An income-tailored energy efficiency rebate policy: Multi-dimensional benefit evaluation approach for upgrading heating furnaces in Ontario, Canada
Bibliographic record
Abstract
A framework for evaluating the economic, environmental, and health benefits of upgrading residential heating furnaces in Ontario , Canada, is presented focusing on income-based disparities across seven groups. Energy efficiency programs often overlook income-based differences, limiting access to rebates. Key objectives include assessing benefits for consumers and society, and designing an income-tailored rebate policy. Benefits assessed include reductions in natural gas consumption, greenhouse gas emissions (CO 2 , methane, nitrous oxide), primary and secondary particulate matter (PM 2.5 ) contaminants, and the prevention of premature mortality. The methods involve estimating energy consumption reductions and accounting for efficiency declines over time, emission factors, global warming potentials , intake fraction, concentration–response function, and a baseline health endpoint for environmental and health impact assessments. Natural gas price modeling, carbon taxes , and the value of statistical life are used for monetary benefit calculations. Findings reveal significant differences in per-household energy-saving benefits among income groups. Gas consumption reductions range from 7015 (lowest-income) to 19,416 m 3 (highest-income), greenhouse gas reductions vary from 13.32 to 36.86 tons of CO 2 e, and PM 2.5 reductions range from 0.85 to 2.36 kg (primary) and 8.27 to 22.90 kg (secondary). Savings (consumer and societal) range from $2669 to $7388 CAD. Collectively, 10 to 55 premature deaths are avoided. These disparities suggest that uniform rebate policies may not equitably support all income groups. An income-based tax rebate structure is recommended allocating 71.26% of the furnace price to the lowest-income group and 20.62% to the highest-income group, utilizing income tax data for eligibility to enhance upgrade uptake and optimize rebate distribution.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".