Opportunities of Reinforcement Learning in South Africa's Just Transition

TL;DR

This paper explores how Reinforcement Learning can support South Africa's Just Transition by improving land use, energy management, and transportation to address socio-economic and climate challenges.

Contribution

It highlights the overlooked potential of Reinforcement Learning in supporting sustainable development and climate resilience in South Africa's socio-economic context.

Findings

RL can improve land-use practices and agriculture.

RL can optimize decentralized energy networks.

RL can enhance transportation and logistics efficiency.

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.