绿色智慧交通

基于多情景推演的上海市出租网约碳减排策略

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  • 1.同济大学 道路与交通工程教育部重点实验室,上海 201804;

    2.上海基础设施建设发展(集团)有限公司,上海 200032

网络出版日期: 2026-02-27

Carbon Emission Reduction Strategies for Taxi and Ride-Hailing Services in Shanghai Based on Multi-Scenario Projection

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  • 1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804;

    2. Shanghai Infrastructure Construction and Development (Group) Co., Ltd., Shanghai 200032

Online published: 2026-02-27

摘要

随着道路交通领域碳排放持续增长和出租网约行业的蓬勃发展,网约车在城市交通碳减排中的作用逐渐受到关注。本研究构建了居民出行行为多特征联合预测模型,对上海市2025~2035年出租网约车碳排放进行多情景仿真,提出碳减排策略及目标。基于上海市17400户家庭的人口经济特征和出行调查数据,采用SOCSIM人口微观仿真与多任务学习神经网络相结合的方法,实现未来家庭人口经济特征与出行行为(频次、方式、距离)的耦合推演。基于需求推演,探讨多种出租网约需求情景、有效里程占比及对其他出行方式的替代情景,并基于多情景排放仿真结果提出决策建议及目标:1)出租网约车电动化优势受空驶里程抵消效应显著,维持减排优势需将有效里程占比提升至85.5%(2025-2035);2)如果出租网约实现1.8人/次的平均载荷,则2025~2035年最高需达到的有效里程占比降低至71.4%;3)公交走廊重叠订单等对更环保出行方式的替代效应将产生1628万吨额外碳排放,需通过差异化定价调控。研究提出“技术增效-模式优化-系统协同”调控,为超大城市出行结构低碳转型提供了决策范式。

本文引用格式

范宇杰, 徐子强, 梁霄, 等 . 基于多情景推演的上海市出租网约碳减排策略[J]. 华南理工大学学报(自然科学版), 0 : 1 . DOI: 10.12141/j.issn.1000-565X.250456

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.

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