Achieving rapid decarbonisation of Canada’s residential sector requires a strategic approach
Bibliographic record
Abstract
Decarbonisation of Canada’s residential sector in line with our Paris Agreement will be challenging. Conventional decarbonisation strategies involve deep energy efficiency upgrades to the building envelope and adoption of low carbon heating systems such as electric heat pumps. However, past retrofit programs have failed to achieve either the rate of upgrades, or the size of energy savings required for this approach. This study used a database of 38,607 home energy audits from the Waterloo Region to model the energy and emissions impacts of deep energy efficiency upgrades by date of home construction. Modeling demonstrated the greatest potential energy efficiency gains for homes built before 1940. Building envelope upgrades had diminishing returns for homes built between 1940 and 1980, with the lowest energy efficiency improvement potential found in homes built after 1980. Furthermore, directly electrifying a home with heat pumps for space and water heating is the single most impactful measure examined for reducing emissions. Policies and programs should support direct electrification of all homes and target programs for building envelope upgrades to homes built before 1980 and especially before 1940. Such policies can accelerate decarbonisation efforts and maximize the energy, emissions and energy poverty impacts of limited retrofit resources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 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".