Impact of ESG practices on European Bank Lending for Sustainability: The Role of Culture and Institutions

Impact of ESG practices on European Bank Lending for Sustainability: The Role of Culture and Institutions | 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 Impact of ESG practices on European Bank Lending for Sustainability: The Role of Culture and Institutions Mohsin Shabir, Özcan Işık, Shujahat Haider Hashmi, Ghulam Mujtaba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4343842/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 impact of Environmental, Social and Governance (ESG) practices on bank lending in the European Union banking sector, using a sample of 53 banks from 19 European Union countries for the period 2004–2018. Our findings show that ESG activities play a significant role in increasing bank lending. Interestingly, the ESG pillars follow a different pattern. Environmental and governance-friendly activities impact bank lending more than social. Moreover, the national culture and institutional quality significantly affect the ESG activity and bank lending nexus. Our outcomes demonstrate that national cultural dimensions significantly impact bank lending and mediate the relationship between ESG activity and bank lending. In comparison, good quality institutional supports banks in increasing their lending capacity and enhancing the ESG practice's constructive impact. The empirical findings are quite robust to using the various ESG dimensions, alternative estimation techniques, and control for the regulatory environment and macroeconomic conditions. JEL Classification: G20, G21 Finance Environmental Economics ESG Bank Lending National culture Institutional quality European banks 1. Introduction Over the decades, environmental activists' criticisms and increasing awareness of environmental and social issues have forced firms to involve in environmentally and socially responsible performance (Shahbaz et al., 2020 ). Especially in the face of issues like the 2008 Financial Crisis, COVID-19, international conflicts, and global warming, protecting the environment and respecting human rights interests are gradually affecting modern societies, leading to key societal changes (Chen and Xie, 2022 ; Garel and Petit-Romec, 2021 ; Nirino et al., 2021 ). To respond to society's evolving demands, firms are increasingly considering how they can reduce their negative impact on the environment and community (Nirino et al., 2021 ). Therefore, firms are looking for a more ethical and sustainable way to do business in the long term. This leads to the environmental social and governance (hereafter ESG) 1 practices being the focus of corporate strategies worldwide and researchers' and regulators' debate in recent years (Chen and Xie, 2022 ). Primarily in the banking sector, which has played a growing role in promoting a culture of environmental and social responsibility and becoming more conscious of the need to include ESG considerations into strategy, procedures, and financial instruments for long and midterm goals (Azmi et al., 2021 ). However a few researchers have recently examined the impact of ESG practices on a firm's performance, and value creation in non-financial is significantly inconsistent. As shown that ESG practices increase firm competitiveness, enhance their performance, support their reputation in the eyes of stakeholders, decrease systematic risk, and improve company values (Broadstock et al., 2020 ; Chen and Xie, 2022 ; Del Giudice et al., 2017 ; Landi and Sciarelli, 2019 ; Nirino et al., 2021 ). While on the other hand, few researchers argued that ESG practices might result in capital inefficiency, which leads to opportunity costs and adverse relationships with financial performance (Oikonomou et al., 2012 ; Santis et al., 2016 ). However, regardless of the valuable literature on the impact of ESG on non-financial firms, whether ESG activities influence financial organizations, particularly banks, has received the least attention. The 2008 crisis decreased public trust in the financial sector and highlighted the negligence of similar banks and regulatory authorities. Although few researchers have examined the effect of banks' ESG on bank profitability and stability, they also report mixed findings (Anginer et al., 2018 ; Berger et al., 2016 ; Chiaramonte et al., 2021 ; Di Tommaso and Thornton, 2020 ). This suggests that a deeper understanding of the circumstances under which ESG activities are more probable to increase bank lending is required. However, there are two main theoretical approaches to describe the connection between ESG and firm value and stability (i) Stakeholder theory (i.e., risk mitigation view) states that increased investments in ESG benefit all stakeholders and have the potential to generate moral capital or goodwill, provides a competitive advantage, fosters long-term value creation, improves portfolio quality, strengthens stakeholder relationships, reducing asymmetric information, ensures regulatory compliance, decreasing the cost of capital, enhances risk assessment, and reducing operational, environmental and social risks (Chiaramonte et al., 2021 ; El Ghoul and Karoui, 2017 ; Godfrey et al., 2009 ; Luo and Bhattacharya, 2006 ) (ii) Managerial opportunism theory suggests that managers overinvest in ESG to take advantage of agency theory, which acts as a waste of resources, reducing wastes. Moreover, several factors, such as national culture and institutional quality, can impact the relationship between ESG and bank credit growth. National culture encompasses the shared values, beliefs, norms, and practices within a country, and these cultural factors can shape the integration of ESG factors in lending practices. In cultures that place a high value on environmental sustainability, social responsibility, and corporate governance, banks are more likely to prioritize ESG factors in their lending decisions. This can increase credit allocation to projects that align with ESG principles, such as renewable energy initiatives or socially responsible businesses. Moreover, national culture also shapes the risk appetite of individuals and institutions within a country. Some cultures may exhibit a higher tolerance for risk-taking, while others may be more risk-averse. This cultural predisposition can influence investment behavior, lending practices, and bank risk management strategies. Besides this, institutional quality plays a crucial role in shaping the impact of ESG practices on bank credit growth. Institutional quality refers to a country's institutions' effectiveness, transparency, and stability, including regulatory bodies, legal frameworks, and governance mechanisms. Strong institutional quality promotes trust, transparency, and accountability, which are crucial for effectively integrating ESG practices into bank lending strategies. A robust regulatory framework and clear guidelines facilitate the implementation of ESG criteria, ensuring that banks align their lending practices with sustainable objectives. Institutions that enforce ESG standards and provide reliable information can enhance the credibility and effectiveness of ESG practices in the banking sector. This study examined the effect of ESG activities on bank lending in the European Union banking sector. Because European countries are the leading countries that advocate sustainable development (Chiaramonte et al., 2021 ). Besides, deregulation, globalization, technological change, and European integration have structurally reshaped the banking sector in the last two decades (Goddard et al., 2007 ). Additionally, the banking sector has close-fitting profit margins and is highly monitored and organized. Therefore, ESG practices can widen the competitors' financial performance differences, whereas the agency costs related to ESG activity might influence this nexus (Azmi et al., 2021 ). Therefore, we were limiting our sample to the E.U. banking sector to minimize internal and external variation that forces banks to engage in ESG practices (Azmi et al., 2021 ; Simpson and Kohers, 2002 ). To examine ESG activity's effect on bank lending using a sample of 53 European commercial banks from 19 countries from 2004 to 2018. We used the panel fixed-effects model in our baseline estimations, while two stages least square regression (2SLS) and generalized methods of moments (GMM) were applied to control for endogeneity. Our findings show that ESG activities play a significant role in the European Union banking sector by significantly increasing bank lending. The importance of these dimensions is not surprising as awareness of environmental and good governance matters has become the focus of attention of all stakeholders in the last decade. Stakeholders and investors gradually identify the impact of global warming and climate change and understand that banks have a significant role in funding environmentally conscious projects and reducing financing for dirty industries. Moreover, the national culture and institutional quality significantly affect the ESG activity and bank lending nexus. Our outcomes show that national cultural dimensions significantly impact bank lending and mediate the relationship between ESG activity and bank lending. In comparison, good quality institutional supports banks in increasing their lending capacity and enhancing the ESG practice's constructive impact. The empirical findings are quite robust to using the various ESG dimensions, alternative estimation techniques, and control for the regulatory environment and macroeconomic conditions. This study contributes to this stream of literature in the following ways: First, despite the few studies examining the connection between ESG and bank risk and lending, this is the first to examine the ESG activity in the European commercial banking sector and bank lending. The focus on Europe is particularly appropriate countries have advanced sustainability practices, with European union banks leading the way compared to those headquartered elsewhere (Chiaramonte et al., 202). Moreover, from the regulatory perspective, significant actions have been taken to increase the disclosure of diversity and non-financial information (Chiaramonte et al., 2021 ). In 2014, the European Union approved the Non-Financial Reporting Directive (2014/95/E.U.), requiring larger corporations and banks to reveal how they operate and address social and environmental problems. The European Banking Authority (EBA) also issued a discussion paper on the management and supervision of ESG risks in October 2020 for credit institutions and investment companies(Chiaramonte et al., 2021 ; European Banking Authority, 2021 ). Recently the European Union has strongly committed to intensifying diligence to address these problems (Chiaramonte et al., 2021 ). Second, we analyzed whether the effect of ESG activity on bank lending depends on the underlying institutional quality. Institutional theory shows that the countries' institutional environment leads the activities of economic actors and countries' economic performances (Çam and Özer, 2021 ). A strong institutional framework increases the effectiveness of the accounting standards and judicial system, enhances financial development, improves corporate governance and transparency, increases bank stability, and promotes and strengthens the flexibility of bank funding (Acemoglu and Johnson, 2005 ; Doidge et al., 2007 ; La Porta et al., 1998 ; Nguyen, 2022 ; Shabir et al., 2021 ). In contrast, it decreases the risk of expropriation and repudiation of contracts, agency problems, asymmetric information issues, and the cost of debt and equity(Álvarez-Botas and González, 2021 ; Alves and Ferreira, 2011 ; La Porta et al., 1998 ). Thus, these studies have emphasized the importance of countries' legal and institutional quality and banking structures when making lending decisions. Therefore, in this context, it objects to studying the effect of institutional quality on ESG activity and bank lending nexus. Third, we add to the increasing body of knowledge about the role of national culture in ESG activity and bank lending decisions. Diverse cultural systems have made diverse assumptions regarding society, business, and the environment. Earlier studies have shown that national culture not only affects corporate decisions, such as capital structure, dividend policy, cash holdings, financial reporting, corporate innovation, R&D investments, bank risk-taking, and mergers and acquisitions (Ahern et al., 2015 ; Ashraf et al., 2016; Boubakri et al., 2021 ; Chang et al., 2019 ; Choi, 2019 ; Li et al., 2013 ) but also impact the country's financial systems and its global investing approaches (Hooghiemstra et al., 2015 ; Chui et al., 2010 ). Few researchers have shown that environmental attitudes and behaviors are deeply rooted in personal human values (Soyez, 2012 ). Crossland and Hambrick ( 2011 ) reported that diverse cultural attributes directly impact managerial preference, which they conceptualized as the latitude of managerial action. Waldman et al. ( 2006 ) have shown that directors in cultural environments that differ in institutional power distance and collectivity made diverse CSR decisions. Cheung et al. ( 2020 ) and Jackson and Apostolakou ( 2009 ) argued the differences in national-level institutions affect varying degrees of pressure and incentives for corporations to engage in CSR initiatives. Buhr and Freedman ( 2001 ) have shown that, compared to individualist U.S. society, collectivist Canadian society involves higher environmental disclosure. Therefore, considering this evidence, this study enhances the literature on the critical role of national culture in banking by investigating whether national culture moderates the relationship between ESG activities and bank lending. The remainder of this paper is organized as follows. Section 2 shows a detailed literature review. Section 3 explains the sample selection, variable measures, and empirical models. Section 4 describes the empirical results and discussion. Section 5 presents the results of a series of robustness tests, while Section 6 presents conclusions. 2. Literature Review Various researchers have recently investigated the impacts of ESG practices on firm performance and value (Azmi et al., 2021 ). However, results have been inconsistent partly due to data constraints, model misspecification, and measurement concerns (Azmi et al., 2021 ). Trade-off and stakeholder theories are the two main streams theories that predict the relationship between ESG practices and firm performance and value. However, both theories make conflicting predictions while supported by empirical evidence. Agency theory suggests a similar outcome to those of trade-off theory. In contrast, stewardship and resource-based theories provide results like stakeholder theory. All five approaches are described below. Agency theory states that the manager is involved in ESG practices to fulfill their desires (Azmi et al., 2021 ), which is usually true for inappropriately incentivized managers (Jensen & Meckling, 1976 ). Publicity and media awareness of ESG practices can support managers to increase their reputations and benefit personally from the firm's expenditure.Jiraporn and Chintrakarn ( 2013 ) show that less strengthened CEOs are more likely to intensify ESG practices than other CEOs because of the personal and reputational advantage of ESG activity (Azmi et al., 2021 ). Therefore, Agency theory suggests that ESG integration can help mitigate agency conflicts and align the interests of managers and shareholders. By incorporating ESG factors into decision-making processes, banks can establish guidelines and performance metrics that promote responsible behavior and long-term value creation. This can improve bank performance and risk management by reducing potential conflicts between short-term profit maximization and long-term sustainability objectives. According to the trade-off theory, implementing ESG practices may involve expenses such as conducting environmental assessments, adopting sustainable technologies, or implementing social welfare programs. These costs can temporarily reduce a bank's profitability, which could be perceived as a trade-off between financial returns and ESG commitments in the short run.Galant and Cadez ( 2017 ) argued that expanding resources to meet social and environmental goals increases costs and undermines profitability and competitive advantage. Devinney ( 2009 ) points out that while firms can make more "virtuous," virtue always comes at a cost. Hence,Aupperle et al. ( 2017 ) indicate that socially sensible companies experience higher costs and lower profitability compared to socially unconscious businesses. However, the theory argues that, over time, adopting ESG practices can contribute to various long-term benefits that positively impact bank performance and risk management. These benefits include enhanced brand reputation, improved stakeholder relationships, reduced regulatory risks, increased customer loyalty, and access to sustainable funding sources. By integrating ESG practices, banks can mitigate environmental and social risks, strengthen governance structures, and align their activities with societal expectations. These factors can lead to improved financial performance, reduced operational risks, and enhanced long-term sustainability. Stakeholder theory argues that a firm should create value for all stakeholders and develop a practical, well-organized, efficient, and ethical approach to guiding firms in extremely complex and turbulent situations. Azmi et al. ( 2021 ) have shown evidence that firms involved in ESG practices indirectly express their consent to meet stakeholder demands and abstain from the costs associated with tight compliance with formal contractual agreements. Porter and Kramer ( 2017 ) have shown evidence that ESG practices are a source of economic and competitive advantage and business innovation instead of an expense or charitable act. Stewardship theory is a framework that argues managers, as stewards of the company dedicated to enhancing the company's long-term value by addressing the competing fascination of all stakeholders (Azmi et al., 2021 ). Stewardship theory suggests banks should embrace their role as stewards of societal and environmental well-being. By integrating ESG practices into their strategies and operations, banks can demonstrate their commitment to responsible stewardship, leading to improved financial performance, and create long-term value, mitigating risks, achieve sustainable financial performance and stakeholder trust. The resource-based theory considers ESG practices to be a strategic investment (Azmi et al., 2021 ). This investment can help companies gain a competitive advantage by acquiring new resources and skills that would otherwise be challenging, leading to improved financial performance(Ruf et al., 2001 ; Russo and Fouts, 2017 ). By integrating ESG considerations into their operations, banks can develop unique capabilities and build a positive reputation as responsible and sustainable institutions. These ESG-related resources can enhance a bank's performance and risk management. Institutional theory suggests that the institutional context significantly shapes banks' adoption and implementation of ESG practices. By conforming to prevailing norms and meeting regulatory requirements, banks can enhance their legitimacy, manage risks, gain a competitive advantage, and access valuable resources. Corporate Governance Theory suggests that strong governance mechanisms, including board oversight, accountability, transparency, shareholder engagement, and risk management practices, are essential for promoting the integration of ESG considerations within banks. By embracing ESG practices, banks can enhance their reputation, attract responsible investors, manage risks, and achieve sustainable financial performance. Moreover, effective corporate governance supports the alignment of ESG considerations with a bank's strategic objectives, ensuring responsible behavior and long-term value creation. The Sustainable Banking Theory suggests that banks that embrace ESG practices can achieve a balance between financial performance, social responsibility, and environmental stewardship. By integrating sustainability principles into their strategies, risk management practices, and product offerings, banks can enhance their reputation, identify new business opportunities, mitigate risks, and achieve long-term financial success while contributing to a more sustainable economy and society. The impact of ESG activity on firm financial performance and value has been widely examined from theoretical and empirical views and reported mixed results. It has shown positive, negative, and neutral effects, while others indicate a U-shaped relationship. The positive relationship of ESG activity with firm financial performance and value proposed that higher CSR gives firms a competitive edge by mitigating the risk, improving the credit rating, enhancing financial strength, cashing their reputation to obtain financing at lower borrowing costs, and increasing profitability (Cavaco and Crifo, 2014 ; Pornsit Jiraporn et al., 2014 ; Sheikh, 2019 ; Sun and Cui, 2014 ). In contrast, the negative relationship aligns with the trade-off theory, suggesting that ESG increases costs (Baird et al., 2012 ; Gilley et al., 2016 ; Servaes and Tamayo, 2013 ; Surroca et al., 2010 ). The neutral association recommended that being socially responsible does not impact firm profitability and value because the positive effects offset the negative effects (Baird et al., 2012 ; Gilley et al., 2016 ; Servaes and Tamayo, 2013 ; Surroca et al., 2010 ). Brammer and Millington (2008) show a U-shaped connection between ESG activity and financial performance, which implies that this relationship depends on ESG activity investment level. In the early stage, ESG practices negatively affect financial performance as the costs outweigh the benefits, while the relationship reverses and becomes positive later. Banking sector-related studies on ESG activity have primarily been concerned with financial performance. They usually report a significant effect on profitability, which is anticipated to increase bank value (Brogi & Lagasio, 2019 ; Di Tommaso & Thornton, 2020 ; Shen et al., 2016 ). Gangi et al. ( 2020 ) environmentally friendly banks experience lower degrees of risk of insolvency. Neitzert and Petras ( 2022 ) argued that ESG practices decrease default and portfolio risk. Siueia et al. ( 2019 ) and Wu and Shen ( 2013 ) reported that bank CSR helps improve financial performance and reduces banks' non-performing loans. A recent study by Azmi et al. ( 2021 ) shows a non-linear nexus among ESG activity and bank value. At the same time, environmentally friendly activities affect the bank's value the most. 3. Data and Methodology 3.1. Data The study aims to examine the impact of ESG activity and bank lending strategy and, more intensely, determine the role of national culture and institutional quality. The sample comprises 53 banks from 19 European Union member countries from 2005–2018 2 3 . We collected ESG-related data from the Thomson Reuters ASSET4 database, while the bank-level financial and accounting data from the Bankscope database. However, the data related to country-specific controls, such as GDP per capita, inflation, and dummy crisis taken from World Bank GFDD. The series of the institutional quality index (Political Stability, Rule of Law, Control of Corruption, Government Effectiveness, and Regulatory Quality) and national culture is attained from the Worldwide Governance Indicators (WGI) 4 and Hofstede Insights 5 . Appendix Table A presents a detailed explanation of the studied variables. 