Artificial Intelligence (AI) and machine learning (ML) can help support climate change mitigation and adaptation, as well as climate science, across many different areas, for example energy, agriculture, forestry, climate modeling, and disaster response (for a broader overview of the space, please refer to Climate Change AI’s interactive topic summaries and materials from previous events). However, impactful research and deployment have often been held back by a lack of data and other essential infrastructure, as well as insufficient knowledge transfer between relevant fields and sectors.

The relationship between AI and climate change is also nuanced, and can manifest in various ways that either contribute to or counteract climate action. Thus, the use of AI for climate action must be performed responsibly, and ideally with quantifiable impacts.

Research projects shall leverage AI or machine learning to address problems in climate change mitigation, adaptation, or climate science, or shall consider problems related to impact assessment and governance at the intersection of climate change and machine learning.

Along with the project, the grantees must publish a documented dataset (or simulator), which was created by collating, labeling, and/or annotating existing data, and/or by collecting, simulating, or otherwise making available new data that can enable further research. The dataset is required with the FAIR Data Principles (Findable, Accessible, Interoperable and Reusable).

Projects are expected to result in a deployed project, scientific publications, or other public dissemination of results, and should include a carefully considered pathway to impactful deployment. All grant IP — e.g., the dataset/simulator produced and (if applicable) trained models or detailed descriptions of architectures and training procedures — must be made publicly available under an open license.

Relevant research includes but is not limited to the following topics:

• ML to aid mitigation approaches in relevant sectors such as agriculture, buildings and cities, heavy industry and manufacturing, power and energy systems, transportation, or forestry and other land use
• ML applied to societal adaptation to climate change, including disaster prediction, management, and relief in relevant sectors
• ML for climate and Earth science, ecosystems, and natural systems as relevant to mitigation and adaptation
• ML for R&D of low-carbon technologies such as electrofuels and carbon capture & sequestration
• ML approaches in behavioral and social science related to climate change, including those anchored in climate finance and economics, climate justice, and climate policy
• Projects addressing AI governance in the context of climate change, or that aim to assess the greenhouse gas emissions impacts of AI or AI-driven applications, may also be eligible for funding. (Studies addressing this area may be exempt from the dataset publication requirement.)

• Others (see text field entitled "Additional Eligibility Criteria" for clarification)

Estimated Total Program Funding: