ESG Performance Empowers Financial Flexibility in Manufacturing Firms—Empirical Evidence from China

Abstract

In the context of slowing economic growth and increasing uncertainty, enhancing the financial flexibility of manufacturing enterprises is a critical foundation for promoting the high-quality development of the real economy. This study selects a sample of Chinese A-share-listed manufacturing firms from Shanghai and Shenzhen, spanning the years 2012 to 2022, and constructs a fixed-effects model to examine the impact of ESG performance on the financial flexibility of these firms and its underlying mechanisms. The study finds that: (1) ESG performance significantly enhances corporate financial flexibility. (2) ESG performance promotes financial flexibility primarily through mechanisms such as alleviating financing constraints, improving competitive advantages, and attracting analysts’ attention. (3) Heterogeneity analysis reveals that the positive effect of ESG performance on financial flexibility is more pronounced in high-tech firms and non-heavily-polluted firms. (4) Sub-dimensional analysis shows that corporate governance has a more significant impact on financial flexibility enhancement than social responsibility, while environmental investment exerts an inhibitory effect on financial flexibility. (5) The uncertainty associated with ESG ratings weakens the contribution of ESG practices to the financial flexibility of manufacturing firms. Based on these findings, this paper suggests that enterprises should be encouraged to actively adopt ESG practices, accelerate the improvement of their ESG disclosure systems, and support firms with strong ESG performance to foster high-quality development.

1. Introduction

As a global leader in manufacturing, China’s manufacturing value added accounts for approximately 30 percent of the world’s total, holding the top position for 14 consecutive years [1]. The manufacturing sector is a crucial pillar of China’s economic and social development, playing an indispensable role in driving economic growth, expanding employment opportunities, and fostering technological innovation. However, external shocks such as global financial crises, geopolitical conflicts, and the impact of the COVID-19 pandemic have not only tested the resilience of enterprises, but have also raised higher demands for their financial stability [2,3]. The core of financial flexibility lies in the ability to quickly raise funds at lower costs, enabling enterprises to effectively seize investment opportunities [4], strengthen their position in turbulent environments [5], and mitigate the impact of adverse shocks [6]. Therefore, exploring ways to enhance corporate financial flexibility in the current economic climate is a significant and practical challenge we face.

Since the concept of ESG was first introduced by the United Nations Global Compact in the “Who Cares Wins” report in 2004, it has garnered increasing attention. Chinese government departments, regulators, and industry associations have implemented a series of policies aimed at enhancing ESG disclosure by listed companies, improving corporate ESG performance, and guiding investors toward ESG investment practices. According to data from China Securities Net, by the end of March 2023, the total investment in China’s ESG funds had reached RMB 117.667 billion, with performance significantly outpacing that of non-ESG funds [7]. This indicates that ESG has become a crucial channel for companies to attract market and investor attention, helping them increase cash reserves, enhance financing capacity [8], and thus improve financial flexibility. Therefore, there is a strong correlation between ESG performance and financial flexibility.

Although a small body of literature has addressed the subtle link between the Social (S) and Governance (G) dimensions and corporate financial flexibility, research on the impact of social responsibility on financial flexibility has not yet reached a unanimous conclusion. The positive effects of corporate governance on financial flexibility [9,10] are widely recognized by academics. However, Zhang et al. [11] found that fulfilling social responsibility has a substitution effect on firms’ financial flexibility, while Gupta and Krishnamurti [12] discovered that behaviors such as improving employee benefits enhance firms’ financial performance. The results of studies on environmental investment and business operations are also mixed. Chen et al. [13] argue that increased environmental investment can improve financial performance, while Fisher-Vanden and Thorburn [14] suggest that environmental investment may conflict with a firm’s pursuit of enterprise value and financial performance. Vashisht et al. [15] confirmed that the contribution of environmental investments to financial performance is observed in only a few companies. Even heavily polluting firms, which may potentially benefit from environmental investments through cost savings, experience financial losses in the short term. The study by Zhang and Liu [16] is undoubtedly a significant milestone, as they examine ESG as a whole and find that ESG performance significantly improves firms’ financial flexibility, with financing constraints playing a mediating role. While the existing literature provides valuable insights into the relationship between ESG performance and financial flexibility, there is a lack of research on ESG as a whole, its sub-dimensions, and their impact on financial flexibility. Moreover, most of the related studies do not deeply explore the multiple mechanisms through which ESG affects corporate financial flexibility, leaving certain gaps in the logical framework.

Building on the theoretical perspectives of stakeholders, principal-agent theory, and resource dependence, this paper explores the intrinsic relationship between corporate ESG performance and financial flexibility, along with the mechanisms of action. Using a sample of Chinese A-share listed manufacturing companies from 2012 to 2022, the study strongly confirms that ESG performance significantly contributes to corporate financial flexibility, particularly for high-tech and non-polluting firms. Examining the mechanisms, it is found that the positive impact of ESG performance arises from its ability to reduce financing constraints, enhance competitive advantages, and attract the attention of analysts. Among the three ESG dimensions, corporate governance plays the most prominent role in enhancing financial flexibility, while the social responsibility dimension has a relatively limited effect. Furthermore, firms’ environmental investments may slightly weaken their financial flexibility. Additionally, the uncertainty surrounding ESG ratings may send misleading signals, harm corporate reputation, and disrupt stakeholders’ judgments, thus diminishing the role of ESG in enhancing financial flexibility.

