Artikel
Firm‐Level Climate Change Exposure
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.
- Language
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Englisch
- Bibliographic citation
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Journal: The Journal of Finance ; ISSN: 1540-6261 ; Volume: 78 ; Year: 2023 ; Issue: 3 ; Pages: 1449-1498 ; Hoboken, NJ: Wiley
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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SAUTNER, ZACHARIAS
VAN LENT, LAURENCE
VILKOV, GRIGORY
ZHANG, RUISHEN
- Event
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Veröffentlichung
- (who)
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Wiley
- (where)
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Hoboken, NJ
- (when)
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2023
- DOI
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doi:10.1111/jofi.13219
- Last update
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10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- SAUTNER, ZACHARIAS
- VAN LENT, LAURENCE
- VILKOV, GRIGORY
- ZHANG, RUISHEN
- Wiley
Time of origin
- 2023
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