Papers
Work that is in progress, published, and/or deployed; must include experimental or theoretical validation and a clear pathway to climate impact. Limited to four pages (references not counted).
Machine Learning/CORE Unranked
Tutorials TCCML NeurIPS 2026
[Tutorials track] Tackling Climate Change with Machine Learning: workshop at NeurIPS 2026
The NeurIPS 2026 Workshop on Tackling Climate Change with Machine Learning brings together researchers applying machine learning to climate mitigation, adaptation, and science. The workshop emphasizes ground-up innovation, interdisciplinary collaboration, and real-world impact, encouraging submissions that address climate challenges through novel applications of ML techniques, community-driven data, and open-source tools.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Proposals
Early-stage work and detailed ideas for future work; must justify the problem's importance, inadequacy of current methods, proposed approach, and pathway to climate impact. Limited to three pages (references not counted).
Tutorials
Interactive executable notebooks demonstrating ML methods for climate-relevant challenges; must include learning outcomes, code, and a requirements.txt file. Submitted in two rounds with feedback.
Research areas in scope
01
Application Areas
Agriculture and foodBehavioural and social scienceBuildingsCarbon capture and sequestrationCities and urban planningClimate finance and economicsClimate justiceClimate science and climate modelingDisaster management and reliefEarth observations and monitoringEarth scienceEcosystems and biodiversityExtreme weatherForestry and other land useHealthHeavy industry and manufacturingLocal and indigenous knowledge systemsMaterials science and discoveryOceans and marine systemsPower and energy systemsPublic policySocietal adaptation and resilienceSupply chainsTransportation
Policies worth checking twice
- Submissions must be made via OpenReview.
- All submissions are double-blind; authors must anonymize their PDFs.
- Papers and proposals must use the NeurIPS 2026 style template (LaTeX).
- Tutorial submissions must follow the CCAI NeurIPS 2026 Tutorial Template and include a runnable environment (e.g., requirements.txt).
- Submissions must explain the pathway to positive climate impact.
- Authors are required to make data and code publicly available when possible.
- No CCAI website materials (including the call for papers) may be shared with LLMs during the writing process.
- Submissions must comply with the NeurIPS 2026 LLM Policy on the Use of Large Language Models.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026