Compared to the existing literature, the marginal contributions of this paper are as follows: (1) Focus on Chinese manufacturing enterprises: This study focuses on Chinese manufacturing firms, often referred to as the “factory of the world”, as the research subject. It analyzes the feasible pathways for improving the financial flexibility of these enterprises from an ESG perspective, providing a typical case for studying the industrialization process in the world’s largest developing country. (2) Clarification of the mechanism: This paper elucidates the mechanisms through which ESG performance enhances financial flexibility from three aspects: financing constraints, competitive advantages, and analysts‘ concerns. It uncovers the long-term value creation logic of corporate ESG practices and complements and deepens the existing literature. (3) Dimensional analysis: This study integrates comprehensive ESG ratings with subdimensional ratings to offer a more nuanced analysis of how different ESG dimensions contribute to financial flexibility. It fills research gaps with new samples and provides a theoretical foundation for the targeted management of ESG practices. (4) Exploration of ESG rating uncertainty: Given the absence of standardized ESG rating criteria and the information barriers between companies and rating agencies [11], ESG rating uncertainty has emerged as a critical obstacle affecting corporate ESG practices. While ESG uncertainty has been shown to dampen its influence on portfolio choice, stock returns, and stock price responses to news [17,18,19,20], no literature has addressed whether this uncertainty impacts the relationship between ESG performance and financial flexibility. Using ESG rating data from six organizations, this study is the first to explore the interplay between ESG performance, ESG rating uncertainty, and firms’ financial flexibility, thereby further contributing to the relevant body of research.

The remainder of the paper is organized as follows: Section 2 provides a review of related literature and research hypotheses; Section 3 outlines the research design and variable descriptions; Section 4 presents the empirical analysis of the impact of ESG performance on corporate financial flexibility, including robustness and mechanism tests; Section 5 offers further analysis, focusing on heterogeneity analysis, the impact of each ESG dimension, and the differences in impact under ESG rating uncertainty; Section 6 discusses the research contributions, limitations, and future directions; and Section 7 concludes with the key findings and implications of the study.

2. Theoretical Analysis and Research Hypotheses

2.1. ESG Performance and Financial Flexibility

Sanchez subdivided financial flexibility into two types: financial resource flexibility and financial coordination flexibility [21]. Financial resource flexibility focuses on a firm’s ability to quickly raise financial resources at a low cost in the short term, while financial coordination flexibility emphasizes the firm’s skill in flexibly deploying both internal and external financial resources. Firms typically achieve financial flexibility by increasing cash holdings and maintaining the residual capacity to raise debt [22].

First, from the perspective of stakeholders, corporate managers who demonstrate a spirit of win-win cooperation, reflected in higher ESG ratings, are more likely to gain stakeholders’ trust. ESG performance encompasses information on environmental factors (e.g., energy and water use), social responsibility (e.g., gender equality), and corporate governance (e.g., internal structure), which helps stakeholders assess the social impact and ethical values of firms [23]. This transparency effectively reduces the cost of information acquisition and regulation for creditors and investors, thereby lowering the financing costs for manufacturing enterprises [24]. One study found that, in the context of green finance policies, banks are more inclined to provide larger loans, longer maturities, more favorable interest rates, and more lenient collateral requirements to firms with strong ESG performance [25].

Second, according to principal-agent theory, ESG performance serves as supplementary information to financial reporting, reducing management’s opportunism in manipulating financial data [26]. It helps to alleviate issues such as surplus manipulation and insider trading, which arise from asymmetric internal and external information [27], and effectively prevents management and majority shareholders from misusing internal cash flows for personal gain. Managers who prioritize ESG principles are more likely to make decisions that support long-term financial flexibility, reduce risky financial behaviors driven by short-term gain, and ultimately reduce agency costs. This attracts more long-term investors and creates better conditions for future external financing [28].

From the perspective of signaling theory, ESG practices have become a vital tool for companies to shape their public image, enhancing social awareness and expanding market share in response to the attention of media and stakeholders [29]. Corporate practices related to environmental protection and employee welfare have a publicity effect, which will help to enhance consumer goodwill, raise our company’s priority in product selection and consumer market [30], improve corporate profitability, and, in turn, increase corporate operating cash flow and future cash holdings.

Finally, based on resource dependence theory, implementing an ESG strategy aims to secure external resources that support the firm’s sustainable development. To garner more support from stakeholders, firms use ESG practices to promote resource integration across their supply chain and utilize supply chain financing strategies to improve liquidity management [31]. When firms align their ESG practices with government goals—such as contributing to economic, social, and environmental objectives—the government is incentivized to provide additional financial and tax support, thereby reducing the risk of a capital chain breakdown [32]. Based on the above analysis, the following research hypotheses are proposed:

Hypothesis 1.

Good ESG performance is conducive to improving the financial flexibility of manufacturing enterprises.

2.2. ESG Performance, Financing Constraints, and Financial Flexibility

Due to significant information asymmetry in the capital market, financial institutions impose stricter credit supply management, which inevitably hampers the efficiency of capital allocation. As a result, companies face challenges in raising the funds necessary for investment projects and experience financing constraints. Firms with high financing constraints struggle to secure external funding, limiting their ability to flexibly adjust their capital structure and investment strategies. This often leads them to adopt more conservative policies in response to market changes and competitive pressures [33]. When companies are unable to access the required funds promptly and resort to high-cost, high-risk financing methods such as debt restructuring, it exacerbates their financial distress and further undermines their financial flexibility.

However, strengthening ESG practices offers new solutions to alleviate financing constraints and maintain high financial flexibility. First, the global rise of ESG concepts has shifted investors’ focus from solely economic performance to considering ESG factors, making them more likely to prioritize firms with strong ESG performance for investment [34]. Second, companies with robust ESG practices are more inclined to disclose non-financial information such as environmental governance, social responsibility initiatives, and internal governance practices [35]. This transparency reduces the costs of investigation and monitoring for stakeholders and provides a new criterion for financial institutions to identify reliable firms and projects, thus easing financing constraints. Third, from a risk management perspective, companies excelling in ESG significantly reduce legal, environmental, and regulatory risks, leading to lower borrowing costs and more favorable financing terms [36]. Consequently, we propose the following hypothesis:

Hypothesis 2.

ESG performance can promote the financial flexibility of manufacturing enterprises by easing financing constraints.

