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:
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:
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.
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:
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:
The audited site is therefore not GDPR compatible! To manage this, you must ask the user for consent explicitly:
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.
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.
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
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.
Energy consumption of Instagram features for 1 minute
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
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 case
Energy consumption (mAh)
Exchanged data (MB)
Memory consumption (MB)
Test time (second)
Carbon impact (gEqCO2) per minute
Equivalence in meters of average car in France / minute
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.
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.
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
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.
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
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.
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 appwith 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
This scenario will be used as a basis for the next ones, we are launching a search for the keywords “procrastination definition”.
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.
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
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.
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
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.
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
For this scenario, we activate the homepage newsfeed of some applications and compare with the without newsfeed version.
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
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.
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:
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.
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.
Article updated with new measures on StarLeaf, Rainbow and Circuit by Unify on May 19, 2020. Article updated with new measures on Hangouts and Google Meet on May 4, 2020. Article updated with new measures on Tixeo and Infomaniak Meet on April 23, 2020. Article updated with new measures on JITSI and Teams on April 15, 2020.
The current “stay at home” context mechanically increases the use of online collaboration tools, in particular videoconferencing tools. This leads to a pressure on the network and more particularly to a significant load on the servers of each solution. It is therefore important from an efficiency point of view but also an environmental impact one to choose the simplest and most efficient solution.
For this study, we compare 14 videoconferencing applications: Whereby, Webex (by CISCO), Skype, Zoom, JITSI, Microsoft Teams, Tixeo, Infomaniak Meet, Hangouts,GoToMeeting, Google Meet, StarLeaf, Rainbow and Circuit by Unify.
For each of these applications, measured on an Samsung 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:
Audio conference only in one-to-one
One-to-one audio and video conference (camera activated on each side)
One-to-one audio conference and screen sharing
Each measurement is the average of 3 homogeneous measurements (with a small 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.
Measurement of energy consumption (mAh)
The StarLeaf application consumes the most of all three scenarios. This is due to the fact that the consumption in audio only mode, audio + screen sharing or audio + video is the same. This is a special case of our study. The GoToMeeting application is the least energy-consuming, closely followed by Hangouts, Zoom and Webex.
The energy consumption of all these applications is on average 2.1 times higher when adding video to audio and only slightly higher when adding screen sharing to audio (+14%). It’s not a surprise, avoid sharing with a camera to consume less energy on your devices during your video conferences and save your autonomy and the lifespan of your battery!
Measurement of data exchanged (MB)
GoToMeeting and Webex are the two applications that consume the least data. JITSI and Infomaniak Meet are the ones that consumes the most. Overall and without any surprise we note that the audio scenario is the one that consumes the least data. While the scenario activating both audio + video stream is the most consuming one.
The data consumption of all of these applications is 13 times higher when adding video to audio and almost doubled when adding screen sharing to audio (+77%). It’s not a surprise, avoid sharing video and limit your screen sharing to consume less data on networks in your video conferences!
It should be noted that these significant differences are mainly due to the significant audio-video consumption of the JITSI application with 35 MB transferred in 1 minute compared to 1.13 MB for GoToMeeting! JITSI‘s optimized mode does not reduce this data impact (33.4 MB / minute). Infomaniak Meet based on the JITSI engine meets the same volume anomaly on average and mainly on the audio + video part without improvement with the optimized mode.
Projection of the measured carbon impact data (gEqCO2)
A conference on mobile is 3 times more impactful for the environment when we add video to audio.
The carbon impact projection of all of these applications is on average 3 times greater when adding video to audio and higher when adding screen sharing to audio (+35%). It’s no surprise, avoid sharing video and limit your screen sharing to less impact infrastructure (network, datacenter) and on your devices in the context of video conferences!
Without any surprise, a large part of the carbon impacts are located on the network part (63%) but the impacts part on the device (28%) should not be overlooked!
The JITSI and Infomaniak Meet apps even averages 5.8 times more impact when adding video to audio and 40% more when adding screen sharing.
In the Skype vs Teams battle at Microsoft, the overall results are very close (6%). The Carbon impact is lower for Skype with a lower data impact but higher energy consumption on the mobile than Teams.
Which applications optimize energy and data consumption?
Only Whereby and Webex could be tested on optimization features for the mobile version.
