Understanding future trends and identifying characteristic differences in regional ecosystem services (ESs), in addition to ecological zoning, is vital for promoting the adjustment of ecological policy and the construction of sustainable ecosystems. Based on shared socioeconomic pathways and representative concentration pathways (SSP–RCP), the spatial distribution pattern of land use in the Beijing–Tianjin–Hebei region (BTH) in 2030 was simulated by using a patch-generating land use simulation model (PLUS) in this study. Water yield, carbon storage, habitat quality, and food product were simulated through the comprehensive evaluation model combining ecosystem services and trade-offs (InVEST). The comprehensive supply capacity of ESs was assessed. The ecological risk level was calculated by comparing the changes in the integrated supply capacity of ESs in 2020 and under each SSP–RCP scenario in 2030, and ecological zoning was established using a two-dimensional discriminant matrix. The results are as follows: (1) The degradation of grassland and cropland accompanied by an increase in construction land and forest to varying degrees will be the common characteristics of the three SSP–RCP scenarios in BTH. (2) Water yield and carbon storage services will exhibit an upward tendency only under SSP1-2.6, while habitat quality and food product services will exhibit a downward trend under three SSP–RCP scenarios. Obvious geographical heterogeneity exists in the comprehensive supply capacity of ESs. (3) Zones with low ecological risks will mainly be distributed in some counties of Zhangjiakou city, while zones with high ecological risks will account for a large proportion of the whole. There will be great ecological risks in the BTH overall. (4) The BTH was split into four types of ecological zones based on supply and risk. These zones comprise the ecological enhancement zone, ecological restoration zone, ecological sensitive zone, and ecological conservation zone. Corresponding control measures were also proposed. The findings of this study can be used to inform the formulation and improvement of environmental conservation policies.

Ecological importance evaluation can clearly identify the ecological service functions and ecological values of a region. This paper takes the Yanshan-Taihang Mountain area in Hebei Province as the research area, utilizing 2020 land use data. With the help of various analytical models and GIS spatial analysis methods, this paper selects water conservation, soil and water conservation, biodiversity, carbon sequestration and oxygen release to evaluate the importance of ecosystem services, and selects soil and water loss sensitivity and land desertification sensitivity to evaluate the ecological sensitivity, so as to identify the important areas of ecological protection in the study area, analyze their spatial change characteristics and divide the leading ecological functions according to the results. The results show that the moderately important and highly important areas in the Yanshan-Taihang region of Hebei Province account for more than 70% of the total study area. Based on the importance evaluation results, three types of dominant ecological function zones were obtained using self-organized feature mapping neural network analysis in the R language, and control measures were proposed. The research results can provide strategic support for local ecological protection and regional ecological restoration, as well as serving as a reference for the optimization of land spatial development patterns.

With rapid economic development and the change in land use patterns, the region faces the environmental challenge of increasing carbon emission risk. The research on analyzing and identifying carbon emission risk of land use is helpful to realize regional sustainable development. This study takes the Yanshan-Taihang Mountain area of Hebei Province as a case study. Based on the remote sensing monitoring data of land use in 2010, 2015, and 2020, the carbon emissions of land use are calculated by direct and indirect calculation methods. Then, the carbon footprint pressure index and land use carbon emission risk index are introduced to analyze the temporal and spatial differentiation characteristics of land use carbon emission risk in the study area. The findings indicate that carbon emissions associated with land use in the study area initially exhibited an increasing trend, followed by a subsequent decline over time. In space, the high-value areas of carbon emissions are mainly distributed in the south of the study area, and the low-value areas of carbon emissions are mainly concentrated in the northeast of the study area. The region experiences significant carbon-cycling pressure and environmental risk, with the proportion of counties in carbon balance areas decreasing from 27.27% in 2010 to 18.18% in 2020, and the proportion of counties in carbon imbalance areas increasing from 72.72% in 2010 to 81.82% in 2020. The carbon emission risk of land use is divided into micro-risk area, low-risk area, medium-risk area, high-risk area, and severe-risk area. From 2010 to 2020, there was an 18.18% increase in the percentage of counties classified as high-risk and severe-risk areas. Additionally, the regions identified as hotspots for land use emissions exhibited a trend in expansion. This phenomenon indicates that these areas have not successfully managed to mitigate environmental pollution or ensure the sustainable use of resources in the context of their economic development efforts. This series of dynamic changes shows that the study area is facing the challenge of increasing carbon emission risk from land use. Governments at all levels should strengthen environmental governance in high-risk areas, implement stricter land use policies, and promote green development and cleaner production to attain a mutually beneficial outcome for both economic development and ecological protection.