Use, Storage, and Disposal of Electronic Equipment in Switzerland

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Use, storage and disposal of electronic equipment in Switzerland Esther Thiébaud, Lorenz M. Hilty, Mathias Schluep, and Martin Faulstich Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b06336 • Publication Date (Web): 15 Mar 2017 Downloaded from http://pubs.acs.org on March 16, 2017

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Use, storage and disposal of electronic equipment in

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Switzerland

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Esther Thiébaud*1), Lorenz M. Hilty1),2),3), Mathias Schluep4), Martin Faulstich5)

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1) Empa, Swiss Federal Laboratories for Materials Science and Technology, Technology and

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Society Laboratory, Lerchenfeldstrasse 5, CH-9014 St. Gallen, Switzerland

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2) University of Zürich, Department of Informatics, Binzmühlestr. 14, CH-8050 Zürich, Switzer-

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land

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3) KTH Royal Institute of Technology, Centre for Sustainable Communications (CESC), Lind-

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stedtsvägen 5, S-10044 Stockholm, Sweden

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4) World Resources Forum (WRF), Lerchenfeldstrasse 5, CH-9014 St. Gallen, Switzerland

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5) Clausthal Environmental Institute (CUTEC), Leibnizstraße 21+23, D-38678 Clausthal-

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Zellerfeld, Germany

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TOC Art:

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Abstract

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For an efficient management of these resources, it is important to know where the devices are lo-

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cated, how long they are used and when and how they are disposed of. In this article, we explore

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the past and current quantities of electronic devices in the in-use stock and storage stock in Swit-

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zerland and quantify the flows between the use, storage and disposal phase with dynamic materi-

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al flow analysis (MFA). Devices included are mobile phones, desktop and laptop computers,

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monitors, cathode ray tube and flat panel display televisions, DVD players, and headphones. The

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system for the dynamic MFA was developed as a cascade model dividing the use phase in first,

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second and further use, with each of these steps consisting of an in-use stock and a storage stock

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for devices. Using a customized software tool, we apply Monte Carlo simulation to systematical-

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ly consider data uncertainty. The results highlight the importance of the storage stock, which ac-

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counts for 25% (in terms of mass) or 40% (in terms of pieces) of the total stock of electronic de-

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vices in 2014. Reuse and storage significantly influence the total lifetime of devices and lead to

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wide and positively skewed lifetime distributions.

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Keywords: In-use stock; storage stock, disposal pathways, lifetime, dynamic material flow analysis

Electronic devices contain important resources, including precious and critical raw materials.

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Introduction

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secondary resources. Over the past twenty years, waste electronic equipment (e-waste) has been

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collected in Switzerland and sent for recycling, where, among other materials, base and precious

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metals are recovered. Several rare technical metals such as indium, gallium, tantalum, or the rare

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earth metals are not recycled. Existing efforts to improve recycling processes are hampered by

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missing information on the location and quantities of these metals, the complex structure of elec-

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tronic equipment (EE), low concentrations per device, the metallurgical limits of recovery pro-

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cesses, as well as lack of economic incentives.1–5

Electronic devices we use or store at our homes can be viewed as an anthropogenic stock of

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Stocks and flows of EE and the critical raw materials they contain6 have been subject to vari-

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ous recent investigations. The flows entering collection and recycling schemes are often mod-

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elled quantitatively by combining sales or stock data with estimated product lifetimes, using a

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dynamic material flow analysis (MFA) approach.7–17 Most of these studies are limited to one or a

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few electronic device types and present results either for EE flow only, or together with qualita-

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tive or quantitative recovery potentials of critical raw materials in specific types of EE.

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Many of these studies have shown discrepancies between high sales flows and low collection

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flows or long phase-out periods of technologies that are no longer sold for certain device

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types.10,11,17–21 These findings suggest that there are other significant disposal pathways than the

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official e-waste collection. Other possible disposal pathways are, for example, export for reuse or

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recycling, waste incineration (via the municipal solid waste collection pathway), and illegal

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dumping. Additionally, as we will show in the following, e-waste generation can be significantly

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slowed down due to reuse or storage of obsolete equipment, which is often not explicitly taken

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into account. Only preliminary investigations in this direction can be found in literature so far.

