Research
Data Methodology
GreenCIO combines authoritative public sources with proprietary analytics to deliver investment-grade intelligence on AI data center energy risks and opportunities.
Primary Data Sources
Government & Regulatory
- • Department of Energy (DOE): National energy consumption data, data center electricity projections
- • Lawrence Berkeley National Laboratory (LBNL): Data center energy research and forecasts
- • FERC: Federal energy regulations, Order 2023 interconnection rules
- • State PUCs: Utility tariffs, rate cases, data center-specific regulations
- • EIA: Energy Information Administration statistics and pricing data
Grid Operators (ISOs/RTOs)
- • PJM: Interconnection queue data, capacity auction results, congestion pricing
- • ERCOT: Real-time grid conditions, resource adequacy reports
- • CAISO: Renewable integration data, transmission constraints
- • MISO, SPP, NYISO, ISO-NE: Regional grid data and queue status
Industry & Financial
- • Corporate filings: 10-K/10-Q reports from public data center operators
- • Earnings calls: Management commentary on energy costs and expansion plans
- • M&A databases: Transaction data for infrastructure deals
- • Permit filings: Construction permits and environmental assessments
Update Frequency
Real-Time (< 15 minutes)
- • Grid conditions and pricing
- • M&A announcements
- • Regulatory filing alerts
- • News and press releases
Daily Updates
- • Interconnection queue changes
- • Tariff modifications
- • Permit applications
- • Weather and climate data
Weekly Analysis
- • Queue progression analytics
- • Regional trend reports
- • Policy change summaries
- • Market sentiment indicators
Monthly Deep Dives
- • Comprehensive market reports
- • Regulatory landscape updates
- • Infrastructure development tracking
- • ESG metrics compilation
Analytical Framework
Risk Scoring Methodology
Our proprietary risk scores combine multiple factors:
- • Grid Risk (40%): Queue position, interconnection delays, capacity constraints
- • Regulatory Risk (30%): Current tariffs, proposed changes, political climate
- • Energy Cost Risk (20%): Price volatility, renewable availability, demand charges
- • Environmental Risk (10%): Water stress, extreme weather probability, carbon intensity
Predictive Models
Machine learning models trained on 10+ years of data to forecast:
- • Interconnection approval timelines
- • Energy price trajectories
- • Regulatory change probability
- • Infrastructure build-out patterns
Data Quality Assurance
Validation Process
- Multi-source verification for critical data points
- Automated anomaly detection
- Expert review of outliers
- Historical backtesting
Transparency Standards
- Source attribution for all data
- Confidence intervals provided
- Model assumptions disclosed
- Change logs maintained
Key Differentiators
1. Financial-Grade Accuracy: Our data undergoes the same rigorous validation as investment research, ensuring reliability for high-stakes decisions.
2. Unified View: We're the only platform that combines grid, regulatory, financial, and environmental data into a single coherent risk framework.
3. Forward-Looking Intelligence: Beyond historical data, our models predict future conditions that will impact your investments.
4. Actionable Insights: Raw data is transformed into specific recommendations tied to your portfolio and investment strategy.
For detailed documentation on our data sources and methodologies, or to request access to our data dictionary, please contact data@greencio.com