Ecotourism is a form of responsible travel that seeks to minimize the negative impacts of tourism on the environment and local communities. It focuses on protecting natural areas and promoting sustainable practices, while also contributing to the economic development of host communities. Ecotourism often involves activities such as nature walks, wildlife viewing, and cultural experiences that promote environmental awareness and appreciation. In this article, we examine how to evaluate the potential of sites and activities for ecotourism, assess its impacts, and explore the financing landscape for ecotourism.
Evaluating Potential for Ecotourism
The multi-criteria evaluation (MCE) method is the most widely used approach for assessing a site’s potential for ecotourism. It involves a set of criteria, each with corresponding subcriteria, which can be further broken down into various indicators. The selection of criteria and indicators depends on the specific ecological and socio-economic characteristics of the site being evaluated.
Ghahroudi Taliet al. (2012) presents an example of using MCE for ecotourism site selection in the Miankaleh Peninsula. The study considers three types of criteria: environmental, economic, and geodata. The environmental criteria aim to minimize the negative impact of visitors on the environment, while the economic criteria focus on enhancing visitor satisfaction and reducing construction costs. Geodata criteria ensure that the sites meet the necessary structural and building standards. These criteria were determined through a survey of experts, including professors and environmental organization practitioners, who provided input on their preferences.
The geodata criteria include geology, soil, geomorphology, elevation, and slope. Environmental criteria cover vegetation, wildlife, security zones, dune vulnerability, and climate. Economic criteria address land use (e.g., low dense forest, dense rangeland, agricultural land, fisheries units), proximity to the shoreline, tourist attractions, corrals, municipal services, existing buildings, and road accessibility. The weights for these criteria were determined using the Analytic Hierarchy Process (AHP). Once the weights were assigned, the criteria were integrated using ArcGIS Spatial Analysis to create a final suitability map, identifying the most suitable sites for ecotourism development.
Similarly, Withanage et al. (2024) employs Geographic Information Systems (GIS) and MCE to develop an ecotourism suitability index for the Anuradhapura area. The study identified six main categories of factors contributing to ecotourism potential. These include landscape, which considers the visual and aesthetic appeal; topography, which assesses physical features like elevation and slope; and accessibility. Climate is also significant, as it influences tourist comfort and available activities throughout the year. The presence and diversity of flora and fauna fall under the forest and wildlife category, while negative factors—such as pollution and land-use conflicts—could detract from the area’s suitability for ecotourism. These factors were combined to create a comprehensive ecotourism suitability index.
The first step in the MCE framework often involves gathering expert insights on the selection of criteria and indicators. Various methods are used to organize and aggregate these opinions. Ocampo et al. (2018) highlights the use of the Fuzzy Delphi Method (FDM) for indicator selection within the MCE framework in the Philippines. FDM refines the traditional Delphi Method by applying fuzzy set theory, which helps to manage uncertainties in expert judgments. In the Delphi Method, experts provide their opinions in multiple rounds, gradually refining their responses to reach a consensus. FDM improves this process by allowing experts to express their opinions in degrees, rather than fixed values, through fuzzy numbers.
An Example of the Application of FDM
Suppose a group of experts is evaluating the importance of ‘Accessibility’ as a criterion for ecotourism site suitability. Instead of providing a single, fixed value (e.g., a rating of 8 out of 10), the Fuzzy Delphi Method (FDM) allows experts to express their opinions as fuzzy numbers, capturing uncertainty and varying degrees of preference. In this case, three experts rate the importance of ‘Accessibility’ on a scale of 0 to 10 using Triangular Fuzzy Numbers (TFN). A fuzzy number consists of three components: the minimum value (low), the most likely value (medium), and the maximum value (high), as shown below:
- Expert 1: (7, 8, 9) → Meaning: “Accessibility is likely to be rated around 8, but it could be as low as 7 or as high as 9.”
- Expert 2: (6, 7, 8) → Meaning: “Accessibility is likely around 7, but it could vary between 6 and 8.”
- Expert 3: (8, 9, 10) → Meaning: “Accessibility is likely around 9, but it could be as low as 8 or as high as 10.”
Next, we calculate the group consensus by averaging the three experts’ fuzzy numbers. This is done by taking the average of the low (minimum), medium (most likely), and high (maximum) values from all experts, resulting in the average values (7, 8, 9).
To convert (defuzzify) these fuzzy results into a single crisp value for decision-making, we apply the centroid method, a common defuzzification technique in FDM. In this case, the crisp value is calculated as x_crisp = (7+8+9)/3 = 8 for the triangular fuzzy number. This indicates that the crisp value for the importance of “Accessibility” is 8.
FDM effectively captures the inherent uncertainty in expert assessments and provides a more nuanced analysis.
The results from the FDM can also be used to determine the weights for each criterion (or factor) in a multi-criteria decision-making process.
Enhancing MCE with additional methods can lead to more optimized results. In Akbari et al. (2023), the MCE approach is integrated with the NSGA-II (Non-Dominated Sorting Genetic Algorithm II) to improve ecotourism zoning. The study evaluates three main categories—physical, biological, and socio-economic—further broken down into 13 sub-criteria and 33 indicators, all chosen based on previous research and field visits. The NSGA-II algorithm optimizes the process by generating non-dominated solutions, balancing multiple conflicting objectives such as ecological conservation, economic benefits, and land suitability. This approach improves decision-making by identifying the best trade-offs.
To be continued in Part 2
References:
Akbari, R., Pourmanafi, S., Soffianian, A. R., Galalizadeh, S., & Khodakarami, L. (2023). Enhancing ecotourism site suitability assessment using multi-criteria evaluation and NSGA-II. Environment Development and Sustainability. https://doi.org/10.1007/s10668-023-03835-4
Ghahroudi Tali, M., Sadough, S. H., Nezammahalleh, M. A., & Nezammahalleh, S. K. (2012). Multi-criteria evaluation to select sites for ecotourism facilities: a case study Miankaleh Peninsula. Anatolia, 23(3), 373–394. https://doi.org/10.1080/13032917.2012.712872
Ocampo, L., Ebisa, J. A., Ombe, J., & Geen Escoto, M. (2018). Sustainable ecotourism indicators with fuzzy Delphi method – A Philippine perspective. Ecological Indicators, 93, 874–888. https://doi.org/10.1016/j.ecolind.2018.05.060
Withanage, N. C., Wijesinghe, D. C., Mishra, P. K., Abdelrahman, K., Mishra, V., & Fnais, M. S. (2024). An ecotourism suitability index for a world heritage city using GIS-multi criteria decision analysis techniques. Heliyon, 10(11), e31585. https://doi.org/10.1016/j.heliyon.2024.e31585
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