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Parajuly et al.7 include reuse and storage of IT and telecommunication equipment and consumer

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electronics in their MFA system, but do not quantify related stocks and flows. Similarly, Yoshida

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et al.16 mention both storage and reuse of computers, but only quantify domestic reuse flows.

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Chung et al.22 evaluate the amount of stored televisions (TVs) and computers in China. The

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amount of stored TVs and e-waste in general in US households is analyzed by Milovantseva et

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al.23 and Saphores et al.24. Polák and Drápalová14 include storage times for mobile phones and

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Sabagghi et al.25, as well as Williams und Hatanaka26 report storage times of personal computers

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in their studies. Steubing et al.15 include reuse and storage times in their MFA of computers and

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monitors, and quantify related flows. To the best of the authors' knowledge, there is, however, no

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dynamic MFA research that includes reuse and storage times and differentiates between in-use,

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reuse and storage stocks and the related flows of EE.

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In an attempt to close this research gap, we conducted a survey on the service lifetime, storage

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time, and disposal pathways of EE in Switzerland between 2014 and 2016. The findings high-

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light the influence of storage and reuse on the time a product remains in the use phase27. In this

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article, we present a dynamic MFA of nine different types of EE in Switzerland. We develop a

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model to identify past and current in-use stocks and storage stocks of EE and quantify in detail

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the flows between and from the use, storage and disposal phases. Based on the in-depth 'bottom-

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up' data we are able to differentiate between new and (re)used devices. Such a model is a signifi-

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cant step towards a deeper understanding of the behavior of device types in the use phase, such

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as the above mentioned discrepancy between high sales flows and low collection flows. It facili-

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tates detecting leakages where devices and the incorporated resources leave the system, as well

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as determining their final sink. In combination with assumed or extrapolated sales flows, the

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model can also serve to forecast the composition of future recycling flows, a capability which

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enables recycling system managers to provide appropriate and tailored recycling capacities and

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technologies.27 The present article will introduce the model and the results in terms of mass

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flows; the implied flows of critical raw materials incorporated in EE (with indium, neodymium

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and gold as examples) will be published in a planned subsequent article.

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To implement the model, we developed an open source software tool in Python 3, allowing for

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the calculation of dynamic MFAs in a generic and flexible way. The tool implements 'dynamic

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probabilistic material flow analysis', a combination of dynamic material flow modeling with

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probabilistic modeling, as proposed by Bornhöft.28 The probabilistic approach allows to system-

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atically dealing with data uncertainty in our model.

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Method

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as proposed in Müller et al.29

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Overview The purpose of the dynamic MFA model is to quantify in-use stocks and storage stocks of nine

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types of EE with a high content of indium, neodymium or gold, so that they cumulatively cover

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around 90% of these three metals in private Swiss households30 (see Table 1) and to map and

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evaluate in detail the flows between the use, storage and disposal phases.

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This chapter is structured according to the ODD (overview, design concepts, details) protocol

Table 1: Electronic device types included in the MFA. Device type Description

UNU -Key

Fraction containing Indium

Neodymium

Gold

Magnets in hard disk drive (HDD), optical PWB34 0302 drive30,31 / printed wiring board (PWB)32,33

Desktop

Desktop computer (incl. all-in-one computer, excl. peripherals)

Laptop

Laptop computer/ Notebook

Monitor

Flat panel display (FPD) monitor LCD35

Mobile

Conventional mobile phone

Liquid crystal Magnets in HDD, optical drive, loudspeak- PWB34 0303 display (LCD)35 er30,31 / PWB32,33

LCD35

PWB32,33

PWB34 0309

Magnets in loudspeaker, vibration alarm30 / PWB34 0306

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PWB32

phone Smart phone Smart phone Headset CRT TV

Cathode ray tube (CRT) TV

FPD TV

Flat panel display (FPD) TV

Magnets in loudspeaker, vibration alarm30 / PWB34 0306 PWB32 Magnets in Loudspeaker30,36

Headphones / Headset

DVD player DVD player / Blu-ray player

101 102

LCD35

PWB LCD35

32,33

0401 PWB

34

0407

PWB32,33

PWB34 0408

Magnets in optical drive37 / PBW32,33

PWB34 0404

Unlike Thiébaud et al.27, we do not include loudspeakers, as no sales data are available for such devices.

