Research on a Green Money Laundering Identification Framework and Risk Monitoring Mechanism Integrating Artificial Intelligence and Environmental Governance Data

Focusing on environmental crime proceeds and the "carbon credit/green bond" channel, we construct a weakly supervised AML framework integrating ESG multimodal signals (carbon registries, emissions disclosures, supply chain incidents, negative news) × transaction networks. Using 1,800 cross-border counterparties, 5 years of data, and 14 carbon-related typology rules as label functions, the model achieves ROC-AUC=0.972 and PR-AUC+28% on greenwashing-related capital chains. It provides +2.4 days of advance warning for high-emission anomalies and suspicious carbon credit rotations; False positive rate reduced by 21%, with significantly elevated risk quantiles (p<0.01) for affected account groups within ESG event windows. This approach translates ESG transparency into actionable AML monitoring capabilities, balancing sustainable finance with national ecological enforcement requirements.