ESG Investment Imbalance, Sustainable Corporate Development, and Financial Fraud: Evidence from Chinese Listed Firms

1. Introduction

Because China’s capital market institutions remain imperfect, leading to relatively weak investor protection, and corporate financial fraud still appear in the news from time to time. Corporate financial fraud damages investor confidence, reduces firm value, leads to capital misallocation, and exacerbates capital market instability [1,2,3]. Accordingly, scholars have begun to investigate the determinants of corporate financial fraud. Existing studies have produced abundant evidence from the perspectives of internal corporate governance [4,5,6] and external governance [7,8,9]. However, the effect of the increasingly important ESG performance on corporate financial fraud still requires further investigation.

ESG investment is a core mechanism through which firms translate sustainable development goals into corporate strategy, resource allocation, risk control, and stakeholder accountability. As a development concept and investment strategy, ESG focuses on firms’ comprehensive performance in environmental protection, social responsibility, and corporate governance, with the goal of promoting sustainable corporate development. ESG provides a basis for firms to monitor and regulate their own behavior, and it has also become an important criterion for investors to evaluate firms’ sustainability and fulfillment of social responsibility. Chinese regulators have also strengthened ESG supervision of listed companies. In 2018, the China Securities Regulatory Commission revised the Code of Corporate Governance for Listed Companies, requiring listed firms to disclose information related to environmental protection, social responsibility, and corporate governance. In April 2022, the CSRC issued the Guidelines for Investor Relations Management of Listed Companies, which included ESG information as part of communication between listed firms and investors. Over the past decade, ESG asset investment has grown rapidly. By the end of 2025, China had 471 public ESG funds, including 337 actively managed funds and 134 passive index funds, of which 126 were equity funds. The net asset value of China’s ESG fund products reached RMB 1.16 trillion, with a three-year compound growth rate exceeding 30%. Meanwhile, ESG-themed bank wealth-management products have expanded rapidly, with an outstanding scale of more than RMB 300 billion, becoming an important component of green finance.

However, listed firms may engage in ESG “greenwashing”: they invoke the concept of ESG without making genuine ESG investment, and market investors may recognize such behavior and discount the positive effect of ESG investment on firm value. This paper discusses a special form of ESG greenwashing. Relative to other firms in the same industry, a listed firm may allocate ESG investment unevenly across E, S, and G. As a result, its overall ESG score may be high because one dimension, such as E, S, or G, performs strongly, rather than because the three dimensions develop in a balanced way. We define ESG investment imbalance as the aggregate deviation of a firm’s E, S, and G component scores from the corresponding industry-year averages. This is similar to teachers evaluating students: a student is expected to develop comprehensively in morality, intelligence, physical education, aesthetics, and labor, rather than excelling in one aspect while having deficiencies in others. When ESG investment is imbalanced across environmental, social, and governance dimensions, the resulting sustainability signal may become incomplete or misleading, reducing the ability of capital markets to identify genuinely sustainable firms. This type of greenwashing through ESG investment imbalance has not been addressed in prior research. Taking financial fraud as the research object, this paper empirically examines the impact of ESG investment imbalance on financial fraud. Robustness tests using alternative measures of ESG investment imbalance, alternative ESG data, and high-dimensional fixed effects, as well as endogeneity tests based on instrumental variables, consistently show that ESG investment imbalance increases both the likelihood and frequency of financial fraud by listed firms. We further conduct mechanism tests from the perspectives of perceived pressure, perceived opportunity, and rationalization, perform heterogeneity tests based on ownership, industry competition, and economic policy uncertainty, and discuss the economic consequences of the effect of ESG investment imbalance on financial fraud. Finally, we examine the moderating role of external monitoring by institutional investors and financial media.

This paper may provide three main contributions. First, it is the first to discuss greenwashing through ESG investment imbalance. From the perspective of sustainable development, this paper contributes by showing that the internal structure of ESG investment is an important condition for credible corporate sustainability. A balanced ESG profile reflects coordinated attention to environmental responsibility, social responsibility, and governance quality, whereas an imbalanced ESG profile may reveal a selective sustainability strategy that increases greenwashing risk and weakens the ethical and governance mechanisms that support sustainable value creation. This enriches sustainability research by moving beyond the level of ESG performance to examine whether ESG dimensions develop in a coherent and mutually reinforcing manner. Prior studies define corporate greenwashing based on the gap between ESG disclosure and ESG performance [10], whereas this paper defines greenwashing based on the deviation of the three ESG components from industry averages. We attempt to show that even when a listed firm has strong overall ESG performance, investors and regulators should still pay attention to the imbalance across its ESG component investments, because such imbalance may weaken the benefits that ESG brings to listed firms. Second, although many studies examine the determinants of financial fraud and several studies discuss the impact of ESG performance on financial fraud [11,12,13], these studies generally find that ESG performance reduces corporate financial fraud. In contrast, this paper finds that although ESG investment itself reduces financial fraud, ESG investment imbalance increases both the probability and severity of fraud, thereby weakening the positive effect of ESG investment and enriching the literature on the determinants of financial fraud. Third, this paper examines the moderating role of external monitoring. We show that monitoring by institutional investors and financial media mitigates the negative effect of ESG investment imbalance on financial fraud and strengthens the positive governance effect of ESG investment itself while prior studies have paid relatively limited attention to the role of external monitoring in financial fraud. These findings highlight the importance of external monitoring in identifying potentially misleading ESG practices, constraining fraud risk, and supporting the sustainable development of capital markets.

