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Geo-based model of intrinsic resilience to climate change: an approach to nature-based solution
Authors:Mahmood  Riffat  Zhang  Li  Li  Guoqing  Rahman  Munshi Khaledur
Institution:1.Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
;2.University of Chinese Academy of Sciences, Beijing, 100094, China
;3.Department of Geology and Geography, Georgia Southern University, 1100C Herty Building, Statesboro, GA, 3046, USA
;4.Department of Geography and Environment, Faculty of Life and Earth Sciences, Jagannath University, Dhaka, 1100, Bangladesh
;
Abstract:

A substantial amount of researches have been done on the understanding and assessment of resilience from multiple perspectives, e.g., ecological, social, economic, and disaster management; however, recent international approach is trending toward more systematic and comprehensive risk assessment processes. Pivotal element of such approach is to emphasizing on promoting resilience in the face of climate change impacts. Conceptualization and identification of parameters to assess climate change resilience is one of the remaining challenges that academia is facing. Reviewing the principles of the climate change resilience highlighted in the literature, the goal of this study is to introduce a theoretical model about the climate change resilience concept to facilitate and enhance future climate change resilience-related researches. The model proposed in this study is named as the climate change resilience of place (C-CROP) model, a geo-based model which is designed to assess climate change resilience for any geographic region with an approach to the incorporation of nature-based solution (NBS). C-CROP model considers vulnerability, exposure, sensitivity to climate change on one side; another side is co-benefit, climate proofing, and disservices of proposed NBS. An operational framework of the C-CROP model is also proposed, that allows spatially explicit assessment of climate change resilience in real world by developing an indicator-based framework and comprehensive mapping using the geospatial approach. Therefore, this model includes vulnerability hotspots identification; better understanding of the pathways of resilience; and solutions (i.e., NBS) to infer the impacts and effectiveness of resilience-building interventions.

Keywords:
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