مجتبی عباسیان

Background and Theoretical Foundations:

The economic effects of sea level rise on the growth of tourism is a growing concern. Because the rise of the sea level has affected the reduction of the level of the coasts all over the world, which means the reduction of the capacity of carrying recreation by the coastal areas, which in turn can significantly affect the tourism economy. Rising sea levels have created significant challenges for the growth of tourism in the Middle East, especially in coastal areas, over the past two decades. Therefore, the purpose of this research was to investigate the economic effects of sea level rise on the growth of tourism in the Middle East.

The current research is mixed in terms of data type, applied-developmental in terms of purpose, exploratory in terms of nature, and survey in terms of data collection method. The data of this study has been done in the form of a time series and for the years 2010-2000 with an increase in sea level rise equal to 2.92 centimeters and for the years 2011-2021 with an increase in sea level rise equal to 4.87 centimeters. The statistical population of the research is Middle Eastern countries. The data needed for this research was obtained from official international statistical centers such as the World Bank, United Nations Statistical Division, Tourism Statistics Database, Data Info. To analyze the data of the research, the panel method and EViews software were used.

The research findings showed that the effect of the logarithm of population density variable on the dependent variable of tourism growth is positive and significant, so that at a height of 2.92 centimeters above sea level, the coefficient of the logarithm of population density variable is equal to 0.77 with an increase in sea level to 4.87 0.56 was obtained. The effect of the variable of the logarithm of agricultural area on the variable of tourism growth is positive and significant, so that at the height of 2.92 centimeters above the sea level, the variable coefficient of the logarithm of agricultural area was equal to 0.13 and when the sea level increased to 4.87, it was equal to 0.09. The effect of the logarithm of the GDP variable on the tourism growth variable is positive and strong, so that at a height of 2.92 centimeters above the sea level, the coefficient of the logarithm of the GDP variable is equal to 0.34 and with the increase of the sea level, it is equal to 0.31. The logarithm variable of vegetation has an effect on the growth of tourism, so that at a height of 2.92 centimeters above the sea level, the coefficient of this variable is equal to 0.57 and when the sea level increases to 4.87, it is equal to 0.28. The logarithm variable of carbon dioxide emission per capita is negative and significant on the variable of tourism growth, so that at a height of 2.92 centimeters above sea level, the variable coefficient of carbon dioxide emission per capita is -0.14, and with the increase of sea level, the coefficient of this variable is equal to 0.18 - Was obtained.

The result of the panel method showed that the effects of economic variables during the rise of the sea level over time affect the growth of tourism. Therefore, taking environmentally friendly measures against sea level rise requires a combination of strategies at different scales, from individual actions to initiatives at the community level and broader government policies. As sea level rise affects numerous coastal areas around the world, international cooperation is critical. Sharing knowledge, best strategies and financial measures can help vulnerable areas. Renewable energies are the key source of environmental sustainability, awareness and education can significantly affect the consumption of renewable energies, and making society more aware of the effects of environmental degradation is vital for the production of renewable energies.

Many studies have examined the relationship between economic growth, environmental pollution, and health expenditures by combining different variables. However, the role of ICT and renewable energy consumption in health expenditures has been overlooked. Therefore, the purpose of this study is to investigate the dynamic links between health expenditures, environmental pollution, economic growth, information and communication technology and renewable energy consumption in Iran.

This descriptive-analytical and applied study was performed using time series data extracted from the World Bank and Iran Statistics Center from 1981 to 2019 for Iran. In this study, the Autoregressive Distributed Lag (ARDL) model to evaluate the long-term and short-term dynamic, unit root tests and diagnostic tests CUSUM and CUSUMQ and Canonical Co-Integrating Regression (CCR) Dynamic least squares (DOLS), and fully modified least squares (FMOLS) have been used to validate the results of ADRL estimates in Eviews12 software.

The results of ARDL estimation method showed that in the long run, carbon dioxide emissions and economic growth have a positive effect on health expenditures with a coefficient of 0.829 and 1.065, respectively. Renewable energy consumption with coefficient -0.0808 was a significant negative role in reducing health expenditures. The coefficient of -0.286 obtained for the information and communication technology variable at the level of 95% indicates a reduction in health expenditures in parallel with the development of information and communication technology. Short-term results also showed that COE with a coefficient of 0.193 has a positive effect on HE. In addition, EG has a positive effect on HE with a coefficient of 0.828. Also, ICT and REC with coefficients of -0.199 and -0.080 have a negative effect on HE. CUSUM and CUSUM squares confirmed the stability of the parameters and CCR, DOLS and FMOLS regressions confirmed the results of ADRL model.

Increasing economic growth and carbon dioxide emissions had a positive effect and the consumption of renewable energy and information and communication technology had a short-term and long-term negative effect on health expenditures; Therefore, policies to maintain sustainability, reduce environmental pollution, focus on renewable energy projects, as well as invest in information and communication technology equipment and infrastructure will be beneficial.

Achieving environmentally sustainable development goals requires identifying new sources of environmental degradation. Therefore, in this study, the impact of shadow economy, information globalization, trade and market size on emissions during the years 1990-2020 in developing countries has been investigated using the Threshold (TAR) model. The results showed that in the short and long term, the shadow economy has a negative impact on environmental pollution in developing countries. In the long run, the co-integration results showed a long-term relationship between the variables in the study. The existence of a long-term relationship between the variables showed that with the increase of the shadow economy, the emission of polluting gases also increased. In the long run, policymakers can use the shadow economy as a tool to influence environmental pollution. In addition, the effect of the threshold index of globalization, trade, market size and shadow economy were estimated in four models. In general, the results of the estimation showed that the shadow economy (SE) has more weight in the output and the emission of pollutant gases increases. Therefore, environmental policymakers and planners to reduce emissions should consider informal and covert activities to make regulations more effective for the environment, as well as the globalization of information and trade to improve the performance of companies and individuals.

The water requirement of the urban district of Zahedan City in Iran is met by the transfer of water from Sistan Chah-Nimeh reservoirs that are themselves suffering from severe water crisis. So, the prediction of the urban demand for drinking water can greatly help managers and users of urban water systems to use sound management practices. The present paper uses the artificial neural networks of GMDH and RBF, as two vigorous tools for the analysis and modeling of nonlinear relationship, to estimate monthly demand for drinking water in Zahedan in 2017. The selected parameters include mean monthly temperature, relative humidity percentage, mean precipitation, sunny hours, and previous-month consumption rate. The results of comparing MSE and MAE indicators show that after studying different structures with various number of neurons and hidden layers, the GMDH neural network with three hidden layers that has one neuron in layer 1, three neurons in hidden layer 2, and three neurons in hidden layer 3 yields the best results for the predication of short-term demand for drinking water. The comparison of linear and nonlinear activity functions reveals that in output layer of GMDH and RBF neural models, the nonlinear functions outperform linear functions. As well, among the GMDH model, the models with nonlinear output show better performance than those with linear output. Also, it is shown that the expansion of the network structure cannot improve the results considerably.