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Estimating the Impact of Feed-in Tariff Adoption: Similarities and Divergences among Countries through a Propensity-score Matching Method

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  • Antonio Angelo Romano

    (Department of Management Studies and Quantitative Methods, University of Naple Parthenope , Via Generale Parisi, 13 Naples, Italy)

  • Giuseppe Scandurra

    (Department of Management Studies and Quantitative Methods, University of Naple Parthenope , Via Generale Parisi, 13 Naples, Italy)

  • Alfonso Carfora

    (Department of Management Studies and Quantitative Methods, University of Naple Parthenope Via Generale Parisi, 13 Naples, Italy)

Abstract

Feed-in tariff (FiT) is one of the most popular policy measures for supporting the generation from renewable energy sources. In this paper we individuate the determinants driving a country s choice of adopting FiT and investigate the reasons at the bottom of their distinctions in the choice of energy policies. The novelty of the paper relates to the methodology used to obtain the second aim that is based on the comparison and cross-checking (matching) between national heterogeneous data of several nature among which the country-effects drawn from a panel analysis conducted, at national level, on a dataset of 54 countries. The results allow us to identify some specific features related to the energy choices of the countries

Suggested Citation

  • Antonio Angelo Romano & Giuseppe Scandurra & Alfonso Carfora, 2016. "Estimating the Impact of Feed-in Tariff Adoption: Similarities and Divergences among Countries through a Propensity-score Matching Method," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 144-151.
    • Handle: RePEc:eco:journ2:2016-02-1
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    Cited by:

    1. Alfonso Carfora & Monica Ronghi & Giuseppe Scandurra, 2017. "The effect of Climate Finance on Greenhouse Gas Emission: A Quantile Regression Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 185-199.
    2. Carfora, Alfonso & Romano, Antonio A. & Ronghi, Monica & Scandurra, Giuseppe, 2017. "Renewable generation across Italian regions: Spillover effects and effectiveness of European Regional Fund," Energy Policy, Elsevier, vol. 102(C), pages 132-141.
    3. Carfora, Alfonso & Scandurra, Giuseppe, 2019. "The impact of climate funds on economic growth and their role in substituting fossil energy sources," Energy Policy, Elsevier, vol. 129(C), pages 182-192.
    4. Cafora, Alfonso & Romano, Antonio Angelo & Ronghi, Monica & Giuseppe, Scandurra, 2017. "Substituting fossil energy sources: the role of the climate funds and effects on the economic growth," MPRA Paper 82373, University Library of Munich, Germany.

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    More about this item

    Keywords

    Feed in Tariff; Renewable Energy; Propensity Score Matching; Energy Investments;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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