期刊
RENEWABLE ENERGY
卷 198, 期 -, 页码 213-221出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2022.08.074
关键词
Institutional quality indicators; CO2 emissions; Climate change mitigation; Environmental sustainability; Sub-Saharan Africa; Environmental Kuznets curve
This study examines the impact of institutional quality on CO2 emissions and finds that corruption control, regulatory quality, and the rule of law significantly reduce CO2 emissions. Additionally, there is a one-way causality from CO2 emissions to industrialization, economic growth, and energy consumption, while a two-way causality is observed between CO2 emissions and population growth as well as institutional quality indices.
The motivation of this study stems from the United Nations Sustainable Development Goals on clean and responsible energy consumption, climate change mitigation and sustainable economic growth (UN-SDGs-7, 11, 12 and 13). The present study examines the impact of institutional quality on CO2 emissions in the presence of the Environmental Kuznets Curve (EKC) framework using data on CO2 emissions and the six dimensions of institutional quality from the World Governance Indicators (WGI). The current study focuses on selected 30 Sub-Saharan African (SSA) countries over the annual period from 2000 to 2021. The EKC hypothesis revealed CO2 emissions are substantially reduced by corruption control, regulatory quality, and the rule of law. Findings from the Dumitrescu and Hurlin causality test showed a one-way causality running from CO2 emissions to industrialization. Similar uni-directional causality is observed between economic growth, and energy consumption. On the other hand, we observed a two-way causality flow from CO2 emissions to population growth and all indices of institutional quality over the investigated period. These findings indicate that government agencies should efficiently implement acceptable strategies for pollution control and enact public benefit environmental regulations in the form of a healthier climate for the entire population.
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