Quantifying and Disclosing the Environmental Footprint of AI in Research: Life Cycle–Informed Framework and Open-Access Calculator Development Study

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Abstract
Background: AI research increasingly depends on energy-intensive computation, yet energy use, greenhouse gas emissions, hardware life cycle burdens, and water consumption are rarely reported in a standardized way. This lack of reproducible environmental accounting limits comparisons across studies and obscures the trade-offs among model performance, infrastructure choices, carbon intensity, and cooling water demand. Objective: This study aimed to develop and describe an open-access, life cycle–informed AI Environmental Footprint Calculator and propose a minimum reporting dataset for transparent environmental disclosure in AI research. Methods: We developed a browser-based calculator that combines operational energy, regional grid carbon intensity, power usage effectiveness (PUE), water usage effectiveness, hardware embodied emissions, and workload-specific metrics for training and inference. The framework was evaluated in 3 representative scenarios: a single–graphics processing unit laboratory fine-tuning task, a midsized academic cluster workload, and a large-scale industrial training cycle. Outputs were compared with those of established tools to identify how boundary choices and parameter assumptions affect emission estimates. Results: Across the scenarios, the inclusion of PUE and hardware life cycle allocation increased reported emissions compared with operational-only estimates. In the small laboratory scenario, optimization reduced total emissions from approximately 0.07 kg carbon dioxide equivalents (CO 2 e) to 0.05 kg CO 2 e and improved the proposed label from C to B. In the midsized cluster scenario, carbon-aware scheduling reduced emissions from approximately 665 kg CO 2 e/month to 450 kg CO 2 e/month—a 32% reduction. In the large-scale scenario, shifting to renewable-backed, lower-PUE infrastructure reduced operational emissions by approximately 79% while increasing the importance of water-carbon trade-off reporting. Conclusions: The calculator provides a practical and transparent method for reporting AI environmental footprints using auditable parameters and publication-ready outputs. Routine disclosure of energy use, emissions, water use, hardware assumptions, and regional context can improve reproducibility and support more equitable and sustainable AI research evaluation.