ESG Ratings and Green Innovation

Abstract

This study examines the impact of ESG ratings on corporate green innovation, selecting A-share listed companies in China from 2012 to 2022 as the research sample. Using a multiple-time-point difference-in-differences model, we analyze how ESG ratings influence both the output and efficiency of green innovation. The findings reveal that ESG ratings significantly promote green innovation, particularly by encouraging companies to increase investments in environmental protection, social responsibility, and corporate governance. Additionally, ESG ratings facilitate green innovation by reducing financing pressures, increasing media attention, and mitigating managerial myopia, with the effects most pronounced in highly polluting industries and firms with weaker corporate governance structures. These results offer valuable insights for companies pursuing sustainable development and for policymakers aiming to foster green innovation.

1. Introduction

The pursuit of economic green innovation has garnered global attention as societies aim to balance economic growth with environmental sustainability [1]. Enterprises, as primary economic agents, are pivotal in advancing green and sustainable development. However, companies face significant challenges in green innovation, including high investment risks, uncertain returns, long innovation cycles, extensive capital requirements, and often limited intrinsic motivation. These challenges span financing, technological development, market conditions, and regulatory policies, all demanding continuous corporate commitment and exploration. Thus, a key question arises: how can businesses be effectively incentivized to embrace green innovation, thereby contributing to sustainable development? This question is particularly pressing for government regulators and industry leaders.

Two predominant strategies for encouraging green innovation in corporations have emerged: stringent formal environmental regulation and adaptable informal regulation. Formal environmental policies, increasingly adopted worldwide, use mandatory instruments to compel firms toward green practices, such as government subsidies for environmental protection [2] and enhanced regulatory frameworks like China’s Environmental Protection Law [3]. These policies are intended to steer companies toward innovation that benefits both the economy and the environment. However, excessive formal regulation may raise production costs, reducing firms’ free cash flow and, consequently, their international competitiveness [4,5]. Informal regulation, in contrast, can complement formal measures by stimulating internal motivations and improving firms’ market positioning, thereby supporting their competitiveness and green innovation efforts [6,7].

Given this backdrop, the current study examines the role of environmental, social, and governance (ESG) ratings as an informal regulatory tool influencing corporate green innovation. ESG ratings serve as a bridge between firms and the market, offering a platform for companies to communicate their environmental commitment. By analyzing ESG rating data for listed firms from SynTao Green Finance, this study leverages China as an ideal research setting to explore the relationship between ESG ratings and corporate innovation. Prior studies suggest varied relationships between ESG and green innovation, with some showing a U-shaped relationship [8] and others indicating an inverse link [9]. However, evidence generally supports the positive contribution of ESG ratings to corporate innovation [1,10].

This paper contributes to the literature by examining the nuanced impacts of ESG ratings on green innovation within highly polluting industries, focusing on firms listed on China’s A-shares between 2012 and 2022. We incorporate a difference-in-differences model to assess the effects on innovation outputs and efficiency, accounting for factors like financing constraints, managerial myopia, and media attention. This study’s findings underscore that ESG ratings significantly foster corporate green innovation, particularly among companies in highly polluting sectors and those with lower corporate governance quality. This paper provides a novel perspective by quantifying green innovation across invention and non-invention patents, thereby offering a comprehensive evaluation.

By revealing the interactions between ESG ratings, corporate governance, and green innovation, this research contributes a unified framework that enhances our understanding of market-oriented green innovation mechanisms. These insights offer policymakers and business leaders actionable recommendations to foster sustainable innovation and mitigate environmental impact within China’s corporate sector.

2. Research Hypothesis

ESG ratings are recognized not only by authoritative third-party rating agencies but also reflect the recognition of companies’ achievements in socially responsible investment by governments and financial institutions [11], sending positive signals of a good corporate image to the capital market. Evidence presented by [12] suggests that not only creditors and shareholders value ESG information, but public bondholders are also beginning to value firms’ ESG disclosures. A good ESG rating sends a positive signal of good corporate image to the capital market, as Truong et al. (2021) [13] argue that firms with high customer satisfaction scores in the disclosure scores are able to obtain higher bond yields and lower bank loan spreads compared with firms with lower scores, resulting in a win-win situation of increased revenues and reduced costs. Available funds and the quality of management are the primary factors influencing corporate green innovation [14], forming the essential foundation for achieving sustainable development strategies [15]. The ESG rating mechanism effectively encourages companies to curb short-sighted decision-making by managers, strengthens the transparency of information disclosure, and encourages companies to abandon the goal of pursuing only their own short-term interests [16,17] and instead focus on the long-term healthy development of the company. ESG rating companies can also send positive signals to stakeholders, implying that the company is actively taking on social responsibility, reducing environmental pollution, and responding to national calls to embark on the path of green innovation. Therefore, this study posits that ESG ratings can mitigate information asymmetry, reduce financing constraints and managerial myopia, and convey management’s attitude towards green investment to external stakeholders to attract media attention, thereby increasing the level of corporate green innovation.

Firstly, ESG ratings alleviate the financial constraints faced by companies. Green innovation is known for its distinctive characteristics, including high initial capital investment, long profitability cycles, and risk factors that are difficult to accurately predict [2,18], which collectively shape the unique position and challenges of green innovation in advancing the process of sustainable development. Therefore, for companies to engage in green innovation, they must have sufficient discretionary funds to cope with the uncertain risks of development, indicating that the availability of funds plays a crucial role in green innovation. Under the premise of the capital market information asymmetry theory, companies can disclose high-quality information to show stakeholders the company’s future development prospects, gain the trust of future investors, and obtain resource support from stakeholders, thereby differentiating themselves from competitors [19]. Horn (2023) [20] argues that ESG ratings help mitigate information asymmetries among firms, reduce idiosyncratic risk, and represent a holistic view of corporate social responsibility and long-term sustainability. They help stakeholders fully assess companies, reduce investment risks, and enhance market efficiency. Meanwhile, ESG ratings encourage companies to actively fulfill social responsibilities and promote sustainable development, gaining a deeper understanding of corporate governance, management capabilities, and financial conditions, thereby helping stakeholders better understand financial and non-financial information. This understanding enables companies to receive green funds during credit evaluations, reducing the financing costs of green activities [21].

According to the principal–agent theory, corporate managers will make more proactive environmental management decisions through green innovation behaviors to respond to the public pressure generated by the invisible social contract with stakeholders [22]. ESG ratings internalize the external costs of environmental pollution through information disclosure and replace low-level end-of-pipe governance mechanisms with green innovation technologies that meet stakeholders’ green demands [23]. Additionally, institutional investors prefer ESG investments to avoid adverse selection risks [24]. Companies with higher ESG ratings can improve their ESG performance and information disclosure quality, thereby alleviating market concerns about information asymmetry and enabling companies to attract more external capital [25]. However, companies with lower ESG ratings may face higher financing costs due to the risk of environmental penalties. As ESG rating disclosures become more widespread, rated companies demonstrate higher transparency and lower risk, meeting investors’ risk aversion needs, effectively reducing corporate financing costs, and enhancing market competitiveness [26]. At the same time, it also helps to significantly mitigate the negative impact of managers’ short-sighted behavior on creditors’ and investors’ decision-making, effectively bridges the information gap, and promotes the improvement of market transparency, thus laying a solid foundation for sound growth and sustainable development of enterprises [8].

A strong ESG rating can fully showcase a company’s positive social image, thereby enhancing its reputation and winning the trust of capital providers. This also helps attract media attention and increase corporate transparency. By reducing the impact of managerial myopia on creditor and investor decisions, a high ESG rating effectively mitigates information asymmetry issues, providing strong support for the company’s stable development. In a competitive market, companies seeking more investment need to improve their ESG ratings. High-quality ratings can secure favorable financing, assist in technological improvements and energy-saving innovations, promote green innovation, reduce innovation risks, and achieve a virtuous cycle.

Secondly, ESG ratings not only enhance a company’s market recognition but also stimulate managers’ environmental awareness. A strong ESG rating sends positive development signals, attracting market and media attention and showcasing the company’s business attitude. Within the principal–agent framework, the management, as the key to corporate development, faces multifaceted pressures, such as environmental policies, and will pay more attention to green innovation, driving the company to achieve sustainable strategic goals [27]. Zhang et al. (2015) [28] find that executives’ environmental awareness encourages managers to integrate green elements into daily management activities, increasing corporate green behaviors and reducing the impediments to green sustainability caused by short-sighted behaviors.

Additionally, the external pressure faced by senior management is a key factor driving their innovative behavior. According to upper-echelon theory, managers adapt their business strategies in response to changes in the external environment. ESG ratings provide stakeholders with new ways to monitor companies, enhancing oversight of managers and making them more attentive to sustainable green innovation. This external supervision encourages managers to actively engage in green practices, thereby promoting corporate green innovation. Consequently, ESG ratings not only improve corporate transparency and credibility but also effectively foster corporate green innovation and sustainable development, having profound social impact and significance [29]. ESG rating activities attract significant attention from the media, analysts, and investors, prompting corporate executives to prioritize environmental protection and actively update technologies to meet market challenges. By sending positive signals, companies not only enhance their own competitiveness but also raise the entry barriers for competitors, laying a solid foundation for long-term development. ESG ratings are becoming a crucial force in driving corporate green innovation [30].

