This workshop is dedicated to discuss how Natural Language Processing can be incorporated in Climate Change science, and help mitigating or adapting to climate change.
The final program can be found here.
The workshop takes place on August 16th starting at 9am at the ACL 2024 conference venue.
FAQ
- Q: Who is this workshop for? We invite everybody who's interested in the intersection of NLP and Climate Change, no matter their background. It is a very open field, and this workshop should facilitate a discussion on how NLP methods can and should be used in the context of climate change! :)
- Q: What are the objectives of the workshop? To discuss and learn more about the interesction of NLP and Climate Change. To learn about objectives and goals of Climate Change science. To get to know various interdisciplinary stakeholders such as climate scientists, NGOs, NPOs, policy makers and regulators and to understand their perspective. To foster future collaborations.
- Q: I am an NLP person and want to participate/help, what can I do? We welcome you to submit a short or long paper. We are always looking for PC members to review papers. We appreciate any input regarding the setup of the workshop, and what you would like to see -- please get in touch!
- Q: I am a non-NLP person and want to participate/help, what can I do? We welcome you to submit a short (or long) paper about your ideas. If you want to present your perspective or have specific ideas what this workshop can do to help you achieve your goals -- please let us know about your goals and we try to incorporate them!
- Q: I am preparing a paper to submit, are there specific formatting guidelines? Yes, you must adhere to the ACL formatting guidelines for long and short papers. Find more information here. Easiest is to keep in mind that short papers are 4 pages, and long papers 8 pages, and you should use one of the following templates and take it from there.
Key Dates
| Event | Date |
|---|---|
| Submissions Open | February 5th, 2024 |
| Submission Deadline | May 24, 2024 |
| Acceptance Notification | June 17, 2024 |
| Camera-Ready Deadline | July 1, 2024 |
| Workshop Date | August 16th, 2024 |
All deadlines are specified in AoE (Anywhere on Earth).
Description
In the past few decades, the importance of addressing climate change has been fully adopted by the scientific community. At the same time, most policymakers and regulators around the world and the economy start to catch up. Climate change leads to global, national, and local discussions, spanning various platforms and involving all sectors of society. There is only limited yet growing amounts of work at the intersection of climate change and NLP methods [1]. This workshop's primary objective is to provide a platform for researchers and practitioners (and policymakers and other stakeholders) interested in contributing to the intersection of NLP and climate change. The workshop aims to fill the gap in the existing literature and shed light on the unique challenges, opportunities, and potential methods for applying NLP techniques in helping to combat climate change -- and explore other potential applications in this domain. In contrast to ongoing discussions about tackling climate change with AI [2], we want a more focused discussion on how specifically NLP methods can help here.
NLP has the potential to support faster and scaleable climate mitigation and adaptation action considerably. To explore specific use cases and the most promising approaches in the intersection of NLP and climate change, We propose a workshop to collect insights gained so far, outline potential avenues for future research, and discuss challenges with domain experts from Academia, NGOs, and Industry. A core goal of the workshop is to bring together people from Academia, NGOs, and Industry. Also, we try to understand each others' needs, and want to foster a discussion about the current state of this intersection and where this should be going in the future. Thus, we welcome contributions from NGOs and industry in the form of submitting abstracts about a talk highlighting their goals and perspectives on this intersection.
We are very open to various sorts of submissions at the intersection of Climate Change and NLP, and invite submissions in the form of abstracts for a talk or submitting papers which will be published in the workshop proceedings if submitted arxival. To lower entry barriers, we highlight some specific topics we think are interesting and outline some broad topics which we hope could be relevant. This is not meant to be an exhaustive list, and we welcome submissions covering other topics as well!
To lower the entry-barrier for submitting papers, we provide a set of specific topics and related literature (in no specific order):
- Detection and analysis of Environmental Claims [3]
- QA-systems for Climate Change related Information [4]
- Long Document Analysis of relevant documents [5]
- Stance Detection [6]
- Climate change denial classification [7,8]
- Narratives around Climate Change [9]
- Automated Fact Checking of climate change related claims [10]
- Named entity mapping to asset-level data for spatial finance [11]
- Creation of benchmark datasets for climate and NLP [12]
- Evidence mapping of climate documents: scholarly, corporate, or policy [13]
- Automated knowledge graph creation for climate policy texts [14]
- Analysis of climate-related corporate disclosures with NLP [15, 16, 17]
We also welcome broad topics along the lines of:
- Using machine translation for translating policies or any information related to climate to underrepresented languages to inform local communities better
- The analysis of sentiment and specificity of corporate or sovereign activities related to climate change
- Knowledge integration in assets localization models
- Studies which involve NLP and climate risk, spatial finance or sustainable finance
- Identification of greenwashing indicators identified through NLP methods
- Automated assessment of physical or financial damage due to natural calamities using, e.g., news data or government sources
We are also interested in position and overview papers, new datasets, and new methods on existing training and benchmarking datasets. We encourage the dialogue and submissions for climate-aware NLP and energy-efficient models. And we stress again that this is a non-exhaustive list of possible research questions, and we're looking forward to submissions covering others.
References
[1] The Climate Change Debate and Natural Language Processing, Stede & Patz, 2021;
[2] Tackling climate change with machine learning, Rolnick et al., 2022;
[3] Environmental Claim Detection, Stammbach et al., 2023;
[4] ChatClimate: Grounding Conversational AI in Climate Science, Vaghefi et al., 2023;
[5] chatReport: Democratizing Sustainability Disclosure Analysis through LLM-based Tools, Ni et al., 2023;
[6] Detecting Stance in Media On Global Warming; Luo et al., 2020;
[7] Computer-assisted classification of contrarian claims about climate change, Coan et al., 2021;
[8] Exploring data augmentation for classification of climate change denial, Piskorski et al., 2022;
[9] Analyzing Climate Change Policy Narratives with the Character-Role Narrative Framework, Gehring & Grigoletto, 2023;
[10] Climate-fever: A dataset for verification of real-world climate claims, Diggelmann et al., 2021;
[11] Spatial finance: practical and theoretical contributions to financial analysis, Caldecott et al., 2022;
[12] ClimaBench: A Benchmark Dataset For Climate Change Text Understanding in English, Laud et al., 2023;
[13] Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies, Callaghan et al., 2021;
[14] Beyond modeling: NLP Pipeline for efficient environmental policy analysis, Planas et al., 2022;
[15] ClimateBert: A Pretrained Language Model for Climate-Related Text, Webersinke et al., 2022;
[16] ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets, Schimanski et al., 2023;
[17] Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication, Schimanski et al., 2023;