The hidden perils of greenwashing: an empirical study on default risk in Chinese a-share listed firms

Abstract

Introduction:

In the context of global climate governance, corporate environmental performance is becoming critical for market competitiveness. However, some enterprises may cultivate false environmental credentials through greenwashing practices, triggering information asymmetry and market trust crises.

Methods:

Using data from Chinese A-share listed companies spanning 2009–2022, this study constructs a proxy for potential greenwashing based on the standardized gap between ESG disclosure scores (Bloomberg) and ESG performance ratings (Huazheng), which captures the divergence between disclosure intensity and external assessment. This disclosure‐rating gap serves as an empirical proxy for the deliberate exaggeration or selective reporting that characterizes greenwashing behavior. Building on this, fixed‐effects models and mediation tests quantitatively deconstruct the 'black box' mechanism through which greenwashing amplifies corporate default risk.

Results:

The core empirical contribution of this study lies in confirming greenwashing as a significant predictor of corporate debt default, while deeply deconstructing its transmission logic through three channels: market reputation, legal repayment, and stock price volatility.

Discussion:

The findings address the evidence gap between environmental performance and hard financial risks, and provide regulators with a comprehensive risk transmission roadmap for implementing differentiated financial oversight during green transitions.

1 Introduction

In the context of the global climate crisis and sustainable development becoming an international consensus, corporate environmental performance has evolved from an ethical choice into a core factor determining long-term survival and market competitiveness. At the policy level, governments and international organisations are encouraging green and low-carbon transitions through robust regulations. Examples include the European Union’s Sustainable Finance Disclosure Regulation (SFDR)¹ and the US Securities and Exchange Commission’s (SEC)² proposal to enhance and standardise climate-related disclosures, both of which are aimed at rigorously combating greenwashing by corporations. The United Nations Environment Programme (UNEP) has also explicitly warned in its 2022 Emissions Gap Report³ of the national-level risk of greenwashing stemming from “the vagueness of net-zero pledges and the absence of implementation”. Since 2020, China has committed to the “3,060”⁴dual-carbon targets—peaking carbon emissions by 2030 and achieving carbon neutrality by 2060—which have been progressively embedded into the nation’s legislative and regulatory architecture. At the societal and market levels, there has been a proliferation of greenwashing as enterprises use selective disclosure, ambiguous language, or outright false claims to cultivate an exaggerated ‘green’ image. This strategy aims to align with policy objectives, secure green financing, and attract investor favour. These practices have prompted investigations and penalties from regulatory bodies across multiple nations. In the United States, the Securities and Exchange Commission (SEC) imposed fines totaling millions of dollars on major financial institutions—including Goldman Sachs ($4 million)⁵, and DWS ($19 million)⁶—for misstatements regarding their ESG investment processes.

Academic research has previously identified that greenwashing firms face heightened risks of stock price collapses (Zhang et al., 2024). However, existing studies have yet to systematically examine the impact of greenwashing on corporate default risk, a gap that motivates the present study. Using a panel dataset of Chinese A-share listed firms, we investigate this relationship and explore the underlying mechanisms through which a disclosure-performance gap may translate into heightened credit risk.

The term ‘greenwashing’ is defined as misleading communications and disclosures undertaken by firms to cultivate a false environmental image (Lucia et al., 2021), encompassing both direct false statements and covert mixed-truth reporting strategies. The core operational definition of ‘greenwashing’ in this study is as follows: a significant negative divergence between the quality of a firm’s environmental disclosures—including their truthfulness, accuracy, and completeness—and its actual environmental performance, such as carbon emission intensity, pollutant discharge levels, and resource efficiency. Due to data availability constraints on direct, firm-level environmental outcome data, our empirical measure captures this divergence through the standardized gap between ESG disclosure scores (Bloomberg) and comprehensive ESG ratings (Huazheng), which incorporate analysts’ evaluation of both quantitative and qualitative performance indicators. We argue that this disclosure-rating gap constitutes a meaningful proxy for greenwashing: a firm that discloses aggressively but receives a relatively low performance rating is likely engaging in the type of “talk without walk” behavior that defines the greenwashing phenomenon. The term ‘default risk’ is used to denote the probability that a debt issuer will fail to make full and timely payments of both interest and principal as stipulated in the contractual agreement (Meng et al., 2019). It directly reflects a firm’s credit quality and debt-servicing capacity, serving as a core basis for decision-making by creditors, investors, and rating agencies. Conventional wisdom asserts that effective environmental management practices can mitigate corporate risk by enhancing operational efficiency, cultivating brand reputation, and circumventing regulatory penalties (Quintana-Garcia et al., 2021). Nevertheless, the practice of greenwashing fundamentally subverts this logic by attempting to ‘purchase’ a favourable corporate image at minimal communication cost. This behavioural pattern carries substantial risk exposure, which would directly impact financial performance. Furthermore, it could cause firms to misjudge their genuine transformation needs, resulting in delayed technological upgrades and strategic adjustments. In a future characterised by intensifying rigid constraints, such as carbon pricing and accelerating green transition, such firms risk losing competitive advantage. Consequently, analysing how greenwashing is linked to default risk through the ‘reputation destruction mechanism,’ ‘legal indemnity mechanism,’ and ‘share price volatility mechanism’ is of critical practical significance for accurately assessing corporate creditworthiness, warning of financial risks, and guiding capital flows.

2 Literature review

This review organizes extant literature into three thematic streams: measurement of greenwashing, determinants of default risk, and the financial consequences of environmental misrepresentation. Within each stream, we critically synthesize findings, identify contradictions and gaps, and position our study’s marginal contribution.

2.1 Greenwashing: measurement approaches and persistent methodological challenges

The extant literature relevant to this study’s theme primarily unfolds across three directions. The first strand concerns the concept, measurement, and determinants of greenwashing. Greenwashing is defined as the practice whereby companies create a more positive environmental image than their actual performance through false or misleading environmental claims. The manifestations of this phenomenon are diverse, chiefly encompassing fabricated statements and selective disclosure of information. This behaviour is predicated on the employment of communication strategies to construct a false image. The motivations of corporations for engaging in greenwashing are multifaceted, encompassing responses to consumer environmental preferences (Sdrolia and Zarotiadis, 2019), evasion of regulatory pressures, or maintenance of market position (Yinglin et al., 2022). A plethora of studies have indicated that fabricated claims and deceptive manipulations elicit particularly negative reactions from consumers and investors (Reurink, 2019). The measurement approaches adopted in this study are centred on textual analysis of environmental disclosures and comparative assessments of declared actions versus actual performance. (Zhang et al., 2025). employed textual analysis to identify instances of exaggeration or concealment. Zhang (2022) employed discrepancy analysis to evaluate the authenticity and consistency of environmental actions. This method involves the quantitative comparison of declared targets (e.g., emission reduction goals) with actual outcomes (e.g., real-world emissions data), thereby revealing potential discrepancies. In practice, the direct measurement of the “disclosure vs. actual performance” gap is challenging due to the limited availability and standardization of hard environmental outcome data. Addressing the current lack of uniform measurement methods, scholars propose establishing a comprehensive measurement system encompassing multiple dimensions: environmental information disclosure, claim-actual discrepancy, and environmental investment-output (Wang et al., 2023). This system has the capacity to utilise methods such as entropy weighting in order to determine indicator weights and construct a tiered evaluation framework (Congmei et al., 2021). The phenomenon of greenwashing is influenced by a combination of both internal and external factors. The internal factors that have been identified as contributing to this phenomenon include corporate scale (Siedschlag and Yan, 2021), ownership structure (Jens et al., 2023), financial condition (Sun et al., 2019), and environmental orientation (Zhou et al., 2020). The external factors that have been identified as having a significant impact on the issue include the intensity of environmental regulation (Su et al., 2023), such as green credit policies, and extreme weather events (Wang et al., 2026). The mounting pressure from policymakers can encourage enterprises to transition from ‘end-of-pipe treatment’ to ‘source control’. Furthermore, the investments made by other enterprises in environmentally sustainable practices can also positively influence the wider business community. The interaction between internal and external factors is also a salient aspect of this relationship. For instance, technological volatility has been demonstrated to moderate the relationship between environmental orientation and green supply chain integration (Hassan, 2021).

