Combining choice modelling and multi-criteria analysis for technology diffusion: An application to the uptake of electric vehicles

Efforts to reduce greenhouse gas emissions in the residential sector by adopting technologies such as solar photovoltaics and electric vehicles (EVs) have major implications for the capacity of electricity distribution networks, particularly at local areas with high uptake. Consumer decisions to purchase these technologies are also influenced by several complex criteria such as costs/benefits, performance, appeal/status, risk, psychographics, and demographics. This complexity motivated the development of an innovative diffusion model, incorporating features of multi-criteria analysis and choice modelling, to estimate the adoption of these technology options spatially across the landscape of heterogeneous consumers. We test the model to forecast market share of EVs through to 2030, using the vehicle stock across all 1.5 million households in Victoria, Australia. Seven financial and non-financial criteria were included and calibrated via focus groups and a large‐scale survey. Annual change of criteria values and their elasticity to adoption were incorporated. Geographical differences in uptake of EVs were primarily due to driving distance, employment status and household income, with urban areas having about three times the proportional uptake. By testing the model for a range of incentives, we demonstrate its capability to inform and evaluate policy options.

► A diffusion model, incorporating MCA and choice modelling, with high geographical granularity
► Estimate adoption of technologies with multiple financial and non-financial features
► Implementation across all 1.5 million households in Victoria to forecast market share of electric vehicle options
► Use of a large‐scale pilot survey of 2000 households to calibrate the model criteria
► Evaluation of government policies to accelerate adoption