Banking on Sustainability: ESG Practices and Their Macroeconomic Influence in Europe

Abstract

This paper investigates the relationship between European GDP and firm-level characteristics of European banks, with a particular emphasis on environmental, social, and governance (ESG) performance. Using Ordinary Least Squares (OLS) regression, the analysis incorporates the ESG score, board size, average number of employees, executive members’ gender diversity, and management score grade as predictors of GDP. The results show that the ESG score and executive members’ gender diversity have a positive and statistically significant effect on GDP, while board size, average number of employees, and management score grade are negatively associated with GDP. To further examine the components of ESG, a stepwise regression was conducted including the environmental, social, and governance scores. The findings reveal that only the environmental score significantly contributes to explaining GDP, suggesting that environmental performance is the most economically impactful ESG pillar among European banks. These results underscore the importance of environmental initiatives in driving macroeconomic outcomes.

1. Introduction

In recent years, European policymakers have intensified their efforts to establish a regulatory framework for banks that fully integrates ESG principles to improve the sustainability in the financial system (Bruno & Lagasio, 2021). The push to return to normalcy after recent crises has sparked a new wave of corporate behavioural practices centered on awareness and transparency. In response to a surge of scandals involving reckless social and environmental practices, there is a growing demand for greater attention to ESG disclosures (Elali, 2021). Extensive research has explored the connection between ESG performance and incidents of corporate scandals. One notable study by Buallay et al. (2021), which analysed data from 882 banks across both developed and emerging markets following the 2008 financial crisis, revealed that ESG initiatives played a crucial role in enhancing banks’ financial outcomes, both in terms of accounting metrics and market valuation, in developing regions. These findings lend empirical support to the theory that non-financial elements, including governance standards, risk oversight, and environmental and social responsibility, contribute meaningfully to organizational value creation (Buallay et al., 2020).

Ehrenhard and Fiorito (2018) investigated the ethical principles upheld by the 25 largest European banks in the period following the financial crisis. Their findings indicated that values such as integrity and a focus on customer needs were the most commonly emphasized. Despite this, 15 of the institutions were linked to a variety of corporate misconduct, including enabling money laundering, engaging in dark pool trading, executing unauthorized transactions, evading taxes, manipulating benchmark interest rates like Libor and Euribor, and conducting business with sanctioned individuals or entities. In contrast, banks that prioritized socially inclusive values—such as respect, equality, and solidarity—were not involved in any major scandals (Ehrenhard & Fiorito, 2018).

A study of 93 banks from emerging markets identified a positive relationship between environmental and social performance and financial outcomes (Shakil et al., 2019). Furthermore, Buallay et al. (2020) highlighted that several developing countries are gradually emerging as key contributors to global sustainability efforts. In Pakistan, for instance, initiatives aimed at enhancing the financial sector have included the integration of Corporate Social Responsibility (CSR) practices. Similar patterns have been observed in Turkey, where Akdogan et al. (2020) examined CSR implementation. Their findings, supported by Szegedi et al. (2020), emphasize that social structures and the level of economic development are fundamental determinants in understanding and advancing CSR practices.

ESG practices are essential for all stakeholders in the banking sector. Strong social and governance performance, supported by effective Accounting and MIS, plays a critical role in mitigating financial risk and strengthening banks’ adherence to sound governance and environmental standards (Chollet & Sandwidi, 2018). When risk is low, financial uncertainty diminishes, thereby enhancing CSR engagement. Financial performance plays a pivotal role in management’s long-term decision-making, as banks with strong profitability are less concerned with short-term survival and are better positioned to invest in CSR initiatives (Bătae et al., 2020).

European countries actively support social and economic development, with the banking sector playing a vital role in driving growth and stability across the European economy (Buallay, 2019). According to the European Banking Authority’s (EBA) final report on ESG risk management guidelines, all financial institutions are required to incorporate ESG risks into their risk management frameworks (European Banking Authority, 2025). Banks have a significant impact on long-term economic growth. Given their influence on the broader economy, it is crucial for them to embrace sustainability by adopting ESG practices (Galletta et al., 2022).

This study makes a novel contribution to the field of sustainable finance by being the first, to the best of our knowledge, to examine the impact of the ESG practices of the European banking sector on the European GDP. While existing research has explored ESG factors and economic performance separately, no prior study has specifically focused on how the sustainability practices of banks influence macroeconomic outcomes within the European context. The findings provide timely and valuable insights for policymakers, financial institutions, and stakeholders seeking to align sustainability objectives with economic growth across Europe.

