Air pollution perception bias and its influence on well-being: a multilevel analysis of micro and macro influences in China

Previous research has highlighted a notable connection between perceived air quality and well-being. However, the impact of biases in air quality perception on well-being has been insufficiently examined. This study combines subjective air pollution (SP) data from the 2021 China General Social Survey (CGSS) and China Social Survey (CSS) with objective air pollution (OP) measurements at the provincial level, specifically PM _2.5 and PM _10, to calculate the air quality perception bias (AQPB). We further explore the relationship between AQPB and well-being, considering both individual and broader factors across 5054 Chinese adults. A range of models, including Ordered Probit (O-Probit), Ordered Logit (Ologit), and Ordinary Least Squares (OLS), are employed to test the consistency of the results, with Instrumental Variable Ordered Probit (IV-OPROBIT) models used for robustness checks. The results show that 70.1% of participants underestimate the severity of air pollution, where SP is more favorable than OP. Significant demographic differences in AQPB and well-being were identified. Moreover, AQPB is negatively related to well-being, indicating that larger discrepancies between perceived and actual air quality are linked to lower levels of reported well-being. The analysis incorporates both macro and micro factors. These findings offer valuable insights for policymakers focused on reducing AQPB and enhancing public well-being.