The impact of smart city construction on urban public health: evaluation using dual machine learning models

Global public health is confronted with multiple challenges such as the frequent occurrence of infectious diseases, the increasing burden of chronic diseases, climate change, and the transformation of population structure. These social and environmental factors have exacerbated the complexity and difficulty of public health governance. Digital technologies represented by artificial intelligence and big data are developing rapidly, providing new tools and paradigms for addressing these challenges.

This paper takes the pilot policies of smart cities in China as the standard natural experiment, and uses the panel data of 270 prefecture-level cities in China from 2007 to 2020. It empirically examines the impact of smart city construction (SCC) on public health level (PHL) by adopting a dual machine learning model.

The results show that SCC can significantly improve PHL, and the conclusion remains valid after robustness tests such as resetting the machine learning model, instrumental variable method, and multi-dimensional fixation. Mechanism tests show that SCC mainly enhances PHL by improving the quality of the ecological environment, enhancing the level of innovative services, and optimizing the structure of healthy human resources. Heterogeneity analysis revealed that the promoting effect of SCC on PHL was more pronounced in regions such as western China, large cities and key environmental protection cities.

The research conclusions can not only enrich the theoretical accumulation in the fields of smart cities and public health, but also provide replicable and scalable practical experience for other developing countries to enhance their public health governance capabilities through digital means under resource constraints.