What Drives Sustainable Development of Enterprises? Focusing on ESG Management and Green Technology Innovation
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School of Economics and Management, Weifang University of Science and Technology, Weifang 262700, China
2
College of Business, Gachon University, Seongnam 13120, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(18), 11695; https://doi.org/10.3390/su141811695
Submission received: 11 August 2022
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Revised: 12 September 2022
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Accepted: 14 September 2022
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Published: 18 September 2022
(This article belongs to the Special Issue Strategic Management and Organizational Innovation: Strategic Innovation as a Means to Improve Organizational Sustainability)
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:
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.
where indictes the dependent variable (natural logarithm of the number of green patent and 1), stands for the independent variable (ESG disclosure overall score), represents each control variable, is the coefficient of each variable, represents the different enterprise individuals, represents the research year, is the random disturbance term, and and 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 (LESGi,t) and the mean value of ESG performance of other listed enterprises in the same industry (AESGi,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.
where 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.
Funding
This work was supported by the Gachon University Research Fund of 2022 (GCU-202207100001).
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.
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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 | 350.790 63.403 16.380 | ||
| 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.
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Zhang, C.; Jin, S. What Drives Sustainable Development of Enterprises? Focusing on ESG Management and Green Technology Innovation. Sustainability 2022, 14, 11695. https://doi.org/10.3390/su141811695
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Zhang C, Jin S. What Drives Sustainable Development of Enterprises? Focusing on ESG Management and Green Technology Innovation. Sustainability. 2022; 14(18):11695. https://doi.org/10.3390/su141811695
Chicago/Turabian StyleZhang, Cong, and Shanyue Jin. 2022. "What Drives Sustainable Development of Enterprises? Focusing on ESG Management and Green Technology Innovation" Sustainability 14, no. 18: 11695. https://doi.org/10.3390/su141811695
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