2.3. ESG Performance, Competitive Advantage, and Financial Flexibility

Externally, companies with high ESG ratings tend to have a more diversified supplier base and lower concentration [37], which helps reduce dependence on major suppliers, diversify supplier-related risks, and ensure operational robustness and continuity [38], ultimately enhancing market competitiveness. Internally, employees and management, as direct beneficiaries of ESG practices, experience significant improvements in various benefits, such as enhanced employee benefits, opportunities for salary increases, and better working conditions [39]. These positive changes attract more skilled job seekers, significantly boost employee satisfaction and sense of belonging, and encourage greater proactivity in the company’s operations, further enhancing competitiveness [40]. Additionally, by promoting environmental innovation, improving employee welfare, and enhancing governance efficiency, companies develop more competitive products and services [41] to meet customers’ demands for “higher value” or “lower cost at the same value”, thereby strengthening their competitive advantages.

The strengthening of competitive advantages contributes to improving the financial flexibility of enterprises. On the one hand, with increased market share and brand influence, the company gains stronger bargaining power, securing more favorable supply chain policies [42] as well as achieving higher profit margins and faster capital turnover, all of which enhance financial flexibility. On the other hand, improving competitive advantages creates more growth opportunities and strategic options for the company, enabling managers to make more informed decisions regarding market positioning, product mix, and expansion strategies [43], thereby sustaining performance growth and further enhancing financial flexibility. In summary, the following research proposition is proposed:

Hypothesis 3.

ESG performance can promote the financial flexibility of manufacturing enterprises by enhancing competitive advantage.

2.4. ESG Performance, Analyst Attention, and Financial Flexibility

As key information intermediaries in the capital market, analysts play a crucial role in optimizing the information environment and significantly contribute to external governance. Strong ESG performance reflects a company’s robust development potential and investment value, attracting analysts with extensive information channels and exceptional analytical skills to closely follow and monitor these companies [44]. Furthermore, analysts who are more attuned to ESG information tend to provide more accurate forecasts compared to their peers [45].

Analysts’ in-depth examination of multidimensional ESG data helps to reduce information asymmetry and mitigate the risk of greenwashing [46], particularly as investors increasingly prioritize sustainability indicators. This creates greater opportunities for companies to grow. The transparency in the production and operations of firms enables suppliers to better understand the true intentions of companies, allowing them to more effectively monitor receivable security [6] and offer more favorable credit terms, thereby enhancing the company’s cash flow. Simultaneously, heightened external oversight encourages firms to focus more on improving their long-term sustainability capabilities, reduces the risk of executives engaging in personal gain-seeking behaviors such as excessive investment or surplus management, and lowers the likelihood of debt default [47]. This, in turn, increases creditors’ willingness to provide long-term loans and helps reduce corporate financing costs. Based on this, this paper puts forward the following hypothesis:

Hypothesis 4.

ESG performance can promote manufacturing firms’ financial flexibility by increasing analyst attention.

The research framework of this paper is shown in Figure 1.

3. Research Design

3.1. Sample Selection and Data Sources

In this paper, China A-share listed companies in the manufacturing industry are selected as samples from 2012–2022. ESG rating data were from the WIND data-base, and data on financial flexibility and other variables were from the CSMAR database. Enterprises with special treatment category (ST), abnormal or missing variable data were excluded from data processing, and 20,021 enterprise-annual data were obtained after screening. At the same time, to avoid the interference of extreme values with the accuracy of the results, all continuous variables were subjected to a shrinkage of the upper and lower 1% quartiles.

3.2. Definition of Variables

3.2.1. Explained Variable: Financial Flexibility (FF)

Drawing on the practice of Zeng et al. [48], cash flexibility (the difference between the firm’s cash ratio and the industry average cash ratio) and debt flexibility (the more significant value of the difference between the industry average debt ratio and the enterprise debt ratio compared with 0) are summed up. The larger the value of FF, the higher the firm‘s financial flexibility.

$$Financial Flexibility=Cash Flexibility+Debt Flexibility$$

(1)

$$Cash Flexibility=The {firm}^{’}s cash ratio-The industry average cash ratio$$

(2)

$$Debt Flexibility=Max\left(0,The industry average debt ratio-The fir{m}^{\mathrm{‘}}s debt ratio\right)$$

(3)

3.2.2. Explanatory Variables: ESG Performance (ESG)

Considering the coverage and availability of ESG ratings issued by various organizations, Huazheng ESG ratings were selected, and the values 1 to 9 were used to indicate C to AAA ratings, respectively. The higher the company’s value, the higher the ESG rating, the better its ESG performance, and the lower the ESG-related risks it faces.

3.2.3. Other Control Variables

Referring to an existing study [49], the following control variables were selected: enterprise age (Age), which is the natural logarithm of the establishment of the enterprise plus 1; enterprise size (Size), which is the natural logarithm of total assets at the end of the year; book-to-market ratio (MTB), that is, the ratio of book value to total market value; proportion of fixed assets (FixRatio), which is the ratio of net fixed assets to total assets; leverage (Leverage), the ratio of total liabilities to total assets at the end of the year; nature of shareholding (SOE), that is, the value of state-owned enterprises is 1, otherwise 0; shareholding checks and balances (Balance), representing the proportion of the second to five major shareholders and the proportion of the first major shareholder; independent director ratio (Indep), that is, the ratio of independent directors to the total number of directors; audit opinion (Opinion), where if the annual report is issued with a standard audit opinion, the value is 1, otherwise it is 0; and proportion of independent directors (Duality), that is, the same person of the chairman and the general manager takes 1, and different people take 0. The measurement methods for each variable are shown in

Table 1. Definition and measurement of variables.

