Abstract
The emergence of Bicycle-Sharing Systems (BSSs) has brought about changes in traffic systems and generated economic, environmental, and human health effects. This study took Beijing as the research object, and aimed to examine the economic, environmental, and human health effects of BSSs and the key drivers affecting the environmental performance of shared bicycles. Questionnaire surveys were carried out to provide an overview of BSSs in Beijing by referring to the original data in the impact assessment, and the identification of key drivers. Based on the relationship between leisure-time and economic growth, the economic effects resulted in a statistically significant increase of 79.3 US dollars (612.3 RMB) and 44.4 US dollars (342.7 RMB) per capita GDP per day in the baseline of the United States and Denmark, respectively. The environmental and human health effects were evaluated using the life cycle assessment method to study the substitution of different transport modes during the entire life cycle of bicycle-sharing. The results revealed that reduced adverse environmental effects were proved to be significant and positive on all impact categories and the reduction in human health damage were positive, approximately equal to 500,000 DALYs. The sensitivity analysis demonstrated that the increase of usage rate in sharing bicycle will bring more environment benefits and human health damage reduction. The identification of key drivers was determined by the binary logistic model, and included the following: gender, monthly income, the low cost of BSSs, the location of BSSs in relation to bus stations, metro stations, and residential areas; perceptions of a higher frequency of bicycle-sharing; damaged bicycles as a development barrier, and optimism about the future of BSSs. This study provides empirical evidence for BSS management and policy making by the administrative department.
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Abbreviations
- BSSs:
-
Bicycle-sharing systems
- LCA:
-
Life cycle assessment
- LCI:
-
Life cycle inventory
- GDP:
-
Gross Domestic Product
- GWP:
-
Global warming potential
- SOD:
-
Stratospheric ozone depletion
- IR:
-
Ionizing radiation
- OFH:
-
Ozone formation, Human health
- FPMF:
-
Fine particulate matter formation
- OFT:
-
Ozone formation, Terrestrial ecosystems
- TA:
-
Terrestrial acidification
- FEU:
-
Freshwater eutrophication
- TE:
-
Terrestrial ecotoxicity
- FEC:
-
Freshwater ecotoxicity
- ME:
-
Marine ecotoxicity
- HCT:
-
Human carcinogenic toxicity
- HNCT:
-
Human non-carcinogenic toxicity
- LU:
-
Land use
- MRS:
-
Mineral resource scarcity
- FRS:
-
Fossil resource scarcity
- WC:
-
Water consumption
- DALYs:
-
Disability-Adjusted Life Years
- GWH:
-
Global warming/human health
- WCH:
-
Water consumption/human health
- GHG emission:
-
Greenhouse gas emission
References
Abonazel MR, Ibrahim MG (2018) On estimation methods for binary logistic regression model with missing values. Int J Math Comput Sci 4(3):79–85
Bachand-Marleau J, Lee BH, El-Geneidy AM (2012) Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transp Res Rec 2314(1):66–71. https://doi.org/10.3141/2314-09
Barbour N, Zhang Y, Mannering F (2019) A statistical analysis of bike sharing usage and its potential as an auto-trip substitute. J Transp Health 12:253–262. https://doi.org/10.1016/j.jth.2019.02.004
Beijing Annual Report of Transport Development in 2019 (2019) Beijing: Beijing Transport Institute (in Chinese)
Beijing Annual Report of Transport Development in 2020 (2020) Beijing: Beijing Transport Institute (in Chinese)
