Greenhouse gas (GHG) emission monitoring and forecasting are critical components of efforts to address climate change and reduce GHG emissions. GHG monitoring refers to the ongoing measurement and tracking of GHG emissions from various sources, while GHG forecasting involves predicting future emissions based on current trends and projections. GHG monitoring typically involves the use of remote sensing, ground-based monitoring stations, and atmospheric modelling to track changes in GHG concentrations and identify emission sources. These methods can provide a detailed understanding of the sources and magnitudes of GHG emissions, as well as their spatial and temporal variability.
GHG forecasting uses a range of tools and models including Machine Learning (ML) models to predict future emissions based on various scenarios, such as population growth, economic development, and policy changes. These forecasts can help policymakers and stakeholders anticipate future emissions and develop effective strategies for reducing them. The combination of GHG monitoring and forecasting provides a comprehensive understanding of current and future GHG emissions, allowing for the development of targeted and effective mitigation strategies. This information can be used to identify areas of high emissions, prioritize mitigation measures, track progress towards emission reduction targets, and evaluate the effectiveness of mitigation measures.
Researchers at NCEL are developing robust AI and ML based predictive tools and models for monitoring and reliable forecasting of GHG emission in Ghana. Preliminary models developed by researchers at NCEL have shown great promise for predicting future emission scenarios especially in the energy and transportation sectors.
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