A Data-Driven Analysis of Electric Vehicle Adoption Barriers in the Philippines: Combining SEM and ANNs

Abstract

As the world progresses into the peak of the Fourth Industrial Revolution, the adoption of smart and sustainable technologies, including electric vehicles (EVs), has gained significant momentum. However, the widespread acceptance of EVs is hindered by several unresolved barriers. This study investigates the factors influencing the adoption of electric vehicles in the Philippines, focusing on key barriers through an integrated approach using machine learning and structural equation modeling (SEM). Specifically, artificial neural networks (ANNs) and SEM are employed to analyze data from online surveys and the existing literature, identifying the critical obstacles that impact consumer acceptance. The findings reveal that the availability of charging stations, range anxiety, and vehicle costs are the primary deterrents to EV adoption. By incorporating a sustainability perspective, this study underscores the crucial role of electric vehicles in reducing environmental impacts and achieving carbon reduction targets. The hybrid methodology presented offers new insights to guide policymakers in promoting electric vehicle usage, thereby contributing to the global sustainable development goals.

Keywords

• electric vehicle
• sustainability
• structural equation modeling
• artificial neural network