There are more than 26 million elderly people in China, and due to the Health China strategy proposed in 2020, “Elderly Health” has become an important topic of concern for all sectors of society. Neighborhoods provide important social relationships. However, Chinese researchers have not extensively explored the impact of these relationships on the physical health of the elderly. Based on the data from the 2018 China Health and Retirement Longitudinal Study (CHARLS), we constructed a comprehensive research framework integrating ordinary least square (OLS) regression, heterogeneity analysis, IV-2SLS, robustness testing, and Karlson–Holm–Breen (KHB) mediating effect analysis, which can be used to thoroughly examine neighborhood health effects (NHEs) in relation to the physical health of the elderly. The OLS results showed that the NHEs (B = 0.4689, p < 0.01) had a positive influence on the physical health of the elderly, and were lower than the NHEs estimated by IV-2SLS (B = 0.5018, p < 0.01). The mediating effects of social networks and social relationships were analyzed using KHB, and both the total (B = 0.6056, p < 0.01) and indirect (B = 0.0800, p < 0.01) effects on neighborhood health were significant, with the total effect being 10 times larger than the direct effect and 13.24% of the total effect coming from the mediating variable. Firstly, the NHEs positively influence the physical health of elderly persons, but there are heterogeneous differences. Secondly, the IV-2SLS estimation results suggest that not controlling for endogeneity leads to underestimation of the role of the NHEs. Thirdly, using the county-level NHEs, self-rated health, and health changes to replace variables, and grouping by smokers (small sample) and never smoked (large sample), the influence of the NHEs on the physical health of the elderly is robust. Finally, social networks and social relationships are important transmission mechanisms of the NHEs when it comes to the physical health of the elderly.

The policy of rural collective construction land marketization (RCCLM) signifies that such land can currently be traded commercially. This not only breaks the government’s previous monopoly over the primary land market but also creates opportunities for revitalizing rural industries. This study uses county-level panel data from China between 2010 and 2022, treating the policy as a quasi-natural experiment. A multi-period difference-in-differences model is employed to examine its effects on rural industrial integration and the underlying mechanisms. The results indicate that, compared to non-pilot areas, the level of rural industrial integration in pilot areas has increased significantly by 1.47%. This finding remains robust after addressing potential model estimation biases caused by factors such as sample selection bias and reverse causality. Mechanism analysis indicates that RCCLM promotes rural industrial integration development by fostering county-level technological innovation and entrepreneurial activities. Further analysis shows that the policy exhibits significant effects in the eastern, western, and northeastern regions, as well as in counties with larger populations, while showing no significant effects in the central region and counties with smaller populations. Additionally, the RCCLM generates positive spatial spillovers on rural industrial integration of neighboring areas through resource sharing. This study provides valuable insights for research on RCCLM policy and rural industrial integration.

Industrial prosperity is the prerequisite and foundation of rural revitalization, while rural collective operating construction land (COCL), as an important resource for rural industrial development, has significant theoretical and practical guiding significance regarding whether its market-oriented reform can promote rural industrial integration (RII). This study innovatively combines the synthetic control method (SCM) and mediation effect model to assess the impact of rural collective operating construction land entering the market (COCLEM) on RII, using panel data from 86 counties in Hunan Province (2011–2022), and its underlying mechanisms. This study finds that COCLEM significantly enhances RII, and this conclusion remains robust after a series of tests. A mechanism analysis indicates that COCLEM primarily promotes RII through population agglomeration, fiscal support, and technological upgrading. Furthermore, this study reveals that COCLEM effectively facilitates the extension of the agricultural industry chain and promotes the development of agricultural services. Nevertheless, the improvement of rural infrastructure still requires policy guidance and sustained investment to provide more robust support for RII. Therefore, policymakers should actively formulate a COCLEM policy framework, enhance rural public services, and increase infrastructure investment to continuously promote regional population agglomeration. Local governments should be responsible for ensuring policy implementation and financial support. Additionally, emphasis should be placed on the role of COCLEM in advancing agricultural technology to support RII. Compared with existing research, this study provides new empirical methods and theoretical insights for detailed research on the development of RII.