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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".