Seaweed as a climate fix for meat and dairy production: an LCA perspective
Bibliographic record
Abstract
Livestock supply chains contribute a substantial share of global anthropogenic greenhouse gas (GHG) emissions, with enteric methane (CH₄) from ruminants being a key driver. Seaweed-derived feed additives have been proposed as a CH₄ mitigation strategy, but their broader environmental trade-offs remain unclear. This study applies life cycle assessment (LCA) to evaluate seven seaweed-supplemented scenarios across beef, dairy, and sheep production, assessing climate change, marine and freshwater eutrophication, land use, fossil fuel depletion, and water use. Results indicate that while certain seaweed additives can lower CH₄ emissions in vitro, real-world reductions in total GHG emissions remain modest. Energy-intensive processing and long transport distances can offset CH₄ abatement gains, with only scenarios utilizing low-impact by-products achieving net climate benefits. Sensitivity analyses highlight the importance of Global Warming Potential (GWP) time horizon selection, energy sources, and grazing practices in determining overall environmental performance. Optimizing algae sourcing, processing, and application will be essential to realizing meaningful and scalable mitigation potential in ruminant systems. While seaweed additives can contribute to CH₄ reduction, this study concludes they will not single-handedly deliver transformative climate benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".