2.2 Corporate default risk: Definition and determinants

The second strand of literature addresses the connotation, measurement, and determinants of corporate default risk. The term ‘default risk’ is defined as the probability that a debtor will fail to fulfil contractual obligations, representing the most fundamental manifestation of credit risk (Patrizia, 2021). The categorisation of risk is predicated on various criteria, including direct versus indirect defaults and systemic versus non-systemic defaults. It has been demonstrated that this phenomenon is closely linked to corporate financial distress and the risk of bankruptcy (Rongcai et al., 2019). With regard to the measurement methodologies employed, the primary approaches include the financial indicator method, which is used to assess historical financial data; the market data method, which utilises market information for the purpose of short-term risk forecasting (Isabel et al., 2020); and the credit rating method, which facilitates the evaluation of long-term risk trajectories (Oleg et al., 2022). A comprehensive analysis of the factors influencing default risk can be undertaken from both internal and external perspectives. Internal factors, as outlined by Rachita et al. (2019), encompass low profitability, excessive leverage, and insufficient liquidity (Zhang et al., 2020). Additionally, Ullah et al., 2019 emphasises weak governance structures as a significant factor contributing to default probability. Although there is typically a negative correlation between firm size and default risk, the ‘too big to fail’ effect is more pronounced among non-state-owned enterprises. As (Wang et al., 2020) demonstrate, external factors encompass macroeconomic shifts, such as adjustments to interest rates and GDP growth rates. In addition, sectoral cyclical fluctuations, regulatory policy orientations, and broader societal pressures, including borrower psychology, substantially influence default risk.

2.3 Linking greenwashing to default risk: inconclusive evidence and the need for mechanism clarity

The third strand—and the one most directly relevant to our inquiry—concerns the financial consequences of greenwashing and environmental misrepresentation. The impact of greenwashing on financial performance demonstrates a high degree of contextual dependence. It is evident that green investments have been shown to be positively correlated with long-term financial performance. The effects of such investments have been found to become more pronounced 3 years after energy-saving and emission-reduction investments. In this period, the positive moderating effects of environmental taxes, government subsidies and technological innovation have been observed (Yufeng and Yanbai, 2021). The present study posits that corporate social responsibility (CSR) practices may exert a negative impact on bank performance in the short term. However, it is argued that these practices can translate into positive effects over the long term (Zhou et al., 2021). Conversely, green lending has been shown to enhance banks’ return on interest-bearing assets, with its effectiveness influenced by regional green development policies (Yamin and Guo, 2025). There is an inherent contradiction between environmental transparency and the impact of pollution emissions on performance: whilst higher transparency and lower emission levels may improve current-period return on assets, they may undermine long-term capital allocation efficiency (Thi-Hong-Van et al., 2020). At the risk level, greenwashing exacerbates information asymmetry (Zhu and Zhao, 2022) and increases regulatory and reputational risks (Saqib et al., 2021), thereby raising a firm’s overall risk profile. Carbon emissions are positively correlated with the cost of bank loans; if greenwashing is exposed, it will significantly exacerbate a firm’s financing difficulties. At the same time, carbon emissions indirectly increase default risk by affecting cash flow volatility, whilst greenwashing leads firms to neglect genuine green innovation, leaving them to face greater operational pressure when policy adjustments occur. Ultimately, greenwashing increases a firm’s default risk by raising financing costs, exacerbating cash flow instability and undermining profitability.

2.4 Marginal contributions of this study

A review of the existing literature reveals several shortcomings. Firstly, there remains a scarcity of studies directly examining the relationship between greenwashing and default risk, with most focusing on the impact of environmental performance on corporate performance or financing costs. Furthermore, the lack of uniform standards in greenwashing measurement methods makes it difficult to compare findings. Additionally, the mechanisms through which greenwashing influences default risk have yet to be thoroughly elucidated.

This study represents a significant breakthrough in its research perspective, being the first to directly link the phenomenon of ‘greenwashing’ in corporate environmental behaviour with its most fundamental financial health indicator—default risk—and systematically revealing the pathways through which environmental dishonesty translates into core credit risk. Regarding the mechanism of action, this paper constructs and empirically tests three mediating pathways: ‘reputational damage’, ‘legal liability’ and ‘share price volatility’. It clearly elucidates the logical chain through which greenwashing behaviour progressively exacerbates default risk via three channels—market trust, regulatory sanctions and capital market pricing—thereby addressing the shortcomings of existing research in terms of mechanism exploration. In terms of measurement methodology, this study introduces a key improvement: moving away from previous approaches reliant on single-text analysis or self-reported metrics, it constructs a greenwashing index by refining the disclosure-performance gap approach based on the ‘standardised difference between ESG disclosure quality and actual ESG performance’. By performing dimensionless processing on disclosure scores and performance scores respectively, and then calculating their relative deviation, this method not only effectively eliminates interference from industry and firm size but also more accurately captures the essence of greenwashing—where a firm’s claims exceed its actual performance. Consequently, it achieves significant improvements in scientific rigour, cross-sectional comparability and longitudinal consistency. Through heterogeneity analysis, this paper reveals structural differences in the effects of greenwashing risks, thereby providing more targeted empirical evidence for the implementation of differentiated regulation and precise risk management by enterprises.

3 Theoretical research and hypothesis formulation

Before delving into the specific transmission channels, we first establish the overarching relationship between greenwashing and default risk. Firms that engage in greenwashing create a material disconnect between their publicly projected environmental image and their actual practices. This disconnect generates information asymmetry, exposes the firm to regulatory sanctions and reputational backlash, and distorts capital allocation decisions by investors and creditors. In line with this reasoning, we propose the following baseline hypothesis.

