Dramatic changes in the global energy market due to COVID-19 pandemic, the OPEC+ agreement, and increasing rates of green energy share in the world community have brought negative effects on the oil sector. In the long term, oil will reduce its importance as an energy resource, but for many years, it will continue to play a significant role in the world of energy. The oil industry has huge potential in terms of technical expertise, management, and financial resources to reduce its greenhouse emissions and ensuring an affordable availability of predictable energy. However, nowadays this sector has lost investing attractiveness. It is an interdisciplinary problem with a solution at the intersection of different stakeholders’ interests. The article is a review one and devoted to the issue of the implementation a public-private partnership (PPP) as a key tool that allows the use of the state and the business’ available resources to achieve the sector’s sustainable development and investment attractiveness. Research and analysis of PPP were based on foreign and domestic literature, using classification and generalization methods, retrospective and critical analysis. This paper contains identified groups of drivers, constraints, and recommendations for further successful PPP implementation in the Russian case.

The problem of the excessive CO₂ emitted into the atmosphere is one of the significant problems for the modern world and ecology. This article examines the dynamics of carbon dioxide absorption from thermal power plants, TPP, and waste gases by three types of microalgae, the most typical for the Russian Federation: Chlorella kessleri, Chlorella vulgaris, and Chlorella sorokiniana. The exhaust gases of the TPP contain up to 39% carbon dioxide. In this work, the rate of absorption of carbon dioxide from model exhaust gases with a CO₂ content of up to 39% was studied. As a result of the study, a species of microalgae (Chlorella vulgaris) was identified, characterized by the maximum rate of absorption of CO₂ = 0.412 g/L·day and the maximum volume of CO₂ utilized in 1 day = 8.125 L. The conducted research proved the possibility of utilizing a large content (up to 39%) of carbon dioxide from the exhaust gases of the TPP with the help of microalgae of the genus Chlorella. A scheme for the utilization of CO₂ with the help of microalgae is also proposed, which meets the principles of a circular economy (closed cycle).

Information technologies (IT) architecture and infrastructure is a significant cost item, especially for enterprises with complex production infrastructure and equipment that require automated and digital devices to collect and process primary data on technological and production processes. Most investment models for enterprise-wide development projects usually do not take into account the automation’s costs, including the design and implementation of information systems. The Enterprise Architecture (EA) paradigm has been proposed to bridge the gap between the business and the IT sector. The study aims to develop investment models for projects for the implementation and development of EA solutions, including IT architectures that eliminate the shortcomings of existing approaches. The research methodology is based on the analysis of published approaches to investment models for projects creating and developing EA, IT architectures with the identification of their advantages and limitations, and on the analysis of IT investment assessment practices in Russian infrastructure-intensive companies. As a result, investment and appraisal models are proposed that have advantages associated with the ability to calculate the effect of an integrated approach to the implementation of IT solutions, a more accurate calculation of an investment project cost by taking into account the IT system’s cost, a reduction in the investment cycle of development and implementation of architectural solutions, including physical and IT component.

The COVID-19 pandemic has severely tested humanity, revealing the need to develop and improve the medical, economic, managerial, and IT components of vaccine management systems. The vaccine lifecycle includes vaccine research and development, production, distribution, and vaccination of the population. To manage this cycle effectively the proper organizational and IT support model of the interaction of vaccine lifecycle management stakeholders is needed—which are an innovation ecosystem and an appropriate virtual platform. A literature review has revealed the lack of methodological basis for the vaccine innovation ecosystem and virtual platform. This article is devoted to the development of a complex approach for the development of an innovation ecosystem based on vaccine lifecycle management and a virtual platform which provides the data exchange environment and IT support for the ecosystem stakeholders. The methodological foundation of the solution, developed in the article, is an enterprise architecture approach, CALS technologies, supply chain management and an open innovation philosophy. The results, presented in the article, are supposed to be a reference set of models for the creation of a vaccine innovation ecosystem, both during pandemics and periods of stable viral load.

The consideration of infectious diseases from a mathematical point of view can reveal possible options for epidemic control and fighting the spread of infection. However, predicting and modeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemic of a new type of virus based on the example of COVID-19. The important result of the study is the description of the principle of dynamic balance of epidemiological processes, which has not been previously used by other researchers for epidemic modeling. The new approach is also based on solving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered and dead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the future trajectories of stochastic parameters, which are called the percentage increase in the total number of confirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the considered indicators of the COVID-19 epidemic, in Russia and European countries, during the first wave of the epidemic.

Seventeen sustainable development goals were formulated to create a harmonious world order for the benefit of different nations and peoples. At the same time, economic practice provides a lot of examples of conflicts of an economic nature between individual sustainable development goals. One of these conflicts is the need for environmental imperatives and economic growth when a massive assessment of land used for crop production and green energy projects is needed. The present paper considers a non-traditional approach to the mass evaluation of land plots on the condition that geographic information systems provide the main source of information, such as the case of land allocation for green energy facilities and evaluation of agricultural plots. The novelty of the proposed approach firstly means the development of a comparative approach, which receives much less attention in the valuation literature than cost and income approaches, as it can give an adequate picture of the current state of the market. The model includes the study of the entire dataset, the selection of model distributions and the construction of estimates based on model distributions. The methodology of multivariate lognormal distribution of factors and prices of analogues is used. The peculiarity of the market evaluation of land plots in such cases is, as a rule, the absence of rank predictors and sufficient number of continuous predictors, which provides a base for the application of a novel approach. The method of express testing of hypotheses about joint normality of logarithms of values of pricing factors and prices is proposed. The market value is estimated as an estimate of the modal value of conditional lognormal price distribution. Secondly, the problem of market valuation is solved in case of the almost complete absence of information about price-forming factors in the areas being assessed, and thirdly, the factors are determined based on geoinformation databases (distance to the nearest large city, regional center, federal highway, large rivers, lakes, and solid waste landfills), which allow for market assessment in the absence of information on pricing factors for land plots, except for the offer price and the plot area. The research was necessitated by the claim to determine on a specific date the cadastral value of agricultural land for the purposes of taxation, corresponding to the market value, in the almost complete absence of information on pricing factors in the assessed areas. The value of land reflects a complex combination of factors, so the use of the proposed mathematical toolkit allows for building a consistent model for the evaluation of land where improvements are absent or have no value in terms of land acquisition purposes.