NeurIPS 2025 Workshop: Tackling Climate Change with Machine Learning

About Important Dates Submit Mentorship Program Organizers FAQ

About

Many in the ML community wish to take action on climate change, but are unsure how to have the most impact. This workshop will highlight work that demonstrates that, while ML is no silver bullet, it can be an invaluable tool in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change.

Climate change is a complex problem for which action takes many forms, from advancing theory to deploying new technology. Many of these actions represent high-impact opportunities for real-world change, and simultaneously pose interesting academic research problems.

This workshop is part of a series that aims to bring together those applying ML to climate change challenges and facilitate cross-pollination between ML researchers and experts in climate-relevant fields.

The main workshop will take place on December 6 or 7, 2025 (exact date TBD).

About NeurIPS

This workshop is part of the thirty ninth annual conference on Neural Information Processing Systems, one of the premier conferences on machine learning, which will be held between December 2 and 7, 2025 in San Diego, California. For information on how to attend the NeurIPS conference, please see https://neurips.cc/.

Important Dates

• Mentorship matching application deadline: July 28, 2025
• Notification of mentorship: August 1, 2025
• Submissions (papers, proposals, and tutorials Round I) due via CMT: August 20, 2025
• Accept/Reject notification date: September 22, 2025
• Tutorials Round II submission deadline: October 6, 2025
• Tutorials Round II review feedback released: October 20, 2025
• Tutorial final submissions due: November 3, 2025
• Workshop: December 6 or 7, 2025

Call for Submissions

We invite submissions of short papers using machine learning to address problems in climate mitigation, adaptation, or science, including but not limited to the following topics:

• Agriculture and food
• Behavioural and social science
• Buildings
• Carbon capture and sequestration
• Cities and urban planning
• Climate finance and economics
• Climate justice
• Climate science and climate modeling
• Disaster management and relief
• Earth observations and monitoring
• Earth science
• Ecosystems and biodiversity
• Extreme weather
• Forestry and other land use
• Health
• Heavy industry and manufacturing
• Local and indigenous knowledge systems
• Materials science and discovery
• Oceans and marine systems
• Power and energy systems
• Public policy
• Societal adaptation and resilience
• Supply chains
• Transportation

All machine learning techniques are welcome, from kernel methods to deep learning. Each submission should make clear why the application has (or could have) a pathway to positive impacts regarding climate change. We highly encourage submissions which make their data and code publicly available. Accepted submissions will be invited to give poster presentations, of which some will be selected for spotlight talks.

The theme of this workshop, “Roots to Routes: A Dialogue on Different Machine Learning Methods for Climate Impact,” invites submissions that explore the strengths of diverse machine learning approaches in climate-related contexts. We particularly encourage work that demonstrates the effectiveness of classical ML methods under real-world constraints, such as limited data availability, privacy concerns, or restricted computational resources. At the same time, we welcome contributions that showcase how scaling up data and computing resources combined with modern tools and techniques can unlock new possibilities for tackling global-scale climate prediction challenges. Our goal is to foster a rich and constructive dialogue around when and where small- or large-scale models are most impactful.

The workshop does not publish proceedings, and submissions are non-archival. Submission to this workshop does not preclude future publication. Previously published work may be submitted under certain circumstances (see the FAQ).

All papers, proposals, and tutorial submissions must be through the submission website. Submissions will be reviewed double-blind; do your best to anonymize your submission, and do not include identifying information for authors in the PDF. Authors are required to use the workshop style template (based on the NeurIPS style files), available for LaTeX.

Please see the Tips for Submissions and FAQ, and contact climatechangeai.neurips2025@gmail.com with questions.

The NeurIPS conference Financial Aid and Volunteer applications will be available on the NeurIPS website in late August or early September.

Submission Tracks

There are three tracks for submissions: (i) Papers, (ii) Proposals, (iii) Tutorials. Submissions are limited to four pages for the Papers track, and three pages for the Proposals track, in PDF format (see examples from previous workshops here). References do not count towards this total. Supplementary appendices are allowed but will be read at the discretion of the reviewers. Tutorial submissions are in executable notebook format that follows CCAI’s NeurIPS 2025 Tutorial Template. All submissions must explain why the proposed work has (or could have) positive impacts regarding climate change.

PAPERS Track

Work that is in progress, published, and/or deployed.

