• Energy Research

This study aims to evaluate the innovation adoption performance of the renewable energy alternatives. Within this context, a new model is created that consists of two different stages. Firstly, the decision combinations of innovation adoption life cycle patterns are identified by considering integer code series. On the other side, in the second stage, the innovation adoption life cycle performance of the renewable energy alternatives is ranked. In this framework, interval type-2 (IT2) fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methodology is taken into consideration based on alpha cut levels. Moreover, a comparative evaluation is also conducted by considering IT2 fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). It is identified that the analysis results are quite coherent and reliable. The findings demonstrate that solar energy is the most appropriate renewable energy type to make innovation. Additionally, wind and geothermal energies are also other significant renewable energy types that have a great innovation potential. However, it is also concluded that biomass and hydroelectric energy have lower importance in comparison with the others. Another important point is that the ranking results are quite similar for different alpha cuts. This condition indicates that the analysis results are coherent and reliable. It is obvious that making investment to solar energy alternatives provides opportunity to adopt technologic innovation more easily. Therefore, it is recommended that investors should give priority to solar energy projects so that it can be more possible to increase the efficiency and the profitability of the investments.

Abstract The aim of this study is to identify significant risks for the renewable energy investment projects. In this regard, a novel hybrid multi-criteria decision-making (MCDM) model has been proposed based on hesitant linguistic terms and alpha level sets. This model has three different phases. Firstly, the dimensions and criteria are defined regarding the renewable energy investment risks. In the second part, the significance levels of these factors are calculated with the help of hesitant interval type-2 fuzzy (IT2F) DEMATEL-based ANP (DANP). Finally, the types of investors by the risk awareness entitled high, moderate, and low are ranked while using hesitant IT2F QUALIFLEX. In all analysis, alpha level sets are taken into consideration. The results demonstrate that organizational effectiveness and cost efficiency are the most prominent factors for renewable energy investment. On the other side, moderate risk should be taken in these projects. It is recommended that the departments within the company should be compatible with each other for the renewable energy investment projects to be more successful. For this purpose, necessary actions should be taken to improve communication between departments. Another important point is that cost control should be done effectively to contribute to the performance of the renewable energy investment projects.