The foundation for accurately understanding regional land-use structures and pursuing the coordination of human–land relations is the scientific identification and simulation of temporal and spatial evolution patterns of land-use spatial conflict (LUSC). Based on the production–living–ecological space (PLES) perspective, a land-use spatial conflict identification and intensity diagnosis model (LUCSII) was constructed using a landscape ecology index. The methods of geographic information system (GIS), spatial autocorrelation analysis, and mathematical statistics were used to achieve the spatial pattern of LUSC over the last 20 years, and the GMOP–Markov–PLUS model was used to simulate the evolution of LUSC in the future under various scenarios. The results indicated that our established LUCSII could accurately identify potential land-use spatial conflict areas in geoparks. The GMOP–Markov–PLUS model constructed had also scientifically predicted the future land-use patterns under different scenarios, successfully demonstrating the changing process of spatial conflict pattern evolution. The research proposed three different plans for the long-term land use of YG, including ecological protection, economic development, and long-term development perspectives. Finally, the research further emphasized the importance for sustainable development of geoparks. More attention should be paid to the optimal allocation of land-use structure and the coordinated development of human–land relationships.

The agro-pastoral ecotone of northern China (AENC) is a significant ecological barrier, where the topographical features play basic roles in land-use change. In order to reveal the influence of topographical factors on land-use changes in the AENC, we used land-use transfer matrix, geo-information graphics, terrain niche, distribution index and geographical detector to explore the topographic gradient effect of land-use changes during 2000–2020 in the AENC based on remote-sensing image data from 2000, 2010, and 2020. The findings indicate that: (1) The total areas of land-use changes were 121,744 km², accounting for 17.41% between 2000–2020. This was characterized by increasing amounts of land-use changes in the AENC. The changes in land-use were dominated by the conversions among farmland, forestland, and grassland, which were distributed widely in the mountainous regions of northern, western, and eastern margins. The expansion of construction land was derived mainly from farmland and grassland occurred in river valleys. (2) The pattern of land-use changes was divided into five types including stable type, prophase change, anaphase change, continuous change, and repeated change. Stable type accounted for 559,868.86 km² and 80.09% of the total area. It was dominant in high altitude and complex terrain areas with terrain niches of more than 1.61. Prophase and anaphase changes accounted for 3.95% and 13.03%, respectively, which occupied to dominant positions in the 0.69–1.17 and 0.04–0.69, 1.17–1.61 terrain niches topographic gradient, respectively. Continuous and repeated changes occupied dominant positions in low altitude and flat complex areas with terrain niches of 0.04–1.17. (3) The topographic gradient effect of land-use changes in the AENC was influenced comprehensively by natural, geographical location, socioeconomic, and policy factors. Natural environmental factors and geographical location determined the topographic gradient pattern of land-use structure, while the direction of the topographical gradient pattern of land-use changes in the AENC is influenced by socioeconomic and policy factors. This research can provide a scientific reference for the development and protection of territorial space and optimal allocation of land resources in the AENC.

The foundation for accurately understanding regional land-use structures and pursuing the coordination of human–land relations is the scientific identification and simulation of temporal and spatial evolution patterns of land-use spatial conflict (LUSC). Taking the Yimengshan Geopark (YG) as an example, based on the productional–living–ecological space (PLES) perspective, which constructs a land-use spatial conflict identification and intensity diagnosis model (LUCSII) using the landscape ecology index. We apply geographic information system (GIS) and other methods to achieve the spatial pattern of LUSC over the last 20 years, and we use the GMOP–Markov–PLUS model to simulate the evolution of LUSC in the future under various scenarios. From 2000 to 2020, the LUSC values in the YG were mainly stable and controllable, with mild conflict, while the areas of severe conflict were mainly concentrated in the central urban area of Mengyin County and in low and flat terrain areas such as southern Bailin Town. The LUSC in the YG showed a significant positive spatial correlation, and spatial agglomeration is gradually strengthening. The high–high clusters are found in contiguous areas at the junction of Changlu Town, Gaodu Town, and Mengyin Street, as well as in the southern hilly areas. The low–low clusters were concentrated in Yedian Town, Daigu Town inarea north of the study, and areas surrounding Yunmeng Lake Wetland Park. In the next ten years, the ecological priority scenario (EPD) and sustainable development scenario (ESD) will both be reasonable options for easing and controlling LUSC in YG. Local governments and park management bureaus should determine the three lines and three zones based on the needs of social and economic development, particularly the boundary red line for construction land growth, and plan production and living spaces to alleviate land-use conflicts and stabilize the land-use system. Regional ecological security can be maintained, and future deterioration of the park’s ecological environment avoided, by performing well in terms of ecological isolation.

