Traffic and Transportation Journal

Article

How Do Neighbourhood and Working Environment Affect Green Commuting in China? A Resident Health Perspective

Junlan Chen, Hanshang Du, Meina Zheng, Xiucheng Guo

DOI:https://doi.org/10.7307/ptt.v35i2.49

Keywords:green commuting, commuting mode, neighbourhood environment, working environment, resident health, transportation planning

Abstract

Commuting contributes to high levels of greenhouse gases and air pollution. The recently advocated ‘green commuting’, i.e. active and public modes of transport, will be conducive to low-carbon and environmentally friendly transport. A baseline goal of urban planning is to promote health; however, few studies have explored the health-related impacts of environments at both ends of the commute on residents’ commuting mode choices. To fill the gap, this study proposes to consider the impact of the neighbourhood and working environment on green commuting from a health perspective. Using a sample of 15,886 people from 368 communities in China, three generalised multilevel linear regression models were estimated. Physical and psychological health were combined to further analyse health-related environmental attributes on the commuting choices of residents with different health levels. The results indicate that the working environment exerts more substantial effects on ‘green commuting’ than the neighbourhood environment, especially for workplace satisfaction. Moreover, we found that a good working environment and relationships will significantly encourage the sub-healthy group to choose active commuting. These findings are beneficial for policymakers to consider focusing on reconciling neighbourhood and working environments and meeting the commuting requirements of the less healthy group.