2. Literature Review and Research Hypotheses

2.1. Literature on ESG Greenwashing

With the growing popularity of sustainable development, corporate ESG disclosure has become increasingly normalized. At the same time, ESG greenwashing has become more prominent and has attracted close attention from capital markets, regulators, and academia. ESG greenwashing refers to opportunistic behavior in which firms create a favorable ESG image through exaggerated promotion, selective disclosure, or false statements, while lacking substantive actions to support that image. Its essence is a mismatch between ESG disclosure and actual ESG performance [14]. ESG greenwashing is a sustainability governance problem because it separates the appearance of sustainable development from substantive improvements in environmental, social, and governance practices. Studying ESG investment imbalance therefore helps explain how firms may obtain sustainability-related legitimacy while failing to build the balanced capabilities needed for long-term responsible development. Current mainstream measurement approaches include the disclosure-performance gap method, textual sentiment analysis, and machine-learning models, among which the disclosure-performance gap method is most widely used.

Regarding driving factors, market competition, investor ESG preferences, media attention, and social media pressure may also induce firms to engage in low-cost greenwashing to obtain compliance benefits [15]. Internally, deficiencies in corporate governance significantly promote greenwashing. Excessive executive pay gaps and concentrated power can trigger opportunistic behavior. Financial distress, high leverage, and financing constraints make firms more inclined to maintain external image and obtain development resources through greenwashing. Managerial corruption and agency conflicts also aggravate greenwashing [14,16].

Although ESG greenwashing may bring short-term financial benefits by improving sales and market share [17], it also amplifies operational risks and potential reputation-related costs [18]. These potential risks are ultimately reflected in lower market valuation and greater stock price volatility. ESG greenwashing may reduce financing costs, obtain policy support, and create competitive advantages in the short run [19], but it erodes firms’ long-term value [20]. It not only increases information asymmetry and damages the confidence of creditors and investors, but also triggers reputation and compliance losses such as regulatory penalties and consumer boycotts. More broadly, it may distort capital allocation, distort market efficiency, and hinder the healthy development of the ESG ecosystem [21,22].

2.2. Literature on Financial Fraud

Corporate financial fraud, as a typical opportunistic behavior in the capital market, has long attracted considerable public and academic attention. It causes substantial financial losses, harms investors and shareholders, induces capital misallocation, amplifies financial risks, undermines market stability, and severely damages corporate reputation and market confidence [1,2].

Cressey (1953) proposed the fraud triangle theory, which explains the formation mechanism of financial fraud from the dimensions of perceived pressure, perceived opportunity, and rationalization [23]. In terms of determinants, managerial characteristics are regarded as key drivers. Autocratic leadership style, excessively high living standards, CEO connectedness, CFO gender, and executive compensation all significantly affect the probability of fraud. Excessive managerial power and insufficient moral constraints directly increase the tendency toward misconduct [2,24,25,26]. Internal governance problems, such as inexperienced independent directors, unreasonable shareholder meeting arrangements, and weak internal control mechanisms, expand opportunities for fraud, whereas board diversity and effective external monitoring can play a restraining role [27,28,29]. At the external governance level, regulatory intensity, media monitoring, analyst coverage, institutional investors, and external auditing can all alleviate information asymmetry and constrain financial fraud, while an imperfect institutional environment and weak monitoring aggravate fraudulent behavior [8,9,30]. Meng et al. (2011) and Li et al. (2019) find that employee stock ownership plans reduce fraud risk through interest alignment and internal monitoring [31,32]. Yuhui and Zhang (2023) and Tian et al. (2022) argue that digital finance can leverage big data and cloud computing technologies to alleviate information asymmetry and financing constraints, thereby helping curb financial fraud [33,34].

Meanwhile, most studies confirm that CSR or ESG performance has a significant negative effect on financial fraud [11,13]. However, some studies argue that firms may divert investor attention through false ESG promotion, which may become a tool for concealing financial fraud [35].

2.3. Hypotheses Development

According to the classical Fraud Triangle Theory, fraud is driven by three elements: perceived pressure, perceived opportunity, and rationalization [23]. With respect to fraud perceived pressure, managers may commit fraud to meet performance evaluation targets, raise stock prices before exercising stock options [36], satisfy external financing needs [37], or conceal financial distress and delisting pressure [38]. With respect to fraud opportunities, information asymmetry is a key factor because it makes managerial concealment more difficult to detect [39]. In addition, inefficient corporate governance structures, such as insufficient board independence, a low proportion of independent directors, and ineffective audit committee supervision, weak internal control [40,41,42], and external audit failure provide opportunities for fraud [43]. With respect to excuse, executives’ personal traits, including moral values, attitudes toward fraud, moral hazard, and luck-oriented psychology, affect the probability of corporate financial fraud [44].

