Carbon Intensity, Energy Transition, And Economic Development In Africa: Evidence From A Panel Vector Autoregression Analysis
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International Review of Economics and Finance
Abstract
This study evaluates the dynamic correlations among carbon intensity, energy access, renewable
energy consumption, population growth, economic development, energy intensity, and indus
trialization in Africa using panel data for 52 countries from 2005 to 2023. A Panel Vector
Autoregression (PVAR) framework is used to capture the endogenous interactions and trans
mission mechanisms among environmental, demographic, and economic variables. The results
uncover strong persistence in carbon intensity, energy intensity, population growth, GDP per
capita, and industrialization, supporting the path-dependence and carbon lock-in perspectives.
Energy intensity greatly increases carbon intensity, strongly supporting the energy-efficiency
hypothesis, whereas population growth grows energy intensity levels, revealing the importance
of demographic pressures in determining environmental outcomes. Economic growth directly
determines industrialization, backing the structural transformation theory. Renewable energy
consumption and energy access do not exert significant short-run effects on carbon intensity,
suggesting that the environmental benefits of renewable energy materialize over longer horizons.
Granger causality results indicate that improvements in energy access stimulate renewable energy
consumption, while impulse response and variance decomposition analyses show that renewable
energy and energy efficiency become increasingly important drivers of long-run carbon-intensity
dynamics. The findings underscore the need for integrated policies that promote energy effi
ciency, renewable energy investment, demographic management, and green industrialization to
advance sustainable development and climate resilience in Africa.
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Research Article
Citation
Ahenkan, A., & Alemzero, D. (2026). Carbon intensity, energy transition, and economic development in Africa: Evidence from a panel vector autoregression analysis. International Review of Economics & Finance, 111(C).
