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Previous studies provide empirical evidence for the connection between air pollution and tourism. However, many of them take the nexus as a linear one. It remains unexplored whether any thresholds are required for the nexus to materialize. This study systematically investigates whether PM2.5 concentrations-an essential indicator of air pollution-affect tourism in China at various tourism development levels. We analyze 284 Chinese cities from 2008 to 2018 using the Unconditional Quantile Regression method. Our statistical results reveal that air pollution positively influences tourism (regarding tourist visits and tourism revenue) in areas with low tourism development levels. However, a complex correlation between air pollution and tourism emerges when tourism development has reached a certain level. The correlation is initially negative, then positive, and finally disappears. But, the overall correlation remains negative. The effects of the interaction between air pollution and tourism resources on tourism are inverted U-shaped, implying that tourism resources can mitigate the negative effects of air pollution on tourism only when tourism development has reached a certain level. Based on the above findings, the associated policy implications are discussed., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xiao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study utilizes high-resolution population grid data from 2004 to 2018 to assess the extent of urban polycentricity in 284 Chinese prefecture-level cities. A new method for measuring urban polycentricity is devised using the Clauset-Newman-Moore grey modularity maximization algorithm. Additionally, the Spatial Durbin Model is applied to investigate the effect of urban polycentricity on the mean and spatial variation of PM 2.5 concentrations. Our statistical results show that the increased distance between urban centers and the increased size disparity between the large and small centers effectively reduces air pollution within the city. On the other hand, in cities with a strong clustering of urban centers and significant size disparity between main and sub-centers, the polycentric urban structure cannot help reduce air pollution but merely shift it to other urban centers within the city. Hence, interpreting the severity of urban air pollution solely on the basis of the citywide average of air pollution may be insufficient. It is necessary to consider the possible relocation of air-polluting activities associated with polycentric urban structures. Policymakers should take into account the potential spatial inequality of air pollution driven by urban polycentricity. [Display omitted] • New algorithm for measuring urban polycentricity (distance dispersion and size disparity of urban centers). • New approach for measuring "urban" air pollution. • Suggested method for addressing the spatial spillover effects of air pollution in data analysis. • Polycentric urban structures reduce air pollution but exacerbate the uneven distribution of air pollution. • Benefits and potential drawbacks of urban polycentricity in mitigating air pollution. [ABSTRACT FROM AUTHOR]