Machine learning analysis of greenhouse gas sources impacting Africa's food security nexus.

ABSTRACT: The essential need to identify the most informative sources of greenhouse gas emissions (climate change drivers) impacting the food security nexus in Africa requires a comprehensive and holistic approach. Machine learning method excels in the identification of single-variable importance, our study complements their abilities by deriving principal components using principal component analysis (PCA) to give a significant understanding of the three primary greenhouse gases (carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)). We used data from FAO concerning Africa from 2000 to 2021. Food household consumption emission (CH₄), burning crop residues (N₂O), and food transport (N₂O) are the key variables identified by machine learning as critical contributors to climate change drivers i

SUBMITTER: Bofa A 

PROVIDER: S-EPMC12325927 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature