Backcasting Policies in Transport Systems as an Optimal Control Problem : An Example with Electric Vehicle Purchase Incentives

TL;DR

The paper introduces backcasting as a method to identify optimal policy roadmaps for transport systems, with a focus on the passenger car fleet and greenhouse gas emissions. It formulates the EV purchase incentive as the control input in an optimal control problem aimed at minimizing the total budget while achieving a prescribed $CO_2$ target. A Metropolitan France case study demonstrates the approach and compares alternative policy scenarios. Key contributions include the formalization of backcasting in transport policy, the integration of fleet dynamics with emissions outcomes, and a roadmap for future model refinements such as regional disaggregation, additional vehicle types and modes, LEZ effects, and demand modeling.

Abstract

This study represents a first attempt to build a backcasting methodology to identify the optimal policy roadmaps in transport systems. Specifically, it considers a passenger car fleet subsystem, modelling its evolution and greenhouse gas emissions. The policy decision under consideration is the monetary incentive to the purchase of electric vehicles. This process is cast as an optimal control problem with the objective to minimize the total budget of the state and reach 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.

Figures (1)

• Figure 5: Reference Scenarios with the full model: CO$_2$ emissions (top) and EV stock (bottom) as a function of time.