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The MFA system includes the process 'use phase', divided into 'active use' and 'storage' (Figure

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1). The 'active use' and 'storage' are further divided into the subprocesses 'first use', 'second use'

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and 'further use' as well as 'first storage', 'second storage and 'further storage'. Each subprocess

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includes an in-use stock or storage stock. The system boundary corresponds to the Swiss national

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border. The temporal extent is different for each device type. It starts when an electronic device

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type was first put on the market and ends with the year 2014. The temporal scale is one year.

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Based on the empirical results of our survey, we developed a cascade model, with each step

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consisting of an in-use stock and storage stock. The first step includes the first use and storage of

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new devices, the second step the second use and storage of second-hand devices. The third and

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final step summarizes all possible further uses and storages. The cascade model enables the rep-

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resentation of different service lifetimes, storage times and disposal pathways both for new and

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for used devices.

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The system (see Figure 1) has one inflow corresponding to the sales of new devices and four

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outflows, one for each disposal option. Internal flows include 'reuse' flows from in-use stock and

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storage stock to the next in-use stock and 'storage' flows from in-use stock to storage.

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Figure 1: Cascade model of the process 'use phase', divided into 'active use' and 'storage'.

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Design concepts

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from the net flow by using the balance of masses (equation (1)), with the constant sampling rate

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T = 1 year.29

The model employs a retrospective top-down approach, deriving the stock S[n] at a time n

   

  ∙    1 124 125

(1)

The outflows of the in-use stock and the storage stock are calculated according to equation (2).29 

       ∙ 

(2)



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The model applies different lifetime distribution functions f[m] for the service lifetimes and

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storage times of new and used devices. The service lifetime is defined as the time of active use of

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a device. The storage time is defined as the time between the end of the active use of a device and

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its final disposal or transfer to a different user.27 The flows from the different stocks to the in-

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use stock, storage, or disposal pathways are determined by transfer coefficients. All data include

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changes over time provided that time series are available.

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We accounted for data uncertainty by modelling inflows, transfer coefficients and mass of de-

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vices as probability distributions. We chose either normal or triangular distributions, based on

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the characteristics of the available data. The dependent variables are calculated by Monte Carlo

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simulation and again provided as probability distributions.28,38 The uncertainty of the lifetime

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distribution parameter was taken into account by conducting a sensitivity analysis with a lower

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and upper lifetime distribution scenario.

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Details

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Initial condition In our model, stocks of EE are calculated from inflow data and lifetime distribution functions

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(equation (1) and (2)), starting from a stock = 0. For sales data, the model thus demands infor-

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mation back to the year when a device type was first brought onto the market. In cases where

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such data was not available, we extrapolated existing sales figures.

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Model input data Inflow data for desktops, laptops and flat screen monitors were taken from annual ICT market

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reports for Switzerland, which include both the business and the home segment.39. Sales data of

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television sets (CRT and FPD TVs) and DVD players were collected by the Swiss Consumer

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Electronics Association40 and the market research company GfK41. GfK additionally reports

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sales data for mobile phones and smartphones.41,42 We further assumed that the number of sold

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headsets corresponds to the sum of all sold portable audio and video devices, smart phones and

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mobile phones. The uncertainties of inflow data were modeled as normal distributions. The

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standard deviation (SD) was estimated at 10% of the inflow value for time series with available

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data from Weissbuch, GfK or SCEA.43 For extrapolated time series, we assumed a SD of 20%.

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As the inflow data for headsets are based on rough estimations, we assumed a SD of 30%.

