What Drives Sustainable Development of Enterprises? Focusing on ESG Management and Green Technology Innovation

Abstract

Sustainable development of a company is an important task in corporate management. Enterprises must constantly innovate and change to achieve sustainable development. In China, considering the need for sustainable development of enterprises and the requirement of the dual carbon goals of carbon peaking and carbon neutrality, the environment, social responsibility, and governance (ESG) management and green technology innovation of enterprises are in the spotlight. Therefore, this study aimed to use empirical analysis to verify whether the ESG performance of enterprises promotes corporate green technology innovation and to further explore corporate attributes that promote the relationship between the two. This study selected 933 Chinese A-share listed companies from 2015 to 2019 as the research object and used the fixed effect model to empirically analyze the relationship between ESG performance and the green technology innovation capability of enterprises. The results show that ESG performance plays an important role in promoting green technology innovation capability. Moreover, this study found that, compared to enterprises with low technology levels or short-listing life span, the ESG performance of enterprises with high technology level and long listing life span has a stronger role in promoting the green technology innovation capability of enterprises. Simultaneously, compared with non-state-owned enterprises, state-owned enterprises play a stronger role in the promotion. This study enriches the theoretical mechanism of ESG performance affecting green technology innovation of enterprises, and they have a certain reference value for promoting the sustainable development of enterprises.

1. Introduction

Recently, global energy resource shortages and environmental problems have become increasingly severe. To actively respond to climate change and achieve green and sustainable development, global actions such as the Paris Agreement and the United Nations 2030 Agenda for Sustainable Development have been implemented. The concept of sustainable development based on the principles of ESG has become the consensus of global enterprise development [1].

In China, for the first time, carbon peaking and carbon neutrality have been written into the government work report, and striving to achieve carbon peaking by 2030 and carbon neutrality by 2060 have been incorporated into the overall layout of ecological civilization construction, which will gradually promote the transformation and upgrading of the national economy to low carbon and green [2]. ESG is an effective and powerful evaluation tool for implementing the concept of green development and realizing the overall layout of ecological civilization construction [3].

Furthermore, ESG is a type of financial data, which focuses on the corporate environment (E), social responsibility (S), and internal governance performance (G), rather than just financial information [4]. In contrast to traditional simple financial performance value investment concepts and evaluation criteria, it pays more attention to and examines the contribution of enterprises to environmental protection and social responsibility fulfillment, while promoting sustainable economic development [5]. Specifically, E refers to the resources needed by the enterprise, the consumption and treatment of energy used, the management of waste discharged, and the impact of business activities and investment behaviors of the enterprise on the environment. S focuses on and examines the internal and external relationships between the enterprise and its stakeholders, such as employees, shareholders, and consumers, and whether the enterprise can achieve coordination and balance with its stakeholders. However, G pays attention to and examines the standardization of internal mechanisms, including corporate structure, risk management, management compensation, and business ethics [6].

Green technology innovation is an effective means to solve environmental pollution problems and improve ecological quality. For example, green technology innovation can significantly reduce carbon emissions [7]. The green technology innovation of enterprises is also an important strategy for judging whether an enterprise can achieve green and sustainable development [8]. In addition to minimizing the impact on ecological damage, upgrading and transitioning green technology of enterprises can also help them gain a competitive advantage and long-term sustainable goodwill [9].

Good ESG performance can transmit positive messages to the capital market, increase the transparency of enterprises, and enable enterprises to win the favor of all stakeholders, especially the trust of external investors, which in turn eases corporate financing constraints [10], optimizes the innovation environment of enterprises, and helps enterprises maintain sustainable development. However, serious population, resource, and environmental crises have become prominent problems that hinder high-quality development in China. The key to solving this problem is to develop a green economy and guide enterprises to pay attention to and actively conduct green technology innovation [11]. Therefore, this study aimed to use empirical analysis to verify whether the ESG performance of enterprises promotes green technology innovation, and to further explore the enterprise attributes that promote the relationship between the two.

The innovations of this paper are as follows. First, the research on ESG in China has just started, and the corresponding theoretical support is relatively lacking. Most of the existing related literature mainly has been qualitative analysis and discussion on the relationship between ESG concepts and corporate value. However, there are few studies on the specific impact of ESG performance on business decision-making behavior. From the perspective of green sustainable development, this study analyzed the relationship between ESG performance and the level of green technology innovation of listed companies, which enriches the theory and literature on ESG performance and the impact of corporate environmental protection decisions. Second, from the perspective of three heterogeneities in the nature of corporate property rights, corporate technology level, and corporate listing life span, this study examines whether this will lead to differences in the impact of ESG performance on corporate green technology innovation, which will further deepen the recognition and understanding of ESG performance. It also provides a basis for listed companies to pay more attention to ESG performance and promote corporate green technology innovation.

2. Literature Review and Hypothesis

2.1. Impact of ESG Performance on Corporate Green Technology Innovation

Based on stakeholder theory, enterprises should not only focus on satisfying the economic interests of all stakeholders but also meet their social benefits [12], such as environmental governance. China’s economy has gradually shifted away from high-speed growth at the expense of the environment to high-quality development. Important stakeholders (external investors) of enterprises often have obvious ESG investment preferences, and restrict and supervise whether corporate behavior follows social expectations and meets legitimacy requirements [13]. Only when the enterprise can meet both the economic and social expectations of external investors will external investors provide the corresponding funds and resources for the enterprise [14]. Therefore, to achieve legitimacy goals [15], meet stakeholder needs [16], and satisfy strategic orientation needs [17], enterprises must conduct green innovation. While actively improving ESG performance to attract financial subsidies and external financial support, to further establish and maintain a good image of green responsibility practitioners, enterprises will also actively increase R&D investment to promote innovation activities to meet the demands of various stakeholders [18].

Based on the theory of resource allocation, the goal of strategic resource allocation management is to achieve the best interests of different economic entities [19]. Enterprises hope that the funds and resources obtained will be used to conduct innovation activities to promote the improvement of self-competitiveness and the increment in self-interest; thus, investors and enterprises form mutual needs [20]. ESG performance is an important piece of information exchanged between the two. Investors will increase their investment confidence and willingness based on good ESG performance. Thus, investors choose to invest in enterprises. Accordingly, enterprises will further increase their R&D investment and green innovation activities [21]. Hao et al. (2022) and Xiang et al. (2022) believe that the number of green patent applications and the number of green patent authorizations can represent the green technology innovation capabilities of enterprises [22,23]. In summary, this study proposes research Hypothesis 1:

Hypothesis 1a.

Better ESG performance can increase the number of corporate green patent applications.

Hypothesis 1b.

Better ESG performance can increase the number of corporate green patent authorizations.

2.2. Moderating Effect of the Nature of Corporate Property Rights

The nature of corporate property rights refers to the nature of the rights enterprises enjoy over their assets. In China, there is a division between state-owned and non-state-owned enterprises [24]. First, compared with non-state-owned enterprises, state-owned enterprises find it easier to obtain support from external financial resources (government subsidies, equity financing, and debt financing) to conduct technological innovation when their ESG performance is good [25]. Moreover, China’s state-owned enterprises usually have an administrative monopoly position, and they have inherent advantages in terms of talent, technology, scale, and so on. ESG performance can better promote green technology innovation activities [26]. In addition, the results of their innovation transformation are more likely to form economies of scale [27]. Second, state-owned enterprises find it easier to obtain national legal and policy support through political connections, which can greatly reduce external risks such as policy uncertainty faced in the process of innovation [28]. Therefore, this study believes that when ESG performance is better than that of non-state-owned enterprises, the green technology innovation willingness of state-owned enterprises is stronger, and their performance is also stronger. Therefore, this study proposes research Hypothesis 2:

Hypothesis 2a.

Compared to non-state-owned enterprises, better ESG performance of state-owned enterprises has a greater positive impact on increasing the number of corporate green patent applications.

Hypothesis 2b.

Compared to non-state-owned enterprises, better ESG performance of state-owned enterprises has a greater positive impact on increasing the number of corporate green patent authorizations.

