Revealing the Effects of Corporate Governance on Green Investment and Innovation: Do Law and Policy Matter?

Green investment (GI) and innovation performance are key factors of sustainable green development. GI and innovation have become a trendy solution to minimize environmental issues in the previous few decades. We investigate the effects of corporate governance, environmental law, and environmental policy stringency on GI and environmental innovation (EI) using Chinese time-series data from 1998 to 2020. Short and long-run findings indicate that corporate governance has a positive and significant impact on GI and innovation in China. However, environmental law has positive and significant effects on GI and innovation in the short run and long run. Furthermore, environmental policy stringency has an insignificant impact on GI but stimulates green innovation both in the short and long run. The study also reveals that education has a significant positive impact on green innovation both in the short and long-run. The short and long-run results propose essential policy implications.

Introduction

In recent times, global climate change has emerged as the most daunting challenge that humanity is facing. Even though implementation of the environmental regulations has helped to abate the environment-related issues up to some extent, they have also increased the economic costs of products and services. These deliberations inspire policymakers and empirics to look into the relationship between environmental laws, industry competitiveness, and the firms’ social duties (Greenstone et al., 2012; Kitzmueller and Shimshack, 2012; Arif and Sohail, 2020; Chai et al., 2021; Liu N. et al., 2022). In this regard, the researchers have focused on the various determinants of environmental efficiencies, such as public policies, energy policies, and technological innovations (Johnstone et al., 2010; Amores-Salvadó et al., 2014; Jian et al., 2021; Jiang et al., 2021); however, there are still a lot of variations inside the firms that can affect the environment and its related factors, which need further exploration. To fill this, lacuna researchers have recently started to explore the role of organizational structures in improving environmental efficiency (e.g., Martin et al., 2012; Sohail et al., 2013; Lin and Ho, 2016; Liu Y. et al., 2022). Several research works are available, confirming the positive role of management and organizational structures on environmental efficiency (Martin et al., 2012; Paillé et al., 2014; Sohail et al., 2014a,b). However, the direction of a causal relationship between the governance system and environmental quality is yet to be estimated in detail (Van Kamp et al., 2003; Khan et al., 2019, 2021a,b,2022a,b; Sohail et al., 2019a,b; Zhang et al., 2022; Zhenyu and Sohail, 2022).

Since the industrial revolution, most nations have adopted the process of industrialization but they have paid the environmental cost as well. Once the volume of industrialization increases, people become more and more aware of the environment-related issues (Ullah et al., 2020; Lu and Sohail, 2022; Sohail et al., 2022a,c, 2021d). Similarly, along with increasing environmental concerns, the firms started to focus more on improving their environmental efficiency (Wei et al., 2022). Therefore, the pressure is mounted on the firms and enterprises from all corners of the society, particularly, civil society, and environmentalists, to fulfill their responsibilities concerning environmental safety and protection. In this way, corporate social responsibility has gained popularity and has become a norm for modern enterprises to do business in recent times (Bénabou and Tirole, 2010; Shahab et al., 2016; Yasara et al., 2019; Sohail et al., 2021a,b,c,d; Khan et al., 2022a,b). In other words, the environmentally friendly conduct of the firms has got popularity as a helping hand in achieving sustainable development of the firms (Khan et al., 2021a,b). On one side, external factors are crucial in shedding light on the firm’s corporate responsibility regarding environmental sustainability (Leonidou et al., 2013; Yen et al., 2017, 2021; Yat et al., 2018; Zhao et al., 2019, 2022a,b; Sohail et al., 2020; Zahid et al., 2022). On the other side, several internal factors (e.g., competitive advantage, executive compensation, and corporate governance) are also important in explaining the firms’ environment-related policies (Muhammad et al., 2014: Mahfooz et al., 2017, 2019, 2020; Ji et al., 2021).

Currently, theorists and empirics have not reached any consensus on the matters related to environmental and green investment (GI). In this context, two different opinions have come to the fore. According to traditional opinion, green or environmental investment is purely a cost (Schaltegger and Synnestvedt, 2002); in contrast, the modern view suggests that environmental investment can bring future prosperity (Porter and Van der Linde, 1995; Sánchez-Medina et al., 2015; Jiang and Akbar, 2018). In recent times, GI has become part and parcel of the firm’s environmental strategy. Given the importance of the GI by firms in protecting the environment, various factors have been recorded as a promoter of GI, including green fees, eco-innovation, environmental technologies, waste discharge fees, pollution penalties, and so on. Increasing the number of green investors may positively impact the firm’s environmental strategy and social responsibility (Chuang and Huang, 2018). Clement and Meunie (2010) analyzed the role of social responsibility, GI, and cultural norms played in improving environmental quality. According to Chuang and Huang (2018), corporate social responsibility exerted a positive impact on green information technology, green communication capital structure, and green capital linkage. Moreover, they observed that green capital help improve environmental performance, which consequently improves business competition. Similarly, Guenster et al. (2011) also observed a positive relationship between a firm’s environmental performance and its corporate worth.