Finally, Pelster et al. (2024) [31] point out that the current widespread use of ESG ratings has significantly increased the attention of potential investors and stakeholders to corporate ESG performance. This trend not only encourages managers to reduce short-sighted decision-making behaviors but also shifts their focus to promoting long-term sustainable development and ensuring balanced and harmonious growth in economic, social, and environmental aspects. By considering the reduction in adverse environmental impacts as a corporate responsibility and improving production technologies to reduce pollution, companies not only enhance their image but also gain more opportunities for sustainable development, achieving a simultaneous increase in economic and social benefits [32]. They can accurately capture green resources from stakeholders, such as financial institutions and investors, and effectively utilize them to enhance their green innovation capabilities, producing more green innovation outcomes. ESG ratings not only motivate companies to develop green innovation strategies but also improve managers’ environmental awareness, thereby increasing the quantity and quality of corporate green innovation outcomes. Please see Figure 1.

Therefore, this study proposes the following hypotheses:

H1. 

ESG ratings significantly improve the output of corporate green innovation, measured by the quantity and effectiveness of green innovation activities undertaken by firms.

H2. 

ESG ratings significantly enhance the efficiency of corporate green innovation, defined as the extent to which green innovation investments translate into tangible environmental and social benefits, thereby reflecting the quality of green innovation.

3. Research Design

3.1. Sample and Data

To study the impact of ESG ratings on the level of corporate green innovation, we utilized annual panel data at the company level, including all A-share companies listed on the Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE). With SynTao Green Finance disclosing ESG rating information for A-share listed companies since 2015, our initial sample includes a two-year non-experimental period from 2013 to 2022. We used data from the main board-listed companies on the SHSE and SZSE and processed the collected data as follows: (1) considering the uniqueness of the financial industry, we excluded all listed companies in the financial sector (CSRC industry code J, 2012), as their financial statements differ from those of companies in other industries; (2) we excluded listed companies under special treatment (ST) or delisting risk warning (PT), as their financial data often exhibit anomalies or losses, which may interfere with the research results; and (3) we excluded listed companies with missing financial and market indicators. After the above filtering, we obtained a total of 26,765 observations. ESG rating data were sourced from the WIND database, corporate green innovation information was obtained from the China Research Data Service (CNRDS) database, and other financial and corporate governance data were sourced from the CNRDS database.

3.2. Variable Definition

3.2.1. Dependent Variable

The level of corporate green innovation is a key indicator for measuring the degree of a company’s green innovation. Firstly, the level of corporate green innovation reflects a company’s ability to propose strategies and possess innovation capabilities when facing green innovation challenges. Secondly, the extent and effectiveness of green innovation can demonstrate the company’s investment and achievements in green innovation. Additionally, green innovation can drive continuous improvement and enhancement of a company’s green innovation efforts. Through continuous green innovation, companies can optimize production processes, improve resource utilization efficiency, reduce environmental risks, and continuously elevate their level of green innovation. In summary, using the level of corporate green innovation to measure a company’s green innovation is reasonable and effective.

The measurement of corporate green innovation levels has been approached by some studies using comprehensive alternative metrics. For instance, Tariq et al. (2017) and Qiu (2020) [33,34] measure corporate green product innovation using four composite items, including the usage level of new products or the development of recyclable, reusable, and decomposable products beneficial to the ecological environment. They substituted the degree of green product innovation for the degree of corporate green innovation. However, this measurement approach can lead to an overlap with ESG rating evaluation indicators, causing significant endogeneity with the core explanatory variable ESG in this study. To avoid this, this paper measures corporate green innovation from two perspectives: green innovation and efficiency optimization. Invention patents can demonstrate a company’s innovation capability in green technology and the degree of legal protection, while non-invention patents can reflect the diversity and flexibility of corporate innovation. These two indicators collectively showcase a company’s research and development strength and market competitiveness in the green technology field, thus serving as important indicators for measuring green innovation outcomes [35]. Specifically, the quantity of corporate green innovation outcomes is measured by the number of green invention patents obtained and the number of green non-invention patents obtained [36]. The quality of corporate green innovation is calculated using the DEA-SBM model, based on initial inputs, such as the number of R&D personnel and R&D expenditure, and intermediate output indicators, like the number of green patent applications and green patent grants [37].

3.2.2. Independent Variable

The ESG rating is measured using data published by SynTao Green Finance. As a professional third-party institution, SynTao Green Finance is dedicated to providing comprehensive and objective ESG performance assessments for companies. It not only collects information voluntarily disclosed by companies but also widely gathers data from media, regulatory agencies, and other sources. Utilizing a real-time online monitoring system, SynTao Green Finance accurately identifies ESG controversies among target companies and scientifically assesses risks. Through this approach, SynTao Green Finance can provide in-depth and detailed ESG performance profiles for companies. Furthermore, the target companies selected by SynTao Green Finance are all from the China Securities Index (CSI), effectively avoiding potential company manipulation risks and ensuring the impartiality and accuracy of the assessment results. This is also why ESG ratings are chosen as an exogenous shock in this study. SynTao Green Finance’s professional services are significant for promoting corporate ESG practices and facilitating sustainable development. In 2015, SynTao Green Finance conducted its first ESG evaluation and calculated the ESG evaluation index. Subsequently, with the increasing enthusiasm of listed companies for ESG information disclosure, the disclosed ESG information has also increased annually. These ESG rating data facilitate the construction of treatment and control groups in the multiple-time-point difference-in-differences method. Therefore, this paper constructs the core explanatory variable based on SynTao Green Finance’s ESG rating data. Following the approaches of [1,10], if SynTao Green Finance publishes the ESG rating data for company i in year t, it is considered as the treatment group, ESGit = 1; otherwise, it is considered as the control group, ESGit = 0.

3.2.3. Control Variables

This study controls for important variables that influence corporate green innovation, including firm size (Size), measured as the natural logarithm of total assets; leverage ratio (Lev), used to measure the company’s short-term debt situation; firm age (FirmAge), indicating the time since the company was listed; cash flow ratio (Cashflow), used to measure the ability to repay debt and fulfill commitments; the shareholding ratio of the largest shareholder (Top1), used to describe ownership concentration; the proportion of independent directors (Indep); the number of board members (Board); whether the roles of CEO and chairman are combined (Dual); the degree of ownership balance (Balance2); and the proportion of management shareholding (Mshare). The specific variable definitions are shown in

Table 1. Variable Definitions.

Variable Type	Variable Name	Variable Symbol	Variable Definition
Dependent Variable	Green Innovation Output	Patent	Ln (1 + number of green invention patents obtained)
	Green Non-invention Patent Output	N_Patent	Ln (1 + number of green non-invention patents obtained)
Dependent Variable	Green Innovation Efficiency	Inveffi	The larger the value, the higher the quality of corporate green innovation.
Independent Variable	ESG	ESG	If SynTao Green Finance publishes the rating data for company i in year t, it is the treatment group, ESGit = 1, otherwise, it is the control group, ESGit = 0.
Control Variables	Firm Size	Size	Natural logarithm of total assets
	Leverage Ratio	Lev	Total liabilities/Total assets
	Number of Board Members	Board	Natural logarithm of the number of board members
	Independent Director Ratio	Indep	Ratio of independent directors to the total number of board members
	Dual Role	Dual	1 if the chairman and CEO are the same person, 0 otherwise
	Largest Shareholder Ownership Ratio	Top1	Ratio of shares held by the largest shareholder
	Ownership Balance Degree	Balance2	Sum of shareholding ratios from the second to the fifth largest shareholders divided by the shareholding ratio of the largest shareholder
	Firm Age	FirmAge	Natural logarithm of the number of years since the company was listed
	Management Shareholding Ratio	Mshare	Ratio of shares held by management
Mechanism Variables	Financing Constraints	SA	−0.737 × Size + 0.043 × Size2 − 0.04 × Age
	Managerial Myopia	Managerial Myopia Index	Proportion of the total frequency of 43 “short-term view” words in the MD&A to the total word frequency of MD&A × 100
	Media Attention	Media Attention	Total number of news articles mentioning the company within a year
Grouping Variables	Corporate Governance Level	CorGovindex	Corporate governance level
	Heavy Polluting Enterprises	heavy pollution	1 if the enterprise is heavily polluting, 0 otherwise

.

3.3. Model Specification

The coverage of SynTao Green Finance’s ESG is exogenous, and the ratings are independent and authoritative. Therefore, the exogenous ESG governance mechanism can significantly impact corporate behavior. To verify the impact of ESG ratings on the level of corporate green innovation, and to test the validity of Hypotheses 1 and 2, this study uses the first disclosure of SynTao Green Finance’s ESG information by listed companies as an exogenous shock. Through the multiple-time-point difference-in-differences method, the following model is constructed:

$$G{T}_{it}={\lambda }_0+{\lambda }_{1}ES{G}_{it}+{\lambda }_{2}control{s}_{it}+{\sigma }_i+yea{r}_t+{\epsilon }_{it}$$

(1)

The dependent variable GT_it represents the green innovation level of company i in year t, which includes three components: the number of green invention patents obtained (Patent), the output of green non-invention patents (N_Patent), and green innovation efficiency (Inveffi). The core explanatory variable $ES{G}_{it}$ equals 1 if SynTao Green Finance has published the ESG disclosure data for company i in year t, otherwise = 0. $Control{s}_{it}$ represents a set of control variables; ${\sigma }_i$ denotes individual fixed effects; and $yea{r}_t$ denotes time fixed effects.