2.3. Moderating Effect of Corporate Technology Level

Canepa and Stoneman (2008) noted that the demand for financial resources for innovation in high-tech enterprises is much greater than that of non-high-tech enterprises, and they are easily affected by financial factors [29]. Enterprises with a high level of technology have good basic resources for innovation, such as technologies, equipment, professionals, patents, and knowledge, and their development is highly dependent on constantly upgrading and developing cutting-edge technologies, which is conducive to reducing innovation costs and risks and rapidly achieving breakthroughs in green technology innovation [30]. Therefore, high-tech enterprises are more likely to be favored by investors. Correspondingly, when ESG performs well, high-tech enterprises can gain cost advantages in competition for investment, reduce the financing difficulty of enterprises, and improve their financial environment [31]. High-tech enterprises use financial resources to increase their intensity of investment in green innovation, which further enhances their core competitiveness and improves corporate performance [32]. Simultaneously, when the ESG performance of high-tech enterprises is good, there are more government subsidies and tax-saving incentives, which makes high-tech enterprises more willing to conduct green technological innovation than non-high-tech enterprises [33]. Based on the above analysis, this study proposes Hypothesis 3:

Hypothesis 3a.

Compared with enterprises with a low technical level, better ESG performance of enterprises with a high technical level has a greater positive impact on increasing the number of corporate green patent applications.

Hypothesis 3b.

Compared with enterprises with low technical levels, the better ESG performance of enterprises with high technical levels has a greater positive impact on increasing the number of corporate green patent authorizations.

2.4. Moderating Effect of Corporate Listing Life Span

Based on life cycle theory, listed enterprises in different life cycle stages have obvious differences in innovation willingness and R&D capability [34]. D’Amato and Falivena (2020) also noted that age, as an important characteristic of enterprises, indirectly reflects and affects their experience, knowledge, reputation, resources, human capital, strategic position, and market share [35]. Compared to mature listed companies, young listed companies have larger external financing constraints and tighter internal financial resources [36]. Meanwhile, the R&D investment of young listed companies faces greater risks than that of mature listed companies [37], and they tend to prioritize short-term profit-seeking or value preservation rather than long-term risky innovation strategies [38]. Even with the improvement in ESG performance and the easing of the financial environment, young listed companies’ willingness to innovate is still lower than that of mature listed companies because of the lack of relevant innovation experience and accumulation of knowledge [39], as well as network and collaborative relationships [40]. On the contrary, with the dual support of sufficient external investment and internal financial resources, mature listed companies are better able to use mature organizational practices and previously accumulated experience and knowledge to conduct more green innovation activities [41]. Additionally, mature listed companies have more formal and advanced internal control mechanisms [42]. Perfect internal supervision can control the use of funds and resource allocation within a reasonable range, and its supervision effect directly affects the behavioral decision making of company management. For example, reducing short-term behaviors, such as corporate financing, and increasing long-term behaviors, such as corporate green innovation [43]. In summary, this study proposes Hypothesis 4:

Hypothesis 4a.

Compared with enterprises with short-listing life span, the better ESG performance of enterprises with long-listing life span has a greater positive impact on increasing the number of green patent applications.

Hypothesis 4b.

Compared with enterprises with short-listing life span, better ESG performance of enterprises with long-listing life span has a greater positive impact on increasing the number of green patent authorizations.

Figure 1 is the research model of the study.

3. Research Design

3.1. Data and Samples

This study selected data on Chinese A-share listed companies from 2015 to 2019 as the initial research sample. Recently, Chinese companies have been placing importance on ESG management and green technology innovation and are attracting attention from all over the world. The reason why the research period was set until 2019 was because various financial indicators of companies showed different patterns from the past that to COVID-19, which swept the world from early 2020. To ensure the accuracy and representativeness of the data and avoid the interference of other factors, this study adopts the following processing for the initial sample data: (1) exclude financial, insurance, and real estate companies due to the reason that the report structure of financial listed companies and the financial indicators (control variables) of real estate are significantly different from other industries. The field of research objects includes basic materials, consumer cyclicals, consumer non-cyclicals, energy, health care, industrials, technology and utilities; (2) exclude ST (special treatment, which means listed companies with negative net profit for two consecutive fiscal years), ST* (special treatment*, which represents delisting warning due to loss of listed companies for three consecutive fiscal years), PT (particular transfer, which means listed companies that stop any transactions, clear the price, wait for delisted), and delisted companies. Finally, a total of 933 listed companies and 4149 sample observations were obtained; (3) To eliminate the influence of outliers, this study conducted winsorization on all continuous variables at the upper and lower 1% level; (4) To weaken the collinearity and heteroscedasticity of the model and ensure better data stability, some main continuous variables were logarithmized; and (5) to better explain the meaning of the independent variable coefficients and the problem of collinearity, this paper centralizes the processing of the continuous variables in the interaction term of the moderating effect.

3.2. Definition of the Variables

3.2.1. Dependent Variable

Green technological innovation. Referring to the research method of Zhang et al. [44], this study adopted the total number of green patent applications or authorizations in the current year as a proxy variable for enterprises’ green technological innovation capability. The total number of green patent applications or authorizations includes the number of green invention patents and green utility model patents. We add 1 to the sum of the two and then take the natural logarithm to measure the green technology innovation capability of enterprises. Green patent application and authorization data were obtained from the Chinese Research Data Services Platform (CNRDS).

3.2.2. Independent Variable

Corporate ESG performance. At present, the academic community uses the method of constructing a multidimensional indicator system or employs the grades or scores of a third-party evaluation agency to measure ESG performance. Given the subjective nature of self-built indicators and the fact that there are few reference indicators in line with China’s national conditions at present, the relevant data on the construction of ESG indicator systems by third-party institutions in China are not yet perfect; therefore, this study uses the comprehensive score of ESG performance of listed companies provided by the relatively mature and authoritative Bloomberg Consulting for making quantitative assessments. The score can be subdivided into three types: environmental, social responsibility, and corporate governance. The higher the score, the higher the degree to which the enterprise fulfills its responsibilities. Specific evaluation and evaluation weight standards are listed in Table 1.

3.2.3. Moderating Variables

The Nature of Corporate Property Rights

To further examine the moderating influence of the nature of corporate property rights on the relationship between ESG performance and the level of corporate green technology innovation, this study divided the nature of corporate property rights into state-owned and non-state-owned enterprises according to the type of ultimate controller in the Wind financial database. Drawing on the practice of Dai et al. [45] and previous research, this study selects the nature of corporate property rights as dummy variables to deal with and sets the value of state-owned enterprises as 1 and non-state-owned enterprises as 0.

Corporate Technology Level

To further examine the moderating influence of corporate technology level on the relationship between ESG performance and the level of corporate green technology innovation, based on the practice of Yu et al. [46], this study believes that dividing enterprises into high-tech and non-high-tech enterprises according to their technological heterogeneity can more accurately analyze the influence mechanism. Therefore, referring to the document “Guidelines for the Industry Classification of Listed Companies (revised in 2012)” issued by the China Securities Regulatory Commission, this study classifies 13 types of enterprises as high-tech enterprises, with a value of 1, and other enterprises as non-high-tech enterprises, with a value of 0.

Corporate Listing Life Span

To further examine the moderating influence of corporate listing life span on the relationship between ESG performance and the level of corporate green technology innovation, this study referred to the research of Zhang et al. [47] and used the current year minus the listing year to measure the length of corporate listing life span.

3.2.4. Control Variables

To explore the impact of ESG performance on corporate green technology innovation more accurately, this study drew on the relevant research by Xu et al. [48] and selected a series of other variables that may affect corporate green technological innovation from multiple perspectives to control. Specifically, enterprise size (SIZE), current ratio (CR), current asset turnover (CAT), return on assets (ROA), net asset growth rate (NAGR), asset-liability ratio (LEV), and ISO14000 environmental management certification system (ISO). In addition, the effects of the year variable (Year) and industry variable (Industry) were controlled. Table 2 lists the variables and their measurement methods.

3.3. Model Design

To test whether Hypothesis 1 is true, that is, to test the impact of ESG performance on the level of corporate green technology innovation, Equation (1) was constructed as follows:

(1)$Y_{i,t}=\alpha 0+\alpha 1X_{i,t}+{\alpha }_{k}Contro{l}_{i,t}+Industr{y}_i+Yea{r}_t+{\epsilon }_{i,t}$

To further explore whether there are heterogeneity differences in the nature of corporate property rights, corporate technology level, and corporate listing life span between ESG performance and corporate green technology innovation, this paper constructed Models (2)–(4) to verify whether the nature of corporate property rights, corporate technology level, and corporate listing life span play a moderating role between ESG performance and corporate green technology innovation, that is, to verify Hypotheses 2–4.

