Exploring the green transition of cultivated land use from the perspective of green utilization efficiency evaluation has become an important content of deepening the study of cultivated land use transition, which is of great significance to promote food security and ecological civilization construction. At present, there are few studies on the green utilization efficiency of cultivated land (GUECL), which covers the comprehensive benefits of economy, ecology and society, combined with the requirements of ecological civilization and green development. Taking 65 cities (regions and autonomous prefectures) of the Yellow River Basin as the basic evaluation unit, the GUECL of the Yellow River Basin is evaluated with a Super-SBM model. In general, the GUECL of the Yellow River Basin was not high at four time points of 2000, 2006, 2012 and 2018, which presents a trend of “rising first and then falling”. Analyzing its temporal and spatial evolution pattern, the GUECL in the upper, middle and lower reaches presented an order of the upper reaches area > the lower reaches area > the middle reaches area; and the spatial variation trend showed a decrease from west to east, and a U-shaped change in the south-north direction. Using spatial correlation analysis, except for the year 2000, the GUECL in the Yellow River Basin presents a general distribution characteristic of spatial agglomeration, which is positively correlated in 2006, 2012 and 2018. The change of spatio-temporal pattern is the result of internal and external factors. The former mainly displays in the main characteristics of farmers, family characteristics and farmers’ cognition, while the latter is reflected in natural, social and policy factors.

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.

Clarifying the spatio-temporal patterns of ecosystem services trade-off/synergy relationships (ESTSs) and their attribution in the Yellow River Basin is crucial to constructing ecological civilization in China. This study first analyzed the spatio-temporal change of ecosystem services (ESs) including the water yield, soil conservation, carbon sequestration, and habitat quality in the Yellow River Basin during 2000–2020 based on the InVEST and RUSLE models. Then, the spatial autocorrelation methods were used to quantify the spatio-temporal differentiation of ESTSs, and the Geo-detector method was employed to identify the contributions of driving factors associated with the natural, social-economic, and regional policy aspects of the ESTSs. Finally, the random forest and analysis of variance methods were used to validate the reasonability of major driving factors obtained by the Geo-detector. The main findings include: (1) In 2000–2020, water yield, soil conservation, and habitat quality increased, and carbon sequestration decreased. The ESs had a spatial pattern of high in the east and low in the west. (2) Overall, there were synergistic relationships between the four Ess. In the spatial distribution of ESTSs, the expansion of the synergy zone and trade-off zone occupied the majority. The synergy zones tended to be concentrated in the northwest and southeast of the study area. In contrast, the trade-off zones were more scattered than the synergy zone, mainly focused on the east-central and southwestern parts of the Yellow River Basin. (3) Geo-detector and random forest both showed that natural factors had a strong explanatory power on ESTSs, in which NDVI is a key driver. Both the results of Geo-detector and the analysis of variance showed that the interactions between natural factors exerted the most significant influence on ESTSs, followed by the interaction between natural factors and socio-economic factors.

The Yellow River Basin (YRB) is a significant area of economic development and ecological protection in China. Scientifically clarifying the spatiotemporal patterns of carbon emissions and their driving factors is of great significance. Using the methods of spatial autocorrelation analysis, hot-spot analysis, and a geodetector, the analysis framework of spatiotemporal differentiation and the driving factors of carbon emissions in the YRB was constructed in this paper from three aspects: natural environment, social economy, and regional policy. Three main results were found: (1) The carbon emissions in the YRB increased gradually from 2000 to 2020, and the growth rates of carbon emissions in the different river reaches were upper reaches > middle reaches > lower reaches. (2) Carbon emissions have an obvious spatial clustering character from 2000–2020, when hot spots were concentrated in the transition area from the Inner Mongolia Plateau to the Loess Plateau. The cold spots of carbon emissions tended to be concentrated in the junction area of Qinghai, Gansu, and Shaanxi. (3) From 2000 to 2020, the driving factors of spatial differentiation of carbon emissions in the YRB and its different reaches tended to be diversified, so the impacts of socioeconomic factors increased, while the impacts of natural environmental factors decreased. The influence of the interactions of each driving factor showed double factor enhancement and nonlinear enhancement. This study will provide a scientific reference for green and low-carbon development, emphasizing the need to pay more attention to environmental protection, develop the green economy vigorously, and promote the economic cycle, so as to achieve green development and reduce carbon emissions.