Call for Proposals: Climate Change AI Innovation Grants 2025 Special Track on Energy Dataset Gaps

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 300K in grants across all projects.
• Scope: Datasets for power sector-relevant applications where AI/ML innovations can bring significant impact to accelerate climate change mitigation and adaptation strategies
• Proposal submission deadline: June 15, 2025, 23:59 AOE (Anywhere on Earth time, UTC-12)
• Decision notification by: August 2025
• Submission site: https://cmt3.research.microsoft.com/CCAIGrants2025
• Contact: grants@climatechange.ai

The purpose of this grant

Climate Change AI, with support of Google DeepMind, is pleased to invite select teams to apply for the Climate Change AI Innovation Grants Special Track on Energy Dataset Gaps. This call aims to foster the creation of critical datasets in power and energy systems domains that enable the impactful and responsible use of machine learning (ML) and artificial intelligence (AI) for climate change mitigation and adaptation. By supporting the creation of ML-ready datasets in these target domains, our goal is to unlock new opportunities for research, innovation and deployment in the AI-for-climate community and energy sector, to catalyze downstream development and deployment.

This opportunity is available by invitation only and the project proposal must adhere to the following requirements. Further information on proposal submission format is provided in the Proposal Guidelines.

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.

Requirements:

• Project Duration: One year maximum and concluded no later than November 2026, whichever milestone is reached first.
• Sectoral Themes: Power sector-relevant applications where AI/ML innovations can bring significant impact to accelerate climate change mitigation and adaptation strategies. Examples of topics of interest include, e.g., power supply and demand estimation, power infrastructure assessment, power grid operations, power grid planning, and the nexus between power systems and other societal infrastructure. (Applications focused on other energy-related sectors such as transportation and the built environment are eligible to apply but as they are not the primary focus of this call, may only be considered with lower priority.)
• Themes of downstream ML tasks: Primary areas of interest include areas where the dataset, once available, can spur applications of ML to: (i) Distill raw data into actionable information, (ii) improve data or predictions, (iii) optimize systems for efficiency, (iv) approximate time-intensive simulations or (v) accelerate scientific discovery.
• Dataset creation: Projects should propose the creation of a new ML-ready dataset (or simulator), or propose to improve existing datasets (or simulators) with respect to, e.g., their obtainability, usability, reliability, sufficiency, and/or ML-readiness. The dataset (or simulator) can be produced by collating, labeling, and/or annotating existing data, and/or by collecting, simulating, or otherwise making available new data that can enable further research. Projects are encouraged to build on openly available datasets and tools and/or their previous work.
• Deliverables: All projects must deliver a new ML-ready dataset that will be created and made publicly available open source that will enable development of further work in the broad AI-for-climate community and energy sector applications. Datasets will need to follow the FAIR Data Principles (Findable, Accessible, Interoperable and Reusable) and use datasheets to document their created datasets.

References:

• Tackling Climate Change with Machine Learning - Electricity Systems
• Climate Change AI Data Gaps Taxonomy
• Climate Change AI Data Gaps Examples: Power and & Energy Systems Sector

Eligibility

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).

We do not fund research activity that is currently funded by other grant programs. If other grant proposals for the same project have been submitted and/or are under consideration, the relation of the present proposal to those other proposals needs to be clearly explained. If the proposal is selected for funding, no aspect of a project should be double funded by other funding bodies.

Timeline

Activity	Date
Proposal submission deadline	June 15, 2025
Notification of results	August 2026
Award start date	November 2025
Award end date	November 2026

Selection criteria

Proposals will be reviewed through a single-blind process by independent reviewers.

Projects will be evaluated on the following criteria:

• Climate and power systems relevance: Projects should focus on problems in the power sector, and demonstrate a clear link to climate change mitigation and/or adaptation. The relationship to climate change should be made explicit. (Applications focused on the intersection of climate change and other energy-related sectors such as transportation and the built environment are eligible to apply, but as they are not the primary focus of this call, may only be considered with lower priority.)
• AI/ML relevance: The proposed dataset or simulator to be created should serve to enable the impactful application of AI/ML to further a climate-related challenge in the power sector. The AI/ML challenge addressed by the dataset should be 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. Datasets may (and are encouraged) to foster approaches in addition to AI/ML if relevant, but AI/ML approaches must indeed be among those fostered by the dataset.
• Dataset relevance, usability, and accessibility: Proposals should highlight the particular gap in dataset availability that this project aims to address. We require the dataset to comply with the FAIR Data Principles (Findable, Accessible, Interoperable and Reusable).
• Pathway to impact: Proposals should address how their dataset, if successful, can foster progress in practice on power sector problems with relevance to climate mitigation and/or adaptation. This can be addressed in the form of collaborations 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 and other relevant collaborators: Project teams including relevant organizations through whom the proposed dataset could be impactfully used or disseminated, or organizations that may be on-the-ground beneficiaries of the work the dataset is intended to foster, will be viewed favorably. Other relevant partnerships with, e.g., data owners, data providers, or other entities well-positioned to foster meaningful co-creation, impact, and pathways to deployment will also 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.