To ensure the robustness of the relationship between ESG ratings and the level of corporate green innovation, we refer to [36] and establish a parallel trend test by constructing the following model:

$$\begin{gathered} B{L}_{it}=\alpha +{\beta }_s^{precut}\left[A_i\ast I\left(t-T_A<-3\right)\right]+{\displaystyle \sum }_{s=-3}^{-2}{\beta }_s^{pre}\left[A_i\ast I\left(t-T_A=s\right)\right] \\ +{{\displaystyle \sum }}_{s=0}^2{\beta }_s^{post}\left[A_i\ast I\left(t-T_A=s\right)\right]+{\beta }_s^{postcut}\left[A_i\ast I\left(t-T_A>2\right)\right]+{\lambda }_{2}control{s}_{it}+U_i+yea{r}_t+{\epsilon }_{it} \end{gathered}$$

(2)

where $A_i$ represents the firm, dividing the firms into treatment and control groups. If = 1, it indicates that the firm is in the treatment group; if = 0, it indicates that the firm is in the control group. $\mathrm{I}\left(t-T_A=\right)$ is a function where $T_A$ represents the period when the firm has an ESG rating, and t − $T_D$ represents the difference between the time of ESG rating publication and the current period. In the Formula (2), the variation in ${\beta }_s$ reflects the dynamic impact of the published ESG rating on the level of corporate green innovation.

4. Empirical Results Analysis

4.1. Descriptive Statistics

Table 2. Descriptive Statistics.

	VAR	Mean	Median	Std. Dev.	Min	Max	N
Dependent Variables	Patent	0.033	0.000	0.185	0.000	1.386	26,765
	N_Patent	0.051	0.000	0.242	0.000	1.609	26,765
	Inveffi	0.578	0.555	0.187	0.274	0.986	26,765
Independent Variable	ESG	0.124	0.000	0.330	0.000	1.000	26,765
Control Variables	Size	22.253	22.071	1.279	19.926	26.231	26,765
	Lev	0.427	0.419	0.204	0.058	0.898	26,765
	Cashflow	0.047	0.046	0.067	−0.152	0.240	26,765
	Board	2.120	2.197	0.196	1.609	2.639	26,765
	Indep	0.377	0.364	0.054	0.333	0.571	26,765
	Dual	0.284	0.000	0.451	0.000	1.000	26,765
	Top1	0.337	0.313	0.147	0.084	0.737	26,765
	Balance2	0.750	0.586	0.613	0.030	2.837	26,765
	FirmAge	2.926	2.944	0.314	1.946	3.526	26,765
	Mshare	0.133	0.007	0.193	0.000	0.675	26,765

presents the descriptive statistics for the main variables. The average number of green invention patent grants (logarithmically transformed) during the observation period is approximately 0.033, with a standard deviation of 0.185. The difference between the maximum and minimum values is 1.386, indicating significant differences in green innovation quantities among different firms, with an overall low level of green innovation. The average number of green non-invention patents during the observation period is about 0.051, with a standard deviation of 0.242, and the difference between the maximum and minimum values is 1.609. This data shows that the average number of green non-invention patents is higher than that of green invention patents. The average green innovation efficiency is around 0.578, with a standard deviation of 0.187, and the difference between the maximum and minimum values is 0.712. The average ESG score of companies is approximately 0.124, indicating that 12.4% of companies in the sample period have an ESG rating, with a standard deviation of 0.330, a maximum value of 1, and a minimum value of 0.

4.2. ESG Ratings and Corporate Green Innovation Levels

The regression results in

Table 3. ESG Ratings and Corporate Green Innovation Levels.

	(1)	(2)	(3)
VARIABLES	Patent	N_Patent	Inveffi
ESG	0.034 ***	0.042 ***	0.006 *
	(4.058)	(2.704)	(1.931)
Size	0.010 ***	0.024 ***	0.000
	(2.761)	(2.878)	(0.236)
Lev	−0.016	0.013	−0.007
	(−1.546)	(0.540)	(−1.434)
Cashflow	0.005	−0.017	−0.014
	(0.301)	(−0.512)	(−1.246)
Board	−0.014	0.004	0.002
	(−0.725)	(0.098)	(0.388)
Indep	0.015	0.056	0.000
	(0.289)	(0.493)	(0.002)
Dual	−0.002	−0.010	−0.003
	(−0.502)	(−1.227)	(−1.385)
Top1	−0.016	−0.066	−0.002
	(−0.472)	(−1.001)	(−0.272)
Balance2	−0.009	−0.009	−0.001
	(−1.273)	(−0.681)	(−0.595)
FirmAge	−0.017	0.066	−0.002
	(−0.447)	(0.843)	(−0.662)
Mshare	0.003	−0.000	0.002
	(0.624)	(−0.003)	(0.384)
Constant	−0.111	−0.540 *	0.471 ***
	(−0.903)	(−1.885)	(19.632)
Individual Fixed Effects	YES	YES	YES
Time Fixed Effects	YES	YES	YES
Observations	34,332	34,332	31,264
Number of stkcd	4910	4910	4164
R-squared	0.014	0.015	0.372

Note: *** and * indicate that the estimated coefficients are significant at the 1%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

column (1) show that the regression coefficient of ESG ratings is significantly positive, indicating that even after including a series of control variables, ESG ratings can still significantly increase corporate green invention patent output. The regression coefficient for ESG ratings is 0.034, significant at the 1% level, meaning that ESG ratings result in an average increase of 3.4% in green invention output for the following year. The regression results in column (2) indicate that the coefficient for ESG ratings is also significantly positive, suggesting that ESG ratings can significantly increase corporate green non-invention patent output. The regression coefficient for ESG ratings is 0.042, significant at the 1% level, implying that ESG ratings lead to an average increase of 4.2% in the total number of green non-invention patents granted in the following year. Compared with the coefficient in column (1), the impact of ESG ratings on green non-invention patents is stronger because non-invention patents are generally easier and quicker to apply for compared with invention patents [38]. The regression results in column (3) also show that the regression coefficient for ESG ratings is significantly positive, indicating that ESG ratings can significantly enhance corporate green innovation efficiency. The regression coefficient for ESG is 0.006, significant at the 1% level, meaning that ESG ratings lead to an average increase of 0.6% in green invention efficiency for the following year.

Therefore, based on the data shown above, we can validate Hypotheses 1 and 2, indicating that ESG ratings can significantly increase the quantity and quality of corporate green innovation outcomes.

4.3. Robustness Tests

4.3.1. Parallel Trend Test

To perform the parallel trend test and further examine the dynamic characteristics of the ESG rating effect, this paper re-examines the dynamic impact of ESG ratings on corporate green invention patent innovation, green non-invention patent innovation, and green innovation efficiency using Model (2). The results are shown in Figure 2 and Figure 3.

Before the ESG rating, there were no significant differences in the number of green invention innovations, the number of non-green invention innovations, and green innovation efficiency among the three groups, which satisfies the parallel trend assumption. However, after the ESG rating, the treatment group shows a significant increase in the number of green invention innovations, the number of non-green invention innovations, and green innovation efficiency compared with the control group. ESG ratings have a significantly positive effect on the number of green invention patents obtained, the number of green non-invention patents obtained, and green innovation efficiency. According to the figures below, it is clear that to the left of the red line, there is no significant relationship between ESG ratings and the number of green invention innovations, the number of non-green invention innovations, and green innovation efficiency. To the right of the red line, a significant positive relationship forms between ESG ratings and the number of green invention innovations, the number of non-green invention innovations, and green innovation efficiency. This also indicates that the positive relationship between ESG ratings and the number of green invention innovations, the number of non-green invention innovations, and green innovation efficiency is robust.

4.3.2. Placebo Test

To ensure that the impact of ESG ratings on the level of corporate green innovation is not generated randomly, a placebo test is conducted to re-examine the relationship between the two. Referring to the methods of [39], we construct 500 pseudopolicy variables through random sampling based on the distribution of the ESG rating variable in the baseline regression. We then re-estimate the regression according to Model (1) and examine the distribution of the coefficients and p-values. The results are shown in Figure 4, Figure 5 and Figure 6.

It can be observed that the mean coefficients of the pseudopolicy variables for the number of green invention patents obtained, the number of green non-invention patents obtained, and green innovation efficiency are mostly close to 0 and significantly smaller than the baseline regression coefficients. For example, the coefficient of the ESG rating on the number of invention patents obtained should be around 0.54, while the coefficients of the pseudopolicy variables are distributed between −0.015 and 0.015. The distribution of the estimated coefficients approximates a normal distribution, with most P-values greater than 0.10, indicating insignificance at the 10% level. The coefficient of the ESG rating on the number of green non-invention patents obtained should be around 0.26, while the coefficients of the pseudopolicy variables are distributed between −0.013 and 0.013. The distribution of the estimated coefficients approximates a normal distribution, with most p-values greater than 0.10, indicating insignificance at the 10% level. The coefficient of the ESG rating on green innovation efficiency should be around 0.64, while the coefficients of the pseudopolicy variables are distributed between −0.005 and 0.005. The distribution of the estimated coefficients approximates a normal distribution, with most p-values greater than 0.10, indicating insignificance at a 10% level.

This indicates that the impact of ESG ratings on the number of green invention patents obtained, the number of green non-invention patents obtained, and green innovation efficiency is not due to other random factors. The conclusions of this paper are validated through the placebo test, demonstrating that ESG ratings can significantly increase the quantity of corporate green innovation outcomes and the quality of corporate green innovation, confirming the reliability of these conclusions.

