Category: Applications sobriety

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

Reading Time: 6 minutes

For this new 2021 edition of our ranking, we have completed our 2020 study with new solutions and even extended to web solutions or even to a solution whose main objective is not to make videoconferencing but which integrates this functionality. 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 are 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 + screensharing 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 + screensharing (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 + screensharing 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:

Favor 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 video is added to the audio. Adding screen sharing is not too 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 traveling 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 favorite 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.

The Impact of playing a Canal + video study

Reading Time: 16 minutes

Introduction

Logo_Canal+

This study was carried out by Greenspector, a company specializing in the impact of digital technology, and EVEA, specializing in analyzes of the environmental impact of products. This study is based on:

  • a calculation methodology initiated as part of the CONVINcE project on the energy consumption of video. A project involving several players such as Orange, Sony …
  • energy and resource consumption measurements carried out on the myCANAL application to support hypotheses
  • collection of data on the use and infrastructure of Canal +
  • a bibliographic study to identify and correlate emission factors and energy consumption

Study summary :
Methodology
Results : consumption, overall impact and projection
Areas for improvement
Conclusion – points to remember
Chief Digital Officer – Canal+ testimony

Methodology

The functional unit of this study is defined as follows: “Watch 1 hour of video, live or in a replay, on the Canal + interfaces”.

Disclaimer: The purpose of this study is to estimate, by orders of magnitude, the carbon impact of playing a video on Canal + interfaces. To date, it is based on reliable and robust sources and is intended to be representative of the reality of the Canal + infrastructure. However, this is not a complete Life Cycle Assessment (LCA). We rely on LCA methodologies but, for example, we have not performed a sensitivity analysis that would allow min-max deviations on the values. However, this study made it possible to formalize an analysis benchmark for the carbon footprint of a video service, and it could be used to monitor the evolution of the carbon footprint over time. And all this in no way detracts from the consistency of the data for its primary use.

Means of access to services

We have listed different ways to access myCANAL video services. Depending on this type of access, it is necessary to have more or less material (s). This inventory is needed to assess the impact of accessing video services. We have classified the means by category:

  • Canal + decoder
  • FAI TV Box
  • TNT Box
  • Game console
  • IP TV (Android TV, Samsung TV)
  • PC / Mac Multimedia Gateway with ISP Internet Box
  • Smartphone / Tablet with ISP Internet Box or GSM access

For Decoder, TV / TNT Box, Console, and Multimedia Gateway access, it is necessary to add a TV. We have ruled out all special cases of viewing considered as anecdotal compared to other accesses such as:

  • Display of the PC stream on a TV,
  • Multiroom viewing

Regarding the link between the terminals and the Boxes, we will consider that the Wi-Fi connection and the wired connection (Ethernet) have no impact on the consumption of the terminals (PC and TV).

Definition of visualization

Visualization quality influences end-to-end power consumption. We have taken the following 3 main definitions:

  • SD (Simple Definition)
  • HD (High Definition)
  • UHD / 4K (Ultra High Definition)

The definition depends on 3 factors:

  • compatible program availability
  • material compatibility in terms of quality
  • the quality of the connection (the quality of myCANAL videos is adapted according to the connection speed).

Greenspector’s exploratory measurements on myCanal show the impact of SD, HD, or UHD video consumption on smartphones, and then on laptops:

Impact of consuming SD, HD or UHD video on smartphones and laptops

Bibliographic sources were used for the consumption of other platforms such as Consoles (Source 1), TV (Sources 1 and 2), Set-Top-Box (Sources 1 and 2). The consumption of Canal + decoders supplied by Canal + was also used.

Depending on the different equipment configurations, we obtain energy consumption which varies from 1.3 to 108 Wh / h, i.e. a ratio of 1 to 80:

Consommations d'énergie selon les configurations d'équipements

Technologies

Several parameters were taken into account in the calculations because they influence the consumption:

  • Streaming technology: IPTV (Technology among ISPs), OTT (Over the Top), and Peer To Peer
  • Linear (Live) or Non-linear (Replay / VOD) signal
  • Broadcasting (Hertzian, Satellite, IP)

Means of connection to the network

To access the services, the infrastructure considered takes into account: the user’s equipment (Box among others), access to networks (GSM, Fiber, etc.), and the heart of the IP network.

We used calculation methods that are widespread in the scientific literature and standardized by ETSI. The principle is to take an “energy / typical use” ratio in Wh/Go. Although the network infrastructure has a fairly fixed consumption and does not depend on the use, this calculation method makes it possible to assign a global impact to a use (here one hour of video). In addition, it allows to study the improvement of networks in terms of efficiency. Beyond the search for improved energy efficiency, it also makes it possible to assess pressure on the network and to take into account the improvement in the capacity of the infrastructure.

As explained in the CONVINcE report:

“As we are looking for an order of magnitude in energy saving, we suppose that decreasing by 30% the traffic volume in the core network induces a decrease of same ratio in network dimensioning and consequently a decrease of 30% in energy consumption in the core IP network.”

We have studied the literature and listed metrics ranging from 1.3Wh / Go (figure for FTTH fiber) to 600Wh/Go. These factors fluctuate depending on the infrastructure assessment date, method (Top Down or Bottom Up) and technology. It is clear, however, that the efficiency improves over the years.

We have taken:

  • 13 Wh / Go for the core network (source CONVINcE)
  • 30 to 40 Wh / Go for the fixed network
  • 150 Wh / Go for the GSM network (source CONVINcE)

For TNT and satellite, there are few data. However, we based ourselves on a BBC study for TNT.

Content Delivery Network

Content Delivery Networks (CDNs) are servers used to limit the load on “Top of Head” servers (serving videos) and to provide a flow as close as possible to the user. Canal+ uses CDN providers on the market but also uses its own infrastructures.

The methodology for estimating the energy intensity per GB is to bring the estimated consumption of the data center down to throughput during peak periods. Canal+ indeed knows the number of physical servers, their types as well as their speeds. As the servers are hosted by a host, certain assumptions have been made to estimate the actual consumption: among other things, an assumption of PUE (Power Usage Effectiveness) of 2, consumption per server of 250W, and a server load of 50%. The consumption of routers and storage is considered negligible compared to the consumption of servers. (Sources 1, 2 , and 3)

The energy intensity obtained is 0.13 Wh / Go, with a different value between VOD and Live. Note that this value is calculated in the event of a peak (football match for example). It can be lower (use of the total capacity of the data center) or higher (during periods of low traffic).

For lack of data, the hypothesis of the same energy intensity (Wh / Go) was taken for CDNs outside Canal + (50%). For comparison and verification, the studies listed display values from 0.04 to 1 Wh / GB. These differences can be explained by various factors:

– Improving the efficiency of data centers, old studies, therefore, lead to higher figures,

– Top-Down approaches that have a higher estimate than Bottom-Up studies (like this estimate on CDNs).

AWS application servers Excluding video: using Mycanal (catalog presentation, authentication, etc.) and watching videos involve requesting services hosted on servers (rights management, etc.). The APIs used are hosted on AWS instances.

Canal+ rents AWS instances and knows the number of VMs. A part of these VMs is purely dedicated to providing ancillary services to video.

It is difficult to know the power consumption of AWS instances because no communication or information is provided by Amazon. According to an expert, we took a value of 20W (taking into account a PUE of 2) (Source: Interview of experts on virtualization from the company Easyvirt).

We have assumed a uniform distribution of consumption concerning the number of hours visualized and we obtain a value of 0.14 Wh / h.

Video encoding servers

The “Headend” servers allow you to format videos, “package” them to the user’s format … For the OTT, up to 150 packages may be available for a single video. Here is the workflow for the OTT.

The most consuming parts are video encoding and decoding. The workflow is distributed over specific servers (for encoding) and classic servers (for formatting and packaging).

We took the figures from the CONVINcE project (considering Harmony servers identical to those used by Canal +) to estimate the energy, as well as the Canal + server data:

  • For IPTV: 0.09 Wh for one hour of video
  • For the OTT: 0.30 Wh for one hour of video

This difference is explained by the fact that OTT is encoded in several formats, unlike IPTV which is encoded in a high definition format.

