Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Institutions and carbon emissions: an investigation employing STIRPAT and machine learning methods
Empirical Economics · Eylül 2024
YÖKSİS Kayıtları
Institutions and carbon emissions: an investigation employing STIRPAT and machine learning methods
Empirical Economics · 2024 SSCI
DOÇENT İBRAHİM ÖZMEN →
Makale Bilgileri
DergiEmpirical Economics
Yayın TarihiEylül 2024
Cilt / Sayfa67 · 1015-1044
Scopus ID2-s2.0-85188418207
Erişim🔓 Açık Erişim
Özet
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.
Yazarlar (2)
1
Arusha Cooray
ORCID: 0000-0001-8809-9188
2
Ibrahim Özmen
ORCID: 0000-0003-2632-4217
Anahtar Kelimeler
C87
Carbon emissions
F64
Institutions
Lasso regression
Machine learning
O43
Q53
Ridge regression
STIRPAT
Kurumlar
James Cook University
Townsville Australia
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
2
Atıf
2
Yazar
10
Anahtar Kelime