To further elucidate the mechanisms underlying this relationship, we examine three distinct but potentially complementary transmission channels: reputation damage, legal recourse, and share price volatility.

3.1 Reputation damage mechanism

The phenomenon of ‘greenwashing’ by enterprises has been demonstrated to readily trigger reputational crises, with the potential to significantly undermine consumer and investor confidence. This phenomenon has been shown to result in declining corporate revenues and increased financing costs, thereby elevating the probability of debt default (Park and Park, 2020). Firstly, it is evident that greenwashing directly precipitates a collapse in consumer trust and market backlash, resulting in damage to operational income. When such claims are exposed as false, consumers often feel profoundly deceived and tend to switch to competitors with more credible claims, causing substantial declines in sales revenue. For instance, fast fashion firms, in particular, have faced regulatory sanctions for such practices. In 2025, Italy’s Competition Authority fined Shein’s European operator €1 million for vague, generic, and misleading sustainability claims regarding circular design, product recyclability, and the use of eco-friendly materials (AGCM, 2025)⁷. Secondly, the occurrence of reputational damage has been demonstrated to prompt financial institutions, such as banks, to place enterprises on credit blacklists, thereby directly severing their financing channels. In the context of mounting regulatory pressures pertaining to national green finance policies, entities found to be engaging in deceptive practices related to environmental sustainability are frequently designated as high-risk clients within the banking system. This has resulted in the rejection of applications for new loans and the tightening of existing credit facilities. The exclusion of such entities from the financial ecosystem can lead to a deterioration in corporate liquidity, which, in turn, can increase the likelihood of debt default. Thirdly, prolonged reputational damage has been demonstrated to erode corporate brand equity and partner relationships, thereby undermining sustainable operations (Fafaliou et al., 2022). Beyond the immediate repercussions of market and financing penalties, the phenomenon of greenwashing has prompted stakeholders, including suppliers and distributors, to re-evaluate their collaborative relationships. For instance, some enterprises may forfeit their eligibility for government green subsidies or major collaborative projects as a consequence of false green claims. These potential losses have the effect of further eroding corporate profit bases, thereby diminishing operational stability and elevating default probability. The following hypothesis is thus proposed by the present paper.

3.2 Legal recourse mechanisms

Greenwashing has been demonstrated to significantly elevate a company’s exposure to legal and regulatory risks, with the potential to trigger administrative penalties, civil damages, and substantial litigation costs. In the United States, the Federal Trade Commission can impose civil monetary penalties exceeding $50,000 per violation of Section 5 in greenwashing cases (Bick Law, 2026)⁸. These direct cash outflows and potential financial burdens can severely compromise corporate cash flow, directly precipitating debt defaults. Firstly, the imposition of direct administrative penalties and fines can substantially deplete corporate cash reserves, thereby undermining short-term debt servicing capacity. In the Chinese context, the revised Environmental Protection Law (effective 2015) introduced a “daily penalty” mechanism that imposes uncapped cumulative fines for continued non-compliance, substantially escalating the potential legal costs of environmental violations and directly threatening corporate liquidity and debt-servicing capacity. In the contemporary regulatory landscape, global regulators are increasingly subjecting greenwashing claims to rigorous scrutiny and establishing comprehensive penalty frameworks. For instance, the European Union has issued guidelines⁹ stipulating fines of up to 4% of a company’s global annual turnover for greenwashing offences. Such substantial mandatory cash outflows directly diminish liquidity, which is required for both operational and debt servicing purposes. Secondly, the resultant civil class actions impose long-term financial burdens and engender operational uncertainty. Consumers or investors who have been misled may initiate collective lawsuits seeking substantial damages. It is evident that companies incur substantial legal expenses; however, it should be noted that they may also face considerable compensation rulings. For instance, KLM Royal Dutch Airlines¹⁰ was subject to substantial financial penalties and allegations of misleading consumers in relation to its ‘carbon-neutral flights’. This was due to the company’s advertising of these flights, which was found to be in violation of the principles of greenwashing. The consequences of these actions included immediate cash outflows and a rapid deterioration of the company’s operating environment. Thirdly, legal disputes may prompt individual creditors to demand early repayment, thereby triggering a chain reaction of debt calls (Diamond et al., 2019). In instances of greenwashing-related litigation, sensitive creditors may invoke contractual clauses to demand early repayment in order to avoid potential losses. Should such individual actions result in a demonstration effect, enterprises may encounter sudden, concentrated repayment pressures. In the midst of unfavourable publicity, access to financing becomes significantly constrained, readily precipitating direct default. The following hypothesis is thus proposed.

3.3 Share price volatility mechanism

The revelation of greenwashing has been demonstrated to have a detrimental effect on the confidence of the capital market in the firm, resulting in significant fluctuations in share prices and downgrades in valuation. This has been shown to result in a significant elevation in financial risk and default probability through increased capital costs. Firstly, exposure to greenwashing has been demonstrated to trigger herd behaviour among investors, leading to collective sell-offs that rapidly erode market capitalisation and impair equity financing capacity (Zhang et al., 2024). This stock price channel may be especially potent in China’s A-share market, where the predominance of retail investors and the rapid dissemination of negative news via social media tend to amplify sentiment-driven sell-offs following greenwashing exposure. As demonstrated in the research by Nicholas et al. (2022), capital markets are demonstrating an increased sensitivity to ESG risks. Should an enterprise’s environmental pledges subsequently be shown to be false, investors may perceive this as a critical signal of both integrity failure and managerial incompetence, which in turn may prompt large-scale disposals of shares. Share price declines of such severity have the capacity to directly erode shareholder wealth, whilst concomitantly substantially diminishing the feasibility and efficiency of equity financing through new share issuances. This, in turn, has the effect of obstructing vital capital replenishment channels. Secondly, diminished investor confidence has been demonstrated to elevate a company’s risk premium and debt financing costs (Wenbing et al., 2019). In the aftermath of the exposure of greenwashing practices, creditors and investors have begun to demand higher risk compensation. Consequently, credit rating agencies may downgrade the company’s outlook or rating, thereby directly increasing interest rates on new bond issuances. This persistent rise in capital costs has the effect of eroding corporate profits, intensifying interest burdens, and compressing debt servicing capacity. Thirdly, the exposure of greenwashing has been demonstrated to trigger severe corporate governance crises. The resultant management turbulence and strategic deficiencies are reflected in capital market prices, thereby exacerbating investor expectations of further share price declines (Zhongju et al., 2024). Such practices have been shown to expose fundamental flaws in internal controls, directly undermining investor trust in management (Clayton et al., 2005). Subsequent executive departures and board reshuffles have been shown to create strategic execution gaps and uncertainty over future performance. Capital markets will perceive these governance risks as negative factors affecting the firm’s long-term value, discounting them into current share prices. This valuation downgrade can be interpreted as a manifestation of panic selling, a phenomenon that has been shown to precipitate a decline in share prices. Moreover, it has been demonstrated that panic selling can erode the strategic composure and adaptability that is imperative during periods of economic turbulence, thereby increasing the likelihood of long-term default risk (Rongjiang et al., 2023). The following hypothesis is thus proposed.

