AI-driven real-time carbon footprint tracking and autonomous reduction for sustainable enterprises

Current practice in enterprise carbon reporting is heavily dependent on periodic emissions inventories and disconnected data streams. As a result, drivers of emissions are not detected quickly enough to enable responsive action. Here we build and validate a near-real-time carbon-intelligence pipeline that incorporates facility IoT telemetry, ERP event logs, grid carbon-intensity feeds, and optionally satellite proxies, for near-real-time estimation of Scope 1-2 emissions at sub-hourly granularity and associated recommendations for abatement actions. The system uses attention-based multimodal fusion, an ensemble of Transformer, CNN, and BiLSTM predictors with calibrated uncertainty, and a multi-objective optimizer (NSGA-III with PSO and simulated-annealing refinement) that balances emissions, cost, and operational disruption. SHAP and LIME provide audit-ready explanations for key predictors and recommended actions. An 18-month matched-pair observational evaluation (January 2023–June 2024) across 247 organizations in four sectors compared the proposed approach with conventional quarterly accounting baselines. The proposed pipeline showed stronger agreement with verified emissions (MAPE 3.2% vs. 18.7%; RMSE 0.087 vs. 0.524 tCO 2 e; Pearson r 0.94–0.97). On matched baseline comparisons, sites implementing recommended actions tended to have lower verified emission intensity on average, though results were still impacted by production mix, demand variation, implementation rigor and data quality. Economic impacts, such as estimated ROI, are also derived from observational comparisons without causal attribution. Overall, the findings suggest that explainable and time-resolved emissions intelligence may support enterprise decarbonization planning and operational monitoring.