From short-term fluctuations to long-term improvement: A multi-temporal scale and multi-configurational pathway study on air pollution control

Air pollution has become one of the significant challenges to sustainable development in human society. Based on Air Quality Index (AQI) data from 282 cities in China from 2015 to 2023, this study employs functional data analysis methods and the Hilbert space norm to systematically analyze the dynamic characteristics of AQI across three temporal scales: daily fluctuations, weekly peaks, and monthly averages. Furthermore, using a dynamic panel fuzzy-set qualitative comparative analysis model, exploring multi-configurational pathways for improving AQI at different time scales. The findings indicate that (1) The dynamic characteristics of AQI exhibit significant regional heterogeneity and temporal correlations across different time scales; (2) AQI improvement pathways demonstrate distinct time-scale dependencies: in the short term, the synergy between financial and industrial policies accounts for 85 % of the explained reduction in pollution; in the medium term, green finance and green coverage jointly account for 82.9 % of the explanatory power in controlling pollution peaks; in the long run, the synergy between green finance, urban planning, and ecological construction explains 85.7 % of the sustained improvement. Based on the findings, this study proposes policy recommendations such as enhancing green finance and refining urbanization models to support sustainable economic and environmental development of cities.