Empirical Analysis of Factors Affecting Air Quality in China-Based on the Skew-Normal Spatial Dynamic Panel Data Models

Abstract

To explore the influence factors of air quality, the air quality data of 31 provincial capitals in China from 2014 to 2019 is studied by introducing skew-normal spatial dynamic panel data models in this paper. A Markov Chain Monte Carlo algorithm is developed to estimate the unknown parameters in the model. The main conclusions of the research are followed. (1) The variation of air quality in China could be well reflected by the skew-normal space dynamic panel data model. (2) PM10 had the most significant influence on air quality among the five air quality indexes, such as SO2, CO, NO2, PM10 and O3. (3) There are significant temporal and spatial correlations on air quality among provincial capitals. (4) In China, the air quality in southern and southwestern is excellent, while the air quality in northern is slightly polluted. These conclusions provide theoretical guidance for improving air quality in China and a scientific basis for making decisions of relevant departments.