The Impact of Air Quality on Inbound Tourist Arrivals over China Based on Grey Relational Analysis

Air pollution has a significant impact on tourism; however, research in this area is still limited. In this study, we applied grey relational analysis to panel data from 31 provinces in China and evaluated the relationship between air quality and inbound tourist arrivals. The study focused on provincial-level disparities for the different key air quality evaluation standards during 2009-2012 and 2013-2019. For instance, we considered PM10, SO2, NO2 and the excellent and good ratings of Air Pollution Index (API) during 2009-2012 and the additional PM2.5, CO, O-3 and the excellent and good ratings of Air Quality Index (AQI) from 2013 to 2019. Results indicate that: (1) Inbound tourist arrivals are significantly and positively affected by ambient air quality, and the impact from 2013 to 2019 was greater than that from 2009 to 2012; (2) there is regional diversity in inbound tourist arrivals, and the impact of the different air quality indicators varies; (3) inbound tourists showed greater sensitivity to air pollution under the AQI standard; (4) the impact of air quality indicators on the inbound tourist arrivals shows grey relational order, and the concentration of PM2.5, PM10 and SO2 have less impact than NO2, CO and O-3 on changes in tourism numbers; (5) consistency in the air quality impact on foreign tourists and compatriot tourists from HK, MO and TW varies by air quality indicators. This study highlights the need for appropriate measures to improve air quality for high-quality and sustainable development of inbound tourism.

Automated summary

This study used grey relational analysis on panel data from 31 provinces in China to evaluate the impact of air quality on inbound tourist arrivals. It found that air quality has a significant positive impact on inbound tourist numbers, with a greater impact from 2013 to 2019 compared to 2009 to 2012, and different air quality indicators have varying effects on inbound tourists and show regional diversity.