Improving energy efficiency is considered to be important and cost-effective strategies to reduce greenhouse gas emissions, which is crucial in the face of global climate change. This study hypothesizes that there are significant differences in the impact of energy efficiency on economic and energy systems. Using quantile regression method, the impact of economic and energy characteristics on energy-related greenhouse gas emissions in 120 countries over the period 2010–2020 is investigated. Detailed analyses are conducted by dividing the countries into 12 groups based on gross national income per capita (Atlas method) and the share of resource rents in gross domestic product. A macro-level system of indicators for assessing energy efficiency is proposed, which can be linked to the economic and energy characteristics of the systems: National Energy Efficiency, Energy Intensity Ratio, Primary Energy Production Efficiency, Industrial Sector Energy Efficiency, Share of Low Carbon Energy, Population Energy Efficiency. The findings show that (1) global energy efficiency has remained largely unchanged over the decade; (2) there are differences in energy efficiency between fossil fuel producing and non-fossil fuel producing countries; (3) the factors that have an inverse effect on emissions in fossil fuels dependent countries include industrial energy efficiency and population energy efficiency, which are far from exhausting their potential; (4) despite the recognized importance of energy efficiency, its impact on reducing greenhouse gas emissions globally has been unnoticeable. The study highlights that Sustainable Development Goals 7 and 13 on energy affordability and emission reductions are conflicting and require harmonization of mechanisms to achieve them, such as balancing climate change mitigation investments, identifying the potential for energy efficiency improvements across economy or improving energy saving practices. The overall conclusion suggests that there is an urgent need to review current decarbonization and energy efficiency initiatives, given the lack of significant global progress in reducing greenhouse gas emissions or improving energy efficiency between 2010 and 2020.

The article considers the most modern and most informative methods for diagnosing faults in squirrel-cage induction motors with a short-circuited rotor. The methods are based on the evaluation of vibrations and electrical parameters, as well as coordinates during operation and are normalized when powered from a sinusoidal voltage with frequency of 50 Hz. The main advantages and disadvantages of these methods, as well as the main indicators characterizing the general technical condition of the electric motor, are described. The application of these methods when powered from a non-sinusoidal voltage is impossible due to the complex harmonic composition of current and voltage. The consequence of this composition are the ripple moment and additional power loss. The interaction of the harmonics of the inverter and subharmonics, caused by faults, leads to the occurrence of a resonance phenomenon, and therefore to a decrease in the stability of the electric drive.

This article is devoted to the prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.