How ESG Rating Divergence Undermines Financial Flexibility and Sustainable Resilience

Abstract

Owing to the lack of a unified ESG (environmental, social and governance) rating standard, notable inconsistencies have appeared in ESG ratings assigned to the same firm by various rating agencies. Based on data encompassing Chinese A-share listed companies from 2015 to 2023, this paper investigates the effects of ESG rating divergence on corporate financial flexibility. We find that ESG rating divergence reduces corporate financial flexibility, and the finding remains reliable following various robustness tests. Mechanism tests show that ESG rating divergence diminishes corporate financial flexibility by raising operating leverage and the cost of equity capital, and exacerbating the degree of maturity mismatch between investing and financing. Further, we find that the negative impact of ESG rating divergence on financial flexibility is more pronounced in heavily polluting firms, enterprises with stronger market competitive positions, those facing lower financing constraints and companies with higher analyst earning forecast accuracies. We subsequently explore the economic effects of financial flexibility in reaction to ESG rating divergence. The findings indicate that ESG rating divergence negatively affects corporate sustainable resilience by first reducing financial flexibility. Overall, this study reveals the specific effects and pathways via which ESG rating divergence affects the financial flexibility of firms, holding significant implications for actively promoting the establishment of ESG systems and achieving sustainable corporate growth.

1. Introduction

ESG is an acronym that represents environmental, social, and governance factors. Corporate ESG practices promote the notion of sustainable development and enable enterprises to systematically demonstrate the multifaceted fulfilment of their responsibilities to stakeholders, thereby drawing increasing attention from market investors. For an extended period, policymakers and regulators worldwide have paid increasingly more attention to implementing corporate environmental stewardship and social accountability. Conforming to the global trend, the Chinese government has continuously advanced sustainable development; proposed the national strategic objectives of ‘carbon neutrality and carbon peaking’; and guided enterprises to pay more attention to concerns including environmental pollution, climate change, and their related risks, aiming to achieve industrial green transition and drive high-caliber economic growth. In comparison, the globally prevalent ESG development framework aligns closely with China’s pursuit of high-quality economic development, making the ESG rating an essential institutional mechanism for advancing sustainable governance in China. Chinese companies commonly report their ESG performance through standalone ESG reports, sustainability reports and similar instruments. For instance, in 2024, the number of Chinese A-share listed companies publishing standalone ESG reports reached 2475, reflecting a significant increase compared with 2233 and 1848 companies in 2023 and 2022, respectively. Specifically, ESG disclosure helps mitigate information asymmetry in the market. By utilising external ESG ratings to showcase their ESG performance, firms can gain access to financing [1], enhance operational efficiency [2] and foster green innovation [3].

Fueled by the strong demand for ESG ratings, over 600 ESG rating agencies have appeared worldwide, dedicated to helping investors gain a more comprehensive and systematic understanding of enterprises’ fulfilment of their relevant responsibilities. Examples include MSCI, Bloomberg, Huazheng, SynTao Green Finance and RKS, which are all widely engaged in corporate ESG evaluation and investment activities. Notably, each third-party rating agency independently develops its own ESG rating framework. Variations in evaluation scope, data sources and indicator weightings often result in discrepancies across rating outcomes [4]. Further, the incorporation of region-specific indicators—such as those related to targeting poverty alleviation and rural revitalisation in China—may amplify the divergence in individual ratings. Conversely, based on the cost–benefit principle, enterprises consider ‘flexibility’ in the implementation of regulations and policies during ESG disclosure and flexibly choose the form and content of disclosure [5], which indirectly interferes with the evaluations of third-party rating agencies. This selective disclosure behaviour indirectly affects the accuracy and consistency of third-party evaluations. Taking Kweichow Moutai as an example, Huazheng and SynTao Green Finance rated its 2020 ESG performance as ‘AA’ and ‘C+’, respectively, presenting a stark contrast. Thus, the impact of corporate ESG disclosure is counterproductive: ESG rating divergence generates disclosure ‘noise’ and further exposes disclosing enterprises to reputational risks [6,7]. In response to the increased complexity and ambiguity of information, stakeholders may seek to minimise their information processing costs by adopting a ‘voting with their feet’ strategy, which entails reducing financial support and investment interest in firms with inconsistent ESG ratings [8]. This behaviour, in turn, can trigger a series of adverse consequences, including impacts on managerial decision-making [9], increased risk premiums [10] and reduced stock liquidity [11].

Financial flexibility denotes a firm’s comprehensive capacity to proactively respond to environmental changes or uncertainties, effectively manage financial risks, mobilise financial resources and optimise financial decisions to enhance corporate value [12]. It embodies dual functions of prevention and utilisation: a reasonable reserve of corporate financial flexibility—specifically, maintaining a certain level of cash holdings to directly address future funding needs, and sustaining a low debt level to alleviate financing pressure and thereby indirectly secure rapid capital inflows—not only resolves capital shortages arising from business operations, but also enables enterprises to seize investment opportunities [13]. In fact, significant shifts in both international and domestic environments, coupled with frequent unexpected events, have created increasingly complex economic and trade conditions. These dynamics necessitate that enterprises adopt appropriate financial strategies to manage uncertainties in financing, operational and investment activities. In this context, financial flexibility strategies have become a critical pathway through which enterprises can implement prudent financial policies. As previously discussed, corporate engagement in ESG due diligence can lead to the ‘reconfiguration’ of financial resources, such as reduced financing costs and more stable cash inflows, thereby supporting the effective implementation of financial flexibility strategies. However, the ESG performance of enterprises sometimes fails to gain consistent recognition from rating agencies. Will the ‘noise’ generated by ESG rating divergence further affect enterprises’ corporate financial flexibility reserves? What are the mechanisms through which it influences such reserves? Exploring these questions helps clarify the link between Chinese firms’ ESG practices and their ability to adapt to complex environments, and this carries considerable significance for how the government and market can standardise ESG ratings.

Guided by stakeholder theory, the research employs Chinese A-share listed companies from 2015 to 2023 as the sample to reveal the significant negative impact of ESG rating divergence on corporate financial flexibility and its underlying mechanism. The specialized value of this research is reflected in not only confirming the general correlation between the two, but also precisely identifying three clear transmission mechanisms: namely, ESG rating divergence weaken corporate financial flexibility by increasing operating leverage, raising cost of equity capital, and exacerbating the maturity mismatch between investing and financing. Meanwhile, the heterogeneity analysis further specifies the boundary conditions of this impact, and the findings indicate that the adverse impact is significantly mitigated in enterprises with higher pollution levels, stronger market competitive positions, weaker financing constraints, or more accurate analyst earnings forecasts. Additionally, this study also reveals its far-reaching multiplier effect: the decline in financial flexibility caused by ESG rating divergence does not stop at the short-term financial level; instead, it further serves as a transmission hub that weakens the sustainable resilience of enterprises when responding to long-term challenges. This indicates that ESG rating divergence, which initially emerges as an information measurement issue, generates an amplification effect through financial channels and ultimately escalates into a strategic issue that affects the sustainable growth of enterprises. All conclusions retain their validity following the implementation of multiple robustness tests.

2. Literature Review

2.1. Research on ESG Rating Divergence

Regarding the determinants of ESG rating divergence, the lack of a unified rating standard is the root cause of such divergence [14,15]. Major rating agencies have differing focuses regarding the characteristics, definitions and attributes of ESG [16], which leads to the existence of autonomous judgments when they evaluate the ESG performance of firms. Further, with respect to information disclosure, Christensen et al. argued that increased corporate disclosure may result in greater divergence owing to discrepancies in agency-specific evaluation criteria [17]. In addition, some enterprises engage in selective disclosure of favourable ESG information, while withholding unfavourable data, thereby constructing a ‘sustainable development’ image [18]. This strategic presentation of information, however, tends to amplify discrepancies in ESG ratings. In contrast, Kimbrough et al. asserted that voluntary disclosure of ESG-related information boosts transparency by providing richer information, which facilitates a more thorough and precise assessment of corporate ESG practices and contributes to reduced rating divergence [19].

Regarding the economic implications, such divergence implies vagueness in corporate ESG information, which exacerbates information asymmetry and leads to a series of impacts. In the investment market, ESG rating divergence results in volatility in investor sentiment [20], heightens the likelihood of stock price plunges [21], and diminishes both stock returns [22] and liquidity [11]. In the financing market, affected firms often encounter higher interest rates and stricter lending conditions, which elevate financing expenses [23], intensify financing constraints [24] and limit access to external capital [17]. In corporate management, it strengthens managers’ incentive to engage in earnings manipulation [25], making them more likely to commit financial fraud, conceal negative information and engage in other such behaviours [26]. In addition, it inhibits green innovation output, degrades green innovation quality and gives rise to green innovation ‘bubbles’ [27].

