IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v41y2020i4p153-184.html
   My bibliography  Save this article

On the CO2 Emissions Determinants During the EU ETS Phases I and II: A Plant-level Analysis Merging the EUTL and Platts Power Data

Author

Listed:
  • Benoit Chèze
  • Julien Chevallier
  • Nicolas Berghmans
  • Emilie Alberola

Abstract

This article studies ex-post the CO2 emissions determinants during 2005-2012 by resorting to an original database merging the European Union Transaction Log (EUTL) with the World Electric Power Plants (WEPP) database maintained by Platts. We estimate the main drivers of CO2 emissions for the 1,453 power plants included in the EU ETS using plant-level panel data. During phases I and II, there has been a debate about whether the economic crisis was ultimately the only factor behind the fall in CO2 emissions. We find that the EU ETS kept some degree of effectiveness but only during phase I (2005-07). During phase II (2008-12), its impact has been largely impeded by the deep economic recession in 2008-2009 which became the leading cause of the emissions reduction. We disentangle the analysis not only by periods but also for each type of power plants. We conclude that the EU Commission’s flagship climate policy could and should be enhanced by better coordination of overlapping climate policies.

Suggested Citation

  • Benoit Chèze & Julien Chevallier & Nicolas Berghmans & Emilie Alberola, 2020. "On the CO2 Emissions Determinants During the EU ETS Phases I and II: A Plant-level Analysis Merging the EUTL and Platts Power Data," The Energy Journal, , vol. 41(4), pages 153-184, July.
  • Handle: RePEc:sae:enejou:v:41:y:2020:i:4:p:153-184
    DOI: 10.5547/01956574.41.4.bche
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5547/01956574.41.4.bche
    Download Restriction: no

    File URL: https://libkey.io/10.5547/01956574.41.4.bche?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
    ---><---

    References listed on IDEAS

    as
    1. Campiglio, Emanuele, 2016. "Beyond carbon pricing: The role of banking and monetary policy in financing the transition to a low-carbon economy," Ecological Economics, Elsevier, vol. 121(C), pages 220-230.
    2. JÅ«ratÄ— JaraitÄ— & Corrado Di Maria, 2016. "Did the EU ETS Make a Difference? An Empirical Assessment Using Lithuanian Firm-Level Data," The Energy Journal, , vol. 37(2), pages 68-92, April.
    3. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    4. Aleksandar Zaklan, 2013. "Why Do Emitters Trade Carbon Permits?: Firm-Level Evidence from the European Emission Trading Scheme," Discussion Papers of DIW Berlin 1275, DIW Berlin, German Institute for Economic Research.
    5. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    6. Christoph Böhringer & Henrike Koschel & Ulf Moslener, 2008. "Efficiency losses from overlapping regulation of EU carbon emissions," Journal of Regulatory Economics, Springer, vol. 33(3), pages 299-317, June.
    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. Lessmann, Christian & Kramer, Niklas, 2024. "The effect of cap-and-trade on sectoral emissions: Evidence from California," Energy Policy, Elsevier, vol. 188(C).
    2. Mahmoud Hassan & Marc Kouzez & Ji-Yong Lee & Badreddine Msolli & Hatem Rjiba, 2024. "Does Increasing Environmental Policy Stringency Enhance Renewable Energy Consumption in OECD Countries?," Post-Print hal-04350282, HAL.
    3. Flori, Andrea & Borghesi, Simone & Marin, Giovanni, 2024. "The environmental-financial performance nexus of EU ETS firms: A quantile regression approach," Energy Economics, Elsevier, vol. 131(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. Benoît Chèze, Julien Chevallier, Nicolas Berghmans, and Emilie Alberola, 2020. "On the CO2 Emissions Determinants During the EU ETS Phases I and II: A Plant-level Analysis Merging the EUTL and Platts Power Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 153-184.
    2. Cheng Hsiao, 2007. "Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-22, May.
    3. Bun, Maurice J.G. & Kiviet, Jan F., 2006. "The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 409-444, June.
    4. Kentaro Akashi & Naoto Kunitomo, 2015. "The limited information maximum likelihood approach to dynamic panel structural equation models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 39-73, February.
    5. León-González, Roberto & Montolio, Daniel, 2015. "Endogeneity and panel data in growth regressions: A Bayesian model averaging approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 23-39.
    6. José María ARRANZ & Carlos GARCÍA SERRANO & Virginia HERNANZ, 2013. "Active labour market policies in Spain: A macroeconomic evaluation," International Labour Review, International Labour Organization, vol. 152(2), pages 327-348, June.
    7. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    8. Oberndorfer, Ulrich & Moslener, Ulf & Böhringer, Christoph & Ziegler, Andreas, 2008. "Clean and Productive? Evidence from the German Manufacturing Industry," ZEW Discussion Papers 08-091, ZEW - Leibniz Centre for European Economic Research.
    9. Cristina Guillamón & Enrique Moral-Benito & Sergio Puente, 2017. "High growth firms in employment and productivity: dynamic interactions and the role of financial constraints?," Working Papers 1718, Banco de España.
    10. Kentaro Akashi & Naoto Kunitomo, 2010. "The Limited Information Maximum Likelihood Approach to Dynamic Panel Structural Equations," CIRJE F-Series CIRJE-F-708, CIRJE, Faculty of Economics, University of Tokyo.
    11. Piotr Gretszel & Henryk Gurgul & £ukasz Lach & Stefan Schleicher, 2020. "Testing for the economic and environmental impacts of EU Emissions Trading System: A panel GMM approach," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 21(2), pages 99-125.
    12. Ryan R. Brady, 2011. "Measuring the diffusion of housing prices across space and over time," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 213-231, March.
    13. Lorenzo Pozzi & Griet Malengier, 2007. "Certainty Equivalence and the Excess Sensitivity of Private Consumption," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1839-1848, October.
    14. Gayle, George-Levi & Viauroux, Christelle, 2007. "Root-N consistent semiparametric estimators of a dynamic panel-sample-selection model," Journal of Econometrics, Elsevier, vol. 141(1), pages 179-212, November.
    15. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.
    16. Sun, Yixiao X, 2005. "Estimation and Inference in Panel Structure Models," University of California at San Diego, Economics Working Paper Series qt5tf1231k, Department of Economics, UC San Diego.
    17. Sarafidis, Vasilis & Yamagata, Takashi, 2010. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors under Cross-sectional Dependence," MPRA Paper 25182, University Library of Munich, Germany.
    18. Zhang, Yonghui & Zhou, Qiankun, 2019. "Estimation for time-invariant effects in dynamic panel data models with application to income dynamics," Econometrics and Statistics, Elsevier, vol. 9(C), pages 62-77.
    19. Andrea Bigano & Francesco Bosello & Giuseppe Marano, 2006. "Energy Demand and Temperature: A Dynamic Panel Analysis," Working Papers 2006.112, Fondazione Eni Enrico Mattei.
    20. Majid M. Al-Sadoon & Tong Li & M. Hashem Pesaran, 2017. "Exponential class of dynamic binary choice panel data models with fixed effects," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 898-927, October.

    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:41:y:2020:i:4:p:153-184. 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.