Expressway decarbonization strategy assessment: An integrated system dynamics and multi-agent simulation approach

Road transport significantly contributes to global carbon emissions, with expressways playing a key role due to high capacity and truck traffic. However, research specifically addressing expressway carbon emissions remains limited. This study introduces a novel method combining system dynamics (SD) and multi-agent simulation (MAS) to estimate these emissions. The SD model predicts travel demand under policy scenarios, while the MAS model calculates vehicle-level emissions. By linking macro-level policies with micro-level traffic operations, this integrated approach improves emission quantification accuracy and simulates long-term trends, offering a tool to evaluate decarbonization strategies. Applied to the Hangzhou Ring Expressway, the SD-MAS model evaluates three strategies: optimizing freight demand, promoting new energy vehicles (NEVs), and enhancing energy consumption technology. Results show that managing freight demand is an effective and rapidly deployable measure, while NEV promotion has a more modest initial impact but becomes increasingly important over time. Energy efficiency improvements offer the greatest reductions. Nevertheless, while each strategy reduces emissions, no single strategy can achieve carbon peaking alone. Implementing all three strategies at maximum intensity could peak emissions before 2031, with reductions of up to 47 %. This study offers quantitative support and a robust methodology for policymakers to manage emissions in road transport systems.