Category: Applications sobriety

What is the environmental impact of mobile games in 2023?

Reading Time: 11 minutes

Previously, we provided a ranking of mobile video games :

Since the most recent update (beginning of 2020), downloads and revenues have significantly surged. Today, mobile games account for most of the digital gaming revenue (source: https://www.statista.com/topics/1680/gaming/#topicOverview ), notably experiencing unprecedented increases during the pandemic (source: https://www.statista.com/statistics/511639/global-mobile-game-app-revenue/

If we took at the most downloaded mobile games in 2022, we get the following ranking: 

1- Subway Surfers
2- Stumble Guys
3- Roblox
4- Candy Crush
5- Race MAster 3D
6- 8 Ball Pool
7- FIFA Mobile
8- Merge Master – Dinosaur Game
9- Garena Free Fire
10- Bridge Race

Source : https://www.statista.com/statistics/1285134/top-downloaded-gaming-apps-worldwide/

App namePackage nameVersion
Subway Surferscom.kiloo.subwaysurf3.16.1
Stumble Guyscom.kitkagames.fallbuddies0.54
Robloxcom.roblox.client2.589.593
Candy Crush Sagacom.king.candycrushsaga1.259.0.1
Race Master 3Dcom.easygames.race4.0.4
8 Ball Poolcom.miniclip.eightballpool5.13.3
FIFA Mobilecom.ea.gp.fifamobile18.1.03
Merge Master - Dinoasaur gamecom.fusee.MergeMaster3.11.0
Garena Free Firecom.dts.freefireth1.100.1
Bridge Racecom.Garawell.BridgeRace3.23

The first thing that stands out compared to the previous mobile games ranking is that three games reappear: Subway Surfers, Candy Crush Saga, and 8 Ball Pool. In terms of downloads, Subway Surfers remains in the first position of the ranking 

Looking at the APK deployments, we notice that the frequency of new APK versions varies widely: at most one every 15 days or so for Candy Crush Saga or Race Master 3D, and at least one per week for Roblox. These updates undoubtedly have an environmental impact. For the sake of simplicity, we will focus here on the impact of their usage, assuming the conditions of the initial game launch. The functional unit would thus be: “starting a game session for the first time on a mid-range Android mobile device using Wi-Fi. 

This will allow us to assess the environmental impacts of these games to compare them.

Did you know?

In 2021, Google commissioned and published a study on the future of mobile gaming (https://games.withgoogle.com/reports/beyondreport/ ). 

While the initial intention was to assess the expected increase in revenue generated by mobile games, some interesting facts have emerged: 

  • More than half of mobile gamers are female.  
  • 73% of gamers spend money on these games. 
  •  Most of the gamers are in Asia and the Pacific region. 

Methodology

Definition of the User Journey

For measurement, it was essential but challenging to define a common user journey for all the games. As mentioned earlier, the obvious choice for us was a journey starting with the launch of the application and then the start of a game session. However, things quickly became more complex. 

For most of the games studied, the initial launch involves several intermediate screens: 

  • Gathering GDPR-related consent 
  • Input of the player’s age 
  • Gathering consent related to advertisements 
  • Creating a profile or linking to an account on a third-party service (Google, Facebook, etc…) (It would be interesting to analyze the third-party services used in each game in more detail at a later stage) 
  • Loading content updates 

If the first three items on the list are only filled out once, content updates can occur unexpectedly and independently of the APK update. 

Linking the games to a Google account made automating the Bridge Race game too complex compared to the time we had decided to allocate for this study. This led to the exclusion of the game from the sample studied here. We observed differences in behavior when linking the Google profile depending on the device used. In the future, we will consider conducting a complementary study that directly assesses the impacts of one minute of gameplay using Greenspector’s Testunner tool.

In the end, we still attempted to automate the following user journey for each game: 

Step 1: Launching the application

    Step 2: Loading the title screen 

    Step 3: Starting a game session 

Between these measured steps, some actions for consent validation or login that were not measured because they were absent in some applications. The significant number of elements to click on and intermediate screens during the initial launch of the FIFA Mobile game prevented us from reaching the actual gameplay launch. The measurements and calculations presented here are therefore below the actual values but have been retained to highlight other aspects. 

As part of this study, the data was collected between August 11th and August 16th, 2023, using Greenspector Studio. We utilized the Greenspector Domain-Specific Language (GDSL) to create test scripts that automatically replicate the actions to be performed on a mobile phone. The Testrunner module then enabled us to take measurements on an Android smartphone, providing us with energy and resource consumption (memory, CPU, data exchanged), and response times for each step of the user journey. 

Subsequently, based on these measurements, the impact model integrated into Greenspector Studio assessed the corresponding environmental impact and Eco-Scores. As a reminder, in the case of a user journey measurement, the overall Eco-Score is divided into three Eco-Scores: Performance, Mobile Data, and Energy. Each is rated on a scale from 0 to 100, with 100 being the best score. Each of these scores is derived from the ratings achieved for each measurement step, which depend on predefined thresholds. For instance, in the case of mobile data for loading steps: 

Hypotheses

During this evaluation, we started from the perspective of the initial application launch. This inherently represents a more impactful journey than subsequent uses but provides a better understanding of the game’s operation. It’s worth noting that during actual gameplay, there are often regular updates that can also have an impact. 

Measurement Context 

  • Device: Samsung Galaxy S9, Android 10 
  • Network Connection: Wi-Fi 
  • Screen Brightness: 50% 
  • Tests were conducted over at least 3 iterations to ensure result reliability. 

Hypotheses for Environmental Projections 

  • User Location: 100% World
  • Server Location: 100% World
  • Devices Used: Smartphones only 

The environmental footprint depends on the location and type of application servers, user locations, and the type of devices they use. We have focused our study solely on the use of applications on smartphones. We have also assumed that users and servers are overwhelmingly located outside of France, as precise statistics were unavailable.

Résultats 

App nameAPK size (Mo)Data exchanged (MB)Total battery discharge (mAh)
Subway Surfers177,15,318,3
Stumble Guys19846,444,9
Roblox160,81715,4
Candy Crush Saga92,212,122,3
Race Master 3D182,633,217,1
8 Ball Pool89,907,8
FIFA Mobile180,4161,534,3
Merge Master - Dinoasaur game17526,124,5
Garena Free Fire400,85,719,8
Bridge Race170,9

First and foremost, we observe that APK files for initial downloads are mostly between 160 and 200 MB, which is already quite substantial. Only Candy Crush Saga and 8 Ball Pool are below this range, at around 90 MB. For Free Fire, the file size goes up to 400 MB! 

The duration of usage is estimated to be a little over 20 minutes per day according to this study by Statista: https://www.statista.com/statistics/1272891/worldwide-game-apps-time-spent-daily-age/. The impact of installation and updates, assuming daily or near-daily usage, has therefore been excluded from the present study. However, it’s possible that for a game used sporadically over time, updates might have a more significant impact than actual usage. In such cases, there is a risk for the game publisher that users may lose interest in the game. While the APK update can be managed daily like other mobile applications, it’s the content update during the game launch that can become problematic. As we will discuss later, everything is designed to encourage daily play. 

Regarding the data transferred for the initial launch of the game, most games require a few additional megabytes. Only 8 Ball Pool does not require any data transfer. Conversely, FIFA Mobile, Stumble Guys, Race Master 3D, and Merge Master require downloading a few tens of megabytes. In particular, FIFA Mobile requires downloading over 160 MB, which includes updating the content based on sports news. This is seen as added value by the publisher but is considered a poor practice from a sustainability perspective. Initially, efforts should focus on how to reduce the technical size of the necessary information, and ideally, limit the refresh frequency and the comprehensiveness of the information. 

These substantial data transfers are often correlated with degraded performance. For instance, for Stumble Guys and FIFA Mobile, it sometimes takes 1 to 2 minutes for the screen that allows the game to appear to be launched. For half of the games, simply launching the application takes between 10 and 20 seconds, which is particularly long. In the case of Roblox, the title screen loads quickly, with content loading occurring after selecting a game (and generally taking quite a while). The longest launch time is for 8 Ball Pool, but for this game, no data is transferred, and the impact on the battery is relatively low (particularly due to the limited number of animations). This game ultimately has the best scores. In general, the scores obtained are as follows:

We can observe that 8 Ball Pool is the game with the best scores, mainly due to what we just discussed (few animations, no data transferred). In this regard, its operation during launch is closer to what I would tend to associate with “old-school” mobile games: offline and relatively simple games (though not as basic as Snake and similar games). It’s worth noting that once the onboarding process of 8 Ball Pool is completed, it reverts to a more typical operation, including online gameplay by default, which would need to be measured separately. 

In comparison, the other games have lower scores. Only Race Master 3D and Subway Surfers stand out somewhat. It appears that the games studied here have scores that are at best around the average, with very few exceptions. All of this may indicate concerns regarding efficiency or sustainability but is more likely attributed to the nature of these applications themselves: animation, data exchanges, and often a multitude of persuasive design mechanisms. We will revisit this later. 

