Baibi Chen

Dr. Baibi Chen


Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 May 2024 in Journal of Environmental Management
Verified
Journal Article
Closed access
LWLinsheng Wen
Linsheng Wen
BLBaoyin Li
Baoyin Li
YPYun Peng
Yun Peng
YZYunrui Zhou
Yunrui Zhou
AWAifang Weng
Aifang Weng
BCBaibi Chen
Baibi Chen

21 November 2025 in Forests
Verified
Journal Article
Open access
PDF file
WHWeiping Hua
Weiping Hua
传华传茂 华
传茂 华
思张思恒 张
思恒 张
TQTian Qiu
Tian Qiu
XJXidian Jiang
Xidian Jiang
BCBaibi Chen
Baibi Chen
Accurate estimation of forest stand carbon storage is critical for assessing ecosystem functions and informing sustainable forest management. Most existing models depend heavily on stand age, a strategy that is often unreliable in natural forests, and they typically ignore species interactions, limiting their applicability across forest types. To overcome these issues, we developed a dynamic carbon storage model based on the Richards equation that replaces stand age with a growth interval period (defined as the time difference between two successive growth stages, Tn = T2 − T1) and explicitly incorporates site quality and species composition. This approach enables consistent estimation for both natural and plantation forests. Using field data from six dominant tree species in Wuyishan City, Fujian Province, we calibrated and validated the model through five-fold cross-validation. It achieved high accuracy, with an efficiency coefficient (EA) above 99% and a relative mean absolute error (RMA) under 7%, effectively reflecting how site conditions and mixed-species structures influence carbon dynamics. Total forest carbon storage in the study area was estimated at 7.32 million tons. Simulations show a gradual decline in carbon accumulation over time, consistent with natural growth saturation in aging stands. Scenario analyses further identified practical zones for sustainable harvesting in major plantation types, underscoring the model’s management relevance. The model does not yet include climate variability, disturbances, or below-ground carbon pools. Adding these components in future work would strengthen its utility for regional carbon assessment and support more robust carbon-neutral forestry planning.