Digital transformation and convergence effect as factors of achieving sustainable development

• Published in: IOP conference series. Earth and environmental science 2019-07, Vol.302 (1), p.12102
• Main Authors: Dyatlov, S A, Didenko, N I, Lobanov, O S, Kulik, S V
• Format: Article
• Language: English
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/302/1/012102

The paper introduces the term "digital (neural network) economy", which reveals the content of the processes of digital transformation of the national and world economic systems that are currently going on and makes it possible to form the methodological basis for further research in this field. It is shown that at the technological level the processes are accompanied by NBIC convergence of (nano-, bio-, information and cognitive) clusters of the digital economy, which generates synergy effects. The new structure of the modern economy is being formed. The building blocks of today's economy are: systems of the circular economy, clusters, network, business incubators, and technology platforms. The economic contents and the essence of the structure of the national economy are shown and the directions for its transfer are found out. It has been concluded that in the time of digital economy, as a result of network convergence, synergy effects of converged regional clusters appear. It has been found out that with a growing number of converged clusters of information spaces, the entropy inside them is stably going down. The paper reveals the content of the digital concept, defines the general principles, criteria and indicators of sustainable development. In order to characterize sustainable development, a number of indicators are used. They can be considered at different hierarchical levels: global, national, regional, local and industry-specific ones. The features of the "green economy" developing in the time of digitalization of the world and national economic systems are looked into. Given these features, a model of the regional concept has been developed and proposed. The model embraces sustainable development and organizational and economic mechanisms, which can be used to measure the efficiency of its implementation in the time of transfer to the digital (e-neural network) economy.