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A PRINCIPAL–AGENT MODEL FOR OPTIMAL INCENTIVES IN RENEWABLE INVESTMENTS

René Aïd et al.

International Journal of Theoretical and Applied Finance2025https://doi.org/10.1142/s0219024925500049article
AJG 2ABDC B
Weight
0.44

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.

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https://doi.org/https://doi.org/10.1142/s0219024925500049

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@article{rené2025,
  title        = {{A PRINCIPAL–AGENT MODEL FOR OPTIMAL INCENTIVES IN RENEWABLE INVESTMENTS}},
  author       = {René Aïd et al.},
  journal      = {International Journal of Theoretical and Applied Finance},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219024925500049},
}

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.