Balanced ESG investment can support sustainable corporate development by aligning resource allocation with environmental protection, social responsibility, and effective governance. In contrast, ESG investment imbalance may create sustainability decoupling: firms appear to perform well in ESG terms while the underlying governance and accountability systems remain insufficient to constrain opportunistic behavior. By doing so, it increases information asymmetry, tighten financing constraints, weakens internal control, and shapes an unethical corporate culture. From a sustainability perspective, ESG investment imbalance may undermine corporate sustainability because it can divert managerial attention and resources away from balanced and substantive ESG improvements. To the extent that such imbalance increases financial fraud risk, it may erode stakeholder confidence, weaken firms’ long-term capacity for value creation, and impair the sustainable functioning of capital markets. Therefore, the ESG investment imbalance studied in this paper creates the illusion of superior ESG performance by amplifying specific environmental (E), social (S), or governance (G) factors, rather than implementing genuine sustainability actions. This leads to a disconnect between disclosed ESG information and actual ESG performance, thereby constituting another more covert form of ESG greenwashing based on ostensibly genuine actions.

First, ESG investment imbalance can transmit financial pressure to financial fraud, becoming a pressure factor for listed company management to engage in financial fraud. ESG investment imbalance cannot improve firms’ underlying operational or financial conditions. Moreover, because professional financial institutions and capital-market participants may identify the misleading ESG performance associated with such imbalance, firms may find it difficult to obtain low-cost green credit or external financing, thereby exacerbating financing constraints. At this time, the company's management has to fabricate financial data through financial fraud, forming a transmission chain of “ESG investment imbalance - financial distress - financial fraud.”

Second, the false ESG performance constructed through ESG investment imbalance weakens corporate governance and creates opportunities for managers to commit financial fraud. ESG investment imbalance may cause internal governance mechanisms, such as the board of directors, internal control, and independent directors, to become merely formal. Once internal governance fails, managerial opportunism is difficult to constrain, thereby increasing opportunities for managers to commit financial fraud.

Third, ESG investment imbalance aggravates information asymmetry in listed firms and increases the opportunities for executives to engage in financial fraud. The operation of ESG investment imbalance adds noisy information, misleads external investors, analysts, and auditors, and weakens their ability to identify firms’ true non-financial performance. According to agency theory, managers may use noisy information to conceal operational shortcomings, thereby increasing opportunities to hide losses, manipulate earnings, fabricate revenues, and engage in other forms of financial fraud.

Finally, ESG investment imbalance violates the principle of integrity, reflects managerial opportunism, and increases executives’ rationalization for financial fraud. ESG investment imbalance provides insufficient ethical constraints on managers and reflects weak compliance awareness. When firms obtain short-term benefits through false ESG performance without punishment, managers may form the perception that violations have low costs and high returns, which violates the principle of honest business operations. As a result, false ESG performance generated by ESG investment imbalance becomes a tool for managers to satisfy private interests and strengthens their rationalization for financial fraud.

Therefore, ESG investment imbalance increases financial pressure, weakens internal governance, reduces information transparency, and violates the principle of honest business operations. These effects influence perceived pressure, perceived opportunity, and rationalization in financial fraud, thereby increasing the probability and severity of financial fraud by listed companies. Accordingly, this paper proposes the following hypotheses:

Hypothesis H1: ESG investment imbalance increases financial fraud by listed firms.

Hypothesis H2: ESG investment imbalance increases financial fraud by affecting corporate financial pressure, internal governance, information transparency and the principle of honest business operations.

3. Results

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

3.1. Model Specification

To empirically examine the impact of ESG investment imbalance on financial fraud, this paper specifies the following regression models:

$$\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{b}\mathrm{i}\mathrm{t}({\mathrm{F}\mathrm{d}\mathrm{u}\mathrm{m}}_{i,t}=\mathrm{1,0})={\alpha }_0+\beta \mathrm{*}{BiaESG}_{i,t}+\mathrm{*}{Control\mathrm{s}}_{i,t}+{\lambda }_t+{\mu }_j+{\epsilon }_{i,t}$$

(1)

$$\mathrm{p}\mathrm{o}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{o}\mathrm{n}({\mathrm{F}\mathrm{d}\mathrm{e}\mathrm{g}}_{i,t}=\mathrm{1,0})={\alpha }_0+\beta \mathrm{*}{BiaESG}_{i,t}+\mathrm{*}{Controls}_{i,t}+{\lambda }_t+{\mu }_j+{\epsilon }_{i,t}$$

(2)

where i denotes firm and t denotes year; Fdum is a dummy variable indicating whether a listed firm commits financial fraud; Fdeg denotes the frequency of financial fraud; BiaESG denotes ESG investment imbalance; and Controls_i,t denotes a vector of firm-level control variables; ${\lambda }_t$ and ${\mu }_j$ denote year and industry fixed effects, respectively. models (1) and (2), we include industry fixed effects (${\mu }_j$) to control for unobservable time-invariant industry factors, and year fixed effects (${\lambda }_t)$ to control for factors that vary over time but not across firms.

