Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

Abstract

The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.

Does China’s poverty alleviation policy improve the quality of the ecological environment in poverty-stricken areas?

“…In terms of control variables, the regression coefficients of lnPGDP and ID are significantly negative at the 1% level, which is also largely consistent with the findings of previous scholars (Xu et al, 2022;Ward and Shively, 2012): economic and industrial development will be detrimental to the local environment, especially in underdeveloped areas, the negative environmental impact of industrial development is more pronounced, and the higher the density of enterprises will bring about greater pollution. The regression results of other control variables are also basically consistent with the results of previous scholars (Zhou et al, 2013):The rise of secondary and tertiary industries has brought about improvements in the local environment, probably because of the popularity of the Nature Based Solutions (NBS) concept, and more and more companies and industries have started to transform to a sustainable economic development model.…”

“…It has been suggested that the pursuit of economic performance motivates local governments to devote themselves to areas that can bring promotion, crowding out resource inputs for environmental protection and weakening local environmental control standards, thus undermining the environmental quality of the region (Jiao et al, 2011;Wang and Lei, 2020), so with reference to Zhangchose GDP growth rate as an economic performance indicator (Zhang, 2020). 5) Industrialization level (second): the level of industrialization and environmental quality are interrelated, and the evolution of industrial structure has a significant impact on the ecological and environmental quality in China (Xu et al, 2022), so the ratio of gross secondary industry product (million yuan) to gross regional product (million yuan) was used to represent this indicator (Lin and Zhu, 2019). 6) Enterprise density (ID).…”

Inequality and Heterogeneity in Medical Resources for Children with Autism Spectrum Disorders: A Study in the Ethnic Minority Region of Southern China

“…It is worth noting that the number of enterprises above designated size re ected the number of medium to large enterprises in each region, which were a major source of local government revenue, but it was negatively correlated with the CTPP indicator in this study. For reasons of environmental protection and green economy policies [54,55], many large enterprises in China are deployed in exclusive economic development zones or industrial parks located away from urban areas, which we believe has led to a negative association with CTPP.…”

Machine Learning-Based Predominant Driving Factors Impacting Urban Industrial Wastewater Discharge in the Yellow River Basin

“…On the basis of data from 21 surface water monitoring sites in the Yangtze River basin in China, the spatiotemporal water quality features were explored by machine learning methods . According to the statistical data, 17 economic, environmental, and social variables were determined using the RF algorithm of recursive feature elimination, and the results indicated that environmental pollution had a greater impact on the health of residents …”

“…29 According to the statistical data, 17 economic, environmental, and social variables were determined using the RF algorithm of recursive feature elimination, and the results indicated that environmental pollution had a greater impact on the health of residents. 30 Despite the wide application of machine learning in model prediction, machine-leaning-based EKC regression analysis has rarely been studied. In this work, three machine learning algorithms, including k-nearest neighbor (knn), random forest (RF), and support vector machine (SVM), are used to build an optimal model to explore the main driving factors affecting IWD.…”