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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Published in: | Environmental Research Communications Vol. 6; no. 7; pp. 75019 - 75029 |
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Abstract | 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. |
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AbstractList | 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. |
Author | Choi, Minsoo Nateghi, Roshanak Hardaway, Kanaan C McMillan, Sara K Hardiman, Brady S Ma, Zhao |
Author_xml | – sequence: 1 givenname: Kanaan C orcidid: 0009-0000-3014-5655 surname: Hardaway fullname: Hardaway, Kanaan C organization: Purdue University Environmental and Ecological Engineering, IN, United States of America – sequence: 2 givenname: Minsoo orcidid: 0000-0001-8921-2162 surname: Choi fullname: Choi, Minsoo organization: Purdue University Department of Industrial Engineering, IN, United States of America – sequence: 3 givenname: Roshanak surname: Nateghi fullname: Nateghi, Roshanak organization: Purdue University Department of Industrial Engineering, IN, United States of America – sequence: 4 givenname: Sara K surname: McMillan fullname: McMillan, Sara K organization: Iowa State University Department of Agricultural and Biosystems Engineering, IA, United States of America – sequence: 5 givenname: Zhao orcidid: 0000-0002-9103-3996 surname: Ma fullname: Ma, Zhao organization: Purdue University Department of Forestry and Natural Resources, IN, United States of America – sequence: 6 givenname: Brady S orcidid: 0000-0001-6833-9404 surname: Hardiman fullname: Hardiman, Brady S organization: Purdue University Department of Forestry and Natural Resources, IN, United States of America |
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SubjectTerms | 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 |
Title | Vegetation reduces cooling demand in low-income neighborhoods on hot days in Chicago |
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