Another important relationship that has recently come in limelight is the link between corporate governance and green innovation. Certainly, modern-day firms have become more serious about investing in green research and development activities that would lead them toward the path of eco-innovations (Khan et al., 2020a,b; Chen et al., 2022; Li et al., 2022a,b,c). However, going green is relatively a newer concept that requires a shift in the firm’s research and development culture, the introduction of new production techniques, investment in green technologies, and promotion of novel ideas (Sohail et al., 2014b; Rasool et al., 2017; Khan et al., 2018a,b; Usman et al., 2021; Li and Ullah, 2022; Mustafa et al., 2022). Therefore, Kock et al. (2012) observed that “successfully reducing and preventing waste emissions necessitates a great deal of extra managerial effort because it requires a complex redesign of a firm’s internal processes and the development of green competencies.” The Organization of Economic Cooperation and Development (OECD) underlined that the conventional business model adopted by the traditional firms stops them from being involved in a more radical method of eco-innovation. According to OECD, many existing firms are more than satisfied with their existing business model and do not involve in systematic efforts to bring more radical technological innovations. Therefore, more complex research and development questions are to be answered if the firms want to involve technological innovations. However, it is widely accepted that managerial entrenchment is a hurdle in the way of managerial inclinations toward complex activities; hence, we can confer that worse corporate governance is negatively related to green innovation.

While the environmental benefits are widely considered to be the by-product of environment-related regulations (Magazzino and Falcone, 2022), the debate among the empirics on this topic is still on. The available literature in this regard suggests that firms and industries have to bear the extra cost due to the implementation of strict rules and regulations that may negatively impact the firms’ competitive position, profitability, production, demand, and investment decisions (Kozluk and Zipperer, 2015; Sohail et al., 2015). However, a modern view of the relationship between environmental aims and firms’ benefits may go side by side with each other, and law and regulation may serve as a “win-win” situation for the firm because it can increase its long-term profitability and improve competitive position (Porter and Van der Linde, 1995). Previously, empirics have tried to find the various factors that affect green investment and innovation, but none have focused on the impact of corporate governance on green investment and innovation in China’s economy. This study will fill this gap in the literature and analyze the impact of corporate governance on green investment and innovation in China’s economy, which is the first of its kind. This study will try to answer the following research questions: (1) does corporate governance lead to promote green investment and innovation? (2) Do environmental law and policy matter for green investment and innovation?

This new perspective has given rise to a new debate under the porter hypothesis; the main focus of which is to observe the link between related laws and regulations and innovations. This linkage is considered an important element in improving a firm’s performance. Keep in mind that with the above discussion, we aim to investigate the impact of corporate governance, environmental law, and environmental policy stringency on green investment and innovation in China by using the ARDL method. This technique works better on a small sample and provides relatively more robust results. This approach also provides short and long-run effects of corporate governance, environmental law, and environmental policy stringency on green investment and innovation.

Empirical Methods

The main aim of this paper is to identify the effect of corporate governance, environmental law, and environmental policy stringency on green investment and innovation, using the China data over the period 1998–2020. Our empirical model is based on stakeholder–agency theory and porter’s innovation theory (Porter, 1991; Hill and Jones, 1992). Theoretically, standard green investment and innovation are associated with corporate governance, environmental law, and environmental policy stringency. Therefore, standard time series models will be:

Where the dependent variables are the EI and GI that are assumed to depend on the corporate governance (CG), environmental regulations (ER), environmental policy stringency (EPS), and educational attainment (Education). Equations (1) and (2) are long-run models and estimates of π1, π2,  π3, and π4 reflect long-run effects of variables corporate governance, environmental law, environmental policy stringency, and education on EI and GI. To assess the short-run effects, we need to re-write Equations (1) and (2) in an error-correction format so that we can also judge the short-term effects of corporate governance, environmental law, environmental policy stringency, and education. A method that offers long-term and short-term estimates in one step is that of Pesaran et al. (2001) ARDL bounds testing approach. In doing so, we follow the ARDL method and rely on the following error-correction specification:

In both Equations (3) and (4), the coefficients assigned to the “B” are short-run effects and the estimates of π1 – π5 are the long-run effects. Standard literature proposes two tests for establishing cointegration among the variables, such as F-test and t-test. Previous other co-integration methods do not offer robust estimates and have some limitations. ARDL approach cannot be used when variables are not stationary, as this approach needs all the series to be integrated of either I (0) or I (1), or contains a mixture of the order of integration (Bahmani-Oskooee et al., 2020). The level of integration has been determined by using PP and DF-GLS unit root tests (Zhou et al., 2022). This technique becomes more suitable for limited sample data, as in the case of China. This method directly explores short-run, as well long-run effects, in a single step. This method offers correct estimations of the long-term model. Moreover, ARDL is free from residual correlation and endogeneity. Serial correlation is verified through the Lagrange multiplier (LM). The model specification is checked using the Ramsey RESET test. This study employs the CUSUM and CUSUM-sq tests to check the model stability.

Data

This study examines the effect of corporate governance, law, and policy on green investment and innovation. Table 1 provides information about abbreviations and definitions of variables and their source of data. Table 2 reported descriptive statistics. In this study, green investment is measured through nuclear, renewables, and others in terms of quad Btu, and the data has been taken from EIA. Environment-related technologies data has been explored by OECD. Board independence is used to measure corporate governance which refers to the proportion of independent directors to the total number of directors on a board. The data for board independence is taken from China’s stock market and accounting research. Environmental regulations (measured as environment-related taxes as a percent of total tax revenue) have been used to measure environmental law impact. While environmental policy stringency is used to measure policy impact. The data for environmental regulation and environmental policy stringency is taken from the OECD. Education measured as average years of schooling has been added as a control variable and the data is extracted from the study of Barro and Lee.

TABLE 1

Definitions and data sources.

Variables	Abbreviations	Definitions	Data sources
Green investment	GI	Nuclear, renewables, and other (quad Btu)	EIA
Environmental innovation	EI	Environment-related-technologies	OECD
Board independence	BI	Board Independence refers to the proportion of independent directors to the total number of directors on board.	CSMAR
Environmental regulation	ER	Environmentally related taxes, % total tax revenue	OECD
Environmental policy stringency	EPS	Environmental policy stringency index ranges from 0 (not stringent) to 6 (highest degree of stringency)	OECD
Year of schooling	Education	Average year of schooling	Barro and Lee

TABLE 2

Descriptive statistics.

	Mean	Median	Maximum	Minimum	Std. dev.	Skewness	Kurtosis
GI	9.325	7.157	22.24	2.226	6.578	0.661	2.090
EI	10.35	10.43	12.28	8.210	1.284	–0.175	1.791
BI	0.403	0.360	0.764	0.010	0.227	–0.323	2.246
ER	3.671	3.100	6.360	1.564	1.498	0.411	1.790
EPS	1.257	0.980	2.210	0.520	0.632	0.281	1.407
Education	12.16	12.60	14.65	9.300	1.830	–0.275	1.610

Results and Discussion

Our study is based on time-series data and the prerequisite of applying the regression technique is to confirm the stationarity of variables to be used in the model. Thus, two-unit root tests have been chosen for this exercise. Table 3 reports the results of the PP unit root test and DF-GLS unit root test. Both tests report similar order of integration. It displays that only education is at stationary level, while, GI, EI, BI, ER, and EPS are stationary at first difference. Hence, the precondition to applying the ARDL approach, i.e., the mixed order of integration, is fulfilled. Table 4 is reporting the result estimates of green investment and EI models. The upper panel provides the short-run estimates of both models, the middle panel contains long-run estimates, and the lower panel reports the findings of some important diagnostic tests.

TABLE 3

Unit root testing.

	PP		DF-GLS
	I (0)	I (1)	I (0)	I (1)	PP	DF-GLS
GI	1.754	–2.987*	–1.234	–1.814*	I(1)	I (1)
EI	–1.03	–2.875*	0.123	–4.302***	I(1)	I (1)
BI	–0.754	–3.120**	–0.542	–3.012***	I(1)	I (1)
ER	–1.023	–3.012**	–0.875	–3.012***	I(1)	I (1)
EPS	–0.421	–3.874***	–0.185	-3.012***	I(1)	I (1)
Education	–2.675*		–1.675*		I(0)	I (0)

***p < 0.01; **p < 0.05; *p < 0.1.

TABLE 4

Estimates of green investment and environmental innovation.