Given the widespread discourse surrounding CSR and transparency in environmental and social impact, as well as the limited research on the influence of ESG practices on macroeconomic indicators such as GDP, this study examines whether the ESG performance of European banks is associated with European GDP and broader macroeconomic outcomes.

2. Literature Review and Hypothesis Development

The banking sector plays a central role in economic development through the mobilization of savings and the allocation of financial resources to productive investments. Empirical evidence suggests that banking indicators such as deposits, investments, advances, profitability, and interest earnings contribute positively to economic growth, highlighting the importance of financial institutions in supporting macroeconomic performance (Aurangzeb, 2012). As banks influence investment decisions and capital allocation, their strategic orientation may extend beyond firm-level outcomes and affect broader economic activity.

In recent years, ESG-oriented governance models have become increasingly prevalent within the banking industry. This transition reflects a broader shift from traditional shareholder-oriented approaches toward governance structures that also acknowledge stakeholder interests and sustainability objectives (Leogrande & Costantiello, 2023). The development of ESG frameworks is closely associated with the evolution of Corporate Social Responsibility principles, which gradually incorporated environmental, social, and governance considerations into banking operations and decision-making processes (Ferri & Leogrande, 2021). Furthermore, social, ethical, and environmental concerns have long been embedded in European banking traditions, particularly within cooperative banking institutions (Ferri & Leogrande, 2022).

Despite the growing adoption of ESG practices in the banking sector, evidence regarding their relationship with macroeconomic outcomes remains limited and inconclusive. Buallay (2019) reports that banks operating in lower-GDP countries often exhibit higher ESG scores, while Miralles-Quirós et al. (2019) document a negative association between ESG-related value creation and GDP across an international sample of banks. In contrast, evidence from the West African banking sector indicates a positive, although statistically insignificant, relationship between ESG performance and GDP. These mixed findings highlight the need for further research examining whether the ESG performance of banks is associated with broader economic outcomes, particularly within the European context.

Through their financial intermediation function, banks influence the allocation of capital across sectors of the economy and play a key role in financing productive investment. As a result, ESG-oriented banking practices may affect economic activity indirectly by promoting sustainable projects, improving risk management processes, enhancing financial stability, and supporting more efficient capital allocation. From this perspective, the relationship between banks’ ESG performance and GDP is not expected to be direct, but rather to operate through the banking sector’s contribution to investment, productivity, and long-term economic development.

The risks and consequences of climate change are a growing concern for the banking sector, making risk adjustment and assessment through environmental strategies essential for banks (Basel Committee on Banking Supervision, 2021). Banks mitigate these risks by prioritizing sustainability, reducing their carbon footprint, improving energy efficiency, and investing in renewable energy projects (Weber, 2011; Raut et al., 2017; Gallego-Álvarez & Pucheta-Martínez, 2019; Khattak & Saiti, 2020). Environmental transparency and emission reduction are crucial for a bank’s stability, as shareholders and bondholders increasingly prioritize a bank’s commitment to sustainability (Azmi et al., 2021). Banks’ engagement in corporate social responsibility (CSR) practices fosters more prudent banking structures, strengthens relationships within the financial community, and enhances their reputation (Chiaramonte et al., 2021).

Banks consume significant amounts of resources, placing them under considerable pressure to deliver societal benefits (Azmi et al., 2021). Sound environmental practices play a vital role in supporting the social pillar, as environmentally conscious manufacturing not only benefits the planet but also makes a substantial contribution to human well-being, social development, and overall quality of life (Karia & Davadas Michael, 2022). Moreover, in the event of bankruptcy, banks are more likely to receive taxpayer-funded bailouts. As a result, their activities are subject to heightened scrutiny from regulators, citizens, and the media (Azmi et al., 2021). As such, there is a positive relationship between the ESG activities of European banks and their overall value (Finger et al., 2017). Also, the integration of ESG factors may impact a bank’s cost of capital, cash flow, and operational efficiency. Among these, the cost of capital and cash flow are generally regarded as the primary channels through which firm value is affected (Damodaran, 2012). However, an important question arises: Do ESG activities have a measurable impact on GDP?