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Data regarding the service lifetime, the storage time, and the disposal pathways of EE were

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taken from Thiébaud et al.27 They are provided as histograms of the service lifetimes and storage

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times of new and second hand devices of ten different EE types. The temporal change of the ser-

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vice lifetime of new devices was included where appropriate by dividing the histograms into

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sales year groups as defined in Thiébaud et al.27. For the temporal change of the storage time of

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new devices, we calculated for each device the year it was put to storage and subsequently divid-

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ed the histograms into storage year groups. For the third step of the cascade model, no data on

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the service lifetime or storage time are available, although the survey data confirms that for most

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device types, there are some second-hand devices that are further used.27 Due to the lack of data,

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we assumed that the same service lifetimes and storage times as for the second use apply for all

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further uses in total (modelled as the third step of the cascade model). This assumption is tested

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by a sensitivity analysis.

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In order to estimate the lifetime distribution functions for the dynamic MFA, we fitted Weibull

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distribution functions to the normalized histograms of the service lifetimes and storage times of

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new and second-hand devices. Modeling positively skewed distributions of product lifetimes is

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typically done with Weibull distributions, as they can adopt many different shapes.44,45

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The uncertainty of the service lifetime and storage time is at least ±1 year for all histograms.27

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The tool developed for the simulation of the dynamic MFA supports probabilistic modelling of

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the inflows and transfer coefficients, but not of lifetime distribution parameters. In order to eval-

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uate the sensitivity of our model to the Weibull distribution parameters as well, we shifted the

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histograms provided by Thiébaud et al.27 by ±1 year and fitted the Weibull distribution functions

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again. The model was then run additionally with the resulting lower and upper lifetime distribu-

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tion scenarios for each device type.

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Thiébaud et al.27 further provide transfer coefficients of the flows between the processes 'active

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use' and 'storage' as well as to the different disposal pathways: 'donation', 'collection', 'municipal

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waste' and 'unknown'. We modelled the 'donation' option as an export flow, as most donation

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projects for used electronics are situated abroad.46,47 Due to the limited amount of available data

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sets, the temporal change of transfer coefficients was analyzed for CRT TVs, mobile phones,

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laptops and desktops only. For the third step of the cascade model, the transfer coefficients for

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second-hand devices were adapted by splitting the reuse flows proportionally to the other dispos-

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al pathways. As the uncertainty of the transfer coefficients increases with smaller sample sizes

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per process, we introduced a triangular distribution for each transfer coefficient, varying

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with ±



√

, with n being the total number of devices transferred from a specific process.

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To simulate the distributions of total lifetime, that is, the time devices remain in the use phase

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from sales to disposal, we run an impulse response analysis. For each device type, we fed an im-

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pulse of 100,000 devices in one year into the cascade model and simulated the succeeding peri-

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ods of 50 years. This includes all different service lifetimes and storage times, as well as the per-

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centage of products that enter reuse and storage according to the transfer coefficients provided by

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Thiébaud et al.27 The response at the outflow of the overall use phase, corresponding to the sum

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of all four disposal pathways and adding up to the 100,000 devices, is equivalent to the total life-

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time distribution function.

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The average mass per device of a given type was determined by own measurements and litera-

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ture data.9,17,34,48,49 We introduced a triangular distribution for the mass of each device type, de-

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termined by the weighted mean and the lowest and highest value. The lowest and highest values

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were extended by taking into account the SD of the SD, as suggested in JCGM,

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lower and upper limit of the triangular distribution should lie within the 95% confidence interval.

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For the mass per device type, available data did not show any significant temporal change.

50

so that the

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More information on the inflow data, an overview of all fitted Weibull distribution functions

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including details on the sales year and storage year groups, the sensitivity analysis of the third

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step of the cascade model, the transfer coefficients, the impulse response analysis, and the uncer-

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tainty distributions of mass per device type can be found in the Supporting Information (SI).

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Model output data and evaluation Model output data comprise stochastic time series of all stocks and flows occurring in Figure

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1. The distribution of the time series is visualized by the 10th percentile, mean and 90th percentile.

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Additionally, the lower, mean and upper lifetime distribution scenarios as described above are

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presented. With the exception of Sankey diagrams, the in-use stocks 1, 2 and 3 and storage

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stocks 1, 2, and 3 are aggregated to the total in-use stock and total storage stock.

