The paper seeks to develop a predictive model for assessing the impact of the (COVID-19) pandemic on the economies of Eastern Europe, taking into account quarantine measures. Functions of the dependence on the number of the infected populations in Eastern Europe on pandemic duration were calculated based on trend analysis. Factors affecting the intensity of disease and the number of infected persons have been determined. Integral model of their influence has been built using regression analysis. Based on the values of the factors, the number of infected people and the rate of infection were predicted for each of the Eastern European countries. The prognostic duration of the stage of exponential disease growth and the total duration of quarantine (epidemiological saturation point) are substantiated. The predicted decline in Eastern European GDP due to COVID-19 has been estimated based on the construction of a prognostic regression model. The results obtained can be used by state authorities and economic agents as a tool for active and preventive response. They can also serve as an example of the urgent need to develop, especially in non-standard situations, mechanisms and products of open innovation.

University social responsibility (USR) is an important assessment criterion of the QS Stars. In the context of the COVID-19 pandemic, the social orientation of universities as intellectual leaders in the development of society gains particular importance. The research purpose is to analyze the impact of the COVID-19 pandemic on the university activity directions in the framework of strategies (USR). An empirical assessment of the level and complementary factors of USR in the BRICS countries (Brazil, Russia, India, China, and South Africa) was conducted, using the method of integral and expert assessment. Grounded on scoring according to the principal component analysis, the structure of the factors of the USR development in the BRICS countries was determined. Multifactor regression modeling allowed substantiating the priority of factors stimulating the development of USR in the BRICS countries in modern conditions and arguing the main barriers to introducing the concept of social responsibility into university activities and expanding the stakeholders’ circle in it. The research results showed that the university management creativity, effective communication with the public and stakeholders, the quality of the educational process and the development of scientific activities stimulate USR development in the BRICS countries and should be used as the basis for the strategic planning of activities in the context of the continuing COVID-19 pandemic. Conceptual trends in the USR development can be useful for universities in the studied countries when adapting strategic development plans regarding the social needs of modern society.

The development of scientific approaches to assessing and diagnosing the financial risks of oil industry in the Russian Federation becomes a high priority task in conditions of high level of volatility in oil prices in the world energy market and preservation of sanctions regime. The article shows the main threats to financial stability of oil companies in Russia. Using cluster analysis, a system of indicators is proposed that determines the level of financial risk of oil companies in Russia. Based on the method of expert assessments and fuzzy sets, the classification of financial risk levels of oil industry is proposed. The integrated financial risk level of oil industry was calculated and scenarios of its development for 2018-2020 were forecast by means of regression modeling. The system of measures to improve the stability of oil companies and prevent functional financial risks is argued. The practical implementation of research results will be the basis for timely diagnosis of financial risks and qualitative development of preventive measures to neutralize them in the oil industry of Russia.

The decrease in the economic activity level around the world due to the COVID-19 pandemic spread has led to a sharp decrease in the crude oil price and provoked an oil war outbreak in the global energy market. The current situation has provoked the need for a total decrease in the crude oil production in the world. Considering that Russia is one of the main oil exporters on the world market, the need to determine the supply and demand levels for Russian oil is becoming relevant. The aim of the paper is to model predictive scenarios of Russian oil industry development, considering the specifics of the current economic environment given the COVID-19 pandemic. The multifactor correlation modeling method was used to form the system of indicators determining the level of demand and supply for Russian oil used and the total level of their influence. The functions determine the probability of implementing various scenarios of oil industry development depending on the predicted values of demand and supply. The three-sigma rule and the fuzzy sets method were used to estimate three scenarios of oil industry development for 2020–2021. Changes in revenues of the industry under the influence of forecast indicators of supply and demand for oil have been assessed and the probability of implementation of each of the scenarios has been reasoned. The results obtained are of a practical nature and can be used by government agencies, financial intermediaries, and scientists to diagnose Russian oil industry development. The results will be useful for oil companies to develop a strategy of open innovations for further design of the scientific information field for the effective functioning of the industry in complete uncertainty conditions.

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At the present time, ecological economics is one of the most widely used approaches to the analysis of economic development projects whenever they have bearing on specific eco-systems. This article illustrates possibilities of successfully applying the concepts of ecological economics to the analysis of projects aimed at the economic development of specially protected territories on the example of the economic development of the Kurshskaya Kosa (Curonian Spit) National Park.From the viewpoint of the ecological-and-economic analysis of the Environment-Society-Economy system, the article investigates the economic, ecological, social aspects of the prospective development of the Kurshskaya Kosa (Curonian Spit) national park; the index of degradation is used as a non-financial integral index of the assessment of the prevailing condition of the national park. Based on conducted research, conclusions reached to the effect that it is necessary to render more precise a methodology for determining the recreational capacity of the national park as a specially protected national territory; evidence is provided for the need to reduce a recreational load on the national park. From the viewpoint of the concepts of ecological economics, the article provides rationale for a system of ecological restrictions and indices to be used in designing and building touristand recreation facilities at specially protected natural territories.In the writing of this article, the authors use comparative analysis methods, induction and synthesis methods and mathematical methods.The provisions and conclusions of this paper have a direct bearing on the formation of economic policies for the development of the national park as a specially protected territory and, in the final analysis, on the social status of the population living and working within the territory of the national park.

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This research aims to substantiate the impact of using open innovation (OI) in the energy sector in readiness to implement artificial intelligence (AI) technologies and their effectiveness. The empirical method was proposed to determine the readiness level of OI for the implementation of AI technologies by comparing Russian and French energy companies. Readiness level indicators of companies for AI implementation using the Fibonacci sequence, Student’s t-test, and the method of fuzzy sets were empirically determined. The integrated readiness indicator for AI implementation by companies was calculated using the method of fuzzy sets and expressed through variance, allowing for these significant factors. Russian companies are at a low level of developmental readiness to implement AI, which is in contrast to companies operating in a developed market where the determining factor is the AI technology cost. The example of the innovative business model “Energy-as-a-Service” shows the synergistic effects of OI use and AI technology introduction. This paper is novel because it seeks to contribute to the current debate in the literature, justifying the position that energy companies that have in the past actively applied the concept of open innovation in business, are the most competitive and most efficient in implementing AI technologies.