The institutional determinants of CO2 emissions: a computational modeling approach using Artificial Neural Networks and Genetic Programming

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@Article{Alvarez-Diaz:2011:EM,
  author =       "Marcos Alvarez-Diaz and Gonzalo Caballero-Miguez and 
                 Mario Solino",
  title =        "The institutional determinants of {CO2} emissions: a
                 computational modeling approach using Artificial Neural
                 Networks and Genetic Programming",
  journal =      "Environmetrics",
  year =         "2011",
  volume =       "22",
  number =       "1",
  pages =        "42--49",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, artificial
                 neural networks, ANN, computational methods, CO2
                 emissions, institutional determinants",
  URL =          "https://doi.org/10.1002/env.1025",
  URL =          "https://onlinelibrary.wiley.com/doi/abs/10.1002/env.1025",
  URL =          "https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.1025",
  DOI =          "doi:10.1002/env.1025",
  size =         "8 pages",
  abstract =     "Understanding the complex process of climate change
                 implies the knowledge of all possible determinants of
                 CO2 emissions. This paper studies the influence of
                 several institutional determinants on CO2 emissions,
                 clarifying which variables are relevant to explain this
                 influence. For this aim, Genetic Programming and
                 Artificial Neural Networks are used to find an optimal
                 functional relationship between the CO2 emissions and a
                 set of historical, economic, geographical, religious,
                 and social variables, which are considered as a good
                 approximation to the institutional quality of a
                 country. Besides this, the paper compares the results
                 using these computational methods with that employing a
                 more traditional parametric perspective: ordinary least
                 squares regression (OLS). Following the empirical
                 results of the cross-country application, this paper
                 generates new evidence on the binomial institutions and
                 CO2 emissions. Specifically, all methods conclude a
                 significant influence of ethnolinguistic
                 fractionalization (ETHF) on CO2 emissions.",
  notes =        "Replaces \cite{Alvarez-Diaz:funcas401}?",
}

Genetic Programming entries for Marcos Alvarez-Diaz Gonzalo Caballero Miguez Mario Solino

Citations