Consumer preferences and the influence of networks in electric vehicle diffusion: An agent-based microsimulation in Ireland

118 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Pleasure or profit? Surveying the purchasing intentions of potential electric vehicle adopters in China (2019, background): “Another body of evidence emphasizes the importance of factors such as “interpersonal influence” and social networks as they relate to EV acceptance (Axsen et al. 2013; Axsen and Kurani 2011; Axsen and Kurani 2012; Axsen and Kurani 2013; McCoy and Lyons 2014).”
A review of agent‐based modeling of climate‐energy policy (2020, background): “…a high clustering (Desmarchelier et al., 2013; Niamir et al., 2018); small-world networks which combine short average distance and high clustering (McCoy & Lyons, 2014; Moglia et al., 2018; Palmer et al., 2015; Rai & Robinson, 2015; Sopha et al., 2011); and scale-free networks displaying a highly…”
Adoption of energy efficient technologies by households – Barriers, policies and agent-based modelling studies (2019, background): “Five studies describe the decision of households to adapt a technology by calculating the maximum utility [31,32,39,43,44].”
Assessing the socio-demographic, technical, economic and behavioral factors of Nordic electric vehicle adoption and the influence of vehicle-to-grid preferences (2020, unknown): “they relate to EV acceptance [64], [65].”