Inputs
Adjust technical assumptions to match your storage architecture and region. Outputs estimate energy, cost, and emissions for the chosen period.
Formula used
This calculator estimates operational (electricity) and optional embodied (hardware) emissions. It models replication, storage overhead, utilization, and monthly growth to derive average provisioned capacity.
- Effective TB = Stored TB × Replication × (1 + Overhead%)
- Provisioned TB = Effective TB ÷ Utilization%
- Growth applies monthly: TBₘ = TB₀ × (1 + g)ᵐ
- Average TB = mean(TBₘ) over the selected months
- IT power (kW) = Avg TB × (W/TB) ÷ 1000
- Energy (kWh) = IT power × Hours × PUE
- Net intensity = Grid gCO₂e/kWh × (1 − Renewables%)
- Operational kgCO₂e = Energy × Net intensity ÷ 1000
- Embodied kgCO₂e is amortized across 4 years
Note: Defaults are reasonable placeholders. For best accuracy, replace power-per-TB and carbon intensity using your provider’s disclosures or measurements.
How to use this calculator
- Enter the amount of data you keep stored (GB/TB/PB).
- Select your storage tier and set replication (or an equivalent factor).
- Set overhead for snapshots, erasure coding, metadata, or parity.
- Adjust utilization to reflect how full the storage typically runs.
- Provide PUE, local carbon intensity, and any renewables matching.
- Click Submit. Results appear above the form under the header.
- Download CSV or PDF to keep an audit trail of assumptions and outputs.
Example data table
These examples illustrate common storage patterns and how assumptions can change totals.
| Scenario | Stored Data | Tier | Replication | Overhead | Utilization | PUE | Grid Intensity |
|---|---|---|---|---|---|---|---|
| Analytics bucket | 50 TB | Object Storage | 3× | 15% | 65% | 1.30 | 500 gCO₂e/kWh |
| VM block volumes | 12 TB | SSD (Block) | 2× | 10% | 75% | 1.25 | 350 gCO₂e/kWh |
| Long-term archive | 200 TB | Archive / Cold | 1× | 5% | 80% | 1.35 | 600 gCO₂e/kWh |
Replication and overhead shape real capacity
Carbon impact scales with the physical capacity you must provision, not only the data you store. The calculator converts your stored size into effective capacity by applying replication and overhead. If 50 TB is protected at 3× and you add 15% snapshots, effective capacity becomes 172.5 TB. That single design choice can dominate the footprint. Replacing 3× replication with an equivalent 1.5× erasure-coded overhead can cut the capacity driver close to 50%.
Utilization determines how much hardware stays online
Utilization translates effective capacity into provisioned capacity. When utilization is 70%, you need about 1.43× more provisioned media than the effective requirement. Raising utilization from 60% to 80% can reduce provisioned capacity by 25%. This typically lowers operational energy and the embodied component in roughly the same proportion. Tiering infrequently accessed data and pruning versions are practical ways to raise utilization.
Energy model links watts, time, and facility efficiency
Average provisioned capacity is multiplied by a power-per-terabyte factor to estimate IT power. That power is then converted to energy using hours in the selected period and scaled by PUE to include cooling and facility overhead. Example: 100 TB at 6 W/TB is 0.6 kW. Over 12 months that is about 5,260 kWh, and at PUE 1.3 it becomes roughly 6,838 kWh. If you have telemetry, override W/TB to match your environment and reduce estimation error.
Regional intensity and renewables change net emissions
Operational emissions are calculated by multiplying energy by carbon intensity. If intensity is 500 gCO₂e/kWh, the 6,838 kWh example yields about 3,419 kgCO₂e. Renewables matching reduces the net intensity; 40% matching applies 60% of the original intensity. Sensitivity is linear: cutting intensity from 500 to 300 gCO₂e/kWh reduces operational emissions by 40%.
Growth scenarios support budgeting and optimization
Growth compounds quickly, so the calculator averages provisioned capacity month by month. A 2% monthly growth rate is about 26.8% annualized, while 5% monthly is roughly 79.6%. Use scenarios to test cold tiers, lower replication where safe, improved utilization, and better PUE. Strong plans reduce provisioned capacity first, then improve energy sourcing to cut the remaining footprint. Export results to document quarterly optimization decisions clearly.
FAQs
1) What does “power per TB” mean?
It estimates the average IT power required to keep one terabyte online. Use tier defaults for quick planning, or override with your measured watts per terabyte for higher accuracy.
2) Why does replication increase emissions so much?
Replication increases the physical capacity that must be provisioned. More capacity typically means more hardware online and more energy used, so operational and embodied emissions often scale close to linearly with replication.
3) How should I set overhead?
Include snapshots, versioning, parity, metadata, and retention buffers. If your platform reports “logical vs physical,” use that ratio to estimate overhead, then refine with measurements over time.
4) What is PUE and why is it included?
PUE reflects facility overhead like cooling and power conversion. The model multiplies IT energy by PUE to estimate total facility energy, which better represents real electricity consumption.
5) How does renewables matching affect results?
It reduces net carbon intensity by the matching percentage. For example, 25% matching applies 75% of the grid intensity. It supports market-based accounting comparisons across regions and providers.
6) Should I enable embodied emissions?
Enable it for medium-to-long planning horizons or hardware-heavy deployments. It approximates manufacturing impact and amortizes it over four years, which helps compare “store more” versus “store smarter” strategies.