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 (
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.
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.
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