用强化学习优化南非低碳转型中的农业、能源与交通
Opportunities of Reinforcement Learning in South Africa's Just Transition
- 提出强化学习在农业、能源和交通领域的应用思路
- 指出该技术可提升资源利用效率，支持2050年碳中和目标
- 适合关注可持续发展与AI交叉研究的学者参考
南非正面临贫困、不平等、失业与气候危机等多重挑战。其《公正转型》框架旨在提升气候韧性，实现2050年净零排放，并推动社会包容与减贫。根据总统第四次工业革命委员会报告，人工智能技术具备解决这些难题的巨大潜力。本文探讨了被忽视的强化学习（Reinforcement Learning, RL）在支持南非公正转型中的潜力，分析其在优化农业与土地利用、管理复杂去中心化能源网络、改善交通物流等方面的可行性。研究为其他学者参与此类关键问题提供了路线图。
原文摘要 · Abstract (English)
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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