In recent years, global warming caused by greenhouse gases has seriously affected the living environment of human beings. It is of great significance to study the temporal and spatial evolution and simulation of carbon stocks under land use and climate change to understand the global carbon cycle, formulate policies to reduce emissions and increase sequestration, and achieve the goal of carbon neutrality. However, it is a major problem to obtain carbon density data. Xinjiang is located in the northwest and should protect the ecological service system, while Manas County, located in the hinterland of Xinjiang, is a relatively complete ecosystem service functional area, and it is also an important area that constitutes an ecological environmental protection barrier, so people should pay more attention to protecting the ecological service system in this area. This paper presents a comprehensive analysis of carbon stocks in terrestrial ecosystems in Manas, a typical arid zone, based on an improved carbon density index; the results of the study show that from 2000 to 2020, land use in Manas County has changed dramatically, with the area of unused and forested land decreasing and the area of arable land continuing to increase, which is of great significance for the reclamation of reserve arable land in Xinjiang. In addition, the spatial distribution pattern of total carbon stock in the study area did not differ much in different periods, with an overall increasing trend from southeast to northwest and a continuous decreasing trend in the spatial amount of forest carbon stock. This trend may inhibit the sustainable development of China’s dual-carbon policy. The results of the driving mechanism in Manas County showed the nonlinear enhancement of NDVI and temperature, and the two-factor enhancement of NDVI and precipitation. Finally, this study highlights the necessity of accurately estimating the carbon density corrected for dryland areas with incoming analyses of their driving mechanisms. This is crucial for the validation and parameterization of Chinese and global carbon models.