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Opportunities of Reinforcement Learning in South Africa's Just Transition

Claude Formanek, Callum Rhys Tilbury, Jonathan P. Shock

TL;DR

This paper 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.

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.

Opportunities of Reinforcement Learning in South Africa's Just Transition

TL;DR

This paper 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.

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.

Paper Structure

This paper contains 24 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Artificial Intelligence and Reinforcement Learning
  4. South Africa's Just Transition and Fourth Industrial Revolution
  5. Just Transition.
  6. The Fourth Industrial Revolution.
  7. Motivation
  8. Agriculture
  9. Energy
  10. Transportation
  11. Research Methodology
  12. Reinforcement Learning Applications
  13. Agriculture
  14. Energy
  15. Transportation and Logistics
  16. ...and 9 more sections

Figures (1)

  • Figure 1: On the left is a visual depiction of the Just Transition created by Generative AI model DALL-E ramesh2021zeroshottexttoimagegeneration. On the right, a depiction of the Fourth Industrial Revolution in South Africa was also generated using DALL-E . We prompted DALL-E using the information in the background section of this report.