Data sources

The carbon estimate is only as good as its inputs. This page lists each input, where it comes from, and how often we update it.

Energy formula coefficients (α, β)

• Source: EcoLogits v0.4 — LLM inference methodology.
• Values: α = 8.91 × 10⁻⁵, β = 1.43 × 10⁻³ (Wh per output token).
• Updates: when EcoLogits publishes a new methodology version with new coefficients, we evaluate it, bump the methodology_version, and note the change in the dashboard’s footer with the effective date.

The coefficients were derived from a regression across published benchmarks on a fleet of representative GPUs. They are an average; real hardware varies.

Model active parameters

• Source priority:
  1. EcoLogits registry — models with verified architecture details (accurate).
  2. Provider documentation — values published by the model creator (accurate or medium, depending on whether the statement is unambiguous).
  3. Research papers and credible leaks — peer-reviewed architecture descriptions, technical reports (medium).
  4. Name-based estimates — llama-70b → 70B (gross).
• Updates: when a new model lands, we look up its parameter count in this priority order and tag the accuracy band accordingly. Re-evaluation happens monthly and on demand when a model’s source upgrades.

For Mixture-of-Experts models we use the active parameter count (parameters used per token), not the total parameter count. This distinction matters: a 600B-parameter MoE that activates 20B per token has the energy profile of a 20B dense model, not a 600B one.

Grid carbon intensity

• Source: International Energy Agency electricity statistics — annual averages by country.
• Aggregation: where a region maps to multiple countries (e.g. eu-west covers FR, DE, NL, IE), we use a population-weighted average for the region.
• Updates: annually, when the IEA publishes the new dataset. Switching dataset versions bumps the methodology_version.

We do not use real-time grid carbon intensity (which would require per-request lookups against a service like ElectricityMap). It’s on the roadmap; the trade-off is that real-time numbers introduce sampling noise we’d need to explain. Annual averages are coarse but boring, and “boring” is a feature in a methodology document.

Sample values

Specific values per region are in the dashboard’s settings page; the table above is for orientation.

Pricing data

• Source: each upstream provider’s published price list, refreshed daily.
• Updates: within one business day of an upstream change going live.
• Storage: the price applied at the moment of a request is stored on the generation record, so historical bills are stable.

Pricing isn’t strictly part of the carbon methodology, but it is part of the per-request decision (auto-cheap and tie-break heuristics) so the source is documented here for completeness.

What we deliberately don’t include

• Hardware embodied carbon. Manufacturing emissions for the GPUs serving inference are non-zero but we don’t have a defensible per-token allocation. Until we do, omitting the number is more honest than guessing.
• Cooling overhead. Data-centre cooling adds 10–30% to the energy used by compute (Power Usage Effectiveness, PUE). The EcoLogits formula incorporates an average overhead; provider-specific PUE refinements are pending more data.
• Network transport. Energy used to move bytes between the gateway, the upstream, and the user is small relative to inference and is not counted.
• Training emissions. Documented separately on the limits page.