Behavioral interventions steering CO2-optimal electric vehicle charging: Evidence from a BMW field experiment in the Netherlands

Abstract This study examines how behavioral interventions can support resilient urban energy systems by promoting CO 2 -optimal electric vehicle (EV) charging during periods of lowest grid carbon intensity. We conducted a two-phase field experiment in the Netherlands with 355 BMW EV drivers. We randomly assigned participants to three intervention groups (forecast, statistics, gamification) and a control group. By applying a mixed-methods design, we analyzed 13,153 charging sessions using a difference-in-differences framework and a binary logit model. We complemented these findings with qualitative insights from semi-structured interviews with 14 participants. Our results show a significant increase in CO 2 -optimal charging in the gamification group compared to the control group, suggesting a potential for reducing carbon emissions associated with charging. Hence, the intervention, which combines CO 2 -forecast, charging statistics, and gamification-based badges, is more effective in shifting charging behavior than interventions that include only the forecast or the forecast with statistics. Further analysis indicated a higher likelihood of optimal charging among drivers living in rural areas, driving a battery electric vehicle, and charging on rest days. The interview findings suggest that sustained motivation for optimal charging requires both alignment with individual motives and seamless integration into daily routines. By linking individual charging decisions to urban system benefits, the study highlights the potential of behavioral strategies to complement the integration of renewable energy sources and support resilient charging infrastructures.