Machine-Learning-Driven Lifecycle Carbon Footprint Assessment in Electric Vehicle Batteries

In response to the European Union’s Carbon Border Adjustment Mechanism (CBAM), which mandates stringent carbon footprint standards for imported Electric Vehicle (EV) batteries, accurate and high-resolution carbon accounting across its lifecycle has become essential. This paper presents a Machine-Learning(ML)-driven methodology for comprehensive carbon footprint accounting designed to precisely capture emissions across material extraction, assembly, logistics, and recycling phases in EV battery production. By employing advanced machine learning techniques, our approach enhances the granularity and accuracy of emissions data, aligning with regulatory requirements and supporting sustainable practices in EV battery manufacturing. The model is validated through empirical analysis, demonstrating its capability to support compliance with evolving global carbon policies.