Monitoring Carbon Emission from Key Industries Based on VF-LSTM Model

Abstract

Human activities that generate greenhouse gas emissions pose a significant threat to urban green and sustainable development. Production activities in key industrial sectors are a primary contributor to high urban carbon emissions. Therefore, effectively reducing carbon emissions in these sectors is crucial for achieving urban carbon peak and neutrality goals. Carbon emission monitoring is a critical approach that aids governmental bodies in understanding changes in industrial carbon emissions, thereby supporting decision-making and carbon reduction efforts. However, current industry-oriented carbon monitoring methods suffer from issues such as low frequency, poor accuracy, and inadequate privacy security. To address these challenges, this article proposes a novel privacy-protected “electricity-carbon’’ nexus model, long short-term memory with the vertical federated framework (VF-LSTM), to monitor carbon emissions in key urban industries. The vertical federated framework ensures “usable but invisible” privacy protection for multisource data from various participants. The embedded long short-term memory model accurately captures industry-specific carbon emissions. Using data from key industries (steel, petrochemical, chemical, and nonferrous industries), this article constructs and validates the performance of the proposed industry-level carbon emission monitoring model. The results demonstrate that the model has high accuracy and robustness, effectively monitoring industry carbon emissions while protecting data privacy.