Submissions for the Papers track should describe projects relevant to climate change that involve machine learning. These may include (but are not limited to) academic research; deployed results from startups, industry, public institutions, etc. and climate-relevant datasets.

Submissions should provide experimental or theoretical validation of the method presented, as well as specifying what gap the method fills. Authors should clearly illustrate a pathway to climate impact, i.e., identify the way in which this work fits into broader efforts to address climate change. Algorithms need not be novel from a machine learning perspective if they are applied in a novel setting. Details of methodology need not be revealed if they are proprietary, though transparency is highly encouraged.

Submissions creating novel datasets are welcome. Datasets should be properly documented with regards to their provenance and contents and designed to permit machine learning research (e.g. formatted with clear benchmarks for evaluation).

PROPOSALS Track

Early-stage work and detailed descriptions of ideas for future work.

Submissions for the Proposals track should describe detailed ideas for how machine learning can be used to solve climate-relevant problems. While less constrained than the Papers track, Proposals will be subject to a very high standard of review. Ideas should be justified as extensively as possible, including motivation for why the problem being solved is important in tackling climate change, a discussion of why current methods are inadequate, an explanation of the proposed method, and a discussion of the pathway to climate impact. Preliminary results are optional.

TUTORIALS Track

Interactive notebooks for insightful step-by-step walkthroughs

Submissions for the Tutorials track should demonstrate the use of machine learning methods and tools (e.g., libraries, packages, services, datasets, or frameworks) to address climate-relevant challenges.

Tutorial submissions should include a clear and concise description of user learning outcomes. Tutorial submissions will be reviewed based on their potential impact, pedagogical value, and usability by the climate and AI research community. Submissions of tutorials featuring AI-for-climate applications not yet featured in the CCAI Tutorials repository, are encouraged. Please review our list of CCAI tutorials.

Notebooks will undergo two review cycles to ensure high quality submissions. In the first round, Round I, reviews will be made upon the initial (80% complete) submission (due August 20, 2025) and the second round, Round II, of reviews will be made upon the complete (100%) submission (due October 6, 2025) which should address and revise content based on feedback from Round I. Please see CCAI’s NeurIPS 2025 Tutorials guidelines for more details.

Tutorials should be in the form of executable notebooks that follow CCAI’s NeurIPS 2025 Tutorial Template. Authors must submit the notebook along with an accompanying requirements.txt file to ensure users and reviewers are able to run the tutorial in a self-contained runnable environment both locally and remotely (e.g., Python virtual environments, Colab).

We expect tutorials to be 80% complete by the Round I deadline in order for our reviewers to provide adequate feedback and select tutorials for spotlight presentations.

Notebook submissions will be assessed based on clarity, accessibility, and code quality. We ask that authors emphasize real-world impacts of their ML models by answering questions such as Who will be using the models/outputs, and how will they be used? What decisions will be made based on these models? How will this impact existing systems/the environment/affected communities on the ground? For more information, please review our tutorial development guidelines. For questions with respect to tutorials, please email tutorials@climatechange.ai.

Tips for Submissions

• For examples of typical formatting and content, see submissions from our previous workshops.
• Be explicit: Describe how your proposed approach addresses climate change, demonstrating an understanding of the application area.
• Frame your work: The specific problem and/or data proposed should be contextualized in terms of prior work.
• Address the impact: Describe the practical implications of your method in addressing the problem you identify, as well as any relevant societal impacts or potential side-effects. We recommend reading our further guidelines on this aspect here.
• Explain the ML: Readers may not be familiar with the exact techniques you are using or may desire further detail.
• Justify the ML: Describe why the ML method involved is needed, and why it is a good match for the problem.
• Avoid jargon: Although jargon is sometimes unavoidable, it should be minimized. Ideal submissions will be accessible both to an ML audience and to experts in other relevant fields, without requiring field-specific knowledge. Feel free to direct readers to accessible overviews or review articles for background, where it is impossible to include context directly.

Addressing Impact

Tackling climate change requires translating ideas into action. The guidelines below will help you clearly present the importance of your work to a broad audience, hopefully including relevant decision-makers in industry, government, nonprofits, and other areas.

