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Convergence in road transport CO2 emissions in Europe

Author

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  • Ángel Marrero

    (Universidad de La Laguna)

  • Gustavo A. Marrero

    (Universidad de La Laguna)

  • Marina González

    (Universidad de La Laguna)

  • Jesús Rodríguez-López

    (U. Pablo de Olavide)

Abstract

In Europe the transport sector accounts for more than 27% of total CO2 emissions and, within this sector, road transport is by far the largest polluter. This fact has placed road transport emissions abatement firmly on the agenda of global alliances. In this paper, we examine the convergence in per capita road transport CO2 emissions in a sample of 22 European Union (EU) countries over the 1990-2014 period. We find evidence that EU countries converge to one another but depending on certain structural factors (conditional convergence), and that the convergence speed has increased over time. In light of this evidence, we estimate a conditional convergence dynamic panel data model to examine the structural factors affecting the convergence process and its influence on the convergence speed. Because, in our sample, road transport CO2 emissions depend almost exclusively on (fossil) fuel consumption, we focus on the determinants channelled through the use of energy in the sector. By using alternative econometric approaches (pooled-OLS, fixed-effects and instrumental variables), our results show that the convergence process is conditioned by factors such as economic activity and fuel prices and that some of these factors have a significant effect on the convergence speed. These results may entail policy implications with regards to the geographical impact of the EU policies on climate change currently in place.

Suggested Citation

  • Ángel Marrero & Gustavo A. Marrero & Marina González & Jesús Rodríguez-López, 2020. "Convergence in road transport CO2 emissions in Europe," Working Papers 20.06, Universidad Pablo de Olavide, Department of Economics.
  • Handle: RePEc:pab:wpaper:20.06
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    Keywords

    Road transport; CO2 emissions; Convergence; Dynamic panel data models.;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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