Optimizing Government Investment Strategies in the Electric Vehicle Supply Chains Using Data-Driven Approaches

Abstract

Electric vehicles (EVs) are a promising technology with the potential to significantly reduce emissions in the transportation industry. They have gained considerable attention from the government in recent years as climate change concerns escalate. Thus, government investment offices are seeking guidance on optimal intervention strategies to accelerate EV adoption rates. In this thesis, we develop a data-driven optimization model that provides the government with recommendations and visuals that will aid them in their investment allocation decisions, specifically in the battery manufacturing and consumer stages of the EV supply chain. The findings indicate that consumer subsidy is a more effective strategy compared to investing in battery production facilities. However, both are crucial in increasing EV adoption. The model also recommends optimal battery plant locations in Ontario, Canada, based on different government investment levels.