Methodology¶

SPRUCE uses third-party resources and models to estimate the environmental impact of cloud services. It enriches usage reports — AWS CUR, Azure cost details, or FOCUS exports from either provider — with additional columns, allowing users to do GreenOps and build dashboards and reports.

Unlike the information provided by CSPs (Cloud Service Providers), SPRUCE gives total transparency on how the estimates are built.

The overall approach is as follows:

1. Estimate the energy used per activity (e.g. for X GB of data transferred, usage of an EC2 instance, storage etc.)
2. Add overheads (e.g. PUE, WUE)
3. Estimate water consumption (cooling and electricity generation)
4. Apply accurate carbon intensity factors - ideally for a specific location at a specific time
5. Where possible, estimate the embodied carbon related to the activity

This is compliant with the SCI specification from the GreenSoftware Foundation.

The main columns added by SPRUCE are:

operational_energy_kwh

amount of energy in kWh needed for using the corresponding service.

operational_emissions_co2eq_g

emissions of CO2 eq in grams from the energy usage.

embodied_emissions_co2eq_g

emissions of CO2 eq in grams embodied in the hardware used by the service, i.e. how much did it take to produce it.

The total emissions for a service are operational_emissions_co2eq_g + embodied_emissions_co2eq_g.

SPRUCE also estimates water consumption:

water_cooling_l

volume of water in litres used for data centre cooling

water_electricity_production_l

volume of water in litres consumed during electricity generation.

water_consumption_stress_area_l

total water consumption attributed to regions under high or extremely high water stress.

See the enrichment modules page for details on how each estimate is computed.

Everyday equivalences¶

The dashboard and the static report translate the emissions, energy, and water totals into everyday comparisons ("In everyday terms"). These conversions use documented, order-of-magnitude factors meant to build intuition, not precise accounting. The factors live in reporting/equivalences.py; the table below lists them with their sources.