Over the years, a lot of studies have been deeply engaged in enhancing our understanding of the managerial outcomes related to TMT personality. These studies mainly inspired by the Upper Echelons Theory which posits that corporate strategies are greatly influenced by the cognitive foundations of its TMT (Bromiley and Rau, 2016; Neely Jr et al., 2020). And the theory has been applied to empirically investigate a number of strategic outcomes, such as innovation, digital transformation, financial health (Bedford et al., 2022; Attah-Boakye et al., 2023; Chen et al., 2023). Furthermore, some research examines how CEO cognition styles and metacognitive experiences affect decision-making. (Plambeck and Weber, 2009; Mitchell et al., 2011). Additionally, a modest body of research on TMT investigates how organizational outcomes are impacted by TMT cognitive diversity, cognitive representations, and the complexity of TMTs’ cognitive model of the domestic industry. (Alexiev et al., 2010; Wei and Wu, 2013). These studies directly or indirectly illustrate that TMT cognition influence a variety of corporate outcomes through strategic decisions.

Greenwashing behavior is also a kind of corporate outcomes, which is the result of environmental strategic decisions (Yang et al., 2020). Therefore, TMT cognition has an impact on greenwashing through strategic decisions. According to theory of bounded rationality, strategic decisions are often characterized by the decision makers’ bounded rationality (Abatecola et al., 2013), which is influenced by decision makers’ cognition. So the impact of TMT cognition on greenwashing can be explained from the perspective of bounded rationality.

TMTs with strong environmental cognition are more likely to recognize the harm that greenwashing can bring to the company. This type of cognition is based on TMT members’ understanding of resource and environmental issues, forming their understanding and knowledge structure of resource and environment, as well as their psychological experience when assuming responsibilities for resource conservation and environmental protection (Hameed et al., 2021; Zhou and Jin, 2023). TMTs with strong environmental cognition will prioritize resource and environmental issues and are more likely to be exposed to information about the negative impact of greenwashing on enterprises so that the occurrence of greenwashing behavior can be avoided.

TMTs with high levels of environmental cognition tend to make environmentally friendly decisions and try to promote genuine environmental efforts. As makers of corporate strategic decisions, TMTs’ responses to environmental changes are affected by their own cognition of environmental issues, and they integrate environmental cognition into their daily management activities (Simon, 1979; Wang et al., 2020). They endeavor to fulfill their environmental responsibilities while ensuring the economic profitability of the enterprise, and to meet their own spiritual needs. Thus, we formulate the following hypothesis:

Hypothesis 1:

TMT environmental cognition restrain greenwashing behavior

Hypothesis 2:

TMT environmental cognition promote genuine environmental efforts

Political connections are regarded by resource dependence theorists as boundary-spanning initiatives to lessen external uncertainty between the focus firm and the environment (Tihanyi et al., 2019). They are viewed as an essential resource for the company, one that is difficult for other companies to copy. According to some research, political connections significantly influence the environmental strategies adopted by businesses (Hillman, 2005). For example, they help ease the financial restrictions on environmental investments, reduce the cost of capital for businesses investing in environmental initiatives, and share the government’s risk associated with the development of new environmental technologies.

Firms with political connections have the opportunity to interact with government departments, stay informed about government policy directions, and effectively mitigate innovation risks (Zhang, 2017). Different degrees of political connection intensity will lead to varying levels of resource acquisition. The higher the intensity of an entrepreneur’s political connection, the more favorable it is for resource acquisition. These firms are motivated to obtain additional resources by genuinely implementing environmental protection practices. In addition, from the perspective of local governments, they also tend to seek out more familiar firms to assist in addressing local environmental issues. Therefore, we believe that political connections will reinforce the inhibitory effect of TMT environmental cognition on greenwashing, and accordingly, we put forth the following hypothesis:

Hypothesis 3:

Political connections enhance the inhibitory effect of TMT environmental cognition on greenwashing

Environmental regulation comprises a set of laws and regulations established and enforced with the aim of safeguarding the environment (Testa et al., 2011). Numerous studies have explored the influence of environmental regulation on corporate performance. Among these, several argue that stringent environmental regulations, which restrict business operations, adversely affect productivity and competitiveness (López-Gamero et al., 2010; Shao et al., 2020). Firms incur costs related to environmental protection, potentially resulting in the forfeiture of investment opportunities in other profitable ventures. Conversely, some studies present contrasting findings. According to this research, regulations that are effectively designed and implemented can benefit both the environment and the firm, potentially providing a competitive edge over unregulated firms (Stanwick and Stanwick, 1998; Zhou et al., 2022).

