Spatial-Temporal Embodied Carbon Models With Dual Carbon Attribution for Embodied Carbon Accounting of Computer Systems

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Embodied carbon</i> is the carbon emissions in the manufacturing process of products, which dominates the overall carbon footprint in many industries. Existing studies derive the embodied carbon through life cycle analysis (LCA) reports. Current LCA reports only provide the carbon emission of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">product class</i>, e.g. 28nm CPU, whereas a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">product instance</i> can be made in various regions and time periods. Carbon emissions depend on the electricity generation process, which has spatial-temporal dynamics. Therefore, the embodied carbon of a product instance can differ from its product class. Additionally, different carbon attribution methods (e.g., location-based and market-based) can affect the carbon emissions of electricity, thus further affecting the embodied carbon of products. In this paper, we present new Spatial-Temporal Embodied Carbon (STEC) accounting models with dual attribution methods. We observe significant differences between STEC and current models, e.g., for 7nm CPU the difference is 13.69%. We further examine the impact of STEC models on existing embodied carbon accounting schemes on computer applications, such as Large Language Model (LLM) training and LLM inference. We observe that using STEC results in much greater differences in the embodied carbon of certain applications as compared to others (e.g., 32.26% vs. 6.35%).