A GCC Evidence-Calibrated Nonlinear Decision Framework for Photovoltaic Technology Selection Under Coupled Desert Environmental Stress

Photovoltaic technology selection in Gulf Cooperation Council (GCC) desert environments is affected by coupled dust, thermal, ultraviolet (UV), humidity, and salinity stresses, which are not fully represented by static weighting and additive multi-criteria decision-making models. This study develops a GCC evidence-calibrated nonlinear decision-support framework that integrates published literature-derived GCC/desert-stress calibration, adaptive hybrid entropy–desert weighting, and bipolar fuzzy Einstein aggregation. The framework is applied to compare passivated emitter and rear cell (PERC), tunnel oxide passivated contact (TOPCon), and heterojunction technology (HJT) photovoltaic technologies using calibrated evidence from Qatar, the United Arab Emirates, Saudi Arabia, and Oman. The results show that dust tolerance receives the highest final hybrid weight (0.258), followed by thermal resistance (0.228), UV resistance (0.207), efficiency (0.173), and cost effectiveness (0.134). The nonlinear Einstein aggregation ranks HJT first (0.889), followed by TOPCon (0.861) and PERC (0.742). Benchmark comparison with TOPSIS, VIKOR, and PROMETHEE II shows high rank agreement, while Monte Carlo perturbation analysis indicates that HJT preserves the first rank in 93% of perturbation runs. The proposed framework links PV technology selection with published GCC desert-stress evidence and provides a reproducible basis for technology prioritization in harsh solar energy deployment environments. A stress-to-decision translation table is also provided to clarify how desert degradation mechanisms are converted into decision criteria and reusable selection guidance.