Intuitionistic Fuzzy Hierarchical Multi-Criteria Decision Making for Evaluating Performances of Low-Carbon Tourism Scenic Spots

Abstract

Low-carbon tourism is an effective solution to cope with the goal conflict between developing tourist economy and responding to carbon emission reduction and ecological environment protection. Tourism scenic spots are important carriers of tourist activities and play a crucial role in low-carbon tourism. There are multiple factors affecting the low-carbon performance of a tourism scenic spot, and thus the performance evaluation and ranking of low-carbon tourism scenic spots can be framed as a hierarchical multi-criteria decision making (MCDM) problem. This paper develops a novel method to tackle hierarchical MCDM problems, in which the importance preferences of criteria over the decision goal and sub-criteria with respect to the upper-level criterion are provided by linguistic-term-based pairwise comparisons and the assessments of alternatives over each of sub-criteria at the lowest level are furnished by positive interval values. The linguistic-term-based pairwise comparison matrices are converted into intuitionistic fuzzy preference relations and an approach is developed to obtain the global importance weights of the lowest level sub-criteria. A multiplicatively normalized intuitionistic fuzzy decision matrix is established from the interval-value-based assessments of alternatives and a method is proposed to determine the intuitionistic fuzzy value based comprehensive scores of alternatives. A case study is offered to illustrate how to build a performance evaluation index system of low-carbon tourism scenic spots located at Zhejiang Province of China and show the use of the proposed intuitionistic fuzzy hierarchical MCDM method.

Multicriteria Decision Making in Sustainable Tourism and Low-Carbon Tourism Research

“…In contrast, others are created by merging numerous ways to make a relatively novel method. This third category includes the following methods: Principal Components Analysis (PCA) (Işik & Demir, 2017;Michalena et al, 2009); Linguistic multi-attribute group decision making (MAGDM) (Lin & Wang, 2017); Dive Site Risk Assessment Model (DSRAM), which combines AHP, Fuzzy set, and Evidential Reasoning (FER) (Anuar et al, 2020); Intuitionistic fuzzy preference relations (IFPRs) (Yang & Wang, 2020); Intuitionistic multiplicative UTAS-TAR method (IM-UTASTAR) ; and a thermodynamic feature-based method, the q-rung ortho-pair fuzzy set (q-ROFS) . We also highlight the use of some relatively new method combinations from two different articles by the same primary author, which covers Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Higher-Order Singular Value Decomposition (HOSVD), Self-Organizing Map (SOM), and Classification and Regression Tree Analysis (CART) (Nilashi et al, 2019) as well as ANFIS with Support Vector Machine (SVM) and Naïve Bayes, Support Vector Regression (SVR), Neural Network (NN), and Entropy weight method (EWM) (Nilashi et al, 2021); a Pareto-based method -the multi-objective orienteering problem with Time Windows, Restaurant Selection, and Compulsory POIs (MOPTW-RSCP) (Choachaicharoenkul et al, 2022) -and an unnamed method using compensatory and non-compensatory types of aggregation procedures with the Condorcet-and the Borda-type approach used to obtain a composite indicator value (Blancas & Lozano-Oyola, 2022); and lastly, a developed and generalized sustainable evaluation criteria system based on the effect of favourability (Pomucz & Csete, 2015).…”

“…Most studies are expected to result in the development of new techniques (He et al, 2021) that include a combination of quantitative and qualitative approaches (Latinopoulos & Vagiona, 2013) both in adaptations and in replication case studies of tourist destinations' contexts (Jeong et al, 2014). Several articles have mentioned a combination of methods used to plan tour packages with carbon footprint information (Cho et al, 2016), determine safe diving locations (Anuar et al, 2020), and identify environmental governance solutions as well as low-carbon supplier selection (Yang & Wang, 2020).…”

Diamond Model of Green Commitment and Low-Carbon Travel Motivation, Constraint, and Intention

“…These reasons are specifically called travel constraints. Travel constraints are multi-faceted concepts that refer to factors that prevent or reduce the frequency, proportion, or fun of individuals engaging in specific activities [ 16 , 18 , 28 , 29 ]. Ying, Tang, Wen, Ye, Zhou and Li [ 28 ] argued that travel constraints are a key factor in preventing people from starting or continuing to travel.…”

“…Consumers’ green commitments to the environment is considered an important factor for predicting environmentally responsible behavior in traveling (Safari, Salehzadeh, Panahi and Abolghasemian [ 10 ]). Although some studies have been conducted in the field of tourists’ low-carbon travel behavior [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], additional empirical evidence is needed to be gathered before we can be sure of the causal impacts of the results. Especially, there is a need to focus on the mechanisms through which various personal and contextual antecedents influence tourists’ low-carbon travel behavior.…”

Exploration of Social Benefits for Tourism Performing Arts Industrialization in Culture–Tourism Integration Based on Deep Learning and Artificial Intelligence Technology

“…As time goes by, Chinese tourists can no longer be attracted by purely scenic spots. Industry integration, as a more advanced mode for the vigorous development of the industry, has become an inevitable trend (Kim et al, 2019 ; Urien-Lefranc, 2020 ; Yang and Wang, 2020 ). Tourism performing arts is one of the typical forms in culture–tourism integration development in recent years.…”