Quantifying the prevalence of energy poverty across Canada: Estimating domestic energy burden using an expenditures approach
Bibliographic record
Abstract
Abstract Energy poverty is gaining public attention in Canada. Based on statistical analysis of Statistics Canada's 2016 Survey of Household Spending, we estimate that 7–9% of households spent more than 10% of their income on energy expenditures. Households in the Atlantic provinces faced the most energy poverty and highest energy expenditures. Low income, geography, and dwelling conditions were the main predictors of energy poverty. Households in energy poverty also spent approximately five times more on energy expenditures as a proportion of their total expenditures compared to households not in energy poverty. Our study fills a key research gap as a limited number of studies exist on the extent and drivers of energy poverty in Canada. Further, our study's robust quantitative estimates allow benchmarking and comparative research. These estimates lay the groundwork for evidence‐based solutions—and our findings highlight the need to reconsider current policies. Considering the challenge of the ratio of energy costs to income is vital, especially in the aftermath of events such as COVID‐19 or the 2021 heatwave in western Canada, which result in different energy behaviours and needs. More broadly, in the regular day‐to‐day, energy services should be seen as necessary and decision makers ought to consider the energy burden of Canadians .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".