Green Light or Green Burden: ESG’s Dual Effect on Financing Constraints in China’s Heavily Polluting Industries

Abstract

Using a firm-level panel of China’s heavily polluting industries from 2014 to 2023, this paper employs two-way fixed-effects regressions and a battery of robustness checks to examine how ESG performance affects corporate financing constraints and the channels through which effects operate. We uncover a paradox: overall ESG performance is associated with reduced financing constraint, whereas the environmental subcomponent alone significantly aggravates firms’ financing difficulties. Moderating analyses show that stricter regional environmental regulations and higher persistence in firms’ innovation outputs weaken the easing effect of aggregate ESG performance and may even fully offset it under certain conditions. Mechanism tests reveal that ESG mitigates constraints mainly by enhancing corporate reputation and curbing green agency costs. Heterogeneity analyses further indicate that the environmental-induced tightening effect is more pronounced in state-owned enterprises, firms in eastern provinces, and those located in regions with lower levels of new-quality productivity. These findings point to a trade-off between the short-term compliance costs of environmental investment and the longer-run signaling and informational benefits of ESG disclosure. Policy implications include the need for targeted green-finance support, improved ESG transparency and verification, and measures to accelerate innovation pathways that shorten the payback period for environmental investments.

1. Introduction

To advance high-quality development, China has actively promoted carbon emission reduction, pollution control, the expansion of green initiatives, and economic growth through green transformation. During local inspections in 2023, national leaders underscored the need to balance ecological preservation with industrial restructuring, while leveraging “new-quality productive forces” to modernize traditional industries. Within this policy, heavily polluting sectors occupy a paradoxical position: they remain essential to economic stability yet are among the most challenging to decarbonize [1]. Investigating the relationship between environmental, social, and governance (ESG) performance and financing constraints in these industries is therefore crucial, as their transition is pivotal to achieving China’s broader goals of sustainable development [2].

From both theoretical and empirical perspectives, ESG plays a dual role in corporate finance. On the one hand, strong ESG performance can serve as a credible signal to capital markets, reducing information asymmetry and facilitating access to external financing. This signaling effect is particularly salient for non-state-owned enterprises and in more market-oriented regions [3]. On the other hand, the implementation of ESG practices, especially environmental and social initiatives, requires substantial upfront investment in compliance, technological upgrades, and disclosure systems. These commitments often increase short-term liquidity pressures and aggravate financing challenges. In heavily polluting industries, the interaction of these opposing dynamics is especially pronounced: firms simultaneously face external restrictions on financing access and rising endogenous governance and compliance costs. As a result, ESG practices may either ease financing constraints through signaling mechanisms or worsen them by crowding out financial resources.

Prior work has documented multiple pathways through which ESG influences firm outcomes. By encouraging green innovation and reallocating resources more efficiently, sound ESG practice can enhance operational performance and sustain long-term value creation [4]. At the same time, inaccurate disclosures or “greenwashing” weaken the informational benefits of ESG, exacerbating information frictions, reputational risk, and ultimately financing costs [5]. Moreover, the three ESG pillars are not homogeneous in effect: governance and social practices often reduce agency costs and build stakeholder credibility, thus alleviating financing frictions. In contrast, environmental commitments often impose significant short-term financial burdens, diminishing firms’ cash flow and flexibility [6].

Despite these advances, two critical gaps remain. First, the literature has not fully explained why aggregate ESG scores can appear to both improve and worsen financing constraints when analyzed through the financing channel. This paradox highlights the need to examine how different ESG dimensions interact and generate potentially conflicting effects. Second, most empirical studies have concentrated on static firm-level characteristics, such as size or ownership, while paying less attention to dynamic or contextual moderators. Notably, the intensity of regional environmental regulation and the sustainability of firms’ innovation outputs are likely to reshape the net impact of ESG on financing access. These factors are particularly salient in heavily polluting industries, where regulatory enforcement varies significantly across provinces and where firms’ technological trajectories differ over time.

Despite growing interest in the relationship between ESG performance and corporate finance, several research gaps remain. First, prior studies have not adequately explained the mechanisms behind ESG’s paradoxical effects on financing constraints. Specifically, while aggregate ESG scores can alleviate financing pressure, environmental components often exacerbate it. Existing literature typically treats ESG as a single index, ignoring the internal heterogeneity and conflicting cost–benefit dynamics among its subdimensions. Second, research on contextual moderators in this relationship is limited. Few empirical studies consider how regional environmental regulation intensity and firms’ sustainability of innovation outputs affect ESG’s net financial impact. This is particularly important in heavily polluting industries, where compliance and innovation cycles vary by region. Third, evidence regarding the transmission channels of ESG’s financial influence is fragmented. Few studies have jointly examined how corporate reputation, analyst attention, and green agency costs mediate the ESG–financing nexus within a unified framework.

Accordingly, this study makes the following verifiable contributions:

(1)

It identifies a financing paradox of ESG. Based on firm-level data from 2014 to 2023, the results show that overall ESG performance alleviates financing constraints, while the environmental dimension aggravates them.

(2)

It introduces theoretically grounded moderating factors. Environmental regulation intensity and innovation output sustainability reduce the positive financial impact of ESG and explain when ESG benefits become weaker or reversed.

(3)

It verifies three mediating mechanisms. ESG performance alleviates financing constraints by improving corporate reputation and lowering green agency costs.

(4)

It demonstrates heterogeneity in the ESG–finance relationship. The tightening effect of environmental performance is stronger in state-owned enterprises, in eastern provinces, and in regions with lower emerging productivity. These results indicate that ownership structure and regional development conditions significantly influence the effectiveness of ESG practices.

This study refines the analytical framework linking ESG and financing constraints and provides evidence for balancing environmental compliance costs with sustainable financial development in transition economies.

The remainder of this paper is structured as follows: Section 2 reviews the relevant literature and develops the research hypotheses. Section 3 outlines the sample selection, variable definitions, and empirical methodology. Section 4 presents the baseline regression results and a series of robustness checks. Section 5 provides heterogeneity analyses across ownership types, regions, and regulatory environments, and further discusses the policy implications. Finally, Section 6 concludes the paper with key findings and recommendations for both corporate practice and policymaking.

2. Literature Review and Research Hypotheses

2.1. ESG and the Financing Constraint Paradox

ESG is inherently connected to the broader goals of sustainable financial development [7]. Financing constraints, as a core factor shaping corporate green transformation, directly influence both resource allocation efficiency and long-term competitiveness [8]. Within ESG practice, however, the effects of the three subdimensions on firms’ financing capacity differ substantially [9] and in some cases, their impacts may even diverge in opposite directions [10]. This divergence underscores the complexity of the ESG–finance nexus and highlights the need for more nuanced analysis.

From the perspective of resource dependence theory and cost transmission theory, the environmental dimension often imposes heavy financial burdens on firms in heavily polluting industries. To comply with the “dual carbon” targets and stringent regulatory requirements, these companies must allocate large sums to cleaner technologies, pollution-control equipment, and mandatory disclosure of environmental information [11]. In the short to medium term, such expenditures displace operating cash flows and raise costs significantly [12]. In regions with strong regulatory intensity, firms are particularly vulnerable to falling into a “compliance cost trap,” where environmental investment horizons exceed their financial payback period, thereby aggravating debt pressure and tightening financing conditions [13].

By contrast, the social and governance dimensions tend to relax financing frictions through signaling theory and agency cost theory. Fulfilling social responsibilities builds stakeholder trust networks, reduces information asymmetry, and attracts socially responsible investors, thereby expanding equity financing channels [14]. n parallel, governance improvements such as enhanced internal control and greater financial transparency reduce agency costs, strengthen creditor confidence, and lower the cost of debt financing [15]. These mechanisms demonstrate how the non-environmental pillars of ESG can create tangible financial advantages.

These contrasting dynamics generate an inherent paradox. The “resource synergy” benefits of the social and governance pillars interact with the “cost crowding-out effect” of the environmental pillar. As a result, the financial burden imposed by environmental compliance may partially offset the relief provided by social and governance performance, such as greater transparency and social capital accumulation [16]. Especially in cases where green technologies have yet to yield commercial returns, firms in high-pollution sectors may simultaneously encounter “rigid compliance costs” and “narrower financing channels,” producing a paradoxical pattern of “overall alleviation with local deterioration” [17].

