Search

• The score function is proposed for hesitant Fermatean fuzzy set. • Unify the framework of Regional Green Development Level Evaluation. • Improve MULTIMOORA method is proposed with hesitant Fermatean fuzzy information. • Level Difference Maximization Method to combine subjective and objective weights. • Case study of RGDL is presented to show the potentiality of the developed method. Economic green development has received increase attention. However, the uncertainty decision processes in the development of green economic growth has brought challenges to the evaluation, which leads to the evaluation index system has not yet reached a consensus. In order to solve the above problems, this paper, based on the established and developed regional green development level evaluation (RGDLE) system in existing literature, employed improved MULTIMOORA group decision model under the hesitant fuzzy Fermatean environment (HFFS-MULTIMOORA) and an innovative weighting method to develop the RGDLE process. This evaluation framework describes the complexity of the RGDLE process. The Level Difference Maximization Method to combine the average-based subjective weight-determining method and the Shannon-entropy-based objective weight-determining method is developed to fit RGDLE problems with cognitive complex information. This innovative index system and HFFS-MULTIMOORA method based group decision-making framework is further applied to evaluate the green development level of five provinces and municipalities along the Yangtze River Economic Zone, China. Results showed the optimal green development levels ranking of the five alternative provinces and municipalities of the Yangtze River Economic Zone, China. Moreover, the effectiveness and feasibility of the model framework was verified by robustness and comparative analysis between the existing method. This paper contributes not only to the literature on RGDLE but also to giving decision-makers and researchers inside and outside China valuable inspiration and references for improving the evaluation efficiency and giving policy suggestions. [ABSTRACT FROM AUTHOR]