A multi-criteria decision-making framework for enhancing renewable energy productivity

Productivity plays a critical role for sustainable development of the renewable energy projects. In this process, identifying the most significant indicators is very necessary to use the limited resources more efficiently. This study proposes a novel five-stage decision-making model to generate effective and prioritized strategies for enhancing productivity in renewable energy investments. Firstly, the expert team is selected using Hartigan-Wong algorithm. Secondly, the weights of the experts are computed via dimensionality reduction. In the third stage, the missing opinions of such people are completed with random forest regressor. After that, the importance of the criteria is evaluated by Gaussian fuzzy weighted evaluation of nonlinear subjective logical optimization (WENSLO). Finally, BRICS (Brazil, Russia, India, China and South Africa) countries are examined with respect to the productivity performance of these projects by considering Gaussian fuzzy relative assessment of weighted evaluation criteria (RAWEC). The main contribution of our study is that prior and effective investment strategies can be presented to increase the productivity of the renewable energy projects with a novel model. The use of Gaussian fuzzy sets has a positive influence on handing uncertainty in the analysis process more effectively. In addition to them, owing to Hartigan-Wong algorithm and dimension reduction technique, it can be possible to prioritize the experts. This situation can be very helpful to reach more effective findings. Our findings denote that technological infrastructure and energy storage capacity are the most essential indicators to increase the productivity of renewable energy investments. Moreover, China and Russia are the most successful countries regarding the productivity performance for these investments.