Concentration Beats Coverage: Impact-Aware Expert Review for AI-Assisted Carbon Footprint Estimation

Reducing the rapidly growing carbon footprint of computing at scale will require replacing month-long manual life cycle assessment (LCA) with AI estimates of product carbon footprint (PCF) to inform product design, supplier selection, and portfolio-level decarbonization decisions. These estimates are cheap but often uncertain, while expert review and primary data collection are accurate but costly. This raises a key question: under a fixed expert review budget, which components in a complex design should be escalated to expert review? Across prior literature, we observe electronics products' life cycle carbon impact is often concentrated within a few parameters or components. Leveraging this, we hypothesize that combining uncertain models with selective expert review of a few components can substantially reduce estimation errors. Reviewing the top-3 highest carbon components of an estimator trained on 131 consumer electronics products with detailed a detailed mass breakdown, we cut median aggregate error $4.67\times$ with perfect information and $2.84\times$ with 10\% noise in review. We see similar results and impact concentration of over 80\% in $K=3$ product submodules on a larger dataset of 835 PCFs and more detailed real-world bill of materials from Fairphone. Our results show the important practical finding that even noisy LCA estimators can dramatically reduce experts' verification burden and immediately achieve high quality results with modest expert input. We discuss the implications for the future of AI-LCA and its assurance mechanisms suggesting a shift from focusing on complete verification of the long tail to a subset of high carbon impact components.