3.2. Data Sources

The main explanatory variable, ESG investment imbalance (BiaESG), is constructed using Huazheng ESG data from the Huazheng database. The dependent variables, financial fraud (Fdum and Fdeg), and the control variables are obtained from the CSMAR database. We exclude financial firms, ST and *ST firms with abnormal financial conditions, and observations with missing variable data. The final sample contains 45,763 firm-year observations for Chinese A-share non-financial listed firms in Shanghai and Shenzhen from 2009 to 2024. All continuous variables are winsorized at the 1% and 99% levels.

3.3. Variable Definitions

3.3.1. Dependent Variable: Financial Fraud

Following the “Listed Companies’ Violation and Punishment” database in CSMAR, this paper identifies financial fraud by listed companies based on violation types such as “inflated assets,” “fabricated profits,” and “false statements.”

The financial fraud dummy variable, Fdum, is defined as follows: if a firm is found to have engaged in financial fraud and is simultaneously determined by one or more regulatory authorities to have violated regulations (Yin et al., 2025) [45], Fdum is set to 1; otherwise, it is set to 0.

We also use the frequency of fraud, Fdeg, to measure the severity of financial fraud. If a listed firm receives no penalty in a given year, Fdeg equals 0. If the listed firm or its executives receive multiple penalties in that year, Fdeg equals the cumulative number of penalties.

3.3.2. Independent Variable: ESG Investment Imbalance

This paper mainly uses Huazheng ESG ratings as the original data for measuring ESG investment imbalance by listed firms. Since 2009, Huazheng Index has evaluated the ESG performance of Chinese A-share listed firms and currently covers all A-share listed companies. This index has been widely recognized by both industry and academia. We also use ESG data for listed firms from the Wind database.

We define ESG investment imbalance (BiaESG) as the standard deviation of each ESG component (E, S, and G) from its annual industry average. The specific definition is as follows:

$$\mathrm{BiaE}=\mathrm{E}-\mathrm{mean}\text{\_}\mathrm{industry}\text{\_}\mathrm{year}\left(\mathrm{E}\right)$$

(3)

$$\mathrm{BiaS}=\mathrm{S}-\mathrm{mean}\text{\_}\mathrm{industry}\text{\_}\mathrm{year}\left(\mathrm{S}\right)$$

(4)

$$\mathrm{BiaG}=\mathrm{G}-\mathrm{mean}\text{\_}\mathrm{industry}\text{\_}\mathrm{year}\left(\mathrm{G}\right)$$

(5)

$$\mathrm{BiaESG}=\sqrt{\mathrm{BiaE}*\mathrm{BiaE}+\mathrm{BiaS}*\mathrm{BiaS}+\mathrm{BiaG}*\mathrm{BiaG}}$$

(6)

A larger value of BiaESG indicates a higher degree of ESG investment imbalance by a listed firm (To mitigate the impact of ESG performance itself on ESG unbalanced investment, we incorporate ESG performance as a control variable.).

To verify that ESG investment imbalance is a form of false ESG greenwashing, this paper conducts empirical tests from three dimensions: correlation tests, grouped mean-difference tests, and industry heterogeneity tests. Following prior studies, we measure corporate ESG greenwashing by the distance between ESG performance and ESG disclosure [21], and conduct a series of tests based on this indicator. The indicator is constructed as follows:

$$\mathrm{ESGGW}=\left({\displaystyle \frac{{\mathrm{ESG}}_{\mathrm{dis}\mathrm{i},\mathrm{t}}-\overline{{\mathrm{ESG}}_{\mathrm{dis}}}}{{\mathsf{\sigma }}_{\mathrm{dis}}}}\right)-\left({\displaystyle \frac{{\mathrm{ESG}}_{\mathrm{per}\mathrm{i},\mathrm{t}}-\overline{{\mathrm{ESG}}_{\mathrm{per}}}}{{\mathsf{\sigma }}_{\mathrm{per}}}}\right)$$

(7)

First, we conduct correlation tests for the core variables. The Pearson correlation coefficient between ESG investment imbalance (BiaESG) and corporate ESG greenwashing (ESGGW) is 0.0379 and is significant at the 1% level, preliminarily confirming a significant association between ESG investment imbalance and corporate greenwashing.

Second, we conduct grouped mean-difference tests based on the degree of corporate greenwashing. Using the median ESG greenwashing degree in the full sample as the cutoff, we divide the sample into high-greenwashing and low-greenwashing groups and compare their ESG investment imbalance levels. The results, reported in columns (2)-(4) of Table 1, show that the mean BiaESG of firms with high ESG greenwashing is significantly higher than that of firms with low ESG greenwashing. This indicates that the more pronounced a firm’s greenwashing behavior is, the more evident its ESG investment imbalance becomes, further confirming the positive association between the two.

