Bi-level optimization of carbon emission pricing for urban road traffic and environmental impacts

Abstract Limiting carbon emissions from the transportation sector has become increasingly critical due to the intensification of global climate change. Congestion pricing strategy has received widespread attention as a measure to reduce emissions. However, the pricing affects both traffic conditions and environmental outcomes, which in turn influences the effectiveness of such pricing strategies. To capture these endogenous interactions and derive effective pricing strategy designs, this study proposes a bi-level programming emission reduction congestion pricing model (ERCM), considering the endogenous relationship between pricing strategies, road network operating conditions and vehicle emission factors. The upper-level model represents traffic managers optimizing emission reduction pricing strategies, while the lower-level model represents road users and establishes a congestion-pricing-based user equilibrium model (CSUEM) that incorporates emission reduction charges. In order to distinguish the power sources of different vehicles, gasoline emission factors were calibrated using Motor Vehicle Emission Simulator (MOVES) -based speed bins, while electric vehicle factors were estimated via a power-energy conversion model. A case study conducted in Dongguan, China, evaluated the carbon reduction effects of various congestion pricing strategies. The results indicate that, compared to a no-pricing scenario, the proposed pricing scheme reduced total CO₂ emissions by 3.01% across the city and by 13.06% within the pricing zone, respectively, within the congestion pricing demonstration area. This study provides both a theoretical foundation and empirical evidence for urban traffic managers to formulate effective carbon emission reduction pricing strategies.