Identifying key factors of regional agricultural drought vulnerability using a panel data grey combined method

Huifang Sun (), Yaoguo Dang and Wenxin Mao
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Huifang Sun: Nanjing University of Aeronautics and Astronautics
Yaoguo Dang: Nanjing University of Aeronautics and Astronautics
Wenxin Mao: Southeast University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 98, issue 2, No 13, 642 pages

Abstract: Abstract Regional agricultural drought vulnerability (RADV) is a complex problem caused by the interaction of various factors, and the combination of multiple dimensions of each subregion, factor index and time affects the RADV. Therefore, panel data should be used to reflect the actual situation of the region objectively and comprehensively. Current research on identifying key factors of affecting RADV is relatively scarce from the perspective of panel data. In view of this, in order to classify and identify the key factors, a new panel data grey combined method of comprehensive grey relational analysis (CGRA) and Max-CGRA clustering is proposed, which is applied to identify the key factors of RADV in China’s Henan Province. According to the identification results of key factors, the reasons for the change of RADV are further discovered, and the corresponding drought policies and countermeasures that need to be strengthened and controlled are presented. In addition, these results can also provide scientific basis for regional agricultural drought risk control.

Keywords: RADV; Key factors; Panel data; CGRA; Max-CGRA clustering (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11069-019-03722-0

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