Finally, we conduct heterogeneity tests based on industry attributes. We divide firms into polluting and non-polluting industries (According to the "Directory for the Industry Classification Management of Environmental Protection Verification for Listed Companies" formulated by the Ministry of Environmental Protection in 2008, the subdivided industries of 14 heavily polluting sectors, including thermal power, steel, cement, electrolytic aluminum, coal, metallurgy, building materials, mining, chemical industry, petrochemical industry, pharmaceutical industry, light industry, textile industry, and leather making, are mapped to the corresponding three-digit industry codes of the China Securities Regulatory Commission (CSRC) in 2012 as B06, B07, B08, B09, B10, C15, C17, C19, C22, C25, C26, C27, C28, C29, C30, C31, C32, and D44 respectively, in accordance with the "Guidance on the Industry Classification of Listed Companies (Revised in 2012)" issued by the CSRC.) and compare the mean BiaESG between the two subsamples. The results, reported in columns (5)-(7) of Table 1, show that BiaESG is significantly higher for firms in polluting industries than for firms in non-polluting industries. This further supports the greenwashing nature of ESG investment imbalance from the perspective of industry heterogeneity.

3.3.3. Control Variables

To alleviate the influence of ESG performance itself on the measurement of ESG investment imbalance, this paper includes ESG performance as a control variable. For illustration, consider a student with a high total score: the student’s subject scores may deviate substantially from the mean simply because the student performs well overall. If the formula is applied directly, bias may arise. Therefore, after controlling for the total score, we examine the extent to which the student’s Chinese, mathematics, and foreign-language scores deviate from the average.

In addition, this paper includes firm size (Size), financial leverage (Lev), return on assets (ROA), operating cash flow (CFO), ownership nature (SOE), board size (BoardSize), proportion of independent directors (Indirector), shareholding ratio of the largest shareholder (First), institutional shareholding ratio (Insti), managerial shareholding ratio (Msh), and whether the chairperson and general manager positions are held by the same person (Dual). Definitions of the main variables are summarized in Table 2.

3.4. Descriptive Statistics

The descriptive statistics in Table 3 show that, on average, 22.89% of listed firms are punished by regulators for financial fraud, and each listed firm receives 0.2696 financial fraud penalties per year on average. This indicates that financial fraud remains common in China’s capital market and that governing financial fraud is still a difficult task. The mean value of ESG investment imbalance (BiaESG) is 10.0616 and the median is 8.8074, indicating that Chinese listed firms’ ESG performance deviates from the industry-year ESG mean or median by 13.76% or 12.01%. The mean ESG performance of Chinese listed firms is 73.1203. State-owned listed firms account for 33.58% of the sample. The average shareholding ratio of the largest shareholder is 33.7348%, the average institutional shareholding ratio is 0.4308%, and the average managerial shareholding ratio is 0.0794%. These statistics suggest that, despite the split-share structure reform, Chinese listed firms still exhibit serious ownership concentration, while managerial shareholding remains low. Therefore, agency problems between controlling and minority shareholders, as well as between managers and shareholders, still need to be addressed.

4. Results

4.1. Baseline Regression Results

Table 4 reports the baseline regression results. Columns (1) and (3) do not include control variables but control for industry and year fixed effects. Columns (1) and (2) report the results estimated from model (1). The regression results show that the coefficient on ESG investment imbalance (BiaESG) is positive and significant at the 1% level, indicating that firms with a higher degree of ESG investment imbalance are more likely to engage in financial fraud. Columns (2) and (4) report the results estimated from model (2). The coefficient on BiaESG is again positive and significant at the 1% level, indicating that firms with a higher degree of ESG investment imbalance commit financial fraud more frequently. Therefore, ESG investment imbalance has a significantly positive effect on financial fraud, supporting Hypothesis 1.

In particular, the coefficient on ESG performance itself is significantly negative, indicating that ESG performance significantly reduces financial fraud by listed firms. This finding is consistent with Chu et al. (2023) and Su et al. (2024), who show that corporate ESG or CSR performance significantly restrains financial fraud [11,13].

Taken together, these findings indicate that sustainable development cannot be assessed only through aggregate ESG ratings. For sustainability-oriented investors and regulators, the balance among E, S, and G dimensions provides important information about whether ESG activities are embedded in corporate governance and long-term value creation or are selectively used to create a favorable sustainability image.

4.2. Robustness Tests

4.2.1. Alternative Measure of ESG Investment Imbalance

We replace the industry-year mean deviations of the E, S, and G components in formula (3) with deviations from the annual mean of the E, S, and G components, and redefine ESG investment imbalance as BiaESG_Year. The results are reported in columns (1) and (2) of Table 5. The coefficient on BiaESG_Year remains significantly positive.

4.2.2. Alternative ESG Performance Data

We use ESG performance data from the Wind database to remeasure ESG investment imbalance as BiaESG_Wind. The regression results are reported in columns (3) and (4) of Table 5. The coefficient on BiaESG_Wind remains significantly positive.

