IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v42y2021i1p93-128.html
   My bibliography  Save this article

European Industries’ Energy Efficiency under Different Technological Regimes: The Role of CO2 Emissions, Climate, Path Dependence and Energy Mix

Author

Listed:
  • Eirini Stergiou
  • Kostas Kounetas

Abstract

The assessment of industrial-level energy efficiency’s (EE) development is a critical research topic that has entrenched in the global battle against climate change. Under the Energy Efficiency Directives 2012/27/EU and 2018/2002/EU, European Commission sets specific industrial energy efficiency targets, rules and obligations for the 2020-2030 period aiming, among others, at specific energy intensity reduction and energy efficiency improvements. In this paper we use a balanced panel of fourteen European industries from twenty-seven countries for the period 1995-2011 under a metatechnology framework. The aim of this study is to evaluate, at a first stage, the industrial total factor energy efficiency (TFEE) at a national and European level by incorporating technological heterogeneity through a nonparametric approach. Reflecting the divergent views on the importance of desirable and undesirable outcomes in the pursuit of TFEE, we additionally estimate industrial performance by prioritizing either economic or environmental aspects. At the second stage of our analysis, econometric models are applied to investigate the main factors of industrial TFEE using sector specific and country characteristics while we further proceed with a ft and ^-convergence analysis for our TFEE measures. The results of this study reveal that small-scale economies exhibit persistent high TFEE scores. At the same time, TFEE determinants suggest that path dependence phenomena have a strong presence, climatic characteristics occur while energy mix displays both linear and non-linear relationship. Either considering one desirable output or consolidating the undesirable output in the production function our results indicate a strong evidence of conditional and unconditional convergence in TFEE scores.

Suggested Citation

  • Eirini Stergiou & Kostas Kounetas, 2021. "European Industries’ Energy Efficiency under Different Technological Regimes: The Role of CO2 Emissions, Climate, Path Dependence and Energy Mix," The Energy Journal, , vol. 42(1), pages 93-128, January.
    • Handle: RePEc:sae:enejou:v:42:y:2021:i:1:p:93-128
    DOI: 10.5547/01956574.42.1.este
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5547/01956574.42.1.este
    Download Restriction: no
    File URL: https://libkey.io/10.5547/01956574.42.1.este?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. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    2. Hosier, Richard H. & Dowd, Jeffrey, 1987. "Household fuel choice in Zimbabwe : An empirical test of the energy ladder hypothesis," Resources and Energy, Elsevier, vol. 9(4), pages 347-361, December.
    3. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    4. Han, Lei & Han, Botang & Shi, Xunpeng & Su, Bin & Lv, Xin & Lei, Xiao, 2018. "Energy efficiency convergence across countries in the context of China’s Belt and Road initiative," Applied Energy, Elsevier, vol. 213(C), pages 112-122.
    5. Manuel Llorca & José Baños & José Somoza & Pelayo Arbués, 2017. "A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    6. Kumar, Surender, 2006. "Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index," Ecological Economics, Elsevier, vol. 56(2), pages 280-293, February.
    7. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    8. Azadeh, A. & Amalnick, M.S. & Ghaderi, S.F. & Asadzadeh, S.M., 2007. "An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors," Energy Policy, Elsevier, vol. 35(7), pages 3792-3806, July.
    9. Lin Zhang and Philip Kofi Adom, 2018. "Energy Efficiency Transitions in China: How Persistent are the Movements to/from the Frontier?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    10. S C Ray, 2008. "The directional distance function and measurement of super-efficiency: an application to airlines data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 788-797, June.
    11. Richard G. Newell & Adam B. Jaffe & Robert N. Stavins, 1999. "The Induced Innovation Hypothesis and Energy-Saving Technological Change," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 941-975.
    12. Diakoulaki, D. & Zopounidis, C. & Mavrotas, G. & Doumpos, M., 1999. "The use of a preference disaggregation method in energy analysis and policy making," Energy, Elsevier, vol. 24(2), pages 157-166.
    13. Halkos, George E. & Tzeremes, Nickolaos G., 2011. "A conditional nonparametric analysis for measuring the efficiency of regional public healthcare delivery: An application to Greek prefectures," Health Policy, Elsevier, vol. 103(1), pages 73-82.
    14. Zhang, Ning & Zhou, P. & Choi, Yongrok, 2013. "Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance functionanalysis," Energy Policy, Elsevier, vol. 56(C), pages 653-662.
    15. Anthony Shorrocks, 2013. "Decomposition procedures for distributional analysis: a unified framework based on the Shapley value," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(1), pages 99-126, March.
    16. Gilbert E. Metcalf, 2008. "An Empirical Analysis of Energy Intensity and Its Determinants at the State Level," The Energy Journal, , vol. 29(3), pages 1-26, July.
    17. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    18. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    19. Markandya, Anil & Pedroso-Galinato, Suzette & Streimikiene, Dalia, 2006. "Energy intensity in transition economies: Is there convergence towards the EU average?," Energy Economics, Elsevier, vol. 28(1), pages 121-145, January.
    Full references (including those not matched with items on IDEAS)

