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The comfort rebound from heat pumps and impact on household cooling behaviour and energy security

Nature Energy volume 10, pages 1166–1177 (2025)Cite this article

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Abstract

Adopting clean energy technologies offers households a viable solution to overcome energy insecurity. Heat pumps contribute to this potential by reducing energy expenses and increasing energy services. We examine the role of adopting heat pumps in mitigating energy insecurity, utilizing electricity records from 8,656 households in Phoenix, Arizona. We use a thermal comfort index to examine a household’s energy-limiting behaviour using a temperature–electricity response function. Our regression results show that households with heat pumps initiate cooling at 0.996 °C lower than those without and consume 0.476 kWh less electricity daily per degree increase in temperature. It indicates that heat pumps improve indoor comfort by activating earlier summer cooling. Cost savings from operation have a rebound effect of enabling greater comfort. Furthermore, this adoption reduces the energy equity gap across income groups, resulting in more similar and comfortable cooling start temperatures. This study supports the adoption of clean technologies to reduce energy insecurity.

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Fig. 1: Electricity spending by income group for households with and without heat pumps.
Fig. 2: Comparison of thermal comfort indicators by income level from 2019 to 2021.
Fig. 3: Estimated effects of heat pump adoption on thermal comfort indicators.
Fig. 4: Estimated effects of heat pump adoption using different instrumental variable models.
Fig. 5: Estimated effects of heat pump adoption across income groups.
Fig. 6: Estimated effects of heat pump adoption by ethnic groups.

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Data availability

The raw household smart meter data and residential survey data are not publicly available due to a non-disclosure agreement but can be accessed upon reasonable request to the authors and with permission from the Salt River Project. Historical weather data are open access and can be obtained from the ‘GSODR’ package in R, following the code provided. Source data are provided with this paper.

Code availability

All data, models and figures are processed in R studio (version 4.3.1) and Stata. The code used to perform the data analysis is available via Github at https://github.com/yxf-993/Technology_Adoption.git.

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Acknowledgements

We acknowledge support from the Alfred P. Sloan Foundation.

Author information

Authors and Affiliations

  1. School of Public Policy, University of Maryland at College Park, College Park, MD, USA

    Xiaofeng Ye & Yueming Lucy Qiu

  2. Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA

    Destenie Nock

  3. Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Destenie Nock

  4. Salt River Project, Tempe, AZ, USA

    Bo Xing

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  1. Xiaofeng Ye
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  2. Yueming Lucy Qiu
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Contributions

Y.L.Q. and D.N. jointly designed the research and revised the manuscript. X.Y. performed the research and wrote and revised manuscript. B.X. provided access to the electricity consumption data and institutional support.

Corresponding author

Correspondence to Yueming Lucy Qiu.

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The authors declare no competing interests.

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Nature Energy thanks Yohan Min, Dorothée Charlier and Narasimha Rao for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Three-step workflow to assess energy insecurity and heat pump impacts.

Step 1 involves matching each household’s electricity usage data and survey data with the nearest monitoring station’s temperature records. In Step 2, we calculate the energy burden and use the temperature response function to compute thermal comfort indicators, thereby identifying the energy insecurity level. In Step 3, we analyze the data to explore the effect of clean energy technologies adoption using a random effect panel data regression model, incorporating whether households have adopted clean energy technologies.

Extended Data Fig. 2 Plot of observed data and fitted piecewise linear regression for one household (ID = 441005, Year =2019, R² = 0.8744).

The scatter points represent actual daily electricity consumption, and the fitted segmented regression model is shown as a solid blue line. The red dashed vertical lines indicate the fitted balance points at 17.11 °C and 26.44 °C.

Source data

Extended Data Fig. 3 Illustration of the temperature response relationships.

(a) A U-shape temperature response function with two balance points is well-suited for households that use electricity for both cooling and heating. (b) A segmented function with a single balance point is applicable to households with electric cooling and gas heating. (c) A segmented function with two cooling balance points is applicable to households with electric cooling and gas heating.

Extended Data Fig. 4 Spatial distribution of Heating, Ventilation, and Air Conditioning (HVAC) contractors in the research area (EPSG:4326).

The map shows the study region, with brown polygons representing 92 ZIP Code areas across 22 county-level jurisdictions. Blue dots indicate the locations of HVAC contractors. Each point indicates the location of an individual contractor, geocoded based on publicly available address information (Google Maps). Specifically, we searched for businesses categorized as “HVAC contractors” or “air conditioning contractors” within 22 counties in Arizona that overlap with our study area of 8,656 households. We manually recorded business names and addresses and geocoded the coordinates using the geocode() function in R (with OpenStreetMap as the base source). ZCTA shapefiles from the US Census Bureau (https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html).

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1–3, Figs. 1–11, Tables 1–16 and reference.

Reporting Summary

Source data

Source Data Fig. 1

This dataset contains energy spending records by income group and heat pump adoption status. The raw data are processed, and we compute the first quartile (Q1), median (Q2) and third quartile (Q3) of energy costs for each group by Income and Heat pump. These statistics are used to construct the box boundaries in the plots.

Source Data Fig. 2

This Excel file contains the source data used to generate the boxplots shown in Fig. 2. Each row corresponds to an individual household thermal comfort indicators generated from piecewise linear regression. To protect privacy, all individual identifiers have been removed from the dataset.

Source Data Fig. 3

Baseline regression statistical results.

Source Data Fig. 4

IV regression statistical results.

Source Data Fig. 5

Heterogeneity (Income) regression statistical results.

Source Data Fig. 6

Heterogeneity (Ethnic) regression statistical results.

Source Data Extended Data Fig. 2

Key regression outputs for the household shown in Extended Data Fig. 2 (ID: 441005), including the estimated breakpoints and segment-specific slopes from the piecewise linear regression model, as well as standard errors and confidence intervals.

Source Data Extended Data Fig. 4

Spatial distribution data for HVAC contractors within the study region.

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Ye, X., Qiu, Y.L., Nock, D. et al. The comfort rebound from heat pumps and impact on household cooling behaviour and energy security. Nat Energy 10, 1166–1177 (2025). https://doi.org/10.1038/s41560-025-01845-2

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