Deciphering the Intricate Influence of Greenwashing and Environmental Performance on Financial Outcome Through Panel VAR/GMM Analysis

Abstract

This study explores the intricate interconnections between greenwashing, environmental performance (ESG), firm-specific characteristics, board composition, firm age, size, leverage, carbon emissions (CO₂), and financial performance. By applying a combination of panel VAR/GMM estimation, robust least squares regression, and Granger causality tests, the research draws upon comprehensive data spanning from 2009 to 2022 sourced from the Chinese Research Data Services Platform (CNRDS), Bloomberg, and Refinitiv. The dataset comprises 312 listed Chinese firms, yielding a total of 5335 observations. The findings reveal that past return on equity acts as a reinforcing mechanism for both financial performance and ESG outcomes, as it positively affects subsequent returns and environmental engagement. However, its influence on firm size, board structure, and Tobin’s Q is statistically insignificant. Additionally, greenwashing demonstrates a dual character: while it reflects strong internal consistency, it also significantly shapes environmental outcomes and market perceptions. Firm size stands out as a pivotal determinant. It exhibits high persistence over time and plays a crucial role in shaping governance structures and capital allocation decisions. Moreover, board composition is positively associated with firm size. Leverage and return on assets show consistent temporal persistence and exert substantial influence on various firm attributes. Although leverage may contribute positively under favorable conditions, its overall impact on sustainability and governance practices appears limited. Higher carbon emissions are associated with increased ESG disclosures, whereas stronger ESG performance contributes to emission reduction and modestly enhances financial outcomes. Tobin’s Q also emerges as a critical factor, significantly influencing sustainability practices. This suggests that firms respond to investor expectations by improving their ESG performance. Results from the robust least squares regression underscore the dominant roles of firm size, Tobin’s Q, and leverage in driving financial performance. In contrast, ESG scores, CO₂ emissions, and greenwashing do not exhibit any statistically significant direct effects on financial performance. Granger causality tests confirm unidirectional relationships from financial performance to key structural variables such as size, leverage, firm age, and Tobin’s Q. A notable bidirectional causal link is observed between return on assets and return on equity. However, sustainability and governance-related variables show no causal impact on financial performance. Overall, the study acknowledges limitations and offers policy recommendations along with directions for future research.

1. Introduction

In recent times, the integration of sustainability and environmental accountability has emerged as a central focus within corporate governance, reflecting a significant transformation in the way firms are expected to operate amid growing global environmental awareness. This shift is fueled by increasing regulatory pressures, evolving stakeholder demands, and the urgent necessity to combat climate change, all of which compel companies to align financial objectives with broader environmental and social responsibilities. Consequently, the intersection of sustainability and financial performance calls for a more holistic and multidimensional evaluation framework, where ethical conduct and strategic foresight are integral to long-term value creation. Within this framework, the current study explores key factors influencing financial performance, including greenwashing practices, ESG performance, board structure, firm size, age, leverage, carbon emissions, market valuation (Tobin’s Q), and return on assets, thereby providing a comprehensive understanding of the dynamic relationship between corporate sustainability and financial outcomes. The existing literature reveals diverse perspectives on how these factors impact financial outcomes, with both positive and negative effects observed. For instance, a strong ESG profile has been found to improve return on equity (ROE) by enhancing brand reputation, drawing green investments, and lowering exposure to regulatory risks, thereby fostering a favorable environment for sustainable profitability [1]. As sustainability considerations gain prominence among investors, companies with robust ESG practices are increasingly seen as lower-risk and better positioned to adapt to regulatory changes, making them attractive to stability-focused investors [2,3]. Conversely, excessive greenwashing where companies exaggerate their environmental achievements has been widely criticized for its detrimental effects. Such deceptive practices undermine investor confidence, damage corporate credibility, and increase the likelihood of legal repercussions, all of which can adversely impact ROE as companies grapple with both reputational damage and financial penalties [4]. These findings highlight the potential risks companies face when attempting to falsely enhance their environmental image. Firm size also exhibits complex effects on financial performance. While larger firms often benefit from economies of scale that improve cost efficiency and profitability, they are also subject to greater regulatory scrutiny due to their environmental footprint. This heightened oversight can lead to significant compliance costs, which may offset some profitability gains [5,6]. Additionally, factors such as board diversity, board size, firm age, and leverage introduce further complexity in financial outcomes, as their effects vary depending on the governance structure and strategic focus of each firm. For example, a diverse board can enhance decision-making by bringing multiple perspectives, which may improve financial performance, though the impact often depends on the specific governance context [7]. Moreover, CO₂ emissions are another critical factor influencing ROE, especially as global regulations become more stringent. High emission levels can lead to increased compliance costs, carbon taxes, and reputational risks, all of which exert downward pressure on ROE [8,9]. Conversely, firms that actively invest in reducing emissions or adopting clean technologies may experience improved financial performance through operational cost reductions, appeal to eco-conscious investors, and enhanced brand reputation [10]. This duality underscores the significant role of CO₂ emissions in shaping financial performance, particularly as environmental accountability becomes integral to corporate strategy [11].

Although prior research has offered valuable insights, the complex interrelationships among these variables remain insufficiently examined, particularly within the Chinese context, where corporations must navigate the dual challenges of sustaining rapid economic development while adhering to increasingly rigorous environmental regulations. Importantly, much of the existing literature has centered on Western economies or employed broad international datasets, leaving a notable gap in understanding the distinctive institutional, policy, and economic frameworks that shape the link between ESG performance and financial outcomes in China. This underrepresentation highlights the necessity for a more targeted investigation into how these factors jointly affect financial performance. The core issue this study addresses is the inadequate comprehension of how greenwashing, ESG, and firm-specific attributes such as size, board structure, age, leverage, and carbon emissions collectively influence financial performance, with a particular focus on listed companies in China. This line of inquiry is especially critical, given China’s status as both a global economic and a leading contributor to carbon emissions. Accordingly, this study seeks to answer the central research question: How do greenwashing, environmental performance, firm size, board composition, firm age, leverage, and carbon emissions affect return on equity (ROE) among Chinese-listed firms? To this end, the research advances several original contributions.

Firstly, it uses a panel VAR/GMM estimation method to examine the underlying mechanisms connecting these variables, thereby providing valuable insights for both academic and industry stakeholders. Panel VAR/GMM is particularly well-suited for this analysis as it effectively addresses endogeneity issues where explanatory variables may be correlated with the error term by incorporating lagged variables as instruments [12,13]. This approach not only enhances the robustness of the results but also captures the dynamic relationships among variables, allowing for a more comprehensive understanding of how changes in one variable impact others over time [14]. Secondly, this study employs robust least squares (RLS) and Granger causality tests, which are particularly effective in ensuring the credibility and consistency of estimated relationships, especially in the presence of data anomalies such as heteroskedasticity, outliers, and violations of normality assumptions [15,16]. The RLS approach mitigates the influence of extreme observations and model specification errors, thereby improving the precision and reliability of the results. Meanwhile, the Granger causality technique is instrumental in determining the temporal precedence between variables, allowing the study to identify whether changes in one variable can predict future movements in another. This is crucial for unveiling causal dynamics and directional relationships among key constructs such as corporate financial performance, governance characteristics, and environmental indicators [17]. Third, this research adds to scholarly discourse by focusing on Chinese-listed companies, a setting that has received comparatively less scrutiny in previous studies. As the world’s largest emitter of greenhouse gases and a key player in global supply chains, China represents a crucial yet understudied context for examining sustainability challenges [18]. By investigating Chinese firms, this study seeks to illuminate the unique challenges and opportunities they encounter as they navigate sustainability imperatives within an evolving regulatory landscape. Methodologically, our study utilizes a longitudinal dataset covering 312 Chinese-listed companies from 2009 to 2022, all of which are listed companies sourced from reliable platforms such as the Chinese Research Data Services Platform (CNRDS), Bloomberg, and Refinitiv. This rich dataset enables us to examine the temporal dynamics of greenwashing practices, environmental performance, firm size, board composition, firm age, leverage, and carbon emissions, offering insights into how these variables evolve and cumulatively affect financial outcomes. By employing a rigorous methodological design and a forward-looking analytical framework, this study aims to advance academic understanding while also offering practical guidance for decision-making in the realms of corporate sustainability and financial strategy. Ultimately, the study’s findings enrich the existing body of literature and offer meaningful insights for policymakers and practitioners in these fields, supporting informed decision-making concerning corporate governance, regulatory development, and strategic investment planning.

The remainder of this manuscript unfolds as follows: Section 2 delves into an extensive review of existing literature; Section 3 outlines the research methodology; Section 4 presents the empirical results; Section 5 discusses practical implications and policy recommendations; and Section 6 concludes with study limitations and suggestions for future research directions.

