What impact does the network have on digital services?

Posted on by Olivier PHILIPPOT
Reading Time: 8 minutes

According to the ADEME/Arcep study for 2022, the network in France is responsible for between 2% and 14% of the impact of digital technology. Fixed networks generate more impact than mobile networks (between 75% and 90%). However, given the greater use of fixed networks, the unit impact (per user or per GB of data exchanged, for example) is lower for the fixed network.

This observation has led to a number of recommendations encouraging the use of fixed networks rather than mobile networks. For example, here are ADEME’s recommendations for teleworking:

“8. Using WiFi rather than 4G on mobile phones

On your mobile phone, it’s best to use WiFi when you’re working at home. It puts less strain on the network than 4G. You can also use the wired network to connect your computer to your box.

This impact of the network is even reflected in the AGEC law. Communication operators are required to display the CO2 cost associated with user consumption.

Network evaluation for digital services

When it comes to assessing the impact of a digital service, the network must be taken into account. The commonly used approach is to use the intensity in gEqCo2/Gb. This assumes linearity between the CO2 impact and the data exchanged.

Note: Despite common usage, this approach is criticised. The reality of a network is that there is a constant consumption of energy, consumption which is not dependent on the data in transit. However, the intensity approach is applicable because there is a need to allocate this existing energy. In addition, the impact of manufacturing must also be allocated according to use. Other allocation methodologies by time of use would be preferable. However, this requires more precise data for each part of the network. Allocation by subscriber is also possible, but this metric is ill-suited to the granularity of a unitary digital service.

This impact-accounting methodology makes it possible to take into account the threshold effect caused by an increase in infrastructure and its use if the overall volume increases (new equipment, larger quantities of equipment and electricity to power it).

For certain parts, such as the user box, we have used a time allocation method rather than one based on intensity.

When assessing the digital service, it will also be necessary to have details of the different connections (Wifi, 4G, etc.).

With the AGEC Act, we have two interesting metrics:

  • 50 gEqCO₂ / Go for mobile networks
  • 18 gEqCO₂ / Go for fixed networks

However, the associated assumptions are not made explicit enough. The impact of the network will depend on many factors and assumptions:

  • Scope taken into account (Scope 3 including network operator operations)
  • Whether or not the equipment is manufactured
  • Taking account of the user’s box
  • … 

If we look at other sources that can be used directly, there is no more information. For example, the ADEME database contains Negaoctet data and in particular two metrics on mobile and fixed telephony:

“Fixed-line network; at consumer; xDSL, FFTx average mix; . Data come from equipment installation and energy consumption in 2020 – (…) Sources: French operators, ARCEP, ICT report: European Commission, ICT Impact study, (…), IEA-4E, (…)”.

Even if sourced, there is no information to analyse the data. All the more so when you want to analyse the impact of digital accurately. This is the case with our methodology.

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Analysis of market data

To make our assessments more accurate, we have carried out R&D work to obtain more reliable emission factors.

We have modelled the network in several thirds:

  • The backbone, which interconnects the network
  • The access network, closer to the user, with specific architectures for each type of connection (3G, fibre, etc.).
  • CPE (Customer Permise Equipment): Mainly the box on the user’s premises

We have excluded the user terminal from the modelling. We’ll look at how to deal with it specifically at the end of the article.

For access types, we have grouped :

  • Wired Fibre
  • Wired Copper (xDSL)
  • Old generation GSM (2G and 3G)
  • New generation” GSP (4G and 5G)
  • Public Wifi (hotspot)
  • Wifi corporate LAN
  • Company Ethernet LAN

It would be interesting to go further down the grouping (for example to separate 4G and 5G), but this grouping is adapted to the granularity of the data available.

We analysed 35 public sources (operators’ CSR reports, scientific papers, manufacturers’ data). Each data item identified in the documents was classified according to the 7 types of access, the third of the network, and the scope taken into account (Manufacturing/Usage in particular). 169 data items were identified. We selected 145 (some data did not seem relevant).

The quality of each data item was qualified according to our methodology. 39 parameters were thus qualified (Core network Usage, Core network Manufacturing, etc.) in a format compatible with our methodology (Trapezoidal determination usable in fuzzy logic). For example, for the impact of using the fibre access network, we have the following values: 0.1293 / 0.3181 / 0.7891 / 1.9415. This means that the impact of the fibre access network, according to the literature, is probably between 0.3 and 0.78 Wh/GB.

In the end, the model can be represented as follows:

This model can be used dynamically by specifying certain parameters: EPC lifespan, energy mix, etc. Our API handles this automatically.

What is the likely impact of each network?

Taking the functional unit “Load a site of MB in 1s”, we obtain the following network impact:

Fibre has much less impact than other types of access. The ranking of 4G/5G ahead of ADSL seems counter-intuitive, especially in view of the messages we regularly hear: Favour wired connection over 4G as mentioned above. This data is wrong for a number of reasons:

  • The impact of base antennas and the access network of older GSM technologies is indeed more consumptive. The figures for older studies are based on these findings. It is important to adapt the recommendations according to the technologies and the age of the studies.
  • Some studies talk about the impact of the network on the terminal. For example, the Eco-index documentation states that “(…) a 4G connection requires up to 23 times more energy to transport the same amount of data as an ADSL connection. (..)” However, the source used is a study on the impact of LTE connections on smartphones at cell level. We’ll come back to the reality of smartphones later.

Margins of uncertainty can be observed for XDSL and old generation GSM networks:

This is due, on the one hand, to the older study data (and therefore weighted by our algorithm) and, on the other hand, to a greater diversity of technologies.

The proportion of manufacturing varies according to the technology used:

There has been a marked improvement in the energy efficiency of new-generation networks, and this is a widely cited argument for promoting new architectures.

Critical analysis of network impact data

  • Despite the model, which takes account of data sorting and qualification, the scope of all the data is not identified. We can find figures with the “raw” manufacturing impact and potentially others with the operator’s scope 3 (the impact of the operator’s offices, among other things). This will be taken into account in the model via the uncertainty margins.
  • The Co2/GB network intensity model is used. It is not fully representative of reality. In order to improve representativeness, we need more sources for temporal allocation (network throughput, consumption per user, etc.). We have begun to use this allocation mode for certain metrics such as Box data.
  • There are common elements between networks, and sometimes specific ones (for example, there are specific backbone elements for 5G). This needs to be taken into account.
  • Even though we have a level of granularity that allows us to take account of the energy mix dynamically, some of the data incorporates the mixes of different countries. This potentially overestimates the value of some data.

We compared our metrics with other market data (for 1GB).

Greenspector values are higher than NegaOctet and Ademe values (ARCEP/ADEME values are however higher than the low Greenspector threshold). Telephonica data is higher (for fixed) than the high Greenspector threshold (and identical for mobile).

This difference can probably be explained by the fact that we have incorporated many network manufacturing values. A second explanation is perhaps an underestimation of the values for France, which has set its figures at a low threshold. Without getting into a debate, these figures on the impact of the network are often monitored, so the tendency may be to underestimate the figures rather than overestimate them!

Specific

Are connections other than a private box more sober?

Yes, this is because this type of architecture is more shared. On the one hand, the hardware has a higher bandwidth capacity, so a lower allocation per item of data exchanged, and on the other, the impact of manufacturing is relatively low (in terms of capacity).

It should be noted that wifi has a slightly greater impact than ethernet. This is also true of boxes (for example, +3 Wh/h more on an Orange box).

Impact of the network on the terminal

We measure mobile applications and websites every day for our customers, so we deal with the impact of the network on the terminal and above all on the software. What we can say “from expert opinion”, but based on measurements, is that the impact of GSM networks is not 23 times greater, nor 10 times greater.

Here’s some measurement data on a streaming application (only the launch and connection to the application, not the streaming itself):

As can be seen for the connection (2nd graph), there is some data (~700 KB) and consumption is almost the same, or even slightly higher for the Wifi connection.

When it comes to loading the application (1st graph), WiFi consumes slightly less. However, there is a high level of data consumption (4MB vs 600kB). This can be explained by the different behaviour of the Wifi application (more data is loaded if the connection is Wifi). This has a major impact on loading times (from 4s to 7s for 3G).

The network will ultimately have an impact, but there are no set rules:

  • If the application adapts its behaviour to the type of connection and the speed, then potentially more data will be loaded on connections with a higher throughput. And potentially more CPU to process this data.
  • For 3G/2G connections, the loading time will be potentially longer (sometimes x2 or even x3).
  • Depending on whether or not requests are grouped together, the impact of GSM networks will be greater or lesser.

It is necessary to measure the final application to understand its behaviour in relation to the network. Implementing estimation rules in models is therefore complex and will lead to false data.

Conclusion

Assessing the environmental impact of the network is complex. We need more data from operators and manufacturers. This data needs to be more detailed and more transparent. However, existing data can be used, provided it is properly qualified and used. Given these observations, the use of averaged data is not an ideal approach. This is why we have adopted an approach that includes calculating uncertainties. As soon as we can, we have to measure in order to have contextualised and more accurate data. This is the approach we apply. This provides important clarifications for LCIs (life cycle inventories), digital impact assessments, or more individually for software evaluation.

What is the environmental impact of the 10 most widely used transport applications in France? 2023

Posted on by Maëva Rondeau
Reading Time: 8 minutes

With the emergence of transport apps in France, urban mobility has undergone a significant transformation in recent years. Indeed, these mobile applications are among the most downloaded and used by the French. Every major city has an app published by an urban transport company, offering practical, flexible solutions for getting around town. However, behind this ease of use and convenience lies an aspect that is often overlooked: the environmental impact of these applications.

These companies have understood that the development of mobile applications makes it possible to offer services to passengers (timetables, traffic information, transport maps, intermodality), but also to reduce costs by providing ticket sales and stamping services directly integrated into the application on our phones.

