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Bi-level optimisation of subsidy and capacity investment under competition and uncertainty

Zhang, Z., Chronopoulos, M. ORCID: 0000-0002-3858-2021, Kyriakou, I. ORCID: 0000-0001-9592-596X & Dimitrova, D. S. ORCID: 0000-0003-3169-2735 (2023). Bi-level optimisation of subsidy and capacity investment under competition and uncertainty. European Journal of Operational Research, doi: 10.1016/j.ejor.2024.03.028


In this paper, we develop a bi-level real options framework for deriving the equilibrium Government subsidisation and firm-level capacity investment policy in a duopoly market structure. We find that strategic interactions with the Government may impact a firm’s capacity investment decision significantly and that the equilibrium subsidisation policy depends on both the market structure and the type of duopolistic competition. Interestingly, the provision of greater subsidy to the leader raises the follower’s incentive to invest earlier and in a bigger project. The loss in value of the leader, due to the follower’s entry, relative to the monopolist increases with economic uncertainty and, although a subsidy can mitigate this loss, its effect becomes less pronounced as economic uncertainty increases. We also find that a profit (welfare)-maximising Government does not offer (offers) a subsidy in a highly uncertain environment or upon low tax rate, while higher tax rate does not always decelerate investment. Finally, we find that while competition is always desirable for a social planner, a profit-maximising Government may benefit more under pre-emptive competition.

Publication Type: Article
Additional Information: This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: Investment analysis, Competition, Real options, Subsidy design
Departments: Bayes Business School
Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
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