A multi-dimensional evaluation of upgrading fenestration systems in single-family dwellings and implications for rebate structures
Bibliographic record
Abstract
This paper presents a novel methodology of four interdependent stages to evaluate the economic, environmental, and health benefits of replacing low-energy performing residential windows with higher-performing ones in single-family dwellings. A modeling-based approach is developed to compute and monetize reduction in heating and cooling energy transmittance, greenhouse gas emissions, and premature mortality associated with the decrease in the outdoor fine particulate matter. Linear regression models of energy prices, carbon pricing, and a value per statistical life approach quantify these benefits monetarily. Factors like upgrade type, dwelling window-to-wall ratio, deterioration in energy performance, geographic location, and window placement by orientation are analyzed for their impact on benefits. A case study examining two locations in the province of Ontario, Canada, is carried out. Results show that energy bill saving is sufficient to justify investing in windows with energy-efficient recognition across all dwelling categories in Southern Ontario, though government rebates are necessary for some dwelling types opting to upgrade to the most energy-efficient models. In contrast, rebates are unnecessary in Northern Ontario for any upgraded models due to substantial energy bill savings. The study proposes a per-window rebate policy based on realized benefits, considering various upgrade models, distinct single-detached dwellings, and geographic location eligibility. • New method to evaluate multi-dimensional benefits of upgrading residential windows. • Present a modeling-based approach for estimating heating and cooling energy losses. • Benefit disparities emerge based on dwelling and upgrade types and climate conditions. • For a typical Toronto home, saving is $1995.74 for upgrading to double-glazed windows. • Propose rebate policy tailored to dwelling and upgrade types and geographic locations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".