Introduction

“Green technologies—going green—is bigger than the Internet. It could be the biggest economic opportunity of the 21st century.”

— John Doerr

Green innovation plays a crucial role in driving economic transformation and addressing the ever-changing environmental challenges (UNFCCC, 2023; Wurlod and Noailly, 2018). By fostering continuous innovation, we can better adapt to environmental changes while ensuring the sustainable and healthy development of the economy and society (Bina, 2013; Johnstone et al., 2008). Although the drivers of green innovation are akin to a complex labyrinth, challenging to decipher, their research is of utmost importance (Chen, 2008). Traditional studies typically explore these drivers from the perspectives of technology, market, institutions, and corporate strategy, analyzing policies and market changes to identify the factors that drive corporate green innovation (Brunnermeier and Cohen, 2003; Chang, 2011; Cleff and Rennings, 1999; Horbach, 2008). According to innovation diffusion theory, innovation spreads through interactions among individuals and groups within a social system, where the role of management is pivotal (Miller, 2015).

Existing research has explored the positive role of general management skills in promoting corporate innovation (Custódio et al., 2019). These skills help to stimulate innovative thinking, drive organizational change, and achieve strategic goals (Tether et al., 2005). However, the situation becomes more complex when it comes to green innovation. Green innovation is not just a matter of technical innovation; it also encompasses elements of environmental protection and sustainable development (Takalo and Tooranloo, 2021). This requires managers to possess not only general management skills but also a profound understanding and commitment to environmental protection, social responsibility, and sustainable development (Martínez-Ros and Kunapatarawong, 2019). Consequently, they need to control and allocate organizational resources to drive green innovation (Khanra et al., 2022).

The challenge of green innovation lies in its often-necessary transcendence of traditional business boundaries, involving more complex stakeholder management, higher initial costs, longer investment recovery periods, and the uncertainty of policy and market environments (Adams et al., 2016). Additionally, green innovation may require a change in the core operational methods of a business, including the adoption of new technologies, processes, and materials, which can encounter both internal and external resistance (Chen, 2008). Managers need to guide their businesses toward more sustainable and environmentally friendly directions while maintaining business performance and competitiveness (Hughes et al., 2018). This requires a perspective and managerial ability that goes beyond traditional management skills (Lin et al., 2021).

According to the resource-based view, managerial ability is seen as a unique resource of a company (Mahoney and Pandian, 1992), key to achieving green innovation. Managers can drive the development of green technologies and practices in their companies through effective decision-making and resource allocation (Wernerfelt, 1984). Additionally, the theory of transformational leadership underscores the role of management in guiding organizational culture and motivating employees, especially in inspiring environmental consciousness and innovative thinking among staff (Gumusluoglu and Ilsev, 2009; Jung et al., 2003). Furthermore, corporate social responsibility theories highlight the critical role of management in formulating and implementing a company’s environmental policies and practices, which are directly linked to the company’s green innovation performance (Garriga and Melé, 2004). These theories collectively emphasize that efficient managerial abilities can help companies better utilize and allocate resources to foster green innovation. However, quantifying managerial ability has been a challenge. In the past, scholars often relied on many factors outside the control of management to measure this variable. This situation continued until a new method was proposed by Demerjian et al. (2012), which is based on assessing how management enhances the revenue efficiency of a company. Through this approach, large-scale studies on managerial ability have become more reliable.

Meanwhile, there are perspectives in academia suggesting that the link between managerial ability and green innovation is minimal or non-existent. For instance, technological determinism emphasizes that green innovation is primarily driven by technological advancement and its own evolutionary logic, with limited influence from managerial decisions and abilities (Freeman, 1996). Market orientation theory argues that market demand and consumer preferences are the main drivers of green innovation, overshadowing the role of management (Cheng, 2020; Du and Wang, 2022; Wang, 2020). Additionally, institutional theory views corporate green behaviour as a response to external institutional pressures, such as laws, regulations, and industry standards, rather than because of proactive managerial strategies (Chen et al., 2018; Qi et al., 2021; Shu et al.,