Variable Type	Variable Name	Variable Symbol	Variable Definitions
explained variable	Financial flexibility	FF	Cash flexibility + Debt flexibility
explanatory variable	ESG performance	ESG	Huazheng ESG Rating C-AAA corresponds to assignments 1–9
control variable	Enterprise age	Age	Natural logarithm of the number of years the business has been in existence plus 1
	Enterprise size	Size	Natural logarithm of total assets of the enterprise at the end of the year
	Book-to-market ratio	MTB	Book value/total market value
	Proportion of fixed assets	FixRatio	Net fixed assets/total asset
	Leverage	Leverage	Total liabilities at year-end/total assets at year-end
	Nature of shareholding	SOE	State-owned enterprises take the value of 1, otherwise 0
	Shareholding checks and balances	Balance	The sum of shareholdings of the second to fifth most significant shareholders/shareholding of the first largest shareholder
	Proportion of independent directors	Indep	Number of independent directors/directors
	Audit opinion	Opinion	Takes the value of 1 if the annual report has been issued a standard audit opinion; otherwise, 0
	Duality	Duality	If the chairman and general manager are the same person, take 1; otherwise, take 0.
	Industry effect	Industry	Industry dummy variables
	Annual effect	Year	Year dummy variable

.

3.3. Model

To test Hypothesis 1, the following model is constructed:

$${FF}_{i,t+1}={\alpha }_0+{\alpha }_1{ESG}_{i,t}+{\alpha }_2\sum {control}_{i,t}+{Industry}_i+{Year}_t+{\epsilon }_{i,t}$$

(4)

where i represents the sample individual, t represents the year, FF represents the manufacturing firm’s financial flexibility, ESG represents the ESG performance level, control represents the control variable, and ε represents the random error term. Since the improvement of corporate financial flexibility has a certain lag, this paper front-loads the FF by one period to control for the lagged impact of ESG and the endogeneity problem caused by reverse causation. To minimize the effects of factors such as industry and time on firms’ financial flexibility, the model further controls for industry (Industry) and year (Year) fixed effects.

4. Basic Results

4.1. Descriptive Statistics

From the descriptive statistics in

Table 2. Main variables’ descriptive statistics.

Variables	Sample	Mean	Std. Dev.	Minimum	Maximum
FF	20,021	0.06	0.19	−0.27	0.85
ESG	20,021	4.11	1.06	1.00	8.00
Age	20,021	2.93	0.31	1.61	3.61
Size	20,021	22.11	1.17	19.57	26.45
MTB	20,021	0.85	0.83	0.05	10.09
FixRatio	20,021	0.22	0.13	0.00	0.73
Leverage	20,021	0.39	0.19	0.03	0.93
SOE	20,021	0.26	0.43	0.00	1.00
Balance	20,021	0.77	0.61	0.02	2.96
Indep	20,021	0.38	0.05	0.29	0.60
Opinion	20,021	0.98	0.15	0.00	1.00
Duality	20,021	0.32	0.47	0.00	1.00

, it can be seen that there is a significant difference between the minimum value (−0.27) and the maximum value (0.85) of corporate financial flexibility. With a mean score of 0.06, the listed businesses in the manufacturing sector of the A-share market have a relatively low overall level of financial flexibility, leaving potential for further improvement. The mean value of the ESG performance is 4.11, which reflects that the sample companies have an average rating of ESG ranging from B to BB. The mean leverage value is 39%, but some companies have a gearing ratio of 93%. The mean value of the proportion of independent directors (Indep) is 38%, which meets the SEC’s requirement for the proportion of independent directors on the board. The mean value of Duality is 0.32, indicating that more than one-third of the enterprises have managers holding two positions simultaneously.

4.2. The Benchmark Regression Results

Column (1) of

Table 3. Benchmark regression results.

Variables	(1)	(2)
ESG	0.020 *** (0.001)	0.0038 *** (0.001)
Age		−0.082 *** (0.005)
Size		−0.016 *** (0.002)
MTB		0.002 (0.002)
FixRatio		−0.200 *** (0.011)
Leverage		−0.419 *** (0.009)
SOE		0.016 *** (0.005)
Balance		−0.004 * (0.002)
Indep		−0.062 *** (0.023)
Opinion		0.003 (0.007)
Duality		−0.002 (0.003)
Constant	−0.023 *** (0.005)	0.842 *** (0.037)
INDUSTRY/YEAR	YES	YES
Observes	20,021	20,021
R-squared	0.0122	0.3216

Note: Parentheses indicate clustering standard deviations; * and *** indicate significance at the 10% and 1% levels, respectively.

presents the regression findings without adding control variables, whereas Column (2) provides the regression results after adding control variables. The regression coefficients of corporate ESG are 0.020 and 0.0038, respectively, which are both positively significant at a 1% statistical level, indicating that the ESG advantage promotes the enhancement of the level of financial flexibility of manufacturing enterprises and verifying hypothesis 1. Among the control variables, Age and Size hurt corporate financial flexibility, indicating that with the growth of years of establishment and the size expansion, there are more diversified ways of obtaining funds and loans when facing a crisis. In addition to Age and Size, Leverage and FixRatio also weaken the level of financial flexibility, indicating that a higher proportion of fixed assets as regular capital investment is less favorable to the firm’s ability to respond to unexpected risks promptly. On the other hand, MTB and Opinion have a positive impact on the level of financial flexibility by sending a positive signal to the outside world.

4.3. Robustness Tests

4.3.1. Replacement of Explanatory Variables

To avoid misjudgment of ESG performance due to ESG disclosure quality, three levels of assignment as proxy variables (esg) were applied to the results of Huazheng ESG ratings: C-CCC was assigned a value of 1, B-BBB was assigned a value of 2, and A-AAA was assigned a value of 3. The results are shown in Column (1) of

Table 4. Robustness test results.

