Assessing the Impact of Variations in Energy Rates and Carbon Price Projections on Low-Carbon Building System Designs in Canada
Bibliographic record
Abstract
Achieving Canada’s 2050 net-zero targets requires strategic choices in low-carbon building systems, with projections of natural gas and electricity rates, along with carbon pricing, playing a critical role in their financial feasibility analysis. Currently, many organizations still rely on fixed escalations of energy rates, risking inaccurate feasibility assessments. This research examines how six institutional projection scenarios of end-use energy rates and carbon prices affect GHG emissions and costs of residential building system retrofits in Alberta, British Columbia, and Ontario from 2025 to 2050. Results show substantial cost and emissions variations: Alberta’s low natural gas prices create the largest short-term cost gap for retrofits, while British Columbia and Ontario achieve net-zero emissions by 2050 from retrofits even under modest scenarios. The findings also highlight how the natural gas and electricity cost gap decreases over time, and how utilizing scenario-based projections is important to support cost-effective, low-carbon building system decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".