• Illustrate the link: Many types of work, from highly theoretical to deeply applied, can have clear pathways to climate impact. Some links may be direct, such as improving solar forecasting to increase utilization within existing electric grids. Others may take several steps to explain, such as improving computer vision techniques for classifying clouds, which could help climate scientists seeking to understand fundamental climate dynamics.
• Consider your target audience: Try to convey with relative specificity why and to whom solving the problem at hand will be useful. If studying extreme weather prediction, consider how you would communicate your key findings to a government disaster response agency. If analyzing a supply chain optimization pilot program, what are the main takeaways for industries who might adopt this technology? To ensure your work will be impactful, where possible, we recommend co-developing projects with relevant stakeholders or reaching out to them early in the process for feedback. We encourage you to use this opportunity to do so!
• Outline key metrics: Quantitative or qualitative assessments of how well your results (or for proposals, anticipated results) compare to existing methods are encouraged. Try to convey the significance of your problem and your findings. We encourage you to communicate why the particular metrics you choose are relevant from a climate change perspective. For instance, if you are evaluating your machine learning model on the basis of accuracy, how does improved accuracy on a machine learning model translate to climate impact, and why is accuracy the best metric to use in this context?
• Be clear and concise: The discussion of impact does not need to be lengthy; it just needs to be clear.
• Convey the big picture: Ultimately, the goal of Climate Change AI is to “empower work that meaningfully addresses the climate crisis.” Ensure that from the outset, you contextualize your method and its impacts in terms of this objective.

Q&A

If you have further questions on how to participate in the workshop, you can also contact us via email at climatechangeai.neurips2025@gmail.com. For recordings of informational webinars of previous editions of our workshop, please see here.

Organizers

Hari Prasanna Das (Amazon)
Raluca Stevenson (Microsoft Research)
Joaquín Salas (Instituto Politecnico Nacional Mexico)
Salva Rühling Cachay (UC San Diego)
Nadia Ahmed (UC Irvine)
Yoshua Bengio (Mila, Université de Montréal)

Tutorials Track Organizers

Isabelle Tingzon (The World Bank/GFDRR)
Nadia Ahmed (UC Irvine)

Mentorship Program

We are hosting a mentorship program to facilitate exchange between potential workshop submitters and experts working in topic areas relevant to the workshop. The goal of this program is to foster cross-disciplinary collaborations and ultimately increase the quality and potential impact of submitted work.

Expectations

Mentors are expected to guide mentees during the CCAI mentorship program (August 1 - August 20) as they prepare submissions for this workshop.

Examples of mentor-mentee interactions may include:

• In-depth discussion of relevant related work in the area of the Paper or Proposal, to ensure submissions are well-framed and contextualized in terms of prior work.
• Iterating on the core idea of a Proposal to ensure that the climate change application is well-posed and the ML techniques used are well-suited.
• Giving feedback on the writing or presentation of a Paper or Proposal to bring it to the right level of maturity for submission.

Please keep in mind that mentors are not expected to write code for mentees.

Mentees are expected to initiate contact with their assigned mentor and put in the work and effort necessary to prepare a Paper or Proposal submission by August 20th.

Tips for submissions may prove useful in guiding feedback on the mentee’s project.

We suggest that after the mentor-mentee matching is made, a first (physical or digital) meeting should take place within the first week (August 1 - August 8) to discuss the Paper or Proposal and set expectations for the mentorship period. The mentee is recommended to come to this meeting with an agenda of specific questions or requests for feedback, including the level of feedback (low- or high- level).

In particular, during the first meeting, mentees and mentors should

• Introduce themselves and their professional backgrounds.
• Establish a frequency at which they plan to interact (weekly, biweekly, etc.).
• Form a concrete plan for iterating on the mentee’s project and/or submission during the mentorship period.

Subsequent interactions can take place either through meetings or via email discussions, following the expectations set during the initial meeting, culminating in a final version of a Paper or Proposal submitted via the CMT portal by August 20.

Mentees should continue coming to meetings prepared with an agenda, in order to ensure that mentor-mentee interactions are as productive as possible.

Mentors should not do work for the mentee, but should instead offer concrete guidance. For instance, mentors might point mentees to references about relevant climate change domains or machine learning techniques, or explain why a proposed method or application might be incomplete.

Depending on the extent of interactions between the mentor and mentee, at the end of the mentorship program, a mentee can choose to invite the mentor as a co-author to their submission. The mentor should not initiate this conversation, nor should they expect to be given authorship. We do not expect mentors or mentees to feel obligated to maintain the mentor-mentee relationship after the end of the mentorship program. We nevertheless hope that this program is also able to cultivate meaningful relationships between mentors and mentees that continue beyond the workshop.

