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The Ecological Footprint and Kuznets Environmental Curve in the USMCA Countries: A Method of Moments Quantile Regression Analysis

Author

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  • Mario Gómez

    (Instituto de Investigaciones Económicas y Empresariales (Economic and Business Research Institute), Universidad Michoacana de San Nicolás de Hidalgo-Ciudad Universitaria-ININEE, Morelia 58030, Mexico)

  • José Carlos Rodríguez

    (Instituto de Investigaciones Económicas y Empresariales (Economic and Business Research Institute), Universidad Michoacana de San Nicolás de Hidalgo-Ciudad Universitaria-ININEE, Morelia 58030, Mexico)

Abstract

This article examines the environmental Kuznets curve for the member countries of the United States–Mexico–Canada Agreement (USMCA), using the ecological footprint as a measure of environmental degradation during 1980–2016. Panel data econometric methods are applied in this research, such as the cross-section dependence, unit root, cointegration and causality tests, and the new method of moments quantile regression (MMQR). The results suggest that the variables are characterized by a cross-section dependence, integrated of order one, and cointegrated. The fully modified ordinary least squares (FMOLS) method shows that renewable energy reduces environmental degradation, and the environmental Kuznets curve is validated. In contrast, patents and trade openness do not show a statistically significant relationship. These results are confirmed with the MMQR, where renewable energy reduces environmental degradation in quantiles from 4 to 6, while the environmental Kuznets curve hypothesis is valid in quantiles from 3 to 9, and patents and trade openness do not show a statistically significant relationship in any quantile. Therefore, it is essential to promote renewable energies, cleaner technologies, and environmental regulations to reduce polluting emissions.

Suggested Citation

  • Mario Gómez & José Carlos Rodríguez, 2020. "The Ecological Footprint and Kuznets Environmental Curve in the USMCA Countries: A Method of Moments Quantile Regression Analysis," Energies, MDPI, vol. 13(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6650-:d:463291
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