4.3.3. Changing the Sample Range

To mitigate the impact of variable measurement errors on the research conclusions, this paper adopts the method of changing the sample size to ensure the robustness of the results. The regression results in

Table 4. Robustness Test—Changing Sample Range.

	(1)	(2)	(3)
VARIABLES	Patent	N_Patent	Inveffi
ESG	0.056 ***	0.032 **	0.060 ***
	(4.221)	(1.990)	(9.834)
Size	0.005	0.010	−0.002
	(0.839)	(1.138)	(−0.694)
Lev	0.035	0.006	0.022 *
	(1.524)	(0.212)	(1.740)
Cashflow	−0.055 *	0.010	0.025
	(−1.886)	(0.290)	(1.305)
Board	−0.025	0.018	0.023
	(−0.779)	(0.479)	(1.470)
Indep	−0.037	−0.052	0.058
	(−0.427)	(−0.424)	(1.352)
Dual	−0.013 *	−0.008	−0.004
	(−1.883)	(−0.794)	(−0.834)
Top1	−0.090 *	−0.068	0.056 **
	(−1.697)	(−0.924)	(2.104)
Balance2	−0.015	−0.007	0.014 ***
	(−1.528)	(−0.432)	(2.761)
FirmAge	−0.074	−0.035	0.474 ***
	(−1.206)	(−0.435)	(36.318)
Mshare	−0.000	−0.004	−0.001
	(−0.013)	(−0.468)	(−0.173)
Constant	0.259	0.040	−0.891 ***
	(1.294)	(0.127)	(−11.733)
Individual Fixed Effects	YES	YES	YES
Time Fixed Effects	YES	YES	YES
Observations	21,727	21,727	20,323
Number of stkcd	3579	3579	3391
R-squared	0.014	0.004	0.182

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

indicate that selecting the period after 2018 as a replacement sample is appropriate because 2018 marks the first implementation of the Environmental Protection Tax Law in China. This law, designed as a “green tax system”, signifies a strong measure against polluting enterprises by imposing environmental taxes. Therefore, this paper selects 2018 as the new sample range. After changing the sample size, based on the coefficients in columns (1), (2), and (3) mentioned above, it is found that ESG ratings are significantly positively correlated with the output of green invention patents, the output of green non-invention patents, and green innovation efficiency. Thus, after changing the sample range, ESG ratings can significantly increase the quantity of corporate green innovation outcomes and the quality of corporate green innovation.

4.3.4. Changing Variable Measurement

To mitigate the impact of variable measurement errors on the research conclusions, this paper changes the measurement method of the dependent variables and re-runs the regression for Model (1). For the dependent variables, this paper adopts the method of [40] by using the total number of patent grants to represent the level of corporate innovation output. The larger this number, the stronger the firm’s green innovation output capacity. For the measurement of green innovation quality, the methods of [37] for green outcome conversion efficiency and [9] for green innovation efficiency are adopted. Liu’s method measures green innovation efficiency by the ratio of green innovation output to innovation input; the larger this value, the higher the firm’s green innovation efficiency. This paper replaces the explanatory variable with the ESG evaluation by Runling Global, where a larger value represents better ESG performance of the firm. The specific results are shown in

Table 5. Robustness Test—Changing Variable Measurement 1.

	(1)	(2)	(3)
VARIABLES	Patent	Inveffi_1	Inveffi_2
ESG	0.127 ***	0.038 ***	0.006 ***
	(5.057)	(8.090)	(4.551)
Size	0.052 ***	−0.002	0.002 **
	(3.437)	(−0.895)	(2.456)
Lev	−0.147 ***	0.015	−0.008 ***
	(−3.110)	(1.457)	(−3.062)
Cashflow	−0.029	0.048 ***	−0.003
	(−0.452)	(3.022)	(−0.769)
Board	0.001	0.023 *	−0.001
	(0.017)	(1.801)	(−0.311)
Indep	−0.134	0.068 *	−0.010
	(−0.787)	(1.919)	(−1.086)
Dual	−0.021	0.003	−0.001
	(−1.358)	(0.779)	(−0.962)
Top1	−0.129	0.087 ***	−0.008
	(−1.065)	(4.034)	(−1.267)
Balance2	−0.031	0.019 ***	−0.002 *
	(−1.520)	(4.472)	(−1.729)
FirmAge	−0.067	0.509 ***	−0.005 **
	(−1.397)	(52.007)	(−2.177)
Mshare	0.022	0.000	0.001
	(0.856)	(0.065)	(0.798)
Constant	−0.273	−0.983***	0.013
	(−0.840)	(−16.121)	(0.795)
Observations	23,518	31,264	25,641
Number of stkcd	3748	4164	4275
R-squared	0.007	0.231	0.004

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

and

Table 6. Robustness Test—Changing Variable Measurement 2.

	(1)	(2)	(3)
VARIABLES	Patent	N_Patent	Inveffi
Runling Global ESG	0.032 ***	0.064 ***	0.009 **
	(3.993)	(5.644)	(2.225)
Size	0.012 ***	0.011 **	0.000
	(3.384)	(2.499)	(0.498)
Lev	−0.017 *	−0.022	−0.008
	(−1.662)	(−1.444)	(−1.540)
Cashflow	0.006	−0.018	−0.014
	(0.416)	(−0.846)	(−1.207)
Board	−0.015	−0.014	0.002
	(−0.779)	(−0.632)	(0.380)
Indep	0.014	0.003	0.001
	(0.282)	(0.047)	(0.028)
Dual	−0.003	0.000	−0.003
	(−0.510)	(0.047)	(−1.336)
Top1	−0.019	−0.020	−0.002
	(−0.547)	(−0.521)	(−0.279)
Balance2	−0.010	−0.001	−0.001
	(−1.358)	(−0.168)	(−0.586)
FirmAge	−0.013	−0.006	−0.002
	(−0.348)	(−0.132)	(−0.730)
Mshare	0.003	0.001	0.002
	(0.571)	(0.237)	(0.374)
Constant	−0.160	−0.140	0.467 ***
	(−1.315)	(−0.939)	(20.634)
Observations	34,332	34,332	31,264
Number of stkcd	4910	4910	4164
R-squared	0.014	0.019	0.372

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

.

The regression results in Table 5 indicate that after changing the measurement method of the dependent variables, columns (1), (2), and (3) show that ESG ratings still have a significant positive correlation with the level of corporate green innovation and green innovation efficiency. ESG ratings have a significant positive effect on the quantity of corporate green innovation outcomes. The regression results in Table 6 indicate that after changing the measurement method of the explanatory variables, columns (1), (2), and (3) show that ESG ratings still have a significant positive correlation with the level of corporate green innovation and green innovation efficiency. Therefore, even after changing the measurement method of the explanatory variables, the conclusions of this paper remain robust. ESG ratings have a significant positive effect on the quality of corporate green innovation.

4.3.5. Controlling for Constituent Stock

Effects Since SynTao Green Finance has covered all companies in the CSI 300 Index since 2015 and further expanded the ESG research sample to include all companies in the CSI 500 Index since 2018; the characteristics of constituent stocks may affect the strict exogeneity of ESG ratings. To avoid potential constituent stock effects, this paper adds two new variables to Model 1: 300_i,t and 500_i,t. 300_i,t equals 1 if company i is included in the CSI 300 stocks in year t, and 0 otherwise. 500_i,t equals 1 if company i is included in the CSI 500 stocks in year t, and 0 otherwise.

Table 7. Robustness Test—Controlling for Constituent Stock Effects 1.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	Patent			N_Patent
ESG	0.036 ***	0.036 ***	0.042 ***	0.034 ***	0.026 **	0.040 ***
	(4.045)	(4.175)	(4.307)	(3.062)	(2.436)	(3.343)
Size	0.011 ***	0.010 ***	0.012 ***	0.015 ***	0.011 ***	0.016 ***
	(3.209)	(2.755)	(3.475)	(3.393)	(2.580)	(3.666)
Lev	−0.016	−0.016	−0.016	−0.030 *	−0.028 *	−0.030 **
	(−1.589)	(−1.545)	(−1.622)	(−1.942)	(−1.849)	(−1.962)
Cashflow	0.005	0.004	0.005	−0.018	−0.019	−0.018
	(0.331)	(0.293)	(0.329)	(−0.864)	(−0.916)	(−0.866)
Board	−0.014	−0.014	−0.014	−0.011	−0.012	−0.011
	(−0.715)	(−0.730)	(−0.727)	(−0.512)	(−0.530)	(−0.523)
Indep	0.016	0.014	0.016	0.012	0.009	0.012
	(0.309)	(0.286)	(0.313)	(0.195)	(0.153)	(0.198)
Dual	−0.002	−0.003	−0.002	0.001	0.001	0.001
	(−0.491)	(−0.508)	(−0.503)	(0.119)	(0.097)	(0.107)
Top1	−0.017	−0.016	−0.019	−0.016	−0.012	−0.018
	(−0.515)	(−0.483)	(−0.578)	(−0.405)	(−0.298)	(−0.464)
Balance2	−0.010	−0.009	−0.010	−0.000	0.001	−0.001
	(−1.301)	(−1.288)	(−1.365)	(−0.006)	(0.082)	(−0.073)
FirmAge	−0.017	−0.016	−0.014	−0.008	−0.010	−0.004
	(−0.430)	(−0.426)	(−0.352)	(−0.164)	(−0.223)	(−0.094)
Mshare	0.003	0.003	0.003	0.005	0.004	0.005
	(0.702)	(0.614)	(0.723)	(0.843)	(0.673)	(0.866)
CSI300	−0.015		−0.025 **	−0.042 ***		−0.052 ***
	(−1.484)		(−2.226)	(−3.074)		(−3.509)
CSI500		−0.004	−0.013 *		0.004	−0.014
		(−0.656)	(−1.899)		(0.539)	(−1.583)
Constant	−0.136	−0.116	−0.166	−0.233	−0.161	−0.266 *
	(−1.104)	(−0.941)	(−1.352)	(−1.541)	(−1.052)	(−1.739)
Individual Fixed Effects	YES	YES	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES	YES	YES
Observations	34,332	34,332	34,332	34,332	34,332	34,332
R-squared	0.014	0.014	0.015	0.016	0.017	0.016
Number	4910	4910	4910	4910	4910	4910

