Firm-level Climate Change Exposure

By Zacharias Sautner, Laurence van Lent, Grigory Vilkov, Ruishen Zhang. Social Science Research Network, 2020

692 citations recorded by the source corpus.

We introduce a method that identifies climate change exposure from earnings conference calls of 10,158 firms from 34 countries. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The exposure measures exhibit cross-sectional and time-series variations that align with reasonable priors, and these measures are better at capturing firm-level variation than are carbon intensities or ratings. The exposure measures capture economic factors that prior work has identified as important correlates of climate change exposure. In recent years, exposure to regulatory shocks negatively correlates with firm valuations.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/cd76ca1e6c00b79bfc2777e6bcb99bcfee719acd

Citations in the corpus

• Dissecting Green Returns (2021, neutral): “We find that this measure is significantly related to quarterly stock-level returns but (15)Sautner et al. (2021) provide independent evidence that climate concerns strengthen after 2012.”