(2)$Y_{i,t}=\alpha 0+\alpha 1X_{i,t}+\alpha 2CPR{N}_{i,t}+\alpha 3X_{i,t}CPR{N}_{i,t}+{\alpha }_{k}Contro{l}_{i,t}+Industr{y}_i+Yea{r}_t+{\epsilon }_{i,t}$

(3)$Y_{i,t}=\alpha 0+\alpha 1X_{i,t}+\alpha 2TEC{H}_{i,t}+\alpha 3X_{i,t}TEC{H}_{i,t}+{\alpha }_{k}Contro{l}_{i,t}+Industr{y}_i+Yea{r}_t+{\epsilon }_{i,t}$

(4)$Y_{i,t}=\alpha 0+\alpha 1X_{i,t}+\alpha 2AG{E}_{i,t}+\alpha 3X_{i,t}AG{E}_{i,t}+{\alpha }_{k}Contro{l}_{i,t}+Industr{y}_i+Yea{r}_t+{\epsilon }_{i,t}$

where $Y$ indictes the dependent variable (natural logarithm of the number of green patent and 1), $X$ stands for the independent variable (ESG disclosure overall score), $Control$ represents each control variable, $\alpha$ is the coefficient of each variable, $i$ represents the different enterprise individuals, $t$ represents the research year, ${\epsilon }_{i,t}$ is the random disturbance term, and $Industry$ and $Year$ represent the fixed effects of industry and time, respectively.

The p-values of the Hausman test results of Models (1)–(4) in this study were all <0.05, so the fixed effect regression model was the most appropriate choice [49].

4. Empirical Analysis Results

4.1. Descriptive Statistics

It can be seen from Table 3 that the average and maximum values of corporate green technology innovation (the number of green patent applications GTI1 and the number of green patent authorizations GTI2) are 0.5381, 0.4037 and 4.1897, 3.6889, respectively, indicating that green technology innovation investment among sample enterprises is mostly concentrated at the lower middle level. The mean value of ESG is 20.6854, while the maximum value is 43.6214, indicating that most enterprises perform poorly in ESG. Its standard deviation is 6.2929, indicating that there are great differences in ESG performance among different enterprises, which further shows that enterprises pay different levels of attention to ESG. The mean value of corporate property rights is 0.4623, indicating that the number of non-state-owned enterprises in the sample is slightly higher than that of state-owned enterprises. The average and maximum values of the technological level of enterprises are 0.133 and 1, respectively, which indirectly indicates that most of the sample enterprises are non-high-tech enterprises. The mean, minimum, and maximum values of corporate listing life span are 2.5419, 0.6931, and 3.2958, respectively, indicating fewer young enterprises and more mature enterprises in the sample. In addition, the standard deviation of some control variables is relatively large, indicating that there are significant differences in the observed values among the sample enterprises, which may affect the level of corporate green technology innovation.

4.2. Correlation Analysis

As seen in Table 4, the ESG performance of the companies in the sample is significantly positively correlated with the number of green patent applications and the number of green patent authorizations at the level of 1%. The results of the correlation analysis preliminarily support Hypothesis 1 of this study to a certain extent. The variance inflation factors (VIF) value of each variable was <3, indicating no multicollinearity problem.

4.3. Regression Result Analysis

As can be seen from the results of Model (1) (columns 1 and 2) in Table 5, the score of ESG performance is significantly and positively correlated with the number of corporate green patent applications and the number of corporate green patent authorizations at the 1% level, with regression coefficients of 0.0251, and 0.0161, respectively, indicating that ESG performance positively impacts performance of corporate green technology innovation, that is, the better the ESG performance, the stronger the corporate green technology innovation capability. Therefore, Hypothesis 1 is tenable. This also shows that under good ESG performance, corporate financing constraints are eased and subject to the supervision pressure of the corporate external environment; furthermore, enterprises have stronger motivation to increase their R&D investment in green technology innovation, and further release to the positive signal of practicing the concept of green sustainable development to the outside world, which will help the enterprise win the recognition and support of the stakeholders to obtain higher market evaluation and maintain a good corporate image and reputation.

Model (2) (columns 3 and 4) shows that the regression coefficients between the score of ESG performance and the number of corporate green patent applications and corporate green patent authorizations are significantly positive (0.0178***, 0.0085**) at the 1% and 5% levels, respectively. Simultaneously, the interaction term of ESG and corporate property rights is significantly positively correlated with the number of corporate green patent applications and authorizations at the 5% and 1% levels, respectively, and the regression coefficients are 0.0198** and 0.0204***. This suggests that when ESG performance is good, state-owned enterprises are more willing to carry out green technology innovation activities than non-state-owned enterprises, which supports Hypothesis 2. The reason for this difference may be that state-owned enterprises can significantly reduce financing difficulties and costs while actively disclosing ESG information. Under the government’s call, state-owned enterprises should lead by example, actively implement various environmental protection policies and systems promulgated by the state, undertake more social responsibilities, and participate in more R&D in green technology innovation. Therefore, the ESG performance of state-owned enterprises plays a relatively large role in promoting corporate green technology innovation.

Model (3) (columns 5 and 6) shows that the regression coefficients between the score of ESG performance and the number of corporate green patent applications and corporate green patent authorizations are both significantly positive (0.0219***, 0.0134***) at the 1% level. Simultaneously, the interaction term of ESG and corporate technology level is significantly positively correlated with the number of corporate green patent applications and authorizations at the 1% and 5% levels, respectively, and the regression coefficients are 0.0557*** and 0.0468**. This indicates that when ESG performance is better, compared with enterprises having low technical levels, enterprises having high technical levels play a more obvious role in promoting investment in green technology innovation. Thus, Hypothesis 3 is supported. The reason for this difference may be that non-high-tech enterprises lack the advanced technical equipment and related professional and technical personnel required for R&D work, and the difficulty, cost, and risk of innovation are relatively high. In contrast, the basic advantages of high-tech enterprises’ technological resources can help them overcome this adverse phenomenon as much as possible. To maintain competitiveness, they will have greater motivation to innovate and will then invest more capital resources in projects that are conducive to the long-term sustainable development of the enterprise. Therefore, the ESG performance of enterprises with strong internal technical capabilities have stronger green innovation capabilities when ESG performs better.

Finally, Model (4) (columns 7 and 8) shows that the regression coefficients between the ESG performance score and the number of corporate green patent applications and corporate green patent authorizations are both significantly positive (0.0254***, 0.0162***) at the 1% level. Simultaneously, the interaction term of ESG and corporate listing life span is significantly positively correlated with the number of corporate green patent applications and authorizations at the 1% level, and the regression coefficients are 0.0206*** and 0.0162***. This shows that when ESG performance is good, enterprises with long listing life span are more willing to invest in green technology innovation than enterprises with short listing life span, indicating that Hypothesis 4 is true. The reason for this difference may be that enterprises with long listing life span are more concerned about loss and risk in reputation and, simultaneously, are more likely to attract media attention. Therefore, with the improvement of ESG performance and the resulting external financial support, enterprises with long listing life span have a greater incentive to participate in R&D for green innovation to obtain higher green innovation performance to maintain healthy public relations [50]. Although enterprises with short listing life span may also engage in green technology innovation to seek differentiation advantage and gain legitimacy from stakeholders, their limited internal finances may not be able to allocate sufficient resources to achieve higher levels of green innovation activities compared with enterprises with long listing life span [51]. Therefore, the ESG performance of enterprises with long listing life span has a relatively greater impact on green technology innovation.

As a result of empirical analysis, although the adjusted R square is low, it is judged that the correlation between the independent variable and the dependent variable will not be affected. In future studies, one of the ways to solve this problem is to add control variables.

4.4. Robustness Test

Considering the possible endogeneity problem caused by omitted variables and bidirectional causality (i.e., ESG performance can promote corporate green technology innovation, and conversely, green technology innovation can also promote corporate ESG performance), as well as other factors, to overcome the estimation bias brought by this possible endogeneity problem to the empirical results, this study refers to the practice of Gao et al. [52], selects the one-period lag of ESG performance (LESG_i,t) and the mean value of ESG performance of other listed enterprises in the same industry (AESG_i,t) as instrumental variables, and uses the two-stage least squares (2SLS) method to test the robustness. Equations (5) and (6) are the first- and second-stage models of 2SLS, respectively.

(5)$X_{i,t}=\alpha 0+\alpha 1LES{G}_{i,t}+\alpha 2AES{G}_{i,t}+{\alpha }_{k}Contro{l}_{i,t}+Industr{y}_i+Yea{r}_t+{\epsilon }_{i,t}$

(6)$Y_{i,t}=\alpha 0+\alpha 1{\overline{X}}_{i,t}+{\alpha }_{k}Contro{l}_{i,t}+Industr{y}_i+Yea{r}_t+{\epsilon }_{i,t}$

where $\overline{X}$ indictes the fitted value of LESG and AESG in the first stage.

