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 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.
Cite
@article{arxiv.2502.02622,
title = {Backcasting the Optimal Decisions in Transport Systems: An Example with Electric Vehicle Purchase Incentives},
author = {Vinith Lakshmanan and Xavier Guichet and Antonio Sciarretta},
journal= {arXiv preprint arXiv:2502.02622},
year = {2025}
}
Comments
This paper has been submitted to the hEART 2025 conference and is under review. arXiv admin note: substantial text overlap with arXiv:2502.02204