Reducing Emissions, Not Cows: Regulatory Policies to Mitigate Methane
Bibliographic record
Abstract
Enteric fermentation is a process that occurs in ruminant animals, such as cattle, during the digestive process. Methane gas is a by-product of enteric fermentation, expelled when cattle exhale or belch. Methane gas is ~28-34 times more potent than carbon dioxide, and with its short lifespan, can provide an effective short-term way to prevent additional warming. Enteric fermentation causes 43% of Canada’s agricultural methane emissions; however, there are no policies in place to regulate these emissions. Here we show that market-based instruments can encourage farmers to uptake effective mitigation techniques. We conduct a rapid review, analyzing literature to inform mitigation techniques and a jurisdictional scan to examine agricultural policies in other countries. The review of literature reveals two effective mitigation techniques: adding Asparagopsis taxiformis (red seaweed) to feed and efficiency breeding/genetic selection. The jurisdictional scan reveals that market-based instruments, specifically subsidies and offset protocols, are a widely used tool to mitigate methane from enteric fermentation. Our results demonstrate that Canada has regulatory options available to reduce methane from enteric fermentation. This paper recommends Canada create offset protocols within its new offset system that incentivize the uptake of Asparagopsis taxiformis as a feed additive and efficiency breeding/genetic selection.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".