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The Function of Geopolitical Risk on Carbon Neutrality Under the Shadow of Russia-Ukraine Conflict: Evidence from Russia's Sectoral CO2 Emissions by High-Frequency Data and Quantile-Based Methods

Author

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  • Mustafa Tevfik Kartal
  • Ugur Korkut Pata

Abstract

By considering the ongoing Russia-Ukraine conflict, this study analyzes the function of the geopolitical risk index (GPR) on carbon neutrality. So, the study focuses on Russia because the recent literature has focused on mainly European countries and ignored Russia. In this context, Russia's GPR is used as the core explanatory variable, and Russia's sectoral CO emissions are considered as the dependent variables and carbon neutrality indicator. Also, high-frequency daily data from January 2, 2019, to June 30, 2023, is used and novel quantile methods are performed for empirical uncovering. The results present that (i) in the domestic aviation sector, GPR decreases CO emissions at all quantiles except for some upper ones; (ii) in the international aviation sector, GPR increases CO emissions at middle and upper quantiles except for lower ones; (iii) in both transport and power sectors, GPR has an increasing effect at higher quantiles; (iv) in the industry sector, GPR has a mixed effect on CO emissions; (v) in the residential sector, increasing GPR stimulates CO emissions; (vi) there are generally causal effects from GPR to sectoral CO emissions at all quantiles except for some middle (0.50-0.55) ones; (vii) the results are consistent based on the alternative econometric method. Overall, at higher quantiles, GPR stimulates all sectoral CO2 emissions except the industry sector and the effects of GPR on sectoral CO2 emissions vary based on quantiles. Accordingly, various policy caveats for Russia are discussed in detail.

Suggested Citation

  • Mustafa Tevfik Kartal & Ugur Korkut Pata, 2023. "The Function of Geopolitical Risk on Carbon Neutrality Under the Shadow of Russia-Ukraine Conflict: Evidence from Russia's Sectoral CO2 Emissions by High-Frequency Data and Quantile-Based Methods," Journal of Sustainable Development Issues (JOSDI), SDIjournals, vol. 1(1), pages 1-12, December.
    • Handle: RePEc:arv:journl:v:1:y:2023:i:1:p:1-12
    DOI: 10.62433/josdi.v1i1.7
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    Cited by:

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    3. Hasan Dinçer & Serhat Yüksel & Serkan Eti & Merve Acar, 2024. "Pathways to Energy Independence: Spherical Fuzzy Modeling for Decision Making in Energy Investments," Journal of Sustainable Development Issues (JOSDI), SDIjournals, vol. 2(1), pages 1-10, June.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • N54 - Economic History - - Agriculture, Natural Resources, Environment and Extractive Industries - - - Europe: 1913-
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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