Environmental regulation refers to the collection of laws and regulations created and put into effect with the intention of protecting the environment (Testa et al., 2011). Numerous studies have examined the impact of environmental regulation on corporate performance. Among these, several studies contend that onerous environmental regulations that impose limits on business activity have a negative impact on productivity and competitiveness (López-Gamero et al., 2010; Shao et al., 2020). Firms face costs related to environmental protection, which may lead to the loss of investment opportunities in other profitable projects. Some studies have reached conclusions that differ. According to these researches, regulations that are well designed and implemented can be advantageous for both the environment and the firm, potentially providing a competitive advantage over unregulated firms (Stanwick and Stanwick, 1998; Zhou et al., 2022).

Some studies suggest that it is under varying environmental regulatory intensities that the behaviors or outcomes of firms differ (Buysse and Verbeke, 2003; Berrone and Gomez-Mejia, 2009; Li and Ramanathan, 2018; Zhang, D., 2023). These studies consider environmental management benefits as a crucial tool to explain this phenomenon. When the intensity of environmental regulation is at a low level, the environmental management costs of enterprises are low, and the environmental management benefits brought by TMT environmental cognition exceed those of greenwashing, which weakens motivation of the TMT to engage in greenwashing. Conversely, as environmental regulatory intensity increases, this situation reverses. Consequently, we propose the following hypothesis:

Hypothesis 4:

Environmental regulations may weaken the inhibitory effect of TMT environmental cognition on greenwashing.

This study’s basic econometric formula was previously modified in accordance with (Zhang, 2022; Zhang, 2023), which is shown as Equation 1: GW i , t = β 0 + β 1 × EC i , t + γ ′ × X i , t + ε i , t (1)

To show the role of the political connection and environmental regulation mechanism on the relationship between TMT environmental cognition and greenwashing, we construct Equations 2, 3: GWi , t = β 0 + β 1 × EC i , t + β 2 × PCL i , t + β 3 × EC i , t × PCL i , t + γ ′ × X i , t + ε i , t (2) GWi , t = β 0 + β 1 × EC i , t + β 2 × ER i , t + β 3 × EC i , t × ER i , t + γ ′ × X i , t + ε i , t (3)

In these equations, the firm-individual is denoted by the subscript i, and the time interval t is from 2012 to 2020., the variable GW indicates greenwashing level, EC indicates TMT environmental cognition, PCL indicates political connection level, ER indicates environmental regulation intensity. In addition, X_i,t contains a series of control variables, specifically the Size, Lev, ROA, Board, Indep, Dual, ListAge, and SOE. Additionally, the industry fixed effect and the fixed time impacts are recorded, and the idiosyncratic errors are represented by ε.

The China Stock Market and Accounting Research Database (CSMAR) provides the fundamental variables. Environmental regulation intensity was obtained via the provincial statistical yearbook. The data used to measure greenwashing is derived from Bloomberg’s and Huazheng’s ESG Database. The data regarding the political connections of firms are manually gathered from executives’ personal documents in the CSMAR database, and some empty values are filled in by visiting the websites. Interestingly, financial and other ST corporations are not included.

Regarding the degree of greenwashing, this study adheres to previous literature (Yu et al., 2020; Zhang, 2023), designs the greenwashing variable following Equation 4: GW = ED − EP = ESGDis − ESGDis ¯ σ ESGDis − ESGPer − ESGPer ¯ σ ESGDis (4)

In this paper, ED represents a firm’s standing relative to its competitors in the ESG disclosure score distribution, which gauges the degree of environmental disclosure. EP represents a firm’s position relative to its competitors in the distribution of its ESG real performance scores, which measures the level of genuine environmental behavior.

For EC, this study selects pertinent terms for word frequency data, performs a textual analysis of listed firms’ annual reports, and creates an index representing the environmental cognition of listed firms’ TMT. (Duriau et al., 2007).

For ER, this study uses an index calculated as 100 times the ratio of an industry’s industrial output value to its pollution control costs (Wang and Shen, 2016).

For PC, this paper uses a dummy variable to quantify corporate political connections: assigning it a value of one if the chairman or CEO of a listed company is a former or current government figure, and zero otherwise (Deng et al., 2018). PCL is an ordinal variable, which is assigned values of 4, 3, 2, and 1 based on the administrative levels of government agencies with which senior executives establish political connections, categorized into four types: ministerial, departmental, divisional, and sectional (Faccio et al., 2006; Fan et al., 2007).

Table 1 indicates that the mean and median values of greenwashing are −0.47 and −0.53, showing that the average level of greenwashing in the sample we used is relatively low; however, the maximum value is 5.69 and the minimum value is −4.71, showing significant variations in the greenwashing behavior of the sampled firms. The same applies to EC, ER and PC. For PC and SOE, the mean values are 0.34 and 0.51 respectively, which means that 34% of the firms in the sample are politically connected, while 51% are state-owned.

