The health impacts caused by chemical fertilizer utilization have challenged long-term sustainable development in many countries, particularly developing countries. Based on the emergy analysis method, we estimated the temporal and spatial variations of the health costs, through atmospheric, water, and soil pathways, of chemical fertilizer utilization in China during the period from 1990 to 2012. The results showed an obvious increasing trend of health costs from 1.8 billion Yuan in 1990 to 23.0 billion Yuan in 2012, while the ratio of health costs to agriculture output value declined slowly and became stable in recent years. Regional differences were remarkable and were significantly correlated to the levels of economic development (r = 0.843 and p < 0.001) and crop-sown area in the region (r = 0.588 and p < 0.001). Economically developed regions, especially the eastern coastal provinces, had much higher costs than the western regions. Meanwhile, fertilizer consumption shifted from the eastern to the northwest region, which was the same as the health costs. This study provides a reference to estimate the health costs of fertilizer utilization, and the results highlight the importance of sustainable development in China.

The evaluation of housing conditions is a crucial aspect of determining the well-being of residents and the sustainable development of settlements. Assessing housing conditions at a macro-level is imperative to understand the differences in well-being and livability among residents in various regions within a country. Unfortunately, the spatial variation characteristics of housing conditions in China have not been extensively studied at the county scale. Thus, this study examines the housing conditions in China by using 2846 counties as the basic research unit. A housing condition evaluation index system, comprising seven indicators, is constructed based on three aspects: housing spaciousness, internal facilities, and elevator configuration. The entropy value method is used to determine the weights of the indicators, and the spatial difference patterns and spatial autocorrelation characteristics of the housing conditions and types of housing conditions in China are analyzed. The correlation analysis method is used to analyze the correlation between the subtypes of housing conditions and county fundamentals (population density, urbanization, foreign population, and rental housing). The results show that: (1) the configuration of elevators is the most important indicator of the differences in housing conditions in China; (2) the better housing conditions in China are distributed on the southeast side of the “Hu Line”, while the worse areas are distributed on the northwest side of the “Hu Line”, showing significant spatial clustering characteristics, while the distribution of the different subtypes of housing conditions and their distribution in the H–H and L–L zones also have significant variability; (3) housing conditions in China’s urban areas are generally better than those in non-urban areas, and the internal infrastructure conditions of urban housing and the degree of elevator configuration are better than those in non-urban areas; and (4) the correlation between housing conditions and county fundamentals varies depending on the regional level. At the national and urban levels, a negative correlation exists between county fundamentals and housing spaciousness, although a positive correlation exists with internal infrastructure and elevator configuration. Urbanization has the greatest impact on housing conditions in these areas. In non-urban areas, there is significant variability in the correlation between county fundamentals and housing conditions.

Rural industrial integration land has emerged with the rise of new industries in rural China, exhibiting spatial distribution patterns distinct from traditional industrial land. However, research on this land use remains limited. This paper offers an exploratory definition of rural industrial integration land, takes three typical villages in Qingdao, Shandong Province as examples, establishes a model for measuring the diversity, complexity, and compatibility of land for industrial integration development based on the Hill numbers diversity measurement model, the information entropy index, and the mixedness index (WVMDI_i) to explore the spatial layout characteristics of rural industrial integration land, and introduces the grey relational model to analyze its driving mechanism. The findings reveal that: (1) Rural industrial integration land is marked by structural diversity, multifunctionality, and compatibility in use. (2) From 2015 to 2023, the land’s diversity, complexity, and compatibility increased. The spatial layout characteristics of rural industrial integration land in the three villages, which integrates its diversity, complexity, and compatibility, increased from 1.3842, 1.5786, and 1.2127 to 1.8500, 1.9123, and 2.0224. (3) Between 2015 and 2019, key factors influencing spatial distribution included resource endowment, industrial foundation, and socioeconomic conditions. From 2019 to 2023, the most significant factors shifted to industrial foundation, farmers’ demand, and regional policies. Understanding the spatial distribution of rural industrial integration land is essential for guiding rural industrial spatial planning and promoting rural industrial revitalization.