4.2.3. Controlling for Industry-Year High-Dimensional Fixed Effects

Considering that financial fraud may vary across industries over time, we include industry-year high-dimensional fixed effects in the empirical analysis. The results are reported in columns (5) and (6) of Table 5. The coefficient on BiaESG_Year remains significantly positive. Therefore, ESG investment imbalance significantly increases financial fraud by listed firms.

4.2.4. Instrumental-Variable Test

Considering the influence of ESG itself on the empirical results, we include two endogenous variables, BiaESG and ESG, and use two corresponding instrumental variables: the lagged value of ESG investment imbalance and the shareholding ratio of ESG funds, denoted L.BiaESG and ESG_Fund. Listed firms make structural adjustments based on their ESG investment imbalance in the previous period, so current ESG investment imbalance is affected by lagged ESG investment imbalance. “Pan-ESG” funds pay greater attention to the ESG performance of portfolio firms and can influence portfolio firms’ ESG performance by participating in corporate governance. At the same time, the shareholding ratio of “Pan-ESG” funds does not directly affect corporate financial fraud. Therefore, the shareholding ratio of “Pan-ESG” funds is included as an instrumental variable for ESG performance. Columns (7) and (8) of Table 5 report the instrumental-variable test results. The coefficient on BiaESG is positive and remains significant at the 1% level.

4.3. Mechanism Tests

Cressey (1953) provides an analytical framework for financial fraud based on three factors: perceived pressure, perceived opportunity, and rationalization [23]. Specifically, financial pressure becomes a motive factor that induces managers to commit financial fraud; deficiencies in internal control, weak supervision, and unclear division of responsibilities provide opportunity factors; and the lack of personal integrity or biased moral judgment provides rationalization that make financial fraud appear justifiable.

Accordingly, this paper conducts mechanism tests from five aspects: net profit growth pressure (Netprofit_growth_pressure) and financing constraints (KZ), which capture financial pressure and fraud motives; internal control defects (isdeficiency) and information transparency (companyopacity), which capture fraud opportunities; and earnings management (ABSdisacc), which captures fraud excuse. Netprofit_growth_pressure is defined as a dummy variable equal to 1 if a firm’s net profit growth rate in the previous year is lower than the industry median, and 0 otherwise. Internal control is measured using whether internal control defects exist in the CSMAR database (isdeficiency) and the internal control index from the Dibo database (BDIndex). isdeficiency is a dummy variable equal to 1 if a listed firm has internal control defects and 0 otherwise, so a larger value indicates a lower level of internal control. The Dibo internal control index (BDIndex) is quantified based on five elements: internal environment, risk assessment, control activities, information and communication, and internal supervision. A larger BDIndex indicates a higher level of internal control. Financing constraints are measured by KZ. For information transparency, companyopacity equals 4 when the disclosure quality rating by the Shenzhen or Shanghai Stock Exchange is excellent, 3 when it is good, 2 when it is qualified, and 1 when it is unqualified. Earnings management (ABSdisacc) is the absolute value of discretionary accruals calculated using the modified Jones model.

Table 6 reports the mechanism test results. Columns (1) and (2) use net profit growth pressure and financing constraints, respectively, to test fraud motives. The coefficients on BiaESG are significantly positive, indicating that ESG investment imbalance significantly increases listed firms’ net profit growth pressure and financing constraints, thereby increasing financial pressure and strengthening motives for financial fraud. Columns (3) and (4) use internal control levels measured by the CSMAR and Dibo databases to test fraud opportunities. The regression result of the independent variable BiaESG in column (3) is significantly positive while that in column (4) is significantly negative, indicating that ESG investment imbalance significantly increases internal control defects. Column (5) uses information transparency to test fraud opportunities, and the coefficient on BiaESG is significantly negative, indicating that ESG investment imbalance significantly reduces information transparency and thus increases opportunities for financial fraud. Column (6) uses earnings management to test fraud excuse. The coefficient on BiaESG is significantly positive, indicating that ESG investment imbalance significantly increases earnings management, lowers executives’ ethical standards, and increases excuse for financial fraud.

In summary, the empirical results show that ESG investment imbalance increases net profit growth pressure, financing constraints, internal control defects, and earnings management, while reducing information transparency. Through the three channels of perceived pressure, perceived opportunity, and rationalization, ESG investment imbalance increases the occurrence of financial fraud.

4.4. Heterogeneity Tests

4.4.1. Ownership Nature

Compared with non-state-owned enterprises, state-owned enterprises are subject to stricter supervision. They are directly supervised by state-owned assets supervision and administration authorities at the central or local level and are also more closely monitored by the public. In addition, executives of state-owned enterprises often cannot directly benefit from financial fraud because of salary restrictions, low shareholding ratios, or restrictions on the tradability of their shares. Therefore, state-owned enterprises are less likely than non-state-owned enterprises to engage in financial fraud.

