Path to Green Development: How Do ESG Ratings Affect Green Total Factor Productivity?

Abstract

Global environmental issues are becoming increasingly prominent and environmental, social and governance (ESG) ratings may play a key role in green development by stimulating informal environmental regulation from stakeholders. As a pivotal criterion for measuring green development, green total factor productivity (GTFP) refers to maximizing output while minimizing the environmental pollution for the required input production factors. Existing research neglects the impact of ESG ratings on GTFP that indicates the balance between economic growth and ecological protection. This study examines the impact of ESG ratings and mechanisms on GTFP using a sample of Chinese A-share listed manufacturing firms between 2010 and 2021. The findings indicate that ESG ratings promote corporate GTFP, a result which remains robust after a series of robustness tests. The mechanism analysis reveals that ESG ratings improve corporate GTFP by alleviating financial constraints, mitigating managerial myopia, and enhancing supply chain efficiency. A moderating analysis verified that managerial power weakens the positive impact of ESG ratings on corporate GTFP. The positive effect of ESG ratings on GTFP is more pronounced among non-state-owned firms and firms in non-heavily polluting and highly competitive industries. This study confirms that ESG ratings can achieve the benefits of productivity growth, energy conservation, and pollution reduction at the micro-enterprise level, offering a policy foundation for promoting ESG disclosure and achieving green development.

1. Introduction

Global climate disasters are driving countries to revamp their outdated “high input, high consumption and high pollution” development paradigm and to prioritize green development that balances economic growth and environmental protection [1]. China, the largest developing country, has emerged as the largest emitter of carbon dioxide and the largest consumer of energy. This is due to China’s rapid economic growth, which poses environmental and resource constraints to sustainable economic development. In this regard, during the 75th session of the United Nations General Assembly in 2020, China proposed a target of reaching a carbon peak by 2030 and carbon neutrality by 2060 in the pursuit of green development. It is of great theoretical and practical significance to explore effective approaches to the promotion of green development that are consistent with dual-carbon goals. When the resource constraints required for production and the cost of end-of-pipe pollution disposal are low, firms tend to maintain the “high-input and high-pollution” development mode [2]. Signal theory [3] states that environmental, social, and corporate governance (ESG) ratings diminish information asymmetry between firms and stakeholders like investors, upstream and downstream firms, and shareholders. Such invisible pressure from stakeholders constitutes informal environmental regulation and compels firms to make more proactive environmental management decisions to obtain legitimacy and external resources [4]. However, ESG ratings generate undesired economic challenges, as they impose additional costs on businesses for environmental management and thus crowd out productive investment. Therefore, it is pertinent to empirically investigate whether and how ESG ratings affect corporate green development that emphasizes mutually beneficial outcomes for both the environment and growth.

The existing literature focuses on investigating the economic effects of ESG ratings and draws inconsistent conclusions. There is a need to supplement the literature on the impact of ESG ratings on green development that examines the balance between the economic and environmental consequences of ESG ratings within the same framework. Some studies confirm the positive impact of ESG ratings on corporate risk taking [5], financial performance [6,7], creditworthiness [8], stock returns [9], green technology innovation [2,10,11], reduced financial risk [12], total factor productivity [13,14] and environmental pollution control [15]. However, other scholars argue that ESG ratings drive firms to symbolically comply with external requirements for financial support or brand reputation, resulting in information masking and the potential misleading of stakeholders [16,17]. Khan et al. [18] propose that the financial materiality that refers to the relevance of information for stakeholder analysis and decision making affects the informativeness of ESG ratings and that firms’ investments in sustainable practices result in financial outperformance only when these investments are linked to sustainability issues that are financially material to the firms. Madison and Schiehll [19] further reveal that financial materiality affects the values of ESG ratings and that considering financial materiality can provide a better basis for investment decisions based on ESG ratings. The relatively imperfect institutions in emerging countries, such as immature capital markets, which suffer from a lack of corporate transparency and weak legal enforcement, make it difficult for firms from emerging countries to achieve the economic benefits of ESG ratings [20]. The conventional total factor productivity (TFP) indicator, which is based on maximizing economic benefits and ignores environmental pollution as a negative output, has proved to be inadequate for accessing green development. Whether ESG ratings can promote green development with the constrained environment and resources remains to be explored. Investigating this question can provide evidence and practical guidance for the green development of countries, especially emerging countries.

Green total factor productivity (GTFP) offers a powerful criterion for measuring green development as it examines the equilibrium achieved between economic development and environment conservation in order to reflect the quality of economic growth [21,22]. Incorporating energy consumption and environmental pollution emissions into the TFP analysis framework, GTFP aims to achieve maximum output while minimizing environmental pollution for the required input production factors [23]. Previous studies on the determinants of GTFP have verified the positive effect of innovation efficiency [24], the digital transformation [3], technological innovation [25], green technological innovation [26], green credit [27], information and communication technology [28] and the digital economy [21,29,30] on GTFP. Existing research on environmental regulation and GTFP draws inconsistent conclusions and ignores the impact of informal environment regulation denoted by ESG ratings on GTFP. Some scholars have found that environmental regulation improves GTFP [31,32]. In contrast, Yuan and Xiang (2018) propose that environmental regulation has no significant stimulating impact on GTFP [33]. Tang et al. (2020) have empirically revealed that environmental regulation imposes additional costs on businesses for environmental management, thus decreasing GTFP [34]. Wang et al. (2019) believe that the impact of environmental regulation on GTFP is non-linear [35]. Informal environmental regulation has been regarded as a critical complement to formal environmental regulation in cases where gaps in policy implementation may undermine effectiveness and give rise to problems such as greenwashing behavior and patent bubbles [36]. ESG ratings stimulate more scrutiny by stakeholders and can be regarded as informal environmental regulation from stakeholders. It remains to be explored whether and how ESG ratings, considered as informal environmental regulation, affect the GTFP of firms in developing countries with relatively weak formal environmental regulation.

According to signal theory, there is information asymmetry between firms and stakeholders and effective signaling processes can bridge the information gap between them to facilitate collaboration and the achievement of common goals [37]. With regard to ESG ratings, firms send non-financial signals in order to inform stakeholders, including investors, upstream and downstream firms, and shareholders. Superior ESG ratings increase access to green credit and investor confidence due to fewer regulation risks and diminished information asymmetry between firms and investors. ESG ratings decrease the incentive and increase the conditions for management to implement short-sighted behavior by alleviating information asymmetry between management and shareholders. Good ESG ratings reduce upstream and downstream firms’ concerns about the future operational risks of firms and increase their willingness to cooperate in financing and technological innovation by mitigating the information asymmetry between firms and their upstream and downstream counterparts. Given this, ESG ratings may alleviate financial constraints, mitigate managerial myopia and improve supply chain efficiency, thus affecting corporate GTFP. In addition, considering that the complex principle–agent relationship may lead to a conflict between the interest of the firm and the personal interest of the manager, the impact of ESG ratings on corporate GTFP could be moderated by managerial power. Managers face a trade-off between the benefits—attracting capital inflow, alleviated managerial myopia and enhanced supply chain efficiency—that are obtained by signaling to stakeholders and the costs—compromised self-interest due to the increased stakeholder scrutiny—that are the result of such signaling. The impact of ESG ratings on corporate GTFP is contingent upon the constraints that such non-financial information disclosure can impose on management’s self-interest behavior.

This study empirically investigates the impact of ESG ratings and mechanisms on the GTFP based on signal theory using data from Chinese listed manufacturing firms between 2010 and 2021. The findings indicate that ESG ratings improve corporate GTFP by alleviating financial constraints, mitigating managerial myopia and enhancing supply chain efficiency which remains robust after a series of robustness checks. Moreover, we further examine the boundary condition of ESG ratings affecting corporate GTFP. Our examination revealed that managerial power weakens the positive impact of ESG ratings on corporate GTFP. Furthermore, this paper examines the heterogenous effects of ESG ratings on the GTFP of firms with different corporate ownerships, levels of industry pollution, and degrees of industry competition. The results indicate that the positive impact of ESG ratings on GTFP is particularly pronounced for non-heavily polluting firms, non-state-owned firms, and firms in highly competitive industries.

This study contributes to the existing literature in the following aspects. First, it enriches the literature on the impact of EGS rating on green development by incorporating ESG ratings and corporate GTFP into the same framework for empirical analysis through the lens of informal environmental regulation. Additionally, it conducts heterogeneity analysis from the perspectives of corporate ownership, degree of industry pollution, and industry competition. Existing studies focus on the economic consequences of ESG ratings and remain controversial, ignoring the prospect of analyzing the combined measures of ESG ratings, the environment, and the economy within the same framework so as to balance the economic and environmental consequences of ESG ratings. Previous research on the effect of environmental regulations on GTFP reached mixed conclusions and few studies have explored the impact of ESG ratings, representing informal environmental regulations, on corporate GTFP. The findings of this paper extend the research on the determinants of GTFP and on the consequences of ESG ratings that are considered as informal environmental regulations from stakeholders, based on signal theory. Second, it investigates mechanisms by which ESG ratings promote corporate GTFP, revealing that alleviated financial constraints, mitigated managerial myopia, and improved supply chain efficiency are critical paths for advancing the effectiveness of ESG ratings. Third, we argue that the impact of ESG ratings on corporate GTFP is contingent upon the constraints that such information disclosure can impose on management’s self-interested behavior, and we explore the moderating effect of managerial power in order to verify the boundary condition. The empirical evidence provides a reference for the green development of firms in emerging countries.