The regression results of 2SLS are shown in columns 1–3 of Table 6. In the first stage (column 1), the regression coefficients of LESG and AESG with ESG are all significantly positive at the 1% level (0.3284***, 0.4480***). In the second stage (columns 2 and 3), the regression coefficients between the ESG performance score after fitting by LESG and AESG in the first stage and the number of corporate green patent applications and authorizations are also both significantly positive at the 1% level (0.0765***, 0.0348***). The above results show that, after considering the endogeneity problem, corporate ESG performance is still significantly positively correlated with corporate green technological innovation capability, which once again verifies the correctness of Hypothesis 1. In addition, with regard to the 2SLS test, the under-identification test (Kleibergen–Paap rk LM statistic) in Model (6) was 23.545, and the corresponding p-value was 0.0000, indicating that the instrumental variables were identifiable [52]. The weak identification tests (Cragg–Donald Wald F statistic) (Kleibergen–Paap rk Wald F statistic) in Model (6) were 350.790 and 63.403, respectively, both of which are larger than the Stock–Yogo weak ID test critical values at the 10% level of judgment of 16.380, indicating that there is a strong correlation between the instrumental variables and the independent variable and that there is no weak instrumental variable problem [52]. The overidentification test (Hansen J p-value) is 0.1169, which is >0.05 or even 0.1, indicating that the instrumental variables are not directly related to the dependent variable and are not related to the disturbance term. The instrumental variables were all exogenous variables [53]. Therefore, there is no overidentification problem.

5. Conclusion and Implications

5.1. Conclusions

With the continuous growth in the global ESG investment scale, the government, regulatory authorities, stakeholders, and enterprises are paying increasing attention to ESG. Based on the research samples of 933 Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchange from 2015 to 2019, this study empirically tests the impact of ESG performance on corporate green technology innovation and further explores the mechanism of the nature of corporate property rights, corporate technology level, and corporate listing life span on the relationship between ESG performance and corporate green technology innovation.

This study draws the following research conclusions. First, good ESG performance can promote enterprises to conduct green technology innovation. This conclusion echoes Tsai et al.’s (2017) view [54], which suggests that sustainability strategy is positively related to environmental innovation. Furthermore, previous study has also shown enterprises that seek ESG development will benefit with regard to corporate reputation, employee satisfaction, investor attractiveness and technological innovation (In et al., 2019) [55]. Therefore, under the hard constraints of external environmental regulations and the soft constraints of social public environmental requirements as well as multi-stakeholder needs, enterprises are willing to improve ESG performance and further increase R&D investment in green innovation to maintain the sustainable competitive advantage and high-quality development (Wang et al., 2022) [56], which is similar to the basic conclusion. Second, the effect of ESG performance on corporate green technological innovation is affected by differences in the nature of corporate property rights, corporate technology level, and corporate listing life span. In state-owned enterprises and enterprises with high technology level and long listing life span, ESG performance is more prominent in promoting enterprises’ green technology innovation capability. Prior research suggested that the sufficient degree of funds, resources, equipment, talent, technical knowledge and relationship network are required for effective green innovation (De Marchi., 2012; Bai et al., 2021) [57,58]. These are precisely the advantages of heterogeneity of resources owned by state-owned enterprises, high-tech enterprises and mature enterprises compared to non-state-owned enterprises, non-high-tech enterprises and young enterprises (Nunes et al., 2012; Boeing et al., 2016; Yin et al., 2022) [59,60,61]. Hence, for state-owned enterprises and enterprises with high technology level and long listing life span, they are more willing to conduct green technology innovation through using these advantages while ESG performance is well.

5.2. Implications

This study has the following implications. First, at the enterprise level, to maintain sustainable development and competitive advantage, enterprises should focus on non-financial performance, such as ESG. For example, enterprises can improve operations and management by protecting the ecological environment, undertaking social responsibilities, and improving the internal governance of companies, thereby enhancing competitive advantage and effectively alleviating financing constraints to obtain sufficient R&D funding support. Correspondingly, enterprises will further increase investment in R&D for green technology innovation to promote and achieve their own high-quality development in the future. From the perspective of the moderating effect analysis, non-state-owned listed enterprises with low technology levels and short listing life span should form a green and sustainable development system as soon as possible and pay more attention to the cognitive shaping of the ESG concept. Simultaneously, they should incorporate ESG non-financial performance into their corporate strategy and allocate resources rationally to contribute to high-quality economic development. Second, at the external investor level, external investors should consider corporate ESG performance as an important indicator to measure the investment potential of enterprises to effectively evaluate the sustainability of investment returns and risks, and encourage enterprises to improve ESG performance and enhance green technological innovation capability. Third, at the government level, government departments should establish a sound ESG information disclosure and evaluation system. While actively publicizing and guiding enterprises to standardize information disclosure and improve their own ESG performance, the government should also issue preferential policies and incentives to promote the high-quality development of enterprises.

5.3. Limitations and Future Prospects

One of the limitations of this study is that it only examines the impact of listed companies’ ESG performance on corporate green technological innovation, and the research conclusions are not necessarily applicable to Chinese non-listed companies. Simultaneously, in view of the data availability and integrity, this study only used the ESG score of Bloomberg Consulting for research, so there is a lack of comparative data. With the development and improvement of China’s local ESG indicator system, future research could adopt an evaluation system that is more in line with China’s national conditions and conduct related research on the ESG performance of non-listed companies. Simultaneously, the relevant impact of the COVID-19 epidemic on ESG performance in future research is also worthy of attention.

Author Contributions

Data curation and draft, C.Z.; methodology, review, and editing, S.J. 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

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figure and Tables

Figure 1. Research model.

Table 1

ESG Evaluation Criteria.