2. Literature Review

2.1. The Prevailing Scenario of Greenwashing Practices Within the Context of China

China offers a compelling empirical context for investigating greenwashing due to the intricate balance between economic advancement and environmental conservation, which both the government and businesses grapple with. “Since the adoption of the reform and opening-up policy in the 1970s, China has experienced remarkable economic growth”. It has ascended to “become the largest emerging economy globally and the second-largest economy overall” [19,20]. However, China continues to confront significant environmental pollution and ecological degradation challenges. Despite its economic achievements, the nation remains the world’s leading carbon emitter. The environmental issues plaguing China have resulted in economic losses amounting “to approximately 8% of the annual gross domestic product (GDP)” [21,22]. “In 2006, the Fifth Plenary Session of the 16th CPC Central Committee established harmonious society as a primary objective within the Five-Year Plan”. This marked a significant shift in China’s development approach, transitioning from a singular focus on economic growth to one that prioritizes the equilibrium between economic prosperity, societal well-being, and environmental sustainability. Embracing green policies brings environmental concerns to the forefront of public awareness and compels Chinese enterprises to prioritize environmental responsibility [23]. With diverse expectations shaping their strategies, businesses are increasingly asserting their commitment to environmental stewardship. The number of “Chinese-listed companies disclosing social responsibility information surged from 371 to 851 between 2009 and 2018” [24]. Some firms even assert surpassing governmental environmental standards to underscore their dedication to sustainability. However, skepticism persists regarding the genuineness of firms’ portrayals of environmental responsibility. Over the past decade, the media has played a pivotal role in unveiling various instances of greenwashing by Chinese companies [25]. Notably, Southern Weekend, a prominent unofficial Chinese newspaper, has highlighted 151 instances of greenwashing companies listed and exposed since 2009. The prevalence of such practices among Chinese local firms has escalated notably since 2014, with many failing to offer genuinely green products but instead resorting to deceptive tactics to mislead consumers [26]. While the media serves as a potent watchdog, with the Chinese stock market often responding negatively to firms listed for greenwashing, its coverage remains limited. However, such comprehensive guidelines or regulations for environmental claims are yet to be established in China. As a result, identifying instances of greenwashing remains a significant challenge for Chinese regulators, highlighting the need for more robust regulatory frameworks and enforcement mechanisms in environmental advertising.

2.2. Importance of Exploring the Intersection Between Greenwashing, Environmental Performance and Financial Performance

Greenwashing, a deceptive practice wherein a company presents itself as environmentally friendly despite its actions contradicting such claims, has garnered significant attention in academic and business spheres alike [27]. This practice has raised concerns due to its potential to mislead stakeholders and its implications for firms’ economic outcomes. Studies investigating the relationship between greenwashing and financial performance aim to delve into the effects of misleading environmental marketing on companies’ financial well-being. Understanding this link is crucial for several reasons. Firstly, it contributes to ongoing discussions surrounding business ethics and corporate social responsibility. By shedding light on the ethical implications of misleading environmental claims, research in this area helps to foster a more transparent and responsible corporate landscape [28]. Moreover, by identifying and highlighting the risks associated with greenwashing, such as legal liabilities and reputational damage, this research assists firms in implementing measures to mitigate these risks effectively. Secondly, studying the link between greenwashing and financial performance provides valuable insights for investors and financial analysts. By assessing companies’ environmental practices and their impact on financial outcomes, stakeholders can make more informed investment decisions and allocate capital to firms that prioritize genuine sustainability efforts [29]. As shown in Figure 1, the relationships among return on equity, Tobin’s Q, greenwashing, environmental performance, and return on assets reveal key interactions between financial and environmental indicators.

3. Hypothesis Development

3.1. The Intersection Between Greenwashing and Financial Performance

Greenwashing, the practice of conveying a false impression or providing misleading information about how a company’s products or practices are environmentally sound, has garnered increasing attention in the domains of sustainability and corporate finance. Empirical studies have attempted to examine the consequences of greenwashing not only on stakeholder trust but also on a firm’s financial performance. The findings are mixed, with variations depending on the methodology, region, industry, and the scope of greenwashing behavior. Empirical evidence suggests that greenwashing can have both short-term financial benefits and long-term detrimental effects [30,31]. For instance, ref. [32], using a sample of A-share-listed companies in China’s capital market from 2009 to 2021, contend that greenwashing has a significant negative impact on corporate market value. Similarly, ref. [33] empirical analysis, based on data from listed companies in China between 2010 and 2018, revealed a negative relationship between greenwashing and financial performance, thereby lending support to signaling theory. Another recent study by [34], analyzing data from Chinese enterprises over the period 2008 to 2022 and utilizing a rigorous methodological framework, also finds that greenwashing exerts a detrimental effect on financial performance. Additionally, the impact of greenwashing on consumer and financial markets demonstrates that the widespread decline in investor confidence severely hampers the growth of sustainable finance and discourages financial market investments. This is further evidenced by negative consumer responses to greenwashed products, as emphasized by [35,36]. Moreover, ref. [37], drawing on data from Chinese-listed companies between 2017 and 2018, explores the greenwashing practices employed by corporations. The results indicate a high prevalence of greenwashing, as evidenced by the fact that only 13.6% of environmental penalties were disclosed during the analysis period. Furthermore, ref. [38] conducted an online survey by distributing 220 questionnaires and utilized regression analysis through SPSS 24.0 and AMOS 24.0 to evaluate their hypotheses. The results demonstrate that consumers’ perceptions of greenwashing significantly undermine their willingness to engage in green purchasing. Finally, ref. [39] introduced and validated a novel method for the automatic detection of greenwashing, utilizing linguistic indicators derived from tweets posted by a diverse set of firms operating in two highly polluting industries. Their analysis revealed a negative correlation between greenwashing practices and financial market performance among the sampled firms.

3.2. The Correlation Between Environmental Social and Governance (ESG) and Financial Performance

In recent years, an increasing body of literature has delved into exploring the connection between environmental, social, and governance (ESG) factors and financial performance. Researchers and scholars have aimed to grasp the extent to which a firm’s environmental performance influences its financial performance, and conversely. Various theoretical frameworks have emerged to elucidate the relationship between ESG and different levels of financial performance such as return on equity, Tobin’s Q, and return on asset. According to the stakeholder theory, firms actively involved in environmental initiatives and displaying “corporate social responsibility (CSR) practices are more likely to garner support from stakeholders”, thereby potentially promoting financial performance [40]. Similarly, the resource-based view (RBV) argues that ESG activities could serve as a source of competitive advantage, consequently leading to enhanced financial performance [41,42]. Moreover, empirical investigations have yielded diverse outcomes concerning the ESG and financial performance relationship. While some studies have uncovered a positive correlation between ESG performance and various financial performance metrics, including ROE, Tobin’s Q, and ROA [43,44,45], others have found no notable relationship or even negative correlations between ESG and financial performance measures [18,46,47]. For instance, ref. [48] delves into the relationship between ESG and CSR attitudes and financial performance in Europe, providing a quantitative re-evaluation, offering a fresh perspective on the impact of environmental, social, and governance factors on financial outcomes, contributing valuable insights to the existing literature. Moreover, ref. [49] offer valuable insights into the complex interplay between corporate behavior, ESG practices, and financial outcomes. By examining the moderating role of ESG practices, they provide a nuanced understanding of how companies navigate controversies and their impact on financial performance, contributing to the evolving discourse on corporate sustainability and profitability. Lastly, ref. [50] offers “valuable insights into the intricate dynamics between financial outcomes, ESG controversies, and ESG performance”, contributing to our understanding of how companies manage ESG-related challenges and their implications for financial performance, and shedding light on the complexities of sustainable business practices in a global context. Several mediating and moderating factors have been proposed to account for the mixed findings in the literature.

3.3. The Relation Between CO₂ Emissions and Financial Performance

The relationship between CO₂ emissions and financial performance has garnered increasing attention in the context of corporate sustainability and environmental responsibility. Empirical evidence generally suggests that higher carbon emissions are associated with adverse financial outcomes, largely due to regulatory risks, reputational damage, and increased operational costs [51,52]. For instance, ref. [53] analyzed firms across the U.S. and found that companies with better carbon disclosure and lower CO₂ emissions tend to achieve superior financial performance, as they are perceived as more transparent and environmentally responsible by investors. Their findings support the view that proactive environmental strategies can serve as a competitive advantage. Similarly, ref. [54] found a negative relationship between the level of CO₂ emissions and firm value, showing that the market penalizes firms with higher emissions due to expected future liabilities and compliance costs. The study emphasizes the financial materiality of carbon performance, particularly in sectors exposed to environmental scrutiny. Moreover, ref. [55] conducted a longitudinal study of U.S. firms and revealed that higher greenhouse gas emissions are negatively correlated with return on assets (ROA), especially among companies that fail to couple emissions with meaningful reductions or improvements in efficiency. This suggests that merely emitting without strategic environmental planning can erode profitability. Lastly, ref. [56] examined firms in carbon-intensive industries and found that investors react negatively to high emission disclosures, implying that CO₂ emissions serve as a red flag for financial underperformance and environmental risk exposure. Overall, elevated CO₂ emissions can impair financial performance, particularly in an era where environmental accountability and climate-related financial risks are becoming central to investment decisions and regulatory frameworks.

