The relationship between ESG disclosure and credit risk: Evidence from Vietnamese commercial banks

The relationship between ESG disclosure and credit risk: Evidence from Vietnamese commercial banks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The relationship between ESG disclosure and credit risk: Evidence from Vietnamese commercial banks Tuan Anh Vu, Thi Nhan Duong, Quang Khai Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8255045/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the relationship between environmental, social, and governance (ESG) disclosure and credit risk in Vietnamese commercial banks. Using a panel dataset of 34 banks from 2016 to 2023, we employ both fixed-effects and system GMM estimations to address unobserved heterogeneity and potential endogeneity concerns. The findings indicate that greater ESG disclosure is associated with lower levels of credit risk. However, the magnitude of this effect varies by bank size. Specifically, the impact of ESG disclosure is more pronounced among smaller banks compared to their larger counterparts. These results highlight the importance of ESG practices in strengthening financial stability, particularly for banks with limited scale. JEL code: G21, G32, Q56 ESG disclosure Credit risk Commercial banks Financial stability 1. Introduction The disclosure of environmental, social, and governance (ESG) information is now seen as an important part of how firms and financial institutions are judged. Prior studies show that ESG disclosure signals a move toward sustainable growth and can lower financial risks, especially credit risk. For example, Atif and Ali ( 2021 ) find that firms with stronger ESG disclosure face lower default risk, while Yang et al. ( 2021 ) show that ESG reporting reduces corporate bond spreads in China. Wang and Yang ( 2023 ) point out that the impact of ESG on credit risk can change depending on the stage of the corporate life cycle. Other research also broadens the view, such as Bruno and Henisz ( 2024 ) note that ESG factors matter for municipal credit risk, and Kiesel and Lücke ( 2019 ) show that credit ratings respond to ESG integration in financial markets. In the banking sector, ESG disclosure is especially relevant. Banks play a central role in financial systems, and weak risk management in banks can spread quickly to the wider economy. Zhou et al. ( 2022 ) find that green lending rules reduce credit risk in Chinese banks. Bannier et al. ( 2022 )d ck et al. ( 2020 ) also show that sustainable banking practices improve loan quality and cut financial risk. In Taiwan, Ding et al. ( 2025 ) provide further evidence that ESG performance is closely tied to credit risk and financial distress. These studies show that the connection between ESG and credit risk has become a major focus in global research. In many emerging markets, however, ESG disclosure has grown under legal systems that are still changing and not yet complete. Unlike developed economies, where mandatory rules are more common, many emerging markets rely on soft guidance or voluntary reporting (Gallucci et al., 2025 ; Liu et al., 2025 ; Nguyen, 2024c ). This is consistent with the Vietnamese context. While some listed firms have started reporting on corporate social responsibility (CSR) and ESG, the legal framework is still limited. ESG disclosure is mostly encouraged rather than required. Since 2016, Decree No. 155/2015/NĐ-CP and Circular No. 155/2015/TT-BTC have asked listed firms to report some environmental and social issues in their annual reports. Yet these rules are narrow in scope and place little burden on commercial banks, even though banks are key players in the economy. Some studies have looked at ESG and banks in Vietnam. Minh et al. ( 2024 ) analyze the link between ESG disclosure and profitability, while Nguyen Bích ( 2025 ) studies how ESG performance and credit quality are connected in emerging markets. Other work has examined ESG practices in large Vietnamese banks (Hùng & Khiêm, 2024 ; Nguyen, 2025a ) and how sustainable development affects financial results (Nguyen, 2024a , 2024b ; Phương et al., 2025 ). Still, important gaps remain. First, there is little evidence on how ESG disclosure affects credit risk in Vietnamese banks, even though credit risk is the most serious type of risk for them. Second, most local studies focus on profitability or performance rather than risk. Third, no study has explored whether the effect of ESG disclosure differs between large and small banks, even though bank size may shape both ESG adoption and risk control. This study addresses these gaps by examining the link between ESG disclosure and credit risk in Vietnamese commercial banks from 2016 to 2023. It makes three contributions. First, it adds to the global research on ESG and credit risk by providing new evidence from Vietnam, where disclosure rules are still developing. Second, it considers the role of bank size in shaping how ESG disclosure influences credit risk. Third, it offers policy suggestions for improving ESG rules in banking, which can help strengthen financial stability in Vietnam. The structure of the paper includes the following Section 2 reviews existing studies, Section 3 details the methodological approach, Section 4 examines and interprets the results, while Section 5 summarizes the conclusions along with key insights and implications. 2. Literature review When judging credit risk, lenders now look not only at financial numbers but also at ESG disclosure, which is becoming more important in their decisions. Prior studies show that transparent reporting helps reduce uncertainty, builds confidence, and lowers the cost of financing. Atif and Ali ( 2021 ) report that disclosure lowers default risk, while Ding et al. ( 2025 ) find that ESG performance reduces financial distress in Taiwan. Hajek et al. ( 2024 ) confirm that ESG data help improve the accuracy of credit ratings. Evidence from bond markets also supports this view, such as Yang et al. ( 2021 ) show that ESG disclosure reduces bond spreads in China, and Raimo et al. ( 2021 ) find that it decreases the cost of debt financing in Europe. Together, these studies suggest that ESG disclosure serves as a positive signal of risk management and long-term stability. Several theories explain why disclosure matters for credit risk. Agency theory emphasizes that managers often have more information than creditors and may hide problems or focus on short-term goals (Jensen & Meckling, 2019 ; Nguyen et al., 2025a ). This lack of information makes it harder for creditors to judge the true risk. By reporting on ESG activities, firms provide clearer insights into their operations, which reduces information asymmetry and lowers perceived risk. Stakeholder theory broadens this view, noting that firms must meet the expectations of employees, customers, regulators, and society at large (Heath & Norman, 2004 ; Nguyen, 2025b ). ESG disclosure demonstrates that firms are responsive to these wider groups, which strengthens trust and enhances their reputation with lenders. Legitimacy theory highlights the need for social approval to ensure survival and growth (Deegan, 2019 ). Through ESG disclosure, firms show alignment with social norms and regulations, which reduces the chance of penalties and reputational damage, and ultimately lowers credit risk. This relationship is also influenced by the policy environment. In developed economies, mandatory disclosure rules make information more reliable, while in emerging markets disclosure is often voluntary and less standardized (Gallucci et al., 2025 ; Liu et al., 2025 ; Tran & Nguyen, 2025 ). Nevertheless, studies in China show that the Green Credit Policy strengthens the link between ESG practices and reduced credit risk (Zhou et al., 2022 ). Bruno and Henisz ( 2024 ) further show that ESG outcomes affect municipal credit ratings in the United States. These results suggest that ESG disclosure has become an important element in creditors’ risk assessment across different settings. Importantly, the COVID-19 pandemic also