Machine Learning for Sustainable Development: A Cross-Disciplinary Framework

Abstract

Machine learning (ML) has the potential to address some of the world’s most pressing sustainability challenges. This paper presents a cross-disciplinary framework for applying ML to areas such as climate modeling, renewable energy optimization, and biodiversity conservation. By integrating data science with environmental science, engineering, and policy studies, we explore how ML can provide actionable insights for sustainable development. Case studies from Australia highlight successful applications, such as predicting bushfire risks and optimizing water resource management. The paper emphasizes the need for collaboration between data scientists and domain experts to ensure ethical and effective solutions.