq. ( 1 ). The coefficient of concern in this paper is β a , which represents the difference in the level of environmental investment between the treatment group enterprises and the control group enterprises. It can be seen from the figure that when a < 0, the coefficients in each period are not significant. This indicates that before the emergence of green factory enterprises, there is no significant difference in the level of environmental investment between the treatment group and the control group enterprises. The parallel trend assumption is verified to be valid. 4.4.2 Heckman Two-Stage Regression and Alternative Measurement of the Dependent Variable (1) Heckman two-stage regression. The Heckman two-stage regression is used to mitigate the problem of sample selection bias caused by selecting only listed company samples. This analysis utilizes the industrial enterprise database of CSMAR spanning from 2014 to 2021. The first stage uses this dataset, with the dependent variable being whether the company is listed in a given year. The explanatory variables include total assets, sales return rate, and company age. With fixed effects for the year, industry, and province controlled, Probit estimation is then performed to obtain the Inverse Mills Ratio (IMR). In the second stage, IMR was incorporated into Eq. ( 1 ) as a control variable. The regression results of the second stage are reported in column (1) of Table 7 . The coefficient of IMR is not significant, indicating that there is no sample selection bias problem. Moreover, after controlling for IMR, the coefficient of Green is significant at the 5% level, indicating that the results are still robust. (2) Alternative measurement of the dependent variable. First, the environmental investment amount of the company is standardized using the main business income (Env_1). Second, the pollution discharge fees, greening fees, and other items in management expenses are also included in the company’s environmental investment amount (Env_2). The regression results shown in columns (2) and (3) of Table 7 indicate that the conclusions remain unchanged. Table 7 Heckman Two-Stage Regression and Alternative Measurement of the Dependent Variable Heckman two-Stage regression Alternative measurement of the dependent variable (1) (2) (3) EnvInvest Env_1 Env_2 Green 0.1793 ** (0.0791) 0.5133 ** (0.2556) 0.1798 ** (0.0803) IMR 0.2686 (0.2415) _cons -0.2243 (3.0230) 6.0001 (7.5927) 2.0864 (2.6533) Control Yes Yes Yes Firm Yes Yes Yes City-year Yes Yes Yes Industry-year Yes Yes Yes N 2240 2240 2240 R-squared 0.0174 0.0156 0.0155 4.4.3 Other Robustness Tests The specific verifications are carried out from the following aspects: Sensitivity test of the shock time. First, if the list of green factory enterprises is announced in the second half of the year, the shock start time is calculated from the next year, and the green factory certification shock variable Green_1 is reconstructed accordingly. Second, the official announcement time point of green factory enterprises is taken as the emergence node of green factory enterprises, and the shock variable Green_2 is reconstructed. Eliminate enterprises whose registration location or industry affiliation has changed during the observation period. Eliminate all the observation values of enterprises that have been rated as green factories during the observation period. In the construction of the green manufacturing system, the certification work at the enterprise level, in addition to the green factory certification, also includes the “green supply chain management enterprise demonstration” certification. The green supply chain management demonstration enterprises in the sample are eliminated for estimation. The results reported in Table 8 indicate that the conclusion is reliable. Table 8 Other Robustness Tests (1) (2) (3) (4) (5) Sensitivity test of the shock time Sensitivity test of the shock time Eliminate enterprises whose location or industry has changed Eliminate all the observed values of green factory enterprises Eliminate the demonstration impact of green supply chain management Green_1 0.1139 * (0.0642) Green_2 0.1851 ** (0.0838) Green 0.1630 ** (0.0689) 0.1806 ** (0.0806) 0.1808 ** (0.0807) Control Yes Yes Yes Yes Yes Firm Yes Yes Yes Yes Yes City-year Yes Yes Yes Yes Yes Industry-year Yes Yes Yes Yes Yes N 2240 2240 2105 2174 2187 R-squared 0.0130 0.0154 0.0159 0.0154 0.0161 4.5 Mechanism Analysis 4.5.1 Resource Acquisition of Enterprise Based on the data of A-share manufacturing listed companies in the Pearl River Delta region from 2014 to 2021, this study investigates the impact of green factory certification on enterprise resource acquisition. The data screening method is consistent with that used in prior studies, and no samples of green factory enterprises are excluded. The specific model setup is as follows: In this model, Resource it denotes the resource acquisition of enterprises, while Green represents the core explanatory variable. Green takes a value of 1 in the year when the enterprise is certified as a green factory and in all subsequent years, and 0 otherwise. π j , ω i , and σ t respectively represent the fixed effects of the industry, enterprise, and establishment years of the enterprise. The definitions of other variables are consistent with those in Eq. ( 1 ). Table 9 , columns (1) to (4), respectively examine the effects of green factory certification on government subsidy, bank credit, corporate tax burden, and enterprise performance for certified enterprises. Specifically, government subsidies are measured as the ratio of subsidy amounts to total assets; bank credit is defined as the ratio of long-term and short-term borrowings to total assets; corporate tax burden is calculated as the ratio of corporate income tax expenses to operating revenue; and enterprise performance is indicated by Tobin’s Q value. The estimation results show that green factory certification has a significant positive impact on bank credit and enterprise performance and a significant negative impact on corporate tax burden. This improves the enterprises’ resource acquisition and verifies hypothesis 2. However, green factory certification does not have a significant impact on government subsidies. The reason for this result may be the differences in policies among cities in the Pearl River Delta region. The economic development levels, industrial structures, and policy orientations of each city in the Pearl River Delta region are different, and there are also differences in subsidy intensities. This makes the subsidies that enterprises obtain in different cities vary, affecting the consistency and significance of the overall subsidy effect of green factory certification within the region. For example, the subsidy for green factories in Guangzhou is relatively high, while that in Zhongshan is relatively low. This disparity may lead enterprises to be more inclined to apply for certification in regions with high subsidies, while they show low enthusiasm for certification in other regions, thereby weakening the significance of the impact of government subsidies on green factory certification. Table 9 Resource Acquisition of Enterprise (1) (2) (3) (4) Government subsidy Bank credit Corporate tax burden Enterprise performance Green’ 0.0765 (0.0703) 0.0227 *** (0.0085) -0.3241 ** (0.1598) 0.3529 ** (0.1712) N 2423 2443 2440 2411 R-squared 0.0123 0.4552 0.1384 0.0630 4.5.2 Driven by Competitive Pressure This paper reflects the competitive pressure faced by enterprises through the market concentration in the city-industry dimension. It calculates the proportion of the main business income share of the top 4 and top 8 enterprises in the market share in the city-industry dimension to the total main business income of the city-industry (Liu and Hu 2024 ). The results in Table 10 show that the green factory certification is negatively correlated with CR4 and CR8 and is significant, which intensifies market competition, supporting hypothesis 3. Table 10 Competitive Pressure of Enterprise (1) (2) CR4 CR8 Green -0.0223 *** (0.0083) -0.0217 *** (0.0063) N 2240 2240 R-squared 0.0333 0.0759 4.5.3 Perspective of Information Disclosure Information transparency (DA) is used to measure the level of environmental information disclosure of enterprises after being certified as green factories, with the absolute value of discretionary accruals used as its inverse proxy indicator (Liu and Hu 2024 ). The results in column (1) of Table 11 indicate that after being certified as a green factory, the information transparency of enterprises significantly improves, and the level of information disclosure increases. Meanwhile, this paper also examines the impact of information disclosure and information quality on the environmental protection investment of non-certified enterprises by investigating whether the awarded enterprises issue statements and the increment of statement information. The results in columns (2)–(3) of Table 11 show that when the awarded enterprises issue statements, the environmental protection investment of non-certified enterprises significantly increases. The coefficient of the group with a high information increment in column (4) is much higher than that of the group with a low information increment. This indicates that high-quality information disclosure has a more obvious effect on improving the environmental protection investment of non-certified enterprises. In conclusion, enterprises certified as green factories significantly enhance the environmental investment level of non-certified enterprises through high-quality information disclosure, and this effect depends on the improvement in information transparency. Hypothesis 4 is validated. Table 11 Perspective of Information Disclosure (1) (2) (3) (4) (5) Information transparency Statement No statement High information increment Low information increment DA EnvInvest EnvInvest EnvInvest EnvInvest Green’ -0.0239 ** (0.0094) Green 0.1152 * 0.1191 0.3103 ** 0.2009 ** (0.0651) (0.1432) (0.1291) (0.0766) N 2136 2222 457 1097 1569 R-squared 0.1027 0.0131 0.1162 0.0590 0.0210 4.6 Heterogeneity Analysis 4.6.1 Enterprise Scale Large-scale enterprises typically possess advantages in resource endowment, including more substantial financial resources and technical reserves. This enables them to translate green factory certification into tangible environmental protection investments, such as upgrading pollution control equipment and adopting clean production technologies. Small-scale enterprises may be constrained by limited funds, outdated technology, and insufficient management capabilities. This makes it difficult for them to effectively translate green factory certification into actual environmental actions. As a result, the effect of environmental investment protection improvement is not as good as that of large-scale enterprises. To explore whether the impact of green factory certification on environmental protection investment varies by enterprise size, this study divides the sample into large-scale and small-scale enterprise groups based on the median enterprise size for group testing (Su et al 2025 ). The results, as shown in Table 12 , indicate that green factory certification significantly enhances environmental investment in large-scale enterprises, consistent with the previous analysis. Table 12 Enterprise Scale (1) (2) Large-scale enterprises Small-scale enterprises Green 0.1632 ** -0.0162 (0.0676) (0.0337) _cons 4.2555 -2.8722 (7.3678) (2.3563) Control Yes Yes Firm Yes Yes City-year Yes Yes Industry-year Yes Yes N 1066 1030 R-squared 0.0228 0.0197 4.6.2 Industry Nature To examine the industry heterogeneity effect of green factory certification on the environmental investment of non-certified enterprises, this paper categorizes the samples into three groups: the overall industry, heavy-pollution industries, and non-heavy-pollution industries. Furthermore, according to the different nature of “construction in progress” projects, enterprise environmental investment is classified into emission reduction investment ( EnvInv_em ) and energy-saving investment ( EnvInv_en ) (Liu and Hu 2024 ). The results in Table 13 show that in the overall industry sample, the green factory certification mainly increases the energy-saving investment of non-certified enterprises. In heavy-pollution industries, green factory certification also has a certain promoting effect on energy-saving investments. However, in non-heavy-pollution industries, the impact on both emission reduction and energy-saving investments is not significant. This may be because heavy-pollution industries face greater environmental regulatory pressure, giving them more incentive to draw on the experience of green factory certification to enhance environmental investments. In contrast, non-heavy-pollution industries face relatively lower environmental pressures, and the demonstration effect of green factory certification is weaker. Table 13 Industry Nature Overall industry Heavy-pollution industries non-heavy-pollution industries (1) (2) (3) (4) (5) (6) EnvInv_em EnvInv_en EnvInv_em EnvInv_en EnvInv_em EnvInv_en Green 0.0213 0.1487 ** -0.0131 0.3264 * 0.0390 0.0240 (0.0184) (0.0731) (0.0242) (0.1761) (0.0235) (0.0284) _cons -0.1232 2.0904 1.1137 7.1502 -0.2362 1.0162 (0.3831) (2.4028) (1.1569) (5.4760) (0.3484) (2.3767) Control Yes Yes Yes Yes Yes Yes Firm Yes Yes Yes Yes Yes Yes City-year Yes Yes Yes Yes Yes Yes Industry-year Yes Yes Yes Yes Yes Yes N 2240 2240 424 424 1790 1790 R-squared 0.0087 0.0158 0.0807 0.1665 0.0179 0.0091 Ⅴ. Extended Results 5.1 The Impact of the Number of Green Factories To explore the dynamic impact of the number of green factories on environmental protection investment in non-certified enterprises, this study constructs the variable Green_Depth (by adding one to the number of green factories within a city-industry dimension and then taking the logarithm), and also introduces its squared term to test the nonlinear relationship between the two. The results in column (1) of Table 14 indicate that, in the early stages, an increase in the number of green factory enterprises within a city industry significantly promotes environmental protection investment in non-certified enterprises. After introducing the squared term of Green_Depth , the results in column (2) show that the coefficient of the squared term is significantly negative. The U-test’s extremum point is 2.32, which lies within the range of Green_Depth values. This indicates that there is an inverted U-shaped relationship between the number of green factories and environmental protection investment by enterprises. That is, once the number of green factories reaches a certain level, environmental investment by non-certified enterprises tends to