As for the environmental impacts, the calculations lead to the following results: 

App nameGHG emissions in gCO2eWater consumption in LLand use in cm2
Subway Surfers1,10,11,3
Stumble Guys6,50,63,6
Roblox2,50,21,6
Candy Crush Saga1,90,21,6
Race Master 3D4,40,41,9
8 Ball Pool0,200,6
FIFA Mobile20,11,66,1
Merge Master - Dinoasaur game3,80,42,4
Garena Free Fire1,10,11,3

The high impacts observed here are correlated with findings made previously, particularly significant data loads that lead to performance degradation and increased battery consumption on the device. Consequently, the games with the highest impacts are FIFA Mobile (far ahead of the others), as well as Stumble Guys and Merge Master. Unsurprisingly, 8 Ball Pool is also the least impactful game in this regard. 

Analyse 

The measurements conducted highlight certain design choices that directly contribute to increased environmental impacts. 

As mentioned earlier, the length of onboarding processes often highlights three interrelated concerns: the collection of personal data, advertising, and more broadly, the use of persuasive design mechanisms. As a reminder, these mechanisms are aimed at keeping the user engaged with the application for as long as possible and are present in many applications and websites. Ethical Designers, among others, have a keen interest in this matter. However, let’s delve into the case of the games studied here. 

The RGPD consent, as well as that related to advertisements (and sometimes age verification), aim to minimize the display of ads during the game. This, of course, serves as a source of revenue for game developers. The level of intrusiveness of advertisements can vary. Often, players are offered the option to watch video ads in exchange for rewards or to continue playing. Sometimes, it’s even possible to purchase the game to get rid of these ads. The vigilance and attention of players make them more susceptible to such suggestions, which is linked to persuasion mechanisms. 

In the field of design, particularly in terms of user interface and interaction design, the concept of “gamification” has been discussed for several years. This involves incorporating game elements to increase user engagement and make the experience more enjoyable. This shouldn’t be underestimated, as some games are very skilled at attracting and retaining players. Furthermore, by taking this approach further, it’s possible to encourage players to spend money. 

For example (among many others), let’s consider Candy Crush Saga. It would be possible to offer this game in a more limited form, focused solely on the gameplay itself: progressing through levels of varying difficulty with the objective of clearing candies or obstacles. The rules and challenges are simple and form the core of the game as such. However, various artificial elements have been added here: 

  • Bright colors, visual effects, and sounds are used to enhance the impact of user actions. This is referred to as feedback. 
  • Limited resources (lives, bonuses, etc.) are introduced to create the need and, more importantly, a sense of scarcity. Often, it’s possible to replenish these resources by watching videos or spending money (virtual or real). 
  • Progress bars (level or otherwise), trophies, rewards, avatars, and scores are incorporated to encourage constant improvement and enable users to compare themselves with others. This leads us to another important element of gamification and attention capture: 
  • Social connection: seeing what others are doing, comparing oneself with them, and allowing them to see what one is doing. Like social networks (often through a connection with them), this mechanism encourages more and better gameplay but also creates attachment to the game through connections with other players. This is accomplished through online gameplay or by connecting with social networks (for sharing achievements, finding players, etc.). 
  • Randomness to create addiction (analogous to slot machines). Correlated with limited resources, this factor appears in the form of random rewards, sometimes in the form of loot boxes (https://medium.com/behavior-design/hooked-on-loot-boxes-how-design-gets-us-addicted-79c45faebc05 ). This is expected to increasingly lead to legal framework changes for regulatory purposes: https://www.gamesindustry.biz/european-parliament-votes-to-take-action-against-loot-boxes-gaming-addiction-gold-farming-and-more

All of this contributes to the player’s motivation, particularly through a subtle balance of their frustration between two states that the player consciously or unconsciously seeks to achieve: 

  • Flow: The player’s state of concentration where objectives seamlessly follow one another. This is typically the period when time seems to fly by. Consequently, this can encourage the player to invest (time, virtual resources, real money) to maintain this state and avoid frustration. 
  • Fiero: A state of satisfaction linked to overcoming a particularly formidable obstacle (in non-mobile video games, this is one of the basic mechanics in what is sometimes called “Souls-like” games: games that emphasize player mastery to offer a very high challenge). 

The game design often revolves around these two states, directly or indirectly (gradual increase in difficulty, rewards, etc.). This delicate balance will be crucial for the quality of a game and, more importantly, the gaming experience. For publishers, it also affects the time players spend on the game and the money they spend. This can sometimes lead to higher environmental impacts. 

Directly, it encourages players to spend as much time as possible on the game by utilizing online play and links with social networks, displaying advertisements, and designing interfaces that are rich in information and visual effects. 

Indirectly, it promotes consumption (spending money) but also overconsumption (wanting more and generating frustration). This leads to environmentally unfriendly behaviors that go against the principles of environmental sustainability. 

It’s worth noting that the video game industry is becoming increasingly aware of its environmental impacts. 

Polygon has shown interest in the topic: https://www.polygon.com/features/22914488/video-games-climate-change-carbon-footprint

This growing awareness often leads to actions as well as greenwashing efforts (example: https://www.ouest-france.fr/high-tech/jeux-video/ecologie-le-secteur-du-jeu-video-fait-il-du-greenwashing-355c56fc-0c3d-11ee-8e71-2cd44afe92ef?utm_source=pocket-newtab-bff [FR]). 

However, more and more relevant resources are emerging on the subject: https://playing4theplanet.org/resources. Some manufacturers are looking into the environmental impact of their hardware, while publishers are trying to assess their environmental footprint. The regular turnover of gaming machines and the release schedules of games themselves pose inherent environmental challenges. Online gaming and digitization do not necessarily make things better. In this regard, you can refer to the study by ADEME (French Agency for Ecological Transition) on the digitization of cultural services. Similar to what can be observed, for example, with websites, optimizations are possible (efficiency), but the real challenge lies in sustainability (in contrast to the extravagance of open-world games). There are also broader issues to consider:   

  • How video games can implement systems that do not encourage excessive consumption (accumulation logic, power race, etc.) 
  • How to strengthen our connection to nature? How to introduce game mechanics that go beyond accumulation and competition? How to portray possible (e.g., The Climate Trail ) or desirable futures? 

Conclusion 

Mobile games are an integral part of many people’s daily lives, making it even more essential to study their environmental impacts (although other aspects of Responsible Digital, such as the attention economy, accessibility, and personal data management, remain relevant). While technical optimizations are still possible, the crux of the issue lies in their design and the behaviors they induce (including the risk of addiction and financial risks for players). 

Video games, in general, could and should become a catalyst for raising awareness about ecological issues. Just as with accessibility, some initiatives have already begun, and it will be interesting to see how they evolve in response to the urgent challenges at hand. 

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

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.

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

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

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.

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.

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 most used dating applications in France?

Reading Time: 5 minutes

Article Summary

In 2022, a third of the French population used a dating application. But what is the environmental impact of looking for love?  

We measured the 10 most popular mobile dating apps (on a common user journey).  

Here is the Flop 3 (the most impactful apps): Lovoo, Grindr and OkCupid.  

Here is the Top 3 (the least impactful apps): Bumble, Tinder and Happn.  

The impactful number: Projected over a month, the impact of these 10 dating apps is 9614 tons EqCO2. That’s equivalent to driving around the Earth 1102 times in a combustion engine car (counting 23 million French users with an average use of 55 minutes per day). 

Note that many people use several applications (even if it means maximizing their chances 😉 ) 

Since the creation of the geolocation feature for smartphones, dating applications have become very popular. Today, there are more than 8,000 of them in the world, including 2,000 in France. Nearly a third of French people use or have used a dating application. Moreover, users spend 55 minutes a day on them. 

Given these figures, we can wonder about the impact of these applications on the environment.

The dating applications we chose to measure 

Three criteria were used to choose the 10 applications we evaluated: popularity, free access, and availability on Android. Indeed, we chose to evaluate applications that are free to use because they are among the most used. Less than 3% of French people use paid applications.  

Regarding the evaluation, a common scenario was determined that is compatible with all applications, namely consulting profiles, liking or not liking the profile, and sending a message. This allows us to get as close as possible to the real use of the application.

Step 1: Launching the application

Step 2: Dislike a profile (by swiping or clicking on “reject”)

Step 3: Like a profile (or send a tap) (by swiping or clicking on “like”)

Step 4: View the details of a profile (by scrolling)

Step 5: Like the profile (or send a tap) (by swiping or clicking on “like”)

Step 6: View the last conversation

Step 7: Send a message in this conversation

Ranking of the environmental footprint of the 10 most used free dating applications in France

The 3 most impactful applications: are Lovoo, Grindr, and OkCupid.

The 3 applications with the least impact: are Bumble, Tinder, and Happn.

We note that the most impactful application (Lovoo) has an impact 5 times greater than that of the least impactful application (Bumble).   

