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The institutional determinants of CO 2 emissions: a computational modeling approach using Artificial Neural Networks and Genetic Programming
Author(s) -
ÁlvarezDíaz Marcos,
CaballeroMiguez Gonzalo,
Soliño Mario
Publication year - 2011
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.1025
Subject(s) - artificial neural network , ordinary least squares , econometrics , genetic programming , set (abstract data type) , negative binomial distribution , fractionalization , computer science , economics , empirical research , process (computing) , mathematics , artificial intelligence , sociology , statistics , ethnic group , poisson distribution , anthropology , programming language , operating system
Understanding the complex process of climate change implies the knowledge of all possible determinants of CO 2 emissions. This paper studies the influence of several institutional determinants on CO 2 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 CO 2 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 CO 2 emissions. Specifically, all methods conclude a significant influence of ethnolinguistic fractionalization (ETHF) on CO 2 emissions. Copyright © 2009 John Wiley & Sons, Ltd.