Estimation and prediction of ecological footprint using tourism development indices top tourist destination countries

During the last two decades, the ecological footprint (EF) has had various fluctuations and has been associated with an upward trend, which can be a concern. This research aims to statistically examine tourism development indices and their effect on the EF during the last two decades in eight top tourism countries (France, United States, China, Italy, Turkey, Mexico, Thailand, and Germany). For this purpose, indices (extracted from the World Bank and Global Footprint Network databases) were used. Also, repeatability models were used to check the time and place and penalized regression models were used for the fit and accuracy of tourism development indices. The research findings showed that the amount of EF in the countries of China, France, the United States of America, Mexico and Thailand had an upward trend. The predictive accuracy of the penalized regression models of Ridge, LASSO and Elastic Net were reported as 0.910, 0.908, and 0.908, respectively. The difference is that the LASSO model acted more strictly and provided a more economical model by selecting the variable. We believe that a deeper statistical look can effectively apply an efficient strategy in better management of the EF challenge.