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

presents the results of the empirical study of the impact of ESG on the number of green innovations of companies after controlling for constituent stock effects. Specifically, column (1) presents the regression results after adding CSI300 as a control variable in the model, which shows that the regression coefficient of ESG rating is 0.036 and significant at the 1% level, indicating that ESG rating leads to an average increase of 3.6% in firms’ green innovation output in the following year. Column (2) shows the regression results after adding CSI500 as a control variable in the model, and again, the regression coefficient of ESG rating is 0.036 and significant at the 1% level, which means that ESG rating enables firms to increase their green invention output by 3.6% on average in the following year. Column (3), on the other hand, controls for both the CSI300 and the CSI500 and performs a regression analysis, which shows that the regression coefficient of ESG ratings is 0.042, again significant at the 1% level, implying that ESG ratings enable firms to increase their green invention output by an average of 4.2% in the following year. These results consistently show that ESG ratings significantly increase firms’ patent output for green inventions, whether controlling for CSI300 alone, CSI500 alone, or both CSI300 and CSI500.

Columns (4) to (6) show the regression results after controlling for CSI300, controlling for CSI500, and controlling for both CSI300 and CSI500. The regression coefficients for ESG ratings are all significant at the 1% level under these different scenarios, a result that strongly suggests that ESG ratings remain a significant contributor to firms’ green non-invention patent output. The overall results in columns (1)–(6) indicate that the positive relationship between ESG and the number of firms’ green innovations remains robust after controlling for component effects.

Table 8. Robustness Test—Controlling for Constituent Stock Effects 2.

	(1)	(2)	(3)
VARIABLES	Inveffi
ESG	0.005 ***	0.005 ***	0.005 ***
	(3.677)	(3.749)	(3.529)
Size	0.002 *	0.008 ***	0.002 *
	(1.849)	(19.920)	(1.851)
Lev	−0.005 **	0.009 ***	−0.005 **
	(−1.987)	(5.218)	(−1.990)
Cashflow	−0.002	−0.002	−0.002
	(−0.678)	(−0.555)	(−0.677)
Board	0.001	0.004 *	0.001
	(0.249)	(1.877)	(0.248)
Indep	−0.007	0.008	−0.007
	(−0.738)	(1.175)	(−0.737)
Dual	−0.001	0.001 **	−0.001
	(−1.034)	(2.051)	(−1.035)
Top1	−0.013 **	−0.008 ***	−0.013 **
	(−2.093)	(−2.687)	(−2.101)
Balance2	−0.003 ***	−0.001	−0.003 ***
	(−3.017)	(−0.913)	(−3.031)
FirmAge	−0.005	−0.013 ***	−0.005
	(−0.765)	(−12.205)	(−0.761)
Mshare	0.001	0.004 *	0.001
	(0.881)	(1.811)	(0.879)
CSI300	−0.002		−0.002
	(−0.866)		(−0.877)
CSI500		−0.003 ***	−0.000
		(−3.080)	(−0.128)
Constant	0.014	−0.125 ***	0.013
	(0.541)	(−12.021)	(0.523)
Individual Fixed Effects	YES	YES	YES
Time Fixed Effects	YES	YES	YES
Observations	23,420	23,420	23,420
R-squared	0.030	0.070	0.030
Number	3745	3745	3745

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

presents the empirical findings on the impact of ESG on the quality of firms’ green innovations after controlling for component stock effects. Columns (1) to (3) show the regression results after controlling for the CSI300, after controlling for the CSI500, and after controlling for both the CSI300 and the CSI500. The regression coefficients of ESG ratings are all significant at the 1% level under these different scenarios, providing strong evidence that ESG ratings continue to make a significant contribution to the quality of firms’ green innovation.

4.3.6. Lagged Treatment of Corporate Green Innovation

To ensure that the positive impact of ESG ratings on corporate green innovation (quantitative and qualitative) can be consistently and accurately assessed, we consider lagging the green innovation metrics. Specifically, we analyze companies’ green innovation lagged by one, two, three, and four periods, respectively, in order to capture its long-term effects and possible lagged effects.

Table 9. Robustness Test—Lagged Treatment of Corporate Green Innovation 1.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Patent				N_Patent
VARIABLES	Lagged One Period	Lagged Two Periods	Lagged Three Periods	Lagged Four Periods	Lagged One Period	Lagged Two Periods	Lagged Three Periods	Lagged Four Periods
ESG	0.018 **	0.019 **	0.019 **	0.016 **	0.045 ***	0.038 ***	0.046 ***	0.047 ***
	(2.444)	(2.468)	(2.551)	(2.018)	(3.282)	(2.921)	(3.401)	(3.400)
Size	0.010 ***	0.041 ***	0.038 ***	0.036 ***	0.056 ***	0.059 ***	0.057 ***	0.057 ***
	(2.772)	(9.414)	(8.027)	(6.927)	(12.113)	(11.301)	(10.364)	(9.858)
Lev	−0.015	−0.068 ***	−0.067 ***	−0.067 ***	0.134 ***	0.119 ***	0.111 ***	0.118 ***
	(−1.430)	(−5.373)	(−4.747)	(−4.268)	(7.371)	(5.860)	(5.165)	(5.224)
Cashflow	−0.014	0.008	0.005	−0.003	−0.003	−0.059	−0.009	−0.001
	(−0.852)	(0.511)	(0.295)	(−0.176)	(−0.079)	(−1.326)	(−0.191)	(−0.011)
Board	−0.015	0.035 **	0.045 ***	0.060 ***	0.055 **	0.060 **	0.055*	0.057 **
	(−0.677)	(2.361)	(2.711)	(3.274)	(2.363)	(2.323)	(1.946)	(1.993)
Indep	0.006	0.115 **	0.133 **	0.158 ***	0.090	0.176 **	0.142	0.162 *
	(0.108)	(2.349)	(2.416)	(2.597)	(1.192)	(2.088)	(1.540)	(1.728)
Dual	−0.000	0.010 **	0.007 *	0.008 *	0.028 ***	0.021 **	0.017*	0.017 *
	(−0.018)	(2.540)	(1.810)	(1.788)	(3.634)	(2.401)	(1.873)	(1.844)
Top1	−0.028	0.030	0.037	0.047	−0.004	−0.048	−0.082**	−0.080*
	(−0.736)	(1.299)	(1.435)	(1.636)	(−0.130)	(−1.303)	(−2.029)	(−1.948)
Balance2	−0.010	0.003	0.002	0.003	−0.000	−0.009	−0.016**	−0.016**
	(−1.185)	(0.731)	(0.578)	(0.611)	(−0.044)	(−1.256)	(−2.020)	(−2.008)
FirmAge	−0.025	−0.017 **	−0.017 **	−0.015 **	−0.165 ***	−0.171 ***	−0.174 ***	−0.175 ***
	(−0.549)	(−2.574)	(−2.435)	(−2.200)	(−13.805)	(−11.668)	(−10.582)	(−10.422)
Mshare	0.002	−0.003	−0.003	−0.002	0.035**	0.024	0.012	0.012
	(0.641)	(−1.158)	(−1.109)	(−0.758)	(2.023)	(1.590)	(1.087)	(1.064)
Constant	−0.091	−0.938 ***	−0.913 ***	−0.913 ***	−0.855 ***	−0.894 ***	−0.826 ***	−0.827 ***
	(−0.641)	(−8.954)	(−7.958)	(−7.224)	(−7.194)	(−6.566)	(−5.689)	(−5.522)
Individual Fixed Effects	YES	YES	YES	YES	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES	YES	YES	YES	YES
R-squared	0.009	0.041	0.038	0.036	0.041	0.041	0.041	0.041
Number	4510	3975	3545	3263	4510	3560	3360	3144

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

shows the results of the lagged treatment regressions on firms’ green innovation output. Specifically, columns (1) to (4) show the treatments for firms’ green invention patents lagged from one to four periods, from which it can be seen that the coefficients of ESG ratings are significantly positive, indicating that ESG ratings have a significant contribution to firms’ green invention patents in the following one to four years. Similarly, columns (5) to (8) show the treatment of firms’ green non-invention patents lagged by one to four periods, and the coefficients of ESG ratings are still significantly positive, indicating that ESG ratings have a significant promotion effect on firms’ green non-invention patents one to four years later.

Table 10. Robustness Test—Lagged Treatment of Corporate Green Innovation 2.

