Invisible footprints, visible insights: machine learning reveals Scope 3 emissions

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 R 2 , mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE). Results XGBoost achieved the highest accuracy (R 2 = 0.85, MAPE = 15%, RMSLE = 0.20), outperforming random forest (R 2 = 0.80, MAPE = 20%) and AdaBoost (R 2 = 0.78), while K-NN had the lowest accuracy (R 2 = 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.