If It Matters, Measure It: A Review of Methane Sources and Mitigation Policy in Canada
Bibliographic record
Abstract
Methane, a potent greenhouse gas, is critically underregulated in Canada. We review the sources of methane emissions in Canada, policies in place, policy coverage, and mitigation options for each source. Three sectors account for 96 per cent of Canada’s methane emissions: oil and gas, agriculture, and waste. The oil and gas sector is the largest contributor to national methane emissions, as well as the only sector with methane mitigation regulations and a methane reduction target. Agriculture is the largest source of unregulated and unpriced methane, mainly because livestock is the largest single source of methane emissions in Canada. In a best case scenario, direct regulatory coverage is approximately 58 per cent of methane emissions, with indirect regulatory coverage via offset markets accounting for 14 per cent. However, data gaps and policy exemptions and gaps make this measure of potential coverage an overestimate. Emissions measurement challenges hinder methane emissions management for all sectors. Due largely to these challenges, 28 per cent of Canada’s methane emissions are unregulated and policy options are limited. Better methane management, relying on better measurement, is crucial to achieving Canada’s 2030 and 2050 emissions reduction goals. Key short-term policy actions are improving and standardizing current emissions estimates, matching emissions to policy coverage, and identifying unregulated sources. Longer-term actions require further study of cost-effective regulatory options across all sources, to support stricter regulations or well-defined market-based approaches with measurable outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".