A hybrid weighting method with a new score function for analyzing investment priorities in renewable energy

The transition to renewable energy sources is a critical challenge that emerging economies are facing today. Investment decisions related to the renewable energy transition involve multiple criteria and objectives that need to be analyzed and evaluated before making informed choices. This study develops a score function, the partitioned dual Muirhead mean (PDMM), the q-rung orthopair fuzzy weighted PDMM (q-ROFWPDMM), and a hybrid weighting technique based on Shannon entropy and decision-making trial and evaluation laboratory (DEMATEL) methods for q-rung orthopair fuzzy information. Additionally, a new multi-criteria group decision-making (MCGDM) technique is constructed using these developed approaches. This approach is applied to analyze the investment priorities of the renewable energy transition for emerging economies (E7 countries). It considers twelve criteria in four dimensions: (i) regulations to increase the renewable potential; (ii) supply of renewable materials; (iii) encouragement of private sector investors in the renewable industry; and (iv) awareness of renewable energy consumption. The results of the hybrid weight finding technique indicate that the “reducing market risk” criterion is the most important for investment priorities. The application of the novel MCGDM technique reveals compelling results, indicating that China has emerged as the frontrunner in effectively bolstering renewable energy investment projects. Moreover, a sensitivity analysis is presented, followed by a comparative analysis in which four conventional and three partition based aggregation operators are considered to show the effectiveness of the developed approach. The proposed approach provides a comprehensive analysis of investment priorities for the renewable energy transition, aiding decision-makers in making informed investment choices.University Grants Commissio