Green Investor Shareholding and Financial Distress Risk of Manufacturing Companies: Exacerbate or Mitigate?

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Dapeng Tang, Mingyao Li, Yichen Liu, Haizhi Ren, Yue Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6390011/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Accelerating the promotion of green economic development relies on the infusion of green capital. In this context, can the green investor (GI) serve as a 'stabilizer' for the green transformation and development of manufacturing companies? This article empirically examines the impact of green investor shareholding (GIS) on financial distress risk (FDR) by constructing a refined measure that better captures the FDR of manufacturing companies. The research findings indicate that: (1) GIS can significantly mitigate the FDR of manufacturing companies; (2) two possible channels are the enhanced levels of green governance performance and alleviation of financing constraints; (3) from the institutional logic perspective, the strength of shareholder protection enhances the role of GIS in mitigating FDR, while the intensity of environmental regulation exhibits a U-shaped moderating effect, initially suppressing it and subsequently promoting it. Furthermore, we explore long-term, multifaceted impact of GIS, highlighting their broader implications for corporate sustainable development. JEL classification G23, G23, Q56 Business and commerce/Business and management Social science/Economics Social science/Environmental studies Social science/Finance green investor shareholding financial distress risk green governance performance financing constraints institutional logic China Figures Figure 1 Figure 2 Figure 3 1. Introduction In recent years, the Chinese central government has launched a series of groundbreaking initiatives to improve the ecological and environmental conditions. As of 2023, China has fostered 2,783 green factories, 296 green supply chain enterprises, and 223 green industrial parks at the national level, and has progressively established a full-chain green product supply system spanning from basic raw materials to end-consumer goods 1 . Against this backdrop, the Chinese government has gradually shifted the responsibility for pollution control to manufacturing companies, encouraging them to bear the costs of pollution abatement through market-based mechanisms independently. However, this approach may further intensify the pressure on manufacturing companies to undergo green transformation (Zhang et al., 2019 ). Existing research indicates that while the green transformation of manufacturing firms can reduce environmental pollution, enhance production efficiency, and improve market competitiveness, the required investments in green R&D, pollution control, patented technologies, and human capital cannot be ignored in their impact on the financial sustainability of these companies. (Khan et al., 2019 ). The Bank for International Settlements (BIS) posits that 'green swan events,' which arise from the accumulation of environmental issues, may become the next significant risk exposure in capital markets and trigger severe systemic financial crises. The Research Institute of the Bank of China, in its report titled "Challenges and Responses to Risks Caused by Global Climate Change", also highlighted that restrictions on energy consumption may result in financing difficulties for related enterprises and an increase in credit risk. Furthermore, stricter environmental regulations are likely to raise production and operational costs for manufacturing enterprises, such as expenses for upgrading equipment or transitioning to clean energy. These substantial short-term capital outflows are indicative of an increase in financial distress risk (FDR) 2 . Meanwhile, as the focus of environmental regulation, manufacturing companies have a greater demand for green management and green investment during their green transformation process compared to other sectors, which may expose them to greater FDR (Zhang et al., 2022 ). Therefore, to achieve the goal of economic green transformation and development while maintaining stable economic growth, the capital market needs to find the 'stabilizer' that can promote industrial green transformation while minimizing FDR of market entities. According to classical financial management theory, generating returns and controlling risks are the two driving forces of corporate value growth. Therefore, exploring how to effectively prevent companies from falling into financial distress is of great significance for reducing systemic market risk and minimizing the likelihood of shareholders suffering substantial wealth losses (Godfrey et al., 2009 ). The existing literature primarily examines the factors influencing FDR from two perspectives. At the macro perspective, factors include country risk (Altman et al., 2017 ), financial environment (Almeida & Philippon, 2007 ), political corruption (Khieu et al., 2023 ), and climate change (Rudebusch, 2021 ). At the micro-enterprise perspective, factors include board structure (Fich & Slezak, 2008), management turnover (Gilson, 1989 ), institutional investors shareholding (Tarighi et al., 2022 ) and strategy formulation (Tykvová & Borell, 2012 ). Whether manufacturing firms fall into financial distress depends not only on their own operational conditions, which is a critical factor, but also to a large extent on the flow of market capital, particularly the allocation of 'green' funds (Tchorzewska, 2024 ). The significance of 'green' funds extends beyond providing liquidity to invigorate firms; more importantly, they convey expectations of a firm's commitment to green development to the market. This, in turn, attracts more investors to continuously inject capital, fostering a virtuous cycle that enhances the firm's financial stability. However, existing literature has not explored this issue from this perspective. Barnea et al. ( 2005 ) classify institutional investors based on the 'green' attributes of their investment philosophy. They argue that, compared to non-green investors, green investors (GIs) not only seek financial returns but also require the investee firms to place greater emphasis on environmental governance responsibilities, ultimately achieving a dual investment objective of sustainability and financial returns (Ng & Zheng, 2017). This provides a new perspective for this study. Existing literature has validated the impact of green investor shareholding (GIS) on corporate financial performance, including the promotion of green investments and innovation outputs (Chung et al., 2012 ; Yan et al., 2021 ; Tang et al., 2024 ), influencing stock price fluctuations (Cheng et al., 2024 ), and affecting corporate profitability (Jiang et al., 2021 ; Zhang et al., 2024 ). Notably, current studies have overlooked the critical role of FDR as a threshold for corporate survival. Therefore, exploring the specific pathways through which GIs influence corporate FDR can not only fill the research gap in the risk management dimension of environmental finance theory but also provide decision-making references for preventing and mitigating systemic environmental financial risks. Compared to previous literature, this paper makes contributions primarily in the following aspects: At the theoretical level, existing literature primarily explores the impact of GIS on corporate financial performance but often overlooks the critical role of risk management in corporate financial sustainability. With the growing market demand for green manufacturing products, manufacturing enterprises exhibit significant transformation potential. Against this backdrop, this study adopts the perspective of green capital infusion and focuses on manufacturing firms to empirically examine, for the first time, the relationship between GIS and corporate FDR. Furthermore, by investigating the internal mechanisms through which GIS influences FDR, as well as the external constraints affecting this relationship, this study expands the theoretical scope of risk management theory and signaling theory. At the practical level, this paper offers theoretical guidance on how the current capital market can promote green development and how manufacturing companies can cope with FDR. Developing countries with significant pollutant emissions are typically undergoing or preparing to undergo a green transition; meanwhile, their green capital markets still requiring further development. The findings of this study, derived from data on China—the largest developing country—can assist government agencies in such nations in improving policy frameworks to guide the green development of capital markets. Simultaneously, these findings help corporate managers better understand the relationship between GIs and corporate FDR, encourage manufacturing firms to prioritize FDR management and fosters the development of sustainable market economy. 2. Theoretical Analysis and Research Hypothesis 2.1. The Internal Logic of How GIS Influences FDR FDR arises from the uncertainties in both the external operating environment and internal business activities of companies. It manifests as the possibility of a company falling into financial crisis, where cash flow is insufficient to repay maturing debts, making it difficult to maintain normal production and operations (Beaver, 1966 ; Altman, 2002 ). For the manufacturing industry, the pollution characteristics of enterprises in this sector—namely, high energy consumption and high-emission production models—result in sources of FDR that exhibit distinct industry-specific characteristics. External factors include the uncertainty of environmental policies and regulations, social opinions, and market's increasing demand for 'greening;' internal factors include the uncertainty of technological R&D, energy supply costs, pollution-related penalties (Dal Maso et al., 2024 ). As entities that prioritize environmental and socially responsible investments, GIs tend to invest in companies with green development potential (Chung et al., 2012 ). Meanwhile, as large-scale market participants, they not only influence companies' capital allocation decisions but also shape market expectations through their green investment attributes, thereby optimizing corporate financing conditions. Under this mechanism, companies will place greater emphasis on environmental performance while pursuing economic benefits, aiming to reduce litigation risks and environmental penalties. Consequently, this enhances financial sustainability, increases long-term corporate value, and ultimately leads to a 'win-win' situation, where GIs also achieve excess investment returns (Jiang et al., 2021 ). 2.1.1. GIS, Green Governance Performance, and FDR By participating in corporate green governance, GIs guide companies to embed green development principles into their resource allocation processes and operational procedures (Jiang et al., 2021 ). This involvement helps companies 'greenify' their original production capacities with high market conversion value, thereby balancing the dual objectives of financial performance and environmental performance (Li et al., 2020 ). They can improve green governance performance (GGP) by adopting investment strategies that involve either 'voting with hands' or 'voting with feet' to participate in corporate green governance (Mallin, 2012 ). Specifically, GIs can participate in corporate green governance through 'voting with hands' in the following three forms: The first is exercising voting rights and soliciting proxy voting rights. Voting rights are fundamental rights enjoyed by all shareholders of a company, which can be exercised by shareholders themselves or delegated to other shareholders for execution 3 . Article 3 of the "Code of Corporate Governance for Listed Companies" suggests that 'listed companies should implement the development principles of innovation, coordination, green, openness, and sharing.' The environmental preferences of GIs align with the environmental protection demands of small shareholders who have environmental protection preferences in manufacturing companies, making it easier for GIs to garner the proxy voting intentions of other small shareholders (Kim et al., 2019 ), thus influencing corporate GGP. The second is shareholder proposals. Shareholders individually or collectively holding more than 1% of a company's shares can submit written proposals to the board of directors 4 . This allows GIs to push companies to pay more attention to green production and environmental compliance (Chen et al., 2020 ), thereby influencing corporate GGP. Lastly, GIs can engage in public proposals and joint actions. Joint actions typically involve institutional investors publicly and collectively urging a company to address issues and enhance governance when faced with challenges (Appel et al., 2016 ), compelling the company to enhance its governance practices. GIs can also adopt the 'voting with feet' approach, expressing dissatisfaction with a company or distrust of its governance practices by selling or reducing their holdings (Gürerk et al., 2014 ). In the capital market, it is generally believed that institutional investors possess superior investment strategies due to their information and financial advantages, and their stock selling behavior may trigger a 'herding effect,' leading to the risk of corporate stock prices falling and reputation damage (Hsieh et al., 2020 ). Therefore, if a company refuses to meet the environmental demands of GIs, they may sell or reduce their shareholdings to convey their dissatisfaction to other stakeholders and force the company to improve its GGP. Enhancing corporate GGP can also mitigate FDR. On the one hand, GIs can leverage governance measures such as optimizing a company's environmental strategies and risk management frameworks (Hyatt & Berente, 2017 ). These efforts help improve resource utilization efficiency and reduce the costs associated with green transformation, enabling firms to better withstand external shocks, such as fluctuations in energy prices, that impact production and operations. On the other hand, GIs can strengthen internal oversight systems by improving monitoring mechanisms over management's allocation of resources for environmental governance and by implementing performance evaluations for green management. Such measures reduce the likelihood of penalties or legal actions stemming from non-compliance with environmental standards or inadequate pollution control facilities. By minimizing cash outflows due to environmental fines and mitigating reputational damage that could adversely affect corporate profitability (Moore & Ghahramani, 2013 ), these actions help prevent firms from falling into financial distress. 2.1.2. GIS, Financing Constraints, and FDR Green credit policies require banks and other financial institutions to consider environmental factors as crucial criteria for credit assessment, raising the financing threshold for manufacturing companies (Wang & Wang, 2023 ). This paper proposes that GIS can partially bridge the funding gap for corporate green transition through capital infusion. At the same time, they transmit a 'green' signal to the capital market, improving financing conditions and attracting additional green funding support, thereby reducing FDR. Firstly, GIS directly provides capital support to enterprises, effectively serving as a cash injection that enhances their liquidity, reduces financial leverage, and ensures financial security for green technology research and development as well as the transition to sustainable production (Zhang et al., 2024 ). Secondly, GIS can signal to the market a company's commitment to environmental sustainability and its transition toward green manufacturing, thereby strengthening investor confidence in the company's long-term growth potential. Based on this, other investors may choose to invest in the company in anticipation of excess returns driven by its green transition potential, thereby expanding its financing channels and alleviating its financing constraints (FC) (Hao, 2023 ). Once FC are effectively alleviated, companies can opt for more suitable financing instruments and structures, reducing their dependence on a single source of financing. This not only lowers financing costs but also diversifies financing risks, which enhancing corporate liquidity, improving overall financial stability, and reducing FDR (Mertzanis et al., 2024 ). Based on the above analysis, this article proposes the following hypotheses: H1: GIS can effectively mitigate FDR of manufacturing companies. H2a: GIS can mitigate FDR of manufacturing companies by improving GGP. H2b: GIS can mitigate FDR of manufacturing companies by alleviating FC. 2.2. The Moderating Role of Institutional Logic in the Relationship Between GIS and FDR Exploring how different regional 5 institutional environments moderate the effectiveness of green investment has become a critical theoretical issue, as the urgent task of addressing environmental challenges and ensuring the smooth transition of real-economy companies has surpassed the capacity of government or market forces alone (Bartley, 2007 ). The institutional logic perspective in institutional theory provides a theoretical basis for addressing this issue. Institutional logic originates from different and relatively enduring institutional orders (such as the state, market, family, etc.), which are perpetuated through integration into stable practices (Lee & Lounsbury, 2015 ). Yan et al. ( 2021 ) divides institutional logic into financial logic and environmental logic. The financial logic aims at maximizing shareholder wealth, while the environmental logic aims at environmental protection and sustainable development. Both operate based on the government's legal regulations and enforcement system. As the builder of the core institutional order of society, the government usually supports a variety of policy objectives, including market economic development and ecological environmental protection, by promulgating laws and regulations and implementing regulatory reviews. In the research scenario of this article, GIs are important participants in the Chinese capital market with dual investment objectives of financial and environmental performance. Their investment behavior can be defined as a hybrid practice of using financial logic to serve environmental logic objectives (Yan et al., 2021 ). The government can support the objectives of environmental logic by enacting environmental protection policies, while maintain market order and supporting the goals of financial logic by issuing regulatory laws and enhancing the protection of shareholders' legitimate rights and interests (Thronton et al., 2012).The mitigating effect of GIS on FDR depends on the 'legitimacy' conferred upon them, i.e., to what extent green investment practices are recognized and supported by the local public under financial or environmental logic (Alrazi et al., 2015 ). From the perspective of financial logic, since green investment operates under financial logic, stronger strength of shareholder protection (SSP) may promote green investment practices by providing more instrumental support for achieving the environmental objectives of GIs. Additionally, the cultural normative influence of green investment may also be stronger. Specifically, as the government improves the SSP—thus increasing the legitimacy of financial logic—GIs may express their demands more directly when entering companies. In turn, companies may become more willing or compelled to pay attention to these demands and make changes. Therefore, in regions where shareholder legitimacy is higher, the normative influence of green investment may be more substantial and effective in mitigating FDR. From the perspective of environmental logic, environmental protection policies and green investment practices share the same ultimate institutional goal: the preservation of the ecological environment. However, the central and dominant role of the government may institutionalize a homogeneous 'belief' that environmental protection is primarily driven by government regulations. Strong governmental regulations on environmental protection might undermine the legitimacy of environmental initiatives from private sectors, such as investment institutions, thereby leaving less space for the practice of green investment. For instance, Arjaliès & Durand ( 2019 ) argued that the appropriate tool to combat excessive carbon emissions is the rule of law through democratic action, rather than environmentally conscious investors. Therefore, as the intensity of environmental regulation (IER) increases, the marginal governance role played by GIs tends to be weakened. However, as the environmental logic receives broader attention and acceptance in society through various mechanisms (including the normative influence of laws), government environmental policies are gradually perceived as the minimum requirements that companies need to meet for survival. This, in turn, reduces the crowding out of the legitimacy of other participants (such as GIs), lessening the normative influence on green investment practices, and potentially creating a synergistic effect with green investment practices in preventing and controlling FDR. Based on the analysis above, the article proposes the following hypotheses: H3a: SSP can positively moderate the negative relationship between GIS and the FDR of manufacturing companies. H3b: IER can exhibit a U-shaped moderating effect on the negative relationship between GIS and the FDR of manufacturing companies, initially inhibiting and then promoting it. The conceptual model shown in Fig. 1 explains the relationships among the proposed constructs. 