Pillar (Weight)	Field ID	Field Description	Units	Disclosure Frequency	Weight (% of Overall Score Weight)
Environmental (33%)		Air Quality	Percentage		4.78%
	ES007	Nitrogen Oxide Emissions	Thousand Metric Tonnes	Annual	0.96%
	ES009	VOC Emissions	Thousand Metric Tonnes	Annual	0.96%
	ES010	Carbon Monoxide Emissions	Thousand Metric Tonnes	Annual	0.96%
	ES013	Particulate Emissions	Thousand Metric Tonnes	Annual	0.96%
	F0949	Sulfur Dioxide/Sulfur Oxide Emissions	Thousand Metric Tonnes	Annual	0.96%
		Climate Change	Percentage		4.70%
	ES036	Emissions Reduction Initiatives	Y/N	Annual	0.11%
	ES071	Climate Change Policy	Y/N	Annual	0.11%
	ES105	Climate Change Opportunities Discussed	Y/N	Annual	0.11%
	ES106	Risks of Climate Change Discussed	Y/N	Annual	0.11%
	ES001	Direct CO2 Emissions	Thousand Metric Tonnes	Annual	0.47%
	ES002	Indirect CO2 Emissions	Thousand Metric Tonnes	Annual	0.47%
	ES012	ODS Emissions	Thousand Metric Tonnes	Annual	0.47%
	ES076	GHG Scope 1	Thousand Metric Tonnes CO2e	Annual	0.47%
	ES077	GHG Scope 2	Thousand Metric Tonnes CO2e	Annual	0.47%
	ES078	GHG Scope 3	Thousand Metric Tonnes CO2e	Annual	0.47%
	ES255	Scope 2 Market-Based GHG Emissions	Thousand Metric Tonnes CO2e	Annual	0.47%
	ES262	Scope of Disclosure	Nominal (1–3)	Annual	0.47%
	ES399	Carbon per Unit of Production	Metric Tonnes/Unit of Production	Annual	0.47%
		Ecological & Biodiversity Impacts	Percentage		4.79%
	ES088	Biodiversity Policy	Y/N	Annual	0.28%
	ES032	Number of Environmental Fines	Count	Annual	1.13%
	ES033	Environmental Fines (Amount)	Million Reporting Currency	Annual	1.13%
	SA231	Number of Significant Environmental Fines	Count	Annual	1.13%
	SA359	Amount of Significant Environmental Fines	Million Reporting Currency	Annual	1.13%
		Energy	Percentage		4.73%
	ES035	Energy Efficiency Policy	Y/N	Annual	0.14%
	ES014	Total Energy Consumption	Thousand Megawatt Hours	Annual	0.57%
	ES015	Renewable Energy Use	Thousand Megawatt Hours	Annual	0.57%
	ES080	Electricity Used	Thousand Megawatt Hours	Annual	0.57%
	ES107	Fuel Used—Coal/Lignite	Thousand Metric Tonnes	Annual	0.57%
	ES108	Fuel Used—Natural Gas	Thousand Cubic Meters	Annual	0.57%
	ES109	Fuel Used—Crude Oil/Diesel	Thousand Cubic Meters	Annual	0.57%
	ES384	Self-Generated Renewable Electricity	Thousand Megawatt Hours	Annual	0.57%
	ES494	Energy Per Unit of Production	Megawatt Hours/Unit of Production	Annual	0.57%
		Materials & Waste	Percentage		4.74%
	ES039	Waste Reduction Policy	Y/N	Annual	0.16%
	ES019	Hazardous Waste	Thousand Metric Tonnes	Annual	0.65%
	ES020	Total Waste	Thousand Metric Tonnes	Annual	0.65%
	ES021	Waste Recycled	Thousand Metric Tonnes	Annual	0.65%
	ES025	Raw Materials Used	Thousand Metric Tonnes	Annual	0.65%
	ES026	% Recycled Materials	Percentage	Annual	0.65%
	ES104	Waste Sent to Landfills	Thousand Metric Tonnes	Annual	0.65%
	ES498	Percentage Raw Material from Sustainable Sources	Percentage	Annual	0.65%
		Supply Chain	Percentage		4.79%
	ES037	Environmental Supply Chain Management	Y/N	Annual	4.79%
		Water	Percentage		4.79%
	ES247	Water Policy	Y/N	Annual	0.28%
	ES081	Total Water Discharged	Thousand Cubic Meters	Annual	1.13%
	ES082	Water per Unit of Production	Liters/Unit of Production	Annual	1.13%
	ES269	Total Water Withdrawal	Thousand Cubic Meters	Annual	1.13%
	SA484	Water Consumption	Thousand Cubic Meters	Annual	1.13%
Social (33%)		Community & Customers	Percentage		5.53%
	ES059	Human Rights Policy	Y/N	Annual	0.34%
	ES332	Policy Against Child Labor	Y/N	Annual	0.34%
	ES369	Quality Assurance and Recall Policy	Y/N	Annual	0.34%
	ES370	Consumer Data Protection Policy	Y/N	Annual	0.34%
	ES055	Community Spending	Million Reporting Currency	Annual	1.39%
	ES120	Number of Customer Complaints	Count	Annual	1.39%
	ES488	Total Corporate Foundation and Other Giving	Million Reporting Currency	Annual	1.39%
		Diversity	Percentage		5.49%
	ES058	Equal Opportunity Policy	Y/N	Annual	0.13%
	ES479	Gender Pay Gap Breakout	Y/N	Annual	0.13%
	ES046	% Women in Management	Percentage	Annual	0.52%
	ES047	% Women in Workforce	Percentage	Annual	0.52%
	ES048	% Minorities in Management	Percentage	Annual	0.52%
	ES049	% Minorities in Workforce	Percentage	Annual	0.52%
	ES091	% Disabled in Workforce	Percentage	Annual	0.52%
	ES480	Percentage Gender Pay Gap for Senior Management	Percentage	Annual	0.52%
	ES481	Percentage Gender Pay Gap Mid & Other Management	Percentage	Annual	0.52%
	ES482	Percentage Gender Pay Gap Employees Ex Management	Percentage	Annual	0.52%
	ES483	% Gender Pay Gap Tot Empl Including Management	Percentage	Annual	0.52%
	ES484	% Women in Middle and or Other Management	Percentage	Annual	0.52%
		Ethics & Compliance	Percentage		5.57%
	ES069	Business Ethics Policy	Y/N	Annual	0.93%
	ES197	Anti-Bribery Ethics Policy	Y/N	Annual	0.93%
	ES067	Political Donations	Million Reporting Currency	Annual	3.72%
		Health & Safety	Percentage		5.58%
	ES057	Health and Safety Policy	Y/N	Annual	0.15%
	ES052	Fatalities—Contractors	Count	Annual	0.60%
	ES053	Fatalities—Employees	Count	Annual	0.60%
	ES054	Fatalities—Total	Count	Annual	0.60%
	ES092	Lost Time Incident Rate	Lost Time Incidents/200,000 h Worked or 100 Full-Time Employees	Annual	0.60%
	ES121	Total Recordable Incident Rate	Recordable Incidents/200,000 h Worked or 100 Full-Time Employees	Annual	0.60%
	ES260	Lost Time Incident Rate—Contractors	Lost Time Incidents Contractors/200,000 h Worked or 100 Contractors	Annual	0.60%
	ES261	Total Recordable Incident Rate—Contractors	Recordable Incidents Contractors/200,000 h Worked or 100 Contractors	Annual	0.60%
	SA201	Total Recordable Incident Rate—Workforce	Recordable Incidents/200,000 h Worked or 100 Employees & Contractors	Annual	0.60%
	SA202	Lost Time Incident Rate—Workforce	Lost Time Incidents/200,000 h Worked or Employees & Contractors	Annual	0.60%
		Human Capital	Percentage		5.55%
	ES068	Training Policy	Y/N	Annual	0.21%
	ES070	Fair Renumeration Policy	Y/N	Annual	0.21%
	ES043	Number of Employees—CSR	Count	Annual	0.86%
	ES044	Employee Turnover %	Percentage	Annual	0.86%
	ES045	% Employees Unionized	Percentage	Annual	0.86%
	ES094	Employee Training Cost	Million Reporting Currency	Annual	0.86%
	ES199	Total Hours Spent by Firm—Employee Training	Hours	Annual	0.86%
	ES258	Number of Contractors	Count	Annual	0.86%
		Supply Chain	Percentage		5.54%
	ES118	Social Supply Chain Management	Y/N	Annual	0.26%
	ES116	Number of Suppliers Audited	Count	Annual	1.06%
	ES117	Number of Supplier Audits Conducted	Count	Annual	1.06%
	ES119	Number Supplier Facilities Audited	Count	Annual	1.06%
	ES250	Percentage of Suppliers in Non-Compliance	Percentage	Annual	1.06%
	ES499	Percentage Suppliers Audited	Percentage	Annual	1.06%
Governance (33%)		Audit Risk & Oversight	Percentage		4.17%
	ES101	Audit Committee Meetings	Count	Annual	0.83%
	ES182	Years Auditor Employed	Years	Annual	0.83%
	ES299	Size of Audit Committee	Count	Annual	0.83%
	ES300	Number of Independent Directors on Audit Committee	Count	Annual	0.83%
	ES304	Audit Committee Meeting Attendance Percentage	Percentage	Annual	0.83%
		Board Composition	Percentage		4.16%
	SA198	Company Conducts Board Evaluations	Y/N	Annual	0.19%
	ES061	Size of the Board	Count	Annual	0.79%
	ES065	Number of Board Meetings for the Year	Count	Annual	0.79%
	ES066	Board Meeting Attendance %	Percentage	Annual	0.79%
	ES194	Number of Executives/Company Managers	Count	Annual	0.79%
	ES284	Number of Non-Executive Directors on Board	Count	Annual	0.79%
		Compensation	Percentage		4.16%
	SA193	Company Has Executive Share Ownership Guidelines	Y/N	Annual	0.23%
	SA213	Director Share Ownership Guidelines	Y/N	Annual	0.23%
	ES305	Size of Compensation Committee	Count	Annual	0.93%
	ES306	Num of Independent Directors on Compensation Cmte	Count	Annual	0.93%
	ES310	Number of Compensation Committee Meetings	Count	Annual	0.93%
	ES311	Compensation Committee Meeting Attendance %	Percentage	Annual	0.93%
		Diversity	Percentage		4.17%
	ES098	Board Age Limit	Years	Annual	0.83%
	ES290	Number of Female Executives	Count	Annual	0.83%
	ES292	Number of Women on Board	Count	Annual	0.83%
	ES294	Age of the Youngest Director	Years	Annual	0.83%
	ES295	Age of the Oldest Director	Years	Annual	0.83%
		Independence	Percentage		4.18%
	ES062	Number of Independent Directors	Count	Annual	4.18%
		Nominations & Governance Oversight	Percentage		4.18%
	ES312	Size of Nomination Committee	Count	Annual	1.05%
	ES313	Num of Independent Directors on Nomination Cmte	Count	Annual	1.05%
	ES317	Number of Nomination Committee Meetings	Count	Annual	1.05%
	ES318	Nomination Committee Meeting Attendance Percentage	Percentage	Annual	1.05%
		Sustainability Governance	Percentage		4.18%
	ES073	Verification Type	Y/N	Annual	2.09%
	ES093	Employee CSR Training	Y/N	Annual	2.09%
		Tenure	Percentage		4.18%
	ES064	Board Duration (Years)	Years	Annual	4.18%

Table 2

Variable Definitions.