3.2. Measurement of variables 3.2.1 Dependent variable We followed the existing studies of Shabir et al. ( 2022 ), Hu and Gong ( 2019 ), and Ҫolak and Öztekin ( 2021 ). We used the natural logarithm of the annual growth rate of loans as the primary dependent variable for measuring loan activity, which is widely used in the existing banking literature (Shabir et al., 2022 ). 3.2.2 Independent variable Our main explanatory variable is Environmental, social, and governance (ESG) obtained from the Thomson Reuters ASSET4 database. The database comprises 35 million individual indicators across all key asset classes, together with 8.5 million economic indicators. Since 2002, this database has measured a company's environmental, social, and governance performance and dedication. These scores are based on publicly available company-level data, such as yearly financial and corporate social responsibility reports and news media coverage. The scores are classified into ten groups., forming three pillars Environmental pillar (ENV), the social pillar (SOC), and the governance pillar (GOV), and collective ESG scores. This study has chosen the ESG combined score (ESG) as the main explanatory variable ranging from 1 to 100. A high ESG score indicates superior performance. Moreover, we also use the three pillars ENV, SOC, and GOV for robustness. Recently researchers have extensively used the ESG score of Refinitiv Eikon in their studies, such as (Bae et al., 2021 ; Bătae et al., 2021 ; Gaies and Jahmane, 2022 ; Shakil, 2021 ) among them. 3.2.3. Measuring national culture In this study, we followed the existing literature of Ashraf et al. (2016). Boubakri et al. ( 2017 ), Li et al. ( 2013 ), and Zheng et al. ( 2012 ), and used the four cultural dimensions, namely, uncertainty avoidance (UAI), power distance (PDI), individualism/ collectivism (CLT), and masculinity/femininity (MAS), to capture the national cultural dimensions of each country. Although the literature has raised criticisms of Hofstede's scores over time, this has not reduced their demand and applicability (Guermazi and Halioui, 2020 ).Hooghiemstra et al. ( 2015 ) show that Hofstede's scores are "the most widely used measures of national culture and have produced a widely accepted, well-defined, empirically based terminology to characterize culture." 3.2.4 Institutional environments A growing body of literature indicates that the quality of institutions substantially impacts the financial industry's functioning and performance in different countries (Bai and Ho, 2022 ; Nguyen, 2022 ; Shabir et al., 2022 ). Bermpei et al. ( 2018 ) argued that stronger institutional quality improves regulators' enforcement capacity, thereby increasing the effect of bank regulation and supervision on bank stability. Giannetti ( 2003 ) argued that better institutions decrease information asymmetry and reduce agency problems. In this study, we followed the existing literature (Bermpei et al., 2018 ; Shabir et al., 2022 ) and used the Worldwide Governance Indicators (WGI) such as political stability, government effectiveness, regulatory quality, control of corruption, and the rule of law to capture the institutional quality environment of each country. 3.2.5 Bank and Country-specific variables We follow the existing literature on bank lending and credit growth (Nguyen et al., 2020; Shabir et al., 2022 ; Ҫolak and Öztekin, 2021 ) and contain two types of control variables (bank-specific and country-specific) which potentially explain bank lending behavior and could affect the ESG activity and bank lending relationship. Bank-specific control variables are capitalization (CAP), calculated as the ratio of capital to assets; bank size (SIZ), measured as the natural logarithm of total assets; liquidity (LIQ) is estimated as the ratio of liquid assets to total assets, the net loan to total assets determines loan share (LTA), credit risk (NPL) is measured as non-performing loans, and bank efficiency (EFF) determined as the cost to income ratio. While the two country-specific control variables are GDP per capita (GDPpc) and inflation (INF) to account for the macroeconomic environment because the countries are diverse in economic and financial development, technological capacity, and geography. We also include a dummy variable for bank crisis which equals 1 for 2007–2012, which captures the global crisis of 2007–2009 and the sovereign debt crisis of 2010–2012 and zero otherwise. 3.3. Empirical framework To explore the impact of ESG activity and bank lending strategy in this study, we used the following baseline regression model, which was built based on existing literature in banking and finance (Hu and Gong, 2019 ; Nguyen et al., 2020; Shabir et al., 2022 ; Ҫolak and Öztekin, 2021 ) $${LND}_{\text{i},\text{t}}= {{\alpha }}_{\text{i}}+ {{{\beta }}_{1}\text{E}\text{S}\text{G}}_{\text{i},\text{t}}+{{\gamma }\text{i} \text{X}}_{\text{i},\text{t}}+{{\delta }}_{\text{k}}{\text{Z}}_{\text{j},\text{t}}+{{{\theta }}_{1}\text{C}\text{R}\text{S}}_{,\text{t}}+{\text{ʎ}}_{\text{t}}+ {{\epsilon }}_{\text{i},\text{t}} \left(1\right)$$ where i, j, and t represent bank, country, and year. LND is our dependent variable which represents bank lending. \({ESG}_{it}\) is our primary independent variable, which indicates the overall bank-level ESG scores. \({X}_{it}\) is a vector of our bank-related control variables, including the capital ( CAP ), credit risk (NPL), bank efficiency (EFF), liquidity ( LIQ ), size ( SIZ ), and loan share (LTA); \({Z}_{jt}\) is a vector of country-related control variables, including GDP per capita (GDPpc) and inflation (INF); CRS is a crisis dummy. α, β , \({\theta },\) δ, and \({\gamma }\) are the estimated coefficients of interest. \({{\alpha }}_{\text{i}}\) and \({\text{ʎ}}_{\text{t}}\) represent the bank and time fixed effects. ε is the error term that indicates unobserved error terms. Eq. (1) is an estimate with the fixed-effects mode. 6 Lastly, we use the robust standard error clustered at the bank level. Furthermore, we observe the moderating impact of national culture and institutional quality on the relationship between ESG and bank lending. For this purpose, we expand our baseline model (1) by incorporating the interaction term between our moderators (M) and ESG as follow $${LND}_{\text{i},\text{t}}= {{\alpha }}_{\text{i}}+ {{{\beta }}_{1}\text{E}\text{S}\text{G}}_{\text{i},\text{t}}+{\beta }_{2}{M}_{it}+{\beta }_{3}{\text{E}\text{S}\text{G}\text{*}M}_{it}+{{\gamma }\text{i} \text{X}}_{\text{i},\text{t}}+{{\delta }}_{\text{k}}{\text{Z}}_{\text{j},\text{t}}+{{{{\theta }}_{1}\text{C}\text{R}\text{S}}_{,\text{t}}+ \text{ʎ}}_{\text{t}}+ {{\epsilon }}_{\text{i},\text{t}} \left(2\right)$$ In Eq. (2), we include ESG*M to observe the interactive impact of ESG and moderating variables (e.g., institutional quality and national culture). Therefore, the coefficients \({\beta }_{1}\) and \({\beta }_{3}\) are our primary interest. The rest of the variables and specifications are the same as our baseline model (1). 4. Results 4.1. Descriptive statistics Table 1 shows the descriptive statistics of all variables used in this study. The average credit growth of banks in our sample is 0.0517 for 19 countries, ranging between − 3.747 and 4.135, with a standard deviation of 2.679. Regarding our main explanatory variable, ESG has an average value of 49.219 and 20.383 standard deviations. At the same time, the mean value of the sub-pillar of ESG, such as environmental, social, and governance, is 52.893, 53.895, and 48.129, respectively. Other banks and country-specific control factors also illustrate substantial variation around the sample means. Our undocumented correlation analysis implies no problem with multicollinearity in the regression estimation. Table 1 Descriptive statistics Variables N Mean Sd Min Max LND 742 6.517 17.679 -33.747 94.135 ESG 742 49.219 20.383 1.573 89.531 ENVD 742 52.893 23.786 0 97.551 SOCD 742 53.895 27.488 0.82 97.336 GOVD 742 48.129 30.285 1.921 97.373 SIZ 663 11.25 2.097 6.297 14.33 CAP 662 7.349 2.777 3.22 12.74 LIQ 663 14.900 7.264 5.026 29.106 LTA 662 61.13 10.79 37.53 78.25 NPL 638 8.612 8.734 0.59 34.78 EFF 661 59.35 12.7 30.58 103.9 GDPpc 742 10.268 0.561 9.013 11.114 INF 742 1.721 1.415 -0.665 4.239 PDI 728 52.75 19.36 18 90 MAS 728 52.94 22.23 5 88 UAI 728 73.62 25.07 23 94 IDV 728 61.826 14.342 30 80 PSB 742 0.656 0.414 -0.381 1.393 COC 742 0.925 0.797 -0.227 2.446 REG 742 1.135 0.418 0.238 1.925 ROL 742 1.032 0.612 -0.0136 2.042 GEF 742 1.015 0.619 -0.36 2.354 CRS 742 0.168 0.375 0 1 Note: This table indicates the summary statistics for the variables used in this analysis 4.2. Baseline regression results The primary objective is to examine bank ESG activity's impact on bank lending in the European Union region banking sector. For this purpose, firstly, we regress the bank lending variable on bank ESG activity scores along with bank and country-specific control variables. Table 2 shows our baseline regression model results, which observe bank ESG activity's impact on bank lending. Firstly, we observed the influence of ESG on bank lending has the expected sign. In column 1, we regress the ESG with bank lending along cross-sectional and time fixed-effects, but we do not incorporate bank and country-related control variables. We include the bank and country-related control variables in the next two columns. In column 4, we include all bank and country-related variables with cross-sectional and time fixed-effects to examine the effect of observing bank ESG activity's impact on bank lending. We observe that the coefficients of ESG are significant and positive at the 1% level. This outcome illustrates that the ESG has significantly impacted banking lending behavior. Regarding economic magnitude, we observe that a standard-deviation rise in ESG increases bank lending by 3.2% (19.50*0.164%). Overall, the positive effect of ESG practices on bank lending can be explained by the fact that better ESG performance helps to reduce information asymmetry, generates a certain signal to the capital market, lower Idiosyncratic risk and agency costs, and builds a good corporate reputation that creates a unique competitive advantage (Chiang et al., 2019 ; El Ghoul et al., 2011 ; Reber et al., 2021 ). This helps banks adjust their investment strategies, maintain better lending relationships, and support their borrowers (Chiang et al., 2019 ; He et al., 2022 ). This improves bank credit growth and stability and is consistent with stewardship and good governance theories. Table 2 ESG impact on Bank lending (1) (2) (3) (4) VARIABLES LND LND LND LND ESG 0.304*** 0.042** -0.681* 0.421*** (0.108) (0.017) (0.350) (0.090) SIZ 1.807*** 1.916*** (0.370) (0.415) CAP 0.146** 0.120* (0.064) (0.060) LIQ 2.591*** -0.493 (0.281) (2.790) LTA 3.269*** 1.722*** (0.228) (0.491) NPL -0.072*** -0.073** (0.024) (0.031) EFF -0.008 -0.009 (0.009) (0.009) GDPpc -0.967*** -2.325*** (0.343) (0.472) INF 0.019 -0.137 (0.129) (0.132) CRS -0.873** -1.283*** (0.364) (0.371) Constant -0.147 -21.64*** -3.227** -22.99*** (0.272) (5.156) (1.294) (6.508) Observations 742 637 742 637 R-squared 0.553 0.662 0.567 0.674 Bank F.E. Yes Yes Yes Yes Time F.E. Yes Yes Yes Yes Note: This table demonstrates the results of our baseline regression Eq. (1) using fixed effects to investigate the effect of ESG on bank lending. Robust Standard errors are clustered at the bank level and reported in parenthesis; ***, **, and * indicate statistically significant at 0.01, 0.05, and 0.1. Regarding the bank-level control variables, our results mostly have the expected signs. Table 2 shows that the liquidity and assets quality coefficient significantly positively affect bank lending. This indicates that the banks with a more liquid portfolio and better asset quality are more likely to expand their supply of loans. This outcome is consistent with theory and empirical data from the literature that suggests that greater liquidity and better asset quality create a greater capacity for banks to provide credit to the private sector(Altunbas et al., 2009 ; Mishra and Burns, 2017 ; Nguyen et al., 2020). Meanwhile, the bank's size, capital, and credit risk affect bank lending negatively. The negative coefficient of bank size indicates that larger large-size banks have lower loan growth rates. This may be because large-sized banks focus more on the diversification in their asset portfolios, engaging in nontraditional banking activities and increasing market share stability rather than only on credit activities which lead to lower loan sizes(Demsetz and Strahan, 1997 ; DeYoung and Torna, 2013 ; Nguyen et al., 2020; Shabir et al., 2021 ). Furthermore, the negative link between capitalization and bank lending indicates that banks might decrease their assets to meet capital levels, which reduces bank credit availability(Fang et al., 2022 ). The credit portfolio's riskiness adversely impacts the banks' capacity to enhance credit growth. Other things being equal, high non-performing loans reduce a bank's profitability and capital adequacy and negatively impact credit growth. This aligns with previous studies by (Ananou et al., 2021 ), which argued the significant adverse correlation between credit risk and bank lending behavior. For the country-specific control variables, the coefficient of economic growth and inflation is significantly positive and negative, which suggests that bank lending is higher for countries with high GDP growth and low inflation. These findings align with the theoretical possibility that an expanding economy will require more funds for new investment projects and the consumption of goods (Shabir et al., 2022 ). At the same time, the demand for loans increases, enhancing bank lending. 4.3 ESG activity and bank lending: Role of national culture As recently viewed that legal and cultural differences across countries drive different stakeholder activities and management behavior(Brammer et al., 2006 ). In particular, the difference in national culture is widely recognized as a key factor in shaping changes between individuals' and organizations' values, belief systems, customs, and traditions(Hofstede, 2001 ). In addition, Thanetsunthorn ( 2015 ) highlighted that national culture significantly influences attitudes related to the corporate decision-making process regarding ESG strategies and activities. Doidge et al. ( 2007 ) indicated how country-specific factors override organizational structure in defining corporate governance behavior. Therefore, in this study, we use Hofstede's cultural dimensions to determine the role of national culture in ESG activity and bank lending nexus. The results reported in Table 3 show that the coefficient of UAI and its interaction term ESGUAI is significantly negative at a 1% significant level in column 1. This indicates that banks operating in a country with a culture of uncertainty avoidance, stricter laws, regulations, and regulations lead to a reduction in bank lending. In comparison, the higher degree of uncertainty avoidance reduces the positive impact of ESG activity on bank lending. This adverse relationship may be because they are more likely to engage in less risky lending, and the cost of information disclosure and ESG engagement is higher than the benefit. This finding aligns with Aggarwal and Goodell ( 2009 ) and Ashraf et al. (2016), who show that countries with higher scores on UAI are comparatively more risk averse. Table 3 ESG impact on Bank lending: Role of national culture (1) (2) (3) (4) VARIABLES LND LND LND LND ESG 0.110** 0.097* 0.133*** 0.100* (0.052) (0.049) (0.048) (0.051) SIZ 1.625*** 1.913*** 1.602*** 1.622*** (0.465) (0.496) (0.459) (0.456) CAP 1.405*** 1.738*** 1.387*** 1.404*** (0.399) (0.428) (0.357) (0.411) LIQ -0.677 -0.169 -0.267 -0.566 (1.839) (1.952) (1.876) (1.920) LTA 0.021* 0.020* 0.023** 0.021* (0.011) (0.011) (0.011) (0.011) NPL -0.083*** -0.088*** -0.088*** -0.082*** (0.013) (0.013) (0.013) (0.013) EFF -0.006 -0.008 -0.007 -0.006 (0.006) (0.006) (0.006) (0.006) GDPpc -1.130** -0.799* -1.235*** -1.079** (0.448) (0.430) (0.414) (0.429) INF -0.147 -0.149 -0.157 -0.144 (0.116) (0.118) (0.117) (0.116) CRS -1.118*** -1.136*** -1.225*** -1.101*** (0.344) (0.341) (0.333) (0.337) UAI -0.110*** (0.004) ESGUAI -1.387*** (0.357) PDI -0.089*** (0.000) ESGPDI -1.389*** (0.120) MAS 1.252** (0.513) ESGMAS 0.0001 (0.000) IDV 0.965* (0.495) ESGIDV -0.799* (0.430) Constant 3.872*** 3.846*** 3.841*** 4.143*** (0.609) (0.580) (0.583) (0.614) Observations 625 625 625 625 R-squared 0.608 0.610 0.610 0.608 Note: This able displays the outcomes of our baseline regression Eq. (1) to examine the moderating role of national culture on the relationship between ESG and bank lending. All regression models were estimated by using OLS regression. P-values calculated using heteroskedastic-robust standard errors and reported in parentheses. ***, **, and * indicate statistically significant at 0.01, 0.05, and 0.1. Column 2 in Table 3 represents the findings on the effect of the PDI and its interaction term ESGPDI on bank lending. The coefficient estimates on PDI and ESGPDI are significantly negative at the 1% level, indicating that a large degree of power distance decreases bank lending and mitigates ESG activity's impact. This outcome is consistent with Kreiser et al. ( 2010 ) and Mihet ( 2013 ). They show that power distance has a significant adverse effect on corporate risk-taking. Ferrary ( 2003 ) and Zheng et al. ( 2012 ) argued that elevated levels of power distance society's bank-firm relationship are formal, and firms consider banks distant institutions that cannot be trusted. In such societies, firms are reluctant to make information readily available, and bank credit rejection rates are higher because banks do not have access to information (Berger and Udell, 2006 ). Column 3 in Table 3 illustrates the impact of MAS and its interaction term with ESG on bank lending and shows that the coefficient on MAS is significantly positive at the 5% level. This indicates that banks in high masculinity societies increase bank lending. One interpretation of this finding is that females show less risk-seeking behavior than men in financial decision-making, regardless of contextual factors such as familiarity, costs, or ambiguity (Powell & Ansic, 1997 ; Zheng et al., 2012 ). Comparatively, managers in highly masculine cultural environments pursue risky investments and accelerate overinvestment issues (Zheng et al., 2012 ). In contrast, its interaction term ESGMAS is insignificant. Column 4 of Table 3 reports the coefficients of IND and ESGIND are statistically significant positive and negative at the 10% level. This suggests that individualistic national culture increases bank lending while mitigating ESG activity's impact. This implies that highly individualistic cultures overestimate their abilities and are excessively optimistic about their prediction's accuracy (Chen et al., 2015 ; Zheng et al., 2012 ). Hence, they can be involved in risky activities that increase bank lending (Ashraf et al., 2016). These results are consistent with Ashraf et al. (2016), who show that banks operating in individualistic societies are likely to take the higher risk. Chui et al. ( 2010 ) argued that high individualism cultures remunerate individual performance and achievements, and encouragement for individual risk-taking is more critical than in collective societies where collective interest is paramount. 4.4 ESG activity and bank lending: Role of institutional quality The quality of institutions is a key factor that is normally considered an external mechanism to remove agency issues (Bai and Ho, 2022 ; Hunjra et al., 2020 ) and significantly impacts the functioning and performance of the financial sector in various countries (Bai and Ho, 2022 ; Nguyen, 2022 ; Shabir et al., 2022 ). As La Porta et al. ( 2002 ) explain countries with better institutional quality have strong investor protection and highly efficient financial markets. Recent studies have determined the impact of institutional quality on bank lending (Shabir et al., 2022 ). Mishkin ( 2009 ) shows that poor institutional quality and the legal system make it harder for lenders to enforce restrictive contracts. In comparison, Levine ( 2005 ) argued that the legal system reform that strengthens and stabilizes creditor rights, contract enforcement, and accounting practices enhances financial development. Bae and Goyal ( 2009 ) and Demirgüç-Kunt & Detragiache ( 1998 ) show that financial instability is significantly linked to poor institution quality. A higher quality institution could reduce adverse selection, borrow moral hazard, and increase bank loan payment and credit growth. Thus, for more detailed insight into the ESG and bank lending relationship, we consider whether better institutional quality moderates the effect of ESG on bank lending. For this purpose, following the prior studies and used regulatory quality (RQL), control of corruption (COC), political stability (PST), the rule of law (ROL), and government effectiveness (GEF) to capture the institutional quality. Column 1–5 in Table 4 shows the role of institutional quality on ESG activities and bank lending relationship. Institutional quality and interaction terms coefficient are statistically significant and positive in all estimation models. This implies that the better quality of institutions contributes significantly to improving bank lending and enhancing ESG activities' impact. This finding aligns with previous literature (Shabir et al., 2022 ). Table 4 ESG impact on Bank lending: Role of institutional quality (1) (2) (3) (4) (5) VARIABLES LND LND LND LND LND ESG 0.320*** 0.568* 0.266*** 0.428*** 0.484*** (0.622) (0.328) (0.002) (0.003) (0.003) SIZ 1.920*** 2.109*** 1.911*** 1.961*** 1.931*** (0.408) (0.424) (0.395) (0.399) (0.401) CAP 0.125** 0.137** 0.122** 0.119* 0.123* (0.058) (0.059) (0.059) (0.061) (0.061) LIQ -0.598 -0.437 -0.535 -0.531 -0.462 (2.793) (2.849) (2.716) (2.752) (2.766) LTA 0.042** 0.047*** 0.042** 0.043** 0.042** (0.018) (0.017) (0.017) (0.017) (0.017) NPL -0.074** -0.075** -0.073** -0.074** -0.073* (0.031) (0.029) (0.033) (0.031) (0.037) EFF -0.010 -0.009 -0.009 -0.008 -0.009 (0.009) (0.008) (0.009) (0.009) (0.009) GDPpc -1.683*** -1.852*** -1.650** -1.913*** -1.755*** (0.604) (0.522) (0.654) (0.551) (0.619) INF -0.130 -0.155 -0.138 -0.135 -0.136 (0.130) (0.134) (0.131) (0.132) (0.126) CRS -1.366*** -1.205*** -1.292*** -1.312*** -1.314*** (0.394) (0.377) (0.376) (0.366) (0.351) PSB 0.584*** (0.002) ESGPSB 1.175* (0.664) COC 1.175* (0.664) ESGCOC 0.689*** (0.033) REG 1.369** (0.585) ESGREG 1.650** (0.654) GEF 1.207* (0.666) ESGGEF 1.054*** (0.005) ROL 1.079** (0.429) ESGROL 0.021** (0.012) Constant -22.76*** -25.69*** -23.40*** -24.30*** -24.28*** (6.567) (6.530) (6.387) (6.506) (6.677) Observations 637 637 637 637 637 R-squared 0.674 0.678 0.674 0.675 0.675 Bank F.E. Yes Yes Yes Yes Yes Time F.E. Yes Yes Yes Yes Yes Note: This table expresses the outcomes of our baseline regression Eq. (1) to examine the moderating role of institutional quality on the relationship between ESG and bank lending. Robust Standard errors are clustered at the bank level and reported in parenthesis; ***, **, and * indicate statistically significant at 0.01, 0.05, and 0.1. 