Carbon Footprint Analysis and Emission Reduction for Electric Vehicles

The increasing global carbon footprint has made it clear that we need better solutions for sustainable transportation. Electric Vehicles (EVs) are key to reducing Greenhouse Gas Emission (GHG), but their production—especially the manufacturing of lithium-ion batteries—poses significant environmental and ethical issues. This paper investigates how Generative Artificial Intelligence (Gen AI) can help address these issues and improve the sustainability of EVs.The study uses a comprehensive synthetic datasets that includes details on manufacturing processes, battery life cycle, and performance metrics to develop AI-driven methods for enhancing EV sustainability. Machine learning algorithms such as Linear Regression, Neural Networks, and XGBoost are applied to predict environmental impacts and optimize resource efficiency.Root Mean Square Error (RMSE) is a metric used to assess the efficacy of prediction models; the smaller the RMSE, the more efficient the model is. Along with the technical analysis, choice of biodegradable, plant-based batteries as a sustainable alternative to traditional lithium-ion batteries is explored. The new direction in EV technology intends to reduce environmental effects through sustainable methods of making and disposing of batteries. The research along with its paper establishes that artificial intelligence tools advance electric vehicle sustainability at a substantial level. The goal pursues predictive enhancement and new battery research that leads to improved sustainability across transportation domains.