Literature review and hypothesis development

Managerial ability and green innovation

When exploring the impact of managerial ability on green innovation, the resource-based view (RBV) provides a powerful theoretical framework. According to this view, managerial ability is considered a unique resource of the company, crucial for achieving green innovation (Kraaijenbrink et al., 2010; Mahoney and Pandian, 1992; Wernerfelt, 1984). For instance, Assensoh-Kodua (2019) emphasized the key capabilities of the resource-based perspective in providing a competitive advantage in their study. The effectiveness of green innovation hinges critically on management’s ability to adeptly integrate internal and external knowledge sharing, which not only influences organizational performance but also navigates the potential risks associated with knowledge transfer (Ben Arfi et al., 2018). Similarly, Baia et al. (2020) discussed the rarity of resources and capabilities as sources of competitive advantage and superior performance for companies. Additionally, transformational leadership theory highlights the role of management in guiding organizational culture and motivating employees, particularly in terms of influencing environmental awareness and innovative thinking among staff (Gumusluoglu and Ilsev, 2009; Jung et al., 2003). Albort-Morant et al. (2016) suggested that dynamic and ordinary capabilities significantly enhance green innovation performance, with dynamic capabilities directly improving innovation by adjusting learning relationships. This resonates with the study by Gibson et al. (2021), which explored the importance of incorporating community resources into the RBV. Corporate social responsibility theories also point out the critical role of management in formulating and executing a company’s environmental policies and practices, which are directly linked to the company’s green innovation performance, as noted by Andersen (2021).

Recent research underscores the pivotal role of coopetition strategy, open innovation, and digitalization capabilities in enhancing sustainable performance within business ecosystems (Y. Jiang et al., 2023b; Lee and Roh, 2023a, 2023b; Lu et al., 2023). The synergistic effect of these strategies provides a nuanced framework for understanding how managerial abilities can foster an environment conducive to green innovation. Studies have shown that businesses that adeptly navigate the complexities of coopetition—collaborating with competitors while simultaneously competing—and leverage digitalization capabilities are better positioned to achieve sustainable outcomes (Lee and Roh, 2023b). This suggests that the managerial ability to integrate and balance these strategic elements is crucial for driving sustainable performance and, by extension, green innovation. Furthermore, the interaction between inbound and outbound open innovation, facilitated by digital technologies, acts as a catalyst for sustainable performance, highlighting the importance of managerial ability in these areas (Lee and Roh, 2023a). Moreover, empirical evidence from studies on energy green efficiency across various regions further emphasizes the role of managerial ability in measuring and implementing energy efficiency initiatives, showcasing a direct link to green innovation (Lu et al., 2023).

However, quantifying managerial ability has always been a challenge. In the past, scholars often relied on many factors outside the control of management to measure this variable. For example, Grant and Verona (2015) discussed the challenges and potential solutions in assessing organizational capabilities in empirical research. The method proposed by Demerjian et al. (2012), using data envelopment analysis (DEA) to evaluate managerial ability based on revenue efficiency, marked a significant advancement in quantifying managerial ability in academic research. It facilitated more accurate comparisons across firms and industries and encouraged further studies on the interplay between managerial ability and corporate governance, enhancing our understanding of managerial efficiency and innovation.

Overall, RBV suggests that firms with superior managerial abilities can better leverage their resources towards innovative ends, including green innovation. Managers play a crucial role in resource allocation, strategic planning, and fostering an organizational culture that embraces sustainability. Their ability to sense, seize, and transform opportunities into green innovations is a critical driver of a firm’s environmental performance and sustainable competitive advantage. By applying the RBV framework, we argue that managerial ability acts as a strategic resource that facilitates the development and implementation of green innovations. Thus, we propose the following research hypothesis:

H1a: Managerial ability is positively correlated with green innovation.

Meanwhile, there are also perspectives suggesting that the correlation between the two may be minimal or even non-existent. For instance, technological determinism advocates that green innovation is primarily driven by technological advancement and its own evolutionary logic, with limited influence from managerial decisions and capabilities (Freeman, 1996). This suggests that green innovation is more driven by technological developments rather than management strategies or decisions. According to technological determinism, the pace and direction of green innovation are determined by the inherent trajectory of technological progress, independent of individual managerial actions.

