An integrated SWARA-CODAS decision-making algorithm with spherical fuzzy information for clean energy barriers evaluation

Abstract

Clean energy has various environmental and economic benefits, including reducing air pollution. Also, due to its characteristics, it can be more compatible with the environment than fossil fuels. Industry leaders believe that high costs, technological shortcomings, and constraints on transmission infrastructure are hampering the growth of clean energy. In this paper, a new approach is presented to cover some of the drawbacks of the Failure Mode and Effects Analysis (FMEA) technique. The presented approach addresses the shortcomings of the FMEA method and applies complete prioritization for organizations. In this paper, a developed approach based on multi-criteria decision-making (MCDM) methods in a Spherical fuzzy set (SFS) to assess barriers to clean energy development has been stated. Three main criteria, severity, detection, and occurrence, are used to assess clean energy barriers and are weighed using the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on SFS. In the next phase, clean energy development barriers in five categories: technical, economic, social and cultural, administrative, legal, and political, are prioritized using the Combinative Distance-based Assessment (CODAS) method in the SFS environment, which reduces uncertainty. The results show that incomplete markets and consecutive changes in the administrative system are critical barriers. Finally, to confirm the effectiveness of the results, the sensitivity analysis was performed with other common methods. The results show the performance of the developed approach and its ranking stability. Also, the results of the MOORA, COPRAS and CODAS methods were compared and CODAS was superior to the other methods with a correlation of 0.981.