A Comprehensive Dataset of Residential Air Conditioning Prevalence in the Continental United States

This dataset presents the most comprehensive estimate of residential air conditioning (AC) prevalence across the continental United States. Using property-level data for over 103 million housing units from the Dewey database, we imputed and classified four AC types: central, other, evaporative cooler, and none, using XGBoost models optimized for performance. Housing characteristics, socioeconomic indicators, and environmental conditions, such as Cooling Degree Days and elevation, informed predictions. The final product offers national coverage with spatial resolution at the census tract, ZIP code, and metropolitan levels. Model validation was conducted using American Housing Survey data, with strong alignment observed for the central and no air conditioning (AC) categories. This dataset addresses longstanding gaps in understanding the geographic and demographic disparities in AC access, critical for public health, climate adaptation, and energy equity research. Users may integrate these data into epidemiological modeling, resilience planning, and policy analysis to support heat vulnerability assessments and infrastructure interventions.

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Publication Details

Journal
Scientific Data
Published
2025-10-31
DOI
https://doi.org/10.1038/s41597-025-06104-3
Primary Topic
Climate Change and Health Impacts
Type
article
Field-Weighted Citation Impact
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article

A Comprehensive Dataset of Residential Air Conditioning Prevalence in the Continental United States

Christopher K. Uejio, Yoonjung Ahn
Scientific Data
Climate Change and Health Impacts
article

A Comprehensive Dataset of Residential Air Conditioning Prevalence in the Continental United States

Christopher K. Uejio, Yoonjung Ahn
article en

Abstract

This dataset presents the most comprehensive estimate of residential air conditioning (AC) prevalence across the continental United States. Using property-level data for over 103 million housing units from the Dewey database, we imputed and classified four AC types: central, other, evaporative cooler, and none, using XGBoost models optimized for performance. Housing characteristics, socioeconomic indicators, and environmental conditions, such as Cooling Degree Days and elevation, informed predictions. The final product offers national coverage with spatial resolution at the census tract, ZIP code, and metropolitan levels. Model validation was conducted using American Housing Survey data, with strong alignment observed for the central and no air conditioning (AC) categories. This dataset addresses longstanding gaps in understanding the geographic and demographic disparities in AC access, critical for public health, climate adaptation, and energy equity research. Users may integrate these data into epidemiological modeling, resilience planning, and policy analysis to support heat vulnerability assessments and infrastructure interventions.

Scientific DataVol. 12(1)
Florida State University (US), University of Kansas (US)
Openalex Percentile: Top 59%
Climate Change and Health Impacts
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