A Study of the Diffusion of Alternative Fuel Vehicles: An Agent-Based Modeling Approach

This paper demonstrates the use of an agent-based model (ABM) to investigate factors that can speed the diffusion of eco-innovations, namely alternative fuel vehicles (AFVs). The ABM provides the opportunity to consider the interdependencies inherent between key participants in the automotive industry: manufacturers, consumers, and governmental agencies. Agent-based models allow for tackling these interdependencies in a very elegant way. Because ABMs allow convenient modeling of the interactions between multiple agents, each with unique optimization goals, it provides a method for understanding consumer and manufacturer response to diverse environmental changes. Grounding the agent-based model on empirical data further improves the validity of the model results. We use choice-based conjoint data of more than 7,000 respondents to elicit heterogeneous consumer preferences. Further, information about manufacturers' cost structure is available from an established vehicle design tool, AVCEM. In three experiments, mechanisms are considered for speeding the adoption of AFVs: technology push, market pull, and regulatory push. Simulation results support the idea that technology push can be an important mechanism for speeding the diffusion of AFVs. Market pull, that is, word of mouth, also has a positive impact on the diffusion of AFVs and increases the social good by decreasing the preference for fuel-inefficient vehicles. Furthermore, word of mouth leads to a higher willingness to pay for AFVs, which indicates that the perceived value of AFVs increases with word of mouth. In contrast, a governmental push that focuses on the manufacturers (fuel economy mandates) leads to a decrease in the social good (air pollution improvement) because market share for fuel-inefficient vehicles increases. This article provides insights into how to set up an ABM to evaluate factors that influence the diffusion of alternative fuel vehicles. These insights can be applied to other types of eco-innovations.