Logo image
Climate-responsive urban planning through generative models: Sensitivity analysis of urban planning and design parameters for urban heat island in Singapore's residential settlements
Journal article   Peer reviewed

Climate-responsive urban planning through generative models: Sensitivity analysis of urban planning and design parameters for urban heat island in Singapore's residential settlements

Elif Esra Aydin, F. Peter Ortner, Song Peng, Anna Yenardi, Zebin Chen, Jing Zhi Tay and Peng Song
Sustainable cities and society, Vol.114, p.105779
01/11/2024

Abstract

Construction & Building Technology Energy & Fuels Green & Sustainable Science & Technology Science & Technology Science & Technology - Other Topics Technology
The Urban Heat Island (UHI) effect exacerbates the sustainability and well-being challenges of extreme heat events. While city planning and design measures have been shown to mitigate UHI severity, the complex interaction among these measures has limited the ability of previous research to assess their impact holistically and across urban scales. To investigate the cross-scalar effectiveness of multiple UHI mitigation measures, this study applies sensitivity analysis (SA) to nine parameters in an urban generative model. Previously unstudied planning parameters, land parcel area and road network density, are included in the analysis. From the SA of 21,000 model solutions for a 100 ha case study site in Singapore, building density, podium density, and land parcel area are found to have greatest impacts on UHI. This finding supports a hypothesis that urban planning parameters have a high potential for UHI mitigation. Key findings include that a high green plot ratio (>50 %) combined with a low site coverage ratio (<50 %) permits even high-density model solutions (gross plot ratio >4) to maintain annual UHI below 0.89 degrees C. The conclusion discusses the implications of the findings for heat-resilient city planning and demonstrates that performance-based evaluation of generative urban models can improve upon prescriptive planning approaches.
url
https://doi.org/10.1016/j.scs.2024.105779View
Published (Version of record) Open

Metrics

1 Record Views

Details

Logo image