Overall, research concerning ESG rating divergence has shown a gradual growth trend in recent years, with studies focusing primarily on the drivers and economic implications of such divergence. Among these studies, discussions on the economic impacts have centred mainly on capital market performance, financing constraints and corporate management performance, while relatively little attention has been paid to how divergence influences corporate financial decision-making.

2.2. Research on Corporate Financial Flexibility

From a functional and strategic perspective, corporate financial flexibility represents a systematic and holistic competence allowing organizations to swiftly and effectively respond to evolving external environments, rapidly mobilise financial resources and effectively mitigate financial risks [12]. Through sustaining a reasonable degree of financial flexibility, enterprises can achieve a balance between the internal and external resources needed for sustained operations or investments through the synergistic effect of their preventive and utilisation attributes, thereby enhancing corporate value [28]. Within the context of globalisation, financial flexibility functions not only as an intrinsic and objective characteristic embedded in corporate financial practices, but also as a strategic governance mechanism for navigating environmental uncertainties and improving the efficiency of financial decision-making. This capability significantly contributes to strengthening a firm’s long-term sustainability and resilience [29,30].

In terms of influencing factors, corporate financial flexibility is shaped by a combination of external environmental dynamics and internal firm-specific characteristics. External environmental changes focus primarily on their uncertain attributes, such as the macroeconomic environment [31], policy uncertainty [32] and intensity of market competition [33]. Factors at the firm-specific characteristic level are more diverse: management traits and managerial capabilities [34] directly alter corporate financial policies from the perspective of managers’ behavioural characteristics, while the corporate life cycle [12] and digital transformation [35] affect financial flexibility by driving the reallocation of financial resources to adapt to the specific developmental stages or requirements of firms.

In summary, scholarly inquiry into financial flexibility has a well-established foundation. However, in the context of evolving risk environments, limited academic attention has been paid to examining how ESG rating divergence influences financial flexibility.

3. Methods and Analysis

3.1. Sample Selection

This study utilizes data from Chinese A-share listed firms, spanning the years 2015 to 2023. On this basis, financial industry companies, firms designated as ST (Special Treatment), *ST (Delisting Risk Warning), or PT (Particular Transfer), along with those containing missing data, are removed from the sample, yielding a final dataset of 21,161 observations. To mitigate the impact of extreme values, all continuous variables are winsorised at the 1% and 99% levels. Among them, ESG rating data are collected from the Wind database, which provides information from Huazheng, Wind, Menglang, and SynTao Green Finance. All other financial data come from the China Stock Market & Accounting Research Database.

3.2. Model Specification and Variable Selection

To test whether ESG rating divergence reduces financial flexibility, this paper constructs the following Model (1):

$${\mathrm{FFI}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_0+{\propto }_1{\mathrm{ESGdif}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Controls}+\mathrm{ID}+\mathrm{Year}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(1)

where ESGdif_i,t denotes ESG rating divergence for firm i in year t; FFI_i,t measures financial flexibility of the firm; Controls denote a group of control variables; Year and ID denote year and firm fixed effects, respectively; ε_i,t is the random disturbance term; and $\propto$ signifies the coefficient of interest in the regression equation. To account for potential within-firm correlation, the estimation employs robust standard errors clustered at the firm level. The definitions of the variables are outlined in detail below.

3.2.1. Dependent Variable

The dependent variable is financial flexibility (FFI). Drawing on the research conducted by Zeng et al. [36], FFI is measured by integrating cash flexibility and debt financing flexibility. Specifically, cash flexibility is calculated as the difference between the firm’s cash ratio and the industry average cash ratio; debt flexibility is defined as max {0, industry average debt ratio − firm debt ratio}.

3.2.2. Independent Variable

The independent variable is ESG rating divergence (ESGdif). Drawing on the research conducted by Avramov et al. [37] and Sun et al. [21], ESG rating divergence is measured by the standard deviation of ESG scores conferred on the same firm by four rating agencies, namely Huazheng, Wind, Menglang and SynTao Green Finance. To ensure comparability across different rating scales, we standardise all ESG scores to a common 0–10 point scale. For example, Menglang employs 19 incremental rating levels ranging from weak to strong, which are first assigned numerical values from 1 to 19, respectively. These values are then standardised by multiplying them by 10/19. As a result, the standardised ESG scores from all four agencies fall within the 0–10 range, thereby satisfying the requirement of cross-rating comparability.

3.2.3. Control Variables

Building upon the work of Mu et al. [38], this study includes the following control variables: average ESG rating (AveESG), firm size (Size), Big 4 audit (Big4), proportion of independent directors (Indep), market share (Ms), firm growth (Growth), firm age (Age), shareholding ratio of the largest shareholder (Top1), cash flow ratio (Cashflow), CEO duality (Dual) and return on equity (ROE). Complete definitions are presented in

Table 1. Summary of variables.

Variable Types	Variable Symbols	Variable Names	Variable Definitions
Dependent Variable	FFI	Financial flexibility	(Firm’s cash ratio − Industry average cash ratio) + max (0, Industry average debt ratio − Firm debt ratio)
Independent Variable	ESGdif	ESG rating divergence	The standard deviation of ESG ratings for a firm assigned by four rating agencies, namely Huazheng, Wind, Menglang, and SynTao Green Finance
Control Variables	AveESG	Average ESG rating	The average ESG rating of a firm provided by four rating agencies, namely Huazheng, Wind, Menglang, and SynTao Green Finance
	Size	Firm size	The log of total assets at the end of the reporting period
	Big4	Big 4 audit	1 if audited by a Big Four accounting firm, 0 otherwise
	Indep	Proportion of independent directors	(Number of independent directors)/(Total number of board directors)
	Ms	Market share	(Firm’s operating income in the current year)/(Sum of operating income of all firms in its industry in the current year)
	Growth	Firm growth	(Revenue in current period/Revenue in previous period) − 1
	Age	Firm age	Firm age
	Top1	Shareholding ratio of the largest shareholder	(Total number of shares owned by the top shareholder)/(Total number of shares outstanding), with the unit being %
	Cashflow	Cash flow ratio	(Net cash flow from operating activities)/(Total assets)
	Dual	CEO duality	Code as 1 if the chairman and general manager are held by the same individual; otherwise, code as 0.
	ROE	Return on equity	(Net profit)/(Average balance of shareholders’ equity)
	ID	ID	Firm fixed effect
	Year	Year	Year fixed effect

.

3.3. Theoretical Analysis and Research Hypotheses

3.3.1. The Impact of ESG Rating Divergence on Corporate Financial Flexibility

Currently, differences among rating agencies regarding evaluation standards, coverage and measurement methodologies, combined with varying levels of proactivity, completeness and accuracy in corporate non-financial information disclosure, contribute to uncertainty in ESG rating outcomes. Consequently, given the reality of ESG rating divergence, investors, creditors and other stakeholders must exercise caution when interpreting these ratings and may need to adjust contractual terms and relationships to mitigate potential adverse effects [39]. In response to such divergence, firms often feel compelled to ‘demonstrate their credibility’ by allocating additional financial and material resources to secure more consistent future ratings. This reallocation of resources is likely to carry important consequences for corporate operational activities and financial conditions.

From the perspective of resource acquisition, creditors need to evaluate various metrics when assessing the creditworthiness of firms during the lending process. Inconsistencies in ESG ratings may, to a certain degree, act as a negative factor in this evaluation. To safeguard their interests and mitigate potential losses, creditors typically demand additional risk premiums as compensation [37]. In the short term, this leads to increased financing costs for firms [10]. Over the long term, creditors’ cautious or ‘wait-and-see’ stance may reduce their willingness to extend long-term credit, directly making it difficult for borrowing enterprises to maintain their existing corporate financial flexibility reserves, leading to a dual decline in the elasticity of financing interest rates and future cash inflows. As external credit conditions and borrowing capacity become more constrained, firms may increasingly rely on internal financing sources, which accelerates the depletion of cash reserves. In the event of economic recessions or industry downturns accompanied by a sharp decline in market demand, firms may face dual pressures from reduced internal cash generation (‘blood-making’) and restricted access to external financing (‘blood-transfusion’), further straining their financial flexibility. Additionally, institutional investors frequently depend on ESG ratings to pinpoint high-potential investment targets with a strong ESG performance [40]. However, ESG rating divergence disrupts valuation mechanisms in capital markets, leading to lower future stock returns [22], increased price volatility and a higher risk of stock price crashes [21]. As a result, firms may lose access to high-quality institutional investors and the associated resource spillovers. To stabilise stock prices, they may resort to measures such as stock buybacks, which draw on internal cash reserves and further challenge their financial planning and capital allocation strategies [41].

