Why the Cost of Carbon Capture and Storage Remains Persistently High
Bibliographic record
Abstract
Carbon capture and storage (CCS) costs depend on the process type, capture technology, carbon dioxide (CO2) transport, and storage location. CO2 capture costs are projected to range from CAD 27–48/tCO2 for processes with concentrated CO2 streams to CAD 50–150/tCO2 for diluted gas streams. The actual cost of CCS projects in Canada indicates that costs are in the upper range of what is predicted in the literature. The persistent high costs of CCS are attributed to high design complexity and the need for customization that limits the deployment of CCS. Comparing the experience rates—or the decrease in cost with increased development and deployment—of CCS with other energy technologies, such as solar and wind, shows that CCS cost reductions have been slow, despite being in use commercially for more than 50 years. The economic viability of CCS for the oil and gas sector continues to rely heavily on federal and provincial government financial support. This is in contrast to renewable technologies, which have generally required government subsidies only in the initial development phases. CCS may play an important role in hard-to-decarbonize industrial sectors such as cement and steel, where substitute materials or fully matured decarbonization technologies are not yet available or fully developed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".