The Whereby application with its “Mobile mode” setting which limits the resolution of the stream as well as other resource consumption optimizations. And Webex thanks to its configuration allowing to deactivate High-definition playback of videos only. Whereby saves 21% on energy consumption on the audio side, 15% on the audio and video side and 3% on the screen sharing side. These gains are nevertheless low considering the results obtained by the application that consumes the most audio and video mode from our panel.
On the Webex side, the gains are barely visible as limited only to the video part. It’s barely 5% gain in the Audio and Video scenario for the energy and carbon impact. The exchanged data is even slightly higher in audio and video mode.
The optimized JITSI application does not improve the data or energy impact. Power consumption is even higher in screen sharing mode in this optimized mode. However, there is a reduction in the data loaded in this optimized but minor mode (11% on average). Same findings for Infomaniak Meet.
The optimization of the TIXEO parameters makes it possible to considerably reduce the impact in audio + video mode and allows it to be classified as the 2nd least impacting application in the panel.
As for the Zoom application, a notification of battery overconsumption appeared several times during the measurements. Although we can optimize the video quality on PC, it does not seem possible to configure this on mobile. Without this optimization, the application becomes the most energy-consuming application in audio + video mode.
GREENSPECTOR advices:
Promote audio only during your conferences: the video stream will tend to consume much more especially when sharing video via camera.
We recommend that publishers provide optimization options to the user and make them as accessible as possible even by default.
Prefer videoconferences over travel by car!
Comparison for 2 people who talk to each other for 3 hours in video + video (1 gEqCO2 on average per minute) when one of the two made 20 kms (112 g eqCO2 / km in France) round trip for a face-to-face face,
In videoconference: 180 * 1 * 2 = 360 gEqCO2.
By car: 112 * 20 = 2.4 kg EqCO2. Or 6.7 times more for the physical one-to-one.
Google unveiled the best of 2019 Google Playstore in early December last year. Thanks to the GREENSPECTOR App Mark technology, we are revealing the ranking of these applications according to the App Mark, which assesses applications according to 5 criteria: performance, sobriety, discretion, inclusion and, of course, ecology.
Favorite 2019 applications ranking
At the top of this ranking we find the following three applications: Boosted, 21 Buttons and Omio. Only 1/3 of the applications are below the global ecoscore average. Among the 3 least sober applications of this ranking, 2 are strongly impacted by a zero score on at least one of the key indicators. In fact, the Plant Nanny application presents a score of zero on the ecological side (32 MB of data loaded during our evaluation), for the Curio application, it is the performance indicator which is zero, impacted by the time of application loading more than 23 seconds.
Focus on Boosted vs Music Zen
Here we compare the application with the best GREENSPECTOR App Mark (Bosted) versus the lowest ranked application in this ranking (Music Zen).
Even if the energy consumption is not the strong point of Boosted, MusicZen consumes 2 times more battery than Boosted on an identical route … what not to be “Zen” For its autonomy! Simple explanations: there are many trackers in this application which alters its performance and its sobriety. As a reminder, an added tracker also means an average of 8.5% more resource consumption.
Favorite 2019 games apps ranking
On the podium of the highest ranked game applications, we find: Assassin’s Creed Rebellion, Brawl Stars and Fishing Life. We observe that the last three game applications have a zero score for the ecology indicator. This is explained by two highly high technical criteria: the volume of data loaded as well as the CO2 impact. By the way, gaming applications are also more impactful on average than other applications.
Focus on Assassin’s Creed Rebellion vs Diner Dash Adventures
This time we are comparing the Assassin’s Creed Rebellion application versus Diner Dash Adventures.
The 2021 edition of this ranking is available! Read the study
The web browser is a main tool on a mobile device. Not only for websites but also for new applications based on web technologies (progressive web app, games, …).
In our 30 most popular mobile apps ranking, among the mails, direct messaging, social networks, browsers categories, web browsing and social networks are on average more consuming than games or multimedia applications. The ratio would be 1 to 4 between the least and most energy consuming applications.
Decreasing the environmental impact of the digital life and increasing the autonomy of phones go in part through the choice of a good browser. Just as if you want to reduce the impact of your mode of transport, it is important to take the most efficient vehicle.
Last year we published the 2018 ranking of the least energy-consuming browsers, we made a new edition for 2020, more complete, made with our GREENSPECTOR App Mark.
