Applying equity principles leads to higher carbon removal obligations in Canada
Bibliographic record
Abstract
Despite net-zero pledges, consensus on national responsibilities for carbon dioxide removal (CDR) strategies is lacking. Here, we use integrated assessment modeling to examine equity-informed estimates of Canada’s remaining carbon budgets, exploring CDR’s role at net-zero and beyond. Gigaton-scale CDR efforts post-2050 are needed to address Canada’s carbon debt under various burden-sharing principles. Cumulative negative emissions (2050-2100) could increase from 7.5 GtCO2 in the Net-Zero scenario to 20.3 GtCO2 in equity-informed scenarios. By 2100, a CDR portfolio, including bioenergy with carbon capture and storage, direct air capture, and enhanced weathering could contribute up to ~500 MtCO2/year of removals. The projected average CDR growth rates, 2.8%-16%/year, align with the historical adoption rates of ammonia synthesis and biomass consumption in Canada, underscoring the importance of drawing lessons from past successes. Socio-economic and technological sensitivity analysis highlights that, despite variations in the role of individual CDR technologies, CDR remains essential for Canada’s post-net-zero commitments. By 2100, bioenergy with carbon capture and storage, direct air capture, and enhanced weathering can contribute to removing 500 megatons of carbon dioxide emissions per year in Canada and are key to commitment under burden-sharing principles, according to an integrated assessment model analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".