Variables	Replacing Explanatory Variables		Reconsidering Lagged Effects	Replacing Fixed Effects	Replacing Regression Model
	FF		ff	FF	FF	FF	FF
	(1)	(2)	(3)	(4)	Q(25)	Q(50)	Q(75)
ESG			0.003 * (0.001)	0.003 * (0.001)	0.005 *** (0.002)	0.008 *** (0.001)	0.005 *** (0.002)
esg	0.007 *** (0.002)	0.013 *** (0.004)
Constant	0.835 *** (0.037)	0.804 *** (0.043)	0.821 *** (0.033)	0.957 *** (0.121)	0.526 *** (0.045)	0.362 *** (0.033)	0.526 *** (0.045)
Control variable	YES	YES	YES	YES	YES	YES	YES
INDUSTRY	YES	YES	YES		YES	YES	YES
FIRM				YES
YEAR	YES	YES	YES	YES	YES	YES	YES
Observes	20,021	20,021	13,516	20,021	16,512	16,512	16,512
R-squared	0.3213	0.3203	0.3637	0.2750
Pseudo R2					0.2514	0.1940	0.2514

Note: Parentheses indicate clustering standard deviations; * and *** indicate significance at the 10% and 1% levels, respectively.

. In addition, this paper also classifies ESG according to the upper quartile of the “Industry − Year,” assigning a value of 1 when the firm’s ESG rating belongs to the top 25%, and otherwise assigning a value of 0. The results are shown in Column (2) of Table 4, and the conclusions remain unchanged.

4.3.2. Reconsidering Lagged Effects

In the benchmark regressions, this paper front-loads FF by one period to control for the lagged impact of ESG and endogeneity issues due to reverse causality. However, the positive effect of ESG on financial flexibility is also constrained by multiple factors, such as the efficiency of information transmission and the speed at which stakeholders adjust their financial policies. Referring to Fang and Hu [50], the financial flexibility data in years t, t + 1 and t + 2 are weighted 0.3, 0.4, and 0.3, respectively, and summed up as the explanatory variables (ff). The regressions are re-run and the results are presented in Column (3) of Table 4 with the same conclusions.

4.3.3. Examining the Fixed Effects of Enterprises

To control the differences caused by the firms’ characteristics, this paper adopts the replacement fixed effects approach to test the research findings again by replacing the industry fixed effects with firm fixed effects, the results of which are shown in Column (4) of Table 4, and the ESG performance is still significantly positive at the 10 percent level.

4.3.4. Replacement of Regression Model

To exclude the effects of extreme values, this paper chooses 0.25, 0. 5, and 0.75 as the quartiles to construct the panel quantile regression model, respectively, and the estimation results are shown in the last three columns of Table 4. For the financial flexibility of the 25%, 50%, and 75% quantile groups, ESG performance can significantly improve the financial flexibility of manufacturing companies, which is consistent with the benchmark regression results.

4.4. Endogeneity Test

4.4.1. GMM Estimation Method

In this paper, referring to the method of Ma and Cui [51], the second-order and higher-lagged terms of financial flexibility are introduced into the model as instrumental variables for GMM estimation, and the results are shown in Column (1) of

Table 5. Endogeneity test results.

Variables	SYS-GMM	Panel IV-2SIS
	(1)	(2) Phase I (ESG)	(3) Phase II ( FF)
L.FF	0.452 *** (0.054)
ESGQ		0.007 *** (0.002)
ESG	0.005 ** (0.002)		0.124 *** (0.022)
Constant	0.603 ** (0.301)	−1.806 *** (0.291)	0.746 *** (0.075)
Control variable	YES	YES	YES
INDUSTRY/YEAR	YES	YES	YES
Observes	20,021	19,250	19,250
R-squared		0.1148
AR(1)	0.000
AR(2)	0.115
Hansen-P value	0.694
Wald F-statistic		21.57 ***

Note: Parentheses indicate clustering standard deviations; ** and *** indicate significance at the 5% and 1% levels, respectively.

. It can be seen that the AR(1) p-value is less than 0.1 and the AR(2) p-value is more significant than 0.1, indicating that the model passes the first-order serial correlation test and there is no second-order serial correlation; the Hansen-p-value is more significant than 0.1, indicating that the model passes the instrumental variable validity test. From the regression coefficients of the variables, the regression coefficient of the lagged term of the explained variable Financial Flexibility is significantly positive, indicating that the improvement of Financial Flexibility has an apparent dynamic continuity, and it is appropriate to use the dynamic panel data model in this paper; the ESG coefficient is also significantly positive, which is consistent with the benchmark regression results.

4.4.2. Instrumental Variable Approach

To further deal with the possible endogeneity problem, the instrumental variable is the number of shares held by “Broad ESG” funds (ESGQ), following the approach of Xie and Lv [52]. The reason is that, on the one hand, “Broad ESG” fund ownership fulfills the relevance principle by playing the role of “active shareholders” to promote ESG practices in firms. On the other hand, the financial flexibility of enterprises mainly relies on the judgment and deployment of internal managers on the market. At the same time, “Broad ESG” fund companies tend to make investment decisions based on the ESG performance of enterprises and do not directly interfere with the financial decisions of listed companies, which is in line with the assumption of homogeneity. The results of the instrumental variables regression are shown in Table 5, Column (2) to Column (3). The first-stage results show that the instrumental variables strongly correlate with the core explanatory variables. The estimation passes the weak instrumental variables test with an F-statistic of 21.57 (>10). Since the number of instrumental variables is precisely equal to the number of endogenous variables, no over-identification test is needed. The fitted coefficient of ESG performance in the second stage is positive at the 1% statistical level, which is consistent with the results of the benchmark regression, confirming that ESG performance can contribute to the financial flexibility of manufacturing firms.