The aim of this study is to measure the environmental impact of transport applications in France’s 10 most populous cities, according to the Statista website:

  • Bonjour RATP for the Paris region
  • RTM in Marseille
  • TCL in Lyon
  • Tisséo for Toulouse
  • Lignes d’azur in Nice
  • TAN in Nantes
  • TAM in Montpellier
  • CTS in Strasbourg
  • TBM in Bordeaux
  • Ilevia in Lille

These applications differ in terms of user interface, but they all meet a set of essential user needs. We have therefore determined a common user path, enabling us to compare these applications in terms of carbon impact, energy consumption and data exchanged. Finally, in the second part, we analyze the causes of these results.

Ranking of France’s 10 most populous cities in 2020

Methodology

User path definition

For the measurement, we determined a common scenario compatible for all applications, namely the search for a route from point A to point B (geolocation activated), with the following steps.

  • Step 1: Launch the application
  • Step 2: Access to search page
  • Step 3: Enter route
  • Step 4: Display results
  • Step 5: Route selection
  • Step 6: Application background (30 sec)

For this study, data was measured on June 19, 2023, using Greenspector Studio. We used GDSL (Greenspector Domain-Specific Language) to write test scripts, which automatically reproduce the actions to be performed on a phone. The Testrunner module then enabled us to carry out the measurements on an Android smartphone: we thus obtained energy and resource consumption (memory, CPU, exchanged data) and response times for each step of the journey. Finally, based on these measurements, the impact model integrated into Greenspector Studio evaluates the corresponding environmental impact.

Hypothesis

For this evaluation, we decided to study the behavior of a user who regularly uses the application and therefore searches for his itinerary with as few clicks as possible.

Measurement context

  • Samsung Galaxy S10, Android 10
  • Network: Wi-Fi
  • Brightness: 50%.
  • Tests carried out over at least 3 iterations to ensure reliability of results

Environmental projection hypothesis

  • User location: 100% in France
  • Server location: 100% in France
  • Devices used: smartphones only

The environmental footprint depends on the location of the application servers, their type, the location of the users and the type of devices they use. We have studied the use of applications only on smartphones and on users present on French soil, as their use is intended only for this part of the population. In the absence of better information, servers were considered to have a medium level of complexity.

Results

After a detailed analysis, we drew up a comparative table of the results, highlighting the applications with the lowest GHG emissions and those with the largest environmental footprint.

The following results are expressed in g of CO2 equivalent per trip.

The soberest application

Lille’s Ilévia and Montpellier’s Tam are the applications with the lowest impact according to our results. They consume very little energy. The fact that the route measured contains a small number of images and animations explains this figure in particular.

Least sober application

Bonjour RATP comes last in the ranking, but that’s no great surprise. In fact, the application consumes a lot of energy. This enormous power consumption is due in particular to the integration of third-party geolocation services and the large amount of multimedia content (photos, icons, etc.). What’s more, the application offers a host of features right from the home screen, such as scooter scanning.

The application preloads a wide range of content. Even if the user is offline, they can still access the interactive map to search for a station. This is a negative point for the application, as this pre-loading is not a critical step for the rest of the journey. It is irrelevant for the user to load a map that goes beyond the borders of Paris.

Projection for 10,000 regular users

Most apps have between 100,000 and 500,000 downloads on the Playstore. For each city, let’s take 10,000 regular users who use the app every day to make a round trip: this equates to 600,000 monthly visits.

Application (Ville) Impact per visit (g CO2e)Impact per day for 10000 users (2x/day) (kg CO2e)Impact total par an (kg CO2e)
TAM (Montpellier) 1,1 22 8030 
Ilévia (Lille) 1,1 22 8030 
CTS (Strasbourg) 1.2 24 8760 
Tisseo (Toulouse) 1.2 24 8760 
RTM (Marseille) 1.2 24 8760 
TCL (Lyon) 1.2 24 8760 
TAN (Nantes) 1.3 26 9490 
Azur (Nice) 1.5 30 10950 
TBM (Bordeaux) 1.5 30 10950 
RATP (Paris) 2.4 48 17520 

The table shows the carbon impact of a single visit in g CO2e and presents the projection of twice-daily use for 10,000 users in kg CO2e. Finally, the projection is made over a one-year period using the same unit.

For low-impact applications such as TAM or CTS, such annual use represents 8.03 tonnes of CO2e. This is equivalent to more than 36,903 km driven in a light vehicle, according to Ademe’s Impact CO2 website.

For the RATP, by far the biggest contributor, the impact is more than double, amounting to 17.5 tonnes of CO2e per year. This is equivalent to over 80,000 km in a light vehicle.

According to the Ministry of Ecological Transition’s Bilan annuel des transports en 2019, a car registered in mainland France has driven an average of 12,200 km over the year. The impact of a sober transport app used by 10,000 people 2 times a day represents the annual emissions of more than 2 light vehicles, while the impact of the RATP represents the annual emissions of around 7 vehicles!

One-year impact projection

According to the RATP Group website, the Bonjour RATP application is visited by 2.5 million unique monthly visitors and generates over 20 million monthly visits. If we assume that each visit includes at least one route search, we can obtain the app’s monthly carbon impact.

This represents 48 t CO2e per month, or more than 220,000 km by car.

But what causes these impacts?

In this second part, we analyze where these environmental impact values may come from. Using energy consumption and data exchanged over the network during the user’s journey, applications are again ranked according to their energy consumption.

ApplicationlaunchIncative foregroundAccess route pageInput departure/arrivalResults displayroute selectionInactive background
TAM0,41,20,11,30,30,21,1
TCL0,61,10,310,30,51,2
ILévia0,610,21,40,30,41,2
TAN0,61,10,11,70,40,52
CTS0,510,22,20,40,41,1
RTM1,51,10,31,60,20,31,1
Tisseo1,31,10,21,9101,1
Azur1,610,21,811,51
TBM0,81,10,62,70,41,51,1
RATP1,61,10,75,81,80,71,2

The graph above compares the different stages (apart from a few pauses) of each route measured in terms of energy consumed.

We notice that the pauses in the foreground are generally consuming, i.e. the user is present on the application’s home screen but without performing a single action. This can be explained by the fact that the launch is not long enough to generate all the content, so that even when inactive after being launched, it continues to generate content such as the little bus station icons, for example. It’s also possible that the user’s location is constantly being sought, as evidenced by the activity on the background pause stage.

We also note that background applications consume almost the same amount of energy in all measurements.

The most time-consuming step is the entry of the start and end points of the user’s itinerary, due to the search and loading of the itineraries entered for the section. Indeed, on many transport applications, it is necessary to perform several actions, or even load new pages for each entry step, whereas on other applications, entry is directly accessible from the home page. For example, CTS and Ilevia.

A disparity in consumption is also observed at the route selection stage in the applications. Some applications, such as Tisseo, directly propose the only route available in the next few minutes. 

Moreover, RATP displays a route page access step that consumes much more power than the others. Some applications that display zero consumption at this stage simply don’t load a new page, as this functionality is present on the home page. The user’s journey is optimized by reducing actions, thus reducing energy consumption. This is the case with Tisséo, which has no results page to display the different routes. Instead, the application directly suggests the shortest route, as seen in the screenshot below.

One notable observation concerns the route entry stage, where Ratp stands out for its higher energy consumption, being 5.8 times more power-hungry than the TCL. This excessive consumption could be attributed to trackers and integrated third-party services.

Finally, on the Azur application from Nice and TBM from Montpelier, the route display stage consumes more energy than the others. This may be due to the map generated for this display being uncompressed or loading beyond what is necessary, i.e. beyond the limits of the city’s transport network.

In terms of data exchanged, the CTS, Tisseo and TAM applications are the least frugal. TAM exchanges 2.4 MB, twice the average for all applications. The best performers in terms of data exchanged are Azur, Ilevia, TCL, RTM and TBM, which consume less than 0.5 MB.

According to Green IT, the average size of an e-mail is 81 Kb. So, on average, a route search is equivalent to the exchange of 12 e-mails.

According to our tool, during the launch stage of most applications, a significant amount of data exchange occurs to ensure a smooth and responsive user experience. However, some applications, such as TAN, have chosen to adopt a progressive data loading approach. This means that only essential information is retrieved initially, while other data is loaded as the application is used.

As mentioned earlier, the RATP application loads a lot of content at launch, as does TAM. This can be seen when the application is launched offline, with the map already loaded with metro and bus stations and stops, for example.

Are all these third-party services necessary?

The integration of third-party services will depend on the specific benefits they bring, their relevance to end-users and the overall impact on application performance and technical complexity. Testing, performance monitoring and user feedback are recommended to assess the effectiveness of third-party services and make informed decisions.

Conclusion

A study of the environmental impact of transport applications in France’s 10 largest cities reveals contrasting results. Some applications, such as RATP, TBM and Azur, have less sober journeys and consume more energy, which can have a negative impact on the environment. On the other hand, applications such as Azur, Ilevia and TAM stand out by consuming less data and energy.

It is essential that designers and product owners of transport applications become aware of the impact their solutions have on the environment, and look for ways to reduce their ecological footprint. Adopting best practices in terms of digital sobriety and carbon emissions reduction can help mitigate the environmental impact of these applications.

Discover our measurement solutions

Sources  

https://www.statistiques.developpement-durable.gouv.fr/sites/default/files/2020-12/datalab_78_comptes_transports_2019_circulation_novembre2020.pdf

https://impactco2.fr/convertisseur

GreenIT

A closer look

Posted on by Laurent DEVERNAY
Reading Time: 4 minutes

Just ten years ago, the subject of the environmental impact of digital technology was confined to a handful of specialists. Over the past few years, however, the subject has gained considerable momentum, particularly in France but also internationally. While some people are (rightly) concerned about the preponderance of discourse around net zero and carbon neutrality, this trend is merely a symptom of a biased approach to the subject.