CO² emission factors

For energy, we used the values provided by the Open Data Networks Energies database (Work of distributors such as RTE and ADEME). We used intermittent usage which corresponds more to video usage in terms of the period (evening), ie 60g CO2eq / kWh. Part of the broadcasts for the rest of the world has been added taking into account the fact that some users are outside France.

For terminal and server manufacturing emission factors, we used factors provided by Shift Project / IEA.

These factors were brought to the time of viewing by taking into account the lifespans associated with each material.

Note: Regarding the impacts of DTT and satellite, there is a lack of data on infrastructure and emission factors. However, we have integrated this part to understand the orders of magnitude of the impacts. Certain analyzes were carried out only on the IP part in certain cases (for comparison with other studies for example).

Likewise, the impact of the data center manufacturing phase outside of the servers and the network infrastructure was not taken into account as it was considered to be shared and low given the lifespan of the buildings.

Results – global projection

The supply of renewable energy to AWS infrastructures and Canal + CDNs hosted by Interxion has not been taken into account.

In one hour, on average, here are the flows that pass through the network:

500 000 hours of video
900 TB of data
3.6 GB / hr average throughput

The number of viewing hours for Canal+ subscribers has been broken down according to the parameters listed above.

The unit data obtained previously are then projected on these uses to obtain the overall consumption. By taking these results, we can obtain the usage intensities of each part (Terminal, Network, Server) to check the consistency of the overall consumption.

Intensity analysis

The usage intensities obtained can be compared to the Shift Project and IEA studies. This intensity is calculated by taking the distribution of devices, the use of Canal +, and keeping only the IP transfer part (without Satellite or TNT).

It is clear (and shared by the IEA) that the Shift Project study overestimates grid consumption. For servers, we have a fairly low value but are quite confident considering that it is based on data from a known infrastructure (with the uncertainty from Amazon servers and third-party CDNs). The estimate of the terminal part of the Shift Project seems to us to have been underestimated (shared by IEA). It seems that the IEA also underestimated many parameters and seems not to have taken into account the influence of the definition on the consumption of the terminal, certain elements such as boxes, and the consumption of new TVs that consume more. The CONVINcE study and the Sauber/Koomey analysis also validate the consistency of our study.

Impact of playing an hour of video

Across the Canal+ fleet, the average end-to-end consumption is 214 Wh per hour of video. For comparison, the IEA study announces consumption between 120 and 240Wh per hour. The breakdown is as follows:

The impact in average CO2 equivalent by type of access is as follows (with the assumptions specified in the paragraph CO2 emission factors where the manufacture of Satellite, DTT, network and server hosting infrastructures has not been taken. into account)

By taking the type of connection:

The impact ranges from 20 to 66 g CO2 eq, it depends on several parameters:

  • the more or less energy consumption of the device (for example Smartphone vs TV),
  • embodied energy and material life,
  • the means of access to the network (for example GSM access has a greater impact than wired access),
  • the viewing quality which will influence above all the share of the network (depending on a cost per Wh / Go),
  • the number of materials to access the service.

By taking real uses (see the end of the document), the average is 28g CO2eq. If we take only the IP part, the average value is 37g CO2eq.

For comparison, the IEA study (2020) announces 8g CO2eq for France (for the use phase only). Another study for the US (2014) announces 360g CO2eq on the use phase (and 420g CO2eq with manufacturing). If we take a US energy mix, we get 202g which brings us closer to this study. The lower value can be explained by the differences in assumptions, in particular on the energy intensity of the network. For example, if we take for our study a TV with 4K reception, IEA announces 20g CO2eq while we rather estimate 14g (37g with the manufacturing phase).

If we look at the usage / manufacture ratio, in some cases the use has more impact (UHD in particular), while in others it is the manufacturing (IP TV or PC in HD for example).

In this same study, the estimate of the purchase of a DVD is 400g EqCO2, which therefore leads to the impact of watching a 2-hour video. 5 times less than that of buying a DVD in France and equivalent in the US.

Consumption and overall impact

The result of the total energy consumption of viewing Canal+ videos over IP is 900 GWh per year with a greenhouse gas impact of 159,000 tonnes CO2eq.

For comparison, annual French consumption is 473 TWh per year (Source RTE) and France’s 2017 carbon footprint (national emissions + imports) is 749 Mt CO2 eq (source: Haut Conseil pour le Climat – 2019 report). The consumption of the Canal + fleet by IP is therefore 0.18% of energy consumption and 0.016% of the French carbon footprint.

The end-to-end distribution is as follows:

Most of the consumption is on the user’s premises (terminal and part of the network access). Indeed, it is necessary to have the equipment (TV, Box, Smartphone …) which is not shared like the servers.

To validate the consistency of this projection, we took the consumption of CDNs specific to CANAL as well as to suppliers. We have a consumption of Canal + CDNs of 2.6 GWh (and estimated at 7 GWh for suppliers) excluding video head-ends which are not at Interxion. We get 12 GWh with the projection, which validates the model.

If we look at the distribution of the impact in greenhouse gases, we have the following distribution:

répartition de l’impact en gaz à effet de serre

A large part of the impact comes from the manufacture of user terminals. On the network part, the manufacture of Boxes (FAI, Satellite …) also has a significant impact.

Areas for improvement to limit the impact of services

General strategy

As we have seen in the network part, efficiency improves. The same is true for terminals. But on the other hand, 4K will become widespread, networks will continue to increase their capacity and therefore increase the overall impact of consumption. By taking one of the hypotheses of a 30% improvement in network efficiency (according to the trends set out in the studies listed in the Networks section of this report) and an increase in average throughput of 20% over 3 years as well as of 20% of the hours viewed, as well as a transfer of 75% of the hours viewed from all interfaces to that of the OTT, we estimate an increase in the energy consumed by 39% and the impact of greenhouse gases greenhouse by 23%. This simplistic projection allows us to approach a more realistic consumption in 3 years.

To offset this increase, and optimize the impact of video playback, we examined the impact of the following projects carried out by Canal+:

  1. Switching from H264 encoding to HEVC
  2. Switching to multicast for live
  3. Switch audio encoding from AAC to AC4
  4. Strengthen bitrate downsizing
  5. Improve the interface and the software layer
  6. Help the user on their digital impact

These projects are not exhaustive. Other possible areas for improvement have been identified which could be launched subsequently by Canal+.

The measurements, estimates, and models that allowed us to obtain the overall impact were used to quantify the estimated gains.

Note on optimizing video servers:

The energy consumption of video servers is very low, as is their impact, as the hardware has a high lifespan (10 years). Several optimizations are under study (video passage Just-in-Time among others). However, these optimizations provide very little end-to-end gain. They are however necessary to optimize management and reduce storage size (impact not taken into account in this study because it is low). Optimizations such as switching to HEVC have a stronger impact (this is confirmed by the CONVINcE study).

“The “Just In Time Transcoding” approach will allow to reduce the number of video representations stored in the CDN and thus its power consumption. This is an end-to-end approach to be compared to the global abovementioned one consisting of reducing the bandwidth of the network by using the most efficient encoding technology (HEVC/AVC).

On CDNs in the same way, even if the impact is low, certain actions such as increasing the rate of use of Canal + infrastructures (using a transfer of flows from supplier CDNs to own CDNs) will improve the efficiency.

Switching from H264 encoding to HEVC

The HEVC video codec is about 20% more efficient than the H264, and a large number of devices are now compatible. For a 3-year projection of consumption, we have taken an increase in the market share of HEVC compatible devices (Box, Smartphone, etc.).

For users who are on 1080p 5Mbits to date (34% of users currently) as for users restricted to 720p for technical reasons (for example a limitation of the network), a 20% drop in consumption is expected. For other 1080p compatible users, there is no decrease in consumption, but an increase in quality with superior grip.

A new even more efficient format begins to appear (AV1), saving an additional 20%. However, very rare equipment is compatible: the transition is largely premature, we would hardly gain anything to date because the fleet is almost zero.

