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Exploring the macro environment determinants behind the diffusion of electric Light Commercial Vehicles
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Energy Systems.ORCID iD: 0000-0001-9383-9187
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Energy Systems.ORCID iD: 0000-0001-5742-6457
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Energy Systems.ORCID iD: 0000-0003-0253-3380
2025 (English)In: Transportation, ISSN 0049-4488, E-ISSN 1572-9435Article in journal (Refereed) Epub ahead of print
Abstract [en]

Rising transport emissions undermine urban sustainability goals, exposing a widening gap between climate ambition and emissions trajectories. Amid these trends, in the European Union (EU), electric vehicles account for only a very small proportion of new registrations for Light Commercial Vehicles (LCVs), below the levels seen in the passenger vehicles segment. While previous studies have investigated country-specific factors, this research adopts a macro-level perspective by examining aggregate diffusion patterns of electric Light Commercial Vehicles (eLCVs) across 27 EU member states. To identify the underlying determinants of this variation, this study employed a series of panel data regression models to evaluate how a set of socioeconomic, energy, mobility, and innovation-related variables shape eLCV diffusion and, more specifically, to assess the explanatory power of these variables. Among the models tested, the Fixed Effects Model proves to be most effective in capturing these relationships, reinforcing the value of a multifactorial approach to understanding eLCV adoption dynamics. The findings enhance the understanding of structural diffusion patterns and provide an empirical basis for better aligning policy and industry efforts with the EU’s regional decarbonisation objectives.

Place, publisher, year, edition, pages
Springer Nature , 2025.
Keywords [en]
Electric vehicles, Light commercial vehicles, Innovation diffusion, Panel data regression model, Electrification policies
National Category
Engineering and Technology
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-369448DOI: 10.1007/s11116-025-10656-zISI: 001522400600001Scopus ID: 2-s2.0-105009631921OAI: oai:DiVA.org:kth-369448DiVA, id: diva2:1995550
Funder
KTH Royal Institute of Technology
Note

QC 20250908

Available from: 2025-09-05 Created: 2025-09-05 Last updated: 2025-10-03Bibliographically approved

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Gil Ribeiro, CarolinaThakur, JagrutiHenrysson, Maryna

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