Reducing a global crisis to a technical problem

The climate emergency is a key issue that has gained enormous momentum in recent years. The digital sector has not been spared, and studies and tools have made many people aware of the issue. The problem is alarming, but also complex, which is why some aspects have been lost along the way in favor of broader awareness.

In the case of digital services, it is understood that an LCA (Life Cycle Assessment) is an excellent way of estimating environmental impacts, but the process can prove cumbersome and costly. Defining the scope, selecting the indicators, collecting and analyzing the data. The complexity is all the more difficult to take into account when you want results quickly and, preferably, easily communicated. So, to gain in efficiency, some choose to measure only part of their digital services, thanks to easy-to-use tools. In just a few clicks, you have your answer and can share it.

Sound familiar? It’s called technological solutionism, as expounded by Evgeny Morozov in his seminal work “To save everything, click here“.

This is also why solutions are being developed that analyze code to suggest ways of improving it to reduce its environmental impact. Some are even beginning to rely on artificial intelligence for this purpose.

It’s also what prompts some to optimize where their code will be executed, to move towards a location where energy has less impact from an environmental point of view (taking into account, of course, only greenhouse gas emissions). And what can’t be avoided or reduced can always be compensated for.

In the end, it’s all very human. Faced with a complex and urgent problem, we try to simplify and adopt or find a quick solution. That’s not a bad thing, but we can’t stop there. All the more so when some people rely on claims of “net zero” and carbon neutrality to artificially draw a finish line that can be reached via clever calculations and investments, whereas the problem is systemic by nature.

The risk here is of optimizing one indicator while degrading others that we didn’t have in mind (for example, requesting a data center presented as carbon neutral without taking into account its impact on water resources). As a result, we’re increasingly asking ourselves whether a sober site is necessarily ugly, without realizing that it’s not always accessible. Or really sober, for that matter.

Reminder

The environmental impacts of digital technology are not limited to greenhouse gas emissions. As we see in LCA, the indicators to be taken into account are much more numerous and varied. Little by little, we are also having to take into account the criticality of certain mineral resources, as well as that of water (as we saw recently with ChatGPT and Google’s data centers).

The environmental impact of digital services doesn’t just come from the code. In fact, according to GreenIT.fr, only around 20% of the impact comes from the code. Which makes perfect sense. Through code, we seek to improve efficiency (doing better with less). The real levers for reduction are to be found in the other stages of the lifecycle, notably design, strategy and content production. In this way, we can move towards sobriety for good.

Finally, the impacts of digital technology are not only environmental, and this is the heart of Responsible Digital. We need to keep in mind the impact on the individual (via accessibility, security, personal data management, the attention economy, ethics and inclusion). So, managing the climate emergency can only be done with an intersectional approach.

But how?

The technical approach is not necessarily bad in itself. It’s a good thing to have effective solutions to improve the efficiency of digital services (as long as we keep in mind the possible side-effects). Sometimes, it’s even an excellent starting point for taking initial action, initiating a continuous improvement process and getting to grips with the subject.

On the other hand, it’s essential to go further. This is what we see today in movements around Sustainable UX, responsible communication and even responsible digital marketing, for example. We are also seeing the emergence of resources and books on “green service design” and systemic design.

This is also the reason why the GreenIT collective’s 115 best practices have evolved over time, and why other, more comprehensive reference frameworks have emerged, such as RGESN and GR491.

Beyond this, it is also important to ask ourselves more general questions about what we eco-design, and how the services we create can induce more environmentally-friendly behavior.

Conclusion

As we’ve already seen when examining the offerings of web hosting providers, the reality of the environmental impact of digital technology is more complex than it might seem. The problem won’t be solved with a single click, and perhaps that’s just as well. In fact, it’s an opportunity to rethink digital technology, the way we use it and the way we think about it. These constraints may well give rise to a digital world that is more respectful not only of the environment, but also of individuals.

What is the environmental footprint of social networking applications? 2023 Edition

Posted on by Yosr Ben Ammar
Reading Time: 8 minutes

Introduction 

The uses and functionalities of social networks are expanding, as are their communities and the time spent on our screens.
Trends, corporate marketing and new channels of influence are all factors that are multiplying user connection and usage time.

We are social’s Digital report France 2023 estimates that 92.6% of French people are connected to the Internet. This represents an increase of +1% compared to 2022, or 600,000 people, over 80.5% of whom are present on social networks.
The environmental impact generated by social networks is evolving with the increase in the number of people and time spent on applications. This implies a greater level of responsibility on these massively used digital services to assess and reduce their generated impacts. Is there an eco-responsible social network in the world? How can we raise the awareness of application publishers, and perhaps even their users? To answer these 2 questions, there’s nothing like a little consumption measurement and impact projection.

As not all these networks work in the same way, we chose to remeasure a use case common to all of them, namely, browsing and reading a news feed from the 10 most popular social network mobile applications in France.

 
Méthodology

Choice of social networks studied

The 10 most popular social networking applications among the French are: Facebook, Instagram, LinkedIn, Pinterest, Reddit, Snapchat, TikTok, Twitch, Twitter and Youtube. We have used We are social statistics from January 2023 to project environmental impacts.

Given the use case selected, we’ve focused on social networks with a news feed, which excludes messaging applications such as Whatsapp, Messenger, Imessage, Skype, Discord and Telegram. You’ll probably find them in a future article 😉

User path definition

We evolved the user journey by creating a news feed scrolling scenario with the following steps

  • Step 1: launch the application
  • Step 2: read news feed without scrolling (30 sec)
  • Step 3: News feed scrolls with pauses.
  • Step 4: application background (30 sec)
Step 1: launch the application Step 2: read news feed without scrolling (30 sec) Step 3: News feed scrolls with pauses. Step 4: application background (30 sec)

This path consists of a 2-second scroll followed by a 1-second read (pause), all repeated and weighted over a 1-minute duration.

Regarding Snapchat, its operation forced us to consider a click and not a scroll scenario, but not calling into question the pause and content scroll times. What’s more, the chosen news feed is the stories page, which is not the application’s home page. In order to have comparable scenarios, the step of accessing the stories page was not measured on this path and therefore not included in the impact generated.

The pauses in scrolling through the news feed simulate the most realistic reading behavior possible.
This path does not transcribe the most frequent uses on these platforms (reading a post or associated rich content, a video, reaction, generated exchange, ….) but it does give us an indication of the level of sobriety of the applications.

For this study, data was measured using our Greenspector Test Runner solution, which enables automated tests to be carried out locally on smartphones.

We measured resource consumption (energy, memory, data) and response times. These data were then used to calculate the environmental impact of the applications.

Please note that the methodology used in this study compares only the scrolling of the most common news feeds. This means that the comparison is not necessarily equivalent, as some news feeds focus on video scrolling and others on multimedia posts (text, image, video, animated gif, etc.).

Measurement context

  • Samsung 10, Android 10
  • Network: Wi-Fi
  • Brightness: 50%.
  • Tests carried out over at least 3 iterations to ensure reliability of results

Assumptions used for environmental projections

  • User location: 100% France
  • Server localization: 100% worldwide (in the absence of information for each application)
  • Devices used: 100% smartphone
  • Server type: 100% complex

The environmental footprint depends on the location of application servers, their type, the location of users and the type of devices they use. We have decided to study the use of applications only on smartphones and on the share of French users.

Top and flop apps in France according to results

The graph below ranks the various social networking applications according to the environmental footprint of the path we defined above.

Ranking of the environmental impact of mobile social networking applications 1 - linkedIn :0.47gEqCO2/min 2- twitch : 0.51 gEqCO2/min 3- Twitter : 0.52 gEqCO2/min 4- Facebook: 0.63 gEqCO2/min 5- Snapchat: 0.65 gEqCO2/min 6- Pinterest: 0.66 gEqCO2/min 7- Instagram: 0.87 gEqCO2/min 8- Youtube: 0.87 gEqCO2/min 9- Reddit: 0.92 gEqCO2/min 10 - Tiktok: 0.96 gEqCO2/min The measurements were taken by Greenspector on 13/04/2023

The less sober app

Tiktok comes last in the ranking, but that’s no great surprise. In fact, the application is very energy-hungry, consuming 22.4 mAh at launch and exchanging a lot of data as it scrolls through the news feed. This enormous exchange is due in particular to the constantly running video launch and the many advertisements present on the application.

The application preloads a wide range of content, so if the user is offline, he or she can still access the videos. Tiktok loads around 5 MB of data for 30s after launch, equivalent in this test to 10 preloaded videos.

The most sober application

LinkedIn is the least impactful application according to our results. It exchanges a very low volume of data when the application loads, as well as when scrolling through the news feed. The fact that the social network is focused on sharing text-based posts with a low amount of photos and videos explains this score in particular. What’s more, LinkedIn consumes 13.9 mAh of energy, 15% less than the other applications on the panel.

 

Other applications preload less content, and often less volume. A preloaded video consumes more energy and generates more data exchanges than a preloaded text post.

One-year projection of the impact of the 2 applications most used by the French

According to the We Are Social annual report, the average time spent on social networks is 1h55 per day. When we project the environmental impact over one year for each application, the environmental impact represents 20 to 40 kg eqCO2 depending on the social network. This represents 185km by car for the least sober network.

According to Ademe’s Impact CO2 website, which offers an online converter, approximately 200g CO2eq = 1km. This includes direct emissions, vehicle construction (manufacture, maintenance and end-of-life) and the production and distribution of fuel and electricity. Infrastructure construction (roads, railways, airports, etc.) is not included in this calculation.

