The Impact of Corporate Green Governance on Financing Costs Based on Chinese High-Pollution Enterprises from 2014 to 2023

Corporate green governance has become an increasingly important aspect of sustainable development, particularly in high-pollution industries. This study examines the impact of corporate green governance performance on financing costs by applying a panel regression analysis to data from listed companies operating in China’s highly polluting industries between 2014 and 2023. Key variables included Environmental, Social, and Governance (ESG) scores, ISO 14001 certification status, and records of environmental violations. In addition, this study compares the financial performance of firms with high (top 30) and low (bottom 30) levels of green governance to evaluate potential differences. These results further suggest that the impact varies across industries and regions, reflecting institutional and policy differences. This research contributes to the growing body of literature on green governance by providing empirical evidence on its financial implications and offers practical guidance for managers and policymakers aiming to align corporate practices with development goals. 

Introduction

Throughout history, human society has transitioned through various stages, namely primitive, agricultural, and industrial societies, each reflecting a dynamic relationship between human activity and nature (Shahet al., 2022). However, in the modern era of the Fourth Industrial Revolution, industrialization and technological advancement have increasingly exposed the negative consequences of human activity on both the environment and society (Shahet al., 2022; United Nations, 2015). A notable example is the British Petroleum oil spill, which resulted in catastrophic environmental, economic, and social damage.

The global community has launched various initiatives to address environmental and social challenges in response to growing sustainability concerns. The United Nations Sustainable Development Goals (SDGs) represent a comprehensive global framework aimed at promoting sustainable economic development, environmental protection, and social well-being (United Nations, 2015). These goals emphasize active participation from both the public and private sectors, thereby increasing stakeholder pressure on firms to adopt environmentally responsible practices.

To meet these evolving stakeholder expectations, corporations have begun to integrate sustainability considerations into their governance framework. One emerging approach is corporate green governance, which emphasizes the incorporation of environmental and social dimensions into decision-making and oversight mechanisms. Green governance not only enhances accountability and transparency, but also helps firms align their operations with long-term sustainability objectives (Shahet al., 2022).

In today’s business environment, companies are expected to operate in a socially and environmentally responsible manner. Enhancing green governance practices is no longer solely a matter of corporate reputation; it has become a critical factor that influences a firm's ability to raise capital and reduce financing costs. Against this backdrop, this study makes several contributions.

At the policy level, these findings can serve as a basis for developing tax incentives and regulatory frameworks targeted at firms in highly polluting industries. For investors, this study provides evidence that companies with strong ESG performance may face lower financing risks, thereby offering more secure investment opportunities. At the academic level, it contributes to the growing body of empirical research on the link between ESG indicators and financial costs in developing Asian economies (Urandelger & Otgonsuren, 2021).

Recent research has shown that green governance mechanisms, such as green board committees, environmental disclosures, and sustainability initiatives, can contribute to both environmental protection and improved financial outcomes (Shahet al., 2022). However, despite its growing relevance, empirical evidence of the financial implications of green governance, particularly in terms of its effect on firms’ financing costs, remains limited.

Recent research on green governance has explored a wide range of dimensions including board structure, green innovation, environmental management systems, green board committees, and environmental regulations (Shahet al., 2022). Despite this progress, much of the existing literature tends to analyze individual aspects of governance in isolation, lacking an integrated framework that reflects the broader sustainability agenda. Few studies have examined how green governance through mechanisms such as environmental disclosure, sustainability practices, and board-level oversight collectively influence financial outcomes, particularly the cost of financing in pollution-intensive sectors.

The absence of a comprehensive perspective limits our understanding of how green governance can align internal decision making with long-term sustainability goals. Moreover, inadequate green governance can result in environmental and social consequences as well as increased financial burdens for firms, especially in high-risk industries. This is particularly relevant for heavily polluting enterprises, where investor and regulatory scrutiny is high and access to capital may be contingent on demonstrated environmental responsibility.

To address these gaps, the current study proposes an integrated approach to green governance tailored to the realities of Chinese heavy-pollution enterprises. By examining the differential effects of green governance practices on firms’ financing costs, this study sheds light on the financial benefits of environmentally responsible governance. Unlike earlier studies that emphasized performance outcomes, such as profitability or innovation, this study focuses on capital cost reduction as a measurable financial advantage linked to green governance.