The remainder is structured as follows. Section 2 outlines the theoretical hypotheses. In Section 3, the sample, variable selection, and model construction are delineated. Section 4 displays the empirical findings, including baseline regression, robustness tests, further mechanism analysis, and heterogeneous analysis. Finally, Section 5 presents the conclusions, policy implications and limitations.

2. Theoretical Analysis and Research Hypotheses

2.1. The Impact of ESG Ratings on Corporate GTFP

ESG ratings are a means of communicating a firm’s proficiency in environmental impact, social responsibility, and corporate governance to external stakeholders [38]. This furnishes a market-driven governance mechanism by which to promote productivity growth, energy conservation and pollution reduction, ultimately improving corporate GTFP. As the informal environmental regulation from stakeholders, ESG ratings incentivize firms to implement green transition programs [39]. Firms with high ESG ratings are more likely to receive low-cost financing support from both banks and governments due to their lower regulatory risks and green initiatives [13]. Actively engaging in social responsibility, such as by making charitable donations, can enhance legitimacy and cultivate better public reputations to acquire external resources [1]. These available resources create favorable conditions for production optimization and research and development (R&D), leading to higher green productivity [2].

Furthermore, ESG ratings reduce inefficient investment by alleviating information asymmetry and principal–agent problems, thus improving capital allocation efficiency. Moreover, firms with high ESG ratings tend to prioritize employee well-being and professional development, which increases their appeal to top-tier talent and boosts labor productivity [40]. Effective corporate governance improves management decision making and facilitates the coordination of internal and external resources, resulting in greater production and operational efficiency. Furthermore, ESG ratings actively trigger informal regulation from corporate stakeholders, reshaping the market-oriented governance framework so as to reduce corporate pollution emissions [39]. This informal regulation urges firms to optimize production technologies and processes in order to reduce energy consumption and pollution emissions. Therefore, this study proposes the following hypothesis:

Hypothesis 1 (H1).

ESG ratings improve corporate GTFP.

2.2. The Mediating Effect of Financial Constraints

ESG ratings mitigate financial constraints by lowering financing costs, thus improving corporate GTFP. Improvement of GTFP requires substantial capital investment, which usually relies on external financial support. According to signal theory, there is information symmetry between enterprises and stakeholders, such as investors. As a critical signal, ESG ratings can be transmitted to investors and affect their decisions, which are primarily driven by return and risks. Superior ESG ratings attract more potential investors for firms and increase access to green financial support due to environmental friendliness and diminished information asymmetry between firms and their investors [41]. Financial institutions and corporate creditors wield ESG ratings as a tool for granting favorable green credits so as to avoid the risk of adverse selection [42]. Firms that prioritize sustainability and social responsibility tend to be rated with higher credit scores, which enables them to secure low-interest loans from banks [13]. Environmentally conscious investors exhibit a stronger willingness to pay a premium for firms with high ESG ratings or to retain their stocks with a lower expected excess return.

In addition, firms with excellent ESG ratings are exposed to fewer regulatory risks and establish stable business models [43], which strengthens investor confidence in the company. ESG ratings provide a large amount of non-financial information so as to reduce information risks for investors [8], ultimately attenuating the requisite rate of investor compensation. Conversely, firms with lower ESG ratings face higher financing costs due to risk premiums or the penalties and legal risks associated with environmental pollution [10]. Sufficient capital creates the conditions for firms to implement technological improvements, energy-saving programs, and other pollution control measures, thus improving corporate GTFP. Therefore, we propose the following hypothesis:

Hypothesis 2 (H2).

ESG ratings promote corporate GTFP by mitigating financial constraints.

2.3. The Mediating Effect of Managerial Myopia

ESG ratings restrain both motivation and the conditions for the implementation of managerial myopia, thus improving corporate GTFP. Signal theory indicates that there is information symmetry between management and stakeholders, which incurs an information advantage on behalf of management over stakeholders and creates the conditions for managerial myopia. Managerial myopia is the opportunistic behavior of a management that is motivated by self-interest, i.e., reputation and appraisal performance, and so focuses on the short-term performance of their firm and potentially neglecting its long-term development [44]. Managers who are subjected to short-term performance targets tend to prefer upholding a relatively conservative business philosophy and lack the intrinsic motivation to improve corporate GTFP. On the one hand, ESG ratings reduce career risks of management and the incentive for management to engage in short-sighted behaviors. Firms with high ESG ratings are inclined to adopt sustainable and innovative business models, creating higher investor returns. Investors tend to understand the short-term performance fluctuations of firms with high ESG ratings that are less risky to operate and less likely to be subject to penalties and lawsuits, and support their managers in making risky long-term investments for long-term value growth [27,45].

In addition, ESG ratings improve accounting information quality and raise the conditions for management to implement myopia behavior [45]. Accounting information quality is determined by internal supervision, the external environment and management traits. In terms of internal supervision, good ESG ratings imply sound internal corporate governance mechanisms, such as independent supervisory boards and audit committees, which can strengthen internal oversight of managerial myopia. From the viewpoint of external supervision, issued by authoritative third-party rating agencies, ESG ratings directly reduce the monitoring cost of the external stakeholders through non-financial information disclosure and significantly reduce the potential for earnings management and manipulation. This strengthens external supervision and encourages management to conduct activities that contribute to corporate long-term value [2]. In terms of management traits, good ESG ratings indicate that management actively fulfills social responsibilities that reflect altruistic tendencies, thus reducing earnings management behaviors that are detrimental to the legitimate interests of stakeholders. Therefore, we propose the following hypothesis:

Hypothesis 3 (H3).

ESG ratings promote corporate GTFP by mitigating managerial myopia.

2.4. The Mediating Effect of Supply Chain Efficiency

ESG ratings improve supply chain efficiency by fostering collaboration between upstream and downstream firms, thus improving corporate GTFP. Reflected in the frequent trade exchanges between upstream and downstream enterprises [46], supply chain efficiency aims at maximizing resource utilization from raw material acquisition to disposal, including processing, packaging, storage, and transportation [47]. Given limited internal resources, acquiring and optimizing technological and capital resources through cooperation with upstream and downstream firms in order to improve supply chain efficiency contributes to enhancing corporate GTFP. Upstream and downstream firms within the supply chain are more willing to collaborate in financing and technological innovation with firms that accord significance to partner interests and uphold a sustainable business philosophy [48]. Good ESG ratings indicate to stakeholders that the firm values environmental protection, actively fulfills social responsibilities and improves corporate governance structure. These favorable signals alleviate upstream and downstream firms’ concerns about the future operational risks of firms and increase their willingness to cooperate in financing and technological innovation [49].

Moreover, high ESG ratings can strengthen a firm’s position in the supply chain relationship, helping it obtain more commercial credit from upstream and downstream firms. Relationship-based transactions typically grant large customers a dominant position and strong bargaining power in the transaction process. A large customer may require firms to reduce production prices or extend payment terms so as to maximize its own interests. In such cases, firms may encounter liquidity difficulties due to the sunk costs associated with specialized assets. ESG ratings break down information barriers between different nodes of the supply chain to expand the choice of upstream and downstream partners for firms [50]. Good ESG ratings shape a responsible and reliable corporate image and generate social reputation capital for the firm, which facilitates the establishment of trust relationships with stakeholders and their contributions to the competitiveness of the firm in its production and operation activities. Accordingly, it improves the bargaining power of firms in negotiations, creating the conditions for the extension of payment terms to upstream partners and for taking advance receipts from downstream partners, thus improving supply chain efficiency.

Hypothesis 4 (H4).

ESG ratings enhance corporate GTFP by improving supply chain efficiency.

2.5. The Moderating Effect of Managerial Power

The impact of ESG ratings on corporate GTFP is contingent upon the constraints that such information disclosures impose on management’s self-interested behavior. Management encounters a trade-off between the benefits—attracting capital inflows, alleviated managerial myopia and improved supply chain efficiency—obtained by signaling to stakeholders their firm’s capabilities and the cost—a compromised self-interest due to the increased scrutiny by external stakeholders that such signaling entails [51]. Managerial power refers to the ability of management to reflect individual will in business operations [52]. As for the separation between ownership and management, management has the ability and motivation to engage in power rent-seeking and even manipulate their own compensation [53]. The greater the managerial power, the more likely it is that management will prioritize their own interests over those of shareholders and potentially abuse power to achieve self-defined compensation [54]. Management may manipulate information disclosure and set compensation in accordance with their own interests, which can ultimately reduce the quality of the disclosed information [55].

