Assessment and projection of carbon density in China: An integrated approach combining Boruta-SHAP-machine learning and structural equation modeling

Abstract

Carbon density is a key indicator of the carbon sequestration capacity of terrestrial ecosystem. As one of the world's largest carbon emitters, gaining a clear understanding of China's carbon density is essential for achieving the strategic goals of “carbon peak” and “carbon neutrality.” Therefore, this study used machine learning to estimate the current carbon density of vegetation in China. However, machine learning often suffer from the “black box” problem. To address this, the Boruta-SHAP variable selection method was employed to enhance the interpretability of feature importance. Based on the current estimation of China's vegetation carbon density, this study used a structural equation model to explore the driving factors influencing carbon density. The model further predicts China's vegetation carbon density for 2030 and 2060 based on the identified key driving factors. The results of the study indicate the following: (1) Boruta-SHAP effectively visualizes the relative importance of feature variables. Among various vegetation indices, the Leaf Area Index (LAI) is the most important variable for carbon density modeling, with a mean SHAP value of 4.86. (2) In estimating China's carbon density, Boruta-SHAP-Random Forest (R² = 0.636) and Boruta-SHAP-XGBoost (R² = 0.629) outperform Multiple Linear Regression (R² = 0.559). (3) The average vegetation carbon density in China is 48.27 Mg/ha. The Moderately Low Carbon Density Zone accounts for the largest proportion of the total area, primarily located in the northeast region (e.g., Jilin and Heilongjiang), the southern fringe, and the eastern hilly areas. (4) Climate is identified as the main driving factor influencing China's vegetation carbon density, with a path coefficient of 0.72. Among climate variables, maximum temperature exerts the strongest influence, with a path coefficient of 0.85. (5) The average vegetation carbon density in China is projected to be 52.34 Mg C/ha in 2030 and 53.08 Mg C/ha in 2060. The findings of this study are expected to provide theoretical support for optimizing forest management policies and addressing climate change risks, including carbon sink fluctuations caused by extreme events.