CEFGroup’s Professor Ivan Diaz-Rainey and Dr Quyen Nguyen presented to Stanford University‘s Sustainable Finance and Investment Seminar today. They introduced how transition risk is measured and highlighted associated challenges with particular focus on corporate carbon footprints,
Diaz-Rainey and Nguyen showed that the application of machine learning algorithms to fill the carbon data gaps lead to improved prediction accuracy especially in Scope 1 and Scope 2 emissions. For Scope 3 emissions, firms’ voluntary disclosures have measurement divergence and incomplete composition problems, and prediction accuracy is primarily limited by low observations in particular categories.
Finally, they used actual and predicted corporate carbon footprint data to examine US banks’ exposure to climate transition risk using a bottom-up, loan-level methodology incorporating climate stress test based on the Merton probability of default model and transition pathways from the Intergovernmental Panel on Climate Change.
The presentation drew from several research projects of Diaz-Rainey and Nguyen, including their work with EMMI.
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