Xi Chang, Fang Xinxing, Ren Chen, Cao Shi-Jie
School of Architecture, Southeast University, 2 Sipailou, Nanjing, 210096, China; Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control, Southeast University, 2 Sipailou, Nanjing, 210096, China.
School of Architecture, Southeast University, 2 Sipailou, Nanjing, 210096, China; Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control, Southeast University, 2 Sipailou, Nanjing, 210096, China; Global Centre for Clean Air Research, Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey, Guildford, GU2 7XH, United Kingdom.
J Environ Manage. 2025 Sep;392:126723. doi: 10.1016/j.jenvman.2025.126723. Epub 2025 Aug 1.
Rapid urbanization has led to massive carbon dioxide emissions, which exacerbates global climate change, and further causes systemic issues such as ecological degradation, public health risks and socio-economic instability. Transportation is a major source of urban carbon emissions, and its dynamic emission characteristic poses a challenge for carbon mitigation. Most transportation carbon reduction strategies treat the cities within urban agglomeration equally or focus on specific influencing factor. There is still a lack of quantitative evidence on coordinated transportation management strategies for diverse cities in urban agglomeration and their complex impact mechanism on carbon reduction. This work aims to develop an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model, to analyze the impacts of transport intensity (passenger and freight volume), demand (bus and taxi ownership and bus passenger volume), and layout (commuting time, expressway mileage, and urban road area) on carbon reduction of the Yangtze River Delta (YRD) urban agglomeration. The maximum carbon emission reductions by optimizing transportation layout, intensity, and demand are 15 %, 7 %, and 5 %, respectively. By jointly reducing commuting time, highway mileage and road area, carbon emissions of core, transportation hubs, secondary and peripheral cities can be reduced by up to 10.04 %, 11.51 %, 13.45 % and 15.18 %, respectively. It suggests shifting from road to rail and waterway transport in YRD urban agglomerations, increasing the allocation of new energy vehicles, and optimizing traffic networks according to city characteristics. A management framework should be established through policy guidance, industrial collaboration, and public participation. The findings contribute to achieving the goals of carbon neutrality and peaking, further advancing urban environmental management and sustainable development strategies.
快速城市化导致大量二氧化碳排放，加剧了全球气候变化，并进一步引发生态退化、公共卫生风险和社会经济不稳定等系统性问题。交通是城市碳排放的主要来源，其动态排放特征给碳减排带来了挑战。大多数交通碳减排策略对城市群内的城市一视同仁，或侧重于特定影响因素。对于城市群中不同城市的协同交通管理策略及其复杂的碳减排影响机制，仍缺乏定量证据。这项工作旨在开发一个扩展的随机人口、富裕程度和技术回归影响模型（STIRPAT），以分析交通强度（客运和货运量）、需求（公交车和出租车保有量及公交客运量）和布局（通勤时间、高速公路里程和城市道路面积）对长江三角洲城市群碳减排的影响。通过优化交通布局、强度和需求，最大碳减排量分别为15%、7%和5%。通过共同减少通勤时间、高速公路里程和道路面积，核心城市、交通枢纽城市、二级城市和周边城市的碳排放量可分别最多减少10.04%、11.51%、13.45%和15.18%。这表明长江三角洲城市群应从公路运输转向铁路和水路运输，增加新能源汽车的配置，并根据城市特点优化交通网络。应通过政策引导、产业协作和公众参与建立一个管理框架。这些研究结果有助于实现碳中和和碳达峰目标，进一步推进城市环境管理和可持续发展战略。
