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  11. Monitoring Carbon Emission From Key Industries Based On Vf-lstm Model

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

Yang Wang1, Tianchun Xiang1, Shuai Luo2

  • 1China State Grid Tianjin Electric Power Company Tianjin Hebei District, Tianjin, China.

Big Data
|November 22, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a privacy-preserving VF-LSTM model to monitor industrial carbon emissions, enhancing accuracy and data security for urban sustainability goals.

Area of Science:

  • Environmental Science
  • Data Science
  • Industrial Ecology

Background:

  • Greenhouse gas emissions from industrial activities threaten urban sustainable development and carbon neutrality goals.
  • Current carbon emission monitoring methods lack frequency, accuracy, and privacy security.
  • Effective monitoring is crucial for informed decision-making in carbon reduction strategies.

Purpose of the Study:

  • To propose a novel privacy-protected model for monitoring carbon emissions in key urban industries.
  • To address limitations of existing methods, including low frequency, poor accuracy, and inadequate privacy.
  • To develop an effective tool for achieving urban carbon peak and neutrality.

Main Methods:

  • Development of a privacy-protected "electricity-carbon" nexus model using Long Short-Term Memory (LSTM) with a Vertical Federated Framework (VF-LSTM).
Keywords:
artificial intelligencedata privacy protectionkey industries carbon emission monitoringvertical federated learning

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  • Implementation of the VF framework to ensure "usable but invisible" privacy protection for multisource data.
  • Utilizing LSTM to accurately capture industry-specific carbon emission patterns.

    • Main Results:

    • The VF-LSTM model demonstrated high accuracy and robustness in monitoring carbon emissions across steel, petrochemical, chemical, and nonferrous industries.
    • The model effectively monitored industry-level carbon emissions while ensuring data privacy.
    • Validation confirmed the model's performance in real-world industrial data.

    Conclusions:

    • The proposed VF-LSTM model offers a significant advancement in industry-level carbon emission monitoring.
    • It provides a viable solution for accurate and privacy-secure carbon tracking, supporting urban sustainability.
    • This approach aids governmental bodies in decision-making for effective carbon reduction efforts.