In cities’ policy-making, it is a hot issue to grasp the determinants of carbon dioxide emission in Chinese cities. And the common method is to use the STIRPAT model, where its coefficients represent the influence intensity of each determinants of carbon emission. However, less work discusses estimation accuracy, especially in the framework of non-normal distribution and heterogeneity among cities’ emission. To improve the estimation accuracy, this paper employs a new method to estimate the STIRPAT model. The method uses a mixture of Asymmetric Laplace distributions (ALDs) to approximate the true distribution of the error term. Meantime, a designed two-layer EM algorithm is used to obtain estimators. We test the robustness via the comparison results of five different models. We find that the ALDs Mixture Model is more reliable the others. Further, a significant Kuznets curve relationship is identified in China.

Previous studies have shown there are no consistent and robust associations between socioeconomic status and morbidity rates. This study focuses on the relationship between the socioeconomic status and the morbidity rates in China, which helps to add new evidence for the fragmentary relationship between socioeconomic status and morbidity rates. The National Health Services Survey (NHSS) and China Health and Retirement Longitudinal Study (CHARLS) data are used to examine whether the association holds in both all-age cohorts and in older only cohorts. Three morbidity outcomes (two-week incidence rate, the prevalence of chronic diseases, and the number of sick days per thousand people) and two socioeconomic status indicators (income and education) are mainly examined. The results indicate that there are quadratic relationships between income per capita and morbidities. This non-linear correlation is similar to the patterns in European countries. Meanwhile, there is no association between education years and the morbidity in China, i.e., either two-week incidence rate or prevalence rate of chronic diseases has no statistically significant relationship with the education level in China.