Abstract for: Charging Forward: Unlocking The Acceleration of Electric Vehicles (EVs) Adoption In Indonesia

Driven by rapid urbanization, increasing transportation demand, and concerns over carbon emissions, electric vehicles (EVs) have emerged as one of the promising solutions to reduce pollution in Indonesia. Despite rising awareness, EV adoption remains limited due to inadequate charging infrastructure, high purchase cost, and behavioral barriers. This study seeks to analyze the dynamic interaction among multi-drivers influencing early EV adoption in Indonesia, focusing on charging infrastructure, vehicle purchase price, social/peer influence, and environmental concern. A System Dynamics approach, incorporating Bass Diffusion in the model, is employed to develop a stock-flow framework that captures interdependencies and feedback mechanisms among the drivers: infrastructure availability, EV purchase price, social imitation, and environmental concern. The model is then used to explore policy recommendations that could accelerate EV adoption. Baseline simulations show that while EV adoption grows exponentially, it remains far below government targets, primarily constrained by the high EV purchase prices and limited charging infrastructure. Policy simulations reveal that infrastructure expansion alone is insufficient to address affordability barriers, while price subsidies alone do not directly resolve infrastructural gaps. In contrast, integrated policy, combining EV price subsidies and charging station expansion, substantially improves EV adoptions. The findings underscore the need for Indonesia to pursue integrated policy that balances short-term incentives with long-term commitments. Coordinated efforts between government agencies, private sector, and utility providers are essential to address systemic adoption barriers. Future research should refine the model by incorporating deaveraged behavioral dynamics, consumer segmentation, technological variables (e.g., vehicle range, type), operational costs, and production constraints to improve policy relevance and model realism.