| Title |
Solving barrier ranking in clean energy adoption: An MCDM approach with q-rung orthopair fuzzy preferences |
| ID_Doc |
21694 |
| Authors |
Krishankumar, R; Pamucar, D |
| Title |
Solving barrier ranking in clean energy adoption: An MCDM approach with q-rung orthopair fuzzy preferences |
| Year |
2023 |
| Published |
International Journal Of Knowledge-Based And Intelligent Engineering Systems, 27.0, 1 |
| DOI |
10.3233/KES-230048 |
| Abstract |
With a growing focus from the United Nations to eradicate the ill effects of climate change, countries around the world are transforming to green and sustainable habits/practices. Adoption of clean energy for demand satisfaction is a prime focus of many countries as it reduces carbon trace and promotes global development. In developing countries like India, there is an urge for sustainable global development. Literature shows that direct and complete adoption of clean energy incurs some barriers, which impede the sustainable development of the nation. Grading such barriers supports policymakers to effectively plan strategies, which motivates authors to put forward a novel decision model with integrated approaches. First, qualitative rating data on barriers and circular economy (CE) factors are collected from experts via questionnaires, which are transformed into q-rung orthopair fuzzy information (qRFI). Second, the weights of experts and CE factors are determined by the proposed variance measure and CRITIC. Third, barriers are graded by the proposed ranking algorithm that considers modified WAPAS formulation. Finally, these approaches are integrated into a model that is testified for practicality by using a case example from India. Sensitivity and comparative analyses are performed to realize the merits and limitations of the model for extant works. |
| Author Keywords |
Clean energy; sustainability barriers; evidence measure; CoCoSo method; q-Rung orthopair fuzzy; circular economy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001029119100003 |
| WoS Category |
Computer Science, Artificial Intelligence |
| Research Area |
Computer Science |
| PDF |
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