This study will demonstrate, through an econometric approach, the relationship among per capita GDP, CO 2 emissions, and energy use in Vietnam. Using annual data for years 1970-2014, stationarity, structural breaks, Toda-Yamamoto test, Johansen and Juselius approach, and Variance decomposition have been conducted. The causality results in our analysis highlight that the presence of unidirectional causality was running from economic growth to energy consumption. This result will be significant since it supports the conservation hypothesis for the economy of Vietnam. Finally, the results of the variance decompositions reject the hypothesis that energy is neutral for growth, but that there is a relationship link even if the effects last for a short time.

Foreign exchange market has been subject of studies and discussions for many years. They were created modern theories and models to understand and predict the evolution of the price of money, and embarked on new discussions and new frontiers of study.In this paper we test the hypothesis of non-linearity and behavior chaotic the latest developments of the markets, to arrive at a solid and unambiguous conclusion on this type of dynamic systems analyzed. In particular, we introduce mathematical concepts and to study the properties of chaotic dynamics and non-linear in nature. It will delve into topics not therefore always present in economics courses in order to base the tests carried out on solid considerations from the point of view of formal mathematical. It will be followed, finally, a scientific rigor during the course of the analysis in order to give an interpretation of the results of logistic type can lead to scientific considerations different from econometric modeling.

In this study, we used an image neural network model to assess the relationship between economic growth, pollution (PM2.5, PM10, and CO2), and deaths from COVID-19 in the Hubei area (China). Data analysis, neural network analysis, and deep learning experiments were carried out to assess the relationship among COVID-19 deaths, air pollution, and economic growth in China (Hubei province, the epicenter of the COVID-19 pandemic). We collected daily data at a city level from January 20 to July 31, 2020. We used main cities in the Hubei province, with data on confirmed COVID-19 deaths, air pollution (expressed in µg/m3 as PM2.5, PM10, and CO2), and per capita economic growth. Following the most recent contributions on the relationship among air pollution, GDP, and diffusion of COVID-19, we generated an algorithm capable of identifying a neural connection among these variables. The results confirmed a strong predictive relationship for the Hubei area between changes in the economic growth, fine particles, and deaths from COVID-19. These results would recommend adequate environmental reforms to policymakers to contain the spread and adverse effects of the virus. Therefore, there is a requirement to reconsider the system of transport and return to production by combining it with economic growth to protect the planet.