5. Robustness 5.1 Individual ESG dimensions and bank lending Next, for further insights and robustness, we evaluate the individual ESG dimension to explore whether the component of ESG scores (e.g., environmental, social and governance) differently affect lending. For this purpose, we use the ESG sub-pillar, which is environmental (ENV), social (SOC), and governance (GOV), as independent variables of interest, respectively. Our main objective for investigating the different dimensions of ESG activity arises from the fact that every industry has unique characteristics and faces different pressures from different stakeholders. Therefore maybe, the advantage of focusing only on one of the three ESG factors(Azmi et al., 2021 ). The results reported in Table 5 show that the coefficient of environmental and governance pillars is statistically significant and positive at 1%, while a social pillar is significantly positive at 10%. This indicates that only environmental and governance factors are more relevant to the banking industry. This result aligns with the previous finding by Chiaramonte et al. ( 2021 ) and Azmi et al. ( 2021 ). They asserted that the environmental and governance components of ESG have a significant effect on the value and stability of banks. This may be because the rising awareness of environmental issues, climate change, rising temperatures, increasing sea levels, and the introduction of Sustainable Development Goals has been progressively increasing. Therefore, banks consider environmental and governance problems more when lending capital to firms. Table 5 ESG impact on Bank lending: Individual dimensions (1) (2) (3) Variables Environmental Social Governance E/S/G 0.685*** 1.233* 0.374*** (0.004) (0.678) (0.006) SIZ 1.913*** 1.906*** 1.925*** (0.418) (0.415) (0.418) CAP 1.977*** 2.255*** 1.963*** (0.475) (0.556) (0.437) LIQ -0.518 -0.653 -0.584 (2.791) (2.787) (2.782) LTA 0.041** 0.042** 0.041** (0.017) (0.017) (0.017) NPL -0.073** -0.074** -0.073** (0.030) (0.031) (0.030) EFF -0.009 -0.009 -0.009 (0.009) (0.009) (0.009) GDPpc -1.130** -0.799* -1.235*** (0.448) (0.430) (0.414) INF -0.138 -0.137 -0.133 (0.134) (0.132) (0.132) CRS -1.280*** -1.276*** -1.286*** (0.369) (0.366) (0.368) Constant 22.84*** 22.29*** 23.21*** (6.397) (6.559) (6.517) Observations 637 637 637 R-squared 0.674 0.674 0.674 Bank F.E. Yes Yes Yes Time F.E. Yes Yes Yes Note: This Table shows the results of our baseline regression equation (1) to examine the impact of each pillar of ESG on bank lending. In column (1), our main explanatory variable is the environmental pillar of ESG. In column (2), our main explanatory variable is the social pillar of ESG. In column (3), our main explanatory variable is the governance pillar of ESG. Robust Standard errors are clustered at the bank level and reported in parenthesis; ***, **, and * indicate statistically significant at 0.01, 0.05, and 0.1. 5.2 Alternative econometric methodology Although we incorporate a wide variety of control variables, we are still interested in endogeneity, which affects our previous findings. Our model might suffer from endogeneity problems because of reverse causality and omitted variables. Hence, we will now use alternative economic methods to address this issue and determine whether the different assumptions in the data generation process will affect our key findings. Therefore, we follow the earlier literature for this purpose and use the 2SLS and two-step System Generalized Method of Moments (System GMM) to deal with potential endogeneity (Azmi et al., 2021 ). 5.3.1 2SLS estimation techniques Firstly, we re-estimate our regressions using a two-stage least squares (2SLS) regression and instrument the ESG activities for accounting for potential endogeneity due to a potential reverse causality issue. For this reason, we employ three instruments to handle the endogeneity linked with ESG activity. First, we follow the study of Azmi et al. ( 2021 ) and employ the legal origins as an instrument for ESG activities. Recent studies by Beck et al. ( 2003 ) and La Porta et al. ( 2008 ) argued that legal origins describe dissimilarities in international financial development. Second, following previous literature by Benlemlih and Bitar ( 2018 ), Azmi et al. ( 2021 ), and Bhandari and Javakhadze ( 2017 ), using the initial ESG score to instrument ESG. Third, we followed the literature of Shabir et al. ( 2022 ) and used the predictor variable (ESG activity) lag values as a potential instrumental variable. Table 6 , Panel A and B show the results of the first and second-stage regressions, respectively. In the first stage, we use ESG as the dependent variable and all our exogenous and instrument variables to predict ESG activity. In the second stage, we estimated value of ESG activities rather than the actual value. Panel B shows that the predicted ESG coefficients keep their significant and positive coefficient, verifying the earlier results. The essential condition for the validity of an instrument is to satisfy the instrumental variables exogeneity test, which confirms that it must affect our dependent variable (bank lending) through its impact on the endogenous variable (Bhandari and Javakhadze, 2017 ; Shabir et al., 2022 ) Table 6 ESG impact on Bank lending: Two-stage least squares (2SLS) estimation. (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES LND LND LND LND LND LND LND LND ESG Environmental Social Governance ESG Environmental Social Governance Panel A: First-stage regression Panel B: Second-stage regression LOR 0.065*** 0.055*** 0.030** 0.006** (0.017) (0.017) (0.012) (0.002) IESG 0.492*** 0.006*** 0.428*** 0.310*** (0.106) (0.001) (0.105) (0.087) L.ESG 0.361*** 0.291*** 0.518*** 0.144** (0.076) (0.063) (0.104) (0.072) ESG/E/S/G 0.894*** 0.772*** 0.406*** 0.675*** (0.260) (0.032) (0.089) (0.065) Bank Controls Yes Yes Yes Yes Yes Yes Yes Yes Country Controls Yes Yes Yes Yes Yes Yes Yes Yes Constant 5.354*** 4.939*** 1.714*** 4.395*** 2.814*** 0.894*** 3.827*** 1.870*** (0.364) (0.388) (0.407) (0.356) (0.427) (0.260) (0.425) (0.465) Observations 637 637 636 637 637 636 637 637 R-squared 0.092 0.131 0.522 0.041 0.117 0.616 0.106 0.214 Tests of endogeneity Durbin (score) 17.291*** 65.372*** 9.815*** 6.512*** Wu–Hausman 17.291*** 65.370*** 9.813*** 6.510*** Tests of overidentifying restrictions Sargan (Score) 17.523 Basmann 17.269 First-stage regression summary statistics Min. eigenvalue 3469.35*** 4145.81*** 438.63*** 2013.77 *** Note: This table presents the result of our baseline regression models using the two-stage instrumental variable egression. Panel A presents the first-stage regression. At the same time, Panel B presents the second-stage regression findings. Standard errors reported in parentheses; ***, **, and * stand for statistical significance at 0.01, 0.05, and 0.1. 5.3.2 GMM estimation technique Furthermore, we employ the two-step System GMM suggested by (Blundell and Bond, 1998 ), which is especially more suitable for addressing the inconsistency caused by endogeneity. (Azmi et al., 2021 ; Bilgin et al., 2021 ; Nguyen, 2022 ). This approach has been widely used in banking and finance studies to eliminate the endogeneity bias (Demir and Danisman, 2021 ; Wu et al., 2020 , 2021 ; Zhang et al., 2021 ). This model is based on two essential conditions. Firstly, the Hansen test for over-identification restrictions was used to confirm the validity of the instruments. At the same time, the second test applies to validate the non-autocorrelation hypothesis. However, the first-order autocorrelation did not show inconsistencies in the measure. This one is confirmed by second-order autocorrelation. For further accuracy, the two-step system GMM approach is appropriate for dealing with endogeneity issues (Al-Shboul et al., 2020 ; Albaity et al., 2019 ). Table 7 reports our results by using GMM estimators. This finding is like the previous outcome and shows that ESG engagement enhances bank lending and remains unchanged. Moreover, they satisfy the instruments' validity, showing no second-order autocorrelation among errors and overidentifying restrictions are valid, respectively(Bilgin et al., 2021 ). Table 7 ESG impact on Bank lending: GMM approach (1) (2) (3) (4) VARIABLES LND LND LND LND ESG Environmental Social Governance L.CRD 0.183*** 0.172* 0.197*** 0.188** (0.061) (0.088) (0.068) (0.077) ESG/E/S/G 0.504*** 0.645*** 0.466*** 0.390*** (0.088) (0.134) (0.114) (0.083) SIZ 0.397*** 0.282*** 0.096 0.515*** (0.107) (0.096) (0.109) (0.111) CAP 0.459*** 0.341*** 0.030 0.463*** (0.110) (0.101) (0.110) (0.106) LIQ -4.127 -7.970 -2.996 1.116 (7.420) (10.570) (9.758) (10.351) LTA 0.426*** 0.098 0.609*** 0.492*** (0.099) (0.080) (0.107) (0.090) NPL -0.083*** -0.091*** -0.096*** -0.095*** (0.027) (0.029) (0.023) (0.026) EFF -0.016 -0.016 -0.010 -0.018* (0.010) (0.010) (0.009) (0.010) GDPpc -0.357*** -0.024 -0.605*** -0.463*** (0.097) (0.068) (0.118) (0.098) INF -0.135 -0.086 -0.243 -0.321* (0.279) (0.299) (0.181) (0.169) CRS -2.154** -2.212* -2.211* -2.344* (1.013) (1.291) (1.190) (1.227) Constant 8.758 4.001 6.282 7.178 (5.315) (4.772) (4.411) (5.065) Obs. 636 636 636 636 AR (1) 0.000 0.005 0.001 0.002 AR (2) 0.589 0.467 0.376 0.537 Hansen 0.801 0.834 0.864 0.910 Note: This table presents the result of our baseline regression models using the two-step System GMM regression. Standard errors reported in parentheses; ***, **, and * stand for statistical significance at 0.01, 0.05, and 0.1. 5.3 Role of the regulatory environment Banks are expected to affect by a country's regulatory environment, which fluctuates from country to country. Therefore, better bank regulation quality is useful for financial sector stability and soundness. (Mirzaei & Samet, 2022 ). It can put a stop to unnecessary risk-taking and encourage them to maintain larger capital buffers, enhancing their capacity to absorb losses effectively(Le et al., 2020 ; Mercieca et al., 2007 ). A more stringent framework for banking capital regulation and supervision lowers systemic risk, enhances financial institutions' performance and securities, improves lending technology, and increases loan availability(Qian and Strahan, 2007 ; Deli and Hasan, 2017 ; Shabir et al., 2021 ). Moreover, recently researchers have highlighted that it affects the operations and performance of banks in many ways, such as a tighter regulatory environment that discourages more risky bank lending and risk-taking behavior by imposing penalties on banks (Shabir et al., 2021 , 2022 ). Hirtle et al. ( 2020 ) determine the impact of supervision on bank profitability, riskiness, and growth. They argued that banking supervision plays a significant role in reducing the risk of the banking sector. Therefore, we took into account the whole banking regulatory environment and looked at how ESG activity affected the bank lending nexus to draw a more precise picture. In this regard, following the studies of Nguyen ( 2021 ) and Shabir et al. ( 2021 ) and we incorporate (i) activity restrictions index (RES), (ii) capital stringency index (CSI), and (iii) private monitoring index (PMI) to control the bank regulatory environment that directly affects bank lending policies. 7 The activity restriction index takes into account the extent to which a country restricts banks' involvement in insurance, securities, and real estate operations. The capital stringency index (CSI) determines the minimum amount of capital banks must keep regarding their risk-weighted assets. The private monitoring index (PMI) measures how well a country's regulatory and supervisory policies enable private investors to watch the actions and governance of banks. Columns 3–6 in Table 8 represent the influence of ESG activity on bank lending after controlling the regulatory environment. This empirical finding remains unchanged after incorporating the bank regulatory variables in the model. Table 8 ESG impact on Bank lending: Additional Test (1) (2) (3) (4) (5) (6) (7) (8) (9) VARIABLES LND LND LND LND LND LND LND LND LND ESG 0.540*** 0.419*** 0.084*** 0.495*** 0.357*** 0.376*** 0.542*** 0.366*** 0.149** (0.100) (0.080) (0.009) (0.096) (0.095) (0.099) (0.110) (0.101) (0.065) SIZ 0.123* 0.053 2.287*** 2.017*** 1.920*** 1.911*** 1.905*** 2.029*** 2.426*** (0.073) (0.083) (0.462) (0.384) (0.415) (0.416) (0.408) (0.411) (0.420) CAP 0.015 0.058 0.157** 0.122* 0.111* 0.120* 0.123** 0.123** 0.127** (0.049) (0.068) (0.060) (0.061) (0.061) (0.061) (0.060) (0.060) (0.059) LIQ -4.892* 1.839 -0.944 -0.349 -0.675 -0.490 -0.399 -0.684 -0.473 (2.545) (2.411) (2.894) (2.769) (2.858) (2.806) (2.823) (2.849) (3.109) LTA 0.029** 0.016 0.045** 0.041** 0.041** 0.042** 0.043** 0.044** 0.045** (0.012) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.018) (0.017) NPL -0.074*** -0.083*** -0.078** -0.078** -0.076** -0.076** -0.070** -0.076** -0.088*** (0.017) (0.019) (0.031) (0.031) (0.031) (0.030) (0.033) (0.031) (0.027) EFF -0.019** 0.012 -0.006 -0.009 -0.009 -0.009 -0.010 -0.009 -0.009 (0.008) (0.017) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) GDPpc -1.442* 2.487*** 1.042 -1.202* -1.610*** -1.677*** -1.592*** -1.048* 1.829*** (0.729) (0.828) (0.697) (0.630) (0.556) (0.520) (0.529) (0.581) (0.519) INF -0.188 -0.067 -0.177 -0.140 -0.129 -0.137 -0.153 -0.122 -0.152 (0.190) (0.134) (0.129) (0.132) (0.131) (0.132) (0.129) (0.133) (0.131) CRS -1.478*** -0.832* -1.137*** -1.288*** -1.301*** -1.285*** -1.255*** -1.206*** -0.911** (0.472) (0.462) (0.381) (0.369) (0.373) (0.370) (0.369) (0.379) (0.362) RES -0.200*** (0.074) CRI -0.456*** (0.122) OSP -0.364*** (0.099) PMI 0.140 (0.125) TOP -0.409*** (0.121) DCP -0.304*** (0.108) EFR -0.162*** (0.0473) Constant 7.408** -4.541 -26.46*** -24.31*** -22.35*** -23.04*** -22.93*** -23.07*** -18.99*** (3.146) (2.762) (7.048) (6.069) (6.869) (6.544) (6.440) (6.631) (6.844) Observations 318 319 637 637 637 637 637 637 637 R-squared 0.620 0.639 0.678 0.675 0.674 0.674 0.674 0.675 0.684 Bank F.E. Yes Yes Yes Yes Yes Yes Yes Yes Yes Time F.E. Yes Yes Yes Yes Yes Yes Yes Yes Yes Note: This table shows the results of our baseline regression Eq. (1) using fixed effects to examine the effect of ESG on bank lending after including the regulatory environment and additional macroeconomic variables. 5.4 Additional macroeconomic factors Finally, for further robustness and to evaluate the sensitivity of our relationships between ESG activity and bank lending by containing macroeconomic factors as control variables: trade openness, financial development, and financial freedom. Trade openness increases market competition by driving in more foreign firms and pressuring domestic companies to invest more in their infrastructure(Khan et al., 2021 ). Higher trade openness enhances the bank's borrower selection, decreasing risk and stability. Thus, regulatory authorities are encouraged to transform the financial sector to facilitate credit availability. Those financial reforms shape the banking sector to be more competitive, lower the cost of credit for firms and enhance the availability of credit in the economy(Khan et al., 2021 ). Moreover, the level of financial development plays a significant role in enhancing the efficiency and effectiveness of the financial system infrastructure in a nation. It improves the smooth functioning of the payment system, financial intermediation process, and settlement system (Sanfilippo-Azofra et al., 2018 ). At the same time, financial freedom measures banking efficiency and independence from government control and interference in the financial sector. Hence, we evaluate the sensitivity of our results by including trade openness, financial development, and financial freedom in the regression. We also obtain the significance level of the concerns variable that remains unaffected. At the same time, the coefficient on trade openness, financial development, and financial freedom is significant. 6. Conclusion This study investigates the effect of environmental, social and governance (ESG) activities on bank lending in the European Union banking sector. We use data from 53 banks in 19 nations from 2004 to 2018. Moreover, we also analyze the significant role of national culture and institutional environment in banking by exploring whether national culture and institutional environment moderate the connection between ESG activities and bank lending. For this reason, we used the panel fixed-effects model in our baseline estimations, while 2SLS and GMM were applied to control for endogeneity. Our findings reveal that ESG activities play a significant role in the European Union banking sector, significantly increasing bank lending. The importance of these dimensions is not surprising as awareness of environmental and good governance matters has become the focus of attention of all stakeholders in the last decade. People and investors gradually identify the impact of global warming and climate change and realize that banks have a significant role in funding environmentally conscious projects and reducing financing for dirty industries. Moreover, the national culture and institutional quality significantly affect the ESG activity and bank lending nexus. Our outcomes show that national cultural dimensions significantly impact bank lending and play a moderating role in the relationship between ESG activity and bank lending. In comparison, better institutional quality supports banks in increasing their credit availability and enhancing the ESG practice's constructive impact. The findings are robust to using the different ESG dimensions, alternative estimation techniques, and control for the regulatory environment and macroeconomic conditions. Our empirical results support regulatory efforts to transition banks to ESG activity and improve non-financial information disclosure. Our findings support recent regulatory changes and policy developments in Europe brought about by the Non-Financial Reporting Directive. Big banks must disclose information about their social and environmental operations and how much they value human rights and diversity on their boards of directors. The primary limitation of this research is the small number of banks in our sample. We only use data from the 53 banks whose ESG information is available on Reuters. Therefore, more favorable results may have emerged if our sample size had been larger. A second limitation is that other variables (e.g., economic policy uncertainty and market competition) may influence the relationship between ESG and bank lending. References Acemoglu, D., & Johnson, S. (2005). Unbundling institutions. Journal of Political Economy , 113 (5), 949–995. https://doi.org/10.1086/432166 Aggarwal, R., & Goodell, J. W. (2009). 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Journal of Banking and Finance . https://doi.org/10.1016/j.jbankfin.2021.106207 Footnotes ESG and CSR are gradually used interchangeably in the literature. The time of this study is based on the data availability. We limited our sample to banks for which we have ESG data. The institutional quality indicator ranges from − 2.5 to 2.5, with higher values related to better institutional quality. For a more detailed, please visit Kaufmann et al. (2009) and https://info.worldbank.org/governance/wgi/ For further information, please visit https://hi.hofstede-insights.com/national-culture Confirmed by the Hausman test. Our primary objective is to control the bank regulation and supervision system, not to assess their impact on the banking sector of the sample countries. Additional Declarations The authors declare no competing interests. 