Assessing the long-term asymmetric relationship between energy consumption and CO2 emissions: Evidence from the Visegrad Group countries
DOI:
https://doi.org/10.18559/ebr.2024.1.1082Keywords:
asymmetric panel data, Visegrad Group, energy transition, asymmetric causality, renewable energy, CO2 emissionsAbstract
This study investigates the impact of renewable (REW) and non-renewable (NREW) energy usage, along with econom-ic growth (GDP), on carbon dioxide (CO2) emissions in the Visegrad countries, which rely heavily on traditional energy sources. Using data from 1991 to 2021, the analysis employs a panel asymmetric regression with Driscoll-Kraay and FGLS standard errors. The latent cointegration test reveals long-term relationships with asymmetry among the variables. Real GDP fluctuations exhibit a negative impact on CO2emissions for both positive and negative shocks. A reduc-tion in conventional energy source consumption leads to a greater CO2 emission reduction, confirming asymmetry. Conversely, an increase in consumption positively impacts CO2 reduction. However, non-conventional energy sources show no asymmetries. The OLS-based model proposed by Driscoll-Kraay showed reduced standard errors, but lower significance in the estimated parameters compared to the FGLS model. The findings recommend a sustainable energy transition for Visegrad countries by eliminating traditional sources and promoting renewable resources.
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Adedoyin, F. F., Ozturk, I., Bekun, F. V., Agboola, P. O., & Agboola, M. O. (2021). Renewable and non-renewable energy policy simulations for abating emissions in a complex economy: Evidence from the novel dynamic ARDL. Renewable Energy, 177, 1408–1420. https://doi.org/10.1016/j.renene.2021.06.018 DOI: https://doi.org/10.1016/j.renene.2021.06.018
View in Google Scholar
Ahmad, M., Işık, C., Jabeen, G., Ali, T., Ozturk, I., & Atchike, D. W. (2021). Heterogeneous links among urban concentration, non-renewable energy use intensity, economic development, and environmental emissions across regional development levels. Science of The Total Environment, 765, 144527. https://doi.org/10.1016/j.scitotenv.2020.144527 DOI: https://doi.org/10.1016/j.scitotenv.2020.144527
View in Google Scholar
Ali, F., Huang, S., & Cheo, R. (2020). Climatic Impacts on Basic Human Needs in the United States of America: A Panel Data Analysis. Sustainability, 12(4), Article 4. https://doi.org/10.3390/su12041508 DOI: https://doi.org/10.3390/su12041508
View in Google Scholar
Allison, P. D. (2019). Asymmetric Fixed-effects Models for Panel Data. Socius, 5, 2378023119826441. https://doi.org/10.1177/2378023119826441 DOI: https://doi.org/10.1177/2378023119826441
View in Google Scholar
Alvarado, R., Deng, Q., Tillaguango, B., Méndez, P., Bravo, D., Chamba, J., Alvarado-Lopez, M., & Ahmad, M. (2021). Do economic development and human capital decrease non-renewable energy consumption? Evidence for OECD countries. Energy, 215, 119147. https://doi.org/10.1016/j.energy.2020.119147 DOI: https://doi.org/10.1016/j.energy.2020.119147
View in Google Scholar
Ambroziak, Ł., Chojna, J., Miniszewski, M., Strzelecki, J., Aleksander, S., Śliwowski, P., Święcicki, I., & Wąsiński, M. (2021). Visegrad Group—30 years of transformation, integration and development. Polish Economic Institute. https://pie.net.pl/grupa-wyszehradzka-30-lat-transformacji-integracji-i-rozwoju/
View in Google Scholar
Antonakakis, N., Chatziantoniou, I., & Filis, G. (2017). Energy consumption, CO2 emissions, and economic growth: An ethical dilemma. Renewable and Sustainable Energy Reviews, 68, 808–824. https://doi.org/10.1016/j.rser.2016.09.105 DOI: https://doi.org/10.1016/j.rser.2016.09.105
View in Google Scholar
Apergis, N., & Ozturk, I. (2015). Testing Environmental Kuznets Curve hypothesis in Asian countries. Ecological Indicators, 52, 16–22. https://doi.org/10.1016/j.ecolind.2014.11.026 DOI: https://doi.org/10.1016/j.ecolind.2014.11.026
