Integrating social narratives of flood events into a text network analysis-based decision support framework to reduce vulnerability to climate change in Africa
Viable actions that can help address the impacts of climate change are often impeded by a lack of accessible and usable information needed to underpin evidence-based decision-making. Compounding this, disaster risk management systems often fail to account for context-specific drivers of social vulnerability and environmental risks, which are crucial for enhancing social resilience to flood impacts.
This paper outlines an attempt to overcome these impediments by using community-based narratives of flood risk in Lusaka, Zambia. The researchers relied on natural language processing (NLP) and text network analysis (TNA) of residents’ narratives of their experiences with flooding to fine-tune flood maps that had been created by using global rainfall and global information system (GIS) datasets.
The researchers detail the specific procedures that underpinned the conduct of “learning labs”, which were held with stakeholders to solicit input on the scope for enhanced flood resilience across Lusaka, with an emphasis on issues affecting its unplanned settlements. These sessions served as a sounding board for determining which issues and solutions should be prioritized. The most prominent issue that emerged was the pressing need for local government to become more adaptive and inclusive, with a better representation of community voices in decision-making processes.
The authors argue that the approach provides a dynamic entry point for enhancing stakeholder engagement. In their experiences in Lusaka, the use of this approach presented opportunities to discuss social vulnerability to floods and climate change, and highlighted future challenges and opportunities for resilience planning.
The authors indicate that the work demonstrates the value of convening stakeholders to discuss the interdisciplinary challenges of climate resilience, offers a benchmark for better use of available resources, and enables a swift evaluation of needs and tailored measures for resilience building.
Distribution channels: Environment
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