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

TL;DR

This paper introduces a backcasting methodology to determine optimal transport policies, exemplified by electric vehicle incentives in France, aiming to meet CO2 reduction targets cost-effectively.

Contribution

It develops a novel backcasting approach for transport policy planning, specifically optimizing electric vehicle incentives to achieve emission goals.

Findings

Optimal incentive policies reduce CO2 emissions effectively.

Backcasting provides clear policy roadmaps for future objectives.

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. 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.