Exploring Site‐Specific Carbon Dioxide Removal Options With Storage or Sequestration in the Marine Environment – The 10 Mt CO <sub>2</sub> yr <sup>−1</sup> Removal Challenge for Germany
Bibliographic record
Abstract
Abstract Marine carbon dioxide removal (mCDR) and geological carbon storage in the marine environment (mCS) promise to help mitigate global climate change alongside drastic emission reductions. However, the implementable potential of mCDR and mCS depends, apart from technology readiness, also on site‐specific conditions. In this work, we explore different options for mCDR and mCS, using the German context as a case study. We challenge each option to remove 10 Mt CO 2 yr −1 , accounting for 8%–22% of projected hard‐to‐abate and residual emissions of Germany in 2045. We focus on the environmental, resource, and infrastructure requirements of individual mCDR and mCS options at specific sites, within the German jurisdiction when possible. This serves as an entry point to discuss main uncertainty factors and research needs beyond technology readiness, and, where possible, cost estimates, expected environmental effects, and monitoring approaches. In total, we describe 10 mCDR and mCS options; four aim at enhancing the chemical carbon uptake of the ocean through alkalinity enhancement, four aim at enhancing blue carbon ecosystems' sink capacity, and two employ geological off‐shore storage. Our results indicate that five out of 10 options would potentially be implementable within German jurisdiction, and three of them could potentially meet the challenge. Our exercise serves as an example on how the creation of more tangible and site‐specific CDR options can provide a basis for the assessment of socio‐economic, ethical, political, and legal aspects for such implementations. The approach presented here can easily be applied to other regional or national CDR capacity considerations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".