3. Material and Methods 3.1. Sample Collection This article selects A-share listed manufacturing companies in China from 2012 to 2022 as the sample, excluding ST, ST*, and PT samples, financial and insurance samples, as well as samples with missing data. GIS-related data are collected manually, and all data come from CSMAR and CNRDS databases. After matching the above data, 17,025 annual observations are finally obtained, including 4,596 observations for firms with GIS and 12,429 observations for firms without GIS. To eliminate the impact of extreme values on causal identification, we also perform 1% winsorization on the main continuous variables. Due to the significant imbalance in the sample sizes between firms with and without GIS, as well as potential differences in firm characteristics and market features, there may be bias in the estimates from the regression models. Therefore, this article further applies the Propensity Score Matching (PSM) method to select a control group of samples with similar characteristics and a comparable number to the treatment group. Specifically, (1) all control variables in Model (1) are set as matching variables; (2) two datasets are constructed using two different methods: a cross-sectional PSM is created by applying the nearest neighbor matching method to find the optimal control group without GIS for each firm with GIS that satisfies the common support condition; (3) a year-by-year matching approach is used to match the firm samples annually. As a result, 7,046 observations are obtained, including 3,437 samples with GIS ownership and 3,609 samples without GIS ownership (the balance test for the two matched datasets are presented in Supplementary Table S1 ). 3.2. Model Construction To test the impact of GIS on FDR, this article establishes the following model: $$\:\begin{array}{c}{AFDR}_{i,t}={\beta\:}_{0}+{\beta\:}_{1}{GIS}_{i,t-1}+{\beta\:}_{k}{Controls}_{i,t-1}+{\theta\:}_{i}+{\omega\:}_{r}\times\:{\tau\:}_{j}+{\tau\:}_{j}\times\:{\mu\:}_{t}+{\mu\:}_{t}+{\epsilon\:}_{i,t}\end{array}$$ 1 In Model (1), the dependent variable AFDR represents the FDR of manufacturing firms adjusted for their 'pollution attributes.' 6 The calculation formula is as follows: $$\:\begin{array}{c}{AFDR}_{i,t}={GC}_{i,t}\times\:{FDR}_{i,t}\end{array}$$ 2 Measurement of a firm's pollution attributes ( GC ): The first step involves scraping data on corporate pollution emissions from annual reports, sustainability reports, social responsibility reports, and other sources for listed companies 7 . Since the units of various pollutants may differ, the following approach is used in this study: (1) Standardize the raw data of pollutant indicators using the extreme value method, where represents the standardized emissions of the k -th pollutant; (2) Calculate the adjustment coefficient for each pollutant: $$\:{\rho\:}_{ikt}=\frac{{pollut}_{ikt}}{\stackrel{-}{{pollut}_{kt}}}$$ 3 Here, \(\:\stackrel{-}{{pollut}_{kt}}\) represents the mean emission level of the k -th pollutant in the sample. The comprehensive pollution emission indicator for firm i can be expressed as: $$\:{P}_{it}=\frac{1}{5}\sum\:({pollut}_{ikt}\times\:{\rho\:}_{ikt})$$ 4 In the second step, to eliminate differences in production scale across firms and enable a fair assessment and comparison of their pollution attributes, this study adopts the indicator construction approach proposed by Ge et al. ( 2020 ) and Li & Lu ( 2018 ). The pollution attributes of a firm are measured as the ratio of its standardized output value to the standardized pollutant emission indicators: $$\:{GC}_{it}={V}_{it}/{P}_{it}$$ 5 In essence, this metric reflects how much output a firm can generate for each unit of pollutant emitted. A higher value of GC indicates greater production cleanliness (or resource use efficiency) and weaker pollution attributes. For measuring overall FDR, this study employs the modified Z-score model developed by Altman ( 2002 ). Agarwal & Taffler ( 2008 ) demonstrated that accounting-based Z-score models outperform market-based models in predicting bankruptcy. Altman et al. ( 2017 ) further validated the utility of the Z-score as a reliable indicator for bankruptcy risk prediction. A higher Z-score indicates a lower overall FDR faced by the firm. GIS represents the two key explanatory variables of this article: whether GIs hold shares ( GIS_dum ) and the proportion of shares held by GIs ( GIS_pct ) 8 . The variable GIS_dum is assigned a value of 1 when a firm has GIS and 0 otherwise, to test whether GIS influences a firm's FDR. The variable GIS_pct is measured as the average proportion of GIS over the four quarters of the year, to examine the extent to which varying levels of GIS affect the reduction in a firm's FDR. The specific method to screen whether a fund is a 'green investor' is: (1) Obtain the 'Fund Entity Information Table' and 'Stock Investment Details Table' from the CSMAR database Fund Market series, and match the data from these two tables to obtain detailed information on the investment objectives, scope, and strategies of existing funds investing in listed companies; (2) Manually search the 'Investment Objectives' and 'Investment Scope' in the fund details for keywords, including 'ecology,' 'green,' 'low carbon,' 'energy saving,' 'environmental protection,' 'new energy,' and 'emission reduction,' and other related words with clear green orientation. If any of these keywords appear in the investment objectives and scope of the fund, the fund is defined as a 'green investor.' Following the practices in existing literature (Yan et al., 2021 ; Borochin & Yang, 2017 ), this article introduces a series of control variables at both macro and micro levels. Variable definitions are presented in Table 1 . The article also controlled for firm fixed effects (FirmFE), industry 9 -region fixed effects (IndFE&PrvnFE), industry-year fixed effects (IndFE&YearFE), as well as year fixed effects (YearFE), and conducts clustering at the firm level. Subscripts i, j, r, and t denote firm, industry, province, and year respectively. Furthermore, to ensure the reliability of causal identification as much as possible, this paper lag the explanatory variables and all control variables by one period. Table 1 Variable definitions. Variables Definition Data AFDR The FDR of manufacturing firms adjusted for their 'pollution attributes.' Manully collected GIS_dum 1 when a firm has GIS and 0 otherwise Manully collected GIS_pct The average proportion of GIS over the four quarters of the year Manully collected Size The total assets by logarithm. CSMAR Lev The ratio of total liabilities to total assets. CSMAR Age 2021 minus the founding year plus one followed by logarithm. CSMAR Shr3 The ratio of shareholding held by the top three shareholders. CSMAR Indep The ratio of the number of independent directors to the total number of board members. CSMAR Board The total number of board members by logarithm. CSMAR Msalary Total compensation of top three executives divided by one million. CSMAR GDPgrow Regional GDP growth rate. CNRDS 3.3. Statistical Analysis During the transition of manufacturing companies from producing 'brown' to 'green' products, the magnitude of improvement in environmental performance may be greater compared to other sectors, thus offering higher investment returns (Zhong & Peng, 2022 ). According to existing industry data (Fig. 2 10 ), the proportion of manufacturing listed companies in China with GIS increased from 8.80% in 2011 to 44.28% in 2022. Over this period, the scale and scope of GIs expanded annually, and the 'green' issues of manufacturing companies have progressively garnered attention from the capital market. In Table 2 , in the matched sample of listed manufacturing firms, the maximum value of AFDR is 0.2120, with a standard deviation of 0.0352 and a mean of 0.0208, indicating considerable variability in the FDR adjusted for pollution attributes. The mean value of GIS_dum is 0.5049, suggesting a satisfactory matching outcome. The maximum value of GIS_pct is 9.9375, with a standard deviation of 1.9775, reflecting substantial variation in the GIS proportion across the sample, with the highest shareholding ratio being approximately 10%. Other variables are generally consistent with existing literature and will not be repeated here. Table 2 Descriptive statistics. VarName Obs Mean SD Median Min Max AFDR 7046 0.0208 0.0352 0.0082 0.0000 0.2120 GIS_dum 7046 0.5049 0.5000 1.0000 0.0000 1.0000 GIS_pct 7046 0.8782 1.9775 0.0000 0.0000 9.9375 Size 7046 22.1821 1.1253 22.0491 19.3012 25.9940 Lev 7046 0.3948 0.1831 0.3916 0.0523 0.9136 Age 7046 2.0930 0.7564 2.0794 0.6931 3.3673 Shr3 7046 13.1873 21.8828 0.5560 0.1859 75.7893 Indep 7046 0.3842 0.0929 0.3750 0.0000 0.6250 Board 7046 2.2950 0.2393 2.3026 1.6094 2.8904 Msalary 7046 0.0287 0.0234 0.0220 0.0020 0.1440 GDPgrow 7046 0.0828 0.0410 0.0844 -0.0361 0.2460 To ensure the validity of parameter estimation, this paper conducts Pearson correlation analysis on the main variables in the sample data, and the results are presented in Table 3 : Without considering the influence of other factors, GIS_dum , and GIS_pct have significant positive correlations with AFDR , which preliminarily verifies H1. Additionally, the correlation coefficients between the main variables are all below 0.6, indicating that there is no serious multicollinearity problem. Table 3 Correlation Statistics. Variables 1 2 3 4 5 6 7 8 9 10 1.AFDR 2.GIS_dum 0.089*** 3.GIS_pct 0.169*** 0.440*** 4.Size 0.488*** -0.004 0.141*** 5.Lev 0.159*** -0.004 0.049*** 0.496*** 6.Age 0.287*** 0.001 0.048*** 0.553*** 0.333*** 7.Shr3 0.002 0.004 -0.010 0.084*** 0.010 -0.022* 8.Indep 0.027** -0.009 -0.027** -0.033*** -0.006 -0.038*** -0.020* 9.Board 0.108*** -0.009 -0.006 0.216*** 0.127*** 0.194*** -0.217*** -0.065*** 10.Msalary 0.266*** 0.002 0.088*** 0.348*** 0.071*** 0.143*** 0.290*** -0.032*** 0.008 11.GDPgrow 0.004 -0.003 0.008 -0.047*** -0.016 -0.039*** 0.091*** -0.059*** -0.031*** -0.030** 4. Empirical results and analysis 4.1. Baseline Results This study controls for a range of factors that may influence a firm's FDR; however, there inevitably exist unobservable third-party factors that may affect the model, leading to potential omitted variable bias. Additionally, there may be reverse causality between FDR and GIS, resulting in an endogeneity issue. To address the potential endogeneity problem in the model, this study constructs instrumental variables for GIS and applies two-stage least squares (2SLS) estimation. Using exogenous policy shock is one of the main approaches in the existing literature to find a instrumental variable (Giannetti et al., 2015 ). In the research context of this paper, the "Green Investment Guidelines (For Trial Implementation)" (hereinafter referred to as the "Guidelines") issued by the Asset Management Association of China in 2018, define the concept of 'green investment' 11 and provide universal guidance for fund managers on their green investment activities and internal institutional development. On one hand, the "Guidelines" aim to help fund managers establish standards for green investment management based on their own conditions, providing important guidelines for GIs' investment practices. On the other hand, institutional investors or companies cannot foresee whether or when the "Guidelines" will be issued, nor can they predict or intervene in the content of the "Guidelines" in the short term, making the "Guidelines" an exogenous policy that can impact GIS. This creates a valuable opportunity for constructing the instrumental variable in this article. We use the intersection of the dummy variable post ( post = 1 for 2018 and onwards, post = 0 otherwise) indicating the period before and after the release of the "Guidelines," and the greenness level of GIs ( gl ) 12 as the instrumental variable for GIS ( Instrument = post × gl ). Table 4 shows that in the first-stage regression, the coefficients of Instrument for both GIS_dum and GIS_pct are significantly positive. In the second-stage regression, the coefficients of GIS_dum and GIS_pct are also significantly positive. The KP rk Wald F-statistic exceeds the critical value for the Stock-Yogo weak instrument identification F-test at the 10% significance level, indicating that there is no weak instrument problem. Additionally, the Hansen J-statistic is close to zero, and we cannot reject the null hypothesis that the instrument is valid, suggesting that the selected instrumental variables are appropriate. These results indicate that GIS can effectively mitigate the FDR of manufacturing companies. Table 4 The results of the baseline regression. Variables (1): First stage (2): Second stage (3): First stage (4): Second stage GIS_dum AFDR GIS_pct AFDR GIS_dum 0.0263*** (6.77) GIS_pct 0.0027*** (7.03) Instrument 0.0669*** 0.6447*** (14.52) (24.65) Size -0.1215*** 0.0105*** -0.0872 0.0075*** (-5.69) (8.60) (-1.19) (6.93) Lev 0.0084 -0.0135*** -0.3550 -0.0123*** (0.11) (-3.89) (-1.55) (-4.01) Age 0.0479* -0.0048*** 0.0499 -0.0037*** (1.72) (-3.44) (0.68) (-2.94) Shr3 0.0010*** -0.0000* -0.0042*** 0.0000 (2.67) (-1.73) (-3.54) (0.19) Indep -0.1133 0.0092*** -0.4485* 0.0074*** (-1.51) (2.90) (-1.79) (2.86) Board 0.0794** -0.0013 -0.0695 0.0010 (2.45) (-0.86) (-0.66) (0.81) Msalary -1.1782** 0.1215*** -1.0973 0.0936*** (-2.57) (4.92) (-0.77) (4.24) GDPgrow -0.0591 0.0074 1.6321*** 0.0014 (-0.41) (1.19) (3.68) (0.27) FirmFE YES YES YES YES PrvFE&YearFE YES YES YES YES IndFE&YearFE YES YES YES YES YearFE YES YES YES YES Obs 7046 7046 7046 7046 KP rk Wald F 210.90 607.68 Hansen J 0.000 0.000 4.2. Robustness Tests 4.2.1. Heckman two-stage analysis Theoretically, better financial performance indicates fewer developmental bottlenecks and better growth potential for a company, making it more likely for GIs to choose such companies as investment targets. This suggests that stock selection by GIs may not be random but rather endogenously determined by the companies themselves. To control for this 'self-selection' problem as much as possible, we employ the Heckman ( 1979 ) two-stage model. Existing research has found that the momentum effect of stocks can affect the proportion of institutional investor shareholding (Gompers & Metrick, 2001 ). Furthermore, evaluations by external rating agencies also affect the choice of investment targets by institutional investors, but neither has a significant impact on FDR. Therefore, in the first stage of regression, this paper introduces Mom (Cumulative monthly return of the company over the past 2 to 12 months) and Escore (the E-score in the ESG rating of the company over the past year) as exogenous explanatory variables. The dummy variable GIS_dum is used as the dependent variable in the Probit regression and the inverse Mills ratio ( IMR ) is calculated. The results are presented in Column (1) of Panel A in Table 5 . In the second stage, the IMR is incorporated into Model (1) for subsequent regression. The results are presented in Columns (2) and (3), where the coefficients of GIS_dum and GIS_pct are both significantly positive, indicating that, after adding the IMR to the regression model to address 'self-selection' problem, the baseline results remain robust. Table 5 Robustness Tests. Panel A: Robustness tests based on the Heckman two-stage model, replacing the key explanatory and dependent variables. Variables (1) (2) (3) (4) (5) (6) (7) (8) AFDR AFDR AFDR AFDR AFDR oscore AFDR oscore AFDR zmscore AFDR zmscore GIS_sum 0.0011*** (6.64) GIS_dum 0.0023*** -0.0049** -0.0015*** (3.99) (-2.53) (-3.31) GIS_pct 0.0013*** -0.0037*** -0.0008*** (5.16) (-4.54) (-4.32) Mom 0.5631*** (15.45) Escore 0.0044* (1.73) IMR -0.0103*** -0.0090*** (-6.22) (-5.78) Size 0.0371* 0.0106*** 0.0099*** 0.0089*** -0.0591*** -0.0569*** -0.0059*** -0.0054*** (1.77) (7.10) (6.72) (6.27) (-8.85) (-8.66) (-5.45) (-5.05) Lev -0.2392** -0.0105** -0.0104** -0.0144*** 0.0147 0.0128 0.0230*** 0.0226*** (-2.29) (-2.54) (-2.53) (-3.62) (1.13) (0.99) (6.19) (6.07) Age -0.0123 -0.0038 -0.0033 -0.0001 0.0272*** 0.0271*** 0.0017 0.0016 (-0.41) (-1.53) (-1.34) (-0.08) (3.60) (3.60) (1.18) (1.14) Shr3 0.0033 0.0000 0.0000 0.0001 -0.0002 -0.0003 -0.0000 -0.0000 (1.56) (0.26) (0.42) (1.21) (-1.04) (-1.09) (-0.30) (-0.36) Indep -0.3240* 0.0031 0.0026 0.0000 0.0108 0.0112 0.0020 0.0021 (-1.69) (1.01) (0.86) (0.02) (1.09) (1.14) (0.88) (0.94) Board 0.0247 0.0007 0.0010 0.0004 -0.0023 -0.0032 0.0001 -0.0002 (0.34) (0.48) (0.72) (0.30) (-0.48) (-0.66) (0.04) (-0.12) Msalary -0.6153 0.1072*** 0.1019*** 0.1065*** -0.4508*** -0.4310*** -0.0716*** -0.0671*** (-0.79) (4.09) (3.94) (3.98) (-4.46) (-4.34) (-3.41) (-3.23) GDPgrow -0.4836 0.0152* 0.0143 0.0132 -0.0480 -0.0459 -0.0040 -0.0035 (-0.79) (1.66) (1.64) (1.54) (-1.57) (-1.54) (-0.50) (-0.45) Cons -0.8203 -0.2012*** -0.1873*** -0.1768*** 1.1911*** 1.1463*** 0.1058*** 0.0954*** (-1.60) (-6.16) (-5.84) (-5.75) (8.27) (8.06) (4.34) (3.94) FirmFE YES YES YES YES YES YES YES YES PrvFE&YearFE YES YES YES YES YES YES YES YES IndFE&YearFE YES YES YES YES YES YES YES YES YearFE YES YES YES YES YES YES YES YES Obs 7,023 7,019 7,019 7,046 7,046 7,046 7,046 7,046 Adj_R2 / Pseudo R2 0.159 0.889 0.891 0.845 0.902 0.903 0.827 0.828 Panel B: Robustness tests based on controlling for time trends. Variables (1) (2) (3) (4) AFDR AFDR AFDR AFDR GIS_dum 0.0027*** 0.0027*** (5.32) (5.26) GIS_pct 0.0014*** 0.0015*** (6.41) (6.45) Controls×f(T) YES YES Controls×T_dummy YES YES Size 0.0167*** 0.0160*** 0.0111*** 0.0103*** (6.60) (6.45) (6.48) (6.16) Lev -0.0231** -0.0203** -0.0171*** -0.0171*** (-2.45) (-2.17) (-3.51) (-3.64) Age 0.0008 0.0001 0.0077 0.0073 (0.22) (0.03) (0.90) (0.86) Shr3 -0.0276* -0.0289** 0.0000 0.0001 (-1.94) (-2.04) (0.52) (0.78) Indep 0.0043 -0.0001 -0.0097 -0.0096 (0.23) (-0.01) (-0.60) (-0.61) Board -0.0047 -0.0044 0.0029 0.0027 (-0.70) (-0.66) (0.56) (0.53) Msalary 0.3261*** 0.3234*** 0.0751* 0.0656* (2.74) (2.74) (1.91) (1.73) GDPgrow 0.0763 0.0712 0.0322 0.0067 (1.34) (1.28) (0.71) (0.81) Cons -0.2254*** -0.2051*** -0.2304*** -0.2117*** (-6.75) (-6.38) (-6.77) (-6.45) FirmFE YES YES YES YES PrvFE&YearFE YES YES YES YES IndFE&YearFE YES YES YES YES YearFE YES YES YES YES Obs 7,046 7,046 7,046 7,046 Adj_R 2 0.845 0.848 0.846 0.849 4.2.2. Alternative measures of key explanatory variables To further enhance the reliability of the research findings, this article selects the logarithm of the number of GIs present in the company during the year ( GIS_sum ) to re-measure the explanatory variables. The regression results are presented in Columns (4) of Panel A in Table 5 , where the coefficients of GIS_sum is significantly positive, indicating that the baseline results remain robust. 4.2.3. Alternative measures of explained variable In addition to the Z-score, the O-score and ZM-score are also accounting-based models for measuring FDR (Tykvová & Borell, 2012 ). This article follows the methods of Ohlson ( 1980 ) and Zmijewski ( 1984 ) to remeasure the dependent variable using the O-score and ZM-score and perform regression analysis 13 . The coefficients of GIS_dum and GIS_pct are both significantly negative in Columns (5) to (8) of Panel A in Table 5 , validating the robustness of the baseline regression results. 4.2.4. Control for time trends Other influencing factors, such as the time trend of control variables, may also impact the regression results when there are differences between sample companies with or without GIS (with high or low proportion of GIS). Therefore, referring to the research design of Moser & Voena ( 2012 ), this paper constructs interaction terms between all control variables and a third-order polynomial of time trends ( \(\:f\left(T\right)=T+{T}^{2}+{T}^{3}\) ), as well as interaction terms between all control variables and time dummy variables ( T_dummy ). These two sets of interaction terms are separately added to the baseline regression model. As shown in Columns (1) to (4) of Panel B in Table 5 , after adding the control interaction terms mentioned above, the coefficients of the main explanatory variables remain significantly positive, indicating that the previous regression results are still robust. 