Impact of mobile and fix network

[da-ekchajzer]

Problem

We want to retrieve the impacts of manufacture and usage of data transfer over the internet (mobile and fix network). Private network are outside the scope of this issue.

Solution

Several strategies exist in the literature to measure the impact of a data transfer over a mobile or fix network. This issue is highly inspired by this article. Feel free to read it if you need a complete explanation of the different approaches.

ADEME - NEGAOCTET approach

Source : ADEME PCR

Criteria : Carbon Life phase(s) : all life cycle Primary objective : Reporting for FAI clients

Mobile network : impact of a client is linear as a function of the consumed data

Empreinte carbone (en gCO2e/mois) = Quantité de données consommées par l’utilisateur (en Go/mois) x Ratio moyen majorant représentatif de l’impact du « Réseau Mobile France » (en gCO2e/Go)*

*au 1er janvier 2022 cette valeur est estimée à 49.4gCO2e/Go (gramme CO2 équivalent par Gigaoctet)

Fix network : impact of a client is linear as a function of time (month)

Empreinte carbone (en gCO2e/month) = Impact moyen de la consommation Internet fixe d’un Français (en gCO2e/mois)**

** au 1er janvier 2022 cette valeur est estimée à 4.1 kgCO2e/mois par abonné. L’utilisation des réseaux fixes est à privilégier dès que possible.

Exemple of querry using default factors :

{
"network_type": "mobile"
"data": 45
}

{
"network_type": "fix",
"subscriber_number": 1,
"hour_use": 1
}

+ :

• Uses all the life cycle
• Impacts factors used to apply a French legislation

- :

• Mono criteria
• In reality, impact and data are not proportional
• No documentation on the methodology:
  • usage and manufacture impact can't be distinguished
  • specific carbon intensity can't be used (french carbon intensity will always be applied)

POWER MODEL

Source : Malmodin's paper : page 87, DIMPACT study

Criteria : Power consumption (multiple impact can be retrieved with electrical impact factors) Life phase(s) : Usage only Primary objective : Measure the marginal effect on power consumption of a change in data consumption

Fix network : per user (1 user = 1 device) per line

• Access network + CPE :

(idle_power/nb_users_per_line) + (((idle_power-max_power)/100)*average_bit_usage_per_second)/nb_user_in_usage 

With malmodin's power factors :
(16.5 W/nb_users_per_line) + (0.02 W/Mbps / nb_user_in_usage) * average_bit_usage_per_second 

• Core network :

SUM((((idle_power/nb_line)/nb_users_per_line) + ((((idle_power-max_power)/100)*average_bit_usage_per_second)/nb_lines_in_usage)nb_user_in_usage) FOREACH hops) 

With malmodin's power factors :
(1.5 W/nb_users_per_line) + (0.03 W/Mbps / nb_user_per_line) * average_bit_usage_per_second 

Mobile network : per line (1 line = 1 device)

• Access network (Base station) :

(idle_power/nb_device) + ((idle_power-max_power)/100)*average_bit_usage_per_second))

With malmodin's power factors :
1 W + 0.02 W/Mbps * average_bit_usage_per_second

• Core network :

SUM(((idle_power/nb_device) + ((idle_power-max_power)/100)*average_bit_usage_per_second) FOREACH hops)

With malmodin's power factors :
0.2 W + 0.03 W/Mbps * average_bit_usage_per_second

Exemple of querry using default factors :

{
"network_type": "mobile",
"average_bit_usage_per_second": 45,
"hour_use": 1
}

{
"network_type": "fix",
"average_bit_usage_per_second": 45,
"hour_use": 1,
"nb_users_per_line": 4,
"nb_user_in_usage": 2
}

+ :

• Quite detailed components of the network
• Marginal approach, taking in consideration fix and variable impacts.

- :

• Only usage is measured
• Data to apply the model for a specific service will be hard to gather. Maldmodin's data don't apply to all location/services.
• The allocation of "unused" or "under used" fix power (when the network is not used 100% of the time) is not considered (on-purpuse)

Alternatives

• 1 bytes model

Additional context or elements

The power model seems best suited to represent the impact of fix and mobile network. Using Maldmodin's power factor at first could push stakeholders to challenge those data and create more specific factors.

How could we account for manufacture impact ? Since network is always up and power model usage impacts gives a promising allocation principle, I suggest making an estimation of manufacture impact based on usage impact : usage_impact*manufacture_impact_facor. We can check the coherence of manufacture_impact_facor with "ADEME - NEGAOCTET" results