Institutions and carbon emissions: an investigation employing STIRPAT and machine learning methods
Cooray, Arusha, and Özmen, Ibrahim (2024) Institutions and carbon emissions: an investigation employing STIRPAT and machine learning methods. Empirical Economics, 67. pp. 1015-1044.
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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.
| Item ID: | 83529 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 1435-8921 |
| Keywords: | C87, Carbon emissions, F64, Institutions, Lasso regression, Machine learning, O43, Q53, Ridge regression, STIRPAT |
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| Copyright Information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Date Deposited: | 04 Sep 2024 00:23 |
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