A Mathematical Investigation of Fuel Subsidy Removal and its Effects on Nigerian Economy
Keywords:
Absolute Random Error, ASDDE, Fuel Subsidy Removal, HESDBBDFM, Nigerian Government, UncertaintyAbstract
Communication in Physical Sciences, 2024, 11(3): 398-428
Authors: Chigozie Chibuisi, Bright O. Osu, Kevin Ndubuisi C. Njoku and Chukwuka Fernando Chikwe
Received: 14 January 2024/Accepted: 09 May 2024
The objective of this paper is to investigate and provide a solution to the adverse effects of fuel subsidy removal on the Nigerian Economy through a mathematical investigation. Recently, the removal of fuel subsidies by the Nigerian Government is of good interest to strengthen the economy of the country but it resulted in information and macroeconomic adverse effects of uncertainties directly to petroleum marketers, fuel dealers, transport operators, production companies and marketers of produced products and the general public thereby inflicting sufferings on the masses through inflation. The adverse effects of these uncertainties are capable of resulting in future delay and uncertainty noise in the financial market during business transactions which are therefore modeled mathematically as an Advanced Stochastic Delay Differential Equation (ASDDE). The modelled equation is solved using the Hybrid Extended Second Derivative Block Backward Differentiation Formulae Method (HESDBBDFM) with three new theorems of mathematical expressions developed for the evaluations of the delay term and noise term. To reduce the adverse effects of uncertainties of fuel subsidy removal, the government of Nigeria should diversify and develop other economies and conduct a well-designed communication campaign to highlight and tackle the negative impact of fuel subsidy and the benefits from its removal and compensating measures for the poor to cushion the adverse effects of fuel subsidy removal. This can be expressed mathematically by solving some examples of ASDDE numerically using the proposed method. The analysis of the numerical solutions of the modelled equation with its comparison and graphical presentations proved that the proposed method performs better by producing the Least Minimum Absolute Random Errors (LMAREs) at Lesser Computational Processing Unit Time (LCPUT) which indicates a reduction in the adverse effects of uncertainties of fuel subsidy removal for better economy than other existing methods in terms of accuracy and efficiency.
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