Carbon dioxide emissions and economic activities: A mean field variational Bayes semiparametric panel data model with random coefficients

Badi H Baltagi, Georges Bresson, Jean Michel Etienne

Research output: Contribution to journalArticle

Abstract

This paper proposes semiparametric estimation of the relationship between CO2 emissions and economic activities for a panel of 81 countries observed over the period 1991-2015. The observed differentiated behaviors by country reveal strong heterogeneity as well as different trends across countries and years. This is the motivation behind using a mixed fixed- and random-coefficients panel data model to estimate this relationship. Following Lee and Wand (2016a), we apply a mean field variational Bayes approximation to estimate a log model with structural breaks between CO2 emissions per capita and GDP per capita including control covariates such as energy intensity and use, energy consumption, population density, urbanization and trade. Results reveal a strong “CO2 emissions - GDP elasticity”, close to one, confirming the increasing but complex link between these two variables. The use of this methodology enriches the estimates of climate change models underlining a large diversity of responses across variables and countries.

Original languageEnglish (US)
Pages (from-to)43-77
Number of pages35
JournalAnnals of Economics and Statistics
Issue number134
DOIs
StatePublished - Jan 1 2019

Keywords

  • Carbon Dioxide Emissions
  • Energy Intensity
  • Environmental Kuznets Curve
  • GDP
  • Greenhouse Gas Emissions
  • Mean Field Variational Bayes Approximation
  • Panel Data
  • Random Coefficients
  • Semiparametric Model

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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