	Green investment				Environmental innovation
Variable	Coefficient	Std. error	t-statistic	Prob.*	Coefficient	Std. error	t-statistic	Prob.*
Short-run
BI	2.904***	0.827	3.512	0.177	3.216***	0.203	15.85	0.040
BI (–1)	2.584***	0.735	3.514	0.177	1.358***	0.174	7.796	0.081
BI (–2)	0.045	0.127	0.357	0.782	1.933***	0.204	9.466	0.067
ER	0.425***	0.091	4.684	0.134	0.030***	0.006	4.888	0.129
ER (–1)	1.388***	0.100	13.88	0.046	0.008	0.015	0.508	0.701
ER (–2)	0.621***	0.231	2.692	0.226	0.074***	0.016	4.636	0.135
EPS	0.196	0.290	0.676	0.622	0.082***	0.028	2.964	0.207
EPS (–1)	0.479*	0.274	1.750	0.331	0.377***	0.036	10.56	0.060
EPS (–2)	–5.215***	0.387	–13.46	0.047	–0.305***	0.064	–4.757	0.132
EPS (–3)	0.537	0.567	0.946	0.518	0.245***	0.039	6.342	0.100
EDUCATION	3.122***	0.447	6.983	0.091	0.142**	0.058	2.439	0.248
EDUCATION (–1)	3.398***	0.410	8.295	0.076	0.056	0.046	1.199	0.443
EDUCATION (–2)	–1.712***	0.332	–5.155	0.122	0.077	0.052	1.484	0.378
Long-run
BI	2.533***	10.43	0.243	0.812	2.294***	0.063	36.22	0.018
ER	2.730***	0.796	3.428	0.004	0.009***	0.003	3.477	0.178
EPS	0.854	3.040	0.281	0.783	0.184***	0.018	10.05	0.063
EDUCATION	6.834***	2.591	2.638	0.020	0.478***	0.005	98.14	0.007
C	11.37***	0.871	13.05	0.004	4.137***	0.054	76.26	0.008
Diagnostics
F-test	8.021***				14.02***
ECM (–1)*	–0.725***	0.007	–98.24	0.007	–0.409***	0.138	–2.972	0.010
LM	2.012				1.065
RESET	1.032				0.701
CUSUM	S				S
CUSUM	S				S

***p < 0.01; **p < 0.05; *p < 0.1.

The long-run findings show that board independence is significantly and positively associated with green investment and EI displaying that corporate governance tends to enhance green investment and EI in the case of China. It infers that a 1% upsurge in board independence increases green investment by 2.533% and EI by 2.294% in the long run. This finding is supported by Hill and Jones (1992) stakeholder–agency theory, which noted that corporate governance increases return on shareholder green investment by minimizing pollution levels. This theory provides a positive effect of corporate governance on environmental performance. This finding also infers that green innovation and investment are executive’s strategic decisions. This finding is also in line with Abebe and Myint (2018), who noted that the board of directors encourages companies to adopt an innovation-based green business model. This means that the board of directors cannot separate the economic objectives from the environmental issues. Therefore, companies increase profits in an ethical and environmentally responsible manner to meet the interests of the green economy. A similar finding is also suggested by previous studies (see Amore and Bennedsen, 2016; Asni and Agustia, 2022). This means that corporate governance is an essential organ in green development by encouraging green investment and innovation.

Environmental regulation is also significantly and positively linked with green investment and EI in the long run, demonstrating that implementation of environmental law tends to improve green investment and EI in the case of China. It concludes that a 1% upsurge in implementation of environmental regulation increases green investment by 2.730% and EI by 0.009% in the long run. This finding is backed by Porter’s Hypothesis (Porter, 1991), which noted that stricter environmental regulation fosters green innovation, which in turn increases green productivity and firm’s competitiveness. This means that environmental laws and regulations enforce firms for green investment and innovation. This also infers that environmental laws and regulations trigger society and firms for dynamic green efficiency. This finding is also backed by Huang and Lei (2021), who suggest that environmental regulation is positively related to green investment in China due to legal requirements.

