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The 2024 call closed on September 15, 2024. No active open call found on the page.
Climate Change AI Innovation Grants is sponsored by Climate Change AI. This program supports projects that address research and deployment challenges in climate change mitigation, adaptation, and climate science by leveraging AI and machine learning. It also focuses on creating publicly available datasets and tools to catalyze further work.
Projects can cover areas such as climate change, biodiversity conservation, agriculture, and water.
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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. 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 projects over a broad range of application areas, with investigators from institutions across countries on 6 continents. A list of our accepted projects can be seen below, with links for more information.
Madagascar energy insights: a multi-layered ground-truth and geospatial dataset for AI-powered solar forecasting and energy planning Fabienne Rafidiharinirina (MAIDI, Madagascar); Mamisoa Ramanitrarivo (MAIDI, Madagascar); Fabrice Ramamonjy (MAIDI, Madagascar); Mamy Rakotoarisoa (CNIA Madagascar); Fandresena Rajerison (Solar Madagascar Power, Madagascar) Computer Vision & Remote Sensing SEER - Sustainable Electricity Expansion Roadmaps: Democratizing power systems planning for a robust and sustainable energy transition Mohini Bariya (Rhiza Research, United States); June Lukuyu (University of Washington, United States); Joshua Adkins (Rhiza Research, United States); Genevieve Flaspohler (Rhiza Research, United States) Unsupervised & Semi-Supervised Learning CO-AI: Bridging Local Knowledge and AI through Coproduced Tools for Disaster Risk Reduction Timon McPhearson (New York University, United States); Christopher Kennedy (New York University, United States); Brian Palmer (ICLEI Africa, South Africa); Clara Marais (ICLEI Africa, South Africa); Kate Strachan (ICLEI Africa, South Africa) Disaster Management and Relief Climate Science & Modeling Can AI technologies increase farmer’s resilience to climate change?
Impact evaluation of Croppie Marcela Ibanez Diaz (University of Göttingen, Germany); Christian Bunn (CGIAR, Colombia); Romain Gaoutron (CGIAR, Colombia); Juan Carlos Muñoz-Mora (CGIAR, Colombia) Climate Finance & Economics Computer Vision & Remote Sensing DNA_DRV; the DNA biodiversity drive Richard O’Rorke (University of Auckland, New Zealand); Aimee van der Reis (University of Auckland, New Zealand); Jacqueline Beggs (University of Auckland, New Zealand); Greg Holwell (University of Auckland, New Zealand); Andrew Jeffs (University of Auckland, New Zealand); Gillian Dobbie (University of Auckland, New Zealand); Yun Sing Koh (University of Auckland, New Zealand); Daniel Wilson (University of Auckland, New Zealand); Holly Fleming (Terra Pura Consulting, New Zealand); Louise Weaver (The NZ Institute for Public Health & Forensics Science (PHF Science), New Zealand); Annette Bolton (The NZ Institute for Public Health & Forensics Science (PHF Science), New Zealand); Daniel Pritchard (Te Tiaki Mahinga Kai (TMK), New Zealand); Gretchen Brownstein (Bioeconomy Science Institute, New Zealand) Ecosystems & Biodiversity Societal Adaptation & Resilience Using Earth Observation and AI/ML technologies to support climate change adaptation for sustainable coral reef management and shoreline defence Meredith Roe (University of Queensland, Australia); Willie Kenneth (Pele Island Environmental Livelihood Solutions Network, Vanuatu); Mitchell Lyons (University of New South Wales, Australia); Gillian Rowan (University of Queensland, Australia); Daniel Harris (University of Queensland, Australia); Kathryn Markey (University of Queensland, Australia); Steven Micklethwaite (University of Queensland, Australia); Glarinda Andre Tanearu (Live and Learn Vanuatu, Vanuatu); Reilly Williamson (University of Queensland, Australia) Ecosystems & Biodiversity Earth Observation & Monitoring Computer Vision & Remote Sensing CLIMAI: Anticipating and reducing climate-driven mosquito-borne disease risks through data and collaboration Clara Bermúdez-Tamayo (University of Granada, Spain); Puerto López del Amo González (University of Granada, Spain); Demetrio Carmona Derqui (University of Granada, Spain); Josué Martínez de la Puente (Doñana Biological Station EBD-CSIC, Spain); Jordi Figuerola (Doñana Biological Station EBD-CSIC, Spain); Valéry Ridde (French National Research Institute for Sustainable Development (IRD), France); Emmanuel Bonnet (French National Research Institute for Sustainable Development (IRD), France); Belén Rodríguez-Fonseca (Complutense University of Madrid, Spain); Irene Polo Sánchez (Complutense University of Madrid, Spain); Lyda Osorio (Universidad del Valle, Colombia); Mabel Carabali (McGill University, Canada); Gina Polo Infante (Pontificia Universidad Javeriana, Colombia); Jaime Jiménez-Pernett (Andalusian School of Public Health, Spain); Ainhoa Ruiz Azarola (Andalusian School of Public Health, Spain); Olga Leralta Piñán (Andalusian School of Public Health, Spain); Marta Martín del Rey (Complutense University of Madrid, Spain); Fabio Augusto González-Osorio (National University of Colombia, Colombia); Fabián Méndez Paz (Universidad del Valle, Colombia); Mario Rivera-Izquierdo (University of Granada, Spain); Ana Eduviges Sancho Jiménez (Ministry of Health, Costa Rica) Behavioral and Social Science Societal Adaptation & Resilience FieldValAI: analysis ready AI training data for smallholder climate change adaptation strategies Berber Kramer (International Food Policy Research Institute, Kenya); Koen Hufkens (BlueGreen Labs, Belgium); Lilian Waithaka (ACRE Africa, Kenya); Senthilkumar Sankarraju (Dvara E-Registry, India) Climate Finance & Economics Earth Observation & Monitoring Societal Adaptation & Resilience Computer Vision & Remote Sensing A Foundational Methane Detection Dataset: Transparent Access to Cloud-Optimized Spatio-Temporal Datasets (TACO) Luis Gómez-Chova (University of Valencia, Spain); Cesar Aybar (University of Valencia, Spain); Julio Contreras (University of Valencia, Spain); Luis Guanter (Polytechnic University of Valencia, Spain); David Montero (Leipzig University, Germany); Miguel Mahecha (Leipzig University, Germany) Earth Observation & Monitoring Computer Vision & Remote Sensing Machine Learning and Decision Modeling for Climate-Smart Beef Production in South Africa Karun Kaniyamattam (Texas A&M University, United States); Abubecker Hassen (University of Pretoria, South Africa); Serinmary Pulikkottil Rejimon (Texas A&M University, United States); Abuye Tulu Demisse (University of Pretoria, South Africa); Ziyanda Goli (University of Pretoria, South Africa); Demian Johnson (University of Pretoria, South Africa); Georgette M.
