Abstract
Achieving net-zero emissions by mid-century demands highly accurate environmental monitoring, reporting, and verification (MRV) systems. Traditional MRV methods are labour-intensive and lack sufficient spatial-temporal coverage. This paper explores how Artificial Intelligence (AI), through machine learning, computer vision, and remote sensing, transforms environmental governance. By integrating data from satellites, ground sensors, IoT devices, and citizen science, AI enables real-time monitoring and near-instant verification using algorithms such as random forests, support vector machines, and deep learning. Applications include industrial emissions tracking, nature-based solution verification, and hazardous environment assessment, potentially saving over $2 trillion annually through improved efficiency. Key challenges include data quality, algorithmic bias, AI energy use, skill shortages, and financial constraints for small enterprises. The proposed framework emphasises strategic assessment, phased deployment, and ongoing evaluation. Success depends on equity, collaboration, continuous improvement, emerging technology adoption, and ethical commitment to achieve global net-zero goals.