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.

We propose a text‐based method for measuring the cross‐border propagation of large shocks at the firm level. We apply this method to estimate the expected costs, benefits, and risks of Brexit and find widespread reverberations in listed firms in 81 countries. International (i.e., non‐U.K.) firms most exposed to Brexit uncertainty (the second moment) lost significant market value and reduced hiring and investment. International firms also overwhelmingly expected negative first‐moment impacts from the U.K.'s decision to leave the European Union (EU), particularly related to regulation, asset prices, and labor market impacts of Brexit.