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Introduction","content":"\u003cp\u003eOver the decades, environmental activists' criticisms and increasing awareness of environmental and social issues have forced firms to involve in environmentally and socially responsible performance (Shahbaz et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Especially in the face of issues like the 2008 Financial Crisis, COVID-19, international conflicts, and global warming, protecting the environment and respecting human rights interests are gradually affecting modern societies, leading to key societal changes (Chen and Xie, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garel and Petit-Romec, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nirino et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To respond to society's evolving demands, firms are increasingly considering how they can reduce their negative impact on the environment and community (Nirino et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, firms are looking for a more ethical and sustainable way to do business in the long term. This leads to the environmental social and governance (hereafter ESG)\u003csup\u003e1\u003c/sup\u003e practices being the focus of corporate strategies worldwide and researchers' and regulators' debate in recent years (Chen and Xie, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Primarily in the banking sector, which has played a growing role in promoting a culture of environmental and social responsibility and becoming more conscious of the need to include ESG considerations into strategy, procedures, and financial instruments for long and midterm goals (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever a few researchers have recently examined the impact of ESG practices on a firm's performance, and value creation in non-financial is significantly inconsistent. As shown that ESG practices increase firm competitiveness, enhance their performance, support their reputation in the eyes of stakeholders, decrease systematic risk, and improve company values (Broadstock et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen and Xie, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Del Giudice et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Landi and Sciarelli, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nirino et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While on the other hand, few researchers argued that ESG practices might result in capital inefficiency, which leads to opportunity costs and adverse relationships with financial performance (Oikonomou et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Santis et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, regardless of the valuable literature on the impact of ESG on non-financial firms, whether ESG activities influence financial organizations, particularly banks, has received the least attention. The 2008 crisis decreased public trust in the financial sector and highlighted the negligence of similar banks and regulatory authorities. Although few researchers have examined the effect of banks' ESG on bank profitability and stability, they also report mixed findings (Anginer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Berger et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chiaramonte et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Di Tommaso and Thornton, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This suggests that a deeper understanding of the circumstances under which ESG activities are more probable to increase bank lending is required.\u003c/p\u003e \u003cp\u003eHowever, there are two main theoretical approaches to describe the connection between ESG and firm value and stability (i) Stakeholder theory (i.e., risk mitigation view) states that increased investments in ESG benefit all stakeholders and have the potential to generate moral capital or goodwill, provides a competitive advantage, fosters long-term value creation, improves portfolio quality, strengthens stakeholder relationships, reducing asymmetric information, ensures regulatory compliance, decreasing the cost of capital, enhances risk assessment, and reducing operational, environmental and social risks (Chiaramonte et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; El Ghoul and Karoui, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Godfrey et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Luo and Bhattacharya, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) (ii) Managerial opportunism theory suggests that managers overinvest in ESG to take advantage of agency theory, which acts as a waste of resources, reducing wastes.\u003c/p\u003e \u003cp\u003eMoreover, several factors, such as national culture and institutional quality, can impact the relationship between ESG and bank credit growth. National culture encompasses the shared values, beliefs, norms, and practices within a country, and these cultural factors can shape the integration of ESG factors in lending practices. In cultures that place a high value on environmental sustainability, social responsibility, and corporate governance, banks are more likely to prioritize ESG factors in their lending decisions. This can increase credit allocation to projects that align with ESG principles, such as renewable energy initiatives or socially responsible businesses. Moreover, national culture also shapes the risk appetite of individuals and institutions within a country. Some cultures may exhibit a higher tolerance for risk-taking, while others may be more risk-averse. This cultural predisposition can influence investment behavior, lending practices, and bank risk management strategies.\u003c/p\u003e \u003cp\u003eBesides this, institutional quality plays a crucial role in shaping the impact of ESG practices on bank credit growth. Institutional quality refers to a country's institutions' effectiveness, transparency, and stability, including regulatory bodies, legal frameworks, and governance mechanisms. Strong institutional quality promotes trust, transparency, and accountability, which are crucial for effectively integrating ESG practices into bank lending strategies. A robust regulatory framework and clear guidelines facilitate the implementation of ESG criteria, ensuring that banks align their lending practices with sustainable objectives. Institutions that enforce ESG standards and provide reliable information can enhance the credibility and effectiveness of ESG practices in the banking sector.\u003c/p\u003e \u003cp\u003eThis study examined the effect of ESG activities on bank lending in the European Union banking sector. Because European countries are the leading countries that advocate sustainable development (Chiaramonte et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Besides, deregulation, globalization, technological change, and European integration have structurally reshaped the banking sector in the last two decades (Goddard et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Additionally, the banking sector has close-fitting profit margins and is highly monitored and organized. Therefore, ESG practices can widen the competitors' financial performance differences, whereas the agency costs related to ESG activity might influence this nexus (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, we were limiting our sample to the E.U. banking sector to minimize internal and external variation that forces banks to engage in ESG practices (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Simpson and Kohers, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo examine ESG activity's effect on bank lending using a sample of 53 European commercial banks from 19 countries from 2004 to 2018. We used the panel fixed-effects model in our baseline estimations, while two stages least square regression (2SLS) and generalized methods of moments (GMM) were applied to control for endogeneity. Our findings show that ESG activities play a significant role in the European Union banking sector by significantly increasing bank lending. The importance of these dimensions is not surprising as awareness of environmental and good governance matters has become the focus of attention of all stakeholders in the last decade. Stakeholders and investors gradually identify the impact of global warming and climate change and understand that banks have a significant role in funding environmentally conscious projects and reducing financing for dirty industries. Moreover, the national culture and institutional quality significantly affect the ESG activity and bank lending nexus. Our outcomes show that national cultural dimensions significantly impact bank lending and mediate the relationship between ESG activity and bank lending. In comparison, good quality institutional supports banks in increasing their lending capacity and enhancing the ESG practice's constructive impact. The empirical findings are quite robust to using the various ESG dimensions, alternative estimation techniques, and control for the regulatory environment and macroeconomic conditions.\u003c/p\u003e \u003cp\u003eThis study contributes to this stream of literature in the following ways: First, despite the few studies examining the connection between ESG and bank risk and lending, this is the first to examine the ESG activity in the European commercial banking sector and bank lending. The focus on Europe is particularly appropriate countries have advanced sustainability practices, with European union banks leading the way compared to those headquartered elsewhere (Chiaramonte et al., 202). Moreover, from the regulatory perspective, significant actions have been taken to increase the disclosure of diversity and non-financial information (Chiaramonte et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In 2014, the European Union approved the Non-Financial Reporting Directive (2014/95/E.U.), requiring larger corporations and banks to reveal how they operate and address social and environmental problems. The European Banking Authority (EBA) also issued a discussion paper on the management and supervision of ESG risks in October 2020 for credit institutions and investment companies(Chiaramonte et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; European Banking Authority, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently the European Union has strongly committed to intensifying diligence to address these problems (Chiaramonte et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, we analyzed whether the effect of ESG activity on bank lending depends on the underlying institutional quality. Institutional theory shows that the countries' institutional environment leads the activities of economic actors and countries' economic performances (\u0026Ccedil;am and \u0026Ouml;zer, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A strong institutional framework increases the effectiveness of the accounting standards and judicial system, enhances financial development, improves corporate governance and transparency, increases bank stability, and promotes and strengthens the flexibility of bank funding (Acemoglu and Johnson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Doidge et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; La Porta et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shabir et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, it decreases the risk of expropriation and repudiation of contracts, agency problems, asymmetric information issues, and the cost of debt and equity(\u0026Aacute;lvarez-Botas and Gonz\u0026aacute;lez, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Alves and Ferreira, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; La Porta et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Thus, these studies have emphasized the importance of countries' legal and institutional quality and banking structures when making lending decisions. Therefore, in this context, it objects to studying the effect of institutional quality on ESG activity and bank lending nexus.\u003c/p\u003e \u003cp\u003eThird, we add to the increasing body of knowledge about the role of national culture in ESG activity and bank lending decisions. Diverse cultural systems have made diverse assumptions regarding society, business, and the environment. Earlier studies have shown that national culture not only affects corporate decisions, such as capital structure, dividend policy, cash holdings, financial reporting, corporate innovation, R\u0026amp;D investments, bank risk-taking, and mergers and acquisitions (Ahern et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ashraf et al., 2016; Boubakri et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Choi, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) but also impact the country's financial systems and its global investing approaches (Hooghiemstra et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chui et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Few researchers have shown that environmental attitudes and behaviors are deeply rooted in personal human values (Soyez, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Crossland and Hambrick (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported that diverse cultural attributes directly impact managerial preference, which they conceptualized as the latitude of managerial action. Waldman et al. (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) have shown that directors in cultural environments that differ in institutional power distance and collectivity made diverse CSR decisions. Cheung et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Jackson and Apostolakou (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) argued the differences in national-level institutions affect varying degrees of pressure and incentives for corporations to engage in CSR initiatives. Buhr and Freedman (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) have shown that, compared to individualist U.S. society, collectivist Canadian society involves higher environmental disclosure. Therefore, considering this evidence, this study enhances the literature on the critical role of national culture in banking by investigating whether national culture moderates the relationship between ESG activities and bank lending.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section 2 shows a detailed literature review. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e explains the sample selection, variable measures, and empirical models. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the empirical results and discussion. Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of a series of robustness tests, while Section \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents conclusions.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eVarious researchers have recently investigated the impacts of ESG practices on firm performance and value (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, results have been inconsistent partly due to data constraints, model misspecification, and measurement concerns (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Trade-off and stakeholder theories are the two main streams theories that predict the relationship between ESG practices and firm performance and value. However, both theories make conflicting predictions while supported by empirical evidence. Agency theory suggests a similar outcome to those of trade-off theory. In contrast, stewardship and resource-based theories provide results like stakeholder theory. All five approaches are described below.\u003c/p\u003e \u003cp\u003eAgency theory states that the manager is involved in ESG practices to fulfill their desires (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which is usually true for inappropriately incentivized managers (Jensen \u0026amp; Meckling, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Publicity and media awareness of ESG practices can support managers to increase their reputations and benefit personally from the firm's expenditure.Jiraporn and Chintrakarn (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) show that less strengthened CEOs are more likely to intensify ESG practices than other CEOs because of the personal and reputational advantage of ESG activity (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, Agency theory suggests that ESG integration can help mitigate agency conflicts and align the interests of managers and shareholders. By incorporating ESG factors into decision-making processes, banks can establish guidelines and performance metrics that promote responsible behavior and long-term value creation. This can improve bank performance and risk management by reducing potential conflicts between short-term profit maximization and long-term sustainability objectives.\u003c/p\u003e \u003cp\u003eAccording to the trade-off theory, implementing ESG practices may involve expenses such as conducting environmental assessments, adopting sustainable technologies, or implementing social welfare programs. These costs can temporarily reduce a bank's profitability, which could be perceived as a trade-off between financial returns and ESG commitments in the short run.Galant and Cadez (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) argued that expanding resources to meet social and environmental goals increases costs and undermines profitability and competitive advantage. Devinney (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) points out that while firms can make more \"virtuous,\" virtue always comes at a cost. Hence,Aupperle et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) indicate that socially sensible companies experience higher costs and lower profitability compared to socially unconscious businesses. However, the theory argues that, over time, adopting ESG practices can contribute to various long-term benefits that positively impact bank performance and risk management. These benefits include enhanced brand reputation, improved stakeholder relationships, reduced regulatory risks, increased customer loyalty, and access to sustainable funding sources. By integrating ESG practices, banks can mitigate environmental and social risks, strengthen governance structures, and align their activities with societal expectations. These factors can lead to improved financial performance, reduced operational risks, and enhanced long-term sustainability.\u003c/p\u003e \u003cp\u003eStakeholder theory argues that a firm should create value for all stakeholders and develop a practical, well-organized, efficient, and ethical approach to guiding firms in extremely complex and turbulent situations. Azmi et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have shown evidence that firms involved in ESG practices indirectly express their consent to meet stakeholder demands and abstain from the costs associated with tight compliance with formal contractual agreements. Porter and Kramer (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) have shown evidence that ESG practices are a source of economic and competitive advantage and business innovation instead of an expense or charitable act.\u003c/p\u003e \u003cp\u003eStewardship theory is a framework that argues managers, as stewards of the company dedicated to enhancing the company's long-term value by addressing the competing fascination of all stakeholders (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Stewardship theory suggests banks should embrace their role as stewards of societal and environmental well-being. By integrating ESG practices into their strategies and operations, banks can demonstrate their commitment to responsible stewardship, leading to improved financial performance, and create long-term value, mitigating risks, achieve sustainable financial performance and stakeholder trust.\u003c/p\u003e \u003cp\u003eThe resource-based theory considers ESG practices to be a strategic investment (Azmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This investment can help companies gain a competitive advantage by acquiring new resources and skills that would otherwise be challenging, leading to improved financial performance(Ruf et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Russo and Fouts, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By integrating ESG considerations into their operations, banks can develop unique capabilities and build a positive reputation as responsible and sustainable institutions. These ESG-related resources can enhance a bank's performance and risk management.\u003c/p\u003e \u003cp\u003eInstitutional theory suggests that the institutional context significantly shapes banks' adoption and implementation of ESG practices. By conforming to prevailing norms and meeting regulatory requirements, banks can enhance their legitimacy, manage risks, gain a competitive advantage, and access valuable resources.\u003c/p\u003e \u003cp\u003eCorporate Governance Theory suggests that strong governance mechanisms, including board oversight, accountability, transparency, shareholder engagement, and risk management practices, are essential for promoting the integration of ESG considerations within banks. By embracing ESG practices, banks can enhance their reputation, attract responsible investors, manage risks, and achieve sustainable financial performance. Moreover, effective corporate governance supports the alignment of ESG considerations with a bank's strategic objectives, ensuring responsible behavior and long-term value creation.\u003c/p\u003e \u003cp\u003eThe Sustainable Banking Theory suggests that banks that embrace ESG practices can achieve a balance between financial performance, social responsibility, and environmental stewardship. By integrating sustainability principles into their strategies, risk management practices, and product offerings, banks can enhance their reputation, identify new business opportunities, mitigate risks, and achieve long-term financial success while contributing to a more sustainable economy and society.\u003c/p\u003e \u003cp\u003eThe impact of ESG activity on firm financial performance and value has been widely examined from theoretical and empirical views and reported mixed results. It has shown positive, negative, and neutral effects, while others indicate a U-shaped relationship. The positive relationship of ESG activity with firm financial performance and value proposed that higher CSR gives firms a competitive edge by mitigating the risk, improving the credit rating, enhancing financial strength, cashing their reputation to obtain financing at lower borrowing costs, and increasing profitability (Cavaco and Crifo, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pornsit Jiraporn et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sheikh, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sun and Cui, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, the negative relationship aligns with the trade-off theory, suggesting that ESG increases costs (Baird et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gilley et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Servaes and Tamayo, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Surroca et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The neutral association recommended that being socially responsible does not impact firm profitability and value because the positive effects offset the negative effects (Baird et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gilley et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Servaes and Tamayo, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Surroca et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Brammer and Millington (2008) show a U-shaped connection between ESG activity and financial performance, which implies that this relationship depends on ESG activity investment level. In the early stage, ESG practices negatively affect financial performance as the costs outweigh the benefits, while the relationship reverses and becomes positive later.