Scenario analysis of subsidy policies on electric motorcycle market in Indonesia using system dynamics simulation

• Electric motorcycle subsidies and incentive policies have not been effective in achieving government targets. • Policy scenario analysis was conducted using a dynamic systems model simulation. • Consideration of the subsidy amount, target, and duration is needed when formulating the subsidy policy. With over 130 million motorcycles, Indonesia faces a critical challenge in transitioning to electric mobility to meet its carbon emission reduction commitments under the Paris Agreement. Despite the existing government incentives and subsidies, the adoption of electric motorcycles (EM) remains critically low, only 0.18% of the 13 million units targeted by 2030. This study aims to evaluate the effectiveness of current EM subsidy and incentive policies and to determine the suitable strategies for achieving the 2030 target. It adapts PTTMAM model, a system dynamics (SD) model that captures the complex interactions among four market agents: users, manufacturers, infrastructure providers, and government, to the context of EM in Indonesia. It also enriches the willingness to consider (WTC) framework within the model by incorporating behavioral variables such as lifestyle and awareness of future trends. The model is calibrated and validated using historical data (2013–2023) through sensitivity and extreme case analysis. A total of 72 subsidy and incentive policy scenarios involving the market agents were constructed to assess the achievement of current policies and identify optimal strategies to reach government’s target. Simulation results of the scenarios reveal that current policies are insufficient, projecting only 15.9% achievement of the 2030 target. More aggressive interventions, including extended subsidies, carbon taxes, and electricity incentives, could enable reaching the target by 2033. Hence, the existing 2030 goal appears overly ambitious without strategic adjustments. This study underscores the need for policy redesign and offers a robust, behaviorally informed SD framework to guide Indonesia’s electric mobility transition.