CO2 emissions analysis for East European countries: the role of underlying emission trend

  • Received May 11, 2020;
    Accepted June 4, 2020;
    Published June 5, 2020
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ee.11(1).2020.07
  • Article Info
    Volume 11 2020, Issue #1, pp. 67-81
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This work is licensed under a Creative Commons Attribution 4.0 International License

This paper aims to analyze the impact of energy consumption, economic structure, and manufacturing output on the CO2 emissions of East European countries by applying the Structural Time Series Model (STSM). Several explanatory factors are used to construct the model using annual data of the 1990–2017 period. The factors are: total primary energy supply, GDP per capita and manufacturing value added, and, finally, a stochastic Underlying Emission Trend (UET). The significant effects of all variables on CO2 emissions are detected. Based on the estimated functions, CO2 emissions of Belarus, Ukraine, Romania, Russia, Serbia, and Hungary will decrease, by 2027, to 53.2 Mt, 103.2 Mt, 36.1 Mt, 1528.2 Mt, 36 Mt, and 36.1 Mt, respectively. Distinct from other countries, CO2 emissions of Poland will extend to 312.2 Mt in 2027 due to the very high share of fossil-based supply (i.e., coal and oil) in Poland. The results also indicate that the most forceful factor in CO2 emissions is the total primary energy supply. Furthermore, for Poland, Romania, Hungary, and Belarus, the long-term impact of economic growth on CO2 emissions is negative, while it is positive for Russia, Ukraine, and Serbia. The highest long-term manufacturing value-added elasticity of CO2 emissions is calculated for Serbia and Belarus.

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    • Figure 1. Global CO2 emissions shares of East European countries, China, and India
    • Figure 2. Changes in released CO2 emissions in terms of fuel resource
    • Figure 3. Change in TPES and emissions during the estimation period
    • Figure 4. Long-term GDP per capita, manufacturing value added and TPES
    • Figure A1. Underlying CO2 emissions trends
    • Figure A2. Forecast results and UETs for each country
    • Table A1. Estimated results for each country
    • Formal Analysis
      Denizhan Guven
    • Methodology
      Denizhan Guven
    • Visualization
      Denizhan Guven
    • Writing – original draft
      Denizhan Guven, M. Özgür Kayalica
    • Supervision
      M. Özgür Kayalica
    • Writing – review & editing
      M. Özgür Kayalica, Gülgün Kayakutlu