IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p383-d1015602.html

Some searches may not work properly. We apologize for the inconvenience.

   My bibliography  Save this article

How Plot Spatial Morphology Drives Surface Thermal Environment: A Spatial and Temporal Analysis of Nanjing Main City

Author

Listed:
  • Zidong Zhao

    (School of Architecture, Nanjing Tech University, Nanjing 211816, China)

  • Ruhai Ye

    (School of Architecture, Nanjing Tech University, Nanjing 211816, China)

  • Yingyin Wang

    (School of Architecture, Nanjing Tech University, Nanjing 211816, China)

  • Yiming Tao

    (School of Architecture, Nanjing Tech University, Nanjing 211816, China)

Abstract

Rapid urban development has changed urban substrate conditions, greatly affecting urban ecology and heating urban environment. Mitigating urban temperature rises by optimizing urban morphology is considered a promising approach; most studies ignore spatial and temporal heterogeneity. This study analyzes how plot spatial form influences urban thermal environment in the main Nanjing area from 2001, 2006, 2011, 2016, and 2021, based on geographically weighted regression models (spatio-temporal- and multi-scale). Results show that: 1. The formation of geothermal heat islands matches the direction of urban expansion, mainly due to changes in land substrate; 2. the spatio-temporal model performs best, indicating that urban morphology and surface thermal environment have obvious spatio-temporal heterogeneity; obvious scale differences exist in each index influencing the heat island effect; and 3. floor area ratio (FAR) and building density (BD) negatively and positively correlate with surface thermal conditions, with gradually increasing effect, respectively. Normalized difference vegetation index (NDVI) and distance from the nearest water body (Dis_W) negatively and positively correlate with surface thermal conditions separately; good ecological infrastructure reduces surface temperatures but shows a gradually weakening effect. Proximity to roads is associated with warmer thermal environment. This study elucidates how urban form influences surface thermal environments and suggests measures to reduce surface temperatures in the main urban Nanjing area.

Suggested Citation

  • Zidong Zhao & Ruhai Ye & Yingyin Wang & Yiming Tao, 2022. "How Plot Spatial Morphology Drives Surface Thermal Environment: A Spatial and Temporal Analysis of Nanjing Main City," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:383-:d:1015602
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/383/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/1/383/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yunfei Li & Sebastian Schubert & Jürgen P. Kropp & Diego Rybski, 2020. "On the influence of density and morphology on the Urban Heat Island intensity," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    2. Jing Kong & Yongling Zhao & Jan Carmeliet & Chengwang Lei, 2021. "Urban Heat Island and Its Interaction with Heatwaves: A Review of Studies on Mesoscale," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    3. Lin, Boqiang & Xu, Bin, 2018. "How to promote the growth of new energy industry at different stages?," Energy Policy, Elsevier, vol. 118(C), pages 390-403.
    4. William P. Anderson & Pavlos S. Kanaroglou & Eric J. Miller, 1996. "Urban Form, Energy and the Environment: A Review of Issues, Evidence and Policy," Urban Studies, Urban Studies Journal Limited, vol. 33(1), pages 7-35, February.
    5. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    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. Rakin Abrar & Showmitra Kumar Sarkar & Kashfia Tasnim Nishtha & Swapan Talukdar & Shahfahad & Atiqur Rahman & Abu Reza Md Towfiqul Islam & Amir Mosavi, 2022. "Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    2. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    3. Marcus Adolphson, 2010. "Kernel Densities and Mixed Functionality in a Multicentred Urban Region," Environment and Planning B, , vol. 37(3), pages 550-566, June.
    4. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    5. Changchun Feng & Hao Zhang & Liang Xiao & Yongpei Guo, 2022. "Land Use Change and Its Driving Factors in the Rural–Urban Fringe of Beijing: A Production–Living–Ecological Perspective," Land, MDPI, vol. 11(2), pages 1-18, February.
    6. Wang, Xianzhu & Huang, He & Hong, Jingke & Ni, Danfei & He, Rongxiao, 2020. "A spatiotemporal investigation of energy-driven factors in China: A region-based structural decomposition analysis," Energy, Elsevier, vol. 207(C).
    7. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    8. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    9. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    10. Hongli Liu & Xiaoyu Yan & Jinhua Cheng & Jun Zhang & Yan Bu, 2021. "Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    11. Banister, David, 2011. "Cities, mobility and climate change," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1538-1546.
    12. Michiel Fremouw & Annamaria Bagaini & Paolo De Pascali, 2020. "Energy Potential Mapping: Open Data in Support of Urban Transition Planning," Energies, MDPI, vol. 13(5), pages 1-15, March.
    13. Li Gao & Mingjing Huang & Wuping Zhang & Lei Qiao & Guofang Wang & Xumeng Zhang, 2021. "Comparative Study on Spatial Digital Mapping Methods of Soil Nutrients Based on Different Geospatial Technologies," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    14. Juliane Große & Christian Fertner & Niels Boje Groth, 2016. "Urban Structure, Energy and Planning: Findings from Three Cities in Sweden, Finland and Estonia," Urban Planning, Cogitatio Press, vol. 1(1), pages 24-40.
    15. Kang-Rae Ma & David Banister, 2007. "Urban Spatial Change and Excess Commuting," Environment and Planning A, , vol. 39(3), pages 630-646, March.
    16. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    17. Diana Saadi & Emanuel Tirosh & Izhak Schnell, 2021. "The Relationship between City Size and Carbon Monoxide (CO) Concentration and Their Effect on Heart Rate Variability (HRV)," IJERPH, MDPI, vol. 18(2), pages 1-13, January.
    18. Moore, David & Webb, Amanda L., 2022. "Evaluating energy burden at the urban scale: A spatial regression approach in Cincinnati, Ohio," Energy Policy, Elsevier, vol. 160(C).
    19. Jack C. Yue & Ming-Huei Tu & Yin-Yee Leong, 2024. "A spatial analysis of the health and longevity of Taiwanese people," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 384-399, April.
    20. Andrea CIRILLI & Paolo VENERI, 2010. "Spatial Structure and CO2 Emissions Due to Commuting: an Analysis on Italian Urban Areas," Working Papers 353, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

    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:15:y:2022:i:1:p:383-:d:1015602. 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.