Opportunities of Reinforcement Learning in South Africa's Just Transition

Overview

South Africa faces significant socio-economic challenges and a looming climate crisis, which its Just Transition framework aims to address by enhancing climate resilience and achieving net-zero greenhouse gas emissions. This paper explores the potential of Reinforcement Learning (RL), an Artificial Intelligence technology, to support this transition. It examines how RL can optimize agriculture, manage decentralized energy networks, and improve transportation/logistics. The paper provides a roadmap for researchers to apply RL solutions to these critical areas, contributing to a just and equitable low-carbon future for all South Africans.

Recommendations

Reinforcement Learning (RL) holds significant, yet overlooked, potential to advance South Africa's Just Transition by optimizing agriculture, decentralized energy networks, and transportation. While global RL applications are growing, there's a critical need for context-specific solutions tailored to South Africa's unique challenges. A roadmap emphasizes developing local simulators, collating South African data, and improving real-world RL deployment. Acknowledging limitations like the digital divide and ethical concerns, the paper highlights RL as a promising avenue for researchers to contribute to a just and equitable low-carbon future.

Publisher: SACAIR 2024

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