Modeling the electricity generation dynamics of Ghana: a structural vector autoregression regression approach

Sampson Agyapong Atuahene () and Qian Xu Sheng ()
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Sampson Agyapong Atuahene: Zhejiang Normal University
Qian Xu Sheng: Zhejiang Normal University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 2, No 50, 3927-3946

Abstract: Abstract This study estimates endogenous parameters to ascertain the dynamics of the electricity generation sector in Ghana. An unrestricted vector autoregression regression (VAR) model is employed to examine the empirical ramifications of the Ghanaian electricity energy sector and power consumption. The data period ranges from 2002 to 2021. Precisely, the results detected structural long-run and short-run headwinds for the unrestricted models. The findings depict that the reactions of the GDP growth rate and electricity from fossil fuels react directly to headwinds based on their correlation. Also, the Granger causality analysis shows a feedback relationship between the gross domestic product (GDP) growth rate and electricity from fossil fuel sources since they are significant. The impulse function indicates that the GDP growth rate responds to outside forces, and the effects last for a long time. Furthermore, the variance decomposition results backed up this analysis, showing that renewable energy without hydropower explains less than 1% of the variance due to shocks and total global greenhouse emissions, further explaining about 85% of the variance due to headwinds in the time period. Electricity from fossil fuel sources explains more than 100% of the variance owing to headwinds hitting the system, implying the nation’s overreliance on conventional energy sources. Installed renewable energy will grow to over 2500 MW by 2036, at 57.8%. As a recommendation, the Energy Commission of Ghana needs to exploit renewable energy sources.

Keywords: Ghana; Vector autoregression regression; Electricity; Energy; Conventional energy; Renewable energy sources (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10668-023-04050-x

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