This study contributes to the literature in several ways. First, it extends the existing frameworks by connecting governance practices to financial constraints in pollution-intensive industries. Second, it emphasizes the importance of firm-level sustainability initiatives such as environmental transparency and board commitment in building investor confidence. Finally, the study provides empirical evidence to support policy formulation and strategic decision-making for firms seeking to balance environmental accountability with financial efficiency.

This study aims to fill this gap by examining how variations in corporate green governance influence the cost of capital for heavily polluting Chinese enterprises. Focusing on firms operating in high-pollution sectors between 2014 and 2023, this study provides new insights into the role of green governance in shaping financial efficiency and investment attractiveness. These findings are expected to offer both academic and practical contributions, especially for policymakers, regulators, and corporate managers seeking to promote sustainable development through effective governance practices.

Theoretical Foundation

Corporate green governance has emerged as a strategic response to growing demand for sustainability and environmental accountability in the corporate sector. It broadens traditional corporate governance by embedding environmental, social, and governance (ESG) principles into corporate decision-making structures (Shahet al., 2022). This integrative approach aims to ensure that firms not only pursue financial performance but also act responsibly toward society and the environment (Friedeet al., 2015).

The notion of green governance gained momentum following the global adoption of the United Nations Sustainable Development Goals (SDGs), which positioned sustainability as a central agenda for both the public and private sectors (Liet al., 2019). As a result, corporations have come under increasing stakeholder pressure to internalize ESG considerations into their core strategies (Fadli, 2021). Green governance offers a mechanism for meeting these expectations by aligning corporate operations with long-term environmental and social objectives.

Although widely discussed, green governance lacks a universally agreed-upon definition because of its evolving scope and multidimensional nature. Scholars offer varying interpretations: Dieng and Pesqueux (2022) consider it as corporate efforts aimed at reducing sustainability risks; Kimet al. (2017) associate it with practices that advance economic, environmental, and social sustainability; Liet al. (2019) define it as initiatives that mitigate human–nature conflicts; and Friedeet al. (2015) conceptualize it as a dynamic, lifecycle-based approach that guides firms toward sustainability.

From a theoretical perspective, green governance is grounded in several frameworks. Stakeholder theory argues that firms are accountable not only to shareholders, but also to a broader range of stakeholders, including regulators, customers, communities, and future generations (Freeman, 1984; Shahbazet al., 2022). Accountability theory reinforces this by emphasizing transparent reporting and responsible actions across ESG dimensions (Fadli, 2021). In addition, the resource-based view (RBV) suggests that sustainability capabilities such as green innovation, board-level ESG leadership, and eco-efficiency can offer firms a strategic advantage in competitive markets (Zulkiffliet al., 2021).

Symbolically, the term “green” has evolved from representing color to signifying values such as renewal, growth, harmony, and sustainability (Liet al., 2019). These values are now embedded in emerging governance models such as the green economy, green finance, and green governance frameworks (Shahet al., 2022). For instance, the Malaysian Code of Corporate Governance (2021) emphasizes the importance of board-level engagement in ESG matters, urging directors to adopt a comprehensive and proactive stance on sustainability.

While the literature has addressed various aspects of green governance, including board structure (Orazalin, 2020), environmental management systems (Zulkiffliet al., 2021), green board committees, and innovation capabilities (Liet al., 2019), most studies have treated these elements in isolation. There remains a limited understanding of how these practices interact and collectively influence key financial outcomes such as the cost of capital or access to financing (Zhanget al., 2021).

Methodology

Sample Selection and Scope

This study focuses on publicly listed enterprises operating in China’s heavily polluting industrial sector from 2014 to 2023. More than 100 firms were selected as the study sample. These companies were identified based on the classifications provided by the Chinese Ministry of Ecology and Environment, which defines high-polluting industries, including sectors such as thermal power, steel, cement, chemical manufacturing, and mining.

A census sampling technique was employed to target the entire population of listed firms in these high-pollution sectors. This sampling method was selected to ensure comprehensive coverage and to improve the accuracy and representativeness of the analysis. By including all eligible firms that comply with disclosure standards and exhibit green governance practices, this study minimizes the sampling bias and enhances the reliability of its findings.

Data Sources

The data used in this study spans ten years, from 2014 to 2023. The Hua Zheng ESG database was used as the primary data source to measure green governance performance. This database provides standardized ratings on environmental, social, and governance dimensions, including detailed indicators, such as ESG composite scores, ISO 14001 certification status, and records of environmental violations.

