Modelling Electricity Generation and Capacity with CO₂ Emissions for Sub Saharan Africa

Abstract

In this investigation the electricity generation and the electricity capacity of energy mix for sub Saharan Africa from 2020 to 2040 including CO 2 emission from (coal, oil, gas) (Total Final Consumption, transport) and power generation were analyzed. These energy sources include conventional and renewable energy sources such as coal, oil, gas, hydro, nuclear, bioenergy, solar PV, and other renewables. We developed a linear regression equation based on the least-square method of estimation to forecast the value of energy and CO 2 emission. We fit a linear trend to the energy time series including CO 2 emission to show how simple linear regression analysis can be used to forecast future value. The predicted results from 2020 to 2040 show that the electricity capacity and the electricity generation from gas, hydro, solar PV and other renewables will dominate compared to nuclear and bioenergy. Some forms of energies contributions such as nuclear and bioenergy will remain insignificant. The gas will continue to emit a lot carbon dioxide compared to the emission from oil and coal. The emission of CO 2 from total final consumption (TFC) of oil will be high compared to its emission from power generation (PG) and transport. The least squares estimated regression equation adequately describes the relationship between Energy or CO 2 emission and time period with a high R-square d. This approach of modeling in a linear regression , the energy and CO 2 emission simplifies significantly the analysis to help policy makers under lying reasons for the trends to develop appropriate strategies for the future , may be useful to assess the sustained economic development and transformation that require a definition of electricity access in those countries.