Potential Role for Consumers to Reduce Canadian Agricultural GHG Emissions by Diversifying Animal Protein Sources
Bibliographic record
Abstract
The discussion of diversified protein sources triggered by the 2019 Canadian Food Guide has implications for Canada’s livestock industry. In response to this discussion, a scenario analysis is conducted on the potential impact of reducing red meat consumption on the greenhouse gas (GHG) emissions from Canadian livestock production. This analysis uses medical recommendations as a proxy for healthy servings of red meat. For simplicity, it was assumed that red meat is either beef or pork and that broilers are the only nonred meat choice. The medical scenario is combined with four livestock production scenarios for these three livestock types. Broiler consumption is allowed to expand to maintain national protein intake in all four scenarios. Under the medical scenario, red meat consumption in Canada would decrease from 2.5 Mt to 1.9 Mt of live weight. A feedlot diet for slaughter cattle, and a 50:50 split of the medically recommended red meat intake of beef and pork (Scenario 1), reduced GHG emissions by 3.9 Mt CO2e from the 20.6 Mt CO2e (carbon dioxide equivalent) for current consumption. Replacing the feedlot beef diet by grass fed beef (Scenario 2) increased GHG emissions by 1.5 Mt CO2e over Scenario 1. Halving the consumption of grass fed beef and increasing pork by 50% (Scenario 3) reduced GHG by 7.7 Mt CO2e. Reverting back to the feedlot diet, and the same 25:75 beef–pork ratio (Scenario 4), increased the GHG emissions reduction to 8.9 Mt CO2e. Without including the emission savings from the medical scenario, GHG reductions from Scenarios 3 and 4 dropped to 3.8 Mt and 5.0 Mt CO2e, respectively. No scenario exceeded the feed grain area required to meet the 2017 consumption of these commodities, but Scenario 2 required more forage area compared to consumption in 2017.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".