The average carbon impact of these applications is 0.25 gEqCO2 for an average duration of 25 seconds. This is equivalent to driving 1 meter in a combustion engine car.

Projecting carbon impacts over one month

To project the impact of the applications in this ranking, we will use the following assumptions  

  • 100% of users are in France   
  • 100% of users are on mobile phones  

Various studies show that one-third of the French population (23 million individuals) used dating applications in 2022 with an average of 55 minutes spent on them.   

Projected over a month, the impact of these 10 dating apps is 9614 tons of CO2e. This is equivalent to driving around the Earth’s circumference 1102 times in a combustion engine car.   

For the highest-ranked application (Bumble), for an average of 55 minutes per day for a month, a user has an impact of 579 gEqCO2. This is equivalent to driving 2.7 kilometers in a combustion-engine car.   

For the lowest ranked application (Lovoo), for an average of 55 minutes per day for a month, a user has an impact of 2.7 kgEqCO2. This is equivalent to driving 12.4 kilometers in a combustion-engine car.

Greenspector experts focus on the soberest application: Bumble

The Bumble application is particularly sober during the launch and opening phases of the message page. During these phases, little data is loaded and there is no major energy overconsumption.   

On the page where the profiles are displayed, only about 4 are pre-loaded. Animations are also limited during actions on the application and a delayed loading of photos has been implemented for those not seen on the screen. 

Greenspector experts focus on the least sober application: Lovoo

The Lovoo application is at the bottom of the ranking with a much higher impact than its competitors. Although the performance is excellent except for the rather long opening time of the application, the energy and data consumption is very high. The integration of ubiquitous advertising with carousels or videos contributes greatly to this over-consumption.   

Opening the page to swiping loads an average of 1.9 MB of data. Many profiles are pre-loaded to be displayed smoothly after each swipe compared to competing applications. More than 20 profiles are preloaded compared to about 4 on other applications.

Summary 

Between the soberest dating application and the least sober application, we note that the impact is 5 times more important.   

Note that the differences are mostly due to animations, the consequent loading of data (photos, videos…), and the management of the elements displayed on the screen. By applying good practices, it is possible to reduce the environmental impact of applications.   

As a user, we can reduce our impact by using only one dating application if we have several.   

And to go further in digital sobriety, it is always possible to meet people in real life!

Results table

Applications Version Energy (mAh)Data exchanged (MB)Carbon impact (gEqCO2)Water Footprint (Litres)Soil Footprint (cm²)Scenario duration (seconds)
Lovoo 143.0 58,95,30,70,10,225,3
Grindr 9.2.0 44,53,30,40,00,221,0
Happn 26.31.2 36,60,90,20,00,130,7
Fruitz 3.6.2 41,40,90,20,00,127,6
Hinge 9.15.1 36,61,00,20,00,129,0
Adopte un mec 4.9.21 86,00,60,20,00,230,4
Badoo 5.306.0 37,21,50,20,00,123,6
OKCupid 74.1.0 42,11,50,20,00,120,2
Bumble 5.307.0 35,60,80,10,00,123,2
Tinder 14.2.0 55,20,60,10,00,121,4

 

For each of the applications, measured on a Samsung Galaxy S9 (Android 10), the measurements were carried out using scripts based on GDSL (Greenspector Domain-Specific Language). This language allows automated actions to be performed on a phone. The measurements were carried out in February 2023.  

Details of the scenario :   

  • Disliking a profile   
  • Liked a profile   
  • View the details of a profile (see the information and the different photos) and like it   
  • Go to the inbox and send a message in the last conversation held  

Each measurement is the average of 5 homogeneous measurements (with a small standard deviation). The consumption measured on a given smartphone according to a wifi type network can be different if one is in 4G or 3G.  

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

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

The impact of our videoconferencing uses on mobile and PC! 2022 edition

Reading Time: 5 minutes

We have decided to proceed differently for this year’s ranking 2022. Unlike the 2021 edition, we have reduced the number of applications measured. We have both taken measurements on the phone and also on the PC to meet demand. It is the purpose of these new measures to compare how the solutions stack up in terms of environmental impact (Carbon) on different user scenarios but also on two platforms: PCs and phones.

We compared 10 apps: BlueJeans, Google Meet, Go To Meeting, JITSI, Skype, Teams, Webex, Whereby, Zoho Meetings et Zoom.

For each of its applications, measured on an S7 smartphone (Android 8) and on a computer, the following three scenarios were carried out through our Greenspector Test Runner, allowing manual tests to be carried out over a period of 1 minute in one- one-to-one: :

  • Audio conference only
  • Audio and video conferencing (camera activated on both sides)
  • Audio conferencing and screen sharing

Learn more about the methodology.

Projected ranking in the carbon impact of videoconferencing apps (gEqCO2) on mobile

Scenario / Year1 min audio videoconference1 min audio + camera videoconference1 min audio + screen sharing videoconference
20220.31 gEqCO21.10 gEqCO20.54 gEqCO2
Equivalent in meters travelled in a light vehicle2,76 meters9,82 meters4,82 meters

Audio video conferencing has an average impact of 71% less than cameras active and 42% less than sharing a screen.    

The Top 3 of one minute of videoconference on average: Zoho Meeting (0.49 gEqCO2), Microsoft Teams (0.513 gEqCO2) and Whereby (0.533 gEqCO2). Zoho Meeting, first in this ranking on the carbon impact side, has an impact 2.2 times less than GoToMeeting, the last in this ranking. The average of this ranking is 0.657 gEqCO2, 4 solutions are above.

The main part of the Carbon impacts is on the user device part (61%), followed by the Server part (23%) and finally the Network part (16%).

Here are the three mobile applications with the least impact in terms of Carbon according to the scenario: 

Audio (Top 3)Audio + camera (Top 3)Audio + Screen Sharing (Top 3)
Microsoft TeamsWherebyMicrosoft Teams & Zoho Meeting
Cisco Webex MeetingZoho MeetingZoom & Google Meet
JITSI MeetTeams & ZoomCisco Webex Meeting

Energy consumption of videoconferencing apps (mAh) on mobile

Here are the power consumption averages for the three phone scenarios: 

Scenario / Year1 min audio videoconference1 min audio + camera videoconference1 min audio + screen sharing videoconference
20226.68 mAh14.29 mAh7.82 mAh

A single minute of audio video conference consumes 53% less energy (or 2.1 times less) than with cameras activated, and 14.5% less than sharing a screen.

Energy consumption of videoconferencing apps (mAh) on PC

Here are the computer energy consumption averages:

Average: 1mn of audio videoconferenceAverage: 1mn of audio + camera videoconferenceAverage: 1mn of audio + scree sharing videoconference
17.25 mAh23.65 mAh22.82 mAh

Here, one minute of audio video conferencing consumes 27% less (or 1.4 times less) energy than with cameras activated and 24% less than sharing a screen. Therefore, depending on the scenario, we see a much more significant difference in energy consumption on the telephone.

Zoho Meeting (76.21 mAh), BlueJeans (81.70 mAh) and Microsoft Teams (83 mAh) are the top 3 in energy consumption (all scenarios combined). First in this ranking in terms of energy consumption, Zoho Meeting consumes 1.4 times less energy than the last.

Here are the three mobile applications with the least impact in terms of Carbon according to the scenario: 

Audio (Top 3)Audio + camera (Top 3)Audio + Screen Sharing (Top 3)
Blue JeansZoho MeetingZoho Meeting
Cisco Webex MeetingZoomTeams
Google MeetTeamsBlueJeans

Exchanged data from videoconferencing apps (MB) on mobile

Here are the averages of the data exchanged for the three scenarios:

Scenario / Year1 min audio videoconference1 min audio + camera videoconference1 min audio + screen sharing videoconference
20220,88 MB10,34 MB4,49 MB

Data consumption is where the gap between tools and uses is widening.

On average, one minute of audio video conferencing consumes 91% less (or 12 times less) data exchanged than with activated cameras and 80% less than sharing a screen.

The Top 3 (all scenarios combined) in data exchange: Whereby (4.54 MB), Zoho Meeting (8.39 MB) and Skype (9.68 MB). Whereby (via Firefox) first in this ranking in terms of data exchanged consumes 9.2 times less than the last in this ranking: GoToMeeting.

Here are the three least data-consuming apps according to the scenario:

Audio (Top 3)Audio + camera (Top 3)Audio + Screen Sharing (Top 3)
Blue JeansWherebyZoho Meeting
Zoho MeetingZoho MeetingSkype
WherebySkypeZoom

For our daily use of videoconferencing: 

In the study carried out in 2021, we indicated that videoconferencing is preferable to travelling by car. Indeed, this solution is less polluting, however, beware of rebound effects! Since the global pandemic, working from home has become widespread in many companies, but this practice encourages more people to live away from their workplaces. As a result, the environmental benefits of working from home can be offset depending on the pace of remote working and the means of transport used.

