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Equilibrium between Road Traffic Congestion and Low-Carbon Economy: A Case Study from Beijing, China

Author

Listed:
  • Shuxia Yang

    (Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China
    School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yu Ji

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Di Zhang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jing Fu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

China has allocated low-carbon targets into all regions and trades, and road traffic also has its own emission reduction targets. Congestion may increase carbon emissions from road traffic. It is worthwhile to study whether it is possible to achieve the goal of road traffic reduction by controlling congestion; that is, to achieve the equilibrium between traffic congestion and a low-carbon economy. The innovation of this paper is mainly reflected in the innovative topic selection, the introduction of a traffic index, and the establishment of the first traffic congestion and low-carbon economic equilibrium model. First, the relevant calculation method of the traffic index is introduced, and the traffic index is used to quantify the traffic congestion degree. Using the traffic index, GDP, and road passenger traffic volume, a nonlinear regression model of road traffic carbon emissions is constructed. Then, the calculation method of the carbon emission intensity of road traffic in the region is proposed. The equilibrium model of traffic congestion and a low-carbon economy is constructed to look for the degree of road traffic congestion that may occur under the permitted carbon emission intensity. Taking Beijing, where electric vehicles account for less than 3% of the total vehicles, as an example, it is difficult to achieve the equilibrium target between road traffic congestion and a low-carbon economy by alleviating traffic congestion in 2020. If the target of traffic carbon emission reduction in 2020 is adjusted from 40%–45% to 19.7% based on 2005, the equilibrium will be achieved. A negative correlation between road traffic carbon emissions and the reciprocal of the traffic index (1/TI) is found after eliminating the effects of GDP and PTV (road passenger traffic volume). As the traffic index decreases by units, the carbon emission reduction accelerates. The results show that carbon reduction targets cannot be simply allocated to various industries. The results of the research on the degree of the impact of traffic congestion on carbon emissions can be used as a basis for carbon reduction decisions of the traffic sector. The research method of this paper can provide a reference for the study of the equilibrium of traffic congestion and a low-carbon economy in other regions.

Suggested Citation

  • Shuxia Yang & Yu Ji & Di Zhang & Jing Fu, 2019. "Equilibrium between Road Traffic Congestion and Low-Carbon Economy: A Case Study from Beijing, China," Sustainability, MDPI, vol. 11(1), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:1:p:219-:d:194850
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    References listed on IDEAS

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    1. Loureiro, Maria L. & Labandeira, Xavier & Hanemann, Michael, 2013. "Transport and low-carbon fuel: A study of public preferences in Spain," Energy Economics, Elsevier, vol. 40(S1), pages 126-133.
    2. Ross Morrow, W. & Gallagher, Kelly Sims & Collantes, Gustavo & Lee, Henry, 2010. "Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the US transportation sector," Energy Policy, Elsevier, vol. 38(3), pages 1305-1320, March.
    3. Shuxia Yang & Di Zhang & Jing Fu & Shujing Fan & Yu Ji, 2018. "Market Cultivation of Electric Vehicles in China: A Survey Based on Consumer Behavior," Sustainability, MDPI, vol. 10(11), pages 1-23, November.
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    Cited by:

    1. Xueting Zhao & Liwei Hu & Xingzhong Wang & Jiabao Wu, 2022. "Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems," Sustainability, MDPI, vol. 14(24), pages 1-23, December.
    2. Zong, Fang & Li, Yu-Xuan & Zeng, Meng, 2023. "Developing a carbon emission charging scheme considering mobility as a service," Energy, Elsevier, vol. 267(C).
    3. Yaping Dong & Jinliang Xu & Menghui Li & Xingli Jia & Chao Sun, 2019. "Association of Carbon Emissions and Circular Curve in Northwestern China," Sustainability, MDPI, vol. 11(4), pages 1-15, February.
    4. Jiaqi Wu & Wenbo Li & Wenting Xu & Lin Yuan, 2023. "Measuring Resident Participation in the Renewal of Older Residential Communities in China under Policy Change," Sustainability, MDPI, vol. 15(3), pages 1-24, February.
    5. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    6. Gabriele Cepeliauskaite & Benno Keppner & Zivile Simkute & Zaneta Stasiskiene & Leon Leuser & Ieva Kalnina & Nika Kotovica & Jānis Andiņš & Marek Muiste, 2021. "Smart-Mobility Services for Climate Mitigation in Urban Areas: Case Studies of Baltic Countries and Germany," Sustainability, MDPI, vol. 13(8), pages 1-19, April.
    7. Weijia Li & Yuejiao Wang, 2023. "Optimization of Urban Road Green Belts under the Background of Carbon Peak Policy," Sustainability, MDPI, vol. 15(17), pages 1-17, August.

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