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<p>Accurate assessment of carbon footprints is essential for regulatory compliance and sustainable planning. This research introduces a cutting-edge AI-based tool that advances current methodologies by addressing all three scopes of the Greenhouse Gas (GHG) Protocol (Scopes 1 to 3). It is a hybrid AI framework using LSTM, AdaBoost, and Optuna for accurate emissions forecasting and analysis. The tool uses a hybrid machine learning approach that combines LSTM networks with AdaBoost. It also uses the Optuna framework to improve its performance and make predictions more accurate. It features a user-friendly Streamlit interface for visualizing data and producing reports that adhere to the European Union’s Carbon Footprint Measurement and Analysis (MACF) standards. Additionally, the tool incorporates a financial module to estimate carbon taxes in systems such as the Border Carbon Adjustment Mechanism (BCAM). The tool demonstrates scalability across various industrial sectors, maintaining high predictive accuracy as data volume and system complexity increase. Case studies indicate up to a 15% reduction in GHG emissions and approximately 20% savings in associated carbon taxes. Case studies reveal that the tool aids in achieving a 10–15% reduction in emissions across various sectors. With its real-time analysis, automated reporting, and adaptable use across industries, this tool offers a significant enhancement over previous, less predictive methods.</p>

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An AI-based carbon footprint assessment tool for comprehensive greenhouse gas emissions management

  • 期刊论文

  • 发表日期：

    2026年1月29日

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  • Sana Elhidaoui,
  • Rajaa El Hanna,
  • Manoj Kumar

摘要

Accurate assessment of carbon footprints is essential for regulatory compliance and sustainable planning. This research introduces a cutting-edge AI-based tool that advances current methodologies by addressing all three scopes of the Greenhouse Gas (GHG) Protocol (Scopes 1 to 3). It is a hybrid AI framework using LSTM, AdaBoost, and Optuna for accurate emissions forecasting and analysis. The tool uses a hybrid machine learning approach that combines LSTM networks with AdaBoost. It also uses the Optuna framework to improve its performance and make predictions more accurate. It features a user-friendly Streamlit interface for visualizing data and producing reports that adhere to the European Union’s Carbon Footprint Measurement and Analysis (MACF) standards. Additionally, the tool incorporates a financial module to estimate carbon taxes in systems such as the Border Carbon Adjustment Mechanism (BCAM). The tool demonstrates scalability across various industrial sectors, maintaining high predictive accuracy as data volume and system complexity increase. Case studies indicate up to a 15% reduction in GHG emissions and approximately 20% savings in associated carbon taxes. Case studies reveal that the tool aids in achieving a 10–15% reduction in emissions across various sectors. With its real-time analysis, automated reporting, and adaptable use across industries, this tool offers a significant enhancement over previous, less predictive methods.