IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i11p4539-d1402853.html
   My bibliography  Save this article

Exploring the Spatio-Temporally Heterogeneous Impact of Traffic Network Structure on Ride-Hailing Emissions Using Shenzhen, China, as a Case Study

Author

Listed:
  • Wenyuan Gao

    (Pollutant and Emergency Monitoring Department, Hunan Provincial Ecological Environment Monitoring Center, Changsha 410001, China)

  • Chuyun Zhao

    (Smart Transport Key Laboratory of Hunan Province, School of Transport and Transportation Engineering, Central South University, Changsha 410075, China)

  • Yu Zeng

    (Pollutant and Emergency Monitoring Department, Hunan Provincial Ecological Environment Monitoring Center, Changsha 410001, China)

  • Jinjun Tang

    (Smart Transport Key Laboratory of Hunan Province, School of Transport and Transportation Engineering, Central South University, Changsha 410075, China)

Abstract

The rise of ride-hailing services presents innovative solutions for curbing urban carbon emissions, yet poses challenges such as fostering fair competition and integrating with public transit. Analyzing the factors influencing ride-hailing emissions is crucial for understanding their relationship with other travel modes and devising policies aimed at steering individuals towards more environmentally sustainable travel options. Therefore, this study delves into factors impacting ride-hailing emissions, including travel demand, land use, demographics, and transportation networks. It highlights the interplay among urban structure, multi-modal travel, and emissions, focusing on network features such as betweenness centrality and accessibility. Employing the COPERT (Computer Programme to Calculate Emissions from Road Transport) model, ride-hailing emissions are calculated from vehicle trajectory data. To mitigate statistical errors from multicollinearity, variable selection involves tests and correlation analysis. Geographically and temporally weighted regression (GTWR) with an adaptive kernel function is designed to understand key influencing mechanisms, overcoming traditional GTWR limitations. It can dynamically adjust bandwidth based on the spatio-temporal distribution of data points. Experiments in Shenzhen validate this approach, showing a 9.8% and 10.8% increase in explanatory power for weekday and weekend emissions, respectively, compared to conventional GTWR. The discussion of findings provides insights for urban planning and low-carbon transport strategies.

