IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v40y2019i1_supplp55-80.html
   My bibliography  Save this article

The Impact of Energy Market Uncertainty Shocks on Energy Transition in Europe

Author

Listed:
  • Mehmet Balcilar
  • David Roubaud
  • Muhammad Shahbaz

Abstract

We study effects of energy market uncertainty shocks on energy transition on the 28 European Union countries from 1990 to 2015 using annual frequency data. We assess the effects of oil price as well as the energy market supply, demand, and residual price shocks using a time-varying parameter panel data stochastic volatility model. We show the importance of reducing energy market uncertainty for the success of a clean energy transition in Europe as uncertainties have strong time-varying effects on the transition from fossil fuels to renewable energy. The oil price and residual energy price uncertainties are the key factors encouraging renewable energy transition that reduces the vulnerability of economies to energy shocks. Energy supply shocks affect the transition negatively while the demand shocks work similarly to residual energy prices shocks, requiring a robust energy base that is less volatile. The paper also discusses policy recommendations.

Suggested Citation

  • Mehmet Balcilar & David Roubaud & Muhammad Shahbaz, 2019. "The Impact of Energy Market Uncertainty Shocks on Energy Transition in Europe," The Energy Journal, , vol. 40(1_suppl), pages 55-80, June.
  • Handle: RePEc:sae:enejou:v:40:y:2019:i:1_suppl:p:55-80
    DOI: 10.5547/01956574.40.SI1.mbal
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5547/01956574.40.SI1.mbal
    Download Restriction: no

