JOURNAL ARTICLE
A PRINCIPAL–AGENT MODEL FOR OPTIMAL INCENTIVES IN RENEWABLE INVESTMENTS.
Published In: International Journal of Theoretical & Applied Finance, 2025, v. 28, n. 1/2. P. 1 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: AÏD, RENÉ; KEMPER, ANNIKA; TOUZI, NIZAR 3 of 3
Abstract
In this paper, we investigate the optimal regulation of energy production in alignment with the long-term goals of the Paris Climate Agreement and analyze the optimal regulatory incentives to foster the development of nonemissive electricity generation when the demand for power is met either by a single firm or by two interacting agents. The regulator aims to encourage green investments to limit carbon emissions while simultaneously reducing the intermittency of total energy production. We find that the regulator can achieve a higher certainty equivalent by regulating two interacting firms, each investing in one technology, rather than a single firm managing both technologies. This higher value is achieved thanks to a greater degree of freedom in the incentive mechanisms, which involve cross-subsidies between firms. Moreover, we find that it is optimal to compensate firms for shutting down their emissive production assets. We provide closed-form expressions of the second-best contracts and show that they take a rebate form, involving time-dependent prices for each state variable. A numerical study quantifies the impact of the designed second-best contract in both market structures compared to the business-as-usual scenario. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Theoretical & Applied Finance. 2025/02, Vol. 28, Issue 1/2, p1
- Document Type:Article
- Subject Area:Law
- Publication Date:2025
- ISSN:0219-0249
- DOI:10.1142/S0219024925500049
- Accession Number:186751847
- Copyright Statement:Copyright of International Journal of Theoretical & Applied Finance 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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