The need to explore the potential of marine CDR – A guide for policy makers
Bibliographic record
Abstract
Limiting global warming to 1.5°C or 2°C in the midst of a climate emergency requires rapid, deep, and sustained emission reductions, alongside annual CDR at the billion-tonne (Gt) scale. CDR is essential for addressing hard-to-abate, residual emissions and reducing atmospheric CO₂. Achieving the billion-tonne CDR target demands a holistic approach that includes both land and ocean – which we term One-Earth CDR. One-Earth CDR is critical because all CDR methods face a "CDR tax" due to feedbacks from the human-altered Earth System. These feedbacks release stored anthropogenic CO₂ from land and ocean reservoirs, which partially offsets the effectiveness of CDR. Therefore, to reach the billion-tonne goal, CDR must be applied sustainably in all feasible environments. One-Earth CDR also serves as a safeguard against over-reliance on land-based CDR, which faces challenges such as side effects (e.g., mega-fires) and sustainability limits (e.g., land and water use). Marine CDR (mCDR) using innovative methods offers a large potential for carbon storage. Proving the effectiveness and safety of mCDR will likely take at least a decade. Ensuring its integrity is crucial for verifiable CDR. Before large-scale deployment, knowledge gaps must be addressed, including risks, sustainability, scalability, cost, permanence, side effects, monitoring, verification, social acceptance, and governance frameworks.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.016 | 0.039 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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".