Questionnaire - e-Waste. This guide

Acknowledgments
First, I would like to thank my supervisor Martin Streicher-Porte from the EMPA St-Gallen for
the efforts and the time he spent to give me the opportunity of realising this diploma thesis
project, and for all the help, inputs and feedbacks throughout the five months. Additionally, it
was very interesting to gain insight into the EMPA-program “Knowledge Partnerships in E-
Waste Recycling”.
I wish to thank my diploma professor Stefanie Hellweg for her fast reaction, support and
flexibility at the time when the study had to be redirected in consequence to missing data.
I am very grateful to the following institutions for their generous financial contributions to my
stay in China:
• Swiss Agency for Development and Cooperation (SDC)
• “Stipendiendienst” of the ETH Zurich
Special thanks go to Ruth Scheidegger and Hans-Peter Bader from the EAWAG for
accepting on short notice to see me through the modelling with the Simbox simulation
program. I learned a lot in your office and particularly appreciated your helpfulness and
friendly ways.
Xiexie - many thanks in Chinese – to my new friends Jonah and Windy, teachers at Jiaojiang
High School No.1, who helped me to organise the data collection and introduced me into
local environmental problems.
I hope that in the future the frogs in Taizhou may have neither more nor less than four legs.
Jean-Paul Würth met the challenge to look over my work, improving more than just my
English syntax, so that it gained substantially in quality. Thank you very much!
I owe the design of the title page to Hans Nievergelt. Thank you for the great job.
Last but not least, I would like to express my gratitude to all the persons that supported me
during this time: my family, friends and fellow students.
I

Abstract
Electrical and electronic waste (or e-waste for short) is one of the fastest growing waste
streams in China due to increasing domestic amounts and imports. The collection,
processing and recycling of e-waste is actually dominated by a well-established informal
recycling sector. This results in serious damages to environment and human health, since e-
waste contains both valuable as well as hazardous substances. Formal recycling pilot
programs of the Chinese government have failed up to now, not being able to compete with
the informal sector, notably in the collection of the e-waste. Therefore, it is important to learn
more about the upcoming quantities of e-waste and the disposal behaviour at the householdlevel.
This is the purpose of this study, based on surveys carried out in high income urban
households of the Taizhou prefecture. Informal collection and storage were identified as main
disposal channels for these households. Future stock and e-waste flow were simulated over
time for the 24 different kinds of electrical and electronic appliances found in the studied
households. Three scenarios were defined to assess the possible range of resulting e-waste
flows. The calculated value of 4 kg per inhabitant de facto arising for recycling is comparable
to average collection amounts of public authorities in Europe. An increase to 9-11 kg was
estimated for the year 2060. The five main appliance types, refrigerator, washing machine,
air conditioner, TV and PC, already included in non-implemented Chinese legal measures,
were assessed to account for more than 50% of the total e-waste. In addition, the electroscooter
and appliances with high content of precious metals were identified as appliances
which would be particularly rewarding for formal recycling.
By including households of lower social strata in the model and taking into account
population dynamics, future amounts of e-waste could be prognosticated, helping to set up
formal e-waste management programs.
II

Summary
The use of Electrical and Electronic Equipment (EEE) has rapidly increased in Chinese
households since the 1980s, resulting in growing amounts of electrical and electronic waste
(or e-waste for short). In addition, China has to face flows of illegally imported e-waste, the
recovery of valuable metals contained in the e-waste (e.g. gold, copper, iron and aluminium)
having become a profitable business. E-waste is categorized as hazardous waste, as it also
contains hazardous substances like lead, cadmium and mercury. Incorrect disposal of e-
waste, as it has been performed by the informal recycling sector in China up to now, causes
serious damage to environment and human health. To address this problem, central Chinese
governmental departments started developing legislations and testing models for a Chinese
e-waste management system. Recycling pilot programs failed to be implemented, as they
encountered collection difficulties, the informal sector actually monopolising e-waste
collection and processing. Collection as interface between domestic e-waste production and
recycling is therefore a central issue for establishing formal e-waste recycling and has to be
investigated. To this end, e-waste amounts and disposal channels of private urban high
income households were examined in this study.
The study is based on a household survey carried out in Taizhou, a prefecture of the
Zhejiang province located near Shanghai on the East China Sea coast, where the metal
scrap and e-waste recycling business represents an important part of the economy. The
survey consisted in questionnaires which were developed on the findings of previous oral
interviews. Out of 600 questionnaires distributed to students and teachers of a local high
school, 225 were retained. 24 types of EEE and five main disposal channels were surveyed
with the questionnaires.
A stock-driven dynamic material flow analysis (MFA) model was applied to analyse the
material balance of EEE in households over time.
Data captured allowed only for the analysis of household use and disposal of new appliances
(first use), as there was hardly any second-hand appliance in use in the investigated high
income households. The flow of obsolete appliances out of the first use process was divided
into three virtual appliance age groups, which allowed to differentiate the e-waste that will be
reused from the one that will not. The different disposal channels used for each of these
three age groups represent the flows out of the system. In order to estimate the e-waste
volume qualifying for recycling, the calculated outputs were considered depending on which
disposal channel and age group they belong to.
The development of the stock and the residence time distribution of EEE in the households
had to be determined for each of the 24 appliance types through calibration of the model with
the collected data. The logistic growth curve was chosen to describe the increasing stocks as
a function of time and the residence time was assumed to follow a Gaussian distribution. The
transition ages between the three age groups were defined according to this distribution.
The model was used to simulate three potential scenarios predicting future developments of
e-waste disposal between 1980 and 2060. For the base scenario, the parameters defined in
the calibration were used. Stock saturation value, mean residence time and disposal transfer
coefficients, being the most sensitive parameters, were varied in the other two scenarios.
The best-case scenario depicts a system evolving to a more sustainable consumption and a
legally regulated e-waste management. Starting in 2010, residence time increases over time
and the informal collection is replaced through formal collection. In the worst-case scenario
no e-waste management is implemented and consumption increases. A higher stock
saturation value was assumed and residence time decreases from the year 2000.
The mathematical model was calculated with the computer simulation program SIMBOX.
III

Informal collection, with about 20-80% of the total e-waste output, and storage, with 20-50%,
were identified as the main actual disposal channels over all appliances. Depending on the
appliance type, the disposal channel into reuse can attain fractions up to 30%. Formal
collection is not much developed, attaining values just over 10% for appliances of the IT and
telecommunication and the consumer equipment categories.
EEE-Stock and e-waste flows into the disposal channels were calculated according to the
three scenarios for all 24 appliances:
The resulting actual total annual e-waste output of 17 kg per high income inhabitant is
comparable to the theoretically expected annual potential of about 20 kg for the European
Union ten years ago. Depending on the scenario, this total e-waste output was assessed to
attain between 17- 50 kg in 2060.
The calculated amount of 4 kg per inhabitant arising for recycling is comparable to average
collection amounts of public authorities in Europe. An increase to 9-11 kg was estimated for
the year 2060.
The five appliances refrigerator, washing machine, air conditioner, TV and PC, which are
listed in non-implemented e-waste management measures of the Zhejiang province, were
assessed to account for 50-60% of the present and future e-waste having to be recycled.
The electro-scooter, characterised by big e-waste amounts and problematic lead content, as
well as appliances from the IT and telecommunication and the consumer equipment
categories with high contents of precious metals, were identified as appliances worth to be
included into formal recycling measures.
Some points were not taken into account in this study, therefore modelling results must be
considered with due caution. New appliances that may be thrown on the market in the future
will probably add to the calculated future e-waste amounts. Stored appliances will eventually
leave the households after some time, contributing to the e-waste streams. This implies that
the study is located on the conservative side.
The exact total e-waste amounts arising for recycling cannot be predicted, as only a part of
all inhabitants was investigated. By including households of lower social strata in the model
and taking into account population dynamics, future amounts of e-waste could be
prognosticated for the Taizhou region, helping to set up formal e-waste management
programs.
Since it would not be reasonable to plan a formal collection and recycling system without
appropriate financing, this issue has to be thoroughly investigated.
IV

