Ensure Energy Efficiency Research Articles

Development of methods for monitoring and optimization of underground drainage systems using wireless sensor networks and ultra-wideband antennas

This research focuses on optimizing ultra-wideband (UWB) antennas, which are critical in modern communication systems due to their wide frequency range (3.1–10.6 GHz) and high data transmission capabilities. The study addresses the challenge of optimizing key antenna parameters – such as return loss, peak gain, and radiation efficiency – while also ensuring energy efficiency and network longevity. Traditional optimization methods, such as LEACH-C, often fail to balance these factors, leading to suboptimal performance. To solve this problem, the researchers developed the Generalized Position-based Optimization Neural Network (GPON) for UWB antenna optimization. They also evaluated the Position-based Hybrid Neural Network (PAN) method, comparing its performance with existing algorithms including LEACH-C, Firefly Algorithm (FA), HFAPSO, FA-ANN, and HWOABCA. The GPON model reduced return loss to 25.5 dB at 3.5 GHz and improved peak gain to 4.2 dB i, while maintaining 92 % radiation efficiency. In contrast, PAN demonstrated a 15–25 % improvement in residual energy and extended network lifetime by 20 % compared to LEACH-C. These improvements were due to the integration of advanced neural network techniques in GPON and the effective use of positional data in PAN, enabling more precise and adaptive optimization. The ability to balance multiple performance metrics simultaneously – a challenge previous models struggled with – is a key feature. This balance is crucial for UWB antennas in communication systems where both performance and energy efficiency are vital. The findings are especially relevant for practical applications in wireless sensor networks, mobile communications, and radar systems, requiring long-term network reliability and optimal antenna performance

Reconfigurable Intelligent Surface (RIS)-Assisted Non-Terrestrial Network (NTN)-Based 6G Communications: A Contemporary Survey

This article examines the transformative potential of integrating reconfigurable intelligent surfaces (RISs) into sixth-generation (6G) wireless non-terrestrial networks (NTNs). The focus is on the RIS’s capability to address diverse user requirements, including secure data transmission, power efficiency, extended coverage, and enhanced data rates. The paper delves into the synergy between RISs and NTNs, emphasizing key components like multiple-input multiple-output (MIMO) systems and advanced radio communications. Additionally, it highlights the crucial role of artificial intelligence (AI) and machine learning (ML) in optimizing RIS-based beamforming to solve scientific and engineering challenges while ensuring energy efficiency and sustainability in NTN operations. By positioning RISs as a key enabler in shaping the future of wireless communication systems, this research underscores their significance in unlocking the full potential of NTNs and advancing next-generation wireless communications. This paper contributes valuable insights and projections for future research directions, highlighting RISs’ potential to revolutionize NTNs for 6G technologies.

Long-term climate-based sizing and economic assessment of air-water heat pumps for residential heating

This study presents a novel methodology for sizing air–water heat pumps (ASHP) for space heating and hot water supply in buildings. Utilizing long-term local climatic data, the developed tool determines the coefficient of performance (COP) and feasibility of ASHP. The sizing algorithm incorporates peak heat demand, unitary final energy demand, and seasonal heat demand across various detached house sizes. Annual local heating system operation time and key economic indicators are derived for each case across 3 European countries. A new approach is precise investigation of 10-year outdoor temperatures and apply it to heat pump (HP) modeling, which enabled a distinction between so-called warm and cold years. The results underscore substantial variations in net present value (NPV) between warm and cold years, illustrating the critical influence of precise climate data on the performance of HPs. The study revealed that for a 160 m2 house in Poland, annual electricity consumption by HPs ranged from 3736 kWh during warm years to 12,908 kWh during cold years, showing a significant variation in performance based on climate conditions. The comprehensive tool allows for precise adjustments of heating systems to actual local climate conditions, improving both economic and environmental outcomes. This comparative assessment aids in the optimal selection of HPs, ensuring energy efficiency and sustainability in residential heating systems. Building on studies from various climatic regions, this work addresses the knowledge gap in performance and explores the feasibility of HP in a changing climate using precise modeling techniques.

