The need to explore the potential of marine CDR – A guide for policy makers
Bibliographic record
Abstract
Rapid, deep and sustained reductions in carbon dioxide (CO₂) emissions are essential to achieve the goals of the Paris Climate Agreement of keeping the long-term global average surface temperature increase well below 2°C above pre-industrial levels and pursue efforts to limit it to 1.5°C1. In addition, the 2021 IPCC Report explains that carbon dioxide removal (CDR) will be needed to offset residual CO₂ emissions from activities and sectors that are difficult to decarbonize by 2050 (Arias et al., 2021). The objective of CDR is removal of atmospheric CO2 from residual emissions and its durable storage in reservoirs, which is an additional critical element towards achieving carbon neutrality by 2050 and thereby ensure less than 2°C global warming.The annual estimates of CDR required in 2030 and by 2050 are 3.6 Gt and 9.4 Gt, respectively (Lamb et al., 2024), leaving a CDR gap of 1 Gt by 2030 and 6.8 Gt by 2050. How much of this gap can be filled sustainably by land-based CDR is unknown. Novel CDR methods include direct air carbon capture and storage (DACCS), biochar, and various marine approaches. Although these novel methods currently account for <0.1% of CDR worldwide, many are being tested through model simulations and small-scale pilot projects. Despite the ocean’s critical role in regulating Earth’s climate, mCDR offers substantial untapped opportunities that have so far been overlooked. Modeling indicates that several mCDR methods could scale to a billion tonnes annually, but their potential ecological side-effects are poorly known. Exploration of the potential of safe, durable and verifiable mCDR and its scalability within sustainability limits is urgently required, even though the process of testing, refining, verifying, and scaling mCDR will take at least a decade. (Boyd et al., 2023a).Time is short, and policymakers must therefore prioritize an ambitious timeline to deliver safe, sustainable, durable, and verifiable mCDR solutions that can potentially scale in parallel with land-based efforts, together with a regulatory framework for deployment.
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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".