Index
Acknowledgments .....................................................................................................................I
Abstract ....................................................................................................................................II
Summary .................................................................................................................................III
Index........................................................................................................................................ V
1 Introduction .......................................................................................................................1
1.1 Definition of e-waste .................................................................................................1
1.2 E-waste in China : background.................................................................................2
2 Research objective............................................................................................................4
3 Methods and study area....................................................................................................5
3.1 Study area ................................................................................................................5
3.2 Data collection ..........................................................................................................5
3.3 Dynamic MFA model ................................................................................................6
3.3.1 System analysis....................................................................................................6
3.3.2 Mathematical model..............................................................................................8
3.3.3 Calibration...........................................................................................................10
3.3.4 Scenarios............................................................................................................13
3.4 Assessment of the WEEE quantities arising for recycling ......................................17
4 Results ............................................................................................................................19
4.1 Exemplary appliance types.....................................................................................19
4.1.1 EEE household stock over time..........................................................................19
4.1.2 WEEE output flows.............................................................................................20
4.1.3 Sensitivity and uncertainty analyses...................................................................23
4.2 All appliance types..................................................................................................24
4.2.1 EEE stock in households ....................................................................................24
4.2.2 WEEE output flows.............................................................................................25
4.3 WEEE arising for recycling .....................................................................................27
4.3.1 Total flow ............................................................................................................27
4.3.2 Flows of appliance clusters.................................................................................29
5 Discussion & conclusion .................................................................................................30
5.1 EEE-stock...............................................................................................................30
5.2 WEEE output flows.................................................................................................30
5.2.1 Exemplary appliance types.................................................................................30
5.2.2 All appliance types..............................................................................................32
5.3 WEEE arising for recycling .....................................................................................32
5.3.1 Total flow ............................................................................................................32
5.3.2 Flow of appliance clusters ..................................................................................33
5.4 Limitations of the study and directions for further research....................................34
6 References......................................................................................................................35
Annexe A................................................................................................................................38
Questionnaire #1 ................................................................................................................39
Questionnaire #2 ................................................................................................................40
Questionnaire #3 ................................................................................................................41
Annexe B................................................................................................................................42
B.1 Household income distribution................................................................................43
B.2 Disposal transfer coefficients..................................................................................44
B.3 Household stock assessment.................................................................................48
B.4 Residence time.......................................................................................................50
B.5 Assumed errors ......................................................................................................51
B.6 Flows of appliance clusters ....................................................................................51
V

Introduction
1 Introduction
1.1 Definition of e-waste
E-waste, used synonymously to Waste Electrical and Electronic Equipment (WEEE) in this
study, has not yet a standard definition (Widmer et al. 2005). Selected definitions are listed in
Table 1. The EU Directive on WEEE defines ten WEEE categories (see Table 2). According
to Liu (Liu et al. 2006 a), the same definition and categories of e-waste as in the EU Directive
are used in Chinese regulations, so that the WEEE definition of the EU Directive was chosen
in this study.
Table 1 Overview of selected definitions of WEEE or e-waste (Widmer et al. 2005)
Reference
EU WEEE Directive
(EU 2003)
Basel Action Network
(Puckett and Smith, 2002)
OECD (2001)
SINHA (2004)
StEP (2005)
Definition
”Electrical or electronic equipment which is waste. ... including all
components, sub-assemblies and consumables, which are part of the
product at the time of discarding.”
Directive 75/442/EEC, Article 1(a) defines “waste” as “any substance or
object which the holder disposes of or is required to dispose of pursuant
to the provisions of national law in force.”
“E-waste encompasses a broad and growing range of electronic
devices ranging from large household devices such as refrigerators, air
conditioners, cell phones, personal stereos, and consumer electronics
to computers which have been discarded by their users.”
“Any appliance using an electric power supply that has reached its endof-life.”
"An electrically powered appliance that no longer satisfies the current
owner for its original purpose."
E-waste refers to “. . .the reverse supply chain which collects products
no longer desired by a given consumer and refurbishes for other
consumers, recycles, or otherwise processes wastes.”
Table 2 WEEE categories as defined in the EU WEEE Directive
No.
Category
1 Large household appliances
2 Small household appliances
3 IT and telecommunications equipment
4 Consumer equipment
5 Lighting equipment
6 Electrical and electronic tools (with the exception of large-scale stationary industrial tools)
7 Toys, leisure and sports equipment
8 Medical devices (with the exception of all implanted and infected products)
9 Monitoring and control instruments
10 Automatic dispensers
1

Introduction
E-waste is categorized as hazardous waste, as it contains both valuable substances, such as
gold, copper, iron and aluminium, and hazardous substances like lead, cadmium and
mercury. Incorrect disposal of e-waste causes serious damage to environment and human
health (Allsopp et al. 2006).
A Swiss study comparing the flows of e-waste and municipal solid waste (Morf et al. 2006)
confirmed the growing importance of WEEE regarding secondary resource metals and
potential toxic substances. The e-waste flow, though 30 times smaller than the domestic one,
was found to turn over larger mass flows of copper and considerable amounts of iron,
cadmium, lead and aluminium.
1.2 E-waste in China: background
Like in the rest of the world, the use of Electrical and Electronic Equipment (EEE) in Chinese
households has rapidly increased since the 1980s due to rising living standards and
affordability. In addition to the growing amounts of domestic e-waste, China has to face flows
of illegally imported e-waste, the recovery of the valuable metals having become a profitable
business.
Exports of hazardous wastes from member states of the OECD, the EU and Liechtenstein to
all other countries are prohibited through the Basel Ban Amendment of the Basel Convention
(Basel convention 1995). As only OECD country, the United States have not ratified the
Basel Convention. 80% of the e-waste collected in the USA is reported to be exported to
Asia, 90% of that to China, as labour costs are much lower and environmental and
occupational regulations are lax or not enforced (Puckett and Smith, 2002).
To address this issue, central Chinese governmental departments are developing legislations
(see Kummer 2007). A national pilot program was started in 2003 by the National
Development and Reform Commission to determine a suitable model for a Chinese WEEE
management system (Hicks et al. 2005). None of the pilot programs are implemented, as all
encountered the same difficulties. In addition to financial problems, pilot programs were
confronted with collection difficulties, as the strong informal sector actually monopolises e-
waste collection and processing.
Alarmed by a television report in 2002 about WEEE dismantling in the Taizhou region, the
local government imposed local import bans, issued operating permits and prohibited
informal work on PC, TVs and monitors (BAN and Greenpeace 2004). In spite of these
efforts, investigators from Greenpeace China and the Basel Action Network reported that
WEEE was still imported illegally by ship into the port of Taizhou in 2004. Their field
investigation conducted in Taizhou describes the situation in relation to metal scrap
recycling. According to this report, dismantling of electric and mechanical equipment has
been performed for over 20 years and increased dramatically in the last few years. The
amount of domestically produced and imported WEEE has both grown substantially.
Dangerous use of cutting torches and harmful burning of e-waste to separate metals from
plastic were still usual procedures employed by waste scrap yards. Widespread in rural
areas, small-scale, primitive e-waste processing allowed farmers financial survival.
2

