Investigating the greenhouse gas emissions of grass-fed beef relative to other greenhouse gas abatement strategies
Bibliographic record
Abstract
Beef is often identified as one of the foods with the largest greenhouse gas (GHG) emissions, causing climate-conscious persons to seek changes in their diets. This study evaluated the ability of a household to reduce its GHG emissions by replacing conventional US beef with grass-fed beef and compared its effectiveness to three other strategies: replacing beef with chicken, becoming a vegetarian, and purchasing carbon offsets. These potential GHG-reducing strategies were considered within a model of a typical US household, using a framework that accounts for all household expenditures and carbon emissions. Replacing beef with chicken and adopting vegetarianism reduced the household’s GHG emissions by 1% and 3%, respectively. Grass-fed beef only reduced emissions if the GHG sequestration rate for pastureland and/or the price of grass-fed beef was high. It is shown that persons paying higher prices for grass-fed beef with the goal of smaller GHG emissions might want to consider buying conventional beef instead and using the savings to purchase carbon offsets. Also, although vegetarianism is often touted as a climate-friendly diet, the model shows that meat-eaters can achieve the same GHG reduction by spending only US$19 per year on carbon offsets. These results assume that additional land for grazing is acquired from recently abandoned cropland, which gives grass-fed beef its best chance at being climate-friendly. Alternative land-use assumptions would only reinforce the result that grass-fed beef does not emit less GHG emissions than conventional beef.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".