Market orientation theory emphasizes that market demand and consumer preferences are the main drivers of green innovation (Wang, 2020). This theory argues that the success of green innovation initiatives is more closely linked to a firm’s ability to understand and meet market needs rather than the strategic competencies or insights of its managers, which indicates that corporate green innovation is more market-driven and has little association with the strategic choices and capabilities of management (Du and Wang, 2022). This contrasts with the theoretical framework of the resource-based view, attributing the impetus for green innovation more to external market factors than internal management resources.

Furthermore, institutional theory views corporate green behaviour as a response to external institutional pressures, such as laws, regulations, and industry standards (Chen et al., 2018; Qi et al., 2021; Shu et al., 2016). This means that even if management has the necessary abilities, a firm’s green innovation actions may be more a reaction to changes in the external environment rather than the result of proactive managerial strategies. Recent research acknowledges that institutional pressures related to sustainability and green performance exert a significant influence on organizational behaviour and outcomes. For example, Liang et al. (2023) documented the ability of managers to innovate business models digitally in response to these pressures plays a mediating role in achieving green performance. This highlights the strategic importance of managerial responsiveness to external sustainability demands as a means to secure green innovation.

In summary, these theories collectively suggest that the drivers of green innovation may originate more from external technological, market, and institutional environments, rather than relying on managerial abilities and decisions. Based on these perspectives, we propose the following hypothesis:

H1b: Managerial ability is not correlated with green innovation.

Firm characteristics

The existing literature indicates that green innovation has its uniqueness in terms of environmental externalities and long-term aspects, characteristics that distinguish green innovation from regular technological innovation (Brunnermeier and Cohen, 2003; Kim et al., 2021; **ang et al., 2022). Environmental externality refers to the environmental benefits of a company’s green innovation activities not being limited to the company itself but also positively impacting society and the natural environment (**e and Teo, 2022). For example, when a company develops low-carbon emission technologies or sustainable production methods, these innovations not only enhance the company’s environmental standards but also reduce environmental pollution for the entire society, bringing widespread environmental benefits. The existence of such externalities means that relying solely on market mechanisms may not be sufficient to fully incentivize companies to engage in green innovation. In this context, the role of government becomes particularly crucial, with policy support and incentives such as tax reductions, subsidies, and R&D funding support becoming important tools to promote corporate green innovation (Cai et al., 2022; Huang et al., 2019). These measures can help alleviate the initial cost burden for companies in green innovation, reduce risks, and provide additional motivation, encouraging companies to participate more actively in green innovation.

In such a policy environment, the facilitating role of managerial ability in green innovation may be more pronounced in state-owned enterprises. State-owned enterprises are often closely linked to government policies (Lin et al., 1998). Therefore, when the government provides support and incentives for green innovation, the management of state-owned enterprises usually has stronger motivation and ability to respond to these policies. They can effectively utilize the resources provided by the government to formulate and implement green innovation strategies (Cheng et al., 2023). Moreover, due to the unique nature of state-owned enterprises, they often bear more social responsibilities, including environmental protection and sustainable development (Lin et al., 2020). Based on these viewpoints, we hypothesize:

H2a: The positive relationship between managerial ability and green innovation is more pronounced for state-owned companies.

On the other hand, the presence of institutional investors has a significant impact on a company’s governance structure and strategic choices (Graves and Waddock, 1990). These investors often hold a longer-term investment perspective (McCahery et al., 2016), making them more inclined to support strategies that can bring long-term sustainable growth, such as green innovation (Aghion et al., 2013). Due to their typically deeper professional knowledge and resources, institutional investors can more effectively evaluate and support management efforts in green innovation (Dyck et al., 2019). At the same time, the influence of institutional investors in corporate governance enables them to push management to focus on and implement green innovation through mechanisms such as site visits (Jiang and Yuan, 2018), and the board of directors (Tihanyi et al., 2003).

Furthermore, the sensitivity of institutional investors to risks and their emphasis on corporate reputation also prompt them to support companies that can effectively manage environmental risks and enhance brand value through green innovation (Amore and Bennedsen, 2016; García-Sánchez et al., 2020). In the current context where Environmental, Social, and Governance (ESG) standards are increasingly valued, this attitude of institutional investors is particularly important. Based on the literature review, the following hypothesis can be proposed:

H2b: The positive relationship between managerial ability and green innovation is more pronounced in companies with a higher proportion of institutional investors.

