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

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...

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Autores principales: Yang, Liu, Wang, Yuanqing, Lian, Yujun, Guo, Zhongming, Liu, Yuanyuan, Wu, Zhouhao, Zhang, Tieyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657302/
https://www.ncbi.nlm.nih.gov/pubmed/36360636
http://dx.doi.org/10.3390/ijerph192113746
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author Yang, Liu
Wang, Yuanqing
Lian, Yujun
Guo, Zhongming
Liu, Yuanyuan
Wu, Zhouhao
Zhang, Tieyue
author_facet Yang, Liu
Wang, Yuanqing
Lian, Yujun
Guo, Zhongming
Liu, Yuanyuan
Wu, Zhouhao
Zhang, Tieyue
author_sort Yang, Liu
collection PubMed
description 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.
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spelling pubmed-96573022022-11-15 Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China Yang, Liu Wang, Yuanqing Lian, Yujun Guo, Zhongming Liu, Yuanyuan Wu, Zhouhao Zhang, Tieyue Int J Environ Res Public Health Article 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. MDPI 2022-10-22 /pmc/articles/PMC9657302/ /pubmed/36360636 http://dx.doi.org/10.3390/ijerph192113746 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Liu
Wang, Yuanqing
Lian, Yujun
Guo, Zhongming
Liu, Yuanyuan
Wu, Zhouhao
Zhang, Tieyue
Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China
title Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China
title_full Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China
title_fullStr Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China
title_full_unstemmed Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China
title_short Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China
title_sort key factors, planning strategy and policy for low-carbon transport development in developing cities of china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657302/
https://www.ncbi.nlm.nih.gov/pubmed/36360636
http://dx.doi.org/10.3390/ijerph192113746
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