# solvers.SustainabilityModel { #mlsysim.solvers.SustainabilityModel }

```python
solvers.SustainabilityModel()
```

Calculates Datacenter-scale Sustainability metrics.

Handles Power Usage Effectiveness (PUE), Carbon Intensity,
and Water Usage Effectiveness (WUE) across different regional grids.
This model simulates the 'Infrastructure Tax' — the energy spent on
cooling and power delivery rather than on neural computation.

Literature Source:
1. Patterson et al. (2021), "Carbon Emissions and Large Neural Network
   Training."
2. Belkhir & Elmeligi (2018), "Assessing ICT Global Emissions Footprint."
3. Wu et al. (2022), "Sustainable AI: Environmental Implications,
   Challenges and Opportunities."

## Methods

| Name | Description |
| --- | --- |
| [solve](#mlsysim.solvers.SustainabilityModel.solve) | Calculates energy, carbon, and water footprint for a fleet operation. |

### solve { #mlsysim.solvers.SustainabilityModel.solve }

```python
solvers.SustainabilityModel.solve(
    fleet,
    duration_days,
    datacenter=None,
    mfu=1.0,
    embodied_carbon_per_device=0.0,
)
```

Calculates energy, carbon, and water footprint for a fleet operation.