JOURNAL ARTICLE
Producing Green or Brown? A Game of Green Innovation in an Asymmetric Oligopoly.
Published In: Journal of Environmental Assessment Policy & Management, 2024, v. 26, n. 2. P. 1 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Carrilho-Nunes, Inês; Catalão-Lopes, Margarida 3 of 3
Abstract
As consensus grows for low-carbon development, innovation becomes vital for green growth, given the substantial technology adoption needed for long-term environmental targets. This paper explores market-driven incentives for green product innovation in an asymmetric duopoly using a game theory approach without relying on direct government intervention. The roles that market dynamics and rivalry have on the supply of green or brown products are explored in simultaneous and sequential scenarios, considering technology costs, demand-creating effects, rivalry gains, and information availability. Results reveal that a fully green market is contingent on low innovation costs, depending especially on the magnitude of demand creation effects. Notably, product differentiation occurs only under extreme cost asymmetry. Recommendations underscore the importance of incentivising low-cost innovations, assessing the impact of product differentiation and diversity, especially concerning lower-income consumers, and proposing measures to improve the transparency of firms' strategic choices. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Environmental Assessment Policy & Management. 2024/06, Vol. 26, Issue 2, p1
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:1464-3332
- DOI:10.1142/S1464333224500078
- Accession Number:177204700
- Copyright Statement:Copyright of Journal of Environmental Assessment Policy & Management is the property of World Scientific Publishing Company 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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