State of Charge Estimation of Lithium-Ion Batteries for Electric Vehicle Application Using Gaussian Process Regression Approach

For the purpose of ensuring a secure, dependable and affordable performancealong with clean energy in electric vehicles, the estimation of the precise state of charge of LIB is very important. In this article, Gaussian Process Regression with different kernel functions-based SOC prediction is proposed and their performance with good health and well-beingare evaluated and analyzed. A useful benefit of employing GPR is the ability to quantify and estimate uncertainties, allowing for the evaluation of the SOC estimate's dependability. The kernel function serves as a crucial hyperparameter that improves GPR performance. GPR considers the temperature and voltage of the battery, which are independent of one another, as their respective input parametersthat relates Industry, innovation and infrastructure where target-dependent variable is battery SOC. Initially, the training process involves determining the ideal hyperparameters of a kernel function to accurately represent the characteristics of the data. The accuracy of predicting SOC of the battery is evaluated using test data. According to the simulation outcomes, the squared exponential kernel function-based GPR estimates SOC with high accuracy and lower RMSE and MAE which ensures energy efficiency and quality education.

Технологии информационного моделирования в концепции устойчивого строительства автомобильных дорог

Based on the analysis of open statistical data, the authors conclude that the volume of industrial and household waste is growing rapidly, most of which is not recycled. The growing environmental impact of the construction industry is leading to the development of green standards for highway design and construction at the state level. The paper considers the peculiarities of the construction sector in the context of sustainable road development. One of the tools for such development is the application of information models that ensure energy efficiency and environmental friendliness of the project. The paper characterises the ecologically-oriented directions of development of design, construction and operation of automobile roads. The authors present One Click LCA software as an example. It allows to assess the environmental impact of the constructed object - a road - throughout the entire life cycle.

Murine nuclear tyrosyl-tRNA synthetase deficiency leads to fat storage deficiency and hearing loss

Aminoacyl-tRNA synthetases are fundamental to the translation machinery with emerging roles in transcriptional regulation. Previous cellular studies have demonstrated tyrosyl-tRNA synthetase (YARS1 or TyrRS) as a stress response protein through its cytosol-nucleus translocation to maintain cellular homeostasis. Here, we established a mouse model with a disrupted TyrRS nuclear localization signal, revealing its systemic impact on metabolism. Nuclear TyrRS deficiency (YarsΔNLS) led to reduced lean mass, reflecting a mild developmental defect, and reduced fat mass, possibly due to increased energy expenditure. Consistently, YarsΔNLS mice exhibit improved insulin sensitivity and reduced insulin levels, yet maintain normoglycemia, indicative of enhanced insulin action. Notably, YarsΔNLS mice also develop progressive hearing loss. These findings underscore the crucial function of nuclear TyrRS in the maintenance of fat storage and hearing and suggest that aminoacyl-tRNA synthetases' regulatory roles can affect metabolic pathways and tissue-specific health. This work broadens our understanding of how protein synthesis interconnects metabolic regulation to ensure energy efficiency.

МАТЕМАТИЧНА ТА КОМП’ЮТЕРНА МОДЕЛІ АСИНХРОННОГО ДВИГУНА З ФАЗНИМ РОТОРОМ У РЕЖИМІ ДИНАМІЧНОГО ГАЛЬМУВАННЯ З САМОЗБУДЖЕННЯМ

One of the important challenges of today is to ensure energy efficiency and improve the performance of the most common induction electric drives (IEDs) for production and lifting and handling machines. Increasing the overall control range and stable speed control in AEDs is often ensured by the use of pulse-key or phase-pulse control systems for the speed of rotation of the AD FR, in which the control is carried out in the rectified rotor current circuit. The presence in such schemes of a rotor winding voltage rectifier creates conditions for the use of the dynamic self-excitation braking (DSB) mode. The paper proposes a mathematical model of the AD FR in the DBS mode in the system of coordinate axes (d, q) fixed relative to the rotor, taking into account the saturation characteristics of the magnetic system of the machine. The model was tested by computer simulation using the capabilities of the Simulink Matlab package. The created model is of practical value; in the formation of energy-efficient braking modes of AEPs, it will allow analysing complex electromag-netic transients and reasonably choosing the structure and parameters of their electrical circuits. Ref. 4, fig. 6.

