Exploring attributes of global CCS projects and the key factors to their accomplishment based on the CCUS project database
Bibliographic record
Abstract
In recent decades, the serious excessive level of carbon emissions has become the worthiest of human consideration to alleviate the problem. The negative impacts of carbon emissions on human beings involve a variety of aspects, such as sea level rise, deforestation, air pollution, global warming, etc. Any one of these issues could cause serious negative impacts on human society. In a large number of relevant studies, Carbon Capture and Storage (CCS) programs are considered to be the most promising and effective approach. The carbon produced during production is captured and transported to rock formations deep underground where it is centrally stored. There are nearly 300 CCS plants in operation around the world that demonstrate the feasibility of such projects. However, one relevant question is whether the project is costly and has barriers to deploy at a scale. We gathered a comprehensive list of large-scale CCS projects globally by utilizing the CCUS Projects Database. We then conducted a comparative analysis of these projects across various categories of project status, ensuring comparability by standardizing cost and extraction figures for each project. We found that the cost of Capture and Storage Projects is the highest, followed by just Capture Projects and just Storage Projects. These plants predominantly exist in developed regions: the U.S. hosts the most, then Europe, parts of Australia, with fewer plants scattered globally. Based on detailed project-specific information, we found that that the two most common reasons for suspended or closed plants are high costs without sufficient financial support and the impact of government agencies’ permissions and regulation. As such, improvement in the capital market and more policy support would be crucial for the deployment and operation of Carbon Capture and Storage projects.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.027 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".