Ambient air pollution is an important environmental problem that impacts the health and sustainable development of human beings. Many measures have been taken by governments to decrease air pollution. This paper focuses on whether government investment has a positive effect on air quality. Based on China’s environmental statistics from 2003 to 2020, the Spatiotemporal Weighted Regression Model is used to observe the spatiotemporal correlation between environmental governance investment and air quality in different provinces in China, finding that there is a negative time-space correlation between environmental governance investment and air quality. In addition, environmental governance investment will not immediately improve air quality, and air pollution has the characteristics of spatial overflow that the pollution between regions affect each other. Then, to further research governments how to deal with environmental protection, configuration analysis has been used, and finds out four high-performance paths for environmental governance of China’s provinces. At the end of this research, we put forward four suggestions for air protection. Firstly, government should formulate long-term air governance policies. Secondly, government environmental governance of air pollution should pay attention to the cooperativity of environmental governance between regions. Thirdly, the third sectors, companies and the public should be encouraged in air protection. Fourthly, government should build a whole-process air governance strategy.

The old-age dependency ratio (ODR) is an important indicator reflecting the degree of a regional population’s aging. In the context of aging, this study provides a timely and effective method for predicting the ODR in Chinese cities. Using the provincial ODR from the Seventh National Population Census and Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data, this study aims to predict and analyze the spatial correlation of the municipal ODR in Chinese cities. First, the prediction model of the ODR was established with curve regression. Second, the spatial structure of the municipal ODR was investigated using the Moran’s I method. The experimental results show the following: (1) the correlation between the sum of the nighttime light and ODR is greater than the mean of nighttime light in the study areas; (2) the Sigmoid model fits better than other regression models using the provincial ODR in the past ten years; and (3) there exists an obvious spatial agglomeration and dependence on the municipal ODR. The findings indicate that it is reasonable to use nighttime light data to predict the municipal ODR in large and medium-sized cities. Our approach can provide support for future regional censuses and spatial simulations.