Tackling extreme urban heat: a machine learning approach to assess the impacts of climate change and the efficacy of climate adaptation strategies in urban microclimates
Grant Buster, Jordan Cox, Brandon N. Benton, and Ryan N. King

TL;DR
This paper introduces machine learning methods to accurately estimate urban heat impacts and assess climate adaptation strategies, revealing significant potential for energy savings through engineered cooling solutions in Los Angeles.
Contribution
The study develops open-source machine learning models that improve urban temperature estimates and evaluate climate adaptation strategies' effectiveness in reducing energy demand.
Findings
Cooling demand could increase by over 50% midcentury without mitigation.
Engineered high-albedo surfaces can reduce cooling demand increase by more than half.
Total energy use benefits from cooling strategies are maintained under future climate scenarios.
Abstract
As urbanization and climate change progress, urban heat becomes a priority for climate adaptation efforts. High temperatures concentrated in urban heat can drive increased risk of heat-related death and illness as well as increased energy demand for cooling. However, estimating the effects of urban heat is an ongoing field of research typically burdened by an imprecise description of the built environment, significant computational cost, and a lack of high-resolution estimates of the impacts of climate change. Here, we present open-source, computationally efficient machine learning methods that can improve the accuracy of urban temperature estimates when compared to historical reanalysis data. These models are applied to residential buildings in Los Angeles, and we compare the energy benefits of heat mitigation strategies to the impacts of climate change. We find that cooling demand is…
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Taxonomy
TopicsUrban Heat Island Mitigation
