E-factor augmentation: a method to quantify the environmental factor in systemic risk analysis

Abstract

Purpose – The paper presents a new method that quantifies environmental risk in systemic risk measurement based on the exposure approach using an existing E-score as the source of information about bank exposure to environmental risks. Our method allows us to base the impact of environmental risk exposure on individual characteristics of banks and their systemic risk levels. Design/methodology/approach – We extract the environmental factor (E-factor) from each bank’s environmental score (part of the ESG score) and augment systemic risk measurement with it. We apply econometric systemic risk models to quantify systemic risk, and for each, we add the E-factor using a conditional sensitivity function. We demonstrate our method empirically on two systemic risk models: CoVaR and SRISK, using a sample of 20 systemically important European banks from 12 European countries between 2007 and 2023. Findings – Our method captures a bigger impact of the environmental risk factor in periods of instability. Moreover, the E-factor records higher impacts on more fragile banks. This observation holds equally for banks from developed and emerging countries, regardless of whether they are global or local systemically important financial institutions. With the E-CoVaR and E-SRISK rankings constructed, we illustrate the contrasts between Western Europe and the CEE region. Higher environmental risk is quantified for the latter, with Russian, Romanian and Polish banks at the bottom of the environmental risk exposure ranking.Research limitations/implications – The presented risk quantification methods are universal in the technical sense and applicable to other systemic risk measures and other environmental scores, while the ranking methods may be of value for the regulators as they allow them to identify the banks that are most prone to losses based on their systemic-risk-based environmental exposure. Practical implications – Regulators and financial institutions can leverage the proposed ranking methods to identify environmentally vulnerable banks, encouraging them to implement more targeted interventions to mitigate climate-related financial risks. Enhanced monitoring of weak links and exposures within the banking sector can help regulators anticipate systemic disruptions and require banks to strengthen buffers against climate-induced shocks. Social implications – Over the long term, this research could influence regulatory frameworks by encouraging the integration of climate risk considerations into financial stability assessments, ultimately reducing spillover effects and systemic crises that produce significant environmental and social costs. Originality/value – The paper addresses a research gap by proposing a novel method of environmental risk measurement and its application to, inter alia, the CEE region.