Assessing coastal vulnerability and evaluating the effectiveness of natural habitats in enhancing coastal resilience: a case study in Shanghai, China
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Author(s)
Zhiyi, Lin
Singh, Minerva
Type
Journal Article
Abstract
This research evaluates the coastal vulnerability of Shanghai, evaluates the effectiveness of existing natural habitats in reducing that vulnerability, and, finally, provides recommendations to improve the resilience of the coastal areas. Shanghai is an important economic center that is home to a large population. However, the combination of ground subsidence, rising sea levels, and more frequent coastal flooding due to tropical cyclones poses escalating climate risks for Shanghai, demanding urgent mitigation measures. The InVEST Coastal Vulnerability Model was used in this study to assess Shanghai’s coastal vulnerability under the current situation and various scenarios that simulated the absence of natural habitats. The assessment results were analyzed through a comparison between different scenarios and spatial aggregation analysis. This study pinpointed highly vulnerable areas, primarily located on the east coast of Chongming Island, the east and northeast coasts of Hengsha Island, and the east coast of the mainland of Shanghai. These areas need to be prioritized for intervention. Also, it demonstrated the effectiveness of existing natural habitats in reducing coastal vulnerability, with large green spaces and salt marshes playing a greater role compared to small green spaces. This is the first study applying the InVEST Coastal Vulnerability Model to Shanghai, demonstrating the model’s potential in providing valuable information regarding coastal protection against the impacts of climate change in Shanghai. Insights from the findings of this study are useful in crafting sustainable land-use policies and plans for Shanghai.
Date Issued
2024-01-10
Date Acceptance
2024-01-08
Citation
Sustainability, 2024, 16 (2)
ISSN
2071-1050
Publisher
MDPI AG
Journal / Book Title
Sustainability
Volume
16
Issue
2
Copyright Statement
© 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
License URL
Publication Status
Published
Article Number
ARTN 609