HIP-251 Draft Meta
AI Compute Carbon Footprint Methodology for measuring and reporting carbon emissions from AI training and inference.
sustainability carbon compute emissions
Requires: HIP-200, HIP-250
HIP-251: AI Compute Carbon Footprint
Abstract
This HIP establishes the methodology for measuring, calculating, and reporting the carbon footprint of Hanzo AI's training and inference operations. It aligns with the GHG Protocol and provides AI-specific guidance for accurate carbon accounting.
Scope
Operational Boundary
Activity Scope Included Model training Scope 2/3 ✅ Yes Model inference Scope 2/3 ✅ Yes Data storage Scope 2/3 ✅ Yes Development compute Scope 2/3 ✅ Yes Employee devices Scope 3 ✅ Yes Cloud services Scope 3 ✅ Yes
Emissions Categorization
GHG Scope AI-Relevant Sources Scope 1 On-site generation (if any) Scope 2 Purchased electricity for owned compute Scope 3, Cat 1 Cloud compute purchases Scope 3, Cat 3 Fuel-related activities Scope 3, Cat 11 Customer inference (if applicable)
Training Emissions
Calculation Methodology
Energy Consumption
Formula :
E_training = Σ(GPU_hours × TDP × PUE) / 1000
Where:
GPU_hours = GPU hours usedTDP = Thermal Design Power (kW)PUE = Power Usage Effectiveness
Carbon Emissions
Formula :
CO2e_training = E_training × EF_grid × (1 - R%)
Where:
E_training = Energy consumption (kWh)EF_grid = Grid emission factor (kgCO2e/kWh)R% = Renewable energy percentage
Reference Values
GPU Power Consumption
GPU TDP (W) Typical Utilization Effective (W) H100 SXM 700 80% 560 H100 PCIe 350 80% 280 A100 SXM 400 80% 320 A100 PCIe 300 80% 240
Data Center PUE
Provider/Region PUE Source Hyperscaler average 1.1-1.2 Provider reports Colocation average 1.3-1.5 Industry benchmarks On-premise average 1.5-2.0 Industry benchmarks
Training Tracking
Required Metrics
For each training run:
Metric Collection Method GPU type Cluster configuration GPU hours Job scheduler logs Data center location Cluster metadata Time period Job timestamps
Aggregation
Period Aggregation Per run Individual training job Weekly Development activity Monthly Reporting period Annually Annual report
Inference Emissions
Calculation Methodology
Per-Request Emissions
Formula :
CO2e_request = E_request × EF_region / 1000
Where:
E_request = (GPU_power × latency_seconds) + memory_energy + network_energy
Estimation Approach
For large-scale inference:
CO2e_inference = Total_GPU_hours × GPU_power × PUE × EF_avg / 1000
Efficiency Metrics
Metric Definition Target CO2e/1K tokens Emissions per 1,000 tokens Track and reduce CO2e/request Emissions per API request Track and reduce CO2e/MAU Emissions per monthly active user Track and reduce
Geographic Distribution
Track inference by region:
Region % of Requests Grid Factor Weighted Factor US-West X% 0.35 Calculated US-East Y% 0.42 Calculated EU Z% 0.28 Calculated Asia W% 0.50 Calculated
Data & Emission Factors
Grid Emission Factors
Source Coverage Update Frequency EPA eGRID US regions Annual EEA EU countries Annual IEA Global Annual Provider-specific Cloud providers As published
Cloud Provider Data
Provider Data Available Source AWS Region carbon intensity AWS Customer Carbon Footprint Tool GCP Carbon-free energy % Google Cloud Carbon Footprint Azure Emissions reporting Microsoft Sustainability Calculator
Data Quality
Level Definition Use Primary Measured data Preferred Secondary Provider-reported Acceptable Tertiary Industry average Gap-filling
Reporting Standards
Internal Reporting
Monthly Dashboard
Metric Display Total compute emissions CO2e (tonnes) Training vs inference split % breakdown YoY change % change Efficiency trend CO2e/request over time
Quarterly Report
Section Contents Summary Total emissions, trends Training Major training runs, emissions Inference Volume, efficiency Initiatives Reduction progress
External Reporting
Annual Disclosure
Report Contents ESG Report Summary metrics, targets CDP Response Detailed methodology Model Cards Per-model training emissions
Model Card Emissions
For each model release:
training_emissions:
total_co2e_tonnes: X
gpu_hours: Y
energy_kwh: Z
data_centers: [list]
renewable_percentage: W%
methodology: "HIP-251"
Reduction Strategies
Training Efficiency
Strategy Impact Implementation Efficient architectures 10-50% reduction Architecture research Mixed precision 30-50% speedup Training configuration Gradient checkpointing Memory vs compute Based on model size Curriculum learning 10-30% reduction Training methodology
Inference Efficiency
Strategy Impact Implementation Model quantization 2-4x efficiency INT8/INT4 deployment Speculative decoding 2-3x speedup Inference optimization Batching Improved utilization Request aggregation Caching Variable Response caching
Infrastructure
Strategy Impact Implementation Green regions 30-90% reduction Region selection Renewable PPAs Up to 100% reduction Energy procurement Efficient hardware 20-50% per generation Hardware refresh Cooling optimization PUE improvement Data center ops
Targets
Absolute Targets
Year Target Baseline 2025 Establish baseline Measure all emissions 2027 -30% vs baseline Reduction 2030 Net zero Reduction + offsets
Intensity Targets
Year CO2e/1K tokens CO2e/request 2025 Baseline Baseline 2027 -50% -50% 2030 -80% -80%
Renewable Energy
Year Renewable % 2025 50% 2027 80% 2030 100%
Verification
Internal Verification
Activity Frequency Data validation Monthly Calculation review Quarterly Methodology audit Annual
External Verification
Activity Frequency Standard Third-party audit Annual ISO 14064-3 CDP verification Annual CDP methodology
Related HIPs
HIP-200 : Responsible AI PrinciplesHIP-250 : Sustainability Standards AlignmentHIP-260 : Efficient Model PracticesHIP-270 : AI Supply Chain ResponsibilityHIP-290 : Evidence Locker Index
Changelog
Version Date Changes 1.0 2025-12-17 Initial draft
Copyright
Copyright and related rights waived via CC0 .