Table 2 presents the Pearson’s correlation matrix, illustrating the relationships among the dependent, independent, and primary control variables used in our econometric analysis. Multicollinearity among the explanatory variables is indicated by a correlation coefficient greater than 0.9 (Zhang, 2023). Given that every coefficient in Table 2 is below 0.5, it is evident that there are no issues with the main explanatory variables in our analysis, in addition to correlation coefficient for PC and PCL. However, since PC and PCL will not appear in the regression equation simultaneously, they do not have an impact on the regression results.

Additionally, Table 2 reveals a significant negative correlation between EC and GW, and a significant positive correlation between EC and both ED and EP. ER shows a significant negative correlation with GW and ED. PCL is significantly and positively correlated with ED and EP but is not related to GW. In terms of control variables, most of them are related to GW, ED, and EP.

This part presents the findings of our empirical regression after Equation 1 in order to confirm the connection between TMT environmental cognition and greenwashing. Table 3 columns 1-4 show that TMT environmental cognition has a negative impact on firm greenwashing at the 1% significance level. This suggests that the stronger the TMT environmental cognition, the more effective the inhibition of greenwashing behavior, validating the Hypothesis 1. Columns 5-6 show that the impact of TMT environmental cognition on the environmental scores disclosed is not significant, but it has a significantly positive effect on the actual environmental scores. Thus accepting hypothesis 2. Moreover, the table also indicates that Size and Dual has a positive effect on greenwashing behavior. Lev and ListAge has a negative impact on greenwashing behavior. Given the potential significant behavioral differences among SOE firms in the Chinese context and the unique nature of government-enterprise relations in China, a further heterogeneous analysis will be conducted in Section 4.2.

Table 8 reveals that the interaction coefficient between TMT environmental cognition and political connection level is −0.005, significant at the 1% level (Columns 1). This means that political connections can significantly enhance the inhibitory effects of TMT environmental cognition on greenwashing, thereby validating hypothesis 3. In addition, columns 2-3 also indicate that the moderation effects may be achieved by promoting the genuine environmental efforts.

Column 4 shows that the interaction coefficient between TMT environmental cognition and environmental regulation intensity is 0.037, significant at the 5% level. This implies that environmental regulation can significantly weaken the inhibitory effects of TMT environmental cognition on greenwashing, thereby validating hypothesis 4. Furthermore, Columns 5-6 also suggest that this moderation effects may be achieved by reducing genuine environmental efforts.

To show that the results of this paper are independent of the measurement of greenwashing, we reference Li and Wang (2021), who used a dummy variable to represent whether a firm exhibits greenwashing behavior. The estimation findings obtained by evaluating greenwashing behavior indirectly using these alternative measurements are presented in Table 9. Column 1 shows that TMT environmental cognition decreases greenwashing at the 1% significance level.

Furthermore, we use the lagged 1-period of EC as the explanatory variable to verify the existence of reverse causality. The regression results shown in Column 2 indicate that TMT environmental cognition decreases greenwashing at the 1% significance level.

Here, EC is lagged by one and two units to address the endogeneity problem using 2SLS and GMM with an instrument variable. A researh design is also introduced to ensure the reliability of the findings in this research (Chen et al., 2022; Zhang et al., 2023). Table 10 presents the relationship between TMT environmental cognition and greenwashing, showing that TMT environmental cognition decreases greenwashing at the 1% significance level.

Based on Upper Echelons Theory and Strategic Recognition Theory, this study investigates the impact of TMT environmental cognition on greenwashing behavior in firms. After empirical testing, this article uses the imbalanced panel dataset of Chinese listed firms from 2012 to 2020 and draws the following conclusions:

Firstly, TMT environmental cognition restrains greenwashing behavior significantly by promoting genuine environmental efforts. Secondly, the inhibitory effect of TMT environmental cognition on greenwashing behavior is more pronounced in firms without political connections than those with political connections. Thirdly, TMT environmental cognition has a significantly positive impact on environmental disclosure scores for private owned or political connection firms, but not for state-owned or no political connection firms. Fourthly, the level of political connection of firms and the intensity of environmental regulations significantly have respectively strengthened or weakened the linkage between TMT environmental cognition and greenwashing behavior.

From current studies, it can be seen that greenwashing will have a series of adverse consequences. Regulators, therefore, tend to suppress greenwashing. Based on research findings, this paper proposes the following suggestions from enhancing TMT environmental cognition in China:

Firstly, facilitate the dissemination of environmental information between government and firms. Local governments can invite firms to participate in the convening of environmental committees, and share internal information such as the ‘Yangtze River Economic Belt Ecological Environment Warning Film’ with relevant firms. Secondly, strengthen the political connections of firms. United Front Work Department, Political Consultative Conference, National People’s Congress, and other departments should actively support private entrepreneurs and executives to participate in environmental protection, giving them more political identities. Thirdly, increase financial penalties for greenwashing. The government can impose severe financial penalties on firms engaaging in greenwashing, which links their misconduct to access to economic resources such as policy loans, thereby increasing their financing costs in multiple aspects.