With regard to signal transmission, the increasing severity of global climate risks compels firms to expedite their ESG initiatives and cultivate a responsible corporate image in the public domain. Their products, labelled as resource-efficient and environmentally sustainable, are more likely to attract consumer preference [42]. These consumers also exhibit higher brand loyalty, and such ‘brand stickiness’ helps secure market share and profitability, thereby ensuring a stable and continuous future cash flow for firms [43]. Conversely, owing to limited analytical capabilities and technical knowledge, consumers struggle to interpret the uncertainties associated with ESG rating divergence. This lack of clarity may translate into distrust, which can spill over into perceptions of product quality and internal management practices, prompting consumers to switch to products from firms with consistent and stable ESG ratings. Moreover, ESG performance is transmitted across the supply chain [44]. When suppliers face ESG rating divergence, the operational resilience of downstream focal firms may be compromised, gradually undermining the stability of the entire supply chain [45]. In response, focal firms may resort to measures such as reducing procurement volumes, cutting commercial credit extensions or even replacing suppliers [46], thus exacerbating financial pressures on firms with divergent ESG ratings. Further, analysts leverage their professional expertise and industry experience to analyse both financial and non-financial information, providing investors with valuable research insights. However, the ‘noise’ generated by ESG rating divergence diminishes the informational usefulness of ESG disclosures and disrupts the professional judgment of analysts [19]. This results in reduced accuracy in earnings forecasts [47] and increases the likelihood of analysts conducting on-site visits and other due diligence activities, which in turn raises the probability of detecting corporate ‘greenwashing’. Ultimately, firms may lose high-quality investors, suffer damage to their equity capital base [20] and become increasingly reliant on debt financing, thereby narrowing their overall financing options.

In terms of principal–agent relationships, the sustainable development of enterprises requires managers to not only fulfil their economic fiduciary responsibilities to improve the enterprises’ operational, profitability and debt-paying capabilities, but also undertake environmental and social fiduciary activities that support long-term sustainability. As one of the prevailing approaches to fulfilling corporate sustainable responsibilities, ESG practices involve managers implementing long-term projects such as biodiversity conservation, rural revitalisation and green innovation. These initiatives necessitate substantial long-term capital investment, which inevitably draws on firms’ financial flexibility reserves. Meanwhile, if third-party rating agencies provide inconsistent evaluations of the performance of sustainable investment projects, it will undoubtedly exacerbate information asymmetry among the board of directors, shareholders and managers [48]. In the short term, managers may even lose the trust of the board and shareholders, or face relevant performance ‘penalties’, which can affect their access to salaries and incentives, and thus alter their behaviour. To address inconsistent ratings, managers may use external public relations to maintain corporate reputation and take measures such as inviting star analysts to conduct on-site research and organising media reports to eliminate rating divergence as much as possible. Such ‘crisis management’ methods directly increase the current cash expenditures of enterprises. When ESG rating divergence reduces enterprises’ access to long-term funds, managers tend to prioritise short-term interests [26] and choose short-term borrowing to ‘fix’ projects with divergent ratings, or invest in more explicit ESG construction projects. This induces a maturity mismatch between investing and financing, causes imbalances between financing arrangements and fund utilisation, and accelerates expenditures such as principal and interest. Therefore, this study hypothesizes that:

H1. 

Holding other factors constant, ESG rating divergence negatively affects corporate financial flexibility.

3.3.2. Mechanisms Through Which ESG Rating Divergence Affects Financial Flexibility

(1)

Operating leverage

The presence of fixed costs in corporate production and operation leads to a situation where the rate of profit fluctuation exceeds the rate of change in output and sales volume. Specifically, when sales decline, profits fall at an even greater rate, resulting in significant fluctuations in operating cash flow. From the perspective of sales volume, ESG rating divergence can undermine an enterprise’s long-term accumulated reputation. Reputation risks are transmitted across firms through business and interest linkages within the supply chain, forcing downstream focal firms to bear the risk of supply chain management failure [49], which may reduce their willingness to cooperate or prompt them to seek alternative suppliers. Moreover, inconsistent ESG ratings are more likely to attract media scrutiny or negative coverage, creating the dual pressures of public scrutiny and regulatory oversight [48]. In the context of intense competition in consumer markets, loyal customer segments cultivated by the firm may easily switch to substitute products. In relation to changes in fixed costs, firms may attempt to restore their reputation and retain supply chain partners by adopting best practices from high-performing ESG firms, such as upgrading production equipment, investing in environmental protection infrastructure or improving employee welfare facilities, which directly increase their fixed cost expenditures. Consequently, owing to operating leverage, a decline in sales volume amplifies profit reductions and reduces cash inflows. The complexity of forecasting declining sales further compounds the uncertainty in profit projections [50], making it increasingly difficult for firms to estimate future cash flow declines. This dynamic weakens the capacity of firms to maintain financial flexibility. Therefore, this study hypothesizes that:

H2. 

ESG rating divergence weakens corporate financial flexibility through an increase in operating leverage.

(2)

Cost of equity capital

According to the capital asset pricing model, investors determine the required rate of return by evaluating the quantitative relationship between risk and expected return. Inconsistent ESG ratings widen the information gap between companies and external investors [51], leading investors to question the ESG performance of companies and triggering a crisis of trust. Given that most investors are risk averse, they demand higher expected returns when exposed to elevated information risk to ensure capital preservation and obtain risk compensation [37]. Further, ESG rating divergence may exacerbate agency conflicts between shareholders and management. In particular, in regions where ESG information disclosure is still voluntary, or where the standardized framework for the content and rules of ESG information disclosure have not yet been unified, management may engage in strategic disclosure by leveraging information asymmetry under the guise of moral hazard, or even conduct ESG ‘greenwashing’ under the influence of peer effects [52], which increases the probability of corporate supervision and confiscation, thus creating operational risks. Similarly, to mitigate agency conflicts and cater to the board of directors and shareholders, management may use corporate funds to deploy long-term ESG construction projects in the short run. However, this long-run nature and low return of such projects mean that enterprises become highly vulnerable to financial risks. Consequently, investors, factoring in both operational and financial risks, will require higher returns on their investments. This translates into an increased cost of equity capital, resulting in higher dividend payouts and reduced retained earnings [10]. The consequent outflow of cash weakens the ability of firms to maintain financial flexibility. Therefore, this study hypothesizes that:

H3. 

ESG rating divergence weakens corporate financial flexibility through an increase in the cost of equity capital.

(3)

Maturity mismatch between investing and financing

In general, long-term investment projects ought to be supported by long-run financing options, while short-term investments should be supported by short-term financing. When firms resort to short-run financing to fund long-run investment initiatives, a maturity mismatch between investing and financing arises [53]. ESG rating divergence implies that enterprises may face substantial problems in regard to sustainable project investments, such as environmental protection and business ethics, which lead to operational risks [37]. Suppliers and customers, owing to risk assessment and avoidance, will exercise prudence in their existing trade credit policies and tend to reduce trade credit supply, exposing enterprises to liquidity crises. Faced with both liquidity constraints and ESG-related uncertainties, firms are compelled to seek greater external financing, leading to rising financing costs. However, under such conditions, firms often gain access to primarily short-term, rather than long-term, capital. This limited access further hampers the ability of firms to secure long-run financing for ESG-related long-run projects, thereby exacerbating the degree of maturity mismatch between investing and financing [54]. Consequently, the inherently low returns of long-term ESG projects result in a struggle to meet the repayment obligations associated with short-term debt, triggering a series of adverse outcomes—including increased debt burdens and persistent liquidity constraints [55]. These dynamics ultimately erode the financial flexibility of firms. Therefore, this study hypothesizes that:

H4. 

ESG rating divergence weakens corporate financial flexibility through an increase in the degree of maturity mismatch between investing and financing.

4. Results

4.1. Descriptive Statistics

The descriptive statistics for the key variables are displayed in

Table 2. Summary of descriptive statistics.