Switching to multicast for live

Broadcasting in hybrid multicast / unicast adaptive streaming for the OTT will greatly reduce the use of bandwidth, and therefore power consumption.

Usable only for live stream, it ensures that a single stream is sent for all customers to the final delivery point. This project will be accompanied by other ancillary projects such as the switch to CMAF packaging. The CMAF audio/video packaging format allows you to have exactly the same video files for all platforms.

Switch audio encoding from AAC to AC4

Currently we mainly use AAC audio, from 96kbits to 128kbits.

The EAC3 + is widely compatible to date and improves audio quality at equivalent bitrate while allowing 5.1. Switching to EAC3+ would be at a fixed rate, without hoping to save bandwidth (while 20% more efficient than EAC3 +).

On the other hand, the new AC4 format is 50% more efficient than the EAC3 + and would allow the audio bit rate to be divided by 2. Even if the share of the video is greater, the gains at the global level are not negligible.

Strengthen bitrate downsizing

To date, except in Africa, or on a cellular network (so as not to empty the customer’s subscription), the quality of the video is adequate . On PC, quality adaptation is possible but access is not necessarily easy.

Making the possibility of reducing the size more accessible by different means (improvement of the interface, communication, etc.) would make it possible to redirect some of the users to a lower but sufficient quality.

Taking into account the increase in video quality and that of network performance, quality caping by users of Canal + services should represent in 3 years a gain of 20% in average speed compared to that generated by uncapped use.

In this axis, several elements have not been included in the gain projection but are possible to help the user in his impact. For example, the Greenspector mobile measurements show that it is better (under favorable conditions such as wifi) to use downloading rather than streaming. Indeed, here is the comparison for a 45mn video (with 5mn download):

Consommation d'énergie d'un streaming vidéo vs d'un téléchargement et lecture vidéo

This is partly explained by the fact that when viewing the downloaded video, the radio cell is not used while it is much more during streaming.

Improve the interface and the software layer

Part of the impact of viewing an hour of a video comes from the interface. Indeed, viewing the catalog, managing subscriptions (via APIs), processing playlists … are necessary functions. Greenspector measurements showed that this could represent 10% of terminal consumption:

Improving the interface on all platforms (smartphone, PC, etc.) would therefore make it possible to obtain significant gains. It would also allow, beyond reducing the impact, to limit the exclusion of certain people as well as the obsolescence of platforms.

Among the actions identified:

  • Switch to the dark mode
  • Reduce the overall impact of the software layer
  • Improve UX
  • Limit the integration of third-party libraries

Help the user on his digital impact

Much of the impact of video playback is not directly related to video playback. Canal +, through its large audience, can act by making users aware of the impact of digital technology. If some of the users take these actions into account, the impact can be reduced. Among these actions:

  • Extending the life of the user’s equipment,
  • Use Ethernet rather than Wifi, rather than 4G,
  • Extinction of equipment out of use.

Results

Here is the projection of the various energy gains:

The improvements compensate for the increase in energy consumption and even allow a gain of 14% compared to the current situation. For the greenhouse gas impact, we obtain a gain of 8%.

There are many ways to reduce the carbon impact of streaming activities for Canal + by 30%, and the actions put in place will make it possible to make a gain of 26% on the environmental footprint of services and in particular -31% for the consumption in OTT only.

Conclusion – to remember

This study has uncertainties on future use, on the emission factors of each element as well as on the potential gains. However, the objective is to challenge the choices or even to rule them out (some actions not listed in this document have, for example, already been discarded), if they had no gain. One of the first actions is to be able to measure yourself in order to improve. This is the goal of this phase. Now comes that of improvement.

The environmental interest of the sites identified by Canal+ has been confirmed. Their implementation will make it possible to offset the effects of the growth in uses, or even more, in the years to come.

The conclusions of this evaluation evolve with many technological parameters but in summary:

  • Work on video and audio compression formats while taking into account new formats optimized by manufacturers.
  • Optimize the consumption of user functions to access the content.
  • Involve/guide users in the choices according to the context of use (streaming, type of network, device screen format, etc.) and lead them to extend the life of the equipment to reduce the impact.
  • Implement hybrid multicast / unicast adaptive streaming for OTT to dramatically reduce bandwidth usage and power consumption.
  • Pursue end-to-end measures to avoid future decisions that shift the impact without reducing it

Pierre-Emmanuel Ferrand, Chief Digital Officer at CANAL+

Testimony

– How does this assessment and improvement process fit into the Group’s strategy?

CANAL+ is anchored in its time. We have decided to become more involved in a major issue of our time, dear to our subscribers and users of myCANAL: the protection of our environment. CANAL+ was very early on as a pioneer in eco-responsible approaches. first. In addition to the rental model of decoders, which is very virtuous from the point of view of the circular economy, Canal+ organized in 1988 the recovery of old decoders to ensure their return to service or their recycling. Moreover, as a publisher, We have produced eco-responsible productions including series that have been exemplary on this subject, Baron Noir, L’Effondrement, and more recently OVNI (S).

-What main lessons/contributions do we draw from this work (on the design of services)?

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. 

– What role do users play?

Protecting the environment is a priority for them. Their role is central to our approach. Our challenge is to offer them a platform offering the best content and the best video quality adapted to their equipment and their reception capacities to reduce the effects on the environment. By raising their awareness and supporting them in responsible actions, they too will be able to get involved, as they do in other areas for a more eco-responsible society.

Digital sobriety comparison of 3 direct messaging apps for business.

Reading Time: 6 minutes

Introduction

Today and more than ever, communication is essential in business. Since the start of the Covid-19 crisis, many companies and employees have discovered remote work. This exceptional situation has led to a change in our interaction habits: making teams communicate effectively with each other, remotely, instantly. We decided to compare the 3 most popular direct messaging apps for business: Skype, Slack and Teams.

6 scenarios were carried out on the basis of an average user journey:

– Launch of the application
– Opening of a blank one-to-one conversation
– Sending a text message
– Send an image (.jpg)
– Sending an attachment (.pdf)
– Send an animated image or GIF (.gif)

Consult the methodology and the details of the scenarios.

Carbon impact projection

Carbon impact (graph) of apps: Skype, Slack and Teams

During the application launching stage, the carbon impact of Skype (0.038 gEqCO2) and Slack (0.039 gEqCO2) are pretty similar. Teams exchanges 77% more data compared to Skype, therefore increasing its carbon impact where in terms of energy, the Teams application has a consumption similar to the other two apps.

On the part of sending a text message and sending an image, Slack is the most efficient application oscillating between -30% (message) and -60% (image) less than Skype, less good application on these two scenarios.

Generally speaking, Teams consumes a lot more data. Indeed, on average it is nearly 196 KB where Skype is at 134 KB and Slack is at 113 KB.

The application with the best carbon impact average is Slack (0.035 gEqCO2) followed closely by Skype (0.043 gEqCO2) then Teams (0.055 gEqCO2), a difference of 36% between the best and the worst.

Focus on background consumption

On the background idle side of the application, we notice several things:

Slack consumption in IDLE Background

Slack consumption in the background is higher than the other two apps. Especially in terms of data exchanged where Skype and Teams do not exchange any data in this user step. Slack also consumes in terms of CPU (1.16%) where Skype consumes 10x less and Teams is once again at zero. This consumption is not linked to the background setting (state change processing) but lasts over time both on background inactivity states and in foreground inactivity.

AppsEnergy consumption par second (µAh/s)Exchanged Data (KB)CPU (%)
Skype45.1700.11
Slack57.1442.61.16
Teams44.0800

The Slack app performs processing in the background, impacting battery, power and resource consumption throughout the day. If we project this impact for a user who puts his Slack application in the background on his phone for a whole working day (7 hours), we obtain an impact of 26 gEqCO2, or approximately the Carbon impact of an average light vehicle driven in 230 meters! At the scale of the year (220 days): this behavior for a person is equivalent to 50 km of the same vehicle. Probably a mess that could be taken care of and avoided.

