Who Finances the Carbon Transition? Financial Structure, Institutional Quality, and Emissions in OECD Economies

Angelo Leogrande, Fabio Anobile (), Alberto Costantiello, Carlo Drago and Massimo Arnone ()
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Alberto Costantiello: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro
Massimo Arnone: Unict - Università degli studi di Catania = University of Catania

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Abstract: This study aims to examine the interrelated effects of finance structure, institutional quality, and macro-demographics on CO₂ emissions per capita in OECD countries from 2004 to 2021. Building on the conventional linear and aggregate nature of the finance-environment relationship, this study suggests an improved methodology based on a hybrid framework combining panel estimation, machine learning-based clustering, and nonlinear modeling. The empirical findings support a positive relationship between bank-based intermediation structure, represented by private credit and credit quality, and CO₂ emissions per capita, which could be explained by a scale effect. At the same time, a negative relationship is found between non-performing loans and CO₂ emissions per capita. In addition, a negative relationship is found between the assets of pension funds and mutual funds and CO₂ emissions per capita. This suggests a critical role played by long-horizon investors in offsetting the carbon footprint of economic activity. Government effectiveness is found to have a positive relationship with CO₂ emissions per capita. This could reflect development stage considerations rather than institutional failure. Finally, a weak positive relationship is found between population density and CO₂ emissions per capita. This supports scale efficiencies. The K-means clustering methodology reveals a strong structural heterogeneity in the finance-environment relationship. This supports the view that there are unique structural regimes in which similar CO₂ emissions per capita outcomes are influenced by a variety of interrelated finance structure and institutional quality drivers. In addition, the Random Forest methodology outperforms other machine learning techniques. This suggests a strong nonlinear nature in the finance-environment relationship. Finally, the empirical findings support a relatively stronger emphasis placed on structural finance structure and institutional quality variables rather than short-run macroeconomic variables in explaining variations in CO₂ emissions per capita.

Keywords: C45; C23; Q56; Q54; Machine learning G20; Sustainable finance; Institutional quality; CO₂ emissions; Financial structure; Financial structure CO₂ emissions Institutional quality Sustainable finance Machine learning G20 Q54 Q56 C23 C45 (search for similar items in EconPapers)
Date: 2026-02-25
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