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The output can be compared to statistical data from various sources for model validation. From

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the Swiss Federal Statistical Office (FSO), time series data on the in-use stocks of desktops, lap-

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tops, CRT TVs, FPD TVs and DVD players are available from 2006 to 2013.51 The Swiss Feder-

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al Office of Communications (OFCOM) provides in-use stock data for mobile phones from 1990

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to 201452. Swico Recycling, the collection system for EE in Switzerland, publishes numbers and

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tonnages of desktops, FPD monitors, laptops, mobile phones, CRT and FPD TVs they collected

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from 2010 to 2014.48 The uncertainty of these statistical data is not quantified by their providers

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but it is assumed to be considerably high. These data are compared to the simulated stocks and

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outflows to collection.

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Detailed model description The conceptual model, as described in Figure 1, was implemented using our own MFA simula-

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tion tool called 'pymfa' (written in Python 3, using the numpy, scipy, and matplotlib libraries),

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which supports the calculation of dynamic MFAs in a flexible way. The core of the tool can be

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used as a Python library and provides the necessary functionality to run analyses through a

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command line interface and an interactive web application that can also be run locally. Model

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descriptions are specified using a simple data-driven approach: the researcher creates a spread-

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sheet where each row represents a link in the MFA. Links can represent inflows, unit conver-

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sions, transfer rates, delays or the split of a fraction entering a process into multiple fractions

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leaving the process. For each link, time series of data including information on the probabilistic

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distribution have to be provided. As such, model descriptions can simply be stored and forward-

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ed as spreadsheets or CSV files. Model description source files can be uploaded through the web

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interface, upon which the tool calculates the time series of resulting stocks and flows by means

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of Monte Carlo simulation, visualizes them and offers the resulting data as a CSV download. A

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screenshot of the web interface as well as an example of a model description source file and out-

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put plots can be found in the SI.

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For each device type, the model was simulated for numbers of devices and for physical mass,

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producing results in pieces and in kg, respectively. In addition to the standard scenario, we run a

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lower and upper lifetime scenario and an impulse scenario for each device type. Each simulation

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run was repeated 10000 times, which we considered a sufficient sample size for the Monte Carlo

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simulation

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Results and discussion

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Total lifetime

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to the median service lifetime of new devices, the median total lifetime, derived from the im-

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pulse response analysis, increases, for example, from 3 to 7 years for conventional mobile

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phones and smartphones, from 5 to 8 and 9 years for desktops and laptops, respectively and from

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9 to 11 years for CRT TVs. Existing studies from the Netherlands53 and Switzerland54 show

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similar results regarding the average or median total lifetime. The median and average total ser-

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vice lifetimes as well as a comparison to existing studies are listed in Table S6 in the SI.

Reuse and storage significantly extend the time a device remains in the use phase. Compared

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The total lifetime distribution functions, compared to the service lifetime distribution functions

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of new devices, show a shifted mode towards longer lifetimes, and a wider and more positively

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skewed distribution. An example of four device types is illustrated in Figure 2 (for the remaining

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distributions see Figure S24 in the SI). Only few sources present Weibull parameter for the total

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lifetime of a large range of EE17,53.As we assume that Dutch usage patterns are similar to those of

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the Swiss, we compare our results to Wang et al.53 Their Weibull distribution functions for the

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year 2005 match well our findings for most device types. Exceptions are laptop computers, with

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a Weibull distribution function more similar to our service lifetime distribution, and mobile

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phones, with a sharply and monotonically decreasing Weibull distribution function. The blue and

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the red curve for mobile phones have similar means (9.6 and 8.5 years, respectively) but different

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medians (4.5 and 7.0 years, respectively). As both laptops and mobile phones are often stored

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and reused in Switzerland, our findings of wider and more positively skewed distributions seem

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to be more realistic.

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CRT TV SL

0.2

TL TL Wang et al.

0.1 0

Relative Frequency [-]

0

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Laptop computer

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Desktop computer

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Relative Frequency [-]

Relative Frequency [-]

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10 20 30 Mobile/Smartphone d) SL TL TL Wang et al.

