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Title article: Energy intensity and energy efficiency of the domestic economy in the context of socio-economic development of the regions
UDC: 338.2
Section: ECONOMICS
Authors: Shvetsov Andrey V.;Shvetsova Natalia K.
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Introduction. Energy intensity is the amount of energy required to perform a job. This concept is used in various fields, including physics, chemistry, ecology and economics. In economics, energy intensity is the amount of energy needed to produce a unit of a good or service. It can be measured in kilowatt hours (kWh) per US dollar or other currency. Energy intensity is an important indicator of energy efficiency and can help in determining energy conservation and sustainable development strategies. The article characterizes the state of the domestic economy in terms of energy intensity and energy efficiency based on statistics from Rosstat in 2021, and also shows the structure of the dependence of GRP energy intensity on a number of socio-economic factors. The purpose of the study is to analyze the dynamics of the energy intensity of the GDP of the Russian Federation, as well as the dependence of the dynamics of the energy intensity of the GRP of the subjects of the Russian Federation on the main socio-economic factors in the available time interval. Materials and methods. The work uses materials from periodicals, mass media, official data on the socio-economic state of the domestic economy, including in the context of the subjects of the Russian Federation. Research results, discussions. Due to the fact that for a long time the domestic economy was considered a raw material, it became necessary to evaluate this conclusion at the present stage of economic and geopolitical development. Since the energy intensity can be different for different types of work or products and depends on many factors, such as production technology, materials used, climate, etc., it is necessary to investigate this indicator both in time and geographical (territorial) aspect. Conclusion. The results show that such dependencies exist, however, in order to obtain more accurate and high-quality models, it is necessary to more accurately determine the subject composition for each model using clustering.

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