Firm‐Level Climate Change Exposure

ABSTRACT 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.

• IF
  Institute for New Economic Thinking
• DF
  Deutsche Forschungsgemeinschaft

  Awards: TRR 266, 403041268
• NN
  National Natural Science Foundation of China
• HE
  Higher Education Discipline Innovation Project

  Award: B18033
• UO
  University of California, San Diego