Decarbonizing transportation: A data-driven examination of ICE vehicle to EV transition

Abstract

Transportation is one of the sectors with the highest CO2 emissions, accounting for 23% globally and significantly contributing to climate change. To address this challenge, the authorities have proposed new stringent policies that lead to decarbonization. From this perspective, this work proposes a multi-scenario analysis for the electrification of a fleet of private users. The scenarios differ on the type of charging mode adopted: slow charging (charging modes 1 and 2) and fast charging (charging modes 3 and 4). The model aims to identify the percentage of potential users who can shift from Internal Combustion Engine (ICE) to Electric Vehicles (EVs) in different scenarios. Furthermore, the model will highlight the average expenditure of users for charging, highlighting how the cost of energy could be a driver for the electrification of the sector. Finally, the model will allow us to evaluate the savings of up to 220 tons of CO2/year thanks to the electrification of the sector with Long Range vehicles, in best case scenario. The use of a multi-scenario analysis allowed several possible electrification solutions to be explored, highlighting the strengths and weaknesses of the charging mode used, supported by quantitative results. This data-driven approach allows us to identify optimal locations for public charging stations in region of northern Italy region, where the data was sourced, which will help to encourage the switch to EVs.