Suggested Citation

  • Wenyuan Gao & Chuyun Zhao & Yu Zeng & Jinjun Tang, 2024. "Exploring the Spatio-Temporally Heterogeneous Impact of Traffic Network Structure on Ride-Hailing Emissions Using Shenzhen, China, as a Case Study," Sustainability, MDPI, vol. 16(11), pages 1-31, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4539-:d:1402853
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/11/4539/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/11/4539/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pei Zhang & Tao Zhang & Hiroatsu Fukuda & Moheng Ma, 2023. "Evidence of Multi-Source Data Fusion on the Relationship between the Specific Urban Built Environment and Urban Vitality in Shenzhen," Sustainability, MDPI, vol. 15(8), pages 1-24, April.
    2. Rodier, Caroline, 2018. "The Effects of Ride Hailing Services on Travel and Associated Greenhouse Gas Emissions," Institute of Transportation Studies, Working Paper Series qt2rv570tt, Institute of Transportation Studies, UC Davis.
    3. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Testing for Spatial Autocorrelation among the Residuals of the Geographically Weighted Regression," Environment and Planning A, , vol. 32(5), pages 871-890, May.
    4. Song, Ying & Miller, Harvey J. & Stempihar, Jeff & Zhou, Xuesong, 2017. "Green accessibility: Estimating the environmental costs of network-time prisms for sustainable transportation planning," Journal of Transport Geography, Elsevier, vol. 64(C), pages 109-119.
    5. Allen, W. Bruce & Liu, Dong & Singer, Scott, 1993. "Accesibility measures of U.S. metropolitan areas," Transportation Research Part B: Methodological, Elsevier, vol. 27(6), pages 439-449, December.
    6. Mikkel Thorhauge & Akshay Vij & Elisabetta Cherchi, 2021. "Heterogeneity in departure time preferences, flexibility and schedule constraints," Transportation, Springer, vol. 48(4), pages 1865-1893, August.
    7. Jenelius, Erik, 2009. "Network structure and travel patterns: explaining the geographical disparities of road network vulnerability," Journal of Transport Geography, Elsevier, vol. 17(3), pages 234-244.
    8. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    9. Li, Peilin & Zhao, Pengjun & Brand, Christian, 2018. "Future energy use and CO2 emissions of urban passenger transport in China: A travel behavior and urban form based approach," Applied Energy, Elsevier, vol. 211(C), pages 820-842.
    10. Kittisak Jermsittiparsert & Thitinan Chankoson, 2019. "Behavior of Tourism Industry under the Situation of Environmental Threats and Carbon Emission: Time Series Analysis from Thailand," International Journal of Energy Economics and Policy, Econjournals, vol. 9(6), pages 366-372.
    11. Meead Saberi & Taha H. Rashidi & Milad Ghasri & Kenneth Ewe, 2018. "A Complex Network Methodology for Travel Demand Model Evaluation and Validation," Networks and Spatial Economics, Springer, vol. 18(4), pages 1051-1073, December.
    12. Brand, Christian & Goodman, Anna & Rutter, Harry & Song, Yena & Ogilvie, David, 2013. "Associations of individual, household and environmental characteristics with carbon dioxide emissions from motorised passenger travel," Applied Energy, Elsevier, vol. 104(C), pages 158-169.
    13. Jean Dub� & Di�go Legros, 2014. "Spatial econometrics and the hedonic pricing model: what about the temporal dimension?," Journal of Property Research, Taylor & Francis Journals, vol. 31(4), pages 333-359, December.
    14. Gyo-Eon Shim & Sung-Mo Rhee & Kun-Hyuck Ahn & Sung-Bong Chung, 2006. "The relationship between the characteristics of transportation energy consumption and urban form," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 40(2), pages 351-367, June.
    15. Yash Babar & Gordon Burtch, 2020. "Examining the Heterogeneous Impact of Ride-Hailing Services on Public Transit Use," Information Systems Research, INFORMS, vol. 31(3), pages 820-834, September.
    16. Alejandro Tirachini, 2020. "Ride-hailing, travel behaviour and sustainable mobility: an international review," Transportation, Springer, vol. 47(4), pages 2011-2047, August.
    17. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    18. Lyu, Tao & Wang, Yuanqing & Ji, Shujuan & Feng, Tao & Wu, Zhouhao, 2023. "A multiscale spatial analysis of taxi ridership," Journal of Transport Geography, Elsevier, vol. 113(C).
    19. Rodier, Caroline & Michaels, Julia, 2019. "The Effects of Ride-Hailing Services on Greenhouse Gas Emissions," Institute of Transportation Studies, Working Paper Series qt4vz52416, Institute of Transportation Studies, UC Davis.
