Climate change is a significant challenge that has sparked discussions across various fields. The detrimental effects of climate change on the environment, economy, and society have driven these conversations.

The Centre for Biological Diversity estimates that if greenhouse gas emissions are not reduced by 2050, one-third of plant and animal species may face extinction.

Climate change is already causing health issues such as heat-related diseases and tick-borne illnesses, as well as threatening livelihoods, particularly for vulnerable populations. Furthermore, climate change-related hazards such as flooding and drought have displaced people, especially in Africa, where agriculture is heavily impacted.

According to the African Union’s Climate Change and Resilient Action Plan, 83% of Africans depend on agriculture for their livelihoods. As such, the effects of climate change on agriculture raise concerns about food security and the livelihoods of both current and future generations.

However, there is potential to enhance climate change mitigation and adaptation in Africa by building climate resilience through artificial intelligence (AI) models. AI refers to tools that replicate human intelligence and can perform tasks beyond human capabilities.

AI tools and models have proven effective in creating and analysing complex systems for climate action, especially with the application of machine learning (ML). ML, a complex subset of AI, uses algorithms to learn, adapt, and improve on previous data inputs, making it capable of building on present data to make projections for the future.

ML models have been instrumental in tracking, monitoring, and analysing complex climate change data, including carbon emissions, temperature fluctuations, and early warning indicators. Some of the key uses of AI/ML for climate change mitigation and adaptation include tracking temperature changes, monitoring weather conditions, and tracking carbon emissions.

Tracking Temperature Changes
Given the large amounts of carbon dioxide trapped in the atmosphere and the consequent increase in the greenhouse effect and global warming, it is crucial to monitor temperature changes in ecosystems.

This helps to assess the extent of climate change and identify the factors contributing to temperature fluctuations. For example, in southern Africa, 24 million people face hunger, disease, and floods due to droughts and flooding.

It is projected that 2.7 million Zimbabweans will experience hunger this year due to drought, while in eastern Africa, 3.8 million people in Somalia have been displaced due to similar conditions. Other countries also face alarming figures triggered by temperature changes from climate change.

Machine learning models, such as general circulation models (GCMs), help meteorologists, climatologists, and researchers pinpoint areas that need intervention to sustain the ecosystem.

Monitoring Weather Conditions and Early Warning Systems
In addition to tracking temperature changes, AI tools are used to predict weather conditions. The International Business Machines Corporation's (IBM) Deep Thunder project utilises ML to observe and predict weather conditions, such as heavy rainfall, floods, and other natural disasters. Early predictions allow the public to prepare for these events.

Unfortunately, in underdeveloped countries, unforeseen natural disasters often have devastating social and economic impacts, largely due to a lack of AI expertise and tools for climate mitigation.

In 2020, for example, 45,000 Ghanaians were negatively affected by floods, resulting in an economic loss of 95 million USD, with projections indicating that these losses will continue to rise without mitigation strategies. In contrast, in more technologically advanced countries, the public is informed of forecasted weather conditions and receives guidance on how to stay safe, which is a key element in promoting climate resilience.

Tracking Carbon Emissions
Tracking carbon emissions is another critical aspect of mitigating climate change. AI tools can monitor emissions from industries, transport, and agriculture, helping to analyse the extent of carbon emissions and inform policymaking.

Advanced Emissions Tracking Systems, for instance, provide detailed data on emissions, allowing governments to implement accurate regulations to limit emissions from companies and institutions contributing to climate change.

However, for such systems to be effective in Africa, AI models capable of tracking emissions need to be developed. Unfortunately, research indicates that these models are currently lacking in many parts of Africa.

Conclusion
The potential benefits of AI/ML for climate change mitigation are significant and cannot be ignored in the global climate change conversation. However, since Africa is not as technologically advanced as the Global North, the widespread use of ML systems will require a gradual shift in innovation.

This will necessitate further support, collaboration, technical assistance, and funding. Such support should focus on training and building the capacity of AI experts, enabling the development of context-specific tools for climate action and fostering resilience to climate change across the continent.