- Sustainability
The detrimental effects of global warming are increasingly evident, necessitating urgent action to reduce Greenhouse Gas emissions. This alarming situation has called for the collective contributions of all actors, i.e. nations, firms, and individuals, to limit the annual warming to below 2 degrees Celsius. Our sponsor company, with a global fleet of over 25,000 vehicles primarily comprised of Internal Combustion Engine (ICE) vehicles, is committed to significantly decarbonizing its fleet by 2030 to mitigate its CO2 emissions footprint and contribute to global warming reduction. This goal is to be achieved while maintaining operational excellence and within the company’s economic and operational constraints. To this end, our study first identified optimal locations for transitioning fleets from ICEs to Electric Vehicles (EVs), considering the geographical scope of the 50 US states plus the District of Columbia. Using Machine Learning Clustering techniques, we included endogenous factors (age of fleet, number of vehicles ) and exogenous factors (laws and incentives, temperature, gas price, and electricity price) to identify how to rank states according to their impact. Then, a logistic growth function, with a growth rate factor derived from 5 metrics, was applied to model the timing and strategy of EV implementation: Total Cost of Ownership (TCO), driving range, refueling, CO2 emissions, value-perception. We found that the adoption of EVs in a global corporation with a significantly large fleet is equally dependent on both endogenous and exogenous factors. Furthermore, to reap optimal benefits, the number of EVs in the company’s fleet mix should be gradually increased over the target period. Combining these 2 approaches allows the company to maintain control over operational performance objectives and predict future TCO and decarbonization implications. The model's applicability extends beyond the studied region to other geographical, political, and economic contexts, such as Europe or East Asia.