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Network

Telecommunications network carrying traffic between users and the servers — Wi-Fi, fixed broadband, cellular. Modelled by its energy intensity per gigabyte transferred.

Params

name

A human readable description of the object.

bandwidth_energy_intensity

Electricity consumed per gigabyte transferred end-to-end through the network. Multiplied by the data transferred by jobs to obtain hourly energy use.

Unit: kilowatt_hour / gigabyte.

Calculated attributes

energy_footprint_per_job

Hourly carbon emissions caused by network traffic, broken down by job. Equal to data transferred times bandwidth energy intensity times the country's grid carbon intensity.

Example value: {
EcoLogitsGenAIExternalAPIJob Generative AI model job (f7b526): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in mg:
first 10 vals [0.149, 0.0425, 0.0637, 0.0213, 0.106, 0.149, 0.127, 0.085, 0.17, 0.106],
last 10 vals [0.127, 0.0637, 0.0637, 0.085, 0.0213, 0.106, 0.0425, 0.127, 0.0637, 0.106],
VideoStreamingJob Video streaming job (c36fe0): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in g:
first 10 vals [27.8, 7.93, 11.9, 3.97, 19.8, 27.8, 23.8, 15.9, 31.7, 19.8],
last 10 vals [23.8, 11.9, 11.9, 15.9, 3.97, 19.8, 7.93, 23.8, 11.9, 19.8],
Job Manually defined job (66f104): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in kg:
first 10 vals [0.00000893, 0.00000255, 0.00000383, 0.00000128, 0.00000638, 0.00000893, 0.00000765, 0.000642, 0.00128, 0.00192],
last 10 vals [0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636, 0],
GPUJob Manually defined GPU job (230897): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in mg:
first 10 vals [4.46, 1.28, 1.91, 0.638, 3.19, 4.46, 3.83, 2.55, 5.1, 3.19],
last 10 vals [3.83, 1.91, 1.91, 2.55, 0.638, 3.19, 1.28, 3.83, 1.91, 3.19],
}

Depends directly on:

through the following calculations:

You can also visit the link to Generative AI model job network energy footprint’s full calculation graph.

instances_fabrication_footprint

Network fabrication footprint, currently always empty: e-footprint does not account for the embodied carbon of network infrastructure since it is shared across countless services.

Example value: no value

Depends directly on:

through the following calculations:

You can also visit the link to no value’s full calculation graph.

energy_footprint

Total hourly carbon emissions caused by network traffic, summed across all jobs that route through this network.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in kg:
first 10 vals [0.0278, 0.00794, 0.0119, 0.00397, 0.0198, 0.0278, 0.0238, 0.0165, 0.033, 0.0217],
last 10 vals [0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly energy footprint’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of fabrication impact repartition weights, used as the denominator when normalising into per-container shares.

Example value: no value

Depends directly on:

through the following calculations:

You can also visit the link to Fabrication impact repartition weights sum’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of usage impact repartition weights, used as the denominator when normalising into per-container shares.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in kg:
first 10 vals [0.0278, 0.00794, 0.0119, 0.00397, 0.0198, 0.0278, 0.0238, 0.0165, 0.033, 0.0217],
last 10 vals [0.00319, 0.00318, 0.00319, 0.00319, 0.00319, 0.00255, 0.00191, 0.00127, 0.000636, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Usage impact repartition weights sum’s full calculation graph.

usage_impact_repartition

Normalised share of usage-phase emissions that this object attributes to each container.

Example value: {
EcoLogitsGenAIExternalAPIJob Generative AI model job (f7b526): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000536, 0.00000515, 0.00000515, 0.00000489],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
VideoStreamingJob Video streaming job (c36fe0): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 0.961, 0.961, 0.912],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
Job Manually defined job (66f104): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.000321, 0.0389, 0.0389, 0.0881],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
GPUJob Manually defined GPU job (230897): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000161, 0.000154, 0.000154, 0.000147],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
}

Depends directly on:

through the following calculations:

You can also visit the link to usage impact attribution to Generative AI model job’s full calculation graph.