DOI: 10.33917/mic-2.121.2025.106-110

The energy system is a complex multi-dimensional and multi-criteria optimization system, and the energy development levels in different regions and at different stages of development are different. The Development and Reform Commission of China and the Energy Administration in the «Strategy for Energy Production and Revolution (2016–2030)» proposed that China’s inter-regional energy transformation should be based on security and focus on high-quality development, so as to promote the spatial effect of energy development on economic growth. This paper selects data from 30 provinces in China from 2000 to 2022, and uses Moran’s index and spatial Durbin’s error model to study the impact and spatial effect of China’s energy industry development on economic growth.

References:

1. Tobler W.R. A computer movie simulating urban growth in the Detroit region. Economic geography. 1970;46:234-240.

2. Cressie N. Statistics for spatial data. John Wiley & Sons. 2015. pp. 633-649.

3. LeSage J., Pace R.K. Introduction to spatial econometrics. Chapman and Hall, 2009.

4. Elhorst J.P. Spatial econometrics: from cross-sectional data to spatial panels. Heidelberg: Springer, 2014. 119 p.

5. Demidova O.A. Spatial aspects of wage curve estimation in Russia / O.A. Demidova, E.A. Timofeeva. Journal of the New Economic Association. 2021; 3(51):69-101.

6. Fingleton B. Estimates of time to economic convergence: an analysis of regions of the European Union. International regional science review. 1999;22(1):5-34.

7. Rey S.J., Montouri B.D. US regional income convergence: a spatial econometric perspective. Regional studies. 1999;33(2):143-156.

8. Guo Shouting, Jin Zhibo. Research on the spatial spillover effect of digital inclusive finance on regional industrial structure upgrading. Economic Perspectives. 2022;6:77-87.

9. Li Jiang, Wu Yuming. A review of cutting-edge theories, methods and applications of spatial econometrics. Contemporary Economic Management. 2024;06:30-41.

10. Anselin L. Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geographical analysis. 1988;20(1):1-17.

11. Su Yi, Lin Zhouzhou. Research on the spatial effect and influencing factors of regional innovation activities. Journal of Quantitative and Technical Economics. 2017;11:63-80.

12. Tang Baojun, Wu Yun, Wang Chongzhou, Zou Ying. Research on Provincial Energy High-quality Development Index (2012-2022). Journal of Beijing Institute of Technology (Social Sciences Edition). 2023;02:17-23.

DOI: 10.33917/es-1.199.2025.34-43

The author explores and compares relationship between the energy industry and economic growth of China and Russia through the cointegration equation and the VECM model. The results prove that China’s energy production hasn’t got any long-term or short-term impact on GDP fluctuations and growth, while oil and natural gas consumption has a long-term impact on GDP growth, coal and natural gas consumption has a short-term positive impact on GDP growth. Energy production and consumption in Russia have an impact on GDP growth. Production of renewable energy, coal and natural gas as well as consumption of coal and natural gas have long-term equilibrium effects on GDP growth, while production of coal and natural gas as well as consumption of coal, oil and natural gas cause short-term positive fluctuations in GDP growth.

References:

1. Kraft J., Kraft A. On the Relationship Between Energy and GNP. The Journal of Energy and Development, 1978, no 3(2), pp. 401–403, available at: http://www.jstor.org/stable/24806805

2. Yu E., Choi J.Y. The Causal Relationship between Energy and GNP: An International Comparison. Journal of Energy Finance & Development, 1985, no 10, pp. 249–272.

3. Kuzovkin A.I. Prognoz energoemkosti VVP Rossii i razvitykh stran na 2020 g. [Forecast of Energy Intensity of GDP in Russia and Developed Countries for 2020]. Problemy prognozirovaniya, 2010, no 3, pp. 144–148.

4. Wang Xuhui, Liu Yong. China’s energy consumption and economic growth: based on cointegration analysis and Granger causality test. Resources Science, 2007, no 29(5), p. 6.

5. Lola I.S., Gluzdovskiy S.V. Primenenie metodov vektornoy avtoregressii v issledovanii vliyaniya malogo roznichnogo predprinimatel’stva na dinamiku torgovli [Application of Vector Autoregression Methods in the Study of the Impact of Small Retail Businesses on Trade Dynamics]. Voprosy statistiki, 2018, vol. 25, no 11, pp. 3–12, EDN VNBZMD

6. Matveev M.G. Parametricheskaya identifikatsiya modeley vektornoy avtoregressii [Parametric Identification of Vector Autoregression Models]. Sovremennaya ekonomika: problemy i resheniya, 2015, no 5, pp. 133–142, available at: ht tps://journals.vsu.ru/meps/article/view/4347

7. Yue Xiaowen. Restoration of oil and gas industry: A review of Russia’s oil and gas industry in 2021. International Petroleum Economics, 2022, no 30(5), pp. 47–52.

8. Hou Meifang. The current situation, challenges and countermeasures of China’s energy transformation and energy security under the goal of carbon neutrality. Journal of Southwest Petroleum University (Natural Science Edition), 2023, no 45(2): 1-1, DOI: 10.11885/j.issn.1674-5086.2023.01.12.02