0 0

10 20 30 Lifetime [years]

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Figure 2: Comparison of own results of service lifetime (SL) with total lifetime (TL) and TL of

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Wang et al.,53 for a) cathode ray tube television (CRT TV), b) desktop computer, c) laptop com-

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puter and d) mobile phone/smartphone.

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The results show that data on mean or median lifetimes do not provide conclusive information

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on the actual lifetime distribution function. They further highlight the importance of including

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flexible lifetime distribution functions in dynamic MFA models of products that are reused and

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stored. Assuming average, median or normally distributed lifetimes may, in case of technology

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change, often result in underestimated phase-out times. Whether lifetime distributions functions

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are favorably divided into different service lifetime and storage time distribution functions or

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merged to total lifetime distribution functions depends on the question to be answered and the

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required level of detail.

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Stocks and flows

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3 for nine device types between 1980 and 2014.

The cascade model produces a cumulated in-use stock and storage stock as illustrated in Figure

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CRT TV

FPD TV

Mobile phone

Smartphone

Desktop

Laptop

Headset

DVD player

10th percentile

90th percentile

Monitor

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Figure 3: a) Cumulated in-use stock in 1000 pieces, b) cumulated storage stock in 1000 pieces,

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c) cumulated in-use stock in 1000 tonnes and d) cumulated storage stock in 1000 tonnes, for nine

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device types between 1980 and 2014

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The cumulated in-use stock has grown in the past 20 years to around 44 million devices or

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220 000 tonnes in 2014. In terms of devices, the 10th and 90th percentiles, resulting from the

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Monte Carlo simulation, account for ±10% or a range of roughly 9 million devices. As the mass

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of each device type increases uncertainty, the 10th and 90th percentile for the in-use stock in

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tonnes accounts for ±12%. The storage stock comprises 30 million ±8% devices or 75 000 ±9%

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tonnes in 2014. The storage stock thus accounts for 40% of the total stock in terms of number of

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devices and 25% in terms of mass. Both in-use stock and storage stock are dominated by small

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devices such as mobile phones, smartphones and headsets. Regarding mass, however, these de-

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vices types are insignificant and CRT as well as FPD TVs and desktop computers are prevalent.

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The in-use stock of CRT TVs and conventional mobile phones is declining as these device types

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are replaced by FPD TVs and smartphones, respectively. The stock of smartphones is increasing

294

with a growth rate of 20 – 30%, while the in-use stocks of most other device types are slowly ap-

295

proaching or have already reached saturation. Thus, the overall growth rate of the in-use stock

296

and storage stock in pieces as well as the storage stock's mass have been declining in the past 10

297

years. Mostly due to the replacement of CRT TVs with lighter FPD TVs, the in-use stock's mass

298

is declining.

299

The simulated stocks and flows to, from and within the cascade model are depicted in Figure 4

300

for mobile phones and FPD TVs in 2014. For the sake of clarity, we refrain from including un-

301

certainty ranges here. The Sankey diagrams for the remaining seven device types can be found in

302

the SI (Figures S25-S31). The disaggregation of the total stock to the in-use stock and storage

303

stock of new devices (cascade step 1), second-hand devices (cascade step 2) and all the rest (cas-

304

cade step 3) shows large discrepancies among device types. Mobile phones, CRT TVs and head-

305

sets have a total storage stock that is larger than or close to the size of the total in-use stock. The

306

smallest total storage stock, compared to the total in-use stock, have FPD TVs and smartphones.

307

The in-use stock 2 of DVD players, CRT TVs and mobile phones accounts for over 15% of the

308

total stock in 2014, while for smartphones and headsets, it is below 5%. Various trends can be

309

derived from these findings: device types that are phased out, such as CRT TVs, mobile phones

310

but also DVD players (replaced by streaming services) are more frequently stored or used as se-

311

cond-hand devices. New device types such as smartphones or FPD TVs are still located mostly

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in the in-use stock 1. Furthermore, small devices such as mobile phones and headsets are more

313

frequently stored than large devices.27 The storage stock 2 compared to the total stock are largest

314

for monitors, mobile phones and desktops. The decision to store a second-hand device therefore

315

does not seem to be influenced by the size of the device, but more by the prospective utility. The

316

small in-use stock 3 and storage stock 3 show that further uses occur, but are irrelevant.