decrease, possibly due to factors such as market competition saturation and changes in resource allocation. Table 14 The Impact of the Number of Green Factories (1) (2) Green_Depth 0.1095 * 0.2420 ** (0.0608) (0.1124) Green_Depth 2 -0.0521 ** (0.0240) N 2240 2240 R-squared 0.0140 0.0159 5.2 Evaluation of the Binding Mechanism Effect of Green Factory Certification This part further assesses the effect of the above-mentioned binding mechanism. The model is now set as follows: The dataset corresponding to Eq. ( 3 ) was utilized to analyze the impact of green factory certification. To avoid the influence of non-binding mechanisms on the estimation results, non-certified enterprises within the same city industry of green factory enterprises were eliminated before the regression. The results in column (1) of Table 15 indicate that there was no significant increase in enterprises’ environmental protection investment after obtaining certification. However, firms that had prior environmental protection investments were more likely to be selected as green factories. This potential selection bias may affect the reliability of the conclusion and result in an underestimation of the binding mechanism’s effect. To address this, two treatment measures were taken in this paper: first, enterprises that had made environmental protection investments in the three years before being selected as green factories were excluded from the treatment group; second, the time trend term of the environmental protection investment predetermined variable with propensity score matching was included and controlled. After these treatments, the coefficient of Green remains insignificant, indicating that the restrictive mechanism of green factory certification has not effectively played a role. Table 15 Evaluation of the Binding Mechanism Effect of Green Factory Certification (1) (2) (3) Eliminate enterprises affected by the spillover effect Eliminate enterprises that had made environmental protection investments in the three years before being selected Add the time trend term of the predetermined variable Green' -0.3689 0.1161 -0.4769 (0.4309) (0.1205) (0.4631) _cons 6.3058 6.0191 361.8354 ** (5.2055) (5.5198) (179.0432) Control Yes Yes Yes Firm Yes Yes Yes City-year Yes Yes Yes Industry-year Yes Yes Yes N 683 622 564 R-squared 0.1379 0.0987 0.3640 5.3 Dynamic Synergistic Effect of Binding Mechanism and Non-binding Mechanism Most of the existing literature focuses on the impact of the binding mechanism of mandatory environmental regulations on green development. As confirmed in prior studies, the existence of voluntary environmental regulations with non-binding mechanisms has been established. Based on this, this paper further explores the non-binding effects under different intensities of mandatory environmental regulations. Firstly, the dummy variable ER1 is defined as follows. Using the ratio of “completed investment in industrial pollution control to industrial added value,” if this ratio is not lower than the annual median, the variable is assigned a value of 1; otherwise, it is assigned a value of 0. Secondly, referring to the study by Chen and Chen ( 2018 ), the intensity of environmental regulation (ER2) is quantified by calculating the proportion of word frequency related to environmental regulation relative to the total word frequency in each city’s government work report. In selecting the vocabulary, this study refers to the research by Liu and Hu ( 2024 ) and is based on the government work report texts of cities in the Pearl River Delta region. In conjunction with the core requirements of the Implementation Guide for Green Manufacturing Engineering (2016–2020) and Made in China 2025 , new energy efficiency-related vocabulary such as “energy conservation,” “circular economy,” and “clean production” has been added to the existing keywords related to pollution reduction and emission control. The empirical results in Table 16 show that in areas with a lower intensity of mandatory environmental regulation, the non-binding influence of voluntary environmental regulation is more prominent. Table 16 Non-binding Impacts under Different Intensities of Environmental Regulation ER1 ER2 (1) (2) (1) (2) The low intensity of environmental regulation High intensity of environmental regulation The low intensity of environmental regulation High intensity of environmental regulation Green 0.1631 ** 0.3093 0.2123 * 0.0594 (0.0666) (0.1909) (0.1166) (0.1982) _cons 0.1139 5.0786 1.4844 -0.4272 (5.8823) (5.5381) (2.3520) (2.0404) Control Yes Yes Yes Yes Firm Yes Yes Yes Yes City-year Yes Yes Yes Yes Industry-year Yes Yes Yes Yes N 1537 599 1664 231 R-squared 0.0119 0.0621 0.0303 0.0505 VI. Conclusions and Policy Implications 6.1 Conclusions This study finds strong evidence that voluntary green factory certification exerts a positive spillover effect on non-certified firms’ environmental investments, confirming the proposed diffusion mechanisms. In particular, we find support for the resource acquisition, market competition, and information disclosure channels. Additionally, an interesting non-linear effect emerged: as the number of certified firms in an industry grows, the marginal influence on other firms follows an inverted-U pattern. Finally, the influence of voluntary regulation is more pronounced where mandatory regulation is weaker, indicating a complementary dynamic. From a theoretical perspective, this study makes contributions to two core academic fields. First, it enriches the literature on institutional legitimacy. The research shows that voluntary certification, as a non-mandatory institutional practice, can promote the legitimacy-seeking behaviors of uncertified enterprises through spillover effects. This expands the academic understanding of how informal institutions influence corporate environmental strategies. Second, it advances the research in the field of the diffusion of voluntary standards. By identifying three specific transmission paths, namely resource acquisition, market competition, and information disclosure, as well as an inverted U-shaped non-linear diffusion pattern, it improves the existing linear diffusion analysis framework. This study also has limitations that need to be noted. First of all, the analysis focuses on a specific type of green factory certification, and the research conclusions may not be fully generalized to other voluntary environmental standards (such as product-level certifications). Secondly, due to the limitation of data availability, the study fails to capture the internal micro-decision-making process of uncertified enterprises, and this process may further clarify how the spillover effect is transformed into actual environmental investment. 6.2 Policy Implications 6.2.1 Construct a Differentiated Collaborative Governance System of “Voluntary + Mandatory” Based on the research conclusion that in regions with relatively weak mandatory supervision, the influence of voluntary certification is more significant, it is necessary to design a differentiated governance combination according to the differences in supervision intensity among different cities in the Pearl River Delta. In regions where the mandatory supervision system is not yet perfect, take the green factory certification as a core supplementary tool. Through incentives such as tax preferences and green credit, enhance the enthusiasm of enterprises to participate in voluntary certification. In regions with more mature supervision, promote the connection between voluntary certification and mandatory standards (such as incorporating the certification results into the enterprise environmental credit evaluation), so as to form a collaborative pattern of voluntary practices deepening the effect of mandatory measures and mandatory supervision ensuring the quality of voluntary actions. 6.2.2 Improve the Whole-process Supervision of Certification Focusing on the core goal of ensuring that the spillover effect of certification is effectively converted into corporate environmental investments, two regulatory mechanisms should be optimized: First, strengthen the government’s dynamic supervision. Regularly monitor the core indicators of certified enterprises such as environmental protection investment and pollutant emissions, and link the monitoring results with policy incentives to avoid the post-certification decline. Second, build a cross-regional information disclosure platform and force certified enterprises to publicly disclose key information such as the application of environmental protection technologies and cost-benefit. This not only echoes the information disclosure transmission path confirmed in this study, but also provides a practical reference sample for uncertified enterprises. At the same time, introduce the supervision of the public and industry associations, and establish a closed loop of “certification-disclosure-supervision-feedback” to ensure the precise implementation of policies. Declarations Declaration of conflicting interest The authors declare that there is no conflict of interest. Funding No funding was received for this work. Author Contribution YANG WANG and SHAOJING ZHANG wrote the main manuscript text and HONGSHENG ZHANG and XINYI JIANG prepared figures 1-3. All authors reviewed the manuscript. Acknowledgements We would like to express our sincere acknowledgements to all the individuals and institutions who have contributed to this research project. Data Availability The data from our surveillance are not available in the public domain due to privacy and ethical restrictions, but anyone interested in using the data for scientific purposes is free to request permission from the corresponding authors. Email: [email protected] . References Borck J C and Coglianese C 2009 Voluntary environmental programs: assessing their effectiveness Annu. Rev. Environ. Resour. 34 305–24 Bu X and Zhao L 2022 Voluntary Environmental Regulations and Enterprise Pollution Emission: Based on the Empirical Test of Government Energy-saving Procurement Policy Financ. Econ. Res. 48 49–63 Chen L, Wang W and Gao T 2025 One Firm’s Green, Another Firm’s Mean: Green Factory Certification and Corporate Debt Costs J. Clean. 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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-8053608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":572723583,"identity":"c2b7f0a2-07e6-44cf-856e-ae6dbfdeec75","order_by":0,"name":"YANG WANG","email":"","orcid":"","institution":"Shanghai Dianji University","correspondingAuthor":false,"prefix":"","firstName":"YANG","middleName":"","lastName":"WANG","suffix":""},{"id":572723584,"identity":"7c68c3f0-ada2-4f77-826b-1e6ac64d4ee6","order_by":1,"name":"SHAOJING 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1","display":"","copyAsset":false,"role":"figure","size":129047,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8053608/v1/3803bc9002067a4c1852c339.png"},{"id":100367089,"identity":"321e4167-c512-4459-bd2c-9fd56e2a8944","added_by":"auto","created_at":"2026-01-16 07:56:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25391,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8053608/v1/5648a6d27eb3962a8181e297.png"},{"id":100546059,"identity":"cdfed608-81c6-4fdc-9198-139c9ca9ea5b","added_by":"auto","created_at":"2026-01-19 07:39:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2142255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8053608/v1/8d895b61-5c06-4e43-83a1-2bd3d1975699.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Institutional Legitimacy and Voluntary Environmental Certification: Diffusion Mechanisms of Green Factory Standards in China","fulltext":[{"header":"Ⅰ. Introduction","content":"\u003cp\u003eDriven by the \u0026ldquo;dual carbon\u0026rdquo; goal, the construction of a green manufacturing system has become the core task of the green transformation of the manufacturing industry. As a non-mandatory policy tool, voluntary environmental regulation has been widely applied globally. For example, the \u0026ldquo;33/50\u0026rdquo; program and the \u0026ldquo;Energy Star\u0026rdquo; program in the United States (Borck and Coglianese 2009), the \u0026ldquo;Responsible Care\u0026rdquo; system launched by the chemical industry associations in the United States and Canada (Prakash \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and the ISO 14001 environmental management system certification standard issued by the International Organization for Standardization (ISO) all confirm their roles in green development. Against this backdrop, since the State Council released the \u003cem\u003eMade in China 2025\u003c/em\u003e in 2015 and listed the \u0026ldquo;Green Manufacturing Project\u0026rdquo; as one of its core implementation projects, China has gradually advanced the construction of the green manufacturing system (Chen et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In 2016, the Ministry of Industry and Information Technology (MIIT) issued the \u003cem\u003eNotice on the Development of Green Manufacturing System\u003c/em\u003e, which laid the foundational framework for the construction of the green manufacturing system (Chen et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Since 2017, green factory certification has been implemented annually. As a typical representative of voluntary environmental regulation in China, it promotes the green transformation of industries through the non-binding mechanism that combines corporate self-declaration with policy incentives.\u003c/p\u003e \u003cp\u003eExisting research on voluntary environmental regulation mostly focuses on certified enterprises themselves, exploring the direct impact of regulation on internal environmental protection behaviors and performance. Some research has been extended to the macro-effects of environmental regulation, but still takes mandatory supervision as the core analysis object, focusing on the implementation effects of binding policies such as administrative orders and sewage charges. Neither type of research has adequately recognized the unique value embedded in the \u0026ldquo;non-binding\u0026rdquo; nature of voluntary regulation\u0026mdash;that is, their spillover effect on the behaviors of non-certified enterprises, which makes it difficult to fully reveal the regulatory impact within industrial clusters. As the core of manufacturing agglomeration and the engine of economic growth in China, the Pearl River Delta (PRD) region not only features a high concentration of industries such as electronics and machinery, but also has long faced the prominent contradiction of the coexistence of \u0026ldquo;high production capacity and high pollution\u0026rdquo;. This dual attribute of \u0026ldquo;high industrial density and high environmental pressure\u0026rdquo; makes it an ideal setting for testing the spillover effect of voluntary environmental regulation. On the one hand, the rapid information transmission and strong competitive interaction among enterprises within the industrial cluster provide natural conditions for the spillover of non-binding mechanisms. On the other hand, the need to balance environmental protection demands and economic development in the region also makes it more practically significant to explore the environmental protection investment behaviors of uncertified enterprises. At present, the number of green factory certified enterprises has been continuously increasing since the implementation of the policy, providing a typical scenario for exploring the regional effects of voluntary environmental regulation.