Overall Ranking
The average rating is 36/100 which is pretty mediocre. It can be explained by low notes for each of the metrics. The three least energy-consuming browsers are: Vivaldi, Firefox Preview, Duck Duck Go.
Overall energy consumption (in mAh)
The median is 47 mAh and a large part of the browsers are in this consumption level (8/18 are in the 2nd quartile). Note that the last 3 browsers in the ranking are recognized by a consumption 75% higher than the median. Firefox, Qwant and Opera Mini are indeed very energy intensive.
Energy consumption of navigation (in mAh)
The last 3 browsers of the global ranking (Opera Mini, Firefox and Qwant) as well as Mint consume much more than the average (between 20 and 35 mAh against 16 mAh).
It is sufficient to say that for most browsers (apart from the previous exceptions), pure navigation is not going to be the reason for the difference in overall consumption. This is mainly due to the use of visualization engines. Most browsers use the Chromium engine. For Opera Mini, the specificity is that a proxy is used and can compress the size of the sites. It seems that this proxy is not good for the energy, in fact the decompression on the user’s phone consumes a lot of energy.
For the Firefox app, over-consumption of energy is a known and shared thing, this is one of the reasons why Mozilla is under development of a new browser. Internal code name is Fenix and public one is Preview. The measures in this ranking are rather encouraging on consumption (in the average). For Qwant, this is due to the use of the Firefox engine! The measurements between Qwant and Firefox are indeed very close.
Power consumption of features (in mAh)
The main feature that is browsing the web also requires other important features: new tab opening, enter an address in the taskbar … Indeed, when we open a new tab, each browser offers different features: mainly used websites, latest news, …
On pure navigation, browsers differ little, there are significant differences in energy consumption on other features with a ratio of more than 3 (between 4 mAh and 12 mAh).
Note that the first 3 (Firefox Focus, Firefox Preview and Duck Duck Go) have a simple homepage. The consumption of the browser in Idle (inactivity) is then very low. Functional sobriety pays the consequences!
When launching browsers, the energy consumptions are quite similar to each other. Note, however, that opening a tab and writing a URL are actions that are performed several times. If we take a daily projection of 30 new tabs and 10 URL entries, we can still see the difference between browsers and the large advance of Firefox Preview and Focus!
The basic features are not insignificant in the overall consumption.
Projection of autonomy (in number of hours)
If we take this energy data and project it for a navigation of several websites, we identify the maximum time that the user can navigate to the complete discharge of his battery:
Data consumption (in MB)
The difference in data consumption between browsers (8 MB difference) is explained by the pure navigation and the different features.
On the navigation, we explain this difference:
some applications do not manage the cache at all for reasons of data protection and confidentiality (Firefox Focus)
proxy usage that optimizes data (Opera Mini)
a difference in the implementation of cache management. It is possible that some browsers invalidate the cache and that data is loaded while they are cached.
additional data consumption continues in the background (idle tabs, background data not blocked …)
download performance differences that increase the duration of the measurement. Indeed, if a browser is powerful, the counterpart is that many more data are potentially loaded in the background.
The difference in overall consumption can also be explained by the data consumption of the basic functionalities:
Many browsers are very consuming. We note the 3 MB of Qwant that seem abnormal! It can be considered that for browsers, this consumption should be close to 0. Indeed, the main feature of a browser is to display a website, any feature (and associated consumption) can be considered as “over-consumption”. In this context, many browsers consume data when writing the URL. This is mainly explained by the URL proposal features. There is indeed exchange between the mobile and the servers, either directly by the browser or by the associated search engine.
For example, for the Yandex browser below, the details of data exchanges when writing a URL show more than 400 KB of data exchanged.
In contrast, below, trading for Brave is frugal with less than 2 KB.
Browser performance (in seconds)
The measures allow us to evaluate the performance of the key features:
Launching the browser
Adding a tab
Writing a URL
Removing the cache
Mozilla Kraken Bench
NB: This study does not evaluate the display performance of websites. However, the Mozilla Kraken benchmark allows this in part by evaluating the functionality of browsers.
Efficiency of browsers (in mAh/s)
We can evaluate the efficiency of browsers by taking the performance of the Mozilla Kraken benchmark and the associated energy. Efficiency is the energy consumption per unit of time:
Samsung, Opera Mini and Opera are the most efficient browsers. This ranking is different from that of overall energy consumption. For Samsung Internet, this first place in terms of efficiency on a Samsung hardware can be explained by the optimized link that can have the manufacturer with a pre-installed software. The Opera browser has a good positioning (2nd for overall consumption and 3rd for efficiency).