highlighted the role of ESG disclosure in managing financial stability (Adams & Abhayawansa, 2022 ). The crisis worsened credit quality worldwide by increasing default risk and weakening borrowers’ repayment capacity. In such conditions, transparent ESG reporting helped banks reassure stakeholders, restore confidence, and demonstrate resilience in the face of systemic shocks. This suggests that ESG disclosure not only matters in normal times but also plays a stabilizing role during periods of heightened uncertainty. Vietnam is beginning to follow this trend. Existing studies examine ESG and profitability (Minh et al., 2024 ), ESG practices in large state-owned banks (Hùng & Khiêm, 2024 ) and the role of sustainability indicators in firm performance (Phương et al., 2025 ). Bích ( 2025 ) directly studies ESG performance and bank credit quality, while other works consider climate risk in lending Tuyết and Hồng ( 2025 ) or broader ESG–financial performance links (Thường et al., 2023 ). These findings suggest that ESG disclosure can improve both financial performance and credit outcomes, even though research on the direct link between disclosure and credit risk in Vietnam is still developing. Based on this reasoning, we propose the following hypothesis: H1. ESG disclosure is negatively associated with credit risk in Vietnamese banks. 3. Methodology 3.1 Data The dataset is constructed from 34 Vietnamese commercial banks over the period 2016–2023. Prior to 2016, there were no clear guidelines or policy initiatives on ESG disclosure in the banking sector, making this timeframe particularly relevant for examining the effects of recent sustainability practices. Financial data were obtained from banks’ audited annual reports and official publications, while ESG disclosure information was manually collected and coded based on established indicators. After excluding banks with missing values, the final sample consists of an unbalanced panel of 34 banks, yielding 267 bank-year observations. 3.2 Model and estimation method The regression model is specified as follows: NPL it = β 0 + β 1 ESG it + β 2 ESGD it + β 3 BAGE it + β 4 BIND it + β 5 ROA it + β 6 BSIZE it + β 7 GDP t + β 8 COVID t + ε it (1) The regression model is designed to examine the effect of ESG disclosure on bank credit risk, measured by the non-performing loans (NPL) ratio (Nguyen, 2024a ; Nguyen et al., 2025b ). The dependent variable ​ NPL it represents the share of non-performing loans for bank i in year t . ESG disclosure is captured by two measures. First, a dummy variable ​ ESG it equals 1 if the bank discloses any ESG information in its annual report, and 0 otherwise. Second, the extent of ESG disclosure ESGD it is measured as the natural logarithm of the number of ESG-related mentions in the annual report, providing a more detailed indicator of reporting intensity. Several control variables are included. Bank age BAGE it is calculated as the number of years since the bank’s establishment. Board independence BIND it is the proportion of independent board members, reflecting governance quality. Profitability ROA it is measured as return on assets, while bank size BSIZE it is the natural logarithm of total assets. At the macroeconomic level, GDP t is measured as the natural logarithm of GDP per capita, obtained from World Bank data. To account for the COVID-19 shock, a dummy variable COVID t equals 1 for the years 2021–2022 and 0 otherwise. The control variables are used that based on large literature (Houston et al., 2010 ; Nguyen, 2022 , 2025b ; Richard et al., 2008 ). The error term ε it captures unobserved factors affecting bank credit risk. The main coefficients of interest are β 1 ​ and β 2 ​ , which indicate whether ESG disclosure (both existence and extent) is significantly associated with lower non-performing loans, thereby reducing bank credit risk. The definitions and sources of all variables are summarized in Table 1 Table 1 Variable Definitions Variable Definition Source NPL Non-performing loans ratio Annual report ESG Dummy variable, equals 1 if ESG information is disclosed, 0 otherwise Annual report ESGD Extent of ESG disclosure, measured as ln(number of ESG mentions in annual report) Annual report BAGE Bank age, calculated from the year of establishment Annual report BIND Proportion of independent board members Annual report ROA Return on Assets ratio Financial statement BSIZE Bank size, measured as ln(total assets) Financial statement GDP Natural log of GDP per capita Worldbank COVID Dummy variable, equals 1 for years 2021–2022, 0 otherwise - 4. Result 4.1 Descriptive statistic and correlation matrix Table 2 presents the descriptive statistics of the variables used in the analysis. The mean value of the non-performing loans ratio (NPL) is 2.65, with a minimum of 0.10 and a maximum of 5.20, suggesting that Vietnamese banks face a moderate level of credit risk. Compared with developed countries, where banks usually have lower NPL ratios, this result shows that Vietnamese banks still face higher credit risk, similar to what is often seen in other emerging markets (Kuzucu & Kuzucu, 2019 ). The dummy variable for ESG disclosure (ESG) has a mean of 0.82, indicating that the majority of banks report some ESG information in their annual reports. The extent of ESG disclosure (ESGD) has an average of 1.36, showing variation across banks in how much detail they provide. Regarding bank characteristics, the average bank age (BAGE) is about 5.54 years, with a wide range from new entrants to banks established more than a decade ago. The proportion of independent board members (BIND) averages 0.25, meaning that about one-fourth of board members are independent. Bank profitability, measured by ROA, has a mean of 5.37, but also shows high variation, reflecting differences in performance across banks. The average bank size (BSIZE), measured as the logarithm of total assets, is 28.08, indicating that the sample mostly consists of medium to large banks. At the macroeconomic level, the average GDP per capita is 8.60 (in natural log), reflecting Vietnam’s status as a developing economy. The COVID variable has a mean of 0.25, meaning that around one-fourth of the observations fall within the pandemic years 2021–2022. Overall, the descriptive statistics indicate that while ESG disclosure is relatively common among Vietnamese banks, there remains variation in the extent of disclosure and in financial performance, with moderate levels of credit risk observed during the study period. Table 2 Descriptive statistic Variable Obs Mean Std. dev. Min Max NPL 267 2.650 1.481 0.100 5.200 ESG 267 0.820 0.385 0.000 1.000 ESGD 267 1.356 0.976 0.000 4.000 BAGE 267 5.543 1.401 0.000 13.000 BIND 267 0.246 0.187 0.000 0.800 ROA 267 5.368 6.181 -20.940 33.050 BSIZE 267 28.081 1.272 26.027 31.678 GDP 267 8.599 7.816 0.032 45.111 COVID 267 0.247 0.432 0.000 1.000 Note : Variable definitions are presented in Table 1 Table 3 reports the pairwise correlations between the main variables. The non-performing loans ratio (NPL) is strongly and negatively correlated with both ESG disclosure (ESG, -0.665) and the extent of ESG disclosure (ESGD, -0.895), suggesting that greater ESG transparency is associated with lower credit risk. Among the control variables, bank profitability (ROA) shows a positive correlation with GDP (0.294), meaning that stronger macroeconomic conditions support better bank performance. Bank size (BSIZE) is weakly and negatively correlated with board independence (BIND, -0.152), while other correlations remain small in magnitude. The highest correlation in the matrix is between NPL and ESGD (-0.895). Although this value is relatively high, most other correlations are well below the conventional 0.80 threshold, indicating that multicollinearity is unlikely to be a serious concern in the regression analysis. Table 3 Correlation matrix Pairwise correlations Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) NPL 1.000 (2) ESG -0.665 1.000 (3) ESGD -0.895 0.602 1.000 (4) BAGE 0.019 0.119 0.029 1.000 (5) BIND -0.097 0.059 0.059 -0.012 1.000 (6) ROA -0.008 0.041 -0.051 -0.066 -0.066 1.000 (7) BSIZE 0.014 -0.187 0.069 0.020 -0.152 -0.061 1.000 (8) GDP -0.017 0.118 -0.011 -0.056 -0.054 0.294 -0.119 1.000 (9) COVID -0.135 0.065 0.112 0.088 0.015 -0.131 0.029 -0.056 1.000 Note : This table presented the Pairwise correlations matrix of the main variables. Variable definitions are presented in Table 1 . 