The Role of Pigs in the Carbon Footprint of Red Meat in Canada
Bibliographic record
Abstract
Global livestock production is a major driver of climate change. Lumping beef and pork together as red meat masks important differences in their carbon footprints, land uses, and social status. These two red meat choices in Canada were compared by using a meta-model of the Unified Livestock Industry and Crop Emissions Estimation System (ULICEES). ULICEES calculated fossil CO2, N2O and CH4 emissions for beef, dairy, pork, poultry, and sheep production in Canada, based on both the livestock and their supporting land base in 2001. The dynamic drivers of the meta-model were crop yields, breeding female populations, tillage practices, nitrogen fertilizer use, and the crop complex of each livestock industry. When the potential carbon sequestration in the land growing harvested perennial forage is credited to beef production, the CO2e emissions offset does not reduce the carbon footprint of beef enough to match the lower carbon footprint of pork. Most of the land required to grow hay for beef would not be needed to feed a protein-equivalent pig population. In a hypothetical conversion of all beef production to pork production for 2021, 4.5 Mha of land under perennial forage was freed and 10.0 MtCO2e per year was mitigated when that area was re-cultivated for annual crops—a GHG mitigation equal to 12% of the GHG emissions budget of Canadian agriculture. Leaving that area under a perennial ground cover mitigated 19.8 MtCO2e per year, the equivalent of 23% of the sector’s GHG emissions budget.
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 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".