Variables	Sample Size	Mean	Median	Std. Dev.	Min	Max
FFI	21,161	0.088	0.043	0.196	−0.240	0.714
ESGdif	21,161	0.799	0.764	0.669	0.000	2.615
AveESG	21,161	5.095	5.154	0.941	1.111	8.408
Size	21,161	22.506	22.321	1.291	20.204	26.425
Big4	21,161	0.068	0.000	0.251	0.000	1.000
Indep	21,161	0.377	0.364	0.053	0.333	0.571
Ms	21,161	0.020	0.004	0.048	0.000	0.334
Growth	21,161	0.322	0.123	0.804	−0.605	5.515
Age	21,161	11.649	10.000	7.796	1.000	29.000
Top1	21,161	33.801	31.700	14.249	9.130	72.220
Cashflow	21,161	0.058	0.054	0.064	−0.118	0.250
Dual	21,161	0.289	0.000	0.453	0.000	1.000
ROE	21,161	0.092	0.077	0.073	−0.017	0.385

. The mean of FFI is 0.088, suggesting that, in general, firms possess a moderate capacity to rapidly mobilise and reallocate financial resources in response to uncertainties or unexpected events. However, its minimum value is −0.240, and the maximum value is 0.714, suggesting substantial variation in financial flexibility across the sample firms. Notably, some firms exhibit negative financial flexibility, implying poor financial resilience and potentially higher exposure to financial risks. The average of ESGdif is 0.799, with a median of 0.764. This indicates the prevalence of ESG rating divergence and suggests that discrepancies in ESG ratings are common among different rating agencies. The descriptive statistics of remaining control variables are within acceptable bounds and align with results documented in previous research.

4.2. Baseline Regression

To address potential multicollinearity concerns, this study utilizes the variance inflation factor (VIF) to rigorously evaluate each variable, thereby ensuring the data model’s robustness. All variables are found to have VIF values substantially below 5, suggesting no substantial multicollinearity. Drawing on the result, the research conducts multiple regression analysis to investigate the potential relationships and interactions between ESG rating divergence and financial flexibility.

Table 3. Baseline Regression Results.

	(1) FFI	(2) FFI
ESGdif	−0.046 ***	−0.010 ***
	(−3.12)	(−4.68)
AveESG		0.004 *
		(1.84)
Size		−0.050 ***
		(−8.93)
Big4		0.013
		(0.96)
Indep		0.008
		(0.22)
Ms		−0.155 *
		(−1.73)
Growth		−0.002
		(−1.02)
Age		−0.005
		(−0.54)
Top1		0.001 **
		(2.07)
Cashflow		0.247 ***
		(12.55)
Dual		−0.002
		(−0.45)
ROE		0.060 ***
		(2.89)
_cons	0.412 ***	1.232 ***
	(35.21)	(7.40)
ID	YES	YES
Year	YES	YES
N	21,161	21,161
r2	0.771	0.779
r2_a	0.724	0.733

Note: T-statistics are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. Standard errors are clustered by firm. The same applies to the following tables.

presents the baseline regression results examining the association between ESGdif and FFI. Column (1) presents the regression outcomes without including control variables but including firm and year fixed effects. The coefficient on ESGdif is −0.046 and statistically significant at the 1% level, suggesting that higher ESG rating divergence is associated with lower corporate financial flexibility. Column (2) incorporates relevant control variables, and the coefficient on ESGdif stays negative at −0.010 and significant at the 1% level. These findings provide empirical support for Hypothesis H1.

4.3. Robustness Tests

4.3.1. Alternative Measures of the Explanatory Variable

To alleviate potential measurement errors in the explanatory variable, this study employs alternative measures of ESG rating divergence. Drawing on the methodology proposed by Zhou et al. [56], ESG rating divergence is calculated as the range of ESG ratings observed within the sample year (ESGrange). As shown in column (1) of

Table 4. Robustness tests: alternative measures of the explanatory variable, propensity score matching, subsample regression, lag effect test, excluding the impact of the COVID-19 pandemic and extended fixed-effects specifications.

	(1) FFI	(2) FFI	(3) FFI	(4) FFI	(5) FFI	(6) FFI
ESGrange4	−0.004 ***
	(−3.12)
ESGdif		−0.007 ***	−0.004 *		−0.010 ***	−0.010 ***
		(−3.27)	(−1.78)		(−4.570)	(−4.603)
L.ESGdif				−0.005 **
				(−2.06)
AveESG	0.004 *	0.004 **	0.009 ***	0.003	0.004 *	0.003
	(1.94)	(2.13)	(3.60)	(1.24)	(1.930)	(1.303)
Size	−0.050 *** (−8.817)	−0.050 ***	−0.050 ***	−0.058 ***	−0.051 ***	−0.054 ***
	(−8.82)	(−8.85)	(−7.11)	(−7.83)	(−8.877)	(−9.559)
Big4	0.013	0.013	0.016	0.012	0.014	0.018
	(0.97)	(0.97)	(0.87)	(0.63)	(0.935)	(1.294)
Indep	0.008	0.008	−0.042	0.003	0.024	0.001
	(0.24)	(0.22)	(−1.20)	(0.09)	(0.646)	(0.039)
Ms	−0.157 *	−0.158 *	−0.228 ***	−0.178 *	−0.135	−0.106
	(−1.76)	(−1.76)	(−2.88)	(−1.91)	(−1.532)	(−1.382)
Growth	−0.002	−0.002	−0.002	−0.002	−0.003	−0.003
	(−1.01)	(−1.01)	(−1.05)	(−0.79)	(−1.627)	(−1.611)
Age	−0.005	−0.005	0.003	−0.000	−0.005	−0.006
	(−0.55)	(−0.54)	(0.46)	(−0.05)	(−0.468)	(−0.624)
Top1	0.001 **	0.001 **	0.000	0.001	0.001 **	0.001 ***
	(2.06)	(2.08)	(0.54)	(1.64)	(2.243)	(2.680)
Cashflow	0.247 ***	0.245 ***	0.273 ***	0.261 ***	0.255 ***	0.239 ***
	(12.54)	(12.47)	(13.26)	(12.33)	(11.988)	(12.295)
Dual	−0.002	−0.002	−0.003	−0.002	−0.003	−0.003
	(−0.42)	(−0.42)	(−0.66)	(−0.41)	(−0.680)	(−0.687)
ROE	0.060 ***	0.060 ***	0.062 ***	0.036	0.071 ***	0.059 ***
	(2.87)	(2.90)	(2.79)	(1.50)	(3.241)	(2.872)
_cons	1.215 ***	1.214 ***	1.123 ***	1.359 ***	1.238 ***	1.315 ***
	(7.30)	(7.31)	(6.41)	(6.55)	(7.241)	(8.068)
ID	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES
Industry	No	No	No	No	No	YES
N	21,161	21,154	16,340	15,480	18,629	21,161
r2	0.779	0.779	0.842	0.809	0.778	0.789
r2_a	0.733	0.733	0.799	0.763	0.724	0.744

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

, the regression coefficient of ESGrange stays significantly negative at the 1% significance level. This confirms the robustness of our earlier conclusions and suggests that the negative impact of ESG rating divergence on financial flexibility is not driven by the specific measurement approach of the core explanatory variable.

4.3.2. Propensity Score Matching

To mitigate potential omitted variable bias, we recognise that firms characterized by high ESG rating divergence may differ from those with low divergence in observable characteristics—such as financial features—that could distort the association between ESGdif and FFI. To address this issue, this study employs the propensity score matching method as a robustness check. Specifically, we define the treatment group as firms with an ESGdif above the median value (coded as 1), and the control group as those below the median (coded as 0). All control variables are included as covariates in the matching process. Using nearest neighbour matching, each treated firm is matched with one control firm, resulting in a total of 21,154 matched pairs. Robustness tests using the matched samples are reported in Column (2) of Table 4. The estimated coefficient of ESGdif is negative at the 1% level, and the research conclusion remains unchanged. This indicates that after accounting for the characteristic factors influencing ESGdif, the conclusion that higher ESG rating divergence significantly reduces financial flexibility remains robust.

4.3.3. Subsample Regression

Since some firms are rated by only one ESG rating agency, the ESG rating divergence measure (ESGdif) contains a large number of zero values, which could lead to bias in the baseline regression outcomes. To minimise potential bias arising from sample selection, this research restricts the sample to listed firms that have been assessed by two or more ESG rating agencies. The results are presented in Column (3) of Table 4. The coefficient of ESGdif is negative at the 10% significance level, further supporting the robustness of our main conclusion.

4.3.4. Lag Effect Test

To ensure that the regression outcomes are not influenced by the potential lag effect of ESG rating divergence, and to avoid interference caused by endogeneity issues between contemporaneous variables, this paper conducts a robustness test using the lag effect. Specifically, we lag the explanatory variable by one period (L.ESGdif) and substitute it into the baseline model for regression. Column (4) of Table 4 presents the results of this treatment. L.ESGdif is negative at the 5% level. This result attests to the robustness of the research conclusions in this paper.