4.2 Main results Table 4 presents the fixed-effect estimation results on the relationship between ESG disclosure and credit risk. The coefficient of ESG is negative and strongly significant (-2.870, p < 0.01), showing that banks disclosing ESG information have lower non-performing loan (NPL) ratios. Likewise, the coefficient of ESGD is also negative and highly significant (-1.299, p < 0.01), suggesting that the greater the extent of ESG disclosure, the lower the level of credit risk. Together, these results confirm hypothesis H1, highlighting that ESG disclosure acts as a credible signal to creditors by reducing information asymmetry and increasing confidence in banks’ stability. These findings are in line with international evidence. Ding et al. ( 2025 ) show that ESG performance lowers financial distress among Taiwanese firms, while Hajek et al. ( 2024 ) demonstrate that ESG information improves the accuracy of credit ratings. Similarly, Atif and Ali ( 2021 ) report that ESG disclosure reduces default risk, and Yang et al. ( 2021 ) find that stronger ESG transparency decreases bond spreads in China. Raimo et al. ( 2021 ) also confirm that ESG disclosure lowers the cost of debt in European markets. Together, these studies support the view that transparent ESG reporting enhances creditors’ trust and reduces financing costs. Evidence from policy-driven contexts further reinforces this argument. Zhou et al. ( 2022 ) show that China’s Green Credit Policy strengthens the link between ESG practices and reduced credit risk, while Bruno and Henisz ( 2024 ) highlight how ESG outcomes affect municipal credit ratings in the United States. These results imply that ESG disclosure not only improves access to finance but also plays a role in broader financial stability. Table 4 Fixed-effect results for the impact of ESG disclosure on credit risk NPL Coefficient t-stat Coefficient t-stat ESG -2.870*** -13.92 ESGD -1.299*** -37.77 BAGE -0.002** -2.04 -0.019** -1.95 BIND 0.403 1.46 -0.075 -0.54 ROA 0.002 0.21 0.004* 1.75 BSIZE -0.337** -2.31 -0.038 -0.52 GDP -0.005 -0.75 0.000 -0.09 COVID 0.223** 2.25 0.205*** 4.12 Cons 14.481*** 3.53 5.432*** 2.61 Note : This table presents the estimation results of Eq. 1, using the fixed-effects method. The regressions use Q as the dependent variable (Dep.var). Variable definitions are presented in Table 1 . *** p < 0.01, ** p < 0.05, * p < 0.1. In Vietnam, research has only recently begun to explore this relationship. Bích ( 2025 ) directly examines ESG performance and bank credit quality, finding results consistent with the negative link between ESG and credit risk. Other studies, such as Minh et al. ( 2024 )d ng and Khiêm (2024), highlight that ESG practices in Vietnamese banks are still evolving, with disclosure often less standardized than in developed economies. The strong and significant coefficients found in this study provide new empirical evidence for Vietnam, confirming that ESG disclosure is an important factor in reducing credit risk, in line with global findings. 4.3 Robustness test Robustness tests are necessary to ensure that the results are not affected by endogeneity or other hidden biases. In the relationship between ESG and non-performing loans (NPLs), it is possible that ESG performance is influenced by financial outcomes or other unobserved firm characteristics. If this problem exists, the estimated results may not be reliable. To address this concern, we apply the System GMM method, which helps control for endogeneity and improve the reliability of the estimates. In addition, we separate ESG into two measures between ESG and ESGD. The reason is that the way ESG is measured can capture different aspects. ESG is a dummy variable, which equals 1 if a firm discloses any ESG information and 0 otherwise. This measure only reflects the presence of ESG disclosure. By contrast, ESGD measures the extent of disclosure, calculated as the natural log of the number of ESG mentions in the annual report. This measure captures how much information firms actually disclose. Separating these two allows us to test whether the effect on bank risk comes simply from the existence of ESG disclosure or from the intensity of that disclosure. Table 5 reports the results of the robustness test using the System GMM method. Both ESG (–3.603, t = − 5.33) and ESGD (–1.474, t = − 10.86) are negative and highly significant, confirming that ESG disclosure, whether measured as presence or extent, reduces non-performing loans (NPLs). This suggests that ESG practices and greater transparency help improve credit quality and lower default risk. Among the control variables, profitability (ROA) and board size (BSIZE) are consistently significant, showing that financial strength and governance structure play an important role. The COVID variable is also positive and significant, reflecting the effect of external shocks on loan quality. The diagnostic tests support the validity of the model. The AR(2) values indicate no second-order autocorrelation, while the Hansen test confirms that the instruments are valid. Overall, these results provide strong evidence that the negative association between ESG, ESGD, and NPLs is robust. Table 5 Robustness test results for System GMM method NPL Coefficient t-stat Coefficient t-stat ESG -3.603*** -5.33 ESGD -1.474*** -10.86 BAGE -0.111* -1.78 -0.016 -1.16 BIND -0.086 -0.21 -0.204 -0.80 ROA 0.001** 2.13 0.009** 1.98 BSIZE -0.162 -1.50 0.086** 2.09 GDP 0.005 0.63 0.000 -0.04 COVID 0.055* 1.73 0.157*** 2.71 _cons 10.730 3.76 2.462 1.00 Observation 267 267 AR(2) test 0.728 0.568 Hansen test 0.432 0.411 Note. This table presents the estimation results of Eq. (1), using the system GMM method. The regression uses credit risk as the dependent variable (Dep. var). Variable definitions are provided in Table 1 . *** p < 0.01, ** p < 0.05, * p < 0.1. These findings are consistent with prior studies that emphasize the role of ESG in reducing financial risk. For example, Gallucci et al. ( 2025 ) show that firms with stronger ESG performance enjoy better credit conditions because investors and creditors perceive them as less risky. Similarly, Atif and Ali ( 2021 ) find that ESG disclosure improves transparency and reduces information asymmetry, which helps lower the probability of default. In the context of emerging markets like Vietnam, this result is particularly relevant. ESG disclosure is still relatively new, and many firms may disclose ESG information mainly to comply with regulations or improve reputation. However, our results suggest that both the presence and the intensity of ESG disclosure matter for credit quality. This indicates that investors and banks in Vietnam are increasingly attentive to ESG factors when assessing firm risk, especially in the face of external shocks such as the COVID-19 pandemic. 