Water-saving irrigation behavior (WSIB) is important for sustainable economic and social development in river basins and is promoted by improving water-saving awareness. Understanding the factors of WSIB could facilitate water demand management and information campaigns. Using the theory of planned behavior, this paper analyzes the influence of subjective attitude, perceived behavioral control and subjective norms on behavioral intention and final behavior with a structural equation model (SEM). Moreover, comparative study of the upper, middle and lower reaches of a river basin is also carried out to examine the regional differences. A survey of 546 rural residents in Heihe River Basin (HRB), which is located in northwest China, shows that (1) water-saving expectations and subjective norms have a significant impact on WSIB in upstream areas, and perceived behavioral control and subjective norms have positive effects in the middle and lower reaches; (2) the transformation of awareness into WSIB is slow and non-significant in all areas, mainly hindered by expected economic benefits; and (3) family water-saving experiences and social networks promote WSIB in the midstream and downstream areas. Compared with the midstream and downstream reaches, historical water-saving experience has no obvious effect on WSIB in the upper reaches. These findings highlight policies that (1) strengthen economic interests and increase the transformation of water-saving awareness into WSIB; (2) strengthen public awareness and neighborhood interaction, setting good examples to promote WSIB; and (3) increase farmer participation in relevant decision-making.

The water resources carrying capacity (WRCC) strongly determines the agricultural development in arid areas. Evaluation of WRCC is important in balancing the availability of water resources with society’s economic and environmental demands. Given the demand for sustainable utilization of agricultural water resources, we combine the water stress index and comprehensive index of WRCC and use multi-source data to evaluate agricultural WRCC and its influencing factors at the township scale. It makes up for the deficiencies of current research, such as the existence of single-index evaluation systems, limited calibration data, and a lack of a sub-watershed (i.e., township) scale. By applying multi-source data, this study expands the spatial scale of WRCC assessment and establishes a multidimensional evaluation framework for the water resources in dryland agriculture. The results indicate water stress index ranges from 0.52 to 1.67, and the comprehensive index of WRCC ranges from 0.25 to 0.70, which are significantly different in different types of irrigation areas and townships. Water quantity and water management are key factors influencing WRCC, the water ecosystem is an area requiring improvement, and the water environment is not a current constraint. Different irrigation areas and different types of townships should implement targeted measures to improve WRCC.

Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions hold greater operational significance for achieving the 2030 Agenda for Sustainable Development from the bottom up; thus, urgent attention should be given to data deficiencies and inadequate analyses related to SDG impact attribution. This study, conducted in the National Innovative Demonstration Zone for Sustainable Development of Lincang City, investigates multisource data sources such as integrated statistics, survey data, and remote sensing data to analyze the progress and status of SDG 6 achievement from 2015–2020, and employs the LMDI decomposition model to identify influential factors. The assessment results demonstrate that the SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020. The SDG 6 indicators and SDG 6 composite index have significant spatial heterogeneity. The water resources indexes in wealthy countries are high, the water environment and water ecology indexes in developing countries are comparatively high, and the SDG 6 composite index is high in undeveloped counties. Technological and economic advances are the main positive drivers impacting the SDG 6 composite index, and the relative contributions of technology, economy, structure, and population are 61.84%, 54.16%, −4.03%, and −11.96%, respectively. This study shows that integrated multisource data can compensate for the lack of small-scale regional statistical data when quantitative and comprehensive multi-indicator evaluations of the SDGs are conducted. And, policies related to SDG 6.1.1, SDG 6.2.1, and SDG 6.3.1 can be a priority for implementation in undeveloped regions with limited funding.

Improving production efficiency can help overcome the constraints of resource scarcity and fragile environments in oasis agriculture. However, there are few studies about the effect of farmers’ cognition of resources and the environment on their production efficiency. Taking farmers in the Ganzhou District of Zhangye—a typical representative of oasis agriculture in an inland river basin in Northwest China—this study empirically analyzed the effect of farmers’ cognition of resources and the environment on agricultural production efficiency. The average agricultural productivity of the surveyed farmers is 0.64, which is much lower than the average level in China. Farmers’ cognition of resources and the environment is related to green production willingness and behavior. Green production willingness, green production behavior between cognition of resources and the environment, and agricultural production efficiency play a chain mediating role, showing that farmers’ cognition of resources and the environment indirectly affects production efficiency. Green planting willingness is formed based on cognition of resources and the environment; when farmers translate willingness into behavior, it will further improve agricultural production efficiency. Recommendations are made based on the findings, such as strengthening the cognition of resources and the environment, mobilizing enthusiasm for green production, and promoting the practice of green planting.