Provide real-time, location- and time-specific grid carbon-intensity signals as the conversion factor
#00169
Every emissions estimate multiplies energy by a carbon-intensity factor, but annual national averages miss regional and hourly variation. An API exposing live, historical, and forecast intensity—both average and marginal—per zone and per hour lets any tool convert energy to accur
Parent issue
#00164 The carbon footprint of software and AI compute is rarely measured, so it cannot be managed
Sustainable Development Goals
Location
Description
The proposal
Model grid carbon intensity per zone and per hour, exposed via API as historical data, real-time signals, and short-term forecasts. Any measurement tool can then multiply energy by an accurate, location- and time-specific factor instead of a coarse annual average.
Why it works
The intensity factor (I) is the shared input beneath every approach: self-run measurement, cloud attribution, and the SCI metric all need it. Getting it right per region and per hour is often the difference between a rough and a usable number. The same signal also enables carbon-aware scheduling that shifts flexible load to cleaner periods.
Where it fits
This is infrastructure that other solutions consume — a data dependency for CodeCarbon-style trackers, cloud-attribution tools, the SCI metric, and carbon-aware schedulers — not an end-user tool.
Trade-offs and limitations
Average (attributional) intensity suits reporting and is what most regulation requires. Marginal (consequential) intensity better reflects what actually changes when load is added or moved, but is harder to produce and verify, and its use is contested. Coverage, temporal resolution, and forecast accuracy vary by region.
Sub-issues
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