5. Conclusion This study investigates the relationship between ESG disclosure and credit risk in Vietnamese commercial banks, using panel data from 34 institutions over the period 2016–2023. By applying both fixed-effects and system GMM estimations, the analysis addresses unobserved heterogeneity and potential endogeneity, producing robust findings. The results confirm that greater ESG disclosure is associated with lower credit risk, suggesting that banks which communicate their ESG practices more transparently are perceived as more stable and trustworthy. The strength of this relationship differs by bank size, reflecting structural features of Vietnam’s banking system. Smaller banks, which typically face higher agency costs, weaker reputations, and limited market visibility, benefit more strongly from ESG disclosure. Agency theory helps explain that creditors are more concerned about opportunistic behavior in small banks due to their shorter track records and fewer monitoring mechanisms, but transparent disclosure reduces such concerns by signaling responsible management. From the perspective of stakeholder and legitimacy theories, ESG reporting enables smaller banks to demonstrate responsiveness to regulators, customers, and local communities, which is especially important in Vietnam where trust in smaller institutions is less established and reputational shocks can have outsized effects. For larger banks, the marginal effect is weaker because these institutions already enjoy established brand reputation, close regulatory oversight from the State Bank of Vietnam, and better access to capital markets. For them, ESG disclosure mainly reinforces existing transparency practices and sustains stakeholder confidence, rather than fundamentally altering perceptions of credit risk. This substitution effect suggests that disclosure matters most where reputation and size cannot provide adequate assurance. The findings also carry practical implications for stakeholders in Vietnam. Bank managers should view ESG disclosure not merely as compliance with emerging sustainability standards but as a strategic mechanism for lowering funding costs and enhancing long-term resilience, especially for smaller banks that depend heavily on depositors’ trust and local lending relationships. Regulators and policymakers can strengthen disclosure by introducing clearer guidelines, harmonizing ESG reporting with international frameworks, and offering targeted support for smaller or rural banks that lack reporting capacity but stand to benefit the most from stronger credibility. For investors and creditors, incorporating ESG factors into credit assessments provides a fuller understanding of bank stability, especially in a market where financial reporting is often less standardized than in developed economies. This study contributes to the growing literature on ESG and financial stability by offering new evidence from Vietnam, an emerging market where ESG frameworks are still developing but where the government is actively promoting green finance and responsible banking. The results underscore that ESG disclosure not only enhances resilience at the bank level but also contributes to System stability in Vietnam’s financial sector, where smaller banks play a critical role in channeling credit to households and small businesses. This study has some limitations. The measure of ESG disclosure is based on textual analysis of annual reports, which may not fully reflect the quality or credibility of ESG practices. Moreover, the focus on commercial banks limits the generalizability of the results. 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Tạp chí Kinh tế và Phát triển (331), 2–11 Wang L, Yang L (2023) Environmental, social and governance performance and credit risk: Moderating effect of corporate life cycle. Pac-Basin Financ J 80:102105 Yang Y, Du Z, Zhang Z, Tong G, Zhou R (2021) Does ESG disclosure affect corporate-bond credit spreads? Evidence from China. Sustainability 13(15):8500 Zhou XY, Caldecott B, Hoepner AG, Wang Y (2022) Bank green lending and credit risk: an empirical analysis of China's Green Credit Policy. Bus Strategy Environ 31(4):1623–1640 Unsectioned Paragraphs Note : Variable definitions are presented in Table 1 Note : This table presented the Pairwise correlations matrix of the main variables. Variable definitions are presented in Table 1 . Note : This table presents the estimation results of Eq. 1, using the fixed-effects method. The regressions use Q as the dependent variable (Dep.var). Variable definitions are presented in Table 1 . *** p < 0.01, ** p < 0.05, * p < 0.1. Note. This table presents the estimation results of Eq. (1), using the system GMM method. The regression uses credit risk as the dependent variable (Dep. var). Variable definitions are provided in Table 1 . *** p < 0.01, ** p < 0.05, * p < 0.1. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8255045","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553759043,"identity":"75da6bde-3f88-4bd8-bac3-b30267d2e861","order_by":0,"name":"Tuan Anh Vu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tuan","middleName":"Anh","lastName":"Vu","suffix":""},{"id":553759044,"identity":"5c36282a-16ef-4144-9311-70f270dca7be","order_by":1,"name":"Thi Nhan 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Introduction","content":"\u003cp\u003eThe disclosure of environmental, social, and governance (ESG) information is now seen as an important part of how firms and financial institutions are judged. Prior studies show that ESG disclosure signals a move toward sustainable growth and can lower financial risks, especially credit risk. For example, Atif and Ali (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that firms with stronger ESG disclosure face lower default risk, while Yang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that ESG reporting reduces corporate bond spreads in China. Wang and Yang (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) point out that the impact of ESG on credit risk can change depending on the stage of the corporate life cycle. Other research also broadens the view, such as Bruno and Henisz (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) note that ESG factors matter for municipal credit risk, and Kiesel and L\u0026uuml;cke (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) show that credit ratings respond to ESG integration in financial markets. In the banking sector, ESG disclosure is especially relevant. Banks play a central role in financial systems, and weak risk management in banks can spread quickly to the wider economy. Zhou et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) find that green lending rules reduce credit risk in Chinese banks. Bannier et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)d ck et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also show that sustainable banking practices improve loan quality and cut financial risk. In Taiwan, Ding et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provide further evidence that ESG performance is closely tied to credit risk and financial distress. These studies show that the connection between ESG and credit risk has become a major focus in global research.\u003c/p\u003e\u003cp\u003eIn many emerging markets, however, ESG disclosure has grown under legal systems that are still changing and not yet complete. Unlike developed economies, where mandatory rules are more common, many emerging markets rely on soft guidance or voluntary reporting (Gallucci et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e). This is consistent with the Vietnamese context. While some listed firms have started reporting on corporate social responsibility (CSR) and ESG, the legal framework is still limited. ESG disclosure is mostly encouraged rather than required. Since 2016, Decree No. 155/2015/NĐ-CP and Circular No. 155/2015/TT-BTC have asked listed firms to report some environmental and social issues in their annual reports. Yet these rules are narrow in scope and place little burden on commercial banks, even though banks are key players in the economy.\u003c/p\u003e\u003cp\u003eSome studies have looked at ESG and banks in Vietnam. Minh et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) analyze the link between ESG disclosure and profitability, while Nguyen B\u0026iacute;ch (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) studies how ESG performance and credit quality are connected in emerging markets. Other work has examined ESG practices in large Vietnamese banks (H\u0026ugrave;ng \u0026amp; Khi\u0026ecirc;m, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e) and how sustainable development affects financial results (Nguyen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Phương et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Still, important gaps remain. First, there is little evidence on how ESG disclosure affects credit risk in Vietnamese banks, even though credit risk is the most serious type of risk for them. Second, most local studies focus on profitability or performance rather than risk. Third, no study has explored whether the effect of ESG disclosure differs between large and small banks, even though bank size may shape both ESG adoption and risk control.