Do green innovation and governance limit CO2 emissions: evidence from twelve polluting countries with panel data decision tree model

We examine the effectiveness of green innovation on CO2 emissions in the top twelve polluting nations—China, the US, India, Russia, Japan, South Korea, Canada, Mexico, Turkey, Italy, Poland, and the UK—from 1996 to 2020. Using panel data fixed and random effect model and decision tree analysis, we found that industrialization, urbanization, and economic growth increase CO2 emissions, whereas green energy consumption and governance decrease CO2 emissions. In the panel data tree-based model, governance is in the second and third positions in the decision tree fixed, and random effect model. Green innovation is not statistically significant despite the expected negative sign. The findings suggest that policymakers should encourage investment in green energy production and governance to combat environmental degradation. Investment in green energy should be escalated to ensure energy efficiency in the long term.

A case study on integrating energy‐efficient technologies for display devices

AbstractFor liquid crystal display‐based devices, the memory sub‐system and backlight consume a notable amount of energy, posing a critical concern, especially for portable devices. This work introduces an integrated display system (IDS) aimed at combining technologies for energy‐efficient memory usage and backlight power optimization, which have been addressed independently until now in the existing literature. Two modifications of approximated memory are proposed to find an optimal scheme for energy‐efficient memory usage as part of the proposed IDS schemes. Additionally, three distinct IDS schemes are proposed and compared to identify an optimum solution, ensuring energy efficiency maximization while maintaining image quality for each image. Experimental results demonstrate that 42.19% of energy consumption in memory access and 37.57% of the backlight power consumption can be reduced, while maintaining the image quality in terms of two different metrics.

TE-Spikformer:Temporal-enhanced spiking neural network with transformer

The integration of Spiking Neural Networks (Maass, 1997) and Transformers (Vaswani et al., 2017) has significantly enhanced performance in the field, achieving substantial improvements while ensuring energy efficiency. This amalgamation bears significant exploratory significance. This paper aims to enhance the network’s capacity for extracting temporal information from neuromorphic datasets. By leveraging the Spikformer (Zhou et al., 2023b) architecture, we introduce a novel network named TE-Spikformer. Through thorough analysis, we identified an imbalance in the Average Firing Rates(AFR) of neurons in the layer preceding the classification head across temporal steps. We thoroughly investigate the root cause of this issue, attributing it to the limitations of the network’s use of Batch Normalization (BN) (Ioffe and Szegedy, 2015) layer. To address these challenges, we propose a Batch Group Normalization (BGN) layer. While maintaining the stability of temporal characteristics in the data, we also introduce the Spike Spatio-Temporal Attention(SSTA) module to enhance the network’s ability to capture temporal information. To validate the effectiveness of our approach, we conducted multiple experiments using neuromorphic datasets, including DVS-CIFAR10, DVS128 Gesture, and N-Caltech101. The experimental results demonstrate that our algorithm consistently outperforms baseline methods, achieving accuracy rates of 99.30%, 89.60%, and 87.42%, respectively, thereby attaining state-of-the-art performance in the field.

Adaptation of indoor ornamental plants to various lighting levels in growth chambers simulating workplace environments

Despite the growing interest in indoor greenery and its positive effects on occupants’ well-being, there is limited knowledge on the optimal light levels for indoor plants that ensure energy efficiency and sustainable growth. This study explored the survival of ornamental plants under low-light conditions typical of indoor workplaces without daylight and investigated the impact of increased light intensity or extended day length on their growth. Three species of foliage plants (Epipremnum aureum, Pachira aquatica, and Rhaphidophora tetrasperma) were cultivated in growth chambers with three different lighting schemes. The results showed that plants sustained growth with 6.8 μmol m−2 s−1 white LED light for 9 h/day, suggesting that extra lighting might not be necessary for shade-tolerant species in offices. In this environment, plants maintained efficient photosynthesis under low illumination by increasing their specific leaf area. Elevating the light to 20.1 μmol m−2 s−1 and extending the day length to 18 h/day enhanced the plants’ relative growth rate. Climbing plants allocated more biomass to stems, resulting in a lower leaf weight ratio and noticeably altering their appearance. This study demonstrates that customized lighting strategies effectively support indoor greening goals, like adjusting intensity for energy savings or adding light for greening large spaces.

