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.
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.
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
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
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
Fix network : impact of a client is linear as a function of time (month)
Exemple of querry using default factors :
+ :
- :
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
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
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
(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
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
+ :
- :
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 ofmanufacture_impact_facor
with "ADEME - NEGAOCTET" results