Research Information System
Renewable Energy, vol.256, 2026 (SCI-Expanded, Scopus)
Improving solar panel recycling processes increases energy efficiency while minimizing negative environmental impacts. Recycling processes often require high-cost investments and complex processes. It is critical to determine which criteria and investment areas are prioritized for the most efficient use of these limited resources. The purpose of this study is to generate effective investment strategies to improve the performance of the recycling processes in solar panel projects by establishing a new model. Firstly, more significant indicators are selected by classifying with zero-shot learning (ZSL) analysis. Secondly, expert weights are calculated via machine learning approach. Thirdly, selected indicators are evaluated with p, q-quasirung orthopair fuzzy sets (p, q-QROFS)-based entropy. Fourthly, investment alternatives are ranked by p, q-QROFS simple additive weighting (SAW) technique. Finally, a comparative examination is conducted for alternative ranking with the help of technique for order preference by similarity to ideal solution (TOPSIS) methodology. The main contribution of this study is the identification of the effective investment strategies for the improvements of recycling processes in solar panel projects by establishing a novel decision-making model. The main superiority of this proposed model is the integration of ZSL to the fuzzy decision-making modelling by classifying the criteria. Reducing the number of criteria with this technique minimizes uncertainties in the analysis process. It is concluded that improving recycling technologies and providing incentives and support are the most essential items. Moreover, China and Russia are the most successful countries with respect to the recycling performance of solar panel projects.