- Location: Ireland, Northern Ireland
- Institution: Queen's University Belfast, Trinity College Dublin, Ulster University, University of Limerick
- Status: Active
- Type: Independent
- Theme: Evidence Discovery & Integration
- Timeframe: 2024 - 2029
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Keeping up with the latest environmental research is a huge challenge as thousands of new scientific studies are published every year. This project aims to find ways to use artificial intelligence (AI) to do that job accurately and efficiently. The AI will be focused on finding and assembling the relevant evidence, allowing the human researchers to evaluate the evidence and make informed decisions and recommendations.
The project focuses specifically on understanding the literature on how carbon, water, and biodiversity behave across different landscapes, from individual farms up to whole regions. This includes how carbon is stored and released, how water quality changes, and how wildlife fares across key target habitats such as peatlands, forests, saltmarshes, and agricultural grasslands.
One of the project’s key goals is honesty about uncertainty. Not all scientific evidence is equally strong, and decision-makers need to know when findings are well-established versus when more research is still needed. To address this, the project is adopting the same confidence-rating framework used by the Intergovernmental Panel on Climate Change (IPCC), the world’s leading body on climate science, to critically examine and categorise the evidence assembled
Throughout the project, the team will work closely with land managers, conservationists, and policymakers to make sure the system is practical and genuinely useful to the people who need it most.
Project Goals
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Build an AI-powered system for automatically gathering and summarising environmental research.
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Collect and organise a large library of scientific studies on climate change, biodiversity, and water quality.
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Check the AI’s work by comparing its outputs against expert-reviewed research and develop a clear way of rating how strong or reliable each piece of evidence is.
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Gather and assess evidence on how peatlands, forests, saltmarshes, and agricultural grasslands and soils store carbon, regulate water, and support wildlife.
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Use the AI to evaluate which land management practices work best for storing carbon, improving water quality, and protecting biodiversity across these habitats.
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Connect the system to existing tools already used by policymakers and researchers, so findings are easy to access and act on.
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Work with stakeholders such as land managers, conservationists, and policymakers, to gather feedback and make sure the system is practical and genuinely useful.
