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Optimising Investment Decisions under Uncertainty: A Study of Risk, Subsidies, Competition, and Technological Learning

Zhang, Z. (2023). Optimising Investment Decisions under Uncertainty: A Study of Risk, Subsidies, Competition, and Technological Learning. (Unpublished Doctoral thesis, City, University of London)


The primary objective of this thesis is to offer effective methods for enhancing investment decision-making under escalating market uncertainty and the deregulation of numerous industries. Specifically, the first part of the thesis delves into the valuation and optimal planning of a multi-stage project, while the subsequent part addresses the strategic interaction between private firms and the Government under uncertainty. Various aspects including risk management, Government support, duopolistic competition, technological learning and subsidy retraction are thoroughly considered.

In this thesis, we begin by taking the perspective of a private firm interested in the sequential capacity expansion of a project and develop a framework for assessing the downside risk of the serial project and optimising the sequence of the stages. Under general distributional assumptions for the duration of each stage, we consider the trade-off between maximising the expected NPV and minimising the risk exposure, and obtain the optimal schedule for risk-averse decision-makers. Results show that both the duration variability of each stage and the decision-maker’s risk preferences can significantly affect the optimal sequence of the stages and that high duration variability is not always undesirable, even for risk-averse decision-makers.

Subsequently, we bridge the gap between optimal subsidisation policy-making and duopolistic competition by constructing a bi-level real options framework for analysing the non-cooperative game between a Government and two symmetric firms under uncertainty and subsidy. We derive and compare the optimal investment and subsidisation strategies for the case of a profit and social welfare-maximising Government, and provide policy and managerial insights based on analytical and numerical results. Our results indicate that both the market structure and the type of duopolistic competition can have a significant impact on the equilibrium subsidisation and capacity investment policy. In addition, we show that a profit (welfare)-maximising Government does not offer (offers) a subsidy in a highly uncertain environment or when the tax rate is low, while a higher tax rate does not always decelerate investment.

Meanwhile, although traditional literature indicates that Governments tend to withdraw subsidies as the cost of alternative energy technologies approaches commercial maturity due to the learning effect, models for analysing the impacts of technological learning on capacity investment and optimal subsidy retraction remain underdeveloped. Therefore, we extend our model to account for the trade-off that although a higher learning rate enhances cost reduction and incentivises greater investment, it also triggers earlier subsidy retraction. Indeed, our results confirm that the appearance of technological learning and subsidy retraction may result in an ambiguous effect on a firm’s investment capacity.

Publication Type: Thesis (Doctoral)
Subjects: H Social Sciences > HJ Public Finance
Q Science > QA Mathematics
Departments: School of Policy & Global Affairs > International Politics
Bayes Business School > Actuarial Science & Insurance
Bayes Business School > Bayes Business School Doctoral Theses
Doctoral Theses
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