Optimal Low Emission Zones scheduling as an example of transport policy backcasting

TL;DR

This paper introduces a backcasting method to design optimal Low Emission Zones policies by setting emission targets first and then identifying policies to meet these goals, demonstrated through a case study in Ile-de-France.

Contribution

It develops a novel backcasting approach for transport policy planning that considers fleet dynamics and uses genetic algorithms to identify optimal LEZ schedules.

Findings

Optimal LEZ schedules can significantly reduce vehicle scrappage.

Backcasting effectively aligns policies with long-term emission targets.

Genetic algorithms provide viable solutions for complex policy optimization.

Abstract

This study presents a backcasting approach that considers the passenger car fleet dynamics to determine optimal policy roadmaps in transport systems. As opposed to the scenario-based approach, backcasting sets emission reduction targets first, then identifies policies that meet the constraint. The policy is the implementation of Low Emission Zones (LEZs), in the Ile-de-France region as a case study. The aim is to minimize the number of scrapped vehicles due to LEZs under CO2 emission targets and to deduce an interdiction schedule of polluting vehicles by 2050. To explore potential solutions, we use a genetic algorithm that provides a first insight into optimal policy pathways.