\u003c/p\u003e \u003cp\u003eBanking sector-related studies on ESG activity have primarily been concerned with financial performance. They usually report a significant effect on profitability, which is anticipated to increase bank value (Brogi \u0026amp; Lagasio, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Di Tommaso \u0026amp; Thornton, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Gangi et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) environmentally friendly banks experience lower degrees of risk of insolvency. Neitzert and Petras (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) argued that ESG practices decrease default and portfolio risk. Siueia et al. (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Wu and Shen (\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reported that bank CSR helps improve financial performance and reduces banks' non-performing loans. A recent study by Azmi et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) shows a non-linear nexus among ESG activity and bank value. At the same time, environmentally friendly activities affect the bank's value the most.\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Data\u003c/h2\u003e\n\u003cp\u003eThe study aims to examine the impact of ESG activity and bank lending strategy and, more intensely, determine the role of national culture and institutional quality. The sample comprises 53 banks from 19 European Union member countries from 2005\u0026ndash;2018\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e. We collected ESG-related data from the Thomson Reuters ASSET4 database, while the bank-level financial and accounting data from the Bankscope database. However, the data related to country-specific controls, such as GDP per capita, inflation, and dummy crisis taken from World Bank GFDD. The series of the institutional quality index (Political Stability, Rule of Law, Control of Corruption, Government Effectiveness, and Regulatory Quality) and national culture is attained from the Worldwide Governance Indicators (WGI)\u003csup\u003e4\u003c/sup\u003e and Hofstede Insights\u003csup\u003e5\u003c/sup\u003e. \u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table A presents a detailed explanation of the studied variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Measurement of variables\u003c/h2\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.1 Dependent variable\u003c/h2\u003e\n\u003cp\u003eWe followed the existing studies of Shabir et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), Hu and Gong (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Ҫolak and \u0026Ouml;ztekin (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). We used the natural logarithm of the annual growth rate of loans as the primary dependent variable for measuring loan activity, which is widely used in the existing banking literature (Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.2 Independent variable\u003c/h2\u003e\n\u003cp\u003eOur main explanatory variable is Environmental, social, and governance (ESG) obtained from the Thomson Reuters ASSET4 database. The database comprises 35\u0026nbsp;million individual indicators across all key asset classes, together with 8.5\u0026nbsp;million economic indicators. Since 2002, this database has measured a company's environmental, social, and governance performance and dedication. These scores are based on publicly available company-level data, such as yearly financial and corporate social responsibility reports and news media coverage. The scores are classified into ten groups., forming three pillars Environmental pillar (ENV), the social pillar (SOC), and the governance pillar (GOV), and collective ESG scores. This study has chosen the ESG combined score (ESG) as the main explanatory variable ranging from 1 to 100. A high ESG score indicates superior performance. Moreover, we also use the three pillars ENV, SOC, and GOV for robustness. Recently researchers have extensively used the ESG score of Refinitiv Eikon in their studies, such as (Bae et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bătae et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gaies and Jahmane, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shakil, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) among them.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.3. Measuring national culture\u003c/h2\u003e\n\u003cp\u003eIn this study, we followed the existing literature of Ashraf et al. (2016). Boubakri et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), Li et al. (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Zheng et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), and used the four cultural dimensions, namely, uncertainty avoidance (UAI), power distance (PDI), individualism/ collectivism (CLT), and masculinity/femininity (MAS), to capture the national cultural dimensions of each country. Although the literature has raised criticisms of Hofstede's scores over time, this has not reduced their demand and applicability (Guermazi and Halioui, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).Hooghiemstra et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) show that Hofstede's scores are \"the most widely used measures of national culture and have produced a widely accepted, well-defined, empirically based terminology to characterize culture.\"\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.4 Institutional environments\u003c/h2\u003e\n\u003cp\u003eA growing body of literature indicates that the quality of institutions substantially impacts the financial industry's functioning and performance in different countries (Bai and Ho, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nguyen, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bermpei et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) argued that stronger institutional quality improves regulators' enforcement capacity, thereby increasing the effect of bank regulation and supervision on bank stability. Giannetti (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e) argued that better institutions decrease information asymmetry and reduce agency problems. In this study, we followed the existing literature (Bermpei et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and used the Worldwide Governance Indicators (WGI) such as political stability, government effectiveness, regulatory quality, control of corruption, and the rule of law to capture the institutional quality environment of each country.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.5 Bank and Country-specific variables\u003c/h2\u003e\n\u003cp\u003eWe follow the existing literature on bank lending and credit growth (Nguyen et al., 2020; Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ҫolak and \u0026Ouml;ztekin, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and contain two types of control variables (bank-specific and country-specific) which potentially explain bank lending behavior and could affect the ESG activity and bank lending relationship. Bank-specific control variables are capitalization (CAP), calculated as the ratio of capital to assets; bank size (SIZ), measured as the natural logarithm of total assets; liquidity (LIQ) is estimated as the ratio of liquid assets to total assets, the net loan to total assets determines loan share (LTA), credit risk (NPL) is measured as non-performing loans, and bank efficiency (EFF) determined as the cost to income ratio. While the two country-specific control variables are GDP per capita (GDPpc) and inflation (INF) to account for the macroeconomic environment because the countries are diverse in economic and financial development, technological capacity, and geography. We also include a dummy variable for bank crisis which equals 1 for 2007\u0026ndash;2012, which captures the global crisis of 2007\u0026ndash;2009 and the sovereign debt crisis of 2010\u0026ndash;2012 and zero otherwise.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Empirical framework\u003c/h2\u003e\n\u003cp\u003eTo explore the impact of ESG activity and bank lending strategy in this study, we used the following baseline regression model, which was built based on existing literature in banking and finance (Hu and Gong, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nguyen et al., 2020; Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ҫolak and \u0026Ouml;ztekin, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$${LND}_{\\text{i},\\text{t}}= {{\\alpha }}_{\\text{i}}+ {{{\\beta }}_{1}\\text{E}\\text{S}\\text{G}}_{\\text{i},\\text{t}}+{{\\gamma }\\text{i} \\text{X}}_{\\text{i},\\text{t}}+{{\\delta }}_{\\text{k}}{\\text{Z}}_{\\text{j},\\text{t}}+{{{\\theta }}_{1}\\text{C}\\text{R}\\text{S}}_{,\\text{t}}+{\\text{ʎ}}_{\\text{t}}+ {{\\epsilon }}_{\\text{i},\\text{t}} \\left(1\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere i, j, and t represent bank, country, and year. LND is our dependent variable which represents bank lending. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ESG}_{it}\\)\u003c/span\u003e\u003c/span\u003eis our primary independent variable, which indicates the overall bank-level ESG scores. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a vector of our bank-related control variables, including the capital (\u003cem\u003eCAP\u003c/em\u003e), credit risk (NPL), bank efficiency (EFF), liquidity (\u003cem\u003eLIQ\u003c/em\u003e), size (\u003cem\u003eSIZ\u003c/em\u003e), and loan share (LTA); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{jt}\\)\u003c/span\u003e\u003c/span\u003e is a vector of country-related control variables, including GDP per capita (GDPpc) and inflation (INF); CRS is a crisis dummy. \u0026alpha;, \u003cem\u003e\u0026beta;\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta },\\)\u003c/span\u003e\u003c/span\u003e \u0026delta;, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma }\\)\u003c/span\u003e\u003c/span\u003e are the estimated coefficients of interest. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\alpha }}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{ʎ}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003erepresent the bank and time fixed effects. \u003cem\u003e\u0026epsilon;\u003c/em\u003e is the error term that indicates unobserved error terms. Eq.\u0026nbsp;(1) is an estimate with the fixed-effects mode.\u003csup\u003e6\u003c/sup\u003e Lastly, we use the robust standard error clustered at the bank level.\u003c/p\u003e\n\u003cp\u003eFurthermore, we observe the moderating impact of national culture and institutional quality on the relationship between ESG and bank lending. For this purpose, we expand our baseline model (1) by incorporating the interaction term between our moderators (M) and ESG as follow\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$${LND}_{\\text{i},\\text{t}}= {{\\alpha }}_{\\text{i}}+ {{{\\beta }}_{1}\\text{E}\\text{S}\\text{G}}_{\\text{i},\\text{t}}+{\\beta }_{2}{M}_{it}+{\\beta }_{3}{\\text{E}\\text{S}\\text{G}\\text{*}M}_{it}+{{\\gamma }\\text{i} \\text{X}}_{\\text{i},\\text{t}}+{{\\delta }}_{\\text{k}}{\\text{Z}}_{\\text{j},\\text{t}}+{{{{\\theta }}_{1}\\text{C}\\text{R}\\text{S}}_{,\\text{t}}+ \\text{ʎ}}_{\\text{t}}+ {{\\epsilon }}_{\\text{i},\\text{t}} \\left(2\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIn Eq.\u0026nbsp;(2), we include ESG*M to observe the interactive impact of ESG and moderating variables (e.g., institutional quality and national culture). Therefore, the coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}\\)\u003c/span\u003e\u003c/span\u003e are our primary interest. The rest of the variables and specifications are the same as our baseline model (1).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1. Descriptive statistics\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics of all variables used in this study. The average credit growth of banks in our sample is 0.0517 for 19 countries, ranging between \u0026minus;\u0026thinsp;3.747 and 4.135, with a standard deviation of 2.679. Regarding our main explanatory variable, ESG has an average value of 49.219 and 20.383 standard deviations. At the same time, the mean value of the sub-pillar of ESG, such as environmental, social, and governance, is 52.893, 53.895, and 48.129, respectively. Other banks and country-specific control factors also illustrate substantial variation around the sample means. Our undocumented correlation analysis implies no problem with multicollinearity in the regression estimation.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 1\u003c/p\u003e\n\u003cp\u003eDescriptive statistics\u003c/p\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSd\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.679\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-33.747\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94.135\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.219\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.573\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.531\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eENVD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.893\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.786\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.551\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSOCD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53.895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.336\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGOVD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48.129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97.373\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSIZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLIQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.264\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNPL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e638\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.734\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e661\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e59.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDPpc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.268\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.561\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.114\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eINF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.665\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.239\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUAI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIDV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.826\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.342\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePSB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.656\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.393\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCOC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.925\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.446\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.925\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eROL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.042\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGEF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.354\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: This table indicates the summary statistics for the variables used in this analysis\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2. Baseline regression results\u003c/h2\u003e\n\u003cp\u003eThe primary objective is to examine bank ESG activity's impact on bank lending in the European Union region banking sector. For this purpose, firstly, we regress the bank lending variable on bank ESG activity scores along with bank and country-specific control variables. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows our baseline regression model results, which observe bank ESG activity's impact on bank lending. Firstly, we observed the influence of ESG on bank lending has the expected sign. In column 1, we regress the ESG with bank lending along cross-sectional and time fixed-effects, but we do not incorporate bank and country-related control variables. We include the bank and country-related control variables in the next two columns. In column 4, we include all bank and country-related variables with cross-sectional and time fixed-effects to examine the effect of observing bank ESG activity's impact on bank lending. We observe that the coefficients of ESG are significant and positive at the 1% level. This outcome illustrates that the ESG has significantly impacted banking lending behavior. Regarding economic magnitude, we observe that a standard-deviation rise in ESG increases bank lending by 3.2% (19.50*0.164%). Overall, the positive effect of ESG practices on bank lending can be explained by the fact that better ESG performance helps to reduce information asymmetry, generates a certain signal to the capital market, lower Idiosyncratic risk and agency costs, and builds a good corporate reputation that creates a unique competitive advantage (Chiang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; El Ghoul et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Reber et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This helps banks adjust their investment strategies, maintain better lending relationships, and support their borrowers (Chiang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; He et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This improves bank credit growth and stability and is consistent with stewardship and good governance theories.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eESG impact on Bank lending\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVARIABLES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.304***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.681*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.421***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.108)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.350)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.090)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSIZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.807***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.916***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.370)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.415)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.146**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.120*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.064)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.060)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLIQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.591***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.493\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.281)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.790)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.269***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.722***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.228)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.491)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNPL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.072***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.073**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDPpc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.967***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.325***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.343)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.472)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eINF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.129)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.132)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.873**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.283***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.364)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.371)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-21.64***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.227**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22.99***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.272)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.156)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.294)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.508)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObservations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.553\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBank F.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime F.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote: This table demonstrates the results of our baseline regression Eq.\u0026nbsp;(1) using fixed effects to investigate the effect of ESG on bank lending. Robust Standard errors are clustered at the bank level and reported in parenthesis; ***, **, and * indicate statistically significant at 0.01, 0.05, and 0.1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding the bank-level control variables, our results mostly have the expected signs. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the liquidity and assets quality coefficient significantly positively affect bank lending. This indicates that the banks with a more liquid portfolio and better asset quality are more likely to expand their supply of loans. This outcome is consistent with theory and empirical data from the literature that suggests that greater liquidity and better asset quality create a greater capacity for banks to provide credit to the private sector(Altunbas et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mishra and Burns, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nguyen et al., 2020).