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    Analyzing transport demand and environmental degradation: the case of G‑7 countries
    (SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS, 2022) Erdoğan, Sinan; Sarkodie, Samuel Asumadu; Adedoyin, Festus Fatai; Bekun, Festus Victor; Owusu, Phebe Asantewaa
    The debate for green development has been ongoing in the energy and environment literature—especially initiatives to mitigate climate change. On this note, we explore the efects of the air and railway transport demand, fossil-fuel energy consumption, demographic policies, economic growth, and alternative energy consumption on environmental degradation in Group of Seven (G7) economies. Using robust panel estimation techniques that account for cross-sectional dependence, empirical results afrm the presence of long-run relationships among variables. Besides, the results give credence to the environmental Kuznets curve hypothesis (EKC) in G7 countries over the sampled period. We observe that demand for air transport, energy from fossil fuel sources, and economic development dampen environmental quality by 0.12%, 0.33%, and 46.54%, respectively. Interestingly, renewable energy and rail transportation demand improve environmental quality. This outcome resonates with the need for alternative and clean energy production and consumption (Sustainable Development Goals 11 and 12) while enhancing the fght against climate change—especially the adoption of clean energy technologies in the air transport sector for sustainable growth.
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    Energy-Climate-Economy-Population Nexus: An Empirical Analysis in Kenya, Senegal, and Eswatini
    (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2020) Sarkodie, Samuel Asumadu; Ackom, Emmanuel; Bekun, Festus Victor; Owusu, Phebe Asantewaa
    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 CO(2)emissions while the machine learning method confirms the scale effect hypothesis. We find that while CO(2)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 CO(2)emissions are inseparable, but require a collective participative decision in the achievement of the SDGs.