Applications Invited for Climate Change AI Innovation Grants 2024

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About the Organization

Climate Change AI (CCAI) is an organization composed of volunteers from academia and industry who believe that tackling climate change requires concerted societal action, in which machine learning can play an impactful role. Since it was founded in June 2019 (and established as a US domestic non-profit on June 14, 2021), CCAI has led the creation of a global movement in climate change and machine learning, encompassing researchers, engineers, entrepreneurs, investors, policymakers, companies, and NGOs.

About the 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.

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.

Special Track on Methane

• Submissions to the Special Track on Methane should leverage AI or machine learning to address problems in methane-related climate change mitigation in the short/medium term period (well before 2040), including (but not limited to) the areas of:
• Energy (including coal mine methane, ventilation air methane, flaring, methane leak detection, super-emitters, and methane emissions from oil and gas)
• Waste and circular economy (including food loss and waste recovery, food or organic waste separation, dumps/landfill emissions, wastewater treatment, and sludge management)
• Agriculture (including livestock, manure management, biomass burning, and rice cultivation)

Special Track on Dataset Gaps

• Submissions to the Special Track on Dataset Gaps should have, as their primary focus, the creation of a documented dataset (or simulator) by collating, labeling, and/or annotating existing data, and/or by collecting, simulating, or otherwise making available new data that can enable further research. Topics that may be addressed by the dataset or simulator follow the same scope as submissions to the Main Track, and applicants should highlight the particular gap in dataset availability that this project aims to address, and why this is important for climate change mitigation or adaptation.
• Proposals in the Special Track on Dataset Gaps may also request support from a Google DeepMind researcher, in addition to the financial award. Applicants who may be interested in taking advantage of this option will be asked to indicate this in the CMT submission form.
• Note: Proposals in the Special Track on Dataset Gaps may also propose research leveraging the dataset or simulator as part of the project; however, the primary focus of the project should be on the creation of the dataset or simulator itself. By contrast, projects in the Main Track must release a dataset or simulator but this need not be the primary focus of the project.

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.

How to Apply

Proposal submission deadline: September 15, 2024 at 23:59 (Anywhere on Earth time, UTC-12)

Submission site: https://cmt3.research.microsoft.com/CCAIGrants2024

Contact: grants@climatechange.ai

For more information please check the Link

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Tags Environment and Sustainability Innovations and Technology Research Others