Bayesian Spatio-temporal Analysis of Greenhouse Gas Emissions in Africa

Global warming is a driver of climate change and is attributed to the increasing concentration of greenhouse gases in the atmosphere due to human activities. Although Africa contributes the least to global greenhouse gas emissions, its emissions are still on the increase. This study analyzes the spatial effect, temporal effect, and the interaction of these effects on these emissions in Africa. A 27-year greenhouse gas emissions data of some selected African countries was studied using Bayesian Spatio-temporal analysis within a Bayesian framework. Inference was based on integrated nested Laplace approximation implemented using the R-INLA package in R. Various subsets of Spatio-temporal models were fitted, including those that accounted for boundary shared among countries. Results show that models with the Spatio-temporal interaction effect outperform models that did not take this effect into account, confirming findings from existing literature. Findings from this study also revealed that the boundary shared among countries impacts greenhouse gas emissions. Countries that are less likely to have high greenhouse gas emissions but shared boundaries with those likely to have high estimates eventually had a high estimate of emissions over a long period. Controlling and reducing greenhouse gas emissions in Africa should be a collective effort, particularly among countries sharing boundaries.