Imagine a smart map that tells you exactly where to fix a swamp or coastline to trap the most carbon and stop floods. It uses satellite data and AI to predict if a restoration project will actually work before you spend any money. Think of it as a GPS for nature recovery that ensures every euro spent on the environment gives the maximum climate benefit.
Companies and governments struggle to identify which coastal areas provide the best return on investment for carbon sequestration and flood protection. Lack of standardized data makes it risky to invest in nature-based restoration.
A digital Decision Support System (DSS) platform and a set of standardized methodologies for prioritizing wetland restoration based on GHG emissions and biodiversity.
If you are a sustainability consultancy dealing with imprecise carbon offset calculations — this project developed a Decision Support System (DSS) that provides standardized methodologies to prioritize restoration for maximum carbon-storage and GHG abatement.
If you are an insurance provider dealing with rising coastal erosion and flood claims — this project developed tools to assess ecosystem services like flood regulation and coastal erosion protection to better quantify natural risk barriers.
If you are a developer dealing with strict EU Nature Restoration Law compliance — this project developed a digital platform and a Community of Practice to guide the selection of priority sites for restoration.
Based on available project data, no specific pricing or licensing costs are mentioned as the project is EU-funded research.
Yes, the project uses remote sensing and machine learning to upscale results from 6 case pilots to a broader European geographical context.
Based on available project data, specific licensing terms are not provided, but the output is a digital platform intended for interested parties.
The tools are designed to support the implementation of the EU Nature Restoration Law and the European Green Deal.
The project period is from 2023-01-01 to 2025-12-31, indicating a multi-year development and testing cycle.
The consortium is heavily weighted toward research and academia (13 out of 16 partners are universities or research institutes), with a modest 12% industry ratio. This suggests the output is highly scientifically validated but may require further commercial refinement. The presence of 3 SMEs indicates a push toward practical, small-scale business application.
Contact Universidade de Aveiro in Portugal
Contact us to access the Decision Support System (DSS) beta or join the Community of Practice.
