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



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. Copyright © 2009 John Wiley & Sons, Ltd.

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