Energy poverty is a serious problem that increases economic inequalities, especially because individuals living in low-income areas have difficulty accessing energy. The development of renewable energy projects (REP) plays a critical role in reducing energy poverty. However, there is considerable uncertainty in determining strategies that will increase the effectiveness of REP to solve the problem of energy poverty. The purpose of this paper is to identify significant strategies to improve REP for the effective management of energy poverty problems by establishing a novel model. First, dimension reduction methodology is considered to calculate the importance of decision makers. The second stage includes prioritization of criteria using p,q-Spherical fuzzy (SFS) analytic hierarchy process (AHP). The final stage focuses on ranking of renewable energy investment (REI) alternatives using p,q-SFS weighted aggregated sum product assessment (WASPAS). The contribution of this paper to the literature is the determination of critical indicators that will increase the performance of REI to reduce the energy poverty problem with an original and comprehensive decision-making model. Creating a virtual expert is the main superiority of this proposed model. With the help of this issue, it can be possible to reach a sufficient number of experts. Hence, a more diverse and comprehensive evaluation can be conducted. The findings denote that start-up costs and geographical conditions have the highest significance to improve REP for the aim of minimizing energy poverty problem. Rooftop solar panels and micro wind turbines are also found as the most essential REI strategies.

The most critical factors must be determined to effectively manage environmental wastes generated during the extraction of rare elements. Otherwise, businesses may not be able to effectively manage their limited financial and human resources. This situation negatively affects the financial performance of the projects. The limited number of existing studies in the literature causes environmental risks to be insufficiently managed and recycling processes to be unoptimized. This study aims to determine priority strategies to increase the effectiveness of rare element waste management processes. Comprehensive and original decision-making models are created under three different scenarios. Koch Snowflake fuzzy sets, Euclidean based expert weighting and cognitive information modelling and analysis system (CIMAS) approaches are integrated in this model. The main contribution of this study is that a new type of fuzzy numbers called Koch Snowflake fuzzy sets is developed by considering the concept of fractal numbers. Fractal geometry is a powerful tool for modelling complex and dynamic systems. Hence, these new sets provide more flexible and more detailed uncertainty modelling. Moreover, considering different scenarios dynamic strategies can be developed that can adapt to changing conditions, such as pandemics or trade wars. The findings denote that technological developments are determined as the most critical factor under normal conditions. In the scenario where trade wars occur, it is revealed that political and regulatory measures should be addressed as a priority. In the event of a new epidemic disease such as COVID-19, it is concluded that more importance should be given to long-term storage strategies.