Green ICT and Sustainable Computing: Evaluating Carbon Footprint Metrics in Cloud-to-Edge AI Workloads

The exponential expansion of Artificial Intelligence (AI) model training and inference workloads across cloud-to-edge infrastructures has led to staggering energy consumption and operational carbon emissions. While cloud datacenters and industrial edge nodes leverage power usage effectiveness (PUE) metrics, traditional workload scheduling algorithms remain carbon-oblivious, optimizing solely for execution cost or completion latency. This paper introduces the Dynamic Carbon-Aware AI Workload Scheduler (DCAS), a mathematical optimization model designed to minimize operational carbon footprints (gCO2eq) across geo-distributed cloud datacenters and industrial edge processing clusters. DCAS formulates a multi-objective Mixed-Integer Linear Programming (MILP) model that dynamically shifts compute-heavy Deep Learning (DL) training and batch inference jobs in space (spatial routing to regions with cleaner energy grids) and time (temporal deferral to match peak solar and wind availability). Evaluating 1,000 deep learning training jobs across 10 geo-distributed cloud and edge facility nodes over a 30-day simulation window using real-time grid carbon intensity feeds, empirical results show that DCAS reduces total operational carbon emissions by 58.4% compared to static cost-optimized baselines, while restricting average Service Level Agreement (SLA) delay penalties to less than 4.2%. Furthermore, spatial-temporal workload shifting paired with edge battery energy storage systems (BESS) reduces peak grid power demand by 42.1%, delivering a scalable blueprint for sustainable Green ICT infrastructure in Industry 4.0 environments.