Predictive analytics for climate-resilient timber supply chains: Integrating anomaly detection with carbon sequestration and risk scoring

This article examines the problem of applying predictive analytics, anomaly detection, and carbon risk scoring to make timber supply chains more resilient and sustainable in the climate change context. On a predictive model of possible disruption of atmospheric characteristics caused by climate-related outcomes like extreme weather and deforestation, we have utilized 1992-2020 data on Global Forest and Carbon Metrics and made a predictive model with the help of the Random Forest. Anomaly detection was used to detect any deviations of carbon stocks and forest area which disclosed that there were big anomalies in particular areas. Moreover, a carbon risk scoring system has been designed to identify carbon integrity in timber sourcing with more insights given to regions with more sustainability risks. The results indicate that the implementation of these methods in timber supply chain management system is likely to enhance the accuracy of forecasting, early detection of a disruption, and sustainability of sourcing timber. The project proposes additional composite incorporation of granular climatic information and rule structures to enhance wood forest management and forest timber supply mechanics.