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Dependence of CO2 emissions on energy consumption and economic growth in the European Union: a panel threshold model

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PL
Zależność emisji CO2 od zużycia energii i wzrostu gospodarczego w unii europejskiej: model progowy
Języki publikacji
EN
Abstrakty
EN
This work aims to analyse the dependence of carbon dioxide (CO2) emissions on primary energy consumption at different Gross Domestic Product (GDP) levels in 28 European countries. Data for the years 1995-2019 were used to develop the models. Random Effects, Fixed Effects, a nonlinear panel threshold model and a continuous kink model were used in the panel data analysis. The work shows that the dependence of CO2 emissions on energy consumption varies at different levels of GDP. The model with two threshold values, which determine three modes of behaviour, proves to be the most suitable. As GDP levels increase, the regression coefficient of the dependence of CO2 emissions on energy consumption decreases. Understanding the relationship between these variables is essen-tial for informed and evidence-based decision-making and adopting new or revision of existing energy and climate policies and strategies at the EU and national levels.
PL
Niniejsza praca ma na celu analizę zależności emisji dwutlenku węgla (CO2) od zużycia energii pierwotnej przy różnych poziomach produktu krajowego brutto (PKB) w 28 krajach europejskich. Do opracowania modeli wykorzystano dane z lat 1995-2019. W analizie danych panelowych zastosowano efekty losowe, efekty stałe, nieliniowy model progu panelu i model ciągłego załamania. Praca pokazuje, że zależność emisji CO2 od zużycia energii jest różna na różnych poziomach PKB. Najbardziej odpowiedni okazuje się model z dwiema wartościami progowymi, które określają trzy sposoby zachowania. Wraz ze wzrostem poziomu PKB maleje współczynnik regresji zależności emisji CO2 od zużycia energii. Zrozumienie związku między tymi zmiennymi ma zasadnicze znaczenie dla świadomego i opartego na dowodach podejmowania decyzji oraz przyjmowania nowych lub rewizji istniejących polityk i strategii w zakresie energii i klimatu na szczeblu UE i krajowym.
Rocznik
Tom
Strony
73--89
Opis fizyczny
Bibliogr. 32 poz., tab., wykr.
Twórcy
  • Faculty of Management, University of Prešov, Prešov, Slovakia, Konštantínova 16, 08001 Prešov, Slovakia
  • Faculty of Management, University of Prešov, Prešov, Slovakia, Konštantínova 16, 08001 Prešov, Slovakia
  • Faculty of Management, University of Prešov, Prešov, Slovakia, Konštantínova 16, 08001 Prešov, Slovakia
Bibliografia
  • Ahmad, A., Zhao, Y., Shahbaz, M., Bano, S., Zhang, Z., Wang, S., Liu, Y., 2016. Carbon emissions, energy consumption and economic growth: An aggregate and disaggregate analysis of the Indian economy, Energy Policy, 96, 131-143, https://doi.org/10.1016/j.enpol.2016.05.032.
  • Alam, M. J., Begum, I. A., Buysse, J., Rahman, S., Van Huylenbroeck, G., 2011. Dynamic modeling of causal relationship between energy consumption, CO2 emissions and economic growth in India. Renewable and Sustainable Energy Reviews, 15(6), 3243-3251.
  • Armeanu, D. S., Joldes, C. C., Gherghina, S. C., Andrei, J. V., 2021. Understanding the multidimensional linkages among renewable energy, pollution, economic growth and urbanisation in contemporary economies: Quantitative assessments across different income countries’ groups. Renewable and Sustainable Energy Reviews, 142(February), https://doi.org/10.1016/j.rser.2021.110818.
  • Balcilar, M., Ozdemir, Z. A., Tunçsiper, B., Ozdemir, H., Shahbaz, M., 2020. On the nexus among carbon dioxide emissions, energy consumption and economic growth in G-7 countries: new insights from the historical decomposition approach. Environment, Development and Sustainability, 22, 8097–8134, https://doi.org/10.1007/s10668-019-00563-6.
  • Baltagi, B. H., 2005. Econometric Analysis of Panel data. John Wiley & Sons.
  • Caner, M., Hansen, B. E., 2004. Instrumental variable estimation of a threshold model. Econom Theory, 20, 813-843, https://doi.org/10.1017/S026646660420501 1.
  • Chang, M. C., 2015. Room for improvement in low carbon economies of G7 and BRICS countries based on the analysis of energy efficiency and environmental Kuznets curves. Journal of Cleaner Production, 99, 140-151, https://doi.org/10.1016/j.jclepro.2015.03.002.
  • Chovancová, J., Vavrek, R., 2020. (De)coupling Analysis with Focus on Energy Consumption in EU Countries and Its Spatial Evaluation. Polish Journal of Environmental Studies, 29(3), 2091-2100, https://doi.org/10.15244/pjoes/110613.
  • EC, 2019. Towards a sustainable Europe by 2030, Reflection paper, European Commission.
  • EEA, 2019. Trends and projections in Europe 2020, https://www.eea.europa.eu/publications/trends-and-projections-in-europe-1.
  • Godawska, J., 2021. Environmental policy stringency and its impact on air pollution in Poland. Ekonomia i Środowisko-Economics and Environment, 76(1), 52-67, https://ekonomiaisrodowisko.pl/journal/article/view/359.
