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The Potential of Carbon Emissions Reductions of Public Bikes

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Listed:
  • Ting Lu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Yan Xu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Linfan Chen

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Lili Lu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

  • Rui Ren

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China)

Abstract

The reduction of carbon emissions has become a heated background topic in the context of climate change. This paper estimates the potential for carbon reduction from the use of public bikes, on the basis of a travel mode choice model and a carbon emission calculation model. A probability model for the travel mode choice is built to predict travel demands of different modes, and is based on the Logit-based stochastic user equilibrium model. According to this, the generalized travel cost of choosing to walk increases with distance, but the cost of choosing a taxi decreases with distance. When the trip distance is 1.4 km, the walk cost equals to that of the taxi, while if the trip distance is smaller than 1.4 km, the probability of the walk is larger than of a taxi, and vice versa. The case of Ningbo is analyzed. Based on the monthly travel data, the travel characteristics of the public bikes are first analyzed; these indicate that the medium travel distance is 1.44 km, and that the number of trips less than 1.6 km accounts for 70% of all trips. This reveals that the public bike trips are mainly short-distance and in workday rush hour. The related carbon emission reductions of Ningbo on average are 1.97 kg/person and 1.98 kg/km 2 , and the reductions are positively linearly related to the average hourly total turnover rate, which means the turnover rate is a great parameter to reflect the capability of carbon emission reductions.

Suggested Citation

  • Ting Lu & Yan Xu & Linfan Chen & Lili Lu & Rui Ren, 2022. "The Potential of Carbon Emissions Reductions of Public Bikes," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14831-:d:968635
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    References listed on IDEAS

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    1. Yu, Biying & Ma, Ye & Xue, Meimei & Tang, Baojun & Wang, Bin & Yan, Jinyue & Wei, Yi-Ming, 2017. "Environmental benefits from ridesharing: A case of Beijing," Applied Energy, Elsevier, vol. 191(C), pages 141-152.
    2. Yuan, Xinmei & Li, Lili & Gou, Huadong & Dong, Tingting, 2015. "Energy and environmental impact of battery electric vehicle range in China," Applied Energy, Elsevier, vol. 157(C), pages 75-84.
    3. Shafique, Muhammad & Azam, Anam & Rafiq, Muhammad & Luo, Xiaowei, 2021. "Investigating the nexus among transport, economic growth and environmental degradation: Evidence from panel ARDL approach," Transport Policy, Elsevier, vol. 109(C), pages 61-71.
    4. Zhang, Yongping & Mi, Zhifu, 2018. "Environmental benefits of bike sharing: A big data-based analysis," Applied Energy, Elsevier, vol. 220(C), pages 296-301.
    5. Tianjian Yang & Ye Li & Simin Zhou & Yu Zhang, 2019. "Dynamic Feedback Analysis of Influencing Factors and Challenges of Dockless Bike-Sharing Sustainability in China," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    6. Elliot Fishman, 2016. "Bikeshare: A Review of Recent Literature," Transport Reviews, Taylor & Francis Journals, vol. 36(1), pages 92-113, January.
    7. Liu, Yixiao & Tian, Zihao & Pan, Baoran & Zhang, Wenbin & Liu, Yunqi & Tian, Lixin, 2022. "A hybrid big-data-based and tolerance-based method to estimate environmental benefits of electric bike sharing," Applied Energy, Elsevier, vol. 315(C).
    8. Philips, Ian & Anable, Jillian & Chatterton, Tim, 2022. "E-bikes and their capability to reduce car CO2 emissions," Transport Policy, Elsevier, vol. 116(C), pages 11-23.
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