4.5. Mechanism Analysis

The previous theoretical analyses point out that ESG performance can alleviate financing constraints, enhance competitive advantages, and attract analysts’ attention, thus enhancing corporate financial flexibility. To test the above mechanisms, this paper draws on the stepwise test method of Baron and Kenny [53] to sequentially test the regression coefficients of the main variables in model (4), model (5), and model (6), and the Bootstrap method of self-help sampling is utilized 500 times. One of the mediating variables (M) includes three variables: first, financing constraints (KZ)—the KZ index is used as a proxy for financing constraints [54], and a larger index indicates a greater degree of financing constraints faced by the enterprise; second, competitive advantage (PCM), which is expressed in terms of the enterprise’s Lerner’s index [55], and the larger the index, the more significant the enterprise’s ability to compete in the market; the third is Analyst Attention (ANALYST), which is measured using the number of equity analysts following the firm plus one and taking the natural logarithm of the result [56].

$$M_{i,t}={\beta }_0+{\beta }_1{ESG}_{i,t}+{\beta }_2\sum {control}_{i,t}+{Industry}_i+{Year}_t+{\epsilon }_{i,t}$$

(5)

$${FF}_{i,t+1}={\gamma }_0+{\gamma }_1{ESG}_{i,t}+{\gamma }_2{M}_{i,t}+{\gamma }_3\sum {control}_{i,t}+{Industry}_i+{Year}_t+{\epsilon }_{i,t}$$

(6)

Table 6. Results of the mechanism of action tests.

Variables	Financing Constraints Mechanisms		Competitive Advantage Mechanisms		Analyst Attention Mechanisms
	KZ	FF	PCM	FF	ANALYST	FF
	(1)	(2)	(3)	(4)	(5)	(6)
ESG	−0.089 *** (0.013)	0.0023 ** (0.001)	0.009 * (0.001)	0.0036 *** (0.001)	0.080 *** (0.006)	0.0032 *** (0.001)
KZ		−0.017 *** (0.0006)
PCM				0.003 *** (0.002)
ANALYST						0.007 *** (0.001)
Constant	8.359 *** (0.432)	0.960 *** (0.035)	−0.755 *** (0.032)	0.862 *** (0.037)	−8.424 *** (0.213)	0.589 *** (0.041)
Control variable	YES	YES	YES	YES	YES	YES
INDUSTRY/YEAR	YES	YES	YES	YES	YES	YES
Observes	20,021	20,021	20,021	20,021	20,021	20,021
R-squared	0.4447	0.3967	0.1724	0.3214	0.3371	0.3273
Ind. eff	0.000		0.000		0.000
Sobel Z	16.867 ***		7.529 ***		4.376 ***

Note: Parentheses indicate clustering standard deviations; *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

reports the test results of the mechanism of action of ESG performance affecting firms’ financial flexibility. In the regression analysis in Column (1), the coefficient of ESG is significantly negative (−0.089), revealing a significant negative correlation between ESG performance and financing constraints. Further, the results in Column (2) show that the coefficient of ESG decreases from 0.0038 (see Table 3, Column (2)) to 0.0023 for the main effect and remains significant at the 1% statistical level, tentatively implying the presence of a mediating effect. To rigorously verify this effect, this paper adopts the Sobel test, whose Z-statistic is as high as 16.867, which fully confirms hypothesis 2; that is, ESG performance can effectively reduce the cost of financing, alleviate the difficulties of corporate funding, and then positively contribute to corporate financial flexibility.

In the regression analysis in Column (3), the coefficient of ESG is significantly positive (0.009), indicating that ESG performance positively impacts product competitiveness. The results in Column (4) further show that the coefficients of both ESG and PCM are significantly positive at the 1% statistical level, and the ESG coefficient decreases from the main effect of 0.0038 to 0.0036. This mediating effect passes the Sobel test, which confirms that competitive advantage plays a partly mediating role in it. This finding strongly validates Hypothesis 3, i.e., that ESG performance significantly enhances product competitiveness and market competitive advantage of manufacturing firms, which in turn enhances firms’ financial flexibility.

In addition, the result in Column (5) shows that the ESG coefficient is 0.080, which is significant at a 1% statistical level. In Column (6), the coefficients of ESG and ANALYST are also both significantly positive at a 1% statistical level. The ESG coefficient decreases from the main effect of 0.0038 to 0.0032, and this mediating effect also passes the Sobel test. This result suggests that “analysts’ attention plays an important mediating role between ESG performance and firms” financial flexibility, which validates hypothesis 4. Specifically, ESG performance effectively contributes to firms’ financial flexibility by enhancing information transparency and strengthening external monitoring.

5. Expansion Analysis

5.1. Heterogeneity Analysis

5.1.1. Heterogeneity Analysis of Technological Characteristics

The sample is divided into two groups—high-tech and non-high-tech firms—based on the Classification of High-Tech Industries (Manufacturing) (2017) research methodology. The empirical results presented in Columns (1) and (2) of

Table 7. Results of heterogeneity test.

Variables	High-Tech Firms	Non-High-Tech Firms	Heavy Polluting Firms	Non-Heavy Polluting Firms
	(1)	(2)	(3)	(4)
ESG	0.004 *** (0.001)	0.003 (0.002)	−0.002 (0.002)	0.006 *** (0.001)
Fisher’s Combined Test p-value	0.004 ***		0.007 ***
Constant	0.847 *** (0.041)	0.754 *** (0.086)	1.016 *** (0.062)	0.928 *** (0.060)
Control variable	YES	YES	YES	YES
INDUSTRY/YEAR	YES	YES	YES	YES
Observes	15,985	4036	5588	14,433
R-squared	0.3237	0.3245	0.3478	0.3457

Note: Parentheses indicate clustering standard deviations; *** indicate significance at the 1% levels, respectively.

show that the regression coefficients for ESG performance in high-tech firms are significantly positive at the 1% level, while the regression coefficients for non-high-tech firms do not pass the significance test. The empirical p-value, obtained through 500 self-sampling iterations using Fisher’s portfolio test, indicates that the between-group difference is significantly different from zero at the 1% level. This suggests that improving ESG performance has a more pronounced effect on the financial flexibility of high-tech manufacturing firms. The reason for this is that the performance of technological innovations and improvements in high-tech firms often lead to substantial business-level progress, which optimizes the firm’s cash flow position. In addition, high-tech firms are frequently more compatible with advanced green equipment and technology applications, and such positive signals can attract more investor attention and help high-tech firms open up markets and access more market resources, which ultimately contributes to their financial flexibility.