Introduction
E-waste management measures were issued for the Zhejiang province to which Taizhou
belongs. In 2004, the Zhejiang Province Economic and Trade Commission (ZETC) issued
pilot provisional measures for the recycling and treatment of WEEE, measures which where
scheduled to become effective on January 1 st 2005 (ZETC 2004). Fixed locations for
recycling and centralized collection were decided to be implemented in a WEEE recycling
and treatment pilot project. Five types of appliances were specified, namely television,
refrigerator, washing machine, air conditioner and computer, with further types of appliances
to be included in the future.
In the last few years, research was done to predict the amount of e-waste from Chinese
households through household surveys or calculations with sales data and lifetimes of
appliances (see (Liu et al. 2006a), (Liu et al. 2006b), (Li et al. 2006), (Streicher and Yang
2007)). These studies all treated either only one or all of the five main types of EEE
(television, refrigerator, washing machine, air conditioner and computer) also addressed in
the pilot measures for the Zhejiang province. No research could be found in literature
covering more than these electrical and electronic appliance types in Chinese households.
As described before, collection as interface between domestic e-waste production and
recycling is a central issue for establishing formal WEEE recycling. As not much is known
about the amounts and disposal channels of private household WEEE, this study examines
disposal behaviour of specified households in the Taizhou region.
3

Research objective
2 Research objective
The aim of this research is to describe the collection of WEEE from Chinese urban
households, using the example of the Taizhou region and addressing all electrical and
electronic appliances found in the studied households. To the best of our knowledge, such
an evaluation has not been done before.
The main questions to be answered are:
• What types and how many of each type of electrical and electronic appliances are
present in the households, including historical data and expected future trends?
• What happens to obsolete appliances? Which are the main disposal channels?
• How do the generated e-waste flows develop over time?
• Have established legal measures been implemented for the five listed appliance types,
including refrigerator, washing machine, air conditioner, television and personal
computer?
Which fraction of the total arising e-waste do these measures cover?
Are there any appliance types, currently not covered by these measures, that should
absolutely be regulated?
• What is the potential for e-waste recycling?
In addition, directions for further research should be identified.
4

Method
3 Methods and study area
3.1 Study area
Taizhou is a prefecture-level region in Zhejiang Province on the economically fast developing
East China Sea coast, about 300 km south of Shanghai. The population reached over 5.5
millions by the end of 2004, encompassing a land area of 9411 sq. km and representing 75%
of the Swiss population in 2004 and 25% of the Swiss land area (Zhejiang Yearbook 2005,
FSO 2004). The urban population accounts for about 20% of the total population and
households average three people (i.e., one child on average).
Taizhou is divided into three urban districts (Jiaojiang, Huangyan and Luqiao), two countylevel
cities (Linhai and Wenling) and four counties (Yuhuan, Tiantai, Xianju and Sanmen).
Jiaojiang city, located in the Jiaojiang district, is Taizhou’s main locality.
According to an article in the English newspaper “China daily” of June 15 th 2004 (China daily
2004), the recycling business plays a big role in Taizhou’s economy, accounting for 720
million USD annually or 55% of the region’s total revenue. The prefecture also features an
important manufacturing industry for valves, water pumps and plastic moulds.
3.2 Data collection
The research in China has been supported by one of the highest ranking local high schools,
the Jiaojiang High School No. 1. The data collection has been conducted in Jiaojiang city by
the author during a six-week period, from end of March to beginning of May, 2007.
To start with, data about private household ownership of EEE and disposal behavior of
WEEE were collected. This orally conducted survey was based on a written guideline
questionnaire (Questionnaire #1) developed on the methodology of Mieg and Brunner (Mieg
and Brunner 2001). The key objectives of the survey were to find out which appliance types
were present in households of this region and which disposal possibilities existed. In total, six
interviews were carried out among families of students of High School No. 1. At each
interview, at least one of the parents and the student participated. As no formal interpreters
were available, for each family, the respective student translated the interview from Chinese
into English. As a result of this lengthy procedure, only one interview a day could be
conducted. Therefore, to accelerate data acquisition, a second questionnaire was developed
based on the findings of the oral interviews. This questionnaire was translated into Chinese
by an English teacher of Jiaojiang High School No. 1 and designed for self-administration by
the respective households. To expedite data analysis, the questionnaire contained almost
exclusively multiple-choice questions and tables (Questionnaire #2) and was structured in
three parts:
• The first part assessed ownership and time period of acquisition of the 24 appliance
types assessed in the interviews.
• The second part dealt with appliance obsolescence. Type, quantity, age, year and way
of disposal had to be indicated for each appliance type owned, specifying whether the
appliance was purchased new versus second-hand. Space was left to add other
appliance types or ways of disposal that may not have been mentioned in the
interviews.
• The third part captured socio-cultural data like place of living, household size and
income, as well as questions about the household’s opinion about disposal possibilities.
5

Method
The questionnaire was distributed to 300 students and 150 teachers of the aforementioned
high school to be completed at their respective homes. After a first round of evaluations
using this questionnaire, it was slightly simplified (Questionnaire #3) and this simplified
version was handed out to 150 students.
Out of the 600 questionnaires distributed, about 350 were completed. Questionnaires which
were not filled out completely (e.g., missing place of living, etc.) or which contained obvious
errors, were rejected, yielding a final count of 225 questionnaires.
The three questionnaires can be consulted in annexe A.
Additionally, further paths of WEEE disposal were investigated. In interviews with secondhand
traders and a person in charge of a scrap metal disassembling company, only imported
goods were mentioned. It seems that locally produced WEEE are too small in volume to be
of practical importance. Another reason might be that the questioned stakeholders were not
willing to provide information on prevailing recycling patterns.
3.3 Dynamic MFA model
The dynamic material flow analysis (MFA) method (Baccini and Bader 1996) was applied to
analyze the material balance of EEE in high income households over time. This method has
been used in several studies dealing with durable goods (see (Zeltner et al. 1999), (Binder et
al. 2001), (Bader et al. 2003), (Hug et al. 2004), (Bader et al. 2006), (Streicher et al. 2007).
First, a system analysis had to be done to define the system border and relevant processes
and flows. Building up on this analysis, the mathematical model was defined and calibrated
by corresponding data. At last, the model was used to simulate potential scenarios predicting
future developments of WEEE disposal, including sensitivity and uncertainty analyses.
3.3.1 System analysis
Accordingly to data collection, Taizhou was defined as the geographical system border. The
analysis of the household income data showed that almost only high and highest income
households were questioned (cp. Annexe B.1), indicating the survey is only representative of
high income households. Therefore, the system had to be reduced to those households. The
system is represented in Fig. 1.
Data captured allowed only for the analysis of household use and disposal of new appliances
(first use), as there was hardly any second-hand appliance in use in the investigated high
income households.
Therefore, the new EEE is the only flow into the system. The flow of obsolete appliances out
of the first use process is divided into three appliance age groups. These virtual processes
are used to differentiate the e-waste that will be reused from the one that will not, as to
estimate e-waste volume qualifying for recycling (see chapter 3.4). The first age group
contains the youngest appliances, which are disposed of before having reached the effective
product lifetime 1 and supposed to be reused. The oldest appliances (third age group)
reached or past their effective lifetime and it is assumed they cannot be reused. They have to
be recycled or finally disposed of. In between is the second age group, which is assumed to
be partly reusable.
The different disposal channels resulting from each of these age groups are the flows out of
the system.
1 The effective product lifetime, product lifetime or effective lifetime is the age that an appliance can
technically attain.
6