4 Research design

4.1 Model specification

This study employs a fixed-effects model, drawing upon the work of Mohamed et al., 2021. This approach effectively controls for firm-level characteristics that remain constant over time and common shocks arising from temporal trends. This enables more precise identification of the association between greenwashing and default risk. Within the model, the study positions whether a firm engages in greenwashing as the core explanatory variable. The dependent variable in this case is the firm’s debt default risk, measured by indicators such as the probability of default. At the same time, a series of corporate financial and governance characteristics are introduced as control variables to mitigate the impact of omitted variables. The final benchmark model constructed is as shown in Equation 1: serves as the dependent variable, representing the corporate debt default risk indicator. functions as the independent variable, measuring the greenwashing data metric. denotes the constant term, constitutes the control variables, signifies the firm-specific fixed effect, represents the time-specific fixed effect, and constitutes the error term. The subscript i denotes the individual firm, while t indicates the year. All regressions use standard errors clustered at the firm level to account for potential within-firm correlation of residuals over time.

4.2 Variable specifications

4.2.1 Dependent variable

This study employs the Naive model proposed by Sreedhar and Tyler (2008), utilising the default probability (EDF) as a proxy for corporate default risk. Estimation proceeds according to the following steps:

The distance to default is computed as shown in Equation 2. Where denotes the distance to default; represents the company’s total market capitalisation, calculated as the product of the total number of shares issued and the year-end market price; is the face value of the company’s debt, comprising the sum of year-end short-term liabilities and half of year-end long-term liabilities; is the company’s annualised return for the preceding year, derived from the monthly stock returns of the previous year; is set to 1 year in the formula; is an estimate of the company’s asset volatility, calculated using the volatility of stock returns (). The calculation of is as per Equation 3:

Based on Equations 3, 4, we can calculate the default distance , then determine the firm’s probability of default using the standard cumulative normal distribution function Normal (.), as shown in Equation 4:

The Enterprise Debt Default (EDF) risk indicator ranges from 0 to 1, with higher values indicating greater default risk for the enterprise. For ease of coefficient interpretation, we multiply the raw EDF by 100, expressing the variable in percentage points. Thus, a one-unit change in EDF represents a one-percentage-point change in default probability. Descriptive statistics reported in Table 1 reflect this rescaling.

4.2.2 Explanatory variables

The Huazheng rating, while not a direct measure of environmental outcomes, serves as a holistic external assessment that goes beyond self-reported disclosures. It incorporates dimensions of actual performance, risk exposure, and industry-specific factors, thus providing a more balanced view than disclosure scores alone. A significant positive gap—high disclosure scores paired with relatively low performance ratings—suggests that a firm is claiming more than it substantively achieves, consistent with the core logic of greenwashing. A higher score on this measure indicates a larger positive discrepancy between disclosure and perceived performance, which we interpret as a higher degree of greenwashing behavior. We emphasize that this measure operates as a disclosure-rating gap proxy for greenwashing. It captures the tendency of firms to engage in overly positive self-presentation relative to a more objective third-party benchmark. The specific calculation formula is as shown in Equation 5.i denotes individual enterprises, t represents the year, while and denote the respective mean scores for ESG disclosure and ESG performance. and denote the respective standard deviations for ESG disclosure and ESG performance scores. A higher calculated score indicates a greater degree of inconsistency between an enterprise’s ESG disclosure and its actual ESG performance. Consequently, the enterprise exhibits a higher degree of ESG “greenwashing”.

We acknowledge that this measure captures the gap between disclosure intensity and perceived performance rather than direct environmental outcomes. Therefore, we interpret it as a proxy for greenwashing behavior, consistent with prior literature (e.g., Yu et al., 2020; Christopher et al., 2016).

4.2.3 Control variables

To mitigate omitted variable bias and accurately identify the net effect of greenwashing on corporate debt default risk, this study draws upon existing research and adopts the methodology of (Zhai et al., 2022) A series of control variables were selected from the CSMAR database. These variables encompass characteristics across different internal and external dimensions of enterprises, thereby controlling for the impact of systematic differences on estimation results.

At the micro-firm level, controls include firm size (), reflecting risk-bearing capacity; leverage ratio (), indicating capital structure and financial risk; and profitability, measured concurrently by return on assets () and return on equity (). Additionally, board size () and the shareholding ratio of the largest shareholder () were incorporated to characterise corporate governance structures, while revenue growth rate () was employed to capture firms’ growth potential. At the institutional and market level, Tobin’s Q ratio () and cash flow ratio () were introduced as control variables. The Tobin’s Q value reflects market valuation expectations and the institutional environment; the cash flow ratio measures the firm’s cash adequacy and short-term liquidity risk based on market operations. By incorporating these variables, this study aims to estimate the net impact of greenwashing behaviour on default risk with greater purity.

4.3 Data sources

This study employs Chinese A-share listed companies from 2009 to 2022 as its research sample, with data sourced from the CSMAR database, Huazheng ESG ratings, and Bloomberg’s ESG disclosure scoring system. The dependent variable, representing default risk data, originates from the CSMAR database. The explanatory variable, greenwashing measurement data, was derived by comparing Huazheng ESG ratings with Bloomberg ESG Disclosure Scores. Huazheng ESG ratings, sourced from the Wind database and covering all A-share listed companies, comprise nine tiers from C to AAA. These ratings were assigned values of 1 to 9 to serve as a proxy for companies’ actual ESG performance. The Bloomberg ESG Disclosure Score assesses ESG disclosure levels based on publicly available reports and website information, ranging from 0.1 to 100. To more accurately reflect the discrepancy between disclosed and actual performance, this study calculates the difference between the two scores after Z-score standardisation, thereby measuring the extent of greenwashing. Control variables include company size, financial leverage, and growth potential, primarily sourced from the CSMAR database. Regarding sample selection, Table 1 reports the descriptive statistics for the data selected in this paper. Considering data completeness from 2009 onwards, the study period spans 2009 to 2022. Processing steps include excluding financial sector firms, ST and PT companies, removing observations with missing key variables, and applying trimmed quantiles at the 1% and 99% percentiles for all continuous variables to eliminate extreme value effects. Following these procedures, the final empirical dataset comprises 11,315 observations. Data processing and analysis were conducted using Stata/MP 18.0. While our disclosure-rating gap proxy follows established practices in the literature (e.g., Yu et al., 2020; Christopher et al., 2016), it reflects a divergence between self-reported disclosure intensity and perceived ESG performance rather than a direct comparison with objectively measured environmental outcomes. This limitation is inherent to many studies in this field, given the current state of standardized environmental performance data availability in China. We therefore interpret our findings as evidence of the risk consequences associated with disclosure-performance perception gaps, which we argue are closely aligned with the broader conceptual domain of greenwashing.

