Electric vehicles are of great significance in supporting sustainable transportation and sustainability. In parallel with the increasing demand for such vehicles worldwide, the electric vehicle charging stations (EVCSs) market has grown dramatically. The study presents a practical model for selecting EVCS sites integrating multi-criteria decision-making (MCDM), fuzzy, and rough sets. The research aims to bridge the gap in evaluating EVCS locations by leveraging the superiorities of fuzzy and rough set theories to address vagueness effectively. Firstly, assessment criteria cover the environment, economic, technology, and social drivers. Secondly, a fuzzy Defining Interrelationships Between Ranked criteria (F-DIBR) model is applied to determine the weight values of siting factors. Last, for the first time, the Mixed Aggregation by COmprehensive Normalization Technique (MACONT) with hybrid fuzzy rough numbers (FRN-MACONT) model is proposed to obtain the ranking results. Further, a new approach for defining hybrid fuzzy rough numbers is suggested, based on an improved methodology for determining rough numbers' lower and upper limits, allowing consideration of mutual relations between a set of objects and flexible representation of rough boundary intervals depending on the dynamic environmental conditions. The study's novelties reside in deciding the importance of the driving forces used in determining the EVCS site location with a novel method, F-DIBR, and selecting the optimal site with a new FRN-MACONT approach. The results show that “economy” is the most significant criterion, whereas “system reliability” is the most critical sub-criterion. The findings also indicate that the Konak territory performs the best, whereas the Cigli territory is the second best. Comprehensive sensitivity analysis verifies the proposed framework's validity, robustness, and effectiveness. As per the research findings and analyses, some managerial implications are further discussed. The approach introduced has the potential to contribute to the green transport literature.

In order to achieve sustainable energy consumption and development goals, it is of great importance to find suitable locations for the construction of solar power plants. In this study, Geographic Information System (GIS) and Z-Number iteration of Fuzzy Logarithm Additive Weights Methodology (F-LMAW), a recently adopted Multi-Criteria Decision Making Analysis (MCDA) technique, are used to identify the best locations for solar power plant construction in Mersin province. Nineteen criteria were selected for the study and their relative weights and usefulness in ranking the solar power plant locations were estimated. The Weighted Linear Combination (WLC) technique was used to determine the suitability index for solar power plant siting in the study area. According to the analysis made by taking into account the expert opinions for the site selection of solar power plants, the solar radiation criterion was the most important criterion with a weight value of 0,0664, while the distance from the river criterion was the least important criterion with a weight value of 0,0265. A potential suitability map for the solar power plant was produced with the suitability index values. According to the suitability index values, the study area exhibited suitability degrees for solar power plant siting ranging from “suitable (0,0038 %)” to “moderately suitable (0,0034 %)” and “very slightly suitable (0,0033 %)”. Silifke and Mut regions are considered as good locations for solar power plants in Mersin province. The robustness of the proposed technique was determined by sensitivity analysis.

This paper explores the evolution, applications, and prospective developments of a very popular multi-criteria decision-making (MCDM) method called Measurement of Alternatives and Ranking according to COmpromise Solution Method (MARCOS). Employing an extensive bibliometric analysis, the study examines 115 pertinent papers sourced from the Scopus database spanning over the years from 2020 to 2024. This study also provides an evaluation of the methodological significance and outlines potential future directions of MARCOS method. The outcomes indicate "Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)" by Stević et al. (2020) as the most cited paper. Journals such as "Sustainability (Switzerland)", "Mathematics" and "Expert Systems with Applications" stand out among the most cited journals. "University of East Sarajevo" is an institution distinguished for its prolific research in this field. "Stević Ž." Has been identified as the most cited and published author. The most frequently used keywords are "MARCOS", "MARCOS method", and "MCDM". CRiteria Importance Through Intercriteria. Correlation (CRITIC) method is a weighting model often integrated with MARCOS method. The results of the study provide researchers and practitioners in the field of MCDM with an important insight into the current state of the MARCOS methodology, highlighted studies and potential future developments. It also provides a comprehensive overview of the importance of this method in the multi-criteria decision-making literature, shedding light on future research directions.

Storing hydrogen resources underground can accelerate the transition to renewable energy, facilitate energy supply security, and the adoption and expansion of hydrogen energy, a clean energy source. The selection of sustainable underground hydrogen storage systems is a critical research topic for addressing environmental issues caused using fossil fuels. However, decision-makers still lack a consensus-based and sustainability-oriented framework that can comparatively evaluate alternative underground hydrogen storage geological formations under economic, environmental, social, and technical uncertainties, which constitutes a critical barrier to large-scale hydrogen deployment. This issue has become more prominent as fossil-based fuel reserves are gradually decreasing worldwide. In contrast, researchers and practitioners lack a consensus on which underground storage method is most suitable for economical, safe, and efficient hydrogen storage. If this problem is not addressed correctly and reasonable solutions are not obtained, continued dependence on fossil fuels may persist. Alternatively, other renewable energy sources with relatively lower efficiency and performance may be adopted. In both cases, significant delays in achieving the global sustainability goal are likely to occur. We propose an integrated fuzzy decision-making framework (F-WENSLO & Dombi-Bonferroni & F-ARTASI) to address this selection problem under uncertainty. The proposed framework integrates fuzzy WENSLO (Weights by ENvelope and SLOpe) for robust sustainability-based criteria weighting, the Dombi–Bonferroni aggregation operator to model interdependencies among criteria explicitly, and the fuzzy ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) method to provide flexible and stable ranking of geological alternatives beyond rigid distance-based approaches. Key advantages of the proposed model include producing reliable and consistent solutions that accurately reflect real-world conditions for selecting sustainable underground hydrogen storage systems. The results revealed that C14 (job creation and employment opportunities) (0.0603) is the most influential criterion in selecting the most suitable storage system. In addition, salt caverns with an Ωi of 10,5167 have achieved the highest score, placing them in the first position, and it is the most suitable and advantageous underground hydrogen storage option. The suggested decision-making tool can yield reliable and robust solutions in real-world conditions, enabling the planning of infrastructure design for hydrogen energy systems that incorporate sustainability dimensions. In that regard, the developed model possesses the characteristics of an efficient and practical roadmap that can guide policymakers and decision-makers in transitioning from fossil-based energy sources to renewable energy sources. It has been implemented to evaluate underground geological formations that could facilitate the storage of hydrogen energy underground, serving as a case study. The reliability and robustness of this tool have been verified through extensive validation tests.