The commitment of countries to safeguarding future generations by protecting the environment, promoting sustainable social and governance objectives, and prioritizing ESG performance has been associated with economic growth, as reflected in increased GDP (Diaye et al., 2021). At the firm level, Baek and Song (2024) identify a significant relationship between ESG ratings and macroeconomic indicators, including GDP. Firms with lower ESG ratings are more susceptible to inflation, global supply chain disruptions, and volatility in GDP growth. Moreover, improvements in the aggregate ESG performance of firms within a country are positively and significantly associated with higher living standards, as measured by GDP (Zhou et al., 2020).

There is substantial empirical evidence indicating that strong ESG performance positively influences economic growth (Hall & Jones, 1999; N. Stern, 2007; S. Stern et al., 2015; Nordhaus, 2008; Cracolici et al., 2010; Jacobs, 2013; Alam et al., 2017; Albrizio et al., 2016; Dechezleprêtre & Sato, 2017; Cohen & Tubb, 2018). Diaye et al. (2021) provide further support, finding that while the short-term impact of ESG on GDP is relatively modest, its long-term effects are significant. Their findings suggest that ESG performance contributes to GDP growth over time, demonstrating a delayed but meaningful influence.

The literature reviewed above suggests that ESG performance may influence macroeconomic outcomes through several channels, including sustainable investment, financial stability, governance quality, and organizational effectiveness. Accordingly, the following research hypotheses are proposed:

H1. 

The ESG performance of European banks is positively associated with European GDP.

H2. 

Executive members’ gender diversity is positively associated with European GDP.

H3. 

The environmental pillar exerts a stronger influence on European GDP than the social and governance pillars.

3. Methodology

Our analysis is based on annual data covering the period from 2015 to 2023. The data on European GDP were obtained from the International Monetary Fund (2025), while bank-specific data were sourced from the Refinitiv Eikon database. The variables utilized in the analysis include the banks’ ESG scores (ESGS), average number of employees (AVE), executive gender diversity (EMGD), board size (BS), management score grade (MSG), as well as the individual environmental (ENVS), social (SOCS), and governance (GOVS) scores (see

Table 1. Variable Definitions and Measurements.

Variable	Definition	Measurement
GDP	Gross Domestic Product of Europe	Billions of U.S. dollars
ESGS	Overall Environmental, Social and Governance Score	Refinitiv ESG score ranging from 0 to 100, where higher values indicate stronger ESG performance
ENVS	Environmental Score	Refinitiv environmental pillar score ranging from 0 to 100
SOCS	Social Score	Refinitiv social pillar score ranging from 0 to 100
GOVS	Governance Score	Refinitiv governance pillar score ranging from 0 to 100
AVE	Average Number of Employees	Average annual number of employees reported by each bank
EMGD	Executive Members Gender Diversity	Percentage of female executives within the bank’s executive management team
BS	Board Size	Total number of board members
MSG	Management Score Grade	Refinitiv management score assessing management quality and effectiveness

). The dataset comprises information on 50 banks operating across 19 European countries (see

Table 2. Summary Statistics.

Country	Number of Banks	Percent
Austria	2	4.0
Belgium	1	2.0
Czech Republic	1	2.0
Denmark	3	6.0
Finland	1	2.0
France	3	6.0
Germany	1	2.0
Greece	2	4.0
Hungary	1	2.0
Ireland	4	8.0
Italy	5	10.0
Netherlands	1	2.0
Norway	1	2.0
Poland	6	12.0
Portugal	1	2.0
Spain	5	10.0
Sweden	3	6.0
Switzerland	2	4.0
United Kingdom	7	14.0

). The selected period represents the most comprehensive and consistent range for which complete and reliable ESG data were available across the sampled banks, ensuring the robustness and comparability of the results.

Since the objective of this study is to examine the relationship between the ESG performance of major European banks and broader macroeconomic developments, aggregate European GDP was selected as the dependent variable. This measure was considered appropriate because the sampled banks operate within an integrated European financial environment characterized by common sustainability regulations, financial interconnections, and increasing banking market integration. Accordingly, the analysis focuses on the association between bank-level ESG characteristics and overall European economic performance rather than country-specific outcomes.

Table 1 presents the operational definitions and measurements of the variables employed in the empirical analysis. ESG-related indicators were obtained from the Refinitiv Eikon database and are expressed either as composite ESG measures or as pillar-specific scores ranging from 0 to 100, with higher values indicating stronger sustainability performance. In contrast, board size, executive gender diversity, management quality, and the average number of employees were included to capture governance and organizational characteristics that may influence economic outcomes. GDP was used as the dependent variable and measured in billions of U.S. dollars.

