Environmental footprint assessment methodology

Our approach

The chosen modelling is based on the principles of Life Cycle Analysis (LCA), and mainly by the definition given by ISO 14040.

It consists of a complete Life Cycle Inventory (LCI) part and a simplified Life Cycle Assessment (LCA). The LCI is predominant in our model. It will ensure that we have reliable and representative data. In addition, the LCI thus obtained can, if necessary, be integrated into more advanced LCAs.

We assess the environmental impact of digital services on a limited set of criteria::

GreenHouse Gas Emissions (GHG)

expressed in g eqCO2,

as commonly used, indicate the intensity of the impact on climate change

Water Resource

expressed in litres,

as defined by the water footprint network, is an indicator reflecting water consumption (blue water) and the number of pollutants used (greywater). It does not take into account the local scarcity of resources, but it is a meaningful indicator.

Land use

expressed in m2,

as originally defined by Heijungs et al. (1992), is a reflection indicator of the impact on biodiversity. It is not the most comprehensive (usually independent of time, but it is the most used currently.

This methodology has been reviewed by the EVEA firm – a specialist in ecodesign and life cycle analyses.
Note on water resource: Greywater and blue water are taken into account at all stages of the life cycle. Green water is added to the manufacturing cycle of terminals and servers. See the definition of the water footprint.

Quality management of results

The quality of LCA results can be modelled as follows¹ :​​

Quality of input data x Quality of methodology = Quality of results

To improve the quality of the input data, we measure the behaviour of your solution on real devices. It helps to limit the models that are potential sources of uncertainty.​

To manage the quality of the results, we apply an approach that identifies the sources of uncertainties and calculates the uncertainty of the model. Our method of managing uncertainties uses fuzzy logic and its sets².

Ultimately, unlike other tools and methodologies, we can provide margins of error in the results we give you. It ensures more peaceful communication of the environmental impact to stakeholders (users, internal teams, partners, etc.)

¹Quality of results: SETAC (Society of Environmental Toxicology and Chemistry 1992)

_²Although often mentioned in the literature dealing with uncertainties in LCA. This approach is little used. Indeed, stochastic models such as Monte Carlo simulations are often preferred (Huijbregts MAJ, 1998). In our case, the use of fuzzy logic seems more relevant because it allows us to deal with epistemic inaccuracies, especially due to expert estimates.​

Calculation steps

Phases taken into account for the equipment used

Note on the impact model of the terminal part

Classical impact analysis methodologies assume a uniform impact on the software (average consumption regardless of the software or the state of the software). Our innovative approach makes it possible to refine this impact. In addition, we are improving the modelling of the software impact on the hardware manufacturing phase by accounting for battery wear.

The battery of smartphones and laptops is consumable. We model the impact of the software on it.​

Input data for the Life-Cycle Inventory

Example of work on hypotheses:

The methodology of propagation of uncertainties requires us to identify precisely the quality of these assumptions. Here are a few examples, in particular the impact of material manufacturing.

The bibliographic analysis allows us to identify the impacts of different smartphones, and to associate the DQI confidence index. These figures mainly come from the manufacturers.

The average impact calculated from these confidence indices is 52 kg eq Co2 with a standard deviation of 16 kg.

Example of restitution

• In this example: the median impact of 0.14g eqCO2 is mainly on the ‘Network’ part.
  
  
• This impact corresponds to viewing a web page for 20s
  
  
• Uncertainty is calculated by the Greenspector model by applying the principle of propagation of uncertainties from the perimeter and assumptions described above.

Necessary elements

To determine the impact of your solution, we need the following information:

• Smartphone / Tablet / PC viewing ratio
• France / World visualization ratio
• Location of France / World servers
• Simple or complex servers (or number of servers in the solution)

On this estimate, we can carry out a simplified LCA based on this model but adapting other elements to counter particular circumstances. For example:​

• Measurement of the energy consumption of the server part (via a partner)
• Accuracy of server assumptions (PUE, server type)
• Measurement of the PC part (via laboratory measurement)
• Accuracy of the electrical emission factors of a particular country…

Comparison of estimation models

Greenspector calculations are integrated into a web service currently used by our customers. Very soon, find our calculations of the environmental footprint of your mobile applications and websites in a SaaS interface.

Comparison of estimation models

Calculation methods in digital sobriety are often not very accurate and sometimes, at the same time, not very faithful. It potentially leads you to use tools that poorly assess the impact of your solutions. The risk is to make your teams work on areas that have no real impact on the environment.

Some approaches, more used in LCAs (and not in market tools), improve fidelity but pose a risk of giving an unfair result (R. Heijungs 2019).

Our approach is based on an innovative computational method, fuzzy arithmetic, first proposed by Weckenmann et al. (2001).

This approach is very efficient for modelling vague (epistemic) non-probabilistic data, which is often the case of data dealing with digital sobriety. In this way, we aim for accurate and faithful results.

Rival solutions make choices that generally make them inaccurate and unreliable:

• Fidelity: Poor control of the environment, no methodology for managing measurement deviations
  
• Accuracy: Model-based on non-representative metrics such as data consumption or DOM size, no energy measurement…

The more frequently a URL is viewed, the more essential it is to reduce its digital impact. A simple measurement makes it possible to check and react to changes made on the page: modifications linked to the graphic charter, events (e-commerce sites during sales periods) or even technical modifications. All of these changes can have a big impact on the sobriety level of a web page.

