Given the increasing threat of climate change, global warming, and depletion of fossil fuels, the adoption of renewable energy technologies (RETs) has become a crucial issue. Nevertheless, selecting the most appropriate technology is crucial. In this sense, developing decision support mechanisms for the identification of appropriate energy resources is one of the core decision-making challenges in the energy industry. In this paper, we address some renewable energy options for electricity generation in Tunisia namely solar photovoltaics (PV), concentrated solar power (solar CSP), onshore wind, and biomass, taking into account technical, economic, environmental, and social dimensions. This study employed a hybrid multi-criteria decision-making approach, combining CRiteria Importance Through Inter-criteria Correlation (CRITIC) and Evaluation based on Distance from Average Solution (EDAS), The considered alternative RETs were ranked and prioritized according to the proposed model. The results indicate that solar PV is the most promising renewable technology for Tunisia. Biomass is the least viable option. Validation through Monte Carlo simulation (MCS) largely corroborated these results, consistently placing solar PV first, followed by onshore wind. The only minor difference observed was an exchange in ranking between biomass and solar CSP. A sensitivity analysis was conducted to validate the outcomes and demonstrate the effect of changing the input data on the final results, revealing the considerable influence of cost-related factors on nearly all renewable energy technologies. Key differences in sensitivity were apparent, with Solar PV being more sensitive to electricity cost than capital cost, whereas solar CSP showed equal criticality to both. These distinctions imply that decision-making and risk prioritization must be tailored to each specific RET, prioritizing R&D for cost reduction and economies of scale for highly cost-sensitive technologies, and technological innovation and operational optimization for those sensitive to efficiency.
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