In this study, we adopted an econometric approach to investigate the relationship between ESG-related factors and GDP across European countries. The analysis was conducted using Ordinary Least Squares (OLS) regression, implemented through the statistical software EViews 13, to quantify the influence of selected independent variables on economic performance.

OLS is one of the most widely used estimation techniques in econometrics due to its simplicity and efficiency under standard assumptions. It minimizes the sum of squared residuals between the observed and predicted values of the dependent variable, thereby producing the Best Linear Unbiased Estimators (BLUE) of the regression coefficients, provided that the Gauss–Markov assumptions are satisfied (Greene, 2018). This foundational result, known as the Gauss–Markov theorem, underpins much of classical linear regression analysis in applied economic research.

Variable standardization was not applied because the primary objective of the study was the estimation and interpretation of the original coefficients in their natural units of measurement. Furthermore, the multicollinearity diagnostics, including correlation analysis and VIF statistics, indicated no significant collinearity concerns among the explanatory variables. Therefore, the use of non-standardized variables was considered appropriate for the purposes of the analysis.

In the initial phase of the analysis, GDP served as the dependent variable, representing economic output. The independent variables incorporated into the model were ESGS, BS, AVE, EMGD, and MSG. The model specification is presented as follows:

$$\mathrm{G}\mathrm{D}\mathrm{P}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{E}\mathrm{S}\mathrm{G}\mathrm{S}+{\mathsf{\beta }}_2\mathrm{B}\mathrm{S}+{\mathsf{\beta }}_3\mathrm{A}\mathrm{V}\mathrm{E}+{\mathsf{\beta }}_4\mathrm{E}\mathrm{M}\mathrm{G}\mathrm{D}+{\mathsf{\beta }}_5\mathrm{M}\mathrm{S}\mathrm{G}+\mathsf{\epsilon }$$

The primary aim of this regression was to identify the variables with a statistically significant effect on GDP and to measure the direction and strength of these relationships. Following the interpretation of the initial regression results, a second model was developed to conduct a more granular analysis focused on the individual components of ESG.

For this purpose, a stepwise regression method was employed. Stepwise regression allows for the iterative inclusion and exclusion of variables based on statistical criteria, enhancing model efficiency and interpretability. In this second model, GDP remained the dependent variable, while the independent variables were disaggregated into the three principal pillars of ESG: ENVS (Environmental Score), SOCS (Social Score), and GOVS (Governance Score).

This methodological step was designed to determine which of the ESG pillars exerts the most substantial impact on economic performance. By applying the stepwise approach, the analysis was able to isolate the most influential dimension of ESG, thereby offering a more precise understanding of how sustainability factors correlate with GDP fluctuations in the European context.

As a robustness check, fixed-effects and random-effects panel estimations were also performed. Given the panel structure of the dataset, these techniques allow for controlling for unobserved heterogeneity across banks. Subsequently, a Hausman specification test was conducted to determine the most appropriate panel-data estimator and assess the robustness of the baseline OLS findings.

4. Results

Table 3. Descriptive Statistics.

Variables	Mean	Median	Maximum	Minimum	Std. Dev.	Skewness	Kurtosis
GDP1	22,050.73	21,846.88	25,802.40	19,284.23	2164.68	0.29	1.86
ESGS	65.91	70.25	95.54	9.21	18.10	−0.76	3.01
ENVS	79.36	85.99	98.34	15.25	18.71	−1.64	5.23
SOCS	67.89	72.02	97.65	4.86	19.28	−0.85	3.26
GOVS	62.96	67.34	97.47	12.55	21.44	−0.51	2.23
AVE	40,626.18	16,777.50	186,944.50	2052.75	49,914.19	1.70	5.09
EMGD	17.85	16.67	62.50	0.00	13.60	0.51	2.70
BS	13.03	13.00	28.00	3.00	4.07	0.39	3.38
MSG	8.20	9.00	12.00	1.00	3.29	−0.60	2.22

presents the descriptive statistics of the variables employed in the analysis. The average ESG score of the sampled banks is 65.91, indicating a relatively strong level of sustainability performance. Among the individual ESG pillars, the Environmental Score exhibits the highest mean value (79.36), suggesting that environmental considerations receive greater attention than social and governance dimensions. Executive gender diversity remains relatively limited, with an average value of 17.85%, while board size averages 13 members. Furthermore, the large standard deviation observed for the average number of employees reflects substantial differences in organizational size across the sampled banks. Overall, the descriptive statistics indicate considerable variation in both ESG performance and organizational characteristics, supporting their inclusion in the empirical analysis.