4.3.5. Excluding the Impact of the COVID-19 Pandemic

Given that the COVID-19 pandemic constituted a major external shock, which might disrupt business operations, market dynamics and the intrinsic correlations among variables, and that financial flexibility could be directly affected by the pandemic, leading to changes not stemming from ESG rating divergence, this study adopts a robust approach to eliminate potential interferences under the special circumstances of the pandemic. This strategy is incorporated into the core of the robustness test: after excluding the sample data of 2020, the regression analysis is conducted anew. As displayed in column (5) of Table 4, the coefficient of ESGdif is negative at the 1% level, which fully verifies that the research conclusion is robust.

4.3.6. Extended Fixed-Effects Specifications

To tackle potential unobserved heterogeneity, we expand the baseline two-way fixed-effects model by incorporating industry fixed effects, which helps accurately capture industry-specific financial strategies and ESG rating mechanisms. Given the variations in growth potential, regulatory environments, and significance of ESG practices across various industries, corporate financial flexibility may exhibit systematic variation; therefore, accounting for industry-level heterogeneity guarantees that our estimation can accurately reflect this within-industry variation in the impact of ESGdif. As shown in Column (6) of Table 4, the regression results indicate that ESGdif is significant at the 1% level, which confirms that there remains a significantly negative association between ESG rating divergence and financial flexibility.

4.3.7. Instrumental Variable Approach

To address potential reverse causality between ESG rating divergence and financial flexibility, this research adopts the instrumental variable approach. The mean ESG rating divergence within the same province, industry and year is employed as an instrumental variable (IV) [57]. The Kleibergen-Paap rk Wald F statistics exceed the corresponding Stock–Yogo critical values by a large margin, confirming the absence of weak instrument problems. In addition, the Kleibergen-Paap rk LM statistics yield p-values of 0.000, suggesting that the instruments are well identified. Column (1) of

Table 5. Robustness test: instrumental variable approach.

	(1) First ESGdif	(2) Second FFI
IV	0.981 ***
	(72.97)
ESGdif		−0.013 ***
		(−3.52)
AveESG	−0.083 ***	0.003
	(−10.93)	(1.64)
Size	−0.058 ***	−0.051 ***
	(−4.64)	(−8.97)
Big4	−0.031	0.013
	(−0.83)	(0.96)
Indep	0.072	0.008
	(0.63)	(0.22)
Ms	0.266	−0.154 *
	(1.24)	(−1.71)
Growth	−0.001	−0.002
	(−0.20)	(−1.03)
Age	0.026	−0.005
	(0.76)	(−0.53)
Top1	−0.000	0.001 **
	(−0.03)	(2.07)
Cashflow	0.107 *	0.247 ***
	(1.80)	(12.59)
Dual	−0.017	−0.002
	(−1.35)	(−0.48)
ROE	−0.053	0.060 ***
	(−0.83)	(2.89)
ID	YES	YES
Year	YES	YES
N	21,161	21,161

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

presents the first-stage regression results, with the instrumental variable’s coefficient being significantly positive at the 1% significance level, demonstrating a strong association between the IV and the endogenous explanatory variable. Column (2) reports the second-stage regression outcomes, showing that the coefficient of ESGdif continues to be statistically significant and negative. This result confirms the reliability of the primary conclusion after accounting for potential endogeneity.

4.4. Mechanism Test

The theoretical analysis indicates that ESG rating divergence reduces the financial flexibility of enterprises by increasing their operating leverage, equity capital cost and the degree of maturity mismatch between investing and financing. To verify the above analysis, this paper refers to Jiang Ting’s research [58] and constructs Equation (2) for mechanism testing. At the same time, Equation (3) is used to examine the effect of mediating variables on financial flexibility. Here, Med represents the mediating variables, namely the operating leverage (DOL), equity capital cost (PEG), and the maturity mismatch between investment and financing terms (SDLA) mentioned earlier.

$${\mathrm{Med}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{ESGdif}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Controls}+\sum \mathrm{ID}+\sum \mathrm{Year}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(2)

$${\mathrm{FFI}}_{\mathrm{i},\mathrm{t}}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{Med}}_{\mathrm{i},\mathrm{t}}+\sum \mathrm{Controls}+\sum \mathrm{ID}+\sum \mathrm{Year}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(3)

4.4.1. Operating Leverage

Against the backdrop of a complex and challenging global economic environment, along with considerable downward pressure on the domestic economy, operating leverage can amplify the effect of market and production uncertainties on a company’s profit margins [50]. When ESG ratings differ significantly, the reliability of a firm’s non-financial information is weakened, which in turn raises stakeholders’ concerns about potential risks. This further enhances the operating leverage effect and limits the company’s ability to flexibly allocate financial resources. To explore this mechanism, we use the formula {(EBIT + fixed costs)/EBIT} as an indicator of DOL. An increased DOL value suggests a greater level of operating leverage. Column (1) of

Table 6. Mechanism test 1.

	(1) DOL	(2) PEG	(3) SDLA
ESGdif	0.032 **	0.003 ***	1.088 ***
	(2.21)	(3.45)	(3.11)
AveESG	−0.043 ***	0.000	2.062 ***
	(−3.32)	(0.04)	(5.57)
Size	−0.081 ***	0.009 ***	−11.028 ***
	(−2.67)	(6.46)	(−12.65)
Big4	0.039	−0.002	2.740
	(0.54)	(−0.56)	(1.08)
Indep	−0.222	−0.001	2.490
	(−0.86)	(−0.11)	(0.40)
Ms	0.117	−0.020	43.042 **
	(0.28)	(−1.18)	(2.54)
Growth	0.018 *	−0.001	−5.250 ***
	(1.68)	(−1.15)	(−7.88)
Age	0.143 **	−0.001	1.944 **
	(2.46)	(−0.11)	(2.05)
Top1	−0.003	0.000	−0.071
	(−1.38)	(0.73)	(−1.39)
Cashflow	−0.056	−0.022 ***	−95.135 ***
	(−0.40)	(−3.45)	(−29.91)
Dual	−0.021	0.002 *	−0.233
	(−0.82)	(1.84)	(−0.30)
ROE	−6.187 ***	0.032 ***	−89.619 ***
	(−32.63)	(4.50)	(−18.62)
_cons	2.788 ***	−0.096	216.658 ***
	(3.03)	(−1.52)	(10.16)
ID	YES	YES	YES
Year	YES	YES	YES
N	21,161	13,673	21,161
r2	0.5229	0.5389	0.3570
r2_a	0.4238	0.4181	0.2242

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

displays the outcomes of the operating leverage mechanism test. The findings show that the coefficient corresponding to ESGdif is 0.0322, significant at the 5% statistical level. It suggests that ESG rating divergence meaningfully increases a firm’s operating leverage. Further, as indicated in Column (1) of

Table 7. Mechanism test 2.

	(1) FFI	(2) FFI	(3) FFI
DOL	−0.007 ***
	(−5.82)
PEG		−0.119 ***
		(−3.34)
SDLA			−0.001 ***
			(−10.88)
AveESG	0.004 **	0.005 **	0.006 ***
	(2.16)	(2.12)	(3.08)
Size	−0.045 ***	−0.037 ***	−0.058 ***
	(−8.85)	(−5.59)	(−10.18)
Big4	0.013	0.004	0.016
	(0.93)	(0.27)	(1.10)
Indep	0.006	0.010	0.010
	(0.17)	(0.22)	(0.28)
Ms	−0.155 *	−0.121	−0.123
	(−1.73)	(−1.45)	(−1.35)
Growth	−0.002	−0.003	−0.006 ***
	(−1.14)	(−1.19)	(−3.06)
Age	−0.007	−0.001	−0.004
	(−0.74)	(−0.15)	(−0.42)
Top1	0.001 *	0.000	0.001 *
	(1.92)	(0.29)	(1.90)
Cashflow	0.249 ***	0.277 ***	0.176 ***
	(12.57)	(11.45)	(8.52)
Dual	−0.001	0.001	−0.002
	(−0.34)	(0.09)	(−0.41)
ROE	0.020	0.032	−0.005
	(0.87)	(1.33)	(−0.24)
_cons	1.244 ***	0.906 ***	1.368 ***
	(7.52)	(5.18)	(8.17)
ID	YES	YES	YES
Year	YES	YES	YES
N	21,161	13,673	21,161
r2	0.7796	0.7862	0.7835
r2_a	0.7338	0.7302	0.7387

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

, a negative relationship exists between operating leverage and financial flexibility. These regression results support Hypothesis H2, indicating that ESG divergence weakens corporate financial flexibility by increasing operating leverage.

