Accepted 2026 January 30 ; Published 2026 February 4

Journal of Industrial Engineering and Applied Science, 2026, 4(1), 3005-6071.

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Data centers have become a core contributor to global digital carbon emissions, with their carbon footprint growing 19% annually alongside the expansion of AI and cloud services. Traditional carbon accounting methods are either trapped in macro-level rough calculation based on Power Usage Effectiveness (PUE) or limited to micro-level hardware power consumption measurement, failing to establish a traceable correlation between chip-level energy behavior and datacenter-wide carbon emissions. To address this gap, this study proposes a Hierarchical Coupling Carbon Emission Estimation Model (HCCEEM) that integrates physical modeling and graph neural network (GNN)-based statistical aggregation. The model constructs a four-level traceability chain spanning board (chip), server node, rack cluster, and campus datacenter, and introduces a real-time load adaptation module to capture dynamic workload impacts. Validated on a 14-month dataset from a heterogeneous cloud datacenter, HCCEEM achieves an estimation accuracy of 95.7%, reducing mean absolute error (MAE) by 27.1% and 19.3% compared to PUE-based models and single-level machine learning models respectively. Moreover, the model realizes fine-grained attribution of carbon contributions across levels, revealing that chip-level dynamic power consumption drives 65.2% of server emissions, and rack-level cooling losses account for 33.8% of datacenter emissions. This research provides an interpretable, scalable tool for targeted carbon reduction, bridging the gap between hardware-level optimization and datacenter-wide carbon management. Specifically, HCCEEM exhibits remarkable applicability in high-load scenarios such as large language model (LLM) training and inference, where it can reduce carbon accounting errors by over 30% compared to conventional methods. For small and medium-sized datacenters with limited monitoring resources, the model’s modular design allows lightweight deployment by simplifying partial hierarchical modules without significant accuracy loss. Additionally, the hierarchical contribution quantification function of HCCEEM can directly support enterprises’ carbon disclosure and compliance reporting, aligning with the carbon neutrality requirements of the digital industry in various regions.

Carbon Emission Estimation , Hierarchical Traceability , Cross-Level Coupling , Graph Neural Network , Green Datacenter .

APA Style

Liu, W. (2026). Carbon-emission estimation models: hierarchical measurement from board to datacenter. Journal of Industrial Engineering and Applied Science, 4(1), 42-48. https://doi.org/10.70393/6a69656173.333931

Not Applicable.

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