How Floodlight Measures Emissions

Understanding how Floodlight combines bottom-up modeling with top-down satellite verification to deliver accurate, asset-level emissions intelligence with quantified uncertainty.

Overview: Two Complementary Approaches

Floodlight employs a hybrid methodology that combines the strengths of both traditional bottom-up accounting and innovative top-down satellite verification to provide the most accurate and reliable emissions data available.

Bottom-Up Emissions Estimation

The bottom-up approach starts with asset-level activity data and applies emissions factors to calculate estimated emissions. This method is the foundation of traditional carbon accounting and regulatory reporting.

How Bottom-Up Works

• Activity Data Collection: Gather operational data for each asset, including energy consumption, production volumes, fuel usage, and process-specific parameters.
• Emissions Factor Application: Apply scientifically validated emissions factors (e.g., IPCC, EPA, or industry-specific factors) to convert activity data into estimated emissions.
• Asset-Level Baselines: Create a detailed emissions profile for each physical asset, allowing for granular tracking and portfolio roll-up.
• Portfolio Aggregation: Sum asset-level emissions to calculate total portfolio emissions, maintaining traceability to individual sources.

Strengths of Bottom-Up

• Provides detailed, asset-specific emissions data
• Aligns with existing regulatory frameworks (e.g., GHG Protocol, CBAM)
• Enables scenario modeling and what-if analysis
• Supports forward-looking projections based on operational plans

Limitations of Bottom-Up

• Relies on self-reported or estimated activity data
• Emissions factors may not capture site-specific conditions
• Difficult to verify without independent measurement
• Can miss unaccounted sources or leaks

Top-Down Satellite Verification

The top-down approach uses satellite observations of atmospheric greenhouse gas concentrations to independently verify and validate bottom-up estimates. Satellite data provides an objective, third-party measurement of actual emissions occurring at specific locations.

How Top-Down Works

• Satellite Observations: Collect atmospheric measurements of CO₂, CH₄, NO₂, and other pollutants from missions like OCO-2, GOSAT, Sentinel-5P, and TROPOMI.
• Atmospheric Modeling: Use inverse modeling techniques to estimate surface emissions from observed atmospheric concentrations, accounting for wind patterns and atmospheric transport.
• Hotspot Detection: Identify emission sources and anomalies that may not be captured in bottom-up inventories, such as leaks, flaring, or undocumented sources.
• Cross-Validation: Compare satellite-derived emissions with bottom-up estimates to identify discrepancies and improve accuracy.

Strengths of Top-Down

• Provides independent, third-party verification
• Detects unreported or unaccounted emissions sources
• Captures real-world conditions and operational variations
• Improves stakeholder confidence and regulatory compliance

Limitations of Top-Down

• Limited spatial resolution for small or closely spaced assets
• Affected by cloud cover and atmospheric conditions
• Requires complex atmospheric modeling and inversion techniques
• Historical data may be limited for some regions

Floodlight's Hybrid Approach

Floodlight combines the rigor of bottom-up accounting with the independence of top-down verification to deliver the most reliable emissions intelligence available. Our methodology provides the best of both worlds: regulatory alignment and scientific validation.

The Asset Stack

For each asset in your portfolio, Floodlight creates a comprehensive emissions profile through a three-layer stack:

1. Activity Data Layer: Collect operational data from your systems, IoT sensors, financial records, and regulatory filings.
2. Emissions Models Layer: Apply validated emissions factors and process models to calculate estimated emissions for each asset.
3. Satellite Verification Layer: Compare modeled emissions against satellite observations to detect anomalies, validate estimates, and flag discrepancies.

Portfolio Roll-Up

Once asset-level emissions are calculated and verified, Floodlight aggregates the data to provide portfolio-wide insights:

• Total Portfolio Emissions: Sum emissions across all assets, with full traceability to individual sources.
• Sector and Geography Breakdowns: Segment emissions by industry, region, or custom categories for targeted reduction strategies.
• Trend Analysis: Track emissions over time to measure progress toward climate goals and identify emerging risks.
• Scenario Modeling: Evaluate the emissions impact of potential investments, divestments, or operational changes.

Uncertainties, Ranges, and Confidence Scores

No emissions estimate is perfectly accurate. Floodlight provides transparent uncertainty quantification and confidence scores to help you understand the reliability of our data.

Uncertainty Sources

• Activity Data Quality: Incomplete or estimated operational data introduces uncertainty.
• Emissions Factor Variability: Generic emissions factors may not reflect site-specific conditions.
• Satellite Resolution and Coverage: Cloud cover, measurement frequency, and spatial resolution affect satellite-based verification.
• Atmospheric Modeling: Inverse modeling introduces uncertainty due to wind patterns and atmospheric transport.

Confidence Scoring

Floodlight assigns a confidence score to each emissions estimate based on data quality, verification status, and methodology:

• High Confidence: Asset has high-quality activity data and satellite verification shows strong agreement with bottom-up estimates.
• Medium Confidence: Asset has reasonable activity data or satellite verification is partially available.
• Low Confidence: Asset relies on estimated activity data with limited or no satellite verification.

Uncertainty Ranges

All emissions estimates are presented with uncertainty ranges (e.g., ±15%, ±30%) to provide a realistic picture of data reliability. These ranges are calculated using statistical methods and validated against satellite observations.

Anomaly Detection and Validation

One of the most powerful features of Floodlight's methodology is the ability to detect anomalies and validate emissions claims through satellite data.

Anomaly Detection Use Cases

• Unreported Emissions Sources: Identify assets or facilities that are emitting but not included in bottom-up inventories.
• Operational Incidents: Detect sudden spikes in emissions due to equipment failures, process upsets, or flaring events.
• Verification Flags: Flag assets where satellite observations significantly differ from bottom-up estimates, triggering further investigation.
• Third-Party Validation: Provide independent verification for investors, regulators, or auditors who require objective evidence of emissions performance.

Regulatory Alignment

Floodlight's methodology is designed to align with major regulatory frameworks and industry standards:

• GHG Protocol: Asset-level accounting aligns with Scope 1, 2, and 3 categories and organizational boundaries.
• CBAM: Provides the detailed, asset-level emissions data required for Carbon Border Adjustment Mechanism reporting and compliance.
• ISSB Standards: Supports climate risk assessment and scenario analysis required by the IFRS Foundation's ISSB framework.
• EU Taxonomy / SFDR: Enables portfolio screening and alignment with EU sustainable finance regulations.
• Carbon Markets: Provides independent verification for voluntary carbon credits and offset programs.

Downloadable Resources

For a concise summary of Floodlight's emissions methodology, download our one-page overview designed for investors and stakeholders:

Learn More

Explore our other knowledge hub articles to dive deeper into Floodlight's approach:

• Satellite Data Sources →
• Carbon Accounting Methodology Guide →
• Frequently Asked Questions →