The HCI GenAI CO2ST Calculator: A Tool for Calculating the Carbon Footprint of Generative AI Use in Human-Computer Interaction Research
Nanna Inie, Jeanette Falk, Raghavendra Selvan
TL;DR
The paper addresses the challenge of quantifying the climate impact of Generative AI use in HCI research amid opaque cloud-based hardware. It presents the HCI $CO_2$ST Calculator, an input-driven estimator that translates GenAI usage, model type, and data characteristics into energy use $E$ via $E = N \cdot E_p$ and carbon footprint $C$ via $C = CI \cdot E$ with $CI = 0.481$ kgCO2e/kWh, grounded in in-house measurements and CHI 2024 analyses. The work contributes a domain-specific tool and an online platform to promote transparency, awareness, and mitigation strategies across seven-stage HCI pipelines, including prompts for training and evaluation. Its practical impact lies in enabling daily research planning and reporting to reflect GenAI-related emissions, guiding researchers toward more sustainable practices through input-driven design and post hoc reporting.
Abstract
Increased usage of generative AI (GenAI) in Human-Computer Interaction (HCI) research induces a climate impact from carbon emissions due to energy consumption of the hardware used to develop and run GenAI models and systems. The exact energy usage and and subsequent carbon emissions are difficult to estimate in HCI research because HCI researchers most often use cloud-based services where the hardware and its energy consumption are hidden from plain view. The HCI GenAI CO2ST Calculator is a tool designed specifically for the HCI research pipeline, to help researchers estimate the energy consumption and carbon footprint of using generative AI in their research, either a priori (allowing for mitigation strategies or experimental redesign) or post hoc (allowing for transparent documentation of carbon footprint in written reports of the research).