Spatial mismatch and drivers of carbon sequestration services supply-demand in China

• Carbon Sequestration Supply-Demand Analysis: The study assessed carbon sequestration supply and demand in China (2000–2020) using the CASA model and carbon emission coefficient method. • Fluctuating Supply and Rising Demand: Carbon sequestration supply showed an upward trend, while demand continuously increased, leading to widespread mismatches, particularly in eastern and urban regions. • Key Factors: Supply was primarily influenced by the Normalized Difference Vegetation Index (NDVI), and demand was linked to population density (POP) and GDP. • Policy Recommendations: Suggested measures include enhancing ecosystem protection, promoting sustainable land practices, and developing carbon markets to address supply–demand mismatches. • Implications for “Dual Carbon” Goals: The findings offer insights for policies supporting China’s carbon peak and neutrality targets. This study investigates the spatial mismatch between the supply and demand of carbon sequestration services in China, a critical issue for achieving the country’s “dual carbon” goals of peak carbon emissions by 2030 and carbon neutrality by 2060. Using the Carnegie-Ames Stanford Approach (CASA) model and the carbon emission coefficient method, the supply and demand of carbon sequestration services from 2000 to 2020 were quantitatively assessed. The supply–demand ratio index (SDI) and Local Indicators of Spatial Association (LISA) were employed to analyze the spatial distribution and intensity of the supply–demand relationship. The results reveal that while the supply of carbon sequestration services exhibited an overall increasing trend, demand steadily grew, leading to a widening mismatch, particularly in the economically developed eastern regions and urban agglomerations. The supply was primarily influenced by the Normalized Difference Vegetation Index (NDVI) and land use/land cover change (LUCC), while demand was closely linked to population density (POP) and Gross Domestic Product (GDP). To further analyze the factors driving this mismatch, the Structural Equation Model (SEM) and Random Forest (RF) model were applied to identify the impact of both natural and human activity factors. SEM highlighted complex causal relationships, while RF captured nonlinear interactions between these variables. The findings emphasize the growing tension between insufficient supply and rising demand for carbon sequestration services in key regions. To mitigate this spatial mismatch and support the “dual carbon” goals, several policy recommendations are proposed, including strengthening ecosystem protection, promoting sustainable land management practices, establishing carbon markets, and encouraging broader participation in carbon trading. These strategies aim to address the supply–demand imbalance and contribute to sustainable carbon management in China.