Since the beginning of the COVID-19 outbreak and the launch of the “Healthy China 2030” strategy in 2019, public health has become a relevant topic of discussion both within and outside China. The provision of public health services, which is determined by public health expenditure, is critical to the regional public health sector. Fiscal decentralization provides local governments with more financial freedom, which may result in changes to public health spending; thus, fiscal decentralization may influence public health at the regional level. In order to study the effects of fiscal decentralization on local public health expenditure and local public health levels, we applied a two-way fixed effect model as well as threshold regression and intermediate effect models to 2008–2019 panel data from China's 30 mainland provinces as well as from four municipalities and autonomous regions to study the effects of fiscal decentralization on public health. The study found that fiscal decentralization has a positive effect on increasing public health expenditure. Moreover, fiscal decentralization can promote improvements in regional public health by increasing public health expenditure and by improving the availability of regional medical public service resources. In addition, fiscal decentralization has a non-linear effect on public health.

As the country with the largest CO2 emissions in the world, the Chinese government has put forward clear goals of hitting peak carbon emissions by 2030 and carbon neutralization by 2060. Thus, China started piloting carbon emission trading in 2013, and in July 2021 China opened national carbon trading, which is the largest carbon market in the world (China Launches World, 2021). Therefore, it is very important for China to study the role and mechanism of carbon trading at present. Based on the quasi-natural experiment of China’s carbon market pilot, this paper uses panel data of 30 provinces in mainland China from 2008 to 2019 to conduct an empirical study on carbon emission reduction and the economic effects in China’s pilot provinces through a Time-varying Differences-in-Differences method model. The results show that the implementation of a carbon trading policy can significantly inhibit carbon emissions and promote economic growth. At the same time, this paper further analyzes the emission reduction mechanism of the carbon emissions trading policy through the intermediary effect test and finds that the policy mainly realizes carbon emission reduction by changing the energy consumption structure, promoting low-carbon innovation, and upgrading the industrial structure. In addition, innovative research has found the impact of a carbon price signal and marketization on the emission reduction effect of the carbon market. Finally, targeted suggestions are put forward.

BackgroundThe link between the gut microbiota (GM) and Sjögren’s Syndrome (SS) is well-established and apparent. Whether GM is causally associated with SS is uncertain.MethodsThe MiBioGen consortium’s biggest available genome-wide association study (GWAS) meta-analysis (n=13,266) was used as the basis for a two-sample Mendelian randomization study (TSMR). The causal relationship between GM and SS was investigated using the inverse variance weighted, MR-Egger, weighted median, weighted model, MR-PRESSO, and simple model methods. In order to measure the heterogeneity of instrumental variables (IVs), Cochran’s Q statistics were utilized.ResultsThe results showed that genus Fusicatenibacter (odds ratio (OR) = 1.418, 95% confidence interval (CI), 1.072–1.874, P = 0.0143) and genus Ruminiclostridium9 (OR = 1.677, 95% CI, 1.050–2.678, P = 0.0306) were positively correlated with the risk of SS and family Porphyromonadaceae (OR = 0.651, 95% CI, 0.427–0.994, P = 0.0466), genus Subdoligranulum (OR = 0.685, 95% CI, 0.497–0.945, P = 0.0211), genus Butyricicoccus (OR = 0.674, 95% CI, 0.470–0.967, P = 0.0319) and genus Lachnospiraceae (OR = 0.750, 95% CI, 0.585–0.961, P = 0.0229) were negatively correlated with SS risk using the inverse variance weighted (IVW) technique. Furthermore, four GM related genes: ARAP3, NMUR1, TEC and SIRPD were significant causally with SS after FDR correction (FDR<0.05).ConclusionsThis study provides evidence for either positive or negative causal effects of GM composition and its related genes on SS risk. We want to provide novel approaches for continued GM and SS-related research and therapy by elucidating the genetic relationship between GM and SS.

BackgroundEvidence from previous studies have implicated an important association between gut microbiota (GM) and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), but whether there is a definite causal relationship between GM and ME/CFS has not been elucidated.MethodThis study obtained instrumental variables of 211 GM taxa from the Genome Wide Association Study (GWAS), and mendelian randomization (MR) study was carried out to assess the effect of gut microbiota on ME/CFS risk from UK Biobank GWAS (2076 ME/CFS cases and 460,857 controls). Inverse variance weighted (IVW) was the primary method to analyze causality in this study, and a series of sensitivity analyses was performed to validate the robustness of the results.ResultsThe inverse variance weighted (IVW) method indicated that genus Paraprevotella (OR:1.001, 95%CI:1.000–1.003, value of p < 0.05) and Ruminococca- ceae_UCG_014 (OR 1.003, 95% CI 1.000 to 1.005, value of p < 0.05) were positively associated with ME/CFS risk. Results from the weighted median method supported genus Paraprevotella (OR 1.003, 95% CI 1.000 to 1.005, value of p < 0.05) as a risk factor for ME/CFS.ConclusionThis study reveals a causal relationship between genus paraprevotella, genus Ruminococcaceae_UCG_014 and ME/CFS, and our findings provide novel insights for further elucidating the developmental mechanisms mediated by the gut microbiota of ME/CFS.

Energy poverty is a crucial issue faced by countries all around the world, as the largest developing country in the world, China is also experiencing energy poverty problems. In order to explore the health effect of energy poverty in China, this paper first uses the principal component analysis (PCA) to construct a comprehensive index to measure energy poverty, and then adopts the ordinary least square method (OLS), fixed effect model (FE), instrumental variable two-stage least squares (IV-2SLS) regression to study the impact of energy poverty on the physical and mental health of Chinese people based on China Family Panel Studies 2018 (CFPS 2018). The study discovers that energy poverty significantly hampers the mental and physical health of Chinese people, an increase in energy poverty might cause 28.74%、18.69% decrease in mental and physical health respectively. Moreover, this paper further explores the influencing paths of energy poverty by intermediary effect and regulatory effect. It is revealed that in addition to directly affecting physical and mental health, energy poverty also have a negative impact on physical and mental health by affecting the accessibility of a series of resources, such as water and food, reducing the opportunities for physical exercise and increasing medical expenses. However, the impact is restricted by age and family income. Finally, under the national strategy of China, this paper further discusses how to give consideration to the joint implementation of heath and emission reduction strategies, then gives specific policy suggestions based on the results.