When managerial power is excessive, management is less likely to be affected by shareholder supervision and more likely to manipulate non-financial information disclosure to favor its own interests, though ESG ratings have a regulatory role for rated firms that raises GTFP. Excessive managerial power may lead to greater greenwashing, making it difficult for stakeholders to identify abnormal information about ESG ratings. Management has multiple incentives to conceal negative news and avoid the serious consequences of its exposure, such as reputational damage, dismissal, or legal penalties. The greater the managerial power, the more likely management is to utilize their power to disclose positive non-financial information for compensation and reputational benefits. Meanwhile, excessive managerial power can cause firms to overlook valuable investment projects and make inadequate investments. Management tends to reduce long-term and high-risk projects so as to ensure positive performance appraisals and thus consolidate their position, which may result in the neglecting investment in technological innovations that contribute to the long-term development of their enterprise. Furthermore, the greater the managerial power, the more likely management is to be overconfident and overoptimistic in terms of performance expectations. Under such circumstances, management is unable to rationally assess the negative feedback information in the process of project investment and is prone to overestimate the cash flow and underestimate investment risks. Based on the above analysis, we propose the following hypothesis:

Hypothesis 5 (H5).

Managerial power weakens the positive impact of ESG ratings on corporate GTFP.

3. Research Design

3.1. Sample and Data Resources

The manufacturing industry is the main battlefield for China to achieve peak carbon and carbon neutrality. As a major manufacturing country and the largest developing country, China is committed to improving green total factor productivity (GTFP) in manufacturing so as to achieve green development. We select the data of Chinese listed manufacturing firms from 2010 to 2021 and eliminate firms that enjoyed special treatment and firms with missing data. The ESG ratings data are obtained from Hexun.com, while other firm-level data are mainly from the CSMAR and CNRDS databases. The continuous financial data are winsorized at the 1% and 99% levels to eliminate the effect of extreme values. The city-level data are collected from the China Urban Statistical Yearbook. A detailed description of the sample size is shown in Table 1.

3.2. Variables Chosen

3.2.1. Dependent Variable: Corporate GTFP

The relevant literature mainly proposes the parametric stochastic frontier analysis (SFA) and the non-parametric data development analysis (DEA) to measure GTFP [56]. Based on DEA, Pittman (1983) incorporated the undesired output into the calculation of TFP [57]. Effectively dealing with radial and angular problems, the global Malmquist–Luenberger (GML) index with a slack-based model (SBM) based on directional distance function (DDF) has been widely adopted to measure GTFP [56,58]. This study calculates corporate GTFP based on the SBM-DDF-GML model.

• (1). Global production possibility set. First, A-share manufacturing firms in China are taken as decision-making units (DMU). Each DMU_k uses N kinds of production factors to obtain M kinds of expected output factors. N kinds of production factors $\mathrm{x}=({\mathrm{x}}_1,{\mathrm{x}}_2\dots ,{\mathrm{x}}_{\mathrm{n}})\in {\mathrm{R}}_{\mathrm{N}}^{+}$ to obtain M kinds of expected output factors $\mathrm{y}=({\mathrm{y}}_1,{\mathrm{y}}_2\dots ,{\mathrm{y}}_{\mathrm{m}})\in {\mathrm{R}}_{\mathrm{M}}^{+}$ and I kinds of unexpected output factors $\mathrm{b}=({\mathrm{b}}_1,{\mathrm{b}}_2\dots ,{\mathrm{b}}_{\mathrm{i}})\in {\mathrm{R}}_{\mathrm{I}}^{+}$. T is the time variable. The possibility of current production is expressed as follows [59]:

  (1)${\mathrm{p}}^{\mathrm{G}}(\mathrm{x})=\left\{\begin{array}{c}\left({\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}}\right):{\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{y}}_{\mathrm{k}\mathrm{m}}^{\mathrm{t}}\ge {\mathrm{y}}_{\mathrm{k}\mathrm{m}}^{\mathrm{t}},\forall \mathrm{m};{\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{b}}_{\mathrm{k}\mathrm{i}}^{\mathrm{t}}={\mathrm{b}}_{\mathrm{k}\mathrm{i}}^{\mathrm{t}},\forall \mathrm{i};\\ {\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{x}}_{\mathrm{k}\mathrm{n}}^{\mathrm{t}}\le {\mathrm{x}}_{\mathrm{k}\mathrm{n}}^{\mathrm{t}},\forall \mathrm{n};{\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}=1,{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}>0,\forall \mathrm{k}\end{array}\right\}$

  where ${\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}$ is the weight of the cross section of the decision-making unit.

• (2). Overall SBM directional range function. The global SBM directional range function is constructed as follows [60]:

  (2)$$\begin{gathered} {\mathrm{S}}_{\mathrm{V}}^{\mathrm{G}}\left({\mathrm{x}}^{\mathrm{t}\mathrm{k}},{\mathrm{y}}^{\mathrm{t}\mathrm{k}},{\mathrm{b}}^{\mathrm{t}\mathrm{k}},{\mathrm{g}}^{\mathrm{z}},{\mathrm{g}}^{\mathrm{y}},{\mathrm{g}}^{\mathrm{b}}\right)=\underset{{\mathrm{s}}^{\mathrm{x}},{\mathrm{s}}^{\mathrm{y}},{\mathrm{s}}^{\mathrm{b}}}{\max }{\displaystyle \frac{\frac{1}{\mathrm{N}}\sum _{\mathrm{n}=1}^{\mathrm{N}}\frac{{\mathrm{s}}_{\mathrm{n}}^{\mathrm{x}}}{{\mathrm{g}}_{\mathrm{n}}^{\mathrm{x}}}+\frac{1}{\mathrm{M}+\mathrm{I}}(\sum _{\mathrm{m}=1}^{\mathrm{M}}\frac{{\mathrm{s}}_{\mathrm{m}}^{\mathrm{y}}}{{\mathrm{g}}_{\mathrm{m}}^{\mathrm{y}}}+\sum _{\mathrm{i}=1}^{\mathrm{I}}\frac{{\mathrm{s}}_{\mathrm{i}}^{\mathrm{b}}}{{\mathrm{g}}_{\mathrm{i}}^{\mathrm{b}}})}{2}} \\ \mathrm{s}.\mathrm{t}.{\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{x}}_{\mathrm{k}\mathrm{n}}^{\mathrm{t}}+{\mathrm{s}}_{\mathrm{n}}^{\mathrm{x}}\ge {\mathrm{x}}_{\mathrm{k}\mathrm{n}}^{\mathrm{t}},\forall \mathrm{n}; \\ {\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{y}}_{\mathrm{k}\mathrm{m}}^{\mathrm{t}}-{\mathrm{s}}_{\mathrm{m}}^{\mathrm{y}}\ge {\mathrm{y}}_{\mathrm{k}\mathrm{m}}^{\mathrm{t}},\forall \mathrm{m}; \\ {\sum }_{\mathrm{t}=1}^{\mathrm{T}}{\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}{\mathrm{b}}_{\mathrm{k}\mathrm{i}}^{\mathrm{t}}+{\mathrm{s}}_{\mathrm{i}}^{\mathrm{b}}\ge {\mathrm{b}}_{\mathrm{k}\mathrm{i}}^{\mathrm{t}},\forall \mathrm{i}; \\ {\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}=1,{\mathrm{Z}}_{\mathrm{k}}^{\mathrm{t}}>0,\forall \mathrm{k};{\mathrm{s}}_{\mathrm{n}}^{\mathrm{x}}\ge 0,\forall \mathrm{n};{\mathrm{s}}_{\mathrm{m}}^{\mathrm{y}}\ge 0,\forall \mathrm{m};{\mathrm{s}}_{\mathrm{i}}^{\mathrm{b}}\ge 0,\forall \mathrm{i}; \end{gathered}$$

Vectors ${\mathrm{x}}^{\mathrm{t}\mathrm{k}},{\mathrm{y}}^{\mathrm{t}\mathrm{k}},{\mathrm{b}}^{\mathrm{t}\mathrm{k}}$ represent the production input, expected output and unexpected output of firm k at time t, respectively. Vectors ${\mathrm{g}}^{\mathrm{z}},{\mathrm{g}}^{\mathrm{y}},{\mathrm{g}}^{\mathrm{b}}$ are the reduced input, increased expected output and reduced unexpected output, respectively. Vectors ${\mathrm{s}}_{\mathrm{n}}^{\mathrm{x}},{\mathrm{s}}_{\mathrm{m}}^{\mathrm{y}},{\mathrm{s}}_{\mathrm{i}}^{\mathrm{b}}$ are undue input, insufficient expected output and excessive unexpected output, respectively.

• (3). GML index. The global Malmquist–Luenberger exponent is used to further observe the change of GTFP.

(3) ${\mathrm{G}\mathrm{M}\mathrm{L}}_{\mathrm{t}}^{\mathrm{t}+1}=\left[1+{\mathrm{S}}_{\mathrm{V}}^{\mathrm{G}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}};\mathrm{g}\right)\right]÷\left[1+{\mathrm{S}}_{\mathrm{V}}^{\mathrm{G}}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{b}}^{\mathrm{t}+1};\mathrm{g}\right)\right]$

The input factors include the firm’s assets, number of employees and electricity consumption. The expected output factor is the corporate revenue, and the unexpected output factors include the emission of wastewater, SO₂, NO and dust particles. Corporate assets, employees and revenue data are revealed by annual reports. We use the number of employees in the secondary sector of the city where the enterprise is located and the manufacturing firm’s employees to weight the undisclosed data at the firm level [22,61]. At last, we use the Maxdea software (version 9.1) to calculate the GML index and take it as the proxy for GTFP.