Method
System border : High income households in Taizhou
1. age group
Storage
2. use
Informal collection
Formal collection
Other
New
EEE
Consumption
(1. use)
2. age group
…
3. age group
…
Input
Stock
Output flows to
age groups
Flows into
disposal channels
for each age group
Fig. 1 System analysis
This system was used for to all appliance types that were assessed in the households via
questionnaires (see Table 3). A few other appliances such as electric stove or medical
appliance were mentioned only one time and therefore not considered.
Table 3 Investigated appliance types classified after the European Union WEEE
categories (EU 2003). The five appliances which are listed in the Zhejiang province measures
(ZETC 2004) are accentuated.
Large household
appliances
Small
household
appliances
IT and
telecommunication
equipment
Consumer
equipment
Refrigerator Rice cooker Laptop Television set (TV)
Microwave Electric kettle Personal computer (PC) DVD/Video-player
Washing machine Electric iron Printer Radio
Electric boiler Hairdryer Mobile phone Stereo
Air conditioner Vacuum cleaner Telephone Portable MP3/MP4-
Electric fan Sewing machine
player
Digital camera
Other
Electroscooter
7

Method
3.3.2 Mathematical model
The consumption process is defined by three variables: the external input I(t), the output O(t)
and the stock M(t). The model is stock-driven, which means that the input (purchase of new
appliances over time) and the output (arising obsolete appliances over time) were calculated
from the stock over time and the residence time 2 of the appliances in the households. This
was done using balance and model equations:
Balance equation:
(t) = I(t) - O(t)
(t) is the derivative of M(t) with respect to time.
Stock of appliances:
M(t) = P(t)
P(t) is a parameter function describing the assumed diffusion of appliances in the
households in function of time.
Obsolescense of appliances:
t
∫
O(t) = k(t, t’) I(t’) dt’
0
k(t, t’) is the transfer function or residence time distribution of appliances acquired at time t’
and being disposed of at time t.
Corresponding to this residence time distribution, the outputs were divided into the three age
groups according to their age.
P(t) and k(t, t’) were defined through calibration with the collected data, see next section.
Final outputs (disposal channels):
O i, j (t) = c t i, j (t) * O i (t)
O i, j (t) is the output into the disposal channel j of age group i. The three outputs in a disposal
channel (one from each age group) can be added together to the total output in the disposal
channel.
c t i, j (t) is the transfer coefficient for the age group i into the disposal channel j in function of
time.
These transfer coefficients were calculated out of the collected data. For some appliances,
only very little information about the way of disposal was indicated, so that assumptions had
to be made. The definition of the transfer coefficients for all appliance types is described in
annexe B.2.
2 The residence time of an appliance is the time during which the appliance is used in a household.
For first-hand appliances, the residence time corresponds to the appliance age at time of disposal.
8

Method
Ten different ways of disposal options were surveyed with the questionnaires. The principal
ones are listed and explained below:
Given away:
Sold to informal collector:
Sold to second-hand dealer:
Returned at store:
Stored:
Discarded:
Electrical and electronic equipment is often given to family
or acquaintances for second use, for example to the grandparents
on the countryside.
Informal collectors go from door to door and buy obsolete
appliances. The price paid depends on the condition and
metal content of the appliance. The households don’t know
when the collectors will come by and there are long
intervals between two visits. In some guarded residential
areas, informal collectors are not allowed to enter, so that
these households do not use this way of disposal.
Appliances in working conditions can be sold to secondhand
dealers.
Obsolete appliances can sometimes be returned in the
store when buying a new one in exchange for a discount on
the price of the new appliance. This seems to be a newer
way of disposal, as the earliest mention is in 1999.
A lot of households keep old or broken appliances at home.
Different reasons were named for it. Appliances are stored
until an informal collector gets by, until someone takes the
time to go sell it or have it repaired. Another reason is that
some households think they may use the appliances again
later.
WEEE is also discarded together with the domestic waste.
Some other ways of disposal were mentioned but were not so relevant: Households that
moved out of a flat or house they own sometimes left their appliances in it as they rented it. A
few households disassembled appliances to reuse some parts. A couple of appliances were
lost or stolen.
To simplify interpretation, out of the ten disposal channels, five were formed. These are:
“storage”, “second use”, “informal collection”, formal collection” and “others” (see Table 4).
Appliances which are sold to the second-hand trade or given away will both be reused.
Waste pickers scan domestic waste, so that discarded appliances are assumed to end up in
the informal sector.
Quantitatively irrelevant disposal ways were allocated to the category “others”.
Table 4 Disposal channels used for the further calculations
New disposal
channels Storage Second use
Ways of
disposal
assessed with
questionnaire
Informal
collection
Stored Given away Discarded
Sold to
second-hand
dealer
Sold to
informal
collector
Formal
collection
Returned at
store
Others
Left in rented
property
Disassembled
Lost
Stolen
The mathematical model, as described above, has been calculated with the computer
simulation program SIMBOX.
9

Method
3.3.3 Calibration
Calibrating data means defining parameter functions that describe the available data as
accurately and simply as possible and that follow future potential developments. For this
study, the development of the stock and the residence time distribution had to be determined
for each of the 24 appliance types.
Stock parameter function P(t)
Stocks in the past were calculated based on the present stock of appliances and disposed
appliances (see annexe B.3). Six stock values were determined from 1984 to 2007. Stocks
were calculated in number of appliances per household.
The logistic growth curve (or S-curve) was chosen to describe the increasing stock of EEE in
the private households as a function of time. The growth curve has four parameters and is
described by the following equation (Bader et al. 2006):
psat
− pinit
P(
t)
= pinit
+
−α
( t−t )
1+
e turn
p init is the initial value in the distant past (t → - ∞) whereas p sat stands for the saturation value
in the distant future (t → ∞). α is proportional to the maximal growth rate and t turn is the
turning point of the growth curve.
Stock per household P(t)
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
1980 1990 2000 2010 2020 2030 2040
Collected data Fitted curve
t
Fig. 2 Stock data and fitted logistic growth curve for the refrigerator
The S-curve fits the available data as can be seen in the example in Fig. 2. The saturation
value of nearly 1.6 refrigerators per household seems reasonable, as freezers may have
been counted in or people may have two houses. But the curve fit for all four parameters
works only well if saturation is implied in the data time series. If not, the fit gives
unreasonable values so that one parameter has to be assumed. In this case the saturation
value p sat was assumed. This is shown in Fig. 3, where a curve fit with four unknown
parameters would forecast a stock of almost 14 TVs per household in 2030 with continued
rising. An assumed p sat of 3 TVs per household gives a more plausible fit. The estimations
and the parameters from the fits can be found in Table 30 in annexe B.3.
10

