AI-Supported spherical fuzzy decision-making for barriers to renewable energy projects in hospitals: Comparative country analysis

Abstract The purpose of this study is to determine the most important barriers for the improvements of the renewable energy projects in the hospitals. Within this context, a novel artificial intelligence-based fuzzy decision-making model is created. In the first stage, selected barriers are weighted by using artificial intelligence-based Spherical fuzzy CRITIC methodology. In the next process, emerging seven countries are ranked via Spherical fuzzy MAIRCA. An important novelty of the study is the integration of the CRITIC and MAIRCA methodologies with artificial intelligence. Owing to this situation, the weights of experts can be identified based on their qualification. This situation contributes to a more accurate analysis. The findings demonstrate that the most important factor in clean energy projects is operating costs. Similarly, technology and operational infrastructure factor also has an important impact on this situation. On the other side, the ranking results show that the most successful countries in clean energy projects in hospitals are Russia and China. India and Mexico are the last ranks in this regard. To increase the efficiency of projects, systems and equipment need to be analyzed regularly. In this context, the use of current technologies for renewable energy applications allows efficiency to be increased.