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

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  • 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
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    as
    1. 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.
    2. 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.
    3. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, , vol. 32(2), pages 59-80, April.
    4. 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.
    5. 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.
    6. 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.
    7. Manuel Llorca & Jose Banos & Somoza Jose & Pelayo Arbues, 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, , vol. 38(5), pages 153-174, September.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. 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.
    13. Kumar, Surender, 2006. "Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index," Ecological Economics, Elsevier, vol. 56(2), pages 280-293, February.
    14. 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.
    15. Edward A. Hudson & Dale W. Jorgenson, 1974. "U.S. Energy Policy and Economic Growth, 1975-2000," Bell Journal of Economics, The RAND Corporation, vol. 5(2), pages 461-514, Autumn.
    16. 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.
    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. 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.
    20. 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.
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