An intuitionistic fuzzy analytics approach for examining factors in decentralized renewable energy adoption in emerging economies

In many developing countries, heavy reliance on centralized, non-renewable energy sources poses threats to both energy security and the environment. However, there is still a lack of structured evidence on which factors should be prioritized to accelerate the adoption of decentralized renewable energy systems (DRES) in emerging economies such as Bangladesh. This study intends to determine, rank, and examine the factors affecting the adoption of DRES and extends fuzzy MCDM applications by integrating Interval Valued Type 2 Intuitionistic Fuzzy (IVT2IF) with Weighted Influence Non-linear Gauge System (WINGS). The integrated approach provides a way to handle specialist judgments under uncertainty and to reveal both the strength and the direction of relationships among the factors. A total of 15 factors were chosen after a comprehensive review of the existing literature and subsequent specialist validation. The analysis reveals that “Dependability and economic feasibility,” “Supportive policy and regulatory frameworks,” and “Community empowerment and local governance” are the most critical factors, with total engagement values of 0.1587, 0.1585, and 0.1520, respectively. The causal ranking further indicates that “Accessibility of renewable resources” and “Supportive policy and regulatory frameworks” are the most influential factors within the cause group, with total positional values of 0.0047 and 0.0035. The findings suggest that strengthening policy design, improving economic conditions for projects, and empowering local communities are central levers for accelerating DRES deployment. The outcomes provide a practical decision support basis for stakeholders and policymakers who seek to prioritize interventions and enhance the sustainability and resilience of the energy sectors of emerging economies. • Identify factors that influence decentralized renewable energy adoption in emerging economies. • Apply intuitionistic fuzzy analytics with decision-making to rank adoption drivers. • Reveal dependability and economic feasibility as the most critical factors. • Provide decision support for policymakers to enhance energy sustainability. • Offer guidance to improve resilience in emerging economies’ energy systems.