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https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/128242
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| 題名: | Invisible footprints, visible insights: machine learning reveals Scope 3 emissions |
| 作者: | Wang, Szu-Yung;Ye, Nian-Zu |
| 關鍵詞: | Scope 3 emission;carbon accounting;supply chain management;machine learning;AdaBoost;XGBoost;random forest |
| 日期: | 2025-09 |
| 上傳時間: | 2025-11-20 12:05:22 (UTC+8) |
| 出版者: | Frontiers in Sustainability |
| 摘要: | Introduction: Scope 3 greenhouse gas emissions are critical to firms’ carbon footprints yet are often difficult to quantify due to limited direct data, motivating predictive modeling approaches.
Methods: We developed and compared four machine learning algorithms (K-nearest neighbors, random forest, AdaBoost, and XGBoost) to estimate corporate Scope 3 emissions using readily available financial and sustainability performance data. We leverage 10,449 listed firm-level data from 2014 to 2023, covering major industries such as semiconductor, steel, textile, and building materials, evaluating performance of each model by a held-out test set with metrics including R2, mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE).
Results: XGBoost achieved the highest accuracy (R2 = 0.85, MAPE = 15%, RMSLE = 0.20), outperforming random forest (R2 = 0.80, MAPE = 20%) and AdaBoost (R2 = 0.78), while K-NN had the lowest accuracy (R2 = 0.60). The results demonstrate that ensemble tree-based models substantially improve Scope 3 emission prediction accuracy over simpler models.
Discussion: Notably, random forest’s interpretable feature importance provided insight into key emission drivers with only a slight accuracy trade-off, highlighting the balance between predictive accuracy and model interpretability. |
| 關聯: | Frontiers in Sustainability 6 ,p.13 |
| DOI: | 10.3389/frsus.2025.1649150 |
| 顯示於類別: | [會計學系暨研究所] 期刊論文
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