Mapping Environmental Risks of Carbon Capture, Utilization, and Storage (CCUS): A Pre-LCA Approach
Bibliographic record
Abstract
Carbon Capture, Utilization, and Storage (CCUS) is a promising technology to reduce greenhouse gas emissions, presenting environmental opportunities alongside critical risks.The environmental risks of CCUS require preliminary investigation to ensure sustainable development.The current study presents a systematic literature review and a pre-LCA (Life Cycle Assessment) screening matrix to identify and prioritize environmental risks across the CCUS value chain.Using a heat map technique, with likelihood and severity criteria, showing pipeline rupture, carbon dioxide (CO 2 ) and methane leakage as high-priority risks by developing a 5x5 environmental risk matrix.Targeted mitigation strategies such as advanced monitoring, better material selection, and proactive maintenance are proposed to reduce the risks in the project lifecycle.By identifying these risks before full Life Cycle Assessment (LCA), this study provides practical strategies to improve CCUS sustainability.The study contributes to bridge current knowledge gaps for safer and more sustainable CCUS deployment.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.022 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".