Predictive modeling of energy‐related greenhouse gas emissions in Ghana towards a net‐zero future

Abstract

AbstractGhana is determined to reduce greenhouse gas (GHG) emissions by at least 15% by 2030 and attain net‐zero emissions by 2070. However, like many developing countries, Ghana must utilize its limited resources effectively to actualize its climate goals. Currently, climate policies in the country are not driven by emission data, which has important implications on effective utilization of resources and selection of efficient mitigation techniques. We analyzed energy consumption and GHG emission data between 1990 and 2016 from Ghana's energy sector which is responsible for about 36% of the country's total emissions. Predictive models were then developed using machine learning to forecast energy related emissions up to 2030. Based on the analysis and projections, attainable data‐driven recommendations were proposed to direct climate policies in the country. We found that between 1990 and 2016, petroleum fuel consumption increased by about 64.5% and the corresponding GHG emissions increased by 303%. The projections suggests that by 2030, energy sector emissions could increase by 131% compared to 2016 levels. Transport sector emission is also projected to increase by a whopping 219% and fuel consumption could hit 6742 ktoe by 2030, which is about 106% increase from the 2016 benchmark. The findings from this work will direct policy for effective mitigation of GHG emissions in the country while ensuring effective utilization of climate resources to pursue its net‐zero targets. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.