Optimization of Sustainable Tourism Systems: A Fuzzy Com-prehensive Evaluation Framework

Abstract

Juneau, Alaska, with a population of about 30,000, faced significant tourism pressure in 2023, hosting 1.6 million cruise passengers. While tourism brings substantial revenue, it also causes overcrowding and environmental damage, especially to the receding Mendenhall Glacier. This paper focuses on optimizing sustainable tourism in Juneau, using Fuzzy Comprehensive Evaluation (FCE), Entropy Weight Method (EWM), and Grey Relational Analysis (GRA) to address these challenges. The ARIMA model is applied to forecast future tourism trends, and the model is tested in Barcelona with sensitivity analysis to assess its adaptability and scalability.

First of all, the Fuzzy Comprehensive Evaluation (FCE) method is used to address the multi-level decision-making challenges in sustainable tourism development. By setting evaluation criteria and conducting data analysis, we comprehensively consider various influencing factors to derive accurate sustainability assessment results. Next, the Entropy Weight Method (EWM) is applied to automatically calculate the weights of each indicator based on their information contribution. This allows us to accurately reflect the importance of key factors in the sustainable tourism model and focus on the most influential indicators. Then, through Grey Relational Analysis (GRA), the relationships between different factors are identified, helping us uncover the critical drivers of sustainable tourism development and their interactions. Finally, the ARIMA model is used to forecast data for the next five years, providing valuable insights into future tourism trends and informing long-term planning.

Based on the model developed in Task 1, we apply the same methods, including FCE, EWM, GRA, and ARIMA, to evaluate and optimize the sustainable tourism development of Barcelona, Spain. The model is adapted to account for the unique characteristics of Barcelona’s tourism system. The results are then compared with those from Juneau to assess how the model can be effectively applied in different tourism contexts.

In order to evaluate the stability and scalability of the model, we perform a sensitivity analysis. The analysis explores whether the model can be adapted to larger or smaller tourism systems and assesses its applicability in other regions. By testing the model with varying parameters and scenarios, we investigate its robustness and capacity to handle diverse tourism systems, ensuring that the model is flexible enough to accommodate different regions and varying levels of tourism intensity.