The role of indigenous knowledge in integrating scientific and indigenous knowledge for community-based disaster risk reduction: A case of Haikou Village in Ningxia, China. (December 2019) - Wang, Zhiqiang; Liu, Jianbo; Xu, Na; Fan, Chunbo; Fan, Yida; He, Shuoxin; Jiao, Linxi; Ma, Nannan
Abstract: With climate change, the occurrence and severity of disasters has greatly increased over recent decades. The importance of community-based disaster risk reduction (CBDRR) has been emphasized for its vital role in building resilience. It has also been demonstrated that integrating indigenous and scientific knowledge contributes to CBDRR. However, there remain many challenges to further correlate scientific knowledge and local information, such as inadequate communication and poor understanding of local contexts. Few literatures have investigated how indigenous people make efforts to achieve integration of scientific and indigenous knowledge (ISIK) based on their knowledge and practices. This article reveals the theoretical framework of integrating scientific and indigenous knowledge to improve community capacity for disaster risk reduction under certain social development degree. Taking Haikou Village in Ningxia Hui Autonomous Region of China as an example, this research explored the vital role of indigenous knowledge during ISIK. The findings show that ISIK that is led by indigenous knowledge from Haikou villagers' initiative, including management and organization, monitoring methods, information dissemination, and propaganda and education, especially overcomes poor understanding of local circumstances from government and scientists. The degree of social development decides the capacity that a community acquires knowledge for disaster risk reduction, and the understanding and acceptance of scientific knowledge, which further affects the application of knowledge for CBDRR and the mode and depth of ISIK. The study reveals five stage of ISIK varied by social development degree of community and provides some references for choosing proper ISIK model under specific background.