Data-driven algorithmic modeling for forest carbon sequestration and sustainability

Forests play a critical role in mitigating climate change through carbon sequestration. This study develops a forest carbon sequestration model and a comprehensive decision-making framework to optimize carbon storage and ensure sustainable forest management. Using entropy weighting, multiple linear regression, and time series prediction, we assess key factors such as cutting intensity and population size. Applied to the Montreal forest, our model predicts a carbon sequestration of 187,900 tons after 100 years, with a cutting index adjustment from 0.39 to 0.47 increasing sequestration by 9,740 tons in 20 years. Sensitivity analysis confirms model robustness. These findings offer a scalable approach to balancing ecological and economic forest values.