We divide listed firms into state-owned and non-state-owned enterprises according to the nature of the actual controller. Columns (1)-(4) of Table 7 report the ownership heterogeneity test results. Columns (1) and (3) report the results for state-owned enterprises, and columns (2) and (4) report the results for non-state-owned enterprises. The results show that the coefficient on BiaESG is smaller and the corresponding z-value is lower in the state-owned enterprise sample. Therefore, compared with non-state-owned enterprises, ESG investment imbalance in state-owned enterprises has a weaker effect on increasing both the probability and severity of financial fraud.

4.4.2. Industry Competition

Industry competition increases firms’ operating pressure and thus increases the occurrence of financial fraud. Monopolistic firms can enjoy monopoly profits and transfer operating pressure upstream or downstream, so they face lower operating pressure. In contrast, firms in competitive industries face numerous competitors, lower profit margins, and the risk of being eliminated. Therefore, firms in competitive industries have stronger motives to engage in financial fraud.

We divide industries into competitive and non-competitive industries based on the median Herfindahl-Hirschman Index (HHI) calculated using operating revenue. Columns (5)-(8) of Table 7 report the industry competition heterogeneity test results. Columns (5) and (7) report the results for non-competitive industries, and columns (6) and (8) report the results for competitive industries. The results show that the coefficient on BiaESG and the corresponding z-value are larger in the competitive-industry sample. Therefore, compared with firms in non-competitive industries, ESG investment imbalance has a stronger effect on increasing both the probability and severity of financial fraud among firms in competitive industries.

4.4.3. Economic Policy Uncertainty

Economic policy uncertainty increases uncertainty in firms’ operations. This allows executives of listed firms that engage in financial fraud to rationalize their behavior, thereby strengthening the positive effect of ESG investment imbalance on financial fraud.

This paper uses the China Economic Policy Uncertainty Index compiled from the People’s Daily and Guangming Daily, obtained from the CSMAR database. We take the average of the 12 monthly values each year as the annual China Economic Policy Uncertainty Index (EPU). Based on the median over the sample period, we divide the sample into periods with high economic policy uncertainty (Dummy_EPU = 1) and low economic policy uncertainty (Dummy_EPU = 0), and conduct regressions separately. The results are reported in columns (9)-(12) of Table 7. Columns (9) and (11) report the results for high economic policy uncertainty, and columns (10) and (12) report the results for low economic policy uncertainty. The results show that the coefficient on BiaESG is larger and more significant during periods of high economic policy uncertainty than during periods of low economic policy uncertainty. This indicates that higher economic policy uncertainty exacerbates the positive effect of ESG investment imbalance on financial fraud.

4.5. Economic Consequences

This paper further examines whether the increase in financial fraud caused by ESG investment imbalance affects firm value. We discuss this issue from two dimensions: financial value and market value. The empirical regression model is specified as follows:

$${\mathrm{Y}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1\mathrm{*}{\mathrm{B}\mathrm{i}\mathrm{a}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}\mathrm{*}{\mathrm{F}\mathrm{d}\mathrm{u}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_2\mathrm{*}{\mathrm{B}\mathrm{i}\mathrm{a}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_1\mathrm{*}{\mathrm{F}\mathrm{d}\mathrm{u}\mathrm{m}}_{\mathrm{i},\mathrm{t}}+\mathrm{*}\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}+{\lambda }_t+{\mu }_j+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(8)

$${\mathrm{Y}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1\mathrm{*}{\mathrm{B}\mathrm{i}\mathrm{a}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}\mathrm{*}{\mathrm{F}\mathrm{d}\mathrm{e}\mathrm{g}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_2\mathrm{*}{\mathrm{B}\mathrm{i}\mathrm{a}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_1\mathrm{*}{\mathrm{F}\mathrm{d}\mathrm{e}\mathrm{g}}_{\mathrm{i},\mathrm{t}}+\mathrm{*}\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}+{\lambda }_{\mathrm{t}}+{\mu }_{\mathrm{j}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(9)

where Y_i,t uses ROA as the measurement indicator for financial value and TQ as the measurement indicator for market value. If the regression coefficient ${\beta }_1$ of the interaction term BiaESG _i,t * Fdum_i,t or BiaESG _i,t * Fdeg_i,t is significantly positive, it indicates that the increase in financial fraud caused by ESG investment imbalance improves firm value. If the interaction term or regression coefficient is significantly negative, it indicates that the increase in financial fraud caused by ESG investment imbalance reduces firm value.

Table 8 reports the empirical results of economic consequences. Columns (1)-(4) show that the coefficients on the interaction terms BiaESG * Fdum and BiaESG * Fdeg are significantly negative. This indicates that the increase in financial fraud caused by ESG investment imbalance significantly reduces the value of listed firms and damages investors’ interests.

4.6. Moderating Effect of External Monitoring

The preceding analysis shows that ESG investment imbalance increases the probability and severity of corporate financial fraud, thereby reducing listed firms’ financial and market value. To reduce the negative impact of ESG investment imbalance on firms, this paper examines the moderating effects of institutional investor monitoring from the capital market and financial media monitoring. We define the institutional investor shareholding ratio as institutional investor monitoring (Insti), measured as the ratio of shares held by institutional investors to the total shares outstanding of the listed firm. We define negative reports by financial media as media monitoring (Media), measured as Media = ln (number of negative reports by financial media + 1). Table 9 reports the results for the moderating effects of external monitoring. Columns (1) and (2) examine institutional investor monitoring, and columns (3) and (4) examine financial media monitoring.

