Spatially-heterogeneous impacts of surface characteristics on urban thermal environment, a case of the Guangdong-Hong Kong-Macau Greater Bay Area

Ping Yu Fan, Kwok Pan Chun*, Ana Mijic, Daphne Ngar Yin Mah, Qing He, Byron Choi, Cho Kwong Charlie Lam, Omer Yetemen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

One-size-fits-all approach is common in climate-sensitive urban design due to neglecting spatial heterogeneities in urban form and urban climate. This study explores a spatially-varied climate-sensitive urban design based on the Guangdong-Hong Kong-Macau Greater Bay Area (GBA). Three thermal indices, the Wet Bulb Globe Temperature (WBGT), the Apparent Temperature (AT), and the Universal Thermal Climate Index (UTCI) are used to assess the outdoor thermal environments. The local climate zone (LCZ) classification system is used to map urban form including built and land-cover types. Moreover, incorporating spatial effects, geographically weighted regression (GWR) models are used to account for spatially-varied thermal variations due to urban form changes. Our findings indicate that the large low-rise type (LCZ 8) needs more attention in built-up planning for thermal mitigation, and urban low plants type (LCZ D) should be a more effective nature-based climate mitigation strategy compared with the water bodies (LCZ G). The GWR results show a stronger consistency between UTCI and LCZ 8 and LCZ D, compared with WBGT and AT. UTCI is thus suggested for application in future urban climate studies. More importantly, the spatially-varied relationship between UTCI and urban form specifies the strategies and appropriate locations for thermal mitigation in climate-sensitive urban design.

Original languageEnglish
Article number101034
JournalUrban Climate
Volume41
DOIs
Publication statusPublished - Jan 2022

Scopus Subject Areas

  • Geography, Planning and Development
  • Environmental Science (miscellaneous)
  • Urban Studies
  • Atmospheric Science

User-Defined Keywords

  • Geographically weighted regression model
  • Local climate zone
  • Thermal indices
  • Thermal mitigation strategies

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