View in Google Scholar
Baltagi, B. H., Feng, Q., & Kao, C. (2012). A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170(1), 164–177. https://doi.org/10.1016/j.jeconom.2012.04.004 DOI: https://doi.org/10.1016/j.jeconom.2012.04.004
View in Google Scholar
Baum, C. F. (2001). Residual Diagnostics for Cross-section Time Series Regression Models. The Stata Journal, 1(1), 101–104. https://doi.org/10.1177/1536867X0100100108 DOI: https://doi.org/10.1177/1536867X0100100108
View in Google Scholar
Bilgili, F., Koçak, E., & Bulut, Ü. (2016). The dynamic impact of renewable energy consumption on CO2 emissions: A revisited Environmental Kuznets Curve approach. Renewable and Sustainable Energy Reviews, 54, 838–845. https://doi.org/10.1016/j.rser.2015.10.080 DOI: https://doi.org/10.1016/j.rser.2015.10.080
View in Google Scholar
Cialani, C. (2017). CO2 emissions, GDP and trade: A panel cointegration approach. International Journal of Sustainable Development & World Ecology, 24(3), 193–204. https://doi.org/10.1080/13504509.2016.1196253 DOI: https://doi.org/10.1080/13504509.2016.1196253
View in Google Scholar
Coy, D., Malekpour, S., Saeri, A. K., & Dargaville, R. (2021). Rethinking community empowerment in the energy transformation: A critical review of the definitions, drivers and outcomes. Energy Research & Social Science, 72, 101871. https://doi.org/10.1016/j.erss.2020.101871 DOI: https://doi.org/10.1016/j.erss.2020.101871
View in Google Scholar
Daoud, J. I. (2017). Multicollinearity and Regression Analysis. Journal of Physics: Conference Series, 949(1), 012009. https://doi.org/10.1088/1742-6596/949/1/012009 DOI: https://doi.org/10.1088/1742-6596/949/1/012009
View in Google Scholar
Debone, D., Leite, V. P., & Miraglia, S. G. E. K. (2021). Modelling approach for carbon emissions, energy consumption and economic growth: A systematic review. Urban Climate, 37, 100849. https://doi.org/10.1016/j.uclim.2021.100849 DOI: https://doi.org/10.1016/j.uclim.2021.100849
View in Google Scholar
Dissanayake, H., Perera, N., Abeykoon, S., Samson, D., Jayathilaka, R., Jayasinghe, M., & Yapa, S. (2023). Nexus between carbon emissions, energy consumption, and economic growth: Evidence from global economies. PLOS ONE, 18(6), e0287579. https://doi.org/10.1371/journal.pone.0287579 DOI: https://doi.org/10.1371/journal.pone.0287579
View in Google Scholar
Driscoll, J. C., & Kraay, A. C. (1998). Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data. The Review of Economics and Statistics, 80(4), 549–560. DOI: https://doi.org/10.1162/003465398557825
View in Google Scholar
Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. https://doi.org/10.1016/j.econmod.2012.02.014 DOI: https://doi.org/10.1016/j.econmod.2012.02.014
View in Google Scholar
Flanker, R. (2016). The Paris Agreement and the new logic of international climate politics. International Affairs, 92(5), 1107–1125. https://doi.org/10.1111/1468-2346.12708 DOI: https://doi.org/10.1111/1468-2346.12708
View in Google Scholar
Givens, J. E., Huang, X., & Jorgenson, A. K. (2019). Ecologically unequal exchange: A theory of global environmental injustice. Sociology Compass, 13(5), e12693. https://doi.org/10.1111/soc4.12693 DOI: https://doi.org/10.1111/soc4.12693
View in Google Scholar
Granger, C. W. J., & Yoon, G. (2002). Hidden Cointegration (SSRN Scholarly Paper 313831). https://doi.org/10.2139/ssrn.313831 DOI: https://doi.org/10.2139/ssrn.313831
View in Google Scholar
Haberl, H., Wiedenhofer, D., Virág, D., Kalt, G., Plank, B., Brockway, P., Fishman, T., Hausknost, D., Krausmann, F., Leon-Gruchalski, B., Mayer, A., Pichler, M., Schaffartzik, A., Sousa, T., Streeck, J., & Creutzig, F. (2020). A systematic review of the evidence on decoupling of GDP, resource use and GHG emissions, part II: Synthesizing the insights. Environmental Research Letters, 15(6), 065003. https://doi.org/10.1088/1748-9326/ab842a DOI: https://doi.org/10.1088/1748-9326/ab842a