To assess potential multicollinearity among the independent variables, a Pearson correlation analysis and Variance Inflation Factor (VIF) test were conducted. The correlation matrix (see

Table 4. Correlations.

		GDP	ESGS	BS	AVE	EMGD	MSG
GDP	Pearson Correlation	1	0.255 **	−0.013	−0.025	0.211 **	0.132 **
	Sig. (2 − tailed)		0.000	0.777	0.590	0.000	0.005
	N	450	450	450	450	450	450
ESGS	Pearson Correlation	0.255 **	1	0.378 **	0.592 **	0.259 **	0.685 **
	Sig. (2 − tailed)	0.000		0.000	0.000	0.000	0.000
	N	450	450	450	450	450	450
BS	Pearson Correlation	−0.013	0.378 **	1	0.359 **	−0.137 **	−0.034
	Sig. (2 − tailed)	0.777	0.000		0.000	0.004	0.469
	N	450	450	450	450	450	450
AVE	Pearson Correlation	−0.025	0.592 **	0.359 **	1	−0.007	0.383 **
	Sig. (2 − tailed)	0.590	0.000	0.000		0.882	0.000
	N	450	450	450	450	450	450
EMGD	Pearson Correlation	0.211 **	0.259 **	−0.137 **	−0.007	1	0.278 **
	Sig. (2 − tailed)	0.000	0.000	0.004	0.882		0.000
	N	450	450	450	450	450	450
MSG	Pearson Correlation	0.132 **	0.685 **	−0.034	0.383 **	0.278 **	1
	Sig. (2 − tailed)	0.005	0.000	0.469	0.000	0.000
	N	450	450	450	450	450	450

Note: *** p < 0.01; ** p < 0.05; * p < 0.10.

) reveals no high correlations exceeding the 0.8 threshold, with the strongest correlation observed between ESGS and MSG (r = 0.685, p < 0.01). These results suggest that the independent variables are not excessively correlated with each other, reducing the concern for multicollinearity. In addition, the VIF values (

Table 5. Collinearity Statistics.

Variables	Tolerance	VIF
ESGS	0.303	3.295
BS	0.646	1.548
AVE	0.610	1.640
EMGD	0.843	1.186
MSG	0.429	2.332

) were examined to further evaluate multicollinearity. The VIFs for all independent variables are below the commonly accepted threshold of 10, with values ranging from 1.186 (EMGD) to 3.295 (ESGS). These results indicate that multicollinearity is not a significant issue in the model, as all VIF values are well within acceptable limits. Therefore, both the correlation and VIF tests suggest that the independent variables provide unique contributions to explaining GDP without problematic overlap.

Following the multicollinearity checks, an OLS regression was conducted to examine the relationship between the independent variables (ESGS, BS, AVE, EMGD, and MSG) and GDP (see

Table 6. OLS Regression Results.

Variable	Coefficient	Prob.
ESGS	63.32587	0.000 ***
BS	−66.26759	0.023 **
AVE	−0.009767	0.000 ***
EMGD	16.75088	0.029 **
MSG	−117.1064	0.008 ***

Note: *** p < 0.01; ** p < 0.05; * p < 0.10.

).

The coefficient for ESGS is 63.33 (p < 0.001), meaning that for every one-unit increase in ESGS, GDP is expected to increase by 63.33 units, holding all other variables constant. This relationship is statistically significant at the 1% level. Similarly, the coefficient for BS is −66.27 (p = 0.023), indicating that a one-unit increase in BS is associated with a decrease of 66.27 units in GDP, controlling for other variables. This effect is statistically significant at the 5% level. The coefficient for AVE is −0.0098 (p < 0.001), suggesting that for each unit increase in AVE, GDP decreases by 0.0098 units. This relationship is statistically significant at the 1% level.

For EMGD, the coefficient is 16.75 (p = 0.029), meaning that a one-unit increase in EMGD corresponds to an increase of 16.75 units in GDP, holding other variables constant. This effect is statistically significant at the 5% level. Finally, the coefficient for MSG is −117.11 (p = 0.008), indicating that for each unit increase in MSG, GDP decreases by 117.11 units, with this relationship being statistically significant at the 1% level.

