PREDICTION OF BATTERY ELECTRIC VEHICLE (BEV) ADOPTION IN INDONESIA USING AGENT-BASED MODELLING AND SIMULATION

• Main Author: Aji Wasesa, Haidar
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/86505
• Institution: Institut Teknologi Bandung

Air pollution reduction is a critical goal that requires a shift to cleaner alternative to conventional vehicles. The Indonesian government has supported this transition by introducing various policies and incentives to boost Electric Vehicles (EVs) adoption. Advances in battery technology and increasing number of supporting infrastructures may promote its adoption. However, many obstacles still hinder people from choosing EVs for personal use. These include the insufficient infrastructure in some areas, the uncertainty about the reliability and performance of EVs, and the high costs of owning and operating EVs. These influencing factors have been extensively studied using different theoretical models, such as the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT). However, it is not clear how these models can be used to simulate EV adoption using Agent-Based Modelling (ABM), a technique that can capture the complex and dynamic interactions among potential adopters and their environment. It is also unclear how well these models reflect the real-world situation of EV adoption. This study aims to integrate TPB, TAM, and UTAUT/UTAUT2 into ABM, offering proof of concept to underscore the viability of technology adoption theories for simulation and visualization of its impact on a given population. In addition, this study also aims to predict future Battery Electric Vehicles (BEVs) adoption in Indonesia based on available conceptual model. Leveraging a previous integrated adoption model based on previosly mentioned theories to discern customers' intentions to adopt electric vehicles in Indonesia, this study constructs a simulation capturing the dynamic interaction of identified determinants. This research not only contributes to refining the understanding of the applicability of TPB in ABM simulations, particularly for BEV adoption dynamics, but also underscores the importance of calibration in enhancing the model's accuracy and predictive power. Additionally, it provides valuable insights for decision-makers, highlighting ABM as an alternative tool for shaping regulations in the context of technology adoption during a green transition. The findings suggest that to significantly increase BEV adoption rate, supporting infrastructure, manufacturers' support, and promotional policies regarding BEVs must be constantly increased, allowing adoption to eventually reach a greater portion of the population who still harbor skepticism toward BEVs. Among the four factors examined, supporting government regulations have the most positive impact on BEV adoption in Indonesia. Furthermore, the study found an interesting pattern in which there is a discrepancy between the simulated sales figure and empirical data during the height of the COVID-19 pandemic, implying the negative impact of widespread global events and economic downturns on the domestic green transition.