\u003c/p\u003e\u003cp\u003eThis study addresses these gaps by examining the link between ESG disclosure and credit risk in Vietnamese commercial banks from 2016 to 2023. It makes three contributions. First, it adds to the global research on ESG and credit risk by providing new evidence from Vietnam, where disclosure rules are still developing. Second, it considers the role of bank size in shaping how ESG disclosure influences credit risk. Third, it offers policy suggestions for improving ESG rules in banking, which can help strengthen financial stability in Vietnam.\u003c/p\u003e\u003cp\u003eThe structure of the paper includes the following Section 2 reviews existing studies, Section 3 details the methodological approach, Section 4 examines and interprets the results, while Section 5 summarizes the conclusions along with key insights and implications.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eWhen judging credit risk, lenders now look not only at financial numbers but also at ESG disclosure, which is becoming more important in their decisions. Prior studies show that transparent reporting helps reduce uncertainty, builds confidence, and lowers the cost of financing. Atif and Ali (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) report that disclosure lowers default risk, while Ding et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) find that ESG performance reduces financial distress in Taiwan. Hajek et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) confirm that ESG data help improve the accuracy of credit ratings. Evidence from bond markets also supports this view, such as Yang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that ESG disclosure reduces bond spreads in China, and Raimo et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that it decreases the cost of debt financing in Europe. Together, these studies suggest that ESG disclosure serves as a positive signal of risk management and long-term stability.\u003c/p\u003e\u003cp\u003eSeveral theories explain why disclosure matters for credit risk. Agency theory emphasizes that managers often have more information than creditors and may hide problems or focus on short-term goals (Jensen \u0026amp; Meckling, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). This lack of information makes it harder for creditors to judge the true risk. By reporting on ESG activities, firms provide clearer insights into their operations, which reduces information asymmetry and lowers perceived risk. Stakeholder theory broadens this view, noting that firms must meet the expectations of employees, customers, regulators, and society at large (Heath \u0026amp; Norman, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). ESG disclosure demonstrates that firms are responsive to these wider groups, which strengthens trust and enhances their reputation with lenders. Legitimacy theory highlights the need for social approval to ensure survival and growth (Deegan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Through ESG disclosure, firms show alignment with social norms and regulations, which reduces the chance of penalties and reputational damage, and ultimately lowers credit risk. This relationship is also influenced by the policy environment. In developed economies, mandatory disclosure rules make information more reliable, while in emerging markets disclosure is often voluntary and less standardized (Gallucci et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tran \u0026amp; Nguyen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nevertheless, studies in China show that the Green Credit Policy strengthens the link between ESG practices and reduced credit risk (Zhou et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bruno and Henisz (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) further show that ESG outcomes affect municipal credit ratings in the United States. These results suggest that ESG disclosure has become an important element in creditors\u0026rsquo; risk assessment across different settings. Importantly, the COVID-19 pandemic also highlighted the role of ESG disclosure in managing financial stability (Adams \u0026amp; Abhayawansa, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The crisis worsened credit quality worldwide by increasing default risk and weakening borrowers\u0026rsquo; repayment capacity. In such conditions, transparent ESG reporting helped banks reassure stakeholders, restore confidence, and demonstrate resilience in the face of systemic shocks. This suggests that ESG disclosure not only matters in normal times but also plays a stabilizing role during periods of heightened uncertainty.\u003c/p\u003e\u003cp\u003eVietnam is beginning to follow this trend. Existing studies examine ESG and profitability (Minh et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), ESG practices in large state-owned banks (H\u0026ugrave;ng \u0026amp; Khi\u0026ecirc;m, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the role of sustainability indicators in firm performance (Phương et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). B\u0026iacute;ch (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) directly studies ESG performance and bank credit quality, while other works consider climate risk in lending Tuyết and Hồng (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) or broader ESG\u0026ndash;financial performance links (Thường et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings suggest that ESG disclosure can improve both financial performance and credit outcomes, even though research on the direct link between disclosure and credit risk in Vietnam is still developing.\u003c/p\u003e\u003cp\u003eBased on this reasoning, we propose the following hypothesis:\u003c/p\u003e\u003cp\u003eH1. ESG disclosure is negatively associated with credit risk in Vietnamese banks.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data\u003c/h2\u003e\u003cp\u003eThe dataset is constructed from 34 Vietnamese commercial banks over the period 2016\u0026ndash;2023. Prior to 2016, there were no clear guidelines or policy initiatives on ESG disclosure in the banking sector, making this timeframe particularly relevant for examining the effects of recent \u003cb\u003esustainability\u003c/b\u003e practices. Financial data were obtained from banks\u0026rsquo; audited annual reports and official publications, while ESG disclosure information was manually collected and coded based on established indicators. After excluding banks with missing values, the final sample consists of an unbalanced panel of 34 banks, yielding 267 bank-year observations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Model and estimation method\u003c/h2\u003e\u003cp\u003eThe regression model is specified as follows:\u003c/p\u003e\u003cp\u003eNPL\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eESG\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003e ESGD\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003eBAGE\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e4\u003c/sub\u003eBIND\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e5\u003c/sub\u003eROA\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e6\u003c/sub\u003eBSIZE\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e7\u003c/sub\u003eGDP\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e8\u003c/sub\u003eCOVID\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit\u003c/sub\u003e \u003cb\u003e(1)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe regression model is designed to examine the effect of ESG disclosure on bank credit risk, measured by the non-performing loans (NPL) ratio (Nguyen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). The dependent variable \u003cb\u003e​\u003c/b\u003e NPL\u003csub\u003eit\u003c/sub\u003e represents the share of non-performing loans for bank \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. ESG disclosure is captured by two measures. First, a dummy variable \u003cb\u003e​\u003c/b\u003e ESG\u003csub\u003eit\u003c/sub\u003e equals 1 if the bank discloses any ESG information in its annual report, and 0 otherwise. Second, the extent of ESG disclosure ESGD\u003csub\u003eit\u003c/sub\u003e is measured as the natural logarithm of the number of ESG-related mentions in the annual report, providing a more detailed indicator of reporting intensity. Several control variables are included. Bank age BAGE\u003csub\u003eit\u003c/sub\u003e is calculated as the number of years since the bank\u0026rsquo;s establishment. Board independence BIND\u003csub\u003eit\u003c/sub\u003e is the proportion of independent board members, reflecting governance quality. Profitability ROA\u003csub\u003eit\u003c/sub\u003e is measured as return on assets, while bank size BSIZE\u003csub\u003eit\u003c/sub\u003e is the natural logarithm of total assets. At the macroeconomic level, GDP\u003csub\u003et\u003c/sub\u003e is measured as the natural logarithm of GDP per capita, obtained from World Bank data. To account for the COVID-19 shock, a dummy variable COVID\u003csub\u003et\u003c/sub\u003e equals 1 for the years 2021\u0026ndash;2022 and 0 otherwise. The control variables are used that based on large literature (Houston et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Richard et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The error term ε\u003csub\u003eit\u003c/sub\u003e captures unobserved factors affecting bank credit risk. The main coefficients of interest are β\u003csub\u003e1\u003c/sub\u003e\u003cb\u003e​\u003c/b\u003e and β\u003csub\u003e2\u003c/sub\u003e\u003cb\u003e​\u003c/b\u003e, which indicate whether ESG disclosure (both existence and extent) is significantly associated with lower non-performing loans, thereby reducing bank credit risk. The definitions and sources of all variables are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Definitions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-performing loans ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual report\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDummy variable, equals 1 if ESG information is disclosed, 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual report\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESGD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExtent of ESG disclosure, measured as ln(number of ESG mentions in annual report)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual report\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBank age, calculated from the year of establishment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual report\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProportion of independent board members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual report\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReturn on Assets ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFinancial statement\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBank size, measured as ln(total assets)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFinancial statement\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNatural log of GDP per capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWorldbank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDummy variable, equals 1 for years 2021\u0026ndash;2022, 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Result","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Descriptive statistic and correlation matrix\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics of the variables used in the analysis. The mean value of the non-performing loans ratio (NPL) is 2.65, with a minimum of 0.10 and a maximum of 5.20, suggesting that Vietnamese banks face a moderate level of credit risk. Compared with developed countries, where banks usually have lower NPL ratios, this result shows that Vietnamese banks still face higher credit risk, similar to what is often seen in other emerging markets (Kuzucu \u0026amp; Kuzucu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The dummy variable for ESG disclosure (ESG) has a mean of 0.82, indicating that the majority of banks report some ESG information in their annual reports. The extent of ESG disclosure (ESGD) has an average of 1.36, showing variation across banks in how much detail they provide. Regarding bank characteristics, the average bank age (BAGE) is about 5.54 years, with a wide range from new entrants to banks established more than a decade ago. The proportion of independent board members (BIND) averages 0.25, meaning that about one-fourth of board members are independent. Bank profitability, measured by ROA, has a mean of 5.37, but also shows high variation, reflecting differences in performance across banks. The average bank size (BSIZE), measured as the logarithm of total assets, is 28.08, indicating that the sample mostly consists of medium to large banks. At the macroeconomic level, the average GDP per capita is 8.60 (in natural log), reflecting Vietnam\u0026rsquo;s status as a developing economy. The COVID variable has a mean of 0.25, meaning that around one-fourth of the observations fall within the pandemic years 2021\u0026ndash;2022. Overall, the descriptive statistics indicate that while ESG disclosure is relatively common among Vietnamese banks, there remains variation in the extent of disclosure and in financial performance, with moderate levels of credit risk observed during the study period.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistic\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESGD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-20.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eVariable definitions are presented in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the pairwise correlations between the main variables. The non-performing loans ratio (NPL) is strongly and negatively correlated with both ESG disclosure (ESG, -0.665) and the extent of ESG disclosure (ESGD, -0.895), suggesting that greater ESG transparency is associated with lower credit risk. Among the control variables, bank profitability (ROA) shows a positive correlation with GDP (0.294), meaning that stronger macroeconomic conditions support better bank performance. Bank size (BSIZE) is weakly and negatively correlated with board independence (BIND, -0.152), while other correlations remain small in magnitude. The highest correlation in the matrix is between NPL and ESGD (-0.895). Although this value is relatively high, most other correlations are well below the conventional 0.80 threshold, indicating that multicollinearity is unlikely to be a serious concern in the regression analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation matrix Pairwise correlations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(7)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(8)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(9)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(1) NPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(2) ESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(3) ESGD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(4) BAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(5) BIND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(6) ROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(7) BSIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(8) GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(9) COVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eThis table presented the Pairwise correlations matrix of the main variables. Variable definitions are presented in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Main results\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the fixed-effect estimation results on the relationship between ESG disclosure and credit risk. The