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.
Backwards links
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 (80be20): 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 [0.0637, 0.17, 0.127, 0.127, 0.0425, 0.0213, 0.0213, 0.191, 0.17, 0.191],
last 10 vals [0.106, 0.191, 0.106, 0.127, 0.127, 0.0213, 0.0213, 0.191, 0.17, 0.106],
VideoStreamingJob Video streaming job (a74768): 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 [11.9, 31.7, 23.8, 23.8, 7.93, 3.97, 3.97, 35.7, 31.7, 35.7],
last 10 vals [19.8, 35.7, 19.8, 23.8, 23.8, 3.97, 3.97, 35.7, 31.7, 19.8],
Job Manually defined job (3a776b): 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 [0.00000383, 0.0000102, 0.00000765, 0.00000765, 0.00000255, 0.00000128, 0.00000128, 0.000648, 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 (19009e): 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 [1.91, 5.1, 3.83, 3.83, 1.28, 0.638, 0.638, 5.74, 5.1, 5.74],
last 10 vals [3.19, 5.74, 3.19, 3.83, 3.83, 0.638, 0.638, 5.74, 5.1, 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.
energy_footprint_per_usage_pattern
Hourly carbon emissions caused by network traffic, broken down by usage pattern. Equal to data transferred times bandwidth energy intensity times the country's grid carbon intensity, summed across the pattern's jobs.
Example value: {
UsagePattern usage pattern (39480c): 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 [11.9, 31.8, 23.8, 23.8, 7.94, 3.97, 3.97, 35.7, 31.8, 35.7],
last 10 vals [19.8, 35.7, 19.8, 23.8, 23.8, 3.97, 3.97, 35.7, 31.8, 19.8],
EdgeUsagePattern Default edge usage pattern (5eea34): 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, 0, 0, 0, 0, 0, 0, 0.000637, 0.00127, 0.00191],
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:
- Hourly data transferred in usage pattern
- bandwith energy intensity
- Average carbon intensity
- Hourly data transferred in usage pattern
- Hourly data transferred in usage pattern
- Hourly data transferred in usage pattern
through the following calculations:
You can also visit the link to usage pattern 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 [0.0119, 0.0317, 0.0238, 0.0238, 0.00794, 0.00397, 0.00397, 0.0363, 0.033, 0.0376],
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:
- Generative AI model job network energy footprint
- Video streaming job network energy footprint
- Manually defined job network energy footprint
- Manually defined GPU job network energy footprint
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 [0.0119, 0.0317, 0.0238, 0.0238, 0.00794, 0.00397, 0.00397, 0.0363, 0.033, 0.0376],
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:
- Generative AI model job network energy footprint
- Video streaming job network energy footprint
- Manually defined job network energy footprint
- Manually defined GPU job network energy footprint
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 (80be20): 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, 0.00000526, 0.00000515, 0.00000508],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
VideoStreamingJob Video streaming job (a74768): 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, 0.982, 0.961, 0.949],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
Job Manually defined job (3a776b): 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, 0.0178, 0.0389, 0.0511],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
GPUJob Manually defined GPU job (19009e): 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, 0.000158, 0.000154, 0.000153],
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.