4.3. Mechanism Analysis 4.3.1. Mediation analysis According to the research hypotheses, the role of GIS in mitigating FDR is primarily achieved through two channels: enhancing GGP and alleviating FC. Corporate green governance refers to a management and decision-making approach designed to integrate environmental sustainability into the core business operations and strategies of a company. Its goal is to minimize adverse environmental impacts while achieving sustainable development (Hussain, 1999 ). GGP, on the other hand, quantitatively reflects various aspects of the effectiveness of corporate green governance, including the evaluation of the achievement of sustainable development goals and the assessment of environmental performance. In the existing literature, the measurement of GGP is not yet standardized. To avoid the bias of 'greenwashing' that may arise from considering only the environmental information disclosed in corporate annual reports, and to comprehensively account for both the extent of a company's internal environmental management systems and the evaluations of it's environmental performance by external stakeholders, including the government and the public, we adopt a more comprehensive and comparable Janis-Fadner coefficient to measure GGP (Table 6 14 ), following the indicator design approach of Bansal & Hunter ( 2003 ) and Li et al. ( 2023 ). The indicator's construction basis is listed in Table 6 . Where p represents the positive score, with a value of 1 if the company meets the conditions, and 0 otherwise; q represents the negative score, with a value of -1 if the company meets the conditions, and 0 otherwise. GGP ranges from − 1 to 1, with a value closer to 1 indicating higher corporate GGP. The calculation formula is as follows: Table 6 Basis for Selection of Corporate GGP Indicators. p q Established a major environmental incident emergency response mechanism. Pollutant emissions did not meet standards. Received honors and awards in the field of environmental protection. Experienced a major sudden environmental incident. Obtained ISO 4000 series certification. Had environmental violations. Participated in environmental protection actions. Involved in environmental petition cases. Ranked in the top 30% of the sample in environmental scores in ESG scoring. Ranked in the bottom 30% of the sample in environmental scores in ESG scoring. Achieved the highest level in environmental rating in ESG rating. Achieved the lowest level in environmental rating in ESG rating. $$\:{GGP}_{i,t}=\left\{\begin{array}{c}\frac{{p}_{i,\:t}^{2}-{p}_{i,t}\times\:\left|{q}_{i,t}\right|}{{\left({p}_{i,t}+\left|{q}_{i,t}\right|\right)}^{2}},\:if\:{p}_{i,t}>\left|{q}_{i,t}\right|\\\:0,\:if\:{p}_{i,t}=\left|{q}_{i,t}\right|\\\:\frac{{p}_{i,t}\times\:\left|{q}_{i,t}\right|-{q}_{i,\:t}^{2}}{{\left({p}_{i,t}+\left|{q}_{i,t}\right|\right)}^{2}},\:if\:{p}_{i,t}<\left|{q}_{i,t}\right|\end{array}\right.$$ 6 The WW index is used in this paper to measure corporate FC (Whited & Wu, 2006 ). Existing research suggests that in the three-step mediation model, the regression in the third step includes both the mediator and the dependent variable, which often leads to a reduction in statistical significance. Moreover, failing to achieve significance in the second or third step does not necessarily indicate the absence of a mediation effect (Hayes, 2009 ). Following the approach of Jiang ( 2022 ), this study adopts a two-step method to construct the mediation model.: Step 1 as shown in Model (1); Step 2 as shown in Model (7): $$\:\begin{array}{c}{Mediator}_{i,t}={\alpha\:}_{0}+{\alpha\:}_{1}{GIS}_{i,t-1}+{\alpha\:}_{k}{Controls}_{i,t-1}+{\theta\:}_{i}+{\omega\:}_{r}\times\:{\tau\:}_{j}+{\tau\:}_{j}\times\:{\mu\:}_{t}+{\mu\:}_{t}+{\epsilon\:}_{i,t}\end{array}$$ 7 Where Mediator represents the mediation variable, and the meanings of the other parameters are the same as described in Model (1). We primarily observe whether the coefficient \(\:{\alpha\:}_{1}\) is significant. If the coefficient \(\:{\beta\:}_{1}\) in Model (1) and the coefficient \(\:{\alpha\:}_{1}\) in Model (7) both show significance, it indicates that GIS_dum and GIS_pct can indeed affect AFDR through the mediation variables. The regression results in Columns (1) and (2) of Table 7 show that the coefficients of GIS_dum and GIS_pct on GGP are both significantly positive, and on FC are both significantly negative. This indicates that an increase in GIS behavior and the proportion of shareholding can both enhance GGP and alleviate FC, thereby mitigating FDR, H2a and H2b are supported. Table 7 The results of mediation analysis. Variable (1) (2) (3) (4) GGP GGP FC FC GIS_dum 0.0798*** -0.0084*** (4.58) (-6.62) GIS_pct 0.0110** -0.0023*** (2.45) (-8.25) Size 0.1718*** 0.1684*** -0.0064** -0.0048 (13.03) (12.58) (-2.19) (-1.64) Lev 0.0796 0.0794 0.0105 0.0098 (1.18) (1.17) (1.20) (1.12) Age -0.0225 -0.0204 0.0068* 0.0062* (-1.32) (-1.19) (1.90) (1.74) Shr3 0.0022** 0.0024** -0.0002*** -0.0002*** (2.36) (2.56) (-2.59) (-2.75) Indep 0.0225 0.0172 -0.0042 -0.0027 (0.23) (0.17) (-0.57) (-0.36) Board 0.0913** 0.0949** -0.0023 -0.0033 (2.27) (2.36) (-0.71) (-0.98) Msalary 0.9557* 0.9127* -0.0778* -0.0602 (1.82) (1.72) (-1.81) (-1.40) GDPgrow 0.0321 0.0196 -0.0045 -0.0046 (0.10) (0.06) (-0.23) (-0.24) Cons -4.1532*** -4.0578*** -0.8968*** -0.9334*** (-15.20) (-14.64) (-14.06) (-14.78) FirmFE YES YES YES YES PrvFE&YearFE YES YES YES YES IndFE&YearFE YES YES YES YES YearFE YES YES YES YES Obs 7,046 7,046 7,046 7,046 Adj_R 2 0.158 0.156 0.730 0.731 4.3.2. Moderation analysis Based on the analysis above, from the perspective of institutional logic, both investors and corporate managers are influenced by normative pressures from two institutional orders——environment and market. This is reflected in the variation of environmental regulations and investor practices, which may affect the effectiveness of GIS in mitigating FDR. In the study by Yan et al. ( 2021 ), SSP is defined as 'the extent to which shareholder rights are adequately enforced across different countries.' This paper aims to examine the degree of shareholder protection across different regions of China. Therefore, we follow the indicator selection method of Yan et al. ( 2021 ) and use the sub-index of 'Development of Market Intermediaries and Legal System Environment' from the 'Marketization Process Index' as a proxy variable for SSP. This sub-index is derived from the comprehensive calculation of 'Development of Market Intermediaries,' 'Maintenance of the Legal Environment for Markets,' and 'Protection of Intellectual Property Rights,' which collectively reflect the degree of investor protection in a region. Additionally, since the indicators in the database are available only up to 2019, this paper extrapolates the data for 2020–2021 based on the average growth rate of the indices from previous years. In selecting the variable for IER, on one hand, higher government investment in environmental governance is often closely linked to stricter environmental laws and regulations; on the other hand, local communities and the public are likely to pay more attention to environmental issues as more government funds are allocated to environmental governance. This, in turn, compels local companies to make adjustments to comply with regulations and meet societal expectations, resulting in greater environmental regulatory pressure (Dabbous et al., 2023 ; Xu & Xu, 2023). Therefore, this paper uses the proportion of annual regional expenditure on air and water pollution control relative to the total industrial output of that year as a proxy variable for IER. This article constructs the following models to test H3a and H3b: $$\:{AFDR}_{i,t}={\gamma\:}_{0}+{\gamma\:}_{1}{GIS}_{i,t-1}+{\gamma\:}_{2}{Moderator}_{r,t-1}+{\gamma\:}_{3}{Moderator}_{r,t-1}\times\:{GIS}_{i,t-1}+{\gamma\:}_{k}{Controls}_{i,t-1}+{\theta\:}_{i}+{\omega\:}_{r}\times\:{\tau\:}_{j}+{\tau\:}_{j}\times\:{\mu\:}_{t}+{\mu\:}_{t}+{\epsilon\:}_{i,t}$$ 8 $$\:{AFDR}_{i,t}={\gamma\:}_{0}+{\gamma\:}_{1}{GIS}_{i,t-1}+{\gamma\:}_{2}{Moderator}_{r,t-1}+{\gamma\:}_{3}{Moderator}_{r,t-1}\times\:{GIS}_{i,t-1}+{\gamma\:}_{4}{Moderator}_{r,t-1}^{2}+{\gamma\:}_{5}{GIS}_{r,t-1}^{2}+{\gamma\:}_{6}{Moderator}_{r,t-1}^{2}\times\:{GIS}_{r,t-1}^{2}+{\gamma\:}_{k}{Controls}_{i,t-1}+{\theta\:}_{i}+{\omega\:}_{r}\times\:{\tau\:}_{j}+{\tau\:}_{j}\times\:{\mu\:}_{t}+{\mu\:}_{t}+{\epsilon\:}_{i,t}$$ 9 Where Moderator represents moderating variable (abbreviated as M in Table 8 ), and the meanings of other parameters are the same as described in Model (1). We mainly observe the significance of the coefficient \(\:{\gamma\:}_{3}\) in Model (8) and the significance of the coefficients \(\:{\gamma\:}_{3}\) and \(\:{\gamma\:}_{6}\) in Model (9). If \(\:{\gamma\:}_{3}\) is significant in Model (8), it indicates that M has a significant moderation effect. If both \(\:{\gamma\:}_{3}\) and \(\:{\gamma\:}_{6}\) are significant and have opposite signs in Model (8), it indicates M exerts a U-shaped moderation effect. Table 8 The results of moderation analysis. Variables (1) (2) (3) (4) (5) (6) (7) (8) M: SSP M: IER AFDR AFDR AFDR AFDR AFDR AFDR AFDR AFDR GIS_dum -0.00013 -0.00214 0.0041*** 0.0043*** (-0.11) (-0.77) (6.21) (5.39) GIS_dum*M 0.00030*** 0.00074 -0.0069*** -0.0108** (2.65) (1.33) (-2.68) (-2.31) GIS_dum*M 2 -0.00002 0.0091* (-0.83) (1.83) GIS_pct 0.00019 0.00103 0.0017*** 0.0019*** (0.43) (1.14) (10.00) (2.96) GIS_pct*M 0.00011*** 0.00004 -0.0015* -0.0047*** (2.93) (0.52) (-1.83) (-3.61) GIS_pct 2 -0.00007 -0.0001 (-0.89) (-0.46) GIS_pct 2 *M 2 0.00001 0.0010*** (0.90) (3.26) M 2 -0.00003 -0.00004 -0.0048 -0.0018 (-0.85) (-1.42) (-1.17) (-0.79) M 0.00003 0.00047 0.00006 0.00085 -0.0024 -0.0004 -0.0058*** -0.0002 (0.14) (0.72) (0.24) (1.37) (-1.14) (-0.10) (-2.85) (-0.07) Size 0.00990*** 0.00992*** 0.00891*** 0.00893*** 0.0101*** 0.0099*** 0.0091*** 0.0073*** (6.91) (6.92) (6.50) (6.55) (13.78) (13.58) (12.50) (5.26) Lev -0.01456*** -0.01428*** -0.01383*** -0.01354*** -0.0138*** -0.0147*** -0.0141*** -0.0120*** (-3.71) (-3.61) (-3.57) (-3.46) (-5.35) (-5.71) (-5.54) (-2.94) Age -0.00009 0.00017 0.00012 0.00038 -0.0010 0.0001 0.0002 -0.0041** (-0.05) (0.10) (0.06) (0.22) (-0.86) (0.05) (0.16) (-2.44) Shr3 0.00004 0.00004 0.00005 0.00005 -0.0000 0.0000 0.0001 -0.0000 (0.92) (0.93) (1.15) (1.17) (-0.93) (1.27) (1.63) (-0.40) Indep -0.00005 -0.00008 -0.00046 -0.00056 0.0054** 0.0006 0.0007 0.0059* (-0.02) (-0.03) (-0.17) (-0.20) (2.07) (0.22) (0.26) (1.79) Board 0.00041 0.00043 0.00080 0.00081 0.0009 0.0003 0.0009 0.0020 (0.31) (0.32) (0.60) (0.60) (0.78) (0.29) (0.76) (1.38) Msalary 0.11637*** 0.11633*** 0.10486*** 0.10394*** 0.1206*** 0.1224*** 0.1100*** 0.1248*** (4.36) (4.35) (4.13) (4.10) (7.29) (7.36) (6.67) (4.61) GDPgrow 0.01322 0.01253 0.01211 0.01156 0.0008 0.0132* 0.0128 0.0011 (1.47) (1.41) (1.42) (1.37) (0.15) (1.67) (1.64) (0.19) Cons -0.20032*** -0.20239*** -0.17947*** -0.18329*** -0.2038*** -0.2004*** -0.1823*** -0.1380*** (-6.40) (-6.50) (-6.02) (-6.21) (-13.02) (-12.78) (-11.70) (-4.98) FirmFE YES YES YES YES YES YES YES YES PrvFE&YearFE YES YES YES YES YES YES YES YES IndFE&YearFE YES YES YES YES YES YES YES YES YearFE YES YES YES YES YES YES YES YES Obs 7,046 7,046 7,046 7,046 6,942 6,943 6,941 6,942 Adj_R2 0.841 0.841 0.842 0.844 0.840 0.841 0.844 0.836 Columns (1) to (4) of Table 8 report the moderation effects of SSP on the relationship between GIS and FDR. The results in Columns (1) and (3) show that the regression coefficients of the interaction term between SSP and GIS_dum (and GIS_pct ) are significantly positive. This indicates that stronger SSP can promote the effectiveness of GIS in mitigating FDR. In the regression results of Columns (2) and (4), the coefficients of the square of the interaction term between SSP and GIS_dum (and GIS_pct ) are not significant. This implies that the promotion effect of SSP on the negative relationship between GIS and FDR is not nonlinear, thus supporting H3a. Columns (5) to (8) of Table 8 report the moderation effects of IER on the relationship between GIS and FDR. The results in Columns (5) and (7) show that the coefficients of the interaction term between IER and GIS_dum (and GIS_pct ) are significantly negative. This indicates that, overall, IER inhibits the effectiveness of GIS in mitigating FDR. In the regression results of Columns (6) and (8), the coefficients of the square of the interaction term between IER and GIS_dum (and GIS_pct ) are significantly negative, while the coefficients of the squared term of the interaction with GIS_dum and GIS_pct are significantly positive. This suggests that IER exhibits a U-shaped moderation effect, thus supporting H3b. 5. Further Discussions: The Economic Benefits of GIS To further investigate whether GIS can drive comprehensive changes within firms, this article expands the discussion by focusing on firms' environmental performance, financial performance, and capital market performance. The objective is to comprehensively assess whether GIs, through their investment behaviors, positively influence firms' long-term development. Given that the year in which each firm is first held by GIs varies in the context of this article, we adopt the construction logic of the Stacked DID model to develop the GIS_dd variable. (Specifically, firms are assigned a value of 1 for the year they are first held by GIs and for subsequent years within the 2012–2022 period, and 0 otherwise.) GIS_dd serves as the core explanatory variable for a series of regressions 15 aimed at observing the long-term effects of GIS on firm development (regression results presented in Table 9 ). Additionally, the article employs the parallel trend test method to conduct regressions and generate trend visualization results (Fig. 3–5). These figures track the periodical changes in firms' environmental performance, financial performance, and capital market performance after GIS. This approach assesses the persistence and volatility of the governance effects attributed to GIS. Table 9 The results of further discussions. Variable (1) (2) (3) Gre ROA Yield GIS_dd 1.0959** 0.0154*** 0.1723*** (2.5328) (4.3067) (6.7056) Size 3.4273*** -0.0080 -0.3468*** (6.5399) (-1.4758) (-11.7997) Lev 1.1372 -0.1712*** 0.3506*** (0.6534) (-8.1682) (3.6366) Age 3.2309*** -0.0162*** -0.0119 (4.1380) (-3.4805) (-0.3064) ShrHolder3 -0.0321 0.0001 0.0001 (-1.0368) (0.9909) (0.0609) Indep -0.3665 -0.0044 0.0034 (-0.2264) (-0.3649) (0.0404) Board 1.1814 -0.0028 0.0474 (1.5104) (-0.4624) (1.2740) Msalary -17.4250 0.6142*** 1.2536** (-1.5060) (7.5700) (2.2499) GDPgrow 12.4737** 0.0563* 0.1843 (2.5107) (1.9099) (0.7939) Cons -81.4583*** 0.3019*** 7.4726*** (-6.8146) (2.6005) (11.8253) FirmFE YES YES YES PrvFE&YearFE YES YES YES IndFE&YearFE YES YES YES YearFE YES YES YES Obs 7046 7039 7045 Adj_R2 0.5721 0.4102 0.3334 5.1. Environmental Performance In this study, corporate environmental performance is measured by the quantity of green innovations ( Gre ). The regression results, presented in Column (1) of Table 9 , indicate that GIS significantly enhance firms' environmental performance. Figure 3 shows that, during the same year of GIS, there is no significant change in environmental performance. However, a significant improvement begins in the second year after shareholding and persists for four years. The positive effect of GIS on environmental performance emerges in the third period after shareholding but is limited in duration. This delayed effect may result from the fact that green innovation requires prolonged R&D investment and technological accumulation. The influence of GIS typically materializes gradually through their influence on resource allocation and innovation strategies, leading to positive effects that become evident in the third period. However, once green innovation reaches a certain threshold, firms may have already met their core environmental goals, reducing the marginal benefits of further innovation. At this stage, technological development shifts from a focus on 'quantity' to 'quality.' Simultaneously, the focus of GIs may shift from promoting green technology development to other corporate social responsibility objectives, such as optimizing governance structures or enhancing social impact. This dynamic transition may also be influenced by factors such as weakened policy incentives, saturated market demand, and the reallocation of corporate resources. As a result, the growth in green innovation slows, and the positive impact of GIS on environmental performance diminishes accordingly. 5.2. Financial Performance Regarding corporate financial performance, this paper uses Return on Assets ( ROA ) for measurement. The regression results, presented in Column (2) of Table 9 , indicate that GIS significantly enhance firms' financial performance. Figure 4 shows that during the year GI acquire shares, corporate financial performance improves significantly, and this positive effect lasts for five years. It is evident that the positive impact of GIS on financial performance emerges earlier than its impact on environmental performance, although the duration of the effect is limited in both cases. The possible reasons for this effect lie in the fact that GIS often lead to rapid improvements in financial performance through the optimization of corporate governance structures and financing conditions. However, as governance mechanisms stabilize, further financial improvements rely more heavily on firms’ endogenous growth capabilities. Additionally, GIs are often more focused on their investment returns or the advancement of ESG goals. Once initial financial improvements are achieved, their level of intervention may diminish. At the same time, firms may reallocate resources to meet long-term ESG objectives, increasing investments in non-financial areas, which could weaken direct support for financial performance. 5.3. Capital Market Performance Regarding corporate capital market performance, this study adopts the annual year-end stock return rate ( Yield ) as the measurement metric. The regression results, presented in Column (3) of Table 9 , indicate that GIS can significantly enhance firms' capital market performance. Figure 5 shows that in the year GIs acquire shares, corporate capital market performance improves significantly and remains elevated throughout the holding period. The possible explanation is that GIS in manufacturing firms sends a signal to the market indicating support for cleaner production processes. Additionally, corporate reputation is enhanced, leading the capital market to recognize the firm's long-term value, thereby driving a sustained improvement in capital market performance, consistent with the results discussed earlier. The difference in the 'continuity' of improvements between capital market performance and financial performance stems from the differing drivers of their growth. Capital market performance is primarily influenced by signaling effects and investor expectations, rather than by the continuous improvement of a firm's intrinsic financial efficiency. 6. Conclusions and Implications 6.1. Conclusions Using data of A-share listed manufacturing companies in China from 2012 to 2022, this article examines the impact of GIS on FDR and its underlying mechanisms. The main conclusions are summarized as follows: (1) GIS can effectively mitigate FDR of manufacturing companies. (2) GIS primarily mitigate FDR by enhancing GGP and alleviating FC. (3) SSP and IER have a moderation effect on the causal relationship between GIS and FDR. Specifically, the stronger the SSP, the greater the role of GIS in mitigating FDR. However, as IER increases, the effect of GIS on FDR prevention first weakens and then strengthens, displaying a U-shaped trend. (4) The impact of GIS on environmental performance shows a lag effect, while its influence on financial performance is immediate but both effects lack persistence. In contrast, GIS has an immediate impact on capital market performance, which persists over a longer period. 6.2. Implications Based on the above conclusions, this paper provides the following policy recommendations: (1) At the government level, clearer green investment standards and evaluation methods should be formulated to enhance investors' awareness and confidence in companies' green projects. This can be achieved by introducing industry certification, green rating, and other measures to help investors in better identifying and selecting green investment opportunities. Meanwhile, the government should promote the establishment of an environmental risk information disclosure framework and improve the disclosure system to raise companies' awareness of risk prevention and control against climate change. This will enable GIs to fully play their role in steadily promoting companies' green transformation and improving the quality of the ecological environment. (2) At the market level, it is necessary to develop more scientific and reasonable green investment indicators and corporate environmental performance evaluation systems to enhance the consistency of information disclosure across companies. This will enable market investors and creditors to better evaluate the actual environmental commitment and investment value of companies, making it easier for environmentally friendly companies to obtain green financing. Consequently, this will compel companies to improve their environmental governance responsibilities mechanisms and environmental risk prevention and control systems. (3) At the firm level, manufacturing companies should fully recognize the long-term value of corporate environmental governance. By leveraging the participation of GI in environmental governance and the role of attracting external capital investment, companies should establish and improve internal governance mechanisms. This includes formulating environmental management policies and establishing environmental protection responsibility systems, thereby improving the efficiency of resource allocation and facilitating a smooth transition towards green production. Declarations Competing interests The authors declare no competing interests. Ethical approval Ethical approval was not required as the study did not involve human participants. Author Contribution All authors were involved in all sections of this work, and should be regarded as joint first authors. Data availability Data will be made available on request or openly available from the Chinese Research Data Services Platform (CNRDS), and China Stock Market Accounting Research Database (CSMAR). References Agarwal V and Taffler R (2008) Comparing the performance of market-based and accounting-based bankruptcy prediction models. J Bank Financ 32(8), 1541-1551. Almeida H and Philippon T (2007) The risk‐adjusted cost of financial distress. J Financ 62(6), 2557-2586. Alrazi B, De Villiers C and Van Staden CJ (2015) A comprehensive literature review on and the construction of a framework for environmental legitimacy accountability and proactivity. J Clean Prod 102, 44-57. 