A Technology Development Matrix for Carbon Capture: Technology Status and R&D Gap Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Numerous carbon capture, utilization, and storage (CCUS) technologies are under development to reduce CO 2 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 CO 2 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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it