\u003c/p\u003e \u003cp\u003eBased on the above, this paper focuses on the non-binding mechanism of voluntary environmental regulation in the PRD region, taking green factory certification as the research subject. It further selects A-share manufacturing listed companies in the PRD region from 2014 to 2021 as the research sample. Drawing on theories of institutional change, externality, and information asymmetry, this study utilizes methods such as the multi-period difference-in-differences model to conduct an in-depth analysis of the impact of green factory certification on the environmental protection investment by non-certified enterprises in the PRD region. Studying the non-binding mechanism of voluntary environmental regulation, represented by green factory certification, in the PRD region helps to systematically reveal the intrinsic mechanisms, implementation effects, and potential issues of this regulatory model in regional development. This research not only rich the exploration of existing literature on the non-binding effects of environmental regulation but also further expands the research boundaries of environmental regulation theory, providing new perspectives and ideas for subsequent related studies. Moreover, it can offer empirical evidence for the government to optimize environmental policies and stimulate the endogenous motivation of enterprises for green transformation, contributing to the collaborative development of the regional economy and ecological environment.\u003c/p\u003e"},{"header":"Ⅱ. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Effects and Driving Factors of Voluntary Environmental Regulations\u003c/h2\u003e \u003cp\u003eAs a non-mandatory policy tool, the core value of voluntary environmental regulation lies in guiding enterprises to internalize environmental externalities through flexible mechanisms. Existing research has primarily focused on its effects and driving factors. In terms of policy impact, current findings have concentrated on validating the positive effects of voluntary regulation on corporate environmental behavior and values. Some studies have also addressed the “non-binding” nature of the policy—that is, promoting corporate action through signal transmission and incentives, rather than through mandatory requirements. For example, the ISO14001 certification improves the efficiency of enterprises’ waste management and reduces pollution emissions through the organizational learning (Franchetti \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Mungai et al 2020), and drives green innovation through the demand for green reputation (Ren et al \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The government's energy-saving procurement policy, through the demand-side signal, encourages enterprises to actively optimize the energy structure to obtain policy dividends (Bu and Zhao \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the economic value dimension, voluntary regulation can alleviate enterprises' financing constraints (Luo and Yu \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Zheng et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and enhance the value of social responsibility information disclosure (Chijoke-Mgbam et al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These effects all rely on the non-binding feature of enterprises can obtain benefits by voluntarily participating, providing a mechanism reference for the subsequent analysis of the role of green factory certification.\u003c/p\u003e \u003cp\u003eAt the driving factor level, the interaction between external pressures and internal characteristics determines the willingness of enterprises to respond to non-binding mechanisms. On the external side, government regulation compels enterprises to participate through differences in compliance costs (Khanna and Anton \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), while public attention amplifies the attractiveness of non-binding regulations through reputation risks (Chen and Chen 2022). On the internal side, CEO openness will strengthen the acceptance of non-binding regulations by enterprises (Luo and Yu \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while the self-interest motivation of senior executives may weaken its effect (Cui and Jiang \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The maturity and profitability of enterprises determine their resource capabilities to participate in non-binding regulations (Zheng et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The organizational culture of enterprises also has a subtle influence on whether enterprises voluntarily carry out environmental governance (Howard-Grenville \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These studies provide variable references for analyzing why non-certified enterprises respond to the green factory certification signal, but have not yet extended to the group of non-certified enterprises. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the main research results of the above-mentioned related literature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eThe Effects and Driving Factors of Voluntary Environmental Regulations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResearch perspective\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMain content\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental performance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFranchetti, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mungai et al., 2020; Bu \u0026amp; Zhao, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVoluntary environmental regulation can reduce pollution emissions and improve the environmental performance of enterprises\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnterprise performance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChijoke-Mgbame et al., 2020;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVoluntary environmental regulation can have a positive impact on enterprise performance, among which the green management and social responsibility information disclosure of enterprises play an important role.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen innovation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLuo \u0026amp; Yu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ren et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVoluntary environmental regulation can promote green technological innovation in enterprises, but the strength of mandatory laws will weaken its effects.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExternal factors\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChen \u0026amp; Chen, 2022; Khanna \u0026amp;Anton, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicy pressure plays a significant role in promoting enterprises’ participation in voluntary environmental regulation.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal factors\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLuo \u0026amp; Yu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chui \u0026amp; Jiang, 2020; Zheng, 2024; Howard-Grenville, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe awareness and decision-making of the management are critical in enterprises’ responses to voluntary environmental regulation.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 International Green Certification Systems and China’s Green Factory Certification\u003c/h2\u003e \u003cp\u003eIn international research, the valuable reference is the design logic of certification systems that is non-mandatory but implementable. For example, As early as 2000, Japan embraced the environmental protection concept of establishing a “circular economy society,” integrating its sustainable development policy into concrete actions(Xinhua News Agency \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Hitachi Group in Japan has the “Selected Environmentally Friendly Factories \u0026amp; Offices” certification. It guides its subordinate units to reduce environmental load through internal incentives rather than mandatory requirements (Yang and Liu 2017). This “incentive-oriented” non-binding design highly aligns with the logic of “voluntary application + policy support” of the Green Factory Certification. Although South Korea’s green certification system is based on a legal framework, its core still attracts enterprises to participate through the transmission of values such as new town development adaptability (Kim and Park \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and improvement of public welfare (Lee and Kim \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), rather than administrative coercion. This further verifies the diffusion path of non-binding mechanisms.\u003c/p\u003e \u003cp\u003eDomestic research has focused on the non-binding effects of green factory certification, and some of the research results have already touched on the potential for “diffusion”. For example, green factory certification alleviates the financial pressure of enterprises’ green transformation through policy subsidies (Cui and Shi 2025, Dai et al 2024). It also improves the quality of enterprises' environmental information disclosure through the “certification signal” (Wang et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), promotes breakthroughs in innovation quality (Zhu et al 2023), and even helps enterprises break through international environmental barriers (Su et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Yu et al \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Zhang et al \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These studies have clarified the non-binding value of green factory certification for certified enterprises, but have not yet explored whether the signal of certified enterprises will spread to non-certified enterprises, that is, the “spillover and diffusion” effect of the non-binding mechanism. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the main research results of the above-mentioned related literature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eInternational Green Certification Systems and China’s Green Factory Certification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResearch perspective\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMain content\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea’s “green certification system”\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYang \u0026amp;\u0026nbsp;Liu, 2017; Kim \u0026amp; Park, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lee \u0026amp; Kim, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe green certification system in South Korea is applicable and practical in new city development and also promotes low-carbon cities and public welfare.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan’s “Circular Economy Society”\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXinhua News Agency, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Yang \u0026amp;\u0026nbsp;Liu, 2017;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJapan embraced the environmental protection concept of establishing a “circular economy society,” integrating its sustainable development policy into concrete actions.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina’s “green factory certification”\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChui \u0026amp; Shi, 2025; Dai et al., 2024; Wang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhu et al., 2023; Zhang, 2024; Su, 2024; Yu et al., (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIn China, green factory certification, as an important tool of voluntary environmental regulation, has promoted the construction of the green manufacturing system.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e2.3 Research on the Adaptability of Regional Environmental Regulation and Voluntary Regulation in the Pearl River Delta\u003c/p\u003e \u003cp\u003eThe core conclusion of the research on regional environmental regulation is that different regions need to be matched with differentiated regulation models. Relevant studies in the Pearl River Delta (PRD) have clearly identified the limitations of traditional mandatory regulation, providing regional necessity support for the introduction of voluntary regulation. Existing regional studies have already recognized the inapplicability of mandatory regulations in the region. For example, Lei et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) pointed out that in the Pearl River Delta, command-based regulations are likely to lead to passive responses from enterprises, and fee-based regulations frequently fail. Only the flexible mechanism of “incentive instead of punitive” is more suitable for the regional needs. Fang et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further found that the industrial structure in the core area of the PRD is highly advanced. Strong regulatory constraints may inhibit innovation, so it is necessary to focus on flexible tools such as green certification. These studies directly provide a regional rationality basis for this paper to select green factory certification in the PRD as the research object, and clarify the premise that voluntary regulation is more suitable for the PRD. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the main research results of the above-mentioned related literature.