Is blue carbon a ‘red herring’? Problems regarding magnitude, cost-effectiveness and timescale
Bibliographic record
Abstract
Avoiding the most catastrophic consequences of climate change will require some amount of carbon dioxide removal, in addition to urgently reducing emissions. Options for such removal include nature-based solutions, such as “blue carbon” burial in the sediment of vegetated coastal ecosystems (mangroves, salt marshes and seagrass meadows). Very high carbon sequestration rates have been claimed for these blue carbon ecosystems, and many media outlets have uncritically endorsed this message. Unfortunately, there seems to be some over-optimism among researchers, the general public and policy-makers, about the actual climate mitigation potential of coastal blue carbon.We identify three main reasons why blue carbon ecosystems are unsuitable for directly offsetting fossil fuel emissions. First, the magnitude of the any climate benefit is likely to be much smaller than claimed, due to a range of methodological issues; second, the cost-effectiveness is low, particularly in developed countries; and third, there is a mismatch in timescale between the emission of ancient fossil carbon and the storage of carbon by vegetated ecosystems. Given these limitations, we suggest that blue carbon may be a misleading distraction (i.e. a ‘red herring’) for climate mitigation purposes. Nevertheless, blue carbon ecosystems provide many benefits. Protection of such habitats and their restoration, where practicable, would protect critical habitat for diverse species, prevent coastal erosion, reduce storm damage, promote food security and provide opportunities for tourism.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".