Firm-Level Climate Change Exposure

We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.

Automated summary

We have developed a method to identify the attention given by earnings call participants to companies' climate change exposures. The method utilizes a machine learning keyword discovery algorithm and captures exposure to opportunities, physical impacts, and regulatory shocks associated with climate change. These measures cover more than 10,000 firms from 34 countries between 2002 and 2020. We demonstrate the usefulness of these measures in predicting important real outcomes related to the net-zero transition, particularly job creation in disruptive green technologies and green patenting, and show that they contain market-priced information.