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Zhang A, Deng R and Wu Y (2022) Does the green credit policy reduce the carbon emission intensity of heavily polluting industries?-Evidence from China's industrial sectors. J Environ Manag 311, 114815. Zhang L, Xie Y and Xu D (2024) Green investor holdings and corporate green technological innovation. Sustain 16(10) 4292. Zhang Q, Yu Z and Kong D (2019) The real effect of legal institutions: Environmental courts and firm environmental protection expenditure. J Environ Econ Manag 98, 102254. Zhong Z and Peng B (2022) Can environmental regulation promote green innovation in heavily polluting enterprises? Empirical evidence from a quasi-natural experiment in China. Sustain Prod Consump 30 815-828. Zmijewski ME (1984) Methodological issues related to the estimation of financial distress prediction models. J Account Res 59-82. Notes Government of China (www.gov.cn/zhengce) Bank of China (www.boc.cn/fimarkets/summarize) See Article 15 of the "Code of Corporate Governance for Listed Companies" (CSRC Announcement [2018] No. 29) for details. See Article 115 of the "Company Law of the People's Republic of China" (Revised for the Second Time by the Seventh Session of the Standing Committee of the Fourteenth National People's Congress on December 29, 2023). 'Region' refers to the provincial administrative region in China. The same applies hereafter. Pollution attributes reflect the additional pressures that manufacturing firms may encounter during environmental governance and green transformation. By using the product of pollution attributes and FDR as a new proxy variable for the FDR of manufacturing firms, we are able to capture both the internal financial changes related to pollution attributes for each firm and the potential impact of the external environment on the firm's financial health. This approach enables a more precise evaluation of the effect of GIS on reducing FDR. The pollution emission data for listed manufacturing firms primarily include five pollutant indicators. For water pollution, these include chemical oxygen demand emissions and ammonia nitrogen emissions. For air pollution, the indicators include sulfur dioxide emissions, nitrogen oxides emissions, and particulate matter emissions. We averaged the data on the proportion of GIS across four quarters of the year to measure the annual shareholding status, aiming to smooth out the bias caused by the different lengths of shareholding within a year. 'Industry' refers to the sub-sectors of manufacturing industry. The same applies hereafter. Data source: CSMAR Database, calculated manually by authors. Green investment refers to the practice of using systematic green investment strategies to invest in enterprises or projects that can generate environmental benefits, reduce environmental costs, and mitigate risks. Under the influence of the "Guidelines," funds with a higher level of greenness may gain greater social recognition and potentially have more influence within a company. Therefore, we argue that such funds are more significantly impacted by the "Guidelines." Referring to the indicator construction method of Xiang et al. (2020), we compile the frequency of keywords used in the screening process for GIs within the investment objectives and scopes of the funds. The higher the keyword frequency, the greater the greenness level of the fund. Due to the change in calculation method, higher values of 'O-Score' and 'ZM-Score' indicates greater corporate FDR. Data source: 'Listed Company Environmental Management Disclosure Table' and 'Listed Company Environmental Regulation and Certification Disclosure Table' from the CSMAR database. By conducting difference-in-differences analyses across multiple time periods to capture the effects for each time interval and then 'stacking' these results to construct a new model for regression analysis. This approach is well-suited to the context in this study, which involves multiple time points and multiple treatment groups. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eIn recent years, the Chinese central government has launched a series of groundbreaking initiatives to improve the ecological and environmental conditions. As of 2023, China has fostered 2,783 green factories, 296 green supply chain enterprises, and 223 green industrial parks at the national level, and has progressively established a full-chain green product supply system spanning from basic raw materials to end-consumer goods\u003csup\u003e1\u003c/sup\u003e. Against this backdrop, the Chinese government has gradually shifted the responsibility for pollution control to manufacturing companies, encouraging them to bear the costs of pollution abatement through market-based mechanisms independently. However, this approach may further intensify the pressure on manufacturing companies to undergo green transformation (Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Existing research indicates that while the green transformation of manufacturing firms can reduce environmental pollution, enhance production efficiency, and improve market competitiveness, the required investments in green R\u0026amp;D, pollution control, patented technologies, and human capital cannot be ignored in their impact on the financial sustainability of these companies. (Khan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Bank for International Settlements (BIS) posits that 'green swan events,' which arise from the accumulation of environmental issues, may become the next significant risk exposure in capital markets and trigger severe systemic financial crises. The Research Institute of the Bank of China, in its report titled \"Challenges and Responses to Risks Caused by Global Climate Change\", also highlighted that restrictions on energy consumption may result in financing difficulties for related enterprises and an increase in credit risk. Furthermore, stricter environmental regulations are likely to raise production and operational costs for manufacturing enterprises, such as expenses for upgrading equipment or transitioning to clean energy. These substantial short-term capital outflows are indicative of an increase in financial distress risk (FDR)\u003csup\u003e2\u003c/sup\u003e. Meanwhile, as the focus of environmental regulation, manufacturing companies have a greater demand for green management and green investment during their green transformation process compared to other sectors, which may expose them to greater FDR (Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, to achieve the goal of economic green transformation and development while maintaining stable economic growth, the capital market needs to find the 'stabilizer' that can promote industrial green transformation while minimizing FDR of market entities.\u003c/p\u003e \u003cp\u003eAccording to classical financial management theory, generating returns and controlling risks are the two driving forces of corporate value growth. Therefore, exploring how to effectively prevent companies from falling into financial distress is of great significance for reducing systemic market risk and minimizing the likelihood of shareholders suffering substantial wealth losses (Godfrey et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The existing literature primarily examines the factors influencing FDR from two perspectives. At the macro perspective, factors include country risk (Altman et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), financial environment (Almeida \u0026amp; Philippon, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), political corruption (Khieu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and climate change (Rudebusch, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the micro-enterprise perspective, factors include board structure (Fich \u0026amp; Slezak, 2008), management turnover (Gilson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), institutional investors shareholding (Tarighi et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and strategy formulation (Tykvov\u0026aacute; \u0026amp; Borell, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhether manufacturing firms fall into financial distress depends not only on their own operational conditions, which is a critical factor, but also to a large extent on the flow of market capital, particularly the allocation of 'green' funds (Tchorzewska, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The significance of 'green' funds extends beyond providing liquidity to invigorate firms; more importantly, they convey expectations of a firm's commitment to green development to the market. This, in turn, attracts more investors to continuously inject capital, fostering a virtuous cycle that enhances the firm's financial stability. However, existing literature has not explored this issue from this perspective. Barnea et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) classify institutional investors based on the 'green' attributes of their investment philosophy. They argue that, compared to non-green investors, green investors (GIs) not only seek financial returns but also require the investee firms to place greater emphasis on environmental governance responsibilities, ultimately achieving a dual investment objective of sustainability and financial returns (Ng \u0026amp; Zheng, 2017). This provides a new perspective for this study.\u003c/p\u003e \u003cp\u003eExisting literature has validated the impact of green investor shareholding (GIS) on corporate financial performance, including the promotion of green investments and innovation outputs (Chung et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), influencing stock price fluctuations (Cheng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and affecting corporate profitability (Jiang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, current studies have overlooked the critical role of FDR as a threshold for corporate survival. Therefore, exploring the specific pathways through which GIs influence corporate FDR can not only fill the research gap in the risk management dimension of environmental finance theory but also provide decision-making references for preventing and mitigating systemic environmental financial risks.\u003c/p\u003e \u003cp\u003eCompared to previous literature, this paper makes contributions primarily in the following aspects: At the theoretical level, existing literature primarily explores the impact of GIS on corporate financial performance but often overlooks the critical role of risk management in corporate financial sustainability. With the growing market demand for green manufacturing products, manufacturing enterprises exhibit significant transformation potential. Against this backdrop, this study adopts the perspective of green capital infusion and focuses on manufacturing firms to empirically examine, for the first time, the relationship between GIS and corporate FDR. Furthermore, by investigating the internal mechanisms through which GIS influences FDR, as well as the external constraints affecting this relationship, this study expands the theoretical scope of risk management theory and signaling theory.\u003c/p\u003e \u003cp\u003eAt the practical level, this paper offers theoretical guidance on how the current capital market can promote green development and how manufacturing companies can cope with FDR. Developing countries with significant pollutant emissions are typically undergoing or preparing to undergo a green transition; meanwhile, their green capital markets still requiring further development. The findings of this study, derived from data on China\u0026mdash;the largest developing country\u0026mdash;can assist government agencies in such nations in improving policy frameworks to guide the green development of capital markets. Simultaneously, these findings help corporate managers better understand the relationship between GIs and corporate FDR, encourage manufacturing firms to prioritize FDR management and fosters the development of sustainable market economy.\u003c/p\u003e"},{"header":"2. Theoretical Analysis and Research Hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. The Internal Logic of How GIS Influences FDR\u003c/h2\u003e \u003cp\u003eFDR arises from the uncertainties in both the external operating environment and internal business activities of companies. It manifests as the possibility of a company falling into financial crisis, where cash flow is insufficient to repay maturing debts, making it difficult to maintain normal production and operations (Beaver, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1966\u003c/span\u003e; Altman, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For the manufacturing industry, the pollution characteristics of enterprises in this sector\u0026mdash;namely, high energy consumption and high-emission production models\u0026mdash;result in sources of FDR that exhibit distinct industry-specific characteristics. External factors include the uncertainty of environmental policies and regulations, social opinions, and market's increasing demand for 'greening;' internal factors include the uncertainty of technological R\u0026amp;D, energy supply costs, pollution-related penalties (Dal Maso et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs entities that prioritize environmental and socially responsible investments, GIs tend to invest in companies with green development potential (Chung et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Meanwhile, as large-scale market participants, they not only influence companies' capital allocation decisions but also shape market expectations through their green investment attributes, thereby optimizing corporate financing conditions. Under this mechanism, companies will place greater emphasis on environmental performance while pursuing economic benefits, aiming to reduce litigation risks and environmental penalties. Consequently, this enhances financial sustainability, increases long-term corporate value, and ultimately leads to a 'win-win' situation, where GIs also achieve excess investment returns (Jiang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. GIS, Green Governance Performance, and FDR\u003c/h2\u003e \u003cp\u003eBy participating in corporate green governance, GIs guide companies to embed green development principles into their resource allocation processes and operational procedures (Jiang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This involvement helps companies 'greenify' their original production capacities with high market conversion value, thereby balancing the dual objectives of financial performance and environmental performance (Li et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They can improve green governance performance (GGP) by adopting investment strategies that involve either 'voting with hands' or 'voting with feet' to participate in corporate green governance (Mallin, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecifically, GIs can participate in corporate green governance through 'voting with hands' in the following three forms: The first is exercising voting rights and soliciting proxy voting rights. Voting rights are fundamental rights enjoyed by all shareholders of a company, which can be exercised by shareholders themselves or delegated to other shareholders for execution\u003csup\u003e3\u003c/sup\u003e. Article 3 of the \"Code of Corporate Governance for Listed Companies\" suggests that 'listed companies should implement the development principles of innovation, coordination, green, openness, and sharing.' The environmental preferences of GIs align with the environmental protection demands of small shareholders who have environmental protection preferences in manufacturing companies, making it easier for GIs to garner the proxy voting intentions of other small shareholders (Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), thus influencing corporate GGP. The second is shareholder proposals. Shareholders individually or collectively holding more than 1% of a company's shares can submit written proposals to the board of directors\u003csup\u003e4\u003c/sup\u003e. This allows GIs to push companies to pay more attention to green production and environmental compliance (Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thereby influencing corporate GGP. Lastly, GIs can engage in public proposals and joint actions. Joint actions typically involve institutional investors publicly and collectively urging a company to address issues and enhance governance when faced with challenges (Appel et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), compelling the company to enhance its governance practices.\u003c/p\u003e \u003cp\u003eGIs can also adopt the 'voting with feet' approach, expressing dissatisfaction with a company or distrust of its governance practices by selling or reducing their holdings (G\u0026uuml;rerk et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the capital market, it is generally believed that institutional investors possess superior investment strategies due to their information and financial advantages, and their stock selling behavior may trigger a 'herding effect,' leading to the risk of corporate stock prices falling and reputation damage (Hsieh et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, if a company refuses to meet the environmental demands of GIs, they may sell or reduce their shareholdings to convey their dissatisfaction to other stakeholders and force the company to improve its GGP.\u003c/p\u003e \u003cp\u003eEnhancing corporate GGP can also mitigate FDR. On the one hand, GIs can leverage governance measures such as optimizing a company's environmental strategies and risk management frameworks (Hyatt \u0026amp; Berente, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These efforts help improve resource utilization efficiency and reduce the costs associated with green transformation, enabling firms to better withstand external shocks, such as fluctuations in energy prices, that impact production and operations. On the other hand, GIs can strengthen internal oversight systems by improving monitoring mechanisms over management's allocation of resources for environmental governance and by implementing performance evaluations for green management. Such measures reduce the likelihood of penalties or legal actions stemming from non-compliance with environmental standards or inadequate pollution control facilities. By minimizing cash outflows due to environmental fines and mitigating reputational damage that could adversely affect corporate profitability (Moore \u0026amp; Ghahramani, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), these actions help prevent firms from falling into financial distress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. GIS, Financing Constraints, and FDR\u003c/h2\u003e \u003cp\u003eGreen credit policies require banks and other financial institutions to consider environmental factors as crucial criteria for credit assessment, raising the financing threshold for manufacturing companies (Wang \u0026amp; Wang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This paper proposes that GIS can partially bridge the funding gap for corporate green transition through capital infusion. At the same time, they transmit a 'green' signal to the capital market, improving financing conditions and attracting additional green funding support, thereby reducing FDR.\u003c/p\u003e \u003cp\u003eFirstly, GIS directly provides capital support to enterprises, effectively serving as a cash injection that enhances their liquidity, reduces financial leverage, and ensures financial security for green technology research and development as well as the transition to sustainable production (Zhang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Secondly, GIS can signal to the market a company's commitment to environmental sustainability and its transition toward green manufacturing, thereby strengthening investor confidence in the company's long-term growth potential. Based on this, other investors may choose to invest in the company in anticipation of excess returns driven by its green transition potential, thereby expanding its financing channels and alleviating its financing constraints (FC) (Hao, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnce FC are effectively alleviated, companies can opt for more suitable financing instruments and structures, reducing their dependence on a single source of financing. This not only lowers financing costs but also diversifies financing risks, which enhancing corporate liquidity, improving overall financial stability, and reducing FDR (Mertzanis et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the above analysis, this article proposes the following hypotheses:\u003c/p\u003e \u003cp\u003eH1: GIS can effectively mitigate FDR of manufacturing companies.\u003c/p\u003e \u003cp\u003eH2a: GIS can mitigate FDR of manufacturing companies by improving GGP.