Embodied carbon is the total amount of carbon released from the processes associated with a product from cradle to gate. In many industry sectors, embodied carbon dominates the overall carbon footprint. Embodied carbon accounting, i.e., to estimate the embodied carbon of a product, has become an important research topic. Existing studies derive the embodied carbon through life cycle analysis (LCA) reports. Current LCA reports only provide the carbon emission of a product class, e.g., 28nm CPU, yet a product instance can be manufactured from diverse regions and in diverse time periods, e.g., in the winter in Ireland (Intel). It is known that carbon emissions depend on the electricity generation process, which has spatial and temporal dynamics. Therefore, the embodied carbon of a specific product instance can largely differ from its product class. In this paper, we present new Spatial-Temporal Embodied Carbon (STEC) models for embodied carbon accounting. We observe significant differences between current embodied carbon models and STEC models, e.g., for 7nm CPU the difference can be 13.69%. We further examine the impact of STEC models on existing embodied carbon accounting schemes on key computer applications, such as Large Language Model (LLM) inference and LLM training. We observe that using STEC models leads to much greater differences in the embodied accounting of certain applications as compared to others (e.g., 32.26% vs. 6.35%). This is because the hardware requirements of certain applications allow for a wider range of hardware choices, while others have greater restrictions.