Electrification of Agricultural Machinery: One Design Case of a 4 kW Air Compressor

In response to the global pursuit of net-zero carbon emissions, the electrification of agricultural machinery is becoming a significant research and development trend. This study introduces the overall design of a 4 kW air compressor aimed at achieving a green vision for agricultural machinery. The design focuses on providing continuous and stable power and air output using a lithium-ion battery. Durability and cost-effectiveness are prioritized, with a particular emphasis on the Arduino system for integrating battery and motor systems to withstand harsh conditions and ensure ease of maintenance. A permanent magnet brushless motor was selected as the power source, paired with an optimized pulley to supply the proper torque to the air compressor. The system employs an Arduino-based feedback control sensor for air pressure regulation, ensuring energy efficiency. The primary energy source is a 48 V lithium iron phosphate battery, known for its high energy density and safety. The battery design focuses on system integration, addressing specific environmental discharge requirements. The embedded battery management system provides thermal and lifecycle parameter estimation, guaranteeing long-duration power supply and safe operation under various conditions. Unlike traditional fuel-driven systems, lithium iron phosphate batteries do not emit harmful gases, aligning with environmental standards. System integration testing demonstrated that the air pressure feedback control effectively meets the energy-saving requirements by digitally reducing power output as air accumulates in the chamber. Bench testing confirmed that the system performs as designed, achieving the desired results and advancing the goal of sustainable agricultural machinery.

Green Transformation of the Real Sector in Turkey: A priority Evaluation with DEMATEL Methodology

Increasing awareness of global changes in the natural environment, such as climate change and increasing biodiversity loss, has led to the concept of green transformation. Green transformation is a broad concept that includes objectives such as environmental sustainability, conservation of natural resources, combating climate change and ensuring energy efficiency. While the transition to green transformation provides advantages such as reducing greenhouse gases from fossil fuels, increasing energy efficiency, increasing the use of clean energy resources, reducing dependence on natural resources that increase carbon emissions, and new business opportunities, green transformation projects have disadvantages such as high initial investment costs, the installation of renewable energy infrastructure, the variable structure of renewable energy resources can cause problems in energy storage, and the need for energy storage technologies due to the fact that resources such as wind and solar are not continuous and predictable, imposing extra costs on businesses. Given the disadvantages of green transformation, it is important to pay attention to policies and strategies to overcome these challenges. A sustainable green transformation requires balanced and comprehensive policies to mitigate these challenges and maximize the benefits. This study aims to examine the green transformation of the real sector in Turkey and to rank the most effective strategies for transition to green transformation. It is aimed to rank the criteria that play the most role in the transition to green transformation and to offer solutions to the criteria. The critical success factors of the green transformation of the real sector in Turkey are evaluated in line with the answers given by three expert opinions. The data obtained from the expert opinions were analyzed by DEMATEL method, which is one of the multi-criteria decision-making methods. In the study, the eight most frequently mentioned criteria in the literature were identified. In the study, the criteria for the green transformation of the real sector in Turkey; increasing legal regulations for green transformation, increasing green financing opportunities, consumer preferences and behaviors, qualified workforce / green collars specialized in environmental issues, use of recyclable materials, increasing environmental social responsibility projects, combating air and environmental pollution, increasing investment in environmentally friendly energy resources factors were evaluated. As a result of the analysis, it was found that the most important criterion for the green transformation of the real sector in Turkey is the need to increase legal regulations for green transformation. The second most important criterion for the green transformation of the real sector in Turkey is to increase green financing opportunities for green transformation. Turkey needs to achieve a more stable legal framework and more supportive financing opportunities to transition to green transformation.

