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Demand for light fuels in Brazil: an approach using spatial panel data models [Demanda por combustíveis leves no Brasil: uma abordagem usando modelos espaciais de dados em painel]

Author

Listed:
  • Leonardo C. B. Cardoso

    (UFV)

  • Maurício V. L. Bittencourt

    (UFPR)

  • Alexandre A. Porsse

    (UFPR)

Abstract

As mudanças na atual matriz energética são necessárias por conta da futura escassez dos fósseis e pelos danos causados por seu consumo. A correta prescrição de políticas públicas para os mercados de energia necessita do conhecimento das elasticidades da demanda. Por isso, o objetivo deste estudo é estimar os principais determinantes da demanda para o etanol e para a gasolina no Brasil. Estimadores dinâmicos e não dinâmicos foram utilizados e os resultados indicam que ambas as demandas respondem mais a mudanças nos preços da gasolina do que a mudanças nos preços do etanol. Por isso, políticas públicas focadas em mudar hábitos de consumo (redução do consumo de gasolina, por exemplo) têm no preço da gasolina um bom instrumento.

Suggested Citation

  • Leonardo C. B. Cardoso & Maurício V. L. Bittencourt & Alexandre A. Porsse, 2020. "Demand for light fuels in Brazil: an approach using spatial panel data models [Demanda por combustíveis leves no Brasil: uma abordagem usando modelos espaciais de dados em painel]," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 30(1), pages 231-256, January-A.
  • Handle: RePEc:nov:artigo:v:30:y:2020:i:1:p:231-256
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    More about this item

    Keywords

    econometria especial; etanol; gasolina; dados em painel;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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