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Economic Analysis: Theory and Practice
 

Neural network analysis of energy efficiency of the regional economy as a factor of Russia's sustainable development under conditions of big challenges

Vol. 22, Iss. 2, FEBRUARY 2023

Received: 23 January 2023

Received in revised form: 30 January 2023

Accepted: 7 February 2023

Available online: 28 February 2023

Subject Heading: ECONOMIC ADVANCEMENT

JEL Classification: 45, O30, R11

Pages: 206234

https://doi.org/10.24891/ea.22.2.206

Nikolai P. LYUBUSHIN Voronezh State University (VSU), Voronezh, Russian Federation
lubushinnp@mail.ru

https://orcid.org/0000-0002-4493-2278

Elena N. LETYAGINA National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
len@fks.unn.ru

https://orcid.org/0000-0002-6539-6988

Valentina I. PEROVA National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
perova_vi@mail.ru

https://orcid.org/0000-0002-1992-5076

Subject. We consider the energy efficiency of Russias regional economy and its impact on sustainable development of the country in the face of big challenges.
Objectives. The focus is on solving the multidimensional task of analyzing the development of energy efficiency of the economy of Russian regions, which relates to difficult-to-formalize tasks and harmonizes with modern requirements of competitiveness.
Methods. We employ the cluster analysis based on neural networks, which are a relevant component of artificial intelligence. We also use the toolkit of artificial neural networks, i.e. Kohonen self-organizing maps. The said tools are free from model limitations and external interference in the functioning of the neural network, and enable to visualize the clustering results of multidimensional data space on the plane.
Results. The cluster analysis of heterogeneous data enabled to distribute Russian regions across eight cluster formations. The considered indicators characterizing the energy efficiency of Russias regional economy had different effects on the creation of clusters. We obtained a significant unevenness of the distribution of Russian regions by cluster: the number of regions in clusters varied more than fourfold. We determined different levels of energy efficiency of the regions economy according to the studied indicators on cluster scale. This requires the application of different economic development strategies for regions of the Russian Federation in the focus of cluster formations.
Conclusions. The paper shows the influence of big challenges on the development of energy efficiency of Russia. The findings indicate that to improve the sustainable and progressive development of Russias economy, innovative organizational and managerial methods are needed that generate a vector of orientation towards effective solution of urgent challenges facing the country.

Keywords: energy efficiency, energy policy, big challenge, cluster analysis, neural network

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