Introduction to the Company Level Climate Change Exposure Index

Firm-level Climate Change Exposure

• Contributors: Zacharias Sautner, Laurence van Lent, Grigory Vilkov, Ruishen Zhang
• Date created: 2020-06-29 10:50 PM | Last Updated: 2021-10-08 05:02 PM
• Identifier: DOI 10.17605/OSF.IO/FD6JQ

• Description: We introduce a method that identifies firm-level climate change exposures from conversation in earnings conference calls of more than 10,000 firms from 34 countries between 2002 and 2020. The method captures exposures related to opportunity , physical, and regulatory shocks associated with climate change. The exposure measures exhibit cross-sectional and time-series variations which align with reasonable priors, and are better in capturing firm-level variation than carbon intensities or ratings. factors that prior work has identified as important correlates of climate change exposure (e.g., public climate attention). Exposure to regulatory shocks negatively correlates with firm valuations, but only in recent years.
• Background: For an extensive description of this data, please read the data and methodology sections of the paper cited below. For convenience, here is a brief description.The exposure measures count the frequency with which certain climate change bigrams occur in the transcript, scaled by the total number of bigrams in the transcript. We construe these measures as indicating the occurrence of climate change events or shocks at the firm. Our method also allows us to construct measures of the first and second moment associated with these shocks. In other words, whether the events represent (in expectation) good or bad news to the firm and whether the shocks are uncertain. For the first moment, we construct "sentiment'' measures, which count the relative frequency of climate change bigrams that occur in the vicinity of positive and negative tone words (Loughran and McDonald2011). For the second moment, or risk measures, we count the relative frequency of climate change bigrams mentioned in the same sentence as the words "risk " or "uncertainty" (or their synonyms). Following prior practice in Hassan et al. (2019), we interpret these sentiment and risk measures as components of the exposure measures.

• Updates [2021-05-14]: We have updated our data to 2020Q4.
• Updates [2021-04-03]: Last update missed 2019 Q3 and Q4. We added the data of these two quarters in the latest version.
• Updates [2021-01-19]: We have updated our data to 2020Q3.

 Please cite the following study when using this Climate Change Exposure/Risk/Sentiment data:

• Sautner, Z., L. van Lent, G. Vilkov, and R. Zhang, Firm-level Climate Change Exposure, 2020. Available at SSRN: https://ssrn.com/abstract=3642508

 The data is publicly available and free to use, provided its source is acknowledged as follows:

• Sautner, Z., L. van Lent, G. Vilkov, and R. Zhang, 2020. ”Data for ‘Firm-level Climate Change Exposure’”. https://doi.org/10.17605/OSF.IO/FD6JQ

 and both data and the associated paper are cited in any work that uses these resources.

With the authorization of Professor Zhang Ruishen from the School of Accountancy of Shanghai University of Finance and Economics, CnOpenData has established a display area and data index for this data to facilitate scholars’ browsing.

 To download data, please click Firm-level Climate Change Exposure, or download directly at the end of this page.

Time interval

2001-2020

Field display

Sample data

firm-year-level Climate Change Exposure

firm-quarter-level Climate Change Exposure

References

• Sautner, Zacharias and van Lent, Laurence and Vilkov, Grigory and Zhang, Ruishen, Firm-level Climate Change Exposure (May 10, 2021). European Corporate Governance Institute–Finance Working Paper No. 686/2020, Available at SSRN : https://ssrn.com/abstract=3642508 or http://dx.doi.org/10.2139/ssrn.3642508

Data update frequency

Updated from time to time

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