Modeling and policy analysis of ride-sharing considering carbon costs from an ESG perspective

SUN Sujun; FANG Diman; LUO Runkang; LI Yuehua

doi:10.11860/j.issn.1673-0291.20240082

Abstract：Under the"Dual-Carbon"policy framework,there is a notable lack of multidimensional per-formance evaluations for ridesharing behaviors under various incentive measures and carbon reduction policies.To address this gap,this study proposes a Logit-based multi-class stochastic user equilibrium ridesharing assignment model.The model integrates both carbon costs and additional costs associated with specific policies,such as congestion pricing,into user travel costs.An evaluation system based on Environmental,Social,and Governance(ESG)criteria is developed.Lastly,numerical experi-ments are conducted on the model using the Sioux-Falls road network.The results demonstrate that congestion pricing policy generally benefit the environmental dimension.It also exhibits positive ef-fects on social and governance dimensions at lower rates,but these benefits diminish with higher rates,potentially adversely affecting platform governance.Traffic restriction policy enhances ESG composite indicators when the network accommodates a high number of vehicles,though stricter re-strictions on private car use reduce social and governance benefits.High-Occupancy Vehicle(HOV)lane policy improves the environmental dimension but has limited social impacts and can negatively affect governance indictors.Carbon cost reduction policy significantly promotes environmental ben-efits but also negatively affects governance.Among the four policies examined,traffic restriction policy outperforms in environmental dimensions,congestion pricing policy is optimal in social dimen-sions,and under lenient carbon emission constraints,congestion pricing policy also performs well in governance.Under stricter carbon emission constraints,however,HOV lane policy is more appro-priate.