	(1)	(2)	(3)	(4)
	Inveffi
VARIABLES	Lagged One Period	Lagged Two Periods	Lagged Three Periods	Lagged Four Periods
ESG	0.010 ***	0.008 **	0.003 **	0.003 *
	(2.984)	(2.376)	(2.371)	(1.832)
Size	−0.001	−0.002 *	0.008 ***	0.009 ***
	(−1.129)	(−1.676)	(17.249)	(14.996)
Lev	−0.001	0.001	0.013 ***	0.020 ***
	(−0.111)	(0.241)	(6.183)	(6.301)
Cashflow	0.022 *	0.017	−0.010 *	−0.003
	(1.747)	(1.330)	(−1.930)	(−0.434)
Board	−0.000	0.000	0.004 *	0.007 **
	(−0.043)	(0.024)	(1.685)	(2.158)
Indep	0.018	0.013	0.024 ***	0.030 ***
	(0.892)	(0.600)	(2.821)	(2.741)
Dual	−0.001	−0.003	0.001	0.001
	(−0.565)	(−1.276)	(0.642)	(0.698)
Top1	−0.005	−0.003	−0.014 ***	−0.019 ***
	(−0.521)	(−0.354)	(−3.620)	(−3.574)
Balance2	−0.002	−0.001	−0.002 **	−0.003 **
	(−0.744)	(−0.520)	(−2.194)	(−2.424)
FirmAge	−0.001	−0.001	−0.015 ***	−0.009 ***
	(−0.199)	(−0.143)	(−10.023)	(−4.485)
Mshare	0.001	0.001	0.004	0.015 ***
	(0.390)	(0.296)	(1.337)	(4.702)
Constant	0.493 ***	0.508 ***	−0.133 ***	−0.182 ***
	(18.754)	(17.963)	(−10.183)	(−11.403)
Individual Fixed Effects	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES
Number of stkcd	3960	3554	3098	2653
R-squared	0.3785	0.3891	0.071	0.077

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

, on the other hand, reports the regression results for the lagged treatment of firms’ green innovation quality. Among them, columns (1) to (4) are the treatments for firms’ green invention patents lagged by one to four periods, respectively, and the coefficients of ESG ratings are significantly positive, suggesting that ESG ratings also have a significant contributing effect on the quality of firms’ green invention patents from one to four years later.

Combining the results in Table 9 and Table 10, we can conclude that the impact of ESG ratings on firms’ green innovation still has a significant facilitating effect.

4.3.7. Zero-Inflation Test

Green patent data are in accordance with the characteristics of the counting model, and the existing literature mostly uses Poisson regression or negative binomial regression model to conduct empirical research. However, in terms of the actual situation of the data used in this paper, the proportion of zero value in green invention patents and green non-invention patents is as high as more than 60 percent, which is a significant feature showing that the lack of research and development activities of many enterprises in the field of green innovation is a common phenomenon in a given year. This high proportion of zero-value distributions goes far beyond the natural explanation of standard discrete models, such as Poisson or negative binomial distributions, and represents a typical “zero-inflated” phenomenon. If we continue to use the traditional Poisson or negative binomial regression model to deal with these types of data, it will inevitably lead to a significant bias in the estimation of low-value (especially zero-value) observations, which may affect the accuracy and reliability of the research conclusions. In view of this, this paper introduces a zero-inflated (ZI) regression model in the robustness test to more accurately capture the zero-inflated characteristics in the data and provide effective regression estimation of them to ensure the robustness of the results.

This ultimately translates into the following econometric regression model form:

$$Y_{i,t}=\alpha +\beta X_{i,t}+\gamma Z_{i,t}+{\sigma }_i+yea{r}_t+{\epsilon }_{i,t}$$

(3)

i denotes individual firms, t denotes year, and ${\sigma }_i$ and $yea{r}_t$ are individual fixed effects, time fixed effects, and ${\epsilon }_{i,t}$ is error terms, respectively. $Z_{i,t}$ and $X_{i,t}$ are the set of covariates that affect firms’ green innovation output, and the variables included in both are all explanatory variables in the specific regression.

According to the zinb regression model, it can be found that the confidence intervals of green invention patents and green non-invention patents’ alpha value are between 2.489–3.545 and 1.670–1.910, respectively, which do not include 0. Therefore, it indicates that there is indeed a zero-inflated phenomenon in the green patent data used in this paper, and the fitting effect of zinb is superior to the ordinary negative binomial regression. Therefore, it indicates that there is indeed a zero-inflated phenomenon in the green patent data used in this paper, and the fitting effect of zinb is superior to the ordinary negative binomial regression. We analyze the relevant results based on the above model.

According to the regressions in columns 1 and 2 of the

Table 11. Zero-inflation Test.

	(1)	(2)
VARIABLES	Patent	N_Patent
ESG	0.168 *	0.089 *
	(1.796)	(1.873)
Size	0.688 ***	0.241 ***
	(20.277)	(15.071)
Lev	−1.173 ***	0.616 ***
	(−6.548)	(7.870)
Cashflow	0.629 *	−0.034
	(1.699)	(−0.179)
Board	0.467 **	0.246 ***
	(2.209)	(2.614)
Indep	0.018	−0.011
	(0.030)	(−0.037)
Dual	0.146 *	0.140 ***
	(1.926)	(4.258)
Top1	−0.390	−0.158
	(−1.229)	(−1.170)
Balance2	−0.085	−0.027
	(−1.083)	(−0.853)
FirmAge	−0.228 **	−0.731 ***
	(−2.142)	(−16.870)
Mshare	−0.215	0.080 ***
	(−0.865)	(3.359)
Constant	−18.982 ***	−6.128 ***
	(−21.312)	(−15.199)
Individual Fixed Effects	YES	YES
Time Fixed Effects	YES	YES
Logit (zero-inflated) regression
/lnalpha	1.089 ***	0.500 ***
	(12.081)	(10.970)
Pseudo R2	0.161	0.050
Log pseudolikelihood	−5467.031	−19,283.519
Wald chi2	1470.330	1472.470
Prob > chi2	0.000	0.000
Observations	34,332	34,332

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

, it can be seen that the regression coefficients of both green invention patents and green non-invention patents show a significant positive relationship; therefore, the conclusion of this paper is still robust after using the zero-inflated negative binomial regression model, and ESG ratings have a significant positive impact on firms’ green innovation outputs.

5. Further Analysis

5.1. Moderating Analysis: Heavily Polluting Enterprises

To encourage corporate green innovation, the Chinese government has issued a series of green credit and green finance policies in recent years. These policies aim to direct capital flow to resource-efficient and environmentally friendly enterprises, urging them to assume more social responsibility. As of now, these policies have shown positive effects, specifically reflected in the increasing financing costs faced by Chinese heavily polluting enterprises [41]. In this context, heavily polluting enterprises, to reduce financing costs and gain a competitive advantage, are highly likely to attract investors and financial institutions through ESG practices, thereby promoting corporate green innovation. However, there may also be a “greenwashing” phenomenon, where firms obtain low-cost funds through green practices but do not use the capital for substantial green innovation activities, thereby nullifying the original intent of the policies [42]. To analyze the heterogeneous impact of industry attributes, this paper follows the approach of [43] and divides the sample into heavily polluting enterprises (HPI = 1) and other enterprises (HPI = 0) for group regression.

In

Table 12. Moderating Analysis: Heavily Polluting Enterprises.

VAR	(1)	(2)	(3)	(4)
	Patent		Inveffi
	Heavily Polluting Enterprises	Non-Heavily Polluting Enterprises	Heavily Polluting Enterprises	Non-Heavily Polluting Enterprises
ESG	0.030 ***	0.021 ***	0.012 **	0.004
	(2.694)	(2.901)	(2.005)	(1.036)
	(−4.525)	(−4.569)	(10.293)	(14.072)
Constant	−0.700 ***	−0.296 ***	0.489 ***	0.428 ***
Controls	YES	YES	YES	YES
Individual Fixed Effects	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES
Observations	8814	22,438	8814	22,438
R-squared	0.1372	0.040	0.379	0.370
Chow Test	8.31 ***		2.61 ***
	(0.000)		(0.000)
ESG	0.030 ***	0.021 ***	0.012 **	0.004
	(2.694)	(2.901)	(2.005)	(1.036)
	(−4.525)	(−4.569)	(10.293)	(14.072)

Note: ***, ** indicate that the estimated coefficients are significant at the 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

, the coefficients of ESG in columns (1) and (2) are both significantly positive. The regression coefficient of ESG ratings in column (1) is 0.03, significant at the 1% level. The regression coefficient of ESG ratings in column (2) is 0.21, significant at the 1% level. This indicates that ESG ratings significantly promote corporate green innovation behaviors in both heavily polluting and non-heavily polluting enterprises. Furthermore, the Chow Test reveals a coefficient of 8.31, significant at the 1% level, indicating that this effect is more pronounced in heavily polluting enterprises. The coefficients of ESG in columns (3) and (4) are also positive. The regression coefficient of ESG ratings in column (3) is 0.012, significant at the 1% level. The regression coefficient of ESG ratings in column (4) is 0.004, significant at the 1% level. Additionally, the Chow Test shows a coefficient of 2.61, significant at the 1% level, indicating that this effect is also more pronounced in heavily polluting enterprises. Therefore, ESG ratings have a stronger impact on the quantity and quality of corporate green innovation in heavily polluting enterprises. This may be because heavily polluting enterprises seek to gain a competitive advantage by reducing financing costs; thus, they are more likely to engage in green innovation behaviors through ESG ratings to demonstrate their commitment to social responsibility.