Variable Types	Variables Names	Variables Symbols	Measurement Methods
Dependent variable 1	Green technology innovation capability	GTI 1	Ln (Number of green patent applications + 1)
Dependent variable 2		GTI 2	Ln (Number of green patent authorizations + 1)
Independent variable	Comprehensive indicators of environment, society, and corporate governance	ESG	Bloomberg ESG Disclosure Overall Score
Moderating variable 1	The nature of corporate property rights	CPRN	State-owned enterprise = 1, Non-state-owned enterprise = 0
Moderating variable 2	Corporate technology level	TECH	High-tech enterprise = 1,Non-high-tech enterprise = 0
Moderating variable 3	Corporate listing life span	AGE	Ln (Current year—listing year + 1)
Control variables	Enterprise size	SIZE	Ln (The book value of total assets at the end of the year)
	Current ratio	CR	Current ratio of assets to liabilities
	Current assets turnover	CAT	Sales revenue/Average balance of current assets
	Return on assets	ROA	Net profit/Average balance of total assets
	Net assets growth rate	NAGR	(Net assets at the end of the year—Net assets at the beginning of the year)/Net assets at the beginning of the year
	Asset-liability ratio	LEV	Total liabilities/Total assets
	ISO14000 environmental management certification system	ISO	ISO14001 certification = 1, Non-ISO14001 certification = 0
	Industry	Industry	Dummy variable
	Year	Year	Dummy variable

Table 3

Descriptive Statistics.

VarName	Obs	Mean	Sd	Min	Max
GTI1	4149	0.5381	0.9584	0	4.1897
GTI2	4149	0.4037	0.7893	0	3.6889
ESG	4149	20.6854	6.2929	10.7438	43.6214
CPRN	4149	0.4623	0.4986	0	1
TECH	4149	0.133	0.3397	0	1
AGE	4149	2.5419	0.5513	0.6931	3.2958
SIZE	4149	22.9982	1.2078	20.4512	26.6568
CR	4149	1.9381	1.6913	0.2219	10.837
CAT	4149	1.4572	1.0586	0.2082	5.9407
ROA	4149	4.4531	6.5932	−23.5162	24.5694
NAGR	4149	15.168	35.0479	−51.7246	210.855
LEV	4149	44.9503	19.3184	7.2096	87.7762
ISO	4149	0.2642	0.4409	0	1

Table 4

Correlation Analysis.

VarName	GTI1	GTI2	ESG	CPRN	TECH	AGE	SIZE	CR	CAT	ROA	NAGR	LEV	ISO
GTI1	1
GTI2	0.829 ***	1
ESG	0.242 ***	0.223 ***	1
CPRN	0.023	0.030 *	0.269 ***	1
TECH	0.111 ***	0.072 ***	−0.125 ***	−0.137 ***	1
AGE	−0.079 ***	−0.061 ***	0.203 ***	0.394 ***	−0.137 ***	1
SIZE	0.238 ***	0.242 ***	0.440 ***	0.329 ***	−0.071 ***	0.264 ***	1
CR	−0.090 ***	−0.096 ***	−0.165 ***	−0.172 ***	0.072 ***	−0.201 ***	−0.365 ***	1
CAT	0.023	0.025	0.186 ***	0.130 ***	−0.146 ***	0.132 ***	0.213 ***	−0.334 ***	1
ROA	−0.021	−0.048 ***	−0.013	−0.145 ***	0.022	−0.156 ***	−0.049 ***	0.295 ***	0.075 ***	1
NAGR	−0.013	−0.029 *	−0.076 ***	−0.114 ***	0.078 ***	−0.170 ***	−0.027 *	0.125 ***	0.007	0.359 ***	1
LEV	0.120 ***	0.135 ***	0.180 ***	0.248 ***	−0.090 ***	0.230 ***	0.491 ***	−0.673 ***	0.196 ***	−0.409 ***	−0.141 ***	1
ISO	0.155 ***	0.148 ***	0.217 ***	−0.040 ***	0.037 **	−0.030*	−0.048 ***	−0.005	−0.059 ***	−0.019	−0.021	0.005	1

Notes: 1. *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 5

Regression Results.

	Model (1)		Model (2)		Model (3)		Model (4)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
VARIABLES	GTI1	GTI2	GTI1	GTI2	GTI1	GTI2	GTI1	GTI2
ESG	0.0251 ***	0.0161 ***	0.0178 ***	0.0085 **	0.0219 ***	0.0134 ***	0.0254 ***	0.0162 ***
	(6.0807)	(4.5417)	(3.6991)	(2.1508)	(5.5364)	(3.7014)	(6.4172)	(4.9755)
CPRN			0.1442 ***	0.1096 ***
			(3.6914)	(2.9264)
ESG * CPRN			0.0198 **	0.0204 ***
			(2.5126)	(3.0371)
TECH					0.3745 ***	0.3916 **
					(2.8023)	(2.3901)
ESG * TECH					0.0557 ***	0.0468 **
					(3.0110)	(2.2743)
AGE							0.3883 ***	0.2163 ***
							(3.6948)	(2.6491)
ESG * AGE							0.0206 ***	0.0162 ***
							(3.9672)	(4.0854)
SIZE	0.0008	−0.0007	0.0030	0.0014	−0.0027	−0.0037	−0.0015	0.0003
	(0.0345)	(−0.0399)	(0.1242)	(0.0781)	(−0.1116)	(−0.2016)	(−0.0627)	(0.0168)
CR	−0.0018	0.0020	−0.0034	0.0005	−0.0019	0.0020	−0.0025	0.0010
	(−0.2328)	(0.3155)	(−0.4223)	(0.0825)	(−0.2396)	(0.3111)	(−0.3207)	(0.1632)
CAT	0.0252 *	0.0235 *	0.0251 *	0.0233 *	0.0259 *	0.0242 *	0.0231	0.0220 *
	(1.7324)	(1.7983)	(1.7449)	(1.7966)	(1.7840)	(1.8466)	(1.5975)	(1.6814)
ROA	−0.0027	−0.0014	−0.0028*	−0.0015	−0.0026	−0.0013	−0.0029 *	−0.0016
	(−1.6174)	(−1.0551)	(−1.7037)	(−1.1601)	(−1.5335)	(−0.9690)	(−1.7232)	(−1.2255)
NAGR	0.0002	−0.0001	0.0001	−0.0001	0.0002	−0.0001	0.0001	−0.0001
	(0.7158)	(−0.4315)	(0.6376)	(−0.5751)	(0.6947)	(−0.4275)	(0.6391)	(−0.6037)
LEV	−0.0008	0.0000	−0.0009	−0.0001	−0.0008	0.0000	−0.0010	−0.0001
	(−0.6800)	(0.0304)	(−0.7820)	(−0.0828)	(−0.6748)	(0.0528)	(−0.8703)	(−0.1051)
ISO	−0.0291	−0.0093	−0.0282	−0.0084	−0.0271	−0.0076	−0.0250	−0.0059
	(−1.1059)	(−0.4000)	(−1.0729)	(−0.3597)	(−1.0335)	(−0.3291)	(−0.9471)	(−0.2536)
Constant	−0.0355	0.0378	−0.0072	0.0851	0.0737	0.1199	−0.9123	−0.5039
	(−0.0674)	(0.0944)	(−0.0137)	(0.2134)	(0.1400)	(0.2993)	(−1.6176)	(−1.1563)
Industry FE	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
Observations	4149	4149	4149	4149	4149	4149	4149	4149
Adjusted R-squared	0.0282	0.0112	0.0298	0.0140	0.0313	0.0148	0.0332	0.0149

Notes: 1. T-statistics in parentheses. 2. *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 6

Robustness test.