\u003c/p\u003e \u003cp\u003eBased on the above research, it is evident that most existing studies take “certified enterprises” as the core and explore the impact of voluntary regulation on themselves. However, it has not addressed whether “non-certified enterprises” will respond to the signals of non-binding mechanisms—especially the lack of research on how non-certified enterprises in industrial clusters respond to certification signals through information transmission and competitive interaction. This leaves the spillover effect of non-binding regulations insufficiently explained. Furthermore, the current implementation status, challenges, and potential advantages of voluntary environmental regulation in the PRD have not been fully explored. Additionally, it remains unverified whether these regulations can be adapted to the regional heterogeneity within the PRD. Based on this, this study will take green factory certification as the representative of non-binding mechanisms and focus on non-certified enterprises in the PRD. Through a multi-period difference-in-differences model, the study will empirically test the spillover effect of certification signals on the environmental investment of uncertified enterprises, so as to improve the regional diffusion theory of voluntary environmental regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eThe Dynamic Impact of Environmental Regulation in the Pearl River Delta on Green Development\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResearch perspective\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMain content\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eclassification and effect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLei et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe limitations of traditional mandatory regulations provide a theoretical basis for the introduction of voluntary mechanisms.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregional heterogeneity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe regional heterogeneity within the Pearl River Delta requires that environmental regulation policies have dynamic adaptability.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e "},{"header":"III. Theoretical Foundations and Research Hypothesis","content":"\u003ch2\u003e3.1 Theoretical Foundations\u003c/h2\u003e\u003cp\u003eIn the context of the accelerated advancement of the global “dual carbon” goals and the continuous intensification of domestic ecological and environmental governance, corporate green transformation has evolved from a passive response to external pressure into an inevitable strategy for achieving sustainable development. From the perspective of institutional change theory, green factory certification is a government-led formal institutional innovation. It not only clarifies corporate environmental compliance standards through regulatory provisions, but also uses its “certification-incentive-supervision” closed-loop design. This design breaks the traditional perception that “environmental investment = cost burden.” It encourages enterprises to shift from passively meeting environmental bottom lines to proactively embedding green concepts throughout the entire production, R\u0026amp;D, and supply chain management processes. According to the externality theory, this certification enhances the returns of environmental protection investment through positive incentives such as tax relief and green credit. At the same time, relying on restrictive measures such as public supervision and environmental credit evaluation, it makes the costs of violations explicit. These dual mechanisms address the market failure where “the negative environmental externalities are borne by society” and promote the “internalization of external costs” to improve environmental performance. Based on information asymmetry theory, certification serves as an authoritative third-party evaluation system recognized by the state. By requiring companies to disclose environmental goals and technological pathways, and by quantifying “environmental efforts” through standardized indicators such as energy consumption per unit product and pollutant emission intensity, it assigns a “green credibility label” to businesses. This effectively reduces the information barriers between companies, investors, consumers, and regulatory bodies, providing crucial endorsement for obtaining external resources and trust.\u003c/p\u003e\u003ch2\u003e3.2 Research Hypothesis\u003c/h2\u003e\u003cp\u003eBased on the integration and deepening of the above three theories, this paper breaks through the limitations of a single perspective and constructs a theoretical analysis framework of four-dimensional linkage of “institutional promotion—resource acquisition—market competition—information disclosure”. It systematically explains the transmission path and internal logic of the green factory certification on the environmental protection investment behavior of non-certified enterprises. Based on this, the following four research hypotheses are proposed:\u003c/p\u003e\u003cp\u003eH1: Green factory certification can significantly encourage non-certified companies to increase their investment in environmental protection.\u003c/p\u003e\u003cp\u003eThe benchmark effect in institutional change theory indicates that when a formal institution is promoted by the government and granted legitimacy, it will generate strong normative pressure and imitation pressure. On the one hand, as the pioneers of the system in the industry, the certified enterprises can obtain policy recognition and social reputation through certification, which will become a reference benchmark for non-certified enterprises. This positions them as reference benchmarks for non-certified firms, fostering among the latter a perception that failure to emulate these leaders may result in being perceived as non-compliant. On the other hand, when local governments implement environmental protection policies, they often take the standards of certified enterprises as an “implicit threshold” (e.g., giving priority to certified enterprises in government procurement and project approval). This further strengthens the imitation motivation of non-certified enterprises. Eventually, this prompts them to increase environmental protection investment to conform to the certification standards.\u003c/p\u003e\u003cp\u003eH2: Green factory certification can help companies improve resource acquisition and enhance non-certified companies’ willingness to invest in environmental protection.\u003c/p\u003e\u003cp\u003eBased on the “cost-benefit reconstruction” logic of the externality theory, the green factory certification is essentially a process of resource redistribution within the industry. To encourage enterprises to participate in the certification, the government usually tilts limited policy resources (such as special subsidies, tax exemptions, and low-interest loans) towards certified enterprises (Nie and Wang \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This directly changes the cost-benefit structure of non-certified enterprises: if they do not make environmental protection investments, non-certified enterprises not only cannot obtain the above-mentioned resources but also have to bear higher environmental compliance costs (such as fines and production restrictions). In addition, certified companies will also demonstrate stronger competitiveness in the supply chain, making suppliers more inclined to establish long-term and stable partnerships with them. This differentiated resource allocation will significantly enhance the environmental protection investment willingness of non-certified enterprises and encourage them to actively invest in green projects such as equipment upgrades and technological research and development.\u003c/p\u003e\u003cp\u003eH3: The resource advantages of certified companies intensify competition pressure within the local market, forcing non-certified companies to increase environmental protection investment to maintain their market position.\u003c/p\u003e\u003cp\u003eOn the premise that resources such as customers, channels, and supply chain cooperation opportunities in the regional market are limited, certified enterprises will form an obvious competitive barrier by virtue of the resource advantages brought by the certification. At the same time, green-certified enterprises are increasingly favored by consumers for their products that meet environmental protection standards and occupy a larger share in the market.This trend has progressively reduced the market space available to non-certified enterprises, placing their survival and development under unprecedented challenges. To maintain competitiveness in the intense market competition, non-certified companies are compelled to increase their investment in environmental protection, enhancing their environmental technologies and production capabilities to avoid being eliminated from the market.\u003c/p\u003e\u003cp\u003eH4: The information disclosure of certified companies reduces the environmental trial-and-error costs of non-certified companies and promotes their imitation and innovation.\u003c/p\u003e\u003cp\u003eHigh-quality environmental information disclosure can enhance the reputation of enterprises (Deegan and Rankin \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and reduce information asymmetry (Leuz and Verrecchia 2000). The public knowledge spillover effect in information asymmetry theory suggests that the environmental information disclosed by certified enterprises during and after the certification process becomes a public knowledge resource within the industry. For non-certified enterprises, one of the biggest risks of environmental investment is the trial-and-error cost. The information disclosure by certified enterprises is equivalent to providing ready-made learning samples for non-certified enterprises. By accessing the environmental practices and information shared by certified enterprises, non-certified firms can reduce this risk, making it easier for them to adopt effective and proven environmental strategies without the need to experiment from scratch. This kind of low-cost learning will prompt non-certified enterprises to imitate and innovate in environmental protection investment more quickly, and accelerate their green transformation process. Figure\u0026nbsp;1 illustrates the proposed impact mechanism.\u003c/p\u003e"},{"header":"Ⅳ. Empirical Research","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Model Specification and Data Description\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Sample Selection\u003c/h2\u003e \u003cp\u003eThis paper takes the A-share manufacturing listed companies in the Pearl River Delta region from 2014 to 2021 as the research sample. To ensure the quality of the research sample and the reliability of the research results, strict screening and processing were carried out on the initial sample: First, enterprises marked as ST or PT were excluded due to their typically severe issues in operational and financial performance, which could distort the results and compromise the conclusion\u0026rsquo;s accuracy; enterprises with negative net assets were also excluded to avoid the influence of extreme financial conditions on the validity of the study. Since this study focuses on the impact of green factory certification on non-certified enterprises, listed companies that were the first (batch) of green factories in the industry dimension in the Pearl River Delta region were excluded. For enterprises that were not the first (batch) of green factories, the observation values in the year of their selection as a green factory and thereafter were excluded. Additionally, Enterprise samples with missing data on key variables were also excluded to ensure the integrity and continuity of the data.\u003c/p\u003e \u003cp\u003eAfter the above screening steps, a final sample of 460 companies and 2,343 observations was obtained. The green factory data was manually collected and organized through the public list from the Ministry of Industry and Information Technology and the Tianyancha platform. The environmental investment data of the companies was obtained by reviewing the \u0026ldquo;Projects Under Construction\u0026rdquo; section in the notes of the annual reports of the listed companies. Relevant new capitalized investments related to environmental protection subjects were manually selected, and the data was then summed and standardized. Other financial data and company characteristics data were sourced from the CSMAR database. To eliminate the influence of extreme values on the research results and ensure the stability and validity of the data, the main continuous variables were winsorized at the 1% percentile at both the upper and lower ends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Research Variables\u003c/h2\u003e \u003cp\u003eDependent variable: Environmental protection investment (EnvInvest). Referring to existing relevant studies, the environmental investment was identified by manually reading the \u0026ldquo;Projects Under Construction\u0026rdquo; section in the annual reports and selecting newly capitalized investments related to environmental protection subjects such as dust removal, desulfurization, denitrification, wastewater, waste gas, and solid waste treatment, water and energy conservation, clean energy utilization, and waste heat and pressure recovery. The new capitalized investments for each subject were summed up and divided by the company\u0026rsquo;s total assets, and then the result was multiplied by 100 to serve as an indicator for measuring the company\u0026rsquo;s level of environmental investment. This indicator can intuitively and accurately reflect the company\u0026rsquo;s capital investment intensity in environmental protection, demonstrating the company\u0026rsquo;s actual investment and attention to environmental protection.\u003c/p\u003e \u003cp\u003eIndependent variable: Green factory certification (Green). In the current year and subsequent years when green factory enterprises emerge in the industry dimension in the Pearl River Delta region, Green is assigned a value of 1; otherwise, it is 0. This variable is used to precisely capture the time point and scope of the impact of green factory certification on non-certified enterprises. By comparing the changes in non-certified enterprises before and after certification, the mechanisms and effects of green factory certification are deeply revealed.\u003c/p\u003e \u003cp\u003eControl variables: Capital structure (Lev), company size (Size), profitability (ROE), government attention (Pollution), degree of financialization (Fin), commercial credit financing (Cred), number of subsidiaries in different locations (SubDiff). The specific definitions of these variables are provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eMajor Variables\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\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental protection investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEnvInvest\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe new capitalized investments for each subject were summed up and divided by the company\u0026rsquo;s total assets, and then the result was multiplied by 100.