Climate transition risk metrics: Understanding convergence and divergence across firms and providers - Julia Anna Bingler; Chiara Colesanti Senni; Pierre Monnin (ETH Zurich, CER-ETH - Center of Economic Research, 2021)
Climate risks are now fully recognized as financial risks by asset managers, investors, central banks, and financial supervisors. Against this background, a rapidly growing number of market participants and financial authorities are exploring which metrics to use to capture climate risks, as well as to what extent the use of different metrics delivers heterogeneous results. To shed a light on these questions, we analyse a sample of 69 transition risk metrics delivered by 9 different climate transition risk providers and covering the 1,500 firms of the MSCI World index. Our findings show that convergence between metrics is significantly higher for the firms most exposed to transition risk. We also show that metrics with similar scenarios (i.e. horizon, temperature target and transition paths) tend to deliver more coherent risk assessments. Turning to the variables that might drive the outcome of the risk assessment, we find evidence that variables on metric's assumptions and scenario's characteristics are associated with changes in the estimated firms' transition risk. Our findings bear important implications for policy making and research. First, climate transition risk metrics, if applied by the majority of financial market participants in their risk assessment, might translate into relatively coherent market pricing signals for least and most exposed firms. Second, it would help the correct interpretation of metrics in financial markets if supervisory authorities defined a joint baseline approach to ensure basic comparability of disclosed metrics, and asked for detailed assumption documentations alongside the metrics. Third, researchers should start to justify the use of the specific climate risk metrics and interpret their findings in the light of the metric assumptions.
Keywords: C83; D53; D81; G12; G32; Q54; 330; financial climate risks; corporate finance; climate risk metrics; climate transition risk; spearman's rank correlation; hierarchical cluster analysis; Ward's minimum variance criterion; Lasso regression analysis