\u003c/p\u003e \u003cp\u003eH2b: GIS can mitigate FDR of manufacturing companies by alleviating FC.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. The Moderating Role of Institutional Logic in the Relationship Between GIS and FDR\u003c/h2\u003e \u003cp\u003eExploring how different regional\u003csup\u003e5\u003c/sup\u003e institutional environments moderate the effectiveness of green investment has become a critical theoretical issue, as the urgent task of addressing environmental challenges and ensuring the smooth transition of real-economy companies has surpassed the capacity of government or market forces alone (Bartley, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The institutional logic perspective in institutional theory provides a theoretical basis for addressing this issue. Institutional logic originates from different and relatively enduring institutional orders (such as the state, market, family, etc.), which are perpetuated through integration into stable practices (Lee \u0026amp; Lounsbury, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Yan et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) divides institutional logic into financial logic and environmental logic. The financial logic aims at maximizing shareholder wealth, while the environmental logic aims at environmental protection and sustainable development. Both operate based on the government's legal regulations and enforcement system. As the builder of the core institutional order of society, the government usually supports a variety of policy objectives, including market economic development and ecological environmental protection, by promulgating laws and regulations and implementing regulatory reviews. In the research scenario of this article, GIs are important participants in the Chinese capital market with dual investment objectives of financial and environmental performance. Their investment behavior can be defined as a hybrid practice of using financial logic to serve environmental logic objectives (Yan et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe government can support the objectives of environmental logic by enacting environmental protection policies, while maintain market order and supporting the goals of financial logic by issuing regulatory laws and enhancing the protection of shareholders' legitimate rights and interests (Thronton et al., 2012).The mitigating effect of GIS on FDR depends on the 'legitimacy' conferred upon them, i.e., to what extent green investment practices are recognized and supported by the local public under financial or environmental logic (Alrazi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom the perspective of financial logic, since green investment operates under financial logic, stronger strength of shareholder protection (SSP) may promote green investment practices by providing more instrumental support for achieving the environmental objectives of GIs. Additionally, the cultural normative influence of green investment may also be stronger. Specifically, as the government improves the SSP\u0026mdash;thus increasing the legitimacy of financial logic\u0026mdash;GIs may express their demands more directly when entering companies. In turn, companies may become more willing or compelled to pay attention to these demands and make changes. Therefore, in regions where shareholder legitimacy is higher, the normative influence of green investment may be more substantial and effective in mitigating FDR.\u003c/p\u003e \u003cp\u003eFrom the perspective of environmental logic, environmental protection policies and green investment practices share the same ultimate institutional goal: the preservation of the ecological environment. However, the central and dominant role of the government may institutionalize a homogeneous 'belief' that environmental protection is primarily driven by government regulations. Strong governmental regulations on environmental protection might undermine the legitimacy of environmental initiatives from private sectors, such as investment institutions, thereby leaving less space for the practice of green investment. For instance, Arjali\u0026egrave;s \u0026amp; Durand (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) argued that the appropriate tool to combat excessive carbon emissions is the rule of law through democratic action, rather than environmentally conscious investors. Therefore, as the intensity of environmental regulation (IER) increases, the marginal governance role played by GIs tends to be weakened. However, as the environmental logic receives broader attention and acceptance in society through various mechanisms (including the normative influence of laws), government environmental policies are gradually perceived as the minimum requirements that companies need to meet for survival. This, in turn, reduces the crowding out of the legitimacy of other participants (such as GIs), lessening the normative influence on green investment practices, and potentially creating a synergistic effect with green investment practices in preventing and controlling FDR. Based on the analysis above, the article proposes the following hypotheses:\u003c/p\u003e \u003cp\u003eH3a: SSP can positively moderate the negative relationship between GIS and the FDR of manufacturing companies.\u003c/p\u003e \u003cp\u003eH3b: IER can exhibit a U-shaped moderating effect on the negative relationship between GIS and the FDR of manufacturing companies, initially inhibiting and then promoting it.\u003c/p\u003e \u003cp\u003eThe conceptual model shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e explains the relationships among the proposed constructs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Material and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sample Collection\u003c/h2\u003e \u003cp\u003eThis article selects A-share listed manufacturing companies in China from 2012 to 2022 as the sample, excluding ST, ST*, and PT samples, financial and insurance samples, as well as samples with missing data. GIS-related data are collected manually, and all data come from CSMAR and CNRDS databases. After matching the above data, 17,025 annual observations are finally obtained, including 4,596 observations for firms with GIS and 12,429 observations for firms without GIS. To eliminate the impact of extreme values on causal identification, we also perform 1% winsorization on the main continuous variables.\u003c/p\u003e \u003cp\u003eDue to the significant imbalance in the sample sizes between firms with and without GIS, as well as potential differences in firm characteristics and market features, there may be bias in the estimates from the regression models. Therefore, this article further applies the Propensity Score Matching (PSM) method to select a control group of samples with similar characteristics and a comparable number to the treatment group. Specifically, (1) all control variables in Model (1) are set as matching variables; (2) two datasets are constructed using two different methods: a cross-sectional PSM is created by applying the nearest neighbor matching method to find the optimal control group without GIS for each firm with GIS that satisfies the common support condition; (3) a year-by-year matching approach is used to match the firm samples annually. As a result, 7,046 observations are obtained, including 3,437 samples with GIS ownership and 3,609 samples without GIS ownership (the balance test for the two matched datasets are presented in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Model Construction\u003c/h2\u003e \u003cp\u003eTo test the impact of GIS on FDR, this article establishes the following model:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{AFDR}_{i,t}={\\beta\\:}_{0}+{\\beta\\:}_{1}{GIS}_{i,t-1}+{\\beta\\:}_{k}{Controls}_{i,t-1}+{\\theta\\:}_{i}+{\\omega\\:}_{r}\\times\\:{\\tau\\:}_{j}+{\\tau\\:}_{j}\\times\\:{\\mu\\:}_{t}+{\\mu\\:}_{t}+{\\epsilon\\:}_{i,t}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Model (1), the dependent variable \u003cem\u003eAFDR\u003c/em\u003e represents the FDR of manufacturing firms adjusted for their 'pollution attributes.'\u003csup\u003e6\u003c/sup\u003e The calculation formula is as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{AFDR}_{i,t}={GC}_{i,t}\\times\\:{FDR}_{i,t}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMeasurement of a firm's pollution attributes (\u003cem\u003eGC\u003c/em\u003e): The first step involves scraping data on corporate pollution emissions from annual reports, sustainability reports, social responsibility reports, and other sources for listed companies\u003csup\u003e7\u003c/sup\u003e. Since the units of various pollutants may differ, the following approach is used in this study: (1) Standardize the raw data of pollutant indicators using the extreme value method, where represents the standardized emissions of the \u003cem\u003ek\u003c/em\u003e-th pollutant; (2) Calculate the adjustment coefficient for each pollutant:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\rho\\:}_{ikt}=\\frac{{pollut}_{ikt}}{\\stackrel{-}{{pollut}_{kt}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{pollut}_{kt}}\\)\u003c/span\u003e\u003c/span\u003e represents the mean emission level of the \u003cem\u003ek\u003c/em\u003e-th pollutant in the sample. The comprehensive pollution emission indicator for firm \u003cem\u003ei\u003c/em\u003e can be expressed as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{P}_{it}=\\frac{1}{5}\\sum\\:({pollut}_{ikt}\\times\\:{\\rho\\:}_{ikt})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the second step, to eliminate differences in production scale across firms and enable a fair assessment and comparison of their pollution attributes, this study adopts the indicator construction approach proposed by Ge et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Li \u0026amp; Lu (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The pollution attributes of a firm are measured as the ratio of its standardized output value to the standardized pollutant emission indicators:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{GC}_{it}={V}_{it}/{P}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn essence, this metric reflects how much output a firm can generate for each unit of pollutant emitted. A higher value of \u003cem\u003eGC\u003c/em\u003e indicates greater production cleanliness (or resource use efficiency) and weaker pollution attributes.\u003c/p\u003e \u003cp\u003eFor measuring overall FDR, this study employs the modified Z-score model developed by Altman (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Agarwal \u0026amp; Taffler (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) demonstrated that accounting-based Z-score models outperform market-based models in predicting bankruptcy. Altman et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) further validated the utility of the Z-score as a reliable indicator for bankruptcy risk prediction. A higher Z-score indicates a lower overall FDR faced by the firm.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGIS\u003c/em\u003e represents the two key explanatory variables of this article: whether GIs hold shares (\u003cem\u003eGIS_dum\u003c/em\u003e) and the proportion of shares held by GIs (\u003cem\u003eGIS_pct\u003c/em\u003e)\u003csup\u003e8\u003c/sup\u003e. The variable \u003cem\u003eGIS_dum\u003c/em\u003e is assigned a value of 1 when a firm has GIS and 0 otherwise, to test whether GIS influences a firm's FDR. The variable \u003cem\u003eGIS_pct\u003c/em\u003e is measured as the average proportion of GIS over the four quarters of the year, to examine the extent to which varying levels of GIS affect the reduction in a firm's FDR. The specific method to screen whether a fund is a 'green investor' is: (1) Obtain the 'Fund Entity Information Table' and 'Stock Investment Details Table' from the CSMAR database Fund Market series, and match the data from these two tables to obtain detailed information on the investment objectives, scope, and strategies of existing funds investing in listed companies; (2) Manually search the 'Investment Objectives' and 'Investment Scope' in the fund details for keywords, including 'ecology,' 'green,' 'low carbon,' 'energy saving,' 'environmental protection,' 'new energy,' and 'emission reduction,' and other related words with clear green orientation. If any of these keywords appear in the investment objectives and scope of the fund, the fund is defined as a 'green investor.'\u003c/p\u003e \u003cp\u003eFollowing the practices in existing literature (Yan et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Borochin \u0026amp; Yang, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), this article introduces a series of control variables at both macro and micro levels. Variable definitions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The article also controlled for firm fixed effects (FirmFE), industry\u003csup\u003e9\u003c/sup\u003e-region fixed effects (IndFE\u0026amp;PrvnFE), industry-year fixed effects (IndFE\u0026amp;YearFE), as well as year fixed effects (YearFE), and conducts clustering at the firm level. Subscripts i, j, r, and t denote firm, industry, province, and year respectively. Furthermore, to ensure the reliability of causal identification as much as possible, this paper lag the explanatory variables and all control variables by one period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable definitions.\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\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe FDR of manufacturing firms adjusted for their 'pollution attributes.'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManully collected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 when a firm has GIS and 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManully collected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_pct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe average proportion of GIS over the four quarters of the year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManully collected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe total assets by logarithm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSMAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe ratio of total liabilities to total assets.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSMAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021 minus the founding year plus one followed by logarithm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSMAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe ratio of shareholding held by the top three shareholders.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSMAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe ratio of the number of independent directors to the total number of board members.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSMAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe total number of board members by logarithm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSMAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMsalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal compensation of top three executives divided by one million.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSMAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegional GDP growth rate.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCNRDS\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Statistical Analysis\u003c/h2\u003e \u003cp\u003eDuring the transition of manufacturing companies from producing 'brown' to 'green' products, the magnitude of improvement in environmental performance may be greater compared to other sectors, thus offering higher investment returns (Zhong \u0026amp; Peng, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to existing industry data (Fig.\u0026nbsp;2\u003csup\u003e10\u003c/sup\u003e), the proportion of manufacturing listed companies in China with GIS increased from 8.80% in 2011 to 44.28% in 2022. Over this period, the scale and scope of GIs expanded annually, and the 'green' issues of manufacturing companies have progressively garnered attention from the capital market.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in the matched sample of listed manufacturing firms, the maximum value of \u003cem\u003eAFDR\u003c/em\u003e is 0.2120, with a standard deviation of 0.0352 and a mean of 0.0208, indicating considerable variability in the FDR adjusted for pollution attributes. The mean value of \u003cem\u003eGIS_dum\u003c/em\u003e is 0.5049, suggesting a satisfactory matching outcome. The maximum value of \u003cem\u003eGIS_pct\u003c/em\u003e is 9.9375, with a standard deviation of 1.9775, reflecting substantial variation in the GIS proportion across the sample, with the highest shareholding ratio being approximately 10%. Other variables are generally consistent with existing literature and will not be repeated here.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics.\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\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\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\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0082\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\u003e0.2120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0000\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\u003eGIS_pct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9775\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\u003e9.9375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.1821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.0491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.3012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.9940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.3673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.1873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.8828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.7893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3750\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\u003e0.6250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.8904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMsalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo ensure the validity of parameter estimation, this paper conducts Pearson correlation analysis on the main variables in the sample data, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Without considering the influence of other factors, \u003cem\u003eGIS_dum\u003c/em\u003e, and \u003cem\u003eGIS_pct\u003c/em\u003e have significant positive correlations with \u003cem\u003eAFDR\u003c/em\u003e, which preliminarily verifies H1. Additionally, the correlation coefficients between the main variables are all below 0.6, indicating that there is no serious multicollinearity problem.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.AFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.GIS_dum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.089***\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.GIS_pct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.169***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.440***\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.488***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.Lev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.159***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.287***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.333***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.Shr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.022*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.Indep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.027**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.027**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.038***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.020*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.Board\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.108***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.216***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.127***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.194***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.217***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.065***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.Msalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.266***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.348***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.143***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.290***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.032***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.GDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.091***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.059***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.030**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical results and analysis","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Baseline Results\u003c/h2\u003e \u003cp\u003eThis study controls for a range of factors that may influence a firm's FDR; however, there inevitably exist unobservable third-party factors that may affect the model, leading to potential omitted variable bias. Additionally, there may be reverse causality between FDR and GIS, resulting in an endogeneity issue. To address the potential endogeneity problem in the model, this study constructs instrumental variables for GIS and applies two-stage least squares (2SLS) estimation.