	First Stage	Second Stage
	Model (5)	Model (6)	Model (6)
	(1)	(2)	(3)
VARIABLES	ESG	GTI1	GTI2
ESG		0.0765 ***	0.0348 ***
		(4.7499)	(3.1975)
SIZE	0.6707 ***	0.0018	−0.0179
	(3.4423)	(0.0415)	(−0.5366)
CR	−0.0572	0.0151	0.0073
	(−1.2442)	(1.4272)	(0.8947)
CAT	0.0588	0.0158	0.0307 **
	(0.6557)	(1.0122)	(2.1394)
ROA	0.0167 ***	−0.0027	−0.0002
	(2.8911)	(−1.3502)	(−0.1556)
NAGR	0.0001	−0.0000	−0.0001
	(0.0512)	(−0.0250)	(−0.7643)
LEV	−0.0084	0.0012	−0.0013
	(−1.5734)	(0.6941)	(−0.9830)
ISO	0.0670	−0.0107	0.0138
	(0.5222)	(−0.3060)	(0.5076)
LESG	0.3284 ***
	(7.9620)
AESG	0.4480 ***
	(6.7427)
Constant	−10.3784 **	−1.1930	0.0744
	(−2.5466)	(−1.2791)	(0.1064)
Industry FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	3104	3104	3104
Adjusted R-squared	0.2547	0.0150	0.0046
Underidentification test (Kleibergen–Paap rk LM statistic)		23.545 (Chi-sq(1) p-value = 0.0000)
Weak identification test (Cragg–Donald Wald F statistic) (Kleibergen–Paap rk Wald F statistic) 10% maximal IV size		350.79063.40316.380
Over identification test (Hansen J p-value)		0.1169
Hausman test p-value	0.0000	0.0000	0.0000

Notes: 1. T-statistics in parentheses. 2. *** p < 0.01, ** p < 0.05.

References

1. Ensign, P.C. Business models and sustainable development goals. Sustainability; 2022; 14, 2558. [DOI: https://dx.doi.org/10.3390/su14052558]

2. Wang, W.; Liang, S.; Yu, R.; Su, Y. Theoretical evidence for green innovation driven by multiple major shareholders: Empirical evidence from Chinese listed companies. Sustainability; 2022; 14, 4736. [DOI: https://dx.doi.org/10.3390/su14084736]

3. Jun, W.; Shiyong, Z.; Yi, T. Does ESG disclosure help improve intangible capital? Evidence from A-share listed companies. Front. Environ. Sci.; 2022; 10, 858548. [DOI: https://dx.doi.org/10.3389/fenvs.2022.858548]

4. Aybars, A.; Ataünal, L.; Gürbüz, A.O. ESG and financial performance: Impact of environmental, social, and governance issues on corporate performance. Managerial Thinking in Global Business Economics; IGI Global: Hershey, PA, USA, 2019; pp. 520-536. [DOI: https://dx.doi.org/10.4018/978-1-5225-7180-3.ch029]

5. Li, T.T.; Wang, K.; Sueyoshi, T.; Wang, D.D. ESG: Research progress and future prospects. Sustainability; 2021; 13, 11663. [DOI: https://dx.doi.org/10.3390/su132111663]

6. Amel-Zadeh, A.; Serafeim, G. Why and how investors use ESG information: Evidence from a global survey. Financ. Anal. J.; 2018; 74, pp. 87-103. [DOI: https://dx.doi.org/10.2469/faj.v74.n3.2]

7. Castellacci, F.; Lie, C.M. A taxonomy of green innovators: Empirical evidence from South Korea. J. Clean. Prod.; 2017; 143, pp. 1036-1047. [DOI: https://dx.doi.org/10.1016/j.jclepro.2016.12.016]

8. Huang, J.W.; Li, Y.H. Green innovation and performance: The view of organizational capability and social reciprocity. J. Bus. Ethics; 2017; 145, pp. 309-324. [DOI: https://dx.doi.org/10.1007/s10551-015-2903-y]

9. Hernandez-Vivanco, A.; Bernardo, M.; Cruz-Cázares, C. Sustainable innovation through management systems integration. J. Clean. Prod.; 2018; 196, pp. 1176-1187. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.06.052]

10. Ceccarelli, M.; Glossner, S.; Homanen, M. Catering through transparency: Voluntary ESG disclosure by asset managers and fund flows. SSRN; 2022; 1, pp. 1-51. [DOI: https://dx.doi.org/10.2139/ssrn.4110596]

11. Wang, Q.; Qu, J.; Wang, B.; Wang, P.; Yang, T. Green technology innovation development in China in 1990–2015. Sci. Total Environ.; 2019; 696, 134008. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.134008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31465930]

12. Alsayegh, M.F.; Abdul Rahman, R.; Homayoun, S. Corporate economic, environmental, and social sustainability performance transformation through ESG disclosure. Sustainability; 2020; 12, 3910. [DOI: https://dx.doi.org/10.3390/su12093910]

13. Dyck, A.; Lins, K.V.; Roth, L.; Wagner, H.F. Do institutional investors drive corporate social responsibility? International evidence. J. Financ. Econ.; 2019; 131, pp. 693-714. [DOI: https://dx.doi.org/10.1016/j.jfineco.2018.08.013]

14. Raimo, N.; Caragnano, A.; Zito, M.; Vitolla, F.; Mariani, M. Extending the benefits of ESG disclosure: The effect on the cost of debt financing. Corp. Soc. Responsib. Environ. Manag.; 2021; 28, pp. 1412-1421. [DOI: https://dx.doi.org/10.1002/csr.2134]

15. Berrone, P.; Fosfuri, A.; Gelabert, L.; Gomez-Mejia, L.R. Necessity as the mother of ‘green’ inventions: Institutional pressures and environmental innovations. Strateg. Manag. J.; 2013; 34, pp. 891-909. [DOI: https://dx.doi.org/10.1002/smj.2041]

16. Delgado-Ceballos, J.; Aragón-Correa, J.A.; Ortiz-de-Mandojana, N.; Rueda-Manzanares, A. The effect of internal barriers on the connection between stakeholder integration and proactive environmental strategies. J. Bus. Ethics; 2012; 107, pp. 281-293. [DOI: https://dx.doi.org/10.1007/s10551-011-1039-y]

17. Schmitz, E.A.; Baum, M.; Huett, P.; Kabst, R. The contextual role of regulatory stakeholder pressure in proactive environmental strategies: An empirical test of competing theoretical perspectives. Organ. Environ.; 2019; 32, pp. 281-308. [DOI: https://dx.doi.org/10.1177/1086026617745992]

18. Xiao, Z.; Peng, H.; Pan, Z. Innovation, external technological environment and the total factor productivity of enterprises. Account. Financ.; 2022; 62, pp. 3-29. [DOI: https://dx.doi.org/10.1111/acfi.12779]

19. Maritan, C.A.; Lee, G.K. Resource allocation and strategy. J. Manag.; 2017; 43, pp. 2411-2420. [DOI: https://dx.doi.org/10.1177/0149206317729738]

20. Chen, S.; Shen, T. Does ESG rating affect corporate innovation?. Front. Bus. Econ. Manag.; 2022; 4, pp. 94-99. [DOI: https://dx.doi.org/10.54097/fbem.v4i1.510]

21. Tang, H. The effect of ESG performance on corporate innovation in China: The mediating role of financial constraints and agency cost. Sustainability; 2022; 14, 3769. [DOI: https://dx.doi.org/10.3390/su14073769]

22. Hao, X.; Chen, F.; Chen, Z. Does green innovation increase enterprise value?. Bus. Strategy Environ.; 2022; 31, pp. 1232-1247. [DOI: https://dx.doi.org/10.1002/bse.2952]

23. Xiang, X.; Liu, C.; Yang, M. Who is financing corporate green innovation?. Int. Rev. Econ. Financ.; 2022; 78, pp. 321-337. [DOI: https://dx.doi.org/10.1016/j.iref.2021.12.011]

24. Bai, Y.T. The influence of the nature of enterprise property rights on enterprise innovation performance: The mediating effect of corporate external financing and the moderating effect of corporate information disclosure. Proceedings of the International Conference on Global Business and Management Science; Kunming, China, 16–17 October 2021.

