Plantation Carbon Stock Predictor
Estimate carbon storage, CO₂ sequestration, and potential carbon credit value using five calibrated growth models derived from 2,626 trees across Myanmar's teak plantations.
Start Predicting →| Model | R² | RMSE | AICc | MPE (%) | C/tree (Mg) | C/ha (Mg) | Recommendation |
|---|
Adjust the parameters and click Calculate to see tailored management recommendations.
Scale plot-level carbon estimates to national plantation area. Compare rotation strategies and assess REDD+ and NDC implications.
This app is developed by Dr. Nophea Sasaki, Professor of ESG and Sustainability, Sasin School of Management, Chulalongkorn University. It was based on a master thesis by Cho Cho Naing at the Asian Institute of Technology (AIT).
Naing, C.C., Sasaki, N., Tsusaka, T.W., & Xue, W. (2026). Evaluation of optimal growth models for predicting carbon stocks and sequestration in teak plantations of Myanmar. Master Thesis, Asian Institute of Technology.
Models calibrated from 2,626 teak trees across 14 plots in 7 age classes (3–26 years), Lewe Township, Naypyitaw Union Territory, Myanmar. Parameters fitted using CurveExpert Professional v2.7.3.
Designed for three user groups: Researchers (model comparison & validation), Plantation Managers (carbon stock estimation & rotation planning), and Policymakers (national-scale projections & REDD+/NDC assessment).
TeakCarbon is freely available at teakcarbon.mahakru.com.
Built with HTML5/CSS3/JavaScript and Chart.js for interactive visualization. No server-side dependencies — runs entirely in the browser.
This tool provides estimates based on empirical models from a specific study area. Results should be validated against local field data. Carbon credit values are indicative and depend on market conditions and certification standards.