How (Not) to Measure Companies' Climate Transition Risk: A Framework and Categorized Literature Review

The climate transition is increasingly acknowledged as a major risk driver for companies, financial institutions, and investors. At the same time, there is widespread confusion about how to best measure climate transition risk, since different measurements lead to significantly different risk profiles. I show empirically that, to date, the two most common proxies for climate transition risk are CO2 and E(SG) score data. I further contribute to the transition risk literature by proposing a comprehensive 10‐category framework, specifically tailored toward assessing climate transition risk proxies. I apply the framework by executing the first category‐led literature review on the quality of both CO2 data and E‐scores as proxies for climate transition risk. I find that both data types are dynamic and strong in terms of granularity as well as usability; but have shortcomings across a multitude of categories: bias, availability, comparability, the backward‐looking nature of the metrics, and transition risk specificity being the most severe issues. I urge scholars to reflect on these shortcomings as they could significantly distort results. As a minimum, scholars should test for the robustness of their results when relying solely on ESG or CO2 data to classify transition risk. Therefore, I propose both within and between transition risk metric robustness tests, which are not yet commonly used in the literature. I close by introducing and discussing alternative proxies for climate transition risk, such as EU taxonomy alignment, sector/technology classifications, or innovative combinations of risk metrics. I argue that scholars should consider these alternatives since they are potentially less biased, more specific to transition risk, comparable, and available. I thereby contribute to a better measurement of companies' transition risk, which is a key prerequisite for accurately managing and correctly pricing climate transition risk.