The coordination and coupling of new urbanization and the ecological environment is of great significance for the high-quality development of the Yellow River basin, China. In this paper, the urbanization efficiency and ecological efficiency of seven urban agglomerations in the Yellow River basin, China, from 2006 to 2019 are calculated at the city level. Spatial correlation analysis between urbanization efficiency and ecological efficiency is carried out by applying the spatial association model. Then, the spatio-temporal coupling relationship is estimated based on the relative development and spatial coupling models. The results show that urbanization efficiency has been rising, from 0.83 in 2006 to 0.91 in 2019. Ecological efficiency first stabilized and then rose from 1.03 in 2006 to 1.23 in 2019. Spatially, cities with high urbanization efficiency development are increasing from west to east. Cities with high eco-efficient developments are increasing from northwest to southeast. The coupling relationship between the two is an N-type relationship, with the trend of first rising, then falling, and then rising. Spatially, the coupling degree of the upper and lower reaches of the Yellow River basin, China, is higher than that of the middle reaches of the Yellow River basin, China. Finally, this paper puts forward policy recommendations on the coordinated and coupling development of urban agglomerations in the Yellow River basin, China, from the perspective of the future differentiated development of different urban agglomerations.

Stable and sustainable food production is an important guarantee for national security and social stability. The uneven distribution of cultivated land and water resources will threaten national food security. In this study, we adopt the Gini coefficient and water–land matching coefficient for exploring the water–land nexus in the main grain-producing areas of North China Plain (NCP) from 2000 to 2020. The water–land–food nexus considering grain crop production structure is further explored from spatial and temporal multi-scales. The results show the following: (1) The Gini coefficient presents an increasing trend in the NCP, indicating an increasing imbalance in the water–land matching degree among inter-regions. (2) There are significant differences in the WL nexus and WLF nexus among regions, showing a spatial pattern of “worse in the north and better in the south”. (3) The cities which belonged to the low WL-low WLF and high WL-low WLF should be considered as key targets when formulating policies. (4) Adjusting the wheat–maize biannual system, optimizing the grain cultivation structure, promoting semi-dryland farming, and developing low water-consuming and high-yielding crop varieties are essential measures for these regions. The research results provide significant reference for the optimal management and sustainable agricultural development of agricultural land and water resources in NCP.

Household carbon emissions (HCEs) in urban communities are significant sources of China’s total carbon emissions and contribute to global warming and climate change dramatically. This study aims to estimate the HCEs and investigate their influential factors based on a total of 185 household survey data collected from three typical types of urban communities in Beijing: traditional communities, unit communities, and commercial housing communities with the application of the consumer lifestyle approach analysis and econometrics model. The results show that unit communities contribute to the highest direct carbon emissions and the commercial housing communities produce the most indirect carbon emissions, with the traditional communities emitting the lowest carbon emissions both directly and indirectly. The highest direct carbon emissions of households are found in unit communities at 723.79 kgCO₂ per month, followed by commercial communities at 580.01 kgCO₂, and finally the traditional communities with 526.44 kgCO₂ direct carbon emissions monthly. And the highest monthly indirect carbon emissions of households are found in commercial communities at 707.70 kgCO₂, followed by unit communities at 669.38 kgCO₂, and finally with 554.85 kgCO₂ indirect carbon emissions monthly in traditional communities. It concludes that the community type affects HCE characteristics and their driving factors significantly. Household income, household population, and the ownership of cars increase HCE in more than one type of community. Scientific research work-related population, community environment satisfaction, housing area have positive effects, while community convenience has negative impacts on HCEs in one certain type of community. Policy implications tailored to general and specific community types are proposed as the guidance of carbon reduction and community transformation finally. This study contributes to the understanding of the impact of community attributes on HCEs and proposes some methods for microregional carbon emission reduction and the ecological transformation of urban communities.