JOURNAL ARTICLE

Communicating clean technology: Green premium, competition, and ecolabels.

  • Published In: Journal of Economics & Management Strategy, 2024, v. 33, n. 3. P. 605 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Sengupta, Aditi 3 of 3

Abstract

In markets where differences in the environmental performance of competing firms arise due to differences in technology that cannot be altered in the short run and firms have private information about their own current technology, I show that market competition creates a strategic disincentive for adopting ecolabels (even if the cost of adoption is negligible) to directly and credibly communicate this private information to environmentally conscious consumers. Firms adopt ecolabels only if the green premium that buyers are willing to pay is large relative to the production cost advantage of dirty firms; ecolabels reduce market power, increase the market share of clean firms, and reduce expected environmental damage. I analyze firms' strategic (long‐run) incentive to invest in the development of clean technology where the outcome of such investment is uncertain. The availability of an ecolabel to directly communicate private information about the final outcome of such an investment enhances the expected net surplus whereas it reduces the ex ante strategic incentive to invest which in turn lowers industry investment in cleaner technology, relative to the case with no ecolabels. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Economics & Management Strategy. 2024/08, Vol. 33, Issue 3, p605
  • Document Type:Article
  • Subject Area:Marketing
  • Publication Date:2024
  • ISSN:1058-6407
  • DOI:10.1111/jems.12587
  • Accession Number:178854231
  • Copyright Statement:Copyright of Journal of Economics & Management Strategy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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