Climate change remains one of the most pressing global challenges, with far-reaching impacts on ecosystems, economies, and communities worldwide. Africa is disproportionately affected by these impacts despite contributing minimally to global greenhouse gas (GHG) emissions (AfDB, 2019). This heightened vulnerability is attributed to the continent’s over-dependence on climate-sensitive sectors, coupled with limited institutional, technological, and financial capacities to reduce emissions and build climate resilience (Doku et al., 2021a, 2021b; Mekonnen et al., 2021; Phiri & Doku, 2024).
As climate risks intensify, there is a growing urgency for innovative, data-driven solutions. Artificial intelligence (AI) has emerged as a critical tool for developing and implementing climate resilience strategies (Ferrari, 2024). AI supports the design of models, forecasts, and decision-making systems essential for understanding, predicting, and mitigating climate risks. It strengthens climate information systems and predictive capabilities, enabling more effective resilience planning (Amiri et al., 2024).
AI’s capacity for data analysis, prediction, and decision support underpins the development of early warning systems that alert communities to impending climate-related disasters. By analyzing large datasets from satellites, weather stations, and other sources, AI-powered systems can detect patterns and identify early signs of extreme weather events. This includes predicting changes in temperature and precipitation patterns, which are vital for planning in key sectors such as agriculture. The ability to deliver timely and accurate information supports critical planning efforts for farmers, communities, and governments (Jain et al., 2023; Weaver et al., 2022).
However, significant challenges hinder the potential of AI in climate resilience. Chief among them is the lack of adequate skills to deploy and interpret AI-driven climate modeling tools for resilience planning and resource allocation. This skills gap is driven by two main factors:
- Limited training opportunities in AI-related Science, Technology, Engineering, and Mathematics (STEM) subjects within Africa.
- Persistent gender disparities in AI fields, with a significantly low representation of women in academia and the AI workforce.