In addition, it is necessary to draw attention to the increase in energy consumption of the generated hearths. It is possible that this increase might compensate for the drop in energy needs on business premises, but this must be considered on a case-by-case basis.

Examples:

Measured versions:

  • Microsoft Teams (4.10.1) / computer (1.5.00.10453) 
  • Zoom (5.10.4) / computer (5.10.7.3311)
  • Google Meet (2022.05.15.450927857) / light version on Firefox
  • Cisco Webex Meetings (42.6.0.239) / computer (42.5.0.22187) 
  • GoToMeeting (4.8.1) / light version on Chrome
  • BlueJeans (2.2.0.142) / computer (2.29.1-3) 
  • Skype (8.82.0.403) / computer (8.83.0.411
  • Whereby (2.3.0) / light client on Firefox
  • Zoho Meeting (2.2.1) / light version on Firefox
  • Jitsi Meet (21.3.0)/ light version on Firefox

For each of its applications, measured on an S7 smartphone (Android 8), user scenarios were carried out using our Greenspector Test Runner, allowing manual tests.

Once the app is downloaded and installed, we run our measurements on the app’s baseline and original settings. No changes are made (even if some options allow you to reduce energy or resource consumption: data saving mode, dark theme, etc. However, we encourage you to check the settings of your favourite application in order to optimize its impact.

The average of five homogeneous measurements (with a low standard deviation) is used for each measurement. The consumption measured on the given smartphone according to a Wi-Fi type network may be different on a laptop PC with a wired network for example. For each of the iterations, the cache is first emptied.

As for computer measurements, the Yocto-Watt from YoctoPuce was used to measure energy consumption. In the same way as for mobile applications, heavy clients, when one existed, were downloaded and then installed without changing the basic parameters. This chip, therefore, only allows you to have energy consumption, which is why the carbon impact and the data exchanged in this article only concern the telephone part. Additional resource on the Yoctopuce.

Find out how Greenspector assesses the environmental footprint of using a digital service. (Full methodology)

What is the environmental footprint of the ten most-visited m-commerce sites and applications in France?

Reading Time: 7 minutes

Introduction

The e-commerce market in France is experiencing very significant growth. E-commerce sales have increased from 57 billion in 2014 to 112.2 billion euros in 2021. The health crisis is partly responsible for this increase. It has considerably increased online shopping and created habits among the French. Among these sales, those on mobile have experienced impressive growth. They increased by 13% in 2021. Nearly one in two French people (48%) now make purchases via their phones. One in three French people (34%) buys via their phone at least once a month.

Shopping cart abandonment accounts for $18 billion in lost sales every year. Mobile abandonment rates are higher (97%) than desktop abandonment rates (between 70 and 75%). The reasons are multiple: the price, the shipping costs, the speed of delivery, the discounts available, but also the loading time of the site (Source). M-commerce sites and applications, therefore, have every interest in demonstrating performance and digital sobriety.

What about the ten most visited e-commerce sites and applications in France? What are the most frugal sites and applications on which to do your e-shopping in complete sobriety?

Choice of sites and applications

To determine which sites and applications to study, we relied on the ranking of e-commerce sites and applications produced by Médiamétrie and the Fevad (Federation of e-commerce and distance selling). These are the most popular sites and applications in France in the 2nd quarter of 2021.

This study’s distinguishing feature is that it is based on a common scenario that goes from the search and consultation of a product to the consultation of the basket before payment. Thus, we can be as close as possible to the real uses of Internet users and mobile users.

Ranking of the environmental footprint of the ten most popular e-commerce sites in France

The three least impacting sites are Leclerc, Cdiscount et eBay. 

The three most impactful sites are Amazon, Carrefour et Veepee. 

We observe more than 2.7 times more impact between the least impacting site (Leclerc) and the most impacting site (Amazon) in this ranking.

The average carbon impact of these ten websites is 0.92 gEqCO2 for an average duration of the scenario of 1 minute and 54 seconds, i.e. the equivalent of 8 meters travelled in a light vehicle.

Projection of carbon impacts over one month

It is estimated that 55% of global e-commerce traffic is done on mobile devices, compared with 39% on PCs and 6% on tablets (Source).

If we project the carbon impact of these ten e-commerce sites for an average of 37.41 million visits per month lasting 5 minutes and 54 seconds, this impact would be 439.6 tonnes of CO2eq per month (87.5 tonnes on mobile, 341.0 tonnes on PC and 11.1 tonnes on tablet). It is the equivalent of 98 times the circumference of the Earth travelled by light vehicle.

Regarding the best website in this ranking (Leclerc) for 9.99 million visits/month with an average duration of 3 minutes, this impact would be 37.0 tonnes of CO2eq per month (5.9 tonnes on mobile, 30 .3 tons on PC and 0.8 tons on a tablet). It is the equivalent of 8 times the circumference of the Earth travelled by light vehicle.

For the worst website in this ranking (Amazon) with 164,32 million monthly visits and an average duration of 8 minutes, the carbon impact would be 2639.4 tonnes of CO2eq (588.1 tonnes on mobile, 1978.4 tons on PC and 72.9 tons on tablet). It is the equivalent of 588 times the circumference of the Earth travelled by light vehicle.

The Leclerc website stands out especially in terms of the stages of research, viewing a product and viewing the basket. Indeed, this site consumes little energy on these phases compared to its competitors. Only essential information is present on this search page (name of products, price, availability). When viewing the product sheet, customers have the option of adding the item quickly to their basket, and drop-down menus are provided for additional information. Also, this site exchanges the least amount of data to complete the scenario.

 On the search page of a product, many good practices are applied. There are few network exchanges with 12 HTTP requests and a single CSS file. The images are lazy-loaded.

The Amazon website gets its poor ranking mainly from the research and viewing stages of a product. Indeed, this site consumes a lot of energy during these phases, and exchanges a lot of data. There are 9.10 MB of data exchanged for the research phase (compared to 1.03 MB for Leclerc), and 5.58 MB of data exchanged for the product sheet (compared to 0.18 MB for Leclerc). When searching, a lot of information appears (indication “Suggestions”, “Sponsored”, “Amazon Choice” or “No. 1 in sales”, product name, rating, number of reviews, price, reduction, date of delivery). An autoplay advertising video even appears in the middle of the products. 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…). In addition, 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 109 HTTP requests and 9 CSS files. The deferred loading (lazy-loading) of images is not applied, which implies a loading of images not visible on the screen. This practice should be avoided because the user will not necessarily scroll to these images.

Ranking of the environmental footprint of the then most popular m-commerce apps in France

The three least impacting apps are eBay, Leclerc et Carrefour. 

The three most impactful apps are Amazon, AliExpress et ManoMano.

We observe more than 2.8 times more impact between the least impacting app (eBay) and the most impactful app (Amazon) in this ranking.

The average carbon impact of these 10 applications is 0.69 gEqCO2 for an average duration of the scenario of 2 minutes and 1 second, i.e. the equivalent of 6 meters travelled in a light vehicle.

The eBay application takes its first place in the phases of launching the application and viewing the basket. Indeed, during these phases, this application consumes little energy. In addition, it exchanges little data over the entire duration of the scenario (only 0.53 MB).

Amazon once again finds itself in last place in the ranking, far behind its competitors. This result can again be explained by the research and visualization phases of the product, where this application consumes much more energy than the others. In terms of data exchanged, we observe 8.22 MB for research (compared to 0.23 MB for eBay) and 2.6 MB for the product sheet (compared to 0.13 MB for eBay).

Conclusion

Whether for the most visited websites or m-commerce applications in France, we see an impact almost three times greater between the soberest platform and the most impactful one.

It shows that by managing the information visible on the screen, the loading of images, the consumption of scripts and the data exchanged during the scenario differently, it is possible to reduce the environmental impact of a website and a mobile app.

As an e-shopper using his mobile, it is better to go through applications than websites. Indeed, websites have an average impact of 39% more on the scenario studied. Only AliExpress has a higher consumption on the app than on the website. However, applications have an effect on their download and updates. They are therefore to be preferred only in the event of regular orders.

In terms of digital sobriety, nothing will equal buying food products at the market and other products from local traders, of course!

