A decision framework with q-rung fuzzy preferences for ranking barriers affecting clean energy utilization within healthcare industry

In this article, we aim to rank barriers hindering clean energy adoption within the healthcare industry by proposing a new framework with q-rung orthopair fuzzy data (q-ROFD). Energy is paramount in health industry, and it is estimated by the World Health Organization that nearly a billion people are treated globally with limited/no electricity. United Nation strongly recommends cutting dependencies on fossil fuels, but to meet demand, clean energy is focused. Studies on clean energies reveal that direct adoption is tough, owing to diverse barriers and ranking these barriers will provide policymakers clarity on the strategic plans. Existing studies reveal gaps in uncertainty modeling by not adequately exploring orthopair variants, human intervention reduction by failing to methodically determine diverse decision parameters, consideration of subjective attitude and interactions among entities that are essential for experts and attributes, and accounting for attribute type and yielding ranks comparable with a human decision. Motivated by the gaps, in this article, a combined q-ROFD model is presented where weights of attributes are determined via criteria importance through intercriteria correlation and rank sum and experts’ weights are obtained by rank sum. A ranking algorithm is developed with CODAS formulation for determining the barriers’ grades with risk aversion trait. The significance of the study lies in rational ranking of barriers, reduced human intervention, and methodical determination of decision parameters. The usefulness of the model is testified via a case study of barrier ranking within the Indian healthcare industry and comparison/sensitivity studies reveal the pros and cons of the developed model.