IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i4p1156-d208132.html
   My bibliography  Save this article

Association of Carbon Emissions and Circular Curve in Northwestern China

Author

Listed:
  • Yaping Dong

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Jinliang Xu

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Menghui Li

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Xingli Jia

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Chao Sun

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

Carbon emissions, produced by automobile fuel consumption, are termed as the key reason leading to global warming. The highway circular curve constitutes a major factor impacting vehicle carbon emissions. It is deemed quite essential to investigate the association existing between circular curve and carbon emissions. On the basis of the IPCC carbon emission conversion methodology, the current research work put forward a carbon emission conversion methodology suitable for China’s diesel status. There are 99 groups’ test data of diesel trucks during the trip, which were attained on 23 circular curves in northwestern China. The test road type was key arterial roads having a design speed greater than or equal to 60 km/h, besides having no roundabouts and crossings. Carbon emission data were generated with the use of carbon emission conversion methodologies and fuel consumption data from field tests. As the results suggested, carbon emissions decline with the increase in the radius of circular curve. A carbon emission quantitative model was established with the radius and length of circular curve, coupled with the initial velocity as the key impacting factors. In comparison with carbon emissions under circular curve section and flat section scenarios, the minimum curve radius impacting carbon emissions is 500 m. This research work provided herein a tool for the quantification of carbon emissions and a reference for a low-carbon highway design.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:4:p:1156-:d:208132
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/4/1156/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/4/1156/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Barth, Matthew & Boriboonsomsin, Kanok, 2010. "Real-World Carbon Dioxide Impacts of Traffic Congestion," University of California Transportation Center, Working Papers qt07n946vd, University of California Transportation Center.
    2. Natalia Sobrino & Andres Monzon, 2018. "Towards Low-Carbon Interurban Road Strategies: Identifying Hot Spots Road Corridors in Spain," Sustainability, MDPI, vol. 10(11), pages 1-11, October.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Quan Dai & Hongfei Jia & Yao Liu, 2020. "Private vehicle-based crowdshipping for intercity express transportation: Feasibility assessment," International Journal of Distributed Sensor Networks, , vol. 16(2), pages 15501477209, February.
    2. Xiaodong Zhang & Jinliang Xu & Menghui Li & Qunshan Li & Lan Yang, 2019. "Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO 2 Emissions," IJERPH, MDPI, vol. 16(14), pages 1-14, July.
    3. Jinliang Xu & Yaping Dong & Menghua Yan, 2020. "A Model for Estimating Passenger-Car Carbon Emissions that Accounts for Uphill, Downhill and Flat Roads," Sustainability, MDPI, vol. 12(5), pages 1-21, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Nilanchal PATEL & Alok Bhushan MUKHERJEE, 2014. "Categorization Of Urban Traffic Congestion Based On The Fuzzification Of Congestion Index Value And Influencing Parameters," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 9(4), pages 36-51, November.
    3. Breno Tostes de Gomes Garcia & Diana Mery Messias Lopes & Ilton Curty Leal Junior & José Carlos Cesar Amorim & Marcelino Aurélio Vieira da Silva & Vanessa de Almeida Guimarães, 2019. "Analysis of the Performance of Transporting Soybeans from Mato Grosso for Export: A Case Study of the Tapajós-Teles Pires Waterway," Sustainability, MDPI, vol. 11(21), pages 1-26, November.
    4. Zong, Fang & Li, Yu-Xuan & Zeng, Meng, 2023. "Developing a carbon emission charging scheme considering mobility as a service," Energy, Elsevier, vol. 267(C).
    5. 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.
    6. 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.
    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.
    8. Juan Miguel Vega Naranjo & Montaña Jiménez-Espada & Francisco Manuel Martínez García & Rafael González-Escobar & Juan Pedro Cortés-Pérez, 2023. "Intercity Mobility Assessment Facing the Demographic Challenge: A Survey-Based Research," IJERPH, MDPI, vol. 20(2), pages 1-24, January.
    9. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:4:p:1156-:d:208132. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.