Vladyslav Psyuk

The article explores the evolution of energy transition research through a bibliometric co-occurrence analysis of author keywords extracted from 146 scientific publications. The analysis reveals a shift from the technical aspects of energy systems and traditional decision-support approaches (2014–2018) towards climate change, decarbonization, renewable energy, investments, and energy policy (2018–2021). Recent studies (2022–2024) increasingly emphasize renewable energy, sustainable development, and intelligent decision-support systems, reflecting the growing digitalization of energy systems and the transition towards intelligent energy management. Based on these findings, the study develops a Digital-Twin-Oriented Techno-Economic Decision-Support Framework (DTOTEDSF) for optimizing and managing carbon-reduction strategies under dynamic energy transition conditions. Rather than representing a fully implemented digital twin (DT), the proposed framework constitutes the analytical foundation for its future development. It integrates techno-economic modeling, optimization, scenario analysis, and sensitivity assessment into a unified decision-support methodology. To demonstrate its practical applicability, the framework was applied to four industrial CCS case studies in the cement sector using publicly available technical and economic data. Its analytical core combines technical, economic, and optimization models to evaluate CCS performance under alternative operating conditions. Consequently, the proposed framework provides a methodological basis for the future implementation of fully operational DTs and contributes to the development of intelligent decision-support tools for industrial decarbonization and the sustainable energy transition.

The article explores the evolution of research on the energy transition through a biblio-metric co-occurrence analysis of author keywords extracted from 146 scientific publica-tions. Rather than analysing publication content directly, the study examines the rela-tionships among keywords related to the energy transition and intelligent deci-sion-support technologies. Recent research (2022-2024) increasingly focuses on renewable energy, sustainable development, and intelligent systems, which reflects the shift towards the digitalization of energy systems and the growing importance of sustainable develop-ment. Between 2018 and 2021, research shifted toward a broader understanding of the en-ergy transition, emphasizing climate change, decarbonization, renewable energy, invest-ments, and energy policy within the context of sustainable development. In contrast, pre-vious studies (2014-2018) have mainly focused on the technical aspects of energy systems and traditional decision-support approaches. The study focuses on demonstrating the outcomes of such evolution and considers the decision-making process for building an methodological concept integrated with expert systems to optimize and manage car-bon-reduction strategies under dynamic energy transition conditions. In this study, the concept of a DT is considered as a methodological direction for extending the capabilities of modern expert systems rather than as a fully implemented digital twin. Accordingly, an algorithmic model of an expert system is proposed, in which the analytical core is based on engineering (η), economic (F), and optimization models that are consistent with the digital twin concept. Particular emphasis is placed on the economic module, which ena-bles the assessment of the economic value of CO₂ capture, break-even carbon price, mini-mum functional point, optimal operating conditions, and the sensitivity of results to key economic parameters. The proposed approach contributes to the development of intelli-gent tools to support decarbonization and the transition to sustainable energy supply.

The article presents a multicriterial approach to evaluating the efficiency of the energy transition in EU countries, emphasizing the relationship between resource efficiency and the results of transition. The study uses a data analysis methodology (DEA) to evaluate how effectively countries use resources (inputs), such as energy consumption, investment and innovative development, to achieve the desired results (outputs), including the renewable energy sources, reduction of CO₂ and labour trends. The use of DEA with Python 3.10 software made it possible to obtain objective performance and compare them with the energy transition index (ETI). The DEA and ETI based efficiency matrix has identified four clusters of countries: high efficiency and high transition readiness; high efficiency and low transition readiness; low efficiency and high transition readiness; low efficiency and transition readiness. Validation by means of a solution (DS) confirmed the reliability of the results. The conclusions emphasize that the higher efficiency of resource use does not automatically meet the higher transition indicators, which indicates the need to improve management, innovation spread and investment distribution. The study helps to develop evidence policy by offering a system for monitoring and comparative analysis of the efficiency of the energy transition in EU countries.

