Many countries are prioritizing renewable energy sources in response to fossil fuel depletion, environmental concerns, and the need for energy resilience. This study evaluates five renewable energy alternatives: Biomass, Wind, Solar, Geothermal, and Hydro, with the aim of reducing foreign energy dependency and enhancing flexibility under potential geopolitical disruptions. A three-stage hybrid decision-making framework is proposed, integrating Modified Preference Selection Index (MPSI) and Multi-Attributive Border Approximation Area Comparison (MABAC) methods within an Interval-Valued Spherical Fuzzy (IVSF) environment. In the first stage, expert input is collected. The second stage applies IVSF-MPSI to determine the criteria weights under uncertainty. The third stage employs IVSF-MABAC to rank the alternatives based on these weights. The results indicate that Solar Energy, with a distance value of 0.2783, is the most suitable renewable energy, followed by Wind, Hydro, Geothermal, and Biomass. The proposed IVSF-MPSI-MABAC model equips decision-makers with a mathematically rigorous, uncertainty-resilient evaluation framework that supports quantitative trade-off analysis, prioritization of capital-intensive projects, and alignment of renewable energy portfolios with long-term energy security and sustainability objectives, while the integrated sensitivity analysis ensures ranking stability and robustness against variations in decision parameters.

Air transport plays a pivotal role in enhancing economic development by supporting trade, tourism, and regional competitiveness. The growing environmental concerns and social expectations have necessitated the transition towards sustainable air transport systems. Sustainable air transport refers to aviation activities that balance environmental, economic, and social objectives, aiming to minimize carbon emissions, promote renewable energy usage, and enhance socio-economic welfare. In this study, a novel multi-criteria decision-making (MCDM)-based decision support system (DSS) is proposed to evaluate the sustainable air transport performance of the European countries. The main objective of this research is to develop a comprehensive and integrative framework for measuring and ranking the sustainable air transport performance of nations. A hybrid method, termed fractional fuzzy–ranking comparison-response to criteria weighting (RANCOM)–response to criteria weighting (RECA)–ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS), is introduced. DSS consists of five main stages: expert-based subjective weighting using fractional fuzzy RANCOM, objective weighting via RECA, aggregation of weights, and final performance ranking through the RATGOS method. The results indicate that Germany ranks highest, while Cyprus has the lowest sustainable air transport performance among the evaluated countries. The criterion “commercial aircraft fleet by age of aircraft” is determined to have the highest importance among the sustainable air transport performance indicators. The study provides a comprehensive, replicable framework for policymakers and stakeholders aiming to monitor and improve sustainable aviation systems. It contributes to the literature by addressing the gap in national-level sustainable air transport performance evaluation.

Neuromarketing techniques are increasingly employed to measure emotional responses through brainwaves, eye movements, and facial expressions. The primary motivation of this study is to develop a decision support system capable of evaluating promotional video performance levels based on both eye-tracking data and cognitive assessments. The core objective is to propose a hybrid method that simultaneously integrates neuromarketing insights and cognitive evaluations. To this end, a Fermatean fuzzy (FF)−Hamacher−simple weight calculation (SIWEC)−method based on the removal effects of criteria (MEREC)−alternative ranking using two-step logarithmic normalization (ARLON) decision analytics model is developed and implemented. The FF−SIWEC method is employed to determine subjective criterion weights based on expert judgments, while the FF−MEREC method is used to compute objective weights by analyzing both eye-tracking and qualitative evaluations from viewers. Additionally, the Fermatean fuzzy Hamacher weighted aggregation operator ensures precise aggregation of audience evaluations. The FF−ARLON method is applied to obtain final rankings of promotional videos. A real-world case study is conducted to test the applicability of the proposed method, involving six automobile brands and 10 audiences. Eye-tracking analyses are conducted while audiences view the promotional videos, followed by expert and audience evaluations of seven qualitative and six eye-tracking criteria. Among qualitative criteria, “level of emotional impact” is found to be the most significant, while “saccadic direction” emerges as the most important eye-tracking criterion. The promotional video for the Mercedes brand demonstrates the highest overall performance. This study contributes to the literature by proposing a reliable and consistent hybrid model for evaluating promotional video performance.