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Multi-criteria decision analysis for evaluating carbon capture technologies in power plants

Author

Listed:
  • Sepahi, Nima
  • Ilinca, Adrian
  • Rousse, Daniel R.

Abstract

Power plants are among the largest contributors to CO2 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 CO2 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 CO2 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 CO2 avoidance cost (€33.80/tCO2), 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 CO2 avoidance cost (€19.94/tCO2), and a net efficiency of 37.13%. These findings underscore the importance of balancing economic performance and technological maturity when selecting CO2 capture technologies.

Suggested Citation

  • Sepahi, Nima & Ilinca, Adrian & Rousse, Daniel R., 2025. "Multi-criteria decision analysis for evaluating carbon capture technologies in power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(C).
  • Handle: RePEc:eee:rensus:v:219:y:2025:i:c:s1364032125003727
    DOI: 10.1016/j.rser.2025.115699
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