4.4.2. Cost of Equity Capital

As a key indicator in capital markets for evaluating corporate equity value, cost of equity capital reflects investors’ confidence in a firm’s prospective growth and development. When the cost becomes too high, it can place added financial pressure on firms, potentially leading to fewer high-quality investment opportunities and limiting their ability to grow and thrive. To better understand this dynamic, we follow Easton [59] and apply the PEG model to estimate the cost of equity capital. Column (2) of Table 6 reports the findings of the mechanism test for the cost of equity capital. The findings reveal that the coefficient of ESGdif is 0.0027, with statistical significance at the 1% level. It implies that greater ESG rating divergence meaningfully increases the cost of equity capital. In addition, Column (2) of Table 7 reveals an inverse association between the cost of equity capital and corporate financial flexibility—as the cost rises, financial flexibility tends to decline. These results support Hypothesis H3 and indicate that ESG rating divergence weakens financial flexibility by raising the cost of equity capital.

4.4.3. Maturity Mismatch Between Investing and Financing

The issue of mismatch between investment and financing terms is commonly seen in enterprises. This mismatch not only disrupts normal investment behaviour and increases the risk of financial distress, but also raises the likelihood of cash flow strain or even a broken capital chain when short-run borrowing is utilized to finance long-run investment activities [53]. Consequently, companies may struggle more to manage their financial resources flexibly or respond effectively to uncertainties. To better assess this issue, we use the gap between the funds required for long-run investments and the available long-run capital support as a measure of the degree of mismatch (SDLA). The formula is calculated as follows: [Cash outflow from investment activities − (Increase in long-run loans + Increase in equity + Net cash flow from operating activities + Net cash received from disposal of fixed assets, etc.)]/(Initial total assets). Column (3) of Table 6 reports the results from the mechanism test regarding investment–financing term mismatch. The findings show that the coefficient for ESGdif is 1.0876, statistically significant at the 1% level. Column (3) of Table 7 additionally shows an inverse association between investment–financing term mismatch and corporate financial flexibility—the greater the mismatch, the lower the financial flexibility. These results support Hypothesis H4 and indicate that ESG uncertainty weakens corporate financial flexibility by increasing the degree of investment–financing term mismatch.

4.5. Assessing and Exploring Heterogeneity

The individual characteristics and external environmental features of firms exert differential impacts on their corporate financial flexibility levels. Therefore, this research further examines the influences of firms’ industry attributes, competitive positions, the degree of financing constraints and the accuracy of analyst forecasts on the relationship between ESG rating divergence and financial flexibility.

4.5.1. Firm-Specific Characteristics

(1)

Industry Nature

Owing to the inherent production characteristics of their industries, heavily polluting firms face stricter environmental regulations and more standardised supervisory frameworks. Because environmental pollution represents a critical risk factor, stakeholders tend to adopt a cautious stance when evaluating long-term investment opportunities in such industries. This research categorises the sample into two groups—heavily polluting enterprises and non-heavily polluting enterprises—and conducts grouped regression analyses accordingly. The results are presented in Columns (1) and (2) of

Table 8. Heterogeneity examination: the role of individual firm characteristics.

	Heavily Polluting Enterprises (1) FFI	Non-Heavily Polluting Enterprises (2) FFI	High-Market-Position Enterprises (3) FFI	Low-Market-Position Enterprises (4) FFI
ESGdif	−0.005	−0.013 ***	−0.006	−0.010 ***
	(−1.43)	(−4.96)	(−1.36)	(−3.19)
AveESG	0.001	0.003	0.004	0.006 *
	(0.38)	(1.25)	(1.00)	(1.77)
Size	−0.060 ***	−0.049 ***	−0.044 ***	−0.045 ***
	(−6.58)	(−6.89)	(−3.61)	(−4.08)
Big4	0.046	−0.001	0.032	−0.007
	(1.43)	(−0.03)	(1.21)	(−0.34)
Indep	0.010	0.007	0.082	−0.027
	(0.15)	(0.17)	(1.02)	(−0.57)
Ms	−0.063	−0.210 **	−0.311 *	−0.187
	(−0.48)	(−2.28)	(−1.93)	(−1.59)
Growth	−0.005	−0.002	−0.002	−0.000
	(−1.20)	(−1.05)	(−0.77)	(−0.15)
Age	−0.024	−0.002	0.024	−0.012
	(−1.34)	(−0.19)	(0.85)	(−0.75)
Top1	−0.001	0.001 ***	0.000	0.000
	(−1.55)	(3.73)	(0.78)	(0.84)
Cashflow	0.257 ***	0.243 ***	0.307 ***	0.213 ***
	(7.19)	(10.58)	(7.28)	(7.06)
Dual	−0.012 *	0.001	−0.004	0.002
	(−1.66)	(0.22)	(−0.48)	(0.34)
ROE	0.109 ***	0.033	0.008	0.050
	(3.35)	(1.26)	(0.19)	(1.05)
_cons	1.742 ***	1.151 ***	0.802 **	1.222 ***
	(5.47)	(5.92)	(2.02)	(3.71)
ID	YES	YES	YES	YES
Year	YES	YES	YES	YES
Chow	5.78 ***		11.62 ***
N	6373	14,788	7053	7054
r2	0.774	0.790	0.828	0.806
r2_a	0.728	0.745	0.762	0.728

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

. The findings reveal that the negative effect of ESG rating divergence on financial flexibility is more evident among non-heavily polluting enterprises. A plausible reason is that, compared with the social (S) and governance (G) aspects, heavily polluting firms place greater emphasis on compliance with environmental standards and regulatory requirements to mitigate the risks associated with the environmental (E) dimension. Further, environmental protection authorities closely monitor the operations of these firms through real-time tracking, periodic inspections and specialised audits, and regularly release authoritative reports. Consequently, even in the presence of ESG rating divergence, these official reports help reduce information asymmetry and alleviate the potential negative consequences for corporate financial flexibility.

(2)

Competitive Position

A firm’s competitive position is typically manifested across multiple dimensions, including brand influence, technological advancement, and market share of its products or services. Companies possessing a more robust competitive standing are more inclined to develop monopolistic advantages in scale expansion, profitability and sustainable growth. Additionally, such firms often maintain more stable relationships with suppliers and customers. In this study, the Lerner index (PCM) is employed to assess firms’ market competitive positions—a higher Lerner index indicates a stronger market position. We conduct grouped regression analyses based on tertiles, and the outcomes are reported in Columns (3) and (4) of Table 8. The findings reveal that the negative impact of ESG rating divergence on financial flexibility is more evident among companies with lower competitive positions. One plausible explanation is that firms with weaker market positions typically possess limited bargaining power. When ESG rating divergence arises and leads to information asymmetry, suppliers and customers may more readily seek alternative partners. In contrast, firms with stronger competitive positions benefit from higher levels of market dominance and stronger relational ties with stakeholders. Further, their long-established monopolistic advantages enable them to reallocate resources more efficiently as a reaction to ESG rating divergence.

4.5.2. External Environmental Characteristics

(1)

Degree of Financing Constraints

External fund providers often face difficulties in obtaining comprehensive information about firms, which increases the challenges and expenses of accessing external funding for firms. To examine this effect, we employ the financing constraint index for listed companies and conduct grouped regression analyses based on tertiles. The results are presented in Columns (1) and (2) of

Table 9. Heterogeneity examination: the role of external environmental characteristics.

	Enterprises with High Financial Constraints (1) FFI	Enterprises with Low Financial Constraints (2) FFI	Enterprises with High Prediction Accuracy (3) FFI	Enterprises with Low Prediction Accuracy (4) FFI
ESGdif	−0.020 ***	−0.003	−0.007	−0.009 *
	(−4.69)	(−0.96)	(−1.32)	(−1.87)
AveESG	0.005	0.005 **	0.008 **	0.003
	(1.02)	(2.28)	(2.08)	(0.66)
Size	−0.091 ***	−0.027 ***	−0.040 ***	−0.043 ***
	(−7.15)	(−3.24)	(−4.26)	(−3.31)
Big4	0.046	0.001	0.015	0.024
	(1.27)	(0.05)	(0.48)	(0.88)
Indep	−0.034	−0.018	0.115	−0.006
	(−0.47)	(−0.43)	(1.50)	(−0.07)
Ms	−0.436 **	0.001	−0.070	−0.121
	(−2.04)	(0.01)	(−0.55)	(−0.77)
Growth	−0.007	−0.002	−0.004	−0.005
	(−1.54)	(−0.89)	(−1.18)	(−0.97)
Age	−0.025	0.008	−0.023 **	0.003
	(−0.77)	(1.09)	(−2.32)	(0.27)
Top1	0.001 **	0.000	0.000	0.000
	(2.07)	(0.09)	(0.13)	(0.66)
Cashflow	0.366 ***	0.190 ***	0.244 ***	0.352 ***
	(10.52)	(6.77)	(5.07)	(7.04)
Dual	−0.009	0.002	−0.007	−0.004
	(−1.25)	(0.42)	(−0.77)	(−0.45)
ROE	0.031	0.066 ***	0.120 **	−0.098 *
	(0.71)	(2.77)	(2.20)	(−1.83)
_cons	2.247 ***	0.511 **	1.133 ***	1.010 ***
	(6.18)	(2.35)	(4.60)	(3.11)
ID	YES	YES	YES	YES
Year	YES	YES	YES	YES
Chow	12.22 ***		3.00 ***
N	7053	7054	4811	4812
r2	0.811	0.815	0.867	0.858
r2_a	0.733	0.763	0.737	0.750

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

. The findings indicate that the negative effect of ESG rating divergence on financial flexibility is significantly stronger among companies with high financing constraints. One plausible explanation is that firms facing higher financing constraints typically experience greater information asymmetry in financial markets. As a result, fund providers adopt a more conservative stance towards investment. When such firms exhibit significant ESG rating divergence, financiers become increasingly concerned about the feasibility and value realisation of their sustainable development initiatives. This uncertainty further influences fund providers’ assessments of the firm’s future profitability and debt repayment capacity. Consequently, financing becomes more difficult, which ultimately weakens the firm’s financial flexibility.

