Сучасні підходи до оцінювання енергоефективності національної економіки

Abstract

The article notes that energy efficiency cannot simply be a vague and ambiguous concept that does not have accurate assessment methods. It should have clear measurements and evaluation methods. When the question arises as to measuring or evaluating, the lack of a clear definition of energy efficiency can cause the results to differ and it will be difficult to compare them depending on the needs or objectives. So the need to develop an accurate and unified definition of energy efficiency, as well as its classification and development of measurement methods is highlighted. It is emphasized that highly developed countries of the world have significant experience in achieving a high level of energy efficiency of national economies, but they also do not currently have developed a unified system of energy efficiency indicators at the national level, methods for their assessment and analysis. The energy intensity of GDP is almost the only universal instrument by which international and regional comparisons of energy efficiency levels of national economies are possible. It is shown that in recent years in Ukraine there has been a clear tendency to reduce the energy intensity of the national economy in almost all spheres. The State Agency on Energy Efficiency and Energy Saving of Ukraine has developed tables of primary data and a methodology for calculating energy efficiency indicators of the Ukrainian economy. Primary data and calculation indicators are formed by such sectors as industry, transport, housing, agriculture, services. The article shows that recent methods for evaluating energy efficiency based on modeling the efficiency limit, namely, DEA and SFA models, have become a popular tool for evaluating energy efficiency. Despite a number of its advantages, the DEA method does not take into account statistical noise. In contrast to DEA models, SFA models use the Shepard distance function to determine the energy efficiency index and take into account statistical noise.