External factors

Existing literature indicates that external factors, such as environmental regulation and the degree of marketization, are crucial for understanding and analysing corporate green innovation (Qiu et al., 2020; Zeng et al., 2021; Zhang et al., 2020). For example, Liang et al. (2023) highlighted the role of institutional pressures on corporate green performance. These factors present different challenges and opportunities, but their mechanisms of action differ from internal firm characteristics, such as the type of ownership and the proportion of institutional investors. Therefore, considering these external environmental factors is indispensable when studying the relationship between managerial ability and green innovation.

In the context of high environmental regulation, companies face stricter environmental standards and potential compliance costs, which encourage them to seek innovative solutions to these challenges (Liu et al., 2021). In this scenario, managerial ability becomes a key factor for companies to adapt to environmental regulations and achieve green transformation (Chen et al., 2015). Highly capable management teams are more likely to identify and exploit opportunities for green innovation, effectively integrate resources to support innovative projects and navigate complex regulatory environments (Liao and Long, 2018). They can enhance corporate competitiveness and market performance through green innovation while complying with environmental regulations. Such management teams are usually better at understanding the long-term trends of environmental regulations and market demands, thus making forward-looking decisions (Yang et al., 2019).

Moreover, the level of environmental regulation also affects corporate investment decisions in green innovation (Huang and Lei, 2021). In a highly regulated environment, green innovation is not only a necessity for compliance but also key for maintaining competitiveness in the market (Rubashkina et al., 2015). Therefore, strategic decision-making of the management team is particularly important in promoting green innovation in such an environment (Qian et al., 2023). Based on the above analysis, the following hypothesis can be proposed:

H3a: The positive relationship between managerial ability and green innovation is more pronounced in the context of high environmental regulation.

On the other hand, when reviewing the level of product market development, existing literature reveals several key insights. Du et al. (2018) argued that in environments with a low level of product market development (i.e., low marketization index), companies face weaker market competition, and consumer awareness and demand for green products are less developed than in highly marketized environments. Additionally, the market’s incentive and reward mechanisms for innovation are not sufficiently mature (Aghion et al., 2005). In this context, the ability of management plays an even more critical role in driving companies toward green innovation (Huang and Li, 2017).

Firstly, due to weaker external market incentives, companies deciding whether to invest in green innovation may rely more on internal driving forces (Eyraud et al., 2013). This implies that management teams with high capability are more likely to recognize the potential value and long-term necessity of green innovation, even in the absence of sufficient market incentives (Chen et al., 2015). They might proactively seek to improve operational efficiency, reduce costs, comply with potential future environmental regulations, or prepare for future market changes through innovation (Mishra, 2023). Secondly, strategic vision and managerial ability are particularly important in less marketized environments, where the choice of innovation paths and business models is more complex and challenging (Goldfarb and **ao, 2011). Management needs to make effective resource allocation, market positioning, and technology selection in the absence of clear market guidance. Therefore, the following hypothesis can be proposed:

H3b: The positive relationship between management ability and corporate green innovation is more pronounced in less developed product markets.

Overall, the integration of transformational leadership and RBV provides a theoretical backdrop that supports the importance of managerial ability in driving green innovation. They suggest that the effectiveness of managerial ability in promoting green innovation is contingent upon both the internal attributes of the firm and the external environment in which it operates. This nuanced understanding acknowledges the complexity of green innovation as a multifaceted phenomenon influenced by a range of factors, reinforcing the value of examining these influences through a heterogeneity lens.

Research method

Data and sample

This paper utilizes A-share listed companies from 2008 to 2022 as the research sample. It is important to note that the calculation of the managerial ability index requires data from the previous year. Consequently, the actual sample data used for the years 2008–2022 is derived from the relevant data of the sample companies spanning from 2007 to 2022. With the implementation of new accounting standards in China in 2007, the initial year for this study is established as 2007 to ensure the comparability of financial information. The data selection process for this study is conducted as follows: (1) Excluding listed companies classified in the financial sector according to the Industry Classification Guidelines of the China Securities Regulatory Commission (revised in 2012); (2) Excluding companies listed as ST or *STFootnote 1; (3) Excluding samples with incomplete data. This process yields a final sample size of 2455 companies, amounting to 15,457 observations. To reduce the influence of outliers, this study applies a two-tailed winsorization at the 1% level to the continuous variables annually. Data regarding corporate green innovation is from the Chinese Research Data Services Platform (CNRDS), while other data is obtained from the China Stock Market Accounting Research (CSMAR) database, and Wind database.

