Pricing and incentive design in applications of green technology subsidies and revenue management

Abstract

This thesis addresses three issues faced by firms and policy-makers when deciding how to price products and properly incentivize consumers. In the first part of the thesis, we focus on a firm attempting to dynamically adjust prices to maximize profits when facing uncertain demand, as for example airlines selling flights or hotels booking rooms. In particular, we develop a robust sampling-based optimization framework that minimizes the worst-case regret and dynamically adjusts the price according to the realization of demand. We propose a tractable optimization model that uses direct demand samples, where the confidence level of this solution can be obtained from the number of samples used. We further demonstrate the applicability of this approach with a series of numerical experiments and a case study using airline ticketing data. In the second part of the thesis, we propose a model for the adoption of solar photovoltaic technology by residential consumers. Using this model, we develop a framework for policy makers to find optimal subsidy levels in order to achieve a desired adoption target. The technology adoption process follows a discrete choice model, which is reinforced by network effects such as information spread and learning-by-doing. We validate the model through an empirical study of the German solar market, where we estimate the model parameters, generate adoption forecasts and demonstrate how to solve the policy design problem. We use this framework to show that the current policies in Germany could be improved by higher subsidies in the near future and a faster phase-out of the subsidy program. In the third part of the thesis, we model the interaction between a government and an industry player in a two-period game setting under uncertain demand. We show how the timing of decisions will affect the production levels and the cost of the subsidy program. In particular, we show that when the government commits to a fixed policy, it signals to the supplier to produce more in the beginning of the horizon. Consequently, a flexible policy is on average more expensive for the government than a committed policy.