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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.

The aim of the study is to elaborate an econometric model to determine the conditions for ensuring the balanced development of environmental, economic, and social components of the Russian agricultural sector within the Eastern European region. The method of fuzzy sets was used to build an integral model for assessing the level of sustainable development of the agricultural sector in Eastern Europe on the basis of economic, environmental, and social sustainability indicators. The control indicators (independent variables) and integral economic, environmental, social sustainability (dependent variables) helped build multifactor linear regression models and calculate the indicators of elasticity of dependent variables from independent variables, which characterize the change in sustainable development indicators with the growth of controlling factors by 1%. This model allows us to define and analyze the levels of sustainable development of the industry, both in a specific country and within the region in general. The study shows that, for Russia and Eastern European countries, innovation is one of the crucial factors in ensuring sustainable development of agriculture in the region, taking into account the current state and level of economic development. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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.