An integrated multi-granular distributed linguistic decision support framework for low-carbon tourism attraction evaluation

With the increasing awareness about environmental protection, low-carbon tourism (LCT) is viewed as a new form of sustainable development that can provide greater economic, social and environmental benefits. Evaluating tourism attractions is of great significance for operators of tourism attractions to improve the service quality. Meanwhile, tourists can select the most appropriate LCT scenic spots among the alternatives. To address this decision-making problem, this study develops an integrated multi-criteria group decision-making method within the context of multi-granular linguistic distribution assessments. First, the best-worst method is employed to identify the weights of the criteria. Second, an extended relative entropy-based method that combines a proximity entropy weight and a similarity entropy weight is developed to assign weights to decision-makers in terms of each criterion. Third, an improved multi-granular linguistic distribution ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) is proposed to prioritize LCT attractions. Finally, an illustrative example of LCT attraction evaluation followed by comparative and sensitivity analyses is presented to verify the applicability and effectiveness of the proposed framework.