Embodied carbon footprint modeling has become an area of growing interest due to its significant contribution to carbon emissions in computing. However, the deterministic nature of the existing models fail to account for the spatial and temporal variability in the semiconductor supply chain. The absence of uncertainty modeling limits system designers’ ability to make informed, carbon-aware decisions. We introduce CarbonClarity, a probabilistic framework designed to model embodied carbon footprints through distributions that reflect uncertainties in energy-per-area, gas-per-area, yield, and carbon intensity across different technology nodes. Our framework enables a deeper understanding of how design choices, such as chiplet architectures and new vs. old technology node selection, impact emissions and their associated uncertainties. For example, we show that the gap between the mean and 95thpercentile of embodied carbon per cm2can reach up to 1.6× for the 7nm technology node. Additionally, we demonstrate through case studies that: (i) CarbonClarity is a valuable resource for device provisioning, help maintaining performance under a tight carbon budget; and (ii) chiplet technology and mature nodes not only reduce embodied carbon but also significantly lower its associated uncertainty, achieving an 18% reduction in the 95thpercentile compared to monolithic designs for the mobile application.

Fair-CO 2 is a system for fairly attributing operational and embodied carbon in cloud data centers to user workloads.It leverages the Shapley value, a game theory solution for fair shared cost attribution with theoretical fairness guarantees.We propose the standard Shapley value solution as a ground truth for attribution in cloud data centers, addressing two key gaps in existing carbon attribution methods that lead to unfair attributions: the effect of dynamic demand on embodied carbon, and interference effects in colocated scenarios.However, the computational cost of the Shapley value solution scales exponentially with the number of workloads and becomes intractable for large systems.Fair-CO 2 addresses the scalability challenges of the Shapley value while preserving its fairness benefits via two core components: demand-aware embodied carbon attribution and interference-aware resource cost attribution.Using Monte Carlo simulations of workload schedules and colocation scenarios, we show that Fair-CO 2 can approximate the ground truth Shapley attribution solution at scale.We also show how users, once provided a fair way of estimating their workload carbon footprint, can dynamically optimize workload deployment for carbon savings.