Average carbon impact projection of the scenarios

Carbon impact of direct messaging apps user journeys

The scenario with the lowest carbon impact on average of the 3 applications measured is that of opening a conversation (0.018 gEqCO2) consuming 69% less than sending an attachment (0.061 gEqCO2). The step of sending an image is the second least impactful step with + 10% more than opening a conversation. Finally, sending a text message and loading the app are similar in their impact (less than 1% difference).

Disclaimer : Note that these scenarios do not have the same duration.

Scenarios and their duration (in seconds)SkypeSlackTeams
Launch of the application4,524,135,88
Opening a conversation1,761,828,95
Sending a text message33,331,7832,19
Sending an image15,169,0714,46
Sending an attachment10,1312,1310,99

Below, the ranking of applications according to their carbon impact per second.

Carbon impact per second of direct messaging apps

Projected energy consumption of scenarios over 60 seconds

Energy consumption of direct messaging apps

When it comes to the launch of the application, Skype is in the lead with energy consumption of 31 mAh followed closely by Teams (32.8 mAh) then Slack (35.5 mAh). A difference of 12% between the first and the last application for this step.

For the opening a conversation step, Teams (11.3 mAh) is doing well with a lower consumption of 61% compared to Skype and Slack side by side (29 mAh).

For the 3 scenarios of sending a text message, image or attachment, the ranking does not change: Slack remains in the lead followed by Skype and Teams.

In the end, by adding all the steps, the Teams application is the most efficient (71.3 mAh) followed by Slack in second position (85.6 mAh) then Skype, the last one (86.2 mAh).

Remember that this classification is projected over one minute of use. In real time, Slack is the fastest application (11.7 seconds on average scenario completion time), Teams the slowest (14.49 seconds), however Teams is the most sober in terms of downloading speed on the smartphone (237, 7 on average compared to 285.6 for Slack or 287.2 for Skype).

On average, a minute of writing and sending a text message consumes 3.33 mAh, which is 2x less consumption than a minute spent in videoconferencing (audio only: 6.60 mAh).

Slack vs Teams: send a GIF

Slack

Teams

For the same functionality of finding and sending a GIF via Giphy third-party, the two applications Slack and Teams have a different user journey.

In fact, Slack allows, with a simple command, the search and display of a SINGLE GIF via the keywords typed then offers the possibility of loading a new one if the first one is not suitable. The command used is as follows:

/giphy simpson

Teams meanwhile, displays a search bar that displays new GIFs with each new letter typed. So unnecessarily. loading dozens and dozens of GIFs. After typing the entire keyword “simpson”, the results window will always display a number. For this scenario, we have chosen to select the first gif from the results.

We therefore observe a difference between the two applications:

ApplicationsDuration of the scenario
(in second)
Energy consumption
(mAh)
Exchanged data
(Mo)
Carbon impact projection
(gEqCO2)
Slack24,842,370,3090,065
Teams24,423,9232,336

We can see that the Slack user journey is much more energy and resource efficient than that of Teams. Especially for the part of the data exchanged (a difference of more than 22 MB!), Which can be explained by the quantity of GIFs unnecessarily loaded by Teams.

The difference in power consumption between these two apps is 40% for a similar scenario duration. The carbon impact is multiplied by 36 for the Teams application compared to Slack.

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

Details of the scenarios:

  • Launch the application
  • Opening a one-to-one conversation
  • Sending a 28 character text message “Hello this is a test message”
  • Sending an image (.jpg): 32 KB (350×350)
  • Sending an attachment (.pdf): 188Kb – generated from a Word text file (A4 format)
  • Sending an animated image or GIF (.gif): GIF used for Slack – 225Ko – 500×375; GIF used for Teams – 600Kb – 500×352

Each measurement is the average of 3 homogeneous measurements (with a low standard deviation). The consumption measured on the given smartphone according to a wifi 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 impacts of infrastructure (datacenter, network) in the carbon projection calculations, we relied on the OneByte methodology based on real data measured on the volume of data exchanged. This assessment 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 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 the energy consumption of the user scenario on a real device and in order to integrate the material impact share, we rely on the theoretical wear rate 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.

Integrate a third-party service: is it dangerous for your visitors privacy, which impact on the environment? The Youtube case.

Reading Time: 4 minutes

Third-party service integration makes it easy to quickly add functionality to a site such as a video or social network integration (see the case of Twitter integration). The providers of these tools have worked to make technology integration quick and easy. And the technique is there. But at what cost?


Energy consumption of the third-party service Youtube

We observe an increase in this type of third-party service on our measurements and abnormal overconsumption. This is the case with many sites and even government websites. 

The YouTube integration is a good case study to explain this effect. In just a few lines, it is possible to display a video on any site:

<iframe width=”560″ height=”315″ src=”https://www.youtube.com/embed/WoQHxxxxxxx-E?rel=0″ frameborder=”0″ allow=”autoplay; encrypted-media” allowfullscreen></iframe>

But what is the result in terms of user impact? Here is the result we get in terms of power consumption on a Nexus 6 smartphone:

Consommation d'énergie du services tiers Youtube

Reference: Phone discharge speed in uAh / s (OS, Browser …)
Loading: Speed of the first 20 seconds of loading
Idle Foreground: Inactive site speed in the foreground
Scroll: Speed when the user scrolls down the page
Idle Background: download speed when the browser (and therefore the site) is in the background

This is a government website. Discharge rates exceed our thresholds for many steps. For loading, the speed is more than 2 times that of reference. For the idle foreground or phase of inactivity in the foreground, the consumption should be identical to that of reference. This consumption is abnormal for a site that seems quite light.

Process CPU du services tiers Youtube

We see that Chrome’s CPU process goes up to 10% every second. This explains the overconsumption of energy. By profiling the JavaScript calls in the development tools, we observe processes from base.js which are from the YouTube framework:

Javascript framework Youtube

Note that this processing also impacts scrolling and loading. Is this an expected operation? A bug or a bad implementation? We haven’t been that far into the analysis.

When we look at the page loading, on 1.2MB, nearly 600KB is used for the YouTube plugin. 50kb of CSS and 550kb of Javascript. To the necessary processing, add the heavy CPU usage to parse and run scripts.

Significant point: No video appears on this page. The integration of the plugin is surely necessary for another page. This makes the waste even more critical, it is all the more annoying that the French tested website is public and widely used: Impots.gouv !


Best practices for integrating a video

1 – Directly embed video without third-party services

It is possible to use free solutions without plugins. Integration via HTML5 is native.

2 – Embed an image

Display an image with the same rendering as the video allows to reduce to 1 request. If the user clicks on the video, then the scripts will be loaded and the video launched ultimately lazy loading.

We also did the exercise on a page of our Greenspector website:

On one of our “Case Study” pages was embedded a YouTube video. We replaced this integration by displaying an image (opposite) representing the old integrated video. This modification allowed us to go from a Greenspector ecoscore from 59/100 to 75/100 characterized by an energy gain of -12% in the loading stage, -10% in Idle and -15% in scroll.

Page with embedded video
Page with image

3 – Integrate the plugin only on the desired page

A solution that is not ideal, but preferable to the existing one, is to only use scripts when the page requires a video.

What will it save?

First of all the performance. A large portion of processing related to site wait times is dedicated to third-party services. This is even more true for the YouTube plugin. On the audited site, the size can be reduced by 2, and the loading time reduced by at least 30%.

Power consumption will also be reduced and even more important than data size or performance. In fact, in addition to saving energy from charging, consumption in idle or inactivity phase will be reduced.

Bonus: user privacy

The other problem with this type of project is the use of tracker and user data recovery. Not integrating a third-party service resolves potential issues of data leakage and GDPR non-compliance. By the way, the YouTube plugin seems to allow version without cookies via the call to the URL: https://www.youtube-nocookie.com.

Like any third-party service, it is not that simple. Even with this no-cookie integration, user data is stored:

Données utilisateurs cookies Youtube

The audited site is therefore not GDPR compatible! To manage this, you must ask the user for consent explicitly:

Fenêtre de consentement

The solution of a hosted video or static image will also manage this.