\u003c/p\u003e\n\u003cp\u003eMeanwhile, the bank's size, capital, and credit risk affect bank lending negatively. The negative coefficient of bank size indicates that larger large-size banks have lower loan growth rates. This may be because large-sized banks focus more on the diversification in their asset portfolios, engaging in nontraditional banking activities and increasing market share stability rather than only on credit activities which lead to lower loan sizes(Demsetz and Strahan, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e; DeYoung and Torna, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nguyen et al., 2020; Shabir et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the negative link between capitalization and bank lending indicates that banks might decrease their assets to meet capital levels, which reduces bank credit availability(Fang et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The credit portfolio's riskiness adversely impacts the banks' capacity to enhance credit growth. Other things being equal, high non-performing loans reduce a bank's profitability and capital adequacy and negatively impact credit growth. This aligns with previous studies by (Ananou et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), which argued the significant adverse correlation between credit risk and bank lending behavior.\u003c/p\u003e\n\u003cp\u003eFor the country-specific control variables, the coefficient of economic growth and inflation is significantly positive and negative, which suggests that bank lending is higher for countries with high GDP growth and low inflation. These findings align with the theoretical possibility that an expanding economy will require more funds for new investment projects and the consumption of goods (Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the same time, the demand for loans increases, enhancing bank lending.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3 ESG activity and bank lending: Role of national culture\u003c/h2\u003e\n\u003cp\u003eAs recently viewed that legal and cultural differences across countries drive different stakeholder activities and management behavior(Brammer et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). In particular, the difference in national culture is widely recognized as a key factor in shaping changes between individuals' and organizations' values, belief systems, customs, and traditions(Hofstede, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). In addition, Thanetsunthorn (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) highlighted that national culture significantly influences attitudes related to the corporate decision-making process regarding ESG strategies and activities. Doidge et al. (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) indicated how country-specific factors override organizational structure in defining corporate governance behavior. Therefore, in this study, we use Hofstede's cultural dimensions to determine the role of national culture in ESG activity and bank lending nexus.\u003c/p\u003e\n\u003cp\u003eThe results reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e show that the coefficient of UAI and its interaction term ESGUAI is significantly negative at a 1% significant level in column 1. This indicates that banks operating in a country with a culture of uncertainty avoidance, stricter laws, regulations, and regulations lead to a reduction in bank lending. In comparison, the higher degree of uncertainty avoidance reduces the positive impact of ESG activity on bank lending. This adverse relationship may be because they are more likely to engage in less risky lending, and the cost of information disclosure and ESG engagement is higher than the benefit. This finding aligns with Aggarwal and Goodell (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Ashraf et al. (2016), who show that countries with higher scores on UAI are comparatively more risk averse.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eESG impact on Bank lending: Role of national culture\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVARIABLES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.110**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.097*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.133***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.052)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.049)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.048)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.051)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSIZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.625***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.913***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.602***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.622***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.465)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.496)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.459)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.456)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.405***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.738***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.387***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.404***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.399)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.428)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.357)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.411)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLIQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.677\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.566\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.839)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.952)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.876)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.920)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.011)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNPL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.083***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.088***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.088***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.082***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.013)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.013)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.013)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.013)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDPpc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.130**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.799*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.235***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.079**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.448)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.430)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.414)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.429)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eINF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.144\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.116)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.118)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.117)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.116)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.118***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.136***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.225***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.101***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.344)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.341)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.333)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.337)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUAI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.110***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGUAI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.387***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.357)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.089***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGPDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.389***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.120)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.252**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.513)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGMAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIDV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.495)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGIDV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.799*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.430)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.872***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.846***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.841***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.143***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.609)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.580)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.583)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.614)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObservations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e625\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.608\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.610\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.610\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.608\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote: This able displays the outcomes of our baseline regression Eq.\u0026nbsp;(1) to examine the moderating role of national culture on the relationship between ESG and bank lending. All regression models were estimated by using OLS regression. P-values calculated using heteroskedastic-robust standard errors and reported in parentheses. ***, **, and * indicate statistically significant at 0.01, 0.05, and 0.1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eColumn 2 in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e represents the findings on the effect of the PDI and its interaction term ESGPDI on bank lending. The coefficient estimates on PDI and ESGPDI are significantly negative at the 1% level, indicating that a large degree of power distance decreases bank lending and mitigates ESG activity's impact. This outcome is consistent with Kreiser et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Mihet (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). They show that power distance has a significant adverse effect on corporate risk-taking. Ferrary (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Zheng et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) argued that elevated levels of power distance society's bank-firm relationship are formal, and firms consider banks distant institutions that cannot be trusted. In such societies, firms are reluctant to make information readily available, and bank credit rejection rates are higher because banks do not have access to information (Berger and Udell, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eColumn 3 in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the impact of MAS and its interaction term with ESG on bank lending and shows that the coefficient on MAS is significantly positive at the 5% level. This indicates that banks in high masculinity societies increase bank lending. One interpretation of this finding is that females show less risk-seeking behavior than men in financial decision-making, regardless of contextual factors such as familiarity, costs, or ambiguity (Powell \u0026amp; Ansic, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e; Zheng et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Comparatively, managers in highly masculine cultural environments pursue risky investments and accelerate overinvestment issues (Zheng et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast, its interaction term ESGMAS is insignificant.\u003c/p\u003e\n\u003cp\u003eColumn 4 of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reports the coefficients of IND and ESGIND are statistically significant positive and negative at the 10% level. This suggests that individualistic national culture increases bank lending while mitigating ESG activity's impact. This implies that highly individualistic cultures overestimate their abilities and are excessively optimistic about their prediction's accuracy (Chen et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zheng et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Hence, they can be involved in risky activities that increase bank lending (Ashraf et al., 2016). These results are consistent with Ashraf et al. (2016), who show that banks operating in individualistic societies are likely to take the higher risk. Chui et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) argued that high individualism cultures remunerate individual performance and achievements, and encouragement for individual risk-taking is more critical than in collective societies where collective interest is paramount.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e4.4 ESG activity and bank lending: Role of institutional quality\u003c/h2\u003e\n\u003cp\u003eThe quality of institutions is a key factor that is normally considered an external mechanism to remove agency issues (Bai and Ho, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hunjra et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and significantly impacts the functioning and performance of the financial sector in various countries (Bai and Ho, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nguyen, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). As La Porta et al. (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) explain countries with better institutional quality have strong investor protection and highly efficient financial markets. Recent studies have determined the impact of institutional quality on bank lending (Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mishkin (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) shows that poor institutional quality and the legal system make it harder for lenders to enforce restrictive contracts. In comparison, Levine (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) argued that the legal system reform that strengthens and stabilizes creditor rights, contract enforcement, and accounting practices enhances financial development. Bae and Goyal (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Demirg\u0026uuml;\u0026ccedil;-Kunt \u0026amp; Detragiache (\u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e) show that financial instability is significantly linked to poor institution quality. A higher quality institution could reduce adverse selection, borrow moral hazard, and increase bank loan payment and credit growth. Thus, for more detailed insight into the ESG and bank lending relationship, we consider whether better institutional quality moderates the effect of ESG on bank lending. For this purpose, following the prior studies and used regulatory quality (RQL), control of corruption (COC), political stability (PST), the rule of law (ROL), and government effectiveness (GEF) to capture the institutional quality. Column 1\u0026ndash;5 in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the role of institutional quality on ESG activities and bank lending relationship. Institutional quality and interaction terms coefficient are statistically significant and positive in all estimation models. This implies that the better quality of institutions contributes significantly to improving bank lending and enhancing ESG activities' impact. This finding aligns with previous literature (Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eESG impact on Bank lending: Role of institutional quality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(5)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVARIABLES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.320***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.568*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.266***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.428***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.484***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.622)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.328)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSIZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.920***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.109***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.911***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.961***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.931***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.408)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.424)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.395)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.399)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.401)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.125**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.137**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.122**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.119*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.058)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.059)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.059)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.061)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.061)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLIQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.598\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.531\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.462\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.793)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.849)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.716)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.752)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.766)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.047***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.018)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNPL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.074**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.075**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.073**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.074**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.073*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.033)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.037)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDPpc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.683***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.852***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.650**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.913***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.755***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.604)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.522)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.654)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.551)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.619)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eINF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.136\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.130)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.134)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.131)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.132)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.126)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.366***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.205***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.292***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.312***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.314***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.394)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.377)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.376)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.366)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.351)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePSB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.584***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGPSB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.175*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.664)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCOC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.175*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.664)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGCOC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.689***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.033)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eREG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.369**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.585)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGREG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.650**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.654)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGEF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.207*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.666)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGGEF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.054***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eROL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.079**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.429)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESGROL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.012)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22.76***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-25.69***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-23.40***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-24.30***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-24.28***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.567)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.530)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.387)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.506)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.677)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObservations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBank F.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime F.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: This table expresses the outcomes of our baseline regression Eq.