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- Publisher Website: 10.1111/jofi.13219
- Scopus: eid_2-s2.0-85153734735
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Article: Firm-Level Climate Change Exposure
Title | Firm-Level Climate Change Exposure |
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Authors | |
Issue Date | 2023 |
Citation | Journal of Finance, 2023, v. 78, n. 3, p. 1449-1498 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/345323 |
ISSN | 2023 Impact Factor: 7.6 2023 SCImago Journal Rankings: 19.139 |
DC Field | Value | Language |
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dc.contributor.author | Sautner, Zacharias | - |
dc.contributor.author | Van Lent, Laurence | - |
dc.contributor.author | Vilkov, Grigory | - |
dc.contributor.author | Zhang, Ruishen | - |
dc.date.accessioned | 2024-08-15T09:26:37Z | - |
dc.date.available | 2024-08-15T09:26:37Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Journal of Finance, 2023, v. 78, n. 3, p. 1449-1498 | - |
dc.identifier.issn | 0022-1082 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345323 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Finance | - |
dc.title | Firm-Level Climate Change Exposure | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1111/jofi.13219 | - |
dc.identifier.scopus | eid_2-s2.0-85153734735 | - |
dc.identifier.volume | 78 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 1449 | - |
dc.identifier.epage | 1498 | - |
dc.identifier.eissn | 1540-6261 | - |