Based on the above evidence, this paper proposes Hypothesis 1.

H1. 

ESG performance alleviates financing constraints overall, but environmental performance exacerbates them, resulting in a paradoxical effect of “overall alleviation, local deterioration.”

2.2. The Regulatory Role of Environmental Regulation

As an important form of informal environmental governance, ESG ratings provide external stakeholders with signals that can enhance trust and thereby improve firms’ financing environments [18]. However, when combined with formal instruments such as carbon emissions trading and other high-intensity regulatory mechanisms, ESG compliance may also diminish firms’ financing advantages by extending the chain of cost transmission [19]. Thus, environmental regulation can play a dual role: it may promote sustainable practices, but at the same time intensify financial pressures.

Environmental regulations shape financing constraints primarily through two interrelated mechanisms. First, stricter regulatory requirements compel firms to allocate greater resources to pollution control and prevention, which generates immediate cash flow pressures and reduces financial flexibility [20]. Under these conditions, the reputational benefits associated with ESG performance may be offset, or even outweighed, by escalating mandatory compliance costs. From a resource dependence perspective, stringent environmental regulation intensifies a firm’s dependence on financial resources for non-discretionary environmental expenditures, thereby crowding out productive capital and tightening financing channels [21]. Second, regulatory-driven requirements for technological upgrading and industrial transformation create sustained financial burdens. Firms are often forced to undertake large-scale R&D investments and replace outdated equipment to meet policy standards [22]. These expenditures extend investment payback cycles, increase uncertainty, and heighten the demand for external financing.

Moreover, a feedback mechanism exists between environmental regulation and financing capacity. As regulatory intensity increases, the associated financial restrictions may erode firms’ ability to comply with environmental mandates, creating a cycle in which rising financing constraints further weaken compliance capacity [23]. This dynamic highlights the importance of treating regulation not merely as an exogenous constraint, but as an endogenous factor that interacts with corporate financing strategies and ESG outcomes. In essence, high regulatory intensity transforms the ESG signal from a net benefit to a net cost indicator for capital providers, as it underscores the substantial and often mandatory financial outlays required for environmental compliance.

Based on this reasoning, the following hypothesis is proposed:

H2. 

Environmental regulation intensity negatively moderates the relationship between ESG performance and financing constraints.

2.3. The Moderating Role of Firms’ Sustainability of Innovation Outputs

Innovation output persistence, an internal firm-level factor, serves as a critical boundary condition that moderates the effectiveness of ESG performance. A firm’s sustained innovation capacity diminishes the financing advantages derived from ESG initiatives through two key mechanisms, as posited by Resource Dependence Theory and Signaling Theory [24].

First, from the lens of Resource Dependence Theory, firms with high and persistent innovation outputs are usually less dependent on the external financing channels that ESG performance seeks to unlock [25]. These firms often generate strong internal cash flows from their innovative products [26]. They also have privileged access to alternative, technology-driven financing sources such as government R&D grants, venture capital, or strategic partnerships. These sources are linked to their technological strength and market potential, not their ESG performance [27]. Thus, for these firms, the marginal benefit of a strong ESG signal in easing general financing constraints is smaller [28]. This is because they do not rely primarily on debt or public equity markets where ESG signals have the strongest impact [29].

Second, from the perspective of Signaling Theory, the inherent risks and unique traits of sustained innovation may “drown out” or conflict with the stability signal from strong ESG performance [30]. Innovation is marked by high uncertainty, long gestation periods, and a non-linear path to commercial success [31]. This risk profile can offset the risk-mitigating, stability-focused signal that ESG conveys to more conservative creditors and investors. For capital providers, the volatility and specialization of a high-innovation strategy may overshadow the perceived governance and social stability from strong ESG practices [32]. Thus, for highly innovative firms, the ESG signal is less salient or less influential in financing decisions than the firm’s technological trajectory and innovation pipeline [33].

In summary, innovation output persistence reshapes a firm’s resource dependence patterns and alters the interpretation of its signals in capital markets. It reduces the firm’s reliance on ESG-sensitive financing and introduces a competing, potentially stronger signal rooted in technological capability. This, in turn, attenuates the positive effect of ESG on firms’ ability to access financing.

Based on this reasoning, the following hypothesis is proposed:

H3. 

The sustainability of firms’ innovation outputs negatively moderates the relationship between ESG performance and financing constraints, reducing ESG’s effectiveness in easing financial restrictions.

2.4. Multi-Theory-Based Mediating Mechanism

Building on H1, which shows that overall ESG performance cases financing constraints, this study next examines how this occurs. We identify and test three specific ways through which ESG creates this positive impact.

Signaling theory points out that enterprises’ excellent ESG performance can send multi-dimensional positive signals about environmental compliance, social responsibility fulfillment, and governance soundness to the capital market. It effectively reduces external investors’ negative expectations of enterprises’ long-term risks [34]. For heavy polluting enterprises, systematic investment in environmental management and practice of social responsibility can gradually reshape their negative label of “high pollution and high risk”. This process helps accumulate reputational capital [35]. As an intangible asset, reputational capital can significantly alleviate information asymmetry between enterprises and fund providers. It enhances the trust of creditors and investors in enterprises’ sustainable development capabilities [36]. This further broadens financing channels, reduces financing costs, and ultimately improves the status of financing constraints [37].

Based on the above theoretical mechanism, the following hypothesis is proposed:

H4a. 

ESG performance alleviates financing constraints by improving corporate reputation.

According to agency cost theory, ESG mechanisms can effectively curb opportunistic behaviors in green projects [38]. This is achieved by clarifying green investment responsibilities and strengthening the incentive compatibility arrangement for management. Green transformation investment of heavy polluting enterprises usually has the characteristics of a long cycle, high capital demand and uncertain returns [39]. These characteristics easily induce short-sighted behaviors of management. Examples include embezzling special green funds and covering up inefficient investment issues [40]. Such behaviors further increase agency costs. ESG practices include establishing green audit mechanisms and linking executive compensation to environmental performance [41]. Such practices can enhance the transparency and accountability of green fund utilization. This reduces the risk of resource misallocation, improves investment efficiency, eases enterprises’ cash flow pressure, and thus alleviates financing constraints.

Based on this reasoning, the following hypothesis is proposed:

H4b. 

ESG performance alleviates financing constraints by restraining green agency costs.

2.5. Map of Impact Mechanisms

Based on the above theoretical analysis and research hypotheses, the following impact mechanism analysis diagram is derived, as shown in Figure 1.

3. Research Design

3.1. Sample Selection and Data Sources

This study takes A-share listed companies in heavy-polluting industries on the Shanghai and Shenzhen Stock Exchanges from 2014 to 2023 as the initial sample. Based on the Catalogue for the Classification and Management of Environmental Protection Inspections of Listed Companies issued by the Ministry of Ecology and Environment (MEE) of China, enterprises belonging to heavy-polluting industries were first screened out. Subsequently, the data were processed as follows: (1) excluding the sample of listed companies in the financial sector (banks, insurance companies, etc.) due to their unique capital structures and regulatory environments; (2) excluded listed companies with missing data; (3) excluded ST(Special Treatment)/*ST(delisting risk warning) companies; (4) excluded insolvent companies. To reduce the influence of outliers, continuous variables are winsorized at the 1% level. After these adjustments, the final dataset contains 6513 firm-year observations. Financial and accounting data are obtained from the CSMAR database, while ESG ratings are sourced from the Huazheng ESG Rating Agency. CSMAR is a leading and authoritative data source specializing in Chinese financial and economic research. It provides comprehensive coverage of listed companies in China and is widely utilized in academic studies for its high-quality, standardized data on firm financials, governance, and stock market information. Huazheng is a leading provider in China’s ESG rating field. Its rating coverage is comprehensive and its methodology is tailored to the actual conditions of the Chinese market, which can ensure the accuracy of the experimental results of subsequent sample data processing in this study. The partially missing data in the sample primarily arises from non-disclosure of required information by companies, gaps in source databases, or corporate actions such as mergers or acquisitions.

