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Environmental benefits of bike sharing: A big data-based analysis

Zhang, Y; Mi, Z; (2018) Environmental benefits of bike sharing: A big data-based analysis. Applied Energy , 220 pp. 296-301. 10.1016/j.apenergy.2018.03.101. Green open access

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Abstract

Bike sharing is a new form of transport and is becoming increasingly popular in cities around the world. This study aims to quantitatively estimate the environmental benefits of bike sharing. Using big data techniques, we estimate the impacts of bike sharing on energy use and carbon dioxide (CO 2 ) and nitrogen oxide (NO X ) emissions in Shanghai from a spatiotemporal perspective. In 2016, bike sharing in Shanghai saved 8358 tonnes of petrol and decreased CO 2 and NO X emissions by 25,240 and 64 tonnes, respectively. From a spatial perspective, environmental benefits are much higher in more developed districts in Shanghai where population density is usually higher. From a temporal perspective, there are obvious morning and evening peaks of the environmental benefits of bike sharing, and evening peaks are higher than morning peaks. Bike sharing has great potential to reduce energy consumption and emissions based on its rapid development.

Type: Article
Title: Environmental benefits of bike sharing: A big data-based analysis
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.apenergy.2018.03.101
Publisher version: http://dx.doi.org/10.1016/j.apenergy.2018.03.101
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bike sharing, Sharing economy, Energy consumption, Carbon emissions, Air pollution, Big data
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10046880
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