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Forecasting of Energy and Petroleum Consumption by Motor Transport in the Regions of the Russian Federation

Author

Listed:
  • Leontiy Eder

    (Federal State Budgetary Scientific Institution Trofimuk Institute of Petroleum Geology and Geophysics of Siberian Branch of Russian Academy of Sciences (IPGG SB RAS))

  • Irina Filimonova

    (a) Federal State Budgetary Scientific Institution Trofimuk Institute of Petroleum Geology and Geophysics of Siberian Branch of Russian Academy of Sciences (IPGG SB RAS) b) Novosibirsk State University)

  • Vasiliy Nemov

    (Kemerovo Science Center (SB RAS))

  • Irina Provornaya

    (Federal Research Center of Coal and Coal Chemistry SB RAS)

Abstract

The paper offers the directions for the improvement of methodological approach to forecasting the energy consumption in transport, taking into account special features of Russian regions. The authors developed a multivariate model allowing to predict the motor vehicle rate specified for the regions of the Russian Federation depending on the economic, social and institutional features. We formalized the dynamic (trend) model for predicting the effectiveness of energy consumption per unit of the vehicle in Russia with details on Federal districts. In the study, in predicting the number of motor transport, the authors applied the methods of economic and mathematical simulation modelling based on the results of the econometric analysis for the calculation of the population having motor transport. In determining the potential specific energy consumption, we have aggregated trending patterns and convergence. The study has shown that by 2040, the number of passenger cars in Russia will grow to 57.1 million, and the total number of all types of road transport will grow by 14.9 million units to 66.2 million. The highest growth rates are predicted in the Central regions of Russia and in some areas of Siberia. The smallest growth rates are expected in the Chukotka Autonomous District, Kamchatka and Primorsky regions. Energy efficiency in transport and active introduction of alternative motor fuels, primarily methane, will reduce the consumption of gasoline and diesel fuel by motor transport. Thus, in the forecast period of 2018–2040, the consumption of petroleum products by motor transport will be reduced by 8.9 million tons: from 61,9 million tons of oil to 51.7 million tons of oil. The results of the study can be applied for the formulation of proposals on the creation of scientific and methodological apparatus to predict the development of transport sector and oil products supply in of the regions of Russia.

Suggested Citation

  • Leontiy Eder & Irina Filimonova & Vasiliy Nemov & Irina Provornaya, 2017. "Forecasting of Energy and Petroleum Consumption by Motor Transport in the Regions of the Russian Federation," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(3), pages 859-870.
    • Handle: RePEc:ura:ecregj:v:1:y:2017:i:3:p:859-870
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    References listed on IDEAS

    as
    1. Eder L. V. & Nemov V. Yu. & Filimonova I. V., 2016. "Prospects for Transport Energy Consumption: Methodological Approaches and Results of Forecasting," World of economics and management / Vestnik NSU. Series: Social and Economics Sciences, Socionet, vol. 16(1), pages 25-38.
    2. Liddle, Brantley & Lung, Sidney, 2013. "The long-run causal relationship between transport energy consumption and GDP: Evidence from heterogeneous panel methods robust to cross-sectional dependence," Economics Letters, Elsevier, vol. 121(3), pages 524-527.
    3. Joyce Dargay & Dermot Gately & Martin Sommer, 2007. "Vehicle Ownership and Income Growth, Worldwide: 1960-2030," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 143-170.
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    Cited by:

    1. Baratsas, Stefanos G. & Niziolek, Alexander M. & Onel, Onur & Matthews, Logan R. & Floudas, Christodoulos A. & Hallermann, Detlef R. & Sorescu, Sorin M. & Pistikopoulos, Efstratios N., 2022. "A novel quantitative forecasting framework in energy with applications in designing energy-intelligent tax policies," Applied Energy, Elsevier, vol. 305(C).

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