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen factory certification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGreen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIn the current year and subsequent years when green factory enterprises emerge in the industry dimension, Green is assigned a value of 1; otherwise, it is 0.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapital structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edebt-to-asset ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecompany size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ethe natural logarithm of the total assets of the enterprise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprofitability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereturn on equity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egovernment attention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePollution\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIf the enterprise belongs to a heavily polluting industry, the value is 1; otherwise, it is 0.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edegree of financialization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ethe proportion of financial assets in total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecommercial credit financing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCred\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ethe ratio of accounts payable, advance receipts, and notes payable to total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enumber of subsidiaries in different locations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSubDiff\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ethe natural logarithm of the number of subsidiaries in different locations\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=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Model Specification\u003c/h2\u003e \u003cp\u003eTo accurately examine the impact of green factory certification on the environmental protection investment of non-certified enterprises in the Pearl River Delta region, the following multi-period difference-in-differences model is set:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 401px; height: 33.3622px;\" width=\"401\" height=\"33.3622\"\u003e\u003c/p\u003e\u003cp\u003eIn this model, \u003cem\u003ei\u003c/em\u003e represents the enterprise, \u003cem\u003ej\u003c/em\u003e represents the industry, \u003cem\u003et\u003c/em\u003e represents the year, and \u003cem\u003ek\u003c/em\u003e represents the city. \u003cem\u003eEnvInvest\u003c/em\u003e indicates the environmental protection investment level of the enterprise; \u003cem\u003eGreen\u003c/em\u003e\u003csub\u003e\u003cem\u003ejt\u003c/em\u003e\u003c/sub\u003e is the core explanatory variable, representing whether there are green factory enterprises in the \u003cem\u003ej\u003c/em\u003e industry in the \u003cem\u003et\u003c/em\u003e year; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents a series of control variables, including the previously mentioned capital structure, enterprise scale, and other various factors that affect the environmental protection investment of the enterprise; \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the company fixed effect, used to control individual differences in company characteristics, such as management level, corporate culture, and other factors that do not change over time; \u003cem\u003eν\u003c/em\u003e\u003csub\u003e\u003cem\u003ejt\u003c/em\u003e\u003c/sub\u003e represents the industry-year fixed effect, controlling for common trends across industries over time and industry-specific annual shocks, such as technological advances and policy changes within the industry; \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ekt\u003c/em\u003e\u003c/sub\u003e represents the city-year fixed effect; \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the random error term. By constructing this model, the interference of other factors can be effectively controlled, and the impact of green factory certification on the environmental protection investment of non-certified enterprises can be accurately identified.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Descriptive Statistical Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the descriptive statistics of the main variables. The mean of environmental investment (EnvInvest) is 0.0829, the median is 0, and the standard deviation is 0.6985. This indicates that environmental protection investment among companies in the Pearl River Delta region is at a relatively low level. The mean of green factory certification (Green) is 0.4942, suggesting that 49.42% of the companies in the sample have been impacted by the certification policy. At the same time, the values of the control variables fall within a reasonable range and are generally consistent with existing studies.\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\u003eDescriptive statistical results\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\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\u003eEnvInvest\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.9953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0000\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\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.8856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.1168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.7107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.5054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eROE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePollution\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCred\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubDiff\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.7918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.6107\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Benchmark Regression Analysis\u003c/h2\u003e \u003cp\u003eTo examine whether green factory certification has a positive impact on corporate environmental protection investment, this paper uses a stepwise regression method for analysis. The regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Column (1) does not include control variables but only accounts for fixed effects of enterprises and years. The findings indicate that the impact of green factory certification on enterprises\u0026rsquo; environmental protection investment is significantly positive. In column (2), city-year fixed effects and industry-year fixed effects are added based on column (1). In column (3), control variables are further included. The coefficient of the core explanatory variable, Green, remains significantly positive, validating hypothesis 1. At the economic significance level, taking the results of column (3) as an example, the Green coefficient of 0.1768 means that the green factory certification has increased the environmental protection investment level of non-certified enterprises in the same industry by approximately 17.68%. At the same time, considering the mean of EnvInvest from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which is 0.0829, this policy shock effect is equivalent to doubling the average environmental investment, highlighting the non-binding impact of green factory certification.\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\u003eBenchmark regression 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\"\u003e\u0026nbsp;\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 \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1816\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1640\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1768\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0792)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eFirm\u003c/p\u003e \u003cp\u003eYear\u003c/p\u003e \u003cp\u003eCity-year\u003c/p\u003e \u003cp\u003eIndustry-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2274\u003c/p\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003cp\u003e0.0153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. The values in parentheses are robust standard errors. The same is below.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness Tests\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Parallel Trend Test\u003c/h2\u003e \u003cp\u003eThis paper conducts a robustness analysis using methods such as parallel trend tests, Heckman tests, alternative the measurement of the dependent variable, and other testing methods. Assume that without the green factory certification policy, the environmental investment levels of certified and non-certified enterprises would change along similar trends and there would be no systematic differences. To test whether the parallel trend assumption holds, the following model is set:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 567px; height: 66.4645px;\" width=\"567\" height=\"66.4645\"\u003e\u003c/p\u003e\u003cp\u003eThis paper takes the year before the implementation of the green factory certification policy as the base period (\u003cem\u003eGreen\u003c/em\u003e \u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e). In Fig.\u0026nbsp;2, aft0 represents the current period of policy implementation, and this figure is within the confidence interval at the 90% level. In the model setting, \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e is the fixed effect of the year, and the definitions of other variables are consistent with Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The coefficient of concern in this paper is \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e, which represents the difference in the level of environmental investment between the treatment group enterprises and the control group enterprises. It can be seen from the figure that when a\u0026thinsp;\u0026lt;\u0026thinsp;0, the coefficients in each period are not significant. This indicates that before the emergence of green factory enterprises, there is no significant difference in the level of environmental investment between the treatment group and the control group enterprises. The parallel trend assumption is verified to be valid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Heckman Two-Stage Regression and Alternative Measurement of the Dependent Variable\u003c/h2\u003e \u003cp\u003e(1) Heckman two-stage regression. The Heckman two-stage regression is used to mitigate the problem of sample selection bias caused by selecting only listed company samples. This analysis utilizes the industrial enterprise database of CSMAR spanning from 2014 to 2021. The first stage uses this dataset, with the dependent variable being whether the company is listed in a given year. The explanatory variables include total assets, sales return rate, and company age. With fixed effects for the year, industry, and province controlled, Probit estimation is then performed to obtain the Inverse Mills Ratio (IMR). In the second stage, IMR was incorporated into Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) as a control variable. The regression results of the second stage are reported in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The coefficient of IMR is not significant, indicating that there is no sample selection bias problem. Moreover, after controlling for IMR, the coefficient of Green is significant at the 5% level, indicating that the results are still robust.\u003c/p\u003e \u003cp\u003e(2) Alternative measurement of the dependent variable. First, the environmental investment amount of the company is standardized using the main business income (Env_1). Second, the pollution discharge fees, greening fees, and other items in management expenses are also included in the company\u0026rsquo;s environmental investment amount (Env_2). The regression results shown in columns (2) and (3) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicate that the conclusions remain unchanged.\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\u003eHeckman Two-Stage Regression and Alternative Measurement of the Dependent Variable\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeckman two-Stage regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAlternative measurement of the dependent variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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\u003eEnvInvest\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEnv_1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnv_2\u003c/em\u003e\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\u003eGreen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1793\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5133\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.2556)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1798\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0803)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIMR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2686\u003c/p\u003e \u003cp\u003e(0.2415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e_cons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2243\u003c/p\u003e \u003cp\u003e(3.0230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0001\u003c/p\u003e \u003cp\u003e(7.5927)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0864\u003c/p\u003e \u003cp\u003e(2.6533)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0155\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=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Other Robustness Tests\u003c/h2\u003e \u003cp\u003eThe specific verifications are carried out from the following aspects:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSensitivity test of the shock time. First, if the list of green factory enterprises is announced in the second half of the year, the shock start time is calculated from the next year, and the green factory certification shock variable \u003cem\u003eGreen_1\u003c/em\u003e is reconstructed accordingly. Second, the official announcement time point of green factory enterprises is taken as the emergence node of green factory enterprises, and the shock variable \u003cem\u003eGreen_2\u003c/em\u003e is reconstructed.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEliminate enterprises whose registration location or industry affiliation has changed during the observation period.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEliminate all the observation values of enterprises that have been rated as green factories during the observation period.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn the construction of the green manufacturing system, the certification work at the enterprise level, in addition to the green factory certification, also includes the \u0026ldquo;green supply chain management enterprise demonstration\u0026rdquo; certification. The green supply chain management demonstration enterprises in the sample are eliminated for estimation. The results reported in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e indicate that the conclusion is reliable.