Akademik Məlumat İdarəetmə Sistemi
Journal of Cleaner Production, vol.539, 2026 (SCI-Expanded, Scopus)
The steel industry generates close to 8% of global carbon dioxide (CO2) emissions, releasing about 1.85 tons of CO2 for every ton of steel produced. Tackling this challenge requires decision-support tools that can handle uncertainty and balance competing criteria. This work introduces a hybrid Multi-Criteria Decision-Making (MCDM) model that combines Interval-Valued Bipolar Fuzzy Soft Sets (IVBFSS), the Logarithmic Percentage Change-Driven Objective Weighting method (LOPCOW), and an enhanced COBRA ranking procedure. Assessments from three decision-makers were combined using the IVBFSS Weighted Aggregation Operator and Score function, enabling both linguistic vagueness and differing expert perspectives to be represented. The ranking derived from the model is: Legal and liability framework for storage (1) > Revenue from CO2 utilization (0.593) >CO2 concentration in flue gas (0.584) > Transport infrastructure availability (0.404) > Maintenance requirements (0.153) > Net CO2 emissions reduction (0.097) > Energy consumption per ton of captured CO2 (0). This outcome points to the central role of legal frameworks, utilization revenues, and flue gas concentration, while high energy demand emerges as the least attractive option. To test robustness, the rankings were compared with nine established MCDM approaches using Spearman and Kendall correlation coefficients, along with significance testing. Sensitivity checks were also applied using bootstrap resampling to obtain confidence intervals, Monte Carlo simulations to test stability under weight variations, and comparisons across different objective weighting schemes. Together, these evaluations demonstrated our framework yields consistent and dependable results under a range of conditions.