The London Register of Subsurface CO2 Storage
Bibliographic record
Abstract
Summary The London Register of Subsurface CO2 Storage addresses the critical need for accurate, standardised records of CO2 storage to evaluate CCUS (Carbon Capture, Utilisation, and Storage) projects’ contributions to climate change mitigation. By establishing a comprehensive global database, the Register consolidates fragmented data sources into an accessible and uniform record, supporting robust policy formulation and assessment of technological scalability. This paper presents the methodology behind data compilation from industry reports, government databases, and environmental assessments, adhering to internationally recognised standards. From an initial 0.070 Mt of CO2 stored in 1996, the cumulative global storage reached 304.372 Mt by 2023, reflecting an average annual growth rate of 14.7%. This growth evolved through three distinct phases: a pioneering stage (1996–2007) dominated by Europe and Canada; a North American expansion (2008–2015) involving broader international participation; and a global scaling stage (2016 onwards), marked by significant projects in Asia, Latin America, and the Middle East. This initiative, supported by a diverse consortium, is foundational for informed global climate strategy development.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.019 |
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".