Impact of climate change on hiking: quantitative evidence through big data mining

ABSTRACT

This study measures quantitatively the impact of climate change on hiking across 100 cities in China by analyzing tourist-generated big data with a hybrid method involving the generalized additive model and segmented regression model. The results indicate that temperature, relative humidity, and sunshine duration influence hiking participation nonlinearly, with threshold effects. Results from a simulation study show that hiking in over 90% of the cities studied will be affected negatively by climate change in the future. The hiking duration will drop by 7.17% to 7.39% in 2050 and 7.16% to 7.57% in 2080 under RCP 4.5. The situation is even worse under RCP 8.5. We encourage the use of this approach among nations or regions with such available data for further research.

KEYWORDS:

• tourism
• climate change
• hiking
• big data
• generalized additive model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under [grant number 41771163]; Key Research and Development Program of Sichuan Province under [grant number 2018SZ0373]; Innovation Inspiration Fund of Sichuan University under [grant number 2018hhs-44]; Regional History and Frontier Studies of Sichuan University; Sichuan University Research Fund; Social science project of Sichuan Province under [grant number SC20B047].