(2)

Accuracy of Analyst Forecasts

Analysts conduct comprehensive evaluations based on publicly available information, such as corporate financial reports, as well as proprietary insights derived from in-depth research. They subsequently issue earnings forecasts and investment recommendation ratings, which serve as critical references for investor decision-making. Following the methodology of Ma Yongqiang and Chen Weizhong [60], this study calculates the accuracy of analyst earnings forecasts and performs grouped regression analyses based on tertiles. The results are presented in Columns (3) and (4) of Table 9. The findings reveal that the negative effect of ESG rating divergence on financial flexibility is more evident among firms with lower analyst forecast accuracy. A reasonable explanation is that ESG rating divergence introduces informational ‘noise’, which increases the complexity and cost of information processing for analysts, ultimately reducing the accuracy of their forecasts. This decline in forecast accuracy may lead to downward revisions in analyst expectations, which can trigger a negative market reaction. As a result, investors face greater difficulty in obtaining reliable information for decision-making, and firms with low forecast accuracy encounter heightened challenges in accessing external financing. This further undermines their financial flexibility.

4.6. Economic Consequence Test

Sustainable resilience reflects the outcome of the interaction between a firm’s resource allocation capabilities and its external environment and is manifested in the availability of discretionary resources under conditions of uncertainty or crisis. To enhance resilience, firms must continuously accumulate resources and refine their resource allocation capabilities [61]. From a functional standpoint, sustainable resilience encompasses two core dimensions: ‘prediction’ and ‘recovery’. The predictive function enables firms to proactively develop strategies before a crisis, thereby mitigating the adverse impacts of external shocks on production, financial stability and market competitiveness, and safeguarding the continuity of critical operations [62]. The recovery function facilitates a rapid assessment of crisis-induced losses, enabling firms to mobilise resources for restoration and reconstruction, and to resume normal operations efficiently [63]. Notably, current ESG practices among Chinese enterprises are predominantly centred on addressing global climate change risks. By implementing sustainability-driven initiatives, firms contribute to the cultivation of long-term value. These ESG initiatives typically require substantial and sustained capital input, thereby fostering green technological innovation and enhancing green total factor productivity [64]. Consequently, ESG can be regarded as a strategic process of long-run resource accumulation and reallocation, serving as a viable pathway to strengthening organisational resilience. As indicated by prior empirical studies, inconsistencies or uncertainties in ESG ratings may undermine corporate financial flexibility. A decline in financial flexibility often compels firms to revise their existing financial strategies, thus disrupting the trajectory and outcomes of resource optimisation efforts. These disruptions ultimately exert a negative influence on enterprise resilience.

Specifically, in firms with lower ESG rating divergence, information uncertainty is relatively reduced, resulting in less damage to corporate reputation. Consequently, market expectations regarding the firm’s long-term development and strategic construction tend to be more optimistic. These firms still retain the capacity and potential to adjust their capital structure and optimise capital allocation. By ‘releasing’ their corporate financial flexibility reserves, firms can accelerate the inflow of short-term debt funds and the deployment of cash reserves. From a financial strategy perspective, this helps enhance their predictive defence capabilities and recovery adjustment capabilities before or during a crisis, and even achieve ‘transcendence’; that is, a growth momentum where firms recover from crises and leverage crises to achieve better development [65], thereby strengthening corporate sustainable resilience. Building on this perspective, this study further examines the economic consequences of ESG rating divergence by analysing its effect on enterprise resilience through financial flexibility. The regression results, presented in Column (1) of

Table 10. Economic consequence test.

	Full Sample (1) res	Enterprises with High ESG Rating Divergence (2) res	Enterprises with Low ESG Rating Divergence (3) res
FFI	6.101 ***	2.548	10.961 ***
	(3.16)	(1.34)	(3.30)
AveESG	−1.529 ***	−0.865	−2.761 ***
	(−3.30)	(−1.56)	(−3.24)
Size	4.680 ***	4.091 **	6.982 ***
	(5.51)	(2.32)	(4.74)
Big4	−1.836	−2.936 *	0.298
	(−1.44)	(−1.84)	(0.09)
Indep	−8.846	−4.592	−9.713
	(−1.51)	(−1.54)	(−0.97)
Ms	5.923	15.923 *	31.074
	(0.16)	(1.68)	(0.38)
Growth	3.945 ***	2.810 **	3.972 ***
	(4.03)	(2.52)	(3.15)
Age	−0.674	−0.066	−0.055
	(−0.77)	(−0.05)	(−0.02)
Top1	0.042	0.013	0.110
	(0.69)	(0.16)	(1.15)
Cashflow	7.227 **	14.299 ***	1.402
	(2.31)	(3.53)	(0.31)
Dual	0.285	0.361	−0.150
	(0.42)	(1.54)	(−0.14)
ROE	42.504 ***	24.622 ***	59.024 ***
	(5.48)	(9.56)	(3.94)
_cons	461.325 ***	464.652 ***	405.074 ***
	(21.80)	(11.59)	(10.94)
ID	YES	YES	YES
Year	YES	YES	YES
N	21,117	10,557	10,560
r2	0.345	0.488	0.294
r2_a	0.209	0.260	0.005

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

, reveal a statistically significant positive correlation between financial flexibility and enterprise resilience (res) at the 1% significance level. This indicates that an increase in financial flexibility contributes to greater enterprise resilience. We further conduct a grouped regression analysis based on the median value of ESGdif to explore how the effect of financial flexibility on enterprise resilience differs among firms with varying levels of ESG rating consistency. The results are reported in Columns (2) and (3) of Table 10. As shown, the positive impact of financial flexibility on enterprise resilience is more pronounced in firms with lower ESG rating divergence. The finding suggests that reducing ESG rating divergence strengthens the contribution of financial flexibility in reinforcing enterprise resilience.

5. Discussion

In terms of specialized scientific contributions, this study empirically examines the association between ESG rating divergence and financial flexibility, thus helping to enrich the theoretical framework surrounding these two constructs and their connections. First, it extends the current body of literature related to ESG ratings and their divergence [66]. By moving beyond the traditional focus on ESG ratings from individual rating agencies, it incorporates the heterogeneous evaluation outcomes across multiple rating agencies, thus enhancing the understanding of ESG ratings from a divergence-based perspective. Moreover, while prior research has explored the association between ESG rating divergence and ESG information disclosure [16], corporate green innovation [67] and the audit fees of accounting firms [68], this paper shifts the focus to the financial consequences of such divergence, particularly its impact on financial resource allocation. By analysing how ESG rating divergence affects corporate financial flexibility, this research contributes to the literature on ESG rating outcomes from a corporate financial strategy perspective. Second, it expands the body of research on the determinants of financial flexibility. Regarding its content and function, financial flexibility denotes the strategic reallocation of financial resources by firms in response to external uncertainties. Prior studies have primarily examined environmental uncertainty [31] or analysed the impact of specific uncertain events, such as financial crises [69] and corporate digital transformation [35]. Today, the current reality of corporate ESG responsibility fulfilment and the divergence in ESG ratings present a novel source of uncertainty, offering a fresh theoretical viewpoint for understanding the implementation of financial flexibility strategies.