Brand J, Hoogendoorn S, Van Oort N, Schalkwijk B (2017) Modelling multimodal transit networks integration of bus networks with walking and cycling. 2017 5th IEEE international conference on models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, pp 750–755. https://doi.org/10.1109/MTITS.2017.8005612
Bureau of Labor Statistics (BLS) (2021) Data of leisure time in United States. https://www.bls.gov/data/. Accessed 10 May 2021
Caggiani L, Camporeale R, Ottomanelli M, Szeto WY (2018) A modeling framework for the dynamic management of free-floating bike-sharing systems. Transp Res C Emerg Technol 87:159–182. https://doi.org/10.1016/j.trc.2018.01.001
Cerutti PS, Martins RD, Macke J, Sarate JAR (2019) “Green, but not as green as that”: An analysis of a Brazilian bike-sharing system. J Clean Prod 217:185–193. https://doi.org/10.1016/j.jclepro.2019.01.240
Chang X, Wu J, He Z, Li D, Sun H, Wang W (2020) Understanding user’s travel behavior and city region functions from station-free shared bike usage data. Transp Res F Traffic Psychol Behav 72:81–95. https://doi.org/10.1016/j.trf.2020.03.018
Chen J, Zhou D, Zhao Y, Wu B, Wu T (2020) Life cycle carbon dioxide emissions of bike sharing in China: Production, operation, and recycling. Resour Conserv Recycl 162:105011. https://doi.org/10.1016/j.resconrec.2020.105011
Clockston RLM, Rojas-Rueda D (2021) Health impacts of bike-sharing systems in the US. Environ Res 202:111709. https://doi.org/10.1016/j.envres.2021.111709
Czepkiewicz M, Ottelin J, Ala-Mantila S, Heinonen J, Hasanzadeh K, Kyttä M (2018) Urban structural and socioeconomic effects on local, national and international travel patterns and greenhouse gas emissions of young adults. J Transp Geogr 68(C):130–141. https://doi.org/10.1016/j.jtrangeo.2018.02.008
D’Almeida L, Rye T, Pomponi F (2021) Emissions assessment of bike sharing schemes: The case of Just Eat Cycles in Edinburgh, UK. Sustain Cities Soc 71:103012. https://doi.org/10.1016/j.scs.2021.103012
Du M, Cheng L (2018) Better understanding the characteristics and influential factors of different travel patterns in free-floating bike sharing: Evidence from Nanjing, China. Sustainability 10(4):1244. https://doi.org/10.3390/su10041244
Eren E, Uz VE (2020) A review on bike-sharing: The factors affecting bike-sharing demand. Sustain Cities Soc 54:101882. https://doi.org/10.1016/j.scs.2019.101882
Evaluation methods for road traffic congestion levels (GA/T 115-2020) (2020) Beijing: Ministry of Public Security of China (MPSC) (in Chinese)
Finnveden G, Hauschild MZ, Ekvall T, Guinée J, Heijungs R, Hellweg S, Suh S (2009) Recent developments in life cycle assessment. J Environ Manag 91(1):1–21. https://doi.org/10.1016/j.jenvman.2009.06.018
Fishman E (2016) Bikeshare: A review of recent literature. Transp Rev 36(1):92–113. https://doi.org/10.1080/01441647.2015.1033036
Fishman E, Schepers P, Kamphuis CBM (2015) Dutch cycling: quantifying the health and related economic benefits. Am J Public Health 105(8):e13–e15. https://doi.org/10.2105/AJPH.2015.302724
Ge XY, Ye TF, Hu CB (2014) Income taxation and leisure time’s impact on China’s economy growth. Econ Surv 31:126–131 (in Chinese)
Guo Y, Zhou J, Wu Y, Li Z (2017) Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China. PloS one 12(9):e0185100. https://doi.org/10.1371/journal.pone.0185100
Hosford K, Fuller D, Lear SA, Teschke K, Gauvin L, Brauer M, Winters M (2018) Evaluation of the impact of a public bicycle share program on population bicycling in Vancouver, BC. Prev Med Rep 12:176–181. https://doi.org/10.1016/j.pmedr.2018.09.014
Hsu CC, Liou JJ, Lo HW, Wang YC (2018) Using a hybrid method for evaluating and improving the service quality of public bike-sharing systems. J Clean Prod 202:1131–1144. https://doi.org/10.1016/j.jclepro.2018.08.193
