The Value of Carbon Capture, Utilization, and Sequestration
Bibliographic record
Abstract
Carbon capture, utilization, and sequestration (CCUS) represents a class of technologies that directly capture carbon dioxide, either before or after combustion, and then either permanently store it in underground deposits or recycle it for further use. As of now, CCUS has been deployed only in isolated pilot projects; most of which sell the resultant stream of carbon dioxide to oil producers as a tool to increase production in older wells. This market is both geographically and economically limited, particularly if oil prices remain low. However, growing concern around climate change has ignited recent interest in CCUS technologies and a series of studies on its global market potential. A 2017 International Energy Agency report suggests that to meet the 2-degree-Celsius target, CCUS must account for at least 20% of the reduction in annual global emissions by 2060. In the United States, Congress has approved generous tax credits for CCUS investments, generating new interest from investors. The number of CCUS projects is increasing in many countries, from the U.S. and Canada to China and Norway. This policy brief poses the following questions. First, what is the value of CCUS technologies from the public perspective, and how might that change over time? Second, how can governments most effectively pursue that value?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".