Integration of Environment Ontology

[jravenel]

Description / Rationale

The introduction of the ENVO Environment Ontology into ABI can significantly enhance how organizations track, map, manage sustainability metrics, regulatory compliance, and resource consumption.

ABI can unify data from multiple sources (e.g., manufacturing processes, operational activities, supplier materials) into a consistent framework to feed the current ENVO ontology (from OBO foundry, so BFO compliant). This can enable sophisticated analytics, such as emissions tracking, water usage, or lifecycle analysis, to be automated and supports better decision-making around environmental initiatives and policies for organizations.

Moreover, the ENVO Ontology can bring greater transparency for stakeholders, customers, regulators, suppliers, by offering standardized descriptions of environmental indicators (e.g., CO₂ emissions, waste outputs, recycled material usage). This to bring trust and supports the organization’s sustainability credentials. Equally important, having a semantic model for environmental data opens the door to future integrations with third-party environmental services, certification bodies (like ISO 14001), and compliance systems. This approach meets our vision for data governance and reporting but also positions the organization to adapt more rapidly to evolving sustainability standards over time.

Suggestions

1. Identify Key Environmental Data Points
   Pin down the most relevant metrics (e.g., carbon footprint, energy usage, resource provenance) aligned with both business and sustainability objectives.

2. Evaluate Existing Ontologies
   Look at standardized or widely adopted ontologies such as:

   • [Environment Ontology (ENVO)](https://www.obofoundry.org/ontology/envo.html)
   • [W3C PROV-O](https://www.w3.org/TR/prov-o/) for provenance
   • ISO-specific models (e.g., [ISO 14001](https://www.iso.org/iso-14001-environmental-management.html)) to ensure compatibility with broader industry and academic norms.

3. Design or Integrate “EnvironmentOntology Module ”
   Incorporate or adapt suitable classes and properties from ENVO, linking them to applications schemas (e.g., product, manufacturing, logistics)

4. Pilot the Integrated Model
   Apply the ontology in a targeted use case, such as tracking the lifecycle environmental impact of a specific product line, and gather feedback from internal stakeholders.

5. Scale and Maintain
   Roll out the model across broader business units with version control and maintenance processes to ensure long-term consistency and data integrity.

Pilot idea for ABI core

Modern Data & AI systems, particularly large-scale models, consume vast amounts of computing power. As these models grow in size and complexity, so too does their environmental impact, driven by the energy required to power data centers and manufacture specialized hardware like GPUs. This energy consumption correlates directly with CO₂ emissions, which vary depending on the carbon intensity of the local power grid. By tracking these emissions with standardized vocabularies from an ontology such as ENVO, organizations can accurately measure, compare, and ultimately reduce their carbon footprint over time.

Another key issue is the production and disposal of hardware. Building high-performance GPUs and servers involves extracting and refining rare metals and other materials, which consumes energy and produces industrial waste. Once hardware reaches the end of its life, it becomes electronic waste that can threaten ecosystems if improperly disposed of. Integrating semantic models of waste outputs into AI workflows ensures that data centers and compute clusters are evaluated not just for performance, but also for how responsibly they handle and recycle materials.

Recycled content plays an increasingly critical role in reducing the total environmental impact of compute infrastructure. By explicitly modeling the percentage of recycled materials in server hardware, organizations can factor sustainability into procurement decisions. These details also feed back into tracking and reporting frameworks such as ISO 14001 or the Greenhouse Gas Protocol, enabling consistent, verifiable sustainability assessments. Linking AI compute usage with these environmental metrics, CO₂ emissions, waste outputs, and recycled material utilization, promotes transparency, guides actionable strategies for optimization, and helps align AI innovation with responsible environmental stewardship.