Seawater carbonate chemistry based carbon dioxide removal: towards commonly agreed principles for carbon monitoring, reporting, and verification
Bibliographic record
Abstract
Carbon Dioxide Removal (CDR) from the atmosphere is unavoidable if we are to meet the Paris Agreement’s goal of limiting global warming to 1.5°C, and almost certainly required to limit warming to 2°C. The ocean exchanges carbon dioxide (CO 2 ) with the atmosphere and is a large repository of carbon that could either be partially emptied to allow more CO 2 absorption or have its carbon storage capacity enhanced to allow it to remove additional CO 2 from the atmosphere. Early-stage techniques exist to utilise the ocean in atmospheric CO 2 removal, but typically, the atmospheric CO 2 removal these techniques stimulate happens downstream of their activity. Verifying the carbon removal associated with these techniques, while critical when evaluating the approaches and pricing the removal, is challenging. This study briefly reviews the challenges associated with verifying the carbon removal associated with non-biological (abiotic) engineered marine CDR approaches, specifically Ocean Alkalinity Enhancement and Direct Ocean Carbon Capture and Storage, and presents the findings from a workshop held with interested parties spanning industry to government, focused on their collective requirements for the Monitoring, Reporting, and Verification (MRV) of carbon removal. We find that it is possible to agree on a common set of principles for abiotic marine MRV, but identify that delivering this MRV with today’s understanding and technology could be prohibitively expensive. We discuss focal areas to drive down marine MRV costs and highlight the importance of specification of MRV criteria by an ultimate regulator to stimulate investment into the required work. High-quality MRV is important to correctly price any CO 2 removal, but we identify that accessibility and transparency in MRV approaches are also key in realising the broader benefits of MRV to society.
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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 | 0.164 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".