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)Link to ORCID Index: http://orcid.org/0000-0003-1605-7661Link to ORCID Index: https://orcid.org/0000-0001-9828-7385Link to ORCID Index: https://orcid.org/0000-0001-8548-6377
-
DOIhttp://dx.doi.org/10.21511/ee.11(1).2020.07
-
Article InfoVolume 11 2020, Issue #1, pp. 67-81
- TO CITE АНОТАЦІЯ
-
Cited by3 articlesJournal title: Ekonomi, Politika & Finans Araştırmaları DergisiArticle title: Gelişmekte Olan Ülkelerde Yenilenebilir Enerji Yatırımlarının Finansal Belirleyicileri Üzerine Ekonometrik Bir AnalizDOI: 10.30784/epfad.1020454Volume: 6 / Issue: IERFM Özel Sayısı / First page: 79 / Year: 2021Contributors: Kenan İLARSLANJournal title:Article title:DOI:Volume: / Issue: / First page: / Year:Contributors:Journal title: Environmental Science and Pollution ResearchArticle title: How do logistics and financial ındicators contribute to carbon emissions in Turkiye?DOI: 10.1007/s11356-023-29255-5Volume: 30 / Issue: 43 / First page: 97842 / Year: 2023Contributors: Tuğrul Bayat, Kenan İlarslan, Muhammad Shahbaz
- 711 Views
-
192 Downloads
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.
- Keywords
-
JEL Classification (Paper profile tab)C32, C53, Q53
-
References30
-
Tables1
-
Figures6
-
- 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
-
- Antanasijević, D. Z., Ristić, M. Đ., Perić-Grujić, A. A., & Pocajt, V. V. (2014). Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis. International Journal of Greenhouse Gas Control, 20, 244-253.
- Aris, B. (2019, September 30). The Cost of Carbon in Russia. The Moscow Times.
- Atalla, T. N., & Hunt, L. C. (2016). Modelling residential electricity demand in the GCC countries. Energy Economics, 59, 149-158.
- Dilaver, Z., & Hunt, L. C. (2011). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426-436.
- Eurocoal. (2019). The country profiles: Poland.
- European Union. (n.d.). EU member countries in brief.
- Grytten, O. H., & Koilo, V. (2019). Evidence of the Environmental Kuznets Curve in Emerging Eastern European Economies (NHH Dept. of Economics Discussion Paper SAM 11/2019).
- Harvey, A. C. (1997). Trends, cycles and autoregressions. The Economic Journal, 107(440), 192-201.
- Harvey, A. C., & Koopman, S. J. (1992). Diagnostic checking of unobserved-components time series models. Journal of Business & Economic Statistics, 10(4), 377-389.
- Harvey, A. C., & Shephard, N. (1993). 10 Structural time series models. Handbook of Statistics, 11, 261-302.
- Harvey, A. C., Henry, S. G. B., Peters, S., & Wren-Lewis, S. (1986). Stochastic trends in dynamic regression models: An application to the employment-output equation. The Economic Journal, 96(384), 975-985.
- Hendry, D., & Juselius, K. (2000). Explaining Cointegration Analysis: Part I. The Energy Journal, 21(1), 1-42.
- Hunt, L. C., Judge, G., & Ninomiya, Y. (2000). Modelling technical progress: an application of the stochastic trend model to UK energy demand (99 p.). Guildford: Surrey Energy Economics Centre.
- IEA. (2019). CO2 Emissions from Fuel Combustion.
- Jebli, M. B., Farhani, S., & Guesmi, K. (2020). Renewable energy, CO2 emissions and value added: Empirical evidence from countries with different income levels. Structural Change and Economic Dynamics, 53, 402-410.
- Köne, A. Ç., & Büke, T. (2010). Forecasting of CO2 emissions from fuel combustion using trend analysis. Renewable and Sustainable Energy Reviews, 14(9), 2906-2915.
- Koopman, S. J., Harvey, A. C., Doornik, J. A., & Shephard, N. (2007). STAMP 8.0: Structural time series analyser, modeller and predictor. London: Timberlake Consultants.
- Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Hauck, J., Pongratz, J., ... & Arneth, A. (2018). Global carbon budget 2018. Earth System Science Data, 10(4), 2141-2194.
- Maddala, G. S., & Kim, I. M. (1998). Unit roots, cointegration, and structural change. Cambridge University Press.
- OECD. (2014). Perspectives on Global Development 2014: Boosting Productivity to Meet the Middle-Income Challenge. OECD Publishing.
- Olivier, J. G., Schure, K. M., & Peters, J. A. H. W. (2018). Trends in global CO2 and total greenhouse gas emissions. PBL Netherlands Environmental Assessment Agency.
- Pérez-Suárez, R., & López-Menéndez, A. J. (2015). Growing green? Forecasting CO2 emissions with environmental Kuznets curves and logistic growth models. Environmental Science & Policy, 54, 428-437.
- Ritchie, H., & Roser, M. (2019). CO₂ and Greenhouse Gas Emissions.
- Scripps Institution of Oceanography. (2020). Scripps CO2 Program: Carbon dioxide concentration at Manua Loa Observatory.
- Song, Y., & Zhang, M. (2019). Study on the gravity movement and decoupling state of global energy-related CO2 emissions. Journal of Environmental Management, 245, 302-310.
- Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., ... & Midgley, P. M. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
- UNFCCC. (2015). Intended Nationally Determined Contributions (INDCs).
- Wei, Y., Li, Yan., Wu, M., & Li, Yingbo. (2019). The decomposition of total-factor CO2 emission efficiency of 97 contracting countries in Paris Agreement. Energy Economics, 78, 365-378.
- World Bank. (2019a). World Development Indicators: GDP.
- World Bank. (2019b). World Development Indicators: Manufacturing, Value Added.
-
-
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
-
Formal Analysis
-
The wealth of nations and sustainable development: energy intensity and the environmental Kuznets curve
Ola Honningdal Grytten , Magnus Lindmark , Kjell Bjørn Minde doi: http://dx.doi.org/10.21511/ee.11(1).2020.10Environmental Economics Volume 11, 2020 Issue #1 pp. 110-123 Views: 1710 Downloads: 495 TO CITE АНОТАЦІЯScholars warn that wealth leads to unsustainable environmental development. However, over the last decades, studies have shown an increase in environmental degradation at the initial stage of economic growth, and then a decline when economic growth reaches a certain level. This first acceleration and then deceleration create an inverted U-shaped curve between pollution and economic growth, called the environmental Kuznets curve (EKC). Environmental degradation can be measured by different factors. This paper deals with two of them, i.e. energy consumption and energy intensity (EI). The latter is measured as the ratio between energy consumption and GDP. The relationship of energy consumption and intensity to economic growth can serve as a tool for examining whether an EKC exists. The paper presents continuous series of energy consumption energy intensity and gross domestic product for the Norwegian mainland economy 1835–2019. The series are used to examine the possible existence of relative and absolute environmental Kuznets curves (EKC). Time series are established using available data and annual figures for 1835–2019, which are presented for the first time. They depict a development that, first, reflects an almost constant downward trend in EI, and, second, the existence of EKCs. The paper also proposes a polynomial regression model to discuss the relationship between environmental degradation as measured by energy consumption and intensity on the one hand, and economic growth on the other. It is concluded that there are both relative and absolute EKC-relations between environmental degradation and economic growth, with 1975 as relative and 2002 as absolute turning point.
-
Legal and economic aspects of Ukrainian enterprises activity at the European renewable energy market
Sergiy Bilotskiy , Nicole Danylova , Olena Grinenko , Oleksandra Karmaza , Daria Koucherets doi: http://dx.doi.org/10.21511/imfi.14(2).2017.07Investment Management and Financial Innovations Volume 14, 2017 Issue #2 pp. 71-78 Views: 1123 Downloads: 532 TO CITE АНОТАЦІЯThe article deals with a current trend of the global energy market, which is characterized by rising tension in relations between the performers of the energy market regulation mechanisms, and it leads to the emergence of alternative energy sources. The article is called to identify the causes of renewable energy markets nascence, to make comparative description of Ukrainian and European Renewable Energy Markets attractiveness, and to characterize the state policy change in a renewable energy market. Different interpretation of nature and classification of the field of renewable energy in foreign and Ukrainian approaches shows the problem of legal criteria of renewable energy markets regulation. It is proved the existence of double barrier penetration of the European market for renewable energy for Ukrainian companies, which includes compliance with the accepted EU Directives and compliance with the Rules of each member individually. The presence of clearly defined standards and certificates of quality for the European market allows producers to show the competitiveness of Ukrainian products in the international market and stimulate Ukrainian manufacturers. The presence of clearly formulated laws, stable and balanced political and legal environment of the EU allows Ukrainian producers of renewable energy to develop such a strategy that considers the time factor, as the primary parameter of competitiveness in international business. The market of solid biofuels in EU is under formation, its development timeframe and uncertainty of environmental risks becoming is especially important for Ukrainian producers.
-
Status and prospects for the development of credit unions in Ukraine
Vita Klimenko , Alena Sokolova , Olena Hasii doi: http://dx.doi.org/10.21511/ppm.15(4).2017.11Problems and Perspectives in Management Volume 15, 2017 Issue #4 pp. 124-133 Views: 1064 Downloads: 219 TO CITE АНОТАЦІЯCredit unions increase social orientation and efficiency of the market economy by providing their members with necessary financial services on a non-profit basis. Unfortunately, the role of credit cooperation is underestimated in Ukraine. The article investigates factors, condition and prospects for the credit unions’ development as an important component of Ukrainian economy. Both optimistic and pessimistic scenarios of credit unions’ development in Ukraine have been provided based on forecasts involving methods of analytical equalization of dynamic series, correlation and regression analysis and extrapolation. Analysis of the main indicators of credit unions’ activity in Ukraine between 2004 and 2016 made it possible to detect that between 2008 (when indicators were the highest) and 2016 there was a downward trend in the number of credit unions (by 41%), membership (by 76%), total assets (by 66.5%), deposit and loan portfolios (by 79 and 68%, respectively) with a relative stability of capital ratio (by 39%). Creating a cooperative cluster which will be able to combine the material, monetary and labor resources of all major types of cooperatives in the country at the regional level has been outlined as one of the means to increase the credit unions competitiveness in Ukraine. Therefore, the purpose of the article is to assess the state and forecast trends in the development of credit unions in Ukraine in order to identify the prospects for their functioning in the future.