Indexing and Visualization of Climate Change Narratives Using BERT and Causal Extraction

• URL: http://arxiv.org/abs/2408.01745v1
• Date: Sat, 3 Aug 2024 11:05:41 GMT
• Title: Indexing and Visualization of Climate Change Narratives Using BERT and Causal Extraction
• Authors: Hiroki Sakaji, Noriyasu Kaneda,
• Abstract summary: We use two natural language processing methods, BERT (Bidirectional Representations from Transformers) and causal extraction, to analyze newspaper articles on climate change. The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers.
• Score: 2.7325857919669327
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, we propose a methodology to extract, index, and visualize ``climate change narratives'' (stories about the connection between causal and consequential events related to climate change). We use two natural language processing methods, BERT (Bidirectional Encoder Representations from Transformers) and causal extraction, to textually analyze newspaper articles on climate change to extract ``climate change narratives.'' The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers. Looking at the extracted climate change narratives over time, we find that since 2018, an increasing number of narratives suggest the impact of the development of climate change policy discussion and the implementation of climate change-related policies on corporate behaviors, macroeconomics, and price dynamics. We also observed the recent emergence of narratives focusing on the linkages between climate change-related policies and monetary policy. Furthermore, there is a growing awareness of the negative impacts of natural disasters (e.g., abnormal weather and severe floods) related to climate change on economic activities, and this issue might be perceived as a new challenge for companies and governments. The methodology of this study is expected to be applied to a wide range of fields, as it can analyze causal relationships among various economic topics, including analysis of inflation expectation or monetary policy communication strategy.

Related papers

• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• ClimateNLP: Analyzing Public Sentiment Towards Climate Change Using Natural Language Processing [0.0]
  This paper employs natural language processing (NLP) techniques to analyze climate change discourse and quantify the sentiment of climate change-related tweets. The objective is to discern the sentiment individuals express and uncover patterns in public opinion concerning climate change.
  arXiv  Detail & Related papers  (2023-10-12T07:48:50Z)
• AI For Global Climate Cooperation 2023 Competition Proceedings [77.07135605362795]
  No global authority can ensure compliance with international climate agreements. RICE-N supports modeling regional decision-making using AI agents. The IAM then models the climate-economic impact of those decisions into the future.
  arXiv  Detail & Related papers  (2023-07-10T20:05:42Z)
• AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N [75.67460895629348]
  Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. We introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy. We describe how to use multi-agent reinforcement learning to train rational agents using RICE-N.
  arXiv  Detail & Related papers  (2022-08-15T04:38:06Z)
• 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)
• The Power of Language: Understanding Sentiment Towards the Climate Emergency using Twitter Data [0.0]
  It could be speculated that there is a relationship between Crude Oil Futures and sentiment towards the Climate Emergency. This study shows that it is possible to split the conversation surrounding the Climate Emergency into 3 distinct topics.
  arXiv  Detail & Related papers  (2021-01-25T19:51:10Z)
• 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)
• From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling [0.0]
  We propose a multi-source topic aggregation system (MuSTAS) MuSTAS processes policy makers speech and rhetoric from several publicly available sources into an easily digestible topic summary. This topic digest will serve the general public and civil society in assessing where, how, and when politicians talk about climate and climate policies.
  arXiv  Detail & Related papers  (2020-10-16T12:21:01Z)

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.