Multivariate Analysis of the Effect of Climate Conditions on Gold Production in Ghana

  • Sampson Takyi Appiah University of Mines and Technology, UMaT
  • Albert Buabeng University of Mines and Technology, UMaT
  • N. K. Dumakor-Dupey University of Mines and Technology, UMaT
Keywords: Gold Production, Climate, Multicollinearity, VIF, Regression Models with ARIMA Errors

Abstract

The change in climatic conditions and its catastrophic effect on mining activities has become a source of worry for mining industries and therefore needs due attention. This study examined the effect some climate factors on commercial gold production in Ghana. First, a direct Multiple Linear Regression was applied on the climate factors with the aim of determining the relative effect of each factor on gold production which exhibited a time series structure. The consequence is that the estimates of coefficients and their standard errors will be wrongly estimated if the time series structure of the errors is ignored. In order to eliminate these deficiencies and better understand the effect of these climate factors on gold production, regression with ARIMA errors technique was employed after its appropriateness has been tested. The model was then compared in terms of prediction accuracy which resulted a MAPE of 9.78%. It was concluded that, gold production in Ghana is positively related to Temperature whilst negatively to Rainfall and Precipitate. It was recommended that mine operators in Ghana could base on this analysis to optimise their production planning and scheduling.

The change in climatic conditions and its catastrophic effect on mining activities has become a source of worry for mining industries and therefore needs due attention. This study examined the effect some climate factors on commercial gold production in Ghana. First, a direct Multiple Linear Regression was applied on the climate factors with the aim of determining the relative effect of each factor on gold production which exhibited a time series structure. The consequence is that the estimates of coefficients and their standard errors will be wrongly estimated if the time series structure of the errors is ignored. In order to eliminate these deficiencies and better understand the effect of these climate factors on gold production, regression with ARIMA errors technique was employed after its appropriateness has been tested. The model was then compared in terms of prediction accuracy which resulted a MAPE of 9.78%. It was concluded that, gold production in Ghana is positively related to Temperature whilst negatively to Rainfall and Precipitate. It was recommended that mine operators in Ghana could base on this analysis to optimise their production planning and scheduling.

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Published
2018-06-28