Results

Ranking of the 10 most popular websites in France

WebsitesURLEnergy consumption (mAh)Exchanged Data (Mo)Carbon Impact (gEqCO2)Water footprint (Litres)Surface footprint (cm²)Scenario duration (seconds)
Leclerc e.leclerc 11,57 2,59 0,61 0,10 1,29 102.72
Cdiscount cdiscount.com 13,49 2,62 0,69 0,13 1,50 108.03
eBay ebay.fr 14,00 3,96 0,78 0,14 1,58 122.24
Leroy Merlin leroymerlin.fr 15,47 3,15 0,80 0,14 1,72 112.67
AliExpress fr.aliexpress.com 14,84 3,89 0,80 0,13 1,67 114.46
ManoMano manomano.fr 13,60 6,30 0,87 0,14 1,56 107.33
Fnac fnac.com 16,88 4,20 0,90 0,15 1,90 118.37
Veepee veepee.fr 14,50 8,19 1,00 0,16 1,68 114.95
Carrefour carrefour.fr 20,25 4,14 1,04 0,19 2,25 118.8
Amazon amazon.fr 19,04 18,06 1,68 0,24 2,29 123.84

Ranking of the 10 most popular apps in France

Applications Version Energy consumption (mAh)Exchanged Data (Mo)Carbon Impact (gEqCO2)Water footprint (Litres)Surface footprint (m²)Scenario duration (seconds)
eBay 6.51.0.2 11,69 0,53 0,51 0,09 1,27 119,73
Carrefour 14.4.3 11,67 0,57 0,52 0,10 1,29 108,64
LeclercDrive 15.2.0 11,97 0,45 0,52 0,10 1,30 128,01
Leroy Merlin 7.12.3 12,95 0,51 0,55 0,11 1,41 115,84
Cdiscount 1.43.0-twa 13,81 0,04 0,58 0,11 1,49 115,44
Fnac 5.2.7 12,78 1,14 0,59 0,11 1,41 128,84
Veepee 5.24.2 12,17 2,32 0,63 0,11 1,35 114,47
ManoMano 1.15.2 15,48 0,26 0,65 0,12 1,69 124,59
AliExpress 8.44.0 17,55 2,33 0,84 0,17 1,94 123,71
Amazon Shopping 24.6.0.100 19,74 12,86 1,46 0,22 2,31 126,03

For each site and each application, measured on a Samsung Galaxy Note 8 (Android 9), the measurements were carried out using scripts using the GDSL (Greenspector Domain-Specific Language) language. This language makes it possible to automate actions to be carried out on a telephone. The measurements were taken between March 29 and April 1, 2022.

Detail of the scenario common to the 20 measures:
-Launch of the site or application
-30 second pause on the homepage
-Search for a product using the search bar, then view the products on offer
-Selection of a product, then visualization of its characteristics (details, opinions, etc.)
-Adding the product to the cart
-30 second pause on the shopping cart page

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

On the projection side of the footprint, the parameters taken into account to carry out these classifications are:

Viewing Ratio: 100% Smartphone
Viewing ratio: 100% France
Server location: 100% World

Find out how Greenspector assesses the environmental footprint of a digital service.

What is the environmental footprint for social media applications? 2021 Edition

Reading Time: 4 minutes

The use of social networking-type mobile applications is increasing every year. Like the professional use of videoconferencing tools, these uses have put additional pressure on the network and the servers of these solutions.

How do the players take the environmental impact into account in their strategy? What are the impacts of our activities on social networks? What are the most/least impacting solutions for the environment, network congestion and the autonomy of our smartphones?

For this study, we chose to measure the news feed of the 10 most popular social media applications: Facebook, Instagram, LinkedIn, Pinterest, Reddit, Snapchat, TikTok, Twitch, Twitter, and Youtube. These applications are different in terms of functionality. We have chosen to compare them in terms of carbon impact, consumption of energy and data exchanged.

For each of its applications, measured on an S7 smartphone (Android 8), the scenario for scrolling the news feed was carried out through our Greenspector Test Runner, allowing manual tests to be carried out over 1 minute in one-to-one. Learn more about the methodology and How does Greenspector assess the environmental footprint of digital service use.

Projected ranking in the carbon impact of social network applications newsfeed (g EqCO2)

ApplicationThe carbon impact of newsfeed/min2021 Rank2020 EvolutionWater ResourceLand Use
Youtube0.46 gEqCO2/min1=0.08 Liters0.92 m²
Twitch0.55 gEqCO2/min2+30.09 Liters1.01 m²
Twitter0.60 gEqCO2/min3 +1 0.10 Liters1.14 m²
LinkedIn0.71 gEqCO2/min4-10.10 Liters1.00 m²
Facebook0.79 gEqCO2/min5-30.12 Liters1.38 m²
Snapchat0.87 gEqCO2/min6+10.12 Liters1.29 m²
Instagram1.05 gEqCO2/min7-10.12 Liters1.03 m²
Pinterest1.30 gEqCO2/min8=0.15 Liters1.25 m²
Reddit2.48 gEqCO2/min9=0.23 Liters1.35 m²
TikTok2.63 gEqCO2/min10=0.27 Liters1.88 m²

According to the Global Web Index July 2021, we spend an average of 2 hours and 24 minutes on social networks, i.e. +2 minutes compared to 2019. If we project the average carbon impact of the 10 applications measured (1.15 gEqCO2) out of 60 seconds to the average time spent per user, we obtain for one user/day: 165.6 gEqCO2. It is the equivalent of 1.4 km in a light vehicle. It also corresponds to 60 kgEqCO2 per user per year, or the equivalent of 535 km travelled in an average light vehicle in France. It is equivalent to 1% of the carbon impact of a French (7 Tons).

In May 2021, the number of active users of social networks stood at 4.33 billion (55.1% of the world population), or + 35% compared to 2019. 99% access social networks on a mobile device. 80% of the time (2 hours and 24 minutes) on social networks is spent on a mobile device. If we project our carbon/user impact to these data, we obtain 262 million Tons EqCO2 per year for the 4.33 billion mobile users, i.e. the equivalent of 0.61% of the EqCO2 impacts in the world in 2019 and more than half of France’s carbon emissions (56%).

Energy consumption of social media applications newsfeed (mAh)

ApplicationEnergy Consumption of newsfeed/min2021 Rank 2020 Evolution
Youtube8.58 mAh1=
Instagram8.9 mAh2+5
LinkedIn8.92 mAh3-1
Twitch9.05 mAh4-1
Twitter10.28 mAh5+1
Pinterest10.83 mAh6+2
Reddit11.04 mAh7-4
Snapchat11.48 mAh8+2
Facebook12.36 mAh9-5
TikTok15.81 mAh10-1

When it comes to energy consumption, the news feeds for Snapchat, Facebook and TikTok apps are the worst performers. Good students on the energy side are Youtube, Instagram and LinkedIn. The TikTok news feed here consumes 1.8 times more power than Youtube‘s.

The average established for energy consumption is 10.73 mAh or + 1.2% compared to our 2020 ranking.

Data exchanged from social network applications newsfeed (MB)

ApplicationExchanged Data on newsfeed/min2021 Rank2020 Evolution
Youtube3.09 MB1=
Twitter6.28 MB2+2
Twitch6.87 MB3+2
Facebook11.15 MB4-2
LinkedIn15.34 MB5-2
Snapchat17.26 MB6+1
Instagram32.46 MB7-1
Pinterest40.65 MB8=
TikTok96.23 MB9+1
Reddit100 MB10-1

In terms of the exchanged data, the bad guys are the news feeds of the Reddit, TikTok and Pinterest apps. The good students in terms of data exchanged are Youtube, Twitter and Twitch. Reddit consumes 32 times more data than the Youtube app.

The average established for the data exchanged is 32.93 MB for this use, or 71% more compared to the 2020 edition. Only 3 applications are above this threshold. Watch out for your data plans! Projection in 1 month, you will have consumed 142 Go!

Taking into account the average time spent on the social networks according to the Visionary Marketing blog: if you only use Tik Tok (up to 52 minutes per projected day), you will consume nearly 149 GB per month. While Instagram (up to 53 minutes per day) will make you consume 51 GB! Are you more into Facebook? It will make you consume nearly 19 GB (up to 58 minutes per day) per month.

Pour chacune de ses applications, mesurées sur un smartphone S7 (Android 8), le scénario suivant a été réalisé au travers de notre Greenspector Test Runner, permettant la réalisation de tests manuels sur une durée de 1 minute en one-to-one.

  • Launch the application (wait 20 seconds)
  • Scroll through the news feed or the “Discover” tab of the applications
ApplicationVersionDownloadsGoogle Playstore Grade
YouTube16.40.3510 000 000 000+4,3
LinkedIn4.1.627.1500 000 000+4,3
Reddit2021.41.050 000 000+4,3
Facebook340.0.0.27.1135 000 000 000+2,6
Twitch11.7.0100 000 000+4,5
Twitter9.16.11 000 000 000+3,5
Instagram210.0.0.28.711 000 000 000+3,8
Pinterest9.35.0500 000 000+4,6
TikTok21.6.51 000 000 000+4,4
Snapchat11.50.0.291 000 000 000+4,2

Each measurement is the average of 3 homogeneous measurements (with a low standard deviation). The measured consumption on the smartphone according to a wifi type network can be different on a laptop PC with a wired network. For each of the iterations, the cache is first emptied.

Discover how does Greenspector assess the environmental footprint of digital service use .

Which video conferencing mobile application to reduce your impact? 2021 Edition

Reading Time: 6 minutes

We invite you to consult the 2022 edition. Read the article.