\u003c/p\u003e \u003cp\u003eUsing exogenous policy shock is one of the main approaches in the existing literature to find a instrumental variable (Giannetti et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the research context of this paper, the \"Green Investment Guidelines (For Trial Implementation)\" (hereinafter referred to as the \"Guidelines\") issued by the Asset Management Association of China in 2018, define the concept of 'green investment'\u003csup\u003e11\u003c/sup\u003e and provide universal guidance for fund managers on their green investment activities and internal institutional development. On one hand, the \"Guidelines\" aim to help fund managers establish standards for green investment management based on their own conditions, providing important guidelines for GIs' investment practices. On the other hand, institutional investors or companies cannot foresee whether or when the \"Guidelines\" will be issued, nor can they predict or intervene in the content of the \"Guidelines\" in the short term, making the \"Guidelines\" an exogenous policy that can impact GIS. This creates a valuable opportunity for constructing the instrumental variable in this article. We use the intersection of the dummy variable \u003cem\u003epost\u003c/em\u003e (\u003cem\u003epost\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 for 2018 and onwards, \u003cem\u003epost\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0 otherwise) indicating the period before and after the release of the \"Guidelines,\" and the greenness level of GIs (\u003cem\u003egl\u003c/em\u003e)\u003csup\u003e12\u003c/sup\u003e as the instrumental variable for GIS (\u003cem\u003eInstrument\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003epost\u003c/em\u003e \u0026times; \u003cem\u003egl\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that in the first-stage regression, the coefficients of \u003cem\u003eInstrument\u003c/em\u003e for both \u003cem\u003eGIS_dum\u003c/em\u003e and \u003cem\u003eGIS_pct\u003c/em\u003e are significantly positive. In the second-stage regression, the coefficients of \u003cem\u003eGIS_dum\u003c/em\u003e and \u003cem\u003eGIS_pct\u003c/em\u003e are also significantly positive. The KP rk Wald F-statistic exceeds the critical value for the Stock-Yogo weak instrument identification F-test at the 10% significance level, indicating that there is no weak instrument problem. Additionally, the Hansen J-statistic is close to zero, and we cannot reject the null hypothesis that the instrument is valid, suggesting that the selected instrumental variables are appropriate. These results indicate that GIS can effectively mitigate the FDR of manufacturing companies.\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\u003eThe results of the baseline regression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1): First stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2): Second stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3): First stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4): Second stage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGIS_dum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGIS_pct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0263***\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 \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(6.77)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_pct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0027***\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(7.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstrument\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0669***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6447***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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(14.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(24.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1215***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0105***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0075***\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(-5.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(8.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0123***\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(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-4.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0479*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0048***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0037***\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(1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0010***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0042***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\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(2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0092***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4485*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0074***\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(-1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0794**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0010\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(2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMsalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.1782**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1215***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.0973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0936***\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(-2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6321***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0014\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(-0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmFE\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\u003ePrvFE\u0026amp;YearFE\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\u003eIndFE\u0026amp;YearFE\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\u003eYearFE\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\u003eObs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKP rk Wald F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e607.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\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\u003e4.2. Robustness Tests\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1. Heckman two-stage analysis\u003c/h2\u003e \u003cp\u003eTheoretically, better financial performance indicates fewer developmental bottlenecks and better growth potential for a company, making it more likely for GIs to choose such companies as investment targets. This suggests that stock selection by GIs may not be random but rather endogenously determined by the companies themselves. To control for this 'self-selection' problem as much as possible, we employ the Heckman (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) two-stage model.\u003c/p\u003e \u003cp\u003eExisting research has found that the momentum effect of stocks can affect the proportion of institutional investor shareholding (Gompers \u0026amp; Metrick, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Furthermore, evaluations by external rating agencies also affect the choice of investment targets by institutional investors, but neither has a significant impact on FDR. Therefore, in the first stage of regression, this paper introduces \u003cem\u003eMom\u003c/em\u003e (Cumulative monthly return of the company over the past 2 to 12 months) and \u003cem\u003eEscore\u003c/em\u003e (the E-score in the ESG rating of the company over the past year) as exogenous explanatory variables. The dummy variable \u003cem\u003eGIS_dum\u003c/em\u003e is used as the dependent variable in the Probit regression and the inverse Mills ratio (\u003cem\u003eIMR\u003c/em\u003e) is calculated. The results are presented in Column (1) of Panel A in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In the second stage, the \u003cem\u003eIMR\u003c/em\u003e is incorporated into Model (1) for subsequent regression. The results are presented in Columns (2) and (3), where the coefficients of \u003cem\u003eGIS_dum\u003c/em\u003e and \u003cem\u003eGIS_pct\u003c/em\u003e are both significantly positive, indicating that, after adding the \u003cem\u003eIMR\u003c/em\u003e to the regression model to address 'self-selection' problem, the baseline results remain robust.\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\u003eRobustness Tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel A: Robustness tests based on the Heckman two-stage model, replacing the key explanatory and dependent variables.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAFDR\u003csub\u003eoscore\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAFDR\u003csub\u003eoscore\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAFDR\u003csub\u003ezmscore\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAFDR\u003csub\u003ezmscore\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_sum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0011***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0023***\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 \u003cp\u003e-0.0049**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0015***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e 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align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-4.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-4.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5631***\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e 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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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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(1.73)\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0103***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-6.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0371*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0106***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0099***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0089***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0591***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0569***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0059***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0054***\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(1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-8.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-8.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-5.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2392**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0105**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0104**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0144***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0230***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0226***\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(-2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(6.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0272***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0271***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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colname=\"c1\"\u003e \u003cp\u003eShr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0000\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 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e 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colname=\"c7\"\u003e \u003cp\u003e(-0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMsalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.6153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1072***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1065***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.4508***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.4310***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0716***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0671***\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(-0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-3.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0152*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e 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colname=\"c2\"\u003e \u003cp\u003e-0.8203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1873***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1768***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1911***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1463***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1058***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0954***\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(-1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-6.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(8.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(8.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(3.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmFE\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrvFE\u0026amp;YearFE\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndFE\u0026amp;YearFE\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYearFE\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj_R2 / Pseudo R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel B: Robustness tests based on controlling for time trends.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(5.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_pct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0015***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(6.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(6.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u0026times;f(T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u0026times;T_dummy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0167***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0160***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0111***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0103***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(6.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(6.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(6.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0231**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.0203**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.0171***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.0171***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(-2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(-2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(-3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(-3.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0276*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.0289**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(-1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(-2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.0097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.0096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(-0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(-0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(-0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(-0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(0.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMsalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.3261***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3234***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0751*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0656*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(1.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.2254***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.2051***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.2304***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.2117***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(-6.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(-6.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(-6.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(-6.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmFE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrvFE\u0026amp;YearFE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndFE\u0026amp;YearFE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYearFE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj_R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.849\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=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2. Alternative measures of key explanatory variables\u003c/h2\u003e \u003cp\u003eTo further enhance the reliability of the research findings, this article selects the logarithm of the number of GIs present in the company during the year (\u003cem\u003eGIS_sum\u003c/em\u003e) to re-measure the explanatory variables. The regression results are presented in Columns (4) of Panel A in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, where the coefficients of \u003cem\u003eGIS_sum\u003c/em\u003e is significantly positive, indicating that the baseline results remain robust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3. Alternative measures of explained variable\u003c/h2\u003e \u003cp\u003eIn addition to the Z-score, the O-score and ZM-score are also accounting-based models for measuring FDR (Tykvov\u0026aacute; \u0026amp; Borell, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This article follows the methods of Ohlson (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and Zmijewski (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) to remeasure the dependent variable using the O-score and ZM-score and perform regression analysis\u003csup\u003e13\u003c/sup\u003e. The coefficients of \u003cem\u003eGIS_dum\u003c/em\u003e and \u003cem\u003eGIS_pct\u003c/em\u003e are both significantly negative in Columns (5) to (8) of Panel A in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, validating the robustness of the baseline regression results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4. Control for time trends\u003c/h2\u003e \u003cp\u003eOther influencing factors, such as the time trend of control variables, may also impact the regression results when there are differences between sample companies with or without GIS (with high or low proportion of GIS). Therefore, referring to the research design of Moser \u0026amp; Voena (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), this paper constructs interaction terms between all control variables and a third-order polynomial of time trends (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\left(T\\right)=T+{T}^{2}+{T}^{3}\\)\u003c/span\u003e\u003c/span\u003e), as well as interaction terms between all control variables and time dummy variables (\u003cem\u003eT_dummy\u003c/em\u003e). These two sets of interaction terms are separately added to the baseline regression model. As shown in Columns (1) to (4) of Panel B in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, after adding the control interaction terms mentioned above, the coefficients of the main explanatory variables remain significantly positive, indicating that the previous regression results are still robust.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Mechanism Analysis\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1. Mediation analysis\u003c/h2\u003e \u003cp\u003eAccording to the research hypotheses, the role of GIS in mitigating FDR is primarily achieved through two channels: enhancing GGP and alleviating FC.\u003c/p\u003e \u003cp\u003eCorporate green governance refers to a management and decision-making approach designed to integrate environmental sustainability into the core business operations and strategies of a company. Its goal is to minimize adverse environmental impacts while achieving sustainable development (Hussain, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). GGP, on the other hand, quantitatively reflects various aspects of the effectiveness of corporate green governance, including the evaluation of the achievement of sustainable development goals and the assessment of environmental performance. In the existing literature, the measurement of GGP is not yet standardized. To avoid the bias of 'greenwashing' that may arise from considering only the environmental information disclosed in corporate annual reports, and to comprehensively account for both the extent of a company's internal environmental management systems and the evaluations of it's environmental performance by external stakeholders, including the government and the public, we adopt a more comprehensive and comparable Janis-Fadner coefficient to measure GGP (Table\u0026nbsp;6\u003csup\u003e14\u003c/sup\u003e), following the indicator design approach of Bansal \u0026amp; Hunter (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Li et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe indicator's construction basis is listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Where \u003cem\u003ep\u003c/em\u003e represents the positive score, with a value of 1 if the company meets the conditions, and 0 otherwise; \u003cem\u003eq\u003c/em\u003e represents the negative score, with a value of -1 if the company meets the conditions, and 0 otherwise. \u003cem\u003eGGP\u003c/em\u003e ranges from \u0026minus;\u0026thinsp;1 to 1, with a value closer to 1 indicating higher corporate GGP. The calculation formula is as follows:\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\u003eBasis for Selection of Corporate GGP Indicators.