Energy–Climate–Economy–Population Nexus: An Empirical Analysis in Kenya, Senegal, and Eswatini

Samuel Asumadu Sarkodie, Emmanuel Ackom, Festus Victor Bekun, Phebe Asantewaa Owusu

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Abstract

Motivated by the Sustainable Development Goals (SDGs) and its impact by 2030, this study examines the relationship between energy consumption (SDG 7), climate (SDG 13), economic growth and population in Kenya, Senegal and Eswatini. We employ a Kernel Regularized Least Squares (KRLS) machine learning technique and econometric methods such as Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS) regression, the Mean-Group (MG) and Pooled Mean-Group (PMG) estimation models. The econometric techniques confirm the Environmental Kuznets Curve (EKC) hypothesis between income level and CO2 emissions while the machine learning method confirms the scale effect hypothesis. We find that while CO2 emissions, population and income level spur energy demand and utilization, economic development is driven by energy use and population dynamics. This demonstrates that income, population growth, energy and CO2 emissions are inseparable, but require a collective participative decision in the achievement of the SDGs.
Original languageEnglish
Article number6202
JournalSustainability
Volume12
Issue number15
ISSN2071-1050
DOIs
Publication statusPublished - 2020

Keywords

  • Kernel regularized least squares
  • Environmental Kuznets curve
  • Climate change
  • Kenya
  • Senegal
  • Eswatin
  • Energy–growth–population nexus
  • Panel data
  • Heterogeneity
  • Kaya identity

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