Conclusion

If the integration of a video is necessary, think about it quietly and consider the impacts on resource consumption and GDPR. There are technical solutions more respectful of the user, they are initially perhaps a little more complex to set up, however, the solutions will naturally become simpler and more widespread.

The challenges of eco-responsible digital technology for the public sector

Reading Time: 6 minutes

Public organizations have particular challenges when it comes to digital eco-responsibility. Like private organizations, they respond to a global challenge to limit the environmental impacts of digital services, which are growing as much as public services are modernizing and rapidly becoming computerized.

This subject, like accessibility to the greatest number of people becomes a subject of exemplary nature for public services which, we hope, will have a vocation to have a capacity to train private organizations and more generally society at large. Eco-responsibility has appeared several times in the political sphere since we see elected officials who seized on the subject at the same time as political dissident movements integrated this dimension of eco-responsible digital. As a sign of this enhancement and of the new speeches / political programs, we now have an elected representative in Responsible Digital for the city of Nantes (France).

Beyond the environmental issue, digital sobriety also helps to reduce the digital divide, allowing equal opportunities for online content and services since a more frugal service will also be more accessible to citizens with limited or limited connection. an old-tech, low-tech or cluttered computer / mobile.

An acceleration is underway for the consideration and will become even more visible since article 55 of French Law n ° 2020-105 of February 10, 2020 relating to the fight against waste and the circular economy »Specifies that organizations must promote in the markets software whose energy consumption is limited during the use phase.

As of January 1, 2021, the State services as well as the local authorities and their groups, during their public purchases and as soon as possible, must reduce the consumption of single-use plastics, the production of waste and prioritize goods resulting from reuse or which incorporate recycled materials by providing useful clauses and criteria in the specifications.
When the acquired good is software, the administrations mentioned in the first paragraph of article L. 300-2 of the code of relations between the public and the administration promote the use of software whose design makes it possible to limit the associated energy consumption. to their use.

A digital eco-responsibility approach is a quality approach that comes at a cost

This constraint beyond the gain it brings also represents a cost because it requires integrating this eco-design approach into the manufacturing process and thus training the teams in the production chain in good practices, reflexes but also to measure, analyze, detect over-consumption and therefore spend time controlling, measuring, sometimes correcting; All the more so since this approach must take place beyond its initial manufacture but also during the maintenance phase in a context of technical developments often undergone and functional adjustments.

Should a Project Owner (MOA) expect its Project Manager (MOE) partner to integrate this approach spontaneously? Today no, as this approach comes at a cost, it is often risky to integrate this qualitative dimension without taking the risk of drifting or losing the market for a MOE candidate. The MOA must in this case integrate REQUIREMENTS from its specifications which must allow to qualify the expected “quality”.

The project management of the development project – maintenance of its application heritage must be fixed – and set for these partners (MOE, AMO, COM teams, …) – objectives = REQUIREMENTS

But how to integrate these requirements in the clauses of the contracts, how to manage them and how to verify them?

We can read some requirements:

  • The candidate must demonstrate in his response, his level of maturity of his CSR approach in his activities
  • The candidate will have to demonstrate his ability to integrate the digital eco-design dimension in his response
  • The candidate will have to propose / integrate good eco-design practices for websites

Often awkward or superficial, digital eco-responsibility requirements must be more precise and be oriented towards results objectives rather than means requirements.

What type of requirement?

Results requirements have several advantages over good practices (means requirements) because they absolutely validate that the result is good or not good without an expert debate, without a debate on the applicability of a rule, partial application or not, correct implementation or not.

They are above all not very dependent on technological developments and have the advantage of remaining on intermediate indicators that are easy to collect and are easily verifiable and measurable.

However, they must be accompanied by a repository of best practices, in the appendix, to avoid putting in place corrective actions to optimize after the fact. It is also a good way to involve the design and development teams in the process. These attached documents should not be too heavy and focus on generic standards, not techno or language oriented.

They have 2 complexities: they must be calibrated to set thresholds and they do not transfer all responsibility for the result to the person who “manufactures” (MOE).

What metrics to measure to cover the major impacts of a digital service?

A high level indicator comparable to other areas is the carbon impact expressed in geqCO2. The advantage of this indicator is that it is universal and can be shared by everyone in the company, with its customers, comparable with its competitors, in its ecosystem, with any service / product.

The carbon impact is not a measurable indicator but “projectable” on the basis of flow or intermediate indicators. These include energy consumption, network data consumption, display performance or the number of requests to servers internal or external to the organization.

In any case, the Carbon projection will be all the more reliable if we start from real measurements on real devices than from estimation or measurement on an emulator. This projection can be improved by knowing the routes or the type of material on which the service or the content is used.

How to set thresholds for a result requirement?

Setting thresholds first requires specifying the conditions under which measurements such as:

  • The technical conditions of the measurement: on a device type, on a wired / mobile / wifi type of connectivity, on a given browser, etc.
  • Adjustment parameters: with such brightness, with an empty cache or not,….
  • The use case that can be compared to a functional unit: one or x pages played over a given time, one or x steps, a journey from A to Z.

Setting thresholds also requires benchmarking one’s domain or existing sound in order to understand the values. By fixing such a value, we answer the 2 questions, am I in values of good qualities and are they attainable?

Means objectives can be added as appendices to the results requirements. A repository of good practices will have a positive impact and allows the MOE teams to get it right the first time without additional correction costs, but be careful, these repositories of good practices or Green Patterns must be instantiated by technology or by languages and therefore evolve over time. pace of technological change or remain very generic.

Example 1 of requirement on the result:

The reference environment is a mid-range tablet, with a Wi-Fi type connection, based on an environment …, on the 5 key routes of my web application made up of X steps, X screens, we set 4 requirements of resources. Each step must not consume more than X KB AND each step must not consume more than X times the power consumption of a blank page AND each step must display in less than X seconds based on the last element loaded AND each step should consume less than X MB of memory.

Example 2 of requirement on means:

The candidate will have to demonstrate his ability to measure / manage his indicators during the project and alert to a deviation in this consumption as quickly as possible during the project.

Example 3 of requirement on means:

The candidate must integrate good development practices at a minimum (annexed to the technical document). 

Validate compliance with a requirement in a project

Once the requirements have been set, they must then be managed during the project via intermediate points which make it possible to probe a partial result and thus avoid the tunnel effect. All these intermediate reviews make it possible to develop automatic scripts which will allow measurement to be carried out in a reliable and comparable manner and to be able to consolidate the test heritage over time. These views also make it possible to adjust if necessary and discuss over-functional or over-content or additional on-board intelligence costs between MOA and MOA.

It is in this validation phase that the result requirement finds its full meaning since we do not need to audit all screens / pages on a repository of good practices but just automated scripts during the project which are replayed on the finished product.

The carbon impact of Instagram app features

Reading Time: 4 minutes
Instagram logo

Have you ever wondered what the environmental cost of a post, a story, watching a live or Instagram feed?

The application launched in 2010 has 1 billion monthly active users (Source) including 28 million unique visitors per month. In France, there are 11 million unique visitors per day. Instagram is the most frequented social network behind Facebook.

For this study, we chose to measure the carbon impact, energy consumption, and data on 5 user journeys on the Instagram mobile application (version 148.0.0.33.121):

  • The publication of a photo in story
  • The publication of a photo with filter and description in profile
  • Viewing a live Instagram
  • Hosting a live Instagram
  • Scrolling the news feed

The carbon impact of Instagram features per 1 minute unit of time

carbon-impact instagram

The feature that has the least impact on the environment over one minute is the photo publication (0.154 gEqCO2), this is the carbon equivalent of 1.3 meters made by a light vehicle/minute. This feature consumes 10 times less than the most impactful of our measures.

The most impactful feature over one minute is that of the scrolling of the newsfeed (1.549 gEqCO2). Over one minute, it is the equivalent of 13 meters done in a light vehicle. Composed of photos, videos, and advertisements (for an active account), the functionality does not consume the most energy (see following graphs), but in terms of the data exchanged, it is the one that displays the highest value (14.63 MB for one minute).

