Applying an ABM-LCA framework for analysing the impacts of shared automated electric vehicles across large-scale scenarios

• ABM-LCA integration is proposed for the environmental analysis of SAEVs impacts. • Usage patterns, fleet deployment, and vehicle lifetime influence the SAEVs LCA. • A sole focus on CO 2 emissions will yield biased LCA interpretations. • Human toxicity and mineral resources depletion are key impact categories for SAEVs. • SAEVs can maximize environmental benefits at larger scales. Amidst the pressing need for sustainable transportation, Shared Automated Electric Vehicles (SAEVs) emerge as an increasingly explored solution with the potential to revolutionize mobility. Yet, understanding the environmental impacts of operating this mobility solution at different scales remains sparse. This study addresses this by integrating Agent-Based Modelling (ABM) and Life Cycle Assessment (LCA) to assess the environmental impacts of SAEVs at municipal, subregional and regional scales. ABM simulates travellers’ behaviour and SAEVs deployment strategies, yielding dynamic patterns along a typical day, while LCA provides a structured framework for assessing the life cycle environmental impacts. This process involves creating an ABM that reflects a representative mobility scenario, and a modified ABM scenario where private car and bus trips are replaced with SAEV services. The analysis extends the different scales, providing both short-term and long-term perspectives on LCA impacts. Findings revealed significant reductions in global warming potential (up to 91%), but challenges include increased operational intensity, human toxicity (up to 240%), and mineral resource scarcity (up to 229%). Vehicle kilometres travelled, and fleet replacement needs are key factors influencing long-term environmental impacts. Larger-scale implementation yields greater environmental benefits compared to smaller-scale deployment.