A numerical algorithm for decision-making in sustainable transport projects investment

In this paper, we develop a numerical algorithm that sets rules for investment in hydrogen infrastructure under both demand and costs uncertainties. In fact, the growth of the number of oil-fuled cars (which we call the demand) is a major source of uncertainty, as a high number of cars increases pollution and, on the other hand, makes a migration for a hydrogen transport necessary. In the same time, the investment cost is decreasing stochastically with time due to the worldwide R&D effort. We first develop a model, based on the IPAT method, to calculate the real cost of transport externalities. Using the real options method, we then show how to maximize inter-generational utility by choosing the optimal time to invest. We make use of a dynamic programming approach to develop an algorithm that gives, at each future moment, the thresholds for demand and cost for which it is better to invest. We calculate the expected waiting time until investing and show that we must wait a longer time before investing when the uncertainty is high.