Climate-related credit risk: Rethinking the credit risk framework

Abstract

Climate change and the challenges associated with the transition to a zero-carbon economy pose significant financial risks. Climate-related risks (CRR) indirectly impact banks through their loan portfolios. To examine the integration of CRR into banks' credit risk assessment and monitoring, this article reviews academic and institutional literature using quantitative bibliometric techniques and content analysis of 145 academic documents from policymakers and financial supervisors. A framework emerges that incorporates CRR into credit risk management. We find four thematic areas in the literature: CRR drivers, CRR tools, CRR data and CRR pricing. Overall, uncertainty, non-linearity, geographic and industrial dependency and non-reversibility of CRR difficult climate-related credit risk assessment. Moreover, CRR data present comparability, availability and reliability issues, which Artificial Intelligence can improve. Finally, evidence reveals that current financial prices do not fully reflect CRR. Our findings provide important implications to policymakers for assessing ex-ante the financial impacts of climate transition regulations, the potential for prudential regulatory action, and the need for supra-national policies that facilitate access to reliable and comparable climate data.

Climate change and the challenges associated with the transition to a zero-carbon economy pose significant financial risks. Climate-related risks (CRR) indirectly impact banks through their loan portfolios. To examine the integration of CRR into banks' credit risk assessment and monitoring, this article reviews academic and institutional literature using quantitative bibliometric techniques and content analysis of 145 academic documents from policymakers and financial supervisors. A framework emerges that incorporates CRR into credit risk management. We find four thematic areas in the literature: CRR drivers, CRR tools, CRR data and CRR pricing. Overall, uncertainty, non-linearity, geographic and industrial dependency and non-reversibility of CRR difficult climate-related credit risk assessment. Moreover, CRR data present comparability, availability and reliability issues, which Artificial Intelligence can improve. Finally, evidence reveals that current financial prices do not fully reflect CRR. Our findings provide important implications to policymakers for assessing ex-ante the financial impacts of climate transition regulations, the potential for prudential regulatory action, and the need for supra-national policies that facilitate access to reliable and comparable climate data.