An important goal of building “age-friendly communities” is to help the elderly to access more opportunities for social participation and better health. However, little is known about the complex relationships between neighborhood environment, social participation, and elderly health. This study examined the mediating role of social participation in the area of neighborhood environment affecting elderly health and explored the discrepancy among different age groups in 43 neighborhoods of Shanghai. Both neighborhood environment and social participation had significant positive effects on elderly health in all the samples. Meanwhile, social participation served as a mediator of the relationship between interpersonal environment and elderly health. Furthermore, remarkably, health promotion effects transferred from the physical environment to interpersonal environment and social participation with age; the influence of physical environment on elderly health decreased with the increase of age, while the influence of interpersonal environment and social participation on the health of the elderly increased with the increase of age. This study found that physical environment, interpersonal environment, and social participation had different effects on elderly health of different ages. Different policies should be applied toward improving the interpersonal environment, optimizing of physical environment, and guiding the community activities.

Background Although social network is a known determinant of the elderly’s well-being, it is not clear, in urban-rural and age-comparison, what its structural characteristics are and how it works for well-being. The research aims to discuss the features of the elderly’s social network and the social network efficacies on the well-being of older adults in China’s urban and rural areas as well as revealing the urban-rural disparities among the elderly of different age groups. Methods In this study, descriptive statistical analysis and structural equation Modeling (SEM) were used to make a group comparison between the urban and rural elderly of different age groups. All data are quoted from 2014 China Longitudinal Aging Social Survey (CLASS). The survey adopted the multi-stage probability sampling method, targeting Chinese senior citizens aged 60 and above, the ultimate samples totaled 11,511. Results The social network of the elderly in China feature a “reverse structure” in age sequences: with ageing, family network of the elderly expand while their friend network shrink; also, the expansion scale of the rural elderly’s family network is significantly larger than that of the city’s while the shrinkage scale of their friend network is smaller compared with its urban counterpart. The effect of family network on the rural elderly’s well-being shows a remarkable increase with age. However, there is no noticeable change in urban elderly groups of different ages. Conclusion The social network characteristics of the Chinese elderly are different between different age stages. Namely, the family network and the friend network have the “reverse structure “ in age sequences. Meanwhile, the family network and the friend network have different efficacies on the well-being of the elderly in China, and the differences between urban and rural areas are even more obvious. For rural elderly, family network has very important effects on their well-being. Moreover, With the increase of age, family network’s efficacies increase gradually. For urban elderly, comparatively, family network is just as important as friend network.

Worldwide population aging is currently in acceleration, which is especially true for China. Echoing the advocacy of “active aging” and “age-friendly communities”, governments and researchers across the world are paying more attention to the impact of neighborhoods on the health of older adults. Using the Ecological Model of Aging, this study aimed to discuss the relationships between neighborhood environment, lifestyle, and health of older adults, and to compare the differences among older adults of different age groups. The results showed that landscape environment has a direct effect on the health of older adults, while leisure environment has an indirect effect through lifestyle. Both leisure environment and landscape environment directly encourage older adults to take part in outdoor activity, in which the former mainly promotes the social participation of the high-aged (aged 80+) group, while the latter merely promotes that of the middle-aged (aged 70–79) group. The positive effect of social participation on health is gradually strengthened with the increase of age. Meanwhile, outdoor activity has its greatest effect on the middle-aged (aged 70–79) group, but not the low-aged (aged 60–69) group. To effectively boost the health of older adults and promote active aging, adequate considerations should also be given to the differentiated demands of older adults of different age groups, optimization of neighborhood environment, as well as cultivation of an amicable atmosphere.

The important role of the entity economy, especially manufacturing, has been further highlighted after the outbreak of COVID-19. This study fills a research gap on manufacturing in the Wuhan Metropolitan Area by analyzing the spatio-temporal evolution patterns and characteristics of manufacturing, exploring the major location factors causing spatial reconstruction and comparing the effect intensities of the different factors in the manufacturing sector. From 2003 to 2018, the process of industrial suburbanization in the Wuhan Metropolitan Area continued to strengthen and currently the overall spatial pattern of manufacturing in the Wuhan Metropolitan Area is characterized by spreading in metropolitan areas and aggregation in industrial parks. The results of a spatial metering model showed that the dominant factors affecting the layout of manufacturing included innovation and technical service platforms, industrial parks, the number of large enterprises, living convenience, and air quality. However, the effect intensity of the different location factors varied among industries. The findings may help the government to understand the characteristics of agglomeration and spreading in the manufacturing industry and, in accordance with the dominant factors affecting the location of this industry, rationally develop ideas for adjusting the industrial layout in the post-coronavirus age.