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eq\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstablished a major environmental incident emergency response mechanism.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollutant emissions did not meet standards.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived honors and awards in the field of environmental protection.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperienced a major sudden environmental incident.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObtained ISO 4000 series certification.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHad environmental violations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipated in environmental protection actions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvolved in environmental petition cases.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRanked in the top 30% of the sample in environmental scores in ESG scoring.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRanked in the bottom 30% of the sample in environmental scores in ESG scoring.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAchieved the highest level in environmental rating in ESG rating.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAchieved the lowest level in environmental rating in ESG rating.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ6\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{GGP}_{i,t}=\\left\\{\\begin{array}{c}\\frac{{p}_{i,\\:t}^{2}-{p}_{i,t}\\times\\:\\left|{q}_{i,t}\\right|}{{\\left({p}_{i,t}+\\left|{q}_{i,t}\\right|\\right)}^{2}},\\:if\\:{p}_{i,t}\u0026gt;\\left|{q}_{i,t}\\right|\\\\\\:0,\\:if\\:{p}_{i,t}=\\left|{q}_{i,t}\\right|\\\\\\:\\frac{{p}_{i,t}\\times\\:\\left|{q}_{i,t}\\right|-{q}_{i,\\:t}^{2}}{{\\left({p}_{i,t}+\\left|{q}_{i,t}\\right|\\right)}^{2}},\\:if\\:{p}_{i,t}\u0026lt;\\left|{q}_{i,t}\\right|\\end{array}\\right.$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe WW index is used in this paper to measure corporate FC (Whited \u0026amp; Wu, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Existing research suggests that in the three-step mediation model, the regression in the third step includes both the mediator and the dependent variable, which often leads to a reduction in statistical significance. Moreover, failing to achieve significance in the second or third step does not necessarily indicate the absence of a mediation effect (Hayes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Following the approach of Jiang (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), this study adopts a two-step method to construct the mediation model.: Step 1 as shown in Model (1); Step 2 as shown in Model (7):\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{Mediator}_{i,t}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{GIS}_{i,t-1}+{\\alpha\\:}_{k}{Controls}_{i,t-1}+{\\theta\\:}_{i}+{\\omega\\:}_{r}\\times\\:{\\tau\\:}_{j}+{\\tau\\:}_{j}\\times\\:{\\mu\\:}_{t}+{\\mu\\:}_{t}+{\\epsilon\\:}_{i,t}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eMediator\u003c/em\u003e represents the mediation variable, and the meanings of the other parameters are the same as described in Model (1). We primarily observe whether the coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is significant. If the coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e in Model (1) and the coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e in Model (7) both show significance, it indicates that \u003cem\u003eGIS_dum\u003c/em\u003e and \u003cem\u003eGIS_pct\u003c/em\u003e can indeed affect \u003cem\u003eAFDR\u003c/em\u003e through the mediation variables.\u003c/p\u003e \u003cp\u003eThe regression results in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the coefficients of \u003cem\u003eGIS_dum\u003c/em\u003e and \u003cem\u003eGIS_pct\u003c/em\u003e on \u003cem\u003eGGP\u003c/em\u003e are both significantly positive, and on \u003cem\u003eFC\u003c/em\u003e are both significantly negative. This indicates that an increase in GIS behavior and the proportion of shareholding can both enhance GGP and alleviate FC, thereby mitigating FDR, H2a and H2b are supported.\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\u003eThe results of mediation analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0798***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0084***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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(4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_pct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0110**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0023***\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(2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-8.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1718***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1684***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0064**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0048\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(13.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(12.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0098\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(1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0068*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0062*\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(-1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0022**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0024**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002***\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(2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0027\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(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0913**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0949**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0033\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(2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMsalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9557*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9127*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0778*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0602\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(1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0046\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(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.1532***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.0578***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8968***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.9334***\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(-15.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-14.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-14.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-14.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmFE\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\u003ePrvFE\u0026amp;YearFE\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\u003eIndFE\u0026amp;YearFE\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\u003eYearFE\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\u003eObs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj_R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.731\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\u003e4.3.2. Moderation analysis\u003c/h2\u003e \u003cp\u003eBased on the analysis above, from the perspective of institutional logic, both investors and corporate managers are influenced by normative pressures from two institutional orders\u0026mdash;\u0026mdash;environment and market. This is reflected in the variation of environmental regulations and investor practices, which may affect the effectiveness of GIS in mitigating FDR.\u003c/p\u003e \u003cp\u003eIn the study by Yan et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), SSP is defined as 'the extent to which shareholder rights are adequately enforced across different countries.' This paper aims to examine the degree of shareholder protection across different regions of China. Therefore, we follow the indicator selection method of Yan et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and use the sub-index of 'Development of Market Intermediaries and Legal System Environment' from the 'Marketization Process Index' as a proxy variable for SSP. This sub-index is derived from the comprehensive calculation of 'Development of Market Intermediaries,' 'Maintenance of the Legal Environment for Markets,' and 'Protection of Intellectual Property Rights,' which collectively reflect the degree of investor protection in a region. Additionally, since the indicators in the database are available only up to 2019, this paper extrapolates the data for 2020\u0026ndash;2021 based on the average growth rate of the indices from previous years.\u003c/p\u003e \u003cp\u003eIn selecting the variable for IER, on one hand, higher government investment in environmental governance is often closely linked to stricter environmental laws and regulations; on the other hand, local communities and the public are likely to pay more attention to environmental issues as more government funds are allocated to environmental governance. This, in turn, compels local companies to make adjustments to comply with regulations and meet societal expectations, resulting in greater environmental regulatory pressure (Dabbous et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xu \u0026amp; Xu, 2023). Therefore, this paper uses the proportion of annual regional expenditure on air and water pollution control relative to the total industrial output of that year as a proxy variable for IER.\u003c/p\u003e \u003cp\u003eThis article constructs the following models to test H3a and H3b:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:{AFDR}_{i,t}={\\gamma\\:}_{0}+{\\gamma\\:}_{1}{GIS}_{i,t-1}+{\\gamma\\:}_{2}{Moderator}_{r,t-1}+{\\gamma\\:}_{3}{Moderator}_{r,t-1}\\times\\:{GIS}_{i,t-1}+{\\gamma\\:}_{k}{Controls}_{i,t-1}+{\\theta\\:}_{i}+{\\omega\\:}_{r}\\times\\:{\\tau\\:}_{j}+{\\tau\\:}_{j}\\times\\:{\\mu\\:}_{t}+{\\mu\\:}_{t}+{\\epsilon\\:}_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\:{AFDR}_{i,t}={\\gamma\\:}_{0}+{\\gamma\\:}_{1}{GIS}_{i,t-1}+{\\gamma\\:}_{2}{Moderator}_{r,t-1}+{\\gamma\\:}_{3}{Moderator}_{r,t-1}\\times\\:{GIS}_{i,t-1}+{\\gamma\\:}_{4}{Moderator}_{r,t-1}^{2}+{\\gamma\\:}_{5}{GIS}_{r,t-1}^{2}+{\\gamma\\:}_{6}{Moderator}_{r,t-1}^{2}\\times\\:{GIS}_{r,t-1}^{2}+{\\gamma\\:}_{k}{Controls}_{i,t-1}+{\\theta\\:}_{i}+{\\omega\\:}_{r}\\times\\:{\\tau\\:}_{j}+{\\tau\\:}_{j}\\times\\:{\\mu\\:}_{t}+{\\mu\\:}_{t}+{\\epsilon\\:}_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eModerator\u003c/em\u003e represents moderating variable (abbreviated as \u003cem\u003eM\u003c/em\u003e in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), and the meanings of other parameters are the same as described in Model (1). We mainly observe the significance of the coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{3}\\)\u003c/span\u003e\u003c/span\u003e in Model (8) and the significance of the coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{3}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{6}\\)\u003c/span\u003e\u003c/span\u003e in Model (9). If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{3}\\)\u003c/span\u003e\u003c/span\u003e is significant in Model (8), it indicates that \u003cem\u003eM\u003c/em\u003e has a significant moderation effect. If both \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{3}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{6}\\)\u003c/span\u003e\u003c/span\u003e are significant and have opposite signs in Model (8), it indicates \u003cem\u003eM\u003c/em\u003e exerts a U-shaped moderation effect.\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\u003eThe results of moderation analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eM: SSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eM: IER\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00214\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 \u003cp\u003e0.0041***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0043***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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(-0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.77)\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 \u003cp\u003e(6.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum*M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00030***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00074\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 \u003cp\u003e-0.0069***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0108**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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(2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.33)\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 \u003cp\u003e(-2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dum*M\u003csup\u003e2\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-0.00002\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0091*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.83)\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_pct\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\u003e0.00019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0019***\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(2.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_pct*M\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\u003e0.00011***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0047***\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-3.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_pct\u003csup\u003e2\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0001\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_pct\u003csup\u003e2\u003c/sup\u003e*M\u003csup\u003e2\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0010***\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(3.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003csup\u003e2\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-0.00003\u003c/p\u003e \u003c/td\u003e 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colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0058***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0002\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(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00990***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00992***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00891***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00893***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0101***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0099***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0091***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0073***\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01456***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01428***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01383***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01354***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0138***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0141***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0120***\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(-3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-5.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-2.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0041**\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(-0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0054**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0007\u003c/p\u003e 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\u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0020\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(0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMsalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11637***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11633***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10486***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10394***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1206***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1224***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1100***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1248***\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(4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(7.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(4.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0132*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0011\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(1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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colname=\"c7\"\u003e \u003cp\u003e-0.2004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1823***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.1380***\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(-6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-6.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-6.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-6.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-13.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-12.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-11.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-4.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmFE\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrvFE\u0026amp;YearFE\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndFE\u0026amp;YearFE\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYearFE\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj_R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eColumns (1) to (4) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e report the moderation effects of SSP on the relationship between GIS and FDR. The results in Columns (1) and (3) show that the regression coefficients of the interaction term between \u003cem\u003eSSP\u003c/em\u003e and \u003cem\u003eGIS_dum\u003c/em\u003e (and \u003cem\u003eGIS_pct\u003c/em\u003e) are significantly positive. This indicates that stronger SSP can promote the effectiveness of GIS in mitigating FDR. In the regression results of Columns (2) and (4), the coefficients of the square of the interaction term between \u003cem\u003eSSP\u003c/em\u003e and \u003cem\u003eGIS_dum\u003c/em\u003e (and \u003cem\u003eGIS_pct\u003c/em\u003e) are not significant. This implies that the promotion effect of SSP on the negative relationship between GIS and FDR is not nonlinear, thus supporting H3a.\u003c/p\u003e \u003cp\u003eColumns (5) to (8) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e report the moderation effects of IER on the relationship between GIS and FDR. The results in Columns (5) and (7) show that the coefficients of the interaction term between \u003cem\u003eIER\u003c/em\u003e and \u003cem\u003eGIS_dum\u003c/em\u003e (and \u003cem\u003eGIS_pct\u003c/em\u003e) are significantly negative. This indicates that, overall, IER inhibits the effectiveness of GIS in mitigating FDR. In the regression results of Columns (6) and (8), the coefficients of the square of the interaction term between \u003cem\u003eIER\u003c/em\u003e and \u003cem\u003eGIS_dum\u003c/em\u003e (and \u003cem\u003eGIS_pct\u003c/em\u003e) are significantly negative, while the coefficients of the squared term of the interaction with \u003cem\u003eGIS_dum\u003c/em\u003e and \u003cem\u003eGIS_pct\u003c/em\u003e are significantly positive. This suggests that IER exhibits a U-shaped moderation effect, thus supporting H3b.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Further Discussions: The Economic Benefits of GIS","content":"\u003cp\u003eTo further investigate whether GIS can drive comprehensive changes within firms, this article expands the discussion by focusing on firms' environmental performance, financial performance, and capital market performance. The objective is to comprehensively assess whether GIs, through their investment behaviors, positively influence firms' long-term development. Given that the year in which each firm is first held by GIs varies in the context of this article, we adopt the construction logic of the Stacked DID model to develop the \u003cem\u003eGIS_dd\u003c/em\u003e variable. (Specifically, firms are assigned a value of 1 for the year they are first held by GIs and for subsequent years within the 2012\u0026ndash;2022 period, and 0 otherwise.) \u003cem\u003eGIS_dd\u003c/em\u003e serves as the core explanatory variable for a series of regressions\u003csup\u003e15\u003c/sup\u003e aimed at observing the long-term effects of GIS on firm development (regression results presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Additionally, the article employs the parallel trend test method to conduct regressions and generate trend visualization results (Fig.