Measures of variables

Independent variable: managerial ability

Although there are various methods to measure managerial ability, the approach by Demerjian et al. (2012) is widely adopted. They use data envelopment analysis (DEA) to calculate managerial ability by separating the impact of management on firm efficiency from the overall efficiency of the firm. This method not only allows for the simple and intuitive calculation of operational efficiency for a large sample of companies while avoiding sample omission but also eliminates certain noise, thereby enhancing the reliability of research conclusions (Wang et al., 2017; Yuan and Wen, 2018). Therefore, we draw on the ideas of Demerjian et al. (2012) and adopt DEA to measure the managerial ability of listed companies in China. The specific steps are as follows:

In the first stage, DEA is used to calculate the firm efficiency. According to model (1), the maximum firm efficiency value is calculated for each company by industry (with the manufacturing industry classified at the secondary industry level). The company with the highest efficiency in the same industry is assigned a value of 1, and relative efficiencies for other companies are calculated, with values ranging between [0, 1]. The specific model is as follows:

$${{\rm {Max}}\; {\rm {Firm}}\; {\rm {Efficienc}{y}}}_{t}=\frac{{{\rm {Sales}}}_{t}}{{V}_{1}{{\rm {COG}{S}}}_{t}+{V}_{2}{\rm {S{{\& }}{M}}}_{t}+{V}_{3}{{\rm {PP}{E}}}_{t-1}+{V}_{4}{{\rm {Intan}{g}}}_{t-1}+{V}_{5}{\rm {R{{\& }}{D}}}_{t-1}+{V}_{6}{\rm {G{W}}}_{t-1}}$$
(1)

In this model, Sales represents operating revenue, signifying the output of the company; while COGS (Cost of Goods Sold), S&M (Sales and Management expenses), PPE (Property, Plant, and Equipment), Intang (Intangible assets), R&D (Research and Development expenses), and GW (Goodwill) represent the inputs of the company, encompassing operational costs, sales and management expenses, fixed assets, intangible assets, R&D expenditures, and goodwill, respectively. The subscript t denotes the corresponding value for the listed company in the current year, and t−1 represents the value for the previous year.

Given that a firm’s efficiency is influenced not only by managerial ability but also by the company’s characteristics, the second stage involves conducting a Tobit regression of the company’s efficiency by industry to eliminate the impact of company characteristics. The model for this stage is as follows:

$$\begin{array}{c}{{\text {Firm}}\; {\text {Efficienc}{y}}}_{i}=\alpha +{\beta }_{1}\mathrm{ln}{\left({\rm{Total}}\,{\rm{Assets}}\right)}_{i}+{\beta }_{2}{\rm{Market}}\, {\rm{Share}}_{i}+{\beta }_{3}{{\text {Free}}\; {\text {Cash}}\; {\text {Flow}}\; {\text {Indicato}{r}}}_{i}+{\beta }_{4}\mathrm{ln}{\left({{\text {Age}}}\right)}_{i}\\ +{\beta }_{5}{{\text {Business}}\; {\text {Segment}}\; {\text {Concentratio}{n}}}_{i}+{\beta }_{6}{{\text {Foreign}}\; {\text {Currency}}\; {\text {Indicato}{r}}}_{i}+{{\text {Yea}{r}}}_{i}+{\epsilon }_{i}\end{array}$$
(2)

In the model, Total Assets represent the total assets of the company. Market Share is the company’s market share measured in percentage terms. Free Cash Flow Indicator is a dummy variable that takes the value of 1 when the company’s cash flow is non-negative, and 0 otherwise. Age refers to the number of years the firm has been listed on the stock exchange by the end of year t. Business Segment Concentration indicates the sales concentration of the company’s divisions. Foreign Currency Indicator is a dummy variable, which is assigned a value of 1 if the company operates subsidiaries overseas, and 0 otherwise. Year denotes a dummy variable for the company’s fiscal year. The regression residuals ε obtained from Model (2) represent the managerial ability, which is denoted as MA in the subsequent text.