Track of improvements
It is possible to improve the consumption of navigation.
For the user :
Choosing an efficient browser
Use bookmarks or favorites to avoid going through the entry bar
Configure the energy saving options of browsers (mode or dark theme, data server …)
For developers of sites:
Eco-design their site
Test and measure on different browsers to identify different behaviors and take them into account
For browser editors:
Measure energy consumption and efficiency
Eco-design features
Reduce resource consumption of recurring features (url write, new tab …)
Make the homepage as simple as possible.
Measurement protocol
The measurements were carried out by the laboratory GREENSPECTOR App Mark on the basis of a protocol Standardized: Samsung S7 Smartphone, Android 8, Wi-Fi, 50% brightness. Between 4 and 8 iterations were carried out and the value used is the average of these measurements. Measurement campaigns follow a scenario that evaluates browsers in different situations.
Evaluation of features
Launching the browser
Adding a tab
Writing a URL in the search bar
Remove tabs and clean the cache
Navigation
Launch of 6 sites and wait for 20 seconds to be representative of a user journey
At launch (this allows to evaluate the homepage of the browser)
After navigation
After closing the browser (to identify closing problems)
For each iteration, the following tests are performed:
Deleting the cache and tabs (without measurement)
First measure
Second measure to measure the behavior with cache
Remove cache and tabs (with measure)
System shutdown of the browser (and not only a closure by the user to ensure a real closing of the browser)
The average measurement therefore takes into account a 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 … are measured but will not be displayed in this report. Contact GREENSPECTOR for more information.
In order to improve the stability of the measurements, the protocol is completely automated. We use an abstract GREENSPECTOR test description language that allows us to fully automate this protocol. Browser configurations are the default ones. We have not changed any settings of the browser or its search engine.
Rating
A notation out of 100 makes it possible to classify the browsers between them. It is based on the notation of 3 main metrics:
Metric
Definition
Unit
Performance
Duration required for a test step
seconds (s)
Energy
Battery discharge rate found on the device during the test step, compared to the battery discharge rate of the device before the application is launched
Measurements in uAh / s, then classification in multiples of the reference discharge velocity
Data
Total data volume (transmitted + received) during the test step
kilo-bytes (kB)
A weighting ratio is applied to the 5 step levels (from 5 for dark green to -1 for dark red) as described in the following example table:
The score of this application is then calculated at 61/100 for the energy metric. Once the score of each of the three metrics obtained on 100 points, the total score of the application is calculated with equal weighting of the three metrics: Total Score = (Performance Score + Energy Score + Score Data) / 3
Browsers evaluated
Browser name
Version
Brave
1.5.2
Chrome
78.0.3904.108
Duck Duck Go
5.32.3
Ecosia
39632
Edge
42.0.4.4052
Firefox
68.3.0
Firefox Focus
8.0.24
Firefox Preview
2.3.0
Kiwi
Quadea
Lilo
1.0.22
Maxthon
5.2.3.3241
Mint
37290
Opera
54.3.2672.502
Opera Mini
44.1.2254.143
Qwant
37714
Samsung
10.1.01.3
Vivaldi
2.7.1624.277
Yandex
19.10.2.116
Some browsers were discarded because they did not allow the tests automation. For instance, UC Browser and Dolphin browsers could not be measured. Beyond automation, this is a symptom of a accessibility issue of the application. To improve the accessibility of applications for people with visual impairments (among others), it is necessary to set up buttons labels. The automation that we realized is based on this information. In the end, these browsers do not appear in the ranking, but we can consider that accessibility problems are in all cases a crippling problem.
Note : The 2020 ranking is hardly comparable to that of 2018. Indeed, our protocol having completely evolved, the tests are thus more advanced and automated.
In a previous article on this blog, we introduced you the 5 keys to success of a mobile application. We present today the 12 rules by business indicator to respect that will make the success of your application.
The application should not require a recent OS version like Android to be used. Some users do not follow updates, either voluntarily or because of their platform that does not allow them. According to our “PlayStore Efficiency Report 2019“, only 70% of apps on the store are compatible with all versions of Android.
