· Overcoming challenges and defining the future direction of AI implementation in emissions management

· Integrating diverse data sources to improve model accuracy and transparency

· Using machine learning to identify high-impact reduction opportunities in complex operations

· Ensuring model interpretability and regulatory alignment for trustworthy emissions forecasting