Analyzing Sustainability Reports Using Natural Language Processing
- URL: http://arxiv.org/abs/2011.08073v2
- Date: Tue, 17 Nov 2020 17:20:09 GMT
- Title: Analyzing Sustainability Reports Using Natural Language Processing
- Authors: Alexandra Luccioni, Emily Baylor, Nicolas Duchene
- Abstract summary: 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.
- Score: 68.8204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Climate change is a far-reaching, global phenomenon that will impact many
aspects of our society, including the global stock market
\cite{dietz2016climate}. 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). However, given this abundance of
data, sustainability analysts are obliged to comb through hundreds of pages of
reports in order to find relevant information. We leveraged recent progress in
Natural Language Processing (NLP) to create a custom model, ClimateQA, which
allows the analysis of financial reports in order to identify climate-relevant
sections based on a question answering approach. We present this tool and the
methodology that we used to develop it in the present article.
Related papers
- Towards A Comprehensive Assessment of AI's Environmental Impact [0.5982922468400899]
Recent surge of interest in machine learning has sparked a trend towards large-scale adoption of AI/ML.
There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle.
This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations.
arXiv Detail & Related papers (2024-05-22T21:19:35Z) - EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detection [0.0]
EcoVerse is an annotated English Twitter dataset of 3,023 tweets spanning a wide spectrum of environmental topics.
We propose a three-level annotation scheme designed for Eco-Relevance Classification, Stance Detection, and introducing an original approach for Environmental Impact Analysis.
arXiv Detail & Related papers (2024-04-08T01:21:11Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Analyzing Regional Impacts of Climate Change using Natural Language
Processing Techniques [0.9387233631570752]
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.
arXiv Detail & Related papers (2024-01-11T16:44:59Z) - 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) - Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored
Arabic LLM [77.17254959695218]
Large Language Models (LLMs) like ChatGPT and Bard have shown impressive conversational abilities and excel in a wide variety of NLP tasks.
We propose a light-weight Arabic Mini-ClimateGPT that is built on an open-source LLM and is specifically fine-tuned on a conversational-style instruction tuning Arabic dataset Clima500-Instruct.
Our model surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation.
arXiv Detail & Related papers (2023-12-14T22:04:07Z) - Towards Answering Climate Questionnaires from Unstructured Climate
Reports [26.036105166376284]
Activists and policymakers need NLP tools to process the vast and rapidly growing unstructured textual climate reports into structured form.
We introduce two new large-scale climate questionnaire datasets and use their existing structure to train self-supervised models.
We then use these models to help align texts from unstructured climate documents to the semi-structured questionnaires in a human pilot study.
arXiv Detail & Related papers (2023-01-11T00:22:56Z) - 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) - CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims [4.574830585715129]
We introduce CLIMATE-FEVER, a new dataset for verification of climate change-related claims.
We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet.
We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the textscfever framework.
arXiv Detail & Related papers (2020-12-01T16:32:54Z) - 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.