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The aim of this study is to generate appropriate strategies to improve renewable energy investments. Within this framework, a novel model has also been proposed which includes three different stages. Firstly, incomplete preferences of the relation matrixes are calculated. For this purpose, 4 different decision makers evaluate the balanced scorecard-based criteria. In this stage, missing values are estimated by incomplete preferences to complete the relation matrixes. Additionally, the second stage includes the computing the fuzzy preferences by considering the consensus-based group decision-making (CGDM). The final stage is related to the calculation of the weights of the criteria by considering Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL) methodology. Hence, the main motivation of this study is to identify innovative strategies for the renewable energy investments with a novel multi-criteria decision-making (MCDM) model based on incomplete preferences, CGDM and Pythagorean fuzzy sets. The findings indicate that learning and growth is the most important balanced scorecard-based perspective to improve the performance of renewable energy investments. Additionally, the perspective of internal process is identified as another significant factor for this situation. The biggest problem in renewable energy projects is their high initial costs. Hence, technological developments reduce the production costs of renewable energy sources. Additionally, it is also possible to increase the amount of electricity from renewable energy sources owing to the innovative technologies. Thus, renewable energy investors should follow up-to-date technological developments so that it will be possible to reduce the cost of renewable energy investments.

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An Extended Pythagorean Fuzzy Approach to Group Decision-Making With Incomplete Preferences for Analyzing Balanced Scorecard-Based Renewable Energy Investments