Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/331435 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
Working Paper No. 16.2025
Verlag: 
Fondazione Eni Enrico Mattei (FEEM), Milano
Zusammenfassung: 
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.
Schlagwörter: 
sustainable mobility
electric vehicle
neural networks
shap interpretation
JEL: 
N74
Q55
Q58
R40
C45
Dokumentart: 
Working Paper

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