Application instructions

All applications must be received by June 15th, 2025 at 23:59 (Anywhere on Earth time, UTC-12). Applications should be made via the CMT website, which will require the following information.

Basic information. The CMT submission portal will require the title and abstract of the proposal; the name, affiliation, and country of the institution of the Principal Investigator; the names, affiliations, and countries of the institutions of all co-Investigators; and additional short declarations about the project. The first name in the CMT author list will be treated as the Principal Investigator. Only one Principal Investigator may be named, but there is no limit on the number of co-Investigators. Please note that the institution of the Principal Investigator will be responsible for receipt and any further distribution of the funds if a grant is awarded.

Proposal guidelines

Project Description. A detailed description of the project (maximum 5 pages including figures/tables), with unlimited additional pages allowed for references. The Project Description should be submitted as one PDF attachment via CMT, and include the following subsections (please use the same order and headers to separate the subsections):

• Project title, the name and affiliation of the Principal Investigator, and the names and affiliations of any co-Investigators.
• Summary: A short description of the proposed project of up to 250 words.
• Project Outline: A detailed description of the proposed project. This section should address both the proposed methodology (e.g., machine learning) and application area (a climate change-relevant topic), and should explicitly address what gap the proposed project fills in climate change mitigation or adaptation, as well as why the proposed methodology is useful and appropriate for addressing this gap. Projects in the Special Track on Dataset Gaps should follow the instructions under “Dataset Plan” below when writing this section, since the Project Outline will focus primarily on the gap filled by the dataset and how it will be created.
• Deliverables: A description of what concrete deliverables (e.g., datasets, tools, support documentation) are expected from the project.
• Timeline: A timeline for key milestones of the project, aligned with the deliverables described above.
• Team: A description of the relevant expertise of each team member and how it relates to the project.
• Pathway to Impact: A plan for how the proposed work will have an impact on GHG emissions or societal resilience to climate change. This should be as specific as possible. It is not required that deployment take place within the duration of the project, but all projects should be scoped and developed in such a way as to facilitate impactful deployment in future. At a minimum, this section should address: how the authors plan to engage with end users/other relevant stakeholders during the project, which stakeholders will make use of this work, how exactly it will be useful for these stakeholders, and considerations that are necessary to facilitate impactful deployment (bearing in mind the potentially different incentives for various stakeholders involved).
• Dataset Plan: All projects must propose a new dataset that will be created and made publicly available in compliance with the FAIR Data Principles (Findable, Accessible, Interoperable and Reusable). “Creation” of a dataset may include annotating data with labels, collecting completely new data, collating existing data from multiple sources, creating a data simulator (e.g. for reinforcement learning) that is well-grounded in reality, or open-sourcing existing data that was formerly private. This section of the Project Description should describe the dataset, what it will contribute (as compared to existing datasets), and what will be done to create the dataset. The description should also include a detailed plan for how the data will be documented, shared and preserved, in particular elaborating in detail how compliance with each of the FAIR Data Principles (Findable, Accessible, Interoperable and Reusable) will be ensured. Note that teams will be required to use datasheets to document their created datasets. Projects in the Special Track on Dataset Gaps should include the content described for the “Dataset Plan” section as part of the Project Outline section above, since the Project Outline will focus primarily on the gap filled by the dataset and how it will be created.
• Equity Considerations: This section should describe equity-related considerations related to the project, and how the team will shape the project with these in mind. This discussion may include the nature of the research, composition of the team, and/or nature of the stakeholders outside the team who will be worked with.
• Ethical Considerations: A description of any broader ethical considerations associated with the development and deployment of the work, including but not limited to those connected to climate change. This section should include a description of potential societal impacts or side effects, as well as factors to bear in mind to mitigate negative effects, including important stakeholders to include.

Budget and Budget Justification. An itemized Budget (1 page) indicating the total amount requested and how these funds will be used if a grant is awarded, and a brief Budget Justification (1 page) of these amounts, should be submitted as one PDF file through CMT. Eligible expenses include salaries for Investigators, students, and other research staff; materials, equipment, software, and compute; and expenses associated with conferences and other project-related travel. The Budget should also indicate any institutional overhead, at a maximum rate of 10% of the total amount requested. If this project has other sources of funding, the Budget should make clear which research activities are proposed to be funded by the present grant, and which research activities are funded by other sources. Please note that funds will be contracted solely to the institution with which the Principal Investigator is affiliated (lead institution); any further dissemination of funds to partner institutions must be managed by the lead institution.

We encourage you to use this Budget Template and adapt it to your project needs by adding or subtracting lines and/or columns to it.

CVs of key personnel. CVs for the Principal Investigator and all co-Investigators, as a single PDF file (no page limit).

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. See our website for further details.

