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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 (

In recent decades, the issue of ecological footprint (EF) in the world has become a serious anxiety between environmental stakeholders. This anxiety is more in top tourism attracting countries. The purpose of this research is the performance of mixed and penalized effects models in predicting the value of the EF of tourism in the top eight countries of tourism destinations. The World Bank and Global Footprint Network databases have been used in this study. Penalized regression and MCMC models have been used to estimate the EF over the past 19 years (2000-2018). The findings of the study showed that the amount of ecological footprint in China, France and Italy is much higher than other countries. In addition, a slight improvement in the performance of penalized models to linear regression was observed. The comparison of the models shows that in the Ridge and Elastic Net models, more indicators were selected than Lasso, but Lasso has a better predictive performance than other models on ecological footprint. Therefore, the use of penalized models is only slightly better than linear regression, but they provide the selection of appropriate indices for model parsimoniousness. The results showed that the penalized models are powerful tools that can provide a significant performance in the accuracy and prediction of the EF variable in tourism attracting countries.

This article evaluates nine rural districts in Kuhdasht county, Iran, with a population of 3535 between 2013–2016. We address the following two questions: First, what are the most important criteria and effective indicators in the rural population’s quality life enhancement? Second, is there any significant relationship between the public space indicators and quality life enhancement in the case study area? Six factors, including perceptual vision, buildings skeletons, culture and communities, activities, social interaction, and the environment from local peoples’ perspectives, explained 52.6 percent of the total variable variances. The Friedman test showed a significant difference among criteria of esthetics, semantic-perceptual, and activity-based functional at the alpha level of 0.01. The fitting growth regression model showed that the positive effect of the public space indicators on the rural population’s vitality and dynamism quality enhancement was about 0.723, indicating a significant relationship between them. It also stated a vital role of public space indicators in the rural population’s vitality and dynamism quality enhancement in the study area. The most important indicators were those of economic, social, and cultural dynamism and the body and space indicators.

Paying attention to human activities in terms of land grazing infrastructure, crops, forest products and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. In the present study, data from global databases were used. The ability of the penalized regression approach (PR including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 over the past two decades (1999–2018) was depicted and compared. For this purpose, 10-fold cross-validation was used to assess predictive performance and to specify a penalty parameter for PR models. Based on the results, a slight improvement in prediction performance was observed over linear regression. Using the Elastic Net model, more global macro indices were selected than Lasso. Although Lasso included only some indicators, it still had better predictive performance among PR models. Although the findings using PR methods were only slightly better than linear regression, their interest in selecting a subset of controllable indicators by shrinking the coefficients and creating a parsimonious model was apparent. As a result, penalized regression methods would be preferred, using feature selectivity and interpretive considerations rather than predictive performance alone. On the other hand, neural network-based models with higher values of coefficients of determination (R2) and values lower of RMSE than PR and OLS had significant performance and showed that they are more accurate in predicting EF. The results showed that the ANN network could provide considerable and appropriate predictions for EF indicators in the G-20 countries. predictions