In conclusion, the regression results suggest that ESGS, AVE, and MSG have significant effects on GDP, with ESGS and EMGD having positive impacts, while BS, AVE, and MSG negatively affect GDP. The statistical significance of these coefficients, indicated by their p-values, confirms that these relationships are unlikely to have occurred by chance.

The positive and statistically significant coefficients of ESGS and EMGD provide support for H1 and H2, respectively.

In order to explore the specific ESG pillar that most significantly affects European GDP, a second stepwise regression was conducted. The analysis focused on determining whether the Environmental Score, Social Score, or Governance Score of European banks has the greatest impact on GDP. The results, shown in

Table 7. Stepwise Regression Results.

Variable	Coefficient	Prob.
ENVS	27.206	0.000 ***
Excluded Variables
SOCS	0.13	0.373
GOVS	0.086	0.786

Note: *** p < 0.01; ** p < 0.05; * p < 0.10.

, indicate that the Environmental Score is the only variable that significantly contributes to explaining GDP.

In the final model, the Environmental Score is included, with an unstandardized coefficient of 27.21 (p < 0.001), meaning that for each one-unit increase in the Environmental Score, GDP increases by 27.21 units. The standardized beta for the Environmental Score is 0.235, indicating a moderate effect relative to the other predictors in the model. This relationship is statistically significant (t = 5.120, p < 0.001).

The Social Score and Governance Score were both excluded from the model. The Social Score had a t-value of 1.368 (p = 0.173) and a partial correlation of 0.065, indicating that it did not have a statistically significant impact on GDP. Similarly, the Governance Score had a t-value of 1.666 (p = 0.096) and a partial correlation of 0.079, suggesting that its influence on GDP was minimal in this context. The collinearity statistics for both excluded variables indicate no issues with multicollinearity, as their tolerance values are well above the critical threshold of 0.1.

The finding that only the Environmental Score remains statistically significant supports H3, suggesting that the environmental pillar exerts a stronger influence on GDP than the social and governance dimensions.

To further evaluate the robustness of the baseline OLS estimates, a fixed-effects panel regression was conducted (see

Table 8. Fixed-Effects Robustness Check.

Variable	Coefficient	p-Value
ESGS	154.792	0.000
BS	−100.931	0.033
AVE	−0.055	0.002
EMGD	84.221	0.000
MSG	−252.943	0.000

).

Table 8 presents the results of the fixed-effects robustness analysis. To assess the robustness of the OLS estimates, additional panel-data estimations were performed using fixed-effects and random-effects models. The Hausman specification test strongly rejected the random-effects model (χ² = 195.87, p < 0.001), indicating that the fixed-effects specification is more appropriate for the dataset. Importantly, the fixed-effects results remained consistent with the OLS findings, as ESG Score and Executive Members’ Gender Diversity retained positive and statistically significant coefficients, while Board Size, Average Employees, and Management Score Grade remained negatively associated with GDP. These findings provide additional support for the robustness of the reported relationships.

5. Discussion

The findings of this study suggest that the ESG performance of European banks is associated with macroeconomic outcomes, although the magnitude and direction of the effects differ across the examined variables. The positive relationship identified between ESGS and GDP indicates that sustainability-oriented banking practices may contribute to economic activity through more efficient capital allocation, improved risk management, and greater support for sustainable investments. At the same time, the results highlight that not all ESG dimensions exert the same influence on economic performance, suggesting the existence of heterogeneous transmission channels between bank-level sustainability practices and broader macroeconomic indicators.

The positive and statistically significant coefficient of ESGS suggests that banks with stronger sustainability performance are associated with higher levels of economic activity. A possible explanation is that ESG-oriented institutions tend to adopt more effective risk management practices, improve transparency, and allocate capital more efficiently, factors that may contribute to investment and long-term economic development. This finding supports the view that sustainability initiatives within the banking sector may extend beyond firm-level benefits and influence broader macroeconomic outcomes.

The positive association between ESG performance and GDP is broadly consistent with the findings of Diaye et al. (2021), who report that ESG performance contributes positively to economic growth over time. Similarly, Zhou et al. (2020) identify a positive relationship between aggregate ESG performance and macroeconomic indicators. These findings provide additional support for the argument that sustainability practices may extend beyond firm-level benefits and contribute to broader economic outcomes.

Executive members’ gender diversity was also found to be positively associated with GDP. This result may indicate that more diverse management teams contribute to improved strategic decision-making, broader perspectives, and enhanced governance quality. Previous studies have frequently associated diversity with higher organizational effectiveness and stronger corporate performance, suggesting that diversity-related governance practices may generate benefits that extend beyond the firm level.

In contrast, board size, average number of employees, and management score grade exhibited negative coefficients. One possible explanation is that larger organizational structures may increase operational complexity and reduce flexibility in decision-making. Furthermore, these variables may reflect internal organizational characteristics whose effects on macroeconomic performance are indirect and therefore more difficult to capture through aggregate measures such as GDP.

This finding can be contextualized within the European Union’s strong policy orientation toward environmental sustainability, particularly following the implementation of initiatives such as the European Green Deal and the Sustainable Finance Disclosure Regulation (SFDR). Given that the majority of the sample comprises EU-based banks, these regulatory frameworks may have encouraged both green financing activities and environmentally responsible investments. As a result, the environmental dimension of ESG appears to exert a more visible impact on macroeconomic outcomes than the social and governance pillars.

The exclusion of the Social and Governance scores from the final model suggests that their contribution to GDP may be less direct or may materialize over a longer period. While governance mechanisms and social initiatives remain important for institutional quality, risk mitigation, and corporate reputation, their effects may not translate immediately into measurable changes in economic output. In contrast, environmental investments are more likely to stimulate innovation, infrastructure development, and employment, thereby generating a more direct link with GDP.

However, the present findings differ from those of Miralles-Quirós et al. (2019), who document a negative association between ESG-related value creation and GDP. Such differences may be explained by variations in institutional settings, sample composition, regulatory frameworks, and measurement approaches. Consequently, the relationship between ESG performance and macroeconomic outcomes may be context-dependent.

6. Conclusions

This study examines how various firm-level governance and sustainability indicators of European banks relate to European GDP. Through OLS regression analysis, the results demonstrate that ESGS and EMGD positively influence GDP, while BS, AVE, and MSG have significant negative effects. These findings underscore the complex role corporate structures and ESG orientation play in broader economic performance.

Further analysis through a stepwise regression model reveals that, among the ESG pillars, only ENVS significantly contributes to GDP, with SOCS and GOVS excluded due to a lack of statistical significance. This suggests that environmental efforts by banks—such as sustainability initiatives or climate-related disclosures—may have a more tangible economic impact than social or governance efforts, at least within the scope of this study.

Overall, the results highlight the growing economic relevance of environmental performance in the banking sector and suggest that policies or investment strategies focused on environmental factors may yield broader macroeconomic benefits in Europe.

The findings of this study have important implications for policymakers, financial institutions, and investors. For policymakers, the results suggest that promoting ESG integration within the banking sector may support both sustainability objectives and broader economic development. The significance of the environmental pillar highlights the potential value of policies encouraging green finance and environmentally sustainable investments.

For banking institutions, the findings indicate that ESG initiatives, particularly environmental practices, may contribute not only to organizational sustainability but also to wider economic outcomes. Strengthening ESG performance may therefore enhance banks’ contribution to sustainable growth while improving alignment with evolving regulatory requirements.

For investors, the results provide additional evidence that ESG-related information should be considered when evaluating financial institutions, given its association with sustainability performance and macroeconomic outcomes.

Despite its contributions, this study has certain limitations. First, aggregate European GDP was used as the dependent variable, which may not fully capture cross-country differences across European economies. Second, the analysis is based on contemporaneous annual observations. Although previous studies suggest that the effects of ESG performance may materialize over longer time horizons, the present study focuses on the short-to-medium-term association between banking ESG characteristics and macroeconomic performance. Future research could employ lagged variables and dynamic panel models to further examine the long-term effects of ESG practices on economic growth.

For future research, we intend to expand the scope of analysis by incorporating a larger and more diverse sample that includes banks from across the globe, coupled with an extended temporal coverage. This broader approach would enable a more comprehensive examination of the relationship between ESG practices and economic outcomes within the global banking sector. Moreover, by analyzing data segmented by continent, the study could yield valuable comparative insights into how the impact of banks’ ESG performance varies across different regional contexts, reflecting diverse regulatory environments, market dynamics, and sustainability priorities. Such an expanded framework would enhance the generalizability of findings and provide more nuanced guidance for policymakers, financial institutions, and stakeholders worldwide.