Monitoring the circular economy using emerging data streams.
Date: 2022-03-16
Institutions: European Environment Agency
Subjects: circular economy, collaborative economy, data collection, data transmission, information analysis, maintenance, product life

Publications

Monitoring the Circular Economy with new emerging data
streams
Monitoring the circular
economy using emerging
data streams

The EU is tracking its transition to a more circular economy using a monitoring framework. This
framework is based on existing data and covers only essential elements of the transition. This
enabled the framework to be set up quickly and avoid unnecessary costs. To complement this
overview, the EEA is exploring opportunities to collect and use data from new sources to better
understand how the EU economy is changing. This briefing discusses four data sources used to
develop new indicators for circular economy processes that are difficult to monitor with current
data.

Key messages


Establishing how long products are used for before they are replaced is complex. With more

and more devices being connected to the internet, so-called digital fingerprints can

potentially help to derive the lifespans of such products.


Peoples attitudes to adopting emerging business models or other societal innovations that

increase circularity can often be better monitored using openly accessible digital data offered

by popular search engines or social media platforms. Combining these data with information

from other sources, such as market data, can enable policymakers to judge the potential for

further circular economy initiatives and the need for policy action.


The ongoing digitalisation of many processes can generate new data streams that enable

monitoring of these processes, often at product level. There are clear and increasing

opportunities to use such novel sources of data to better monitor the transition to a more

circular economy.

Monitoring the circular economy

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Monitoring the circular economy

The circular economy is one where the value of products and materials is maintained for as long as

possible, and waste and resource use are minimised, as set out in the European Commissions

circular economy action plan.

The European Commission and Eurostat have established a framework to monitor progress towards

a circular economy using available statistical data. This framework focuses on aspects of the circular

economy related to resource use and waste management. Aspects related to maintaining the value

of products and materials for longer  such as design for circularity, repair, reuse and circular

consumption  are not yet included. The circular economy action plan aims to improve the current

monitoring system. All key aspects of the circular economy should be included in the monitoring

system to fully capture progress and inform decision-makers.

The Bellagio principles on monitoring the circular economy were developed under the auspices of the

Network of the Heads of Environmental Protection Agencies in Europe and were referenced by the

EU Council in December 2020. The principles support the establishment of a comprehensive

monitoring framework capturing all relevant elements of the circular economy. They include the

following key points (see Figure 1):

Principle 3: Follow indicator selection criteria. Indicators included in a transparent monitoring

framework for the circular economy transition should follow the RACER criteria: relevant, accepted,

credible, easy to monitor and robust. However, development of innovative, experimental

indicators should also be encouraged, even if not all RACER criteria are fulfilled at first.

Principle 4: Exploit a wide range of data and information sources. Data underpinning a framework

for monitoring the circular economy transition may include:

official statistics from the European Statistical System or national statistical offices and other

data produced by EU institutions, national or local authorities and international organisations

policy information (from tracking policy developments and implementation, including qualitative

assessments)

data from new sources (i.e. information beyond official statistics, such as data from the private

sector and trade associations, from research models, or from new applications of digital

technologies).

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Figure 1. Bellagio principles

Source: Bellagio Declaration. EPA network.

Thus, while principles 3 and 4 call for using strict criteria and statistics from mainly official sources,

they also fully recognise the need for further development and experimentation as a tool to fill some

of the gaps in the monitoring framework.

Potential innovative ways to measure circularity in Europe

Against this backdrop the EEA has, with the support of the European Topic Centre for Waste and

Materials in the Green Economy, identified examples of new sources of data that can be collected to

provide useful estimates for monitoring important elements of the circular economy transition.

Browser fingerprints

Many devices are connected to the internet to enable them to retrieve or share information and

receive software updates. The most obvious examples are mobile phones, laptops and television

sets, but cars and many home appliances are also gradually being connected as the software

content of these devices increases and the internet of things expands.

When these devices connect to websites, they leave a fingerprint with information that enables sites

to tailor information to that particular device. This information includes the type of device and

software version. By harvesting this fingerprint, it is possible to determine the age distribution of

devices in current use, and these data can be used to estimate the service life of devices.

However, using such data may face a number of obstacles:

Different operating systems vary in how they implement the fingerprint. There is, for example,

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a big difference between the structure and content of the fingerprint left by an Android phone

and an iOS (Apple) phone. This makes harvesting more complex.

In many cases it is difficult to determine a devices geographical location. Therefore, statistics

derived from the analysis will tend to be global in nature rather than European or national. It is

possible to get national estimates by harvesting fingerprints obtained from websites heavily used

by the population (e.g. weather websites), but this method is far from perfect.

The fingerprint, as it is implemented in mobile phones, does not include a date of first use.

Instead, it is possible to establish the maximum age of each device based on when a particular

model came on to the market. This means that data provide only an upper-end estimate of the

age distribution of phones in use. However, as new phone models are frequently put on the

market, it still allows a good approximation of the age distribution.

