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

TL;DR

This paper develops a backcasting optimal control framework to design electric vehicle purchase incentives, aiming to minimize costs while achieving CO2 emission targets in transport systems, demonstrated through a case study in France.

Contribution

It introduces a novel backcasting methodology formulated as an optimal control problem for transport policy planning, specifically for electric vehicle incentives.

Findings

Optimal incentive policies effectively reduce CO2 emissions.

The methodology provides cost-effective policy roadmaps.

Scenario analysis reveals trade-offs between budget and emission targets.

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.