Jia Y, Fu H (2019) Association between innovative dockless bicycle sharing programs and adopting cycling in commuting and non-commuting trips. Transp Res A Policy Pract 121:12–21. https://doi.org/10.1016/j.tra.2018.12.025
Jiang Z, Lei C, Ouyang Y (2020) Optimal investment and management of shared bikes in a competitive market. Transp Res B Methodol 135:143–155. https://doi.org/10.1016/j.trb.2020.03.007
Lai R, Ma X, Zhang F, Ji Y (2021) Life Cycle Assessment of Free-Floating Bike Sharing on Greenhouse Gas Emissions: A Case Study in Nanjing, China. Appl Sci 11(23):11307. https://doi.org/10.3390/app112311307
Leisure Green Paper: 2019–2020 China Leisure Development Report (2020) Beijing: Tourism Research Center, Chinese Academy of Social Sciences (in Chinese)
Li W, Kamargianni M (2018) Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China. Transp Res A Policy Pract 111:277–291. https://doi.org/10.1016/j.tra.2018.01.019
Lin L, He Z, Peeta S (2018) Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach. Transp Res C Emerg Technol 97:258–276. https://doi.org/10.1016/j.trc.2018.10.011
Liu A, Ji X, Xu L, Lu H (2019) Research on the recycling of sharing bikes based on time dynamics series, individual regrets and group efficiency. J Clean Prod 208:666–687. https://doi.org/10.1016/j.jclepro.2018.10.146
Luo H, Kou Z, Zhao F, Cai H (2019) Comparative life cycle assessment of station-based and dock-less bike sharing systems. Resour Conserv Recycl 146:180–189. https://doi.org/10.1016/j.resconrec.2019.03.003
Meddin R, DeMaio P (2021) The bike-sharing world map. http://www.metrobike.net. Accessed 10 May 2021
National Bureau of Statistics of China (NBSC) (2021) China Statistical Yearbook. China Statistical Publishing House, Beijing (in Chinese)
Nigri EM, Barros ACD, Rocha SDF, Romeiro Filho E (2014) Assessing environmental impacts using a comparative LCA of industrial and artisanal production processes: “ Minas Cheese” case. Food Sci Technol 34(3):522–531. https://doi.org/10.1590/1678-457x.6356
Organization for Economic Cooperation and Development (OECD) (2022) OECD Better Life Index. https://www.oecdbetterlifeindex.org/topics/work-life-balance/. Accessed 10 Apr 2022
Otero I, Nieuwenhuijsen MJ, Rojas-Rueda D (2018) Health impacts of bike sharing systems in Europe. Environ Int 115:387–394. https://doi.org/10.1016/j.envint.2018.04.014
Pal A, Zhang Y (2017) Free-floating bike sharing: Solving real-life large-scale static rebalancing problems. Transp Res C Emerg Technol 80:92–116. https://doi.org/10.1016/j.trc.2017.03.016
Pelechrinis K, Zacharias C, Kokkodis M, Lappas T (2017) Economic impact and policy implications from urban shared transportation: The case of Pittsburgh’s shared bike system. PloS one 12(8):e0184092. https://doi.org/10.1371/journal.pone.0184092
Pojani D, Stead D (2015) Sustainable urban transport in the developing world: beyond megacities. Sustainability 7(6):7784–7805. https://doi.org/10.3390/su7067784
Qiu L, He L (2018) Bike Sharing and the Economy, the Environment, and Health-Related Externalities. Sustainability 10(4):1145. https://doi.org/10.3390/su10041145
Richardson JT (1990) Reliability and replicability of the approaches to studying questionnaire. Stud High Educ 15(2):155–168. https://doi.org/10.1080/03075079012331377481
Ru X, Wang S, Chen Q, Yan S (2018) Exploring the interaction effects of norms and attitudes on green travel intention: An empirical study in eastern China. J Clean Prod 197:1317–1327. https://doi.org/10.1016/j.jclepro.2018.06.293
Shaheen SA, Guzman S, Zhang H (2010) Bikesharing in Europe, the Americas, and Asia: past, present, and future. Transp Res Rec 2143(1):159–167. https://doi.org/10.3141/2143-20
