JMIR Publications

ABSTRACT

Artificial intelligence (AI) is being rolled out across the UK National Health Service (NHS) to improve efficiency, yet its carbon footprint is largely invisible within mandatory Green Plan reporting. A review of NHS sustainability guidance, DEFRA conversion factors, and recent evidence on AI energy use shows that current Scope 1–3 accounting omits substantial emissions at three points. First, a lack of granularity provide averages that can obscure the extreme energy intensity of certain AI workloads. Second, life-cycle emissions from specialised hardware (e.g., GPUs) are often excluded unless trusts own the equipment, ignoring upstream manufacturing impacts. Third, widespread use of unprocured generative-AI tools is unmeasured; extrapolating GP survey data suggests that ChatGPT queries alone could release ≈ 448 t CO₂e per year in primary care. To close these gaps we propose three potential ways to help reduce these reporting gaps: (1) AI-specific carbon disclosure clauses in vendor contracts; (2) inclusion of cradle-to-grave emission factors for AI hardware in Scope 3 reporting; and (3) lightweight monitoring of external AI traffic (whilst recognising potential ethical issues with this). Implementing these measures would give healthcare leaders a more accurate baseline against which to judge whether AI supports or undermines the NHS net-zero target.