The current and complex issue addressed in this work was the subject of interdisciplinary and transdisciplinary research carried out within the Doctoral School of Management of the Academy of Economic Studies in Bucharest. Economic growth, development, circular economy, digitization and artificial intelligence are presented, explained and analyzed in different countries in the European Union (EU) and in Romania. The research was carried out with a diversified methodological tool selected from the fields of: economics, statistics, econometrics and artificial intelligence. The main methods used are: Principal Components Analysis, Canonical Correlation Analysis, IBM SPSS Statistics 25TM (Statistical Package for the Social Sciences), Deep Learning Method, Autoregressive Distributed Lag Models, prediction methods and neural networks in the field of artificial intelligence.
The work is structured in four parts and contains nine chapters. The theoretical aspects are in the first part, i.e. in chapters 1,2,3,4, and the applied researches are included in chapters 5,6,7 which form the second part of the work, followed by chapters 8 and 9 which make up the part of third. The last part, the fourth, contains the conclusions of the researches carried out. In the four chapters of the first part, the basic notions, the models and their specific approaches are presented and analyzed. Chapter 5 contains an econometric research centered on the Tobit Model regarding economic growth and the circular economy in the states of the European Union, in which the emphasis was placed on the correlations between the research variables and on the causal relationship between them, using established tests in the field of statistical analysis . Chapter 6 integrates a correlative research on digitization and economic growth. For this, the Canonical Correlation Analysis method was used. Thus, a multiple regression model was developed with which the interdependencies between the research variables were analyzed. The research results were tested using the Statistical Platform for Social Sciences (SPSS), the Pillai and Hotelling tests and the Wilk criteria, in order to highlight the positive and statistically significant relationship between the specific variables for the two parameters on which the research was carried out. Chapter 7 integrates a broad and complex research on economic growth, circular economy, energy consumption and development, conducted through eleven relevant research variables. The originality of the scientific research carried out consisted both in the approach of interdependence and the impact of economic variables on the environment and development, as well as in the development of a new composite index, created to determine and compare the EU states and the impact of economic growth on energy consumption, development and environmental protection in last years. The research methods used were: Principal Components Analysis and SPSS. Chapter 8 includes a correlative research about digitalization and economic growth, in which, for the first time in the specialized literature, methods from the field of artificial intelligence (Deep Learning Method), respectively Neural Network Architectures, were used to analyze the research variables. The results of the research were processed using the Principal Components Analysis method, which helped highlight the way digitalization and economic growth have evolved in the EU states and the pace at which the changes took place. Chapter 9 contains an interdisciplinary and integrated research on economic growth, circular economy, development and digitalization in Romania, based on an integrated econometric model of analysis and prediction. The analysis was developed over a period of twenty years using the Distributed Autoregressive Model and the Error Correction Model and was followed by a prediction for eight years of growth and development in Romania, using the specific indicators processed using the Crystal method Ball.
The work is addressed both to researchers from different fields (economics, environment, sustainable development, artificial intelligence, etc.) as well as to interested representatives from the business environment who are aware of the importance of knowledge and predictions in the context of the growth and development of the sustainable and sustainable intelligent business sector in an environment complex digital.