Modeling Study of Multi-objective Genetic Algorithm for Balancing Carbon Emissions and Economic Benefits in Urban Tourism: A Case of Juneau

With the rapid growth of global tourism, some cities face challenges in balancing economic benefits, environmental protection, and social stability. This study develops a self-adaptive multi-objective optimization model to help govern- ments achieve sustainable tourism development. Taking Juneau (USA) as case studies, a four- dimensional evaluation system covering economic revenue, carbon emissions, infrastructure pressure, and resident satisfaction was constructed. The model integrates a feedback mechanism and genetic algorithm for iterative optimization. Results show that Juneau achieves optimal balance at 8,200 tourists with an expenditure ratio of 1:5:4. Sensitivity analysis identifies economic and resident satisfaction weights as primary determinants, while carbon emissions and infrastructure pressure act as negative factors.