Method
Stock per household P(t)
14
12
10
8
6
4
2
0
1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030
t
Collected data Fitted curve Fitted curve with estimated psat
Fig. 3 Implausible exponential curve fit and curve fit with estimated saturation value p sat for
the TV
Residence time distribution k (t, t’)
As the surveyed households of privileged socioeconomic classes acquired only new
appliances, the stated appliance age at disposal corresponds to the household residence
time. The collected data was fitted with a Gaussian distribution which is described
mathematically by the function:
N 0 is the normalization factor, τ is the average residence time and σ is its standard deviation.
A detailed discussion of this distribution can be found in Baccini and Bader (1996)
The questioned people didn’t always indicate the exact age at disposal but often stated a
rounded value. That is why values like 5 or 10 were more frequently assessed as other
values. For example, over 30% of the refrigerators were stated to be 10 years old at
disposal, whereas 15% were 5 years old and not one was 9 years old (see Fig. 4 a).
Because of this rounding, the frequency distribution of the collected data showed multiple
peaks and the Gaussian curve didn’t fit too well. The fit seemed more plausible once the data
was classified over two ore more years (see Fig. 4 b). Best fit parameters were then used for
further calculations.
11

Method
k (t, t')
0.5
0.4
a
k (t, t')
0.5
0.4
b
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0 5 10 15 20
0.0
0 5 10 15 20
t - t'
t - t'
Fig. 4 Residence time distribution k(t, t’) fitted with the collected data classified over 1 (a) and
4 years (b) for the refrigerator. The parameters for the curve in figure b were retained.
The residence time distribution was used to divide the output flow into the three age groups.
For this, the 99%-quantile was taken as reference. The first age group goes from zero to 1/3
of this value, the second age group from there on to 2/3 of the value. The third age group
comprises all values larger than this (see Fig. 5).
k (t, t')
0.5
0.4
0.3
0.2
0.1
0.0
1. age
group
2. age
group
3. age
group
0 5 10 15 20
t - t'
Fig. 5 Division of the residence time distribution into the 3 age groups for the refrigerator
For some appliance types, it was not possible to collect enough values to perform a curve fit.
For the vacuum cleaner, the digital camera and the printer, only between 3 and 8 values
were indicated. The mean and the standard deviation of these values were calculated and
used for τ and σ, respectively. Only one value was given for the laptop, so that τ and σ had to
be estimated. The estimation and the parameters from the fits are listed in Table 5.
12

Method
3.3.4 Scenarios
To examine how the system could develop in the future, three scenarios were defined. Stock
saturation value p sat , mean residence time τ(t) and disposal transfer coefficients c t i, j (t) were
assumed to be the most sensitive parameters and were therefore chosen to be varied. This
assumption was verified with a sensitivity analysis (see results 4.1.3 ). The parameters were
defined for the time period simulated, i.e., from 1980 until 2060. The scenarios are described
below and summarized in Table 6 at the end of the section.
Base scenario
For the base scenario, the parameters defined in the calibration were used. It was assumed
that there would be no changes in the disposal behaviour of the high income households in
the future, so the parameters were not changed over time. The parameters for the base
scenario are listed in Table 5, excepted for the disposal transfer coefficients which can be
found in annexe B.2. The values for p init and α are not listed here; being of lesser importance,
they are listed in the annexe B.3.
Table 5 Parameters for base scenario
Appliance type
Parameters for logistic
growth curve
Saturation
value p sat
Turning point
t turn
Parameters for residence
time distribution
Average
Standard
residence
deviation
time τ
σ
Transition age
between age groups
1. to 2.
age group
2. to 3.
age group
Mobile phone 3.3 2004 3 1.3 2 4
Electric fan 2.6 2002 3.4 1.6 2 5
Telephone 1.9 2000 3.7 1.4 2 5
Air conditioner 1.8 2003 7.2 4.3 6 11
Refrigerator 1.6 2000 8.8 3.4 5 11
Hairdryer 1.3 2002 2.9 1.6 2 5
Washing machine 1.3 2001 6.5 2.7 4 9
Electric iron 1.2 1999 4.7 2.1 3 7
DVD/Video-player 1.1 2000 3 1.5 2 5
Electric boiler 1.0 2002 4.5 1.8 3 7
Radio 1.0 2000 4.9 4.2 4 10
PC 0.9 2003 5.8 1.5 3 6
Stereo 0.9 2002 3.4 2.1 3 6
Microwave 0.7 2002 3.9 1.9 3 6
Sewing machine 0.7 1987 8.4 3.5 5 11
Vacuum cleaner 0.3 2002 5 4.5 5 10
Printer 0.3 2003 4 2 3 6
Estimated p sat
TV 3 2002 9 4.9 6 13
Electric kettle 2 2008 2.5 2.0 2 5
Portable MP3/MP4-
player
2 2008 2.2 1.6 2 4
Rice cooker 2 2004 4.4 2.4 3 7
Digital camera 1 2008 4.8 3.9 4 9
Laptop 1 2010 4 2 3 6
Electro-scooter 1 2007 3.4 2.3 3 6
13

Method
Best-case scenario
The best-case scenario depicts a system evolving to a more sustainable consumption and a
legally regulated WEEE management. This presumes governmental intervention in form of
establishing a formal collection system for all types of WEEE (take-back from vendors and
producer, collection points, door-to-door collection or a combination of these measures) and
raising public awareness for environmental concerns related to e-waste.
According to Cooper (Cooper 2005), sustainable consumption can be achieved if an efficient
production (industry) and sufficient consumption (society) collaborate on increasing the
product residence time (see Fig. 6). This definition of sustainable consumption is adopted, so
that a longer residence time in households and a production of appliances with longer
effective lifetimes for the purpose of resource preservation were assumed.
For the best-case scenario, it was assumed that the implementation of a formal WEEE
management system would start in 2010.
Fig. 6 Sustainable consumption as defined by Cooper (Cooper 2005). Life span as used here
corresponds to the residence time
Stock saturation value
As a reduction of emerging standards of living would not be realistic, the saturation values
that could be defined by S-curve fit were retained. The estimated saturation values were
replaced by the lowest possible value still fitting the stock data of the past years.
Mean residence time
In consequence of increased product lifetime and households using their appliances over a
longer period of time, a final increase in residence time of 50% was assumed to be reached
in 2060. Such efforts from producers and consumers cannot sensibly be enforced and must
be done voluntarily. Hence the increase was expected to be sharper during and just after
sensitizing information campaigns, only to flatten again afterwards. Therefore, for this
scenario, the residence time was assumed to rise 25% from 2010 to 2020, the other 25%
increasing over the next 40 years.
As the effective product lifetime of appliances was assumed to increase, appliances that
were too old to be reused under the base scenario could be reused in this scenario. This
means that the transition age between two age groups should also have increased in time.
However, this assumption was not included in the model and will be considered when
presenting and discussing the results.
14