5.1.2. Heterogeneity Analysis of Pollution Degree

Against the backdrop of the “dual-carbon” target, the government has introduced a series of policies and measures to urge heavily polluting enterprises to adopt ESG practices and strictly promote green and low-carbon initiatives. However, compared to non-polluting firms, high-polluting companies require higher capital investments to improve ESG performance, which inevitably increases the risk of “greenwashing”. If the market detects such behavior, it can negatively affect these companies. To investigate this, this paper divides the sample into two groups: heavy polluters and non-heavy polluters, based on the CSRC’s 2012 revision of the Guidelines for the Classification of Listed Companies by Industry and the List of Listed Companies in Environmental Verification. The regression results in Columns (3) and (4) of Table 7 show that ESG performance contributes to financial flexibility only in the non-heavy polluting group, and this result passes the Fisher significance test. This suggests that the ESG performance of non-heavy polluting firms has a more significant impact on enhancing their financial flexibility than that of highly polluting firms. This phenomenon can be attributed to two factors: First, high-polluting firms face higher environmental and resource costs due to poor environmental and social responsibility performance, which not only hinders their production and operations but also prevents them from accessing policy funds. Second, compared with non-high-pollution enterprises, high-pollution enterprises are subject to strict regulation by both the government and the market in terms of pollutant emissions, which in itself makes them have a stronger drive for green transformation, thus reducing the sensitivity of stakeholders to the fulfillment of ESG commitments by high-pollution enterprises.

5.2. Impact Analysis of ESG Performance Sub-Dimensions

This paper further conducts a sub-dimensional analysis using the E, S, and G sub-scores corresponding to Huazheng’s ESG, and the results are shown in

Table 8. Sub-dimensional regression results.

Variables	(1)	(2)	(3)
E	−0.003 *** (0.001)
S		0.004 *** (0.001)
G			0.007 *** (0.001)
Constant	YES	YES	YES
Control variable	−0.023 *** (0.005)	0.842 *** (0.0367)	0788 *** (0.037)
INDUSTRY/YEAR	YES	YES	YES
Observes	20,021	20,021	20,021
R-squared	0.3199	0.3216	0.3332

Note: Parentheses indicate clustering standard deviations; *** indicate significance at the 1% levels, respectively.

. In terms of coefficient sign and significance, the coefficients of social responsibility and corporate governance are both significantly positive at the 1% level, suggesting that a firm’s good performance in social responsibility and corporate governance contributes to the enhancement of financial flexibility, with the performance of corporate governance playing a more significant role in that enhancement process. However, the coefficient of the impact of environmental investment on corporate financial flexibility is negative, which indicates that manufacturing firms’ investment in the environment increases the economic cost in the current period and has a dampening effect on financial flexibility in the subsequent period. The possible reason for this is that Chinese manufacturing enterprises will undoubtedly be accompanied by an increase in costs at the beginning of their responsibility to protect the environment, and the investment in environmental management cannot be immediately converted into benefits, thus presenting an unfavorable phenomenon for the increase of financial flexibility.

5.3. Impact Analysis of ESG Rating Uncertainty

The ESG rating market is highly diverse and decentralized, with each rating agency operating according to its own unique assessment framework and tools. As a result, the same company may receive significantly different ratings depending on the rating system used. First, as the differentiation in ESG ratings increases, the degree of information asymmetry both inside and outside the company intensifies. This diminishes the positive impact and reference value of ESG ratings, making it more difficult for investors to identify accurate information about the company, ultimately reducing their willingness to invest [57]. Second, in an effort to attract public attention, some media outlets may exaggerate the negative aspects of ESG rating discrepancies, sometimes portraying these differences as indicators of corporate irresponsibility or poor environmental protection practices [58]. This can damage a company’s reputation, lower consumer support, and block a primary source of corporate cash flow. Finally, since ESG performance is linked to higher corporate credit, better financial performance, and lower operational risks, increased information noise in ESG ratings may interfere with trading partners’ ability to assess a company’s operational status, profitability, and solvency [59]. This can destabilize the relationship between the company and its suppliers, ultimately hindering its ability to secure better pricing.

Drawing on Avramov, Cheng, Lioui, and Tarelli [18], ESG uncertainty indicators are constructed using rating data from six agencies, including Wind, Huazheng, and Bloomberg. The median ESG uncertainty level, calculated by industry and year, is then grouped.

Table 9. Results of the impact of ESG rating uncertainty.

Variables	High Uncertainty Group	Low Uncertainty Group
ESG	0.002 (0.002)	0.007 *** (0.002)
Tests for differences between groups	3.77 *
Control variable	YES	YES
Constant	0.580 *** (0.066)	0.611 *** (0.068)
INDUSTRY/YEAR	YES	YES
Observes	5475	4647
R-squared	0.3485	0.3387

Note: Parentheses indicate clustering standard deviations; * and *** indicate significance at the 10% and 1% levels, respectively.

reports the estimation results, and it is found that there is a significant incentive effect of ESG performance on the financial flexibility of firms with low uncertainty compared to firms with high ESG rating uncertainty, i.e., the uncertainty associated with ESG ratings undermines the booster effect of ESG performance on the financial flexibility of manufacturing firms. This suggests harmonized rating standards can improve market transparency and comparability, reduce confusion and misleading rating results, and help manage ESG risks and opportunities more effectively.

