###### Abstract  
The Taihang Mountains are an important ecological barrier in China, and their ecosystems have good carbon sink capacity. Studying the spatial-temporal variation characteristics and driving factors of carbon storage in the Taihang Mountains ecosystem provides decision-making for the construction of \"dual carbon\" projects and the improvement of ecological environment quality in this region. This paper takes the area in the Taihang Mountains as the research area, based on the land use and carbon density data of 2005, 2010, 2015, and 2019 of the Taihang Mountains, calculates the carbon storage in the region with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, explores the main factors affecting the spatial differentiation of carbon storage in this region, and analyzes their driving mechanisms by Geodetector. The results show that: (1) From 2005 to 2019, the land use of the Taihang Mountains changed somewhat. The area of forest and construction land increased slightly, while the area of farmland and grassland decreased. (2) The current carbon storage in the Taihang Mountains ranges from 1472.91 \(\times\) 10\({}^{6}\) t to 1478.17 \(\times\) 10\({}^{6}\) t (t is the abbreviation of ton), and shows a decreasing trend, which is due to the decrease in forest and the increase in construction land. (3) Slope and Normalized Difference Vegetation Index (NDVI) are the main driving factors affecting the spatial variation of carbon storage in the Taihang Mountains ecosystem. Temperature, precipitation, and population density are the secondary factors affecting the spatial variation of carbon storage. (4) The synergy between the driving factors is more potent than the individual factor, which is the most evident between NDVI and slope. This means some areas may have more abundant carbon storage under the combined effect of slope and NDVI. carbon storage; driving factors; InVEST model; Geodetector; Taihang Mountains 2022  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Analysis of the Driving Force of Spatial and Temporal Differentiation of Carbon Storage in Taihang Mountains  
[PERSON], 1 College of Earth Science and Technology, Southwest Petroleum University, Chengdu 610500, China 1  
[PERSON], 1 College of Earth Science and Technology, Southwest Petroleum University, Chengdu 610500, China 1
Mountains to explore scientific ways to improve the carbon stocks of regional ecosystems, which is of great significance for improving the Taihang Mountain ecosystem and reducing carbon emissions. At present, the methods of studying carbon storage in ecosystems are mainly classified as field survey [10], remote sensing inversion [11], and model simulation [12]. Field survey can be used to estimate carbon storage data accurately, but it has a smaller scope and can be disruptive to the research environment when collecting data [13; 14]. Remote sensing inversion can be used for large-scale carbon storage research, mainly based on specific ecosystems and partial carbon data for research [15; 16; 17]. Model simulation methods can effectively estimate, predict, and evaluate carbon storage at various scales by estimating regional carbon storage with the help of FORECAST, HASM, and other models. Currently, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model is the most widely used model, which has less data, fast calculation speed, and accurate estimation results. Scholars estimate and evaluate carbon storage from different perspectives [18; 19; 20] and scales [21; 22] through the InVEST model. Scholars have used the InVEST model to study carbon storage assessment in different regions from different perspectives and scales [23; 24]. [PERSON] analyzed the variation rules of carbon density and storage in the Taihang Mountains under different gradients of driving factors through the InVEST model [25]. However, there are relatively few studies on the driving mechanism of the spatial distribution of carbon stocks in ecosystems.