The carbon footprint of software and AI compute is rarely measured, so it cannot be managed
#00164
Running software and AI models consumes electricity whose CO2 cost varies by grid and workload, but the default toolchain reports latency, cost and accuracy and almost never emissions. Invisible to the people who could reduce it, the footprint goes unmanaged and unoptimised.
#00168Standardize a single comparable carbon metric per unit of software work
Every tool in this space estimates differently, so numbers are not comparable across teams or vendors. A shared specification expresses software carbon as a rate — emissions per functional unit such as a request, user, or training run — so results can be compared and tracked over
#00165Measure the emissions of compute you run yourself by sampling hardware power and applying local grid carbon intensity
For code you operate, sample the energy drawn by CPU, GPU and RAM during execution, multiply by the carbon intensity of the local electricity grid, and log the resulting CO2 estimate next to other run metrics so it can be tracked and reduced.
#00166Estimate the emissions of third-party AI API calls from model and request characteristics when the hardware is not accessible
When a generative model is called through a provider's API, hardware cannot be metered. Estimate per-request energy and CO2 from model size, token counts, and data-centre assumptions using a life-cycle method, giving API consumers an impact figure they otherwise cannot obtain.
#00167Attribute cloud emissions from provider billing and usage data across multiple providers
Convert itemised cloud billing and usage records into energy and emissions figures using published hardware power coefficients and region-specific grid carbon intensity, producing a per-service, per-region footprint across multiple providers.
#00169Provide real-time, location- and time-specific grid carbon-intensity signals as the conversion factor
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