Multi-objective optimization of PI controller for BLDC motor speed control and energy saving in Electric Vehicles: A constrained swarm-based approach

Proportional Integral Derivative (PID) controllers are widely employed in industrial applications due to their effectiveness, simplicity, and versatility. Within the automotive sector, one notable application of the Proportional Integral (PI) controller is in the realm of electric motors, particularly the Brushless Direct Current (BLDC) motors powering Electric Vehicles (EVs). The precision of speed control over BLDC motors, renowned for their efficiency and reliability, is paramount for optimizing vehicle performance and ensuring energy efficiency. PIDs must be tuned each time they are used in an application to optimize performance. A well-known empirical hit and trial tuning strategy is the Ziegler–Nichols method, which results in sub-optimal performance in the presence of locally optimal solutions of large-dimensional search spaces. Existing Artificial Intelligence methods tailored for PID controller tuning often focus on singular optimization aspects, neglecting potential performance gains achievable through multi-objective considerations. Furthermore, prevalent swarm-based PID optimization methods typically initialize the population randomly without imposing any bounds on input parameters, thus rendering them susceptible to inconsistent, unreliable, sub-optimal solutions in unconstrained search environments. In our research endeavors, we mathematically model the multi-objective optimization of BLDC motor speed control, energy usage, and efficiency using constrained Differential Evolution and Particle Swarm Optimized PID controllers. The Ziegler–Nichols tuning method is used to ascertain the initial PI parameters and define bounds within the input space. Our methodology addresses a critical gap by integrating real-time road gradient data into the EV control system, enhancing precision and minimizing energy consumption, especially during challenging road conditions. The proposed approach significantly improves motor speed control and energy efficiency in EVs. With our proposed swam-based optimization over 30 iterations, MSE decreased by approximately 95.4% (from 3.8834 to 0.1809), while energy consumption reduced by about 3.1% (from 1.015 kWh to 0.984 kWh). These results highlight the effectiveness of our method in enhancing both speed control precision and energy efficiency. In addition, code related data is available at Github.

A knowledge-driven memetic algorithm for the energy-efficient distributed homogeneous flow shop scheduling problem

The reduction of carbon emissions in the manufacturing industry holds significant importance in achieving the national ”double carbon” target. Ensuring energy efficiency is a crucial factor to be incorporated into future generation manufacturing systems. In this study, energy consumption is considered in the distributed homogeneous flow shop scheduling problem (DHFSSP). A knowledge-driven memetic algorithm (KDMA) is proposed to address the energy-efficient DHFSSP (EEDHFSSP). KDMA incorporates a collaborative initialization strategy to generate high-quality initial populations. Furthermore, several algorithmic improvements including update strategy, local search strategy, and carbon reduction strategy are employed to improve the search performance of the algorithm. The effectiveness of KDMA in solving EEDHFSSP is verified through extensive simulation experiments. KDMA outperforms many state-of-the-art algorithms across various evaluation aspects.

USING NEURAL NETWORK TECHNOLOGY IN CONSTRUCTION ACTIVITIES

The article discusses the basics of using neural networks in the activities of construction organizations. The main goal of the study was to substantiate the advantages and prospects of using neural networks in the construction industry. As part of the study, the essence of this variant of machine learning was initially determined, their classical architecture was justified, key elements were identified, and the principle of operation was described. Subsequently, options for using neural networks within the construction industry were systematized (design of construction projects, analysis of costs for the implementation of construction projects, management of construction projects, monitoring of labor and equipment productivity, assessment of the quality of materials used and inventory management, ensuring energy efficiency, risk analysis and ensuring the safety of work performed, etc.) and a distinction was made between the areas of application of neural networks within the stages of the life cycle of construction projects, which formed the basis of scientific novelty. The results of the study can be applied in the activities of construction organizations to increase the efficiency of work carried out at various stages of construction.

Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy Consideration

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.

Транспортная энергоэффективность и экономические принципы организации морского судоходства

Modern problems of ensuring energy efficiency and carbon intensity of marine vessels are considered, taking into account the achievement of profitability of flights. Since January 2023, such issues have been particularly acute for all maritime convention vessels engaged in international transport, taking into account the introduction of amendments to annex VI of the International Convention for the Prevention of Pollution from Ships (MARPOL), developed within the framework of the IMO short-term strategy until 2030 to reduce emissions greenhouse gases from ships. The composition of the world merchant fleet by ship types and deadweight was analyzed, the work of the tram freight market and the features of achieving optimal economic operational speed of the vessel were evaluated, taking into account operational and technical conventional restrictions on carbon intensity. The transport work of the universal vessel Singularity over the past three years has been analyzed, taking into account changes in operational speed and the level of freight rates. An adequate mathematical model has been proposed for assigning an optimal average operating speed to a ship according to a carbon intensity rating. The proposed methodological materials can be used by shipping companies in the commercial and technical operation of the fleet by assigning an operational speed in preparation for the voyage and when carrying out corrective energy efficiency measures to achieve an acceptable level of carbon intensity ratings for marine vessels.

