Changing minds about electric cars: An empirically grounded agent-based modeling approach

TL;DR

This paper introduces an empirically grounded agent-based model to simulate how policy measures and social influence affect electric vehicle adoption, emphasizing the importance of tailored policies for different consumer segments.

Contribution

It presents a novel, spatially explicit agent-based model incorporating emotion-influenced decision-making to analyze EV diffusion under various policy scenarios.

Findings

Exclusive EV zones accelerate early adoption more than financial incentives.

Heterogeneous consumer needs require tailored policy measures.

Simulation results vary across different market segments.

Abstract

The diffusion of electric vehicles (EVs) is considered an effective policy strategy to meet greenhouse gas reduction targets. For large-scale adoption, however, demand-side oriented policy measures are required, based on consumers transport needs, values and social norms. We introduce an empirically grounded, spatially explicit, agent-based model, InnoMind Innovation diffusion driven by changing Minds), to simulate the effects of policy interventions and social influence on consumers transport mode preferences. The agents in this model represent individual consumers. They are calibrated based on empirically derived attributes and characteristics of survey respondents. We model agent decision-making with artificial neural networks that account for the role of emotions in information processing. We present simulations of 4 scenarios for the diffusion of EVs in the city of Berlin, Germany (3 policy scenarios and 1 base case). The results illustrate the varying effectiveness of measures in different market segments and the need for appropriate policies tailored to the heterogeneous needs of different travelers. Moreover, the simulations suggest that introducing an exclusive zone for EVs in the city would accelerate the early-phase diffusion of EVs more effectively than financial incentives only.