7. Assessment and Calculation
The calculation components and their outputs.
7.1 IT Electricity Usage Calculation
IT Electricity Usage is calculated by mapping each unit of cloud usage to its underlying hardware components and summing the energy contribution of each. The energy for a given unit of usage is built up from the energy of the components identified for that SKU.
Component power figures are grounded in authoritative benchmarks and manufacturer data, as set out in Section 5.2. Server power is aligned with methodologies used by the US EPA, the EU Ecodesign Regulation, and the SPEC Power database, and accounts for the whole server rather than the processor alone. Where no utilisation data is available for a unit of usage, a conservative default utilisation assumption is applied.
7.2 Facility Energy and Emissions Calculation
Total Facility Energy is calculated by multiplying IT Electricity Usage by the applicable PUE factor for the cloud region. Where a cloud provider does not publish a region-specific PUE value, the best available published value is used, which may be a global average. Usage Emissions are then calculated by multiplying Total Facility Energy by the Grid Carbon Intensity for the relevant region and time period.
Usage Emissions are reported on a location-based basis in accordance with the GHG Protocol Scope 2 Guidance. Greenpixie follows the US EPA's guidance on indirect emissions from purchased electricity, giving precedence to regional or sub-national GCI factors where available.
7.3 Embodied Emissions Calculation
Embodied emissions are estimated on a component-by-component basis, using hardware specifications for the instance type, semiconductor die-size data, lifecycle assessment data adapted from Boavizta, Gröger et al., ADEME, and Morand et al., and assumed server operational lifespans based on publicly available cloud provider statements.
Components covered include CPU, RAM, SSD storage, HDD and other fixed-value components, GPU where applicable, power supply units, chassis, and motherboard.
For CPUs, the methodology estimates emissions by calculating the die area of the silicon based on processor family, lithography process, and physical core count, then multiplying by per-cm² emission factors from Gröger et al. A baseline value is added for packaging materials.
For RAM, emissions are estimated using memory density figures from DRAM manufacturers to derive effective die size, then multiplying by per-cm² memory carbon intensity factors from Gröger et al. A baseline is added for PCB, connector strip, and packaging.
For SSDs, a similar die-area approach is used based on NAND flash density data. For HDDs, a fixed per-device manufacturing footprint is applied from open lifecycle datasets. Motherboards, power supply units, and chassis are assigned fixed or mass-based emissions values from established lifecycle data.
For GPUs, the methodology applies the same per-cm² silicon die intensity factor used for CPUs, adds the embodied footprint of on-board memory using the RAM methodology, and applies a fixed base impact for remaining components from the Morand et al. NVIDIA graphics card lifecycle assessment.
Estimated allowances for transport, end-of-life processing, and an allocation for data centre shell construction are added to the component manufacturing totals. Total server embodied emissions are the sum of all component emissions and these allowances. They are then allocated to the client based on the ratio of reserved capacity to total server capacity, amortised over the server's assumed operational lifespan, and pro-rated by usage hours.
7.4 Water Consumption Calculation
Total water consumption comprises two components. The first is IT cooling water consumption, the water consumed on site in cooling the IT equipment, estimated as the cloud provider's published WUE multiplied by IT Electricity Usage. The second is electricity generation water consumption, the water consumed during the generation of that electricity, estimated using regional water intensity factors that account for the mix of energy sources powering the local grid, including water withdrawn and consumed for fuel extraction, processing, and power generation.
Where cloud providers publish site-specific or region-specific WUE data, these values are used directly. Where only global averages are available, the global average is applied. Where regional electricity generation water data is not available from the producing country, a global average from the World Resources Institute is used.
The water consumption methodology is developed in collaboration with Dr Shaolei Ren at the University of California, Riverside, and informed by the published work Making AI Less Thirsty by Li et al.