An XGBoost-Based Machine Learning Approach to Simulate Carbon Metrics for Forest Harvest Planning

Bibek Subedi (), Alexandre Morneau, Luc LeBel, Shuva Gautam, Guillaume Cyr, Roxanne Tremblay and Jean-François Carle
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Bibek Subedi: FORAC Research Consortium, Université Laval, Quebec, QC G1V 0A6, Canada
Alexandre Morneau: FORAC Research Consortium, Université Laval, Quebec, QC G1V 0A6, Canada
Luc LeBel: FORAC Research Consortium, Université Laval, Quebec, QC G1V 0A6, Canada
Shuva Gautam: FORAC Research Consortium, Université Laval, Quebec, QC G1V 0A6, Canada
Guillaume Cyr: Bureau du Forestier en Chef, Ministère des Ressources Naturelles et Forêts, Quebec, QC G1P 3W8, Canada
Roxanne Tremblay: Bureau du Forestier en Chef, Ministère des Ressources Naturelles et Forêts, Quebec, QC G1P 3W8, Canada
Jean-François Carle: Bureau du Forestier en Chef, Ministère des Ressources Naturelles et Forêts, Quebec, QC G1P 3W8, Canada

Sustainability, 2025, vol. 17, issue 12, 1-18

Abstract: It has become increasingly important to incorporate carbon metrics in the forest harvest planning process. The Generic Carbon Budget Model (GCBM) is a well-recognized tool to evaluate the potential impact of management decisions on carbon sequestration and storage, supporting sustainable forest management planning. Although GCBM is effective in carbon budgeting and estimating carbon metrics, its computational complexity makes it difficult to integrate into forest planning with multiple scenarios. In this regard, this study proposes using machine algorithms to expedite the output generated by GCBM. XGBoost was implemented to estimate the carbon pool and NEP in managed forests of Quebec. Furthermore, polynomial regression was also implemented to serve as a validation benchmark. Datasets with total sizes of 13.53 million and 7.56 million samples were compiled for NEP and carbon pool forecasting to run the model. The results indicate that XGBoost was able to accurately replicate the performance of the GCBM model for both NEP forecasting (R 2 = 0.883) and carbon pool estimation (R 2 = 0.967 for aboveground biomass). Although machine learning approaches are comparatively faster, GCBM still offers better accuracy. Hence, the decision on which method to use, either machine learning or GCBM, should be dictated by the specific objectives and the constraints of the project.

Keywords: forest carbon modeling; machine learning; XGBoost; net ecosystem productivity; carbon pool estimation; sustainable forest management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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