Environmental policy stringency is significantly and positively attached to EI confirming that environmental policy stringency tends to enhance EI in China in the long-run. It infers that a 1% upsurge in environmental policy stringency enhances EI by 0.184% in the long run. In contrast, the association between environmental policy stringency and green investment is found insignificant in the long run, revealing no impact of environmental policy on green investment in China hereafter. This finding is also empirically supported by Hassan and Rousselière (2022), who suggested that strict environmental policy leads to enhanced green innovation in OECD. Education is significantly and positively associated with green investment and EI, in the long run, inferring that education tends to improve green investment and EI in China. It reveals that a 1% increase in education increases green investment by 6.834% and EI by 0.478% in due course.

In the short run, findings infer that board independence is significantly and positively associated with green investment and EI confirming that corporate governance also contributes significantly to the improvement of green investment and EI in the short term. It reveals that a 1% upsurge in board independence increases green investment by 2.904% and environmental innovation by 3.216% in the long run. Environmental regulation also reports a significant and positive impact on green investment and EI in the short run, showing that implementation of environmental regulations improves green investment and EI in case of China. It concludes that a 1% rise in environmental regulation increases green investment by 0.425% and EI by 0.030% in the short run. Environmental policy stringency is significantly and positively associated with EI, confirming that environmental policy stringency tends to enhance EI in the short run. It infers that 1% rise in environmental policy stringency enhances EI by 0.082% in the short run as well. In contrast, the nexus between environmental policy stringency and green investment is found insignificant in the short run. Education is significantly and positively associated with green investment and EI in the short run, inferring that education tends to improve green investment and EI in the case of China. It reveals that a 1% increase in education increases green investment by 3.122% and EI by 0.142% in the long-run.

The diagnostic tests are performed to validate the findings of ARDL models. The findings of all diagnostics tests are according to our expectations. The findings of the F-test and ECM tests validate the long-run cointegration relationship among variables. LM test reveals that no issue of serial correlation is found in both models. Model specification is confirmed through the results of the Ramsey RESET test. The stability of the results is confirmed by the findings of CUSUM tests in Table 4 and Figures 1, 2.

FIGURE 1

Plots of CUSUM and CUSUM-sq (green investment).

FIGURE 2

Plots of CUSUM and CUSUM-sq (environmental innovation).

Conclusion and Implications

It is essential to encourage green investment and innovation through corporate governance, effectively formulating environmental regulations and environmental policies that stimulate green investment and innovation. Thus, this study intends to explore the impact of corporate governance, environmental regulation, and environmental policy stringency on green investment and innovation. In this study, corporate governance is measured by board independence. Environmental law is measured through environmental regulations, while the environmental policy is measured through environmental stringency policy. Education role has been included as a control variable. For deducing long-run and short-run estimates, the study adopted the ARDL approach and reported the following findings. Firstly, the long-run finding reveals that board independence reports a significant and positive effect on GI and EI, revealing that corporate governance can be adopted as a policy tool to enhance GI and EI in China. Secondly, environmental regulations also show a positive association with GI and EI, confirming that implementation of environmental laws is necessary to enhance GI and EI in China. Thirdly, environmental policy stringency shows a positive association with EI, confirming that the effectiveness of environmental policies can contribute significantly to enhancing EI in China. Lastly, education is proven to be positively associated with GI and EI. Hence, this study concludes that corporate governance, environmental regulation, environmental policy stringency, and education are effective determinants of GI and EI.

Based on these findings, this research offers the following policy suggestions. Based on the positive impact of board independence on GI and eco-innovation, this study suggests that optimization of the mechanism of corporate governance as a policy tool to attain sustainability of the organization through GI and EI according to the expectations of the stakeholders. In this way, the organization can attain the competence of directors, skills, and experience to enhance the performance of EI and GI. There is a need to control the influence of large investors to enhance the implementation of GI and EI as these investors can significantly influence the decision-making powers of management. Additionally, environmental determinants should be involved in the performance measurement procedure of local governments. The findings of this study support policymakers and environmentalists to design more effective environmental laws. The Chinese government should adopt environmental regulations and environmental policy stringency in a manner that promotes the GI in the country and also stimulates the firms to invest more in the R and D sector to enhance EI.

Our study contains a few limitations that need to be considered in future studies. Our study considers only one direction of corporate governance; however, there are various measures of corporate governance, such as senior executive dynamic capabilities, environmental legitimacy, quality management, and market competition. Future studies should incorporate these measures into analysis to get a more accurate effect of corporate governance on innovation and GI. Future studies should make comparisons between developed and developing economies by replicating the objectives of this study. Future studies can also explore the impact of corporate governance on environmental sustainability and clean energy consumption.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

YL gave this idea and read and approved the final version. YL and MS wrote the manuscript. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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