The need for modern infrastructure as a prerequisite for sustainable development, poverty alleviation, and improvement of the quality of life of the population is a global problem that requires searching for and attracting large amounts of long-term investments. The presence of this problem in recent decades has led to the increasing implementation of complex and costly infrastructure projects through the public-private partnership (PPP) mechanism with high potential for attracting investment. This mechanism, in conditions of limited financial opportunities, allows one to combine the financial resources of the public and private parties for the implementation of major infrastructure projects. The limited use of existing tools at different stages of PPP projects and the increasing need for additional resources make it necessary to consider the possibility of using digital tools that complement traditional ones. For this purpose, the authors analyze existing financing tools, revealing their advantages and disadvantages, and identify and justify the possibility of using digital tools in the implementation of PPP projects. However, digitalization includes not only financing tools but also the development of infrastructure, including digital platforms needed to conduct such operations in the digital environment. As a result, a combined financing toolkit can be formed for each phase of project realization, including traditional and digital tools. The results of this study will become a basis for revealing the directions of the digital transformation of the PPP mechanism.

The article deals with the problems of implementation of public-private partnership programs in the energy sector aimed at ensuring the sustainable development of macro-regions. It is determined that in the financing of PPP energy projects, the World Bank institutions evaluate the projects for the selection of the most effective and ready for implementation, based on a variety of criteria. This assessment gives priority to projects that are based on renewable energy sources and those that are more environmentally friendly. The problems and uncertainties associated with the implementation of PPP projects in the energy sector are identified.

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Studies of systems engineering applications have revealed that systems engineering (SE) has a high potential for transferring economically inefficient oil and gas projects into a profitable zone due to preserving the value created at the concept stage right up to the implementation stage. To implement any project, including an organizational one, the company must have an economic justification for innovation. Studies into the global experience of assessing SE efficiency based on projects of various types have revealed the lack of a universal assessment method; however, individual studies have potential to be used in developing a method for quantifying the value of SE in oil and gas projects. Considering this fact, we developed our own method and prototype to assess the economic effect from the introduction of SE into oil and gas projects. The method is based on a decision tree used to calculate the Net Present Value considering the probability of projects’ success and failure in terms of budget and deadlines. This allowed us to predict the effect from introducing SE to an oil company’s capital project. The results obtained demonstrated the model’s performance capability and its possible applications in project resource planning stages.

The case-based reasoning method has a high potential for solving tasks of intelligence decision-support. To implement it, it is necessary to solve the problem of comparing situations and selecting the one that is most similar to the current situation in the knowledge base. The problem arises in the case of heterogeneous objects and situations with many different types of parameters and their possible uncertainty. In this paper, an approach based on machine (deep) learning is investigated for this task. It is proposed to carry out the process of selecting situations and solutions from the knowledge base in two stages: recognition of the states of the elements of a complex object and the relationships between them, then the formation of a representation of the situation in the state space and its use for comparing situations using neural networks. An ensemble neural network model based on a multi-layer network is proposed. It successfully simulates the cognitive functions of a human (expert), correctly selects similar situations and ranks them according to the similarity parameter. Proposed neural network models provide the implementation of a hybrid-CBR approach for decision-making on complex objects.