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2024 call closed September 15, 2024. 2023 call closed March 1, 2023. No active cycle is open; future cycles not yet announced.
Climate Change AI Innovation Grants for AI and Climate Research is sponsored by Climate Change AI with Quadrature Climate Foundation, Schmidt Futures, and Google DeepMind. Climate Change AI Innovation Grants support high-impact projects at the intersection of artificial intelligence and climate change.
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Climate Change AI | Tackling Climate Change with Machine Learning Climate Change AI Innovation Grants As part of our mission to catalyze impactful work at the intersection of climate change and machine learning, Climate Change AI awards seed grants supporting research, deployment, and the creation of datasets and tools.
This Innovation Grants program enables the creation of key partnerships that accelerate the research-to-deployment cycle, creating synergies between academic researchers, non-profits, startups and other companies, and governmental or intergovernmental organizations. The Innovation Grants program closed the application period for its 2024 call for proposals on September 15, 2024.
Please visit the 2024 Innovation Grants page for more information about program and the awards notification timeline. If you are interested in partnering with Climate Change AI and supporting the Innovation Grants program, please get in touch with us via partnerships@climatechange. ai .
The Climate Change AI Innovation Grants program supports projects that address research and deployment challenges in climate change mitigation, adaptation, and climate science by leveraging AI and machine learning, while also creating publicly available datasets and tools to catalyze further work. The program allocates grants typically of up to $150K for projects of one year in duration.
So far, over our past grant calls , we have funded 23 projects over a broad range of application areas, with investigators from 62 institutions across 22 countries on 6 continents. A list of our accepted projects can be seen below, with links for more information.
The CityLearn Challenge 2023 Zoltan Nagy (The University of Texas at Austin); Javad Mohammadi (University of Texas at Austin) EMPIRIC_AI: AI-enabled ensemble projections of cyclone risk for health infrastructure in Pacific Island Countries and Territories Christopher Horvat (The University of Auckland); Berlin Kafoa (The Pacific Community); Michelle McCrystall (The University of Auckland); Elizabeth McLeod (World Health Organization); Craig McClain (Harvard Medical School) Climate Science & Modeling Societal Adaptation & Resilience Developing machine learning tools to rapidly assess the catastrophic impact of a range-extending sea urchin in a global warming hotspot Arie Spyksma (University of Auckland); Kelsey Miller (University of Auckland); Katerina Taskova (The University of Auckland); John Keane (University of Tasmania); Nicholas Perkins (University of Tasmania); Ariell Friedman (Greybits Engineering) Data Extraction and Modelling from Plant Trait Literature Richard Reeve (University of Glasgow); Neil A.
Brummitt (Natural History Museum); Claire L.
Harris (Biomathematics and Statistics Scotland); Ana Claudia Araujo (Natural History Museum); Ben Scott (Natural History Museum); Christina Cobbold (University of Glasgow); Glenn Marion (Biomathematics & Statistics Scotland) Ecosystems & Biodiversity Climate Science & Modeling Natural Language Processing Curbing Illegal Logging Patterns using Sound-Based Detection Techniques Delphine Clara Zemp (Université de Neuchâtel, Labo biologie de la conservation); Henry Muchiri (Strathmore University); Anthony Mwangi (Kenya Forestry Research Institute); Andreas Jedlitschka (Fraunhofer IESE); Fengshou Gu (University of Huddersfield) Carbon Capture & Sequestration Computer Vision & Remote Sensing From Observing Power to Improving Power: Loss Localization in the Distribution Grid through Topology June Lukuyu (University of Washington); Genevieve Flaspohler (nLine Inc.); Mohini S Bariya (nLine); Joshua Adkins (nLine Inc.); Kwame Abrokwah (nLine Inc.); Noah Klugman (nLine Inc.) Societal Adaptation & Resilience Unsupervised & Semi-Supervised Learning Flood Justice and Adaptation in the Rio Grande Valley of Texas with AI and satellite imagery Beth Tellman (University of Arizona); Ana Laurel (Texas RioGrande Legal Aid); Lucas Belury (University of Arizona); Zhijie Zhang (University of Arizona) Societal Adaptation & Resilience Computer Vision & Remote Sensing Disaster Management and Relief Artificial Intelligence for water management in the Red River Delta to meet the water demand and control saline intrusion in a changing climate Ivan Serina (Università di Brescia); Roberto Ranzi (Università di Brescia); Ngo Le An (Dai hoc Thuy Loi); Toan Q Trinh (University of California Davis) Climate Science & Modeling Disaster Management and Relief Mapping Rice Water Management and Methane Emissions in Ghana Sherrie Wang (MIT); Soren Vines (Aya Data); Freddie Monk (Aya Data); Benjamin Adevu (Demeter Ghana); William Hunt (Demeter Ghana) Computer Vision & Remote Sensing Accelerating Material Discovery for High-Performance Chemical Separation using AI Subhransu Maji (University of Massachusetts, Amherst); Peng Bai (University of Massachusetts, Amherst) Carbon Capture & Sequestration Detecting Flooding in Fiji's Croplands John Duncan (University of Western Australia); Bryan Boruff (University of Western Australia); Nathan Wales (University of Western Australia); Solomoni Nagaunavou (School of Geography, Earth Science, and Environment, The University of the South Pacific); Renata Varea (Ministry of Agriculture, Government of Fiji); Kevin Davies (School of Geosciences, The University of Sydney); Eleanor Bruce (School of Geosciences, The University of Sydney) Disaster Management and Relief Earth Observation & Monitoring Computer Vision & Remote Sensing Mitigating Climate Change Impacts on Biodiversity via Machine Learning Powered Assessment Oisin Mac Aodha (University of Edinburgh); Scott Loarie (iNaturalist); Thomas Brooks (IUCN) Ecosystems & Biodiversity Machine Learning-based Dynamic Climate Projections for Power System Planning Datasets Bri-Mathias S Hodge (University of Colorado Boulder); Aneesh Subramanian (University of California, San Diego); Claire Monteleoni (University of Colorado Boulder); Himanshu Jain (IIT Roorkee) Climate Science & Modeling Using Machine Learning and Earth Observation Data to Identify Aquaculture Sites with High Potential for Production Intensification and Mangrove Restoration in Southeast Asia Jack Kittinger (Arizona State University); Dane Klinger (Conservation International); Emily Corwin (Conservation International); Issa Tingzon (Thinking Machines - Philippines); Renavell Flores (Thinking Machines - Philippines); Joshua Cortez (Thinking Machines - Philippines); Pia Faustino (Thinking Machines - Philippines) Carbon Capture & Sequestration Ecosystems & Biodiversity Climate Finance & Economics Computer Vision & Remote Sensing Matching Structured Energy System Data for Policy Making and Advocacy using Weakly Supervised Machine Learning Xu Chu (Georgia Tech); Zane Selvans (Catalyst Cooperative) Adaptive Learning Techniques for Improved Subseasonal Forecasting Steve Easterbrook (University of Toronto); Judah Cohen (AER); Lester Mackey (Microsoft Research); Soukayna Mouatadid (University of Toronto); Genevieve E Flaspohler (MIT); Paulo Orenstein (IMPA); Ernest Fraenkel (MIT) Climate Science & Modeling Learning Power System Dynamics in the Frequency Domain Baosen Zhang (University of Washington); Weiwei Yang (Microsoft Research); Yixing Xu (Breakthrough Energy) Extracting and Discovering New Measurements from Climate Text Sources Taylor Berg-Kirkpatrick (University of California San Diego); Tom Corringham (Scripps Institution of Oceanography) Societal Adaptation & Resilience Natural Language Processing ForestBench: Equitable Benchmarks for Monitoring Verification and Reporting of Nature-Based Solutions with Machine Learning Dava Newman (MIT); Moises Exposito-Alonso (Carnegie Institution for Science); Lucas Czech (Carnegie Institution for Science); David Dao (ETH Zurich); Björn Lütjens (MIT); Lauren Gillespie (Stanford University); Hilary Hao (Climate Reality Project); Andrew Cottam (Restor) Ecosystems & Biodiversity Earth Observation & Monitoring Local and Indigenous Knowledge Systems Estimate the ice volume of all glaciers in High Mountain Asia with deep learning (ICENET) Niccolò Maffezzoli (Institute of Polar Sciences – Italian National Research Council, CNR-ISP); Eric Rignot (University of California, Irvine, UCI); Carlo Barbante (Institute of Polar Sciences – Italian National Research Council, CNR-ISP) Climate Science & Modeling Earth Observation & Monitoring Computer Vision & Remote Sensing Towards greener last-mile operations: Supporting cargo-bike logistics through optimized routing of multi-modal urban delivery fleets Maria Astefanoaei (IT University of Copenhagen); Akash Srivastava (MIT-IBM Watson AI Lab, IBM Research); Kai Xu (University of Edinburgh); Nicolas Collignon (Kale Collective); Esben Sørig (Kale Collective); Soonmyeong Yoon (Kale Collective) Improving Resiliency of Malian Farmers with Yield Estimation: IMPRESSYIELD Esra Erten (Istanbul Technical University); R.
Gökberk Cinbiş (Middle East Technical University); Dr. Traore Haoua (OKO Finance Limited); Osman Baytaroğlu (Agcurate Bilgi Teknolojileri Anonim Şirketi) Climate Finance & Economics Earth Observation & Monitoring Societal Adaptation & Resilience Past Innovation Grants Calls Climate Change AI Innovation Grants 2023 — closed March 1, 2023 Climate Change AI Innovation Grants 2022 — closed October 15, 2021
Based on current listing details, eligibility includes: Researchers, practitioners, and organizations worldwide working on AI applications for climate change. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Individual project grants of up to $150,000 per project for one-year duration. Total program funding pool of up to $1.4 million per annual cycle. The program has funded 23 projects across 62 institutions in 22 countries to date. Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is rolling deadlines or periodic funding windows. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
Federal grant success rates typically range from 10-30%, varying by agency and program. Build a strong proposal with clear objectives, measurable outcomes, and a well-justified budget to improve your chances.
Requirements vary by sponsor, but typically include a project narrative, budget justification, organizational capability statement, and key personnel CVs. Check the official notice for the complete list of required attachments.
Yes — AI tools like Granted can help research funders, draft proposal sections, and check compliance. However, always review and customize AI-generated content to reflect your organization's unique strengths and the specific requirements of the solicitation.
Review timelines vary by funder. Federal agencies typically take 3-6 months from submission to award notification. Foundation grants may be faster, often 1-3 months. Check the program's timeline in the official solicitation for specific dates.
Many federal programs offer multi-year funding or allow competitive renewals. Check the official solicitation for continuation and renewal policies. Non-competing continuation applications are common for multi-year awards.
Research on Circular Economy, Smart Manufacturing, and Energy-Efficient Microelectronics is sponsored by U.S. Department of Energy (DOE) Advanced Materials & Manufacturing Technologies Office (AMMTO). This funding opportunity supports innovative technology R&D across the manufacturing sector with a focus on circular economy, smart manufacturing, and energy-efficient microelectronics. While the stated deadline for full applications has passed, AMMTO frequently issues similar solicitations, and this highlights a relevant area of interest for the DOE.
NIST Small Business Innovation Research (SBIR) Phase II Program - Quantum Information Science is sponsored by National Institute of Standards and Technology (NIST). This program allocates funding to small businesses for prototyping innovative technologies in areas including quantum information science, artificial intelligence, and semiconductors. These Phase II awards follow successful Phase I feasibility studies.