3.4. Influence of Firm Size, Board Composition, Firm Age, and Leverage on Financial Performance

Empirical studies have extensively investigated the influence of firm-specific characteristics, namely firm size, board composition, firm age, and leverage, on financial performance. To begin with, firm size is frequently found to have a positive effect on financial performance, as larger firms tend to benefit from economies of scale, greater access to resources, and stronger market influence, which collectively enhance operational efficiency and profitability [57,58]. For example, research by [59] analyzing a sample of 12,023 acquisitions by publicly traded firms between 1980 and 2001 reveals that larger firms tend to achieve higher returns on assets. In addition, board composition plays a crucial role in shaping financial outcomes. Specifically, firms with a greater proportion of independent directors often experience improved financial performance, as these directors provide more effective oversight and mitigate managerial opportunism [60,61]. This perspective is also supported by [62], who argue that board independence contributes to stronger governance and accountability. Moreover, firm age exhibits a complex relationship with financial performance. On the one hand, older firms may leverage accumulated industry knowledge, stable customer bases, and established reputations, which can positively influence performance [63,64]. On the other hand, aging firms may also encounter challenges such as organizational rigidity and declining innovation, potentially hampering profitability [65,66]. Furthermore, ref. [66] highlights a nonlinear pattern, suggesting that while performance initially improves with age, it may eventually plateau or decline. Lastly, leverage presents both opportunities and risks. While moderate debt levels can enhance performance by imposing financial discipline on management [67,68], excessive reliance on debt can lead to financial distress and deteriorating profitability. Supporting this view in using data from 120 non-financial companies listed on the Tadawul Stock Exchange between 2017 and 2020, ref. [69] applied system GMM and quantile regression methods. The GMM results indicate that leverage has a negative impact on firm performance, reflected in return on assets, return on equity, and Tobin’s Q. Additionally, the quantile regression analysis reveals that this effect varies across different performance levels, highlighting a heterogeneous relationship.

3.5. Conceptual Framework

Figure 2 illustrates the conceptual framework guiding this study, highlighting the complex interplay among greenwashing, environmental performance, firm size, board composition, firm age, leverage, and carbon emissions in relation to financial performance. Grounded in the hypothesis development, the conceptual model guides the analysis by providing a structured approach to examining the multifaceted between these factors and financial outcomes.

3.6. Overview of Previous Research

Table 1. Summary of previous studies.

Authors	Study Period and Country	Methods	Main Findings
[70]	From 2013 to 2020 77 European-listed banks	Feasible generalized least squares (FGLS)	GW negatively affects bank performance.
[33]	From 2010 to 2018 Listed companies in China	D-K (Driscoll–Kraay) standard error method	Negative correlation between environmental performance and greenwashing.
[71]	From 2008–2016 U.S. companies	GMM	A firm’s capability reputation has a negative effect of greenwashing on customer satisfaction.
[72]	From 2014 to 2017 Ukrainian large industrial companies	Partial least-squares structural equation modeling (PLS-PM)	One point increase in greenwashing leads to a 0.56-point decline in the company’s green brand with a load factor of 0.78.
[73]	From 2013 and 2015 Chinese-listed firms	D-K (Driscoll–Kraay)	Greenwashing strongly motivates heavy-pollution firms and results in decreasing product quality.
[74]	From 2018 to 2022 Indonesian firms	GMM	GW has a significant positive effect on financial performance.
[75]	From 2015 to 2021 39 firms	Partial least squares structural equation modeling (PLS-SEM)	ESG performance has a direct and positive affect on firm net profit margin. GW is not associated with firm net profit margin.
[38]	From 18 to 55 years old 220 online questionnaires Changsha, China	Stepwise regression	Consumers’ greenwashing perception negatively influences consumers’ green purchasing intentions.
[76]	2018 to 2022 Chinese firm	Two-way fixed effects model	Greenwashing increases firm value.
[77]	From 2014 to 2020 Chinese-listed firm	Partial least squares structural equation modeling (PLS-SEM)	Green finance motivates ESG performance by mitigating firms’ reactions in alleviating greenwashing.
[78]	From 2013 to 2020 Chinese A-share listed companies	Parallel trend test	Issuance of corporate green bonds leads to an increase in the number of green patent applications.
[37]	From 2017 to 2018 Chinese-listed companies	D-K (Driscoll–Kraay)	Greenwashing is widespread, and only 13.6% of environmental penalties have been disclosed by companies.

Source: Designed by the author from the literature review.

offers a detailed overview of existing literature, including the timeframes, research methods, and main findings related to the drivers of financial performance. This synthesis helps position the present study within the broader academic context by uncovering gaps in the literature and emphasizing the relevance of its methodological and empirical contributions.

3.7. Research Gap

Although prior research has offered valuable insights, the complex interrelationships among these variables remain insufficiently examined, particularly within the Chinese context, where corporations must navigate the dual challenges of sustaining rapid economic development while adhering to increasingly rigorous environmental regulations. Importantly, much of the existing literature has centered on Western economies or employed broad international datasets, leaving a notable gap in understanding the distinctive institutional, policy, and economic frameworks that shape the link between ESG performance and financial outcomes in China. This underrepresentation highlights the necessity for a more targeted investigation into how these factors jointly affect financial performance. The core issue this study addresses is the inadequate comprehension of how greenwashing, ESG, and firm-specific attributes such as size, board structure, age, leverage, and carbon emissions collectively influence financial performance, with a particular focus on listed companies in China. This line of inquiry is especially critical, given China’s status as both a global economic and a leading contributor to carbon emissions. To this end, the research uses a panel VAR/GMM estimation method to examine the underlying mechanisms connecting these variables. Moreover, this study employs robust least squares (RLS) and Granger causality tests. Furthermore, our study utilizes a longitudinal dataset covering 312 Chinese-listed companies from 2009 to 2022, and all of them are listed companies.

4. Method Application

4.1. Economic Method

To fulfill the objective of this research, examining the intricate interconnections among greenwashing, environmental performance, and financial performance, along with additional control variables such as board structure, firm size, firm age, leverage, and carbon emissions, this study employs a panel VAR/GMM approach. Originally developed by [79], this methodology treats all variables as endogenous, allowing for a more comprehensive understanding of their dynamic relationships. Moreover, it enhances the precision of panel-based Granger causality tests, enabling the detection of directional and temporal linkages between variables. The rationale behind selecting the PVAR/GMM technique lies in its superior ability to manage common econometric challenges such as endogeneity, autocorrelation, and unobserved heterogeneity issues often inadequately addressed by conventional panel methods like fixed effects or difference GMM. Unlike these traditional approaches, the PVAR/GMM framework captures bidirectional feedback effects and evolving interactions over time, making it particularly well-suited for modeling the complex, dynamic nature of ESG-related corporate behavior. This ensures that the empirical results are both robust and policy relevant. The traditional specification of the “panel VAR model,” incorporating “fixed effects,” is represented as follows:

$$Y_{i,t}= {\phi }_i+\sum _{j=1}^m{\delta }_j{Y}_{i,t-j}+{\mu }_{i,t}$$

(1)

In our framework, $Y_{i,t}$ “represents a vector of dependent variables with dimensions N × 1, where ${\delta }_j$ signifies an N × N matrix of autoregressive coefficients”. Additionally, “${\phi }_i$ captures country-fixed effects, effectively controlling for unobserved individual heterogeneity”. Furthermore, “${\mu }_{i,t}$ denotes a vector of error terms, with (i = 1, …, N) representing individual countries and (t = 1, …, T) representing periods”. By applying panel data techniques which significantly increase the number of observations, we mitigate the issue of limited degrees of freedom, allowing for a substantial widening of the confidence intervals for impulse response functions (IRFs). To estimate our model, we employ estimators that transform the original VAR model by differencing Equation (1) and applying the GMM approach. This method enhances both the consistency and robustness of our VAR estimates, ensuring more reliable results:

$$Y_{it-s}^{*}={\vartheta }_t[Y_{it-s}-\left(Y_{it-s+1}+\cdots +Y_{iT-s}\right)/(T-t\left)\right] (s=0, 1, 2, 3\dots ,\tau )$$

(2)

Within this framework, “$Y_{it}^{*}$ represent the transformed vector representing dependent variables, T represents the year for a specific country (T = 2009, 2010, 2011, …, 2022)”, “i stands for countries, s indicates the lag order of the panel VAR, and ${\vartheta }_t$ denotes a non-singular weighting matrix”. To achieve the aim of this study, Equation (1) is calculated utilizing “GMM technique combined with the forward orthogonal deviation”. As noted earlier, estimates obtained from the “VAR model may not be reliable”. Therefore, we develop “impulse response functions (IRFs)” with data from 312 Chinese-listed companies, enabling an exploration of the intricate connections between greenwashing, “environmental performance, and financial performance”, along with additional and control variables.