We have chosen to compare the 2 applications most used by the French, namely Facebook, which has around 38.1M users, and Instagram, which has around 30.5M users.

Facebook 

The report states that 52 million people in France are present on social networks. Facebook is the most popular social network among 16-64 year-olds (73.3%). If we multiply Facebook’s environmental impact by the number of French users present on this platform (approx. 38.1M), this represents more than 24 tonnes eqCO2/min (or the production of 773 smartphones/min). That’s almost 1M tonnes of eqCO2 per year!

Instagram 

Instagram is the 2nd most popular social network among 16-64 year-olds after Facebook. If we multiply Instagram’s environmental impact by the number of French users present on this platform (58.6%), this represents more than 26.5 tonnes eqCO2/min (or the production of 853 smartphones/min). That’s over 1.1M tonnes of eqCO2 per year!

We can see that despite a gap of almost 8 million users, Instagram has a greater carbon impact than Facebook.

It’s worth pointing out that the amount of time devoted to social networking varies according to the audience concerned. Some people spend less time on them, while others spend considerably more, sometimes up to 8 hours a day.

The table below projects the carbon impact in terms of uptime.

What about international projection?

With an average time spent on social networks of 2 hours and 31 minutes across all networks, we estimate the consumption of these applications worldwide.

Facebook has 2.958 billion users worldwide, making it once again the most popular network. The daily consumption of a user spending an average of 2h31 on this network would be around 95g eqCO2. For the almost 3 billion Facebook users who spend an average of 2h31 a day on this social network, the platform would have an environmental footprint of more than 281,000 tonnes eqCO2/day, or more than 102 million tonnes eqCO2 a year!

Internationally, Instagram has around 2 billion users. Per day, the consumption of a user spending 2h31 on Instagram would produce around 132g eqCO2. On the scale of 2 billion users, this would represent 262,000 tonnes eqCO2/day, or almost 96 million tonnes per year.

And what happens if we use a dark theme?

We carried out our measurements a second time with the applications in dark mode, so as to be able to compare the energy impacts generated.

The measurements were carried out on a Samsung S10, equipped with AMOLED technology, known for the fact that a dark pixel is actually a partially extinguished pixel, which explains why dark modes reduce power consumption. Conversely, when the screen uses LCD technology, color has no influence on consumption, which explains why dark mode is no more energy-efficient than light mode, see article here.

screenshot feed LinkedIn en white mode Versus Screenshot feed LinkedIn en darkmode
RS visuel – 9

Nowadays, more and more phones are equipped with AMOLED screen technology, and it’s worth activating the dark mode to reduce power consumption and preserve battery discharge.

In this study, we noticed that only 8 of the 10 applications studied offered dark mode. Snapchat and Tiktok didn’t, so we excluded them from the measurements. As their interface is based on scrolling videos and photos only, only a few pages such as messaging would lower the energy consumption measurement.

ApplicationEnergy consumed in light mode /1 min(mAh)Energy consumed in dark mode /1 min(mAh)Energy consumption reduction rate
Twitter12,28,531%
LinkedIn11,78,528%
Facebook12,59,326%
twitch107,525%
Pinterest11,38,822%
Instagram13,211,811%
YouTube13,411,910%
Reddit14,113,18%

It can be seen that activating the dark mode reduces the energy consumption measured on the battery.

We can see that when dark mode is activated on the application, energy consumption is reduced by an average of 20%, and the rate of battery discharge is therefore reduced by an average of 18%, relative to their equivalents in light mode.

On text-heavy applications such as Twitter, LinkedIn and Facebook, dark mode is more energy-efficient, as it inverts the colors of a block of text, turning it into fine white writing on a black background. On the other hand, images and videos will not have their colors inverted, so there will be little difference when displaying multimedia content.

darkmode consumption

Conclusion

In this study, we observe that the GHG impact is around twice as great between the most and least impacting platforms.

Applications with a lot of multimedia content consume a lot of energy and require a lot of data exchange over the network to display this content. Text-based content, on the other hand, is much easier to load and consumes much less energy.

In conclusion, although social networks facilitate the exchange and accessibility of information, they are not as totally virtual as we might think, and raise the question of our relationship to the consumption of these applications. Are we really using them to communicate and inform ourselves, or rather to feed on a barrage of information and content that is generally neither desired nor expected?

At a time when climate change is a matter of urgency, it’s time to examine our relationship with our screens and adopt eco-friendly gestures, such as reducing time spent online and activating dark mode on mobile applications.

If you’re an application publisher, you also have a role to play! Here are a few ways in which you can reduce your impact:

  • Default to dark mode when downloading the application
  • Avoid massive pre-loading of heavy content
  • Avoid auto-starting videos or auto-re-launching at the end of videos

Sources  

For social network usage statistics :

https://wearesocial.com/fr/blog/2023/02/digital-report-france-2023-%f0%9f%87%ab%f0%9f%87%b7/

For equivalents in terms of carbon impact :

https://impactco2.fr/

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From Green Pitch to Green IT: are the Champions League finalists’ apps in the game?

Posted on by Julien Gilbert
Reading Time: 8 minutes

The world of football is one of the most popular and influential sectors of our society. Millions of fans come together every week to support their favourite team and experience moments of passion and excitement. However, it’s time to be aware of the environmental consequences of this all-consuming passion. In this article, we will look at the eco-design practices of the applications of the 4 semi-finalist clubs in the 2022-2023 Champions League.

Calculation methodology

In our comparative study of the mobile applications of the 4 semi-finalists in the Champions League, we examined various aspects, such as the size of the applications, their compatibility and the greenhouse gas (GHG) emissions caused by their use. The results highlight significant differences between the applications, underlining the importance of implementing an eco-design approach.

First of all, the vast majority of a smartphone’s environmental impact is due to its manufacturing phase. A great deal of energy and materials, some of them rare, have to be used in the manufacture of the product. Consequently, to effectively reduce the impact of a mobile application, it is necessary to ensure that it does not force users to change phones in order to obtain a suitable user experience. This involves a number of criteria, including but not limited to the following:

  • Battery use: battery wear and tear is one of the main causes of the need to buy a new phone. One of the factors that wear out the battery is the number of charge/discharge cycles the phone goes through. It is therefore essential that using the application does not require too much energy so as not to accelerate the draining of the battery.
  • Performance: this criterion corresponds to the application’s response time. There are 2 reasons why this criterion needs to be taken into account. Firstly, the aim of an eco-design approach is to enable users who do not wish to renew their phone to have a pleasant user experience, even on older devices. Secondly, longer loading times mean more electricity used, and therefore faster wear and tear on the battery.
  • Application size: this indicator has 2 different impacts. Firstly, when the application is downloaded, a large application requires more data to be exchanged. Secondly, users who want to keep their phone for a long time may have to deal with problems of memory shortage. In order to encourage this sobriety approach, the amount of memory used by the application needs to be as small as possible. In this article we will focus solely on the size of the application, but a sober approach must also be taken to all the data stored on the phone, such as good cache memory management.

During an environmental impact analysis at Greenspector, we examine all these points to provide recommendations that will enable our clients to gain an accurate picture of their situation and reduce their environmental impact.

Analysis of results

Comparison of application sizes

First of all, we evaluated the size of the APK files of the selected applications. We found considerable variations in their size, ranging from light, space-saving applications such as Inter Milan to larger ones such as Real Madrid. These differences can have an impact on mobile device storage memory and data consumption when downloading and updating applications.

The size of the application may vary depending on the phone. The following results were obtained with a Samsung S10 running Android 12.

Application compatibility comparison

Another key criterion we looked at was the compatibility of applications with different versions of Android. We found that some applications were designed exclusively for more recent versions, limiting access for users with older devices. This incompatibility often leads to frequent replacement of devices, which can waste natural resources and increase electronic waste.

ClubMinimum Android version requiredPercentage of Android phone owners able to download the application
Real MadridAndroid 6.097,9%
Manchester CityAndroid 6.097,9%
Inter MilanAndroid 7.096,2%
AC MilanAndroid 5.099,3%

Comparison of greenhouse gas emissions

Explanation of our methodology

To assess the greenhouse gas emissions of applications, we have followed a rigorous methodology based on data measured on real phones concerning the energy consumption of mobile devices, execution time and the quantity of mobile data exchanged. Using this measured data and a model developed by our teams, we are able to estimate CO2 emissions. For a more detailed explanation of the methodology, please see our dedicated article.

Defining the journey

These measurements were carried out on the basis of a user journey that we divided into short steps. The criterion for choosing this journey was that it could be carried out on all 4 applications so that a comparison could be made:

These different steps give us a view of several elements that are typically present in a mobile application, such as a scrollable page, a complex element (a calendar in this case) and a video. The launch steps is also very important, as it can provide us with essential elements of understanding, such as data caching or the time taken to launch the application.

In order to obtain the most reliable measurement possible, we are creating a script to automate the execution of 3 identical series of tests.

The results

After carrying out a detailed analysis, we drew up a comparative table of the results, highlighting the applications with the lowest GHG emissions and those with the largest environmental footprint.

The following results are expressed in tonnes of CO2 equivalent.

The results obtained show a wide disparity between applications, demonstrating the extent to which the way in which an application is designed and developed has an impact on greenhouse gas emissions. In order to keep this article succinct, we are only going to analyse one element that explains this difference. But bear in mind that the analysis can (and should) be taken further to highlight all the critical points of the applications.

In addition to our study on CO2 emissions, it should be emphasised that the environmental impact of applications goes beyond greenhouse gas emissions alone. Experts such as mining geologist Aurore Stéphant have highlighted other aspects to consider when assessing the ecological footprint of the digital sector. In a recent conference entitled “Mining rush in the 21st century: how far will the limits be pushed?“, she addresses crucial issues such as the consumption of natural resources in the production of smartphones, the extraction of the minerals needed to manufacture them, and all the waste resulting from mining. There are many ethical issues to consider in our use of digital tools.