6. Discussion and Future Research Agenda

6.1. Discussion

Firstly, when examining the various ESG dimensions, this paper identifies an intriguing phenomenon: investment in environmental initiatives by manufacturing firms temporarily increases current economic costs, thereby dampening financial flexibility. Regarding the relationship between environmental investments and financial performance, existing studies have indicated that the positive effects exhibit a significant lag. Specifically, Horváthová [60] found that the negative impact of environmental investment on financial performance is delayed by one year, but becomes positive after a two-year lag. Similarly, Hang et al. [61] concluded that, while environmental performance improvements do not affect financial performance in the short term, economic returns emerge after an approximate two-year delay. In line with this, Barnett and Salomon [62] pointed out that although environmental management measures require substantial initial investment, the costs incurred will eventually be compensated as these measures improve relationships between firms and stakeholders. Based on these findings, this paper reasonably infers that, as the effects of environmental management become more apparent, the compensation effect may dominate beyond a certain inflection point. At that stage, the benefits of fulfilling environmental responsibilities will outweigh the costs, establishing a positive correlation between environmental protection and financial flexibility. During this period, policymakers and regulators can encourage increased environmental investment by firms, particularly those that have faced losses due to compliance, by offering incentives such as tax breaks and subsidies, thereby creating a win–win outcome for both the environment and businesses.

Secondly, previous studies have suggested that ESG practices lead enterprises to consume limited internal resources [63]. However, this paper argues that ESG performance can have a compensatory effect on the resources invested, from two perspectives. On the one hand, resource dependence theory highlights that enterprises need to acquire additional resources to sustain their operations and foster development. Engaging in environmental protection and social responsibility helps to establish strong relationships with the government, facilitating access to financial support [32]. Moreover, strengthening corporate governance can enhance supply chain integration, expand access to resources held by stakeholders, and improve competitive advantage [64]. On the other hand, the empirical analysis in this paper shows that ESG performance enhances the financial flexibility of manufacturing firms. As a critical strategic resource, financial flexibility enables firms to respond swiftly to market fluctuations and deploy funds efficiently to fill potential funding gaps [6]. This enhanced financial flexibility, driven by ESG performance, not only reflects the firm’s adaptability and resilience in the face of unexpected events, but also promotes the optimal allocation and efficient use of resources within the organization.

Finally, the economic consequences of ESG rating uncertainty have recently gained significant attention in academic research. Some studies use ESG divergence as an explanatory variable, finding that ESG divergence increases the risk premium [19] and reduces stock returns [65]. Others focus on the impact of ESG rating divergence on economic outcomes. For example, ESG uncertainty affects the contribution of ESG to the innovation levels of listed companies [50], and ESG disagreement weakens the ability of ESG ratings to predict future ESG news [20]. This paper selects six rating agencies commonly focused on in the current Chinese ESG market and constructs appropriate ESG rating uncertainty indicators. The results indicate that the uncertainty surrounding ESG ratings weakens the positive impact of ESG performance on the financial flexibility of manufacturing companies, confirming that ESG rating uncertainty has become a critical obstacle to effective ESG practices. This further underscores the importance of improving and standardizing ESG disclosure systems.

6.2. Limitations and Future Research Directions

This paper has several limitations that need to be addressed and further explored in subsequent research. First, while this study focuses on the drivers of financial flexibility reserves, it does not delve deeply into optimizing the internal structure of financial flexibility. Future research should explore whether firms actively adjust and optimize their financial flexibility strategies while enhancing ESG performance in order to further investigate the potential positive effects of ESG practices on internal financial governance mechanisms. Second, the study period (2012–2022) includes significant market events, such as the introduction of the Governance Code for Listed Companies and the COVID-19 pandemic, which may have influenced firms’ financial situations. Future research could use these events as external shock conditions or extend the study to various economic cycles to examine the dynamics of causality across different economic environments, offering more comprehensive and in-depth evidence to understand the nature and adaptability of financial flexibility. Third, due to significant industry differences, this study focuses solely on A-share manufacturing companies, limiting the generalizability of the findings. Future research should broaden the scope by examining the penetration of ESG concepts in non-manufacturing industries and their differentiated impact on corporate financial flexibility strategies, revealing more diverse ESG practice pathways and effects. This would provide more robust theoretical support and practical guidance for promoting the sustainable development and financial health of enterprises.

7. Conclusions and Implications

Based on a sample of A-share manufacturing companies listed from 2012 to 2022, this study explores the relationship and mechanisms through which ESG performance influences corporate financial flexibility. The findings are as follows: First, ESG practices can effectively enhance the financial flexibility of manufacturing firms, with a more significant effect observed in high-tech and non-heavily polluting firms. Second, ESG practices influence financial flexibility through three main mechanisms: alleviating financing constraints, enhancing competitive advantages, and attracting analysts’ attention. Third, compared to social responsibility, corporate governance has a more substantial impact on financial flexibility, while environmental performance exhibits an inhibitory effect. Fourth, ESG uncertainty weakens the positive impact of ESG practices on corporate financial flexibility. Based on these conclusions, the following recommendations are proposed:

Firstly, enterprises should adapt their financial strategies flexibly and increase investment in environmental, social, and governance (ESG) areas. Companies should seize investment opportunities in the market while effectively managing operational risks to ensure financial soundness and achieve sustainable development. Additionally, they should integrate ESG principles into their daily operations, take on environmental responsibility, and cultivate a positive corporate image to safeguard shareholder interests and ensure long-term growth.

Secondly, the government should focus on improving ESG information disclosure systems and promote the standardization of disclosure requirements. To enhance transparency and comparability, the government should establish clear ESG disclosure standards and encourage companies to comply with them. Furthermore, the government should enforce appropriate penalties for companies that fail to meet their disclosure obligations. Additionally, a dedicated regulatory body or department should be established to oversee ESG disclosure, ensuring the accuracy and compliance of the information provided.

Finally, the government should offer incentives and support to companies with outstanding ESG performance. Specific measures could include: first, providing tax incentives or rewards to reduce operating costs and enhance market competitiveness; second, prioritizing the products and services of these companies through procurement policies to generate more business opportunities; and third, creating an ESG evaluation system to assess and rank corporate ESG performance, offering additional policy support and resources based on evaluation results, such as special funds, project support, or industry collaboration opportunities.