Regarding the Live feature, whether it is on the viewer or host side, the impact is almost the same (13% less for the viewer). The energy consumption is similar, however, the spectator part exchanges fewer data.

If we consider that the average carbon impact of Instagram is 0.664 gEqCO2 / minute (unweighted average of these 5 uses) and that its users spend an average of 28 minutes / day on the social network (Source). So the average impact of a user on Instagram is 18.6 gEqCO2 / day, the equivalent of 166 meters traveled by a light vehicle.

Average Instagram app carbon impact per day and per user

Energy consumption of Instagram features for 1 minute

energy consumption of instagram features

Posting a photo on your Instagram account consumes 1.8 times less energy than posting a photo as a Story (reduced to a one-minute user journey) and 2.4 times less than hosting a Live. The live features are very consuming here since it is a continuous video stream.


Data exchanged from Instagram features for 1 minute

exchanged data from instagram features

The association of photos, videos and advertisements of the newsfeed feature greatly impacts its data exchange since it has to load new elements when scrolling. It also consumes 2.6 times more data than hosting a live and 16 times more than publishing a photo (user journeys reduced to one minute of use) /


Methodology

The application is measured on an S7 smartphone (Android 8), the user scenarios were carried out through our GREENSPECTOR Test Runner, allowing the performance of automated tests.

Each measurement is the average of 3 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.

To assess the impacts of infrastructure (datacenter, network) in the carbon projection calculations, we relied on the OneByte methodology based on real data measured on the volume of data exchanged. This assessment 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 context, to a given tool. For the Carbon projection, we assumed a 50% projection via a wifi network and 50% via a mobile network.

To assess the impacts of mobile phones in the carbon projection calculations, we measure the energy consumption of the user scenario on a real device and in order to integrate the material impact share, we rely on the theoretical wear rate 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.


For those who like numbers

Use caseEnergy consumption (mAh)Exchanged data (MB)Memory consumption (MB)Test time (second)Carbon impact (gEqCO2) per minuteEquivalence in meters of average car in France / minute
Create Stories3,320,522410220,2772,47
Publish a photo on your feed4,750,806425590,1541,37
Timeline scrolling9,714,63460631,54913,83
Hosting a live23,8410,554471190,7166,39
Live viewing23,398,785221190,6225,55

The environmental impact of search engines apps

Reading Time: 13 minutes

Introduction

Almost 93% of all internet traffic comes from search engines. It is estimated that Google receives 80,000 requests per second or 6.9 billion requests per day. (Source: Blog du modérateur). Globally, if Google holds nearly 91% of the market share, in recent years, new alternative solutions have been trying to disrupt this digital monopoly of internet research.

What are the impacts of our activities on web or mobile search engine applications? What are the most / least impacting solutions for the environment, network congestion, and the autonomy of our smartphones? Moreover, what are the parameters that can vary this impact and how can we, consumers, better limit our impact?

For this study, we chose to measure 8 of the most popular search engine applications in France on the web and mobile versions on Android: Bing, DuckDuckGo, Ecosia, Google, Lilo, Qwant, StartPage, and Yahoo.

Logos applications moteurs recherche

Summary:

  1. Website search vs URL search comparison
  2. Local search comparison
  3. Weather Search comparison
  4. Comparison of a basic search according to several criteria
    (auto-completion, dark theme, newsfeed)
  5. Comparison between search engines using a web browser
  6. Our advice for an eco-responsible search
  7. Methodology

Disclaimer: we only measure the user device activity, its inputs/outputs, and project the network and server impacts on the basis of an average impact methodology (see methodology section). We know that some engines use low-energy servers, optimized cooling, “green” electricity… That others better protect your privacy or even finance associations and important causes … We have not had access to the data center of our respondents, and we, therefore, made assumptions based on activity projections based on the volume exchanged. However, since this is a subject that has a direct economic impact, we could imagine that these companies have designed optimized systems so that the purchase of machines and their operation don’t cost them too much!


Website search vs URL

For this first comparison, two scenarios are carried out here: on the one hand we launch a search for the keyword “Fnac” and on the other we launch a search by URL “Fnac.com”, allowing us to directly access the site, without going through the search results. Only two applications do not permit direct URL access: StartPage and Yahoo. StartPage does not appear in this ranking due to a display fault on the Fnac site.

Carbon impact of a basic search vs URL

This is not a surprise and it is always better to measure it, we observe that a search by URL consumes much less on all the measured search engine applications. On average, there is a 35% reduction in the carbon impact. Therefore, prefer a search by URL (if you know it!) without going through the search results page in order to save energy and data!

For this first comparison, we recommend using Ecosia, which is the most efficient engine here, all research combined (0.167 gEqCO2) with a standard deviation of 0.377 gEqCO2 with the least sober in the DuckDuckGo ranking (0.5433 gEqCO2). The second place goes to Google (0.192 gEqCO2) which consumes 13% more than Ecosia.

These results are nevertheless very disparate between the different solutions since if on Ecosia the 2 types of research have almost the same impact, it is 2.3 times more important for Google and 4.4 times more for Lilo for example.

On a basic search, it will cost you a 50% higher battery impact with DuckDuckGo and 6 times more data received than with Ecosia. Nevertheless, we note that the memory consumption used by Ecosia is 1.5 times greater on the user’s smartphone in this scenario than the average of other engines. On this same route, we can also note that the lowest energy consumption for your batteries is that of Qwant (tied with Ecosia) due to a faster route. Here efficiency and user scenario performance go hand in hand. It should also be noted that for the most-used engine on the planet, Google is also the one that has the most autonomy impact on basic search, 28% more than the average for other engines. Special mention for Yahoo, which manages to reconcile a low impact and lower memory consumption (not taken into account in the calculation of the Carbon impact).

On a URL search, DuckDuckGo‘s carbon impact is 2.2 times higher than the average for other engines and almost 4 times higher than the most virtuous Google in this scenario. This is explained by low power consumption on the user device but above all with a data consumption 7.3 times less than the average of the engines and almost 15 times less than DuckDuckGo! Small consolation for DuckDuckGo, it also consumes the least memory on the user device with 50% less than the engine average and up to 93% less than the most memory-intensive Ecosia in this scenario again.


Local search, the impact of an interactive map

Capture d'écran du scénario de recherche locale

For this scenario, we run a local search. The keywords “Restaurant Nantes” are searched, most search engines then display an interactive map with a selection of restaurants.

Carbon impact of local research

For this local search, four applications stand out by not displaying an interactive map on the results page: Ecosia, StartPage, Lilo (display of a Pages Jaunes list), and Yahoo. Although less practical for discovering the suggestions at a glance, we notice that these applications are less “carbon-intensive”. It is therefore not surprising that the display of a presentation cartographic representation is detrimental to the environmental impact.

If we take the averages of applications that do not display a map (0.076 gEqCO2) to those that display one (0.161 gEqCO2): we obtain a carbon impact difference of 52%. Maybe these solutions could offer a 2-step map display and only display the detailed map on user request?

In this ranking, Ecosia is also in the lead (0.055 gEqCO2) followed closely by StartPage (0.078 gEqCO2). The worst applications are Google (0.178 gEqCO2) and Qwant (integration of a PagesJaunes card, 0.216 gEqCO2).

The difference in carbon impact between the best and worst application is 74%. However and again Ecosia is also the one which consumes the most memory on the user device, 50% more than the average of the engines for a local search. In the end, only StartPage manages to combine a low carbon impact and lower consumption of memory resources.

To explain these differences, we can cite the data impact 10 times higher for Qwant compared to Ecosia and 2.7 times higher compared to the average for other engines. On the energy side, the differences measured are smaller, Google and Yahoo are the worst enemies of your battery and of the carbon impact on the user device with 28% more consumption on average than the average for other engines.


Targeted search, the impact of a weather widget

Capture d'écran du scénario de recherche ciblée météo

For this scenario, we launch a weather search for the keywords “Météo Nantes”. All engines work with a weather widget. Only the Lilo and Qwant engines do not display any and do not allow a direct view of the current weather forecast. However, Qwant displays in partnership with Yellow Pages, the nearest meteorological organization, skewing the results.

