Linguistic Multi-Attribute Group Decision Making with Risk Preferences and Its Use in Low-Carbon Tourism Destination...

Area of Science:

• Environmental Science
• Tourism Management
• Operations Research

Background:

• Low-carbon tourism is crucial for emission reduction and environmental protection.
• Selecting sustainable destinations involves complex decisions with multiple criteria.
• Existing models often struggle with linguistic data and incomplete attribute weights.

Purpose of the Study:

• To develop a novel framework for multi-attribute group decision-making (MAGDM) in low-carbon tourism destination selection.
• To handle linguistic terms for attribute evaluations and incomplete attribute weight information.
• To provide a practical methodology for real-world sustainable tourism planning.

Main Methods:

• Established a nonlinear programming model to capture group risk preferences from individual linguistic terms.
• Converted linguistic decision matrices to triangular fuzzy decision matrices.
• Aggregated individual matrices into a group triangular fuzzy decision matrix.
• Developed a linear programming model to determine optimal attribute weights with incomplete information.

Main Results:

• Successfully devised a procedure for linguistic multi-attribute group decision-making.
• Demonstrated the framework's applicability through a low-carbon tourism destination selection case study.
• Provided a robust method for handling subjective evaluations and uncertain weights.

Conclusions:

• The developed framework effectively addresses complex decision-making in low-carbon tourism.
• The methodology offers a practical tool for selecting sustainable tourism destinations.
• This research contributes to advancing decision support systems in environmental management and tourism.