View in Google Scholar
Hatemi-J, A. (2012). Asymmetric causality tests with an application. Empirical Economics, 43(1), 447–456. https://doi.org/10.1007/s00181-011-0484-x DOI: https://doi.org/10.1007/s00181-011-0484-x
View in Google Scholar
Hatemi-J, A. (2020). Hidden panel cointegration. Journal of King Saud University - Science, 32(1), 507–510. https://doi.org/10.1016/j.jksus.2018.07.011 DOI: https://doi.org/10.1016/j.jksus.2018.07.011
View in Google Scholar
Hatemi-J, A. (2022). Dynamic Asymmetric Causality Tests with an Application. Engineering Proceedings, 18(1), Article 1. https://doi.org/10.3390/engproc2022018041 DOI: https://doi.org/10.3390/engproc2022018041
View in Google Scholar
Hatemi-J, A., & El-Khatib, Y. (2016). An extension of the asymmetric causality tests for dealing with deterministic trend components. Applied Economics, 48(42), 4033–4041. https://doi.org/10.1080/00036846.2016.1150950 DOI: https://doi.org/10.1080/00036846.2016.1150950
View in Google Scholar
Inglesi-Lotz, R., & Dogan, E. (2018). The role of renewable versus non-renewable energy to the level of CO2 emissions a panel analysis of sub- Saharan Africa’s Βig 10 electricity generators. Renewable Energy, 123, 36–43. https://doi.org/10.1016/j.renene.2018.02.041 DOI: https://doi.org/10.1016/j.renene.2018.02.041
View in Google Scholar
Iqbal, S., Wang, Y., Shaikh, P. A., Maqbool, A., & Hayat, K. (2022). Exploring the asymmetric effects of renewable energy production, natural resources, and economic progress on CO2 emissions: Fresh evidence from Pakistan. Environmental Science and Pollution Research, 29(5), 7067–7078. https://doi.org/10.1007/s11356-021-16138-w DOI: https://doi.org/10.1007/s11356-021-16138-w
View in Google Scholar
Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1–44. https://doi.org/10.1016/S0304-4076(98)00023-2 DOI: https://doi.org/10.1016/S0304-4076(98)00023-2
View in Google Scholar
LaBelle, M. C., Tóth, G., & Szép, T. (2022). Not Fit for 55: Prioritizing Human Well-Being in Residential Energy Consumption in the European Union. Energies, 15(18), Article 18. https://doi.org/10.3390/en15186687 DOI: https://doi.org/10.3390/en15186687
View in Google Scholar
Mardani, A., Streimikiene, D., Cavallaro, F., Loganathan, N., & Khoshnoudi, M. (2019). Carbon dioxide (CO2) emissions and economic growth: A systematic review of two decades of research from 1995 to 2017. Science of The Total Environment, 649, 31–49. https://doi.org/10.1016/j.scitotenv.2018.08.229 DOI: https://doi.org/10.1016/j.scitotenv.2018.08.229
View in Google Scholar
McGee, J. A., & York, R. (2018). Asymmetric relationship of urbanization and CO2 emissions in less developed countries. PLOS ONE, 13(12), e0208388. https://doi.org/10.1371/journal.pone.0208388 DOI: https://doi.org/10.1371/journal.pone.0208388
View in Google Scholar
Muço, K., Valentini, E., & Lucarelli, S. (2021). The Relationships between GDP growth, Energy Consumption, Renewable Energy Production and CO2 Emissions in European Transition Economies. International Journal of Energy Economics and Policy, 11(4), Article 4. DOI: https://doi.org/10.32479/ijeep.11275
View in Google Scholar
Muhammad, B., & Khan, S. (2019). Effect of bilateral FDI, energy consumption, CO2 emission and capital on economic growth of Asia countries. Energy Reports, 5, 1305–1315. https://doi.org/10.1016/j.egyr.2019.09.004 DOI: https://doi.org/10.1016/j.egyr.2019.09.004
View in Google Scholar
Naqvi, S., Wang, J., & Ali, R. (2022). Towards a green economy in Europe: Does renewable energy production has asymmetric effects on unemployment? Environmental Science and Pollution Research, 29(13), 18832–18839. https://doi.org/10.1007/s11356-021-17093-2 DOI: https://doi.org/10.1007/s11356-021-17093-2
View in Google Scholar