4.2. Panel VAR Granger Causality Analysis

In this inquiry, the research “employs panel VAR Granger causality analysis” to investigate the causal connections between the variables. This analysis becomes relevant since “the panel series utilized in this study are all integrated of order one (I(1)) and cointegrated”. “The framework for performing panel VAR Granger causality analysis plays a pivotal role in uncovering these relationships:

$$X_{i,t}={\rho }_0+\sum _{j=1}^m{\rho }_{1,j}{Y}_{i,t-j}+\sum _{j=1}^m{\rho }_{2,j}{Y}_{it-j}+{\tau }_i+{\mu }_{it}$$

(3)

$$Y_{i,t}={\sigma }_0+\sum _{j=1}^m{\sigma }_{1,j}{X}_{i,t-j}+\sum _{j=1}^m{\sigma }_{2,j}{X}_{i,t-j}+{\rho }_i+{\mu }_{it}$$

(4)

In this regard, “m signify the lag length, while $u_{it}$ denote the error terms assumed to follow a white noise distribution”. Futhermore, “${\tau }_i$ and ${\rho }_i$ represent individual fixed effects consistently applied throughout the analysis”. As established earlier, “i and t refer to the specific country and period”, respectively. The forecasting ability of “the difference in variable Y for the difference in variable X is determined by whether the lagged variable Y contains information about variable X”. On the other hand, if the reverse is observed, we conclude that the variation in variable X has the ability to forecast variable Y. “The null hypothesis for the panel VAR causality” is constructed as follows:

$$H_0: {\rho }_{\mathrm{2,1}}={\rho }_{\mathrm{2,2}}=\dots \dots \dots \dots ={\rho }_{2,m}=0 and H_0: {\sigma }_{\mathrm{2,1}}={\sigma }_{\mathrm{2,2}}=\dots \dots \dots \dots ={\sigma }_{2,m}=0$$

(5)

Figure 3 illustrates the methodological framework adopted in this study, providing a comprehensive depiction of each phase involved in the research process.

4.3. Data Processing

The data for this study were sourced “from the Chinese Research Data Services Platform (CNRDS)”, Bloomberg, and Refinitiv spanning from 2009 to 2022, encompassing 312 Chinese-listed companies. The study examined various factors including financial performance (ROE), Tobin’s Q, return on asset (ROA), greenwashing (GW), firm size (FS), board, firmage (FA), leverage (Lev), carbon emissions (CO₂), and environmental performance (ESG).

Table 2. Overview of variable details.

Variables	Symbols	Definition of Variables	Source
“Return on Equity”	ROE	“Relate is a financial ratio that measures a company’s profitability about its shareholders’ equity. It is a key indicator of a company’s financial performance and its ability to generate a return for its shareholders.”	CNRDS
“Market value”	Tobin’s Q	“Measured as the ratio of the market value of a firm’s assets (calculated as the market value of equity plus the book value of total liabilities) to the book value of the firm’s total assets.”	CNRDS
“Return on Asset”	ROA	“Measured as the ratio of net income to total assets, indicating how efficiently a company utilizes its assets to generate profit.”	CNRDS
“Green Washing”	GW	“Refers to a firm’s peer-relative greenwashing score = (a normalized measure representing a firm’s relative position to its peers in the distribution of the Bloomberg Environment disclosure score)—(a normalized measure representing a firm’s relative position to its peers in the distribution of Refinitiv Environmental performance score).”	Bloomberg
“Environmental Performance”	ESG	“Evaluating the company’s sustainability practices.”	CNRDS
“Firm size	Size	“Measure as the natural logarithm of total assets (SIZE) as a proxy of firm size.”	CNRDS
“Board”	Board	“Measure as the total count of board directors, reflecting the governance structure.”
“Firm age”	FA	“Refers to the number of years since the company’s establishment to the present day, reflecting organizational longevity.”	CNRDS
“Leverage”	Lev	“Determined as the ratio of total liabilities to total assets, assessing financial risk.”	CNRDS
“Carbon Emission”	CO2	“Represent the overall carbon footprint of Chinese-listed firms.’’	CNRDS

Note: “CNRDS; Chinese Research Data Services Platform, Bloomberg and Refinitiv”.

outlines the abbreviations, measurements, and sources of the variables used in this study.

4.4. Measurement of Variables

4.4.1. Dependent Variable

The research “dependent variable is financial performance”, a critical metric gauging a company’s profitability relative to shareholder equity. It serves as a key indicator of a company’s financial health and its capacity to generate returns for shareholders and has gained widespread usage as a metric for assessing “financial performance” [31,70].

4.4.2. Independent Variable

The independent variable under scrutiny is greenwashing (GW), which pertains to a firm’s peer-relative greenwashing score. “This score is calculated as the difference between a firm’s relative position and peers in the distribution of Bloomberg Environment disclosure scores” and Refinitiv Environmental performance scores, both normalized. This score quantifies a firm’s greenwashing tendencies by comparing its position relative to its industry peers in two key metrics: the Bloomberg Environmental disclosure score and the Refinitiv Environmental performance score. In line with [70], greenwashing is quantified through Equations (6) and (7), which standardize the environmental disclosure and performance scores. Subsequently, the greenwashing score is derived as depicted in Equation (8).

$$\mathrm{Standardized} \mathrm{Bloomberg} \mathrm{Environmental} \mathrm{disclosure} \mathrm{score}= {\displaystyle \frac{{BENVD}_{it}-{Ō}_{BENVD}}{{\delta }_{BENDV}}}$$

(6)

$$\mathrm{Adjusted} \mathrm{Refinitiv} \mathrm{Environmental} \mathrm{performance} \mathrm{score} = {\displaystyle \frac{{ENVP}_{it}-{Ō}_{ENVP}}{{\delta }_{ENVP}}}$$

(7)

$$\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}\mathrm{w}\mathrm{a}\mathrm{s}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{g}=\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{d}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d} {BENVD}_{it} - \mathrm{A}\mathrm{d}\mathrm{j}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{d} {ENVP}_{it}$$

(8)

In the formula, ${BENVD}_{it}$ represents “the Bloomberg Environmental disclosure score for a bank i at time t”, while ${ENVP}_{it}$ stands for “the Environmental performance score of Refitiniv for a bank i at time t”. ${Ō}_{BENVD}$ and ${ENVP}_{it}$ denote “the means of the environmental scores”, whereas ${\delta }_{BENDV}$ and ${\delta }_{ENVP}$ represent the standard deviations. The rationale behind normalizing “both environmental disclosure and performance scores is to standardize them onto the same scale”. In the concluding stage (Equation (8)), we deduct “the Standardized Bloomberg Environmental Disclosure score from the Adjusted Refinitiv Environmental Performance Score”, yielding the greenwashing score (refer to Table 2).

4.4.3. Control Variables

In addition to the main variables under investigation, several other independent variables have been incorporated as control measures. These include firm size, which is quantified as the natural logarithm of total assets, thus serving as a proxy for firm size. Moreover, the board variable is assessed by the total count of board directors, thereby reflecting the governance structure. Firm age indicates the number of years since the company’s establishment to the present day, thereby reflecting organizational longevity. Furthermore, leverage (LEV) is determined as the ratio of total liabilities to total assets, thus assessing financial risk. Carbon emissions of listed companies (CO₂) represent the overall carbon footprint of Chinese-listed firms. Lastly, environmental performance (ESG) is quantified as environmental social governance, thereby evaluating the company’s sustainability practices.

5. Findings Analysis

5.1. Descriptive Statistics

The descriptive statistics in

Table 3. Summary of descriptive statistics.

Variables	ROE	GW	FS	Board	FA	LEV	CO2	ESG	Tobin’s Q	ROA
Mean	0.081133	10.70570	21.95452	2.173519	2.879344	0.850072	31.48236	25.57037	0.038426	0.508477
Median	0.057761	5.328800	21.81379	2.197225	2.995732	0.538946	29.90740	23.74920	0.037759	0.004700
Maximum	221.4053	90.07250	29.30278	2.944439	3.806663	877.2559	91.01890	79.32240	62.78953	36.84670
Minimum	−85.64680	0.000000	12.31425	0.000000	0.693147	−0.194698	0.000000	0.047400	−24.97394	0.000000
Std. Dev.	3.455447	13.51201	1.659942	0.239986	0.445408	12.34161	11.50289	9.821334	0.957724	2.828014
Skewness	46.85105	2.152137	0.383712	−0.618017	−0.889733	67.52484	0.765386	0.873980	49.15154	8.794416
Kurtosis	3233.471	7.741389	4.431052	7.681254	3.789215	4771.421	4.207787	3.945280	3569.676	91.36242
Jarque–Bera	2.32 × 109	9115.637	586.1497	5210.944	842.3424	5.06 × 109	845.1557	877.8117	2.81 × 109	1793920
“Probability”	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
“Obs”	5335	5335	5335	5335	5335	5335	5335	5335	5335	5335

Source: Author.

demonstrate considerable variability across all variables, thereby uncovering notable patterns and irregularities within the dataset. Specifically, financial performance metrics, namely return on equity (ROE), return on assets (ROA), and Tobin’s Q, exhibit extreme values and substantial dispersion. ROE averages 8.1%, yet spans from −85.65% to 221.41%, with high skewness and kurtosis signaling the presence of significant outliers and deviation from normality. Likewise, the average ROA stands at a modest 0.51%, indicating limited profitability across the sample, while Tobin’s Q, with a mean of 0.038, points to a generally undervalued market view of firms. Additionally, greenwashing (GW) is positively skewed, with an average value of 10.71 and a peak of 90.07, suggesting that while a number of firms are heavily engaged in misleading environmental claims, others exhibit minimal greenwashing activity.