Video impact

During our comparative analysis, we identified a subject that largely explains the differences in environmental impact: video. Videos have become a central element in many applications, and their growing consumption is contributing to the increase in greenhouse gas emissions from the digital sector. The growing popularity of high-resolution video is leading to intensive use of hardware resources on mobile devices. Smartphones need to be equipped with more powerful processors and batteries to process and display this content, which can lead to more frequent replacement of devices. What’s more, they need servers to store and distribute the content, as well as solid network infrastructures to enable smooth streaming. These servers and infrastructures have material and energy requirements at the time of manufacture, and consume a significant amount of electricity.

In this case, we see the following results for the amount of mobile data exchanged for each application:

ClubAmount of mobile data exchangedTest execution time
Real Madrid12.4 MB3min 52s
Manchester City73.3 MB4min 08s
Inter Milan211.3 MB3min 57sec
AC Milan5.8 MB4min 01s

If we take the case of Inter Milan, we can see that this application consumes much more data than its competitors. There are several reasons for this:

  • Non-optimisation of the video: the results of the 30s video viewing steps are very interesting because they allow us to compare the data exchanged by the applications for a single video.
  • Autoplay: feature commonly used on websites and streaming platforms to automatically launch videos or multimedia content as soon as the user accesses a page or application. This practice has a significant environmental impact. Autoplay leads to increased energy consumption, as videos are launched and loaded automatically, even if the user is not actually watching them. The case of the Inter Milan application is quite striking in this respect, as autoplay is activated on all pages containing video. This is particularly the case on the home page, which means that a lot of data is exchanged each time the application is used, even if the user only wants to watch the score of a match.

Video consumption plays a major role in the differences in environmental impact between applications. Mobile developers can help to reduce this impact by optimising video compression, favouring low-resolution delivery by default and encouraging responsible use of video functionalities. Users, for their part, can adopt more conscious viewing practices and limit their video consumption wherever possible. A combination of efforts from all the players involved can contribute to a more sustainable and responsible use of mobile applications.

Video optimisation solutions

Fortunately, solutions do exist and a more in-depth analysis can drastically reduce the impact of videos on the environment.

One approach is to optimise video compression. By using efficient codecs and advanced compression algorithms, it is possible to reduce the size of video files while maintaining acceptable visual quality. This reduces the demand for bandwidth when broadcasting videos, thereby reducing the CO2 emissions associated with their transmission. Intelligent management of video resolution can also help to reduce the carbon footprint of applications.

Alongside these technical measures, it is also important to encourage responsible use of video. Making users aware of the environmental impact of excessive video broadcasting, and encouraging them to adopt practices such as limiting background streaming and reducing resolution when high quality is not required, can have a significant effect on reducing CO2 emissions.

Finally, by combining technical solutions with responsible practices on the part of users, it is possible to considerably reduce the environmental impact of videos in mobile applications. It is essential that developers, content providers and users work together to encourage more sustainable and responsible use of this popular and ubiquitous feature. By acting collectively, we can preserve the quality of our digital experiences while minimising our impact on the environment.

Conclusion

The 2023 Champions League semi-finalists, Real Madrid, AC Milan, Inter Milan and Manchester City, need to consider the environmental impact of their operations. While these clubs enjoy a global reputation and a passionate fan base, it is essential to recognise the environmental footprint associated with their operations, including the use of mobile applications. However, it is encouraging to see that solutions exist to improve this situation. By better understanding these aspects, we can identify opportunities to reduce the ecological footprint while improving the user experience. We are ready to support these clubs as they move towards greater environmental sustainability. Together, we can develop appropriate strategies, implement innovative practices and promote environmental awareness among fans. The aim is to create a genuine synergy between sport and the protection of our planet.

Players in the world of sport, measure the ecological footprint of your application now and take concrete steps to reduce your environmental impact. Together, let’s score goals for sustainability and protect our sport and our planet.

Take a look at our offers

For each site and each application, measured on a Samsung Galaxy S10 (Android 12), the measurements were carried out using scripts based on GDSL (Greenspector Domain-Specific Language). This language is used to automate actions to be carried out on a phone. The measurements were carried out between 3 and 5 May 2023.

Each measurement is the average of 3 homogeneous measurements (with a small standard deviation). The power consumption measured on a given smartphone using a wifi network may be different on a laptop using a wired network, for example. For each iteration on the websites, the cache is emptied beforehand.

On the footprint projection side, the parameters taken into account for these rankings are :

  • Viewing ratio: 100% Smartphone
  • Viewing ratio: 100% Worldwide
  • Server location: 100% Worldwide

The number of users considered for the calculation is 100,000 per day.

Best practice: optimizing fonts

Posted on by Laurent DEVERNAY
Reading Time: 4 minutes

In recent years, the use of fonts on the web has exploded (both in terms of the number of existing fonts and the number of sites using them).

As usual, the Web Almanac is a mine of information, especially via the chapter dedicated to fonts. We learn that the two main suppliers of web fonts are Google and Font Awesome, the latter consisting in the provision of icons. Beyond the potential cost on performance and environmental impacts, some countries have already established that this could contravene the GDPR (General Data Protection Regulation).

Proportion of websites using web fonts

Let’s see what good practices can reduce the impact of fonts on the web.

Existing reference systems

The fonts are mentioned in the UX/UI family of the RGESN (Référentiel Général d’écoconception de services numériques) :

  • 4.10 – Does the digital service use mostly operating system fonts?

They are also found in GR491 (Responsible Digital Service Design Reference Guide):

Finally, the 115 web ecodesign best practices also mention them:

Good practices

Objectives

In order to reduce the impacts of fonts, several best practices are applicable:

  • Give preference to standard/ system fonts : This avoids additional requests
  • Use an optimal compression format (today, it is the WOFF2 format). Online tools as Everything Fonts can provide this conversion.
  • Limit the number of variants used or use a variable font
  • Load only the characters that are really used (for example via a subset)

When ?

These good practices must be implemented as soon as the visual design of the service is done in order to favor standard fonts as much as possible. If this is not possible, then limit the number of variants to be loaded. Finally, when fonts are integrated, use the woff2 format, variable fonts and make sure to load only the characters or languages actually used.

Ease of implementation

If the site is already online, it can be complicated to change the font used. On the other hand, technical optimizations are easy to implement (format, variable font, Subset).

Estimated gains

These best practices reduce the number of HTTP requests and the volume of data transferred.

Specific cases

Google Fonts

To avoid problems with the RGPD, it is recommended to host Google Fonts yourself.
If variable versions are not available for all, some creators offer these versions for free. In addition, the Google API allows you to directly create a Subset with a request of this type: https://fonts.googleapis.com/css?family=Montserrat&subset=latin

Icons

Icon fonts are quite common. Using them directly may imply loading many icons that will not necessarily be used. The best way to use icons is to use each of them directly in SVG format. In this form they can be embedded directly in the HTML (without any additional HTTP request). If an icon font must be kept for practical reasons, limit the file to the icons actually used.

Case study

As part of the support Docaposte teams receive for their corporate site, fonts are often a separate project.

The fonts used here are two Google Fonts: Montserrat and Barlow. The site being already online, it is complicated to impose the use of standard fonts.

To avoid violating the GDPR and to improve site performance, fonts are hosted directly on Docaposte’s servers. In a second phase, a dedicated subdomain could be set up to eliminate the need for cookies.

The integration in the form of a variable font requires some additional adjustments, especially in the style sheets. In the meantime, it was decided to apply two best practices:

  • Propose the files in woff2 format rather than woff
  • The site being proposed only in French and English, a Subset was created keeping only the Latin alphabet.

Original requests

Requests after Subset and conversion to woff2

The woff2 format offers an average of 30% more compression than the woff format and even more than other formats like ttf.

This change in format, combined with Subset, reduced the total weight of the fonts from just over 400 kb to just under 90 kb, a reduction of about 78%.

To go further

DOM as a metric for monitoring web sobriety?

Posted on by Olivier PHILIPPOT
Reading Time: 3 minutes

Choosing the right metric to assess its impact is critical in a sobriety approach.

We have validated the use of energy in our tools (https://greenspector.com/fr/pourquoi-devriez-vous-mesurer-la-consommation-energetique-de-votre-logiciel/ and https://greenspector.com/fr/methodologie-calcul-empreinte-environnementale/ for more details). We do however use and measure other metrics such as CPU. This metric can however be complex to measure and some tools or teams use other more technically accessible elements. The CPU is an interesting metric to measure the resource footprint on the terminal side. Indeed, we have carried out measurements on several hundred sites and it is clear that the CPU is the most important metric for analysing the impact of software. This is why all the models that use the data exchanged to calculate the impact of the terminal are not consistent. CPU-based models (such as the Power API) are preferred.

However, it is necessary to be rigorous in the analysis of this metric as there may be interpretation biases (Example of criticism on the CPU). The criticism must be even more important on the way to obtain this metric, and more particularly in the case of modelling the CPU. This is the case, for example, with methods for projecting the CPU into the web from DOM elements.

This is based on the assumption that the structure of the DOM has an impact on the resource consumption of the terminal. The more complex the dom, the more it needs to be processed by the browser, the more resources (CPU and RAM) it uses and the more environmental impact it creates.

Assuming that the hypothesis of a correlation between DOM complexity and environmental impact is valid, the metric often used is the number of elements. A DOM with many elements may be complex but not systematically so. To take into account the complexity of the DOM, it would be necessary to take into account the architecture of the DOM, in particular the depth, the type of node (not all nodes have the same impact on the browser…). The choice of the number of DOM elements is therefore debatable.