3.2.2. Independent Variable: ESG Ratings

We collect the ESG ratings data from the Hexun database [5,62], as the Hexun ESG score is a comprehensive evaluation system that is specially designed for Chinese enterprises according to the characteristics of the Chinese market. It contains five perspectives: shareholder responsibility, employee responsibility, supplier, customer and consumer responsibility, environmental responsibility and social responsibility. The ESG ratings range from −100 to 100. The higher the ESG score, the better the firm performs.

3.2.3. Control Variables

We control for both firm-level and city-level characteristics, as described in Table 2 [22,27]. We include enterprise size (Size), asset-liability ratio (Lev), Tobin’s Q value (TobinQ), enterprise age (Age), enterprise ownership (SOE) and institutional shareholding ratio (INST). The city-level characteristics consist of ratio of secondary sector output (Ind2per) and GDP per capita (Lngdp).

3.3. Model Specification

The following formula is constructed to examine the impact of ESG ratings on corporate GTFP.

(4)$\mathrm{G}\mathrm{T}\mathrm{F}{\mathrm{P}}_{\mathrm{i}\mathrm{c}\mathrm{t}}={\mathsf{\beta }}_{\mathrm{o}}+{\mathsf{\beta }}_1\mathrm{E}\mathrm{S}{\mathrm{G}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{{\mathsf{\beta }}_2\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\delta }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{c}\mathrm{t}}$

In Equation (4), GTFP_ict is the dependent variable of corporate GTFP; i, c, and t denote the firm, the city, and the year, respectively; ESG_ict represents the ESG ratings; Control_ict refers to the firm-level and city-level control variables; μ_i is the firm-fixed effect; δ_t refers to the year-fixed effect; and ϵ_ict denotes the random error term.

To investigate the channels through which ESG ratings affect corporate GTFP, this paper formulates Equations (5) and (6) for empirical analysis. Mediator_ict denotes the mediating variables, including financial constraints, managerial myopia, and supply chain efficiency.

(5)${\mathrm{M}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}}_{\mathrm{i}\mathrm{c}\mathrm{t}}={\mathsf{\alpha }}_{\mathrm{o}}+{\mathsf{\alpha }}_1\mathrm{E}\mathrm{S}{\mathrm{G}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{\mathsf{\alpha }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\delta }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{c}\mathrm{t}}$

(6)$\mathrm{G}\mathrm{T}\mathrm{F}{\mathrm{P}}_{\mathrm{i}\mathrm{c}\mathrm{t}}={\mathsf{\gamma }}_{\mathrm{o}}+{\mathsf{\gamma }}_1\mathrm{E}\mathrm{S}{\mathrm{G}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{{\mathsf{\gamma }}_2\mathrm{M}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{\mathsf{\gamma }}_3{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\delta }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{c}\mathrm{t}}$

The moderating effect of managerial power on the relationship between ESG ratings and corporate GTFP is estimated using Equation (7). Moderator_ict represents managerial power. We expect to find the estimate of ρ₃ to be significantly negative to support Hypothesis 5.

(7)$\mathrm{G}\mathrm{T}\mathrm{F}{\mathrm{P}}_{\mathrm{i}\mathrm{c}\mathrm{t}}={\mathsf{\rho }}_0+{\mathsf{\rho }}_1\mathrm{E}\mathrm{S}{\mathrm{G}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{{\mathsf{\rho }}_2\mathrm{M}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{\mathsf{\rho }}_3{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i}\mathrm{c}\mathrm{t}}\times {\mathrm{M}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{\mathsf{\gamma }}_3{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{c}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\delta }}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{i}\mathrm{c}\mathrm{t}}$

4. Results

4.1. Descriptive Statistics

Table 3 reports the statistical characteristics of variables. The maximum and minimum values of GTFP are 0.19 and 5.68, respectively, with an average value of 1.05 and a standard deviation of 0.38. This indicates that there are significant differences between the level of corporate GTFP. The maximum and minimum values of the independent variable ESG rating (ESG) are −17.19 and 90.87, respectively, showing significant differences among firms. Table 4 presents the correlation matrix between the variables in our analysis. All correlation coefficients are less than 0.5 and the values of the variance inflation factor (VIF) are much less than 10, indicating that multicollinearity is not a concern in the estimations.

4.2. The Impact of ESG Ratings on Manufacturing GTFP

Table 5 shows the empirical results of the baseline regression. Column (1) includes only the independent and dependent variables. Column (2) is the univariate regression result. We further control for firm-level characteristics in column (3) and both firm-level and city-level characteristics in column (4). Year and firm fixed effects are controlled in columns (2), (3) and (4). The coefficient of HXESG in column (4) is 0.0022 and significant at the 1% level. These results show that ESG ratings improve corporate GTFP, supporting Hypothesis 1.

4.3. Robustness Tests

4.3.1. Lagged Independent Variable

Considering the potential lag effect of corporate ESG ratings on corporate GTFP, this paper incorporates three lagged explanatory variables, including HXESG lagged one period, HXESG lagged two periods and HXESG lagged three periods, into the baseline regression. The result is shown in column (1) of Table 6. The coefficients of HXESG remain significantly positive at the 1% level after controlling for these lagged terms, which further confirms the robustness of our results.

4.3.2. Alternative Measure

We use the ESG score ranking as an alternative measure for ESG ratings which denotes the corporate relative ESG performance. The result is shown in column (2) of Table 6. HXESGrank is the reverse percentage ranking of ESG ratings of firms, ranging from 0 to 100. The coefficient of HXESGrank is 0.0015 and remains significantly positive after controlling for industry-level and city-level fixed effects.

4.3.3. Multidimensional Fixed Effects

We further control for industry- and city-level fixed effects in order to address concerns about omitted variables at the industry and city level that do not change over time and which might bias the estimated results [5,63]. The result is shown in column (2) of Table 6. The coefficient of ESG ratings remains significantly positive, further supporting Hypothesis 1.

4.3.4. Endogeneity Analysis

As the GTFP level is promoted, firms can publicize their effort toward environmentally friendly transformation and disclose positive information so as to gain a good reputation, consequently triggering a higher ESG rating. We employ the two-stage least squares (2SLS) instrumental variable (IV) method to mitigate this reverse causality problem, taking the mean value of other manufacturing firms in the same industry as the instrument variable for its ESG ratings [64]. The test results are represented in column (3) of Table 6. The coefficient of HXESG is still significantly positive. Furthermore, the results pass the weak identification test, under-identification test, over-identification test and endogeneity test. The F test of excluded instruments (14.21) is larger than 10, indicating the instrumental variable is not a weak instrumental variable. The p value of the Anderson canon. corr. LM statistic (0.0000) shows that the hypothesis of the “under-identification of instrumental variables” is significantly rejected at the 1% level. Likewise, the hypothesis of “over-identification of instrumental variables” is rejected with the p value of the Sargan statistic (0.000). Based on the former tests, which validate the relevance and exclusivity of instrumental variables, the endogeneity test validates the reasonableness of the IV method with the p value of 0.0018.

4.4. Further Analysis

4.4.1. The Mediating Effect Test of Financial Constraints

We use the WW indicator (Whited and Wu, 2006) to measure corporate financing constraints [65].

(8)$\begin{array}{c}\mathrm{W}{\mathrm{W}}_{\mathrm{i}\mathrm{t}}=-0.091\times \mathrm{C}{\mathrm{F}}_{\mathrm{i}\mathrm{j}\mathrm{t}}-0.062\times \mathrm{D}\mathrm{i}\mathrm{v}\mathrm{P}\mathrm{o}{\mathrm{s}}_{\mathrm{i}\mathrm{j}\mathrm{t}}+0.021\times \mathrm{L}\mathrm{e}{\mathrm{v}}_{\mathrm{i}\mathrm{j}\mathrm{t}}-\\ 0.044\times \mathrm{S}\mathrm{i}\mathrm{z}{\mathrm{e}}_{\mathrm{i}\mathrm{j}\mathrm{t}}+0.102\times \mathrm{I}\mathrm{S}{\mathrm{G}}_{\mathrm{j}\mathrm{t}}-0.035\times \mathrm{S}{\mathrm{G}}_{\mathrm{i}\mathrm{j}\mathrm{t}}\end{array}$

In Equation (8) i, j, and t denote the firm, the industry, and the year, respectively; CF_ijt denotes the ratio of cash flow to total assets; DivPos_ijt is the cash dividend payment dummy variable, which equals 1 if a cash dividend is paid in year t, 0 otherwise; Lev_ijt is the ratio of long-term liabilities to assets; Size_ijt denotes the size of the firm’s assets, measured by the natural logarithm of total assets; ISG_jt is the average growth rate of industries’ sales; SG_ijt is the growth rate of firms’ sales. The larger the WW indicator is, the tougher the financial constraints that the firm faces. The results are shown in columns (1) and (2) of Table 7. The coefficient of HXESG in column (1) is significantly negative, that in column (2) remains positive and significant, while that of the WW indicator is significantly negative. These results verify that ESG ratings promote corporate GTFP through alleviating financial constraints, supporting Hypothesis 2.

4.4.2. The Mediating Effect Test of Managerial Myopia

Managerial myopia (Myopia) is measured by the ratio of myopic behavior words to the total number of MD&A words in annual reports [45,66]. A higher proportion of myopic behavioral words represents a more serious managerial myopia level. The coefficients of HXESG in column (3) and column (4) of Table 7 remain significantly positive and that of Myopia in column (4) is significantly negative. These results indicate that ESG ratings promote corporate GTFP by mitigating managerial myopia, verifying Hypothesis 3.

4.4.3. The Mediating Effect Test of Supply Chain Efficiency

We adopt the inventory turnover days (IT) as the proxy variable of supply chain efficiency [67]. IT_it denotes the period required for inventory turnover.

(9)$\mathrm{I}\mathrm{T}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}{\mathrm{o}}_{\mathrm{i}\mathrm{t}}=\mathrm{C}{\mathrm{C}}_{\mathrm{i}\mathrm{t}}\times {\left(\mathrm{E}\mathrm{B}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}+\mathrm{E}\mathrm{B}{\mathrm{I}}_{\mathrm{i}\left(\mathrm{t}-1\right)}\right)}^{-1}\times 2$

(10)$\begin{array}{c}{\mathrm{I}\mathrm{T}}_{\mathrm{i}\mathrm{t}}=365\times \mathrm{I}\mathrm{T}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}{\mathrm{o}}_{\mathrm{i}\mathrm{t}}^{-1}\end{array}$

In Equations (9) and (10), i and t denote the firm and year, respectively. CC_it is cooperation costs. EBI_it denotes the ending balance of inventory. ITRatio_it is the inventory turnover ratio. The coefficient of HXESG in column (5) of Table 7 is significantly negative. The coefficient of HXESG in column (6) remains positive and significant, while that of IT is significantly negative. These results indicate that ESG ratings promote corporate GTFP by improving supply chain efficiency.

4.4.4. The Moderating Effect Test of Managerial Power

We use the principal components analysis method to obtain a proxy variable of managerial power (Power1) [68]. This covers five dimensions, including years of service as CEO, whether the chairman serves as CEO, size of board, proportion of internal directors and management’s shareholding ratio. The coefficient of HXESG is significantly positive in column (7) of Table 7 and the coefficient of the interaction term of managerial power and ESG ratings is significantly negative. These results prove that the managerial power weakens the positive effect of ESG ratings on corporate GTFP, supporting Hypothesis 5.

4.5. Heterogeneity Analysis

4.5.1. Effects of SOEs and Non-SOEs

We run grouped regressions to explore the effect of corporate ownership characteristics. State-owned enterprises (SOEs) are born with policy favoritism and greater social responsibility obligations [2,69]. Non-state-owned firms face greater market pressure to obtain external resources by improving ESG ratings and are more in need of external incentives for the improvement of GTFP. Thus, ESG ratings may be more effective for the GTFP of non-state-owned enterprises. The coefficients of ESG ratings in column (1) and column (2) of Table 8 are both significantly positive at the 1% level, while the coefficient of HXESG for non-state-owned firms is about twice that of state-owned firms. This proves that the positive impact of ESG ratings on corporate GTFP is more pronounced for non-state-owned firms.

4.5.2. Effects of Heavily and Non-Heavily Polluting Industries

Enterprises in the heavily polluting industries are subject to stricter formal environmental regulations [70], which increases their costs of pollution reduction to improve GTFP relative to firms in non-heavily polluting industries. Besides, investors generally believe that improving environmental performance is the inherent responsibility of heavy polluters, thus reducing the sensitivity to the fulfillment of heavy polluters’ ESG responsibilities. Thus, the positive impact of ESG ratings on corporate GTFP is more pronounced for firms in non-heavily polluting industries. The coefficients of HXESG in column (3) and column (4) of Table 8 are both significantly positive while the coefficient of HXESG of firms in non-heavily polluting industries is larger than that in heavily polluting industries.

4.5.3. Effects of Industry Competition Intensity

Based on signal theory, fierce market competition creates adversarial relationships between firms and competitors are more inclined to assert their dominance in various ways. Superior ESG ratings as positive signals transmitted to stakeholders for resources support become more crucial for optimizing resource utilization and reducing pollution, thus improving GTFP. Therefore, the positive impact of ESG ratings on GTFP may be more pronounced for firms that confront greater market competition. We divide the sample by the industry competitive intensity using the mean of the Lerner index as the cutoff. Although the coefficients of ESG ratings are both significantly positive in column (5) and (6) of Table 8, the coefficient of ESG ratings of firms in highly competitive industries is higher than that of firms in low competitive industries. These results confirm that the impact of ESG ratings on corporate GTFP is stronger in highly competitive industries.

5. Conclusions, Policy Implications and Limitations

5.1. Conclusions

The implementation of ESG ratings has become a crucial measure by which to improve GTFP for the achievement of green development in the context of a dual-carbon goal. The economic effect of ESG ratings remains controversial in existing studies and there is a lack of research exploring the impact of ESG ratings on corporate GTFP. Most research emphasizes the impact of formal national policies or environmental regulations on GTFP, but few studies have delved into informal environmental regulations. This study contributes to the existing literature on the determinants of GTFP and the consequences of ESG ratings that are considered as informal environmental regulations. It unifies ESG ratings and GTFP into a single framework to provide further theoretical and empirical support for the impact and mechanisms of ESG ratings on GTFP from the perspective of informal environmental regulation. The study further verifies the moderating role of managerial power on the impact of ESG ratings on GTFP and heterogenous analysis from the perspective of corporate ownership, industry pollution level and industry competition intensity.

Using data from Chinese A-share listed manufacturing firms from 2010 to 2021, we reveal that ESG ratings can significantly improve corporate GTFP, a finding that holds after a series of robustness tests. The mechanism analysis indicates that ESG ratings promote corporate GTFP by mitigating financial constraints, alleviating managerial myopia, and enhancing supply chain efficiency. The moderating analysis verifies that managerial power weakens the positive impact of ESG ratings on corporate GTFP. The heterogeneity analyses indicate that the positive effect of ESG ratings on the corporate GTFP is more pronounced in non-state-owned firms, non-heavily polluting firms and highly competitive firms. Conclusively, these findings provide evidence for supporting ESG ratings as an excellent approach by which to promote corporate GTFP and set a direction for further deepening green development.

5.2. Policy Implications

The policy implications of this study for utilizing the contribution of ESG rating to GTFP growth are as follows:

Firms, particularly those in developing countries, are supposed to deepen ESG concepts and engage in the improvement of ESG ratings so as to promote GTFP, in compliance with green development. As the informal environmental regulation from stakeholders, ESG ratings promote the green productivity and pollution reduction of firms. Existing ESG ratings provided by third-party organizations differ significantly in system design and indicator selection, leading to investor confusion. Although listed companies are increasingly issuing ESG reports, the lack of uniform disclosure requirements has led to uneven quality of ESG reports and situations where companies exaggerate or misrepresent their ESG ratings. This greatly increases the cost of analysis for investors and weakens the reference value of ESG ratings. As the largest emerging economy, China plays a pioneering role in integrating the novel ESG rating paradigm so as to promote sustainable development. Regulatory authorities should promote the standardized development of corporate ESG ratings and establish a unified ESG rating system to promote the sustainable development of firms. It is necessary to guide more firms to improve their ESG ratings and provide stakeholders with valid and reliable information in order to obtain external resources for the improvement of GTFP, thus realizing the harmonization of economic, social and ecological benefits.

Stakeholders and corporate management should actively emphasize the positive impact of ESG ratings on GTFP. It is imperative to improve the incentives of financial institutions and creditors in ESG practice. ESG ratings send a positive signal, which helps secure financial support from stakeholders and alleviate financial constraints. Given this, preferential measures can be provided for firms with superior ESG ratings in order to reduce financial constraints. Moreover, the improvement of GTFP requires a shift in the managerial view of ESG ratings and the overcoming of managerial myopia. This study focuses on ESG ratings in terms of the transmission mechanism involved in reducing managerial myopia, which is a crucial factor that management cannot ignore when making decisions to improve GTFP. With the gradual development of ESG ratings, management should change the one-sided view that investment in environmental protection, social responsibility and corporate governance only increases costs and instead pay attention to the role of ESG ratings in reducing managerial myopia and encouraging corporate GTFP. Furthermore, both upstream and downstream counterparts within the supply chain should take ESG ratings as an effective tool with which to evaluate whether to provide financing and carry out technological innovation in the process of procurement, production and sales, thus improving supply chain efficiency.

The heterogeneity effect of ESG ratings on GTFP from the perspective of companies, industries, and the market warrants full consideration. In view of the negative moderating effect of managerial power on the positive relationship between ESG ratings and GTFP, it is necessary to clarify the authority of management and enhance the checks and balances on managerial power to effectively alleviate the agency problem. In addition, this study demonstrates that ESG ratings have a more positive effect on the GTFP of non-state-owned, non-heavily polluting and highly competitive firms. Therefore, non-state-owned and non-heavily polluting firms are supposed to be more actively engaged in advancing ESG ratings for the improvement of GTFP. Market competition can be carefully guided to assist with resource allocation to make the best of the positive impact of ESG ratings on corporate GTFP.

