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Pengxiang Zhao


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High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data


  • Aoyong Li
  • Kun Gao
  • Pengxiang Zhao
  • Xiaobo Qu
  • Kay W. Axhausen

Summary, in English

Dockless bike-sharing systems (DLBS) have gained much popularity due to their environmentally friendly features. This study puts forward a distinctive framework for assessing the environmental influences of DLBS in high resolution based on DLBS transaction data. The proposed framework firstly estimates the transport mode substituted by DLBS for each recorded bike-sharing trip by utilizing the route planning techniques of online maps and a well-calibrated discrete choice model. Afterward, greenhouse gases (GHG) emission reductions in every recorded DLBS trip are quantified using Life Cycle Analysis. The proposed framework is applied to an empirical dataset from Shanghai, China. The empirical results reveal that the substitution rates of DLBS to different transport modes have substantial spatiotemporal variances and depend strongly on travel contexts, highlighting the necessity of analyzing the environmental impacts of DLBS at the trip level. Moreover, each DLBS trip is estimated to save an average 80.77 g CO2-eq GHG emissions versus than the situations without DLBS in Shanghai. The annual reduced GHG emissions from DLBS are estimated to be 117 kt CO2-eq, which is substantial and equals to the yearly GHG emissions of over 25,000 typical gasoline passenger vehicles. Additionally, the associations among built environments and GHG emission reductions from DLBS are quantitatively investigated to shed light on the spatial variances in the environmental impacts of DLBS. The results can efficiently support the benefit-cost analysis, planning, and management of DLBS.


  • Centre for Geographical Information Systems (GIS Centre)
  • Dept of Physical Geography and Ecosystem Science

Publishing year





Journal of Cleaner Production



Document type

Journal article




  • Transport Systems and Logistics


  • Big data
  • Built environment
  • Environmental benefit
  • Greenhouse gases
  • Shared mobility




  • ISSN: 0959-6526