Simulation-based evaluation of low emission zone-rail impacts on emissions and logistics efficiency: Case study of Rotterdam, The Netherlands

Aligned with sustainable development goals (SDGs) and European Union policy goals, including the Green Deal, this study examines the integration of low-emission zones (LEZs) with rail logistics to enhance sustainable urban freight transport. While existing research focuses on standalone LEZs policies for passenger vehicles, the role of rail in freight decarbonization has been underexplored. The study develops a dynamic simulation model to compare the policies of only LEZs, LEZs + Rail, and LEZs + Multimodal, to assess their impact on emissions and logistics efficiency, focusing on achieving the SDGs. A case study from the European logistics sector was conducted in Rotterdam, The Netherlands To ensure robustness of the simulation, the freight data and emission factors for validating the model were adopted from the Covenant-Zero-Emission-City-Logistics-Rotterdam and STREAM report for freight transport (CE Delft). This study is structured into four phases. The first phase involves data collection, system characterization, input of the simulation model, and assumption validation. The second phase is emission estimation using the Groot-Rijnmond model. The third phase includes the formulation of the dynamic simulation model. Finally, the fourth phase involves the development of scenarios and the testing of policies. The results highlight the potential of the LEZs-rail synergy to enhance the efficiency of the last mile logistics and significantly reduce transportation-related emissions in dense urban settings. The findings indicate that dynamic access pricing and multimodal logistics hubs facilitate a 55 percent shift toward rail freight, improving economic efficiency and resilience to supply chains. Furthermore, while a fixed congestion charge reduces freight emissions approximately 20 percent by 2030, a fully optimized dynamic pricing + rail strategy achieves a 50-55 percent reduction. Sensitivity analysis confirms the robustness of the results, demonstrating the applicability of the model to complex and dynamic decision-making contexts. This study offers theoretical contributions and practical insights for decision makers looking to improve the sustainability of urban logistics while ensuring economic viability and operational efficiency.