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Simulating electric vehicle uptake in Italy in the small-to-medium car segment: A system dynamics/agent-based model parametrized with discrete choice data.

handle: 11368/3001298
The main goal of the paper is to provide a simulation of the potential uptake of electric vehicles in Italy up to the year 2030, as a base for transport and energy planning by public and private decision makers. We develop a hybrid model, integrating an agent-based approach for the demand module and a system dynamics approach for the supply module. The demand module is parametrized with data derived from a discrete choice survey to car users (N = 1521), representative of the Italian population. The supply module interacts with the demand module and incorporates the available data on the evolution of battery production costs. Because of the characteristics of the choice data, the model is parametrized with data relative to the small-to-medium sized car segment only, and does not include PHEVs. Word-of-mouth and advertisement induce a growing number of potential buyers to include BEVs in their choice set. Car buyers choose between the two propulsion systems based on the relative utility. We estimate that in the period 2019–2030 BEVs will gradually overtake conventional vehicles (CVs) in Italy. In terms of annual sales, the share of BEVs will be equal to that of CVs in July 2030. By the end of 2030, BEVs will represent 52.4% of new sales. A total fleet of almost 5 million BEVs will be on the Italian roads by 2030, i.e. about a sixth of the Italian car fleet. Scenario analyses lead us to conclude that BEV subsidies are important but that they are likely sub-optimal.
- University of Trieste Italy
Agent-based model, Supply-demand model, Electric car, System dynamics model, Electric car; Simulation; Supply-demand model; Agent-based model; System dynamics model, Simulation
Agent-based model, Supply-demand model, Electric car, System dynamics model, Electric car; Simulation; Supply-demand model; Agent-based model; System dynamics model, Simulation
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