The results in columns (1) and (2) of Table 9 show that the coefficients on the interaction term between ESG investment imbalance (BiaESG) and institutional investor shareholding (Insti), BiaESG * Insti, are significantly negative, while the coefficients on BiaESG are significantly positive. This indicates that a higher institutional investor shareholding ratio significantly weakens the positive effect of ESG investment imbalance on corporate financial fraud. The results in columns (3) and (4) show that the coefficients on the interaction term between ESG investment imbalance (BiaESG) and financial media monitoring (Media), BiaESG * Media, are significantly negative, while the coefficients on BiaESG are significantly positive. This indicates that stronger financial media monitoring significantly weakens the positive effect of ESG investment imbalance on corporate financial fraud. Therefore, external monitoring can significantly mitigate the positive effect of ESG investment imbalance on corporate financial fraud.

In addition, in columns (1) and (2) of Table 9, the coefficients on ESG * Insti are significantly negative, and the coefficients on ESG are also significantly negative, indicating that institutional investor monitoring strengthens the fraud-reducing effect of ESG. However, in columns (3) and (4) of Table 9, the coefficients on ESG * Media are not significant, indicating that financial media monitoring does not significantly strengthen the fraud-reducing effect of ESG.

Therefore, external monitoring is important for sustainable capital markets because it strengthens the credibility of ESG information and reduces the space for symbolic sustainability practices. Institutional investors and financial media can encourage firms to maintain more balanced ESG investment and disclose sustainability performance in a more transparent and accountable manner.

5. Conclusions

Because financial fraud has severe negative effects on the capital market, scholars have focused on the determinants of financial fraud. Although many studies examine the inhibiting effect of ESG on financial fraud, this paper defines a more concealed form of ESG greenwashing, namely ESG investment imbalance, based on the deviations of the three ESG components, E, S, and G, from the corresponding industry-level component values of listed firms. Such ESG investment imbalance may create a misleading impression of strong overall ESG performance while concealing weaknesses in individual ESG dimensions and a lack of substantive ESG engagement. We then examine the impact of ESG investment imbalance on financial fraud. Using data on Shanghai and Shenzhen A-share non-financial listed firms, this paper finds that ESG investment imbalance significantly increases financial fraud by listed firms and offsets the inhibiting effect of ESG investment on financial fraud. This finding suggests that ESG investment imbalance may undermine sustainable corporate development by increasing financial fraud risk. The conclusion remains valid after using alternative measures of ESG investment imbalance, ESG data from the Wind database, industry-year high-dimensional fixed effects, and instrumental-variable tests. Mechanism tests use net profit growth pressure and financing constraints, internal control and information transparency, and earnings management as measures to capture perceived pressure, perceived opportunity, and rationalization, respectively. The results show that ESG investment imbalance significantly increases perceived pressure, perceived opportunity, and rationalization for financial fraud, thereby increasing financial fraud by listed firms. Heterogeneity tests based on ownership nature, industry competition, and economic policy uncertainty show that the effect of ESG investment imbalance on increasing the probability and severity of financial fraud is more pronounced among non-state-owned enterprises, firms in competitive industries, and firms operating under high economic policy uncertainty. Economic consequence tests show that financial fraud caused by ESG investment imbalance reduces not only short-term performance but also long-term performance. Finally, this paper shows that monitoring by institutional investors from the capital market and monitoring by traditional financial media mitigate the positive effect of ESG investment imbalance on financial fraud. In short, these conclusions reinforce the relevance of the study to sustainability research. Financial fraud damages the trust, transparency, and governance quality that sustainable corporate development requires. ESG investment imbalance further intensifies this problem by weakening the reliability of ESG signals and by allowing firms to present an incomplete form of sustainability performance. Therefore, reducing ESG imbalance is not only a matter of improving ESG ratings, but also a practical pathway for strengthening corporate sustainability, investor protection, and the healthy development of sustainable capital markets.

Therefore, this paper is the first to propose a new measure of ESG greenwashing and to discuss the impact of ESG greenwashing on financial fraud, thereby theoretically enriching the literature on ESG greenwashing and the determinants of financial fraud. In practice, this paper provides empirical evidence for investors and regulators to evaluate ESG performance more comprehensively. Regulators may consider requiring more disaggregated and comparable ESG information so that stakeholders can evaluate whether environmental, social, and governance dimensions develop in a balanced way. Investors can incorporate ESG balance into sustainability screening and stewardship activities to identify firms with more credible long-term value creation. Managers should integrate ESG investment into strategic planning rather than emphasizing a single dimension to obtain short-term legitimacy. These actions can help curb ESG greenwashing, improve sustainability governance, and support corporate sustainable development.