Accelerating the 1.5°C energy transition for Canadian residential buildings through selective direct electrification with heat pumps
Bibliographic record
Abstract
Limiting global warming to 1.5–2.0°C in line with our climate commitments will require decarbonization of residential buildings. The traditional approach starts with major energy efficiency upgrades to the building envelope followed by switching to low‐carbon fuel sources for space and water heating. Building envelope retrofits have been a policy goal for over two decades in Canada and elsewhere, yet historical rates and associated emission reductions fall far short of what is required if we are to meet our climate targets. Alternatively, we propose direct fuel switching to electric heat pumps for space and water heating in regions with low‐carbon electricity. Using a database of 44,463 home energy profiles in Waterloo Region, Canada, we modeled the energy efficiency and greenhouse gas emission impacts of building envelope retrofits and direct electrification. While all retrofit plans achieved significant energy efficiency gains (17‐40%), so did direct electrification (70%) and building envelope retrofits plus electrification combined (70‐80%). Only plans that included electric heat pumps achieved greenhouse gas emission reductions of 90% or more. Compared to the conventional approach, direct electrification with heat pumps may be a simpler, more effective, and more realistic approach for policies aiming to decarbonize the residential sector.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".