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Record W7064512708

Cold homes and tight budgets: measuring energy poverty and the coping strategies used by households in the Town of Bridgwater, Nova Scotia

2024· dissertation· en· W7064512708 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2024
Typedissertation
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsnot available
FundersInfrastructure CanadaMcGill University
KeywordsNova scotiaPovertyFuel povertyEnergy povertyCoping (psychology)Population
DOInot available

Abstract

fetched live from OpenAlex

Objective.Energy poverty occurs when households are unable to afford or access the energy services they need to meet their material and social needs.In 2019, the Town of Bridgewater, Nova Scotia, estimated that around 40% of their households were facing energy poverty.Embedded in a larger research project assessing the extent of energy poverty in Bridgewater and its well-being implications, my thesis aims to answer the research questions: Who experiences energy poverty in the Town of Bridgewater?How do they cope?To do so, the objectives of the thesis were to measure the prevalence of energy poverty using different indicators, identify the socioeconomic and housing characteristics associated with energy poverty, and describe the coping strategies used by households with a focus on the 'heat or eat' trade-off, which occurs when households are forced to decrease their energy consumption or their grocery spending to balance their finances.Methods.Data were collected using a community-based survey on energy needs and well-being conducted in the spring of 2022 in the Town of Bridgewater.Energy poverty was assessed using eight expenditure-based and self-reported measured.Survey respondents reported on their socioeconomic and housing characteristics, as well as answered questions about their use of coping strategies to increase thermal comfort and manage finances.Overall, 516 residents of Bridgewater completed the survey.Cross-tabulations and logistic regressions were used to analyse the data.Answers to open-ended questions on coping strategies were also analysed included in the results.Results.Within the sample, 17% of households were spending more than 10% of their income (before housing costs) on energy expenditures, and 46% had a share of energy expenditures to household income (after housing costs) over twice the national median share.Twenty-one percent reported being unable to maintain an adequate temperature in their dwelling.The prevalence of energy poverty was higher among women, households with children, lower-income households, renters, and those in dwellings in need of major repairs.The use of coping strategies was higher among women compared to men, young adults compared to other age groups, and in households with children.Accounting for socioeconomic and housing characteristics, participants facing energy poverty based on the self-reported inability to afford energy needs were 10.33 times more likely to resort to the 'heat or eat' trade-off than participants not facing energy poverty. III Conclusion.According to all measures considered in this study, at least 30% of households in the sample from the Town of Bridgewater are facing energy poverty.Several participants reported a reliance on strategies to cope with energy poverty, namely to increase thermal comfort in their home and decrease financial strain.These findings reveal the burden energy poverty represents for certain households.Better understanding the experience of households facing energy poverty will help guide effective pathways for interventions for other small towns such as Bridgewater across Canada.energy poverty three years ago, inviting me to be a part of the BridgES study, and encouraging me to pursue research at the graduate level.A further thanks

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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