China has shown good momentum on the road of digital economy development, however, it is also rapidly entering an aging society. Exploring the health effects of the digital economy is of positive significance for realizing healthy aging in China. This paper focuses on the relationship between the digital economy and the health of middle-aged and older people using microdata from the China Health and Retirement Longitudinal Study (CHARLS) 2011–2018 and macrodata from Chinese cities. The study found that the digital economy showed a significant inverted U-shaped relationship on the health of middle-aged and older people. The results of subgroup regressions indicated heterogeneity in this effect across gender, education level, urban/rural and region. Individual health in female, highly educated, and urban groups is more closely related to the digital economy. Middle-aged and old groups in the western region are better able to enjoy the dividends of the digital economy, while middle-aged and old groups in the eastern region are more negatively affected by the digital economy. In the lead-up to the development of the digital economy, individual health can be promoted by narrowing the urban–rural income gap and increasing basic medical resources, while in the later stage of the development of the digital economy, it manifests itself in inhibiting the level of individual health by widening the urban–rural income gap and lowering the level of basic medical resources. In addition, air pollution exhibits a positive moderating effect between the digital economy and individual health, suggesting that air pollution reinforces the impact of the digital economy on health. Expansive analyses indicate that the digital economy has a negative impact on physiological health.

China has promised to meet the “carbon peaking and carbon neutrality” goals. Exploring the relationship between innovative industrial agglomeration and environmental pollution plays an important role in the realization of these goals and sustainable development. Based on the panel data of 277 prefecture-level cities in China from 2007 to 2019, this paper uses the multi-period difference-in-difference (DID) model to examine the impact and mechanism of the innovative industrial agglomeration pilot (IIAP) policy on the environment. Furthermore, we explore the spatial effect of the IIAP using spatial DID. The findings show that, firstly, the IIAP policy significantly contributes to environmentally-friendly development in terms of enhancing carbon emission efficiency (CEE) and reducing environmental pollution index (EPI). Secondly, the IIAP policy promotes environmentally-friendly development mainly by improving the technological innovation level. Additionally, the heterogeneity analysis shows that the environmentally-friendly effect of the IIAP policy is primarily in the eastern region, large cities, and non-resource-based cities. Finally, there is a significant spatial effect of the IIAP policy on environmentally-friendly development, mainly reflected in the weak siphon effect between treatment group cities and neighboring regions. We suggest that the government should continue to maintain the steady growth of the IIAP cities and improve the energy utilization efficiency through a policy of green technology innovation. The government should also optimize the spatial layout of the pilot cities and make full use of the energy advantages of each region to better promote environmentally-friendly development.

Background: A decade ago, we proposed an index of physiological dysregulation based on Mahalanobis distance (DM) that measures how far from the norm an individual biomarker profile is. While extensive validation has been performed, focus was mostly on Western populations with little comparison to developing countries, particularly at a physiological system level. The degree to which the approach would work in other sociocultural contexts and the similarity of dysregulation signatures across diverse populations are still open questions. Methods: Using 2 data sets from China and 3 from Western countries (United States, United Kingdom, and Italy), we calculated DM globally and per physiological system. We assessed pairwise correlations among systems, difference with age, prediction of mortality and age-related diseases, and sensitivity to interchanging data sets with one another as the reference in DM calculation. Results: Overall, results were comparable across all data sets. Different physiological systems showed distinct dysregulation processes. Association with age was moderate and often nonlinear, similarly for all populations. Mahalanobis distance predicted most health outcomes, although differently by physiological system. Using a Chinese population as the reference when calculating DM for Western populations, or vice versa, led to similar associations with health outcomes, with a few exceptions. Conclusions: While small differences were noticeable, they did not systematically emerge between Chinese and Western populations, but rather diffusively across all data sets. These findings suggest that DM presents similar properties, notwithstanding sociocultural backgrounds, and that it is equally effective in capturing the loss of homeostasis that occurs during aging in diverse industrial human populations.