5.3. Limitations and Future Recommendations

This study provides a preliminary discussion of ESG ratings promoting corporate GTFP, but much remains for further investigation. First, there is no uniform standard on the measurement of corporate ESG ratings, which may lead to different interpretations of the consequences of ESG ratings. Future studies can adopt multiple approaches to the measurement of ESG ratings. In particular, seeking to integrate financial materiality in ESG scores and incorporate the proposed GTFP framework in order to further investigate the robustness of our findings. Second, we focus on exploring the impact of ESG ratings on corporate GTFP in the context of China. It is essential to further examine the generalizability of our study and test whether the findings are applicable to developed countries as well as other developing countries. Furthermore, this study relies on the availability and quality of data related to ESG ratings and GTFP. Future research could complement this quantitative analysis with qualitative research methods such as interviews and case studies so as to explore the additional mechanisms through which ESG ratings affect corporate GTFP.

Author Contributions

Conceptualization, S.W. and M.F.; methodology, S.W. and M.F.; software, M.F.; validation, S.W. and M.F.; formal analysis, M.F. and Y.X.; investigation, S.W. and M.F.; resources, S.W. and M.F.; data curation, S.W., M.F., L.W. and Y.X.; writing—original draft preparation, S.W., M.F. and L.W.; writing—review and editing, S.W. and M.F.; visualization, M.F. and L.W.; supervision, Z.L.; project administration, S.W.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Tables

Table 1

Details of the sample.

Type	Number of Firms	Number of Years	Number of Observations
Firms collected by Hexun.com	4666	2010–2021	40,058
Manufacturing firms	3624	2000–2022	32,641
ST firms	774	2000–2022	3357
Final net sample	2462	2010–2021	16,560

Table 2

Variable definitions.

Type	Variables	Sign	Definition
Dependent variable	Green total factor productivity	GTFP	Data calculated by Maxdea software according to the SBM-DDF-GML model
Explanatory variables	ESG ratings	ESG	ESG ratings from Hexun.com
Control variables	Enterprise size	Size	Natural logarithm of total assets
	Asset–liability ratio	Lev	Total liabilities at year-end divided by total assets at year-end
	Tobin’s Q value	TobinQ	(market value of outstanding shares + number of non-outstanding shares × net assets per share + book value of liabilities)/total assets
	Enterprise ownership	SOE	State-controlled enterprises take the value of 1, others 0
	Enterprise age	Age	ln (current year − year of incorporation + 1)
	Institutional shareholding ratio	INST	Total number of shares held by institutional investors divided by outstanding share capital
	GDP per capita	Lngdp	Natural logarithm of GDP per capita of the city where the enterprise operates
	Ratio of secondary sector output	Ind2per	The proportion of secondary sector output to the total output in the city where the enterprise operates

Table 3

Descriptive statistics.

Variables	Observation	Mean	Std. Dev.	Min	Max
GTFP	16,560	1.05	0.38	0.19	5.68
HXESG	16,560	22.87	15.12	−17.19	90.87
Size	16,560	22.02	1.16	19.57	26.4
Lev	16,560	0.4	0.19	0.27	0.93
TobinQ	16,560	2.13	1.37	0.8	16.65
SOE	16,560	0.29	0.45	0	1
Age	16,560	2.84	0.34	1.1	3.61
INST	16,560	0.37	0.23	0	0.89
Lngdp	16,560	11.37	0.54	8.88	13.06
Ind2per	16,560	43.21	10.66	11.7	89.75

Table 4

Correlation matrix.

Variable	GTFP	HXESG	Size	Lev	TobinQ	SOE	FirmAge	INST	Lngdp	Ind2per
GTFP	1
HXESG	0.05 ***	1
Size	0.01	0.21 ***	1
Lev	0.06 ***	−0.10 ***	0.48 ***	1
TobinQ	0.03 ***	0.00	−0.32 ***	−0.22 ***	1
SOE	0.00	0.09 ***	0.32 ***	0.29 ***	−0.08 ***	1
Age	−0.03 ***	−0.09 ***	0.18 ***	0.12 ***	0.03 ***	0.15 ***	1
INST	0.00	0.18 ***	0.40 ***	0.18 ***	0.12 ***	0.35 ***	0.11 ***	1
Lngdp	0.03 ***	−0.07 ***	0.03 ***	−0.05 ***	−0.00	−0.20 ***	0.09 ***	−0.03 ***	1
Ind2per	0.01	0.06 ***	−0.11 ***	0.03 ***	−0.05 ***	−0.03 ***	−0.14 ***	−0.05 ***	−0.31 ***	1

Note: *** p < 0.01.

Table 5

Baseline regression.

Variables	(1)	(2)	(3)	(4)
	GTFP
HXESG	0.0012 ***	0.0021 ***	0.0021 ***	0.0022 ***
	(5.9711)	(7.4673)	(7.4038)	(7.5487)
Size			0.0302 ***	0.0263 ***
			(3.0458)	(2.6492)
Lev			0.2438 ***	0.2486 ***
			(6.8131)	(6.9570)
TobinQ			0.0167 ***	0.0167 ***
			(4.3768)	(4.3710)
SOE			−0.0920 ***	−0.0846 ***
			(−3.7154)	(−3.4137)
Age			0.0978 *	0.1044 *
			(1.7719)	(1.8938)
INST			−0.0006	−0.0025
			(−0.0242)	(−0.1038)
Lngdp				0.1358 ***
				(7.1705)
Ind2per				−0.0006
				(−0.6129)
Observation	16,560	16,386	16,386	16,386
Adjusted R-squared	0.0021	0.0196	0.0271	0.0306
Firm fixed effect	NO	YES	YES	YES
Year fixed effect	NO	YES	YES	YES

Note: t values in parentheses. *** p < 0.01, * p < 0.1.

Table 6

Robustness tests and endogeneity analysis.

Variables	(1)	(2)	(3)	(4)
	Lagged HXESG	Rank	HDFE	IV
HXESG	0.0029 ***		0.0022 ***	0.0285 **
	(7.0992)		(7.7389)	(2.4818)
L.HXESG	0.0024 ***
	(6.2563)
L2.HXESG	0.0008 **
	(2.0590)
L3.HXESG	0.0005
	(1.3568)
HXESGrank		0.0015 ***
		(9.0009)
Controls	YES	YES	YES	YES
Observation	13,509	16,386	16,385	16,379
Adjusted R-squared	0.0281	0.0322	0.0424	−0.8183
Firm fixed effect	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES
Industry fixed effect	NO	NO	YES	NO
City fixed effect	NO	NO	YES	NO

Note: “L”, “L2” and “L3” denote lagging the variables by one, two and three periods, respectively. t values in parentheses. *** p < 0.01, ** p < 0.05.

Table 7

Further analysis.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	WW	GTFP	Myopia	GTFP	IT	GTFP	GTFP
HXESG	−0.0006 ***	0.0009 ***	−0.0133 **	0.0020 ***	−0.4989 ***	0.0021 ***	0.0026 ***
	(−13.3618)	(3.0396)	(−2.3720)	(6.5470)	(−3.1569)	(7.3440)	(7.4219)
WW		−1.6756 ***
		(−26.0505)
Myopia				−0.0016 ***
				(−3.2689)
IT						−0.0001 ***
						(−8.3315)
Power1							0.0075
							(1.0295)
Power1_HXESG							−0.0000 ***
							(−2.6568)
Controls	YES	YES	YES	YES	YES	YES	YES
Observation	13,607	13,607	13,782	13,782	16,382	16,382	14,547
Adjusted R-squared	0.5898	0.0861	0.3375	0.0337	0.4726	0.0350	0.0248
Firm fixed effect	YES	YES	YES	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES	YES	YES	YES

Note: t values in parentheses. *** p < 0.01, ** p < 0.05.

Table 8

Heterogeneity analysis.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Non-SOE	SOE	Non-Heavily	Heavily	Low Intensity	High Intensity
HXESG	0.0030 ***	0.0015 ***	0.0026 ***	0.0019 ***	0.0018 ***	0.0030 ***
	(7.2906)	(3.8985)	(6.0871)	(4.9371)	(4.8506)	(5.2555)
Controls	YES	YES	YES	YES	YES	YES
Observation	11,563	4760	9310	7017	9195	6750
Adjusted R-squared	0.0299	0.0423	0.0293	0.0508	0.0460	0.0274
Firm fixed effect	YES	YES	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES	YES	YES

Note: t values in parentheses. *** p < 0.01.