\u0026nbsp;(1) to examine the moderating role of institutional quality on the relationship between ESG and bank lending. Robust Standard errors are clustered at the bank level and reported in parenthesis; ***, **, and * indicate statistically significant at 0.01, 0.05, and 0.1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Robustness","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e5.1 Individual ESG dimensions and bank lending\u003c/h2\u003e\n\u003cp\u003eNext, for further insights and robustness, we evaluate the individual ESG dimension to explore whether the component of ESG scores (e.g., environmental, social and governance) differently affect lending. For this purpose, we use the ESG sub-pillar, which is environmental (ENV), social (SOC), and governance (GOV), as independent variables of interest, respectively. Our main objective for investigating the different dimensions of ESG activity arises from the fact that every industry has unique characteristics and faces different pressures from different stakeholders. Therefore maybe, the advantage of focusing only on one of the three ESG factors(Azmi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe results reported in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e show that the coefficient of environmental and governance pillars is statistically significant and positive at 1%, while a social pillar is significantly positive at 10%. This indicates that only environmental and governance factors are more relevant to the banking industry. This result aligns with the previous finding by Chiaramonte et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Azmi et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). They asserted that the environmental and governance components of ESG have a significant effect on the value and stability of banks. This may be because the rising awareness of environmental issues, climate change, rising temperatures, increasing sea levels, and the introduction of Sustainable Development Goals has been progressively increasing. Therefore, banks consider environmental and governance problems more when lending capital to firms.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eESG impact on Bank lending: Individual dimensions\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEnvironmental\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSocial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGovernance\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE/S/G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.685***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.233*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.374***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.678)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSIZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.913***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.906***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.925***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.418)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.415)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.418)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.977***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.255***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.963***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.475)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.556)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.437)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLIQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.518\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.653\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.584\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(2.791)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(2.787)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(2.782)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.041**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.042**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.041**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNPL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.073**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.074**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.073**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.030)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.030)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDPpc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-1.130**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.799*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-1.235***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.448)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.430)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.414)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eINF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.133\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.134)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.132)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.132)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-1.280***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-1.276***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-1.286***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.369)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.366)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.368)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e22.84***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e22.29***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e23.21***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(6.397)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(6.559)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(6.517)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObservations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBank F.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime F.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e This Table shows the results of our baseline regression equation (1) to examine the impact of each pillar of ESG on bank lending. In column (1), our main explanatory variable is the environmental pillar of ESG. In column (2), our main explanatory variable is the social pillar of ESG. In column (3), our main explanatory variable is the governance pillar of ESG. Robust Standard errors are clustered at the bank level and reported\u0026nbsp;in parenthesis; ***, **, and * indicate statistically significant at 0.01, 0.05, and 0.1.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e5.2 Alternative econometric methodology\u003c/h2\u003e\n\u003cp\u003eAlthough we incorporate a wide variety of control variables, we are still interested in endogeneity, which affects our previous findings. Our model might suffer from endogeneity problems because of reverse causality and omitted variables. Hence, we will now use alternative economic methods to address this issue and determine whether the different assumptions in the data generation process will affect our key findings.\u003c/p\u003e\n\u003cp\u003eTherefore, we follow the earlier literature for this purpose and use the 2SLS and two-step System Generalized Method of Moments (System GMM) to deal with potential endogeneity (Azmi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.1 2SLS estimation techniques\u003c/h2\u003e\n\u003cp\u003eFirstly, we re-estimate our regressions using a two-stage least squares (2SLS) regression and instrument the ESG activities for accounting for potential endogeneity due to a potential reverse causality issue. For this reason, we employ three instruments to handle the endogeneity linked with ESG activity. First, we follow the study of Azmi et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and employ the legal origins as an instrument for ESG activities. Recent studies by Beck et al. (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e) and La Porta et al. (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) argued that legal origins describe dissimilarities in international financial development. Second, following previous literature by Benlemlih and Bitar (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), Azmi et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Bhandari and Javakhadze (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), using the initial ESG score to instrument ESG. Third, we followed the literature of Shabir et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and used the predictor variable (ESG activity) lag values as a potential instrumental variable.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Panel A and B show the results of the first and second-stage regressions, respectively. In the first stage, we use ESG as the dependent variable and all our exogenous and instrument variables to predict ESG activity. In the second stage, we estimated value of ESG activities rather than the actual value. Panel B shows that the predicted ESG coefficients keep their significant and positive coefficient, verifying the earlier results. The essential condition for the validity of an instrument is to satisfy the instrumental variables exogeneity test, which confirms that it must affect our dependent variable (bank lending) through its impact on the endogenous variable (Bhandari and Javakhadze, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shabir et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eESG impact on Bank lending: Two-stage least squares (2SLS) estimation.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(5)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(6)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(7)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(8)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVARIABLES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eESG\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEnvironmental\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSocial\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eGovernance\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eESG\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEnvironmental\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSocial\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGovernance\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A: First-stage regression\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B: Second-stage regression\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.065***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.055***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.006**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.012)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIESG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.492***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.006***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.428***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.310***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.106)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.105)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(0.087)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eL.ESG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.361***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.291***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.518***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.144**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.076)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.063)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.104)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(0.072)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eESG/E/S/G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.894***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.772***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.406***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.675***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.260)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.032)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.089)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.065)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBank Controls\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCountry Controls\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5.354***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4.939***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.714***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e4.395***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.814***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.894***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3.827***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.870***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.364)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.388)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.407)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(0.356)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.427)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.260)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.425)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.465)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eObservations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.616\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.214\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eTests of endogeneity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDurbin (score)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17.291***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e65.372***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e9.815***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.512***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eWu\u0026ndash;Hausman\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17.291***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e65.370***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e9.813***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.510***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eTests of overidentifying restrictions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSargan (Score)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.523\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBasmann\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.269\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eFirst-stage regression summary statistics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMin. eigenvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e3469.35***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4145.81***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e438.63***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2013.77 ***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"17\"\u003eNote: This table presents the result of our baseline regression models using the two-stage instrumental variable egression. Panel A presents the first-stage regression. At the same time, Panel B presents the second-stage regression findings. Standard errors reported in parentheses; ***, **, and * stand for statistical significance at 0.01, 0.05, and 0.1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.2 GMM estimation technique\u003c/h2\u003e\n\u003cp\u003eFurthermore, we employ the two-step System GMM suggested by (Blundell and Bond, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e), which is especially more suitable for addressing the inconsistency caused by endogeneity. (Azmi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bilgin et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nguyen, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This approach has been widely used in banking and finance studies to eliminate the endogeneity bias (Demir and Danisman, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This model is based on two essential conditions. Firstly, the Hansen test for over-identification restrictions was used to confirm the validity of the instruments. At the same time, the second test applies to validate the non-autocorrelation hypothesis. However, the first-order autocorrelation did not show inconsistencies in the measure. This one is confirmed by second-order autocorrelation. For further accuracy, the two-step system GMM approach is appropriate for dealing with endogeneity issues (Al-Shboul et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Albaity et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e reports our results by using GMM estimators. This finding is like the previous outcome and shows that ESG engagement enhances bank lending and remains unchanged. Moreover, they satisfy the instruments' validity, showing no second-order autocorrelation among errors and overidentifying restrictions are valid, respectively(Bilgin et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eESG impact on Bank lending: GMM approach\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVARIABLES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLND\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eESG\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEnvironmental\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSocial\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGovernance\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL.CRD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.172*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.197***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.188**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.061)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.088)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.068)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.077)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESG/E/S/G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.504***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.645***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.466***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.390***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.088)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.134)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.114)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.083)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSIZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.397***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.282***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.515***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.107)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.096)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.109)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.111)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.459***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.341***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.463***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.110)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.101)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.110)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.106)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLIQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.116\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(7.420)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10.570)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(9.758)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10.351)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.426***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.609***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.492***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.099)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.080)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.107)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.090)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNPL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.083***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.091***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.096***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.095***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.027)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.023)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.026)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.010)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDPpc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.357***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.605***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.463***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.097)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.068)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.118)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.098)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eINF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.243\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.321*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.279)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.299)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.181)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.169)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.154**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.212*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.211*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.344*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.013)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.291)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.190)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.227)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.758\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.178\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.315)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(4.772)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(4.411)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.065)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObs.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e636\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAR (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAR (2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.467\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.537\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHansen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.801\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.864\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.910\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote: This table presents the result of our baseline regression models using the two-step System GMM regression. Standard errors reported in parentheses; ***, **, and * stand for statistical significance at 0.01, 0.05, and 0.1.