A dataset for understanding self-reported patterns influencing residential energy decisions
Abstract Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05335-8 |
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| _version_ | 1849333738050158592 |
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| author | Tracy L. Fuentes Kieren H. McCord Max J. Martell Chrissi A. Antonopoulos |
| author_facet | Tracy L. Fuentes Kieren H. McCord Max J. Martell Chrissi A. Antonopoulos |
| author_sort | Tracy L. Fuentes |
| collection | DOAJ |
| description | Abstract Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes. |
| format | Article |
| id | doaj-art-b5b14267c0cf489fbfb7e0ee9f35f458 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-b5b14267c0cf489fbfb7e0ee9f35f4582025-08-20T03:45:45ZengNature PortfolioScientific Data2052-44632025-07-0112111110.1038/s41597-025-05335-8A dataset for understanding self-reported patterns influencing residential energy decisionsTracy L. Fuentes0Kieren H. McCord1Max J. Martell2Chrissi A. Antonopoulos3Pacific Northwest National LaboratoryPacific Northwest National LaboratoryPacific Northwest National LaboratoryPacific Northwest National LaboratoryAbstract Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.https://doi.org/10.1038/s41597-025-05335-8 |
| spellingShingle | Tracy L. Fuentes Kieren H. McCord Max J. Martell Chrissi A. Antonopoulos A dataset for understanding self-reported patterns influencing residential energy decisions Scientific Data |
| title | A dataset for understanding self-reported patterns influencing residential energy decisions |
| title_full | A dataset for understanding self-reported patterns influencing residential energy decisions |
| title_fullStr | A dataset for understanding self-reported patterns influencing residential energy decisions |
| title_full_unstemmed | A dataset for understanding self-reported patterns influencing residential energy decisions |
| title_short | A dataset for understanding self-reported patterns influencing residential energy decisions |
| title_sort | dataset for understanding self reported patterns influencing residential energy decisions |
| url | https://doi.org/10.1038/s41597-025-05335-8 |
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