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

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.

Evaluating the Environmental Phillips Curve Hypothesis in the STIRPAT Framework for Finland

“…In another study [10], an enhanced STIRPAT model, the environmental Kuznets curve, and machine learning techniques such as ridge and lasso regression were used to analyze the impact of institutional quality on carbon emissions across 22 European Union countries from 2002 to 2020. The analysis differentiates between countries with strong and weak institutions to compare effects.…”

“…In Equation (10), Δ is the first difference operator, and n, p, q, r, and s are the lag lengths of the ARDL model.…”