Optimal switch from a fossil-fueled to an electric vehicle

TL;DR

This paper develops a real options model to determine the optimal timing for switching from fossil-fueled to electric vehicles, considering costs, incentives, and traffic bans, with calibration to Italian data.

Contribution

It introduces a novel real options framework for EV adoption, incorporating stochastic opportunity costs and policy effects, with empirical calibration and analysis.

Findings

Incentives and traffic bans effectively encourage EV adoption.

Optimal switching time depends on model parameters and policy measures.

Calibration shows policy tools can significantly influence switching behavior.

Abstract

In this paper we propose and solve a real options model for the optimal adoption of an electric vehicle. A policymaker promotes the abeyance of fossil-fueled vehicles through an incentive, and the representative fossil-fueled vehicle's owner decides the time at which buying an electric vehicle, while minimizing a certain expected cost. This involves a combination of various types of costs: the stochastic opportunity cost of driving one unit distance with a traditional fossil-fueled vehicle instead of an electric one, the cost associated to traffic bans, and the net purchase cost. After determining the optimal switching time and the minimal cost function for a general diffusive opportunity cost, we specialize to the case of a mean-reverting process. In such a setting, we provide a model calibration on real data from Italy, and we study the dependency of the optimal switching time with respect to the model's parameters. Moreover, we study the effect of traffic bans and incentive on the expected optimal switching time. We observe that incentive and traffic bans on fossil-fueled transport can be used as effective tools in the hand of the policymaker to encourage the adoption of electric vehicles, and hence to reduce air pollution.