The application must comply with the accessibility rules and must not exclude users with disabilities.
The app should work well on older phones too only on recent and latest models. This criterion will be degraded if you do not respect that of sobriety. 1/4 of the Google PlayStore applications are 10% of the oldest mobiles. (Source: PlayStore Efficiency Report 2019)
The application must limit its resource consumption (number of CPUs, memory occupied, data exchanged) in order to avoid any slowness or pollution of the other applications (for instance because of the memory leak). 50% of Google PlayStore apps continue to process after the app closes. (Source: PlayStore Efficiency Report 2019)
The application must limit its network consumption in order to not involve any load on the data centers and thus avoid the additional costs related to the unnecessary congestion of the servers.
Performance
The first launch of the application must be fast: otherwise, it is possible that your users won’t go further, the inclusion criterion will not be respected either.
The loading times of the application must be acceptable in all network situations.
Discretion
The application requires few or no permission. Do you really need to consult the list of contacts of your user? It’s all the more important to optimize this since the more permissions there are, the more the application consumes resources. This will therefore negatively influence the performance criterion.
The application has little or no tracker. The integration of a large amount of trackers implies a greater consumption of resources but can also cause bugs. This observation is even more true that the connection is degraded. On average, adding a tracker causes an over-consumption of resources of 8.5%.(Source : PlayStore Efficiency Report 2019)
According to our “PlayStore Efficiency Report 2019“, trackers, analytics and permissions are ubiquitous (44% applications have more than 5).
Ecology
The application must respect the sobriety criterion, the CO2 impact linked to the use is lower as well as the pressure of the resources on the components of the equipment of the user (battery obsolescence, loss of performance). As a result, the user is less likely to renew their equipment, which reduces the risk of obsolescence of his material. Our latest study shows that mobile apps contribute at least 6% of CO2 emissions digital.
Some tracks for the improvement of its GREENSPECTOR App Mark score
Directly improve the application
Several metrics are evaluated by the GREENSPECTOR App Mark and can be directly improved.
Minimum SDK version: Allow Android older versions to avoid the exclusion of users using older generation platforms.
Number of trackers: the fewer trackers the application has, the more it will respect the user’s data as well as the protection of his privacy. In addition, trackers via processing and data exchange increase the consumption of the application.
APK size: the bigger the binary of the application, the more the network is solicited and the less efficient the application. In addition, a large application size will use the limited storage space of some users.
Loaded data: number of loaded data throughout the test run. Limiting this data will reduce the consumption of resources on both the smartphone and the network.
Data loaded in the background: when the application is not used, it must limit its impact and send or receive as less data as possible.
More global metrics
Some metrics are directly related to the impact of the application and its efficiency. It is possible to act on it via the previous metrics, see by other axes (functional optimization, improvement of the source code …)
CO2: the more the application consumes energy, the more the battery is solicited and become obsolete. This may lead to a premature renewal of the battery or even the smartphone and therefore to a higher environmental impact. Let’s not forget that most of the environmental impact of a smartphone is predominant in its manufacturing phase than in its use phase: keeping it longer reduces its overall impact.
Energy Overconsumption: if the application overconsumes, it increases the environmental impact but also creates discomfort for the user especially on the loss of autonomy and generates an additional stress factor.
Performance after the first installation: applications sometimes perform additional treatments during the first launch, so the launch time is sometimes increased. It is necessary to limit its treatments because this loss of performance can be inconvenient for the user.
Performance: the launch time of the application is an important data for the user. It is necessary to reduce it to the maximum while consuming the least possible resources.
3G Performance: in poor network conditions, it is necessary to master the performance to maintain a good user experience. It is even possible that some users do not have access to the application in the case of degraded performance. Having a frugal service that takes into account the constraints of mobility is therefore a key to success.
What about now?
You are certainly wondering how your application is doing on these 5 indicators. Is it rather virtuous? Is there any risk? How is it ranked against its competitors? Do you have quick progress actions? If you ask us, we will tell you! Contact-us, and we will introduce you to your own inclusive, sober, fast, ecological and discreet evaluation – just like your application very soon.
During the Mobile One event, GREENSPECTOR announces a survey of the major mobile consumer trends of the Google Play Store. More than 1000 applications were sifted through for Performance, Sobriety and Inclusion by the measurement tools developed by GREENSPECTOR.
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