3.2. Selection of Variable

3.2.1. Dependent Variable: Financing Constraints (KZ)

Common methods and indicators for measuring corporate financing constraints include the SA index, KZ index, WW index, and key financial ratios. Considering both comparability and comprehensiveness, this study selects the KZ index proposed by Wei et al. as the primary measure [42]. The KZ index integrates multi-dimensional firm-level financial information, such as cash flow, dividend payout ratio, leverage ratio, and Tobin’s Q, thereby enabling a more comprehensive reflection of the financing constraint status faced by energy enterprises. To enhance the robustness of the research results, this study additionally adopts the WW index proposed by Wu as an alternative measure, ensuring that the empirical conclusions are not subject to sensitivity bias due to the choice of financing constraint indicators. For more applications and discussion of the WW index, we refer to [43,44]. See Appendix A for details of indicators. Details are presented in

Table 1. Financing constraint indices.

Index	Constituent Indicators	Theoretical Basis
KZ Index	Operating Net Cash Flow, Tobin’s Q, Debt-to-Assets, Dividends, Cash Holdings	Based on indicators related to firms’ internal fund availability and external debt financing capacity.
WW Index	Debt-to-Assets, Cash Flow, Tobin’s Q, Dividend Payout, Sales Growth	Derived from the Euler equation estimates of investment.
SA Index	Firm Size, Firm Age	Relative to mature industry players, emerging firms exhibit intensified financing constraints.

.

3.2.2. Independent Variable: ESG Performance (ESG)

This study measures the ESG performance of listed companies using the Huazheng ESG Rating. This method helps reduce inconsistencies caused by different assessment criteria across rating agencies. The Huazheng ESG Rating is selected for three key reasons: its authority, reliable methodology, and extensive data coverage in China. Compared with global rating providers such as MSCI and FTSE Russell, the Huazheng ESG framework is designed to reflect China’s regulatory environment and market characteristics. This gives it stronger local relevance [45]. When compared with other early-stage domestic ESG systems such as Business Gateway China, the Huazheng system has four advantages: more mature methodology, wider coverage of listed firms, better data accessibility, and closer alignment with national strategies such as “common prosperity” and the “dual carbon” goals. The Huazheng ESG Rating uses two types of data: corporate disclosures and survey data. It also applies differentiated weights across industries to account for sector-specific risks and opportunities. Huazheng classifies ESG performance into nine grades, ranging from C (lowest) to AAA (highest). Each grade is assigned a numerical score from 1 to 9, where higher scores indicate better ESG performance. This scoring system is applied uniformly across all ESG dimensions, enabling consistent and quantitative evaluation of corporate sustainability.

3.2.3. Control Variables

Considering that there are many factors affecting financing constraints, such as firm size, firm age, governance structure, and so on. Combined with existing studies, the control variables selected in this paper mainly include enterprise asset size (Size), cash flow ratio (Cashflow), the company’s age of establishment (FirmAge), the proportion of the first largest shareholder (Top1), the growth rate of operating income (Growth), and the proportion of independent directors (ID).

3.2.4. Moderating Variables

Firm Innovation Output Persistence (OIP). In this paper, we measure firms’ innovation persistence [46], and the specific Formula (1) is as follows:

$${\mathrm{OIP}}_{\mathrm{t}} = {\displaystyle \frac{\mathrm{OI}{\mathrm{N}}_{\mathrm{t}}}{\mathrm{OI}{\mathrm{N}}_{\mathrm{t}-1}}}\times \mathrm{O}{\mathrm{IN}}_{\mathrm{t}},$$

(1)

where denotes the sustainability of the firm’s innovation output in year t, ${\mathrm{OIN}}_{\mathrm{t}}$ is the sum of the firm’s innovation outputs in year t and $\mathrm{t} - 1$, ${\mathrm{OIN}}_{\mathrm{t}-1}$ is the sum of the corresponding firm’s innovation outputs in year $\mathrm{t} - 1$ and $\mathrm{t} - 2$.

For example, consider a firm (see

Table 2. Example of OIP.

Year	t − 2	t − 1	t
patent	10	15	20

) with the following patent counts:

$$\mathrm{O}{\mathrm{IN}}_{\mathrm{t}}=20+15=35, \mathrm{OI}{\mathrm{N}}_{\mathrm{t}-1}=15+10=25, \mathrm{OI}{\mathrm{P}}_{\mathrm{t}}=(35/25) \times 35=1.4 \times 35=49$$

Thus, the firm’s innovation output persistence in year t is 49.

This metric integrates two key dimensions of innovation. It captures the growth rate of innovation output through the ratio of consecutive cumulative outputs. Simultaneously, it reflects the absolute scale of innovative activity through multiplication by the current output level. This dual approach provides a composite measure of both the momentum and magnitude of a firm’s innovation efforts. The three-year window stabilizes the measure against annual fluctuations. This offers a balanced perspective on short-term persistence. The use of patent data is well-established in innovation research. Its objectivity and widespread availability make it a reliable proxy. Consequently, the OIP metric robustly assesses the consistency of a firm’s innovation output generation.

Environmental regulation intensity (Eri). As a core component of social regulation, environmental regulation refers to government interventions in corporate economic activities through policy tools and control measures. Its aim is to mitigate the negative environmental externalities caused by industrial production and promote coordinated economic and environmental development. In terms of measurement, no unified standard has been established in academia. Three representative methods are commonly used:

The first type involves pollution control costs and emission fees. This approach uses relevant expenditures or charges as the key indicator. Its main advantage lies in directly reflecting the policy’s impact on corporate costs, though data comprehensiveness requires attention [47]. The second type is based on pollution control performance. Yan Wenjuan et al. adopted the ratio of pollution control investment to industrial wastewater discharge [48]. However, this method has limitations, as pollution control investments are not solely directed at wastewater treatment and thus fail to fully capture overall regulation intensity. The third method uses a comprehensive index. Scholars such as Li Mengjie constructed a composite environmental regulation index by integrating indicators such as sulfur dioxide removal rate and industrial dust removal rate [49]. Nonetheless, this approach is often criticized for subjective indicator selection and weight assignment.

Considering that both industrial pollution control investment and emission fees influence corporate employment and other economic behaviors through production costs, and to avoid the inherent drawbacks of the above methods, this study selects the ratio of total industrial pollution control investment to industrial added value as the measure of environmental regulation intensity. This indicator directly reflects policy enforcement intensity and corporate compliance costs, and its value corresponds closely to the actual environmental expenditure burden on firms.

3.2.5. Mechanism Variables

Corporate reputation (CR). This study refers to the research design of Ye et al. and measures corporate reputation by taking the natural logarithm of the sum of a firm’s annual positive news reports from online platforms and newspapers plus 1 [50]. This media-based measure effectively captures the firm’s external image and stakeholder perceptions, serving as a valid proxy for reputational capital.

Green Agency Cost (GAC). Following the approach of Wang et al., we collected data on environmental governance costs, such as greening and sanitation fees, from the “administrative expenses” account of the sample firms. The green agency cost is calculated as the ratio of these environmental governance costs to total operating revenue. A higher GAC value indicates more severe green agency problems [51]. Using explicit environmental expenditures from administrative accounts provides an objective measure of resource allocation to environmental governance, where inefficiencies or excess costs directly reflect agency problems in green initiatives.

The main variables in this paper are defined, as shown in

Table 3. Variables.

Variable Type	Variable Name	Variable Symbol	Variable Definition
Explanatory Variable	Financing constraints	KZ	Financing constraints based on operating cash flow, cash dividends, cash holdings, gearing, Tobin’s Q
Explanatory Variable	ESG performance	ESG	The Huazheng ESG rating is assigned from low to high as “1 to 9” (1 = CCC, 9 = AAA)
	E performance	E	The Huazheng E rating is assigned from low to high as “1 to 9” (1 = CCC, 9 = AAA)
	S performance	S	The Huazheng S Rating is assigned from low to high as “1 to 9” (1 = CCC, 9 = AAA)
	G performance	G	The Huazheng G rating is assigned from low to high as “1 to 9” (1 = CCC, 9 = AAA)
Moderator Variable	Sustainability of innovation outputs	OIP	OIP denotes the firm’s innovation output persistence in year t
	Intensity of environmental regulation	Eri	Completed investment in industrial pollution control/industrial value added
Mechanism Variables	Corporate reputation	CR	The natural logarithm of the sum of a firm’s annual negative news reports from online platforms and newspapers plus 1
	Green Agency Cost	GAC	Environmental Governance Expenses/Total Operating Revenue
Control Variable	Enterprise size	Size	Natural logarithm of total assets for the year
	cash flow ratio	Cashflow	Net cash flows from operating activities/total assets
	Years of Establishment	FirmAge	Natural logarithm of the difference between the current year and the year the enterprise was established, plus 1
	Percentage of the largest shareholders	Top1	Number of shares held by the largest shareholder/total number of shares
	Revenue growth rate	Growth	Current year’s operating income/previous year’s operating income) − 1
	Percentage of independent directors	ID	Number of independent directors/directors
	Industry	Industry	Industry fixed effect
	Year	Year	Annual fixed effect

.

3.3. Regression Models

3.3.1. Baseline Regression Model

In this paper, we construct a two-way fixed effects model to explore the impact of corporate ESG performance on financing constraints, and the benchmark regression model is shown in Equations (2) and (3):

$${\mathrm{KZ}}_{\mathrm{it}} = {\mathrm{\alpha }}_0 + {\mathrm{\alpha }}_1\mathrm{ES}{\mathrm{G}}_{\mathrm{it}} + {\mathrm{\alpha }}_2\mathrm{Control}{\mathrm{s}}_{\mathrm{it}} + \sum \mathrm{Ind} + \sum \mathrm{Year} +{ \mathrm{\epsilon }}_{\mathrm{it}},$$

(2)

$${\mathrm{KZ}}_{\mathrm{it}}={ \mathrm{\beta }}_0+{ \mathrm{\beta }}_1{\mathrm{E}}_{\mathrm{it}}+{ \mathrm{\beta }}_2{\mathrm{S}}_{\mathrm{it}}+{ \mathrm{\beta }}_3{\mathrm{G}}_{\mathrm{it}}+{\mathrm{\beta }}_4\mathrm{Control}{\mathrm{s}}_{\mathrm{it}}+\sum \mathrm{Ind}+\sum \mathrm{Year}+{\mathrm{\epsilon }}_{\mathrm{it}},$$

(3)

In model (1), $\mathrm{ES}{\mathrm{G}}_{\mathrm{it}}$ captures the ESG performance of firm i in year t,$\mathrm{Control}{\mathrm{s}}_{\mathrm{it}}$ comprises a set of firm-level control variables. The model incorporates industry and year fixed effects, represented by $\sum \mathrm{Ind}$ and $\sum \mathrm{Year}$, respectively. The error term is denoted by ${\mathrm{\epsilon }}_{\mathrm{it}}$.

In model (2), ${\mathrm{E}}_{\mathrm{it}}$ captures the E performance of firm i in year t. ${\mathrm{S}}_{\mathrm{it}}$ captures the S performance of firm i in year t. ${\mathrm{G}}_{\mathrm{it}}$ captures the ESG performance of firm i in year t. The settings of the other variables are consistent with those in Model (1).

3.3.2. Moderating Effects Model

In order to test the moderating effects of environmental regulation intensity and firms’ innovation output sustainability on the relationship between firms’ ESG performance and financing constraints, models (4) and (5) are constructed.

$${\mathit{KZ}}_{\mathit{it}}={ \gamma }_0+{\gamma }_1\mathit{ES}{G}_{\mathit{it}}+{\gamma }_2{\mathit{ESG}}_{\mathit{it}} \times { \mathit{Eri}}_{\mathit{it}}+{ \gamma }_3{\mathit{Eri}}_{\mathit{it}}+{ \gamma }_4\mathit{Control}{s}_{\mathit{it}}+\sum \mathit{Ind}+\sum \mathit{Year}+{ \epsilon }_{\mathit{it}},$$

(4)

Eri is a moderator variable representing the environmental regulation intensity of each province in year t. ESG $\times$ Eri is the interaction term between ESG performance and environmental regulation intensity. The settings of the other variables are consistent with those in Model (1).

$${\mathit{KZ}}_{\mathit{it}}={\delta }_0+{ \delta }_1\mathit{ES}{G}_{\mathit{it}}+{ \delta }_2{\mathit{ESG}}_{\mathit{it}} \times { \mathit{OIP}}_{\mathit{it}}+{\delta }_3{\mathit{OIP}}_{\mathit{it}}+{ \delta }_4\mathit{Control}{s}_{\mathit{it}}+\sum \mathit{Ind}+\sum \mathit{Year}+{ \epsilon }_{\mathit{it}},$$

(5)

$\mathrm{OIP}$ is the moderator variable representing firm i’s innovation output capacity in year t. ESG $\times$ OIP is the interaction term between ESG performance and firm’s innovation output sustainability. The settings of the other variables are consistent with those in Model (1).

3.3.3. Mechanism Model

It draws on existing academic research to analyze the causal relationship between ESG performance and mechanism variables. The model for this mechanism test is presented in Equation (6) as follows:

$$M_{\mathit{it}} ={ \mu }_0 +{ \mu }_1\mathit{ES}{G}_{\mathit{it}} +{ \mu }_3\mathit{Control}{s}_{\mathit{it}} + \sum \mathit{Ind} + \sum \mathit{Year} + {\epsilon }_{\mathit{it}},$$

(6)

In this model, ${\mathrm{M}}_{\mathrm{it}}$ denotes the mediating variables. The settings of the other variables are consistent with those in Model (1).

4. Empirical Analysis

4.1. Descriptive Statistics

Table 4. Descriptive statistics.

Variable	Obs	Mean	SD	Min	Max
KZ	6513	0.934	2.220	−5.702	6.151
ESG	6513	4.088	1.036	1.000	6.000
E	6513	2.228	1.212	1.000	8.000
S	6513	4.557	1.625	1.000	9.000
G	6513	5.205	1.300	1.000	8.000
Size	6513	22.593	1.393	20.122	26.518
Cashflow	6513	0.062	0.063	−0.115	0.246
FirmAge	6513	3.028	0.273	2.197	3.555
Top1	6513	0.344	0.150	0.092	0.756
FIXED	6513	0.321	0.164	0.027	0.758
Growth	6513	0.143	0.366	−0.460	2.282
ID	6513	0.378	0.065	0.188	0.750
OIP	5528	4.119	1.518	0.118	7.678
Eri	6513	0.002	0.002	0.000	0.009
CR	6513	4.711	1.348	0.693	12.687
GAC	6513	0.763	3.786	−14.009	164.721

presents the descriptive statistics of key variables from 2014 to 2023. The KZ index, which measures financing constraints, shows significant differences across the sample. The gap between its minimum and maximum values is approximately 11.85. This indicates that firms vary greatly in their ability to obtain external financing. Some companies face significant financing pressure, while others have relatively sufficient funds. Regarding ESG performance, firms in heavily polluting industries generally have low overall ESG levels. Their average score does not reach a “good” level. This is consistent with the environmental protection pressures and transformation challenges they face. From the perspective of sub-dimensions, the environmental dimension has the weakest performance, with an average score of only 2.228. This reflects the practical difficulties firms encounter in environmental compliance. In contrast, the social and governance dimensions perform relatively better. Some firms excel in these two dimensions, showing they have certain advantages in non-environmental ESG management. The variance inflation factor (VIF) test reveals no multicollinearity issues, where we refer to [52,53,54] for the application of VIF test. This provides a reliable basis for subsequent regression analyses. Overall, key variables show significant differences in both financing constraints and ESG performance. This lays the foundation for further in-depth research.

4.2. Baseline Regression

Table 5. Benchmark Regression.

	(1)	(2)	(3)
	KZ	KZ	KZ
ESG	−0.429 *** (−16.76)	−0.345 *** (−16.47)
E			0.0766 *** (4.16)
S			−0.0870 *** (−6.28)
G			−0.355 *** (−21.41)
Size		0.280 *** (14.81)	0.248 *** (13.19)
Cashflow		−21.21 *** (−57.16)	−21.07 *** (−57.78)
FirmAge		0.579 *** (6.72)	0.575 *** (6.71)
Top1		−0.870 *** (−5.72)	−0.683 *** (−4.58)
Growth		−0.365 *** (−5.10)	−0.367 *** (−5.23)
ID		−0.587 * (−1.82)	−0.158 (−0.50)
Constant	2.688 *** (24.91)	−3.846 *** (−8.02)	−2.696 *** (−5.60)
Industry FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
N	6513	6513	6513
Adj r2	0.100	0.470	0.492

Note: * and *** indicate significance at the 10% and 1% levels, respectively; t-statistics are shown in parentheses.

reports the baseline regression results for Equations (2) and (3), where we refer to [55,56] for the applications of baseline techniques. Columns 1 and 2 present the results of Equation (2). Column 1 shows that the overall ESG performance has a significantly negative effect on financing constraints at the 1% level when no control variables are included. Column 2 incorporates control variables and the coefficient remains negative and highly significant, indicating that improvements in overall ESG performance effectively alleviate corporate financing constraints. Economically, a one-unit increase in overall ESG performance reduces the KZ index by approximately 0.35, equivalent to a 9.8% decline in financing constraints relative to the sample mean, indicating a material economic effect. Column 3 reports the results of Equation (3), which examines the ESG sub-dimensions. The results reveal a polarization effect. The environmental dimension has a significantly positive coefficient at the 1% level, suggesting the presence of a “green paradox” where stronger environmental performance increases financing constraints. In contrast, the social and governance dimensions have significantly negative coefficients at the 1% level, demonstrating that improvements in these areas help reduce financing constraints. Among the control variables, cash flow, equity concentration, and firm growth significantly mitigate financing constraints at the 1% level, highlighting the importance of high liquidity, shareholder support, and growth prospects in attracting financing. Conversely, firm size and firm age significantly exacerbate financing constraints at the 1% level, likely due to expansion needs and transformation pressures. The effect of independent directors is weak and statistically insignificant.

Overall, the baseline regression results support Hypothesis H1, confirming that higher overall ESG performance alleviates financing constraints, while environmental performance alone may intensify financing difficulties in certain contexts.

4.3. Robustness Tests

4.3.1. Variable Lag

To further verify the robustness of the baseline regression results and address potential reverse causality, lagged variables of ESG performance and its sub-dimensions are introduced. We refer to [24,57] for more details on applying lagged variables. The lagged variable models

Table 6. Lag test.

	Lag 1		Lag 2
	(1)	(2)	(3)	(4)
L.ESG	−0.269 *** (−12.00)
L.E		0.0729 *** (3.50)
L.S		−0.0541 *** (−3.74)
L.G		−0.292 *** (−16.21)
L2.ESG			−0.229 *** (−10.24)
L2.E				0.0710 *** (3.16)
L2.S				−0.0446 *** (−2.93)
L2.G				−0.244 *** (−12.85)
Size	0.248 *** (11.90)	0.224 *** (10.73)	0.204 *** (9.01)	0.184 *** (8.06)
Cashflow	−21.75 *** (−52.78)	−21.71 *** (−53.22)	−21.82 *** (−49.13)	−21.80 *** (−48.92)
FirmAge	0.435 *** (4.53)	0.441 *** (4.58)	0.356 *** (3.26)	0.357 *** (3.26)
Top1	−0.794 *** (−4.78)	−0.631 *** (−3.84)	−0.600 *** (−3.35)	−0.453 ** (−2.55)
Growth	−0.417 *** (−5.21)	−0.412 *** (−5.19)	−0.410 *** (−4.75)	−0.408 *** (−4.74)
ID	−0.437 (−1.23)	−0.198 (−0.57)	−0.689 * (−1.82)	−0.409 (−1.07)
Constant	−3.040 *** (−5.72)	−2.172 *** (−4.06)	−1.860 *** (−3.19)	−1.178 ** (−2.02)
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	5466	5466	4633	4633
Adj r2	0.467	0.482	0.467	0.478

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-statistics are shown in parentheses.

reports the regression results for one-period lag and two-period lag models. The results confirm that the mitigating effect of ESG performance on financing constraints remains highly significant. For lag one, the coefficient of lagged ESG is −0.269 at the 1% significance level. For lag two, the coefficient is −0.229, also significant at the 1% level. These findings are consistent in direction with the benchmark regressions, reinforcing the conclusion that higher ESG performance alleviates financing constraints. The sub-dimension analysis shows that the environmental dimension continues to exacerbate financing constraints, with positive coefficients of 0.0729 for lag one and 0.0710 for lag two. This persistent effect confirms the presence of the green paradox observed in the baseline regression. In contrast, the social and governance dimensions maintain negative coefficients in both lag specifications, indicating their continued role in mitigating financing constraints. The economic magnitude remains stable, as a one-unit rise in lagged ESG performance lowers financing constraints by about 0.27 in the one-period lag model, corresponding to roughly 7% of the sample mean. The economic magnitude remains stable, as a one-unit rise in lagged ESG performance lowers financing constraints by about 0.27 in the one-period lag model, corresponding to roughly 7% of the sample mean. Control variables display stable effects across lagged models. Firm size and firm age significantly increase financing constraints, while cash flow, equity concentration, and firm growth significantly reduce them. The effect of independent directors remains weak and statistically insignificant.

Overall, the lagged variable tests provide strong evidence that the baseline regression results are robust. The findings support Hypothesis H1 and indicate that the observed relationships between ESG performance and financing constraints are not driven by reverse causality.

4.3.2. Regressions on the Pre-2020 Sample

To examine the stability of the baseline findings, employing similar techniques as [58,59], regressions are conducted using the sample preceding the 2020 COVID-19 outbreak. Table 5 presents the results. The core explanatory variable, ESG performance, retains a significantly negative effect on financing constraints across all specifications, with coefficients of −0.369 and −0.301 at the 1% significance level. These results are consistent with the benchmark regressions, confirming that improvements in ESG performance continue to alleviate financing constraints. The coefficient of −0.301 implies that a one-unit improvement in ESG performance reduces financing constraints by about 8.5%, confirming that the effect is not only statistically but also economically meaningful. The analysis of ESG sub-dimensions reveals that the environmental dimension maintains a positive coefficient of 0.0989, indicating that stronger environmental performance may exacerbate financing constraints, consistent with the green paradox observed in the baseline regression. In contrast, the social and governance dimensions remain negative and highly significant, suggesting that performance improvements in these areas effectively reduce financing constraints. Control variables also show stable effects. Firm size and firm age continue to increase financing constraints, while cash flow and growth significantly reduce them. Equity concentration exhibits a negative effect, though slightly weaker in magnitude, and the role of independent directors remains limited. Minor fluctuations in coefficient estimates are observed due to the adjustment in the sample period, but do not alter the overall patterns or statistical significance.

Overall, the regressions using the pre-2020 sample in

Table 7. Sample period preceding the 2020 COVID-19 outbreak.

	(1)	(2)	(3)
	KZ	KZ	KZ
ESG	−0.369 *** (−12.36)	−0.301 *** (−11.85)
E			0.0989 *** (4.15)
S			−0.105 *** (−6.23)
G			−0.273 *** (−13.28)
Size		0.294 *** (11.79)	0.273 *** (10.99)
Cashflow		−18.82 *** (−38.73)	−18.86 *** (−39.15)
FirmAge		0.708 *** (6.48)	0.703 *** (6.39)
Top1		−0.339 * (−1.71)	−0.260 (−1.32)
Growth		−0.364 *** (−4.14)	−0.369 *** (−4.24)
ID		−0.779 * (−1.80)	−0.457 (−1.07)
Constant	2.419 *** (19.14)	−4.948 *** (−7.97)	−4.162 *** (−6.71)
Industry FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
N	3479	3479	3479
Adj r2	0.090	0.439	0.454

Note: *, and *** indicate significance at the 10% and 1% levels, respectively; t-statistics are shown in parentheses.

provide robust support for Hypothesis H1 and confirm that the core relationships between ESG performance and financing constraints are not driven by the COVID-19 pandemic or other sample-specific shocks.

4.3.3. Substitution of Explanatory Variables

To further assess the robustness of the baseline regression results, the measure of financing constraints is replaced with the WW index.

Table 8. Replacement of explanatory variables.

	(1)	(2)	(3)
	WW	WW	WW
ESG	−0.0212 *** (−25.68)	−0.00527 *** (−13.80)
E			0.00101 *** (3.06)
S			−0.00218 *** (−8.20)
G			−0.00435 *** (−14.28)
Size		−0.0457 *** (−137.21)	−0.0460 *** (−137.93)
Cashflow		−0.163 *** (−25.93)	−0.164 *** (−26.08)
FirmAge		0.00699 *** (4.58)	0.00653 *** (4.26)
Top1		−0.0109 *** (−4.04)	−0.00939 *** (−3.49)
Growth		−0.0487 *** (−28.58)	−0.0486 *** (−28.44)
ID		−0.00752 (−1.32)	−0.00306 (−0.54)
Constant	−0.954 *** (−274.83)	0.0246 *** (2.88)	0.0394 *** (4.58)
Industry FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
N	5669	5669	5669
Adj r2	0.290	0.867	0.870

Note: *** indicate significance at the 1% levels, respectively; t-statistics are shown in parentheses.

reports the regression outcomes using the WW index as the dependent variable. The results demonstrate that the core explanatory variables maintain their direction and remain statistically significant at the 1% level, indicating that the main conclusions are not sensitive to the choice of financing constraint measure. Although the goodness of fit exhibits numerical changes due to the substitution of the explanatory variable, the explanatory power of the core variables is unaffected. Industry and year fixed effects continue to hold, confirming that the empirical results are not driven by unobserved temporal or sectoral heterogeneity. Economically, a one-unit increase in ESG performance decreases the WW index by 0.005, equivalent to about 6% of its standard deviation, showing that the effect is economically relevant despite the smaller numerical scale of the WW index. The environmental dimension continues to positively influence financing constraints, reflecting the green paradox, while the social and governance dimensions negatively affect financing constraints, indicating their role in alleviating financing difficulties. Control variables retain stable effects, with firm size, cash flow, firm age, equity concentration, and growth demonstrating impacts consistent with prior models. The role of independent directors remains limited.

Overall, the substitution of the dependent variable reinforces the robustness of the benchmark regression findings and provides additional support for Hypothesis H1.

4.3.4. Endogeneity Test

This study uses an instrumental variable. As is widely believed, instrumental variables could also matter in the variable-relation justification, as shown in [43,60]. It is the average ESG performance of other listed companies in the same industry and province as the sample firm. The rationale for selecting this instrumental variable and its test results are as follows.

From the perspective of relevance, other listed companies in the same industry and province as the sample firm share the same region. They face the same institutional environment and industrial policy impacts. Thus, their ESG performance has an inherent correlation with that of the sample firm. From the perspective of exogeneity, the ESG performance of other firms in the same industry and province only affects the sample firm indirectly. The influence comes through the industry’s common environment. It cannot directly affect the sample firm’s financing constraints KZ. There is no direct causal relationship between them. This meets the exogeneity criterion for instrumental variables. The results of the instrumental variable validity test can be seen from

Table 9. Endogeneity Test.

Variable	First Stage ESG	Second Stage KZ
IV.ESG	0.661 *** (22.75)
ESG		−0.573 *** (−7.89)
Size	0.166 *** (15.68)	0.329 *** (13.10)
Cashflow	1.045 *** (5.23)	−20.928 *** (−54.70)
FirmAge	−0.221 *** (−4.46)	0.499 *** (5.46)
Top1	0.275 *** (3.27)	−0.798 *** (−5.16)
Growth	−0.055 (−1.63)	−0.383 *** (−5.36)
ID	0.875 *** (4.88)	−0.330 (−0.99)
Industry FE	Yes	Yes
Year FE	Yes	Yes
N	6508	6508
Kleibergen–Paap rk LM statistic		374.502 ***
Kleibergen–Paap rk Wald F statistic		517.438
Weak instrumental variables test		63.51 ***
Critical values: 10%		16.38

Note: *** indicate significance at the 1% levels, respectively; t-statistics are shown in parentheses.

as follows. The Kleibergen–Paap rk LM statistic is 374.502, with a p-value below 0.001. It shows that the instrumental variable can sufficiently identify the endogenous variable. The Kleibergen–Paap rk Wald F statistic is 517.438. It is much higher than the 10% critical value of 16.38 in the Stock–Yogo weak identification test (refer to [61,62]). The Weak instrumental variables test statistic is 63.51, with a p-value below 0.001. These results fully confirm that the selected instrumental variable is not weak. The coefficient estimation is reliable.

4.4. Moderating Effects Test

Table 10. Moderating effects test.

	(1)	(2)
	KZ	KZ
ESG	−0.349 *** (−16.09)	−0.315 *** (−13.81)
ESG × Eri	31.17 *** (3.44)
ESG × OIP		0.0578 *** (4.05)
Size	0.278 *** (14.74)	0.297 *** (12.73)
Cashflow	−21.18 *** (−57.16)	−21.29 *** (−54.53)
FirmAge	0.584 *** (6.80)	0.366 *** (4.03)
Top1	−0.850 *** (−5.63)	−0.661 *** (−3.98)
Growth	−0.363 *** (−5.05)	−0.441 *** (−5.47)
ID	−0.519 (−1.61)	−0.379 (−1.11)
Constant	−5.277 *** (−10.90)	−5.166 *** (−9.01)
Industry FE	Yes	Yes
Year FE	Yes	Yes
N	6513	5528
Adj r2	0.472	0.477

Note: *** indicate significance at the 1% levels, respectively; t-statistics are shown in parentheses.

presents the results of the moderating effects tests. Notably, all moderating variables (Eri and OIP) in the regression models have been mean-centered (tackled similarly as in [63,64]) to mitigate potential multicollinearity issues, ensuring the reliability of the interaction effect results. The interaction between ESG performance and environmental regulation intensity is positive and significant at the 1% level, with a coefficient of 31.17. This indicates that stricter environmental regulations partially offset the mitigating effect of ESG performance on financing constraints. In regions with stringent regulations, even firms that improve their ESG performance experience a weaker reduction in financing constraints, providing empirical support for Hypothesis H2.

Similarly, the interaction between ESG and Firm Innovation Output Persistence is positive and significant at the 1% level, with a coefficient of 0.0578. The moderating effect of environmental regulation intensity increases financing constraints by about 31 units of the KZ index, while high innovation capability raises it by 0.06, both indicating substantial economic influence. This finding suggests that firms with higher innovation capability experience a reduced effect of ESG performance in alleviating financing constraints. As firms strengthen their innovation capacity, the marginal impact of ESG performance on financing constraints diminishes, confirming Hypothesis H3.

As shown in Figure 2, the left panel reveals Eri’s negative moderating effect. Under low environmental regulation, high ESG performance substantially reduces financing constraints. This alleviating effect weakens considerably under strict regulation. The narrowing gap confirms that stringent environmental regulations diminish ESG’s financing advantages. The right panel demonstrates OIP’s negative moderating role. When innovation persistence is low, ESG strongly eases financing constraints. This effect weakens significantly as innovation persistence increases. The converging trend lines visually support that sustained innovation output reduces ESG’s financing benefits.

Control variables retain stable effects across models. Firm size and firm age exacerbate financing constraints, while cash flow, equity concentration, and growth mitigate them. The influence of independent directors remains limited. Overall, the results indicate that external institutional pressures and internal innovation capabilities significantly moderate the relationship between ESG performance and financing constraints, highlighting the context-dependent nature of ESG effectiveness.

4.5. Mechanism Tests

4.5.1. Mechanism Effects of Reputation

Column (1) of

Table 11. Mechanism tests.

	(1) Reputation	(2) GAC
ESG	0.0448 *** (4.55)	−0.162 ** (−2.44)
Size	0.395 *** (39.43)	−0.0323 (−1.07)
Cashflow	1.342 *** (8.06)	−1.218 (−1.11)
FirmAge	−0.124 *** (−2.91)	0.108 (0.49)
Top1	−0.266 *** (−3.22)	0.165 (0.76)
Growth	0.162 *** (5.14)	−0.397 *** (−3.08)
ID	0.394 ** (2.44)	0.814 (0.83)
Constant	−4.203 *** (−16.00)	1.595 * (1.70)
Industry FE	Yes	Yes
Year FE	Yes	Yes
N	6513	6513
Adj r2	0.626	0.021

Note: *, ** and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-statistics are shown in parentheses.

presents the results for the reputation mechanism. The coefficient on ESG is 0.0448 and statistically significant at the 1% level. This indicates that superior ESG performance builds a stronger corporate reputation. Economically, the coefficient of 0.045 indicates that a one-unit improvement in ESG performance enhances corporate reputation by about 4.5%, implying a meaningful increase in investor trust and financing access. A positive reputation, in turn, makes firms more attractive to investors and creditors, and this enhanced appeal facilitates access to capital. The result empirically validates that reputation serves as a significant transmission channel. Therefore, Hypothesis H4a is supported.

4.5.2. Mechanism Effects of Green Agency Cost

Column (2) of Table 11 reports the results for the green agency cost mechanism. The coefficient on ESG is −0.162 and significant at the 5% level. This finding demonstrates that improved ESG performance effectively curbs managerial opportunism in green projects. The coefficient of −0.162 suggests that better ESG performance reduces green agency costs by about 16%, showing a clear economic contribution to easing financing pressure. By mitigating resource misallocation and improving investment efficiency, it preserves internal financial resources. This preservation of capital directly reduces the firm’s reliance on external financing and eases funding pressures. Consequently, Hypothesis H4b is confirmed.

5. Heterogeneity Analysis

5.1. Heterogeneity of Enterprise Ownership

Table 12. Moderating effects test.

	(1)	(2)	(3)	(4)
	State Owned	Nonstate Owned	State Owned	Nonstate Owned
ESG	−0.384 *** (−13.03)	−0.278 *** (−9.94)
E			0.131 *** (5.00)	0.0305 (1.25)
S			−0.0945 *** (−4.74)	−0.0427 ** (−2.35)
G			−0.423 *** (−18.74)	−0.313 *** (−13.08)
Size	0.207 *** (6.92)	0.243 *** (9.43)	0.138 *** (4.68)	0.225 *** (8.67)
Cashflow	−21.96 *** (−44.82)	−19.03 *** (−34.80)	−21.62 *** (−44.90)	−18.95 *** (−35.40)
FirmAge	0.405 *** (3.58)	0.212 (1.50)	0.369 *** (3.29)	0.224 (1.61)
Top1	−1.298 *** (−5.73)	−0.995 *** (−4.73)	−1.172 *** (−5.36)	−0.773 *** (−3.72)
Growth	−0.368 *** (−3.71)	−0.185 * (−1.82)	−0.350 *** (−3.56)	−0.187 * (−1.91)
ID	−0.178 (−0.39)	−0.976 ** (−2.32)	0.180 (0.41)	−0.421 (−1.01)
Constant	−1.797 ** (−2.43)	−1.756 ** (−2.42)	0.375 (0.51)	−1.010 (−1.38)
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	3752	2676	3752	2676
Adj r2	0.458	0.483	0.489	0.503
Empirical p-value	0.006 ***		0.004 ***

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-statistics are shown in parentheses. Empirical p-values test the significance of differences in dimension-level coefficients using 1000 bootstrap samples.

presents regression results examining the heterogeneity of ESG effects on financing constraints between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). Overall ESG performance significantly reduces financing constraints in both ownership types, with coefficients of −0.384 for SOEs and −0.278 for non-SOEs, both at the 1% significance level. The mitigating effect is more pronounced in SOEs, suggesting that ownership structure amplifies the effectiveness of ESG initiatives in alleviating financing constraints. ESG performance reduces financing constraints by about 0.38 in SOEs and 0.28 in non-SOEs, corresponding to 10% and 7% of the sample mean, respectively, confirming that ownership amplifies the ESG effect.

Dimension-level analysis reveals further differences. In SOEs, environmental performance significantly increases financing constraints, with a coefficient of 0.131, highlighting the higher compliance costs associated with green transition initiatives. By contrast, social and governance dimensions exhibit stronger mitigating effects in SOEs. The coefficients for social and governance are −0.0945 and −0.423. Respectively, substantially higher than the corresponding values of −0.0427 and −0.313 in non-SOEs. These results indicate that SOEs leverage their institutional positioning and governance capabilities more effectively to reduce financing constraints through social and governance improvements.

The observed patterns can be explained by differences in institutional roles, resource endowments, and market incentives. SOEs often integrate social responsibility into their political and social mandates, enabling them to secure greater government support and policy incentives. Non-SOEs are generally more market-driven, resulting in a less pronounced effect. At the governance level, SOEs are subject to stricter administrative oversight, limiting self-interested behavior and enhancing the positive impact of governance practices. In contrast, non-SOEs may face challenges such as family control or managerial short-termism, which constrain the effectiveness of governance improvements in reducing financing constraints.

5.2. Regionalization

Table 13. Regional heterogeneity.

	(1)	(2)	(3)	(4)
	East	Midwest	East	Midwest
ESG	−0.326 *** (−11.82)	−0.387 *** (−11.74)
E			0.0892 *** (3.81)	0.0311 (1.04)
S			−0.0645 *** (−3.59)	−0.127 *** (−5.85)
G			−0.377 *** (−17.11)	−0.314 *** (−12.24)
Size	0.301 *** (13.00)	0.221 *** (6.67)	0.267 *** (11.47)	0.200 *** (6.06)
Cashflow	−21.59 *** (−45.82)	−20.02 *** (−33.06)	−21.31 *** (−46.29)	−20.07 *** (−33.38)
FirmAge	0.385 *** (3.77)	0.941 *** (5.91)	0.406 *** (4.00)	0.893 *** (5.62)
Top1	−1.055 *** (−5.53)	−0.646 ** (−2.57)	−0.746 *** (−3.96)	−0.614 ** (−2.48)
Growth	−0.425 *** (−4.53)	−0.297 *** (−2.72)	−0.419 *** (−4.61)	−0.309 *** (−2.84)
ID	−0.255 (−0.63)	−0.796 (−1.55)	0.124 (0.31)	−0.350 (−0.69)
Constant	−3.969 *** (−6.61)	−3.356 *** (−4.14)	−2.786 *** (−4.62)	−2.359 *** (−2.87)
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	3986	2526	3986	2526
Adj r2	0.475	0.474	0.501	0.487
Empirical p-value	0.072 *		0.062 *

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-statistics are shown in parentheses. Empirical p-values test the significance of differences in dimension-level coefficients using 1000 bootstrap samples.

reports regression results examining the heterogeneity of ESG effects on financing constraints across different regions in China. ESG performance significantly alleviates financing constraints in both the eastern and central-western regions, with coefficients of −0.326 and −0.387, respectively, both significant at the 1% level. ESG performance reduces financing constraints by 0.33 in the eastern region and 0.39 in the central-western region, implying a 9–11% decrease relative to the mean KZ index. This indicates that, irrespective of regional context, higher ESG performance generally contributes to reducing financing constraints.

However, sub-dimension analysis reveals notable regional differentiation. Environmental performance significantly increases financing constraints in the eastern region, with a coefficient of 0.0892 at the 1% significance level, reflecting the higher environmental compliance costs and stricter regulatory requirements in more developed areas. In contrast, the mitigating effect of the social is more pronounced in the central and western regions, likely due to higher poverty rates and greater emphasis on social stability. In these regions, corporate social responsibility initiatives help secure governmental and community support, thereby alleviating financing constraints.

The governance shows stronger effects in the eastern region, where more institutionalized governance mechanisms and higher corporate transparency increase the perceived value of governance by financial institutions. This enhances the ability of firms in the East to reduce financing constraints through governance improvements.

Control variables maintain expected effects across regions. Firm size and age generally exacerbate financing constraints, while cash flow, equity concentration, and growth effectively mitigate them. The effect of independent directors remains weak. Empirical p-values obtained through bootstrap sampling (see [65,66,67] for more examples of application) confirm the statistical significance of regional differences in sub-dimension effects, highlighting the importance of considering regional institutional and economic contexts when evaluating ESG impacts.

5.3. Emerging Productivity

The development of new productive forces enhances regional economic quality by promoting sustainable growth and industrial upgrading. Technological innovation, digital transformation, and green low-carbon transitions improve enterprise competitiveness and generate synergistic environmental and economic benefits [68].

