Call for Proposals: Climate Change AI Innovation Grants 2024

Ednah Carrick August 30, 2024

0 586 7 minutes read

Closing Date: 15 September 2024

Call for Proposals: Climate Change AI Innovation Grants 2024

About Climate Change AI

Climate Change AI is a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning. Since it was founded in 2019, CCAI has inspired, informed, and connected thousands of individuals from across academia, industry, and the public sectors, through its foundational reports on AI and climate change, networking and knowledge-sharing events, educational initiatives, and global grants programs.

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.

Grant information

This Climate Change AI Innovation Grants 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. Climate Change AI 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.

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
Contact: [email protected]

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.)

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. Such projects do not need to use AI or machine learning directly; rather, the goal is to establish a dataset that will enable AI or machine learning work in tackling climate change. 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.

Eligibility

Each application must have a Principal Investigator (PI) who is affiliated with an accredited university in one of the 38 OECD Member Countries. The PI must be eligible to hold grants under their name at their accredited university; this may include, e.g., faculty, postdocs, or research scientists (depending on the institution). Co-Investigators can be located outside OECD Member countries and can be affiliated with non-research institutions, and indeed multi-country and multi-sectoral collaborations are encouraged. However, co-Investigators cannot be affiliated with an organization on the Consolidated Screening List) or an organization in a sanctioned country (see FAQ for additional information).

Current members of the Climate Change AI Board of Directors and Climate Change AI staff cannot apply to this grant as a PI, and they may not receive funds towards their own salary. Program Chairs and Meta-Reviewers for this grant may not apply or receive funds in any way (however, Reviewers may, and conflicts of interest will be appropriately managed during the review process).

Selection criteria

Climate Change AI Innovation Grants Proposals will be reviewed through a single-blind process by independent reviewers.

Climate Change AI Innovation Grants Projects will be evaluated on the following criteria:

Climate relevance: Projects should demonstrate a clear link to climate change mitigation and/or adaptation. Given the cross-cutting nature of climate change, this can include a wide range of topics with which climate change interacts and intersects, but the relationship to climate change should be made explicit.
AI/ML relevance: Projects should employ or address AI or ML in a way that is well-motivated and well-scoped for the problem setting. This includes both projects where AI or ML are a central component, as well as those where AI or ML are one among many components. Projects proposing the implementation of AI/ML techniques will not be penalized if other techniques or approaches are found to be better-suited as the project progresses; negative results are welcome if well-tested.
Dataset: The proposed dataset or simulator to be created should serve to enable further impactful work at the intersection of climate change and machine learning beyond the project being proposed. We require the dataset to comply with the FAIR Data Principles (Findable, Accessible, Interoperable and Reusable).

Pathway to impact: Proposals should address how their work, if successful, can be deployed or implemented in practice to aid climate mitigation and/or adaptation. This can be addressed in the form of deployments planned as part of the project itself, or via a concrete plan for disseminating the work among relevant sectors or organizations.
Ethics: Proposals should explicitly discuss ethical considerations and implications of their work. This includes discussion of relevant stakeholders and equity considerations of the problem addressed, as well as the scope and potential negative social or environmental impacts of the proposed solution, including how these risks will be avoided or mitigated in the project’s execution. (See, e.g., the NeurIPS ethics guidelines for a discussion of ethical considerations pertinent to ML.)
Feasibility: The scope of the proposed project should be realistic with respect to the associated timeline and budget.

Expertise of team: The proposed team should have demonstrated expertise in areas of relevance to the development and execution of their project, notably the relevant area(s) of climate change mitigation and adaptation and in AI/ML. Interdisciplinarity and diversity within the proposed team will be viewed favorably.

In addition, the following aspects will be considered favorably during the review process:

Deployment partners: Project teams including relevant organizations through whom the proposed work could be impactfully deployed will be viewed favorably.

Traditionally under-funded areas of work: Projects that are impactful but may not be traditionally covered through other funding streams will be given priority as part of this call. Examples include projects that may not fit neatly into one discipline or area of study, or projects serving stakeholders with limited access to capital.
Equity: Projects that explicitly incorporate equity-related considerations — e.g., through the choice of problem addressed, or stakeholders that are partnered with — will be viewed favorably.