Problem
Research questions and friction points this paper is trying to address.
Carbon Accounting
AI Inference
Cross-Border
Request-Level
Carbon Footprint
GreenPassport: Request-Level Carbon Accounting for Cross-Border AI Inference
🤖 AI Summary
本文提出GreenPassport方法，针对跨境AI推理的碳排放问题进行请求级碳核算，通过关联服务、站点、路线等信息，提高碳排放估算精度。
📝 Abstract
AI inference often crosses regional boundaries as prompts travel to remote data centers and generated tokens return to users. Regional averages cannot represent the resulting differences in serving hardware, electricity, and network delivery. Request-level accounting needs a common boundary for the service, serving site, route, local comparator, uncertainty, and data provenance.GreenPassport Carbon Accounting (GPCA) associates these inputs with each request. It estimates serving and route carbon, then selects a reporting level from the available documentation. Our 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. It produced zero rule overstatement in the deterministic conformance tests. In the buyer case, the clean-electricity CN-West scenario produced $0.0148$ gCO$_2$e /request, 88\% below the local service at $0.1220$ gCO$_2$e /request.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Request-Level Carbon Accounting
Cross-Border AI Inference
Carbon Emission Estimation
Serving and Route Carbon
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