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Carbon Dioxide Emissions and Economic Activities: A Mean Field Variational Bayes Semiparametric Panel Data Model with Random Coefficients

Author

Listed:
  • Badi Baltagi

    (Syracuse University)

  • Georges Bresson

    (CREDEN - Centre de Recherche en Economie et Droit de l'ENergie - UM1 - Université Montpellier 1)

  • Jean-Michel Etienne

    (CEPS - Centre d'Economie de l'ENS Paris-Saclay - Université Paris-Saclay - ENS Paris Saclay - Ecole Normale Supérieure Paris-Saclay)

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Badi Baltagi & Georges Bresson & Jean-Michel Etienne, 2019. "Carbon Dioxide Emissions and Economic Activities: A Mean Field Variational Bayes Semiparametric Panel Data Model with Random Coefficients," Post-Print hal-04129289, HAL.
    • Handle: RePEc:hal:journl:hal-04129289
    DOI: 10.15609/annaeconstat2009.134.0043
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    Cited by:

    1. Alina Wilke & Zhiwei Shen & Matthias Ritter, 2021. "How Much Can Small-Scale Wind Energy Production Contribute to Energy Supply in Cities? A Case Study of Berlin," Energies, MDPI, vol. 14(17), pages 1-20, September.
    2. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change," Center for Policy Research Working Papers 254, Center for Policy Research, Maxwell School, Syracuse University.
    3. Cho-Hoi Hui & Andrew Wong, 2021. "Do countries adjust the carbon intensity of energy towards targets? The role of financial development on the adjustment," SN Business & Economics, Springer, vol. 1(10), pages 1-30, October.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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