Stylized heterogeneous dynamic links among healthcare expenditures, land urbanization, and CO emissions across economic development levels.

This research examines the heterogeneous dynamic links among healthcare expenditures, land urbanization, and CO emissions across the development levels of China. To this end, data of 27 Chinese provinces are considered from 1999 to 2018. Theoretically, this research developed a healthcare expenditures-augmented Stochastic Impacts of Regression by Population, Affluence, and Technology (STIRPAT) model to incorporate healthcare expenditures as a determinant of affluence. Empirically, this research established a system of simultaneous equations based on the healthcare expenditures-augmented STIRPAT model to estimate the links among the variables. As a pre-analysis, second-generation Westerlund cointegration is applied and found the long-term equilibrium association among the variables. The long-run estimations and short-run causality are done by employing dynamic common correlated effects mean group method (DCCEMGM) and Dumitrescu-Hurlin causality. A heterogeneous long-run equilibrium linkage is confirmed to exist among the variables of interest. Concerning the long-run estimates, firstly, the healthcare expenditures growth and land urbanization exhibited a bilateral positive link. Secondly, CO emissions and healthcare expenditures growth manifested the existence of a bilateral positive link. And thirdly, a unilateral positive (negative) link is revealed to exist from a linear term (squared term) of land urbanization to CO emissions. Concerning the short-run results, firstly, a bilateral causal bond exists between the land urbanization and healthcare expenditures growth. Secondly, a bilateral causal bond prevails between CO emissions growth and healthcare expenditures growth. Finally, a unilateral causal bond is operational from land urbanization to CO emissions growth. In terms of the nature of the link, the long-run findings are consistent across the data samples. However, considering the degree of influence, heterogeneity is confirmed across the development levels for both long- and short-run. It infers that relatively more (less) developed regions showed relatively strong (weak) influence. Based on empirical findings, relevant policies are recommended.