The purpose of the article is to characterize the relevance, structure and background of business ecosystem model working out and its implementation at energy companies and to determine the ways of its interconnected and interdependent components transformation into enterprises’ strategy. It’s proved that formation of a business ecosystem model of energy company is an activity based on combining the efforts of interested parties, which, on the one hand, is aimed to achieve business goals, meet the energy resource needs of various groups of consumers, and on the other hand, on the complying with conditions that ensure the balance of their activities for the realization of the goals of sustainable development and the development of measures to maintain the ability of the business ecosystem to preserve its structure and functionality under the influence of external factors, taking into account global energy trends and energy development strategies. The focus is maid on the business ecosystem model those components integrate business processes, natural ecosystems and people efforts. It is emphasized that the transformation is a process of fundamentally changes based on the application of transformation tools, the choice of which depends on the selected type of transformation: emergency, innovative and evolutionary. Transformation processes require stakeholders’ cooperation with, that shifts the traditional orientation of energy companies’ activities mainly from economic results and business interests to the taking into account the benefits of an ecosystem approach, which accomplish with the added value by reducing the harm to the environment as well as the creating, in some way the business opportunities. It is justified that consideration of issues of collaboration and communication in transformation acquires special importance, since the achievement ofstrategic goalsinvolvesthe interested participants and the achievement of understanding and interaction in the process of transformation business ecosystem model into strategy.

The study explores the transformation of energy resource consumption centered on energy efficiency within the global energy transition framework. The growing global energy demand, driven by industrialization, urbanization, and rising living standards, has led to increased fossil fuel consumption. This, in turn, contributes to higher greenhouse gas emissions, global warming, and other adverse environmental impacts. The study emphasizes the efforts of developed nations to curb these emissions, particularly by expanding renewable energy use, and notes the declining share of coal and other fossil fuels in national energy balances. The Energy Transition Index (ETI) is introduced as a measure of countries’ progress in advancing the energy transition, including efforts to enhance energy efficiency and mitigate environmental harm. The study also highlights Ukraine’s challenges in energy supply due to military conflict and infrastructure damage, underscoring the critical role of energy efficiency in rebuilding the country’s energy system and securing its energy future. Energy efficiency is identified as a key strategy for reducing dependence on fossil fuels, decreasing energy imports, and bolstering the economy’s resilience to external pressures.

The achievements of Industry 4.0 are penetrating more and more widely and deeply into various spheres of economic activity. The article discusses the directions of using artificial intelligence (AI) in solving the problems of development in mining enterprises. Methods of achieving results in various directions using AI have been determined. Based on the characteristics of neural networks formation components, the model of the integration of neural networks into the information system of the mining enterprise as well as the main components of this model, their connection and dependencies are determined. The architecture of the proposed information system is described, which consists of four zones: the corporate zone, the operational zone, the control zone, and the intermediate zone. It was hightnlined that the functioning of the operating system of this model depends on the sensors that are installed on the mining equipments in the operational area of the enterprise. It is noted that the number of such sensors depends on the amount of data accumulated because of the activity of the enterprise’s equipment and the efficiency of the construction and functioning of neural networks. The factors that determine the effectiveness of the model and the precision of neural networks in the activity of mining enterprises are substantiated. It was established that the main criterion is the amount of information needed to analyze the object’s behavior and the possibility of predicting it in the future. The dependence of the effectiveness of the application of AI technologies on the level of digitization of the enterprise was considered, and it was also proposed to determine the indicators of the accuracy and efficiency of the functioning of neural networks in the information systems of the enterprise.

Today, the energy sector requires special attention, and the widespread introduction of digital technologies, including gamification, allows key problems related to the energy policies of the European Union to be solved. The literature analysis presented here discusses the use of gamification among home energy production customers. However, there is a lack of analysis on the use of this tool in the management of energy sector companies. When considering the advantages of gamification elements in the training process, there was mention of this tool potentially being able to be used in HR management to realize the EU’s energy goals. The relevance of the topic has been explored in international studies that have shown that gamification increases employee productivity and motivation. In the article, the directions of gamification in the practices of Ukrainian organizations are investigated. The main principles of effective gamification implementation in HR management, at energy enterprises in particular, are highlighted. A survey on gamification perception by Ukrainians from educational and related areas has been organized and analyzed. The survey results are supplemented by the results of the digitalization state analysis conducted for energy companies, which represent an important foundation for the implementation of gamification into practice. The prototype of a simulation model in which a solar panel must be connected to a controller is worked out as an example of the element usage incurred by gamification in the learning and training process, both in enterprise and in university activity. The present research underlines the importance of an interdisciplinary approach in gamification utilization, as it allows the skill-gaining processes in managerial and technical fields to be combined. The main advantages of gamification in HR management in enterprises are described by impact categories, such as staff appraisal, motivation, and corporate culture and internal communications. The use of gamification in companies in the energy sector is expected to contribute to the support of energy policy, as well as to the efficient integration of Ukrainian energy companies into the EU energy market.