Signal GreenPassport: Request-Level Carbon Accounting for Cross-Border AI Inference

Summary

AI inference often crosses regional boundaries as prompts travel to remote data centers and generated tokens return to users, and regional averages cannot represent the resulting differences in serving hardware, electricity, and network delivery. This paper presents GreenPassport Carbon Accounting (GPCA), which associates service, serving site, route, local comparator, uncertainty, and data provenance with each request, estimating serving and route carbon and selecting a reporting level from available documentation. The public-data implementation covers data-center instances, accelerators, model families, electricity mixes, routes, and cloud-region carbon intensity. Against six accounting baselines and four energy-prediction baselines, GPCA reduced median absolute percentage error by 56.3% and median absolute error by 15.5% relative to EcoLogits under the aligned accelerator-energy boundary, and produced zero rule overstatement in deterministic conformance tests. In a buyer case study, the clean-electricity CN-West scenario produced 0.0148 gCO2e per request, 88% below the local service figure of 0.1220 gCO2e per request.