Apply Now: $1.4 Million in Grants for AI Innovation in Climate Change

climate change ict4d problem

Artificial intelligence (AI) and machine learning (ML) can contribute to climate change mitigation and adaptation, as well as to climate science, in many different areas, for example energy, agriculture, forestry, climate modelling and disaster management.

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However, effective research and implementation are often hampered by a lack of data and other essential infrastructure, and by insufficient knowledge transfer between relevant disciplines and sectors.

The relationship between AI and climate change is also nuanced and can manifest in different ways that contribute to or hinder climate action. Therefore, the use of AI for climate action must be done with considerations of impact, responsibility and equity at its core.

$1.4M in Grants for AI Innovation in Climate Change

Climate Change AI Innovation will award grants of up to USD 150K to implement 1-year projects. Grantees must publish a documented dataset (or simulator) created by collecting, labeling, and/or annotating existing data, and/or by collecting, simulating, or otherwise making available new data that can enable further research.

  • Projects for the Main Railway should deploy AI or machine learning to address climate change, climate adaptation or climate science problems, or consider problems related to impact assessment and governance at the intersection of climate change and machine learning.
  • Entries for the Methane Special Track should deploy AI or machine learning to address methane-related climate change issues in the short/medium term (well before 2040).
  • Entries for Dataset Gaps Special Track The primary focus of the research group should be on creating a documented dataset (or simulator) by collecting, labeling, and/or annotating existing data, and/or by collecting, simulating, or otherwise making available new data that can enable further investigation.

Projects are expected to result in an implemented project, scientific publications or other public dissemination of results, and to include a carefully considered path to impactful implementation. All granted IP — for example the produced dataset/simulator and (if applicable) trained models or detailed descriptions of architectures and training procedures — must be made publicly available under an open license.

Each application must have a Principal Investigator (PI) affiliated with an accredited university in one of the 38 OECD member countries. Duplicate submissions made to multiple tracks may be disqualified.

Apply now! Deadline is September 15, 2024

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