Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques

• URL: http://arxiv.org/abs/2401.06817v1
• Date: Thu, 11 Jan 2024 16:44:59 GMT
• Title: Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques
• Authors: Tanwi Mallick, John Murphy, Joshua David Bergerson, Duane R. Verner, John K Hutchison, Leslie-Anne Levy
• Abstract summary: We use BERT (Bidirectional Representations from Transformers) for Named Entity Recognition (NER) to identify specific geographies within the climate literature. We conduct region-specific climate trend analyses to pinpoint the predominant themes or concerns related to climate change within a particular area. These in-depth examinations of location-specific climate data enable the creation of more customized policy-making, adaptation, and mitigation strategies.
• Score: 0.9387233631570752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Cross-Country Comparative Analysis of Climate Resilience and Localized Mapping in Data-Sparse Regions [0.0]
  Agriculture is the most vulnerable to climate change in low-income countries (LICs) This paper introduces a framework for cross-country comparative analysis of sectoral climate resilience. The study identifies shared vulnerabilities and adaptation strategies across LICs, enabling more effective policy design.
  arXiv  Detail & Related papers  (2024-09-13T12:12:26Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study [94.07737890568644]
  As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages. Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
  arXiv  Detail & Related papers  (2023-10-24T15:15:28Z)
• Climate Change Policy Exploration using Reinforcement Learning [0.0]
  We use four different Reinforcement Learning agents varying in complexity to probe the environment in different ways. We use a reward function based on planetary boundaries that we modify to force the agents to find a wider range of strategies.
  arXiv  Detail & Related papers  (2022-10-23T18:20:17Z)
• ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods [89.61670857155173]
  We present our solution to simulate photo-realistic floods on authentic images. We propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation.
  arXiv  Detail & Related papers  (2021-10-06T15:54:57Z)
• Remote sensing, AI and innovative prediction methods for adapting cities to the impacts of the climate change [0.0]
  I propose an AI-based framework which might be useful for extracting indicators from remote sensing images. I underline that this is an open field and an ongoing research for many scientists, therefore I offer an in depth discussion on the challenges and limitations of AI-based methods.
  arXiv  Detail & Related papers  (2021-07-06T15:55:26Z)
• Quantum technologies for climate change: Preliminary assessment [0.0]
  Climate change presents an existential threat to human societies and the Earth's ecosystems. Quantum technologies in computing, sensing, and communication could become useful tools to diagnose and help mitigate the effects of climate change. This report aims to identify potential high-impact use-cases of quantum technologies for climate change with a focus on four main areas.
  arXiv  Detail & Related papers  (2021-06-23T18:02:19Z)
• Applications of physics-informed scientific machine learning in subsurface science: A survey [64.0476282000118]
  Geosystems are geological formations altered by humans activities such as fossil energy exploration, waste disposal, geologic carbon sequestration, and renewable energy generation. The responsible use and exploration of geosystems are thus critical to the geosystem governance, which in turn depends on the efficient monitoring, risk assessment, and decision support tools for practical implementation. Fast advances in machine learning algorithms and novel sensing technologies in recent years have presented new opportunities for the subsurface research community to improve the efficacy and transparency of geosystem governance.
  arXiv  Detail & Related papers  (2021-04-10T13:40:22Z)
• ClimaText: A Dataset for Climate Change Topic Detection [2.9767565026354186]
  We introduce textscClimaText, a dataset for sentence-based climate change topic detection. We find that popular keyword-based models are not adequate for such a complex and evolving task. Our analysis reveals a great potential for improvement in several directions.
  arXiv  Detail & Related papers  (2020-12-01T13:42:37Z)
• Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
  In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG) We present this tool and the methodology that we used to develop it in the present article.
  arXiv  Detail & Related papers  (2020-11-03T21:22:42Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.