Canada's Fair Share of 1.5 °C-Consistent Global Mitigation Through 2035
Bibliographic record
Abstract
This technical report serves to outline the methodological and normative-ethical choices taken by members of Climate Action Network Canada – Réseau action climat Canada (CAN-Rac) in deriving their current position on Canada’s fair share through 2035 towards a global mitigation effort consistent with limiting warming to 1.5 °C, based on the findings of the IPCC Sixth Assessment Report (IPCC AR6). It also outlines the methodology applied and the results obtained. Their work concluded that Canada’s total fair share of a 1.5 °C-consistent global mitigation effort is equivalent to a 160 % reduction in emissions below 2005 levels by 2035, when accounting for Canada’s shortfall over the post-Paris period 2016-2023 between its actual emissions and its previously calculated fair share (a total shortfall of 1,180 MtCO2eq). Utilizing the analysis of the International Energy Agency’s Net Zero Emission scenario, implies a domestic reduction of 73 % below 2005 levels in 2035. Applying a portion of Canada’s post-Paris shortfall to this figure increases it to a total domestic reduction of 80% below 2005 levels in 2035. The gap between the 160 % total fair share and the 80 % domestic portion constitutes the international portion of Canada’s total mitigation fair share. Expressed as climate finance contribution for mitigation, the international portion of Canada’s fair share implies, in 2025, an annual total public climate finance contribution for mitigation of USD $ 19 billion (CAD $ 25bn). This amount increases to USD $ 64 billion (CAD $ 86bn) per year by 2035, for an average of USD $ 43 billion (CAD $ 58bn) per year during the 2025-2035 period. If the ratio between Canada's bilateral public climate finance and its attributed multilateral climate finance for mitigation remains constant and matches the 2016-2020 average for OECD donors, the bilateral public climate finance portion of this total would be USD $ 9 billion (CAD $ 12 bn) annually in 2025, increasing to USD $ 28 billion (CAD $ 38 bn) per year by 2035, averaging USD $ 20 billion (CAD $ 27 bn) per year over this period.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".