Naseer, S., Song, H., Chupradit, S., Maqbool, A., Hashim, N. A. A. N., & Vu, H. M. (2022). Does educated labor force is managing the green economy in BRCS? Fresh evidence from NARDL-PMG approach. Environmental Science and Pollution Research, 29(14), 20296–20304. https://doi.org/10.1007/s11356-021-16834-7 DOI: https://doi.org/10.1007/s11356-021-16834-7
View in Google Scholar
Pastukhova, M., & Westphal, K. (2020). Governing the Global Energy Transformation. In M. Hafner & S. Tagliapietra (Eds.), The Geopolitics of the Global Energy Transition (pp. 341–364). Springer International Publishing. https://doi.org/10.1007/978-3-030-39066-2_15 DOI: https://doi.org/10.1007/978-3-030-39066-2_15
View in Google Scholar
Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312. https://doi.org/10.1002/jae.951 DOI: https://doi.org/10.1002/jae.951
View in Google Scholar
Rahman, M. M., & Kashem, M. A. (2017). Carbon emissions, energy consumption and industrial growth in Bangladesh: Empirical evidence from ARDL cointegration and Granger causality analysis. Energy Policy, 110, 600–608. https://doi.org/10.1016/j.enpol.2017.09.006 DOI: https://doi.org/10.1016/j.enpol.2017.09.006
View in Google Scholar
Sadiq, M., Chau, K. Y., Ha, N. T. T., Phan, T. T. H., Ngo, T. Q., & Huy, P. Q. (2023). The impact of green finance, eco-innovation, renewable energy and carbon taxes on CO2 emissions in BRICS countries: Evidence from CS ARDL estimation. Geoscience Frontiers, 101689. https://doi.org/10.1016/j.gsf.2023.101689 DOI: https://doi.org/10.1016/j.gsf.2023.101689
View in Google Scholar
Şanlı, D., Muratoğlu, Y., Songur, M., & Uğurlu, E. (2023). The asymmetric effect of renewable and non-renewable energy on carbon emissions in OECD: New evidence from non-linear panel ARDL model. Frontiers in Environmental Science, 11. https://www.frontiersin.org/articles/10.3389/fenvs.2023.1228296 DOI: https://doi.org/10.3389/fenvs.2023.1228296
View in Google Scholar
Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer. https://doi.org/10.1007/978-1-4899-8008-3_9 DOI: https://doi.org/10.1007/978-1-4899-8008-3_9
View in Google Scholar
Suproń, B., & Myszczyszyn, J. (2023). Impact of Renewable and Non-Renewable Energy Consumption and CO2 Emissions on Economic Growth in the Visegrad Countries. Energies, 16(20), Article 20. https://doi.org/10.3390/en16207163 DOI: https://doi.org/10.3390/en16207163
View in Google Scholar
Toumi, S., & Toumi, H. (2019). Asymmetric causality among renewable energy consumption, CO2 emissions, and economic growth in KSA: Evidence from a non-linear ARDL model. Environmental Science and Pollution Research, 26(16), 16145–16156. https://doi.org/10.1007/s11356-019-04955-z DOI: https://doi.org/10.1007/s11356-019-04955-z
View in Google Scholar
Ullah, S., Ozturk, I., Usman, A., Majeed, M. T., & Akhtar, P. (2020). On the asymmetric effects of premature deindustrialization on CO2 emissions: Evidence from Pakistan. Environmental Science and Pollution Research, 27(12), 13692–13702. https://doi.org/10.1007/s11356-020-07931-0 DOI: https://doi.org/10.1007/s11356-020-07931-0
View in Google Scholar
White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934 DOI: https://doi.org/10.2307/1912934
View in Google Scholar
Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data. The MIT Press. https://www.jstor.org/stable/j.ctt5hhcfr
View in Google Scholar
York, R., & Light, R. (2017). Directional Asymmetry in Sociological Analyses. Socius, 3, 2378023117697180. https://doi.org/10.1177/2378023117697180 DOI: https://doi.org/10.1177/2378023117697180
View in Google Scholar
Zoundi, Z. (2017). CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach. Renewable and Sustainable Energy Reviews, 72, 1067–1075. https://doi.org/10.1016/j.rser.2016.10.018 DOI: https://doi.org/10.1016/j.rser.2016.10.018
View in Google Scholar
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