Financial, spatial and systemic determinants of ESG scoring assigned to commercial banks

Abstract

The aim is to verify which financial, spatial and systemic importance variables interact with ESG scoring. Based on data from 628 banks from 63 countries, a multinomial ordered logit model was built with the explanatory variables of Sustainalytics and Moody's ESG scores. Results indicate that membership in the EU, being an SIB, capitalisation, and revenues have a positive effect on ESG. In contrast, an increase in leverage, NPL ratio, and profitability are associated with a deterioration in scorings. Results differ in terms of the spatial aspect (in the case of Sustainalytics, additionally, location in the US favours ESG scoring) and the spectrum of systemic importance (in the case of Moody's, it is both global and local dimensions). This study is the first attempt to identify common (to different methodologies) determinants of ESG scoring. Its originality is also determined by the demonstration of a relationship between spatial variables and SIB's membership and ESG scoring.

Determinants of Sustainability Performance of Manufacturing Companies Using Two-stage Data Envelopment Analysis

“…Six machine learning approaches were used to forecast ESG rating of companies using firm-specific and macroeconomic predictors in the research (Chowdhury et al, 2023). ESG rating was also analysed by (Gospodarowicz et al, 2024) using financial, spatial and systemic importance variables observed for banks by employing a multinomial ordered logit model. In order to assess the impact of a particular event, the difference-in-differences (DID) model was used in the study (Zhang et al, 2024).…”