Table 4.

Definitions of control variables.

variable symbol	variable definition	Explanation of indicators
EG	Economic growth. Expressed in GDP per capita.	Pollution concentration is highly correlated with GDP(Wu et al., 2018) [51]. Sustained economic growth makes the need for high-quality development more urgent, reducing pollution emissions, and the expected sign is negative (Zhao et al., 2018) [52].
IS	Industrial structure. It is expressed by the ratio of the added value of the secondary and tertiary industries.	In the process of industrialization, the secondary industry will bring more serious pollution than the tertiary industry (Zhao et al., 2018) [52]. The larger the ratio, the heavier the pollution, and the expected sign is positive.
ES	Employment structure. It is expressed by the ratio of employees in the secondary and tertiary industries	The larger the ratio, the lower the level of urban development, the less pollution, and the expected sign is negative.
EO	The degree of economic openness. It is expressed in terms of the amount of foreign funds actually utilized per capita.	With the rapid growth of foreign direct investment, PM2.5 pollution in Chinese cities is increasing (Pei et al., 2021) [53]. The expected sign is positive.
EDU	Educational level. Expressed as the number of students enrolled in general institutions of higher learning per 10,000 people。	Education level is related to air quality (Bravo et al., 2022) [54], and a higher level of education means an improvement in the quality of residents, which helps to reduce pollution, and the expected sign is negative.
QL	Quality of life. Expressed in terms of per capita disposable income of urban residents.	Per capita disposable income is one of the factors affecting PM2.5 concentration (Chen et al., 2020) [55]. Higher quality of life leads to more consumption, and increased household consumption leads to increased pollution, and the expected sign is positive.