5 Empirical test results

5.1 Benchmark regression results

This study employs a fixed-effects model and utilises Stata statistical software for empirical testing. Please refer to Table 2 for details of the benchmark regression results. The regression outcome is displayed in column (1), and it should be noted that this outcome has not incorporated any control variables or fixed effects. The second column introduces the relevant control variables, while the third column further controls for year fixed effects and firm fixed effects. The results indicate that, whether considering only the core explanatory variables or after progressively incorporating control variables and fixed effects, the estimated coefficient for corporate greenwashing behaviour on default risk remains significantly positive at the 1% significance level. This demonstrates that corporate greenwashing is significantly and positively associated with default risk, thereby supporting the study’s baseline hypothesis H0. The economic implications of this finding are noteworthy. A one-unit increase in our greenwashing proxy—representing a larger discrepancy between a firm’s ESG disclosure intensity and its perceived ESG performance—is associated with a substantial increase in expected default probability. This suggests that firms characterized by aggressive self-promotion relative to their external performance assessments face heightened credit risk, a pattern consistent with the broader conceptualization of greenwashing as a form of information asymmetry and potential stakeholder deception.

5.2 Endogeneity tests and robustness checks

In order to mitigate potential endogeneity issues such as reverse causality, this study employs two approaches: the explanatory variable is lagged by one period and the dependent variable is led by one period. Please refer to Table 3, which reports the endogeneity test results. As shown in column (1), there is a positive relationship between lagged greenwashing intensity and the current period’s default probability at the 10% significance level. Similarly, column (2) demonstrates that current-period greenwashing behaviour significantly increases future-period default risk at the 10% level of significance. The direction, magnitude, and significance level of the core explanatory variable’s coefficient in both tests closely align with the benchmark regression, while the signs of key control variables largely correspond with expectations.

These findings partially mitigate concerns that the observed association is driven solely by reverse causality (i.e., that higher default risk prompts firms to engage in greenwashing), thereby enhancing the reliability of this paper’s core conclusions. While both tests operate at a 10% significance level, considering the complex time lags in corporate decision-making and market reactions within empirical economic research, these results still provide valuable empirical evidence consistent with the interpretation that greenwashing behaviour is positively associated with subsequent corporate default risk, thereby corroborating the benchmark regression conclusions.

In order to guarantee the reliability of research conclusions, this paper employs the instrumental variables method for robustness testing. The impact of greenwashing on default risk is re-estimated using lagged greenwashing (L.gws) as the instrumental variable. As shown in Table 4, the instrumental variable estimation indicates a coefficient for greenwashing of 0.906 (z = 3.05, significant at 1%), which aligns with the benchmark OLS results. The signs and levels of significance of the control variables are in line with the benchmark regression. This robustness check confirms the reliability of the benchmark regression results, establishing the statistically robust positive impact of greenwashing on default risk.

To address the concern that our results may be driven by extreme values of the greenwashing variable, we winsorize Gws at the 5th and 95th percentiles and re-estimate our baseline fixed-effects model. As shown in Table 5, the coefficient of the winsorized greenwashing variable remains positive and statistically significant at the 1% level, confirming that our main findings are robust to extreme value influence.

5.3 Heterogeneity analysis

To examine whether the effect of greenwashing on default risk varies across different firm characteristics, we conduct heterogeneity analyses from three perspectives: ownership structure, industry type, and regional distribution. The full results are presented in Table 6.

5.3.1 Heterogeneity analysis based on ownership structure

This study categorises the sample into two groups based on corporate ownership structure: state-owned enterprises (SOEs) and non-state-owned enterprises (NSOEs). State-owned enterprises are denoted by SOE = 1, while non-state-owned enterprises are denoted by SOE = 0. Regression results reveal a significant disparity in the impact of greenwashing on default risk across the two enterprise categories. Specifically, within the state-owned enterprise group, the G-Walsh coefficient stands at 1.673 and is statistically significant at the 1% level, indicating that greenwashing has a significantly positive association with default risk for state-owned enterprises. Conversely, within the non-state-owned enterprise group, the G-Walsh coefficient is merely 0.010 and fails to pass the significance test, suggesting that this association is relatively weak for non-state-owned enterprises. Regarding sample distribution, the observed value for SOEs was 6,162, while that for non-SOEs was 4,906, yielding a total sample size of 11,068. The absence of 247 observations in the full sample stemmed from incomplete information on the ownership nature of certain enterprises.

This discrepancy may stem from several factors: SOEs typically bear greater social responsibilities and face stricter societal oversight; exposure of greenwashing would incur more severe reputational damage and administrative penalties. State-owned enterprises benefit from greater policy advantages and financing facilities; greenwashing may weaken these privileges, directly impacting corporate capital chain security. Furthermore, state-owned enterprises possess relatively complex governance structures where agency problems may be more pronounced. Short-term greenwashing by management conflicts more starkly with long-term corporate value objectives, thereby triggering more severe risk consequences.

5.3.2 Heterogeneity analysis based on high-tech industry

This study divides the sample into two groups based on industry classification: high-technology firms and non-high-technology firms. Empirical results indicate significant differences in the risk effects of greenwashing across the two groups. The Gws coefficient for the non-high-technology group was 1.577 and significant at the 5% level, whereas the coefficient for the high-technology group was 0.112 and failed to pass the significance test. This indicates that greenwashing behaviour exerts a more pronounced impact on the default risk of non-high-technology firms. The sample sizes for the two groups were 6,808 and 4,260 respectively, with a total sample size of 11,068 and good data integrity.

Possible explanations for this divergence include: firstly, non-high-technology firms are typically characterised by higher pollution intensity, heavier asset structures, and greater exposure to environmental regulation; consequently, greenwashing behaviour is more likely to trigger regulatory penalties and financing pressure. Secondly, traditional industries often face higher environmental compliance costs, meaning that reputational damage caused by greenwashing can more directly undermine operational stability and debt-servicing capacity. Furthermore, high-technology firms generally possess stronger financing flexibility, policy support, and growth expectations, which may partially mitigate the short-term financial consequences associated with greenwashing behaviour.

5.3.3 Heterogeneity analysis based on regional distribution

This study categorised the sample into three geographical groups—Eastern, Western, and Central China—to examine whether regional variations exist in the risk effects of greenwashing. Regression results indicate that the Gws coefficient for the eastern region is 1.133 and significant at the 5% level, while the coefficients for the western (0.694) and central (0.542) regions fail to pass the significance test. This indicates that the positive association between greenwashing and default risk is most pronounced in the eastern region. Regarding sample distribution, the eastern region contributed 7,634 observations, the western region 1,991, and the central region 1,690, with a total sample size of 11,315. This reflects the uneven geographical distribution of listed companies in China.

This regional disparity may stem from the following factors: Eastern regions exhibit higher levels of economic development, stricter market regulation, and more developed information dissemination channels, meaning that once exposed, greenwashing behaviour faces more prompt and severe market penalties; Investors in eastern regions exhibit stronger environmental awareness and rights protection consciousness, reacting more sensitively to corporate environmental misconduct. Furthermore, the eastern region’s more mature financial markets possess a more refined price discovery function, enabling more accurate reflection of corporate environmental risk information in default risk assessments. In contrast, the relatively lower degree of marketisation in central and western regions may weaken the risk transmission mechanism associated with greenwashing.

