Approaches to the Forecast Formation of the Development of the Russian Energy Sector as a Component Part of Scenarios for Decarbonization of the Country’s Economy

S. P. Filippov, F. V. Veselov (), A. V. Keiko and A. A. Khorshev
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S. P. Filippov: Energy Research Institute, Russian Academy of Sciences
F. V. Veselov: Energy Research Institute, Russian Academy of Sciences
A. V. Keiko: Energy Research Institute, Russian Academy of Sciences
A. A. Khorshev: Energy Research Institute, Russian Academy of Sciences

Studies on Russian Economic Development, 2023, vol. 34, issue 6, 769-777

Abstract: Abstract— The article deals with topical issues of creating an integral prognostic system to substantiate decarbonization scenarios for the country’s economy, integrating macroeconomic forecasts with energy development parameters and taking into account the dynamics of trends in the global economy and energy. The main attention is paid to the problems of energy forecasting in the context of decarbonization of the economy and the structure of the corresponding forecasting module. The foreign experience of organizing such forecasting activities is analyzed. The structure of the Russian energy forecasting module is proposed, taking into account the existing scientific background and modeling tools. The module includes calculation blocks: demand for fuel and energy resources, optimization of the development and technological structure of the national energy sector and its industries, assessment of the economic parameters of their development; supplementing blocks: technological information, coordination of calculation results. A scheme of interaction of blocks according to the stages of solving forecast problems for the operational coordination of the forecast parameters of the country’s fuel and energy and inter-sectoral balances and for a more detailed study of the technological and regional structure and financial and economic assessment of the fuel and energy sectors is proposed.

Keywords: energy; fuel and energy resources; greenhouse gases; decarbonization; forecasting; modeling; model-information complex (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1134/S1075700723060035

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