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Clustering environmental performances, energy efficiency and clean energy patterns: a comparative static approach across EU Countries

Author

Listed:
  • Marco Quatrosi

    (University of Ferrara – Department of Economics and Management (Ferrara, Italy);)

Abstract

In the context of convergence of objectives among the single Member States within the European Union, environmental policy has always been considered one pivotal and necessary step towards a cohesive EU. Employing clustering techniques, this work identifies affinities in environmental performances (e.g., CO2 emissions), energy efficiency, and clean energy patterns for European countries. K-medoids clustering will be used for a cross-section of the total carbon dioxide emission in three reference years (2008, 2013, 2018). Data to feed the algorithm have been selected considering the well-established IPAT relationship as an analytical framework. After preliminary analysis, results highlighted the presence of persistent groups of countries over time with marked characteristics in terms of environmental performances, energy efficiency, and clean energy patterns. Considering the limitations of data employed and the potentialities of the methodological approach, this work could shed light on a new perspective of analysis in light of the harmonization path the EU has been undertaking since its foundation. These findings could better address policymakers in terms of convergence of environmental policy implementing new measures to promote low-carbon consumption and production patterns with a specific focus on energy efficiency (e.g., heating and cooling) and sustainable sources (e.g., nuclear power).

Suggested Citation

  • Marco Quatrosi, 2022. "Clustering environmental performances, energy efficiency and clean energy patterns: a comparative static approach across EU Countries," SEEDS Working Papers 0722, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jul 2022.
  • Handle: RePEc:srt:wpaper:0722
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    References listed on IDEAS

    as
    1. Marian R. Chertow, 2000. "The IPAT Equation and Its Variants," Journal of Industrial Ecology, Yale University, vol. 4(4), pages 13-29, October.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Yue, Ting & Long, Ruyin & Chen, Hong & Zhao, Xin, 2013. "The optimal CO2 emissions reduction path in Jiangsu province: An expanded IPAT approach," Applied Energy, Elsevier, vol. 112(C), pages 1510-1517.
    4. Brizga, Janis & Feng, Kuishuang & Hubacek, Klaus, 2013. "Drivers of CO2 emissions in the former Soviet Union: A country level IPAT analysis from 1990 to 2010," Energy, Elsevier, vol. 59(C), pages 743-753.
    5. Marco Quatrosi, 2020. "Analysis of monthly CO2 emission trends for major EU Countries: a time series approach," SEEDS Working Papers 1520, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Nov 2020.
    6. Blindheim, Bernt, 2015. "A missing link? The case of Norway and Sweden: Does increased renewable energy production impact domestic greenhouse gas emissions?," Energy Policy, Elsevier, vol. 77(C), pages 207-215.
    7. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Q50; Q43; C38;
    All these keywords.

    JEL classification:

    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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