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Social outcomes of energy use in the United Kingdom: Household energy footprints and their links to well-being

2022· article· en· W4311092030 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEcological Economics · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsYork University
FundersEngineering and Physical Sciences Research CouncilLeverhulme Trust
KeywordsOverconsumptionEnergy povertyEnergy (signal processing)Context (archaeology)FootprintEconomicsPovertyEcological footprintPublic economicsInequalityNatural resource economicsEnergy consumptionDistribution (mathematics)Environmental economicsDemographic economicsEconomic growthSustainabilityGeographyMicroeconomicsEcologyProduction (economics)

Abstract

fetched live from OpenAlex

How energy relates to human need satisfaction, for whom, and with what wellbeing outcomes has remained under-researched. We address this gap by investigating the relationship between household energy footprint and well-being in the UK. Our results indicate that car and air transportation contributed the most to the total energy footprint of high-income and high-energy users. We find significant inequalities in the distribution of energy use and that the top energy users with high well-being are driving excess energy use. A more detailed analysis reveals that individuals with protected characteristics are particularly vulnerable to energy poverty and that their contribution to overall energy demand is negligible. We find that focusing on well-being steers the attention towards questions of sufficiency, overconsumption as well as the context within which we satisfy needs. Tackling the issues of energy poverty and inequalities are important for lowering energy demand and need to be addressed as a matter of climate justice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.223
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it