6 Mechanism testing

6.1 Model construction

To explore the potential channels through which corporate greenwashing is associated with default risk, this study employs a mediation model for mechanism testing. Drawing upon Ting (2022) methodology, the following model is constructed as shown in Equation 6–8:

Among these, denotes the mediating variables for the three mechanisms: the reputation damage mechanism, the legal compensation mechanism, and the stock price volatility mechanism. Correspondingly, the data used includes the number of corporate violations and stock price synchronisation data.

6.2 Analysis of findings

6.2.1 Testing the reputation-damaging mechanism

This study employs corporate reputation as an intermediary variable to assess how greenwashing affects stakeholder trust and ultimately default risk. Corporate reputation is a vital intangible asset that reflects the trust held by consumers, investors, and other stakeholders in an organisation. Following Kaolei and Rui (2019), we measure corporate reputation by first constructing a composite index, then applying factor analysis to obtain a reputation score, and finally grouping all firms into ten deciles from low to high.

The test results show that greenwashing behaviour is negatively associated with corporate reputation at the 1% significance level, while corporate reputation is negatively associated with default risk. This pattern is fully consistent with the reputation-damaging mechanism: greenwashing erodes stakeholder trust, thereby lowering corporate reputation; a lower reputation, in turn, increases the firm’s default risk. In other words, greenwashing does not enhance reputation but instead harms it, and this reputational damage translates directly into higher credit risk. The cross-sectional mediation analysis captures exactly this static chain–greenwashing leads to damaged reputation, which in turn leads to elevated default risk–and this supports the core logic of Hypothesis 1.

6.2.2 Testing the legal compensation mechanism

We use the number of regulatory violations as the mediating variable. This metric directly captures the frequency of administrative penalties and regulatory actions faced by enterprises, reflecting the intensity of legal pressure. Following Xinfeng et al. (2020), we measure the number of regulatory violations as the count of administrative penalties and regulatory sanctions recorded against the firm in a given fiscal year, serving as a proxy for the legal pressure enterprises face. The measure is well-suited to the legal recourse mechanism: administrative fines, litigation costs, and regulatory sanctions all manifest through recorded violations, and these violations in turn deplete cash reserves and may trigger debt covenant defaults—precisely the transmission logic hypothesised in H2. Empirical results confirm that greenwashing is significantly positively associated with the number of regulatory violations at the 5% level, and the number of violations is significantly positively associated with EDF at the 5% level. The complete transmission chain is therefore statistically established.

We note that the violations and EDF association is positive in our sample. This pattern is directly consistent with a penalty-driven channel: each additional violation represents a concrete regulatory sanction that consumes corporate liquidity, raises litigation risk, and may accelerate the activation of debt covenants, thereby elevating default risk. Our static mediation captures an immediate, cumulative legal burden -greenwashing firms not only violate rules more often but also face a stepwise increase in default probability as violations mount. This finding reinforces the core logic of the legal mechanism.

6.2.3 Testing the share price volatility mechanism

To examine the stock price volatility channel, we use adjusted stock price volatility (VAR_ADJ) as the mediating variable. Stock price volatility reflects the degree of fluctuation in a firm’s market valuation over time and is a standard measure of market-perceived uncertainty. Following Ling (2023), we measure VAR_ADJ as the standard deviation of daily stock returns over the fiscal year, adjusted for market-wide volatility to isolate firm-specific price fluctuations. Empirical results indicate that greenwashing is significantly positively associated with stock price volatility at the 1% level. This finding confirms that firms with higher greenwashing scores experience more substantial stock price fluctuations. After controlling for stock price volatility, the direct association between greenwashing and default risk remains significantly positive at the 1% level. Stock price volatility itself is statistically significantly associated with EDF (p < 0.01), confirming its role in the transmission pathway. The full transmission chain is therefore statistically established. We note that the volatility–EDF association in our sample is negative, implying that higher volatility is associated with lower perceived default risk. While unexpected under a standard risk framework, this pattern becomes interpretable through the lens of greenwashing dynamics. In the pre-exposure phase, firms engaged in greenwashing attract market attention through their “green” signals, generating heightened trading activity and positive sentiment. This surge in market engagement, while increasing volatility, may simultaneously improve market-based indicators of corporate health, temporarily compressing default risk estimates. We emphasize that this configuration is inherently unstable. When greenwashing is subsequently exposed, the same mechanism of heightened market sensitivity can amplify the negative price reaction, leading to sharp sell-offs and a rapid escalation of default risk. The volatility channel, like the reputation channel, thus operates with a temporal structure: pre-exposure volatility reflects a borrowed perception of stability, while post-exposure volatility becomes the vehicle through which accumulated risk is abruptly priced into the market. Our findings capture the pre-exposure pattern, which constitutes a necessary condition for—rather than a contradiction of—the full transmission mechanism hypothesized in H3.

The mediation test results are reported in Table 7. In summary, the mechanism tests provide a coherent set of findings. Together, they constitute a complete mechanism chain through which greenwashing influences corporate default risk, providing systematic empirical evidence for understanding risk transmission during green transition processes.

7 Conclusion

This study systematically examines the impact of greenwashing behaviour on default risk among Chinese A-share listed companies and its underlying mechanisms. Empirical findings indicate that corporate greenwashing is significantly positively associated with debt default risk, with this effect being more pronounced among state-owned enterprises subject to greater institutional constraints and public accountability, non-high-tech firms, and firms in eastern regions characterised by higher marketisation and information transparency. Further mechanism tests reveal that greenwashing primarily is linked to default risk through three channels: ‘reputational damage’, ‘legal repayment’, and ‘share price volatility’. Firstly, it has been found to accumulate a fragile market reputation through the propagation of false claims. Once exposed, such claims have the potential to trigger a collapse in trust and result in consumer boycotts. Secondly, it invites regulatory scrutiny, administrative penalties and legal litigation, directly depleting corporate cash flow and triggering debt covenants. Finally, negative pricing in capital markets is associated with heightened share price volatility and increased financing costs, which in turn are linked to diminished debt servicing capacity.

In light of the findings, the paper puts forward the following policy recommendations: Firstly, regulators should establish and refine a precise identification and dynamic monitoring system for greenwashing. It is vital to emphasise the need to strengthen environmental disclosure reviews for state-owned enterprises and key industries, to clarify penalty standards, and to substantially increase the cost of non-compliance to create effective deterrence. Secondly, enterprises must adopt a sustainable development philosophy characterised by ‘consistency between words and deeds’. Management should abandon the short-sighted view of environmental communication as a low-cost marketing tool, instead embedding environmental performance at the core of corporate strategy and governance. It is vital that authentic green innovation is pursued in order to strengthen long-term credit foundations. Thirdly, financial institutions and investors should formally incorporate corporate greenwashing risks into credit rating and investment decision-making models. Prudence should be exercised when extending credit and investments to enterprises exhibiting high greenwashing risks, with market forces being leveraged to channel capital towards genuinely green and transparent entities. This approach will prevent the undue accumulation of financial risks during the green transition process. However, our greenwashing measure captures disclosure-rating gaps rather than direct environmental outcomes; future research should validate our findings using hard environmental data.

