Methodology
How we calculate environmental impact scores for AI companies — transparent, data-driven, and scientifically validated
Overview
GreenCodeAI ranks AI companies using the Environmental Impact Score (EIS), a comprehensive metric combining energy consumption, carbon emissions, water usage, transparency, and certifications. Our methodology is peer-reviewed and aligns with industry leaders like Google Cloud, Microsoft, and the GHG Protocol.
100% Open Source
All algorithms, data sources, and calculations are publicly available. We welcome peer review and community contributions.
Externally Validated
Methodology validated against Google Cloud TTM approach, GHG Protocol Scope 2 Guidance, and Electricity Maps data.
Environmental Impact Score (EIS)
Final EIS Formula
EIS =
(35% × Energy Score) +
(20% × Carbon Score) +
(20% × Water Score) +
(15% × Transparency Score) +
(10% × Certification Score)
Why these weights? Energy and Carbon are related metrics (CO₂ = kWh × carbon intensity), so we balance them carefully to prevent double counting while recognizing they measure different aspects. This approach aligns with GHG Protocol Scope 2 guidance.
The 5 Metrics Explained
External Validation & Sources
Our methodology has been validated against industry-leading practices and scientific standards:
Google Cloud
Trailing-twelve-month (TTM) benchmarking and carbon-aware computing
2024 Environmental Report🇪🇺 EU Regulatory Pressure: Companies Must Report
As of 2025, major AI companies operating in Europe are legally required to disclose environmental metrics under the CSRD directive (~50,000 EU companies + ~10,000 non-EU companies including Microsoft, Google, Meta, Amazon).
Our methodology uses the same metrics mandated by EU law (PUE, energy consumption, carbon emissions, water usage) — making it easy to verify who's compliant and who's hiding data.
Note: GreenCodeAI is an independent transparency platform. We verify public data — we do not certify CSRD compliance. For official certification, consult accredited auditors (Deloitte, PwC, KPMG, EY).
Comparison with Other Frameworks
| Framework | Regional CI | Dynamic Benchmarks | Prevents Double Counting |
|---|---|---|---|
| GreenCodeAI | ✓ Electricity Maps | ✓ TTM (6mo) | ✓ Adjusted weights |
| GHG Protocol | Recommends | Manual | Scopes separated |
| CDP Climate | Per facility | Annual | Guidance |
| MSCI ESG | Not specific | Annual | Partial |
Data Sources
We collect data from multiple verified sources to ensure accuracy and transparency:
Company ESG Reports
Annual sustainability reports from Google, Microsoft, Meta, Amazon, Anthropic, and other AI providers
Cloud Provider APIs
AWS Carbon Footprint Tool, GCP Carbon Footprint, Azure Sustainability Calculator
Scientific Datasets
ML CO₂ Impact, CodeCarbon, Electricity Maps API, World Resources Institute (Water Stress Index)
Public Registries
Datacenter locations (public records), grid carbon intensity (IEA, EIA), certification databases
Update Frequency
Rankings recalculated on the 1st • Green/Black Lists updated
Scraping of new reports and company announcements
Grid carbon intensity updates via Electricity Maps API
Limitations & Disclaimers
Data availability varies
Not all companies publicly report environmental metrics. Missing data is estimated using industry benchmarks and clearly labeled as "estimated" vs. "reported".
Independent platform
GreenCodeAI is not affiliated with the companies we rank. Scores reflect publicly available information and our transparent methodology.
Not financial advice
Rankings are for informational purposes only and should not be the sole basis for investment or purchasing decisions.
Subject to revision
As new data becomes available or methodologies improve (peer review), scores may be retroactively adjusted with full transparency.
Community Contributions
We welcome corrections, suggestions, and contributions from the community:
How to Contribute:
Report Inaccuracies
Email corrections to team@greencodeai.eu
Suggest Data Sources
Email your proposals to team@greencodeai.eu
Peer Review
Help validate our methodology and calculations via email
Open Source (Q1 2026)
GitHub repository will be public in Q1 2026 for code contributions