FURI | Spring 2025

Using Machine Learning to Assess the Impact of Tires on Carbon Footprint

Electric vehicles (EVs) designed to minimize negative environmental impacts have tires that leave a significant carbon footprint. This research uses datasets from the United States and Finland to develop regression models that predict the global carbon footprint of tires by 2040. Consequently, this research proposes that by using convolutional neural networks (CNN), tires can be sorted based on their age as logistic regression models demonstrate that overused tires have higher weighted impact on carbon footprint. This approach attempts to enhance the sustainability of EVs through better tire management that can be regulated through government policies and enabled by proposed models.