Shelat S, Huisman R, van Oort N (2018) Analysing the trip and user characteristics of the combined bicycle and transit mode. Res Transp Econ 69:68–76. https://doi.org/10.1016/j.retrec.2018.07.017
Si H, Shi JG, Wu G, Chen J, Zhao X (2019) Mapping the bike sharing research published from 2010 to 2018: A scientometric review. J Clean Prod 213:415–427. https://doi.org/10.1016/j.jclepro.2018.12.157
Tian ZP, Wang JQ, Wang J, Zhang HY (2018) A multi-phase QFD-based hybrid fuzzy MCDM approach for performance evaluation: A case of smart bike-sharing programs in Changsha. J Clean Prod 171:1068–1083. https://doi.org/10.1016/j.jclepro.2017.10.098
Turoń K, Sierpiński G, Tóth J (2019) Support for pro-ecological solutions in smart cities with the use of travel databases–a case study based on a bike-sharing system in Budapest. Scientific and technical conference transport systems theory and practice. Springer, Cham, pp 225–237. https://doi.org/10.1007/978-3-030-35543-2_18
Wang Y, Sun S (2022) Does large scale free-floating bike sharing really improve the sustainability of urban transportation? Empirical evidence from Beijing. Sustain Cities Soc 76:103533. https://doi.org/10.1016/j.scs.2021.103533
Wang M, Zhou X (2017) Bike-sharing systems and congestion: Evidence from US cities. J Transp Geogr 65:147–154. https://doi.org/10.1016/j.jtrangeo.2017.10.022
Wei X (2014) Is china an inefficient country? a transnational comparison from the perspective of leisure. China Soft Science (in Chinese)
World Bank (2021a) Data of China. https://data.worldbank.org/country/china. Accessed 10 Apr 2022
World Bank (2021b) Data of United States. https://data.worldbank.org/country/united-states. Accessed 26 Aug 2021
World Bank (2021c) Data of Denmark. https://data.worldbank.org/country/denmark. Accessed 10 Apr 2022
Xing Y, Wang K, Lu JJ (2020) Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China. J Transp Geogr 87:102787. https://doi.org/10.1016/j.jtrangeo.2020.102787
Zhang Y, Mi Z (2018) Environmental benefits of bike sharing: A big data-based analysis. Appl Energy 220:296–301. https://doi.org/10.1016/j.apenergy.2018.03.101
Zhang L, Zhang J, Duan ZY, Bryde D (2015) Sustainable bike-sharing systems: characteristics and commonalities across cases in urban China. J Clean Prod 97:124–133. https://doi.org/10.1016/j.jclepro.2014.04.006
Zheng F, Gu F, Zhang W, Guo J (2019) Is bicycle sharing an environmental practice? Evidence from a life cycle assessment based on behavioral surveys. Sustainability 11(6):1550. https://doi.org/10.3390/su11061550
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The financial support is gratefully acknowledged. We thank International Science Editing (http://www.internationalscienceediting.com) for editing this manuscript.
Funding
This work was sponsored by the Youth Scientific and Technological Innovation Programs of Shanxi Agricultural University (2020QC16). The financial support is gratefully acknowledged. We thank International Science Editing (http://www.internationalscienceediting.com) for editing this manuscript.
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HM: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Visualization, Funding acquisition and Writing–Original Draft. XC: Writing–Review & Editing and Investigation. ZZ: Writing–Review & Editing and Supervision. QW: Data collection and Data Curation.
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Ma, H., Chen, X., Zhen, Z. et al. Bicycle-sharing in Beijing: An Assessment of Economic, Environmental, and Health Effects, and Identification of Key Drivers of Environmental Performance. Netw Spat Econ 23, 285–316 (2023). https://doi.org/10.1007/s11067-022-09585-6
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DOI: https://doi.org/10.1007/s11067-022-09585-6