Based on Beijing’s Air Quality Index (AQI) and concentration changes of the six major pollutants from 2019 to 2021, the results are visualized through descriptive statistics, and the air pollution status and influencing factors of Beijing’s AQI are analyzed using the ARIMA model and neural network. A forecast system is built and the fitting effects of the two models are compared. The results show that PM2.5, PM10, and O₃ of the six major pollutants have the greatest impact on AQI. Beijing’s air quality now shows a trend of improvement in recent years; however, there is obvious seasonal evidence that the summer pollution index has been high. Therefore, special attention should be paid to the treatment of ozone pollution in summer. Both models are useful for the forecast of AQI, but the forecast effect of the neural network model is better than that of the ARIMA model. Moreover, when using the additive seasonal model for the long-term forecast of monthly data, it is found that the Beijing AQI still shows seasonal cyclicality and has a slightly decreasing trend in the next two years. This research provides a basis for the forecast of air quality and policy enlightenment for environmental protection departments to deal with air pollution.

The number and field of researches on the application of Multi-Indicator Comprehensive Evaluation (MICE) are increasing. It is important to reflect on the understanding of the MICE method systematically and the issues implied behind it. This paper compares the core concepts and methodological elements of the three papers that systematically study the MICE method. It is found that the views of the three papers on the core issue are consistent and mutually supportive, but there are differences in the step division and sequence of the evaluation content. In addition, this paper considers the historical status of the MICE and holds that the key to solving the quality of weight lies in the "equivalent conversion" problem in the MICE. Taking the Human Development Index as an example, this paper illustrates the absoluteness of the "equivalent conversion" relationship. In addition, there are multiple processing methods for the MICE from the spatial dimension and multiple evaluation results accordingly, therefore, the results of the MICE need to be used carefully. Finally, based on the systematic summary and reflection of the MICE method, three suggestions are given for the application of the MICE method.

Health risks and hazards caused by the environment have long been one of the most important public issues of concern to the state, society, and the public. At the same time, population aging is becoming a global issue, and residents’ health is the most important component of people’s livelihood, and residents can only pursue other rights and interests if they can protect their own health. Therefore, based on the micro data from the fifth round of the China Family Panel Studies (CFPS), this paper uses binary logistic regression with propensity score matching (PSM) to analyze the effect of environmental perception on the health status (including mental health and physical health) of middle-aged and older adults. It was found that environmental perceptions significantly affect the depressive state and sickness status of middle-aged and older adults. Among them, middle-aged and older adults who were female, of rural households, with low education and relatively low income were more affected by environmental shocks on their health. Therefore, we should pay attention to the mental and physical health of middle-aged and older adults and change the existing design concept of aging policy: the government should formulate effective policies and increase corresponding social support; and society and families should also give corresponding care and encourage middle-aged and older adults to exercise more and provide reasonable psychological guidance.

Natural hazards are a common challenge to human survival and development. China is a country, that is, prone to natural hazards because of its vast territory and vast area. In fact, China is one of the countries most affected by natural hazards. The impact of natural hazards is often multidimensional, and the occurrence of natural hazards can have a dramatic impact on government finances and household property structures. However, existing research in China has mainly focused on individual factors and the household level, and there are gaps in the impact of natural hazards on the economic status of households. Based on the China Household Finance Survey-2019 (CHFS) data, this paper investigates the impact of natural hazards on household financial asset allocation using the Probit and Tobit models and the propensity score matching method. It is found that: the occurrence of natural hazards significantly reduces the likelihood of households’ investment in stocks and risky assets; older, more economically advanced, and better educated household heads are more willing to make risky investments; and households with lower financial literacy are more affected by natural hazards in terms of investment in risky financial assets than those with high financial literacy. Therefore, it is necessary for the government to do pre-disaster prevention and post-disaster reconstruction, and further improve the social security mechanism and optimize the risk management system, so that more households can participate in the financial market and thus obtain higher investment returns.