References

[1] Liu YL, Fang FG, Jing Y. How urban land use influences commuting flows in Wuhan, Central China: A mobile phone signaling data perspective. Sustainable Cities and Society. 2020;53. DOI: 10.1016/j.scs.2019.101914.
[2] Ferreira JP, et al. Economic, social, energy and environmental assessment of inter-municipality commuting: The case of Portugal. Energy Policy. 2014;66:411-418. DOI: 10.1016/j.enpol.2013.11.010.
[3] Liu Y, et al. The spatial distribution of commuting CO_2 emissions and the influential factors: A case study in Xi'an, China. Advances in Climate Change Research. 2015;6(1):46-55. DOI: 10.1016/j.accre.2015.09.001.
[4] Ministry of Ecology and Environment of the People's Republic of China. China Mobile Source Environment Management Annual Report. 2020. https://www.mee.gov.cn/hjzl/sthjzk/ydyhjgl/202109/t20210910_920787.shtml [Accessed March 22nd 2023].
[5] Clark B, Chatterjee K, Melia S. Changes to commute mode: The role of life events, spatial context and environmental attitude. Transportation Research Part A - Policy and Practice. 2016;89:89-105. DOI: 10.1016/j.tra.2016.05.005.
[6] Guo YY, et al. Dockless bike-sharing as a feeder mode of metro commute? The role of the feeder-related built environment: Analytical framework and empirical evidence. Sustainable Cities and Society. 2021;65. DOI: 10.1016/j.scs.2020.102594.
[7] Sun BD, Ermagun A, Dan B. Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai. Transportation Research Part D - Transport and Environment. 2017;52:441-453. DOI: 10.1016/j.trd.2016.06.001.
[8] Yang YY, et al. Towards a cycling-friendly city: An updated review of the associations between built environment and cycling behaviors (2007-2017). Journal of Transport & Health. 2019;14. DOI: 10.1016/j.jth.2019.100613.
[9] Zhu ZJ, et al. The impact of urban characteristics and residents' income on commuting in China. Transportation Research Part D - Transport and Environment. 2017;57:474-483. DOI: 10.1016/j.trd.2017.09.015.
[10] Nagy S, Csiszar C. The quality of smart mobility: A systematic review. Scientific Journal of Silesian University of Technology-Series Transport. 2020;109:117-127. DOI: 10.20858/sjsutst.2020.109.11.
[11] Rahman FI. Analysing the factor influencing travel pattern and mode choice based on household interview survey data: A case study of Dhaka city, Bangladesh. Scientific Journal of Silesian University of Technology. Series Transport. 2020;109:153-162. DOI: 10.20858/sjsutst.2020.109.14.
[12] Yang JW, et al. Transport impacts of clustered development in Beijing: Compact development versus overconcentration. Urban Studies. 2012;49(6):1315-1331. DOI: 10.1177/0042098011410336.
[13] Cole-Hunter T, et al. Objective correlates and determinants of bicycle commuting propensity in an urban environment. Transportation Research Part D - Transport and Environment. 2015;40:132-143. DOI: 10.1016/j.trd.2015.07.004.
[14] Fan JX, Wen M, Wan N. Built environment and active commuting: Rural-urban differences in the U.S. SSM - Population Health. 2017;3:435-441. DOI: 10.1016/j.ssmph.2017.05.007.
[15] Piatkowski DP, Marshall WE. Not all prospective bicyclists are created equal: The role of attitudes, socio-demographics, and the built environment in bicycle commuting. Travel Behaviour and Society. 2015;2(3):166-173. DOI: 10.1016/j.tbs.2015.02.001.
[16] Yang L, et al. Longitudinal associations between built environment characteristics and changes in active commuting. BMC Public Health. 2017;17. DOI: 10.1186/s12889-017-4396-3.
[17] Esztergar-Kiss D, et al. Promoting sustainable mode choice for commuting supported by persuasive strategies. Sustainable Cities and Society. 2021;74. DOI: 10.1016/j.scs.2021.103264.
[18] Liu YQ, et al. Spatial pattern of leisure activities among residents in Beijing, China: Exploring the impacts of urban environment. Sustainable Cities and Society. 2020;52. DOI: 10.1016/j.scs.2019.101806.
[19] Chatterjee K, et al. Commuting and wellbeing: A critical overview of the literature with implications for policy and future research. Transport Reviews. 2020;40(1):5-34. DOI: 10.1080/01441647.2019.1649317.
[20] Dinu M, et al. Active commuting and multiple health outcomes: A systematic review and meta-analysis. Sports Medicine. 2019;49(3):437-452. DOI: 10.1007/s40279-018-1023-0.
[21] Zhu J, Fan YL. Commute happiness in Xi'an, China: Effects of commute mode, duration, and frequency. Travel Behaviour and Society. 2018;11:43-51. DOI: 10.1016/j.tbs.2018.01.001.
[22] Cloutier S, Jambeck J, Scott N. The Sustainable Neighborhoods for Happiness Index (SNHI): A metric for assessing a community's sustainability and potential influence on happiness. Ecological Indicators. 2014;40:147-152. DOI: 10.1016/j.ecolind.2014.01.012.
[23] Mao ZD, Ettema D, Dijst M. Commuting trip satisfaction in Beijing: Exploring the influence of multimodal behavior and modal flexibility. Transportation Research Part A - Policy and Practice. 2016;94:592-603. DOI: 10.1016/j.tra.2016.10.017.
[24] Clark B, et al. How commuting affects subjective wellbeing. Transportation. 2020;47(6):2777-2805. DOI: 10.1007/s11116-019-09983-9.
[25] Smith O. Commute well-being differences by mode: Evidence from Portland, Oregon, USA. Journal of Transport & Health. 2017;4:246-254. DOI: 10.1016/j.jth.2016.08.005.
[26] Humphreys DK, Goodman A, Ogilvie D. Associations between active commuting and physical and mental wellbeing. Preventive Medicine. 2013;57(2):135-139. DOI: 10.1016/j.ypmed.2013.04.008.
[27] Tran PTM, Nguyen T, Balasubramanian R. Personal exposure to airborne particles in transport micro-environments and potential health impacts: A tale of two cities. Sustainable Cities and Society. 2020;63. DOI: 10.1016/j.scs.2020.102470.
[28] Ding C, et al. Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach. Transportation Research Part D - Transport and Environment. 2017;51:314-325. DOI: 10.1016/j.trd.2017.02.002.
[29] Yang LY, et al. Driving as a commuting travel mode choice of car owners in urban China: Roles of the built environment. Cities. 2021;112. DOI: 10.1016/j.cities.2021.103114.
[30] Xu HW. Comparing spatial and multilevel regression models for binary outcomes in neighborhood studies. Sociological Methodology. 2014;44:229-272. DOI: 10.1177/0081175013490188.
[31] Cao XY. Heterogeneous effects of neighborhood type on commute mode choice: An exploration of residential dissonance in the Twin Cities. Journal of Transport Geography. 2015;48:188-196. DOI: 10.1016/j.jtrangeo.2015.09.010.
[32] Guo J, Feng T, Timmermans HJP. Co-dependent workplace, residence and commuting mode choice: Results of a multi-dimensional mixed logit model with panel effects. Cities. 2020;96. DOI: 10.1016/j.cities.2019.102448.
[33] Shier ML, Graham JR. Work-related factors that impact social work practitioners' subjective well-being: Well-being in the workplace. Journal of Social Work. 2011;11(4):402-421. DOI: 10.1177/1468017310380486.
[34] Choi B, et al. Sedentary work, low physical job demand, and obesity in US workers. American Journal of Industrial Medicine. 2010;53(11):1088-1101. DOI: 10.1002/ajim.20886.
[35] Li W, Pu Z, Li Y, Tu M. How does ridesplitting reduce emissions from ridesourcing? A spatiotemporal analysis in Chengdu, China. Transportation Research Part D: Transport and Environment. 2021;95. DOI: 10.1016/j.trd.2021.102885.
[36] Pu ZY, et al. Multimodal traffic speed monitoring: A real-time system based on passive Wi-Fi and bluetooth sensing technology. IEEE Internet of Things Journal. 2021;9(14):12413-12424. DOI: 10.1109/Jiot.2021.3136031.
[37] Zhuang YF, et al. Edge-artificial intelligence-powered parking surveillance with quantized neural networks. IEEE Intelligent Transportation Systems Magazine. 2022;14(6):107-121. DOI: 10.1109/Mits.2022.3182358.
[38] Wang S, et al. Estimating crowd density with edge intelligence based on lightweight convolutional neural networks. Expert Systems with Applications. 2022;206. DOI: 10.1016/j.eswa.2022.117823.

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Published

25.04.2023

Issue

2023 (Vol 35), Issue 2

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