For this new 2021 edition of our ranking, we have completed our 2020 study with new solutions and even extended it to web solutions. The objective of these measures is to see how the solutions stand in terms of environmental impact (Carbon) with each other on common user scenarios but also to provide benchmarks on our uses of videoconferencing.

So this time we compared 19 mobile applications: Big Blue Button, BlueJeans, Circuit by Unify, Cisco Webex Meetings, ClickMeeting, Go To Meeting, Discord, Google Meet, Infomaniak kMeet, Jitsi, Pexip, Rainbow, Skype, StarLeaf, Microsoft Teams, Tixeo, WhereBy, Zoho Meeting et Zoom.

For each of its applications, measured on an S7 smartphone (Android 8), the following three scenarios were carried out through our Greenspector Test Runner, allowing manual tests to be carried out over a period of 1 minute in one-to-one:

  • Audio conference only
  • Audio + video conference (camera activated on each side)
  • Audio and screen sharing conference

Learn more about the methodology.

Projected carbon impact ranking of videoconferencing applications (gEqCO2)

Here are the impact averages for the three scenarios:

User scenario / Year1 mn of audio conference1 min of audio + video conference1 min of audio + screen sharing conference
20210.155 gEqCO20.403 gEqCO20.163 gEqCO2

1,38 meters made in a light vehicle3,6 meters made in a light vehicle1,46 meters made in a light vehicle
20200.102 gEqCO20.289 gEqCO20.121 gEqCO2

On average, one minute of audio-video conferencing impacts 61% less (or 2.6 times less) than with activated cameras and 5% less than when sharing a screen. According to our recent study on the impact of streaming a MyCanal video, on average, one hour of video streaming corresponds to an impact of 14g eqCO2. Or 0.233g eqCO2 per minute, or 1.7 less impacting than a minute of video conferencing in audio and camera but 1.5 times more than a minute of video conferencing in audio-only.

The Top 3 for one minute of videoconferencing on average: Google Meet (0.164 gEqCO2), Tixeo (0.166 gEqCO2), and Microsoft Teams (0.167 gEqCO2). Google Meet, first in this ranking on the carbon impact side, has an impact 2.5 times less than, Discord, the last app in this ranking. The average of this ranking is 0.237 gEqCO2, only 7 solutions are above.

The main part of the Carbon impacts is situated on the user device part (72%), followed by the Network part (16%) and finally the Server part (12%).

Here are the three least impacting applications in terms of Carbon depending on the scenario:

Audio (Top 3)Audio + camera (Top 3)Audio + screensharing (Top 3)
Microsoft TeamsBig Blue Buttons (via Chrome)Microsoft Teams
Google MeetClick MeetingGo To Meeting
Infomaniak MeetGoogle MeetTixeo

Energy Consumption of Video Conferencing Applications (mAh)

Here are the energy consumption averages for the three scenarios:

User scenario/year1 mn of audio conference1 mn of audio + video conference1 mn of audio + screen-sharing conference
20219.84 mAh16.26 mAh9.98 mAh
20206.6 mAh14.24 mAh7.50 mAh

On average, one minute of audio-video conferencing consumes 39% less (or 1.6 times less) energy than with activated cameras and 1.5% less than when sharing a screen.

The Top 3 (all scenarios combined) in energy consumption: Microsoft Teams (27.27 mAh), Go To Meeting (28.79 mAh), and Google Meet (30.11 mAh). Microsoft Teams first in this ranking in terms of energy consumption consumes 2 times less than the last in this ranking: Discord.

Here are the three most energy efficient applications depending on the scenario:

Audio (Top 3)Audio + camera (Top 3)Audio + screen-sharing (Top 3)
Microsoft TeamsZoho MeetingMicrosoft Teams
TixeoZoomTixeo
Infomaniak kMeetStarLeafGo To Meeting

Exchanged data from videoconferencing applications (MB)

Here are the averages of the data exchanged for the three scenarios:

User scenario/year1mn of audio conference1 mn of audio + video conference1 mn of audio + screen-sharing conference
20211.15 Mo16.01 Mo1.87 Mo
20200.806 Mo8.44 Mo1.43 Mo

It is on the consumption of data that the gaps are widening between tools and uses.

On average, one minute of audio conferencing consumes 92% less (or 14 times less) data exchanged than with activated cameras and 38% less than when sharing a screen.

The Top 3 (all scenarios combined) in energy consumption: Big Blue Buttons (4.49 MB), Tixeo (6.21 MB), and Google Meet (6.30 MB). Big Blue Buttons (via Chrome) first in this ranking for exchanged data consumes 10 times less than the last in this ranking: Discord.

Here are the three applications that consume the least amount of data according to the scenario:

Audio (Top 3)Audio + camera (Top 3)Audio + screensharing (Top 3)
Cisco Webex MeetingsBig Blue ButtonsCisco Webex Meetings
Blue JeansTixeoInfomaniak kMeet
Google MeetGoogle MeetGoogle Meet

And for our daily use of videoconferencing:

Just like our first study, we advise you during your online conferences to:

Favour audio only during your meetings: the video stream (camera) will tend to consume a lot more. A mobile session is on average 2.6 times more impactful for the environment in terms of carbon impact when the video is added to the audio. Adding screen sharing is not penalizing if it is useful.

Optimize the settings (when possible): adopt the dark theme, activate the data or energy-saving settings (in the case of LED, AMOLED type screens).

Prefer videoconferencing over travelling by car!

  • Comparison for 2 people who talk to each other in a 3-hour session in audio and active cameras (0.403 gEqCO2 per minute) while one of the two has made 20 km (112 gEqCO2 / km in France) round trip for a face to face.
  • By videoconference: 180 * 0.403 * 2 = 145 gEqCO2
  • By car: 112 * 20 = 2.4 kg EqCO2 or approximately 16x more impact.

Measured versions: Big Blue Button via Chrome (87.0.4280.101), BlueJeans (45.0.2516), Circuit by Unify (1.2.5102), Cisco Webex Meetings (41.2.1), ClickMeeting (4.4.6), Go To Meeting (4.6.0.7), Discord (62.5), Google Meet (2021.01.24.355466926), Infomaniak kMeet (2.2), Jitsi (20.6.2), Pexip (3.4.6), Rainbow (1.84.1), Skype (8.68.0.97), StarLeaf (4.4.29), Microsoft Teams (1416.1.0.0.2021020402), Tixeo (16.0.1.2), WhereBy (2.3.0), Zoho Meeting (2.1.4) et Zoom (5.5.2.1328).

For each of its applications, measured on an S7 smartphone (Android 8), the user scenarios were carried out through our Greenspector Test Runner, allowing manual tests to be carried out.

Once the application is downloaded and installed, we run our measurements on the basic and original settings of the application. No modification is made (even if certain options make it possible to reduce the consumption of energy or resources: data saving mode, dark theme, etc. However, we encourage you to check the settings of your favourite application to optimize it. impact.

Each measurement is the average of 5 homogeneous measurements (with a low standard deviation). The consumption measured on the given smartphone according to a Wi-Fi type network can be different on a laptop PC with a wired network for example. For each of the iterations, the cache is first emptied.

To assess the French impacts of infrastructures (datacenter, network) in the carbon projection calculations, we relied on a Greenspector methodology based on real data measured from the volume of data exchanged. This evaluation methodology takes into account the consumption of resources and energy in use for the requested equipment. As this is a very macroscopic approach, it is subject to uncertainty and could be fine-tuned to adapt to a given context, to a given tool. For the Carbon projection, we assumed a 50% projection via a Wi-Fi network and 50% via a mobile network.

To assess the impacts of the mobile in the carbon projection calculations, we measure on a real device, the energy consumption of the user scenario and in order to integrate the material impact share, we rely on the wear rate theory generated by the user scenario on the battery, the first wearing part of a smartphone. 500 full charge and discharge cycles, therefore, cause a change of smartphone in our model. This methodology and method of calculation have been validated by the consulting firm specializing in eco-design, Evea.

In a process of continuous improvement, we are vigilant in constantly improving the consistency of our measurements as well as our methodology for projecting CO2 impact data. Therefore, it is unfortunately not possible to compare a study published a year earlier with a recent study.

1 hour of Netflix viewing is equivalent to 100 gEqCO2. So what?

Reading Time: 7 minutes

Netflix, along with others like the BBC, has researched, with support from the University of Bristol, the impact of its service. The precise figures and the methodology will be published soon, but it appears that one hour of viewing Netflix is equivalent to 100 gEqCO2.

When the communication was released, several digital players took up this figure, but, in my opinion, not for good reasons. Communicating the impact of video through The Shift Project emerges as a systematic point of debate. As of March 2020, the Shift post had been widely disseminated in the media with a significant evaluation error. This error had been corrected in June 2020 but the damage was already done.

