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Stationarity and Long Range Dependence of Carbon Dioxide Emissions: Evidence for Disaggregated Data

Author

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  • Carlos Pestana Barros

    (Technical University of Lisbon)

  • Luis A. Gil-Alana

    () (University of Navarra)

  • Fernando Perez de Gracia

    () (University of Navarra)

Abstract

Abstract This paper examines the stationarity of global carbon dioxide emissions and its components—gas, liquids, solids, cement production and gas flaring, as well as global per capita emissions—for a long span of data using long range dependence techniques. The empirical results suggest that the series are highly persistent with orders of integration which are above 1 in practically all cases. Disaggregating the data by components, cement production displays the highest persistence, and allowing for structural breaks, two breaks are detected (at 1830 and 1946) for the total series and solids, and one single break (at 1946) for gas, liquids and cement production. In general, higher orders of integration are detected after the break at World War II.

Suggested Citation

  • Carlos Pestana Barros & Luis A. Gil-Alana & Fernando Perez de Gracia, 2016. "Stationarity and Long Range Dependence of Carbon Dioxide Emissions: Evidence for Disaggregated Data," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(1), pages 45-56, January.
  • Handle: RePEc:kap:enreec:v:63:y:2016:i:1:d:10.1007_s10640-014-9835-3
    DOI: 10.1007/s10640-014-9835-3
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    References listed on IDEAS

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    Cited by:

    1. José M. Belbute & Alfredo Marvão Pereira, 2016. "Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption," Working Papers 170, Department of Economics, College of William and Mary.

    More about this item

    Keywords

    Carbon dioxide emissions; Long memory; Fractional integration; Stationarity;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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