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

Asmae Alami, Vinith Lakshmanan, Antonio Sciarretta

TL;DR

This work tackles LEZ scheduling to meet horizon CO2 targets while minimizing scrapped vehicles. It proposes a backcasting framework that fixes a target emissions level $\bar{E}$ and derives policy schedules, solved as a constrained optimal control problem using a genetic algorithm. In the Île-de-France case, the approach yields concrete LEZ schedules with substantial emission reductions; for example, targeting $E(T)=471$ ktCO$_2$ and $R=5.2\times10^6$ disposals achieves a 92% reduction relative to $E(0)$ and disposes about 78% of the initial thermal stock $S_1(0)$. The study demonstrates the feasibility of backcasting for transport policy design and outlines avenues for refinement, including finer spatial granularity, explicit thermal vehicle types, improved disposal dynamics, and integration with complementary measures such as incentives for electric vehicles and public transport improvements.

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.

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

TL;DR

This work tackles LEZ scheduling to meet horizon CO2 targets while minimizing scrapped vehicles. It proposes a backcasting framework that fixes a target emissions level and derives policy schedules, solved as a constrained optimal control problem using a genetic algorithm. In the Île-de-France case, the approach yields concrete LEZ schedules with substantial emission reductions; for example, targeting ktCO and disposals achieves a 92% reduction relative to and disposes about 78% of the initial thermal stock . The study demonstrates the feasibility of backcasting for transport policy design and outlines avenues for refinement, including finer spatial granularity, explicit thermal vehicle types, improved disposal dynamics, and integration with complementary measures such as incentives for electric vehicles and public transport improvements.

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.
Paper Structure (13 sections, 17 equations, 7 figures, 2 tables)

This paper contains 13 sections, 17 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Backcasting model
  3. Vehicle fleet model
  4. Decision Variable and disposal function
  5. Age restriction variable
  6. Disposal function and probability of purchase
  7. Optimal Control Problem (OCP)
  8. Case Study : Île-de-France Region
  9. The initial schedule for 2025
  10. Parameters and exogenous inputs
  11. Genetic algorithm
  12. Results
  13. Conclusion and perspectives

Figures (7)

  • Figure 1: Schematic representation of the relationship between $S$, $O$ and $R$
  • Figure 2: Schematic representing the three pathways to vehicle disposal
  • Figure 3: Boundries for the $6$ chosen rigns of the Île-de-France region (red), for the $63$ intermunicipalities (white) and for the current LEZ in the Parisian region (blue)
  • Figure 4: Annual Emissions from 2026 to 2050
  • Figure 5: Number of vehicles that were disposed of because of the LEZ
  • ...and 2 more figures