\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=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOther Robustness Tests\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\u0026nbsp;\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\u003eSensitivity test of the shock time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity test of the shock time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEliminate enterprises whose location or industry has changed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEliminate all the observed values of green factory enterprises\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEliminate the demonstration impact of green supply chain management\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\u003eGreen_1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1139\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0642)\u003c/p\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreen_2\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\u003e0.1851\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0838)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreen\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\u003e0.1630\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1806\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1808\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0807)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0161\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=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Mechanism Analysis\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Resource Acquisition of Enterprise\u003c/h2\u003e \u003cp\u003eBased on the data of A-share manufacturing listed companies in the Pearl River Delta region from 2014 to 2021, this study investigates the impact of green factory certification on enterprise resource acquisition. The data screening method is consistent with that used in prior studies, and no samples of green factory enterprises are excluded. The specific model setup is as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"643\" height=\"55\"\u003e\u003c/p\u003e \u003cp\u003eIn this model, \u003cem\u003eResource\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e denotes the resource acquisition of enterprises, while \u003cem\u003eGreen\u003c/em\u003e represents the core explanatory variable. \u003cem\u003eGreen\u003c/em\u003e takes a value of 1 in the year when the enterprise is certified as a green factory and in all subsequent years, and 0 otherwise. \u003cem\u003eπ\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, and σ\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e respectively represent the fixed effects of the industry, enterprise, and establishment years of the enterprise. The definitions of other variables are consistent with those in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, columns (1) to (4), respectively examine the effects of green factory certification on government subsidy, bank credit, corporate tax burden, and enterprise performance for certified enterprises. Specifically, government subsidies are measured as the ratio of subsidy amounts to total assets; bank credit is defined as the ratio of long-term and short-term borrowings to total assets; corporate tax burden is calculated as the ratio of corporate income tax expenses to operating revenue; and enterprise performance is indicated by Tobin\u0026rsquo;s Q value. The estimation results show that green factory certification has a significant positive impact on bank credit and enterprise performance and a significant negative impact on corporate tax burden. This improves the enterprises\u0026rsquo; resource acquisition and verifies hypothesis 2.\u003c/p\u003e \u003cp\u003eHowever, green factory certification does not have a significant impact on government subsidies. The reason for this result may be the differences in policies among cities in the Pearl River Delta region. The economic development levels, industrial structures, and policy orientations of each city in the Pearl River Delta region are different, and there are also differences in subsidy intensities. This makes the subsidies that enterprises obtain in different cities vary, affecting the consistency and significance of the overall subsidy effect of green factory certification within the region. For example, the subsidy for green factories in Guangzhou is relatively high, while that in Zhongshan is relatively low. This disparity may lead enterprises to be more inclined to apply for certification in regions with high subsidies, while they show low enthusiasm for certification in other regions, thereby weakening the significance of the impact of government subsidies on green factory certification.\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\u003eResource Acquisition of Enterprise\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\u0026nbsp;\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\u003eGovernment subsidy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBank credit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorporate tax burden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnterprise performance\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\u003eGreen\u0026rsquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0765\u003c/p\u003e \u003cp\u003e(0.0703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0227\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3241\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1598)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3529\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1712)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0630\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=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2 Driven by Competitive Pressure\u003c/h2\u003e \u003cp\u003eThis paper reflects the competitive pressure faced by enterprises through the market concentration in the city-industry dimension. It calculates the proportion of the main business income share of the top 4 and top 8 enterprises in the market share in the city-industry dimension to the total main business income of the city-industry (Liu and Hu \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The results in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e show that the green factory certification is negatively correlated with CR4 and CR8 and is significant, which intensifies market competition, supporting hypothesis 3.\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\u003eCompetitive Pressure of Enterprise\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR8\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\u003eGreen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0223\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0217\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0063)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0759\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=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.5.3 Perspective of Information Disclosure\u003c/h2\u003e \u003cp\u003eInformation transparency (DA) is used to measure the level of environmental information disclosure of enterprises after being certified as green factories, with the absolute value of discretionary accruals used as its inverse proxy indicator (Liu and Hu \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The results in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e indicate that after being certified as a green factory, the information transparency of enterprises significantly improves, and the level of information disclosure increases.\u003c/p\u003e \u003cp\u003eMeanwhile, this paper also examines the impact of information disclosure and information quality on the environmental protection investment of non-certified enterprises by investigating whether the awarded enterprises issue statements and the increment of statement information. The results in columns (2)\u0026ndash;(3) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e show that when the awarded enterprises issue statements, the environmental protection investment of non-certified enterprises significantly increases. The coefficient of the group with a high information increment in column (4) is much higher than that of the group with a low information increment. This indicates that high-quality information disclosure has a more obvious effect on improving the environmental protection investment of non-certified enterprises. In conclusion, enterprises certified as green factories significantly enhance the environmental investment level of non-certified enterprises through high-quality information disclosure, and this effect depends on the improvement in information transparency. Hypothesis 4 is validated.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerspective of Information Disclosure\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\u0026nbsp;\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\u003eInformation transparency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo statement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh information increment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow information increment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEnvInvest\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnvInvest\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEnvInvest\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eEnvInvest\u003c/em\u003e\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\u003eGreen\u0026rsquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0239\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0094)\u003c/p\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreen\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\u003e0.1152\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3103\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0651)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0766)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0210\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=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Heterogeneity Analysis\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.6.1 Enterprise Scale\u003c/h2\u003e \u003cp\u003eLarge-scale enterprises typically possess advantages in resource endowment, including more substantial financial resources and technical reserves. This enables them to translate green factory certification into tangible environmental protection investments, such as upgrading pollution control equipment and adopting clean production technologies. Small-scale enterprises may be constrained by limited funds, outdated technology, and insufficient management capabilities. This makes it difficult for them to effectively translate green factory certification into actual environmental actions. As a result, the effect of environmental investment protection improvement is not as good as that of large-scale enterprises. To explore whether the impact of green factory certification on environmental protection investment varies by enterprise size, this study divides the sample into large-scale and small-scale enterprise groups based on the median enterprise size for group testing (Su et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The results, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, indicate that green factory certification significantly enhances environmental investment in large-scale enterprises, consistent with the previous analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnterprise Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge-scale enterprises\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall-scale enterprises\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\u003eGreen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1632\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0676)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0337)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e_cons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.8722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7.3678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.3563)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0197\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=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.6.2 Industry Nature\u003c/h2\u003e \u003cp\u003eTo examine the industry heterogeneity effect of green factory certification on the environmental investment of non-certified enterprises, this paper categorizes the samples into three groups: the overall industry, heavy-pollution industries, and non-heavy-pollution industries. Furthermore, according to the different nature of \u0026ldquo;construction in progress\u0026rdquo; projects, enterprise environmental investment is classified into emission reduction investment (\u003cem\u003eEnvInv_em\u003c/em\u003e) and energy-saving investment (\u003cem\u003eEnvInv_en\u003c/em\u003e) (Liu and Hu \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The results in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e show that in the overall industry sample, the green factory certification mainly increases the energy-saving investment of non-certified enterprises. In heavy-pollution industries, green factory certification also has a certain promoting effect on energy-saving investments. However, in non-heavy-pollution industries, the impact on both emission reduction and energy-saving investments is not significant. This may be because heavy-pollution industries face greater environmental regulatory pressure, giving them more incentive to draw on the experience of green factory certification to enhance environmental investments. In contrast, non-heavy-pollution industries face relatively lower environmental pressures, and the demonstration effect of green factory certification is weaker.