Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques

TL;DR

The paper addresses the challenge of extracting region-specific climate impacts from a rapidly growing climate literature corpus. It presents a geo-centric NLP pipeline that uses BERT-based NER for location extraction, LocationTagger for fine-grained geography, BERTopic for regional topic modeling, and LLMs for concise topic summarization, applied to a climate-focused subset of the S2ORC containing over 600,000 documents. Key contributions include an end-to-end NLP workflow, a large geo-tagged corpus, identification of country and state-level research activity, and an interactive CIACC tool that makes geo-specific insights accessible. The work enables data-driven, location-tailored adaptation and mitigation planning for policymakers, engineers, and environmentalists.

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.

Figures (6)

• Figure 1: Concept diagram
• Figure 2: Figure left: Global distribution of research papers. A choropleth map illustrating the frequency of academic papers by country. Lighter shades represent higher frequencies, with a logarithmic scale emphasizing the range of contributions across nations. Figure right: Top 10 countries by mentions in the climate corpus. The bar chart showcases the top 10 countries based on their research paper output. Please note that the data, sourced from the Semantic Scholar's Open Research Corpus, might reflect some inherent biases due to the dataset's nature or the methods employed during its collection and assessment to identify the subset associated with climate change hazards.
• Figure 3: Frequency of documents mentioning states in the climate corpus
• Figure 4: Topic distributions for climate research papers focusing on California are uncovering dominant themes and areas of study.
• Figure 5: Topic distributions for climate research papers focusing on Alaska are uncovering dominant themes and areas of study.