A risk-based decision-making approach for identifying natural-based tourism potential areas

Natural-based tourism (NBT) as a fast-growing and key part of the tourism industry, plays an important role in the economic development of countries. There has been always a degree of risk in making decisions with regard to selecting potential areas. Due to this challenge, the present study is aimed to propose a novel approach to identify NBT potential areas considering different risk degrees in decision-making. To this end, we used 12 geographical and environmental data layers. ordered weighted averaging (OWA) model was used to analyze the levels of risk in decision making, while the weights of criteria were calculated using analytical hierarchy process (AHP). In this study, for the first time, the OWA model is used to consider the concept of risk in decision making and prepare a fit map in different scenarios for decision making by NBT managers and planners. The results indicate that increasing the degree of the optimistic yields larger area of high potential class, while its decline results in smaller high potential class area. The proposed approach can be useful to minimize uncertainty when decision makers and planners are faced with obstacles in allocating resources. Management implications: Integrating geographic information system (GIS) and ordered weighted averaging (OWA) contributes to more suitable decision making. Managers and decision makers can identify suitable areas e. g. by ranking various alternatives. Applying OWA model offers various strategies for decision making so that managers and planners can consider the risk parameter and related trade-offs in their decision making process to enhance a sustainable tourism development in natural areas and a wise resource management. In summary, the proposed approach can be useful to minimize uncertainty when decision makers and planners are faced with obstacles in allocating resources.

Automated summary

This study proposes a new approach to identify potential areas for natural-based tourism, considering different risk degrees. It uses multiple data layers to analyze risk levels and generates decision maps using an ordered weighted averaging model. The results show that the degree of optimism affects the size of high potential class areas.