Designing effective policies to drive the adoption of electric vehicles: A data-informed approach

The next few years will be crucial in shaping significant transitions within the realm of sustainability, with mobility having for sure a crucial share. COVID-19 will strongly impact post-pandemic mobility, as new working habits will partly reshape urban areas, with possibly many people living outside metropolitan realities. Hence, novel mobility models need to emerge, smart enough to answer the multifaceted needs of their users, and of course sustainable and energy efficient. Electric Vehicles (EVs) are crucial to support the shift towards green mobility models, and governments all around the globe are shaping their policies to support EV mass adoption. This paper provides a network-based adoption model, whose multi-class agents are potential EV users modeled based on their driving habits, derived from data measured on instrumented vehicles. The network connections are based on physical proximity among users, and a cascade model is used to investigate the dynamics of the unforced adoption mechanism. Then, a policy-design framework is proposed based on the open-loop analysis, and its cost/benefit effects quantified and discussed.