Exploring the orientations which characterise the likely public acceptance of low emission energy technologies
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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 [116]
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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
- Carr-Cornish, S., Ashworth, P., Gardner, J., & Fraser, S. J.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Corresponding author
- Carr-Cornish, Simone
- Venue
- Climatic Change
- Published
- 2010-12-16
- Volume
- 107
- Issue
- 3-4
- Pages
- 549-565
- ISSN
- 0165-0009 (ISSN-L)
- 1573-1480
- OpenAlex
- W2007490890
- Cited by
- 12
- References
- 21
- Citations by year
- 2023: 1 · 2022: 2 · 2020: 3 · 2019: 1 · 2018: 1 · 2015: 1 · 2014: 3
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Climatic Change (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Social Acceptance of Renewable Energy
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Social Sciences › Sociology and Political Science › Risk Perception and Management
- Keywords
- public acceptance, public perception, climate change mitigation, nuclear energy, renewable energy, self-organizing maps, Australian public attitudes
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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