Temporal and Vehicle Impacts on Delivery Emissions in Urban Logistics: A Case Study of Rome

Emerging challenges in urban logistics underscore the need for delivery strategies that balance efficiency with environmental responsibility. This study develops a time dependent delivery model using operational data from a beverage logistics company in Rome, utilizing light commercial vehicles (LCVs) differentiated by fuel type (diesel, petrol) and Euro emission standard (Euro 4– Euro 6). We segment daily operations into four time windows: morning, afternoon, evening, and night, to capture the variability of traffic congestion. For each period, we use a congestion factor calibrated by comparing free-flow and congested travel times retrieved from a routing service. These factors adjust the baseline travel times for realistic routing scenarios. Delivery plans are generated for each time window and vehicle configuration, with calculations for both travel time and emissions. Emissions are estimated using the COPERT methodology, employing standardized emission factors based on euro norms and fuel types. By comparing emission outcomes across time slots and vehicle types, we identify delivery schedules and fleet compositions that minimize environmental impact while maintaining acceptable service levels. The findings demonstrate significant variability in emissions across different delivery time slots, with peak congestion periods showing notably higher emission costs, reaching up to €4.10 per delivery route compared to €1.14 during nighttime operations with advanced vehicle technology. Specifically, shifting delivery schedules from morning to night reduces total emissions by approximately 25.57%, highlighting nighttime deliveries as one of the most effective strategies for mitigating environmental impacts. Additionally, the analysis underscores the importance of fleet composition, revealing that advanced diesel LCVs adhering to the Euro 6d-temp standard equipped with DPF+SCR significantly outperform conventional diesel vehicles, achieving emission reductions up to 63.4%. These insights establish a data-driven foundation for optimizing both temporal scheduling and vehicle selection, enabling sustainable and environmentally responsible urban logistics strategies.