Coordinating public and government responses to air pollution exposure: A multi-source data fusion approach

Aligning public demand with government supply of clean air aids in efficient air pollution control and enhancement of public happiness. However, comparative empirical analyses of public and government attention to air quality changes are still sparse due to data and methodological constraints. Here, we adopt multi-source data fusion approaches to assess the impacts of air pollution exposure on public and government attention. Specifically, remote and social sensing data, alongside keywords extracted from textual data, are utilized to quantify air pollution exposure and corresponding public and government attention levels in 273 Chinese cities from 2011 to 2019, and a two-stage least squares regression model is employed to tackle reverse causality issues underlying the exposure-response relationship. Our findings reveal that, on average, a unit increase in PM_2.5 levels would result in a 17.7% growth in public attention and a 12.7% rise in government attention, respectively, suggesting that demand-driven public attention tends to be more sensitive to air quality changes than policy-driven government attention. Results for the spatial-temporal heterogeneity further demonstrate that public attention varies across time and space, whereas government attention remains relatively consistent. Additionally, we have identified 116 cities exhibiting disparities between the public and government responses to air quality changes, calling for environmental policy refinements to better serve the needs of residents. This study emphasizes the necessity of public engagement in environmental governance and offers rich policy implications for air pollution control in China.