Invisible footprints, visible insights: machine learning reveals Scope 3 emissions
學年 114
學期 1
出版（發表）日期 2025-09-01
作品名稱 Invisible footprints, visible insights: machine learning reveals Scope 3 emissions
作品名稱（其他語言）
著者 Szu-Yung Wang; Nian-Zu Ye
單位
出版者
著錄名稱、卷期、頁數 Frontiers in Sustainability 6 ,p.13
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.
關鍵字 Scope 3 emission;carbon accounting;supply chain management;machine learning;AdaBoost;XGBoost;random forest
語言 en
ISSN
期刊性質 國外
收錄於 ESCI
產學合作
通訊作者 Szu-Yung Wang
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128242 )