Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques
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ID: 281674
2024
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Abstract
Understanding the multifaceted effects of climate change across diverse
geographic locations is crucial for timely adaptation and the development of
effective mitigation strategies. As the volume of scientific literature on this
topic continues to grow exponentially, manually reviewing these documents has
become an immensely challenging task. Utilizing Natural Language Processing
(NLP) techniques to analyze this wealth of information presents an efficient
and scalable solution. By gathering extensive amounts of peer-reviewed articles
and studies, we can extract and process critical information about the effects
of climate change in specific regions. We employ BERT (Bidirectional Encoder
Representations from Transformers) for Named Entity Recognition (NER), which
enables us to efficiently identify specific geographies within the climate
literature. This, in turn, facilitates location-specific analyses. We conduct
region-specific climate trend analyses to pinpoint the predominant themes or
concerns related to climate change within a particular area, trace the temporal
progression of these identified issues, and evaluate their frequency, severity,
and potential development over time. These in-depth examinations of
location-specific climate data enable the creation of more customized
policy-making, adaptation, and mitigation strategies, addressing each region's
unique challenges and providing more effective solutions rooted in data-driven
insights. This approach, founded on a thorough exploration of scientific texts,
offers actionable insights to a wide range of stakeholders, from policymakers
to engineers to environmentalists. By proactively understanding these impacts,
societies are better positioned to prepare, allocate resources wisely, and
design tailored strategies to cope with future climate conditions, ensuring a
more resilient future for all.
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| Authors | Tanwi Mallick; John Murphy; Joshua David Bergerson; Duane R. Verner; John K Hutchison; Leslie-Anne Levy |
| Journal | arXiv |
| Year | 2024 |
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