Methodology & Sources
Transparency is key. Here is how we estimate the numbers.
Core Assumptions
Estimating the exact footprint of proprietary closed-source models (like GPT-4) is difficult because companies do not release exact energy data per query. However, using research papers and industry benchmarks, we can create high-confidence estimates.
Energy per Request
- GPT-4:~0.003 - 0.01 kWh / request
- GPT-3.5:~0.0003 kWh / request
- Images:~0.012 kWh / image (Smartphone charge)
Carbon Intensity
We convert Energy (kWh) to Carbon (gCO2e) based on grid intensity:
- Global Avg:~475 gCO2e/kWh
- Green Grid:~20 gCO2e/kWh (Hydro/Wind)
Water Consumption
Data centers consume water for cooling (Scope 1) and electricity generation (Scope 2). Research indicates a significant "water footprint" for AI.
"Making AI Less Thirsty" (Li et al., 2023) estimates that a conversation of 20-50 questions consumes ~500ml of water.
Regional Water Impact (API v2)
Our v2 API uses region-specific data for each data center location. Water impact depends on three factors:
Water Usage Effectiveness (WUE)
Measures liters of water consumed per kWh of IT energy. Ranges from 0.3 L/kWh (Sweden) to 3.0 L/kWh (UAE).
Water Stress Classification
From WRI Aqueduct Water Risk Atlas. Classifies regions as low, moderate, high, or critical.
Cooling Technology
Evaporative cooling uses more water but less energy. Air cooling reduces water use in cooler climates.
Data Sources & References
WRI Aqueduct Water Risk Atlas
Global water stress mapping used for regional water impact classification.
View Atlas âEIA State Electricity Profiles
US Energy Information Administration data on state-level grid carbon intensity.
View Profiles âCorporate Sustainability Reports
Microsoft (2024) & Google (2025) sustainability reports.
Microsoft â Google âDisclaimer: These figures are estimates intended for educational purposes. The AI landscape changes daily. We update our constants as new peer-reviewed data becomes available.