When to integrate the digital sobriety measurement of a URL?

These measurements can be integrated as part of daily monitoring on any page. “Dynamic” pages whose content changes regularly, such as e-commerce home pages or press information sites, are crucial to monitor. Even less “dynamic” pages can also be targeted: updating a CDN library can, for example, impact this type of site. In this case, the measurement will make it possible to ensure that the new skin of the page does not harm the level of sobriety of the website: an image too heavy for a banner can be easily spotted.

Measurement can also be used during the development phase. To test choices before going into production or to correct excessively impactful changes very early. It is often difficult to change the choice of a technology or an implementation once a site goes into production. Measuring a URL during the development phase allows you to test different options early on and see which one corresponds best by taking into account digital sobriety as one of the criteria.

Example of daily monitoring of a web page

How to measure the digital sobriety of a URL?

There are several options available to you at Greenspector to measure a URL.

A first tool allows us to perform a simple first measurement of a URL and obtain rapid observations: the Benchmark tool based on standardized tests.

To go further, we can measure a complete user journey on a website using App Scan. This kind of measurement represents the full path on a website or mobile application, such as a purchase journey or the completion of a bank transfer. It helps identify areas to focus on to achieve significant improvement. As part of an App Scan, the measurement of a URL is also possible via an automated route which will allow obtaining specific metrics beyond the benchmark.

URL measurement vs Benchmark

Here are the different steps measured during a URL measurement vs with the Benchmark tool:

The URL metric contains more steps than the benchmark we will come back to that. Unlike the benchmark, the URL measurement is more precise on the loadings. The measured duration being the actual loading time, unlike the benchmark tool which performs the measurements over a fixed period of 20 seconds. Another difference is that the URL measurement manages the tabs present on the page, in particular those concerning cookies, which the benchmark tool does not do.

Enfin, la mesure URL par Greenspector permet de réaliser des mesures sur d’autres navigateurs que GFinally, the URL measurement by Greenspector makes it possible to carry out measurements on browsers other than Google Chrome. The benchmark tool is limited to the latter browser, but our GDSL expertise allows us to offer another browser such as Firefox to go even further.

The steps of a URL measurement

• Loading without cache: This is the loading of the URL having previously cleared the cache and deleted all cookies. This step measures the loading of the web page when a user goes to it without a cache. It is essential for URLs with a lot of unique visits.
• Pause after loading without cache: Measuring a short pause after loading allows you to recover data exchanges and other operations that are still taking place when your page is displayed. The ideal, of course, is to have none of that. Otherwise, it allows us to make observations and suggest ways to eliminate or reduce these treatments.
• Pause on a page without cache: It represents the action of a user reading the content. No movement on the screen. The idea of ​​this step is to measure the impact of the continuous display of the page.
• Scroll on the page: Scroll to the bottom of the page to make observations on the treatments during the scroll. Here we can make observations on the possible data exchanges (pagination, image download, advertising) as well as the associated fluidity.
• Pause on the page after scrolling: Measure of pause after scrolling allowing to observe processes that continue after the end of user interactions.
• Loading with cache: Measurement of the loading of the URL with the cache of previous interactions (loading, scroll). This step allows you to see the impact of caching on the site. This is important for pages that will be visited a large number of times by known visitors, such as a website home page.
• Pause on the page with cache: The measure of pause on the page allowing to see if despite the cache there are treatments after loading.

Thanks to our tools and our expertise, we can offer reliable and relevant measurements on a URL. Whether it is a simple measurement allowing initial findings using our benchmark tool or a more in-depth measurement with our GDSL language. This regular URL monitoring gradually improves the sobriety level of its website. This approach compared to other approaches commonly used in the web (Analysis only of the loading of the page with Lighthouse or others …), brings more finesse to the knowledge of the consumption of the page.

What is GDSL ?

The term GDSL stands for Greenspector Domain-Specific Language. It is a language created by Greenspector to simplify test automation on Android and iOS. To put it simply, it is an overlay based on the automation frameworks from Google and Apple, embellished with functions to ease test automation.

This language is the result of the Greenspector expertise accumulated over several years. It combines ease of writing with the ability to measure the energy performance of an application or website.

The GDSL principle is to be a language for describing actions that will be performed on the smartphone. In that sense, it can be closer to Gherkin with whom it shares the quality of not requiring developer training to be read.

The GDSL is a series of actions that will be performed in order on the smartphone. It has the basic actions of WAIT, CLICK or PAUSE as well as more complex actions such as launching an application or managing the GPS. 

With GDSL it is possible to quickly automate most of the critical user journeys of your applications or mobile website. 

The Greenspector team works daily to improve the language and add functionalities. The current state automates most of the scenarios required for a complete measurement campaign for an application or website as well as the critical journeys of most applications. In a future article, we will tell you about the Greenspector tools for running GDSL tests.

The Greenspector team is proud to announce its latest release v.2.9.0.

To measure application and website consumption, you can run user journeys on our smartphone farms. In this context, we have improved our simplified test language (GDSL) with, for example, features for preparing browsers but also taking Firefox into account… Unlike many tools that provide you with an environmental impact only on the main page and on simulated environments, these capabilities will allow you to assess and monitor the impact of your digital solution!