Artificial Intelligence Integrated Quantum Picture Fuzzy Rough Decision Framework for Biodiversity-Oriented Ecosystem Investment Evaluation

This study identifies key indicators for strategic biodiversity decisions to improve the effectiveness of environmental ecosystem investments. A novel four-stage model is proposed. First, expert weights are determined using an artificial intelligence-based decision-making method. Second, missing evaluations are estimated for the strategic biodiversity decisions in ecosystem investments using an expert recommender system. Third, criteria weights for strategic biodiversity decisions are computed using quantum picture fuzzy rough set-based modified SWARA (M-SWARA). Finally, investment alternatives are ranked with QPFR-VIKOR. The main contribution of this study is the integration of artificial intelligence into fuzzy decision-making analysis to compute expert weights objectively, thereby enhancing the effectiveness and reliability of the proposed model. Results reveal that technological innovation and financial evaluation are the most significant indicators for strategic biodiversity decisions. Restoration projects and eco-friendly infrastructure are identified as the most suitable investment alternatives. The proposed framework provides managers and policymakers with a transparent, data-driven decision support tool for prioritizing technological capability development and financial feasibility analysis in biodiversity-oriented investment planning, contributing to more sustainable and strategically aligned ecosystem management practices.