A novel fuzzy decision-making approach to pension fund investments in renewable energy

Abstract

Pension fund must consider some significant issues when making renewable energy project investment decisions. It is necessary to determine the most important factors and prioritize the indicators. Accordingly, the purpose of this study is to conduct a priority analysis of the determinants of investment in renewable energy projects by pension funds. This study constructs a novel fuzzy decision-making model. First, five indicators for this process are weighted using an entropy methodology based on sine trigonometric Pythagorean fuzzy sets. The CRITIC methodology is also considered to make a comparative evaluation. Second, five different clean energy investment alternatives for pension funds are ranked using the RATGOS methodology. Similarly, this ranking analysis is also made by considering TOPSIS technique to check the reliability of the results. The main contribution of this study is the creation of a new and comprehensive fuzzy decision-making model to identify the most important factors in renewable energy project investments for pension funds. The proposed model uses the RATGOS technique to rank clean energy investment alternatives for pension funds. By considering the geometrical mean in the RATGOS calculation process, criticisms related to existing ranking techniques can be overcome. The use of sine trigonometric Pythagorean fuzzy numbers provides significant benefits to the quality of the proposed decision-making model. The defuzzification process can be implemented appropriately using these sets. Therefore, this study’s findings pave the way for investors to make investment decisions under these circumstances. It is concluded that the most important criterion is risk minimization. Effective regulations are another critical issue. Furthermore, the ranking results indicate that the most suitable renewable energy alternative is green bonds. The comparative results with STPFY-TOPSIS show that the proposed model generates coherent and reliable findings.