arXiv:2605.20793v1 Announce Type: new Abstract: Recent advances in natural language processing (NLP) and large language models (LLMs) have enabled the systematic use of large-scale textual data from news, social media, and reports to create datasets with socio-economic impacts of climate hazards such as floods, droughts, storms, and multi-hazard events. As the field of text-as-data for impact assessment expands, so does its methodological complexity. Yet research remains fragmented, with no clear guidelines for defining what constitutes an impact, handling temporal and spatial biases, and sele
Advances in NLP and LLMs are enabling the systematic extraction and analysis of socio-economic impacts from large-scale text data, allowing for new forms of climate impact assessment.
This development allows for more granular, real-time understanding of climate impacts, enabling better policy responses and resource allocation for climate resilience and adaptation.
The ability to quantify climate impacts from unstructured text data shifts how socio-economic vulnerability to climate change is measured and reported.
- · Climate scientists and researchers
- · Government agencies (e.g., FEMA, UNEP)
- · Insurance companies
- · NGOs focused on climate resilience
- · Traditional, slow-moving impact assessment methods
- · Regions unprepared for data-driven climate analysis
Systematic, large-scale datasets detailing climate-related socio-economic impacts will become more common.
Improved accuracy in predicting and mitigating the human and economic costs of climate hazards will lead to more effective adaptation strategies.
The integration of these insights into financial models could lead to more nuanced asset valuations and risk assessments in climate-vulnerable regions.
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Read at arXiv cs.CL