Research on the sustainable tourism development of Juno City based on Stackelberg game and GA-PSO-SA algorithm

Balancing tourism's economic benefits with ecological conservation poses a critical challenge for sustainable tourism management. This study proposes a multi-objective optimization model that integrates hidden cost quantification and dynamic policy tools with intelligent algorithms to generate scientific resource allocation schemes for tourism destinations. A case study in Juneau, USA, addresses economic benefits, environmental costs (e.g., carbon emissions), and social satisfaction under constraints such as carrying capacity and infrastructure investment. By combining Stackelberg game equilibrium and hybrid algorithm optimization (GA-PSO-SA), results demonstrate that the optimal carrying capacity reaches 2,165 people/day (game equilibrium), whereas the hybrid algorithm achieves a higher capacity of 2,583 people/day. Implementing stepped taxes and dynamic pricing reduces carbon emissions by 14% and increases resident satisfaction by 23%. Sensitivity analysis identifies environmental carrying capacity as the most influential parameter. This model provides actionable solutions for over-tourism management, demonstrating both theoretical and practical value for sustainable tourism governance.