  • Gökmenoğlu, K., Taspinar, N., 2016. The relationship between CO2 emissions, energy consumption, economic growth and FDI: the case of Turkey. The Journal of International Trade & Economic Development, 25(5), 706-723, https://doi.org/10.10 80/09638199.2015.1119876.
  • Han, J., Du, T., Zhang, C., Qian, X., 2018. Correlation analysis of CO2 emissions, material stocks and economic growth nexus: Evidence from Chinese provinces. Journal of Cleaner Production, 180, 395-406, https://doi.org/10.1016/j.jclepro.2018.01.168.
  • Hansen, B. E., 1999. Threshold effects in non-dynamic panels: estimation, testing, and inference. Econom, 93, 345-368, https://doi.org/10.1016/S0304-4076(99)00025-1.
  • Hansen, B. E., 2017. Regression Kink with an unknown threshold. Journal of Business & Economic Statistics, 35(2), 228-240, https://doi.org/10.1080/07350015.2015.1073595.
  • IPCC, 2007. Climate Change 2007 Synthesis Report. In Intergovernmental Panel on Climate Change [Core Writing Team IPCC, https://doi.org/10.1256/004316502 320517344.
  • Kahouli, B., 2018. The causality link between energy electricity consumption, CO2 emissions, R&D stocks and economic growth in Mediterranean countries (MCs). Energy, 145, 388–399. https://doi.org/10.1016/j.energy.2017.12.136.
  • Khan, M. K., Teng, J. Z., Khan, M. I., Khan, M. O., 2019. Impact of globalisation, economic factors and energy consumption on CO2 emissions in Pakistan. Science of the Total Environment., 688, 424-436, https://doi.org/10.1016/j.scitotenv.2019.06.065.
  • Knutti, R., Rogelj, J., Sedláček, J., Fischer, E. M., 2016. A scientific critique of the twodegree climate change target. Nature Geoscience, 9(1), 13-18, https://doi.org/https://doi.org/10.1038/ngeo2595.
  • Litavcová, E., Popovičová, M., etruška, I., 2020. Threshold analysis of tourism indicators of selected countries of EU. 38th International Conference on Mathematical Methods in Economics. Electronic Conference Proceedings (Eds. Svatopluk Kapounek, Hana Vránová). Mendelova Univerzita v Brně., 350-356.
  • Litavcová, Eva, Chovancová, J., 2021. Economic development, CO2 emissions and energy use nexus-evidence from the danube region countries. Energies, 14(11), https://doi.org/10.3390/en14113165.
  • Ozcan, B., Ari, A., 2017. Nuclear energy-economic growth nexus in OECD countries: a panel data analysis. Journal of Economic & Management Perspectives, 11(1), 138-154.
  • Pakulska, J., 2021. Emissions of major air pollutants as an indicator of quality of life in Poland in 1990-2017. Ekonomia i Środowisko-Economics and Environment, 76(1), 106-119, https://doi.org/10.34659/2021/1/6.
  • Rahman, M. M., Vu, X. B., 2020. The nexus between renewable energy, economic growth, trade, urbanisation and environmental quality: a comparative study for Australia and Canada. Renewable Energy, 155, 617-627, https://doi.org/10.1016/j.renene.2020.03.135.
  • Robalino-López, A., Mena-Nieto, Á., García-Ramos, J. E., Golpe, A. A., 2015. Studying the relationship between economic growth, CO2 emissions, and the environmental Kuznets curve in Venezuela (1980–2025). Renewable and Sustainable Energy Reviews, 41, 602–614, https://doi.org/10.1016/j.rser.2014.08.081.
  • Seo, M., Kim, S., Kim, Y. J., 2019. Estimation of Dynamic Panel Treshold Model using Stata. Stata Journal, 19(3), 685-697, https://doi.org/10.1177/1536867X19874243.
  • Seo, M., Shin, Y., 2016. Dynamic panels with treshold effect and endogeneity. Journal of Econometrics, 195, 169-186, https://doi.org/10.1016/j.jeconom.2016.03.005.
  • Shahbaz, M., Mahalik, M. K., Shah, S. H., Sato, J. R., 2016. Time-varying analysis of CO2 emissions, energy consumption, and economic growth nexus: Statistical experience in next 11 countries. Energy Policy, https://doi.org/10.1016/j.enpol.2016. 08.011.
  • Steffen, W., Crutzen, J., McNeill, J. R., 2007. The Anthropocene: Are humans now overwhelming the great forces of Nature? Ambio, 36(8), 614-621, https://doi.org/10.1579/0044-7447.
  • Wang, Q., 2015. Fixed-effect panel threshold model using Stata. The Stata Journal, 15(1), 121-134, https://www.stata-journal.com/article.html?article=st0373.
  • Wang, S., Li, Q., Fang, C., Zhou, C., 2016. The relationship between economic growth, energy consumption, and CO2 emissions: Empirical evidence from China. Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2015.10.027.
  • Zhang, X. P., Cheng, X. M., 2009. Energy consumption, carbon emissions, and economic growth in China. Ecological Economics, 68, 10, 2706-2712, https://doi. org/10.1016/j.ecolecon.2009.05.011.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-462883d1-6938-4478-b2c0-a3c1fb239d0e
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