As an important natural resource, forest land plays a key role in the maintenance of ecological security. However, variations of forest land in the agropastoral ecotone of northern China (AENC) have attracted little attention. Taking the AENC as an example and based on remote-sensing images from 2000, 2010 to 2020, we explored the spatiotemporal variation of forest land and its driving factors using the land-use transfer matrix, spatial autocorrelation analysis and spatial error model. The results showed that from 2000 to 2020, the total area of forest land in the AENC increased from 75,547.52 to 77,359.96 km 2 and the changes were dominated by the transformations among forest land, grassland and cropland, which occurred mainly in areas with the elevation of 500-2000 m and slope of 15°-25°. There was obvious spatial agglomeration of forest land in the AENC from 2000 to 2020, with hot spots of forest land gathered in the southern marginal areas of the Yanshan Mountains and the low mountainous and hilly areas of the Loess Plateau. The sub-hot spots around hot spots moved southward, the sub-cold spots spread to the surrounding areas and the cold spots disappeared. The spatiotemporal variation of forest land resulted from the interactions of natural environment, socioeconomic and policy factors from 2000 to 2020. The variables of average annual precipitation, slope, terrain relief, ecological conversion program and afforestation policy for barren mountains affected the spatial pattern of forest land positively, while those of annual average temperature, slope and road network density influenced it negatively.

Many qualitative studies have found that mixed conifer–broadleaf forests provide higher ecological benefits than monoculture forests, and the demand for mixed forests is increasing. However, the carbon sequestration benefits of artificial mixed forests remain unclear. In particular, considering specific growth characteristics of plantation trees and capturing the dynamic changes in carbon sequestration over time are necessary. Using 456 tree disks for dendrochronological analyses, we established a dynamic growth model for the carbon stock of Pinus tabuliformis under three afforestation modes in the eastern Tibetan Plateau. Based on the fundamental growth model, nonlinear fixed-effect (NLFE) models with specific parameter combination constraints were established to improve model stability. Compared with other models, the NLFE model based on the Weibull equation, which uses the model parameters n and z as classification parameters, was the optimal model. This model was used to evaluate the potential contribution of afforestation modes to the growth of carbon stock in individual P. tabuliformis trees over 100 years and to predict the carbon sequestration benefits of mixed and pure forests. Conifer–broadleaf forests can bring lower initial returns but higher long-term returns than the other two afforestation modes, and such forests can store more carbon. In addition, this study provides a feasible method for establishing a carbon stock growth model with minimal sample damage as well as evaluation methods and basis for large-scale pure forest transformation and management strategies.