But is the choice of DOM complexity a viable assumption? There are several criticisms of this.

The DOM is a raw structure that is not sufficient for the browser to display the page. The style is used with the DOM to create the CSSOM, a complexity of the style can thus greatly impact the CSSOM, even with a simple DOM. Then the layout tree is a structure that will allow the display to be managed (typos, sizes…), this management is much more complex to handle for browsers.

A DOM can be modified after its creation. This is called reflow and repaint. The browser will recalculate the layout and other things. This can happen several times during loading and after loading. The depth of the DOM (and not the number of elements) can influence but not only: the loading and execution of JS code are to be taken into account.

Independently of the DOM, resource consumption can be impacted by various processes on the terminal. In particular, all the JS processing that will be executed when the page is loaded. This cost is currently the main cost on the CPU in the web. And you can have a DOM with 100 elements (not many) and a JS gas factory.

Graphics animations will increase resource consumption without necessarily impacting the DOM. Even if most of this processing is handled by the GPU, the resource impact is not negligible. We can also put in this category the launching of videos, podcasts (and more generally media files) and ads.

There are also many other sources of resource consumption: ungrouped network requests, memory leaks.

The use of the DOM should therefore be used with great care. It is best used as a software quality metric that indicates “clean HTML”. Reducing the number of DOM elements and simplifying the DOM structure may be a good sobriety practice but not a sobriety reduction or CO2 calculation KPI.

What is the environmental footprint of the 10 most visited m-commerce sites and apps in France in 2023?

Posted on by Thibault TUFFIN
Reading Time: 8 minutes

The e-commerce market in France in 2022 amounted to 146.7 billion euros in sales. This is a growth of 13.8% compared to 2021. Although the turnover (CA) of product sales is down compared to the previous year, the considerable increase (+36%) in the CA of service sales supports the overall growth of the e-commerce sector.

Fast access

Selection method of websites and applications

User path definition

Ranking of the environmental footprint of the 10 most visited m-commerce sites in France

Ranking of the environmental footprint of the 10 most visited m-commerce applications in France

Review of the study

There were 2.3 billion transactions made on the internet in France in 2022, 6.5% more than in 2021. Inflation and the sale of services contributed to an increase in the average basket with 6.9% increase. It was on average 65 euros in 2022.

This article can be used as a comparison with the previous content made on the subject in 2022. The article focused on the e-commerce figures in 2021 and the ranking of m-commerce apps and websites in the 2nd quarter of 2021.

E-commerce refers to all transactions made on the Internet, while m-commerce refers to all types of purchases made on an e-commerce website with a mobile device. M-commerce is therefore a sub-category of e-commerce.

Selection method of websites and applications

For this new version, we based ourselves on the measurement of the 10 most visited m-commerce sites and applications in France (figures of the 4th quarter 2022 exposed by Fevad). Compared to the previous ranking, 2 players have appeared: Rakuten and Darty. It is eBay and ManoMano that are out of the top 10.

User path definition

After refining the selection of the 10 applications and websites to be measured, we went back to the path that was defined in last year’s article.

steps

Implementation of Greenspector’s solution

We used our innovative solution to measure the environmental impact of different stages of the user journey. We ran the automated tests several times on a real device, in this case the Samsung Galaxy Note 8. We measured resource consumption (energy, memory, data) and response times. This data then allowed us to obtain the environmental impact of applications and websites. We explain it all in detail in our methodology.

Learn about our solution

Ranking of the environmental footprint of the 10 most visited m-commerce sites in France

The 3 sites with the least impact are : Leclerc, Leroy Merlin and Fnac.
Compared to the article published last year, Cdiscount is back on the podium while the Fnac site is in the top 3 of the least impactful sites.

The 3 most impactful sites are: Amazon, Rakuten and Darty.
The Amazon site is 2.2 times more impactful than the Leclerc site.
The average carbon impact of these 10 websites is 1.09gEqCO2 for an average duration of the scenario (see methodology at the end of the article) of 1 minute and 58 seconds, which is the equivalent of 5 meters driven by light vehicle.

Projection of global carbon impacts over one month

In last year’s article we based ourselves on the figures presented in the ECN report. For this new study, we used the Fevad barometer enriched by Médiamétrie in order to be as close as possible to reality.

For this projection, we consider that the share of global e-commerce traffic is 55% on mobile, 39% on PC and 6% on tablet (source). We also used the ADEME tool to project the equivalences.

With an average of 16.15 million monthly users and an average visit time of 5 minutes and 50 seconds, these 10 e-commerce sites have an average projected impact of 172 tons of CO2e per month (29 tons on mobiles, 139 tons on PCs, 4 tons on tablets). This is the equivalent of 20 times the circumference of the Earth covered by a light vehicle.

Impact projection for the most and least sober website

Concerning the best website of this ranking (Leclerc) for 14.84 million visits / month with a duration of 3 minutes, the total carbon impact would be 58 tons of EqCO2 per month (9 tons on mobile, 47 tons on PC and 1 ton on tablet). This is the equivalent of 6 times the circumference of the Earth travelled in a light vehicle.
Concerning the worst website of this ranking (Amazon) for 38.29 million visits / month with a duration of 8 minutes, the carbon impact would be 690 tons of EqCO2 per month (121 tons on mobile, 553 tons on PC and 15 tons on tablet). This is equivalent to 79 times the circumference of the Earth travelled by light vehicle.

The fact that the Leclerc site is at the top of the ranking is mainly due to the low energy consumption of the product viewing and shopping cart viewing stages. Only the essential information is present on this search page (product name, price, availability). On the product page, there is the possibility to quickly add the product to the cart, and drop-down menus are proposed if the customer wants more information. This site is also the one that exchanges the least amount of data to complete the scenario.

By analyzing the product search results page with our measurement tool, we can see that many good practices are applied. There are few network exchanges with 19 HTTP requests and only one CSS file. The lazy-loading of images is applied.

The fact that the Amazon site is last in the ranking is explained by the search and product viewing stages. Indeed, this site consumes a lot of energy on these phases. The data exchange is also important. There are 2,62Mb of data exchanged for the search phase, and 5,85Mb of data exchanged for the visualization of the product sheet. During the search, a lot of information appears (indication “Suggestions”, “Sponsored”, “Amazon Choice” or “No. 1 in sales”, product name, rating, number of reviews, price, discount, delivery date). However, unlike last year, we notice that there are no more autoplay video ads on this phase. When viewing the product, a lot of information also appears (offers, delivery dates in case of free or accelerated delivery, product details, products frequently purchased together…). Moreover, the customer is obliged to scroll before being able to access and click on the “Add to cart” button.

Going into more detail on the product search page, there are a lot of network exchanges with 119 HTTP requests and 11 CSS files. These figures are up from last year’s 109 and 9 respectively. The lazy-loading of images is not applied, which implies that the images are not visible on the screen. This practice should be avoided, as the user will not necessarily scroll to these images.

Ranking of the environmental footprint of the 10 most visited m-commerce applications in France

The 3 applications with the least impact are: Carrefour, Darty and Veepee

The 3 most impactful applications are: Amazon, Aliexpress and Leroy Merlin
We observe that the Amazon application has a carbon impact 3 times higher than the Carrefour application.
The average carbon impact of these 10 applications is 0.81 gEqCO2 for an average scenario duration of 1 minute and 58 seconds, or the equivalent of 3 meters driven in a light vehicle.

The Carrefour application takes its place in the phases of viewing the product sheet and adding the product to the cart, which are lower in energy consumption and in the amount of data exchanged. On the add to cart stage, this can be explained by the fact that it does not automatically redirect to the cart and only generates a simple change on the add to cart button which becomes a quantity selector.

This year again Amazon is at the last place of the ranking. This result is explained once again by the product search and visualization phases, during which the application consumes a lot of energy. In terms of data exchanged we observe 4.73MB on the product visualization stage (against 0.05MB for Carrefour) and 4.15MB on the search stage (against 0.19MB for Carrefour).

Review of the study

This year again we observe that the impact is almost three times greater between the most sober platform and the one with the highest impact.
To shop online, it is better to use applications than websites. Indeed, in the scenario studied, websites have on average 44% more impact. Only Leroy Merlin and Leclerc have a greater carbon impact on applications than on the web. We remind you that applications have an impact on their download and their updates. They are therefore to be preferred only in case of regular orders.


We would like to complete this assessment with an observation we made during our tests. Indeed, we observed that on some websites and applications the path could change or be altered. This is the case for Amazon which has implemented AB testing. This method allows the application and the website to vary the displays. For example, on the description of a product, the description can be different from one user to another.

In our case we encountered a phenomenon of change of path on the Amazon website. During a first test we were redirected directly to the shopping cart with the addition of the product to it. In a second test the next day we were no longer automatically redirected to the shopping cart page. Instead, we had to go to the shopping cart ourselves by clicking on the icon provided for this purpose.


Depending on the path, the site or application will consume more or less energy and exchange more or less data. AB testing is a feature used by many digital solutions in the world and we handle it in our path automation thanks to our GDSL language. In the case of our study we have of course taken care to base our measurements on a single path.

