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Cited by 1,328

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Firm‐Level Climate Change Exposure

Open access
SAUTNER, ZACHARIAS, VAN LENT, LAURENCE, VILKOV, GRIGORY, ZHANG, RUISHEN·2023·The Journal of Finance
DOI 10.1111/jofi.13219
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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.

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Metadata

Authors
SAUTNER, Z., VAN LENT, L., VILKOV, G., & ZHANG, R.
Venue
The Journal of Finance
Publisher
Wiley
Published
2023-03-31
Volume
78
Issue
3
Pages
1449-1498
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
DOI
10.1111/jofi.13219
OpenAlex
W4322624932
Cited by
1,328
References
8
FWCI
267.21
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Firm‐Level Climate Change Exposure. https://app.cochat.ai/papers/10.1111/jofi.13219
The paper
SAUTNER, Z., VAN LENT, L., VILKOV, G., & ZHANG, R. (2023). Firm‐Level Climate Change Exposure. The Journal of Finance. https://doi.org/10.1111/jofi.13219

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.