Moreover, examining firm-specific attributes reveals that firm size (FS) is relatively stable, showing limited variance, whereas firm age (FA) is negatively skewed, indicating a predominance of mature firms within the sample. Board composition (Board) exhibits moderate variability and slight negative skewness, which implies a lower incidence of firms with large boards. On the other hand, leverage (LEV) presents the most pronounced extremities, with a mean of 0.85, a maximum of 877.25, and a skewness exceeding 67, highlighting substantial financial risk in certain firms.

From an environmental perspective, CO₂ emissions (CO₂) average 31.48, highlighting a substantial environmental footprint among firms, while ESG scores average 25.57, indicating a moderate level of commitment to sustainability practices. The ESG variable also shows considerable variability, with scores ranging from 0.047 to 79.32, a standard deviation of 9.82, and a positively skewed distribution, suggesting that while some firms perform well, many lag in ESG engagement. Moreover, the significant Jarque–Bera statistic (p = 0.000), along with the presence of elevated skewness and leptokurtic distributions across all variables, confirms strong departures from normality and underscores the need to apply robust econometric methods in subsequent analyses. Crucially, the dataset includes a uniform total of 5335 observations for each variable, which enhances the reliability and consistency of the analysis. This balanced structure minimizes data imbalance and ensures that every variable contributes equally to the empirical evaluation, ultimately reinforcing the credibility and robustness of the study’s results.

5.2. Correction Matrix

The correlation matrix displayed in

Table 4. Correlation matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	9	10
(1) ROE	1.0000
(2) GW	−0.0256	1.0000
(3) FS	0.0003	0.0136	1.0000
(4) Board	−0.0037	0.0290	0.2143	1.0000
(5) FA	−0.0078	0.0071	0.3701	−0.0823	1.0000
(6) LEV	−0.0413	0.0254	−0.1139	0.0016	0.0024	1.0000
(7) CO2	−0.0141	−0.1272	0.0354	0.0219	−0.0081	−0.0062	1.0000
(8) ESG	−0.0288	0.5913	0.0288	0.0333	−0.0180	0.0060	0.3489	1.0000
(9) Tobin’s Q	0.0059	0.0341	−0.0107	0.0142	0.0003	0.0045	0.0080	0.0147	1.0000
(10) ROA	−0.0312	0.0448	0.1226	−0.0398	0.1102	−0.0042	0.0007	0.0290	−0.0003	1.0000

Source: Author.

reveals that return on equity (ROE) exhibits slight negative correlations with GW (−0.0256), leverage (−0.0413), and ESG scores (−0.0288), implying that increases in these variables are marginally linked to declines in firm financial performance. In contrast, GW shows a strong positive correlation with ESG scores (0.5913), suggesting that firms engaging more in greenwashing tend to report elevated ESG ratings, potentially indicating reputational management rather than authentic sustainability efforts. Additionally, GW maintains weak positive associations with board composition (0.0290) and leverage (0.0254), but a moderate negative correlation with CO₂ emissions (−0.1272), pointing to discrepancies between firms’ environmental disclosures and their actual environmental impact. Furthermore, firm size (FS) is positively correlated with both firm age (0.3701) and board composition (0.2143), indicating that older and larger firms tend to have more extensive boards. FS also shows a slight positive correlation with ROA (0.1226), hinting that larger firms may operate with marginally greater efficiency. ESG scores also display a moderate positive relationship with CO₂ emissions (0.3489), which may suggest that firms with higher environmental impact are simultaneously scoring higher on ESG metrics, possibly reflecting compensatory green strategies or image-enhancing disclosures. Lastly, Tobin’s Q and ROA maintain weak or negligible correlations with most variables. Collectively, these results affirm the absence of significant multicollinearity concerns, thereby supporting the robustness of the forthcoming econometric estimations.

5.3. Summary Unit Root Test

Table 5. Summary unit root test results.

Series	Level	1st ∆
“LLC”	0.0000 (−73.5236)	1.0000 (71.9348)
“IPS”	0.0000 (−71.4952)	0.0000 (−66.4403)
“ADF-Fisher”	0.0000 (991.845)	0.0000 (1571.93)
“PP-Fisher”	0.0000 (726.286)	0.0000 (110.524)

Remark: “Statistics is in (); 1st ∆ is the first difference, LLC: Levin, Lin & Chu t*; IPS: Im, Pesaran and Shin W-stat; ADF—Fisher Chi-square; PP—Fisher Chi-square”.

provides a summary of the unit root test results for the series examined in the study. The series analyzed include “LLC (Levin, Lin & Chu t* statistic), IPS (Im, Pesaran and Shin W-statistic), ADF-Fisher Chi-square, and PP-Fisher Chi-square”. The unit root test results indicate whether a time series is stationary or non-stationary. A stationary time series exhibits stable mean and variance over time, while a non-stationary series shows trends or cycles. For each series, the table presents the p-value of the unit root test, denoted as “Level” and “1st ∆” (first difference). A p-value of 0.0000 indicates strong evidence against the presence of a unit root, suggesting that the series is stationary. For the LLC series, “both the level and first difference tests yield statistically significant results with p-values of 0.0000”. This suggests that the series exhibits unit root properties, indicating non-stationarity in the data. The test statistics for the LLC series are presented in parentheses, with the level test statistic (−73.5236) and the first difference test statistic (71.9348). Similarly, for the IPS series, the level test results indicate a statistically significant p-value of 0.0000, confirming the presence of unit root properties. However, the first difference test yields a p-value of 0.0000 as well, indicating stationarity after differencing. The test statistics for the IPS series are provided in parentheses, with the level test statistic (−71.4952) and the first difference test statistic (−66.4403). Furthermore, “both the ADF-Fisher and PP-Fisher tests show statistically significant results with p-values of 0.0000”. The outcome from the LLC and IPS tests also indicate the presence of unit root properties in the series under examination.

5.4. VAR Lag Order Selection Criteria

Table 6. VAR Lag Order Selection Criteria.

“Lag”	“LogL”	“LR”	“FPE”	“AIC”	“SC”	“HQ”
0	−107673.1	NA	47675926	40.38295	40.39283	40.38640
1	−96030.88	23245.23	620212.4	36.04083	36.12969	36.07188
2	−95505.61	1047.195 *	521690.4 *	35.86785 *	36.03569 *	35.92648 *

Source: Author. Note: * “indicates lag order selected by the criterion; statistic (each test at 5% level).”

reveals the outcome of the “VAR Lag Order Selection Criteria which are crucial for determining the appropriate lag order for the VAR model”. The lag order refers to the number of past periods included in the model to predict future values. The table includes several criteria used to evaluate different lag orders: “Log Likelihood (LogL), Likelihood Ratio (LR), Final Prediction Error (FPE), Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn Criterion (HQ)”. For each “lag order (0, 1, 2)”, the table provides the corresponding values of these criteria. These values serve as indicators of model fit and help in selecting the optimal lag order. For instance, when considering a lag order of 0, the Log Likelihood (LogL) value is −107673.1, and other criteria such as AIC, SC, and HQ are also provided. However, in this case, the Likelihood Ratio (LR) and Final Prediction Error (FPE) are not applicable (NA). As the lag order increases to 1 and 2, we observe changes in the values of the criteria. For example, the Log Likelihood (LogL) increases from −96030.88 (lag order 1) to −95505.61 (lag order 2). Similarly, the Likelihood Ratio (LR) and “Final Prediction Error (FPE)” also change accordingly. The selection of the optimal lag order involves comparing these criteria across different lag orders. Lower values of AIC, SC, and HQ indicate better model fit, while higher “Log Likelihood (LogL) and Likelihood Ratio (LR)” values are preferred. Therefore, based on these criteria, a lag order of 2 may be considered optimal in this case, as it yields relatively lower values for “AIC, SC, and HQ compared to lag order 1”.

5.5. PVAR/GMM Outcome

The results of a panel VAR/GMM estimation presented in

Table 7. Panel VAR/GMM outcome.