Dependent variable: Green innovation

Following the method of Quan et al. (2021), green innovation is quantified using two distinct metrics: the number of green patent applications (GP) and the number of green invention patent applications (GIP), both sourced from the Chinese Research Data Services Platform (CNRDS). Drawing on the approach of **ang et al. (2022) and Zheng et al. (2023), we assess green invention patent applications (GIP), categorized as more advanced and demanding than other types, to reinforce the robustness of our analysis.

Empirical model

To test H1a and H1b, we construct the following model:

$${{\text {GreenInnovation}}}_{i,t+1}={\alpha }_{0}+{\alpha }_{1}{{{\text {MA}}}}_{i,t}+{{{\text {Controls}}}}_{i,t}+{{{\text {Year}}}}_{t}+{{{\text {Firm}}}}_{i}+{\varepsilon }_{{it}}$$
(3)

where GreenInnovation represents the state of corporate green innovation, measured by GP, and GIP, respectively; MA denotes managerial ability. Our primary focus is on the relationship between managerial ability and corporate green innovation, namely the sign and magnitude of the coefficient α1. All standard errors in our regression results are adjusted for clustering at the company level.

Drawing on existing literature (Amore and Bennedsen, 2016; Bammens and Hünermund, 2023; Cheng et al., 3. These results show that in companies with a higher proportion of institutional investors, the positive relationship between managerial ability and corporate green innovation is more pronounced, thus supporting hypothesis H2b. This finding aligns with the theoretical views in the literature regarding the role of institutional investors (Dyck et al., 2019; Graves and Waddock, 1990). Institutional investors are often seen as more rational and long-term-oriented investors, and their involvement is commonly associated with better corporate governance, higher transparency, and stronger strategic planning capabilities (Aghion et al., 2013; McCahery et al., 2016). These attributes are particularly important in the field of green innovation, as it often requires substantial initial investments, long-term research and development processes, and a high sensitivity to market and environmental changes.

External factors

Based on the theoretical derivations in the section “External factors”, we test how external factors affect the influence of managerial ability on corporate green innovation. We categorize the sample according to the intensity of environmental regulation (Panel A) and the development level of the product market (Panel B). The results of these tests are displayed in Table 4.

Table 4 Managerial ability and firms’ green innovation—external factors.
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Firstly, to measure the intensity of environmental regulation, we utilize the amount of industrial pollution control investment per thousand yuan of industrial-added value in the province where the firm is located. Grou** is based on the annual median of environmental regulation intensity. We define a dummy variable (HighEnvReg) which is coded as one if the environmental regulation intensity of the firm’s province exceeds the sample median and zero otherwise. The results from Table 4, Panel A confirm that in the context of high environmental regulation, managerial ability plays a more prominent role in driving corporate green innovation, aligning with hypothesis H3a. In such an environment, companies face stricter environmental standards and regulatory requirements, which not only increase operational costs but may also impact the firm’s public image and market positioning. Consequently, managers need to demonstrate adaptability and foresight to ensure the company complies with current environmental regulations while maintaining a competitive edge through innovation. Our findings also suggest that under high environmental regulation, green innovation becomes more critical as it helps businesses reduce compliance costs and opens up new commercial opportunities. In this scenario, a highly capable management team can more effectively integrate resources and implement green innovation strategies, enabling the firm to meet environmental regulations and gain a competitive advantage in the market.

Furthermore, to assess the degree of product marketization, we employ the “Development Level of Product Market” indicator from the China Market Index Database. Grou** is conducted based on the annual median of this indicator, allowing us to categorize firms according to the development stage of their product markets. The results in Table 4, Panel B, reveal the impact of the product market’s development level on the relationship between managerial ability and green innovation. We find that in environments with less developed product markets, the positive effect of managerial ability on corporate green innovation appears more pronounced, supporting hypothesis H3b. In such market conditions, external market incentives and drivers for innovation are relatively weak, so companies rely more on internal and strategic planning to drive green innovation. The high capability of the management team becomes particularly crucial in this context, as they need to identify and seize opportunities for green innovation in the absence of external drivers.