There are close dynamic relationships among the livelihood, well-being, and ecological environment of farmer households. It is of great significance to scientifically clarify the impact of the Grain for Green policy on the livelihoods and well-being of farmer households in mountainous areas. Based on data from a survey of 392 farmer households in Zhangbei County, the system of indicators for livelihood assets and well-being of farmer households were constructed using the sustainable livelihood framework (SLF). The livelihood assets and well-being levels of different types of farmer households were measured, and a multiple linear regression model was used to analyze the impact of the Grain for Green policy implementation on the well-being levels of farmer households. The results showed that (1) the Grain for Green project caused changes in the livelihood of farmer households. The average livelihood diversity of farmer households was 3.008, and the returned farmland households (3.022) were higher than the nonreturned farmland households (2.975) in Zhangbei County. The level of natural assets among the total average livelihood assets of farmer households was the highest at 0.374, while the level of physical assets was the lowest at 0.018. The level of livelihood assets of returned farmland households (0.948) was lower than that of nonreturned farmland households (1.117). (2) The Grain for Green policy had an improving effect on the level of well-being of farmer households, but the effect was not significant. The level of well-being of all farmer households in Zhangbei County was 0.517, with the level of wealth contributing the most to the well-being of farmer households at 40.20% and the quality of the ecological environment contributing the least at 11.99%. The level of well-being of returned farmland households (0.518) was slightly higher than that of nonreturned farmland households (0.514). (3) The influencing degree of each factor on the level of well-being varied significantly. There are three main paths through which the Grain for Green policy affects the well-being of farmer households: by reallocating human assets, optimizing natural assets, and enhancing financial assets. The factor of household size had the highest degree, at 0.366, while educational attainment of household members, household labor capacity, annual household expenditure, livelihood diversity, number of large production tools, and total value of livestock were also important drivers of household well-being, and area of arable land was negatively associated with household well-being. There were also differences in the factors influencing the level of well-being of different types of farmer households.

There are close dynamic relationships among the livelihood, well-being, and ecological environment of farm households. It is of great significance to scientifically clarify the impact of the grain for green policy on the livelihoods and well-being of farm households in mountainous areas. Based on data from a survey of 392 farm households in Zhangbei County, a system of indicators for farm household livelihood assets and farm household well-being was constructed, drawing on a sustainable livelihood framework (SLF). The livelihood assets and well-being levels of different types of farm households were measured, and a multiple linear regression model was used to analyze the impact of the grain for green policy implementation on the well-being levels of farm households. The three main results are as follows: (1) The level of natural assets among the total average livelihood assets of farm households in Zhangbei County is the highest at 0.374, while the level of physical assets is the lowest at 0.018. The level of livelihood assets of returned farmland households (0.948) is lower than that of nonreturned farmland households (1.117). (2) The level of well-being of all farm households in Zhangbei County is 0.517, with the level of wealth contributing the most to the well-being of farm households at 40.20% and the quality of the ecological environment contributing the least at 11.99%. The level of well-being of returned farmland households (0.518) was slightly higher than that of nonreturned farmland households (0.514). (3) The degree of influence of each influence factor on the level of well-being of farm households varies significantly. Household size was the strongest driver, at 0.366, while educational attainment of household members, household labor capacity, annual household expenditure, livelihood diversity, number of large production tools, and total value of livestock were also important drivers of household well-being, and area of arable land is negatively associated with household well-being. There are also differences in the factors influencing the level of well-being of different types of farming households.