In this context, the IEA carried out a contradictory analysis on the subject. In the end, many studies on the impact of the video came out (IEA, the German Ministry of the Environment, ourselves with our study on the impact of playing a Canal + video). It is always difficult but not impossible to compare the figures (for example, whether or not the manufacturing stage is taken into account, the representativeness of the terminals, the different infrastructures, and optimizations between players, etc.), however, if we take things comparable, all studies have similar orders of magnitude. By taking the correction for the Shift Project error (Ratio 8 resulting from an error between Byte and Bit), the numbers are also close.

What do the studies say?

But beyond the discussions on the numbers, if we examine the studies in detail, the conclusions point in the same direction:

  • Regardless of the unit cost, there is a significant growth in usage and overall impact.


Set against all this is the fact that consumption of streaming media is growing rapidly. Netflix subscriptions grew 20% last year to 167m, while electricity consumption rose 84%.

  • The impact of digital services is relatively small compared to the impact of other activities. However, it is necessary to continue to study and monitor this impact.

What is indisputable is the need to keep a close eye on the explosive growth of Netflix and other digital technologies and services to ensure society is receiving maximum benefits, while minimising the negative consequences – including on electricity use and carbon emissions.”

  • The aim of the concerned companies is to better measure their impact and identify the real areas for optimization.

“Netflix isn’t the only company using DIMPACT right now, either. The BBC, ITV, and Sky are also involved. A spokesperson from ITV says that, like Netflix, the tool will help it to find and target hot spots and reduce emissions. Making such decisions based on accurate data is crucial if digital media companies are to get a grip on their carbon footprints.”

“This work allows us first of all to identify the technical projects to prioritize to minimize the carbon footprint of myCANAL video consumption as much as possible. At the same time, the lessons guide us on the awareness messages to relay to our users, throughout our future developments. This commitment to cooperation between our technical developments and our users is the key to consumption that has less impact on the environment. “ (Testimony of the CDO of Canal +, Greenspector study of the impact of playing a video)

  • The impact of the video can be small but it is necessary to measure it well (previous point)...

The most recent findings now show us that it is possible to stream data without negatively impacting the climate if you do it right and choose the right method for data transmission”.

Are the discussions going in the right direction?

The errors of some studies did not help calm the discussions. Neither does the media coverage of these figures. However, we should not be fooled, saying that digital technology has an impact is not necessarily well accepted by all players. This can be a nuisance for a field that for 30 years has been accustomed to a development paradigm without very little constraint and above all very little interest in internal environmental issues. Let us remember that Moore’s Law, which governs this digital world a great deal, is a self-fulfilling prophecy and not a scientific law: the industry is putting in place financial and technical means so that the power of processors increases regularly. We must not be fooled because focusing on certain errors allows the problems to be ignored. I have seen only quotes from the Shift Project error in Netflix’s DIMPACT ad but no quotes about Netflix’s desire to measure and reduce its impact. We must accept the mistakes of the past if we are to move forward on this subject. The study of the Shift has the merit of bringing to the fore an issue that was difficult to be seen. And also accept these own mistakes, how many digital promises have not been (yet) proven? Have the positive digital externalities been scientifically quantified by a sufficient number of studies? This latest analysis shows that the few existing studies (Mainly 2 Carbon Trust studies and the GSMA) deserve much more work to confirm the huge announced benefits of digital technology.

The study of claims of positive impacts of digital on the climate leads to the conclusion that these cannot be used to inform policy decisions or research. They are based on extremely patchy data and assumptions that are too optimistic to extrapolate global estimates. In addition, the two reports studied do not see the avoidances in the same sectors, or even contradict each other.

It is even a shame to focus on one aspect of the impact by dismissing the overall issue. This is the case in the discussion of the impact of the network on the energy part. The calculation method based on the kWh / Gb metric, even if shared by almost all of the studies and internal teams of operators, is criticized by some. This method can in fact be improved, but the church must be put back in the middle of the village: the impact of the network is in all cases weaker than the Terminal part, the material manufacturing part is never discussed in these debates while this is the main issue of the impact of digital technology. Especially since the energy improvement of the network and data centers is based on a principle contrary to the impact of the hardware: the regular renewal of the hardware to put in place new, more efficient technologies.

Google has been criticized for the waste policy of its servers. Practices have been improved but one can wonder about this management: even if the servers are resold and the environmental cost is amortized for the buyer, this does not change anything in the excessive renewal cycle.

“We’re also working to design out waste, embedding circular economy principles into our server management by reusing materials multiple times. In 2018, 19% of components used for machine upgrades were refurbished inventory. When we can’t find a new use for our equipment, we completely erase any components that stored data and then resell them. In 2018, we resold nearly 3.5 million units into the secondary market for reuse by other organizations.” (Google Environmental Report 2019).

One of the first explanations for these clear-cut discussions often comes from the lack of awareness of digital environmental issues. But behind that there is also a more sociological explanation: We reproach certain organizations for “ecological” beliefs. However, we can also speak of belief among certain digital players when we uncritically idolize the benefits of digital technology. In this case, not sure let these discussions go in the right direction. “Technophobic” versus “Techno-béa”, the reasoned find it difficult to take their place in the middle. Several avenues are however useful to progress serenely on the impact of digital!

Let us limit the comparisons between domains

Comparisons of the environmental impact of digital technology with other fields are a trap. It is necessary to understand an abstract CO2 impact. We use it ourselves to carry out this awareness. However, this leads to sometimes biased conclusions.

Here is the brief used by Les Echos! “Netflix claims that one hour of streaming on its platform generates less than 100gCO2e. This is the equivalent of using a 75W fan for 6 hours in Europe, or a 1,000W air conditioner running for 40 minutes.”

So an hour of streaming is low? Yes and no. Because it has to be seen from a “macro” level: worldwide viewing hours are exploding. And Netflix isn’t the only digital service we use. Is it possible to compare it to fan time? A household will be able to visualize 4 flows at the same time for several hours, we are not on the same importance of use with a fan (Maybe if with global warming …).

What is important is that this metric will allow service designers to track their improvement. With the details of this impact, they will be able to identify the hotspots. It will allow you to compare yourself to a competitor and to position yourself.

Using these numbers to say that the impact of digital is huge or zero doesn’t help much in the debate. All areas must reduce their impact, the challenges ahead are enormous and this type of comparison does not necessarily help in the dynamics of improvement. On the other hand, the more this type of study comes out, the more we will have a precise mapping of the impact of digital technology.

Let’s collaborate!

LCA models are criticized for their unreliability. Ok, is that a reason to abandon digital impact analysis? That would suit some well!

Above all, it is necessary to improve them. And this will come through more transparency: public LCAs from equipment manufacturers, energy consumption metrics reported by hosts, and even more information on the renewal of parks … Some players are playing the game, it is is what we were able to do for example with Canal + and this made it possible to have reliable data on the datacenter, CDN and terminal parts. However, the lack of transparency is significant in this sector when it comes to the area of environmental impact.

It is also necessary to avoid always blaming other sectors. In these discussions about the impact of video, and more broadly digital, I continually see “it’s not me, it’s him” arguments. For example, it is the hardware that must be acted upon, implying the software is not responsible for the impact. Once again, the environmental context is critical, there are no quick fixes and everyone must act. To free oneself from actions by pointing fingers at other actors is not serious. The idea of measuring the impact of digital is not to do “digital bashing” but to improve it. So there is no reason not to take these issues into account, unless ” go into a lobbying process and want to move towards total digital liberalization.

Having seen this field evolve over the past 10 years, I can say that there is a real awareness of certain players. The impact of digital can still be denied, but it is a dangerous risk. Dangerous because it is clear that the environmental objectives will be more and more restrictive, like it or not. Not taking this issue in hand is leaving it to other people. This is what we are seeing today: some are complaining about digital laws. But what have they done over the past 10 years when this issue was known? For fear that this will slow down the development of digital technology compared to other countries? Instead, why not see digital sobriety as a competitive factor in our industry? We can see that sobriety is taken into account by many countries (the DIMPACT project is an example). France has a lead with many players dealing with sobriety. It is time to act, to collaborate on these subjects, to criticize the methods to improve them, to measure themselves, for everyone to act in their area of ​​expertise.

This is what guides our R&D strategy, providing a precise tool for measuring energy consumption and the impact of terminals. We are working to improve the reliability of measurements in this area, to try to provide food for thought and metrics. Hoping that the debates will be non-Manichean and more constructive and that the digital sector fully takes environmental issues into account.

What are the best Android web browsers to use in 2021?

Reading Time: 8 minutes

The internet browser is the most important tool on a mobile device. It is the engine for browsing the internet. No longer just for websites but also now for new types of applications based on web technologies (progressive web app, games, etc.).

For this new edition of our ranking, carried out in 2018 and 2020, we have chosen to compare 16 mobile applications: Brave, DuckDuckGo, Chrome, Ecosia, Edge, Firefox, Firefox Focus, Firefox Nightly (formerly Firefox Preview), Kiwi, Mint, Opera, Opera Mini, Qwant, Samsung, Vivaldi et Yandex.