Method
Disposal transfer coefficients
An established formal collection system implicates that selling waste to informal collectors is
not possible anymore, as informal collectors would either have to be included into the formal
system or be banned. Therefore, the fraction of WEEE which is going to the informal sector
in the base scenario was attributed to the formal collection. Further, it was assumed that 50%
of the WEEE discarded with household waste also adds to formal collection due to sensitized
and informed households. As such changes need time, a linear evolution from the old to the
new transfer coefficients was assumed to take ten years. From the year 2020 on, the new
transfer coefficients were held constant until the end of the simulation.
Worst-case scenario
In the worst-case scenario no WEEE management is implemented and consumption is
characterized by the two words “more” and “faster”. The possession of a lot of appliances,
especially of the newest available technologies, is regarded as status symbol.
Stock saturation value
Excepted for appliance types where the saturation value is almost reached (like the sewing
machine), the saturation value was increased by 50%.
Mean residence time
For the mobile phone, a decrease in residence time could be observed in the collected data
(see annexe B.4). In parallel, Streicher and Yang (Streicher and Yang 2007) reported a
shortening in residence time of PCs in Chinese households. Therefore, it was concluded that
mean household appliance residence time is likely to have already been decreasing for some
time and a linear reduction from the year 2000 until the end of simulation in 2060 was
assumed. For trendy goods such as mobile phone, laptop, PC, digital camera and portable
MP3/MP4 player, a decrease by 50% was postulated, for all other appliance types a
decrease of 25%.
Disposal transfer coefficients
Assuming a constant household disposal behaviour over time, the actual transfer coefficients
were kept as in the base scenario for all age groups.
15

Method
Table 6 Definition of the three scenarios
Parameter Base scenario Best-case Worst-case
Stock saturation
value of the logistic
growth curve p sat
As defined with
data calibration
Lowest fitting value instead
of estimated saturation
values
For fitted values same as
base scenario
Increase compared to base
scenario by 50% excepted
for already saturated stocks
Mean residence time
τ of appliances in
households
As defined with
data calibration,
constant over
time
Same as base scenario
until 2010, then linear
growth of 25% until 2020,
then again linear growth of
25% until 2060 (end of
simulation)
Same as base scenario
until 2000, then linear
decrease until 2060 (end of
simulation) by 50% for
trendy appliances, by 25%
for the others
Disposal transfer
coefficients c t i, j (t)
As defined with
data calibration,
constant over
time
Same as base scenario
until 2010, then linear
change until 2020 to the
new values, then constant
(New values: whole fraction
„sold informal“ and 50% of
„discarded“ are added to
„formal collection“)
Same as base scenario
16

Method
3.4 Assessment of the WEEE quantities arising for recycling
For the estimation of the WEEE quantities having to be recycled, the calculated outputs were
considered depending on which disposal channel and age group they belong to (see Fig. 7).
System border : High income households in Taizhou
1. age group
Storage
2. use
Informal collection
Formal collection
Other
Storage
2. use
Informal collection
Formal collection
Other
New
EEE
Consumption
2. age group
Storage
2. use
Informal collection
Formal collection
Other
3. age group
To recycling
50% to recycling, 50% to 2. use
Fig. 7 Definition of WEEE-flows to recycling
All three disposal channels “formal collection” (all age groups) were assumed to accumulate
for recycling. For the “informal collection”, only the third age group and 50% of the second
age group were presumed to go to recycling, as appliances that are still functioning or can be
repaired will be reused, since the financial profit is higher. Stored appliances of the third age
group were assumed to be disposed of soon and were therefore also counted to the WEEE
stream to recycle.
The disposal channel “second use” was not taken into account to calculate recycling
volumes, as the appliances will leave the system of high income households to be reused by
lower income households. The part of the informal collection that is not going into recycling
(first age group and 50% of second age group) was reassigned to “second use”.
The channel “Others” is negligible and would anyway rather be expected to be reused, with
ways of disposal as “disassembled” and “left in rented house”.
It makes no sense to assess the WEEE quantities to recycle in number of appliances per
household, as the analysed appliance types differ considerably in their average weight.
Therefore, the WEEE streams of each type of appliance was be multiplied by its average
weight .
Average weights of EEE that can be found in the literature vary within a wide range.
Differences of up to more than 100% for one kind of appliance can be explained by different
appliance types, changing material composition and development of new product designs
(Lohse et al. 1998).
17

Method
To be on the conservative side, the smallest value found in the literature (Lohse et al. 1998),
(Zhang et al 2000), (Schäfer and Pretz 2002), (Cherry et al. 2007), (SWICO 2007) was
adopted for each type of appliance. For some types, no literature reference for an average
weight could be found. In those cases, the average weight of a comparable appliance type
was taken. The values used for calculation are listed in Table 7.
To estimate the amount of metals and dangerous substances arising for recycling, an
average composition of Swiss e-waste was adopted (see Table 8).
Table 7 Assumed average weights
Appliance type
Average weight
in kg
Washing machine 70
Electro-scooter 66
Refrigerator 35
TV 25
Microwave 23
PC 19
Stereo 15
Printer 11
DVD/Video-player 5
Laptop 3.4
Vacuum cleaner 3
Electric kettle 1
Electric iron 1
Radio 1
Telephone 1
Hairdryer 0.4
Digital camera 0.2
Assumptions :
Weight same as
Rice cooker
Electric kettle
Sewing machine
Vacuum cleaner
Electric boiler
Refrigerator
Air conditioner
Refrigerator
Electric fan
Vacuum cleaner
Portable MP3/MP4-player Digital camera
Mobile phone
Digital camera
Table 8 Average contents of selected
metals and non-metals in Swiss WEEE
(Morf et al. 2006)
Element/ Average value
substance (mg/kg)
Al 49'000
Sb 1'700
Pb 2'900
Cd 180
Cr 9'900
Fe 360'000
Cu 41'000
Ni 10'300
Hg 0.68
Zn 5'100
Sn 2'400
Cl 9'600
P 360
PCB Sum 13
18

Results
4 Results
The development of EEE-Stock and WEEE flows is presented in a first part for two
exemplary appliances over the simulated time period (1980-2060). In a second part, the
presentation is extended to all appliance types, but limited to the years 2007 and 2060.
Finally, in a third part, an estimation of the volume of WEEE designated for recycling is
described.
4.1 Exemplary appliance types
The two appliance types refrigerator and mobile phone are selected as exemplary appliances
in the first part of the result section.
4.1.1 EEE household stock over time
The development over time of the stock of refrigerators and mobile phones is represented in
Fig. 8 for the base and worst-case scenarios. For these two appliances, the stock values for
the best-case scenario are the same as the ones for the base scenario.
In the base scenario, the stock of refrigerators saturates around one and a half appliances
per household. This appears to make sense, as high-income households may have a two
residencies: one close to their work-place and one in their hometown. A stock saturation just
above three mobile phones per household seems also reasonable, as for the typical Chinese
three people household it represents one mobile phone per person.
The saturation values for the exemplary appliance types in the worst-case scenario remain
quite high, likely propelled by the high disposable income of the studied households.
Even though mobile phones have entered the marketplace about a decade later than
refrigerators, their stock saturation is expected to be reached around 2015 (or 2025 for the
worst-case scenario), whereas for refrigerators it will be attained around 2030 (or even
slightly after 2060 for the worst-case scenario).
EEE-Stock (appliances/household)
6
5
4
3
2
1
0
1980 1990 2000 2010 2020 2030 2040 2050 2060
Refridgerator Base & Best case scenario
Mobile phone Base & Best case scenario
Refridgerator Worst case
Mobile phone Worst case
t
Fig. 8 Stock per household of refrigerator and mobile phone for base/best-case and worstcase
scenarios
19