5.2. Moderating Analysis: Corporate Governance Level

The level of corporate governance in listed companies also affects the innovation output of enterprises. Compared with companies with lower levels of corporate governance, companies with higher levels of corporate governance experience fewer instances of low innovation output caused by information asymmetry and agency problems. Therefore, the enhancement effect of ESG ratings on green innovation output is more significant in samples with lower levels of corporate governance. Companies with lower levels of corporate governance are more likely to obtain investment funds through this method, while companies with higher levels of corporate governance inherently have a stronger sense of social responsibility; thus, the motivation to promote green innovation through ESG ratings is not as evident.

Therefore, this paper selects samples with high and low levels of corporate governance as control groups and performs grouped regression on Model (1) to examine whether the level of corporate governance affects the relationship between ESG ratings and corporate green innovation behavior. This paper adopts the measurement methods of [34], using the following indicators to comprehensively measure the level of corporate governance: dual position (dual), the natural logarithm of board size (Board), proportion of independent directors (Dr), total compensation of the top three executives (Mana_Pay), proportion of shares held by executives (Mana_Share), proportion of shares held by the second to tenth largest shareholders relative to the controlling shareholder (Share_Balance), and proportion of shares held by institutional investors (Inst_Share). This method provides a systematic and comprehensive representation of corporate governance oversight, reduces redundant information, and more accurately reflects the overall level of corporate governance. Using principal component analysis, a corporate governance level index (GOVER) is constructed; the larger the GOVER, the higher the level of corporate governance. The entire sample is divided into high and low corporate governance groups based on the median for grouped testing.

In

Table 13. Moderating Analysis: Corporate Governance Level.

VAR	(1)	(2)	(3)	(4)
	Patent		Inveffi
	High CG	Low CG	Hign CG	Low CG
ESG	0.022 **	0.024 ***	0.003	0.009 *
	(1.998)	(3.152)	(0.437)	(1.894)
Constant	−0.310 ***	−0.383 ***	0.445 ***	0.461 ***
	(−4.270)	(−3.737)	(11.551)	(12.186)
Controls	YES	YES	YES	YES
Individual Fixed Effects	YES	YES	YES	YES
Time Fixed Effects	YES	YES	YES	YES
Observations	13,901	13,251	13,901	13,251
R-squared	0.079	0.049	0.377	0.393
Chow Test	3.03 ***		0.383
	(0.000)		(0.678)

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

, the ESG coefficients in columns (1) and (2) are both significantly positive. The regression coefficient of ESG ratings in column (1) is 0.022, significant at the 1% level. The regression coefficient of ESG ratings in column (2) is 0.024, significant at the 1% level. This indicates that ESG ratings significantly promote corporate green innovation behaviors regardless of whether the company has a high or low level of corporate governance. The Chow Test reveals a coefficient of 3.03, significant at the 1% level, indicating that this effect is more pronounced in companies with high corporate governance levels. The ESG coefficients in columns (3) and (4) are also positive. The regression coefficient of ESG ratings in column (3) is 0.003, indicating that ESG ratings increase the number of green patents in the next year by an average of 0.3% in companies with high corporate governance levels. The regression coefficient of ESG ratings in column (4) is 0.009, significant at the 10% level, indicating that ESG ratings increase the number of green patents in the next year by an average of 0.9% in companies with low corporate governance levels. However, the Chow Test does not show that this effect is more pronounced.

According to the above analysis, it can be observed that in companies with low levels of corporate governance, the impact of ESG ratings on the quantity and quality of corporate green innovation is stronger. This may be because companies with low levels of corporate governance are more eager to gain the attention of stakeholders and non-stakeholders alike. They aim to send positive signals to the outside world through their commitment to social responsibility and their compliance with national environmental policies, thereby highlighting their characteristics and gaining a competitive advantage. This, in turn, attracts more potential investors and reduces financing costs.

5.3. Mechanism Analysis

The empirical evidence in the previous sections shows that ESG ratings promote the level of corporate green innovation. This section, based on theoretical analysis, reveals the mechanisms of this effect from three perspectives: financing constraints, managerial attitudes, and media attention. Referring to the research of [44], the following model is used for mechanism testing:

$$M_{it}={\theta }_0+{\theta }_{1}ES{G}_{it}+{\theta }_{3}control{s}_{it}+U_i+yea{r}_t+{\epsilon }_{it}$$

(4)

where $M_{it}$ is the mechanism variable measuring financing constraints, managerial attitudes, and media attention, and the remaining variables are the same as in the baseline regression model.

5.3.1. Mechanism Analysis—Financing Constraints

From the perspective of achieving high ESG performance, good ESG performance firstly indicates that the company actively responds to national calls, adheres to the concept of green innovation, and helps attract more potential investors [45]. This stabilizes financial markets, enhances economic resilience, broadens financing channels, effectively alleviates corporate financing difficulties, and promotes sustained and steady development of the company [46]. Secondly, ESG performance is selected by authoritative third-party institutions, reflecting the company’s achievements in environmental, social, and governance aspects [11]. These positive outcomes send signals to stakeholders that the company is actively fulfilling its social responsibilities [47], which helps enhance corporate reputation, increase external confidence in the company, and reduce default risk and funding costs. Additionally, ESG performance considers sustainable and high-quality development, helping companies better understand non-financial aspects based on their governance, management, and financial capabilities. This makes companies more advantageous when facing external financing, making it more likely for them to obtain specialized “green funds” and reduce financing constraints.

From the perspective of companies with poor ESG performance, investors often adopt a “vote with their feet” approach to address the consequences of poor environmental performance. These consequences are usually reflected in risk premiums or capital exploitation, forcing companies to bear higher financing costs. Companies with good ESG performance can minimize information asymmetry, establish a positive social image, enhance corporate reputation, increase external confidence in the company, and reduce funding costs [25].

Deng (2013) [48] argues that the more discretionary free cash flow a company has, the more likely it is to spontaneously engage in innovative activities, thereby increasing the company’s innovation output. Conversely, when a company’s discretionary free cash flow is insufficient, engaging in innovative activities increases the risk of breaking the company’s capital chain. In such cases, it may not be a rational choice for the company to pursue innovation, and the company may forgo innovation activities to ensure the normal operation of its capital chain. Therefore, for a company to engage in green innovation activities, it must have sufficient funds; having adequate funds plays a crucial role in corporate green technological innovation. Under the premise of information asymmetry in the capital market, companies can distinguish themselves from competitors by disclosing high-quality information to gain the support of stakeholders [19]. ESG performance represents a company’s efforts to internalize the external costs of environmental pollution and improve green technological innovation to meet stakeholder requirements, thereby attracting more potential investments [49].

Therefore, excellent ESG performance can drive capital to flow towards green enterprises, helping to address issues such as path dependency, environmental externalities, and market imperfections, thereby promoting corporate green innovation. Additionally, it helps reduce information and financial risks, alleviating the financing constraints faced by companies and providing strong support for green innovation. Given that green innovation often faces challenges such as difficulties in quantifying the degree of innovation outcome conversion and lack of transparency, leading to financing difficulties and high costs, alleviating financing constraints becomes particularly crucial at this time. Enhancing their ESG ratings will provide companies with a strong opportunity for green innovation, fostering continuous innovation outcomes [34].

Table 14. Mechanism Analysis: Financing Constraints.

	(1)	(2)	(3)
VARIABLES	SA Index	Patent	Inveffi
ESG Indec	−0.066 ***
	(−20.396)
SA Index		−0.114 ***	−0.035 *
		(−3.648)	(−1.828)
Constant	3.060 ***	0.274 *	0.562 ***
	(30.933)	(1.826)	(5.332)
Controls	YES	YES	YES
Individual Fixed Effects	YES	YES	YES
Time Fixed Effects	YES	YES	YES
Observations	27,335	27,335	27,335
R-squared	0.845	0.009	0.382

Note: ***, * indicate that the estimated coefficients are significant at the 1%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

reports the regression results of the financing constraint mechanism test. Column (1) shows a significant negative correlation between ESG and the SA index. The regression coefficient of ESG ratings in column (1) is −0.066, significant at the 1% level, indicating that ESG ratings reduce the financing constraints faced by companies in the following year by an average of 6.6%. This demonstrates that an increase in ESG scores can significantly alleviate the financing constraints faced by companies. In column (2), the coefficient between the SA index and the quantity of corporate green innovation is −0.114, significant at the 1% level. In column (3), the coefficient between the SA index and the efficiency of corporate green innovation is −0.035, significant at the 1% level. The significant negative correlation between the SA index and both green innovation output and green innovation efficiency in column (3) indicates that a reduction in financing constraints can promote the level of corporate green innovation. Therefore, it can be concluded that ESG ratings can mitigate the negative impact of financing constraints on corporate green innovation.

5.3.2. Mechanism Analysis—Managerial Myopia

Managerial myopia reflects a manager’s temporal cognitive traits, influencing decisions and actions and affecting organizational strategies and outcomes. Managerial cognitive characteristics shape thinking frameworks and determine information-processing methods. To optimize organizational behavior and results, it is essential to understand managerial traits, avoid myopic behavior, ensure decisions align with long-term interests, and promote stable organizational development [50].

