Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China

Liu Yang (), Yuanqing Wang, Yujun Lian, Zhongming Guo, Yuanyuan Liu, Zhouhao Wu and Tieyue Zhang
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Liu Yang: Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Shaanxi Key Laboratory for Carbon Neutral Technology, Department of Urban Planning, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Yuanqing Wang: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Yujun Lian: Department of Finance, Lingnan College, Sun Yat-sen University, Guangzhou 510275, China
Zhongming Guo: Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Institute of Remote Sensing and Geographic Information Science, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Yuanyuan Liu: School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Zhouhao Wu: Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China
Tieyue Zhang: Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Shaanxi Key Laboratory for Carbon Neutral Technology, Department of Urban Planning, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
Authors registered in the RePEc Author Service: 玉君 连 ()

IJERPH, 2022, vol. 19, issue 21, 1-14

Abstract: Exploring key impact factors and their effects on urban residents’ transport carbon dioxide (CO 2 ) emissions is significant for effective low-carbon transport planning. Researchers face the model uncertainty problem to seek a rational and better explanatory model and the key variables in the model set containing various factors after they are arranged and combined. This paper uses the Bayesian Model Averaging method to solve the above problem, explore the key variables, and determine their relative significance and averaging effects. Beijing, Xi’an, and Wuhan are selected as three case cities for their representation of developing Chinese cities. We found that the initial key factor increasing transport emissions is the high dependence on cars, and the second is the geographical location factor that much more suburban residents suffer longer commuting. Developing satellite city rank first for reducing transport emissions due to more local trips with an average short distance, the second is the metro accessibility, and the third is polycentric form. Key planning strategies and policies are proposed: (i) combining policies of car restriction based on vehicle plate number, encouraging clean fuel cars, a carbon tax on oil uses, and rewarding public transit passengers; (ii) fostering subcenters’ strong industries to develop self-contained polycentric structures and satellite cities, and forming employment and life circle within 5 km radius; and (iii) integrating bus and rail transit services in the peripheral areas and suburbs and increasing the integration level of muti-modes transferring in transport hubs. The findings will offer empirical evidence and reference value in developing cities globally.

Keywords: transport CO 2 emission; impact factors; variable significance; planning strategy and policy; Bayesian Model Averaging (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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