What Hinders Electric Vehicle Diffusion? Insights from a Neural Network Approach

Monica Bonacina, Mert Demir, Antonio Sileo and Angela Zanoni
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Monica Bonacina: Fondazione Eni Enrico Mattei, Università degli Studi di Milano
Mert Demir: Fondazione Eni Enrico Mattei
Antonio Sileo: Fondazione Eni Enrico Mattei, GREEN – Università Bocconi
Angela Zanoni: Fondazione Eni Enrico Mattei, Università di Roma La Sapienza, Research Institute for Sustainable Economic Growth – National Research Council

No 2025.16, Working Papers from Fondazione Eni Enrico Mattei

Abstract: The transition to a zero-emission vehicle fleet represents a pivotal element of Europe’s decarbonization strategy, with Italy’s participation being particularly significant given the size of its automotive market. This study investigates the potential for battery electric cars (BEVs) to drive decarbonization of Italy’s passenger vehicle fleet, focusing on the feasibility of targets set in the National Integrated Plan for Energy and Climate (PNIEC). Leveraging artificial neural networks, we integrate macroeconomic indicators, market-specific variables, and policy instruments to predict fleet dynamics and identify key factors influencing BEV adoption. We forecast that while BEV registrations will continue growing through 2030, the growth rate is projected to decelerate, presenting challenges for meeting ambitious policy targets. Our feature importance analysis demonstrates that BEV adoption is driven by an interconnected set of economic, infrastructural, and behavioral factors. Specifically, our model highlights that hybrid vehicle registrations and the vehicle purchase index exert the strongest influence on BEV registrations, suggesting that policy interventions should prioritize these areas to maximize impact. By offering data-driven insights and methodological innovations, our findings contribute to more effective policy design for accelerating sustainable mobility adoption while accounting for market realities and consumer behavior.

Keywords: sustainable mobility; electric vehicle; neural networks; shap interpretation (search for similar items in EconPapers)
JEL-codes: C45 N74 Q55 Q58 R40 (search for similar items in EconPapers)
Date: 2025-08
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