ARIES (ARtifical Intelligence for Environment & Sustainability)
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Tool Description
ARIES (ARtificial Intelligence for Environment & Sustainability) is an innovative AI-powered platform for socialenvironmental systems modelling, hosted by the Basque Centre for Climate Change (BC3). Leveraging the semantic web, it integrates diverse data sources and advanced modelling techniques with robust computational capabilities. Powered by k.LAB technology, ARIES delivers holistic outputs for science-based decision-making by ensuring the efficient integration of complex ecological models while continuously assimilating emerging knowledge and data to refine and expand its capabilities. Together, they foster an ecosystem of FAIR data and models to automate, standardise, and scale ecosystem service and natural capital modelling using semantic AI, enabling rapid, transparent, and reproducible assessments for policy, planning, and accounting (including SEEA EA).
Running the Tool
Tool Inputs
Authoritative global and national datasets (land cover, climate, hydrology, biodiversity, socio-economic data, administrative boundaries). Users can optionally provide their own spatial and non-spatial datasets for custom analyses. The k.LAB platform utilizes a robust and extensible resource adapter architecture to integrate a wide variety of scientific datasets and web services into its semantic modeling environment. These software plug-ins act as translators, bridging standardized protocols like STAC for geospatial assets, SDMX for statistical metadata, and OpenDAP for high performance data transport with the platform’s core API. In addition to supporting modern web services and OGC standards (such as WCS and WFS), k.LAB handles conventional file formats—including GeoTIFF, NetCDF, CSV, and shapefiles— while maintaining strict validation requirements for metadata to ensure interoperability and scientific accuracy across distributed nodes.
Resource Requirements
ARIES runs primarily on cloud-based infrastructure, including deployment through the UN Global Platform. Web-based applications require no local installation or significant computing resources. Advanced modelling using the k.LAB environment may require additional technical expertise and configuration. ARIES k.Lab and ARIES for SEEA (k.Explorer) are run from a web-based platform.
time Requirements
<1 day – months | Time requirements vary widely from rapid assessments using pre-built models to longer workflows for custom analyses.
Skill Requirements
Understanding of ES, spatial data, and interpretation of probabilistic outputs. There is no coding required.
Key data sources:
ARIES website
K.lab website
Training materials: e- learning Moodle course, k.IM Quick tips in Confluence & Chat in Notebook LM for quick queries
How does it work?
ARIES is based on k.LAB technology which provides access to an integrated network of accessible web models, catalogued and connected through semantics. The platform allows users to query the network for information about a concept of interest, merging natural science models (e.g., process-based models) with human behaviour models (e.g., agent-based models). The system automatically resolves differences in units or scale, ensuring that outputs are coherent and useful for decision-making.
Using Tool Outputs
Tool Outputs
Maps of ecosystem service supply, demand, flows and beneficiaries; ecosystem extent and condition; uncertainty layers; and SEEA Ecosystem Accounting tables. Results can be explored via web interfaces and downloaded as geospatial data.
How can the tool be used to inform decisions?
The tool identifies priority areas, supports scenario comparison and trade-off analysis, informs planning, policy appraisal, and natural capital accounting.
Limitations of Use
The tool is dependent on data availability and quality; requires technical understanding for advanced use; model assumptions may be complex; may need local calibration.
Case Studies
There are not publicly available UK case studies of utilising ARIES for SEEA.
Other information
ARIES takes a quantitative approach to incorporating uncertainty to allow for improved model precision
Users can develop and run models directly from the world wide web with ARIES explorer (k.Explorer)
ARIES Explorer (k.Explorer) is like the Co$ting Nature tool, but has a better temporal application
Agent-based modelling is an automated part of ARIES Explorer (k.Explorer)
Information or data archives, including spatial and non-spatial data to populate the models, can substantially reduce the time needed to apply the tool.
ARIES Explorer (k.Explorer) provides an easy-to-use interface. A user can click an area and search their storyline or question in the knowledge dictionary e.g. Carbon’. Using a drag and drop approach, the platform will run the model (according to global models or user-specific models uploaded) and provide an output for the study area
Developer Organisations
The ARIES project started in 2007 at University of Vermont. Since 2011 the project has been hosted at the Basque Centre for Climate Change (BC3). See https://aries.integratedmodelling.org/about/