Carbon impact of targeted «weather» research

We observe for this search comparison targeted on the weather, that the Lilo application (0.045 gEqCO2) which does not display a weather widget, is at the top of the ranking. Followed by Ecosia (0.062 gEqCO2), the most efficient application of those which display a weather widget. Between Lilo and Ecosia, the difference in carbon impact amounts to 26%.

If we compare Lilo to the average of the applications displaying the weather widget (0.083 gEqCO2), the difference in carbon impact then amounts to 45%.

The most impacting engine with the weather widget is DuckDuckGo (0.118 gEqCO2), which is 1.9 times more than Ecosia.

For Qwant (0.199 gEqCO2), the research is inconclusive since the engine does not display a widget but the nearest weather station in the form of a Pages Jaunes business and cartographic representation. This practice is clearly more consuming/impacting, 2.5 times more impacting than the average of other engines, and 4 times more impacting than the Lilo engine.

Lilo consumes little energy on the user device and little data. On this indicator, it consumes more than 4 times less data than the engine average and up to 11 times less than Qwant!

On the memory footprint and user battery consumption part, for targeted research, it is again the most efficient StartPage app with 47% less than the average energy consumption of other engines but also 44% less memory than the average. Yahoo, Qwant, and Google are also the most energy-intensive with an average consumption higher with 13% more than the other engines. On the memory side, it is again Ecosia which over-consumes with 50% more than the average of its competitors and almost twice as much as DuckDuckGo!


Search of a definition

In this part, we analyze different ways of approaching a basic search for a definition. We have chosen THE most searched definition on Google in 2019 in France, that of the word “Procrastination”. In addition, in order to save you research, we give you the meaning: Procrastination (feminine name) “tendency to postpone, to put systematically to the next day”. We will check the major research trends of 2020 in a future study!

Definition search

Capture d'écran du scénario de recherche de définition du mot procrastination

This scenario will be used as a basis for the next ones, we are launching a search for the keywords “procrastination definition”.

Carbon impact of a basic definition search

For a simple research, our top 3 carbon impact side consists of: Lilo (0.065 gEqCO2), Ecosia (0.068 gEqCO2) and StartPage (0.076 gEqCO2). Qwant is disadvantaged by its excessive data consumption, it is more economical in the energy consumed on the device since second on the energy consumption side.

StartPage, in addition to having a low impact, is also less “resource-intensive” in memory than the other engines and 2 times less than Ecosia, especially on this use case. StartPage is also the most energy-efficient and 2 times less than Yahoo in the same search scenario.

Qwant is again last in this ranking in terms of carbon impact because it is too expensive in terms of data, almost 3 times more than the average for other engines, and up to 6 times more than Ecosia.

On this same basic research and on the basis of the average impact of the 8 engines, the share of the impact linked to the network and to the mobile is preponderant and in equal share compared to the share of impact on the server which remains low.

Part en pourcentage de l'impact lié au réseau, au mobile et au serveur

However, this projection must be the subject of a more in-depth analysis by placing probes in data centers in particular.

On average, the carbon impact for all search engines is 0.106 gEqCO2. Google‘s, the most widely used engine in the world, is 0.108 gEqCO2, or the carbon impact equivalent of one meter (0.96m) carried out in a light vehicle.

If one projects based on Google usage statistics, here are some interesting numbers:

The carbon impact of the 80,000 requests made in 1 second (if all these requests were basic requests launched from a mid-range smartphone) worldwide is: 8,660 gEqCO2, ie the equivalent of 77 km traveled in a light vehicle. The carbon impact of a day of Google queries is a carbon equivalent of 6.7 million km in a light vehicle.

Definition search with autocompletion

Capture d'écran du scénario de recherche de définition du mot procrastination en auto-complétion

For this auto-completion or “suggestion” scenario, we run a search for the “definition pro” keywords, the engine then displays a “definition procrastination” or “definition procrastinate” suggestion. We click on this proposition. To evaluate this scenario, we had to activate a parameter which allowed us to deactivate the auto-completion mode on the different engines, only 2 engines allow it and are therefore compared here on this scenario.

Carbon impact of basic research vs auto-completion

Only two search engine applications allow you to completely remove suggestions or auto-completion (Ecosia and DuckDuckGo). We note that for Ecosia, for equivalent energy consumption, a basic search without suggestions, the consumption of data exchanged is reduced by 11% compared to a search offering suggestions. On the DuckDuckGo side, a search without suggestions reduces energy consumption by 22% and the volume of data exchanged by 14%.

We observe on average that research using auto-completion reduces the carbon impact by 14%.

Definition search with dark theme

Capture d'écran du thème sombre de Qwant

For this scenario, we activate the dark theme from the settings of the only two apps offering it: DuckDuckGo and Qwant and run the same search for the definition of the word procrastination.

Carbon impact of a light theme vs dark theme research

For these two applications offering the dark theme on mobile, on average the dark theme reduces the carbon impact by 3%. And a little more optimized for DuckDuckGo than for Qwant with an 8% gain on the default theme.

Definition search with active newsfeed

Capture d'écran du scénario de recherche avec newsfeed actif

For this scenario, we activate the homepage newsfeed of some applications and compare with the without newsfeed version.

Carbon impact of research without and with active newsfeed

3 applications allow the activation and deactivation of the news feed present on the home page: Google, Bing, and Qwant. This has the effect of increasing the carbon impact of these three applications by only 3% on average, with an average increase in data of 4% on these 3 engines and a slight increase in local energy consumption. (1%)


Search with a web browser

Capture d'écran de l'application Chrome

For this scenario, we launch the Chrome web browser (version 83.0.4103.106), the measured search engine is previously defined as the default one. The search for definition is always that of the word procrastination.

Carbon impact of an application vs web search

We chose to compare an app search and a browser search. For this measurement, we have chosen the Chrome browser, you can find our “best browsers to use in 2020” study if you’re looking for a browser ranking. For two of the applications measured: DuckDuckGo and Bing, searching via Chrome is less impactful on average by 8%. For the other applications, for which browsing on Chrome is more impactful, this is an average difference of 116% but which goes up to multiply the impact by 5.3 for Lilo. Overall and on average, search through a browser on all of these engines is 64% more impactful than through the mobile application.

For all of these engines,

  • energy consumption is stable and slightly lower on the web by 2% but with large disparities: + 48% for StartPage and minus 28% for Yahoo.
  • Data consumption is growing sharply for web research, with a volume that doubles (+ 119%). There is a strong contrast, however: when Bing consumes 12% less (the only less “data-consuming”), others consume more with a peak for Lilo in particular (13 times more) and Ecosia (4 times more). Google remains in the average of 2 times more data on the web version.
  • Local memory consumption also increases significantly for a mobile web search versus mobile application search with + 48%. Again, there is a strong contrast with Ecosia last on this criterion for the mobile application and first on this web search criterion with a decrease of 2%. For all the others, it is a strong increase within particular for DuckDuckGo (+ 115%) and StartPage (107%).
  • Note that travel times have decreased by 6% partially explaining lower energy consumption in web search.

Our advice for eco-responsible search

When we observe the environmental impact of a search, it is difficult to give with certainty the best advice, a link saved in your favorites to go directly to the right information, good content will always have less impact than launching a new search. We have not tested other related areas such as the security/use of your data or the accessibility of solutions, here is some information that we could summarize:

Conseils de GREENSPECTOR pour une recherche éco-responsable
  • A shorter search process results in less energy/battery impact on your user’s smartphone and can help reduce the overall carbon impact across the chain.
  • The carbon impacts of our research are mainly distributed between the network part and the user’s mobile part equally.
  • A search has more impact via a mobile browser than with a mobile application (64% carbon gain on average).
  • For the engines with the least carbon impact, opt for StartPage or Ecosia even if the latter consumes a lot of memory, a point to correct.
  • To save your battery and your data plan, choose StartPage.
  • If you’re having memory issues on an older smartphone, give DuckDuckGo a try.
  • If you don’t see a need for it, turn off newsfeed widgets, interactive map display, and other weather widgets. Average carbon gain of 48% to 52%.
  • Switch to dark to light displays, when available. Average carbon gain of 3%.

