Institutions and carbon emissions: an investigation employing STIRPAT and machine learning methods

Arusha Cooray and Ibrahim Özmen ()
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Ibrahim Özmen: Selcuk University

Empirical Economics, 2024, vol. 67, issue 3, No 5, 1015-1044

Abstract: Abstract We employ an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model combined with the environmental Kuznets curve and machine learning algorithms, including ridge and lasso regression, to investigate the impact of institutions on carbon emissions in a sample of 22 European Union countries over 2002 to 2020. Splitting the sample into two: those with weak and strong institutions, we find that the results differ between the two groups. Our results suggest that changes in institutional quality have a limited impact on carbon emissions. Government effectiveness leads to an increase in emissions in the European Union countries with stronger institutions, whereas voice and accountability lead to a fall in emissions. In the group with weaker institutions, political stability and the control of corruption reduce carbon emissions. Our findings indicate that variables such as population density, urbanization and energy consumption are more important determinants of carbon emissions in the European Union compared to institutional governance. The results suggest the need for coordinated and consistent policies that are aligned with climate targets for the European Union as a whole.

Keywords: Institutions; Carbon emissions; STIRPAT; Machine learning; Ridge regression; Lasso regression (search for similar items in EconPapers)
JEL-codes: C87 F64 O43 Q53 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00181-024-02579-y

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