coefficient of ESG is negative and strongly significant (-2.870, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), showing that banks disclosing ESG information have lower non-performing loan (NPL) ratios. Likewise, the coefficient of ESGD is also negative and highly significant (-1.299, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that the greater the extent of ESG disclosure, the lower the level of credit risk. Together, these results confirm hypothesis H1, highlighting that ESG disclosure acts as a credible signal to creditors by reducing information asymmetry and increasing confidence in banks\u0026rsquo; stability. These findings are in line with international evidence. Ding et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) show that ESG performance lowers financial distress among Taiwanese firms, while Hajek et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrate that ESG information improves the accuracy of credit ratings. Similarly, Atif and Ali (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) report that ESG disclosure reduces default risk, and Yang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that stronger ESG transparency decreases bond spreads in China. Raimo et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also confirm that ESG disclosure lowers the cost of debt in European markets. Together, these studies support the view that transparent ESG reporting enhances creditors\u0026rsquo; trust and reduces financing costs. Evidence from policy-driven contexts further reinforces this argument. Zhou et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) show that China\u0026rsquo;s Green Credit Policy strengthens the link between ESG practices and reduced credit risk, while Bruno and Henisz (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight how ESG outcomes affect municipal credit ratings in the United States. These results imply that ESG disclosure not only improves access to finance but also plays a role in broader financial stability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFixed-effect results for the impact of ESG disclosure on credit risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-stat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-stat\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.870***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-13.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESGD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.299***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-37.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.019**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.337**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.223**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.205***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.481***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.432***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eThis table presents the estimation results of Eq.\u0026nbsp;1, using the fixed-effects method. The regressions use Q as the dependent variable (Dep.var). Variable definitions are presented in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Vietnam, research has only recently begun to explore this relationship. B\u0026iacute;ch (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) directly examines ESG performance and bank credit quality, finding results consistent with the negative link between ESG and credit risk. Other studies, such as Minh et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)d ng and Khi\u0026ecirc;m (2024), highlight that ESG practices in Vietnamese banks are still evolving, with disclosure often less standardized than in developed economies. The strong and significant coefficients found in this study provide new empirical evidence for Vietnam, confirming that ESG disclosure is an important factor in reducing credit risk, in line with global findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Robustness test\u003c/h2\u003e\u003cp\u003eRobustness tests are necessary to ensure that the results are not affected by endogeneity or other hidden biases. In the relationship between ESG and non-performing loans (NPLs), it is possible that ESG performance is influenced by financial outcomes or other unobserved firm characteristics. If this problem exists, the estimated results may not be reliable. To address this concern, we apply the System GMM method, which helps control for endogeneity and improve the reliability of the estimates. In addition, we separate ESG into two measures between ESG and ESGD. The reason is that the way ESG is measured can capture different aspects. ESG is a dummy variable, which equals 1 if a firm discloses any ESG information and 0 otherwise. This measure only reflects the presence of ESG disclosure. By contrast, ESGD measures the extent of disclosure, calculated as the natural log of the number of ESG mentions in the annual report. This measure captures how much information firms actually disclose. Separating these two allows us to test whether the effect on bank risk comes simply from the existence of ESG disclosure or from the intensity of that disclosure.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the results of the robustness test using the System GMM method. Both ESG (\u0026ndash;3.603, t = \u0026minus;\u0026thinsp;5.33) and ESGD (\u0026ndash;1.474, t = \u0026minus;\u0026thinsp;10.86) are negative and highly significant, confirming that ESG disclosure, whether measured as presence or extent, reduces non-performing loans (NPLs). This suggests that ESG practices and greater \u003cb\u003etransparency\u003c/b\u003e help improve credit quality and lower default risk. Among the control variables, profitability (ROA) and board size (BSIZE) are consistently significant, showing that financial strength and governance structure play an important role. The COVID variable is also positive and significant, reflecting the effect of external shocks on loan quality. The diagnostic tests support the validity of the model. The AR(2) values indicate no second-order autocorrelation, while the Hansen test confirms that the instruments are valid. Overall, these results provide strong evidence that the negative association between ESG, ESGD, and NPLs is robust.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness test results for System GMM method\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-stat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-stat\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.603***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESGD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.474***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-10.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.111*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.086**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.055*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.157***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR(2) test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHansen test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. This table presents the estimation results of Eq.\u0026nbsp;(1), using the system GMM method. The regression uses credit risk as the dependent variable (Dep. var). Variable definitions are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese findings are consistent with prior studies that emphasize the role of ESG in reducing financial risk. For example, Gallucci et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) show that firms with stronger ESG performance enjoy better credit conditions because investors and creditors perceive them as less risky. Similarly, Atif and Ali (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) find that ESG disclosure improves transparency and reduces information asymmetry, which helps lower the probability of default. In the context of emerging markets like Vietnam, this result is particularly relevant. ESG disclosure is still relatively new, and many firms may disclose ESG information mainly to comply with regulations or improve reputation. However, our results suggest that both the presence and the intensity of ESG disclosure matter for credit quality. This indicates that investors and banks in Vietnam are increasingly attentive to ESG factors when assessing firm risk, especially in the face of external shocks such as the COVID-19 pandemic.