A Digital Twin and Edge-AI Framework for Carbon Emission Accounting and Resource Optimization in Supply Chain Clusters

Abstract

Quantifying carbon footprints while dynamically optimizing resource allocation within highly integrated supply chain clusters remains a complex computational task, largely due to data silo vulnerabilities and telemetry granular irregularities. Traditional centralized tracking methodologies frequently present severe limitations when adapting to real-time industrial fluctuations, exposing a significant operational gap in carbon accounting across multi-enterprise ecosystems. To address these systemic bottlenecks, this paper introduces a coupled digital twin and edge-directed artificial intelligence framework tailored for cluster-scale optimization. During our empirical deployment phase, unanticipated calibration misalignments between physical sensors and virtual representations initially caused substantial predictive skewing, necessitating an immediate structural correction through noise-robust pre-training and statistical multi-response regression techniques. Analytical simulations suggest that distributing accounting protocols to localized edge nodes may, to some extent, eliminate core telemetry latencies and uncover hidden processing inefficiencies, though underlying reporting discrepancies across heterogeneous hardware architectures could partially distort absolute sustainability metrics. Considering the above factors, this leads us to further thinking regarding how decentralized carbon ledger validation might ultimately reshape industrial ecological compliance, though further empirical research is explicitly needed to evaluate framework resilience under severe multi-modal data disruptions.