COMPARATIVE STUDY OF RESIDENTIAL HOUSEHOLD ENERGY CONSUMPTION IN NIGERIA: EKITI STATE AS CASE STUDY

Abstract

The study compares the residential household energy consumption pattern between the Nigerians’ densely and sparsely populated areas on the basis of income level classification. This research identified, determined and evaluated the various households’ energy choices, quantities and costs of domestic energy consumption and provided a database for documentation. Data obtained were analyzed using both independent and paired t-tests conducted at 5 and 10% levels of significance between the low and high income earners in the visited areas. The independent t-test analysis indicated a significant difference in the annual energy consumption between the low income earners with 47,016.16 MJ and high income earners as 6,535.80 MJ respectively. Paired t-test showed that, the low income earners had 34,349.90 MJ and 32,908.84 MJ against 19,202.06 MJ and 20,643.98 MJ of high income earners in the ten studied areas. The analysis revealed that, the densely populated area remains the larger consumer of energy content of 827,411.20 MJ (63%) against the sparsely populated areas with 486,267.60 MJ (37%) while on the basis of households’ income; the energy consumed by the low income earners (790,719.30 MJ) is significantly higher than the high income earners (522,959.49 MJ). The study further revealed that, biomass was the most commonly used fuel for cooking in sparsely populated areas; regarding fuel wood (84,789.50 MJ) as poor man’s energy followed by charcoal (147,846.90 MJ) as the last option due to scarcity of fossil fuels at local areas. Conventional fuel - kerosene consumption (388,557.10 MJ), is positively and significantly influenced by income in both locations of comparison. Liquefied petroleum gas (590,055.35 MJ) and electricity (102,429.95 MJ) were predominantly used in the densely populated areas as the most economically viable fuels of choice among others considered in this study. Software was developed to estimate energy consumption per household based on data obtained using java language programming. The performance evaluations of the developed software agree with the obtained data.