{"id":6882,"date":"2026-05-17T22:52:00","date_gmt":"2026-05-17T14:52:00","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6882"},"modified":"2026-05-19T11:35:33","modified_gmt":"2026-05-19T03:35:33","slug":"jase-202609-32-042","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-042","title":{"rendered":"Design of a Carbon Emission Monitoring and Prediction System Based on Big Data"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Wenyu Jiang<\/i><\/a> <\/p>\n\n\n\n

School of Statistics, Beijing Normal University, Beijing, 100875, China<\/p>\n<\/div>\n\n\n\n

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Received: February 14, 2026
Accepted: March 30, 2026
Publication Date: May 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Layered Big Data Architecture for Data Integration and Analytics <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.042<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Accurate monitoring and prediction of carbon emissions has become a key need by industries across the globe due to the growing need for sustainable development. Industrial activities generate large volumes of energy consumption data, creating opportunities for real-time monitoring and predictive analytics using big data technologies. A carbon emission monitoring and prediction systems is developed that integrates real-time electricity consumption data with external variables such as industry type, energy mix, and economic indicators. Big data platforms including Apache Hadoop and Apache Spark support large-scale data ingestion, storage, and processing. Carbon emission prediction is performed using a hybrid model that combines Long Short Term Memory(LSTM)networks for temporal pattern learning with Extreme Gradient Boosting (XGBoost) to capture feature-based relationships. Data preprocessing techniques such as normalization, feature engineering, and missing value imputation improve data quality and model reliability. The dataset consists of large-scale industrial energy consumption and carbon emission records collected at an hourly resolution, comprising approximately 10,000 samples. The data is divided into training and testing sets using an 80:20 split. The LSTM model is configured with two layers and 128 hidden units, using a learning rate of 0.001 with the Adamoptimizer. The XGBoost model employs 100 estimators with a maximum depth of 6 and regularization parameters ( \u03bb = 1.0, \u03b3 = 0.1 ). Experimental evaluation shows that the hybrid LSTM-XGBoost model outperforms alternative approaches including CNN-LSTM-BERT and AdaBoost, achieving MAE = 0.0234, MSE= 0.00086, and R2 = 0.963. The framework supports real-time carbon emission monitoring and forecasting, providing a reliable tool for data-driven industrial emission management and sustainable decision-making.<\/p>\n\n\n\n

Keywords: Carbon Emission Monitoring, Big Data, LSTM, XGBoost, Sustainability<\/em><\/p>\n\n\n\n

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