Abstract
In this paper, an ordinal regression model and a K-means clustering method are employed to directly assess the intensity of snow, low-temperature and frost disasters in non-pasture areas. Then, economic loss caused by the disasters is evaluated quantitatively by static and dynamic input–output models, respectively. Finally, an empirical analysis is conducted taking Beijing as an example. Compared to historical data in Beijing, results show that snowstorms in 2010 occurred more frequently than before when the intensity was weaker. Meanwhile, the frequency of low-temperature and frost disasters occurring did not change much, but serious low-temperature disasters happened due to heavy snowfalls. The indirect economic losses of the snow, low-temperature and frost disasters occurring in Beijing in 2010 are 2.23 times the direct economic losses. And some sectors are very sensitive to the disasters. Furthermore, it is found that the total economic losses obtained by a dynamic input–output model are less than that evaluated by a static model.
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Acknowledgments
This work was supported by the National Social Sciences Foundation of China [Grant Number 15BTJ019], the Special Scientific Research Fund of Public Welfare Profession of China [Grant Number GYHY201506051] and the National Natural Science Foundation of China [Grant Number 71373131].
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Wang, G., Wu, L. & Chen, J. Intensity and economic loss assessment of the snow, low-temperature and frost disasters: a case study of Beijing City. Nat Hazards 84, 293–307 (2016). https://doi.org/10.1007/s11069-016-2429-3
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DOI: https://doi.org/10.1007/s11069-016-2429-3