Results tables

Ranking of the 10 most visited websites in France

Sites weburlEnergy (mAh)Exchanged Datas (Mo)Impact carbone (gEqCO2)Water Imprint (Litres)Ground Imprint (cm2)Scenario length (secondes)
Leclerce.leclerc12,093,970,670,121,37102,71
Leroy Merlinleroymerlin.fr14,854,190,790,141,67123,27
Fnacfnac.com17,264,430,900,161,94109,37
Cdiscounthttps://cdiscount.com/15,077,320,940,161,73123,48
Carrefourcarrefour.fr18,934,740,990,182,12125,94
Aliexpresshttps://fr.aliexpress.com/18,356,751,050,182,08128,71
Veepeeveepee.fr17,0014,271,320,202,03118,11
Dartyhttps://www.darty.com/22,199,981,350,232,54117,84
Rakutenhttps://fr.shopping.rakuten.com/21,5712,121,410,232,50113,64
Amazonamazon.fr24,3811,171,490,252,80123,62

Ranking of the 10 most visited applications in France

ApplicationVersionEnergy (mAh)Data exchanged (Mo)Impact carbone (gEqCO2)Water Imprint (Litres)Ground Imprint (cm2)Length Scenario (secondes)
Carrefour16.9.111,271,350,520,101,25107,77
Darty4.2.511,402,700,590,111,28108,52
Veepee5.43.19,875,700,650,111,15102,25
Fnac5.3.613,462,370,660,121,50126,91
Cdiscount1.62.0-twa16,631,070,730,141,83111,73
Leclerc19.2.014,383,420,740,131,61127,75
Rakuten9.3.013,754,030,740,131,55130,35
Leroy Merlin8.11.213,308,850,930,151,56119,27
Aliexpress8.67.216,179,121,060,171,88120,11
Amazon24.6.0.10021,5914,291,510,242,53130,15

The selection is based on applications and sites where we can define a common path. We therefore discarded some sites and apps that presented a path too different from the one displayed below. Example: Booking.com.
We also discarded 2 solutions based on the purchase between individuals which are Leboncoin and Vinted.
For each site and each application, measured on a Samsung Galaxy Note 8 (Android 9), the measurements were made from scripts using GDSL (Greenspector Domain-Specific Language). This language allows to automate actions to be performed on a phone. The measurements were performed between April 10 and 19, 2023.

The scenario is defined based on the user’s path to purchase a product. We do not go to the payment stage. We stop at the product visualization.

Details of the common scenario for the 20 measures:

-Launching the site or application
-Pause for 30 seconds on the home page
-Search for a product using the search bar, then view the products offered
-Select a product, then view its characteristics (details, reviews…)
-Add the product to the cart
-Pause for 30 seconds on the shopping cart page


Each measurement is the average of 5 homogeneous measurements (with a low standard deviation). The consumptions measured on the given smartphone according to a wifi type network can be different on a laptop with a wired network for example. For each iteration on the websites, the cache is emptied beforehand.

Côté projection de l’empreinte, les paramètres pris en compte pour réaliser ces classements sont : 

  • Ratio de visualisation : 100% Smartphone 
  • Ratio de visualisation : 100% France 
  • Localisation des serveurs : 100% Monde

Learn how Greenspector assesses the environmental footprint of a digital service.

What is the environmental impact of the top 30 websites of the French daily press?

Posted on by Aurélie Boulais
Reading Time: 10 minutes

– 2023 Edition –

Many questions are being asked today about the environmental impact of the press and digital media. Beyond the contents, advertising and data tracking are integrated both to satisfy the economic requirements of a free or semi-free model and to better know the user to better serve him (center of interest). Moreover, the press and media often use rich content to illustrate their articles (videos, images …). All this often implies an overconsumption of digital titles at each passage of a reader. 

A year ago, we have realized a ranking of the carbon impact of the top 100 most visited websites of the French daily press on mobile. Its objective was to quantify the impact of the online press via mobile. Indeed, this impact is becoming more and more important every year.

For this new ranking, we decided to reduce the study to 30 sites and to evolve our approach, taking into account one article per site (dated 13/04/2023) in addition to the homepage. This is all the more interesting because users spend most of their time on the articles. 

We based ourselves on the ranking of the ACPM site (made according to last year’s filters: Fixed Web Site News / News Information / General Information). The benchmarks were performed with a Samsung Galaxy S9 without Sim card and with 3 iterations. The measurements were performed in March and April 2023. 

Condensed Results

Less impactful homepages :

  • Le Monde
  • Actu.fr
  • Ouest-France

Most impactful homepages :

  • Rfi
  • france24
  • L’Est Républicain

Less impactful articles pages:

  • Huffingtonpost
  • Actu.fr
  • France Info

Most impactful articles pages :

  • Sud Ouest
  • la Nouvelle République
  • Rfi

Here is the list of sites on which the study is based:

INSERT TAB

Discover the Greenspector analysis methodology.

Limitations of the measure

One of the problems encountered concerns the methods of access to content. Indeed, some articles are much shorter because their complete access is paying. 25% of the sites in the ranking are concerned. This influences the results of the benchmark for these websites by indirectly benefiting sites that restrict access to their content. 

Also, some sites are only partially loaded until the user has made his choice for the data collection consent popup (RGPD). This does not necessarily prevent scrolling on the page. More generally, we can only regret that such artifices are detrimental to the user experience.

In particular, this is what we see on the first ranking for home pages (Le Monde).

Here is for example the RequestMap (a tool created by Simon Hearne) for lemonde.fr with the requests seen by the measurement tool (Greenspector, Webpagetest or other) we can see that there are few requests and third party services at first sight :

Here is for comparison the complete RequestMap for lemonde.fr, built from a HAR file obtained via Chrome after acceptance of all cookies by the user:

It thus appears that the data transferred and requests made are multiplied after the Internet user’s consent has been obtained. This phenomenon is observed on a large number of similar sites. Here are some additional resources on online advertising, closely related to the online press:

What we have to remember today is that the results of the measurements proposed here (and via other tools available elsewhere) offer a sometimes truncated vision of the reality of online press sites. However, we already have here a good overview of what could be improved on some of these sites and their respective impacts.

Analysis of the results at the global level

For the home pages of the 30 sites measured, the average carbon impact per page and per minute is 0.44 gEqCO2. This average is 0.42 gEqCO2 for the articles.  

On the analysis of home pages, 19 sites are positioned below this average. While on the measurement of the articles, 23 sites are below. 

We noticed a difference in results between the 7 sites offering restricted access to their articles (sometimes paying or obligation to create an account to continue reading) and the 23 others where the articles are 100% accessible.  

The home pages with restricted access have an average impact of 0.42gEqCO2. For the free access sites, the average impact on the home pages is 0.44 gEqCO2.

This difference is explained by the display of numerous advertisements on some sites with free content, which considerably weigh down the pages.

Due to a richer content, the home pages consume in general slightly more than the articles (we notice an increase of more than 0.02 gEqCO2 on average).  

Here is a synthesis of the different metrics measured on the home pages of this ranking of the Press websites consulted on mobile :

MetricsAverage Minimum  Maximum  
CO2 impact per page / min en gEqCO2  0.44 0.26  1.10  
Ecoscore Greenspector  54  31  72  
Energy consumed in mAh  7.2  4.58  21.98  
Data exchanged en Mo  2.63  0.68  7,27  
Number of web requests 76 30  169  

If we compare these results with our previous analysis on homepages, we can see a slight degradation of the results with an average of 53 for the ecoscore against 54 today. On average, the three metrics of data energy consumed and number of requests have decreased. The energy consumed by the homepages went from 4.22 mAh on average to 7.2 mAh, the data consumed went from 2.31 MB to 3.63 MB, and the number of requests from 78 to 76.

Here is a synthesis of the different metrics measured on the article pages of this ranking of press websites consulted on mobile:

MetricsAverageMinimum  Maximum  
CO2 impact per page / min in gEqCO2   0.37  0.21  1.19 
Ecoscore Greenspector   58  24  75 
Energy consumed in mAh   7.15  4.62  21,26 
Data exchanged in Mo   4.52  0.58  31.42 
Number of web requests 66 26 213 

For the study, these articles were compared with articles coming from the same press sites and dating from 05/07/2022. The result is quite negative because we can see on average a lower Ecoscore on the recent articles with a score of 58 contrary to 59 for the articles of 05/07/2022. The energy consumed by these pages has also increased from 6.58 mAh in July 2022 to 7.15mAh for the articles of April 2023. Similarly, the volume of data exchanged increased from 3.46MB in July 2022 to 4.52MB for the April 2023 articles. On the other hand, we can observe a lower CO2 equivalent impact for the April 2023 articles with a score of 0.37 gEqCO2 and 0.42 gEqCO2 for the July 2022 articles. Similarly, we can see an improvement on the number of requests exchanged from 68 on average for the July 2022 articles to 66 for the April 2023 articles.

Home pages

Top 3 (least impactful home pages)

1st

Le Monde 

Le Monde gets the first place thanks to its efforts to reduce the environmental impact of its website. We notice a good application of sobriety practices, especially on the management of images. Also, data and requests are optimized thanks to the progressive loading of the page. Thus, the content is loaded only when it becomes visible. That said, the site can still be improved in terms of third-party services that are not detected in the measurements and yet are very present. Indeed, the collection of data after the acceptance of cookies increases considerably the number of requests to third party services (see above) and has previously raised concerns about privacy and security.

2nd
Actu.fr 

The web page uses progressive page loading. This allows to limit the number of requests. Moreover, actu.fr uses mostly the right formats for its images. However, actu.fr is invaded by advertising. This reduces the accessibility for the users and increases the number of requests considerably. As for the first ranking, the measurements only took into account what happens before cookies are accepted.

3rd
Ouest-France 

Ouest- France loads its page progressively and uses a very good image format, like AVIF or Webp. However, advertising increases the number of requests, thus increasing the environmental impact of the site. The measurements here are also limited to what happens before accepting cookies.

Conclusion for the top 3 homepages

We can notice that the top 3 use the best practices of progressive page loading and image formats. However, the measurements were done via the cookie page and, if we take a closer look, we can see that these sites use a lot of ads. This considerably increases the number of requests to third party sites and therefore the environmental impact.