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndustry Nature\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOverall industry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHeavy-pollution industries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003enon-heavy-pollution industries\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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\u003e\u003cem\u003eEnvInv_em\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEnvInv_en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnvInv_em\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEnvInv_en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eEnvInv_em\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEnvInv_en\u003c/em\u003e\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\u003eGreen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1487\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3264\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0284)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e_cons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.3831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.4028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.1569)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.4760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.3484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.3767)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0091\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"},{"header":"Ⅴ. Extended Results","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.1 The Impact of the Number of Green Factories\u003c/h2\u003e \u003cp\u003eTo explore the dynamic impact of the number of green factories on environmental protection investment in non-certified enterprises, this study constructs the variable \u003cem\u003eGreen_Depth\u003c/em\u003e (by adding one to the number of green factories within a city-industry dimension and then taking the logarithm), and also introduces its squared term to test the nonlinear relationship between the two. The results in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e indicate that, in the early stages, an increase in the number of green factory enterprises within a city industry significantly promotes environmental protection investment in non-certified enterprises. After introducing the squared term of \u003cem\u003eGreen_Depth\u003c/em\u003e, the results in column (2) show that the coefficient of the squared term is significantly negative. The U-test’s extremum point is 2.32, which lies within the range of \u003cem\u003eGreen_Depth\u003c/em\u003e values. This indicates that there is an inverted U-shaped relationship between the number of green factories and environmental protection investment by enterprises. That is, once the number of green factories reaches a certain level, environmental investment by non-certified enterprises tends to decrease, possibly due to factors such as market competition saturation and changes in resource allocation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Impact of the Number of Green Factories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreen_Depth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1095\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2420\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0608)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1124)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGreen_Depth\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\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-0.0521\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0240)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0140\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0159\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Evaluation of the Binding Mechanism Effect of Green Factory Certification\u003c/h2\u003e \u003cp\u003eThis part further assesses the effect of the above-mentioned binding mechanism. The model is now set as follows:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAnoAAAAuCAYAAAC/KrTEAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAABYHSURBVHhe7d1fTFvl/wfwd383mqWVmMMFuCzREiC7UQYrC3NOHSxuwsWyODeXWC6U6TDG0bL4P+pmonNtxT9TJxijfjOYM/EP7YbZIDJISIa0czcGmuHiQstFm4jnXGi8eH4X8hzPeXpO/0BPC+XzSrjgPKenz3nOc55+ej7PObUxxhgIIYQQQkjJ+T9xASGEEEIIKQ0U6BFCCCGElCgK9AghhBBCShQFeoQQQgghJYoCPUIIIYSQEkWBHiGEEEJIiaJAjxBCCCGkRFGgRwghhBBSoijQI2SNa29vR3t7u7iYEEJICaBAj5A1LB6PY3x8HPv27ROLCCGElAAK9AhZ45qbm/HAAw+IiwkhhJQAG/3WLSFrVyQSgSzL2L59u1hECCGkBFCgRwghhBBSoih1S9akaDSK3bt3w2azwWaz4dChQ1AURVyNEEIIWdUo0CNrTiQSQVNTE/755x/IsoyZmRkMDw9jz549GYO9SCSCQ4cOoba2Vg0Sy8vLcejQIUSjUSiKguPHj2fcDlmaYDCotrv2z+/3p12nvLwckUhEty1CCFkLKNAja4qiKDh69CjKysrw1VdfwW63o7q6Gp2dnbh69Sqi0aj4EmAxwGtsbMTOnTvhcDgwOjoKxhgYY7hx4wZ27NiBpqYmOBwOrFu3Dna7XdwEyYO2tjbMzMygubkZAOByuRCLxeD1enXrhMNhSJIEl8uFUCiERCKBTZs2abZECCHZGx8fx/nz58XFORsYGMD4+Li42FIU6JE1JRqN4urVq+js7ERlZaW6/Nq1a0gmk5ibm9OtDwB+vx/19fUAgImJCfj9flRUVKjldrsdBw4cwMWLFyFJEmprazWvJvlWXV2NI0eOAACSyaRYDAD43//+h2effRYjIyN4+OGHxWJCCMlaZ2cn3nzzTWzZskUsytnOnTvR1dWFd955RyyyjGWBXiQSQXl5eUoKxewvGAyKm7CU3+9PqYPNZkNLSwul3UpYT08PksmkLhhTFAVzc3NwOp1oaGjQre/3+9Hd3Q2n04nvv/8e1dXVunKt6upq7N27N2UbJP8aGhrgdDoxOzuLqakpXVlfXx9cLhdee+01urJKCFkWl8sFABgaGoIkSWKxIZfLhV27domLAQCSJGFoaAjnzp1DZ2enWGwJywK9TZs2IZFIwOfzAQCcTidisZia7mKMqSkYSZKwfv16cROW8nq9kGVZTQFJkoRQKIRLly7Rh8MK5ff7l/ULDvzhwGJA99NPP2F4eDjlKl8wGER3dzckScI333yjKzOzZcuWrNYrRcs9PrmorKzEtm3bAADT09Pqcr/fj4WFBRw4cECzdn4Vcj+XajXUUWu11RertM4kNy+//DKqqqrw0UcfiUWm3nnnHfz888/iYh0e7E1OTuL06dNicd5ZFuhx/MrJtm3bUj4Aq6ur8dVXX2Hv3r1pr5RYxW63qwHmiy++SCmeEjc/P4+FhQVdX4xGo3juuefg8/l087zi8Tiee+45YLFvZDO/y26344knnhAXE4vwX/O4cOECFEXBwMAAysrKdMeREEKWYnx8HB9//DFOnTolFpkaHx9HOBwWFxuSJAl9fX14+umn8csvv4jFeWV5oHfu3DkAwN133y0WqbZs2VKUq2g8ZSdJEnbs2CEWr3rBYFB3N2IxnD9/Ho2Njeqdj319fYAmdV7IlP3IyAiSySTuu+8+tLS0oLa2FidPnsTQ0FBKcHDmzBnMzs4uq28oioKxsTEcPXoU9957Ly5fvgxobuwwmyYwMDCgu6u3sbER8XhcXA3IYt1IJIJ3330Xu3fv1u2j9ri0tLSYbt9qwWAQ5eXlKfXmy9944w3d+lrr16+HJEn47bff0Nvbi8nJSTz55JPiaisS7//i+akoClpaWugu4Szw86iqqkq9iUpRFLz77ruGfYqQXHz44Yd44YUXsk7XJpNJdHV15RQY3nPPPTh8+DDeeustsSivLA30sgmkKisri3YVhE/MLysr002uLwUDAwPwer2m7a5lNl8x3Z/4AWXE6/WitbUVGzduhCzLuHHjBi5evIi+vj589NFHBf3pLUVRcOHCBTidTrS2tuLSpUuYnp7G33//jU8++cRwXQBobW3N6mqeSFEU7NmzB9u3b4fP58P8/Dyqq6sRiUSwc+dOTE5Oii9BNBpFY2MjAoEAgsEgGGPo7+/H5OQkXnjhhZzXbW9vR319PTweD4aGhvDggw8Ci3PYpqenMTIyAo/Hg+Hh4ZR5boXAr5omk0lMTk7izJkzatn09DSSySTGxsYMg2EsZgTq6uowOzuLq1evZtUnV4pr164Zjot8TKqrqytKlmO14Hc/bty4EQsLC1AURQ3y2tra0NraisnJyaL0a1Iazp49i61bt4qLTT3zzDN44403sg4MuYMHD+Ls2bOmN5blg6WBntmgFY/H8fXXX+vWLQZ+hccorbxaKYoCr9eLxx57DBs2bMjqw8Lr9ermTmbzJ14BE/n9fgQCATQ3N+PUqVOw2+2w2+14/PHH8fnnn2N2dha7d+8u2JVc3hfvuusuOBwOdfm+ffvUYElcF2muRPMrL2IAbLPZUFVVBVmWcenSJfUxH9u2bcP8/Dx++OEHTExMwOl04siRI+r+82f7bdy4ESMjI+pxq62tTRk4sl33iy++QCwWg9PpVOcl9vX1oaKiAl1dXbDb7UgkEkWZIwsAU1NT6OzsVIPUa9euqWV8ysf69etN+4jdbsfu3bvFxSsenysqjosAMDc3h2QyWdBzY7VRFAX19fWorq7G3Nyc+kX97Nmz6OrqUtu0WP2arH788Sd8HnAm58+fR1VV1ZKmf/H3+PXXX8WivLE00OOBlDhYh0IhrFu3Trcul2uqKRKJ4Pjx46itrU1JA3q9XtOrT9qrNnyuj1ah6oEs0m/QpCl4eTQaRTAY1L2X1+uFw+FAIBAAAAwPD8PhcBR8wnAkEsFbb70FSZJw8uRJ3bFvaGjA/Pw8nE4nDh48qHudlXhfFD9AefqPTzEQmT0qxW6349KlS4Y39Fy/fl394qD94A4Gg/B6vaiursb169fR1tYGCM/2e/vtt3X16+npAQD1cSK5rAthXmIoFEJFRYX6vukCjkJoa2tTz637778ft9xyi3r1jt9Va3RucpFIBJ9++imw2M5mV/5WmqmpKdMvOufOnTO80me15YxfhWa321FRUaF+Idu2bRvGxsbw8MMPw263q/16pWZqtJ8htsUpLV6vd9X0X6I3OzuL999/Hx6PRyxaOZhFZFlmzc3NDAAbHBxUl/f39zOXy8VisZhufcYYc7vdDID6x1/X29vLAoEAk2WZeTwetczn8+nWd7vduu3x8ubmZibLsq4sFosxp9PJnE5nSl0KVY+ZmRnmcrmYy+ViMzMzjC22j7iNcDjMJElibrebybKstq0kSSwcDqvradc1KisU3n5iOzBNW/h8PrFIJbZnNn/ptscW62TUJry9tMdmcHBQ3a6275rh9TXaX/6+3d3dKe/N8ddr30vbx7T7lsu62vX379+fUsb3U1yeiRXHJxaLsY6ODvUYhMNh9uijj6act9zMzAzbv38/u3z5MnM6nYbHdrms2E+Wpi/yMUkcJ9LJRx3FbYj9mJfnUi8z4ntl8yfWl9P2be35wPu1uB9LZUWdOzo6WDweZ2yxL+d63In1ALCrV6+Ki1M89NBDKesBYA899JBuWToA2NjYmLg4bywL9PgHqHgCIMOAIQZgvb29upPYaJDkgYVR0Obz+QxPeD4YmNXF6nqIwZvRco4PDtr3D4fDhgFzPgflpeDtJrYN0wT/RmXZENswW+k+QHl7GR0bsc2N8H0yWpe/b01NDevt7dWVcfz1TqeTzczMsHA4zDo6OpgkSbovALmuq13frD8Y9eHlWOrxYYt1PXbsmFpHj8eT0p5cLBZj+/fvV/s+P+/M1s+35e4nP4biubvUwNvIUuqYy/iVb7luP13fNhovrZBrnfl4IL6G74u4H6S4Nm/ezPr7+8XFOidOnGCffPKJuJghh0AvkUgwZBlULpVlqVuesnK73eq8LlmW4Xa7U1K5WktJNR05cgSSJGFhYQHz8/OarQGxWMww/cNTdUbpE1hcj1zTbzx92NPTo17er6ioQF1dnW6+mTYd/ccff+iWf/bZZ+r/onzejMHbTWwbaJ5XV+iUCk+Vif1O217auXh8kj80/cSMLMv47bffUp7NB01bbNiwwfS5bjz9NDs7i6amJrz00kvYsGEDJiYmcOXKFV0b5rKudn2jFLpZH14J+N2m/HzTisfj6OrqwvHjx9X0OD92mY7VSqBNN4rzgouVtuWyHb9EwWAQVVVV6nSTTONNPvDzTuzb/JxeyfPzxHbk00DEZ7gGg0HdnfmFaFfyH6/Xi1deeUVcrPP888/j6aefTvl8BIAff/wRNpst4y9gXLx4EbfffjvuuecesShvLAv0jB6rYrfbsW/fvpSOrsXnUv39999YWFjQDfZmc1sqKipQVlam/s+ZfWDwD7l0g6qV9Th9+jSGh4fx3nvvqYO9sngTxZdffpny3La2tja43W4MDw9jz549UBQFlZWV+PTTT3Xvr/1g7+3tVcv4XZlm8nkzBg/wjYKqnp4eOJ1OuFyulA8SK/G+KPY7bXtp+4FdM8l/fHw8Zb6kFu8L4k0e0PQh7U0XIu0XokQigQsXLuDVV181DL5yWRea9Y3uHDbrw8WmKAqCwaDhY1UURcGRI0fw/PPP6/Z5x44dkCQp47FaCXifEPtiJBJBKBQqauCd7fglamtr081LzTTe5APvv2LfNrsBcCWorKzEnj17dA/4TqetrU0X/BWiXcl/Dhw4gNtvvz3tr1ecOHHC8A8AqqqqcOLEibR37iaTSXR2dqY8VSHvxEt8+ZAufTc6OpqyjEt3OZ6lSTUZpc/E9I5WprStlfXQpm6ySb9x2m2Ll/45nrLg68RiMfU1hUhlMJM5b0xIp/h8PjY4OMjC4bBpStNIrqkSliFVxlNVRqmybNqbpdlGuvfVMmsvzufzMY/Hk/O6LE0/FcsuX75suL1cLeX4aPX19bHu7m7D9uJz8oz2hbe12b7m21L306xP8DFCkiTm8/mYLMusr69P99pcLaWO2YxfIp/Pp7b7csabXOvLzzvxPdKlv/PdT3KtM1vsx8eOHWNbt25lW7duNa0L349ijePkX4lEgm3evJkdPnyYJRIJsdgUskzd7t+/n23evFlcnHeWBHqZAikz/IPMqPPz4NFsm+KJn26ODw86zE5SK+vBtw2ASZLEdu3axY4dO2Ya4Gnx9zY62bWDdF1dnVoezjCp3Qq8Dfj8hv7+fvVDjC3hZgduKQOrtr09Hg+TF29mef311zP2UW2bdnR0pBwjfjyM+gl/32zqy9uL148tfiB0dHToluWybrp+qi0LhUJsdHSUDQ4O6taVlxBsLOX4aHk8npR2jMfjzOPxMEmSdEGsFr+pCUDaL0vccvd1qftp1F9CoRALBAJqf/H5fGx0dJSNjo6qrxPrm42l1jHT+CWSZZl1dHSogeBSx5tc6svPSzFgZhm+3LDF12rngoplVvUD3k6SJLFQKCQWG3K73UUdx8m/EomEGpBlG+whi0Dv8OHDOW1zOSxJ3Waa/8YFg0H11wKwzFQTTxFPT0/D7/fjjjvuMEw1aOdliekTzsp65JJ+8/v9ukcdVFZW4r333gMM5iPxlEVFRQXWrVunzhcbGRnBrbfealhXq5w6dQoejwePPfYYbDYbAoEAvvzySzXd29DQAJfLVZAHJvNUWX9/PzZu3Ig777wTDocDH3zwAQKBAL777jvTtrEvzp0JhUK4efMmmpqadPMwHnnkEezduxfffvttSj8xS9EZ4e0VCATgcDhQXl6OkydP4ujRo/D7/br6Zbtuun7K5/zcvHkTALB9+/aip4mCwSASiYSuHdvb21FZWYlAIIBkMolAIJDyayLt7e2oqalRH0A9OTmJmpqalPW0irWvU1NTcLvd2Lt3L+rr61FbW4s///wTXV1d2LRpE9rb29Hd3Y3vv/8e27dvV18n1tdKmcYvUTQahcPhUNO2hRhvzOY5ZjPvNBqN4o8//jCsn5X94PTp0+jt7c36pzbj8ThisVhRx3HyL0mS1B8guHjxolics2QyCZfLhdnZWQwNDUHK8QHLSyJGfsuV7qqTVn9/f8o3lHTfxrRlRqkmfpWOX+0ww785G30b5KysB39/s2/oPk36ze12p6QgePuK3yT5+9bV1TG3280uX77MYrGY7lvhWpPumz/5D7/C6i5Smoj3abGvW6GY+7qUc1Fb30LINH6JfD6f7rgtZR/zJZu26u3tZceOHVOv8gNgXq/X8n7gNkk1a6/kag0ODur2o5jtSvKrv78/66u6+ZLXQE/WpMSMAiVZllkoFGK7du1iEOZR5JpqEmlPcvH13MzMjHpCGz2ahBWoHvykT5d+40GKJElqClRefF6aUdu6FwPQ+++/n3V2drLe3l4Wi8WYy+VKWXetMAuKSSrtB0mh00S87xaqnxZjX2OxGGtpaTEcczIp5Id8NuMXJ8sye/TRR9XjVuzxhgepZm0la9LM/As130er+wGvW3Nzszq1gH8eSZKUUmftMS92u5LVL2+pW7/fD4fDgddffx1YvDxZX1+vS3U5HA60traqlyu1dzrmmmoyov25La14PI6qqirU1NRgeHgYWEzx3HHHHSm/GmFlPbhs0m+yLOO+++7DjRs3MDk5qbbf2NgYJiYmUlKF5eXlSCaTCIfDuOuuu3DgwAFMTU3htttug8PhME1jlTJ+LLNJn65lxUwTFfpu02Lt65kzZzA7OysuzkisbyFkGr84WZZRVlamHrdijzf894PNHqvC08yjo6Oora3VTXWwuh94vV709/fj5s2bqKmpgc1mQ1NTE5xOJyYmJnTpcX7M169fD0VRit6upASIkR9ZvcLhMKupqVEvC/t8PuZyuQp+mXgl4FdEKW2bWTHTRPxKh5i6skox9jVdliATsb4riVi3Yo432ZzvPp+P1dTUsF27dqX0t0L0g2zROE7yLW9X9Ejxbdq0CdPT0+pkX6/XiytXrmQ1+bfU8Iczd3Z2pjyYluidO3dOveoZj8fx66+/ml4VsUJzczOeeuopcbElirmv4rMls6Gt70oj1m0ljzeKouDKlSsYGBjAM888gwsXLqhXxwrdDzKhcZzkGwV6pCS1tbWlfbAz+Vex00Rer7dgd5QWa18rKytx/fp1fPHFF2JRWmJ9VwK/34++vj5EIhH89ddflt81ny374h3y2gc3a0WjUWDxV28aGhpgs9kQj8dx/PhxjI6OFqQfEFIsFOgRsobNz8/j999/Rzweh91ux/T0NP7880/MzMwUJPgqpNW2r2J9V4La2lp0dHTgqaeeQk9Pz4qpVyZzc3NobGyE3W6Hw+EAYwyHDx9GV1cX5ubmVnQ/IGS5bMyqBwcRQgghhJCioit6hBBCCCEligI9QgghhJASRYEeIYQQQkiJokCPEEIIIaREUaBHCCGEEFKiKNAjhBBCCClR/w9rbzlquZw7sAAAAABJRU5ErkJggg==\" width=\"634\" height=\"46\"\u003e\u003c/p\u003e\u003cp\u003eThe dataset corresponding to Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was utilized to analyze the impact of green factory certification. To avoid the influence of non-binding mechanisms on the estimation results, non-certified enterprises within the same city industry of green factory enterprises were eliminated before the regression. The results in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab15\" class=\"InternalRef\"\u003e15\u003c/span\u003e indicate that there was no significant increase in enterprises’ environmental protection investment after obtaining certification. However, firms that had prior environmental protection investments were more likely to be selected as green factories. This potential selection bias may affect the reliability of the conclusion and result in an underestimation of the binding mechanism’s effect. To address this, two treatment measures were taken in this paper: first, enterprises that had made environmental protection investments in the three years before being selected as green factories were excluded from the treatment group; second, the time trend term of the environmental protection investment predetermined variable with propensity score matching was included and controlled. After these treatments, the coefficient of \u003cem\u003eGreen\u003c/em\u003e remains insignificant, indicating that the restrictive mechanism of green factory certification has not effectively played a role.