As for Google, which dominates the market, it is in the average carbon footprint but is also the one that on average consumes the most memory (40% more than other engines for all of these uses). Let us keep in mind that an average google request is equivalent to the carbon impact of a journey of 1 meter in an average light vehicle.


Methodology

For each of its applications, measured on an S7 smartphone (Android 8), the user scenario was carried out through our GREENSPECTOR Test Runner, allowing the performance of automated tests.

Each measurement is the average of 4 homogeneous measurements (with a low standard deviation). The consumption measured on the given smartphone according to a wifi type network can be different on a laptop PC with a wired network for example.

To assess the impacts of infrastructure (datacenter, network) in the carbon projection calculations, we relied on the OneByte methodology based on real data measured on the volume of data exchanged. This assessment 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 wifi network and 50% via a mobile network.

To assess the impacts of mobile phones in the carbon projection calculations, we measure the energy consumption of the user scenario on a real device and in order to integrate the material impact share, we rely on the theoretical wear rate 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.

Search engineVersionWeight (MB) Samsung S7Playstore gradeDownloadsFrench Market Share (%)
Bing11.3.2820730292,84,55 000 000+3,83%
DuckDuckGo5.55.134,14,710 000 000+0,86%
Ecosia3.8.11374,65 000 000+1,11%
Google11.10.11.214184,35 000 000 000+ 91,68%
Lilo1.0.2286,74,3100 000+N/C
Qwant3.5.01794,01 000 000+0,79%
StartPage2.1.574,4500 000+N/C
Yahoo5.10.51114,31 000 000+1,32 %

What’s the carbon impact for social network applications?

Reading Time: 6 minutes

The stay-at-home context has mechanically increased the mobile applications use of the social network type in order to keep people connected. Like the professional use of videoconferencing tools, these uses have brought additional pressure on the network and on the servers of these solutions.

What are our activities impact on social networks? What are the most / least impactful solutions for the environment, network congestion and the autonomy of our smartphones?

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

For each of its applications, measured on an S7 smartphone (Android 8), the user scenario lasting 1 minute was carried out through our GREENSPECTOR Test Runner, allowing manual tests to be carried out. For each of its applications, the user scenario corresponds to a scrolling of the contents of the news feed of an active account.

Each data measurement is the average of 3 homogeneous measurements (with a small standard deviation). The consumption measured on the given smartphone according to a wifi type network can be different on a laptop PC with a wired network for example.

To make Carbon projections to assess the impacts of infrastructure (data center, network), we relied on the OneByte methodology based on real data measured by the volume of data exchanged. This evaluation methodology takes into account the consumption of resources and energy in use for the equipment requested. As it is a very macroscopic approach, it is subject to uncertainty and could be refined to adapt a context, a given tool. For the Carbon projection, we have assumed a projection of 50% via a wifi network and 50% via a mobile network.

To make the carbon projection of the mobile, we measure both the energy consumption linked to the use case based on real measurement on real device and to integrate the share of material impact, we rely on the rated theoretical wear and tear generated by the use case on the battery, the first wearing part of a smartphone. 500 cycles of full charges and discharges therefore cause in our model a change of smartphone.

Projected data measured in Carbon impact (gEqCO2)

Youtube (0.66 gEqCO2) is first in the ranking, followed closely by Facebook (0.73 gEqCO2) and LinkedIn (0.75 gEqCO2). This is easily explained, since the only videos launching during the news feed for Youtube are thumbnails and this, after 2 seconds. It should be noted that in our test, the scanning of the news feed was slow enough to launch videos according to this principle of tempo.

On the Environmental Impact side of applications, the social network whose news feed is the most impacting is Tik Tok. Unsurprisingly, this social network is based exclusively on watching videos and preloading videos from the news feed at startup. The Shift Project also presents streaming platforms such as Netflix, Youtube and Tik Tok as being responsible for 80% of digital electricity consumption. We had already noted this significant impact, in particular when the application was launched in 2019.

Only 4 applications (Tik Tok, Reddit, Pinterest and Snapchat) are above the average carbon impact (2.1 qEqCO2) observed for this comparison of the news feed. Moreover, the Tik Tok news feed has a carbon impact of 7.4 times greater than that of Youtube.

What if we were to project all of this at the user level?

If we projected this usage “display and progress of the news feed” as being representative over the duration of daily usage/user, we will obtain this data.

According to the Global Web Index 2019, we spend on average 2 hours and 22 minutes on social networks. If we project the average carbon impact of the 10 applications measured (2.10 gEqCO2) over 60 seconds at the average time spent per user, we obtain for a user/day: 280.5 gEqCO2. Or the equivalent of 2.50 km traveled in a vehicle. This also corresponds to 102 kgEqCO2 per user per year, the equivalent of 914 km traveled by medium vehicle in France. This is equivalent to 1.5% of the carbon impact of a French person (7 Tons).

And globally?

If we projected this usage “display and progress of the news feed” as being representative over the duration of daily usage/user, we will obtain this data.

The 2019 figures of LyfeMarketing and Emarsys announce 3.2 billion social network users (42% of the world population) of which 91% access social networks via a mobile device. 80% of the time spent (2 hours and 22 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 3.2 billion users on mobile, the equivalent of 56% of EqCO2 impacts in France.

Energy consumption measurement (mAh)

In terms of energy consumption, the bad students are the news feeds of the Snapchat, Tik Tok and Pinterest applications. The good energy students are Youtube, LinkedIn, and Reddit. The Snapchat news feed consumes 1.6 times more energy here than that of Youtube.

The average established for energy consumption is 10.6 mAh, only 4 applications are above.

If we assume that the application runs continuously on the smartphone, then we can project the remaining battery life time (graph below). We can observe that with Snapchat running, the battery lasts 3.45 hours. On the Youtube side, the battery autonomy lasts 5.46 hours, i.e a ratio of 1.5 (or a difference of 2 hours) between the best and the least good application of this ranking. The average is 4.8 hours for all of these applications. The reference measurement of the test smartphone is 1.32 mAh, its battery capacity of 3000 mAh, we can estimate its autonomy at 18 hours. The use of social networking applications therefore greatly impacts your battery life.

Measurement of data exchanged (MB)

In terms of data exchanged, the bad students are the news feeds of the Tik Tok, Reddit and Pinterest applications. The good students on the data exchanged side are Youtube, Facebook and LinkedIn. Tik Tok consumes 9 times more data than the Youtube application.

The average established for the data exchanged is 19.2 MB for this use. Beware of your data plans! Projection in 1 month, you will have consumed 74 GB!

Taking into account the real average time spent by social network according to the Visionary Marketing blog: if you only use Tik Tok in social network (up to 52 minutes per projected day), you will consume nearly 71 GB per month, while Instagram (up to 53 minutes a day) will consume 25.6 GB! Are you more connected to Facebook? This will make you consume almost 10 GB (up to 58 minutes per day) per month.

For those who like numbers

ApplicationVersionDownloadsGoogle Play Store GradeEnergy consumption (mAh)Data exchanged (MB)Memory consumption (MB)Carbon Impact (qEqCO2)
Facebook270.1/0.66.1275 000 000 000+4,29,551845870.73
Instagram142.0.34.1101 000 000 000+4,510,917,2503,21.91
LinkedIn4.1.451 500 000 000+4,39,26,15492,40.75
Pinterest8.17.0100 000 000+4,611,133,2432,73.53
Reddit2020.18.010 000 000+4,69,243,4414,04.54
Snapchat10.82.5.0 1 000 000 000+4,314,418505,82.03
Tik Tok16.0.43 1 000 000 000+4,312,146,9385,54.93
Twitch9.1.1100 000 000+4,69,69,4374,41.09
Twitter8.45.0 500 000 000+4,510,76,6421,10.83
Youtube15.19.34 5 000 000 000+4,19,15,1379,30.66