Criteria for Credible AI-assisted Carbon Footprinting Systems: The Cases of Mapping and Lifecycle Modeling

As organizations face mounting pressure to understand their carbon footprints, AI-assisted footprinting systems are proliferating with widely varying levels of rigor and transparency. Standards and guidance have not kept up with the technology. We propose criteria for credible AI-assisted carbon footprinting, developed through a three-step process: (1) identifying needs and constraints, (2) drafting criteria, and (3) refining them through pilots in two cases — emissions-factor mapping and lifecycle modeling. We find that credible AI systems can be built and should be validated using system-level evaluations rather than line-item review, with metrics such as benchmark performance, indications of data quality and uncertainty, and transparent documentation.