Quantifying energy poverty vulnerability with minimal data – A building energy simulation approach
Bibliographic record
Abstract
Energy poverty is a pressing issue that many countries currently face. The income per household and energy use data per household are variables used in widely known methodologies on the topic of energy poverty. However, due to different reasons, this data isn’t made available in many locations of interest, which prevents them from quantifying energy poverty. To overcome this problem, this study introduces a not yet explored asset-based workflow that makes use of income distributions and building energy modelling to quantify a household’s energy poverty vulnerability. This study defines a novel energy poverty vulnerability index (EVI) that makes use of the widely known ten-percent rule’s fundamental concepts but avoids the latter’s main critics through two major improvements; a varying threshold is introduced with a linear function and distortion is prevented using a weighted average. The methodology has been applied to more than 85,000 low-rise apartment buildings in Montreal, Canada to quantify their energy poverty vulnerability, a feat that wasn’t possible using typical methodologies. The case study results have shown that Montreal’s buildings’ energy poverty vulnerability is highly dependent on the household’s income, but also on the insulation level and floor area. A sensitivity analysis has been conducted to determine the EVI’s requirements and limitations. From this analysis, the EVI is most sensitive to variables related to the building’s energy consumption and is least sensitive to income.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".