A novel method to calculate SSP-consistent remaining carbon budgets for the building sector: A case study of Canada
Bibliographic record
Abstract
• We present a novel method to calculate carbon budgets for individual countries. • The method uses open-source datasets, and can be applied to ∼180 countries. • Four effort-sharing methods lead to a budget of -11–16 GtCO 2 for Canada. • The Canadian building sector seems unlikely to meet its allocated budget share. • There are key scope disparities between life cycle assessment and carbon budgets. Decarbonising the built environment is imperative to reach any net zero global GHG emissions targets. However, there remains uncertainty on how to orchestrate the mitigation efforts. Given a remaining global carbon budget, how should it be assigned both nationally and sectorally? Within this paper, we present a method and Python script to calculate country-specific carbon budgets using open-source datasets, for several scenarios and allocation methods. The script is run for Canada as a case study. Grounded in Canada's calculated carbon budget, tentative budget shares are explored for the Canadian building sector. The feasibility of meeting these budgets is broadly assessed using a streamlined calculation. Even under optimistic assumptions, the Canadian building sector is unlikely to meet its allocated budget share. Key limitations, data requirements and research avenues are highlighted to improve upon the presented approach.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".