A central concentrating solar power (CSP) plant is increasing in the power systems as novel technology in the solar energy sources. Also, solar thermal storage unit is combined with the CSP plant to increase flexibility and decrease the dependence on the instantaneous solar radiation. Furthermore, the CSP plant can be obtained the optimal offering strategies to submit to the electricity market in order to sell the produced power and increase the expected profit. In this paper, the uncertainty modeling of solar irradiation and electricity market price is a big challenge for a CSP plant. Therefore, the scenario-based stochastic framework is proposed for optimal scheduling of a CSP plant in the presence of uncertainties to obtain optimal offering curves in order to sell to power market. Also, the risk related to uncertainties is considered via the downside risk constraints (DRC), which leads to obtain risk-constrained stochastic optimization of a CSP plant. The proposed model is formulated as mixed-integer linear programming, which is solved via CPLEX solver under GAMS optimization software. Risk-averse strategy is introduced in comparison with risk-neutral strategy to investigate the impacts of DRC implementation, which leads to decrease the expected profit while the expected risk-in-profit reduced.

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.

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 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.

The COVID-19 pandemic has significantly affected the employee lifecycle management (ELM) sphere, leading to the adoption of new human resource (HR) technologies and policies. This study investigates the impact of megatrends, artificial intelligence, digital technologies, and innovation on ELM and human resource management (HRM) policies in China, Russia, and Indonesia. Data were collected through structured interviews and publicly available information from companies in these countries between 2021 and 2022. The study evaluates the effects of artificial intelligence (AI), digital transformation (DT), and innovations on the sustainable development of ELM and identifies differences in technological responses to ELM in companies depending on their level of digital maturity. The results show that the majority of companies have continued the process of ELM digital transformation, but the percentage varies based on the scope of activity, labor, and readiness of the country to implement new technologies. The study reveals that large companies in each analyzed country with over 10,000 employees have a greater need and opportunity to implement HR digital transformation, whereas small companies with up to 100 people can operate without automation. In addition, the findings of this study provide propositions for designing how AI and innovations contribute to ELM. This article contributes to the current debate in the literature by substantiating the positive impact of AI, digital technology, and innovation on ELM and HRM strategies, offering practical applications for companies to improve productivity. Overall, this study highlights the importance of adopting innovative HR technologies in response to global challenges and workplace trends.

This article empirically assessed new opportunities and provides a conceptual justification for promising areas of trade and financial and economic relations between China and Russia amidst ongoing global turbulence, the post-COVID situation, and sanctions pressure. The study utilized the trade gravity model, taking into account the latest trends in the development of the research subject and object, as well as current challenges and trends in the global economy. The study revealed similarities between the political systems, reforms, and policies of China and Russia, with centralized power structures overlapping and supporting each other at international forums such as the UNSC. The findings suggest that both countries plan to increase trade volume in the next two years, with China focusing more on trade and economic development, while Russia works to promote security and political stability. This study provides valuable insights into the economic relationship between Russia and China, its impact on the US and Europe, and highlights the need for effective policy interventions to address the challenges posed by this relationship. It offers significant theoretical and practical contributions, including the potential to unlock the potential of national economies, increase their competitiveness and help states enter a phase of advanced and sustainable development. This article provides several policy recommendations to ensure the long-term sustainability of the economic relationship between Russia and China and foster mutual understanding and trust between their peoples. These include promoting trade diversification, enhancing financial cooperation, addressing trade barriers, strengthening political and security coordination, mitigating negative impacts on other countries, promoting sustainable development, and fostering people-to-people exchanges.