In terms of contributions with multiplier effects, first, this study offers actionable guidance for aligning corporate ESG practices with financial strategy. By demonstrating that ESG rating divergence undermines financial flexibility, the findings provide clear strategic pathways for corporate managers. Firms can mitigate rating divergence through enhanced standardization of ESG disclosures, such as unifying disclosure standards and refining quantitative indicators, thereby enhancing the flexibility of financial resource allocation to cope with market fluctuations. This is particularly relevant for Chinese A-share listed companies, where the insights support a balanced integration of ESG accountability and financial stability, facilitate the embedding of sustainability objectives into operational decision-making, and in turn help drive the creation of long-term value. Second, the study provides valuable decision-making inputs for investors and rating agencies, thereby promoting greater market information efficiency. For investors, ESG rating divergence emerges as a salient supplementary signal for assessing corporate risk exposure. Higher divergence may indicate elevated levels of information asymmetry, prompting investors to conduct more rigorous evaluations of firms’ financial flexibility and resilience. For rating agencies, the findings underscore the imperative of harmonizing assessment criteria. More significantly, this discovery reaches beyond the domain of corporate financial strategy, providing a novel fulcrum for the integration of sustainable development and corporate finance theories. Previous studies have predominantly regarded sustainable development and corporate finance as relatively discrete fields. This research, however, empirically demonstrates that sustainable development agendas can be intricately woven into the core decision-making logic of corporate finance by shaping the efficiency of corporate financial resource allocation. This achievement constructs a pivotal juncture for the cross-fertilization of these two theoretical frameworks. Simultaneously, our study imparts valuable insights into the overarching issue of promoting sustainable growth within the global ESG ecosystem, sounding a critical alarm: Harmonizing ESG rating standards stands as the linchpin prerequisite for averting market information disarray and safeguarding the orderly functioning of the ecosystem. The prevailing issue of fragmented ESG ratings in the global market not only escalates the compliance burdens borne by enterprises but also runs the risk of distorting capital allocation. This research furnishes both theoretical and empirical underpinnings for addressing this pressing global conundrum.

This research illuminates the influence of ESG rating divergence on financial flexibility and its inherent mechanisms. However, the research conclusions are of a preliminary exploratory nature and have the following limitations. First, the research sample and data dimensions are limited. This research investigates Chinese A-share enterprises, spanning the years 2015 to 2023. On the one hand, the sample does not include non-listed companies, small and medium-sized enterprises, or Chinese-funded enterprises listed overseas. Moreover, during this period, the ESG rating system in China is still in the process of standardization. On the other hand, due to the data verification cycle, data from 2024 are not included in the analysis. Meanwhile, globally, significant heterogeneity exists in the degree, causes, and market reactions to ESG rating divergence, driven by disparate regulatory frameworks, market expectations, and rating agency methodologies. Should future research extend the data timeframe and expand the sample to include major global economies, it will help clarify the scope of applicability of the current findings and provide valuable references for multinational enterprises to comply with region-specific ESG disclosure requirements. Second, While the comprehensive index used in this study effectively captures overall information heterogeneity, it cannot disentangle the distinct effects of divergence in the individual environmental (E), social (S), and governance (G) dimensions, or in specific sub-indicators. Future studies could refine the measurement of ESG rating divergence to unearth the critical pathways through which it affects financial flexibility. Constructing pillar-specific and indicator-specific divergence indices would allow researchers to identify which dimensions are most critical to financial flexibility, thereby offering more targeted practical guidance. Such an approach would help firms prioritize their ESG disclosure efforts and strategically reduce divergence in the most impactful areas.

6. Conclusions

This study employs data encompassing a sample of Chinese A-share listed companies spanning 2015 to 2023 to investigate the impact of ESG rating divergence on corporate financial flexibility and the underlying mechanisms. Our findings reveal that ESG rating divergence significantly constrains corporate financial flexibility. This finding remains robust after potential endogeneity issues are addressed and multiple robustness tests are implemented. Through mechanism analysis, this study has refined the impact pathways. It reveals that ESG rating differences do not exert their influence through a single channel. Instead, they act on the financial flexibility of enterprises via three parallel mechanisms: the elevation of operating leverage, the increase in the cost of equity capital, and the exacerbation of the maturity mismatch between investing and financing. This discovery has transformed the previously general association into a distinct and multi-faceted impact chain. By conducting heterogeneity analysis, this research has accurately identified the boundary conditions of the impact. We have found that this detrimental impacts of ESG rating differences are not uniformly distributed across all enterprises. Specifically, the negative impacts are significantly alleviated in enterprises with higher pollution levels, stronger market competitiveness, lower financing constraints, and more accurate earnings forecasts. This study further uncovers the multiplier effect engendered by ESG rating disparities. It has been found that the reduction in financial flexibility induced by rating differences can further erode the long-term sustainable risk-bearing capacity of enterprises. This phenomenon suggests that ESG rating divergence goes beyond a matter of information disclosure or valuation. Instead, serving as an initial impetus, it can, through the transmission mechanism of diminished financial flexibility, impair an enterprise’s long-term resilience and viability when confronted with future economic volatilities, industry-wide crises, or the impacts of green transformation.

Accordingly, these following policy suggestions are put forward. First, the government should take an active role in establishing ESG rating frameworks and standardisation systems. To mitigate the ‘noise’ generated by ESG rating divergence stemming from various factors, policymakers should prioritise the development of comprehensive and unified ESG rating standards. This would ensure fairness and consistency in evaluating firms’ ESG performance. Meanwhile, incorporating the unique characteristics of ESG practices within the Chinese context is crucial. The government should collaborate with leading domestic and international institutions to co-develop an ESG rating system that reflects China’s specific economic, social and environmental conditions. Second, for enterprises with lower pollution levels, weaker market competitiveness, higher financing constraints and less accurate analyst earnings forecasts, an ESG disclosure assistance program can be launched. On the one hand, free ESG disclosure training and template tools can be provided to help enterprises standardize their disclosure content and reduce rating divergence caused by insufficient disclosure. On the other hand, financial institutions can be guided to introduce special credit for ESG rating optimization, offering interest rate discounts to enterprises that reduce rating divergence through improved disclosure, thereby alleviating the pressure of reduced financial flexibility. Third, Industry associations should be encouraged to take the lead in establishing industry-specific ESG information sharing databases, which integrate ESG practice data of enterprises within the industry and connect with mainstream ESG rating agencies. These databases will provide rating agencies with unified and authoritative industry data support. This initiative can, on one hand, reduce rating divergence caused by scattered data sources and inconsistent data quality among rating agencies. On the other hand, it enables enterprises to benchmark against industry ESG leaders through the platform, optimize their practices in a targeted manner, and thereby indirectly alleviate the constraints of ESG rating divergence on financial flexibility. Further, regulatory enforcement regarding ESG disclosures must be strengthened. For firms that fail to meet disclosure requirements or engage in ‘greenwashing’, appropriate sanctions should be enforced to ensure the integrity and reliability of the information used in ESG ratings.

Regarding micro-level practices, the following suggestions are put forward for enterprises. First, enterprises should strengthen their engagement with stakeholders to mitigate the negative consequences of ESG rating divergence and enhance the substantive value of non-financial information disclosure. To address the disruption in stakeholder relationships caused by rating inconsistencies, firms can proactively organise on-site visits and specialised briefings for key suppliers, major institutional investors and leading analysts, thereby establishing a structured platform for ongoing ESG practice exchange. Moreover, companies should actively participate in ESG excellence initiatives, such as case selection events and benchmarking programs hosted by industry associations and sustainability alliances. By engaging in these networks, firms can expand their peer connections, learn from best-in-class ESG practices and more effectively communicate their own ESG achievements to enhance transparency and credibility. Second, managers should develop comprehensive and systematic business strategies, allocate financial resources efficiently and proactively integrate risk factors arising from inconsistent ESG ratings into decision-making processes. As this study has revealed, ESG rating divergence undermines corporate financial flexibility by elevating operating leverage, increasing equity financing costs and exacerbating the degree of maturity mismatch between investing and financing. To counteract these effects, managers may adopt a product-driven operational strategy to boost sales volume and market share, while simultaneously strengthening long-term relationships with suppliers and customers. Moreover, firms should enhance their capacity to access credit resources by diversifying financing channels. This approach would ensure a stable and diversified capital base, thereby effectively mitigating the liquidity risks associated with financing term mismatches. Third, for rating agencies, efforts should be made to optimize the industry adaptability of rating models and make specific adjustments based on industry characteristics. For instance, greater emphasis should be placed on environmental protection input and compliance indicators for high-pollution industries to avoid rating biases caused by industry heterogeneity. Meanwhile, a dynamic model update mechanism should be established to adjust indicator weights annually in response to emerging trends in market ESG practices (such as requirements for green transformation under the dual-carbon goals), thereby enhancing the timeliness and accuracy of ratings.