Description
UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency
Recommended citation: Fuentes, Tracy L., Kieren H. McCord, Adrienne L. S. Rackley, Saurabh Biswas, Wilfried Kabre, and Chrissi A. Antonopoulos. UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency”. Pacific Northwest National Laboratory. PNNL-SA-198675. https://doi.org/10.25584/2371918.
This work represents the largest and most comprehensive dataset to-date of responses surveyed from U.S. residents regarding home modifications and energy-related decision-making, including both objective measures describing the occupants and their homes and subjective measures describing the more unpredictable human factors behind residential decision-making. First, to develop the survey, we interviewed 121 individual decision-makers within their households regarding planned or completed projects. We used the insights of these interviews to design a survey that was distributed to 10,000 households across the U.S.
The overarching topics approached in the survey include descriptive information about the respondent, their household, their home, home modifications they have made, and the human cognition-based contextual factors involved in home projects and decision-making, such as preferences, motivations, barriers, and information sources. The processed data from the survey was compiled into a dataset entitled UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency and represents the basis for the analyses included in this paper. The dataset represents a rich repository of home energy technology and modification decision-making results, the largest of its kind to-date. The abundance of contextual considerations within this dataset provides a robust resource for continual analysis, with possibilities for considering cross-cuts of data from a variety of perspectives. This work was funded by the U.S. Department of Energy Building Technology Office.
Version 2 replaces version 1, with minor edits and corrections indicated in the data dictionary and technical documentation.
In accordance with PNNL DataHub policies (https://data.pnnl.gov/policy), the data usage license agreement for this dataset is a Creative Commons Zero (“CC0”) Public Domain Dedication Waiver (https://creativecommons.org/publicdomain/zero/1.0/).
Data collected from this project was overseen by PNNL’s Institutional Review Board (IRB). The IRB approved this project (PNNL IRB #2022-03) including the data collection, consent, data cleaning, data storage, and data publication protocols. Personally identifiable information (PII) has been removed, the dataset has been cleared for publication.