The effects of tourism development on the emission of environmental pollutants in top tourist destination countries

BSTRACT
A
In recent decades, tourism development has been recognized as one of the most important drivers of economic growth in many countries. However, the expansion of its activities can have consequences such as increased resource consumption, waste generation, and pollutant emissions. The purpose of this research is to predict the effects of tourism development on environmental pollution emissions in the world’s leading tourist destination countries. Data for the study were obtained from the World Bank databases and the Global Footprint Network, covering an eight-year period (2010–2017). For data analysis, penalized regression models, including Linear Regression, Ridge Regression, Lasso Regression, and Elastic Net Regression, were employed. The results revealed that the levels of environmental pollution were highest in China (2.46), Mexico (0.946), and Thailand (0.857), while the average CO₂ emissions in the United States and China were 0.74336 and 0.21523, respectively. Furthermore, analysis of MSE and R² values showed that the Lasso model performed better than its competing models. Specifically, the predicted value for PM₂.₅ was 1.014, and for CO₂, 0.726 in the Lasso model. A slight improvement in the performance of penalized regression models compared to the standard linear regression was also observed. The model comparison indicated that Ridge and Elastic Net regressions selected a greater number of predictive indicators compared to Lasso, while Lasso demonstrated superior predictive accuracy in estimating environmental pollutants (PM₂.₅, CO₂). Overall, the findings confirm that penalized regression models serve as effective and powerful tools that significantly enhance the accuracy and reliability of predictions regarding the impacts of tourism development on environmental pollution in the examined countries.
Extended Abstract
Introduction
The tourism industry has been cited as a key strategy in boosting global economic development, increasing foreign exchange, and increasing exports. The importance of this industry in the areas of employment and local income, and promoting cultural and environmental values in developed and underdeveloped countries, is essential. According to statistics published by the World Travel and Tourism Council (2019), the tourism industry contributed US$8.8 trillion to global GDP and created one-tenth of all jobs in the world in 2018.
 
Methodology
To examine the indicators of tourism development and environmental pollution, data from (2010 to 2017) has been extracted from (https://databank.worldbank.org,) and (https://data.footprintnetwork.org). To examine this issue, we considered two dependent variables (output), namely CO2 and PM2.5 environmental pollution. Also, from 10 tourism development indicators (predictor variables), including the number of international tourist arrivals (A1), international tourism expenditure (total imports) (A2), international tourism expenditure (current US dollars) (A3), passenger transportation costs (current US dollars) (A4); international tourism travel item costs (current US dollars) (A5), number of international tourist departures (A6); International tourism revenue (total exports) (A7), international tourism receipts (current US dollars) (A8), international tourism passenger transport item receipts (current US dollars) (A9), and international tourism travel item receipts (current US dollars) (A10) have been used. Also, MSE (mean square error), RMSE (root mean square error), and R-square (coefficient of determination) values were used to measure performance. With n as the total number of observations, it should be noted that the analyses mentioned were performed in R 4.0 (R Core Team, 2020) using different packages.
 
 
Results and discussion
In order to answer the purpose and question raised in this research, penalized regression methods were used. One of the methods is the ordinary linear regression (OLS) model, which is the simplest algorithm that outperforms other fancy and complex models. The OLS method can only determine the factors affecting the dependent variable and has serious analytical deficiencies in predicting related variables and groups, and its results are unstable. Therefore, to overcome some of the weaknesses of this method in improving the performance of variables, penalized regression models (Ridge, Lasso, and ElasticNet) were used. The advantages of penalized models can be stated as follows: first, they manage and select the multicollinearity of the models; second, these models allow testing a large number of predictor variables; third, they introduce biases in the estimation of the models and reduce the mean square error of the responding variable.
In this study, we have shown how ML algorithms can be more reliable in estimating production process parameters than classical statistical models. The use of ML models can help in planning environmental pollution reduction in top tourism destination countries, to reduce both economic costs and biological damage.
In this study, it was shown how a wide range of tourism development indicators, including the number of tourist arrivals, transportation costs, travel item costs, number of departures, total export revenue costs, passenger transport item costs, and receipts costs, affect environmental pollution and there is evidence of their positive effects on the emission of environmental pollutants (PM2.5, CO2). In addition, we showed how tourism development has increased environmental pollution in the top tourism destination countries.
 
Conclusion
The results of the study showed that the average PM2.5 emissions in China, Mexico, and Thailand are 2.46, 0.946, and 0.857, and the average CO2 emissions in the United States and China are 0.74336 and 0.21523. The prediction results of the models were also compared using the root mean square error (RMSE), mean square error (MSE), and coefficient of determination (R2). The RMSE results and prediction accuracy for PM2.5 and CO2 values obtained from all competing models are very close. However, overall, the MSE and R2 results showed that the Lasso model performed better than the other competing models. In this model, the value of the PM2.5 index is 1.014, and the CO2 index is 0.726. Although this model showed its potential superiority over other models and is better compatible with training data, its use in selecting indices and prediction methods to achieve high accuracy is accompanied by flexibility. In addition, Elastic Net and RR can play an important role in tourism development and pollution emission, which have a large number of parameters. In such cases, these techniques, used to analyze tourism development on pollution emission, are the best choice for modeling and forecasting such research. In this study, we have shown how ML algorithms can be more reliable in estimating production process parameters than classical statistical models. The use of ML models can help in planning environmental pollution reduction in top tourism destination countries, to reduce both economic costs and biological damage.
 
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not for profit sectors.
 
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
 We are grateful to all the scientific consultants of this paper.