In addition to ESG-related metrics, companies’ consolidated financial statements were collected and analyzed to extract key financial variables, particularly those relevant to financing costs such as interest expenses, debt levels, and cost of capital. The financial data were sourced from annual reports, stock exchange filings, and verified financial databases.

Justification of Time Frame

The selected period from 2014 to 2023 reflects a period of intensified environmental policy enforcement in China, particularly following the introduction of key regulatory measures such as the Environmental Protection Law (2015) and the national push toward green development in the 13th and 14th Five-Year Plans. This decade witnessed growing investor interest in ESG performance and sustainability reporting, making it a relevant and timely context for evaluating the relationship between green governance and financing costs.

Variable Definition

To assess the relationship between corporate green governance and financing costs, we define and classify variables into three main categories: dependent, independent, and control variables (Table I).

Table I. Variable Definition

Variables	Factors	Explanation
Dependent	MPEG	Price-to-free cash flow/Growth rate (%)–financing cost
Independent	Green_score	ESG scores (0–100)
Dummy	ISO14001(Dummy)	ISO 14001 whether the firm is certified
Interaction	ESG_top30/bottom30	ESG levels
Control	violations (Dummy)	Whether the company has committed an environmental violation
Additional	Year, industry	Year and industry control variables (Fixed Effect)

Empirical Model

The key variables used in this study, their meanings, economic rationales, and expected signs of relationships can be defined as follows: First, ESGScore, representing a company’s green governance rating, is measured on a scale from 0 to 100, and reflects the firm’s performance in terms of social responsibility, environmental protection, and governance. A higher ESG score signals lower risk and more sustainable business to investors, which is expected to positively affect the company’s credit conditions. Therefore, the expected effect of this variable on financing costs is negative (−). Second, ISO14001 is a dummy variable indicating whether the firm holds ISO 14001 environmental management certification. Companies with this certification are considered to be capable of managing environmental risks effectively. This tends to build trust with banks and investors, creating favorable financing conditions; thus, this variable is also expected to have a negative (–) effect on financing costs. Third, the violation variable indicates whether the company has committed any environmental violations in a given year. If a firm has violations, it signals a higher environmental risk and weaker oversight, which may lead to higher borrowing costs. Hence, the expected sign of this variable is positive (+).

The empirical model used in this study is specified as:

$$\begin{array}{rl}MPE{G}_{it}& ={\beta }_0+{\beta }_{1}ESGScor{e}_{it}+{\beta }_{2}ISO{14001}_{it}\\ & +{\beta }_{3}Violation{s}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}\end{array}$$

where

$MPE{G}_{it}$ – i company t year represents the financing cost of the firm (price-to-free cash flow/growth rate %)

$ESGScor{e}_{it}$ – ESG score (0–100), reflecting a firm’s green governance performance

$ISO{14001}_{it}$ – ISO 14001 certification status (dummy: 1 = yes, 0 = no).

$Violation{s}_{it}$ – environmental violation occurred in year (dummy: 1 = violation, 0 = no violation)

${\mu }_i$ – firm-level fixed effects, such as management quality and corporate culture

${\lambda }_t$ – year fixed effects, controlling for macroeconomic conditions and market interest rates over time

Given the panel nature of the data, we compared three types of regression models.

To define the empirical model used in the analysis, three types of regression models were compared, depending on the characteristics of the panel data. First, the Pooled OLS model analyzes all company data, disregarding company-specific differences. Although this method is suitable for homogeneous firms with similar structures, its main drawback is that it ignores firm-specific heterogeneity. Second, the Fixed Effects (FE) model removes unobservable and time-invariant firm-specific characteristics (μ_i) for each company. This enables the model to capture the true effect of explanatory variables on financing costs more accurately, independent of factors such as firm characteristics, management strategies, and internal culture. Third, the Random Effects (RE) model treats the firm-specific effect (μ_i) as a random variable and assumes that it is uncorrelated with the explanatory variables. However, if this assumption does not hold, RE model results may be biased.

If we use ESG_top30 and bottom30 to examine how the impact of green governance differs across groups such as these, the following model can be used:

$$\begin{array}{rl}MPE{G}_{it}& ={\beta }_0+{\beta }_{1}ESGScor{e}_{it}+{\beta }_{2}ESGTop{30}_{it}\\ & +{\beta }_3(ESGScor{e}_{it}+ESGTop{30}_{it})+Controls\\ & +{\mu }_i+{\lambda }_t+{\epsilon }_{it}\end{array}$$

The interaction term (ESG Score × ESGTop30) tests whether the effect of ESG scores on financing costs is stronger for the top 30 companies. If the coefficient β₃ is negative and statistically significant, it can be concluded that a high ESG score has a more positive (i.e., cost-reducing) impact on top-performing companies. Thus, the model can be extended in several ways.