Figure 2. Number of instances when a phone with an Android version with a given release

date connected to the website used in a test run by the European Topic Centre on Waste and

Materials in a Green Economy

Note: Small data set used for illustration only and not necessarily representative of the entire phone population.

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Car sharing system  a case study from Germany

Car sharing is seen as an environmentally friendly alternative to car ownership, as it generally

involves the more efficient use of smaller cars than people would otherwise buy. As each shared car

is used more often than an owned car it wears out faster, leading to a faster renewal of the car fleet,

meaning that cleaner vehicles gain a larger market share faster. However, car sharing may be

competing with public transport rather than replacing privately owned cars, and so the lifetime

environmental credentials of car sharing are not always clear.

German car sharing clubs have centralised the collection of data on the use of shared vehicles. The

EEA has assessed whether this data set could be used to develop estimates of car sharing in

countries where no such data exist.

It is possible to see how often people search online for terms related to car sharing using Google

Trends (see Figure 3). It can also be assumed that people search for information, decide to become

a user, and then have little or no need to search again.

Figure 3. Google searches for car sharing-related terms in Germany (top). Cumulative

searches compared with membership of car sharing schemes (bottom)

Note: The 2020 dip is likely to be due to the COVID-19 pandemic, when people may have stopped paying a

subscription because they could not travel anywhere.

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There is a clear correlation between car sharing membership and cumulative searches, and this

indicates that the number of searches could serve as an estimate of the use of such services. Search

results will not provide absolute market performance data. However, they can illustrate knowledge

about and interest in the concept and, together with very limited market data, indicate the

performance of the car sharing sector.

Web scraping electronic and electrical appliances

An important element of EU policy on the circular economy is the right to repair. But having the right

is not the same as having the ability, and an important aspect is having access to knowledge about

how things are repaired. In the context of modern mass production, it is often cheaper to buy a new

product than to get it professional repaired. The ability to extend the lifetime of products by repairing

them may therefore hinge on peoples ability to do their own repairs.

Many people share videos online showing how to repair specific products, ranging from general

videos on, for example, changing a mobile phone screen, to very detailed ones relating to a specific

manufacturers product. Online video sharing services capture the number of views of individual

videos, making it possible to assess interest in particular topics, although it is not known if this

translates to actual repairs (see Figure 4).

The real challenge for indicator development lies in establishing a list of relevant search terms so that

a measure captures a relevant set of videos. Therefore, an index based on the number of videos and

number of views may indicate interest in attempting a repair.

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Figure 4. Number of new videos posted weekly on the video sharing service YouTube under

phone repair, January 2019 to June 2021

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Web scraping could be used for a range of other topics, such as green claims posted by companies

online (e.g. are companies making reference to refurbished elements and repairability?). This may

enable the development of a range of indicators that have not yet been tested.

Repair case study

Sweden is an example of a Member State that offers a tax break for professional repair activities.

Taxpayers are required to report repairs to obtain the tax deduction, and the data are collected by

the tax authority and made publicly available. This is an easy-to-use data source, but one that is not

widely available across EU Member States. Judging by the data from Sweden, it is likely that the rules

changed in 2016, as it is otherwise difficult to explain the significant change in trend in that year (see

Figure 5).

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Figure 5. Total tax deductions (SEK) for repair in Sweden since their introduction in 2009

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Another repair data source is the alliance of repair cafes where people meet and support each other

in repairing different products. These alliances register the types of products repaired (but not

whether it is successful), age of products, common failures, etc. They therefore form a rich database

to inform the development of more product-specific legislation (access to spare parts, etc.). Although

the data are available in a limited number of countries only, repair cafes still represent a good source

of product-specific information (see Figure 6).

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Figure 6. Success of repair activities in repair cafes

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Conclusion

The ongoing digitalisation of many processes in our society has generated new data streams that

facilitate monitoring of these processes, often at product level. While this can be an attractive

alternative to more traditional (and often expensive) data collection, none of the case studies

presented here is perfect. But they do represent examples of relevant sources of complementary

data that can, in many cases, be used to develop novel proxy indicators to increase the granularity of

monitoring.

In other cases, data are collected to support market or regulatory processes. The key challenge here

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is that the processes may not be uniform across the EU and thus data may be representative of only

one or a few Member States. There are, however, increasing opportunities to use novel sources of

data to contribute to better monitoring the EUs transition to a more circular economy.

Identifiers

Briefing no. 23/2021

Title: Monitoring the circular economy using emerging data streams

EN HTML: TH-AM-21-023-EN-Q - ISBN: 978-92-9480-426-6 - ISSN: 2467-3196 - doi: 10.2800/714610

EN PDF: TH-AM-21-023-EN-N - ISBN: 978-92-9480-427-3 - ISSN: 2467-3196 - doi: 10.2800/457940

Published on 16 Mar 2022

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