317

Flows to collection are largest for all device types. Flows to municipal waste incineration

318

(MWI) are only relevant for headsets. With 20-30% of the total outflow, the share of FPD TVs,

319

mobile phones and smartphones entering unknown disposal pathways is high. Unknown flows

320

include, for example, lost or stolen devices and devices disposed of by others than the survey re-

321

spondents. It is thus possible that material reaching unknown disposal pathways still ends up in

322

the collection system. Export flows are highest for laptops and mobile phones. Total outflows

323

compared to sales flows are by a factor 3 to 7 higher for mobile phones and DVD players. For

324

desktops, monitors and headsets, sales and outflows are of the same magnitude. For laptops, FPD

325

TVs and smartphones, sales are still larger by a factor of 1.2, 2 and 5, respectively. Despite long

326

delays due to reuse and storage, this comparison thus reveals for which device types the system

327

is currently saturated. These delays, however, result in still growing outflows, with an average

328

growth rate of 5% over the last 20 years. Figure S32 in the SI shows the cumulated flows to col-

329

lection between 1980 and 2014. In total, the nine device types result in 24 000 tonnes ±10% that

330

reached the collection system in 2014, dominated by CRT TVs, FPD TVs and desktop comput-

331

ers.

332

From a resource point of view, it is not important how long products are delayed in the use

333

phase, as long as they reach the collection system in the end, which is the case for 80% of all de-

334

vices on average. However, if we would aim for more reuse or refurbishment, it would be im-

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portant that devices are not stored for too long, as newer devices are more in demand on the se-

336

cond-hand market.

a)

337

b)

338 339

Figure 4: Stocks and flows of a) mobile phones and b) flat panel display televisions (FPD TVs)

340

in 2014 in tonnes. Mobile phone flows below 5 t and FPD TV flows below 100 t are not labeled.

341 342

Model sensitivity and validation

343

most device types (Figure 3). If the mass per product with its associated uncertainty is included

The 10th and 90th percentiles of the in-use stock indicate an uncertainty of around ±10% for

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344

in the model, the uncertainty range increases up to ±12%. The lower and upper lifetime distribu-

345

tion scenarios of the sensitivity analysis add a further ±10% deviation from the standard lifetime

346

distribution scenario. Considering the 10th and 90th percentiles of the lower and upper scenario,

347

the maximum deviation from the mean in-use stock of the standard scenario amounts to approx-

348

imately ±30% (Figure 5). The storage stock is less sensitive with ca. ±20% maximum deviation.

349

The sensitivity of the flows is presented using the collection flows as an important example.

350

As longer service lifetimes and storage times lead to smaller collection flows, the lower scenario

351

accounts for the highest flows and vice versa. The deviations between the means of the standard,

352

lower and upper lifetime distribution scenario are similar to the deviations of the 10th and 90th

353

percentiles of the standard scenario (ca. ±10%). The maximum deviations between the mean of

354

the standard scenario and the 10th and 90th percentiles of the lower and upper scenario account

355

for ±20%.

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Total collection flow [1000 pieces/year]

Total in-use stock [1000 pieces]

60'000

356

a)

50'000 40'000 30'000 20'000 10'000 0 4'000 1998 2000 2002 2004 2006 2008 2010 2012 2014

b)

3'000

2'000

1'000

0 1998 2000 2002 2004 2006 2008 2010 2012 2014 Year Standard Standard 90th percentile Lower 10th percentile Upper mean

Standard 10th percentile Lower mean Lower 90th percentile Upper 10th percentile

Upper 90th percentile

Data FSO/OFCOM/Swico

357

Figure 5: a) Total in-use stock in 1000 pieces and b) total collection flow in 1000 pieces/year

358

based on the standard, lower and upper lifetime distribution scenarios for desktops, laptops, mo-

