Designing electric vehicle incentives to meet emission reduction targets

Electric vehicles are expected to reduce transportation emissions. We design and allocate rebates and charging infrastructure investments to induce electric vehicle adoption and achieve emission reduction targets. A nonlinear mixed-integer mathematical model is proposed to optimize the investment allocation over a planning horizon. Logistic functions describe the vehicle demand driven by capital and ownership costs and network externalities. A simulated annealing algorithm is used to solve the nonlinear programming problem that is applied using data representative of the United States market. Our analysis indicates that rebates should be provided earlier than chargers due to neighborhood effects of electric vehicle adoption and the minimization of expenditure; availability of home charging influences consumers choice and the drivers electrified travel distance; rebates are more effective for modest drivers while charging stations should be prioritized for frequent drivers; network externalities should be further investigated because of their impact on electric vehicle demand.

Automated summary

This study utilizes a mathematical model to optimize investment in charging infrastructure, aiming to promote the adoption of electric vehicles and reduce emissions. The findings suggest that rebates should be offered earlier than charging stations, availability of home charging affects consumer choices, and rebates are more effective for moderate drivers while charging stations should prioritize frequent drivers.