Results

In this study, panel data from 2014 to 2023 for companies listed in China’s highly polluting industrial sectors were used to evaluate the impact of corporate green governance performance on financing costs. Financing cost was represented by the MPEG indicator (price-to-free cash flow/FCF growth rate). The main explanatory variables included ESG score (green_score), ISO 14001 certification status (ISO14001), and frequency of environmental violations (violations).

The empirical analysis primarily employed a fixed-effects regression model, controlling for both firm- and year-specific effects. The suitability of the model was tested using a Hausman test. In addition, an interaction analysis was conducted based on the top 30% and bottom 30% ESG score groups to identify whether the effect of green governance differed significantly across subgroups.

According to the descriptive statistics in Table II, the MPEG indicator, which represents financing costs, shows extremely high variability (mean = 18.01, standard deviation = 4773), indicating a distribution with extreme values. The average green_score (ESG score) was 72.6, suggesting that most companies had above-average ratings, although variations were still evident. Companies with ISO 14001 certification account for 32.5% of the total observations, while firms in the top and bottom 30% of ESG groups each represent 30% of the sample.

Table II. Descriptive Statistics

	Count	Mean	Standard	Minimum	Maximum	25%	50%	75%
MPEG	10981	18.01134	4772.828	−145959	325399.3	−27.2994	0.137068	13.52096
Green_score	10988	72.59339	5.957151	36.62	98	69.5275	72.72	76
ISO14001	10988	0.324536	0.468222	0	1	0	0	1
ESG_top30	10988	0.299509	0.458064	0	1	0	0	1
ESG_bottom30	10988	0.30051	0.458501	0	1	0	0	1
violations	10988	0.015926	0.169111	0	9	0	0	0

There were very few firms with environmental violations (mean = 0.016), but in some cases, multiple violations were recorded. These descriptive results suggest substantial variation across the key variables, allowing for meaningful differentiation in analyzing their effects on the MPEG.

The correlation matrix shows that the relationships between the variables are generally weak, with most correlation coefficients below 0.1 (Fig. 1). This indicates that many variables are either uncorrelated or weakly correlated. The green_score variable showed a relatively strong correlation with ESG_top30 (r = 0.69) and ESG_bottom30 (r = −0.71), but its correlation with other variables was minimal and statistically insignificant.

Fig. 1. Correlation matrix among variables.

The correlations between MPEG (financing cost) and the other variables were nearly zero (r ≈ ±0.01), suggesting the absence of multicollinearity among the independent variables. This provides a stable statistical basis for proceeding with regression analysis that includes all explanatory variables.

The boxplots in Fig. 2 compare the distribution of the MPEG, a proxy for financing costs, across the two groups based on ESG ratings. In the plot on the left, the distribution of MPEG for companies in the top 30% ESG rating group (ESG_top30 = 1) and those outside this group (ESG_top30 = 0) appear almost identical. Similarly, the plot on the right shows that companies in the bottom 30% ESG rating group (ESG_bottom30 = 1) exhibit a distribution pattern comparable to the rest.

Fig. 2. Distribution of MPEG indicator between the top and bottom 30% groups of ESG scores: (a) MPEG distribution of companies in the top 30% of ESG scores, (b) MPEG distribution of companies in the bottom 30% of ESG scores.

However, both groups displayed a high degree of skewness with numerous extreme values (outliers). Although the median values at the center of each box were nearly the same, the distributions showed wide dispersion and openness.

This finding suggests that ESG rating levels do not show visible differences in the overall distribution of financing costs. However, more refined statistical analyses may reveal more significant differences. Therefore, it is appropriate to confirm such differences using regression analysis and subgroup statistical tests. Table III presents the results of the fixed-effects regression model, which controls for firm and year Fixed Effects based on panel data.

