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| Prediction of Carbon Footprint in Pulp and Paper Based on Random Forest Algorithm and Analysis of Its Influencing Factors |
| Received:December 28, 2025 Revised:February 06, 2026 |
| DOI：10.11980/j.issn.0254-508X.2026.06.016 |
| Key Words:carbon footprint pulp and paper random forest XGBoost life cycle assessment |
| Fund Project:黑龙江省自然科学基金项目（LH2019C009）。 |
| Author Name | Affiliation | Postcode | | SUN Rushen* | 1State Key Lab of Utilization of Woody Oil Resource, Northeast Forestry University, Harbin, Heilongjiang Province,150040
2College of Material Science and Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, 150040 | 150040 | | REN Shixue* | 1State Key Lab of Utilization of Woody Oil Resource, Northeast Forestry University, Harbin, Heilongjiang Province,150040
2College of Material Science and Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, 150040 | 150040 | | WANG Wei* | 3College of Mechanical and Electrical Engineering,Northeast Forestry University, Harbin, Heilongjiang Province, 150040 | 150040 |
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| Abstract:To address the complexity of influencing factors in carbon emissions in pulp and paper industry and the limitation of traditional life cycle assessment (LCA) in rapidly supporting process optimization， this study constructed a carbon footprint prediction framework for paper products based on LCA and machine learning. With a “cradle-to-gate” system boundary， a dataset was developed based on the Ecoinvent v3.11 and FisherSolve databases. The feature space of the dataset covered variables such as raw materials， energy structure， process parameters， and waste treatment， while the target variable was the carbon footprint intensity calculated using the IPCC 2013 GWP 100a method. On this basis， a random forest (RF) model was used to identify key influencing factors， and XGBoost was introduced to relearn the prediction residuals of the RF model. The results showed that drying-section steam consum ption， grid emission factor， fossil fuel ratio， unit electricity consumption， and lime kiln calcination emissions were the dominant factors. The RF-XGBoost stacked model significantly outperformed single model， with an R² increasing from 0.482 to 0.928 and a root mean square error (RMSE) decreasing from 104.3 kg CO₂eq/t to 38.9 kg CO₂eq/t. |
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