Impacts of haze pollution on China's tourism industry: A system of economic loss analysis

Haze pollution not only negatively influences public health but also causes great economic losses. However, most previous studies have mainly focused on health-related economic losses, while the negative effects of haze pollution on the tourism industry are often ignored, leading to the unsustainable development of tourism. In this context, contrasting with previous research perspectives, this article selected several representative tourist cities from East China, South China, Central China, North China, Northwest China, Southwest China, and Northeast China as research objects in an empirical study, developing an economic loss analysis system to quantitatively evaluate the losses in the tourism industry caused by haze pollution. This system uses the satin bower bird optimization-based distribution estimation method to identify the optimal distribution of haze pollution, demonstrating superior performance to the traditional estimation method. Meanwhile, the optimal distribution of haze pollution is employed to measure the probability of different concentration limits in each area. Furthermore, the economic loss formula of the tourism industry is proposed in the devised system, calculating the economic loss caused by haze pollution at different degrees. The results show that haze pollution in different degrees has caused varying degrees of losses to China's tourism industry. In terms of seasonality and regionality, different seasons and different regions produce different results. Compared with summer, autumn and winter haze pollution is more severe, creating obvious seasonal differences. There is also a regional agglomeration effect, whereby the regional distribution of haze pollution is consistent with each region's economic development.

Automated summary

This study investigates the economic losses in China's tourism industry caused by haze pollution in different regions, demonstrating varying degrees of impact. It also reveals the effects of seasonality and regionality, with different seasons and regions producing different outcomes.