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Climate change has necessitated rapid transition to renewable energy, particularly in regions dependent on climate-vulnerable hydropower. Current multi-criteria decision-making approaches struggle to optimize subjective knowledge with objective data-driven analysis for effective renewable energy deployment. This study introduces a novel hybrid weighting scheme for solar PV and wind energy siting in Bhutan by integrating subjective and objective-based weights (InW) with Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE II) multi-criteria decision analysis. Multicollinearity among spatial criteria was addressed using recursive variance inflation factors and unsupervised random forest techniques, ensuring statistically independent criteria for geographic information system (GIS)-based spatial suitability mapping. Of 14 initial criteria, 12 were evaluated for solar PV and 10 for wind energy after addressing collinearity. Results identified 6392.54 km 2 and 621.1 km 2 of feasible areas for solar PV and wind energy post-restriction. Very high suitability areas (732.87 km 2 for solar; 51.06 km 2 for wind) are concentrated in western regions, generating 551.295 GWh/y from mono-Silicon PV, while WTP300 wind turbines could generate 382.68 GWh/y. Uncertainty analysis via Monte Carlo Simulation demonstrated stable criterion weights with absolute relative changes below 6% using normal distribution and below 1% for triangular and uniform distributions. Benchmark validation against existing solar PV installations showed the InW-PROMETHEE II framework performed slightly better than purely subjective-PROMETHEE II and substantially outperformed from objective-PROMETHEE II approaches in site identification. This validated framework provides energy planners with a robust decision-support tool for optimizing renewable energy infrastructure placement in complex terrains, directly applicable to hydropower-dependent climate-vulnerable regions.
Gyeltshen et al. (Tue,) studied this question.