Exploring the Complex Relationships Between Influential Factors of Urban Land Development Patterns and Urban Thermal Environment: A Study on Downtown Shanghai

Hao-Rong Yang (), Yan-He Li, Wen-Jia Wu, Ai-Lian Zhao and Hao Zhang ()
Additional contact information
Hao-Rong Yang: Department of Environmental Science and Engineering, Jiangwan Campus, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China
Yan-He Li: Department of Environmental Science and Engineering, Jiangwan Campus, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China
Wen-Jia Wu: Department of Environmental Science and Engineering, Jiangwan Campus, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China
Ai-Lian Zhao: East China Electric Power Design Institute (ECEPDI), China Power Engineering Consulting Group, Shanghai 200063, China
Hao Zhang: Department of Environmental Science and Engineering, Jiangwan Campus, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China

Sustainability, 2025, vol. 17, issue 19, 1-27

Abstract: The rapid urbanization process has exacerbated the urban heat island (UHI) effect in megacities like Shanghai. Urban green infrastructure (UGI) plays a crucial role in mitigating the UHI effect; however, its cooling capacity is subject to various urban land development patterns. This study examined 39 typical locations in downtown Shanghai to measure how urban land development patterns affect the UGI’s cooling capacity. Using a data-driven framework, we identified 12 key influencing factors and explored 4 interactions for building three regression models: multiple linear regression (MLR), partial least squares regression (PLSR), and support vector regression (SVR). For each of these models, we considered two variations: a basic model neglecting interactions and an enhanced model including interactions. Results showed that all enhanced models outperformed their basic counterparts. On average, the enhanced models increased their predictive power by 14.59% for training data and 32.15% for testing data. Additionally, among the three enhanced models, the SVR-enhanced models show the best performance, followed by the PLSR-enhanced models. Their mean predictive power increased by 8.33−37.43% for training data and 31.77−43.558% for testing data vs. the MLR-enhanced models. Overall, our findings revealed that impervious surfaces contribute positively to urban warming, while UGI acts as a negative contributor. Moreover, we highlighted how urban land development metrics, particularly the UGI’s percentage and spatial arrangements in relation to adjacent buildings, significantly affect the thermal environment. The findings can offer valuable insights for urban planners and decision-makers involved in managing UGI and developing strategies for UHI mitigation and urban climate adaptation.

Keywords: land use patterns; urban green space; urban warming; built environment; metropolitan shanghai (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/19/8547/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/19/8547/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:19:p:8547-:d:1756533

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().