    File URL: https://libkey.io/10.5547/01956574.40.SI1.mbal?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Gales, Ben & Kander, Astrid & Malanima, Paolo & Rubio, Mar, 2007. "North versus South: Energy transition and energy intensity in Europe over 200 years," European Review of Economic History, Cambridge University Press, vol. 11(2), pages 219-253, August.
    2. Lutz Kilian, 2008. "A Comparison of the Effects of Exogenous Oil Supply Shocks on Output and Inflation in the G7 Countries," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 78-121, March.
    3. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    4. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    5. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    6. Claude Henry, 1974. "Investment decisions under uncertainty: The "irreversibility effect"," ULB Institutional Repository 2013/327343, ULB -- Universite Libre de Bruxelles.
    7. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    8. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alola, Andrew Adewale & Akadiri, Seyi Saint, 2021. "Clean energy development in the United States amidst augmented socioeconomic aspects and country-specific policies," Renewable Energy, Elsevier, vol. 169(C), pages 221-230.
    2. Yongliang Zhang & Md. Qamruzzaman & Salma Karim & Ishrat Jahan, 2021. "Nexus between Economic Policy Uncertainty and Renewable Energy Consumption in BRIC Nations: The Mediating Role of Foreign Direct Investment and Financial Development," Energies, MDPI, vol. 14(15), pages 1-29, August.
    3. Su, Chi-Wei & Khan, Khalid & Umar, Muhammad & Chang, Tsangyao, 2022. "Renewable energy in prism of technological innovation and economic uncertainty," Renewable Energy, Elsevier, vol. 189(C), pages 467-478.
    4. He, Ruofan & Wan, Panbing, 2024. "Electricity market integration in China: The role of government officials’ hometown ties," Energy, Elsevier, vol. 303(C).
    5. Akan, Taner, 2023. "Can renewable energy mitigate the impacts of inflation and policy interest on climate change?," Renewable Energy, Elsevier, vol. 214(C), pages 255-289.
    6. Isaac Appiah-Otoo, 2021. "Impact of Economic Policy Uncertainty on Renewable Energy Growth," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 2(1), pages 1-5.
    7. Bruno Di Giusto & Joseph Lavallee & Igor Žilák & Yvonne Hu Di Giusto, 2024. "Public Opinion and the Energy Transition in East Asia: The Case of Taiwan," Sustainability, MDPI, vol. 16(10), pages 1-20, May.
    8. Saadaoui, Zied & BOUFATEH, Talel & JIAO, Zhilun, 2023. "On the transmission of oil supply and demand shocks to CO2 emissions in the US by considering uncertainty: A time-varying perspective," Resources Policy, Elsevier, vol. 85(PB).
    9. Işık, Cem & Kuziboev, Bekhzod & Ongan, Serdar & Saidmamatov, Olimjon & Mirkhoshimova, Mokhirakhon & Rajabov, Alibek, 2024. "The volatility of global energy uncertainty: Renewable alternatives," Energy, Elsevier, vol. 297(C).
    10. Yang, Yi & Zhen, Deyun & Meng, Fuyu, 2024. "Mineral resource management for a sustainable future: Unraveling the role of energy tax in driving green technologies and environmental quality," Resources Policy, Elsevier, vol. 94(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    2. Giorgio Calzolari & Laura Magazzini, 2014. "Improving GMM efficiency in dynamic models for panel data with mean stationarity," Working Papers 12/2014, University of Verona, Department of Economics.
    3. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    4. Efe Can KILINÇ & Cafer Necat BERBEROĞLU, 2019. "The Relationship Between Saving, Profit Rates and Business CyclesAbstract:There are different approaches of economics schools on the sources, causes and determinants of business cycles. These approach," Sosyoekonomi Journal, Sosyoekonomi Society.
    5. Jooste, Charl & Liu, Guangling (Dave) & Naraidoo, Ruthira, 2013. "Analysing the effects of fiscal policy shocks in the South African economy," Economic Modelling, Elsevier, vol. 32(C), pages 215-224.
    6. Christiane Baumeister & Gert Peersman, 2013. "Time-Varying Effects of Oil Supply Shocks on the US Economy," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(4), pages 1-28, October.
    7. Makiela, Kamil & Ouattara, Bazoumana, 2018. "Foreign direct investment and economic growth: Exploring the transmission channels," Economic Modelling, Elsevier, vol. 72(C), pages 296-305.
    8. Kufenko, Vadmin & Prettner, Klaus, 2017. "You can't always get what you want? A Monte Carlo analysis of the bias and the efficiency of dynamic panel data estimators," ECON WPS - Working Papers in Economic Theory and Policy 07/2017, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.
    9. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    10. Alexander Chudik & M. Hashem Pesaran, 2017. "A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels," CESifo Working Paper Series 6688, CESifo.
    11. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
    12. Odunayo Magret Olarewaju & Stephen Oseko Migiro & Mabutho Sibanda, 2017. "Operational Diversification and Financial Performance of Sub-Saharan Africa Commercial Banks: Static and Dynamic Approach," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 13(5), pages 84-106, OCTOBER.
    13. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    14. Athanasoglou, Panayiotis P. & Brissimis, Sophocles N. & Delis, Matthaios D., 2008. "Bank-specific, industry-specific and macroeconomic determinants of bank profitability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(2), pages 121-136, April.
    15. Musson, Anne & Rousselière, Damien, 2020. "Identifying the impact of crisis on cooperative capital constraint. A short note on French craftsmen cooperatives," Finance Research Letters, Elsevier, vol. 35(C).
    16. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    17. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    18. Doran, Howard E. & Schmidt, Peter, 2006. "GMM estimators with improved finite sample properties using principal components of the weighting matrix, with an application to the dynamic panel data model," Journal of Econometrics, Elsevier, vol. 133(1), pages 387-409, July.
    19. Ryo Okui, 2017. "Misspecification in Dynamic Panel Data Models and Model-Free Inferences," The Japanese Economic Review, Japanese Economic Association, vol. 68(3), pages 283-304, September.
    20. Richard Frensch & Jan Hanousek & Evzen Kocenda, 2016. "Trade in Parts and Components across Europe," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(3), pages 236-262, June.

    More about this item

    Keywords

    Energy transition; Uncertainty; Europe; Stochastic volatility;
    All these keywords.

    JEL classification:

    • F0 - International Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:enejou:v:40:y:2019:i:1_suppl:p:55-80. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.