359

bile phones, CRT TVs, FPD TVs and DVD players (in-use stock) and FPD monitors, desktops,

360

laptops, mobile phones, CRT TVs and FPD TVs (collection flow), compared to statistical data

361

available from FSO, OFCOM and Swico Recycling. 48,51,52

362

By comparing the total in-use stock and the total collection flow with available data from FSO

363

and Swico Recycling, we evaluated to what extent the cascade model is able to reproduce the

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behavior of the real system. As can be seen in Figure 5, the model seems to overestimate the total

365

in-use stock. The sum of FSO and OFCOM data lies in the vicinity of the 10th percentile of our

366

standard scenario and between the mean and the 90th percentile of our lower scenario. A poten-

367

tial explanation is that the service lifetimes and storage times we assumed in the model are long-

368

er than those in the real system. However, the comparison of collection flows shows that fewer

369

devices are collected compared to the modeled collection flow, which could be, in contrast, an

370

argument for longer service lifetimes and storage times. Again, the sum of devices collected by

371

Swico Recycling lies in the vicinity of the 10th percentile of our standard scenario, but now also

372

between the mean and the 90th percentile of our upper scenario. Breaking this down to single de-

373

vice types, the discrepancies between modelled and reported collection flows are largest for mo-

374

bile phones, with 60% less collected than what the model calculates, even though we included

375

long reuse and storage times. As our model for mobile phones overestimates the in-use stock by

376

about 20%, we argue against extending the service lifetime in our model. Whether we assumed

377

too few mobile phones being stored for a too short time, or a large amount of them is exported,

378

could not be investigated within this study. Lange19 or Huisman et al.18, among others, report

379

transboundary shipment of EE to Eastern Europe or Africa from Germany and the Netherlands.

380

Although Switzerland has higher EE collection rates than most EU Member States, it might still

381

be possible for small devices such as mobile phones to be formally or informally exported with-

382

out our knowledge. It is well known that mobile phones are the most difficult devices for collec-

383

tion schemes to get hold of, with already various studies investigating potential causes.14,55,56

384

Taking into account the large model uncertainty of up to ±30%, the cascade model is able to

385

map reasonably well the total in-use stock and the total collection flows. This shows that the

386

mixture of detailed bottom-up information on service lifetimes, storage times and transfer coeffi-

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387

cients combined with a top-down approach to calculate stocks and flows is an adequate approach

388

to enhance dynamic MFAs. The cascade model provides important details regarding reuse and

389

storage stocks and flows that have been often neglected in dynamic MFA studies. It further un-

390

veils different behavior of device types within the use phase, for example with regard to technol-

391

ogy changes or phase out periods.

392

The model structure can be applied to any other product that is potentially reused and stored

393

after its first service life. However, in many cases, data required for a cascade model will be dif-

394

ficult to collect. Furthermore, from a resource point of view, it is not important to know exactly

395

the status of products in the use phase, as long as it can be safely assumed that they will reach the

396

collection system in the end. The use phase could thus be modeled as a 'black box' as in earlier

397

studies, but by adapting the total lifetime distribution function, eventual reuse and storage should

398

be taken into account. Such a simplified model can still be useful to simulate outflows, their

399

composition (mix of devices and materials) and the share reaching collection more precisely.

400 401

Associated Content

402

functions, transfer coefficients and the uncertainty distributions of mass per device type. It fur-

403

ther includes more information on the software tool used to implement the model, run the simu-

404

lations and visualize the output. Additional results on the total lifetime distributions as well as

405

stocks and flows by device type are provided as well. This material is available free of charge at

406

http://pubs.acs.org.

407 408

Author Information

409

*Corresponding author phone: +41 58 765 78 44; e-mail: esther.thié[email protected]

The Supporting Information provides more detail about the inflow data, Weibull distribution

Corresponding Author

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410

Notes

411

The authors declare no competing financial interest.

412 413

Acknowledgments

414

designing, implementing and refining the software tool. We further thank the two anonymous re-

415

viewers for their valuable input. The project was funded by Empa.

416

References

417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442

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