With the development of the economy, environmental pollution caused by energy consumption has become increasingly prominent. Improving the efficiency of energy utilization is an important way to solve this problem. Firstly, we used a data envelopment analysis (DEA) model to calculate the energy utilization efficiency of China’s provinces and regions from the perspective of environmental constraints, including four inputs—labor force, capital stock, energy consumption and carbon emission—and one output, GDP. Secondly, an entity fixed effect model of panel data was built to investigate the influence of openness, urbanization, marketization and industrial structure on energy utilization efficiency in the process of economic structure change. The results indicate that China’s energy efficiency shows a trend of first stabilizing and then declining from 2007 to 2017. Meanwhile, the comprehensive energy efficiency of all provinces and regions is not very ideal. Only Beijing, Shanghai and Guangdong constitute the forefront of China’s energy efficiency. The lack of pure technical efficiency in most provinces is the main reason for the low comprehensive efficiency, but there are also obvious differences among provinces and regions. In addition, urbanization, openness and industrial structure have a negative impact on energy efficiency, while marketization has a significant positive impact on energy efficiency. Finally, based on the regional differences, some suggestions were put forward to improve China’s energy utilization efficiency.

Environmental pollution damages public health and affects economic development. Environmental regulation is the main way for the government to solve environmental pollution. So what type of environmental regulation works better for public health and economic development? Can environmental regulation have an influence on economic development through public health? To solve these problems, this research uses China’s provincial panel data from 2013 to 2017 to divide environmental regulation into command-control policy tools and economic incentive policy tools and uses the mediating effect model to examine the relationship among environmental regulation, public health and economic development. The results show that: (1) There is a positive correlation between economic incentive policy tools and economic development; while no significant relationship between command-control policy tools and economic development is founded; (2) The relationship between command-control policy tools and public health is not significant, while the relationship between economic incentive policy tools and public health is positive; (3) Public health does not play a mediating role between command-control policy tools and economic development but plays a partial mediating role between economic incentive policy tools and economic development. Therefore, the government should strengthen the use of economic incentive policy tools to promote public health and sustainable economic development.

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.

Currently, individual artificial intelligence (AI) algorithms face significant challenges in effectively diagnosing and predicting early stage emerging serious diseases. Our investigation indicates that these challenges primarily arise from insufficient clinical treatment data, leading to inadequate model training and substantial disparities among algorithm outcomes. Therefore, this study introduces an adaptive framework aimed at increasing prediction accuracy and mitigating instability by integrating various AI algorithms. In analyzing two cohorts of early cases of the coronavirus disease 2019 (COVID-19) in Wuhan, China, we demonstrate the reliability and precision of the adaptive combined learning algorithm. Employing an adaptive combination with three feature importance methods (Random Forest (RF), Scalable end-to-end Tree Boosting System (XGBoost), and Sparsity Oriented Importance Learning (SOIL)) for two cohorts, we identified 23 clinical features with significant impacts on COVID-19 outcomes. Subsequently, the adaptive combined prediction leveraged and enhanced the advantages of individual methods based on three forecasting algorithms (RF, XGBoost, and Logistic regression). The average accuracy for both cohorts exceeded 0.95, with the area under the receiver operating characteristics curve (AUC) values of 0.983 and 0.988, respectively. We established a severity grading system for COVID-19 based on the combined probability of death. Compared to the original classification, there was a significant decrease in the number of patients in the severe and critical levels, while the levels of mild and moderate showed a substantial increase. This severity grading system provides a more rational grading in clinical treatment. Clinicians can utilize this system for effective and reliable preliminary assessments and examinations of patients with emerging diseases, enabling timely and targeted treatment.

Environmental pollution has become a hot topic of concern for the government, academia and the public. The evaluation of environmental health should not only relate to environmental quality and exposure channels but also the level of economic development, social environmental protection responsibility and public awareness. We put forward the concept of the “healthy environment” and introduced 27 environmental indicators to evaluate and classify the healthy environment of 31 provinces and cities in China. Seven common factors were extracted and divided into economic, medical, ecological and humanistic environment factors. Based on the four environmental factors, we classify the healthy environment into five categories—economic leading healthy environment, robust healthy environment, developmental healthy environment, economic and medical disadvantageous healthy environment and completely disadvantageous healthy environment. The population health differences among the five healthy environment categories show that economic environment plays a major role in population health. Public health in regions with sound economic environment is significantly better than that in other areas. Our classification result of healthy environment can provide scientific support for optimizing environmental countermeasures and realizing environmental protection.