The objective of these measures is to see how the solutions stand in terms of environmental impact (Carbon) in relation to each other on common user scenarios but also to provide benchmarks on our uses of browsers.

For each of the 16 applications measured on a Galaxy S7 (Android 8) smartphone, the scenarios integrating the launch of the browser, browsing on 7 different websites, periods of inactivity, etc. were carried out through our Greenspector Test Runner, allowing the performance of automated tests.

Learn more about our methodology

Total energy consumption (in mAh)

The average power consumption is 49mAh (as a reminder, the 2020 ranking average was 47mAh or -4.2%).

Here is the evolution from last year.

2021 Ranking2020 RankingÉvolution
Firefox Focus1109
Vivaldi242
DuckDuckGo352
Firefox Nighly4106
Yandex53-2
Kiwi682
Opéra72-5
Brave87-1
Ecosia91-8
Chrome106-4
Samsung119-2
Firefox12131
Edge1311-2
Qwant1413-1
Opera Mini1514-1
Mint1612-4

Firefox Focus is the best solution in terms of energy consumption in our comparison. The version evaluated in 2020 was one of the first versions and it seems that Firefox teams have been working on optimizing the power consumption of their browser since. Ecosia loses its leading position on this indicator and finds itself in the middle of the ranking. On the side of the most energy-hungry browsers, we find Mint and Opera Mini. Note that the most popular browsers: Edge, Firefox, Chrome, and Samsung, are quite poorly classified.

This total energy consumption can be evaluated and analyzed in 2 ways: the energy consumption of pure navigation and the energy consumption related to the functionality of the browser.

Energy consumption of navigation (in mAh)

Navigation is the consumption only associated with viewing the page (no consideration of launching the browser, features, etc.).

Most browsers have a fairly similar power consumption on “pure” navigation. This is mainly due to the use of visualization engines. Most browsers use the Chromium view engine.

Compared to the 2020 ranking, it seems that the Firefox engine has improved. Qwant, using this engine too.

Energy consumption of features (in mAh)

The functionalities include browser states such as idle periods, launching the browser, writing URLs in the navigation bar.

By keeping the same classification as for the total energy, we see that the non-navigation functionalities (writing of URLs, inactivity of the browser, etc.) have a significant impact on total consumption.

Autonomy (hours)

Battery life is the number of hours the user can surf before the battery is completely discharged. The ranking does not change with respect to that of energy, as autonomy is directly related to energy.

We observe that the autonomy can double from 5h to 10h between the most consuming browser (Mint) and the least consuming (Firefox Focus).

Data (Volume of data exchanged) (MB)

Some applications do not manage the cache at all for reasons of data protection and privacy, use proxies that optimize data, have a difference in the implementation of cache management. In addition, if a browser is good, the downside is that a lot more data is potentially loaded in the background. In our methodology, we see it for the New York Times site, which is larger in terms of data.

Here is an example of the measurement iterations on the Amazon site (Amazon.com) that shows the difference in data processing between different browsers.

Memory consumption(RAM) by the browser process (MB)

Memory consumption is important to take into account in a digital service because even the variation in memory consumption does not influence the energy impact, it remains very important to integrate because of the effects of overconsumption on already congested devices. in memory, or older, less powerful, this can create instabilities or applications that cannot operate simultaneously because they compete. In ecological terms, this can of course provides a premature change of device on the user side for a more powerful model to satisfy good user comfort.

The variation goes from 400MB to 1.8GB (approximately half the RAM of the Samsung Galaxy S7).

Let us observe more precisely the behavior of the memory following the sequence:

  • Launch browser
  • Browser inactivity
  • Navigation (Average memory consumption)
  • Inactivity following navigation
  • System after closing browser

At the launch of browsers, we have a median memory usage of 413MB. Edge consumes a lot more with 834MB.

If we leave the browser inactive, the memory consumption of most browsers remains fairly stable. Which is pretty good and normal. On the other hand, we see that Edge and Ecosia have a strong increase in memory.

Then, with navigation, the memory consumed increases significantly. This is due to the consumption of navigation engines to analyze and store items. The management of tabs will also play a role. If the browser offloads the memory for the non-active tabs, then the consumption will be lower.

We can note that Firefox Focus, Mint, Duck Duck go, Opera Mini and Qwant overall consume little memory.

When the browser is closed, almost all browsers are no longer in memory. Firefox remains however with 1, GB as well as Chrome and Mint with around 100MB. Probably a bug but it is annoying because elements still occupy the memory and processing operations can also exist: processing operations are confirmed on Firefox and Mint with the rate of CPU consumed by the browser process which remains high.

We can also look at the memory impact of consulting Wikipedia (the basic consumption of the browser is subtracted here).

We understand the difference in memory management between browsers and the potential entropy on heavier sites.

Performance

We measured the time it took to write the URL in the address bar.

This difference in performance can be explained by several factors: network exchanges during entry (auto-completions), processing during entry, search based on known addresses, etc. In the end, for the user, the time to access the site will be longer or shorter. For example on the Wikipedia URL entry on Duck Duck Go a lot of network traffic and CPU processing (peak at 22% CPU).

Unlike the faster Edge which has lower processing in terms of CPU.

By the way, we could have an optimization of all the browsers by limiting its treatments (for example by grouping and spacing the treatments).

Environmental impact

The environmental impact is calculated according to the Greenspector emission factors taking into account the energy consumed and the wear of the battery (impact on manufacturing). The impact of the network and the data center is taken into account with the internet intensity.

This impact is reduced to the consultation of a page.

Firefox Focus by its low consumption is first. Samsung, which has average power consumption, is in second place thanks to good data management.

The most impactful browsers (Ecosia, Edge, Mint and Opera Mini) have high power consumption and poor data management..

Rated browsers

Measured versions : Brave (1.18.75), Chrome (87.0.4280.101), DuckDuckGo (5.72.1), Ecosia (4.1.3), Edge (45.12.4.5121), Firefox (84.1.2), Firefox Focus (8.11.2), Firefox Nightly 201228), Kiwi (Git201216Gen426127039), Opera (61.2.3076.56749), Opera Mini (52.2.2254.54723), Qwant (3.5.0), Vivaldi (3.5.2115.80), Yandex (20.11.3.88), Mint (3.7.2), Samsung (13.0.2.9).

Scenario

For each of its applications, measured on an S7 smartphone (Android 8), the user scenarios were carried out through our Greenspector Test Runner, allowing automated tests to be carried out.

Once the application is downloaded and installed, we run our measurements on the basic and original settings of the application. No changes are made (even if some options reduce the consumption of energy or resources: data saving mode, dark theme, etc.

However, we encourage you to check the settings of your favorite application to optimize the impact. Here is the evaluated scenario:

· Features evaluation
o Browser launching
o Adding a tab
o Writing a URL in the search bar
o Removing tabs and cleaning the cache

· Navigation
o Launch of 7 sites and wait for 30 seconds to be representative of a user journey

· Brower benchmark
o The Mozilla Kraken benchmark allows you to test JavaScript performance

· Evaluation of periods of inactivity of the browser
o On launch (this allows the home page of the browser to be evaluated)
o After navigation
o After closing the browser (to identify closing problems)

For each iteration, the following tests are carried out:
o Removal of cache and tabs (without measurement)
o First measure
o the Second measure to measure behavior with cache
o Removal of cache and tabs (with measurement)
o System shutdown of the browser (and not just a closure by the user to ensure that the browser is actually closed)

The measurement average therefore takes into account navigation with and without cache.

The main metrics analyzed are display performance, power consumption, data exchange. Other metrics such as CPU consumption, memory consumption, system data, etc. are measured but will not be displayed in this report. Contact Greenspector to find out more.

In order to improve the stability of the measurements, the protocol is fully automated. We use an abstract language of Greenspector test description which allows us strong automation of this protocol. Browser settings are the default. We have not changed any settings in the browser or its search engine.

Each measurement is the average of 5 homogeneous measurements (with a low standard deviation).

Impact assessment

To assess the impacts of infrastructures (datacenter, network) in the carbon projection calculations, we relied on our emission factor base (resulting from our R&D, such as the Impact study of the playing of a Canal + video – Greenspector) with as input the actual measured data of the volume of data exchanged. As this is a very macroscopic approach, it is subject to uncertainty and could be refined to adapt to a given context, to a given tool. For the Carbon projection, we assumed a 50% projection via a Wi-Fi network and 50% via a mobile network.

To assess the impacts of the mobile in the carbon projection calculations, we measure on a real device, the energy consumption of the user scenario and in order to integrate the material impact share, we rely on the wear rate theory generated by the user scenario on the battery, the first wearing part of a smartphone. 500 full charge and discharge cycles, therefore, cause a change of smartphone in our model. This methodology and method of calculation have been validated by the consulting firm specializing in eco-design, Evea.

In a process of continuous improvement, we are vigilant in constantly improving the consistency of our measurements as well as our methodology for projecting CO2 impact data. As a result, it is difficult to compare a study published a year earlier with a recent study.