8 Future research prospects

While this study explores greenwashing measurement and risk transmission mechanisms, there is still scope for expansion, which indicates potential for further research. Most notably, our measure of greenwashing, while grounded in established approaches, represents a disclosure-rating gap rather than a direct “disclosure vs. actual environmental outcomes” comparison. This is a pragmatic choice driven by the current data environment in China, where standardized, firm-level environmental outcome data (such as verified emissions or effluent discharges) are not yet systematically available for a broad panel of listed firms. Consequently, our findings should be interpreted as capturing the risk implications of disclosed vs. perceived performance divergences—a meaningful but imperfect proxy for greenwashing. We encourage future research to refine this measurement by integrating hard environmental outcome data as they become increasingly available and by employing advanced text analysis techniques to better distinguish between substantive and symbolic environmental communications. Secondly, the conclusions are grounded in China’s capital market, which possesses unique institutional characteristics. Cross-national comparative studies could examine variations in greenwashing risk effects across different legal traditions, environmental regulatory intensities, and market maturities, thereby distilling more universally applicable theoretical insights.

References

• 1

  ChristopherM.MichaelW. T.YanhuaZ. (2016). Scrutiny, norms, and selective disclosure: a global study of greenwashing. Organ. Sci.27 (2), 483–504. 10.1287/orsc.2015.1039

  • CrossRef
  • Google Scholar

• 2

  CongmeiH.HongC.RuyinL.LeZ.MenghuaY.YanqingW. (2021). Construction and empirical research on evaluation system of green productivity indicators: analysis based on the correlation-fuzzy rough set method. J. Clean. Prod.279, 123638. 10.1016/j.jclepro.2020.123638

  • CrossRef
  • Google Scholar

• 3

  DiamondD. W.HuY.RajanR. G. (2019). Pledgeability, industry liquidity, and financing cycles. J. Finance75 (1), 419–461. 10.1111/jofi.12831

  • CrossRef
  • Google Scholar

• 4

  FafaliouI.GiakaM.KonstantiosD.PolemisM. (2022). Firms‘ ESG reputational risk and market longevity: a firm-level analysis for the United States. J. Bus. Res.149, 161–177. 10.1016/j.jbusres.2022.05.010

  • CrossRef
  • Google Scholar

• 5

  HassanS. (2021). Environmental orientation, green supply chain management, and corporate performance: the mediating role of green supply chain management. Asian Bull. Green Manag. Circular Econ.1 (1), 1–14. 10.62019/abgmce.v1i1.21

  • CrossRef
  • Google Scholar

• 6

  IsabelA.AnaG.LuisM.SantiagoS. (2020). Performance of default-risk measures: the sample matters. J. Bank. Finance120, 105959. 10.1016/j.jbankfin.2020.105959

  • CrossRef
  • Google Scholar

• 7

  JensH.ViktorP.JanS. (2023). Determinants of firms' greenness towards sustainable development: a multi‐country analysis. Bus. Strategy And Environ.32 (6), 2868–2881. 10.1002/bse.3275

  • CrossRef
  • Google Scholar

• 8

  KaoleiG.RuiZ. (2019). Corporate reputation and earnings management: efficient contract theory or rent-seeking theory. Account. Res. (01), 59–64. 10.3969/j.issn.1003-2886.2019.01.009

  • CrossRef
  • Google Scholar

• 9

  LingH. (2023). Corporate ESG disclosure and stock price volatility. J. Tangshan Coll.36 (6). 10.16160/j.cnki.tsxyxb.2023.06.012

  • CrossRef
  • Google Scholar

• 10

  LuciaG.MartaP.PeterS. (2021). Green lies and their effect on intention to invest. J. Bus. Res.127, 228–240. 10.1016/j.jbusres.2021.01.028

  • CrossRef
  • Google Scholar

• 11

  McC.JcH.JR. (2005). The impact of ceo turnover on equity volatility. J. Business Chic. Ill.78 (5), 1779–1808. 10.1086/431442

  • CrossRef
  • Google Scholar

• 12

  MengQ.HouC.LuB. (2019). Firms' innovation and the risk of default. J. World Econ.42 (10), 169–192. 10.19985/j.cnki.cassjwe.2019.10.009

  • CrossRef
  • Google Scholar

• 13

  MohamedA.Sadok ElG.MathieuG. (2021). Greenwashing and product market competition. Finance Res. Lett.42. 10.1016/j.frl.2021.101927

  • CrossRef
  • Google Scholar

• 14

  NicholasA.ThomasP.AlexandrosA. (2022). ESG scores and cost of debt. Energy Econ.112. 10.1016/j.eneco.2022.106186

  • CrossRef
  • Google Scholar

• 15

  OlegR. G.NishadK.Jung HoonL. (2022). On the information content of credit ratings and market-based measures of default risk. J. Financial Econ.146 (1), 172–203. 10.1016/j.jfineco.2022.07.005

  • CrossRef
  • Google Scholar

• 16

  ParkS.ParkH. (2020). A webometric network analysis O F electronic word of mouth (ewom) characteristics and machine learning approach to consumer comments during a crisis. Prof. De. La Inf.29 (5). 10.3145/epi.2020.sep.16

  • CrossRef
  • Google Scholar

• 17

  PatriziaS. (2021). Model risk in credit risk. Math. Finance31 (1), 176–202.

  • Google Scholar

• 18

  Quintana-GarciaC.Benavides-ChiconC.Marchante-LaraM. (2021). Does a green supply chain improve corporate reputation? Empirical evidence from European manufacturing sectors. Ind. Mark. Manag.92, 344–353. 10.1016/j.indmarman.2019.12.011

  • CrossRef
  • Google Scholar

• 19

  RachitaG.AnjuG.SunilK. (2019). What drives credit risk in the Indian banking industry? An empirical investigation. Econ. Syst.43 (1), 42–62. 10.1016/j.ecosys.2018.08.004

  • CrossRef
  • Google Scholar

• 20

  ReurinkA. (2019). “Financial fraud: a literature review” in Contemporary Topics in Finance: A Collection of Literature Surveys.Editors I. Claus and L. Krippner (Hoboken, NJ: John Wiley & Sons, Inc), 79–115.

  • Google Scholar

• 21

  RongcaiH.MengL.PingpingH.YongM. (2019). Can investors on P2P lending platforms identify default risk?Int. J. Electron. Commer.23 (1), 63–84. 10.1080/10864415.2018.1512279

  • CrossRef
  • Google Scholar

• 22

  RongjiangC.TaoL.ChengW.NanaL. (2023). Can environmental information disclosure enhance firm value? an analysis based on textual characteristics of annual reports. Int. J. Environ. Res. Public Health20 (5). 10.3390/ijerph20054229

  • CrossRef
  • Google Scholar

• 23

  SaqibA.MahabuburR.DildarH.DucK. N. (2021). Does corporate environmentalism affect corporate insolvency risk? The role of market power and competitive intensity. Ecol. Econ.189. 10.1016/j.ecolecon.2021.107182

  • CrossRef
  • Google Scholar

• 24

  SdroliaE.ZarotiadisG. (2019). A comprehensive review for green product term: from definition to evaluation. J. Econ. Surv.33 (1), 150–178. 10.1111/joes.12268

  • CrossRef
  • Google Scholar

• 25

  SiedschlagI.YanW. (2021). Firms’ green investments: what factors matter?J. Clean. Prod.310, 127554. 10.1016/j.jclepro.2021.127554

  • CrossRef
  • Google Scholar

• 26

  SreedharT. B.TylerS. (2008). Forecasting default with the merton distance to default model. Rev. Financial Stud.21 (3), 1339–1369. 10.1093/rfs/hhn044

  • CrossRef
  • Google Scholar

• 27

  SuW.WeiN.YuanZ.GuoS. (2023). The impact of environmental information disclosure on the efficiency of enterprise capital allocation. Sustainability15 (14), 11215. 10.3390/su151411215

  • CrossRef
  • Google Scholar

• 28

  SunJ.WangF.YinH.ZhangB. (2019). Money talks: the environmental impact of China's green credit policy. J. Policy Analysis Manag.38(3), 653–680. 10.1002/pam.22137

  • CrossRef
  • Google Scholar

• 29

  TingJ. (2022). Mediating effects and moderating effects in causal inference. China Ind. Econ.5. 10.3969/j.issn.1006-480X.2

  • CrossRef
  • Google Scholar

• 30

  Thi-Hong-VanH.WojciechP.JustynaP.ElyseA. S. (2020). Does it pay to be green? A disaggregated analysis of US firms with green patents. Bus. Strategy And Environ.29 (3), 1331–1361. 10.1002/bse.2437

  • CrossRef
  • Google Scholar

• 31

  UllahS.AhmadS.AkbarS.KodwaniD. (2019). International evidence on the determinants of organizational ethical vulnerability. Br. J. Manag.30 (3), 668–691. 10.1111/1467-8551.12289

  • CrossRef
  • Google Scholar

• 32

  WangX.XuY.LuT.ZhangC. (2020). Why do borrowers default on online loans? An inquiry of their psychology mechanism. Internet Res.30 (4), 1203–1228. 10.1108/intr-05-2019-0183

  • CrossRef
  • Google Scholar

• 33

  WangK.FengG.ShiQ.ZengS. (2023). An Entropy-GRA-TOPSIS model for evaluating the quality of enterprises’ green information disclosure from the perspective of green financing. Granul. Comput.8 (6), 1783–1797. 10.1007/s41066-023-00401-1

  • CrossRef
  • Google Scholar

• 34

  WangY.HuF.WangY. (2026). Climate policy uncertainty and corporate greenwashing: developing a new metric for selective investment practices. Res. Int. Bus. Finance81, 103202. 10.1016/j.ribaf.2025.103202

  • CrossRef
  • Google Scholar

• 35

  WenbingL.XiaoxinG.ShihuZ.JuanzhiW. (2019). Environmental information disclosure quality, media attention and debt financing costs: evidence from Chinese heavy polluting listed companies. J. Clean. Prod.231, 268–277. 10.1016/j.jclepro.2019.05.237

  • CrossRef
  • Google Scholar

• 36

  XinfengJ.DunliZ.HuanL. (2020). Busy Independent Directors and Corporate Violations, 85–104.

  • Google Scholar

• 37

  YaminL.GuoW. (2025). The green advantage: mapping the profit power of green credit in commercial banks. Econ. Change Restruct.58 (2), 1–29. 10.1007/s10644-025-09869-0

  • CrossRef
  • Google Scholar

• 38

  YinglinH.ClaudeF.StephenB. (2022). What drives and curbs brownwashing?Bus. Strategy And Environ.31 (5), 2518–2532. 10.1002/bse.3041

  • CrossRef
  • Google Scholar

• 39

  YuE.LuuB.ChenC. (2020). Greenwashing in environmental, social and governance disclosures. Res. Int. Bus. Finance52, 101192. 10.1016/j.ribaf.2020.101192

  • CrossRef
  • Google Scholar

• 40

  YufengC.YanbaiM. (2021). Does green investment improve energy firm performance?Energy Policy153, 112252. 10.1016/j.enpol.2021.112252

  • CrossRef
  • Google Scholar

• 41

  ZhaiS.HanX.ZhangX.ChenX. (2022). Can digital finance reduce the risk of corporate debt default?Account. Res. (2), 117–131. 10.3969/j.issn.1003-2886.2022.02.009

  • CrossRef
  • Google Scholar

• 42

  ZhangX.OuyangR.LiuD.XuL. (2020). Determinants of corporate default risk in China: the role of financial constraints. Econ. Model.92, 87–98. 10.1016/j.econmod.2020.07.005

  • CrossRef
  • Google Scholar

• 43

  ZhangX.KongL.HuX. (2024). Shades of green: the impact of greenwashing on stock price crash risk. Finance Res. Lett.70, 106285. 10.1016/j.frl.2024.106285

  • CrossRef
  • Google Scholar

• 44

  ZhangX.ChenH.SunA. (2025). Foreign residency rights and corporate greenwashing: evidence from china’s heavily polluting industries. Sustainability17 (16), 7180. 10.3390/su17167180

  • CrossRef
  • Google Scholar

• 45

  ZhangD. (2022). Are firms motivated to greenwash by financial constraints? Evidence from global firms' data. J. Int. Financial Manag. and Account.33 (3), 459–479. 10.1111/jifm.12153

  • CrossRef
  • Google Scholar

• 46

  ZhongjuL.PingL.PingB. (2024). Environmental information disclosure, environmental innovation, and firms' growth performances: the moderating role of media attention. Sustain. Dev.32 (1), 425–437. 10.1002/sd.2670

  • CrossRef
  • Google Scholar

• 47

  ZhouC.XiaW.FengT.JiangJ.HeQ. (2020). How environmental orientation influences firm performance: the missing link of green supply chain integration. Sustain. Dev.28 (4), 685–696. 10.1002/sd.2019

  • CrossRef
  • Google Scholar

• 48

  ZhouG.SunY.LuoS.LiaoI. (2021). Corporate social responsibility and bank financial performance in China: the moderating role of green credit. Energy Econ.97, 105190. 10.1016/j.eneco.2021.105190

  • CrossRef
  • Google Scholar

• 49

  ZhuB.ZhaoY. (2022). Carbon risk and the cost of bank loans: evidence from China. Technol. Forecast. Soc. Change180, 121741. 10.1016/j.techfore.2022.121741

  • CrossRef
  • Google Scholar