Service-aware real-time slicing for virtualized beyond 5G networks

Edge Intelligence is expected to play a vital role in the evolution of 5G networks, empowering them with the capability to make real-time decisions regarding various allocations related to their management and service provisioning to end-users. This shift facilitates the transition from a network-aware approach, where applications are developed to manage network quality fluctuations, to a service-aware network that self-adjusts based on the hosted applications. In this paper, we design and implement a service-aware network managed from the network edge. We utilize and assess various Machine Learning models to classify cellular network traffic flows in the backhaul, aiming to predict their future impact on network load. Leveraging these predictions, the network can proactively and autonomously reallocate slices in the Radio Access Network via programmable APIs, ensuring the demands of the traffic-generating applications are met. The approach integrates innovative MLOps methodologies for distributed and online training, enabling continuous model refinement and adaptation to evolving network dynamics. Our framework was tested in a real-world environment with realistic traffic scenarios, and the results were evaluated in real-time, down to a granularity of 10ms. Our findings indicate that the network can swiftly adjust to traffic, providing users with slices tailored to their application needs. Notably, our experiments show that under the studied settings, the users experienced up to 4 times lower latency (jitter) and nearly 4 times higher throughput when interacting with various applications, compared to the standard non-AI/ML unit. Furthermore, our dynamic scheme significantly optimizes resource allocation, ensuring energy efficiency by avoiding over- and under-provisioning of resources.

ISSUES OF "GREEN" ENERGY TRANSITION: LEGAL ASPECT

In modern socio-economic conditions, the issues of sustainable development, sustainable economy, resource efficiency and reduction of greenhouse gas emissions have become relevant, and in this regard, the issues of «green» economy, energy transition are being discussed. However, there are no established legal definitions of the terms "green" economy and «energy transition». In the conditions of energy transition, the issues of the «green economy» are relevant for the states that are members of the Eurasian Economic Union (hereinafter referred to as the EAEU) and the Union State (Russia – Belarus). In the conditions of energy transition, new objects of property rights appear (for example, carbon units), new contractual structures are applied, energy efficiency requirements are established when organizing purchases for state and municipal needs, etc. In this regard, it seems relevant to conduct an analysis of the regulatory legal framework of the «green» energy transition in order to substantiate proposals for the development of legislative support for entrepreneurial (economic) activities in the conditions of energy transition. The purpose of the study is to reveal the legal aspect of the «green» energy transition based on the analysis of regulatory legal acts, program documents, scientific statements about the «green» economy and energy transition. Materials and methods. Regulatory legal acts, program documents in the field of regulation of relations related to energy transition, and special literature have been collected for the analysis. The study was conducted using the methods of generalization and analysis, including comparative one. The results of the study. The legal aspect of ensuring energy efficiency, the formation of a low-carbon economy and the reduction of greenhouse gas emissions, a closed-cycle economy in Russia, in the states of the Eurasian Economic Union and in the Union State is considered. Examples of regulation in the regions of Russia of relations on the formation of a circular economy are given. Based on the conducted comparative analysis, it was revealed that the category of «green» economy is used in program and strategic documents, subordinate regulatory legal acts. At the same time, different terms «green economy», «green investments», «green financial instruments», «closed-cycle economy», «circular economy», «green energy», etc. are used. Conclusions. It seems appropriate to improve legislation in the field of regulation of relations connected with energy transition. It is advisable to adopt sectoral and regional plans for adaptation to climate change and energy transition. It is expedient to substantiate and develop proposals for the formation of a concept of legal regulation in the field of energy transition, including common approaches, principles of regulation of the «green» economy in terms of energy transition within the general policy of the EAEU member states and within the Union State.