Results
4.1.2 WEEE output flows
For the refrigerator, the calculated total out-of-household flow of WEEE, as well as out-ofhousehold
flows into each of the three age groups, are shown in Fig. 9 for the three
scenarios base, best- and worst-case and as a function of time.
In the base scenario, the total output saturates around 0.18 appliance per household and
year. About two thirds of the appliances belong to the second age group. The first age group
accounts for the least part of the total flow.
In the best-case scenario, the total output reaches a maximum of 0.14 before 2020 and
slowly decreases from there on due to the increasing household residence time. For the
same reason, the fractions of appliances belonging to the small first and large second age
groups decrease to the benefit of the third age group. In 2060, the appliance output mostly
contains “old” and almost no “new” appliances. An increase of the transition ages, as
described in the assumptions to the mean residence time of the best-case scenario (see
page 14), would somewhat lessen the decrease in the fraction of appliances belonging to
either the first or second age groups.
The total WEEE output for the worst-case scenario is almost twice the value for the base
scenario in 2060 due to the decreasing residence time. This is also the reason that the
fraction of appliances in the first age group increases, attaining twice the number of
appliances in the third age group in 2060, but still only half of the fraction in the second age
group.
The development over time of these out-of-household flows shows similar patterns for the
mobile phone (see Fig. 10). Because of a larger stock and shorter household residence time
of mobile phones in households, the total output flow is about six times as big as the one of
the refrigerator. The decrease of the output in the best-case scenario comes about earlier
than with refrigerators, as mobile phones have a shorter residence time.
The fraction of appliances belonging to the first age group is bigger than for the refrigerator,
equivalent to the third age group in the base scenario and even surpassing the second age
group in the worst-case scenario.
WEEE-flow (appliances/hh/a)
0.2
0.2
0.4
Base scenario Best-case scenario
0.35
Worst-case scenario
0.15
0.15
0.3
0.25
0.1
0.1
0.2
0.15
0.05
0.05
0.1
0.05
0
0
0
1980 2000 2020 2040 2060 1980 2000 2020 2040 2060 1980 2000 2020 2040 2060
Total output
Output 1. age group
Output 2. age group Output 3. age group
t
Fig. 9 Output flows for the three age groups for the refrigerator. The scale for the worst-case
scenario is doubled.
20

Results
WEEE-flow (appliances/hh/a)
1.5
1.5
3
Base scenario Best-case scenario Worst-case scenario
1.25
1.25
2.5
1
1
2
0.75
0.75
1.5
0.5
0.5
1
0.25
0.25
0.5
0
0
0
1980 2000 2020 2040 2060 1980 2000 2020 2040 2060 1980 2000 2020 2040 2060
Total output
Output 1. age group
Output 2. age group Output 3. age group
t
Fig. 10 Output flows for the three age groups for the mobile phone. The scale for the worstcase
scenario is doubled.
The two appliance types, refrigerator and mobile phone, differ considerably in terms of
disposal methodology. This is illustrated by the transfer coefficients, describing the
appliances entry into the disposal channels for the three age groups (see Table 9 for
refrigerator and Table 10 for mobile phone). These are the coefficients assessed with the
questionnaires and used for all scenarios for the period from 1980 until 2010.
Refrigerators are essentially disposed of into the informal collection channel whereas mobile
phones are often stored at home. Furthermore, differences in the disposal coefficients
between the three age groups can be detected. For a big appliance like the refrigerator, the
fraction of stored appliance decreases with increasing age. The contrary is true for the
mobile phone. For both appliance types, the fraction going to informal collection increases
with age and the smallest fraction entering the second use channel is found in the third age
group.
Table 9 Refrigerator transfer coefficients of disposal channels for the 3 age groups
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
1. age group 21% 5% 21% 26% 42% 0% 42% 5% 5%
2. age group 19% 6% 14% 19% 50% 0% 50% 11% 0%
3. age group 13% 0% 0% 0% 63% 13% 75% 13% 0%
Others
Table 10
Mobile phone transfer coefficients of disposal channels for the 3 age groups
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
1. age group 40% 10% 5% 15% 5% 10% 15% 20% 10%
2. age group 45% 10% 10% 20% 15% 5% 20% 10% 5%
3. age group 56% 0% 11% 11% 0% 22% 22% 11% 0%
Others
21

Results
The flows into the different disposal channels for the first, second and third age groups are
calculated on the basis of age group fractions and transfer coefficients. By adding the three
age groups of a specific disposal channel, the entire output into this channel is obtained (see
Fig. 11 for refrigerator and Fig. 12 for mobile phone). As informal collection is very important
in the disposal of refrigerators, the effect of the best-case scenario is big and formal
collection clearly emerges as the predominant disposal methodology after the year 2020.
Such a shift in disposal methodology cannot be expected for mobile phones for which, formal
collection is closely followed by second use and is clearly positioned above informal
collection, although storage remains the main disposal methodology.
WEEE-flow (appliances/hh/a)
0.2
Base scenario
0.175
0.15
0.125
0.1
0.075
0.05
0.025
0
1980 2000 2020 2040 2060
0.2
Best-case scenario
0.175
0.15
0.125
0.1
0.075
0.05
0.025
0
1980 2000 2020 2040 2060
0.4
Worst-case scenario
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1980 2000 2020 2040 2060
Storage
Informal collection
t
Second use
Formal collection
Others
Total output
Fig. 11 Refrigerator output flows for the disposal channels. The scale for the worst-case
scenario is doubled.
WEEE-flow (appliances/hh/a)
1.5
Base scenario
1.25
1
0.75
0.5
0.25
0
1980 2000 2020 2040 2060
1.5
Best-case scenario
1.25
1
0.75
0.5
0.25
0
1980 2000 2020 2040 2060
3
Worst-case scenario
2.5
2
1.5
1
0.5
0
1980 2000 2020 2040 2060
Storage
Second use
Others
Informal collection
Formal collection
Total output
t
Fig. 12 Mobile phone output flows for the disposal channels. The scale for the worst-case
scenario is doubled.
22

