The increasing mismatch between the demand and supply of power in Nigeria raises concerns about the ability of this country to meet its vital energy security and sustainability targets in a demography-growing environment. This paper assesses how these three factors comove over the long run. While Nigeria provides an illustrative case, a multivariate framework including population dynamics, the demand for electricity, and CO2 emissions from the power and heating sector is set with actual time-series data spanning the last five decades. Two independent estimation strategies are conducted: a time-series analysis (i.e., Least Squares with breaks regression) is complemented with Machine Learning experiments (i.e., ML Clustering method). In general, both methodologies’ outputs stress the engine role of the population in driving the demand for power over the long run.
Magazzino, C., Drago, C., Schneider, N. (2023). Evidence of supply security and sustainability challenges in Nigeria’s power sector. UTILITIES POLICY, 82(101576) [10.1016/j.jup.2023.101576].
Evidence of supply security and sustainability challenges in Nigeria’s power sector
Magazzino, Cosimo
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2023-01-01
Abstract
The increasing mismatch between the demand and supply of power in Nigeria raises concerns about the ability of this country to meet its vital energy security and sustainability targets in a demography-growing environment. This paper assesses how these three factors comove over the long run. While Nigeria provides an illustrative case, a multivariate framework including population dynamics, the demand for electricity, and CO2 emissions from the power and heating sector is set with actual time-series data spanning the last five decades. Two independent estimation strategies are conducted: a time-series analysis (i.e., Least Squares with breaks regression) is complemented with Machine Learning experiments (i.e., ML Clustering method). In general, both methodologies’ outputs stress the engine role of the population in driving the demand for power over the long run.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.