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Article: Riding towards a sustainable future: an evaluation of bike sharing's environmental benefits in Xiamen Island, China

TitleRiding towards a sustainable future: an evaluation of bike sharing's environmental benefits in Xiamen Island, China
Authors
KeywordsCarbon emission
Greenhouse gases
Multiscale geographically weighted regression
Shared mobility
Travel behavior
Urban mobility
Issue Date17-Jan-2024
PublisherBeijing Normal University Press
Citation
Geography and Sustainability, 2024, v. 5, n. 2, p. 276-288 How to Cite?
AbstractIn the pursuit of sustainable urbanization, Bike-Sharing Services (BSS) emerge as a pivotal instrument for promoting green, low-carbon transit. While BSS is often commended for its environmental benefits, we offer a more nuanced analysis that elucidates previously neglected aspects. Through the Dominant Travel Distance Model (DTDM), we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance. Utilizing multiscale geographically weighted regression (MGWR), we illuminate the relationship between BSS's environmental benefits and built-environment attributes. The life cycle analysis (LCA) quantifies greenhouse gas (GHG) emissions from production to operation, providing a deeper understanding of BSS's environmental benefits. Notably, our study focuses on Xiamen Island, a Chinese “Type II large-sized city” (1–3 million population), contrasting with the predominantly studied “super large-sized cities” (over 10 million population). Our findings highlight: (1) A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO2-eq, accumulating monthly savings of 144.477 t CO2-eq. (2) Areas in the southwest, northeast, and southeast of Xiamen Island, characterized by high population densities, register significant BSS environmental benefits. (3) At a global level, the stepwise regression model identifies five key built environment factors influencing BSS's GHG mitigation. (4) Regionally, MGWR enhances model precision, indicating that these five factors function at diverse spatial scales, affecting BSS's environmental benefits variably.
Persistent Identifierhttp://hdl.handle.net/10722/351066
ISSN
2023 Impact Factor: 8.0
2023 SCImago Journal Rankings: 1.800

 

DC FieldValueLanguage
dc.contributor.authorLiu, Jianxiao-
dc.contributor.authorWang, Meilian-
dc.contributor.authorChen, Pengfei-
dc.contributor.authorWen, Chaoxiang-
dc.contributor.authorYu, Yue-
dc.contributor.authorChau, K W-
dc.date.accessioned2024-11-09T00:35:31Z-
dc.date.available2024-11-09T00:35:31Z-
dc.date.issued2024-01-17-
dc.identifier.citationGeography and Sustainability, 2024, v. 5, n. 2, p. 276-288-
dc.identifier.issn2096-7438-
dc.identifier.urihttp://hdl.handle.net/10722/351066-
dc.description.abstractIn the pursuit of sustainable urbanization, Bike-Sharing Services (BSS) emerge as a pivotal instrument for promoting green, low-carbon transit. While BSS is often commended for its environmental benefits, we offer a more nuanced analysis that elucidates previously neglected aspects. Through the Dominant Travel Distance Model (DTDM), we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance. Utilizing multiscale geographically weighted regression (MGWR), we illuminate the relationship between BSS's environmental benefits and built-environment attributes. The life cycle analysis (LCA) quantifies greenhouse gas (GHG) emissions from production to operation, providing a deeper understanding of BSS's environmental benefits. Notably, our study focuses on Xiamen Island, a Chinese “Type II large-sized city” (1–3 million population), contrasting with the predominantly studied “super large-sized cities” (over 10 million population). Our findings highlight: (1) A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO2-eq, accumulating monthly savings of 144.477 t CO2-eq. (2) Areas in the southwest, northeast, and southeast of Xiamen Island, characterized by high population densities, register significant BSS environmental benefits. (3) At a global level, the stepwise regression model identifies five key built environment factors influencing BSS's GHG mitigation. (4) Regionally, MGWR enhances model precision, indicating that these five factors function at diverse spatial scales, affecting BSS's environmental benefits variably.-
dc.languageeng-
dc.publisherBeijing Normal University Press-
dc.relation.ispartofGeography and Sustainability-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCarbon emission-
dc.subjectGreenhouse gases-
dc.subjectMultiscale geographically weighted regression-
dc.subjectShared mobility-
dc.subjectTravel behavior-
dc.subjectUrban mobility-
dc.titleRiding towards a sustainable future: an evaluation of bike sharing's environmental benefits in Xiamen Island, China-
dc.typeArticle-
dc.identifier.doi10.1016/j.geosus.2024.01.002-
dc.identifier.scopuseid_2-s2.0-85189521655-
dc.identifier.volume5-
dc.identifier.issue2-
dc.identifier.spage276-
dc.identifier.epage288-
dc.identifier.eissn2666-6839-
dc.identifier.issnl2096-7438-

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