References

1. Wang, H.; Qi, S.; Zhou, C.; Zhou, J.; Huang, X. Green credit policy, government behavior and green innovation quality of enterprises. J. Clean. Prod.; 2022; 331, 129834. [DOI: https://dx.doi.org/10.1016/j.jclepro.2021.129834]

2. Wang, J.; Ma, M.; Dong, T.; Zhang, Z. Do ESG ratings promote corporate green innovation? A quasi-natural experiment based on SynTao Green Finance’s ESG ratings. Int. Rev. Financ. Anal.; 2023; 87, 102623. [DOI: https://dx.doi.org/10.1016/j.irfa.2023.102623]

3. Akerlof, G.A. The market for “lemons”: Quality uncertainty and the market mechanism. Q. J. Econ.; 1979; 84, pp. 488-500. [DOI: https://dx.doi.org/10.2307/1879431]

4. Reber, B.; Gold, A.; Gold, S. ESG disclosure and idiosyncratic risk in initial public offerings. J. Bus. Ethics; 2021; 179, pp. 867-886. [DOI: https://dx.doi.org/10.1007/s10551-021-04847-8]

5. He, F.; Ding, C.; Yue, W.; Liu, G. ESG performance and corporate risk-taking: Evidence from China. Int. Rev. Financ. Anal.; 2023; 87, 102550. [DOI: https://dx.doi.org/10.1016/j.irfa.2023.102550]

6. Chen, S.; Song, Y.; Gao, P. Environmental, social, and governance (ESG) performance and financial outcomes: Analyzing the impact of ESG on financial performance. J. Environ. Manag.; 2023; 345, 118829. [DOI: https://dx.doi.org/10.1016/j.jenvman.2023.118829] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37688968]

7. Rahman, H.U.; Zahid, M.; Al-Faryan, M.A.S. ESG and firm performance: The rarely explored moderation of sustainability strategy and top management commitment. J. Clean. Prod.; 2023; 404, 136859. [DOI: https://dx.doi.org/10.1016/j.jclepro.2023.136859]

8. Kanno, M. Does ESG performance improve firm creditworthiness?. Financ. Res. Lett.; 2023; 55, 103894. [DOI: https://dx.doi.org/10.1016/j.frl.2023.103894]

9. Shanaev, S.; Ghimire, B. When ESG meets AAA: The effect of ESG rating changes on stock returns. Financ. Res. Lett.; 2022; 46, 102302. [DOI: https://dx.doi.org/10.1016/j.frl.2021.102302]

10. Tan, Y.; Zhu, Z. The effect of ESG rating events on corporate green innovation in China: The mediating role of financial constraints and managers’ environmental awareness. Technol. Soc.; 2022; 68, 101906. [DOI: https://dx.doi.org/10.1016/j.techsoc.2022.101906]

11. Zhang, H.; Lai, J.; Jie, S. Quantity and quality: The impact of environmental, social, and governance (ESG) performance on corporate green innovation. J. Environ. Manag.; 2024; 354, 120272. [DOI: https://dx.doi.org/10.1016/j.jenvman.2024.120272] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38394870]

12. Fu, C.; Yu, C.; Guo, M.; Zhang, L. ESG rating and financial risk of mining industry companies. Resour. Policy; 2024; 88, 104308. [DOI: https://dx.doi.org/10.1016/j.resourpol.2023.104308]

13. Deng, X.; Li, W.; Ren, X. More sustainable, more productive: Evidence from ESG ratings and total factor productivity among listed Chinese firms. Financ. Res. Lett.; 2023; 51, 103439. [DOI: https://dx.doi.org/10.1016/j.frl.2022.103439]

14. Xue, Q.; Jin, Y.; Zhang, C. ESG rating results and corporate total factor productivity. Int. Rev. Financ. Anal.; 2024; 95, 103381. [DOI: https://dx.doi.org/10.1016/j.irfa.2024.103381]

15. Meng, G.; Li, J.; Yang, X. Bridging the gap between state–business interactions and air pollution: The role of environment, social responsibility, and corporate governance performance. Bus. Strategy Environ.; 2023; 32, pp. 1872-1884. [DOI: https://dx.doi.org/10.1002/bse.3224]

16. Avetisyan, E.; Hockerts, K. The consolidation of the ESG rating industry as an enactment of institutional retrogression: Consolidation of the ESG rating industry. Bus. Strategy Environ.; 2017; 26, pp. 316-330. [DOI: https://dx.doi.org/10.1002/bse.1919]

17. Li, J.; Zhang, X.; Ali, S.; Khan, Z. Eco-innovation and energy productivity: New determinants of renewable energy consumption. J. Environ. Manag.; 2020; 271, 111028. [DOI: https://dx.doi.org/10.1016/j.jenvman.2020.111028] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32778308]

18. Khan, M.; Serafeim, G.; Yoon, A. Corporate sustainability: First evidence on materiality. Account. Rev.; 2016; 91, pp. 1697-1724. [DOI: https://dx.doi.org/10.2308/accr-51383]

19. Madison, N.; Schiehll, E. The effect of financial materiality on ESG performance assessment. Sustainability; 2021; 13, 3652. [DOI: https://dx.doi.org/10.3390/su13073652]

20. Garcia, A.S.; Orsato, R.J. Testing the institutional difference hypothesis: A study about environmental, social, governance, and financial performance. Bus. Strategy Environ.; 2020; 29, pp. 3261-3272. [DOI: https://dx.doi.org/10.1002/bse.2570]

21. Hao, X.; Wang, X.; Wu, H.; Hao, Y. Path to sustainable development: Does digital economy matter in manufacturing green total factor productivity?. Sustain. Dev.; 2022; 31, pp. 360-378. [DOI: https://dx.doi.org/10.1002/sd.2397]

22. Wang, J.; Liu, Y.; Wang, W.; Wu, H. How does digital transformation drive green total factor productivity? Evidence from Chinese listed enterprises. J. Clean. Prod.; 2023; 406, 136954. [DOI: https://dx.doi.org/10.1016/j.jclepro.2023.136954]

23. Zhao, Z.; Yan, J. The impact of investor sentiment on firms’ green total factor productivity—Facilitator or inhibitor. Environ. Sci. Pollut. Res.; 2023; 30, pp. 70303-70314. [DOI: https://dx.doi.org/10.1007/s11356-023-27204-w]

24. Zhang, D.; Vigne, S.A. How does innovation efficiency contribute to green productivity? A financial constraint perspective. J. Clean. Prod.; 2021; 280, 124000. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.124000]

25. Wang, M.; Pang, S.; Hmani, I.; Hmani, I.; Li, C.F.; He, Z.X. Towards sustainable development: How does technological innovation drive the increase in green total factor productivity?. Sustain. Dev.; 2020; 29, pp. 217-227. [DOI: https://dx.doi.org/10.1002/sd.2142]

26. Wang, H.; Cui, H.; Zhao, Q. Effect of green technology innovation on green total factor productivity in China: Evidence from spatial durbin model analysis. J. Clean. Prod.; 2021; 288, 125624. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.125624]

27. Wang, C.; Wang, L. Green credit and industrial green total factor productivity: The impact mechanism and threshold effect tests. J. Environ. Manag.; 2023; 331, 117266. [DOI: https://dx.doi.org/10.1016/j.jenvman.2023.117266]

28. Wang, M.; Zhu, C.; Wang, X.; Ntim, V.S.; Liu, X. Effect of information and communication technology and electricity consumption on green total factor productivity. Appl. Energy; 2023; 347, 121366. [DOI: https://dx.doi.org/10.1016/j.apenergy.2023.121366]

29. Ma, D.; Zhu, Q. Innovation in emerging economies: Research on the digital economy driving high-quality green development. J. Bus. Res.; 2022; 145, pp. 801-813. [DOI: https://dx.doi.org/10.1016/j.jbusres.2022.03.041]

30. Chen, C.; Ye, F.; Xiao, H.; Xie, W.; Liu, B.; Wang, L. The digital economy, spatial spillovers and forestry green total factor productivity. J. Clean. Prod.; 2023; 405, 136890. [DOI: https://dx.doi.org/10.1016/j.jclepro.2023.136890]

31. Li, B.; Wu, S.S. Effects of local and civil environmental regulation on green total factor productivity in China: A spatial Durbin econometric analysis. J. Clean. Prod.; 2017; 153, pp. 342-353. [DOI: https://dx.doi.org/10.1016/j.jclepro.2016.10.042]

32. Xie, R.; Yuan, Y.; Huang, J. Different types of environmental regulations and heterogeneous influence on “Green” productivity: Evidence from China. Ecol. Econ.; 2017; 132, pp. 104-112. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2016.10.019]

33. Yuan, B.; Xiang, Q. Environmental regulation, industrial innovation and green development of Chinese manufacturing: Based on an extended CDM model. J. Clean. Prod.; 2018; 176, pp. 895-908. [DOI: https://dx.doi.org/10.1016/j.jclepro.2017.12.034]

34. Tang, K.; Qiu, Y.; Zhou, D. Does command-and-control regulation promote green innovation performance? Evidence from China’s industrial enterprises. Sci. Total Environ.; 2020; 712, 136362. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.136362]

35. Wang, Y.; Sun, X.; Guo, X. Environmental regulation and green productivity growth: Empirical evidence on the Porter Hypothesis from OECD industrial sectors. Energy Policy; 2019; 132, pp. 611-619. [DOI: https://dx.doi.org/10.1016/j.enpol.2019.06.016]

36. Zhao, X.M.; Ni, J. A study of the effect of informal environmental regulation on the environmental governance from the perspective of subject difference. Soc. Sci. Int.; 2022; 5, pp. 128-139.

