Due to the youth of the cryptocurrency sphere, the logic of interaction between investors, users and protocols is not always precisely defined. Analysis of the impact of ESG on cryptocurrencies proves that the demand for bitcoin network capacity (occupies the main market share) is the main factor in predicting the price of this cryptocurrency and the cryptocurrency market as a whole. The choice of the statistical method of analysis is determined by the purpose of statistically justified determination of the relationship of the data under consideration, and the reliability of the analysis is checked using Fischer and Student tests. In this paper, several innovations are proposed to solve the problem of energy dependence of cryptocurrencies: firstly, the analysis of cryptocurrencies in the paradigm of sustainable development (taking into account the consumption of a huge amount of energy for the functioning of cryptocurrency systems); secondly, feedback logic to explain the interaction of subjects, including the following parties: users, developers, network infrastructure and their interaction; thirdly, statistical analysis with the creation of artificial variables from real data and iterative improvement of the model. This paper proves that sustainable cryptocurrency growth is impossible when viewed from the perspective of “Green Economics” by Molly Scott Cato. The author's approach is relevant compared to other methods of linear transformations for creating artificial variables by selecting data using the VIF test. As a result, several versions of models were obtained using various combinations of the initially proposed factors, on the basis of which the nature of the greatest influence on the price of bitcoin was established in the form of technical factors and energy infrastructure needs.

This article aims to highlight various methods and approaches to grouping countries, according to the behavior of their open innovation indicators. GDP, inflation and unemployment are the most important indicators of the economic and social policies of states, allowing them to be evaluated and models built. To find the relationships between open innovation indicators the paper uses marginal analysis and feature reduction, as well as machine learning methods (shift to the mean, agglomerative clustering and random forest methods). The results showed that, after isolating all groups, the importance of the signs was established and the patterns of behavior of indicators for each group were compared and open innovation dynamics was analyzed. The conclusions showed that it is obvious that increasing the number of variables in the model and using more extensive indicators can greatly increase the accuracy, in contrast to the generally accepted simple classifications. This approach makes it possible to more accurately find the connections between sectors of the economy or between state economies in general. An accompanying result of the study was the clarification of the equality of open innovation indicators for the analysis of their interrelationships between countries.

During recent years the role of energetic security in Russia steadily increases. After deterioration Russian relations with West countries in energy sector stay single stable economic tool, which Russia use for the maintenance of impact in surrounding region. Instability of the importers market which is priority for the country, bounded with excitements about use by Russia theirs position on the energetic, markets for reaching political goal through the development of projects such as Nord Stream 2 and Turkish stream. In its turn in latest decade, Russia aspires to ensure solid positions in Siberia and Far East. American companies refuse to participate in the execution of orders for Russia due to the unstable political situation and stricter restrictive and regulatory measures, which makes the level of risk of cooperation unacceptable. Using alternative sources of electricity, Russia and China can extract many positive effects: financial income, employment, energy security for the domestic market, etc. For example, in the Far East, the import of fuel can be reduced by 40% after the implementation of RAO UES plans to build 178 renewable energy sources with a total capacity of 146 mW.

Background: Some firms with good growth opportunities and additional funds could have difficulties accessing external finance. One possible way to enhance their financial inclusion could be an exciting approach to planning the money reserve collected on a firm’s account. Methods: This article aims to disclose the introduction of financial logistics as the new theoretical field of management science. The authors present, in this paper, the key findings on the development of logistical models of an optimum money reserve calculation taking into account digital transformation and industry 4.0 technologies and optimization methods. Results: The monetary reserve models are analogies of models of storekeeping in supply chains. The specific area of the theoretical research of logistics is shown in this paper, which could be disclosed as the subject of financial logistics as a science. The authors consider the term “Financial Logistics” based on logistics theory and money demand. Conclusions: Authors suggest the methodology of studying the nature of both financial and material flows of resources by comparing the relevant formulas. From the researchers’ points of view, financial logistics could be defined as the theory of managing the cash flows based on the logistical models for calculating a corporation’s cash reserve. The authors find it interesting to expand the conditions for calculating financial flows since the uncertainty of external market conditions always influences actual commercial activity.

This article sheds light on the question of whether it is possible to create fairly accurate forecasts of real oil prices. For this purpose, a multi-level machine learning model has been created to analyze several sources of heterogeneous data to predict future prices. The article uses different types of data: market condition data, titles, and transaction data. Then, they have been processed to be able to load them into the model. The validation of the regression neural network results showed that the model is more accurate than in previous studies. In fact, this paper presents an artificial neural network model that solves the problem of determining the most informative relationship between different types of oil price data.