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arXiv · 2411.15145

Opportunities of Reinforcement Learning in South Africa's Just Transition

Claude Formanek·Callum Rhys Tilbury·Jonathan P. Shock
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Abstract

South Africa stands at a crucial juncture, grappling with interwoven socio-economic challenges such as poverty, inequality, unemployment, and the looming climate crisis. The government's Just Transition framework aims to enhance climate resilience, achieve net-zero greenhouse gas emissions by 2050, and promote social inclusion and poverty eradication. According to the Presidential Commission on the Fourth Industrial Revolution, artificial intelligence technologies offer significant promise in addressing these challenges. This paper explores the overlooked potential of Reinforcement Learning (RL) in supporting South Africa's Just Transition. It examines how RL can enhance agriculture and land-use practices, manage complex, decentralised energy networks, and optimise transportation and logistics, thereby playing a critical role in achieving a just and equitable transition to a low-carbon future for all South Africans. We provide a roadmap as to how other researchers in the field may be able to contribute to these pressing problems.

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Claude Formanek, Callum Rhys Tilbury, Jonathan P. Shock. 2024-11-06. Opportunities of Reinforcement Learning in South Africa's Just Transition. https://arxiv.org/abs/2411.15145

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