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Evaluating the green growth indicators to achieve sustainable development: A novel extended interval‐valued intuitionistic fuzzy‐combined compromise solution approach
Author(s) -
Alrasheedi Melfi,
Mardani Abbas,
Mishra Arunodaya R.,
Streimikiene Dalia,
Liao Huchang,
Alnefaie Abdullah H.
Publication year - 2020
Publication title -
sustainable development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.115
H-Index - 64
eISSN - 1099-1719
pISSN - 0968-0802
DOI - 10.1002/sd.2136
Subject(s) - compromise , ranking (information retrieval) , computer science , sustainable development , fuzzy logic , rank (graph theory) , multiple criteria decision analysis , operations research , set (abstract data type) , management science , mathematics , environmental economics , artificial intelligence , economics , ecology , social science , combinatorics , sociology , biology , programming language
Recent years have witnessed the green growth as a renowned efficient approach to tracking the progress towards sustainable development. The paper is mainly aimed to propose an innovative extended approach based on Combined Compromise Solution (CoCoSo) and Interval‐Valued Intuitionistic Fuzzy Set (IVIFS); thus, it is called IVIF Combined Compromise Solution (IVIF‐CoCoSo). This study is the first effort that used the fuzzy sets theory and decision‐making technique (entitled IVIF‐CoCoSo) to rank and evaluate the indicators of green growth to sustainable manufacturing in the manufacturing sector. To reach the study objectives, a total of five indicators and 30 sub‐indicators for evaluation of green growth are collected from the existing literature review. Subsequently, this study has been extended using the IVIF‐CoCoSo approach for ranking, modeling, and evaluating the green growth indicators into sustainable manufacturing based on experts' opinions. The results of this study showed that the environmental and resource productivity of the economy indicators is more significant than the other indicators in the manufacturing sector. Moreover, findings confirmed the accuracy of the proposed method in solving the green growth problem, and comparison was also made between the results of the proposed method and some other Multi‐Criteria Decision Making (MCDM) methods.