Abstract:

With the continuous growth of carbon emissions in the road transportation sector and the flourishing development of the taxi and ride-hailing industry, the role of ride-hailing services in reducing urban transportation carbon emissions has garnered increasing attention. This study develops a multi-scenario simulation framework for carbon emissions from taxi and ride-hailing services in Shanghai (2025-2035), to propose carbon emission reduction decision-making recommendations and targets. Integrating SOCSIM demographic microsimulation with a multi-task learning neural network, we establish a coupled prediction model for household demographic and economic attributes and travel behaviors (frequency, mode, distance) using household demographic and economic characteristics and resident travel survey data from 17,400 households in Shanghai. Based on the projection of travel demand, this study examines various scenarios of taxi and ride-hailing demand, the proportion of effective mileage, and the substitution effects on other travel modes. Then, the paper proposes decision-making recommendations and targets based on multi-scenario carbon emission projections: 1) The emission reduction advantage of vehicle electrification is significantly offset by empty-run mileage, requiring an effective mileage ratio threshold of 85.5% to maintain decarbonization benefits. 2) If taxis and ride-hailing services achieve an average load of 1.8 passengers per trip, the maximum required proportion of effective mileage from 2025 to 2035 decreases to 71.4%. 3) The substitution of greener alternatives by overlapping public transit corridor orders and related orders could generate 16.28 million tons of additional emissions, necessitating differential pricing regulation. The proposed "technical efficiency-modal optimization-system coordination" tri-level governance framework provides a decision-making paradigm for megacity mobility decarbonization.

Key words: carbon emission, multi-scenario simulation and projection, effective mileage, substitution effects on other travel modes, policy recommendation