Optimization of the territorial spatial patterns can promote the functional balance and utilization efficiency of space, which is influenced by economic, social, ecological, and environmental factors. Consequently, the final implementation of spatial planning should address the issue of sustainable optimization of territorial spatial patterns, driven by multiple objectives. It has two components—the territorial spatial scale prediction and its layout simulation. Because a one-sided study of scale or layout is divisive, it is necessary to combine the two to form complete territorial spatial patterns. This paper took Hefei city as an example and optimized its territorial spatial scale using the multiple objective programming (MOP) model, with four objective functions. A computer simulation of the territorial spatial layout was created, using the patch-generating land use simulation (PLUS) model, with spatial driving factors, conversion rules, and the scale optimization result. To do this, statistical, empirical, land utilization, and spatially driven data were used. The function results showed that carbon accumulation and economic and ecological benefits would be ever-increasing, and carbon emissions would reach their peak in 2030. The year 2030 was a vital node for the two most important land use types in the spatial scale—construction land and farmland. It was projected that construction land would commence its transition from reduced to negative growth after that time, and farmland would start to rebound. The simulation results indicated that construction land in the main urban area would expand primarily to the west, with supplemental expansion to the east and north. In contrast, construction land in the counties would experience a nominal increase, and a future ecological corridor would develop along the route south of Chaohu County–Chaohu Waters–Lujiang County–south of Feixi County.

Under the background of ecological civilization construction and the overall planning of land and space, it is particularly important to explore the land use ecosystem service value and its zoning control. This paper, taking Chayu County, a typical alpine valley area of southeast Tibet as an example and based on the remote sensing interpretation data of three periods in 2000, 2010 and 2020, employs the three-level spatial scale from the village level, the township level to the county level to converge step by step, and uses a series of model algorithms to analyze and calculate the regional ecosystem service value and their dynamic changes, as well as spatial agglomeration and regional type division. The research shows that the land use types mainly consist of forest land, grassland and unused land, whose overall change range is small during the study period. The conversion of land use types is mainly between forest land, grassland and unused land and the land use index generally presents a spatial pattern of “high in the southwest and low in the northeast”, showing a decreasing trend to some degree. ESVI generally presents a differentiation pattern of “high in the west and low in the east”, with obvious spatial differentiation characteristics of kernel density, significant clustering and distribution characteristics and stable variation range, displaying an overall spatial pattern with characteristics of “dense in the west and sparse in the east, high in the north and low in the south”. Based on the administrative village scale, the study area is divided into three different types of land use ecological function areas: habitat maintenance function area, biological protection function area and production support function area. Differentiated approaches to appropriate development and construction and the corresponding optimization paths of ecological protection will be put forward.

The comparative advantage of land use efficiency can provide effective support for upgrading the industrial structure and optimizing the allocation of land resources. According to the agricultural industry and non-agricultural industry, the evaluation index system of land use efficiency is constructed by difference. By using China’s provincial panel data in 2010, 2015 and 2019 as an empirical test and comprehensively employing the comparative advantage model, GIS spatial analysis model, geographical weighted regression model and other algorithms, the land use efficiency and the comparative advantage were measured and evaluated to explore the evolution of spatial and temporal patterns and identify the influencing factors. The results showed the following: (1) The overall efficiency of agricultural land grew steadily, with regional differences transforming from expanding to narrowing, forming a gradually decreasing overall trend from the southeast coast to the northwest inland, and from the eastern plain to the western plateau. The overall efficiency of non-agricultural land was generally low, presenting an overall growth trend, and regional differences were progressively expanding, still showing a gradually decreasing trend in the eastern, central and western regions. (2) The overall comparative advantage of agricultural land efficiency showed a gradual decline trend, and the differences between regions were further narrowed. The high-value regions were still concentrated in the regions with superior agricultural resource endowment and showed a dominant advantage of the agricultural industry. The overall comparative advantage of non-agricultural land efficiency showed an increasing trend. The high-value areas were concentrated in urban agglomeration, metropolitan areas and other areas with high non-agricultural land efficiency, as well as the vast remote areas such as the northwest and southwest where the agricultural land efficiency was extremely low and the comparative advantage of non-agricultural land efficiency improved. (3) The spatial concentration of the comparative advantage of agricultural land and non-agricultural land efficiency was not obvious, which indicates that the comparative advantage of industrial land in China has not yet formed a trend of agglomeration development, but there were dense and obvious high-high agglomeration areas or low–low agglomeration areas in some local regions. (4) Significant regional differences were found to exist in the impact of various factors on the comparative advantages of agricultural land efficiency and non-agricultural land efficiency. The comparative advantage of land use efficiency can be relied on to promote the optimization and adjustment of industrial structure and guide the efficient allocation of land resources.