Backcasting the Optimal Decisions in Transport Systems: An Example with Electric Vehicle Purchase Incentives

Abstract

This study represents a first attempt to build a backcasting methodology to identify the optimal policy roadmaps in transport systems. In this methodology, desired objectives are set by decision makers at a given time horizon, and then the optimal combinations of policies to achieve these objectives are computed as a function of time (i.e., ``backcasted''). This approach is illustrated on the transportation sector by considering a specific subsystem with a single policy decision. The subsystem describes the evolution of the passenger car fleet within a given region and its impact on greenhouse gas emissions. The optimized policy is a monetary incentive for the purchase of electric vehicles while minimizing the total budget of the state and achieving a desired CO${}_2$ target. A case study applied to Metropolitan France is presented to illustrate the approach. Additionally, alternative policy scenarios are also analyzed to provide further insights.

This paper has been submitted to the hEART 2025 conference and is under review. arXiv admin note: substantial text overlap with arXiv:2502.02204