Using provincial-level data from the CSMAR database,

Table 14. Provincial Heterogeneity of Emerging Productivity.

	(1)	(2)	(3)	(4)
	High	Low	High	Low
ESG	−0.272 *** (−6.93)	−0.349 *** (−13.94)
E			0.0523 (1.39)	0.0872 *** (4.17)
S			−0.0717 *** (−2.66)	−0.0839 *** (−5.23)
G			−0.268 *** (−8.92)	−0.376 *** (−19.08)
Size	0.258 *** (6.74)	0.279 *** (12.60)	0.221 *** (5.73)	0.251 *** (11.37)
Cashflow	−19.60 *** (−26.75)	−21.59 *** (−49.96)	−19.55 *** (−27.15)	−21.37 *** (−50.27)
FirmAge	0.641 *** (3.22)	0.579 *** (6.01)	0.609 *** (3.09)	0.582 *** (6.08)
Top1	−1.409 *** (−4.28)	−0.773 *** (−4.42)	−1.318 *** (−4.08)	−0.558 *** (−3.26)
Growth	−0.242 ** (−2.06)	−0.419 *** (−4.78)	−0.251 ** (−2.15)	−0.416 *** (−4.84)
ID	−1.072 * (−1.75)	−0.365 (−0.98)	−0.723 (−1.19)	0.0909 (0.25)
Constant	−3.314 *** (−3.39)	−3.982 *** (−7.13)	−2.045 ** (−2.04)	−2.883 *** (−5.17)
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	1510	5002	1510	5002
r2_a	0.522	0.456	0.536	0.481
Empirical p-value	0.054 *		0.572

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; t-statistics are shown in parentheses. Empirical p-values test the significance of differences in dimension-level coefficients using 1000 bootstrap samples.

shows that ESG performance mitigates financing constraints more strongly in provinces with low levels of emerging productivity. The ESG coefficient is −0.349 in low-productivity regions, larger in magnitude than −0.272 in high-productivity regions, indicating that financial institutions in traditional-industries-dominated areas rely more on ESG as a risk assessment tool. Economically, ESG performance lowers financing constraints by 0.35 in low-productivity regions, about 30% stronger than in high-productivity regions, underscoring the higher marginal benefit of ESG in less developed areas.

Sub-dimension analysis reveals that environmental performance increases financing constraints only in low-productivity regions with a coefficient of 0.0872, reflecting substantial compliance costs due to weaker regulatory foundations. Social responsibility and governance consistently reduce financing constraints, with stronger effects in low-productivity regions, highlighting their role in securing financing in less-developed areas.

These findings indicate that regional productivity moderates the effectiveness of ESG practices. In regions with weaker productive forces, ESG, particularly through social and governance channels, plays a critical role in alleviating financing constraints, while environmental compliance may temporarily increase financial pressure.

6. Conclusions and Recommendations

6.1. Conclusions and Discussion

Using data from listed companies in China’s heavy pollution industry, this study systematically examines the impact of ESG performance on financing constraints and its underlying mechanisms. The results show that overall ESG performance significantly eases corporate financing constraints. However, environmental performance alone worsens such constraints locally, leading to a “overall alleviation but local deterioration” paradox. Mechanism tests confirm that ESG performance mitigates financing constraints mainly through two channels: enhancing corporate reputation and restraining green agency costs. Moderating analyses reveal that environmental regulation intensity and the sustainability of corporate innovation output negatively affect the ESG-financing constraint relationship. Heterogeneity analyses further indicate that ESG’s mitigating effect is stronger for state-owned enterprises, firms in central and western regions, and companies in provinces with lower levels of emerging productive forces.

These findings provide new theoretical insights for addressing green transformation challenges in heavy polluting industries. They also clarify the transmission mechanisms between ESG and financing, and highlight the tension between traditional environmental governance costs and value creation. Additionally, the study offers practical guidance for advancing financial supply side reforms under China’s dual-carbon goals.

Notably, the “overall alleviation but local deterioration” paradox can be explained by the heterogeneous effects of ESG’s three dimensions. Environmental performance often imposes short-term cost burdens on enterprises. Incomplete disclosure of environmental risks may raise creditors’ concerns about potential environmental liabilities, thereby worsening partial financing conditions. In contrast, the social dimension reduces information asymmetry and enhances stakeholder trust. This attracts socially responsible investors and improves access to equity financing. Improved governance strengthens internal controls and financial transparency, which reduces agency costs, boosts creditor confidence, and lowers debt financing costs.

6.2. Recommendations

Based on the research findings, targeted measures should be developed to enhance the role of ESG in alleviating financing constraints and to effectively mitigate related risks. Achieving this goal requires coordinated efforts in policy design and institutional development, with a balance between short-term feasibility and long-term structural transformation.

In the short term, strengthening the linkage between ESG performance and financing costs is essential. Enterprises with superior ESG ratings should be granted preferential policies such as green credit discounts, expedited approval for bond issuance, and priority access to government procurement. Over the long term, establishing a unified national ESG information disclosure platform is crucial. Mandatory disclosure requirements should be gradually expanded, while machine learning techniques can be applied to construct sector-specific ESG benchmark evaluation systems. Integrating ESG factors into the overall financial regulatory framework will further guide long-term capital flows toward green and low-carbon industries, thereby enhancing the financial system’s capacity to support sustainable development.

To improve risk early-warning and information correction mechanisms, near-term actions should focus on promoting comprehensive ESG cost–benefit–risk assessments within firms. Third-party verification and the implementation of a negative list system should be strengthened to prevent greenwashing. Enterprises found to disclose false or misleading ESG information should face financing restrictions and be listed in regulatory blacklists. In the long term, establishing a nationwide, standardized ESG reporting and verification system is imperative. A rating appeal and dynamic update mechanism should be developed, and ESG risks should be incorporated into the macroprudential management framework. Such measures will enable cross-departmental coordination for early risk identification and prevention, providing institutional safeguards for the sustainable development of the financial market. To implement these recommendations, a synergistic policy framework is proposed, as visualized in Figure 3 below.

6.3. Future Perspectives and Limitations

Future research can build on the conclusions and theoretical framework of this study to explore the following practical directions, thereby promoting the deep integration of ESG theory and practice. These research paths can be advanced in a balanced manner:

First, cross-country comparative studies represent a key avenue for extension. The “green burden” of environmental performance identified in this study is closely tied to China’s specific context. Subsequent research can apply this study’s framework to different institutional environments. For instance, in the EU, mature regulations and green technology markets might make environmental performance easier to translate into financing advantages. In the US, market mechanisms and litigation risks may lead to a more market-driven dynamic. Comparing these “government-led,” “rule-oriented,” and “market-driven” logics can clarify how institutions shape the relationship between ESG and corporate financing.

Second, research should deepen the exploration of how carbon trading mechanisms influence the ESG-financing relationship. Future work could quantitatively analyze how carbon price fluctuations alter the impact of ESG performance on financing constraints for heavy polluters. It could also investigate whether carbon finance tools, like carbon quota pledges, can alleviate the “green burden” and how they interact with corporate ESG performance. Such studies can clarify the moderating role of carbon trading and provide precise evidence for optimizing green finance policies.

Third, exploring the application of artificial intelligence (AI) in ESG analysis is a promising frontier. To address issues of data reliability, future research could develop AI platforms that integrate diverse data sources, such as satellite monitoring and IoT sensors, for dynamic and objective ESG assessment. Simultaneously, AI algorithms could be used to identify “greenwashing” in disclosures, enhancing the credibility of ESG information. This direction can refine ESG evaluation methods and provide more reliable data for investors and regulators.

These three directions complement each other: cross-country studies enrich institutional context, carbon trading research deepens policy insights, and AI applications improve assessment methods. Together, they advance ESG research in a more comprehensive and practical direction, offering solutions for China’s green transition and contributing valuable insights to global green finance governance.

Due to data availability constraints, the conclusions of this study are based on a sample of A-share listed companies. While this limitation prevents the direct generalization of conclusions to private SMEs, the study still accurately examines the relationship between ESG and financing constraints in a high-importance context.

Future research can adopt survey methods or case study approaches to explore the understudied but critically important group of non-listed heavily polluting enterprises, further revealing the dynamic relationships in this field.