Classification and benchmark of City Logistics measures: an empirical analysis

TLDR

Urban freight distribution causes significant pollution and congestion, prompting municipalities to adopt City Logistics measures to mitigate these impacts. The study analyzes data from 70 European cities to update the status of City Logistics initiatives, examine their diffusion, and identify contextual factors driving their adoption. The authors use City Logistics Indices to quantify the breadth of CL measures and perform statistical correlations with contextual variables such as pollution, e‑commerce diffusion, and GDP. Results show that pollution levels, e‑commerce diffusion, and GDP are key drivers of City Logistics deployment.

Abstract

Urban freight distribution accounts for a significant share of pollution and congestion in urban areas. To reduce these negative impacts, municipalities have implemented several City Logistics (CL) measures. This paper presents the empirical analysis of a dataset of 70 European cities that have been piloting or rolling out a CL measure, to provide an updated indication of the status of CL initiatives and analyse the diffusion of CL internationally. The research objective is also to help understand the contextual factors might explain their introduction. To this end, a set of City Logistics Indices (CLI) is used as indicators of the breadth and number of CL measures implemented in a city. A statistical correlation of these CLIs with respect to a set of independent variables, namely the contextual factors, is also performed. Results reveal that the level of pollution, the diffusion of e-commerce and GDP are important drivers of CL deployment.