    20. Liu, Jiaguo & Li, Sujuan & Ji, Qiang, 2021. "Regional differences and driving factors analysis of carbon emission intensity from transport sector in China," Energy, Elsevier, vol. 224(C).
    21. Modarres, Ali, 2013. "Commuting and energy consumption: toward an equitable transportation policy," Journal of Transport Geography, Elsevier, vol. 33(C), pages 240-249.
    22. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ong, Felita & Loa, Patrick & Nurul Habib, Khandker, 2024. "Ride-sourcing demand in Metro Vancouver: Looking through the lens of disability," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    2. Song, Weize & Zhang, Xiaoling & An, Kangxin & Yang, Tao & Li, Heng & Wang, Can, 2021. "Quantifying the spillover elasticities of urban built environment configurations on the adjacent traffic CO2 emissions in mainland China," Applied Energy, Elsevier, vol. 283(C).
    3. Tian, Guang & Ewing, Reid & Li, Han, 2023. "Exploring the influences of ride-hailing services on VMT and transit usage – Evidence from California," Journal of Transport Geography, Elsevier, vol. 110(C).
    4. Guanwei Zhao & Zeyu Pan & Muzhuang Yang, 2022. "Marginal Effects and Spatial Variations of the Impact of the Built Environment on Taxis’ Pollutant Emissions in Chengdu, China," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
    5. Liu, Xingjian & Wang, Mingshu & Qiang, Wei & Wu, Kang & Wang, Xiaomi, 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions," Applied Energy, Elsevier, vol. 261(C).
    6. Lena Kilian & Anne Owen & Andy Newing & Diana Ivanova, 2022. "Exploring Transport Consumption-Based Emissions: Spatial Patterns, Social Factors, Well-Being, and Policy Implications," Sustainability, MDPI, vol. 14(19), pages 1-26, September.
    7. Hao Wu & David Levinson, 2020. "Unifying Access," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    8. Shoki Kosai & Muku Yuasa & Eiji Yamasue, 2020. "Chronological Transition of Relationship between Intracity Lifecycle Transport Energy Efficiency and Population Density," Energies, MDPI, vol. 13(8), pages 1-15, April.
    9. Zhao, Chuyun & Tang, Jinjun & Zeng, Yu & Li, Zhitao & Gao, Fan, 2023. "Understanding the spatio-temporally heterogeneous effects of built environment on urban travel emissions," Journal of Transport Geography, Elsevier, vol. 112(C).
    10. Jinjun Tang & Fan Gao & Fang Liu & Wenhui Zhang & Yong Qi, 2019. "Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM," Sustainability, MDPI, vol. 11(19), pages 1-19, October.
    11. Pang, Qinghua & Qiu, Man & Zhang, Lina & Chiu, Yung-ho, 2024. "Congestion effects of energy and its influencing factors: China's transportation sector," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    12. Rong, Peijun & Kwan, Mei-Po & Qin, Yaochen & Zheng, Zhicheng, 2022. "A review of research on low-carbon school trips and their implications for human-environment relationship," Journal of Transport Geography, Elsevier, vol. 99(C).
    13. Cui, Xuezhu & Zhuang, Caigang & Jiao, Zhenzhi & Tan, Zhangzhi & Li, Shaoying, 2023. "How can urban built environment (BE) influence on-road (OR) carbon emissions? A road segment scale quantification based on massive vehicle trajectory big data," Journal of Transport Geography, Elsevier, vol. 111(C).
    14. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    15. Suwan Shen & Xi Feng & Zhong Ren Peng, 2016. "A framework to analyze vulnerability of critical infrastructure to climate change: the case of a coastal community in Florida," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 589-609, October.
    16. Li, Xi & Yu, Biying, 2019. "Peaking CO2 emissions for China's urban passenger transport sector," Energy Policy, Elsevier, vol. 133(C).
    17. Xue, Fei & Yao, Enjian, 2022. "Impact analysis of residential relocation on ownership, usage, and carbon-dioxide emissions of private cars," Energy, Elsevier, vol. 252(C).
    18. Vladimír Konečný & Jozef Gnap & Tomáš Settey & František Petro & Tomáš Skrúcaný & Tomasz Figlus, 2020. "Environmental Sustainability of the Vehicle Fleet Change in Public City Transport of Selected City in Central Europe," Energies, MDPI, vol. 13(15), pages 1-23, July.
    19. Ilir Nase & Jim Berry & Alastair Adair, 2016. "Impact of quality-led design on real estate value: a spatiotemporal analysis of city centre apartments," Journal of Property Research, Taylor & Francis Journals, vol. 33(4), pages 309-331, October.
    20. Adam Millard-Ball & Liwei Liu & Whitney Hansen & Drew Cooper & Joe Castiglione, 2023. "Where ridehail drivers go between trips," Transportation, Springer, vol. 50(5), pages 1959-1981, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4539-:d:1402853. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.