Application of GM (1,1) to predict the dynamics of stand carbon storage in Pinus Kesiya var. langbianensis natural forests - Gu, Chunxi; Zhou, Zhenyan; Liu, Chang; Zhang, Wangfei; Yang, Zhengdao; Zhou, Wenwu; Ou, Guanglong (Frontiers in Forests and Global Change, 2024)
Amid global carbon reduction and climate action, precise forest carbon storage estimation is crucial for comprehending the carbon cycle. This study forecasts P. kesiya var. langbianensis forests’ 2030 stand carbon storage using data from 81 permanent plots across three Yunnan Province forest surveys and remote sensing. Findings: (1) In 2000, storage ranged from 26 to 38 t·hm−2. Central areas had higher values; southwest and southeast exceeded northwest and northeast. By 2010, storage grew eastward, receded northward. By 2020, east storage declined, southwest rose. (2) GM (1,1) model: posterior difference C 0.001, R2 power function model 0.945, GM (1,1) p value 0.999, power function model p value 0.997. (3) Predictions: Cosivarang border forest’s 2030 carbon stock 2850.804 t·hm−2, up 103.463 t·hm−2 from 2000. At 2022’s certified Emission Reduction carbon price of 60 yuan/ton, 2030’s carbon asset value per unit (t·hm−2) approx. 6207.78 Yuan, compared to 2000. Integrating gray system theory, especially GM (1,1) model, robustly addresses “small data and uncertainty” system challenges. Introducing GM (1,1) gray theory in forestry research offers fresh insight into forest carbon sink dynamics.