\u0026nbsp;3\u0026ndash;5). These figures track the periodical changes in firms' environmental performance, financial performance, and capital market performance after GIS. This approach assesses the persistence and volatility of the governance effects attributed to GIS.\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\u003eThe results of further discussions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGre\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYield\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIS_dd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0959**\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\u003e0.1723***\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(2.5328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.3067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.7056)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4273***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3468***\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(6.5399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.4758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-11.7997)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1712***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3506***\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(0.6534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-8.1682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.6366)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2309***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0162***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0119\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(4.1380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-3.4805)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.3064)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShrHolder3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\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(-1.0368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.9909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0609)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0034\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(-0.2264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.3649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0404)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0474\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(1.5104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.4624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.2740)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMsalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-17.4250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6142***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2536**\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(-1.5060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.5700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.2499)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPgrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.4737**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0563*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1843\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(2.5107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.9099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.7939)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-81.4583***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4726***\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(-6.8146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.6005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(11.8253)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmFE\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\u003ePrvFE\u0026amp;YearFE\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\u003eIndFE\u0026amp;YearFE\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\u003eYearFE\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\u003eObs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj_R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Environmental Performance\u003c/h2\u003e \u003cp\u003eIn this study, corporate environmental performance is measured by the quantity of green innovations (\u003cem\u003eGre\u003c/em\u003e). The regression results, presented in Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, indicate that GIS significantly enhance firms' environmental performance. Figure\u0026nbsp;3 shows that, during the same year of GIS, there is no significant change in environmental performance. However, a significant improvement begins in the second year after shareholding and persists for four years. The positive effect of GIS on environmental performance emerges in the third period after shareholding but is limited in duration.\u003c/p\u003e \u003cp\u003eThis delayed effect may result from the fact that green innovation requires prolonged R\u0026amp;D investment and technological accumulation. The influence of GIS typically materializes gradually through their influence on resource allocation and innovation strategies, leading to positive effects that become evident in the third period. However, once green innovation reaches a certain threshold, firms may have already met their core environmental goals, reducing the marginal benefits of further innovation. At this stage, technological development shifts from a focus on 'quantity' to 'quality.' Simultaneously, the focus of GIs may shift from promoting green technology development to other corporate social responsibility objectives, such as optimizing governance structures or enhancing social impact. This dynamic transition may also be influenced by factors such as weakened policy incentives, saturated market demand, and the reallocation of corporate resources. As a result, the growth in green innovation slows, and the positive impact of GIS on environmental performance diminishes accordingly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Financial Performance\u003c/h2\u003e \u003cp\u003eRegarding corporate financial performance, this paper uses Return on Assets (\u003cem\u003eROA\u003c/em\u003e) for measurement. The regression results, presented in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, indicate that GIS significantly enhance firms' financial performance. Figure\u0026nbsp;4 shows that during the year GI acquire shares, corporate financial performance improves significantly, and this positive effect lasts for five years. It is evident that the positive impact of GIS on financial performance emerges earlier than its impact on environmental performance, although the duration of the effect is limited in both cases.\u003c/p\u003e \u003cp\u003eThe possible reasons for this effect lie in the fact that GIS often lead to rapid improvements in financial performance through the optimization of corporate governance structures and financing conditions. However, as governance mechanisms stabilize, further financial improvements rely more heavily on firms\u0026rsquo; endogenous growth capabilities. Additionally, GIs are often more focused on their investment returns or the advancement of ESG goals. Once initial financial improvements are achieved, their level of intervention may diminish. At the same time, firms may reallocate resources to meet long-term ESG objectives, increasing investments in non-financial areas, which could weaken direct support for financial performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Capital Market Performance\u003c/h2\u003e \u003cp\u003eRegarding corporate capital market performance, this study adopts the annual year-end stock return rate (\u003cem\u003eYield\u003c/em\u003e) as the measurement metric. The regression results, presented in Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, indicate that GIS can significantly enhance firms' capital market performance. Figure\u0026nbsp;5 shows that in the year GIs acquire shares, corporate capital market performance improves significantly and remains elevated throughout the holding period.\u003c/p\u003e \u003cp\u003eThe possible explanation is that GIS in manufacturing firms sends a signal to the market indicating support for cleaner production processes. Additionally, corporate reputation is enhanced, leading the capital market to recognize the firm's long-term value, thereby driving a sustained improvement in capital market performance, consistent with the results discussed earlier. The difference in the 'continuity' of improvements between capital market performance and financial performance stems from the differing drivers of their growth. Capital market performance is primarily influenced by signaling effects and investor expectations, rather than by the continuous improvement of a firm's intrinsic financial efficiency.\u003c/p\u003e "},{"header":"6. Conclusions and Implications","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Conclusions\u003c/h2\u003e \u003cp\u003eUsing data of A-share listed manufacturing companies in China from 2012 to 2022, this article examines the impact of GIS on FDR and its underlying mechanisms. The main conclusions are summarized as follows: (1) GIS can effectively mitigate FDR of manufacturing companies. (2) GIS primarily mitigate FDR by enhancing GGP and alleviating FC. (3) SSP and IER have a moderation effect on the causal relationship between GIS and FDR. Specifically, the stronger the SSP, the greater the role of GIS in mitigating FDR. However, as IER increases, the effect of GIS on FDR prevention first weakens and then strengthens, displaying a U-shaped trend. (4) The impact of GIS on environmental performance shows a lag effect, while its influence on financial performance is immediate but both effects lack persistence. In contrast, GIS has an immediate impact on capital market performance, which persists over a longer period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Implications\u003c/h2\u003e \u003cp\u003eBased on the above conclusions, this paper provides the following policy recommendations: (1) At the government level, clearer green investment standards and evaluation methods should be formulated to enhance investors' awareness and confidence in companies' green projects. This can be achieved by introducing industry certification, green rating, and other measures to help investors in better identifying and selecting green investment opportunities. Meanwhile, the government should promote the establishment of an environmental risk information disclosure framework and improve the disclosure system to raise companies' awareness of risk prevention and control against climate change. This will enable GIs to fully play their role in steadily promoting companies' green transformation and improving the quality of the ecological environment. (2) At the market level, it is necessary to develop more scientific and reasonable green investment indicators and corporate environmental performance evaluation systems to enhance the consistency of information disclosure across companies. This will enable market investors and creditors to better evaluate the actual environmental commitment and investment value of companies, making it easier for environmentally friendly companies to obtain green financing. Consequently, this will compel companies to improve their environmental governance responsibilities mechanisms and environmental risk prevention and control systems. (3) At the firm level, manufacturing companies should fully recognize the long-term value of corporate environmental governance. By leveraging the participation of GI in environmental governance and the role of attracting external capital investment, companies should establish and improve internal governance mechanisms. This includes formulating environmental management policies and establishing environmental protection responsibility systems, thereby improving the efficiency of resource allocation and facilitating a smooth transition towards green production.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eEthical approval was not required as the study did not involve human participants.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors were involved in all sections of this work, and should be regarded as joint first authors.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData will be made available on request or openly available from the Chinese Research Data Services Platform (CNRDS), and China Stock Market Accounting Research Database (CSMAR).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgarwal V and Taffler R (2008) Comparing the performance of market-based and accounting-based bankruptcy prediction models. 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Am Econ Rev 102(1), 396-427.\u003c/li\u003e\n\u003cli\u003eNg A and Zheng D (2018) Let\u0026apos;s agree to disagree! On payoffs and green tastes in green energy investments. Energy Econ 69, 155-169.\u003c/li\u003e\n\u003cli\u003eOhlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 109-131.\u003c/li\u003e\n\u003cli\u003eRudebusch GD (2021) Climate change is a source of financial risk. FRBSF Economic Lett 3, 1-6.\u003c/li\u003e\n\u003cli\u003eTang H Tong M and Chen Y (2024) Green investor behavior and corporate green innovation: Evidence from Chinese listed companies. J Environ Manag 366, 121691.\u003c/li\u003e\n\u003cli\u003eTarighi H, Appolloni A, Shirzad A and Azad A (2022) Corporate social responsibility disclosure (CSRD) and financial distressed risk (FDR): does institutional ownership matter?. Sustain 14(2), 742.\u003c/li\u003e\n\u003cli\u003eTchorzewska KB (2024) A Lost Opportunity? 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Empirical evidence from a quasi-natural experiment in China. Sustain Prod Consump 30 815-828.\u003c/li\u003e\n\u003cli\u003eZmijewski ME (1984) Methodological issues related to the estimation of financial distress prediction models. J Account Res 59-82.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Notes","content":"\u003col\u003e\n\u003cli\u003eGovernment of China (www.gov.cn/zhengce)\u003c/li\u003e\n\u003cli\u003eBank of China (www.boc.cn/fimarkets/summarize)\u003c/li\u003e\n\u003cli\u003eSee Article 15 of the \u0026quot;Code of Corporate Governance for Listed Companies\u0026quot; (CSRC Announcement [2018] No. 29) for details.\u003c/li\u003e\n\u003cli\u003eSee Article 115 of the \u0026quot;Company Law of the People\u0026apos;s Republic of China\u0026quot; (Revised for the Second Time by the Seventh Session of the Standing Committee of the Fourteenth National People\u0026apos;s Congress on December 29, 2023).\u003c/li\u003e\n\u003cli\u003e\u0026apos;Region\u0026apos; refers to the provincial administrative region in China. The same applies hereafter.\u003c/li\u003e\n\u003cli\u003ePollution attributes reflect the additional pressures that manufacturing firms may encounter during environmental governance and green transformation. By using the product of pollution attributes and FDR as a new proxy variable for the FDR of manufacturing firms, we are able to capture both the internal financial changes related to pollution attributes for each firm and the potential impact of the external environment on the firm\u0026apos;s financial health. This approach enables a more precise evaluation of the effect of GIS on reducing FDR.\u003c/li\u003e\n\u003cli\u003eThe pollution emission data for listed manufacturing firms primarily include five pollutant indicators. For water pollution, these include chemical oxygen demand emissions and ammonia nitrogen emissions. For air pollution, the indicators include sulfur dioxide emissions, nitrogen oxides emissions, and particulate matter emissions.\u003c/li\u003e\n\u003cli\u003eWe averaged the data on the proportion of GIS across four quarters of the year to measure the annual shareholding status, aiming to smooth out the bias caused by the different lengths of shareholding within a year.\u003c/li\u003e\n\u003cli\u003e\u0026apos;Industry\u0026apos; refers to the sub-sectors of manufacturing industry. The same applies hereafter.\u003c/li\u003e\n\u003cli\u003eData source: CSMAR Database, calculated manually by authors.\u003c/li\u003e\n\u003cli\u003eGreen investment refers to the practice of using systematic green investment strategies to invest in enterprises or projects that can generate environmental benefits, reduce environmental costs, and mitigate risks.\u003c/li\u003e\n\u003cli\u003eUnder the influence of the \u0026quot;Guidelines,\u0026quot; funds with a higher level of greenness may gain greater social recognition and potentially have more influence within a company. Therefore, we argue that such funds are more significantly impacted by the \u0026quot;Guidelines.\u0026quot; Referring to the indicator construction method of Xiang et al. (2020), we compile the frequency of keywords used in the screening process for GIs within the investment objectives and scopes of the funds. The higher the keyword frequency, the greater the greenness level of the fund.\u003c/li\u003e\n\u003cli\u003eDue to the change in calculation method, higher values of \u0026apos;O-Score\u0026apos; and \u0026apos;ZM-Score\u0026apos; indicates greater corporate FDR.\u003c/li\u003e\n\u003cli\u003eData source: \u0026apos;Listed Company Environmental Management Disclosure Table\u0026apos; and \u0026apos;Listed Company Environmental Regulation and Certification Disclosure Table\u0026apos; from the CSMAR database.\u003c/li\u003e\n\u003cli\u003eBy conducting difference-in-differences analyses across multiple time periods to capture the effects for each time interval and then \u0026apos;stacking\u0026apos; these results to construct a new model for regression analysis. This approach is well-suited to the context in this study, which involves multiple time points and multiple treatment groups.\u003c/li\u003e\n\u003c/ol\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":"green investor shareholding, financial distress risk, green governance performance, financing constraints, institutional logic, China","lastPublishedDoi":"10.21203/rs.3.rs-6390011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6390011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccelerating the promotion of green economic development relies on the infusion of green capital. In this context, can the green investor (GI) serve as a 'stabilizer' for the green transformation and development of manufacturing companies? This article empirically examines the impact of green investor shareholding (GIS) on financial distress risk (FDR) by constructing a refined measure that better captures the FDR of manufacturing companies. The research findings indicate that: (1) GIS can significantly mitigate the FDR of manufacturing companies; (2) two possible channels are the enhanced levels of green governance performance and alleviation of financing constraints; (3) from the institutional logic perspective, the strength of shareholder protection enhances the role of GIS in mitigating FDR, while the intensity of environmental regulation exhibits a U-shaped moderating effect, initially suppressing it and subsequently promoting it. Furthermore, we explore long-term, multifaceted impact of GIS, highlighting their broader implications for corporate sustainable development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG23, G23, Q56\u003c/p\u003e","manuscriptTitle":"Green Investor Shareholding and Financial Distress Risk of Manufacturing Companies: Exacerbate or Mitigate?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-19 11:57:40","doi":"10.21203/rs.3.rs-6390011/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-12T08:52:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-30T10:15:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T17:44:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105779780430936356981615090618300434994","date":"2025-07-31T19:13:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189438368600919300562413806828176891003","date":"2025-07-31T11:58:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237135643465071632428810352366595801240","date":"2025-07-31T09:59:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221024767172772660012096062952034231723","date":"2025-07-04T14:03:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181660718365039654337884577773318857164","date":"2025-07-02T14:26:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T19:29:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179994968875004188767162949264825250547","date":"2025-06-17T19:21:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-17T10:43:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-12T10:06:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-12T10:05:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-24T21:55:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-04-07T04:21:43+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":"51e5bc5c-5dbf-490a-a84c-6a24b3ab116b","owner":[],"postedDate":"June 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":50204575,"name":"Business and commerce/Business and management"},{"id":50204576,"name":"Social science/Economics"},{"id":50204577,"name":"Social science/Environmental studies"},{"id":50204578,"name":"Social science/Finance"}],"tags":[],"updatedAt":"2026-05-22T08:54:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-19 11:57:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6390011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6390011","identity":"rs-6390011","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}