The Climate Change AI (CCAI) is excited to announce its Innovation Grants.
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 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 responsibly, and ideally with quantifiable impacts.
With the support of the Quadrature Climate Foundation and DeepMind, they are excited to announce funding of USD 1.2M for projects at the intersection of AI and climate change. They are also grateful to Future Earth International for serving as the fiscal sponsor for this program.
- Relevant research includes but is 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, 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.)
- Proposals focused on using AI/ML to address climate change mitigation in the electric power sector (including, but not limited to Optimal Power Flow and related multi-level problems like Unit Commitment) may also optionally request support from a DeepMind Engineer, in addition to the financial award.
- This program will allocate grants of up to USD 150K for conducting research projects of 1 year in duration. Research projects shall leverage AI or machine learning to address problems in climate change mitigation, adaptation, or climate science, or shall consider problems related to impact assessment and governance at the intersection of climate change and machine learning.
- Each application must have a Principal Investigator (PI) who is affiliated with an accredited university in an OECD Member country. 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). There are no eligibility restrictions on co-Investigators, and multi-country and multi-sectoral collaborations are encouraged (e.g., including members outside OECD Member countries or from non-research institutions).
- 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).
- They 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.
- 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. They 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.
For more information, visit https://www.climatechange.ai/calls/innovation_grants