Speaker
Description
Experimental research increasingly relies on computing to analyze data, model systems, and apply AI, expanding its role across all scientific disciplines. This shift has driven a surge in demand for computational power, alongside higher energy use and material consumption, compounding the already significant environmental footprint of experimental research.
To address this, a student-driven course was developed to assess research-related environmental impacts. Collaborating with a scientific computing team, students analyzed internal infrastructure and external computing use, including AI tools, to estimate carbon and material footprints and compare them with other research activities.
Results show computing is widespread but unevenly used: most researchers consume about 1,000 CPU hours annually, while a small group accounts for over 20% of total energy use. The computing footprint stems mainly from equipment production (57%), followed by services (25%) and storage (12%).