This study identifies the factors accountable for the historical growth trends in kerosene and liquefied petroleum gas (LPG) consumption in Cameroon households, thereby quantifying their short-and long-run effects for the period 1994-2014. ARDL bound test and Granger-causality following Toda-Yamamoto procedure under an augmented VAR framework are estimated. Empirical results validate the presence of a long-run equilibrium relationship on one hand between kerosene consumption, prices, income, and urbanization; and on the other hand between LPG consumption, prices, income, and urbanization. Prices, income and urbanization have significant positive impact on kerosene and LPG consumption both in the short-and long-runs, with evidence of high degree of fuel substitution from kerosene to LPG. Granger causality test show that there exists bidirectional causality between LPG consumption and income at the 5% significance level, whereas there is no causality between kerosene consumption and income. This means that an increase in LPG consumption affects economic growth with feedback effect. Consequently, supporting energy policies aimed at increasing LPG consumption while reducing kerosene consumption is achievable in Cameroon. Other captious policy measures and sensitive issues such as market liberalization, energy accretion programs and market competitiveness to upgrade availability, accessibility, distribution and extension of energy services are discussed.
The mastery of demand for electricity in Cameroon is one of the concerns of the State, which is part of the development plan for the electricity sector by 2025. Thus, this paper identifies the factors that influence the electricity sector and mentions positive government actions. We use in this work data on the basis of previous work on one hand and a survey on the other hand in order to reinforce the analysis. The survey was conducted on a sample of 3,000 households in Douala in order to identify socio-economic factors that influence the electricity sector. For this, face-to-face approach was chosen. Households were randomly selected, then a questionnaire was submitted to them, assisting them in their response options. Results show that though certain factors have a positive influence on the electricity sector, Cameroon's current electricity system still remains unsustainable. A comprehensive view of how various factors influence the electricity sector in Cameroon would help in understanding the challenges for the future development of the sector. Government policies in this area would be more enlightened and undergo reorganization. Different models of electricity consumption could thus be formulated and adopted in order to predict the potential impacts of changes in planning.
PurposeConventional statistical forecasting methods typically need a large sample size or the use of overly confident hypotheses, like the Gaussian distribution of the input data. Unfortunately, these input data are frequently scarce or do no not follow a normal distribution law. A grey forecasting model can be developed and used to predict energy consumption for at least four data points or ambiguous data based on grey theory. The standard grey model, however, may occasionally result in significant forecasting errors.Design/methodology/approachIn order to reduce these errors, this paper proposes a hybrid multivariate grey model (namely Grey Modeling (1,N)) optimized by Genetic Algorithms with sequential selection forecasting mechanism, abbreviated as Sequential-GMGA(1,N). A real case of Cameroon's oil products consumption is considered to demonstrate the effectiveness of the proposed forecasting model.FindingsThe results show the superiority of Sequential-GMGA(1,4) when compared with the results of competing grey models reported in the literature, with a mean absolute percentage error as low as 0.02%.Originality/valueWithout changing the model's basic structure, the suggested framework completely extracts the evolution law of multivariate time series. Regardless of data patterns, Sequential-GMGA(1,4) actively enhances all model parameters over the course of each predicted timeframe. Consequently, Sequential-GMGA(1,4) improves forecast accuracy by resolving the discrepancy between the model's least sum of squares of prediction errors and the parameterization approach based on grey derivative.
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