Items	ROE	GW	Size	Board	Firm Age	Lev	CO2	ESG	Tobin’s Q	ROA
ROE(−1)	0.009109	−0.018193	−0.005053	−0.001275	0.002771	0.029491	−0.004324	0.024408	−0.001156	0.021166
	(0.01379)	(0.04370)	(0.00320)	(0.00067)	(0.00119)	(0.04951)	(0.03601)	(0.03160)	(0.00386)	(0.00609)
	[0.66042]	[−0.41628]	[−1.57896]	[−1.91629]	[2.32701]	[0.59561]	[−0.12005]	[0.77236]	[−0.29975]	[3.47516]
GW(−1)	−0.005081	0.482588	0.000857	0.000264	0.000657	0.001809	−0.026696	−0.011642	0.003352	−0.002662
	(0.00451)	(0.01428)	(0.00105)	(0.00022)	(0.00039)	(0.01618)	(0.01442)	(0.01032)	(0.00126)	(0.00199)
	[−1.12760]	[33.7976]	[0.81940]	[1.21342]	[1.68879]	[0.11180]	[−1.85194]	[−1.12756]	[2.66084]	[−1.33763]
Size(−1)	0.019084	−0.132858	0.886435	0.011575	−0.014813	−0.295224	0.108177	0.018811	0.001600	−0.027222
	(0.03229)	(0.10233)	(0.00749)	(0.00156)	(0.00279)	(0.11593)	(0.19860)	(0.07399)	(0.00903)	(0.01426)
	[0.59096]	[−1.29836]	[118.295]	[7.42984]	[−5.31378]	[−2.54664]	[0.54469]	[0.25424]	[0.17727]	[−1.90894]
Board(−1)	0.003608	0.318267	0.098806	0.688781	0.019831	0.895655	0.087888	0.576194	0.030407	−0.019204
	(0.20599)	(0.65273)	(0.04780)	(0.00994)	(0.01778)	(0.73948)	(0.74729)	(0.47198)	(0.05759)	(0.09096)
	[0.01751]	[0.48759]	[2.06710]	[69.3131]	[1.11520]	[1.21119]	[0.11761]	[1.22081]	[0.52801]	[−0.21112]
Firmage(−1)	−0.079739	−0.220797	−0.121323	−0.036328	0.761915	0.405903	−0.081461	−0.477475	−0.001588	0.052495
	(0.11686)	(0.37029)	(0.02712)	(0.00564)	(0.01009)	(0.41950)	(0.52543)	(0.26775)	(0.03267)	(0.05160)
	[−0.68235]	[−0.59628]	[−4.47419]	[−6.44426]	[75.5287]	[0.96759]	[−0.15504]	[−1.78330]	[−0.04861]	[1.01731]
Lev(−1)	−2.70 × 10−5	−0.017105	0.001854	0.000102	−7.73 × 10−5	0.106808	−0.012866	−0.000870	−0.000659	−0.000310
	(0.00385)	(0.01219)	(0.00089)	(0.00019)	(0.00033)	(0.01381)	(0.01039)	(0.00882)	(0.00108)	(0.00170)
	[−0.00702]	[−1.40296]	[2.07660]	[0.54752]	[−0.23272]	[7.73254]	[−1.23885]	[−0.09869]	[−0.61259]	[−0.18258]
CO2(−1)	−0.000789	−0.038087	−0.000612	−0.000284	−0.000121	−0.001694	0.491314	0.086241	0.000130	−0.003208
	(0.00514)	(0.01628)	(0.00119)	(0.00025)	(0.00044)	(0.01844)	(0.01524)	(0.01177)	(0.00144)	(0.00227)
	[−0.15353]	[−2.33946]	[−0.51322]	[−1.14611]	[−0.27203]	[−0.09183]	[32.2320]	[7.32602]	[0.09036]	[−1.41403]
ESG(−1)	0.001486	0.235671	0.002342	0.000199	−0.000231	0.037680	0.171138	0.521143	−0.001879	0.008319
	(0.00808)	(0.02561)	(0.00188)	(0.00039)	(0.00070)	(0.02901)	(0.02340)	(0.01852)	(0.00226)	(0.00357)
	[0.18385]	[9.20212]	[1.24866]	[0.50946]	[−0.33145]	[1.29867]	[7.31247]	[28.1419]	[−0.83141]	[2.33084]
Tobin’s Q(−1)	0.016606	0.365388	0.071996	0.000586	−0.001671	−0.088510	0.395744	0.305424	0.004118	−0.020728
	(0.04929)	(0.15619)	(0.01144)	(0.00238)	(0.00426)	(0.17695)	(0.14273)	(0.11294)	(0.01378)	(0.02177)
	[0.33690]	[2.33936]	[6.29449]	[0.24657]	[−0.39269]	[−0.50020]	[2.77263]	[2.70433]	[0.29882]	[−0.95230]
ROA(−1)	−0.042965	0.103166	0.000165	−0.002434	0.000858	−0.002589	0.000571	0.064074	−0.000668	0.843210
	(0.01691)	(0.05357)	(0.00392)	(0.00082)	(0.00146)	(0.06069)	(0.04895)	(0.03873)	(0.00473)	(0.00746)
	[−2.54156]	[1.92594]	[0.04210]	[−2.98449]	[0.58770]	[−0.04266]	[0.01166]	[1.65425]	[−0.14124]	[112.957]
C	−0.067438	3.221099	2.569205	0.527075	0.969047	3.188436	9.089339	10.00612	−0.048374	0.455459
	(0.72360)	(2.29285)	(0.16790)	(0.03491)	(0.06246)	(2.59757)	(2.09527)	(1.65791)	(0.20229)	(0.31952)
	[−0.09320]	[1.40485]	[15.3015]	[15.0996]	[15.5137]	[1.22747]	[4.33804]	[6.03539]	[−0.23913]	[1.42543]

Note: t-statistics are in [].

indicate that ROE(−1) is positively associated with ROA (0.0212) and ESG (0.0244), suggesting that past profitability marginally enhances current financial returns and environmental engagement. Although its influence on other variables such as size, board, and Tobin’s Q is negative, the effects are statistically insignificant due to low z-statistics. Conversely, GW(−1) exhibits a strong and highly significant positive effect on itself (0.4826), with a large z-statistic of 33.80, while exerting a negative and significant effect on CO₂ (−0.0267) and a positive impact on Tobin’s Q (0.0034), indicating that better governance perception or greenwashing practices may reduce emissions and influence market valuation. With regard to size(−1), it strongly and positively influences itself (0.8864) and board structure (0.0116), with highly significant z-values (118.30 and 7.43, respectively), although it negatively affects firm age (−0.0148) and leverage (−0.2952), implying that larger firms may be younger and rely less on debt. Interestingly, board(−1) has a profound positive effect on itself (0.6888) with an extremely high z-statistic of 69.31, and positively influences size (0.0988), suggesting that stronger boards correlate with firm growth and structural governance.

Firm age(−1) has a significantly strong positive effect on itself (0.7619), indicating persistence over time, and a negative relationship with size (−0.1213) and board structure (−0.0363), both statistically significant, implying older firms may resist expansion or governance reforms. Leverage(−1) significantly impacts itself (0.1068) and size (0.0019), implying leverage persistence and a potential size effect. However, its negative influence on other variables, such as ESG and CO₂, is largely insignificant. Regarding environmental indicators, CO₂(−1) is highly significant in explaining itself (0.4913), with a robust z-statistic of 32.23, and positively affects ESG (0.0862), indicating that firms with higher emissions may respond with improved ESG disclosures. ESG(−1), in turn, is significantly associated with itself (0.5211) and strongly influences CO₂ (0.1711) and ROA (0.0083), suggesting that strong ESG commitments reduce emissions and support financial outcomes. Furthermore, Tobin’s Q(−1) significantly affects itself (0.0041), ESG (0.3054), and CO₂ (0.3957), highlighting that market valuation dynamics positively influence environmental and sustainability performance. Finally, ROA(−1) shows a strong persistence (0.8432) and significantly influences Tobin’s Q (0.0641) and negatively affects board structure (−0.0024), indicating that profitability leads to better market perception but could reduce governance reform needs. The constant terms across variables are generally significant in explaining levels, particularly for size, board, firm age, and ESG, suggesting strong base effects in the system.

5.6. Robustness Check

Table 8. Robust least square.

Variable	Coefficient	Std. Error	Z-Statistic	Prob.
GW	−7.13 × 10−6	0.000104	−0.068371	0.9455
Size	0.012929	0.000746	17.33918	0.0000
Board	0.019667	0.004760	4.131375	0.0000
Firm age	−0.014466	0.002703	−5.351865	0.0000
Lev	0.000694	8.91 × 10−5	7.789811	0.0000
CO2	−6.77 × 10−5	0.000119	−0.569734	0.5689
ESG	0.000198	0.000187	1.060713	0.2888
Tobin’s Q	0.018764	0.001142	16.43331	0.0000
ROA	0.001599	0.000391	4.085393	0.0000
C	−0.224185	0.016702	−13.42275	0.0000

Source: Author.

robustness checks employing robust least squares (RLS) regression yield critical insights into financial performance determinants. Notably, firm size exhibits the strongest positive association (coefficient = 0.0129, p < 0.001), underscoring that larger firms consistently achieve superior performance. Similarly, both Tobin’s Q (coefficient = 0.0188, p < 0.001) and leverage (coefficient = 0.000694, p < 0.001) demonstrate significant positive effects, implying that market valuation and strategic debt financing enhance financial outcomes. Conversely, firm age exerts a robust negative influence (coefficient = −0.0145, p < 0.001), which may reflect younger firms’ competitive advantages in agility or innovation. Additionally, board size shows a modest yet statistically significant positive effect (coefficient = 0.0197, p < 0.001), reinforcing governance’s role in driving performance. While return on assets (ROA) has a marginal positive impact (coefficient = 0.0016, p < 0.001), surprisingly, sustainability metrics, greenwashing (GW), CO₂ emissions, and ESG performance fail to show statistically significant relationships (p > 0.05), suggesting their limited direct influence in this context. Theoretically, the negative intercept (coefficient = −0.224, p < 0.001) establishes a baseline when all predictors are zero. Crucially, these results remain robust after controlling for heteroskedasticity and outliers, emphasizing the primacy of structural and financial factors over sustainability indicators in shaping performance.

5.7. VAR Pairwise Granger Causality Tests

The Granger causality in

Table 9. VAR pairwise Granger causality test.