Firstly, the higher the ESG rating a company has, the better it can demonstrate its market position and potential for future development, significantly increasing its market attention. Moreover, the behavioral characteristics of the company’s management can be clearly and effectively displayed, thereby gaining stakeholders’ recognition of their decision-making capabilities and sense of social responsibility. According to the upper-echelon theory, the decisions and behaviors of the management can profoundly impact the company’s development strategy and future direction.

Secondly, excellent ESG performance not only reflects a company’s strength but also serves as an important guarantee for its sustainable and stable development. As environmental policies become increasingly stringent, companies will face pressures related to reputation, oversight, and litigation, forcing them to focus more on green governance [27]. The attention brought by ESG performance allows stakeholders to conduct external supervision of the company, which in turn influences the behavior of managers [29]. By attracting the attention of media, analysts, and stakeholders through their ESG performance, companies ensure that managers’ behavior is further constrained, leading to continuous development and updating of green innovation technologies. This pursuit of higher benefits raises the entry barriers for competitors [30].

Furthermore, the benefits of ESG performance are long-term, and managers benefit more than shareholders. This leads managers to focus more on social, governance, and environmental issues, encouraging them to manage companies more creatively. Managers who overly focus on immediate gains may choose to pursue short-term performance at the expense of the company’s long-term development potential. In investment decisions, myopic managers are more likely to prefer projects with short cycles and quick returns rather than investing in green technology innovation and other long-term value-creating activities [51,52,53]. Investments in green technology innovation typically require large amounts of capital, have long cycles, and come with uncertain returns [54]. Myopic managers often use their resources and personal influence to affect the scale and manner of corporate investment, thereby limiting green technology innovation and hindering sustainable development. Managers with long-term vision can actively stimulate green technology innovation, accurately leverage the green resources provided by financial institutions and investors, and effectively utilize these resources to enhance the company’s green innovation capability. Therefore, ESG performance, by reducing managerial myopia, further promotes the formulation of green innovation strategies, strengthens green technology capabilities, and achieves sustainable development goals.

According to the approach by [34], 43 short-term horizon words were set, and the dictionary method was used to calculate the ratio of the total frequency of these words to the total frequency of words in MD&A, then multiplied by 100 to obtain the managerial myopia index. The larger the value of this index, the more myopic the managers are.

Table 15. Mechanism Analysis: Managerial Myopia.

	(1)	(2)	(3)
VARIABLES	Managerial Myopia Index	Patent	Inveffi
ESG Index	−0.001 ***
	(−3.590)
Managerial Myopia Index		−8.159 *	−1.445 **
		(−1.735)	(−2.130)
Constant	−0.000	−3.644 ***	0.455 ***
	(−1.412)	(−25.265)	(19.260)
Controls	YES	YES	YES
Individual Fixed Effects	YES	YES	YES
Time Fixed Effects	YES	YES	YES
Observations	27,152	23,376	27,152
R-squared	0.097	0.092	0.384

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

reports the regression results of the managerial myopia mechanism test. Column (1) shows a significant negative correlation between ESG and managerial myopia, indicating that an increase in ESG scores can significantly suppress managerial myopia. In columns (2) and (3), there is a significant negative correlation between managerial myopia and corporate green innovation output and green innovation efficiency, indicating that a reduction in managerial myopia can promote the level of corporate green innovation. Therefore, it can be concluded that ESG ratings can mitigate the negative impact of managerial myopia on corporate green innovation.

5.3.3. Mechanism Analysis—Media Attention

Currently, ESG information disclosure mainly relies on corporate autonomy, and there are no mandatory disclosure regulations. Therefore, the quality of ESG disclosure is crucial to its reliability, making it a focal point of media attention [55]. Media coverage is increasingly becoming an important channel for disseminating corporate ESG information. It not only helps stakeholders establish good relationships with companies, thereby enhancing overall corporate performance [56], but also can reduce corporate risk by disseminating positive ESG news, encouraging companies to assume more social responsibility, and promoting green and sustainable development behaviors [57]. Good ESG reputation helps improve financial performance [58], offset risks [1], and maintain corporate image [59]. When companies face higher external scrutiny, such as high media attention, it leads to greater financial transparency and amplifies managers’ decision-making behaviors [60], prompting companies to engage in more green innovation behaviors. Therefore, the impact of ESG on increasing external media attention enhances corporate green innovation levels.

Table 16. Mechanism Analysis: Media Attention.

	(1)	(2)	(3)
VARIABLES	Media Attention	Patent	Inveffi
ESG Index	0.093 ***
	(4.875)
Media Attention		0.013 ***	0.002 *
		(8.615)	(1.759)
Constant	1.153 **	−0.588 ***	0.448 ***
	(2.327)	(−14.199)	(20.184)
Controls	YES	YES	YES
Individual Fixed Effects	YES	YES	YES
Time Fixed Effects	YES	YES	YES
Observations	31,204	31,204	32,065
R-squared	0.347	0.042	0.371

Note: ***, **, * indicate that the estimated coefficients are significant at the 1%, 5%, and 10% levels, respectively. The values in parentheses are the corresponding t-statistics or z-statistics.

reports the regression results of the media attention mechanism test. Column (1) shows a significant positive correlation between ESG and media attention, indicating that an increase in ESG scores can significantly increase the level of external media attention on the company. In columns (2) and (3), there is a significant positive correlation between media attention and corporate green innovation output and green innovation efficiency, indicating that an increase in media attention can significantly promote the level of corporate green innovation. Therefore, it can be concluded that ESG ratings can attract external media attention, thereby promoting corporate green innovation.

6. Conclusions

This paper uses the ESG ratings of Chinese listed companies published for the first time by the third-party agency SynTao Green Finance as an exogenous shock. By employing a multiperiod difference-in-differences model, it examines the impact and mechanism of ESG ratings on corporate green behavior from the perspectives of green innovation quantity and green innovation quality. The study finds that ESG ratings have a significant positive effect on promoting corporate green innovation. The results remain significant after a series of robustness tests, confirming the hypotheses. Compared with green invention patents, the effect of ESG ratings is more pronounced for non-green invention patents. Mechanism pathway research reveals that ESG ratings can promote corporate green behavior by alleviating financing constraints, curbing managerial myopia, and increasing external media attention. Additionally, in heavily polluting industries and companies with low corporate governance levels, the effect of ESG ratings on promoting corporate green behavior is more pronounced.

Based on the above empirical research conclusions, the following insights are derived. From the government’s perspective, firstly, relevant policies and regulations can be formulated to provide a solid institutional guarantee for the improvement of the ESG system. These policies and regulations should serve as behavioral guidelines for companies in ESG aspects, clarifying specific requirements and negative behavior lists regarding environmental protection, social responsibility, and corporate governance. By establishing specialized regulatory agencies, the supervision and inspection of corporate ESG behaviors can be strengthened to ensure compliance with relevant regulations. Secondly, cooperation and exchange with the international community can be enhanced to learn from advanced international experiences, promoting the alignment of China’s ESG practices with international standards. By participating in the formulation and promotion of international ESG standards, ESG standards suitable for China’s national conditions can be established, enhancing China’s voice and influence in the global ESG field, and creating favorable conditions for the international development of enterprises. Lastly, incentive measures and financial support can be provided to encourage companies to actively engage in ESG practices. For instance, establishing ESG special funds to support corporate green innovation and social responsibility projects; simultaneously, for companies performing outstandingly in ESG aspects, the government can offer preferential policies such as tax reductions and financing support, motivating more companies to participate in ESG practices.

From the enterprises’ perspective, companies should strengthen their emphasis on ESG, promote ESG behaviors, proactively advance green innovation, and integrate the ESG concept into the entire production and operation process, considering ESG construction as key to achieving sustainable development. By increasing ESG investment and implementing a series of specific ESG projects, companies can actively fulfill social responsibilities and demonstrate a positive corporate image. This can enhance the company’s social reputation and brand image, increase consumer trust, and attract more partners and investors. Adopting greener production methods and reducing pollution emissions are responsibilities to the environment and investments in the future. Increasing investment in green research and development, encouraging innovation, and being tolerant of short-term failures can stimulate employees’ enthusiasm for innovation and inject vitality into the company’s long-term development. Focusing on employee welfare and community development is also a crucial aspect of integrating the ESG concept. By improving working conditions and welfare benefits, and building harmonious labor relations, companies can stimulate employees’ enthusiasm and creativity, enhancing corporate cohesion and centripetal force. Actively participating in community construction and development, and supporting public welfare, helps companies establish a positive social image and enhance social responsibility. Therefore, companies should deepen the ESG concept, integrate it into daily operations, and achieve a win-win situation for both economic and social benefits.

The future outlook for research on ESG ratings and corporate green innovation is filled with vast potential and profound significance. Firstly, regarding interdisciplinary research, the study of the correlation between ESG ratings and corporate green innovation will involve multiple academic fields, including environmental science, economics, and management. In the future, the advancement of interdisciplinary research will help deepen the understanding of the relationship between ESG ratings and corporate green innovation, proposing more effective solutions. Secondly, in terms of strengthening international cooperation, ESG ratings and corporate green innovation are global issues that require joint efforts from all countries. In the future, cooperation between countries will be further strengthened, collectively promoting the improvement of ESG rating systems and the development of corporate green innovation. Countries can share experiences and technologies in ESG ratings and corporate green innovation, jointly addressing global environmental and social challenges.