Optimizing Urban Passenger Transportation Modes to Reduce Carbon Emissions

The transition toward carbon neutrality requires transformative pathways for urban passenger transport, a sector currently dominated by private vehicles. To address this challenge, this study develops a multiobjective optimization model to determine the optimal modal structure that minimizes carbon emissions while balancing costs for key stakeholders. A life-cycle assessment of carbon emissions revealed that fuel-cycle operations constitute the primary emission source across conventional internal combustion engine vehicles (ICVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). The core optimization model simultaneously pursues three objectives: maximizing the cost-effectiveness of carbon reduction per unit investment, minimizing travelers’ costs (including time and monetary expenses), and minimizing operators’ costs (covering vehicle ownership, maintenance, and fuel). The model framework incorporates costs borne by authorities for promoting low-carbon transport and key constraints such as per capita carbon intensity limits and supply-demand equilibrium. An improved Nondominated Sorting Genetic Algorithm (NSGA-II) algorithm was employed to solve this complex model. A case study in Fuzhou City demonstrated the model’s practical effectiveness. The optimized transportation structure showed a 4.1% decrease in private car usage, offset by increases in conventional bus (2.8%), taxi (0.3%), and ride-hailing (1.0%) shares. This restructuring achieved a 6.7% reduction in system carbon emissions and a 2.4% decrease in traveler costs while maintaining operator viability. Policy scenario analysis further identified effective pathways for structural evolution toward decarbonization. The study provides a validated framework for designing efficient urban transport strategies that align with carbon neutrality objectives.