Vegetation reduces cooling demand in low-income neighborhoods on hot days in Chicago

Vegetation reduces cooling demand in low-income neighborhoods on hot days in Chicago

Cooling energy demand is sensitive to urban form and socioeconomic characteristics of cities. Climate change will impact how these characteristics influence cooling demand. We use random forest machine learning methods to analyze the sensitivity of cooling demand in Chicago, IL, to weather, vegetati...

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Bibliographic Details
Published in:Environmental Research Communications Vol. 6; no. 7; pp. 75019 - 75029
Main Authors: Hardaway, Kanaan C, Choi, Minsoo, Nateghi, Roshanak, McMillan, Sara K, Ma, Zhao, Hardiman, Brady S
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01-07-2024
Subjects:
Census
Censuses
Cities
Climate change
Cooling
Cooling loads
Cooling systems
Demand analysis
Energy demand
Environmental justice
Global temperatures
Impact analysis
Income
Low income groups
Machine learning
Neighborhoods
random forest
Sensitivity analysis
Social factors
Socioeconomics
urban heat
Urban planning
Urban populations
urban vegetation
Variables
Vegetation
Weather
Wind speed
United States > US
Chicago Illinois
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Summary:Cooling energy demand is sensitive to urban form and socioeconomic characteristics of cities. Climate change will impact how these characteristics influence cooling demand. We use random forest machine learning methods to analyze the sensitivity of cooling demand in Chicago, IL, to weather, vegetation, building type, socioeconomic, and control variables by dividing census tracts of the city into four groups: below-Q1 income–hot days; above-Q1 income–hot days; below-Q1 income–regular days; and above-Q1 income–regular days. Below-Q1 census tracts experienced an increase in cooling demand on hot days while above-Q1 census tracts did not see an increase in demand. Weather (i.e. heat index and wind speed) and control variables (i.e. month of year, holidays and weekends) unsurprisingly had the most influence on cooling demand. Among the variables of interest, vegetation was associated with reduced cooling demand for below-Q1 income on hot days and increased cooling demand for below-Q1 income on regular days. In above-Q1 income census tracts building type was the most closely associated non-weather or control variable with cooling demand. The sensitivity of cooling demand for below-Q1 income census tracts to vegetation on hot days suggests vegetation could become more important for keeping cities cool for low-income populations as global temperatures increase. This result further highlights the importance of considering environmental justice in urban design.
Bibliography:ERC-102177.R1
ISSN:2515-7620
2515-7620
DOI:10.1088/2515-7620/ad5e3c