Flop 3 (most impactful homepages)

Rfi 

The site has a lot of images, one for each article highlighted. However, we emphasize the good practice of the webp image format. The fonts are not standard fonts, which increases the environmental impact of the site. In the end, the number of requests is considerable and the errors displayed in the console are numerous.

RequestMap of the rfi.fr homepage
France 24 

The structure and the findings are similar for this page.

We are indeed, as for the site of France24 on sites resulting from France Médias Monde thus with a similar architecture and a similar structure.

L’Est Républicain 

The site takes a long time to fully load because it makes a lot of requests to third party sites when the page loads. Moreover, it launches videos automatically. The content of the page is very long compared to other sites, which explains its ranking.

Conclusion for the flop 3 homepages

The main problem of the sites in the flop 3 is the number of requests. Especially made within the site itself and can slow down the loading of the page. In addition to that, we can deplore a lack of sobriety and sometimes even the automatic launch of videos.

Let’s move on to the articles.

The articles

Top 3 ( Less impactful articles)

1st

Huffingtonpost 

The general sobriety of the site is a good point that earned it its first place in the ranking. The site uses little video or image content in its articles and gradually loads the content on its page. But it is especially that unlike many press sites, Huffingtonpost does not have ads on its articles, and this is verified at the level of its third party services which are very few. This strongly reduces the impact of the site. Besides this, the font format used is good, but the site uses too many. It also generates too many files in JavaScript format used to format the site. Finally, there is a very impactful point which is to load a video and launch it automatically on a page. Thus, even the first place of this ranking can still strongly reduce its environmental impact.

2nd
Actu.fr 

We find here the same optimizations as on the home page. However, the page is largely occupied by advertising, including a video that launches automatically. This last point is particularly problematic from the point of view of environmental impact and accessibility, in addition to harming the user experience.

Underneath the article, there is a lot of additional content, mostly advertising. There are more than 40 thumbnails leading to internal and external articles. Despite this, the majority of the requests are internal, which reduces the use of third-party services.

3rd
France Info 

The article is at first sight quite sober, but the end of the article is made of a lot of images for the article recommendations. This number of images can easily be reduced but the images have at least the good point to be in Webp format which is among the least impacting image formats.

What allows to give this place to this article is the number of requests. Indeed, the site has few requests before the recommendations of articles compared to other press sites and loads gradually its page. Considering the number of requests, the number of third party services is rather significant but it remains reasonable compared to other news sites.

Conclusion for the top 3 articles

The articles in this Top 3 have in common that they don’t use many third party services and that they load progressively the content in order to limit the number of requests. The articles are rather sober but the additional content of recommendation weighs down these article pages.

Flop 3 (most impactful articles)

Sud Ouest 

This article is long unlike many other articles in the ranking. However, if it is the most impactful of the ranking, it is because it uses a lot of images in JPEG and PNG format, JS and CSS scripts for formatting, fonts, and contains audio files, and ads. The site is not at all sober and uses a lot of requests and third party services.

La Nouvelle République  

The article contains a lot of images, scripts and links to other articles. These practices add to the environmental impact of the site. With this, the site uses a lot of requests and third party services. This increases the environmental impact but also reduces the performance of the site which takes longer to load. A good point to raise is the number of fonts used which is 1 and in woff2 format.

Rfi 

The article is sober because of its subscription system which hides a good part of it. There are few images but most of them are in png format which is not necessarily ideal from an efficiency point of view. However, we would like to point out that the most impactful image is in Webp format, which is one of the best compression formats. We find here a lot of requests and the multiplication of third party services.

Conclusion for the flop 3 articles

The main problem of the sites in the flop 3 is the number of requests, especially towards third party services. We can also add the lack of sobriety of these pages and question the functional needs.

Conclusion

When we look at the environmental impacts of online press sites, we can see that good efficiency practices are already widely adopted, especially regarding the management of images (which remain an essential vector to attract the Internet user and support the subject).

However, there are still efforts to be made to be more sober, in particular concerning the use of third-party services but also the use of video (and to a lesser extent fonts). As seen above, this is closely linked to the business model behind these newspapers. The model in question, beyond its considerable environmental impact, demonstrates once again its negative effects on the user experience, notably through the collection of personal data and the security risks that this may entail.

It is also worth noting that improvements to reduce the environmental impact of these sites would be all the more effective as some of them are read several hundred thousand times a day.

It would also be interesting to take a closer look at the accessibility of the sites in question (if this has not already been done), as this is fundamental for everyone to be able to access information related to current affairs (which is an essential part of a citizen’s life).

Discover our solutions

Best Practice: Limit the number of third-party services

Posted on by Laurent DEVERNAY
Reading Time: 5 minutes

Content in 3 minutes

What is a third party service? They are services that allow to integrate a functionality or a complex content via code that we have little control over. The 10 most frequent third-party services block the loading of sites for about 1.4s. Google fonts are the most common service even though its use may contravene the RGPD.

The impacts of third-party services are far from negligible. In terms of environment, performance, security but also personal data management, attention grabbing and sometimes even accessibility. It is therefore advisable to use them as little as possible. This last point is supported by the RGESN and GR491 guidelines.

Best practices for limiting third-party services?

  • Your site is under construction: do not include third-party services
  • Your site is already built: limit the addition of content or remove certain third-party services (by checking compatibility with the design, marketing or strategy already in place)
  • Your site will load faster: a bonus for sustainable digital as well as user experience.

A concrete example:
During a client audit, it was decided to remove the twitter feed present on several pages of the site. Results:
A significant improvement in metrics and indicators. In particular:

  • Reduction of the battery discharge rate by 36%.
  • Reduction of the CPU load by 76%
  • Reduced the volume of data transferred by 68%.

Context

Third-party services are present on 94% of websites and represent 45% of the requests made by all websites. These services are used to integrate a functionality or complex content via code most often hosted on another site, with little or no control over it. Thus, it can be an analytics solution, a video, the contents of a social network, a captcha, trackers, etc.
Here is a list (sorted by categories), built by Patrick Hulce.

Tweet by Marcel Freinbichler

You can find more information about them as well as statistics on their use in the Third-parties chapter of the Web Almanac : https://almanac.httparchive.org/en/2022/third-parties

The statistics show that the 10 most frequent third-party services block the loading of sites for an average of 1.4s and that technical optimizations are often possible (minification, compression, delayed loading, but also removal of obsolete JS code). Also, most of the most used third-party services are linked to Google.

It is interesting to note that Google fonts are the most widely used service, even though their use may contravene the GDPR. One way to fix this is to host the corresponding files yourself. Of course, the best thing to do is to stick to the available system fonts as much as possible.

All this shows that the impact of third-party services, especially on websites but also on mobile applications, is far from negligible. In terms of environment, performance, security, but also in terms of personal data management, attention grabbing and sometimes even accessibility (see the Temesis article on this subject [FR]: https://www.temesis.com/blog/contenus-tiers-exemptes-deroges-audites/). It is therefore advisable to use them as little as possible.

At Greenspector, this is something we look at very closely for our customers, using specific tools to list third-party services and estimate their respective impacts. This is a significant added value of measurement. It is also an essential step in reducing environmental impacts and improving the user experience.

Today, let’s look at a best practice of sobriety, which consists of integrating as few third-party services as possible into a digital service.

Existing reference systems

This criterion can be found in the Specifications family of the RGESN [FR](Référentiel Général d’écoconception de services numériques): 2.5 – Has the digital service taken into account the environmental impacts of third-party services used during their selection ?

This is also found in GR491 (Reference Guide to Sustainable Design of Digital Services): Are third-party services (social network feeds, social wall, carousels, google maps, etc.) not used for convenience to compensate for the lack of content production resources ?

Good practice

Objective

This sobriety best practice consists in limiting the number of third-party services in a digital service.

When to use it?

It occurs at the design stage of the service (choose not to include the services in question) but also when adding content (for example, do not integrate directly a Youtube video in an article).

Ease of implementation

It is technically easy to remove a third-party service. On the other hand, this removal may require upstream discussions on design, marketing or strategy. That’s why this decision should be made as early as possible in the project.

Estimated gains

The gains can be significant in several areas related to Sustainable Digital. In all cases, this proves beneficial to the user.

Case study

While working for a client (initial audit then daily measurements and expertise), it was decided to remove the Twitter feed on all pages of the site. The modification took place on February 1st, 2023. The measurements on the homepage highlight the gains linked to this modification, which can be observed by measuring the consumption of IT resources and energy via the Greenspector tool.


Evolution of page load metrics between February 1 and 2, 2023


Ecoscore (web benchmark) of February 1st 2023

Ecoscore (web benchmark) of February 2, 2023

We can see here a significant improvement of the metrics and indicators. In particular:

  • 36% reduction in battery discharge rate
  • 76% reduction in CPU load
  • Reduction in the volume of data transferred by 68%.

Following the environmental projection via the Greenspector methodology, we can see that the impact of this homepage was, before deleting the Twitter feed, 0.95g eqCO2. After deleting this feed, it is 0.54g eqCO2.

The page in question has more than 2.5 million views per year.

For the sake of simplicity, we leave aside here the contributions of the client-side cache (even if they are certainly non-negligible), the disparities of time spent by each person on this page, as well as the modifications made during this time.

We go from an impact of 2.38T eqCO2 to 1.36T eqCO2 over one year, which is a reduction of more than 40% of the impact in terms of greenhouse gas emissions.
For the other impacts assessed, the trend is similar. Thus, over one year, we go from 383047 to 224675 L of water consumed and from 383 to 230 m² of land use.

To go further

For third-party services that are deemed essential, there are methods to reduce their impact (via efficiency). This can include integrating an interactive map or video as a clickable thumbnail.

Some articles approach the subject from a web performance perspective: