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Device

End-user hardware (smartphone, laptop, set-top box, screen) on which a UsageJourney is performed. Contributes both manufacturing and electricity-use emissions to each UsagePattern that runs on it.

Usage from Python

Pass a list of Devices to UsagePattern.devices. Use the archetype helpers (Device.smartphone(), Device.laptop(), Device.box(), Device.screen()) for sensible defaults.

Common pitfalls

Device.fraction_of_usage_time is used to compute effective usage lifespan by multiplying with device lifespan in years. This effective usage lifespan is then used to compute manufacturing amortization.

Params

name

A human readable description of the object.

carbon_footprint_manufacturing

Embodied carbon emitted to manufacture one unit of the hardware. Amortised over the lifespan when computing the hourly manufacturing footprint.

Unit: kilogram.

power

Electrical power drawn by the device while a user is interacting with it.

Unit: watt.

lifespan

Expected time before the hardware is replaced. Embodied carbon is amortised over this duration.

Unit: year.

fraction_of_usage_time

Fraction of each calendar day during which the device is in use across all activities, used to scale lifespan in years to effective usage lifespan.

Unit: hour / day.

Calculated attributes

nb_journeys_in_parallel_per_usage_pattern

Hourly count of journeys concurrently occupying this device, broken down by usage pattern.

Example value: {
UsagePattern usage pattern (e28419e5-c9b): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00: first=3 , last=1.33 , mean=1.67 , min=0.333 , max=3 , std=0.861 ,
}

Depends directly on:

through the following calculations:

You can also visit the link to usage pattern hourly nb of journeys in parallel’s full calculation graph.

use_footprint_per_usage_pattern

Hourly carbon emissions caused by the device's electricity use, broken down by usage pattern. Equal to the energy spent by concurrent journeys times the country's grid carbon intensity.

Example value: {
UsagePattern usage pattern (e28419e5-c9b): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00: first=12.8 g, last=5.67 g, mean=7.09 g, min=1.42 g, max=12.8 g, std=3.66 g,
}

Depends directly on:

through the following calculations:

You can also visit the link to Usage footprint for usage pattern’s full calculation graph.

use_footprint

Total hourly carbon emissions caused by the device's electricity use, summed across all usage patterns that run on this device.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00: first=12.8 g, last=5.67 g, mean=7.09 g, min=1.42 g, max=12.8 g, std=3.66 g

Depends directly on:

through the following calculations:

You can also visit the link to Devices use footprint’s full calculation graph.

instances_manufacturing_footprint_per_usage_pattern

Hourly manufacturing-phase emissions of all devices in use, broken down by usage pattern. Equal to one device's hourly amortised embodied carbon (lifespan and usage-time-adjusted) multiplied by the number of journeys concurrently in progress.

Example value: {
UsagePattern usage pattern (e28419e5-c9b): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00: first=30.5 g, last=13.6 g, mean=17 g, min=3.39 g, max=30.5 g, std=8.76 g,
}

Depends directly on:

through the following calculations:

You can also visit the link to Manufacturing footprint for usage pattern’s full calculation graph.

instances_manufacturing_footprint

Total hourly manufacturing-phase emissions of all devices in use, summed across all usage patterns that run on this device.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00: first=30.5 g, last=13.6 g, mean=17 g, min=3.39 g, max=30.5 g, std=8.76 g

Depends directly on:

through the following calculations:

You can also visit the link to Devices manufacturing footprint’s full calculation graph.