Carbon Capture, Utilization, and Storage Risks from Supply Chain Perspective: A Review of the Literature and Conceptual Framework Development
Bibliographic record
Abstract
The technology called carbon capture, utilization, and storage (CCUS) is important for capturing CO2 emissions before they enter the air. Because everyone wants to stop global warming by reducing CO2 emissions, CCUS is an important and emerging technology that can help slow down climate change, lower emissions in many areas, and support the move toward a sustainable and carbon-neutral future. As CCUS technology and its adaptation increases, it is very important to pay attention to the CCUS risks from a supply chain (SC) point of view. The goal of this study was to identify CCUS supply chain risks and develop a conceptual framework (CF) that provides a structured approach to ensure safe and reliable CCUS supply chain operations. Therefore, this study analyzed the literature related to the SCs of different sectors and identified the SC risks, which was the foundation for CCUS SC risk identification. This study demonstrates that there is no research article that provides a comprehensive CCUS SC risk management framework that connects with risk management strategies. The conceptual framework that is proposed in this study connects CCUS SC functions, risks, and risk management strategies to construct a complete CCUS supply chain risk management system. Moreover, the CF provides guidelines for future research, which will enrich the CCUS supply chain risk management system as well as fight climate change.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".