A Technology Development Matrix for Carbon Capture: Technology Status and R&D Gap Assessment
Bibliographic record
Abstract
Numerous carbon capture, utilization, and storage (CCUS) technologies are under development to reduce CO2 emissions. To evaluate the status of a CCUS technology under development and identify potential gaps for further advancement, we have established a new technology assessment framework and are developing a decision-making tool, the technology development matrix (TDM), starting with available carbon capture technology (CCT) data. TDM is a data inventory system and screening tool. As a screening tool, it can be used for resource allocation decisions in research, development, and deployment (RD&D) by academia, government, and industry. It shares data with techno-economic analysis (TEA) and life-cycle assessment (LCA) tools as an inventory system. By using available data, this TDM framework has been demonstrated on amine-based (monoethanolamine) absorption post-combustion CO2 capture, for pulverized coal (PC) power plant flue gas, as the best available technology (BAT) for comparison. Three groups of promising post-combustion CCTs under development are presented as Alternative Technology (Alt Tech) case studies, including membrane, solid adsorption, and calcium-based chemical looping. By using available data, preliminary analysis enabled technology benchmarking and highlighted knowledge, data, and technology gaps, all providing potential future RD&D focus.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".