Robustness tests

Although this study controls for time effects and corporate individual effects in the baseline regression, thereby mitigating potential omitted variable issues (such as those variables that change over time and are related to both managerial ability and corporate green innovation), the conclusions may still be influenced by other endogeneity issues. To enhance the reliability of the regression results, the following tests were conducted.

Propensity score matching, entropy balancing, and coarsened exact matching methods

To address concerns that our linear OLS model might not capture certain differences influencing our results, we employ propensity score matching (PSM), entropy balancing methods (EBM), and coarsened exact matching (CEM) in our regression analysis. These techniques enhance the robustness of our findings by addressing potential biases. All the corresponding results are consolidated in Table 5.

Table 5 Results based on propensity score matching, entropy balancing, and coarsened exact matching methods.
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First, we selected control variables from the main regression as covariates to estimate the propensity scores and matched the samples based on these scores. For the Green Patent outcome variableFootnote 4, we employ the ‘calliper nearest neighbour matching’ method for both the treatment and control group samples, conducting 1:1 matching within a calliper range of 0.01Footnote 5. Figure 1 illustrates the effect before and after matching; there was a significant bias between covariates of the treatment and control groups before matching, but this bias was notably reduced after matching, indicating that propensity score matching eliminates characteristic differences between the two groups, enhancing the comparability of the samples.

Fig. 1: Kernel density of propensity score before and after PSM.
figure 1

The figure presents the distributions of the propensity score before and after propensity score matching (PSM).

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The matched samples were then re-analysed using Model (3), and the regression results are shown in columns (1) and (2) of Table 5. The regression results demonstrate that, after controlling for firm characteristic heterogeneity, managerial ability (MA) still significantly promotes green innovation in listed companies, further supporting the core conclusion of our study.

Additionally, considering that the PSM method only matches individuals within the common value range and allows for repeated sampling, which may exclude unmatched samples and result in a reduction in the number of samples available for analysis. Following McMullin and Schonberger (2020) and McMullin and Schonberger (2022), we balance the mean and variance of the control variables across the treatment and control firms. The sample matched using the EBM is then re-regressed using model (3), and the results (see Table 5 Panel B) show that the coefficient of MA remains significantly positive, further validating the previous conclusions.

As a typical non-parametric data matching method (monotonic imbalance bounding), CEM reduces variable stratification by recoding, allowing for the application of exact matching algorithms in data processing (King and Nielsen, 2019). Compared to PSM, CEM lowers the imbalance between estimation error and total variance, ensuring matched groups improve sample balance, and is useful in limiting model dependence and estimation error in average treatment effects. Therefore, CEM is considered to balance reducing sample loss and improving matching quality (Iacus et al., 2009). To enhance the similarity between the treatment and control groups, we select SIZE, LEV, and ROA as the characteristic variables for CEM, apply the CEM method to construct paired samples, and re-run the regression. The results (see Table 5 Panel C) show that the conclusions of this study remain unchanged after matching.

Other robustness tests

To further ensure the reliability of our research, we conduct the following additional robustness tests. The results are listed in Table 6, which demonstrates the robustness of our research findings.

Table 6 Other robust checks.
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(1) Alternative independent variables: Firstly, following the approach of Tian and Yang (2021), we use (1-MA industry rank in the current year/total number of companies in the industry that year) as an alternative measure for managerial ability. Secondly, following the study by Gan and Hu (2023), we divide managerial ability into a 0–1 scale annually by industry, based on the industry median of managerial ability for that year. The results of these alternative regressions are shown in columns (3)–(6) of Table 6.

(2) Control for industry fixed effects: To further refine our analysis, we incorporate industry fixed effects into the model (3). This retesting ensures that our findings are robust across various industry sectors. The revised results are displayed in columns (7) and (8) of Table 6.

(3) Control for additional variables: Apart from our control variables, other factors may influence corporate green innovation. We refer to the studies of Quan et al. (2021), Cheng et al. (2019; Quan et al., 2021). These areas represent fruitful avenues for future research that can build upon our study’s foundation, addressing the dynamic interplay between managerial capabilities, technological advancements, and market changes in sha** green innovation strategies and performance.