Mentors and mentees must abide by the Climate Change AI Code of Conduct. If at any point, there are complications or complex circumstances that might impact the mentorship duration, please raise these issues with the workshop organizing committee by emailing climatechangeai.neurips2025@gmail.com.

Application

Applications are due by July 28, 2025.

• Application to be a mentee
• Application to be a mentor:

Sponsors

Bronze Sponsors

Frequently Asked Questions

Mentorship Program FAQ

Q: Are mentors allowed to be authors on the paper for which they provided mentorship?
A: Yes, mentors can be co-authors but not reviewers.

Q: What happens if the mentor/mentee does not fulfill their duties, or if major issues come up?
A: Please email us at climatechangeai.neurips2025@gmail.com and we will do our best to help resolve the situation. Potential breaches of the Code of Conduct will be responded to promptly as detailed therein.

Q: What happens if I apply to be a mentee but do not get paired with a mentor?
A: While we will do our best, we cannot guarantee pairings for everyone. Even if you do not get paired with a mentor, we encourage you to submit a Paper or Proposal to the workshop, and our reviewers will provide you with guidance and feedback on how to improve it.

Q: What happens if my submission does not get accepted to the workshop?
A: While the mentorship program is meant to give early-career researchers and students the opportunity to improve the quality of their work, sometimes submissions will need further polishing and elaboration before being ready for presentation at a CCAI workshop. If this is the case, we invite you to take into account the comments made by the reviewers and to resubmit again to a subsequent CCAI workshop.

Q: I cannot guarantee that I can commit at least 4 hours to the program over the time period. Should I still apply as a mentor?
A: No. While the 4 hour time commitment is a suggestion, we do believe that it is necessary to ensure that all mentees receive the help and guidance they need.

Q: I do not have a background in machine learning; can I still apply to be a mentor/mentee?
A: Yes! We welcome applications from domains that are complementary to machine learning to solve the problems that we are targeting.

Q: What happens if my mentor/mentee wants to continue meeting after the workshop?
A: We welcome and encourage continued interactions after the official mentorship period. That said, neither the mentor nor the mentee should feel obligated to maintain contact.

Submission FAQ

Q: How can I keep up to date on this kind of stuff?
A: Sign up for our mailing list!

Q: I’m not in the field of machine learning. Can I still submit?
A: Yes, absolutely! We welcome submissions from many fields. Do bear in mind, however, that the majority of attendees of the workshop will have a machine learning background; therefore, other fields should be introduced sufficiently to provide context for the work.

Q: What if my submission is accepted but I can’t attend the workshop?
A: You may ask someone else to present your work in your stead.

Q: It’s hard for me to fit my submission on 3 or 4 pages. What should I do?
A: Feel free to include appendices with additional material (these should be part of the same PDF file as the main submission). Do not, however, put essential material in an appendix, as it will be read at the discretion of the reviewers.

Q: Can I send submissions directly by email?
A: No, please use the CMT website to make submissions.

Q: The submission website is asking for my name. Is this a problem for anonymization?
A: You should fill out your name and other info when asked on the submission website; CMT will keep your submission anonymous to reviewers.

Q: Do submissions for the Proposals track need to have experimental validation?
A: No, although some initial experiments or citation of published results would strengthen your submission.

Q: The submission website never sent me a confirmation email. Is this a problem?
A: No, the CMT system does not send automatic confirmation emails after a submission, though the submission should show up on the CMT page once submitted. If in any doubt regarding the submission process, please contact the organizers. Also please avoid making multiple submissions of the same article to CMT.

Q: Can I submit previously published work to this workshop?
A: Yes, though under limited circumstances. In particular, work that has previously been published at non-machine learning venues may be eligible for submission; however, work that has been published in conferences on machine learning or related fields is likely not eligible. If your work was previously accepted to a Climate Change AI workshop, this work should have changed or matured substantively to be eligible for resubmission. Please contact climatechangeai.neurips2025@gmail.com with any questions.

Q: Can I submit work to this workshop if I am also submitting to another NeurIPS 2025 workshop?
A: Yes. We cannot, however, guarantee that you will not be expected to present the material at a time that conflicts with the other workshop.

Acknowledgement

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.