Process Chairs

Maria João Sousa (Cornell Tech, Climate Change AI)
Priya Donti (MIT, Climate Change AI)
Lynn Kaack (Hertie School, Climate Change AI)
David Rolnick (Mila, McGill, Climate Change AI)

Sponsors

Supported By

Fiscal Sponsor

CMT Acknowledgment

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

FAQ

Q: What is an ML-ready dataset? A: An ML-ready dataset is a dataset that is structured in a way that is amenable for use in ML applications. Common examples of such types of datasets include:

• Datasets for supervised learning, in which ML model inputs (features) are presented alongside “ground truth” outputs (labels) that the ML model will aim to predict.
• Datasets for unsupervised learning, where the data can serve as a prototypical example for a well-defined unsupervised ML task.
• Simulation environments for reinforcement learning, providing states, actions, rewards, and control dynamics via a Gymnasium interface.

Q: Am I eligible to apply for these funds if I have applied to other sources for the same research activity? A: The exact same research activity cannot be double funded. However, this grant may be used to fund a component of a project whose other components are under consideration or have received funding from other sources. This structure should be clearly described in your budget.

Q: Can I apply multiple times with different projects? A: Yes, you are welcome to apply multiple times. However, it is unlikely that multiple proposals from the same team will be funded.

Q: Is review of the proposals double-blind? A: No, the review process is single-blind (reviewers’ identities are hidden from proposal authors, but proposal authors’ identities are visible to reviewers). Proposals are encouraged to be very specific about their pathway to impact, and this is likely to contain de-anonymizing information that reviewers would need in order to evaluate the feasibility of the proposed project.

Q: Will you consider proposals that request less than the maximum budget? A: We will consider project proposals with any budget up to USD 150K. The amount of funding requested is not a criterion on which your proposal will be assessed, and it will not influence our evaluation of your project. We recommend you to propose the budget that you actually need to carry out the project.

Q: Are the start and end dates of the grant fixed, or can I propose different dates? A: The project duration must be one year maximum and the project must be concluded no later than November 2026, whichever milestone is reached first. We will not be able to consider any extensions to this end date, so please plan accordingly.

Q: My institution takes an overhead greater than 10% of the grant. Am I still eligible to apply? A: You are still eligible to apply, but you will need to obtain an exemption from your institution regarding overhead, as your institution will not be allowed to take more than 10% overhead.

Q: How is the 10% cap on overhead defined? A: The overhead should be at most 10% of the total amount requested, and this overhead amount should be internal to the total budget requested. For example, if the total budget proposed is $150K, then at least $135K must be direct project costs, and at most $15K can be overhead.

Q: Is my project allowed to be funded by multiple sources? A: The proposed project may have multiple funding sources that fund different aspects of the project and/or different sub-projects. However, no aspect of a project should be double funded by other funding bodies.

Q: Does the grant have a cost sharing requirement (i.e., a requirement that the PI supply some percentage of matching funds from another source within the project budget)? A: No, there is no cost sharing requirement.

Q: What is climate change mitigation? A: Climate change mitigation refers to the reduction of greenhouse gasses in order to reduce the extent of climate change. As described by the IPCC Working Group III, this “is achieved by limiting or preventing greenhouse gas emissions and by enhancing activities that remove these gasses from the atmosphere.” For examples of where AI and ML can help with climate change mitigation, please see Climate Change AI’s report on “Tackling Climate Change with Machine Learning.”

Q: What is climate change adaptation? A: Climate change adaptation refers to activities that aim to prepare for or build resilience to the conditions created by climate change. For more information, please see resources from the IPCC Working Group II. For examples of where AI and ML can help with climate change adaptation, please see Climate Change AI’s report on “Tackling Climate Change with Machine Learning.”

Q: The pathway to impact for my project is highly speculative. Will this hurt my proposal? A: We encourage submissions anywhere on the spectrum from guaranteed-but-small impact to high-risk/high-reward. The important part for evaluation is that you thoroughly and accurately describe the pathway to impact, including your level of uncertainty about any aspects, and take steps to reduce or address uncertainty where possible. E.g., it may hurt your proposal if the speculation is due to lack of prior homework on the climate-related sector at hand, but not if it is due to irreducible uncertainty about future outcomes, physical processes, etc.

Q: Do the projects have to address global-scale problems, or can they address national and/or regional problems? A: We do not have a preference on the particular geographical scope of the project, beyond its implications for evaluation of the selection criteria listed above. Past grant recipients have addressed a diverse range of geographical scopes and locations. We suggest that project teams propose the geographical scope that makes the most sense for their project.

Q: What constitutes publication of a dataset? A: The dataset must be publicly released in a way that complies with the FAIR Data Principles (Findable, Accessible, Interoperable and Reusable). This may take different forms depending on what makes sense for your project (e.g., a static vs. a dynamic dataset).

Q: What organizations are eligible to be deployment partners? A: There are no restrictions on the types of organizations that are allowed to be deployment partners. For example, they can be private companies, public institutions, non-governmental organizations, governmental organizations, or intergovernmental organizations. However, organizations that are subject to United States export control restrictions are not eligible to be deployment partners (see, e.g., the US International Trade Administration Consolidated Screening List). Additionally, per the stipulations of CCAI’s US 501(c)(3) nonprofit status, projects we fund cannot entail political lobbying or campaigning for political candidates.

Q: The project evaluation criteria refer to “equity.” What is meant by “equity” in this context? A: The word “equity” in this case refers to considerations of diversity, equity, and inclusion (rather than, e.g., the financial meaning of the term).