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Title: UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency

Abstract

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 datamore » from a variety of perspectives.« less


Citation Formats

Fuentes, Tracy L, McCord, Kieren H, Rackley, Adrienne LS, Biswas, Saurabh, Kabre, Wilfried W, and Antonopoulos, Chrissi A. UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency. United States: N. p., 2024. Web. doi:10.25584/2371918.
Fuentes, Tracy L, McCord, Kieren H, Rackley, Adrienne LS, Biswas, Saurabh, Kabre, Wilfried W, & Antonopoulos, Chrissi A. UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency. United States. doi:https://doi.org/10.25584/2371918
Fuentes, Tracy L, McCord, Kieren H, Rackley, Adrienne LS, Biswas, Saurabh, Kabre, Wilfried W, and Antonopoulos, Chrissi A. 2024. "UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency". United States. doi:https://doi.org/10.25584/2371918. https://www.osti.gov/servlets/purl/2371918. Pub date:Tue May 21 00:00:00 EDT 2024
@article{osti_2371918,
title = {UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency},
author = {Fuentes, Tracy L and McCord, Kieren H and Rackley, Adrienne LS and Biswas, Saurabh and Kabre, Wilfried W and Antonopoulos, Chrissi A},
abstractNote = {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.},
doi = {10.25584/2371918},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 21 00:00:00 EDT 2024},
month = {Tue May 21 00:00:00 EDT 2024}
}