    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. Kounetas, Konstantinos & Stergiou, Eirini, 2019. "Technology heterogeneity in European industries' energy efficiency performance. The role of climate, greenhouse gases, path dependence and energy mix," MPRA Paper 92314, University Library of Munich, Germany.
    2. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    3. Tajudeen, Ibrahim A., 2021. "The underlying drivers of economy-wide energy efficiency and asymmetric energy price responses," Energy Economics, Elsevier, vol. 98(C).
    4. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    5. Löschel, Andreas & Pothen, Frank & Schymura, Michael, 2015. "Peeling the onion: Analyzing aggregate, national and sectoral energy intensity in the European Union," Energy Economics, Elsevier, vol. 52(S1), pages 63-75.
    6. Zhang, Fan, 2013. "The energy transition of the transition economies: An empirical analysis," Energy Economics, Elsevier, vol. 40(C), pages 679-686.
    7. Michael Schymura & Andreas Löschel, 2012. "Trade and the Environment: An Application of the WIOD Database," EcoMod2012 3948, EcoMod.
    8. Yanni Yu & Yongrok Choi, 2015. "Measuring Environmental Performance Under Regional Heterogeneity in China: A Metafrontier Efficiency Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 375-388, October.
    9. Zhang, Ning & Kong, Fanbin & Choi, Yongrok, 2014. "Measuring sustainability performance for China: A sequential generalized directional distance function approach," Economic Modelling, Elsevier, vol. 41(C), pages 392-397.
    10. Ying Li & Yung-Ho Chiu & Liang Chun Lu, 2018. "Regional Energy, CO 2 , and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach," Energies, MDPI, vol. 11(8), pages 1-20, August.
    11. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    12. Zhang, Ning & Wei, Xiao, 2015. "Dynamic total factor carbon emissions performance changes in the Chinese transportation industry," Applied Energy, Elsevier, vol. 146(C), pages 409-420.
    13. Makridou, Georgia & Andriosopoulos, Kostas & Doumpos, Michael & Zopounidis, Constantin, 2016. "Measuring the efficiency of energy-intensive industries across European countries," Energy Policy, Elsevier, vol. 88(C), pages 573-583.
    14. Gamtessa, Samuel & Olani, Adugna Berhanu, 2018. "Energy price, energy efficiency, and capital productivity: Empirical investigations and policy implications," Energy Economics, Elsevier, vol. 72(C), pages 650-666.
    15. Voigt, Sebastian & De Cian, Enrica & Schymura, Michael & Verdolini, Elena, 2014. "Energy intensity developments in 40 major economies: Structural change or technology improvement?," Energy Economics, Elsevier, vol. 41(C), pages 47-62.
    16. Rabindra Nepal & Tooraj Jamasb & Clement Allan Tisdell, 2013. "Market-Related Reforms and Increased Energy Efficiency in Transition Countries: Empirical Evidence," Energy Economics and Management Group Working Papers 8-2013, School of Economics, University of Queensland, Australia.
    17. Inglesi-Lotz, Roula & Blignaut, James N., 2011. "South Africa’s electricity consumption: A sectoral decomposition analysis," Applied Energy, Elsevier, vol. 88(12), pages 4779-4784.
    18. Broadstock, David C. & Li, Jiajia & Zhang, Dayong, 2016. "Efficiency snakes and energy ladders: A (meta-)frontier demand analysis of electricity consumption efficiency in Chinese households," Energy Policy, Elsevier, vol. 91(C), pages 383-396.
    19. Mulder, Peter & de Groot, Henri L.F., 2012. "Structural change and convergence of energy intensity across OECD countries, 1970–2005," Energy Economics, Elsevier, vol. 34(6), pages 1910-1921.
    20. Zhang, Ning & Choi, Yongrok, 2013. "Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis," Energy Economics, Elsevier, vol. 40(C), pages 549-559.

    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:42:y:2021:i:1:p:93-128. 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.