Achieving low-carbon future through CO2 storage: A comprehensive review of global projects and policies
Bibliographic record
Abstract
Climate change mitigation efforts require innovative solutions to reduce GHG emissions. CCUS is a crucial technology for achieving a low-carbon economy. However, significant research gaps exist in understanding the intersection of CCUS policy and the United Nations' SDGs. This review article addresses these gaps by comprehensively analyzing CO 2 storage projects across six global regions, examining 53 notable CCUS projects, and assessing CCUS policies in 15 leading countries. The primary objectives of this study are to (1) analyze regional trends, challenges, and technological advancements in CO 2 storage projects across diverse geological formations; (2) investigate the integration of CCUS into national strategies across leading economies, including the US, Canada, Brazil, China, Japan, India, the UK, France, the Netherlands, Germany, Australia, KSA, the UAE, and Qatar. The integration of CCUS with renewable energy sources and BECCS is explored, emphasizing its potential to achieve harmful emissions and support net-zero ambitions. Future perspectives focus on advancing CCUS efficiency and economic viability through innovations in sorbents, membranes, and process optimizations. The findings demonstrate significant alignment between CCUS policies and SDG targets, emphasizing the importance of integrated approaches to achieve a low-carbon future. This review serves as a valuable resource for policymakers, researchers, and industry stakeholders involved in the development of CO 2 storage solutions, providing insights into future perspectives and opportunities for CCUS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".