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

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  • Jun Liu
  • Luyu Yang
  • Haiyue Zhou
  • Shenghong Wang

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.

Suggested Citation

  • Jun Liu & Luyu Yang & Haiyue Zhou & Shenghong Wang, 2021. "Impact of climate change on hiking: quantitative evidence through big data mining," Current Issues in Tourism, Taylor & Francis Journals, vol. 24(21), pages 3040-3056, November.
    • Handle: RePEc:taf:rcitxx:v:24:y:2021:i:21:p:3040-3056
    DOI: 10.1080/13683500.2020.1858037
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