The Hidden Environmental Cost of Artificial Intelligence: Empirical Analysis of Carbon Emissions, Operational Energy Scaling, and the Water-Energy Nexus

The rapid scaling of deep neural networks and generative foundation models has transformed modern computing while introducing an unprecedented environmental footprint across global electricity grids and freshwater ecosystems. This research article presents a comprehensive Data Analytics and Visualization (DAV) investigation into the operational electricity consumption (MWh), greenhouse gas emissions (tCO2eq), and cooling water withdrawals across twelve foundational architectures and ten deployment task categories. Synthesizing benchmark measurements from peer-reviewed literature, model disclosures, and international energy agencies, the study evaluates pre-training lifecycles, task-level inference energy, Power Usage Effectiveness (PUE) plateaus, and green AI mitigation pathways.