ECO-SUS-WATER-001¶
Name: Water-stress-blind AI inference placement
Category: Sustainability & Environmental Impact
Family: Water-Aware Computing
Primary layer: ai
System layers: ai
Description¶
AI inference workloads are placed without considering regional water stress or cooling impact.
Impact¶
- type: water
- confidence: 0.65
- notes: Added as part of the 0.3.0 expansion to capture cross-system sustainability and operational waste.
Detection¶
- method: static-or-runtime
- confidence: 0.55
- runtime_validation_required: Yes
Remediation¶
- guidance: Consider region, model size, caching, batching, and workload scheduling to reduce water-stress-weighted impact.
- tradeoffs: May require architecture, product, or operations review rather than a local code change.
Cost Dimensions¶
- compute: high
- memory: medium
- network: medium
- storage: low
- human_time: medium
- carbon: high
- water: high
Amplification¶
- scales_with_users: Yes
- scales_with_data_volume: No
- scales_non_linearly: No
Temporal Behavior¶
- startup_only: No
- steady_state: Yes
- burst_sensitive: Yes
- time_degradation: No
Runtime Evidence¶
- region inventory
- AI usage metrics
- water stress factors
Pattern examples¶
No pattern examples provided.
Remediation examples¶
No remediation examples provided.
Metadata¶
- catalog_version: 0.4.0
- status: draft
- source: catalog expansion recommendations applied 2026-05-21