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Multi-Sectorial Convergence in Greenhouse Gas Emissions

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Listed:
  • Guilherme de Oliveira
  • Deise Bourscheidt

Abstract

Este artigo usa um painel dinâmico multi-setorial para testar a hipótese de convergência per capita na emissão de gases do efeito-estufa. Tal teste tornou-se possível com a recente publicação da World Input Output Database. A estratégia empírica aplica estimadores convencionais de efeitos aleatórios e fixos, e também um GMM de Arellano e Bond (1991), para os principais poluentes relacionados ao efeito estufa. Encontramos evidências robustas de convergência na emissão de CH4 em setores ligados à agricultura, indústria de alimentos, e serviços. Com relação à emissão de CO2, encontramos evidencias moderadas na agricultura, indústria de alimentos, indústria de bens-duráveis e serviços. Em todos os casos, o tempo para convergência foi menor do que quinze anos. Nas emissões relevantes pelo uso de energia, uma das maiores fontes causadoras do efeito estufa, encontramos evidências moderadas apenas na indústria extrativa. Todos os demais poluentes apresentaram evidência fraca ou ausência de evidências.

Suggested Citation

  • Guilherme de Oliveira & Deise Bourscheidt, 2015. "Multi-Sectorial Convergence in Greenhouse Gas Emissions," Working Papers, Department of Economics 2015_34, University of São Paulo (FEA-USP).
    • Handle: RePEc:spa:wpaper:2015wpecon34
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    File URL: http://www.repec.eae.fea.usp.br/documentos/Oliveira_Bourscheidt_34WP.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Greenhouse gas emissions; multi-sectorial convergence; panel data;
    All these keywords.

    JEL classification:

    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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