Creating Synergy for a Stronger Future Grant (United Way of Greater Rochester and the Finger Lakes)

Community Impact Multi-Year Program Grant is sponsored by United Way of Greater Rochester and the Finger Lakes. This grant provides support for impact-driven, multi-year community programs that engage with one or more impact areas. It aims to help nonprofits expand their reach, strengthen operations, and deliver critical services where they're needed most.

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.

Advancing integrated scenarios and prediction models for informing transition to a nature positive society is sponsored by European Commission — Horizon Europe. Expected Outcome: Project results are expected to contribute to all the following expected outcomes: advanced knowledge and understanding how ecosystems respond to multiple plausible futures shaped by human-induced pressures, supporting the design of long-term, nature positive and equitable policies; strengthened collaboration across bio-physical , socio-economic and humanities disciplines, resulting in more integrated and robust scenario frameworks that capture interactions, trade-offs and synergies across biodiversity, climate, food, water, air, health, energy and economy; advancement of comprehensive ecosystem prediction models and scenario-based assessments that integrate direct and indirect drivers of biodiversity loss, applicable across terrestrial, freshwater and marine ecosystems; a consistent and transparent framework for model intercomparison and cross-scale scenario building, improving the relevance and usability of modelling outputs for policy , business and international assessments (e.g., IPBES, International Panel on Climate Change - IPCC). Scope: To address the objectives of the European Green Deal, the EU biodiversity strategy for 2030, the EU Nature Restoration Regulation and the Kunming-Montreal Global Biodiversity Framework (GBF), as well as Sustainable Development Goals, actions funded under this topic should support the scientific development of A) integrated scenarios, B) predictive models and C) model intercomparison experiments for Member States and Associated Countries. Developments should be verified in case studies covering at least 2 European biogeographic regions [1] and spanning key economic sectors to inform systemic policy transformations and enable long-term planning for a nature positive society. Proposals should: design and develop components of a European integrated prediction and scenario framework to forecast how natural ecosystems respond to a wide range of human-induced ecosystem interactions including climate change, pollution, socio-economic transitions such as conflicts, pressures, and policy interventions. These frameworks should link ecosystem functions with key economic sectors, such as agriculture and fisheries, and test their resilience and tipping points under multiple plausible futures; make use of long-term monitoring data across biodiversity, land use, climate, and socio-economic variables to improve reliability and real-world relevance. They should also quantify uncertainty in model projections and ensure scenarios incorporate dynamic feedback loops between ecosystems and socio-economic systems, and identify risk distributions relevant to frontline socio-ecological communities; test and assess the suitability of the IPBES Nature Futures Framework for European application and align modelling approaches with international efforts and assessments under IPBES and IPCC. Proposals should seek to address knowledge gaps identified by the relevant IPBES assessments and if appropriate provide recommendations to policymakers; advance model intercomparison capabilities to improve robustness, consistency and transparency in scenario-based assessments. To this end, the suitability of remote sensing data for scalability and intercomparison between different models and across different regions should be assessed. Proposals should include case studies that cover at least two European biogeographic regions with global relevance to verify the developed scenarios, models and intercomparison experiments. Case studies should consider different geographical scales and explore ecosystem tele coupling across Europe and beyond. Scenario development should involve relevant stakeholders—including public authorities, economic sectors, and civil society—in co-creating narratives and ensuring policy applicability. Socio-economic transitions and equity and justice considerations concerning trade-offs and distributional impacts across regions and communities (i.e. across biodiversity, climate, food, water, Programme areas: Horizon Europe (HORIZON), Global Challenges and European Industrial Competitiveness, Food, Bioeconomy Natural Resources, Agriculture and Environment, Biodiversity and Natural Resources Keywords: Biodiversity conservation, Biological systems analysis, modelling and simulation, Earth Observation / Services and applications, Ecology, EU biodiversity strategy, Kunming-Montreal Global Biodiversity Framework, biodiversity, climate change, drivers of biodiversity loss, earth observation, freshwater ecosystems, intercomparison, marine ecosystems, model, modelling, nature conservation, scenario, terrestrial ecosystems