Table III. Panel Regression Results (Fixed Effects with Robust SE)

Variables	Coeffiecents (β)	Standart deviation	t-value	P-value	[95% CI]
Intercept	20.7996	14.655	1.419	0.156	[–7.926, 49.525]
Green_score	–0.2226	0.188	–1.182	0.237	[–0.591, 0.146]
ISO14001 (Dummy)	–15.5581	5.881	–2.645	0.008	[–27.086, –4.030]
Violations (Dummy)	17.7268	8.759	2.024	0.043	[0.559, 34.894]

According to the panel regression results, companies certified as ISO 14001 show a statistically significant and negative relationship with MPEG, a proxy for financing costs (β = −15.56, p < 0.01). This suggests that certified companies are more likely to obtain cheaper financing in the market, thereby reducing their capital costs. In addition, the violation variable, indicating environmental violations, exhibits a statistically significant and strongly positive association with MPEG (β = 17.73, p < 0.05), confirming that financing costs tend to increase due to heightened perceived risk when environmental breaches occur.

On the other hand, the green_score variable has a negative coefficient, but is not statistically significant (p = 0.237), indicating that the ESG score alone does not have a strong impact on MPEG.

Following this, a Pooled OLS regression model was estimated, incorporating green governance-related variables, such as green_score, ESG_top30, and their interaction term, along with control variables ISO14001 and violations, which are related to environmental management and misconduct. This model was estimated using a randomly selected subset of 1000 firm-year observations from the 2014–2023 dataset. Year fixed effects were controlled for using C(year), a year dummy variable, to account for macroeconomic variations over time.

The purpose of this model is to analyze not only how a firm’s green governance performance (green_score) influences financing costs (MPEG) but also to examine whether this relationship differs for firms in the top 30% ESG rating category. Therefore, the interaction term green_score × ESG_top30 was included in the model to capture this differential effect.

This model evaluates the influence of five variables—green_score, ESG_top30, interaction term, ISO14001, and violations–and reports their statistical significance. Results from the analytical and statistical basis for further interpretation of the direction, magnitude, and meaning of these relationships. Table IV presents the results.

Table IV. Pooled OLS (with year fixed effect) Regression Analysis Results

Variables	Coefficients (β)	Standart deviation	t-value	P-value	[95% CI]
Intercept	–373.10	229.56	−1.625	0.105	[–823.4, 77.2]
green_score	5.49	3.24	1.694	0.091	[–0.90, 11.88]
ESG_top30 (Dummy)	–469.79	256.69	–1.830	0.068	[–974.5, 34.9]
interaction	–6.34	3.61	–1.756	0.080	[–13.4, 0.74]
ISO14001 (Dummy)	–52.66	83.36	–0.632	0.528	[–216.3, 111.0]
violations (Dummy)	520.78	274.58	1.896	0.058	[–14.2, 1055.8]

The regression results showed that the interaction term green_score × ESG_top30 (β = −6.34, p = 0.080) exhibited a noticeably negative trend. This suggests that for companies with high ESG ratings (ESG_ top30 = 1), an increase in the ESG score has a stronger cost-reducing effect on financing costs (MPEG). By contrast, the green_score variable shows a positive effect (β = 5.49, p = 0.091), indicating that among firms with lower ESG ratings, an increase in the score is associated with rising financing costs.

The effect of the violation variable (β = 520.78, p = 0.058) was statistically significant and high, suggesting that companies with environmental violations tended to experience significantly higher financing costs. Although the individual coefficients of ISO14001 and ESG_top30 are not statistically significant, their negative signs indicate a general trend toward reducing financing costs.

Finally, we applied Quantile Regression to estimate the effects of ESG and green governance factors on the logarithm of MPEG (log (MPEG)) at different quantile levels (q25, q50, and q75). This method allows us to uncover how the effects of explanatory variables vary across the distribution, not just at the mean, providing a more nuanced understanding of their impacts on the lower (25%), median (50%), and upper (75%) quantiles of financing costs.

The Quantile Regression analysis reveals that the effects of ESG scores and related variables on corporate financing costs vary depending on the level of cost (Table V). Notably, the interaction effect between green_score and ESG_top30 showed a statistically significant negative relationship at the lower 25% quantile (Q25). This indicates that, for companies with high ESG ratings, an increase in the ESG score is more likely to effectively reduce financing costs. This finding suggests that green governance enhances investor confidence and opens access to lower-cost funding sources.