25. Lu, D.; Thangavelu, S.M.; Hu, Q. Biased lending and non-performing loans in China’s banking sector. J. Dev. Stud.; 2005; 41, pp. 1071-1091. [DOI: https://dx.doi.org/10.1080/00220380500155361]

26. Ruiqi, W.; Wang, F.; Xu, L.; Yuan, C. R&D expenditures, ultimate ownership and future performance: Evidence from China. J. Bus. Res.; 2017; 71, pp. 47-54. [DOI: https://dx.doi.org/10.1016/j.jbusres.2016.10.018]

27. Yu, H. The ascendency of state-owned enterprises in China: Development, controversy and problems. J. Contemp. China; 2014; 23, pp. 161-182. [DOI: https://dx.doi.org/10.1080/10670564.2013.809990]

28. Pfeffer, J. Size and composition of corporate boards of directors: The organization and its environment. Adm. Sci. Q.; 1972; 17, pp. 218-228. [DOI: https://dx.doi.org/10.2307/2393956]

29. Canepa, A.; Stoneman, P. Financial constraints to innovation in the UK: Evidence from CIS2 and CIS3. Oxf. Econ. Pap.; 2008; 60, pp. 711-730. [DOI: https://dx.doi.org/10.1093/oep/gpm044]

30. Song, M.; Nason, R.W.; Di Benedetto, C.A. Distinctive marketing and information technology capabilities and strategic types: A cross-national investigation. J. Int. Mark.; 2008; 16, pp. 4-38. [DOI: https://dx.doi.org/10.1509/jimk.16.1.4]

31. Zhang, D.; Lucey, B.M. Sustainable behaviors and firm performance: The role of financial constraints’ alleviation. Econ. Anal. Policy; 2022; 74, pp. 220-233. [DOI: https://dx.doi.org/10.1016/j.eap.2022.02.003]

32. Akbar, A. Environment, social, and governance performance and enterprise innovation capacity: A study of the top 100 global hi-tech firms. SSRN Electron. J.; 2021; 100, pp. 1-23. [DOI: https://dx.doi.org/10.2139/ssrn.3895080]

33. Liu, C.; Gao, X.; Ma, W.; Chen, X. Research on regional differences and influencing factors of green technology innovation efficiency of China’s high-tech industry. J. Comput. Appl. Math.; 2020; 369, 112597. [DOI: https://dx.doi.org/10.1016/j.cam.2019.112597]

34. Miller, D.; Friesen, P.H. A longitudinal study of the corporate life cycle. Manag. Sci.; 1984; 30, pp. 1161-1183. [DOI: https://dx.doi.org/10.1287/mnsc.30.10.1161]

35. D’Amato, A.; Falivena, C. Corporate social responsibility and firm value: Do firm size and age matter? Empirical evidence from European listed companies. Corp. Soc. Responsib. Environ. Manag.; 2020; 27, pp. 909-924. [DOI: https://dx.doi.org/10.1002/csr.1855]

36. Czarnitzki, D.; Hottenrott, H. R&D investment and financing constraints of small and medium-sized firms. Small Bus. Econ.; 2011; 36, pp. 65-83. [DOI: https://dx.doi.org/10.1007/s11187-009-9189-3]

37. Coad, A.; Segarra, A.; Teruel, M. Innovation and firm growth: Does firm age play a role?. Res. Policy; 2016; 45, pp. 387-400. [DOI: https://dx.doi.org/10.1016/j.respol.2015.10.015]

38. Bianchini, S.; Krafft, J.; Quatraro, F.; Ravix, J.L. Corporate governance and innovation: Does firm age matter?. Ind. Corp. Change; 2018; 27, pp. 349-370. [DOI: https://dx.doi.org/10.1093/icc/dtx031]

39. Miyazaki, K. Search, learning and accumulation of technological competences; The case of optoelectronics. Ind. Corp. Change; 1994; 3, pp. 631-654. [DOI: https://dx.doi.org/10.1093/icc/3.3.631]

40. Calantone, R.J.; Cavusgil, S.T.; Zhao, Y. Learning orientation, firm innovation capability, and firm performance. Ind. Mark. Manag.; 2002; 31, pp. 515-524. [DOI: https://dx.doi.org/10.1016/S0019-8501(01)00203-6]

41. Withers, M.C.; Drnevich, P.L.; Marino, L. Doing more with less: The disordinal implications of firm age for leveraging capabilities for innovation activity. J. Small Bus. Manag.; 2011; 49, pp. 515-536. [DOI: https://dx.doi.org/10.1111/j.1540-627X.2011.00334.x]

42. Krishnan, G.V.; Myllymäki, E.M.; Nagar, N. Does financial reporting quality vary across firm life cycle?. J. Bus. Financ. Account.; 2021; 48, pp. 954-987. [DOI: https://dx.doi.org/10.1111/jbfa.12508]

43. Wang, P.; Bu, H.; Liu, F. Internal control and enterprise green innovation. Energies; 2022; 15, 2193. [DOI: https://dx.doi.org/10.3390/en15062193]

44. Zhang, Y.; Zhang, J.; Cheng, Z. Stock market liberalization and corporate green innovation: Evidence from China. Int. J. Environ. Res. Public Health; 2021; 18, 3412. [DOI: https://dx.doi.org/10.3390/ijerph18073412] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33806099]

45. Dai, D.; Xue, Y. The impact of green innovation on a firm’s value from the perspective of enterprise life cycles. Sustainability; 2022; 14, 1226. [DOI: https://dx.doi.org/10.3390/su14031226]

46. Yu, Y.; Xu, Q. Influencing factors of enterprise R&D investment: Post-subsidy, sustainability, and heterogeneity. Sustainability; 2022; 14, 5759. [DOI: https://dx.doi.org/10.3390/su14105759]

47. Zhang, Y.; Sun, Z.; Sun, M. Unabsorbed slack resources and enterprise innovation: The moderating effect of environmental uncertainty and managerial ability. Sustainability; 2022; 14, 3782. [DOI: https://dx.doi.org/10.3390/su14073782]

48. Xu, J.; Liu, F.; Shang, Y. R&D investment, ESG performance and green innovation performance: Evidence from China. Kybernetes; 2020; 50, pp. 737-756. [DOI: https://dx.doi.org/10.1108/K-12-2019-0793]

49. Zulfikar, R.; STp, M.M. Estimation model and selection method of panel data regression: An overview of common effect, fixed effect, and random effect model. INA-Rxiv; 2018; 1, pp. 1-10. [DOI: https://dx.doi.org/10.31227/osf.io/9qe2b]

50. Przychodzen, J.; Przychodzen, W. Relationships between eco-innovation and financial performance: Evidence from publicly traded companies in Poland and Hungary. J. Clean. Prod.; 2015; 90, pp. 253-263. [DOI: https://dx.doi.org/10.1016/j.jclepro.2014.11.034]

51. Tariq, A.; Badir, Y.F.; Safdar, U.; Tariq, W.; Badar, K. Linking firms’ life cycle, capabilities, and green innovation. J. Manuf. Technol. Manag.; 2019; 31, pp. 284-305. [DOI: https://dx.doi.org/10.1108/JMTM-08-2018-0257]

52. Gao, Y.; Jin, S. Corporate Nature, Financial Technology, and Corporate Innovation in China. Sustainability; 2022; 14, 7162. [DOI: https://dx.doi.org/10.3390/su14127162]

53. Hansen, L.P. Large sample properties of generalized method of moments estimators. Econom. J. Econom. Soc.; 1982; 50, pp. 1029-1054. [DOI: https://dx.doi.org/10.2307/1912775]

54. Tsai, K.H.; Liao, Y.C. Sustainability strategy and eco-innovation: A moderation model. Bus. Strategy Environ.; 2017; 26, pp. 426-437. [DOI: https://dx.doi.org/10.1002/bse.1926]

55. In, S.Y.; Rook, D.; Monk, A. Integrating alternative data (also known as ESG data) in investment decision making. Glob. Econ. Rev.; 2019; 48, pp. 237-260. [DOI: https://dx.doi.org/10.1080/1226508X.2019.1643059]

56. Wang, F.; Sun, Z. Does the Environmental Regulation Intensity and ESG Performance Have a Substitution Effect on the Impact of Enterprise Green Innovation: Evidence from China. Int. J. Environ. Res. Public Health; 2022; 19, 8558. [DOI: https://dx.doi.org/10.3390/ijerph19148558]

57. De Marchi, V. Environmental innovation and R&D cooperation: Empirical evidence from Spanish manufacturing firms. Res. Policy; 2012; 41, pp. 614-623. [DOI: https://dx.doi.org/10.1016/j.respol.2011.10.002]

58. Bai, Y.; Wang, J.Y.; Jiao, J.L. A framework for determining the impacts of a multiple relationship network on green innovation. Sustain. Prod. Consum.; 2021; 27, pp. 471-484. [DOI: https://dx.doi.org/10.1016/j.spc.2021.01.014]

59. Nunes, P.M.; Serrasqueiro, Z.; Leitão, J. Is there a linear relationship between R&D intensity and growth? Empirical evidence of non-high-tech vs. high-tech SMEs. Res. Policy; 2012; 41, pp. 36-53. [DOI: https://dx.doi.org/10.1016/j.respol.2011.08.011]

60. Boeing, P.; Mueller, E.; Sandner, P. China’s R&D explosion—Analyzing productivity effects across ownership types and over time. Res. Policy; 2016; 45, pp. 159-176. [DOI: https://dx.doi.org/10.1016/j.respol.2015.07.008]

61. Yin, C.; Salmador, M.P.; Li, D.; Lloria, M.B. Green entrepreneurship and SME performance: The moderating effect of firm age. Int. Entrep. Manag. J.; 2022; 18, pp. 255-275. [DOI: https://dx.doi.org/10.1007/s11365-021-00757-3]