Is Urbanization Good for the Health of Middle-Aged and Elderly People in China?—Based on CHARLS Data

City Health Examination and Evaluation of Territory Spatial Planning for SDG11 in China: A Case Study of Xining City in Qinghai Province

City health examination and evaluation of territorial spatial planning is a new policy tool in China. However, research on city health examination and evaluation of territorial spatial planning is still in the exploratory stage in China. Guided by sustainable cities and communities (SDG11), a reasonable city health examination and evaluation index system for Xining City in Qinghai Province is constructed in this paper. The improved technique for order preference by similarity to ideal solution (TOPSIS) was used to quantify the evaluation results, and the city health index was visualized using the city health examination signals and warning panel. The results show that the city health index of Xining City continuously rose from 35.76 in 2018 to 69.76 in 2020. However, it is still necessary to address the shortcomings in innovation, coordination, openness and sharing and to improve the level of city space governance in a holistic way. This study is an exploration of the methodology used in city health examination and the evaluation of territorial spatial planning in China, which can provide a foundation for the sustainable development of Xining City and also provide a case reference for other cities seeking to carry out city health examinations and evaluations of territorial spatial planning in China.

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Diabetes, which is a chronic disease with a high prevalence in people over 45 years old in China, is a public health issue of global concern. In order to explore the spatiotemporal patterns of diabetes among people over 45 years old in China, to find out diabetes risk factors, and to assess its risk, we used spatial autocorrelation, spatiotemporal cluster analysis, binary logistic regression, and a random forest model in this study. The results of the spatial autocorrelation analysis and the spatiotemporal clustering analysis showed that diabetes patients are mainly clustered near the Beijing−Tianjin−Hebei region, and that the prevalence of diabetes clusters is waning. Age, hypertension, dyslipidemia, and smoking history were all diabetes risk factors (p < 0.05), but the spatial heterogeneity of these factors was weak. Compared with the binary logistic regression model, the random forest model showed better accuracy in assessing diabetes risk. According to the assessment risk map generated by the random forest model, the northeast region and the Beijing−Tianjin−Hebei region are high-risk areas for diabetes.

The Impact of Low-Carbon City (LCC) on Elderly People’s Health: Evidence from a Natural Experiment in China

Rapid urbanization has increased haze pollution, affecting the health of elderly people. This study uses low-carbon city (LCC) data and examines the effects of LCCs on improving the health of elderly residents. Our main purpose is to explore the following question: Can the new urbanization model presented by the LCC alleviate haze pollution and enhance the health of middle-aged and elderly people? This study uses data from the China Health and Retirement Longitudinal Study (CHARLS) and the 2012 LCC pilot to explore whether the LCC can alleviate haze pollution and improve elderly people’s health. The study found that the building of LCCs can reduce blood pressure, improve vital capacity, decrease obesity, and improve memory levels, including short-term and long-term memory. The building of LCCs also reduces the probability of being exposed to haze pollution by increasing the city’s green total factor productivity and the use of green technologies. The study concludes that elderly people received health dividends as a result of the enhancement of living conditions, transportation, and medical support in the LCCs.

Using a Random Forest Model to Study the Impact of Local Government-Led Urbanization on Urban Sustainable Development

Urbanization has accelerated China’s economic growth, but it has also brought many sustainability issues. This paper selected a random forest model to study the impact of local government-led urbanization on urban sustainable development. Urbanization affected urban sustainable development through government revenue expansion, land resources mismatch, and industrial structure adjustment. The results showed that the adjustment of industrial structure has the greatest impact on urban sustainable development, and the importance of the average output of industrial enterprises confirms it. Government revenue expansion and land resources mismatch are more important to the sustainable development of representative urban agglomerations. The goodness of fit of the random forest model is better than the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model. The generalization ability of the model is improved with the optimization of variables. The main contribution of this paper is that we have established a complete information dynamic game model on government revenue expansion, land resource mismatch, industrial structure adjustment, and urban sustainable development. And the random forest model is used to study the relationship between the above variables.