The PM_2.5 pollution has been globally threatening human health. By monitoring PM_2.5 concentrations and meteorological data, this study estimated the changes in PM_2.5 concentrations before and after the implementation of an environmental protection tax law in 30 provincial capital cities in China by conducting a counterfactual curve-fitting simulation method and studied the effects of the environmental protection tax (EEPT) on PM_2.5. Then, the influencing factors of the EEPT in China were investigated employing a Bayesian LASSO regression model. The environmental protection tax generally reduced the annual PM_2.5 concentrations in China in 2018. The EEPT in various cities are different. Among the seven significant influencing factors, resident unemployment rate (RUR) and gross domestic product (GDP) were the top two influencing factors, with contributions up to 20.7% and 19.2%, respectively. Proportion of the secondary industry (PSI) (7.9%) and urbanisation rate (UR) (6.7%) were the bottom two influencing factors. The median influencing factors were resident average schooling years (RASY) (17.6%), relief amplitude (RA) (16.5%) and waste gas treatment input (WGTI) (11.5%). Furthermore, GDP and UR associated negatively with the EEPT on PM_2.5 pollution, whereas the other five variables associated positively with the EEPT on PM_2.5 pollution.