Climate Change Al Innovation Grants 2024 (Up to $150,000)

Get latest opportunities in your email

About Climate Change Al Innovation Grants

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

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

Tracks

Main Track

Projects in the Main Track should leverage Al 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 Al governance in the context of climate change, or that aim to assess the greenhouse gas emissions impacts of Al or Al-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 Al 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.

 

• Sponsor Organisation: Climate Change AI (CCAI)

Benefits of Climate Change Al Innovation Grants

• Up to USD 150K per proposal, for projects of 12 months in duration. They will award a total of up to USD 1.4M in grants across all projects.

Climate Change Al Innovation Grants Requirements

Each application must have a Principal Investigator (PI) who is affiliated with an accredited university in one of the 38 OECD Member Countries (see list here). 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).

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
Call release date	July 14, 2024
Informational webinars	July 30, 2024 @ 9 am ET/1 pm UTC register  August 15, 2024 @ 12 pm ET/5 pm UTC register
Proposal submission deadline	September 15, 2024
Notification of results	December 2024
Award start date	February 2025
Award end date	February 2026

Selection criteria

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

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.

Across the full cohort of grantees, we will additionally seek to allocate grants to represent multiple sectors of climate change mitigation and adaptation, as well as coverage across many geographic regions.

 

Application Date and Process

All applications must be received by September 10st, 2024 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 used to determine eligibility, and will be responsible for receipt and any further distribution of the funds if a grant is awarded.

Project Description. A detailed description of the project (maximum 12 pages including figures/tables, using no smaller than 12pt font size, single line spacing, and 1 inch margins), 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. papers, code, datasets, deployed systems) 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, 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 accredited university with which the Principal Investigator is affiliated; any further dissemination of funds to partner institutions must be managed by the lead institution.

They 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).

Application Deadline

19 September, 2024

Scholarship Application Portal

View details of Climate Change Al Innovation Grants

Apply for Climate Change Al Innovation Grants