Energy Dependence, Environmental Quality and Banking Sector Capital: New Evidence from OECD Countries

The current study investigates the relationships among environmental variables, energy sector characteristics, and the resilience of the financial sector using a panel dataset of OECD countries covering 2004–2021. For that purpose, information from the World Bank Global Financial Development Database and Sovereign ESG Data was used, along with the indicator of financial stability—bank capitalization, represented by the capital-to-asset ratio. This work uses an integrated empirical framework that includes panel regressions, clustering techniques, and machine learning models. The findings from fixed-effects panel regression indicate that methane emissions, PM2.5 air pollution, and energy dependence are negatively correlated with bank capitalization, whereas renewable energy consumption is positively correlated. Contrariwise, fossil fuel consumption is positively correlated with the dependent variable, perhaps indicating the financial conditions prevailing at the moment, but not accounting for the long-run sustainable perspective. Robustness checks, such as excluding major economies, using lagged specifications, and adding control variables, confirm the robustness of the main empirical relationships, yet the results need to be interpreted conditionally. Through clustering analysis, various regimes are observed across the sample, each characterized by different combinations of environmental, energy, and financial features. On the other hand, the machine learning results obtained using K-Nearest Neighbors and Random Forest algorithms are consistent with the regression analysis, revealing non-linearities in the data.