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FORECASTING TOURISM DEMAND AND SUSTAINABLE MANAGEMENT IN YELLOWSTONE NATIONAL PARK

Bekzot JanzakovSilk Road International University of Tourism and Cultural Heritage, Department of Tourism, Samarkand, UzbekistanKulmamat MirzaevSamarkand Institute of Economics and Service, Department of Digital Economics, Faculty of Economics and Management, Samarkand, UzbekistanBobur SobirovSamarkand Branch of Tashkent State University of Economics, Department of Green economy and International Business, Faculty of Economics, Samarkand, UzbekistanMirzabek JonuzokovTashkent State University of Economics, Economics and management department, Tashkent, UzbekistanSara GhezziUniversity of Wyoming, HAUB School of Environment and Natural Resources, Laramie, USA
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Modelling tourism demand over the long term is essential for the effective and sustainable management of established protected sites. In this study, the recreation visits at Yellowstone National Park were analyzed using a unique historical dataset covering the years 1904 to 2024. Using scenario forecasts through 2030, the results indicate a gradual increase over the last century from a very small beginning to multi-million annual visits. These cumulative trends include the influence of the car age and the expansion of the Mission 66 program, as well as significant disruptors such as the occurrence of World War II and the COVID-19 pandemic. These monthly visitations were modelled using the ARIMA approach. After testing the results using the criteria mentioned above, the final model applied was the SARIMA (2, 1, 1) (1, 1, 1,12), which indicates the dominance of annual seasonality and the visits return to normal rapidly, but lacks autocorrelation and volatility. These scenario-based forecasts indicate a mature demand pattern: the base projection elevation in annual visitations is from 4.19 million in 2026 to 4.36 million in 2030, with a planning range of 3.77 to 5.35 million. These findings illustrate the significance of summer congestion and the effectiveness of coupled time series and scenario modelling to deal with congestion in heavily utilized national parks.

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