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study investigates the relationship between ESG disclosure and credit risk in Vietnamese commercial banks, using panel data from 34 institutions over the period 2016\u0026ndash;2023. By applying both fixed-effects and system GMM estimations, the analysis addresses unobserved heterogeneity and potential endogeneity, producing robust findings. The results confirm that greater ESG disclosure is associated with lower credit risk, suggesting that banks which communicate their ESG practices more transparently are perceived as more stable and trustworthy. The strength of this relationship differs by bank size, reflecting structural features of Vietnam\u0026rsquo;s banking system. Smaller banks, which typically face higher agency costs, weaker reputations, and limited market visibility, benefit more strongly from ESG disclosure. Agency theory helps explain that creditors are more concerned about opportunistic behavior in small banks due to their shorter track records and fewer monitoring mechanisms, but transparent disclosure reduces such concerns by signaling responsible management. From the perspective of stakeholder and legitimacy theories, ESG reporting enables smaller banks to demonstrate responsiveness to regulators, customers, and local communities, which is especially important in Vietnam where trust in smaller institutions is less established and reputational shocks can have outsized effects. For larger banks, the marginal effect is weaker because these institutions already enjoy established brand reputation, close regulatory oversight from the State Bank of Vietnam, and better access to capital markets. For them, ESG disclosure mainly reinforces existing transparency practices and sustains stakeholder confidence, rather than fundamentally altering perceptions of credit risk. This substitution effect suggests that disclosure matters most where reputation and size cannot provide adequate assurance.\u003c/p\u003e\u003cp\u003eThe findings also carry practical implications for stakeholders in Vietnam. Bank managers should view ESG disclosure not merely as compliance with emerging sustainability standards but as a strategic mechanism for lowering funding costs and enhancing long-term resilience, especially for smaller banks that depend heavily on depositors\u0026rsquo; trust and local lending relationships. Regulators and policymakers can strengthen disclosure by introducing clearer guidelines, harmonizing ESG reporting with international frameworks, and offering targeted support for smaller or rural banks that lack reporting capacity but stand to benefit the most from stronger credibility. For investors and creditors, incorporating ESG factors into credit assessments provides a fuller understanding of bank stability, especially in a market where financial reporting is often less standardized than in developed economies. This study contributes to the growing literature on ESG and financial stability by offering new evidence from Vietnam, an emerging market where ESG frameworks are still developing but where the government is actively promoting green finance and responsible banking. The results underscore that ESG disclosure not only enhances resilience at the bank level but also contributes to System stability in Vietnam\u0026rsquo;s financial sector, where smaller banks play a critical role in channeling credit to households and small businesses.\u003c/p\u003e\u003cp\u003eThis study has some limitations. The measure of ESG disclosure is based on textual analysis of annual reports, which may not fully reflect the quality or credibility of ESG practices. Moreover, the focus on commercial banks limits the generalizability of the results. Future research could extend the scope to include non-bank financial institutions, conduct comparative studies with other emerging markets, or assess the role of third-party assurance in strengthening ESG disclosure credibility in Vietnam.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams CA, Abhayawansa S (2022) Connecting the COVID-19 pandemic, environmental, social and governance (ESG) investing and calls for \u0026lsquo;harmonisation\u0026rsquo;of sustainability reporting. Crit Perspect Acc 82:102309\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtif M, Ali S (2021) Environmental, social and governance disclosure and default risk. Bus Strategy Environ 30(8):3937\u0026ndash;3959\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBannier CE, Bofinger Y, Rock B (2022) Corporate social responsibility and credit risk. 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Pac-Basin Financ J 80:102105\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Y, Du Z, Zhang Z, Tong G, Zhou R (2021) Does ESG disclosure affect corporate-bond credit spreads? Evidence from China. Sustainability 13(15):8500\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou XY, Caldecott B, Hoepner AG, Wang Y (2022) Bank green lending and credit risk: an empirical analysis of China's Green Credit Policy. Bus Strategy Environ 31(4):1623\u0026ndash;1640\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Unsectioned Paragraphs","content":"\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eVariable definitions are presented in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eThis table presented the Pairwise correlations matrix of the main variables. Variable definitions are presented in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eThis table presents the estimation results of Eq.\u0026nbsp;1, using the fixed-effects method. The regressions use Q as the dependent variable (Dep.var). Variable definitions are presented in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eNote. This table presents the estimation results of Eq.\u0026nbsp;(1), using the system GMM method. The regression uses credit risk as the dependent variable (Dep. var). Variable definitions are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ESG disclosure, Credit risk, Commercial banks, Financial stability","lastPublishedDoi":"10.21203/rs.3.rs-8255045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8255045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the relationship between environmental, social, and governance (ESG) disclosure and credit risk in Vietnamese commercial banks. Using a panel dataset of 34 banks from 2016 to 2023, we employ both fixed-effects and system GMM estimations to address unobserved heterogeneity and potential endogeneity concerns. The findings indicate that greater ESG disclosure is associated with lower levels of credit risk. However, the magnitude of this effect varies by bank size. Specifically, the impact of ESG disclosure is more pronounced among smaller banks compared to their larger counterparts. These results highlight the importance of ESG practices in strengthening financial stability, particularly for banks with limited scale.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eJEL code:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e G21, G32, Q56\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"The relationship between ESG disclosure and credit risk: Evidence from Vietnamese commercial banks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 07:27:34","doi":"10.21203/rs.3.rs-8255045/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39f3927f-7902-46ef-bf9b-14ca465661ed","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T07:27:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 07:27:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8255045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8255045","identity":"rs-8255045","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}