Pooys (ARC-Irene, Animal Production Institute, South Africa); Michael Scholtz (ARC-Irene, Animal Production Institute, South Africa) Computer Vision & Remote Sensing Methane Emission Estimation from CAFOs with Machine Learning Daniel Ho (Stanford University, United States); Elena Eneva (Stanford University, United States); Evan Cook (Stanford University, United States); Victoria Hollingshead (Stanford University, United States) Earth Observation & Monitoring Computer Vision & Remote Sensing Uncertainty Quantification & Robustness Modeling and Learning Locational Emission Rates for Low-Carbon Power System Planning and Operation Yize Chen (University of Alberta; Canada); Yuanyuan Shi (University of California San Diego; United States); Feng Zhao (ISO New England; United States) 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 2025 — closed call Climate Change AI Innovation Grants 2024 — closed September 15, 2024 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: N/A (Previous grants have funded investigators from various institutions across many countries, indicating broad eligibility. Specifics should be confirmed on their website). Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Typically up to $150,000 for projects of one year duration 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.
Past winners and funding trends for this program
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 enables key partnerships that accelerate the research-to-deployment cycle, creating synergies between academic researchers, nonprofits, startups and other companies, and governmental or intergovernmental organizations. Funded by the Quadrature Climate Foundation, Schmidt Futures, and Google DeepMind, with Future Earth serving as fiscal sponsor, this is one of the few dedicated grant programs specifically targeting the intersection of AI/ML and climate change. Projects typically involve climate modeling, weather prediction, emissions monitoring, energy optimization, biodiversity monitoring, and other environmental applications of machine learning. The 2026 competition opens with a full proposal deadline of September 15, 2026. The program has grown steadily since its inception, funding 23 projects to date across diverse climate domains and geographies.
The Climate Change AI Innovation Grants program supports catalytic projects using AI and machine learning for climate action, funding research and deployment challenges in climate change mitigation, adaptation, and climate science. Projects must create publicly available datasets and tools as digital public goods, and release open-source code. The program builds partnerships between academic researchers, non-profits, startups, companies, and governmental organizations to accelerate the research-to-deployment cycle. Past funded projects span climate modeling, emissions monitoring, renewable energy optimization, and disaster prediction across all continents.
Climate Change AI Innovation Grants support high-impact projects at the intersection of artificial intelligence and climate change. Supported by Quadrature Climate Foundation, Schmidt Futures, Google DeepMind, and the Global Methane Hub with Future Earth as fiscal sponsor, the program funds research and deployment projects addressing climate change mitigation, adaptation, and climate science through AI and machine learning. Eligible project areas include agriculture and food systems, power and energy systems, ecosystems and biodiversity monitoring, disaster management, climate science and modeling, and transportation and urban planning. The program has a strong track record having funded 23 projects across 62 institutions in 22 countries on 6 continents. The 2024 call for proposals closed September 15 2024, with future annual rounds expected. This is distinct from the Bezos Earth Fund AI Grand Challenge which focuses on larger-scale implementation projects and from the Cornell Atkinson AI and Climate Fast Grants which are limited to Cornell-affiliated researchers.
Small Business Innovation Research Program (SBIR) Phase II is sponsored by Administration for Community Living. Small Business Innovation Research Program (SBIR) Phase II is a forecasted funding opportunity on Grants.gov from Administration for Community Living. Fiscal Year: 2026. Assistance Listing Number(s): 93.433. <p>The purpose of the Federal SBIR program is to stimulate technological innovation in the private sector, strengthen the role of small business in meeting Federal research or research and development (R/R&D) needs, and improve the return on investment from Federally-funded research for economic and social benefits to the nation. The specific purpose of NIDILRR's SBIR program is to improve the lives of people with disabilities through R/R&D products generated by small businesses, and to ...
The J.M.K. Innovation Prize is a grant from The J.M. Kaplan Fund recognizing early-stage social entrepreneurs working on environmental, heritage, and social justice challenges. The prize rewards individuals and organizations demonstrating innovative, entrepreneurial approaches to enduring problems. Applications for the 2025 prize were accepted February 11 through April 25, 2025 via an online portal. Spanish-language applications are welcomed, and a Spanish application form is available for download. The prize is biennial and open to a broad range of applicants across the United States working on forward-thinking solutions at the intersection of environment, community, and cultural heritage.