Climate benefits of seaweed farming: estimating regional carbon emission and sequestration pathways
Bibliographic record
Abstract
Abstract Seaweed farming is widely promoted as an approach to mitigating climate change despite limited data on carbon removal pathways and uncertainty around benefits and risks at operational scales. We explored the feasibility of seaweed farms to contribute to atmospheric CO 2 reduction in coastal British Columbia, Canada, a region identified as highly suitable for seaweed farming. Using a place-based, quantitative model, we examined five scenarios spanning a range of industry development. Our intermediate growth scenario sequestered or avoided 0.20 Tg CO 2 e / year, while our most ambitious scenario (with more cultivation and higher production rates) yielded a reduction of 8.2 Tg CO 2 e /year, equivalent to 0.3% and 13% of annual greenhouse gas emissions in BC, respectively. Across all scenarios, climate benefits depended on seaweed-based products replacing more emissions-intensive products. Marine sequestration was relatively inefficient in comparison, although production rates and avoided emissions are key uncertainties prioritized for future research. Our results show how seaweed farming could contribute to Canada’s climate goals, and our model illustrates how farmers, regulators, and researchers could accurately quantify the climate benefits of seaweed farming in local contexts.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".