• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Machine Learning Aided Tracking Analysis of Haze Pollution and Regional Heterogeneity

Vol. 15, No. 6, June 30, 2021
10.3837/tiis.2021.06.005, Download Paper (Free):

Abstract

Not only can air pollution reduce the overall competitiveness of tourist destinations, but also changes tourists' travel decisions, thereby affecting the tourism flows. The study presents a machine learning method to analyze how the haze pollution puts spatial effect on tourism flows in China from 2001 to 2018, and reveals the regional differences in heterogeneity among eastern, central, and western China. Our investigation reveals three interesting observations. First, the Environmental Kuznets Curve of the impact of haze pollution on tourism flows is not significant. In the eastern and western regions, the interaction between haze pollution and domestic tourism flows as well as inbound tourism flows shows an inverted U-shaped curve respectively. Second, there is an significantly positive spillover effect of tourism flows in all of the eastern, central, and western regions. As to the intensity of spillover, domestic tourism flows is higher than that of the inbound tourism flows. Both of the above figures are greatest in the eastern. Third, the Chinese haze pollution mainly reduces the inbound tourism flows, and only imposes significantly negative direct effects on the domestic tourism flows in the central region. In the central and eastern regions, significantly negative direct effects and spillover effects are exerted on inbound tourism.


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Cite this article

[IEEE Style]
F. Gu, K. Jiang and F. Cao, "Machine Learning Aided Tracking Analysis of Haze Pollution and Regional Heterogeneity," KSII Transactions on Internet and Information Systems, vol. 15, no. 6, pp. 2031-2048, 2021. DOI: 10.3837/tiis.2021.06.005.

[ACM Style]
Fangfang Gu, Keshen Jiang, and Fangdong Cao. 2021. Machine Learning Aided Tracking Analysis of Haze Pollution and Regional Heterogeneity. KSII Transactions on Internet and Information Systems, 15, 6, (2021), 2031-2048. DOI: 10.3837/tiis.2021.06.005.