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of the Binding Mechanism Effect of Green Factory Certification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\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\u003eEliminate enterprises affected by the spillover effect\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEliminate enterprises that had made environmental protection investments in the three years before being selected\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdd the time trend term of the predetermined variable\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\u003eGreen'\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3689\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1161\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4769\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.4309)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1205)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.4631)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e_cons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3058\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0191\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e361.8354\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.2055)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.5198)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(179.0432)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity-year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry-year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e683\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1379\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0987\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3640\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Dynamic Synergistic Effect of Binding Mechanism and Non-binding Mechanism\u003c/h2\u003e \u003cp\u003eMost of the existing literature focuses on the impact of the binding mechanism of mandatory environmental regulations on green development. As confirmed in prior studies, the existence of voluntary environmental regulations with non-binding mechanisms has been established. Based on this, this paper further explores the non-binding effects under different intensities of mandatory environmental regulations. Firstly, the dummy variable ER1 is defined as follows. Using the ratio of “completed investment in industrial pollution control to industrial added value,” if this ratio is not lower than the annual median, the variable is assigned a value of 1; otherwise, it is assigned a value of 0. Secondly, referring to the study by Chen and Chen (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the intensity of environmental regulation (ER2) is quantified by calculating the proportion of word frequency related to environmental regulation relative to the total word frequency in each city’s government work report. In selecting the vocabulary, this study refers to the research by Liu and Hu (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and is based on the government work report texts of cities in the Pearl River Delta region. In conjunction with the core requirements of the \u003cem\u003eImplementation Guide for Green Manufacturing Engineering (2016–2020)\u003c/em\u003e and Made in \u003cem\u003eChina 2025\u003c/em\u003e, new energy efficiency-related vocabulary such as “energy conservation,” “circular economy,” and “clean production” has been added to the existing keywords related to pollution reduction and emission control. The empirical results in Table\u0026nbsp;\u003cspan refid=\"Tab16\" class=\"InternalRef\"\u003e16\u003c/span\u003e show that in areas with a lower intensity of mandatory environmental regulation, the non-binding influence of voluntary environmental regulation is more prominent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab16\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 16\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNon-binding Impacts under Different Intensities of Environmental Regulation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eER1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eER2\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\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(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe low intensity of environmental regulation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh intensity of environmental regulation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe low intensity of environmental regulation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh intensity of environmental regulation\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\u003eGreen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1631\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3093\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2123\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0594\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0666)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1909)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1166)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1982)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e_cons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1139\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0786\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4844\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.4272\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5.8823)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.5381)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.3520)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.0404)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity-year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry-year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1537\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1664\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0621\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0303\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0505\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"VI. Conclusions and Policy Implications","content":"\u003ch2\u003e6.1 Conclusions\u003c/h2\u003e\u003cp\u003eThis study finds strong evidence that voluntary green factory certification exerts a positive spillover effect on non-certified firms’ environmental investments, confirming the proposed diffusion mechanisms. In particular, we find support for the resource acquisition, market competition, and information disclosure channels. Additionally, an interesting non-linear effect emerged: as the number of certified firms in an industry grows, the marginal influence on other firms follows an inverted-U pattern. Finally, the influence of voluntary regulation is more pronounced where mandatory regulation is weaker, indicating a complementary dynamic. From a theoretical perspective, this study makes contributions to two core academic fields. First, it enriches the literature on institutional legitimacy. The research shows that voluntary certification, as a non-mandatory institutional practice, can promote the legitimacy-seeking behaviors of uncertified enterprises through spillover effects. This expands the academic understanding of how informal institutions influence corporate environmental strategies. Second, it advances the research in the field of the diffusion of voluntary standards. By identifying three specific transmission paths, namely resource acquisition, market competition, and information disclosure, as well as an inverted U-shaped non-linear diffusion pattern, it improves the existing linear diffusion analysis framework. This study also has limitations that need to be noted. First of all, the analysis focuses on a specific type of green factory certification, and the research conclusions may not be fully generalized to other voluntary environmental standards (such as product-level certifications). Secondly, due to the limitation of data availability, the study fails to capture the internal micro-decision-making process of uncertified enterprises, and this process may further clarify how the spillover effect is transformed into actual environmental investment.\u003c/p\u003e\u003ch2\u003e6.2 Policy Implications\u003c/h2\u003e\u003ch2\u003e6.2.1 Construct a Differentiated Collaborative Governance System of “Voluntary + Mandatory”\u003c/h2\u003e\u003cp\u003eBased on the research conclusion that in regions with relatively weak mandatory supervision, the influence of voluntary certification is more significant, it is necessary to design a differentiated governance combination according to the differences in supervision intensity among different cities in the Pearl River Delta. In regions where the mandatory supervision system is not yet perfect, take the green factory certification as a core supplementary tool. Through incentives such as tax preferences and green credit, enhance the enthusiasm of enterprises to participate in voluntary certification. In regions with more mature supervision, promote the connection between voluntary certification and mandatory standards (such as incorporating the certification results into the enterprise environmental credit evaluation), so as to form a collaborative pattern of voluntary practices deepening the effect of mandatory measures and mandatory supervision ensuring the quality of voluntary actions.\u003c/p\u003e\u003ch2\u003e6.2.2 Improve the Whole-process Supervision of Certification\u003c/h2\u003e\u003cp\u003eFocusing on the core goal of ensuring that the spillover effect of certification is effectively converted into corporate environmental investments, two regulatory mechanisms should be optimized: First, strengthen the government’s dynamic supervision. Regularly monitor the core indicators of certified enterprises such as environmental protection investment and pollutant emissions, and link the monitoring results with policy incentives to avoid the post-certification decline. Second, build a cross-regional information disclosure platform and force certified enterprises to publicly disclose key information such as the application of environmental protection technologies and cost-benefit. This not only echoes the information disclosure transmission path confirmed in this study, but also provides a practical reference sample for uncertified enterprises. At the same time, introduce the supervision of the public and industry associations, and establish a closed loop of “certification-disclosure-supervision-feedback” to ensure the precise implementation of policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYANG WANG and SHAOJING ZHANG wrote the main manuscript text and HONGSHENG ZHANG and XINYI JIANG prepared figures 1-3. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to express our sincere acknowledgements to all the individuals and institutions who have contributed to this research project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data from our surveillance are not available in the public domain due to privacy and ethical restrictions, but anyone interested in using the data for scientific purposes is free to request permission from the corresponding authors. Email: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eBorck J C and Coglianese C 2009 Voluntary environmental programs: assessing their effectiveness \u003cem\u003eAnnu. Rev. Environ. 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The Impact of Voluntary Environmental Regulation on Firm\u0026rsquo;s Green Technological Innovation: Evidence from Green Factory Certification in China \u003cem\u003eSustainability\u003c/em\u003e 17 2498\u003c/p\u003e\n\u003cp\u003eChen Z and Chen G 2022 Research on the Dynamic Strategy of Enterprise\u0026rsquo;s Participation in Voluntary Environmental Regulation Under Dual External Pressure \u003cem\u003eSoft Sci.\u003c/em\u003e 36 130\u0026ndash;7\u003c/p\u003e\n\u003cp\u003eChijoke-Mgbam A M, Mgbam C O, Akintoye S and Ohalehi P 2020 The role of corporate governance on CSR disclosure and firm performance in a voluntary environment \u003cem\u003eCorp. Gov. Int. J. Bus. Soc.\u003c/em\u003e 20 294\u0026ndash;30\u003c/p\u003e\n\u003cp\u003eCui G and Jiang Y 2020 Industrial Policy Support of Environmental Protection and the Environmental Governance Motivation of Enterprises: Based on Empirical Evidence of Listed Companies with Heavy Pollution \u003cem\u003eAudit. Econ. 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Stand. 25\u0026ndash;7\u003c/p\u003e\n\u003cp\u003eYu L, Li Z, Lan X and Ji P 2024 Green-oriented International Investment: Green Factory Certification and Corporate Outward Foreign Direct Investment J. Shanghai Univ. Finance Econ. 26 33\u0026ndash;48\u003c/p\u003e\n\u003cp\u003eZhang B, Wang X and Cao L 2024 Green Factory Recognition and Enterprises\u0026rsquo; Outward Direct Investment Int. Trade Issues 57\u0026ndash;75\u003c/p\u003e\n\u003cp\u003eZheng M Q, Yan S and Zhang Y 2024 Can Voluntary Environmental Regulation Help Reduce Capital Constraints? The Moderating Effect of Agency Costs and Corporate Social Responsibility Financ. Theory Pract. 1\u0026ndash;12\u003c/p\u003e\n\u003cp\u003eZhu Z, Lin W, Zeng A and Hu Y 2023 Research on the Impact of \u0026ldquo;Green Factory\u0026rdquo; Certification on Enterprise Green Innovation Contemp. Finance Econ. 3\u0026ndash;16\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"The Pearl River Delta region, Voluntary Environmental Regulation, Green Factory Certification, Non-binding Mechanism, Investment in Environmental Protection","lastPublishedDoi":"10.21203/rs.3.rs-8053608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8053608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of global sustainable development and the \u0026ldquo;dual carbon\u0026rdquo; goals, environmental protection has become a crucial issue that cannot be ignored in the economic development of various countries. In particular, the Pearl River Delta region, as an important base for China\u0026rsquo;s manufacturing industry, is facing huge pressures and challenges in accelerating transformation and promoting green development. This paper selects the data of A-share manufacturing listed companies in the Pearl River Delta region from 2014 to 2021 as the research sample and uses methods such as the multi-period difference-in-differences model to examine the non-binding impact of green factory certification on non-certified enterprises. Furthermore, through channels such as resource acquisition, market competition, and information disclosure, the study analyzes how green factory certification influences the environmental protection investment behavior of non-certified enterprises. The research finds that green factory certification has a significant positive effect on the environmental investment of non-certified companies. It also reveals an inverted U-shaped relationship between the number of green factories and the environmental investment of non-certified companies. Additionally, the article points out that in regions with weaker mandatory environmental regulations, the non-binding impact of voluntary environmental regulations is more significant.\u003c/p\u003e","manuscriptTitle":"Institutional Legitimacy and Voluntary Environmental Certification: Diffusion Mechanisms of Green Factory Standards in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 10:43:41","doi":"10.21203/rs.3.rs-8053608/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-08T08:39:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T00:00:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284276386900191597446460204423198067399","date":"2026-01-27T15:21:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T07:26:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318106266513366334019372815698892912330","date":"2026-01-20T06:37:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1981994323247463378757874919171804000","date":"2026-01-09T07:08:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-09T02:57:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T09:14:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-13T11:11:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-02T01:07:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-02T01:02:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0677ce48-6b4d-4d17-b367-859cf39b4f64","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60939968,"name":"Earth and environmental sciences/Environmental sciences"},{"id":60939969,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":60939970,"name":"Social science/Environmental studies"}],"tags":[],"updatedAt":"2026-03-26T15:38:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 10:43:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8053608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8053608","identity":"rs-8053608","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}