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003e5.3 Role of the regulatory environment\u003c/h2\u003e\n\u003cp\u003eBanks are expected to affect by a country's regulatory environment, which fluctuates from country to country. Therefore, better bank regulation quality is useful for financial sector stability and soundness. (Mirzaei \u0026amp; Samet, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). It can put a stop to unnecessary risk-taking and encourage them to maintain larger capital buffers, enhancing their capacity to absorb losses effectively(Le et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mercieca et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). A more stringent framework for banking capital regulation and supervision lowers systemic risk, enhances financial institutions' performance and securities, improves lending technology, and increases loan availability(Qian and Strahan, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Deli and Hasan, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shabir et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, recently researchers have highlighted that it affects the operations and performance of banks in many ways, such as a tighter regulatory environment that discourages more risky bank lending and risk-taking behavior by imposing penalties on banks (Shabir et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hirtle et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) determine the impact of supervision on bank profitability, riskiness, and growth. They argued that banking supervision plays a significant role in reducing the risk of the banking sector. Therefore, we took into account the whole banking regulatory environment and looked at how ESG activity affected the bank lending nexus to draw a more precise picture. In this regard, following the studies of Nguyen (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Shabir et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and we incorporate (i) activity restrictions index (RES), (ii) capital stringency index (CSI), and (iii) private monitoring index (PMI) to control the bank regulatory environment that directly affects bank lending policies.\u003csup\u003e7\u003c/sup\u003e The activity restriction index takes into account the extent to which a country restricts banks' involvement in insurance, securities, and real estate operations. The capital stringency index (CSI) determines the minimum amount of capital banks must keep regarding their risk-weighted assets. The private monitoring index (PMI) measures how well a country's regulatory and supervisory policies enable private investors to watch the actions and governance of banks. Columns 3\u0026ndash;6 in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e represent the influence of ESG activity on bank lending after controlling the regulatory environment. This empirical finding remains unchanged after incorporating the bank regulatory variables in the model.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eESG impact on Bank lending: Additional Test\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(5)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(6)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(7)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(8)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(9)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVARIABLES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLND\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eESG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.540***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.419***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.084***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.495***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.357***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.376***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.542***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.366***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.149**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.080)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.096)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.095)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.099)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.110)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.101)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.065)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSIZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.287***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.017***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.920***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.911***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.905***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.029***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.426***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.073)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.083)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.462)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.384)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.415)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.416)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.408)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.411)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.420)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.157**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.122*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.111*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.120*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.123**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.127**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.049)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.068)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.060)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.061)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.061)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.061)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.060)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.060)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.059)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLIQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.892*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.839\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.684\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.473\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.545)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.411)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.894)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.769)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.858)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.806)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.823)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.849)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.109)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.012)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.018)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNPL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.074***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.083***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.078**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.078**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.076**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.076**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.070**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.076**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.088***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.019)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.030)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.033)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.027)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEFF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.019**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDPpc\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.442*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.487***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.202*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.610***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.677***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.592***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.048*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.829***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.729)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.828)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.697)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.630)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.556)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.520)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.529)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.581)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.519)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eINF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.152\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.190)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.134)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.129)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.132)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.131)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.132)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.129)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.133)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.131)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.478***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.832*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.137***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.288***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.301***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.285***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.255***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.206***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.911**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.472)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.462)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.381)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.369)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.373)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.370)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.369)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.379)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.362)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.200***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.074)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.456***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.122)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOSP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.364***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.099)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.125)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.409***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.121)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDCP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.304***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.108)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEFR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.162***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.0473)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.408**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.541\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-26.46***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-24.31***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22.35***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-23.04***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-22.93***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-23.07***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-18.99***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.146)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.762)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(7.048)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.069)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.869)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.544)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.440)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.631)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.844)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObservations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e318\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e637\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.639\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.684\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBank F.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime F.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003eNote: This table shows the results of our baseline regression Eq.\u0026nbsp;(1) using fixed effects to examine the effect of ESG on bank lending after including the regulatory environment and additional macroeconomic variables.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n\u003ch2\u003e5.4 Additional macroeconomic factors\u003c/h2\u003e\n\u003cp\u003eFinally, for further robustness and to evaluate the sensitivity of our relationships between ESG activity and bank lending by containing macroeconomic factors as control variables: trade openness, financial development, and financial freedom. Trade openness increases market competition by driving in more foreign firms and pressuring domestic companies to invest more in their infrastructure(Khan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Higher trade openness enhances the bank's borrower selection, decreasing risk and stability. Thus, regulatory authorities are encouraged to transform the financial sector to facilitate credit availability. Those financial reforms shape the banking sector to be more competitive, lower the cost of credit for firms and enhance the availability of credit in the economy(Khan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the level of financial development plays a significant role in enhancing the efficiency and effectiveness of the financial system infrastructure in a nation. It improves the smooth functioning of the payment system, financial intermediation process, and settlement system (Sanfilippo-Azofra et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, financial freedom measures banking efficiency and independence from government control and interference in the financial sector. Hence, we evaluate the sensitivity of our results by including trade openness, financial development, and financial freedom in the regression. We also obtain the significance level of the concerns variable that remains unaffected. At the same time, the coefficient on trade openness, financial development, and financial freedom is significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study investigates the effect of environmental, social and governance (ESG) activities on bank lending in the European Union banking sector. We use data from 53 banks in 19 nations from 2004 to 2018. Moreover, we also analyze the significant role of national culture and institutional environment in banking by exploring whether national culture and institutional environment moderate the connection between ESG activities and bank lending. For this reason, we used the panel fixed-effects model in our baseline estimations, while 2SLS and GMM were applied to control for endogeneity. Our findings reveal that ESG activities play a significant role in the European Union banking sector, significantly increasing bank lending. The importance of these dimensions is not surprising as awareness of environmental and good governance matters has become the focus of attention of all stakeholders in the last decade. People and investors gradually identify the impact of global warming and climate change and realize that banks have a significant role in funding environmentally conscious projects and reducing financing for dirty industries. Moreover, the national culture and institutional quality significantly affect the ESG activity and bank lending nexus. Our outcomes show that national cultural dimensions significantly impact bank lending and play a moderating role in the relationship between ESG activity and bank lending. In comparison, better institutional quality supports banks in increasing their credit availability and enhancing the ESG practice's constructive impact. The findings are robust to using the different ESG dimensions, alternative estimation techniques, and control for the regulatory environment and macroeconomic conditions.\u003c/p\u003e \u003cp\u003eOur empirical results support regulatory efforts to transition banks to ESG activity and improve non-financial information disclosure. Our findings support recent regulatory changes and policy developments in Europe brought about by the Non-Financial Reporting Directive. Big banks must disclose information about their social and environmental operations and how much they value human rights and diversity on their boards of directors.\u003c/p\u003e \u003cp\u003eThe primary limitation of this research is the small number of banks in our sample. We only use data from the 53 banks whose ESG information is available on Reuters. Therefore, more favorable results may have emerged if our sample size had been larger. A second limitation is that other variables (e.g., economic policy uncertainty and market competition) may influence the relationship between ESG and bank lending.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu, D., \u0026amp; Johnson, S. (2005). Unbundling institutions. \u003cem\u003eJournal of Political Economy\u003c/em\u003e, \u003cem\u003e113\u003c/em\u003e(5), 949\u0026ndash;995. https://doi.org/10.1086/432166\u003c/li\u003e\n\u003cli\u003eAggarwal, R., \u0026amp; Goodell, J. W. (2009). 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The impact of COVID-19 pandemic on bank lending around the world. \u003cem\u003eJournal of Banking and Finance\u003c/em\u003e. https://doi.org/10.1016/j.jbankfin.2021.106207\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e ESG and CSR are gradually used interchangeably in the literature.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The time of this study is based on the data availability.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We limited our sample to banks for which we have ESG data.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The institutional quality indicator ranges from \u0026minus;\u0026thinsp;2.5 to 2.5, with higher values related to better institutional quality. For a more detailed, please visit Kaufmann et al. (2009) and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://info.worldbank.org/governance/wgi/\u003c/span\u003e\u003cspan address=\"https://info.worldbank.org/governance/wgi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For further information, please visit \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hi.hofstede-insights.com/national-culture\u003c/span\u003e\u003cspan address=\"https://hi.hofstede-insights.com/national-culture\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Confirmed by the Hausman test.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Our primary objective is to control the bank regulation and supervision system, not to assess their impact on the banking sector of the sample countries.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National University of Science and Technology","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, Bank Lending, National culture, Institutional quality, European banks","lastPublishedDoi":"10.21203/rs.3.rs-4343842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4343842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of Environmental, Social and Governance (ESG) practices on bank lending in the European Union banking sector, using a sample of 53 banks from 19 European Union countries for the period 2004–2018. Our findings show that ESG activities play a significant role in increasing bank lending. Interestingly, the ESG pillars follow a different pattern. Environmental and governance-friendly activities impact bank lending more than social. Moreover, the national culture and institutional quality significantly affect the ESG activity and bank lending nexus. Our outcomes demonstrate that national cultural dimensions significantly impact bank lending and mediate the relationship between ESG activity and bank lending. In comparison, good quality institutional supports banks in increasing their lending capacity and enhancing the ESG practice's constructive impact. The empirical findings are quite robust to using the various ESG dimensions, alternative estimation techniques, and control for the regulatory environment and macroeconomic conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification: \u003c/strong\u003eG20, G21\u003c/p\u003e","manuscriptTitle":"Impact of ESG practices on European Bank Lending for Sustainability: The Role of Culture and Institutions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 15:43:50","doi":"10.21203/rs.3.rs-4343842/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":"14f900b2-faa5-4960-9e48-61e5d73e0148","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31308817,"name":"Finance"},{"id":31308818,"name":"Environmental Economics"}],"tags":[],"updatedAt":"2024-05-02T15:43:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 15:43:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4343842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4343842","identity":"rs-4343842","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}