Landscape ecological risk assessment can reflect the impact of landscape pattern on ecological processes and functions, and has become an important method for predicting and measuring the quality and dynamic evolution of the ecological environment. Studying the trend of landscape ecological risk evolution is important for optimizing the regional landscape pattern and maintaining the sustainable development of the ecological environment in ecologically fragile areas. Ningxia Hui Autonomous Region is a typical agro-pastoral region in northern China, which is facing prominent ecological and environmental problems such as soil erosion and land desertification. With the environmental problems becoming more and more serious, the ecological risk also poses increasing potential danger, which becomes one of the bottlenecks restricting regional economic development. This paper selects Ningxia Hui Autonomous Region as the study area. Based on land use data in 2000, 2005, 2010, and 2015, the landscape ecological risk assessment model is built from the perspective of landscape ecology, and combines the spatial statistical analysis method and the gravity shift model to explore the spatial–temporal evolution of the spatial distribution characteristics of landscape ecological risk. The results showed that: (1) During the study period, the area of farmland and grasslands has decreased, and the area of forest, water, and settlement has increased; (2) The transformation of land-use types is mainly distributed in the central northern plain and the south valley, where human activities are intensive. The transfer relationship among different land-use types is mainly between grasslands and farmland; (3) The hotspots and aggregation area of landscape ecological risk are mainly distributed along the city belt and irrigated agricultural areas along the Yellow River in the northern Ningxia Plain; (4) From 2000 to 2005, the landscape ecological risk level decreased, and from 2005 to 2015, the landscape ecological risk index continued to rise. We conclude that the natural environment of Ningxia determines the characteristics of the spatial distribution of landscape ecological risk, while the use and modification of land through human activities are the main driving factors of landscape ecological risk change. The results of landscape ecological risk assessment at different scales based on the landscape index will provide support for regional environmental management.

Altay Prefecture plays a vital role as an ecological barrier in Northwest China. Studying the ecosystem service value is of great significance for promoting regional green high-quality development and maintaining ecological security. Based on Global ESA land cover data from 2000 to 2015, the trade-off and synergy relationships and driving force factors between ecosystem services in Altay Prefecture were analyzed in this study. The analysis produced four main results. (1) The ecosystem service value in Altay Prefecture continued to increase from 113.521 × 109 yuan in 2000 to 115.777 × 109 yuan in 2015, for an increase of about 1.98%. (2) The distribution of ecosystem service value had obvious spatial agglomeration characteristics, with hot spot areas mainly concentrated in the ″two rivers and one lake″ and the mountainous areas in the northwest, while the cold spot areas were mainly the forest and grass-covered areas in the northern mountainous areas and within Jimunai County. (3) The trade-off and synergy relationship among ecosystem services was mainly synergistic, with a total of 77.78% of ecosystem service relative relationships showing a significant positive correlation at the 0.01 level. (4) Economic factors and industrial structure are important factors affecting ecosystem service value in Altay Prefecture. Ecosystem service value is positively correlated with per capita GDP and the output value of the tertiary industry, but negatively correlated with the output value of the secondary industry.

In arid agriculture, the effective allocation of scarce water resources and the assessment of irrigation shortage risks are critical water management practices. However, these practices are faced with inherent and unignorable uncertainties affecting multiple variables. This study aims to model the typical uncertainties in these practices and understand how they impact the allocation of scarce water resources. We advocate for a nuanced consideration of variable characteristics and data availability, variation, and distribution when choosing uncertainty representation methods. We proposed a comprehensive framework that integrates the cloud model to delineate scenarios marked by subjective vagueness, such as “high” or “low” prices. Simultaneously, the stochastic method was used for modeling meteorological and hydrological variables, notably precipitation and crop evapotranspiration. Additionally, to navigate subjectivity and imprecise judgment in standards classification, this framework contains a cloud-model-based assessment method tailored for evaluating irrigation shortage risks. The proposed framework was applied to a real-world agricultural water management problem in Liangzhou County, northwest China. The results underscored the efficacy of the cloud model in representing subjective vagueness, both in the optimization process and the subsequent assessment. Notably, our findings revealed that price predominantly influences net benefits, and that precipitation and crop evapotranspiration emerge as decisive factors in determining optimal irrigation schemes. Moreover, the identification of high water storage risks for maize in the Yongchang and Jinyang districts serves as a reminder for local water managers of the need to prioritize these areas. By adeptly modeling multiple uncertainties, our framework equips water managers with tools to discern sensitive variables. We suggest that enhanced precipitation and evapotranspiration forecasts could be a promising way to narrow the uncertainties.

Ecological sustainability quantifies the capacity of an ecological system to sustain its health while fulfilling human survival needs and supporting future development. An accurate projection of ecological dynamics for sustainability is crucial for decision-makers to comprehend potential risks. However, the intricate interplay between climate change and human activity has hindered comprehensive assessments of future ecological sustainability, leaving it inadequately investigated thus far. This study aimed to assess future ecological sustainability shaped by the Shared Socioeconomic Pathways (SSPs) using remote sensing data from a typical arid farming–pastoral zone located at the northern foot of Yinshan Mountain (NFYM), Inner Mongolia, China. Five machine learning models were employed to evaluate the relationship between ecological sustainability and its driving factors. The results indicate that (1) overall ecological sustainability initially decreased and then increased during 2003–2022; (2) the Geophysical Fluid Dynamics Laboratory Earth System Model version 4 (GFDL-ESM4) mode and random forest model demonstrated the best performance in climate and ecological sustainability simulations; and (3) the annual change rates of ecological sustainability from 2023 to 2099 are projected to be +0.45%, −0.05%, and −0.46% per year under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, suggesting that stringent environmental policies can effectively enhance ecological sustainability. The proposed framework can assist decision-makers in understanding ecological changes under different SSPs and calls for strategies to enhance ecosystem resilience in the NFYM and similar regions.