Call summary :

Quick facts
-Grant amount: Up to USD 150K per proposal, for projects of 12 months in duration. We will award a total of up to USD 1.4M in grants across all projects.
- Scope: Projects at the intersection of AI/machine learning and climate change.
- Eligibility: Principal Investigator must be affiliated with an accredited university in one of the 38 OECD Member Countries (see list here). Co-Investigators can be located outside OECD Member countries and can be affiliated with non-research institutions, and there is no limit on the fraction of funding allocated to Co-Investigators.
- Proposal submission deadline: September 15, 2024 at 23:59 (Anywhere on Earth time, UTC-12)
- Submission site: https://cmt3.research.microsoft.com/CCAIGrants2024

The purpose of this grant

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 papers). 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 with considerations of impact, responsibility, and equity at the center.

With the support of Quadrature Climate Foundation, Google DeepMind, and Global Methane Hub, we are excited to announce funding of up to USD 1.4M for projects at the intersection of AI and climate change. We are also grateful to the Canada Hub of Future Earth for serving as the fiscal sponsor for this program.
Grant information

This program will allocate grants of up to USD 150K for conducting projects of 1 year in duration.

As part of 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. We require the dataset to comply 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.

This year, there are two special tracks in addition to the main track. Submissions should be made to one of these three tracks (duplicate submissions made to multiple tracks may be disqualified). Climate Change AI may move submissions between tracks at the discretion of the Process Chairs.
Main Track

Projects in the Main Track should leverage AI or machine learning to address problems in climate change mitigation, adaptation, or climate science, or consider problems related to impact assessment and governance at the intersection of climate change and machine learning.

Relevant topics include but are 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, waste, 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.)

For context, a list of the projects funded during past Innovation Grants cycles is available here.

Eligibility

Each application must h