Householders’ Mental Models of Domestic Energy Consumption: Using a Sort-And-Cluster Method to Identify Shared Concepts of Appliance Similarity
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If in-home displays and other interventions are to successfully influence people's energy consumption, they need to communicate about energy in terms that make sense to users. Here we explore householders' perceptions of energy consumption, using a novel combination of card-sorting and clustering to reveal shared patterns in the way people think about domestic energy consumption. The data suggest that, when participants were asked to group appliances which they felt naturally 'went together', there are relatively few shared ideas about which appliances are conceptually related. To the extent participants agreed on which appliances belonged together, these groupings were based on activities (e.g., entertainment) and location within the home (e.g., kitchen); energy consumption was not an important factor in people's categorisations. This suggests messages about behaviour change aimed at reducing energy consumption might better be tied to social practices than to consumption itself.
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Using ML, we can model and cluster individuals based on their climate knowledge, preferences, demographics, and consumption characteristics, and thus predict who will be most amenable to new technologies and sustainable behavior change.
“Using ML, we can model and cluster individuals based on their climate knowledge, preferences, demographics, and consumption characteristics (e.g., [66,116,166,261,861]), and thus predict who will be most amenable to new technologies and sustainable behavior change.”
From Tackling climate change with machine learning (2023) · cites this paper as [261]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Gabe-Thomas, E., Walker, I., Verplanken, B., & Shaddick, G.
- Affiliations
- University of Bath (GB)
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-07-28
- Volume
- 11
- Issue
- 7
- Pages
- e0158949
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4965032
- OpenAlex
- W2493182119
- Cited by
- 33
- References
- 38
- Citations by year
- 2024: 1 · 2023: 5 · 2022: 6 · 2021: 1 · 2020: 7 · 2019: 4 · 2018: 2 · 2017: 7
- FWCI
- 10.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PEARL (University of Plymouth) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- PEARL (University of Plymouth) (submittedVersion)
- Funders
- ESPRC (EP/K002724/1)
- Topics
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Education and Sustainability
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
- Keywords
- household energy consumption, mental models, card sorting, in-home displays, energy behaviour change, social practices, energy communication
- MeSH terms
- Adult, Aged, Electricity, Female, Humans, Male, Middle Aged, Models, Psychological, Cluster Analysis
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