Null Hypothesis	Causal Direction	F-Statistic	Prob.
“GW does not Granger Cause ROE”	No Causal Direction	1.17930	0.3076
“ROE does not Granger Cause GW”		0.32972	0.7191
“Firm size does not Granger Cause ROE”	Unidirectional Causality	0.11454	0.8918
“ROE does not Granger Cause Firm size”		16.8121	5 × 10−8
“Board does not Granger Cause ROE”	No Causal Direction	0.39865	0.6712
“ROE does not Granger Cause Board”		2.03115	0.1313
“Firmage does not Granger Cause ROE”	Unidirectional Causality	0.74121	0.4766
“ROE does not Granger Cause Firmage”		2.93564	0.0532
“LEV does not Granger Cause ROE”	Unidirectional Causality	0.00772	0.9923
“ROE does not Granger Cause LEV”		38.5907	2 × 10−17
“CO2 does not Granger Cause ROE”	No Causal Direction	0.22339	0.7998
“ROE does not Granger Cause CO2”		0.00948	0.9906
“ESG does not Granger Cause ROE”	No Causal Direction	0.45039	0.6374
“ROE does not Granger Cause ESG”		0.80649	0.4465
“Tobin’s Q does not Granger Cause ROE”	Unidirectional Causality	0.03860	0.9621
“ROE does not Granger Cause Tobin’s Q”		2.81038	0.0603
“ROA does not Granger Cause ROE”	Bidirectional Causality	3.89786	0.0203
“ROE does not Granger Cause ROA”		5.63176	0.0036

Source: Author.

reveals unidirectional causality flowing from ROE to both firm size (F = 16.812, p = 5 × 10⁻⁸) and leverage (F = 38.591, p = 2 × 10⁻¹⁷), indicating that profitability drives subsequent growth and capital structure adjustments. Similarly, weaker but still significant unidirectional effects exist from ROE to firm age (F = 2.936, p = 0.053) and Tobin’s Q (F = 2.810, p = 0.060), suggesting that profitability may influence longevity and market valuation. Notably, a bidirectional relationship emerges between ROE and ROA, with significant causality in both directions (ROA→ROE: F = 3.898, p = 0.020; ROE→ROA: F = 5.632, p = 0.004), demonstrating mutual reinforcement between asset efficiency and equity returns. In contrast, several variables show no Granger causal relationships with ROE: greenwashing (GW), board, CO₂ emissions, and ESG performance all exhibit statistically insignificant results in both directions (all p > 0.05), implying these factors operate independently of short-term profitability dynamics in this analysis. The results collectively highlight that while financial and structural factors like size, leverage, and asset returns demonstrate clear predictive relationships with ROE, sustainability metrics and governance characteristics appear disconnected from profitability in the temporal framework examined.

5.8. VAR Stability Test

The stability test is crucial to ensure that the model reliably captures the relationships among variables throughout time. If the model lacks stability, its parameters may not represent the true dynamics within the dataset, which can compromise the accuracy of forecasts. Unstable models risk producing unreliable predictions and misleading outcomes, as they may exhibit spurious relationships, “such as false causality and incorrect parameter estimations”. The “stability tests” thus serve as a safeguard, “confirming that the estimated relationships are robust, significant, and accurately represent true patterns within the data”. A stable VAR model provides dependable estimates of impulse responses and the dynamic interactions between variables, thereby improving the economic interpretation of these relationships. As shown in Figure 4, where all points are contained within “the unit circle, the model meets the stability condition”, confirming its robustness. This stability indicates that the model not only reflects the actual relationships in the data but also maintains its predictive accuracy over time, thereby supporting more reliable decision-making and long-term forecasting.

5.9. Analysis of Impulse Response Functions

The outcome from “the impulse response function (IRF) analysis” conducted within the “VAR/GMM framework” is illustrated in Figure 5 below. In each graph, the solid lines “represent the orthogonal IRF of the respective variable across the entire timeframe”. The responses depicted in Figure 5 highlight the significant impact of various factors such as greenwashing, firm size, board, firm age, leverage, carbon emissions, and environmental, social, and governance (ESG) performance on current financial performance. These findings offer valuable insights into the intricate dynamics that govern financial outcomes. Moreover, by examining the responses shown in Figure 5, it becomes evident how fluctuations in one variable can create ripple effects throughout the system, influencing other interconnected variables. For instance, changes in greenwashing (GW) activities may not only have a direct impact on financial performance but also affect related factors such as size, board, and environmental performance (ESG scores). Similarly, variations in carbon emissions (CO₂) and firmage may trigger cascading effects on financial metrics and governance practices. Furthermore, the visual representation provided by Figure 5 enhances our comprehension of the complex relationships among these variables, thereby emphasizing their interconnectedness and interdependence within the corporate ecosystem. Consequently, understanding these dynamics is crucial for making informed decisions that enhance financial performance while addressing sustainability and governance challenges.

6. Discussion

The results from each analysis offer valuable insights that corroborate existing literature and reinforce well-established theoretical frameworks. A comprehensive discussion is provided in a dedicated sub-section, where these findings are critically examined in terms of their implications, consistency with prior studies, and the underlying transmission channels through which their effects unfold.

6.1. Financial Performance and Firm Characteristics

The results presented in Table 7 demonstrate that ROA(−1) displays strong persistence (0.8432) and exerts a positive influence on Tobin’s Q (0.0641), thereby supporting the premise that enhanced profitability contributes to higher market valuation, consistent with [80]. This reflects a transmission channel in which improved return on assets fosters greater investor confidence, subsequently elevating firm valuation through increased expectations of future earnings potential. In a similar vein, ROE(−1) exhibits a modest yet positive effect on both ROA (0.0212) and ESG performance (0.0244), implying that prior profitability may modestly stimulate improvements in both financial and sustainability outcomes, in line with the findings of [43,44,45]. The implied transmission mechanism suggests that higher equity returns generate internal financial slack, which can be strategically redirected to bolster operational effectiveness and advance ESG initiatives across the 312 Chinese-listed firms. Furthermore, the RLS robustness checks reported in Table 8 reinforce that firm size (0.0129, p < 0.001) and Tobin’s Q (0.0188, p < 0.001) serve as significant determinants of financial performance, echoing existing research that underscores the importance of scale and investor perception as key success enablers [81]. The corresponding transmission channels indicate that larger firms benefit from economies of scale and enhanced resource mobilization, which translate into superior operational efficiency and profitability. Likewise, a higher Tobin’s Q reflects favorable market sentiment, which improves access to external financing and investment opportunities. Conversely, the negative relationship between firm age and financial performance (−0.0145, p < 0.001) suggests that younger firms tend to outperform their older counterparts, potentially due to their greater strategic flexibility and innovation capacity, as noted by [82]. This reveals a transmission channel whereby younger enterprises capitalize on agility and innovation-driven practices to achieve superior financial outcomes relative to more mature and structurally rigid firms. These insights are further corroborated by the Granger causality tests in Table 9, which uncover a bidirectional relationship between ROA and ROE, underscoring a mutually reinforcing dynamic between asset utilization efficiency and equity returns, consistent with the resource-based view of the firm [41]. This bidirectional mechanism reflects a feedback loop in which enhanced profitability strengthens the firm’s reinvestment capability, which, in turn, sustains or amplifies future financial performance.

6.2. Governance and Firm Structure Dynamics

The findings in Table 7 reveal that board(−1) exerts a strong positive effect on both itself (0.6888) and size (0.0988), providing support for agency theory, which asserts that robust governance structures promote firm growth [60,61]. The implied transmission channel suggests that, among the 312 Chinese-listed companies, a well-established board framework strengthens strategic oversight and minimizes agency costs, thus fostering company growth and scalability. Furthermore, size(−1) positively affects board structure (0.0116), reinforcing the notion that larger firms tend to adopt more formalized governance structures [57,58]. This highlights a transmission channel where the increasing complexity of a firm’s operations necessitates a more sophisticated and structured governance system, resulting in more elaborate board compositions to manage an expanding operational scope. However, the Granger causality tests in Table 9 show no significant relationship between ROE and board size, suggesting that governance reforms do not directly impact short-term profitability. This finding is consistent with the mixed evidence in corporate governance research [41,42] and indicates a transmission channel where adjustments in board composition may affect long-term strategic direction and risk management but have minimal immediate influence on return on equity.

6.3. ESG, CO₂, and Greenwashing and Financial Outcomes

The results in Table 7 demonstrate that ESG(−1) significantly lowers CO₂ emissions (0.1711) while simultaneously enhancing ROA (0.0083), lending support to stakeholder theory, which posits that sustainability efforts contribute to long-term financial value [43,44,45]. For the 312 Chinese-listed firms, the transmission channel indicates that active ESG implementation improves environmental performance by curbing carbon emissions, thereby boosting operational efficiency and profitability through better resource utilization and strengthened stakeholder relationships. In contrast, CO₂(−1) exerts a positive influence on ESG (0.0862), implying that firms with higher emissions may intensify ESG disclosures as a compensatory mechanism consistent with legitimacy theory [55,56]. This suggests a transmission pathway where high-emitting firms employ ESG reporting to preserve social legitimacy and maintain investor trust, functioning as a strategic reputational safeguard. Meanwhile, the RLS findings in Table 8 reveal no significant association between sustainability variables (GW, CO₂, ESG) and financial outcomes, which diverges from the VAR results. This contrast highlights a different transmission dynamic: whereas the VAR framework captures evolving and lagged effects of sustainability on financial performance, the static nature of RLS may overlook these delayed impacts indicating that ESG benefits may materialize incrementally over time rather than immediately [30,31]. Additionally, Granger causality results in Table 9 confirm that ESG and CO₂ do not cause ROE, further reinforcing the notion that the financial returns from sustainability initiatives are more likely to emerge over extended periods. This points to a transmission channel whereby ESG efforts shape financial outcomes through indirect, time-lagged mechanisms such as enhanced brand equity, regulatory alignment, and risk reduction, which are not immediately reflected in short-term profitability metrics [48,50].

6.4. Market Valuation (Tobin’s Q) and Leverage Effects

The findings in Table 7 reveal that Tobin’s Q(−1) exerts a positive influence on both ESG (0.3054) and CO₂ emissions (0.3957), indicating that firms with higher market valuation are more inclined to invest in sustainability initiatives, consistent with signaling theory [48,49]. This reflects a transmission channel through which firms with elevated Tobin’s Q seek to reinforce investor trust and sustain long-term market positioning by amplifying ESG reporting and environmental strategies, even when current emission levels remain high. In parallel, Lev(−1) negatively affects size (−0.2952) but demonstrates persistence (0.1068), in line with pecking order theory, which posits that firms prioritize internal financing over external debt [67,68]. This suggests a transmission mechanism where higher leverage may hinder firm growth due to the burden of debt servicing, especially within capital-intensive industries, prompting Chinese-listed companies to rely more heavily on retained earnings to support expansion. Furthermore, the RLS estimates in Table 8 confirm a significant positive effect of leverage on performance (0.000694, p < 0.001), corroborating trade-off theory, which maintains that moderate levels of debt can enhance firm efficiency [69]. The corresponding transmission channel implies that optimal debt structures can promote financial discipline and enable strategic allocation of resources, thereby fostering improved financial performance among the analyzed firms.

7. Conclusions

This study provides an in-depth analysis of the intricate relationships among greenwashing, environmental performance (ESG), firm-specific characteristics, board composition, firm age, size, leverage, and carbon emissions (CO₂) in connection with financial performance. By applying a combination of panel VAR/GMM estimation, robust least squares regression, and Granger causality tests, the research draws upon comprehensive data spanning from 2009 to 2022 sourced from the Chinese Research Data Services Platform (CNRDS), Bloomberg, and Refinitiv. The dataset comprises 312 listed Chinese firms, yielding a total of 5335 observations, thereby offering rich insights into the dynamic interplay among these critical variables.

The results reveal that profitability, particularly past return on equity, serves as a reinforcing factor for both financial and environmental outcomes, as it positively influences subsequent returns and ESG engagement. Nevertheless, its impact on structural dimensions such as firm size, board structure, and market valuation appears limited and statistically weak. Furthermore, governance-related elements, especially greenwashing, exhibit a dual nature. On the one hand, they demonstrate strong internal consistency; on the other, they significantly influence both environmental outcomes and market perceptions. Additionally, firm size, in particular, emerges as a key structural determinant. It not only exhibits strong persistence over time but also significantly shapes governance frameworks and capital structure decisions. At the same time, board dynamics are positively correlated with firm growth and reflect ongoing governance development. Moreover, variables such as leverage and return on assets show consistent persistence and exert notable influence across various firm characteristics. While leverage can positively contribute under favorable conditions, its broader impact on sustainability and governance remains relatively constrained. Similarly, environmental variables also present notable feedback mechanisms. Specifically, higher carbon emissions tend to trigger increased ESG disclosures, potentially as a form of compensatory signaling. In return, stronger ESG engagement contributes to emission reductions and modestly enhances financial performance, thus illustrating a pathway through which environmental responsibility aligns with long-term profitability. Furthermore, market valuation measured through Tobin’s Q also plays a central role. It significantly influences sustainability behaviors, indicating that firms actively respond to investor expectations by improving ESG performance.

The robust least squares result further emphasizes the dominant role of structural and financial fundamentals, particularly firm size, market valuation, and leverage, in driving financial performance. In contrast, sustainability metrics such as ESG scores, emissions, and greenwashing indicators do not demonstrate any statistically significant direct influence, suggesting that short-term financial outcomes remain primarily governed by traditional financial and organizational factors. Lastly, the Granger causality analysis confirms unidirectional relationships from profitability to key structural elements, including size, leverage, firm age, and market valuation. A notable bidirectional relationship between return on assets and return on equity also underscores the mutual reinforcement between operational efficiency and shareholder value. Meanwhile, sustainability and governance-related variables exhibit no causal impact on profitability, implying that their influence may be more indirect or become evident over longer time horizons.

7.1. Practical Policy Implication

Based on the findings, several practical policy implications can be drawn to enhance corporate performance and sustainability integration. Firstly, given the limited direct effect of ESG performance, greenwashing, and CO₂ emissions on profitability, it is essential to enhance ESG accountability by mandating more rigorous disclosure frameworks and strengthening third-party audit mechanisms to ensure genuine integration rather than symbolic compliance. Furthermore, since ESG variables positively influence financial outcomes such as ROA in lagged relationships, policymakers should introduce performance-based incentives, such as tax credits or preferential loans, to reward firms demonstrating sustained ESG improvement. In addition, as older firms are found to resist governance reforms and expansion, targeted governance renewal programs offering regulatory flexibility and leadership development should be promoted. Simultaneously, the positive association between firm size, leverage, and financial performance suggests the need to support younger firms through improved access to capital markets, credit guarantees, and financial literacy programs. Moreover, as Tobin’s Q is positively linked to ESG performance and CO₂ mitigation, aligning market valuation with sustainability commitments via green indexing and sustainability-based benchmarks becomes crucial. Reinforcing governance levers is also important, particularly by issuing guidelines to enhance board diversity, independence, and environmental expertise, especially in high-impact sectors. In line with the observed sectoral variation in ESG impacts, industry-specific sustainability standards and transition frameworks should be developed to address differentiated challenges across sectors. Finally, since ESG and governance variables do not exhibit immediate effects on profitability, policies should shift focus from short-term financial metrics to long-term value creation, thereby encouraging investments in sustainable practices with long-term strategic returns. Collectively, these recommendations call for a comprehensive and forward-looking policy approach that balances financial stability, corporate governance, and environmental responsibility.

7.2. Limit of the Study

Despite the comprehensive scope of this study, several limitations should be acknowledged. First, the analysis focuses exclusively on listed Chinese firms, which may limit the generalizability of the findings to other economies with different regulatory environments, corporate governance standards, or levels of ESG maturity. As such, the results may not fully capture the dynamics of unlisted firms or those operating in emerging or developed markets outside China. Second, while the study employs robust methodologies, including panel VAR/GMM, robust least squares, and Granger causality, its design remains largely quantitative. Consequently, it may not fully capture the qualitative nuances of greenwashing behavior, managerial intent, or stakeholder perceptions, which could provide valuable context to the statistical relationships observed. Third, the temporal scope (2009–2022) covers a period of significant policy and market evolution, especially regarding ESG and climate-related disclosure practices. However, potential structural breaks or regime shifts over this period are not explicitly addressed, which may affect the consistency of some relationships over time. Fourth, the robustness check findings indicate that sustainability-related variables such as greenwashing and ESG scores have no direct impact on profitability. Yet, this may reflect a limitation of the model’s temporal structure, suggesting the need to investigate longer-term horizons or lagged effects that could reveal delayed impacts of sustainability efforts on financial outcomes.

7.3. Future Research Directions

To advance the current body of research, several promising directions can be pursued. Firstly, comparative cross-country analyses involving firms from various institutional backgrounds such as developed economies, transitional markets, or regions like Africa and Latin America could illuminate differences in the ESG–ESG performance relationship and assess whether the observed patterns are globally consistent or context-specific. Secondly, employing longitudinal and regime-sensitive models, including time-varying parameters or regime-switching techniques, would allow researchers to better capture structural transformations in ESG practices, regulatory developments, and shifting investor expectations, particularly during pivotal periods such as the post-Paris Agreement period or the COVID-19 crisis. Thirdly, incorporating qualitative or mixed-method approaches, such as interviews or detailed case studies, could offer nuanced insights into the drivers of greenwashing, the practical effectiveness of ESG governance mechanisms, and how stakeholders influence corporate sustainability behavior. Additionally, industry-specific investigations may highlight sectoral differences in ESG implementation, governance strategies, and environmental responsiveness, especially in high-emission sectors like energy, manufacturing, and transportation. Furthermore, examining the role of intangible assets and innovation, such as brand equity, corporate reputation, or green technology initiatives, could reveal how ESG performance interacts with a firm’s strategic and non-physical resources. Lastly, conducting long-term impact assessments using dynamic panel models or survival analysis would help identify delayed or indirect effects of sustainability initiatives on firm performance that may not be apparent in short-term evaluations. By exploring these directions, future research can offer a more comprehensive understanding of the multifaceted links between environmental responsibility, governance structures, and corporate financial outcomes in an increasingly sustainability-driven global economy.