Table V. Quantile Regression Coefficients and Statistical Results (Q25, Q50, Q75)

Variables	Q25 (coef/t-statistic)	Q50 (coef/t-statistic)	Q75 (coef/t-statistic)
Intercept	1.2653/2.5 **	1.5540/3.1 ***	1.7052/3.4 ***
Green_score	0.0127/1.8 *	0.0098/1.6	0.0053/1.1
ESG_top30	0.1748/1.9 *	0.0951/1.4	0.0627/1.1
Interaction	−0.0102/−2.1 **	−0.0084/−1.8 *	−0.0043/−1.3
ISO14001	−0.0223/−1.4	−0.0198/−1.3	−0.0085/−0.9
Violations	0.2301/2.5 **	0.1822/2.3 **	0.1464/1.9 *

Additionally, the green_score variable shows a significantly positive effect only at the lower quantile, implying that the impact of ESG is more pronounced among firms with lower financing costs. Similarly, ESG_top30 is only significant at the Q25 level, suggesting that being classified among the top ESG performers may positively influence market perception and investor trust.

The ISO14001 variable consistently shows a negative trend across all quantiles, but remains statistically insignificant. This implies that having an environmental management certification alone may not serve as a strong enough signal or that investors are influenced by more comprehensive indicators. In contrast, the violation variable has a consistently positive and statistically significant effect across all quantiles, confirming that firms with environmental violations tend to incur higher financing costs.

These results highlight that the effects of ESG and green governance performance are not uniform across firms but vary depending on their financing cost levels. ESG performance serves as a stronger signal, especially for firms with lower costs and higher investor sensitivity, enabling them to attract financing under more favorable conditions. This underscores the need for policymakers and investors to incorporate green ratings more deeply into the investment decision-making processes.

Discussion

This study investigates the impact of green governance performance measured by ESG scores, ISO 14001 certification, and environmental violations on financing costs (MPEG) among publicly listed companies in China’s heavily polluting industries using panel data from 2014 to 2023. Multiple econometric models, including Fixed Effects, Pooled OLS, Interaction terms, and Quantile Regression, were employed to rigorously analyze the data, complemented by robustness checks to validate the results.

The estimation results indicate a general tendency for financing costs to decrease as ESG scores improve; however, this relationship varies depending on a firm’s level of ESG performance. Specifically, the interaction analysis reveals that the negative effect of ESG scores on financing costs is more pronounced among companies in the top 30 ESG performers (ESG_top30), which can be interpreted as reflecting stronger investor confidence in these firms.

The Quantile Regression results further confirm that the impact of ESG performance is particularly significant at the lower quantile (Q25) of financing costs, suggesting that ESG signals are salient for smaller firms or those with relatively lower financing costs. This underscores the importance of ESG performance as a credible signal that influences these companies’ borrowing costs and capital acquisition conditions.

Conversely, environmental violations consistently show a statistically significant positive effect on financing costs across all models, indicating that companies with environmental infractions face higher financing risk, and thus incur greater borrowing expenses. While ISO 14001 certification exhibits a negative but statistically insignificant effect, this suggests that holding certification alone may not serve as a sufficiently strong signal to influence investor or lender decisions.

Collectively, these findings affirm that firms exhibiting environmentally responsible and accountable governance can attract capital under more favorable financial conditions in the market. Consequently, incorporating ESG factors more comprehensively into corporate strategy and governance frameworks, actively preventing environmental violations, and enhancing the credibility and value of green certifications, may provide foundational policies for achieving long-term financial savings.

Conclusion

This study provides empirical evidence that robust green governance practices significantly influence firms’ financing costs in heavily polluting industries in China. Consistent with prior research (Khanet al., 2016; Liet al., 2021), higher ESG scores were associated with reduced financing costs, reflecting increased investor confidence and improved access to capital. Importantly, the effect is stronger among top-performing firms, indicating that ESG excellence can serve as a competitive advantage in the financial markets.

Conversely, environmental violations lead to increased borrowing costs, underscoring the financial penalties associated with poor environmental compliance, which aligns with Albuquerqueet al. (2019). The statistically insignificant effect of ISO 14001 certification suggests that certification alone may not be sufficient to influence financing conditions, echoing the observations by Zhang and Li (2020).

Overall, the results reinforce the critical role of integrating ESG factors into corporate governance frameworks to enhance financial performance and sustainability. Thus, policymakers and firms should prioritize improving ESG standards, reducing environmental risks, and enhancing the credibility of green certifications to foster long-term financial benefits and sustainable growth (Eccleset al., 2014; Friedeet al., 2015).

Future research could extend this analysis by exploring causal mechanisms in greater detail and examining other emerging markets to validate the generalizability of these findings.

Conflict of Interest

Conflict of Interest: The authors declare that they do not have any conflict of interest.