37. Steigenberger, N.; Wilhelm, H. Extending signalling theory to rhetorical signals: Evidence from crowd-funding. Organ. Sci.; 2018; 29, pp. 529-546. [DOI: https://dx.doi.org/10.1287/orsc.2017.1195]

38. Fang, X.M.; Hu, D. Corporate ESG performance and innovation: Empirical evidence from A-share listed companies. Econ. Res. J.; 2023; 58, pp. 91-106.

39. Miralles, Q.M.M.; Miralles, Q.J.L.; Redondo, H.J. The impact of environmental, social, and governance performance on stock prices: Evidence from the banking industry. Corp. Soc. Responsib. Environ. Manag.; 2019; 26, pp. 1446-1456. [DOI: https://dx.doi.org/10.1002/csr.1759]

40. Broadstock, D.C.; Chan, K.L.; Cheng, L.T.W.; Wang, X.W. The role of ESG performance during times of financial crisis: Evidence from COVID-19 in China. Financ. Res. Lett.; 2021; 38, 101716. [DOI: https://dx.doi.org/10.1016/j.frl.2020.101716]

41. Song, M.; Tao, W.; Shen, Z. Improving high-quality development with environmental regulation and industrial structure in China. J. Clean. Prod.; 2022; 366, 132997. [DOI: https://dx.doi.org/10.1016/j.jclepro.2022.132997]

42. Cornell, B. ESG preferences, risk and return. Eur. Financ. Manag.; 2021; 27, pp. 12-19. [DOI: https://dx.doi.org/10.1111/eufm.12295]

43. Mansouri, S.; Momtaz, P.P. Financing sustainable entrepreneurship: ESG measurement, valuation, and performance. J. Bus. Ventur.; 2022; 37, 106258. [DOI: https://dx.doi.org/10.1016/j.jbusvent.2022.106258]

44. Lin, Y.; Shi, W.; Prescott, J.E.; Yang, H. In the eye of the beholder: Top managers’ long-term orientation, industry context, and decision-making processes. J. Manag.; 2019; 45, pp. 3114-3145. [DOI: https://dx.doi.org/10.1177/0149206318777589]

45. Zhang, J.; Li, Y.; Xu, H.; Ding, Y. Can ESG ratings mitigate managerial myopia? Evidence from Chinese listed companies. Int. Rev. Financ. Anal.; 2023; 90, 102878. [DOI: https://dx.doi.org/10.1016/j.irfa.2023.102878]

46. Zahran, S.K.; Jaber, M. Investigation of a consignment stock and a traditional inventory policy in a three-level supply chain system with multiple-suppliers and multiple-buyers. Appl. Math. Model.; 2017; 44, pp. 390-408. [DOI: https://dx.doi.org/10.1016/j.apm.2017.01.084]

47. Agyabeng-Mensah, Y.; Afum, E.; Acquah, I.S.K.; Baah, C. How does supply chain knowledge enhance green innovation? The mediation mechanisms of corporate reputation and non-supply chain learning. J. Bus. Ind. Mark.; 2023; 38, pp. 852-868. [DOI: https://dx.doi.org/10.1108/JBIM-04-2021-0192]

48. Ahluwalia, S.; Mahto, R.V.; Guerrero, M. Blockchain technology and startup financing: A transaction cost economics perspective. Technol. Forecast. Soc. Chang.; 2020; 151, 119854. [DOI: https://dx.doi.org/10.1016/j.techfore.2019.119854]

49. Dai, R.; Liang, H.; Ng, L. Socially responsible corporate customers. J. Financ. Econ.; 2021; 142, pp. 598-626. [DOI: https://dx.doi.org/10.1016/j.jfineco.2020.01.003]

50. Riedl, A.; Smeets, P. Why do investors hold socially responsible mutual funds?. J. Financ.; 2017; 72, pp. 2505-2550. [DOI: https://dx.doi.org/10.1111/jofi.12547]

51. Duong, H.K.; Wu, Y.; Schiehll, E.; Yao, H. Environmental and social disclosure, managerial entrenchment, and investment efficiency. J. Contemp. Account. Econ.; 2024; 20, 100435. [DOI: https://dx.doi.org/10.1016/j.jcae.2024.100435]

52. Finkelstein, S. Power in top management teams: Dimensions, measurement, and validation. Acad. Manag. J.; 1992; 35, pp. 505-538. [DOI: https://dx.doi.org/10.2307/256485]

53. Bebchuk, L.A.; Fried, J.M. Executive compensation as an agency problem. J. Econ. Perspect.; 2003; 17, pp. 71-92. [DOI: https://dx.doi.org/10.1257/089533003769204362]

54. van Essen, M.; Otten, J.; Carberry, E.J. Assessing managerial power theory: A meta-analytic approach to understanding the determinants of CEO compensation. J. Manag.; 2015; 41, pp. 164-202. [DOI: https://dx.doi.org/10.1177/0149206311429378]

55. Cao, Q.; Yang, F.; Liu, M. Impact of managerial power on regulatory inquiries from stock exchanges: Evidence from the text tone of Chinese listed companies’ annual reports. Pac.-Basin Financ. J.; 2022; 71, 101646. [DOI: https://dx.doi.org/10.1016/j.pacfin.2021.101646]

56. Li, J.; Zhou, K.; Cheng, Z. Does China’s “Belt and Road” Initiative promote green total factor productivity growth in countries along the route?. J. Clean. Prod.; 2022; 367, 133004. [DOI: https://dx.doi.org/10.1016/j.jclepro.2022.133004]

57. Pittman, R.W. Multilateral productivity comparisons with undesirable outputs. Econ. J.; 1983; 93, pp. 883-891. [DOI: https://dx.doi.org/10.2307/2232753]

58. Song, M.; Du, J.; Tan, K.H. Impact of fiscal decentralization on green total factor productivity. Int. J. Prod. Econ.; 2018; 205, pp. 359-367. [DOI: https://dx.doi.org/10.1016/j.ijpe.2018.09.019]

59. Oh, D.H. A global Malmquist Luenberger productivity index. J. Product. Anal.; 2010; 34, pp. 183-197. [DOI: https://dx.doi.org/10.1007/s11123-010-0178-y]

60. Fukuyama, H.; Weber, W.L. A directional slacks-based measure of technical inefficiency. Socio-Econ. Plan. Sci.; 2009; 43, pp. 274-287. [DOI: https://dx.doi.org/10.1016/j.seps.2008.12.001]

61. Wu, J.; Xia, Q.; Li, Z. Green innovation and enterprise green total factor productivity at a micro level: A perspective of technical distance. J. Clean. Prod.; 2022; 344, 131070. [DOI: https://dx.doi.org/10.1016/j.jclepro.2022.131070]

62. Wang, S.; Chen, S.-C.; Ali, M.H.; Tseng, M.-L. Nexus of environmental, social, and governance performance in China-listed companies: Disclosure and green bond issuance. Bus. Strategy Environ.; 2023; 33, pp. 1647-1660. [DOI: https://dx.doi.org/10.1002/bse.3566]

63. Shi, B.; Li, J. Does the Internet promote division of labor: Evidence from Chinese manufacturing firms. J. Manag. World; 2020; 36, pp. 130-149. (In Chinese)

64. Yu, L.; Zhang, B.; Yan, Z.; Cao, L. How do financing constraints enhance pollutant emissions intensity at enterprises? Evidence from microscopic data at the enterprise level in China. Environ. Impact Assess. Rev.; 2022; 96, 106811. [DOI: https://dx.doi.org/10.1016/j.eiar.2022.106811]

65. Whited, T.M.; Wu, G. Financial constraints risk. Rev. Financ. Stud.; 2006; 19, pp. 531-559. [DOI: https://dx.doi.org/10.1093/rfs/hhj012]

66. Hu, N.; Xue, F.J.; Wang, H.N. Does managerial myopia affect long-term investment: Based on text analysis and machine learning. Manag. World; 2021; 37, pp. 139-156. (In Chinese)

67. Wang, S.J.; Wang, W.L.; Huang, C.T.; Chen, S.C. Improving inventory effectiveness in RFID-enabled global supply chain with Grey forecasting model. J. Strateg. Inf. Syst.; 2011; 20, pp. 307-322. [DOI: https://dx.doi.org/10.1016/j.jsis.2011.03.003]

68. Liu, J.; Zhang, L.; Yang, X. Government grants, managerial power and executive excess compensation in state-owned enterprises. Account. Res.; 2019; 08, pp. 64-70. (In Chinese)

69. Liu, Y.; Wang, A.; Wu, Y. Environmental regulation and green innovation: Evidence from China’s new environmental protection law. J. Clean. Prod.; 2021; 297, 126698. [DOI: https://dx.doi.org/10.1016/j.jclepro.2021.126698]

70. Cai, X.; Zhu, B.; Zhang, H.; Li, L.; Xie, M. Can direct environmental regulation promote green technology innovation in heavily polluting industries? Evidence from Chinese listed companies. Sci. Total Environ.; 2020; 46, 140810. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.140810]