A scenario-based integrated life cycle assessment–international vehicle emission framework for environmental assessment of municipal solid waste collection

This study evaluates the environmental impacts and air pollutant emissions associated with municipal solid waste (MSW) collection and transfer operations using an integrated framework combining Life Cycle Assessment (LCA) and the International Vehicle Emission (IVE) model. The analysis is based on field data from an urban waste collection system and considers four operational scenarios: Baseline (B-Sc), Diesel Particulate Filter installation (DPF-Sc), Fleet Renewal (FR-Sc), and Optimized Distance (OD-Sc).Vehicle exhaust emissions as well as non-exhaust emissions from tire wear, brake wear, and road abrasion were quantified using locally collected activity data, the IVE emission model, and life cycle inventory analysis. Results indicate that heavy-duty vehicles contribute disproportionately to particulate emissions, with total suspended particles (TSP) (0.00072 kg/ton waste) exceeding PM10 (0.00047 kg/ton waste) and PM2.5(0.00025 kg/ton waste). Waste collection operations were identified as the dominant source of environmental burdens across both midpoint and endpoint impact categories, primarily driven by diesel fuel consumption. Among the evaluated scenarios, the optimized distance scenario (OD-Sc) provides the most effective and balanced emission reduction strategy, reducing global warming potential (GWP) by approximately 9% and decreasing NOx, CO, VOCs, benzene, and particulate matter formation by about 6–7%, without increasing N2O emissions. Fleet renewal and DPF installation achieved smaller environmental improvements. The results demonstrate that operational strategies focused on route optimization and distance reduction can deliver greater system-wide environmental benefits than technology-based measures alone. The proposed integrated LCA–IVE framework provides a practical tool for evaluating emission reduction strategies in waste collection systems, particularly in data-constrained environments.