Multi-criteria decision analysis for evaluating carbon capture technologies in power plants
Bibliographic record
Abstract
Power plants are among the largest contributors to CO 2 emissions, making carbon capture and conversion into valuable products a key strategy to combat climate change and foster a circular economy . However, selecting the optimal CO 2 capture technology is complex due to the wide range of options — such as pre-combustion, post-combustion, and oxy-fuel combustion — and the various technical, economic, environmental, and social factors involved. This study identifies the most promising CO 2 capture technologies for three power plant types: Natural Gas Combined Cycle (NGCC), lignite , and coal. By applying Multi-Criteria Decision Analysis (MCDA), which integrates a systematic literature review with the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the study ranks existing technologies. For NGCC plants , post-combustion calcium looping emerged as the top choice, with a relative closeness score of 0.790, due to its moderate CO 2 avoidance cost (€33.80/tCO 2 ), high efficiency (48.31%), and mature Technology Readiness Level (TRL) of 7. In lignite plants, post-combustion chemical absorption with MDEA ranked highest, achieving a relative closeness of 0.865 and a TRL of 9. For coal plants, pre-combustion using the Selexol process combined with Mn-based chemical looping was most promising, with a relative closeness of 0.829, low CO 2 avoidance cost (€19.94/tCO 2 ), and a net efficiency of 37.13%. These findings underscore the importance of balancing economic performance and technological maturity when selecting CO 2 capture technologies.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".