Comprehensive analysis of carbon footprint reduction projects in the precast concrete industry using circular intuitionistic fuzzy sets

This study evaluated carbon footprint reduction strategies in the precast concrete industry using a hybrid methodology that combined Circular Intuitionistic Fuzzy Analytic Hierarchy Process (CIF-AHP) and Circular Intuitionistic Fuzzy COmbinative Distance-based Assessment (CIF-CODAS). The research assessed various carbon reduction projects, including energy-efficient technologies, Carbon Capture, Utilization, and Storage (CCUS), and renewable energy integration. The findings showed that CCUS was the most effective strategy, followed by energy-efficient grinding technologies and high-efficiency motors. Waste Heat Recovery (WHR) systems and renewable energy integration ranked lower due to challenges related to large-scale application and cost considerations. The sensitivity analysis demonstrated the robustness of the methodological framework, as it consistently prioritized alternatives across different scenarios. These results emphasized the importance of advanced technologies such as CCUS for achieving substantial reductions in carbon emissions and highlighted the need for ongoing innovation in sustainable concrete production. This study contributed to the literature by offering a systematic approach to decision-making under uncertainty and providing actionable insights for reducing carbon footprints in the precast concrete sector.

کلیدواژه ها:

Carbon footprint reduction ، Precast concrete industry ، Circular intuitionistic fuzzy analytic hierarchy process ، Circular intuitionistic fuzzy combinative distance-based assessment