Results
4.1.3 Sensitivity and uncertainty analyses
The sensitivities of the parameters stock, mean residence time τ, residence time standard
deviation σ and disposal transfer coefficients were examined. Stock and the mean residence
time turn out to be the most sensitive parameters. Transfer coefficients are sensitive only for
the flow into disposal channels they define. Sensitivities for the total output flow of mobile
phones are represented in Fig. 13 for all three scenarios.
worst-scenario mobile
0.5
P1: stock
0
-0.5
0
-0.5
0.5 best-scenario mobile 1980 2000 2020 2040 2060
0
-0.5
0.5 base scenario mobile P3: mean residence
time
P4: standard deviation
of residence time
1980 2000 2020 2040 2060
1980 2000 2020 2040 2060
Fig. 13 Mobile phone sensitivity of total output flow for the for different parameters
Uncertainty is calculated based on the sensitivity and assumed errors for all examined
parameters (errors defined in annexe B.5). Logically, uncertainty is bigger for future
developments than for present or past events. In Fig. 14, the uncertainty of the total output
for the base scenario is compared to the outputs of the best- and worst-case scenarios. For
refrigerator and mobile phone, the values of the worst-case scenario are clearly above the
uncertainty of the base scenario. The values of the best-case scenario just pass underneath
the uncertainty for the refrigerator but are comprised in the uncertainty range for the mobile
phone.
Refrigerator
3
Mobile phone
0.3
0.2
base
uncertainty of base
best
worst
2.5
2
1.5
base
uncertainty of base
best
worst
0.1
1
0.5
0
1980 1990 2000 2010 2020 2030 2040 2050 2060
0
1980
1990 2000 2010 2020 2030 2040 2050 2060
Fig. 14 Total output flow for refrigerator respectively mobile phone as a function of time for the
base, best-case and worst-case scenarios. Base scenario displayed with its uncertainty range
23

Results
4.2 All appliance types
The results for all appliance types are presented for the years 2007 and 2060.
For the period 1980-2007 the three scenarios do not differ significantly, therefore only the
values for the baseline are listed for 2007. For the year 2060, the values of the three
scenarios are opposed to one another.
4.2.1 EEE stock in households
Table 11 outlines the calculated stocks of all appliance types.
Mobile phones are identified as the appliance type with the biggest present and future stock,
followed by TVs and electric fans. Vacuum cleaners and printers are the appliance types
least acquired by households.
Table 11 Stock of EEE per household for the years 2007 and 2060
Large household
appliances
Small household
appliances
IT and telecom.
Consumer
equipment
Stock (nb of appliances/household)
2060
Appliance type 2007
Base Best Worst
Refrigerator 1.1 1.6 1.6 2.1
Microwave 0.7 0.7 0.7 1.0
Washing machine 1.0 1.3 1.3 1.9
Electric boiler 0.9 1.0 1.0 1.5
Air conditioner 1.6 1.8 1.8 2.7
Electric fan 2.2 2.6 2.6 3.7
Rice cooker 1.1 1.9 1.4 2.8
Electric kettle 0.9 2.0 1.5 3.0
Electric iron 0.9 1.2 1.2 1.7
Hairdryer 1.0 1.3 1.3 1.9
Vacuum cleaner 0.3 0.3 0.3 0.5
Sewing machine 0.6 0.7 0.7 0.7
Laptop 0.3 1.0 0.5 1.5
PC 0.8 0.9 0.9 1.5
Printer 0.3 0.3 0.3 0.5
Mobile phone 2.8 3.3 3.3 5.0
Telephone 1.8 1.9 1.9 2.7
TV 2.2 3.0 3.0 4.3
DVD/Video-player 1.0 1.1 1.1 1.5
Radio 0.7 1.0 1.0 1.2
Stereo 0.8 0.9 0.9 1.2
Portable MP3/MP4-
player
0.9 2.0 1.0 3.0
Digital camera 0.4 1.0 0.5 1.5
Other
Electro-scooter 0.5 1.0 0.5 1.5
24

Results
4.2.2 WEEE output flows
For all appliance types, the transfer coefficients for each of the disposal channels of the three
age groups are tabled in annexe B.2.
The resulting total outputs and the relative importance of the disposal channels are assessed
for the year 2007 in Table 12 and for 2060 in Table 13. The disposal channel “Others” is not
listed as it never contributes to more than 10% of the total. Generally, it can be said that
informal collection and storage are the main disposal channels, except in the best-case
scenario in which formal collection replaces the informal one in importance. The rather cheap
appliance types, especially small household appliances, are almost never going into second
use.
Table 12 Total WEEE outputs for all appliance types with percentage of the disposal
channels in 2007
Large household
appliances
Small household
appliances
IT and telecom.
Consumer
equipment
WEEE output in 2007 (nb of appliances/household/year)
and fraction for the disposal channels
Second Informal Formal
Appliance type Total Storage
use collection collection
Refrigerator 0.10 18% 17% 53% 10%
Microwave 0.16 19% 20% 50% 9%
Washing machine 0.13 28% 19% 46% 2%
Electric boiler 0.17 27% 19% 45% 3%
Air conditioner 0.14 42% 31% 26% 1%
Electric fan 0.59 36% 2% 50% 4%
Rice cooker 0.21 27% 6% 55% 8%
Electric kettle 0.29 35% 0% 59% 0%
Electric iron 0.17 23% 2% 65% 8%
Hairdryer 0.32 22% 0% 78% 0%
Vacuum cleaner 0.04 33% 6% 51% 2%
Sewing machine 0.07 47% 19% 27% 7%
Laptop 0.05 43% 17% 18% 15%
PC 0.11 49% 17% 21% 11%
Printer 0.06 35% 9% 39% 13%
Mobile phone 0.82 46% 17% 19% 13%
Telephone 0.49 42% 10% 39% 6%
TV 0.18 34% 9% 39% 13%
DVD/Video-player 0.33 35% 9% 40% 13%
Radio 0.20 46% 4% 43% 0%
Stereo 0.11 46% 4% 42% 0%
Portable MP3/MP4-
player
0.31 43% 17% 17% 15%
Digital camera 0.05 42% 16% 16% 17%
Other
Electro-scooter 0.11 43% 29% 26% 2%
25

Results
Table 13 Total WEEE outputs for all appliance types with percentage of the disposal
channels in 2060 for base, best-case and worst-case scenarios
Total WEEE output in 2060 (nb of appliances/household/year)
and fraction for the disposal channels
Base Best Worst
Appliance type
Total
Storage
Second use
Informal
collection
Formal
collection
Total
Storage
Second use
Informal
collection
Formal
collection
Total
Storage
Second use
Informal
collection
Formal
collection
Large household
appliances
Small household
appliances
IT and telecom.
Consumer
equipment
Refrigerator 0.18 18% 15% 56% 11% 0.12 15% 6% 4% 75% 0.31 19% 19% 51% 10%
Microwave 0.17 19% 19% 51% 10% 0.12 16% 11% 3% 69% 0.31 20% 22% 48% 8%
Washing machine 0.19 27% 19% 48% 2% 0.13 28% 17% 8% 46% 0.36 28% 20% 41% 4%
Electric boiler 0.23 27% 19% 47% 2% 0.16 28% 17% 9% 46% 0.42 28% 20% 39% 4%
Air conditioner 0.24 44% 23% 26% 7% 0.17 46% 14% 4% 36% 0.43 43% 27% 26% 4%
Electric fan 0.74 36% 2% 51% 3% 0.51 31% 0% 14% 50% 1.34 36% 4% 49% 3%
Rice cooker 0.41 27% 6% 54% 9% 0.22 26% 10% 9% 53% 0.75 27% 5% 57% 8%
Electric kettle 0.70 35% 0% 59% 0% 0.39 36% 0% 22% 39% 1.21 34% 0% 59% 0%
Electric iron 0.25 23% 2% 65% 7% 0.17 21% 8% 19% 52% 0.44 23% 1% 64% 8%
Hairdryer 0.43 22% 0% 78% 0% 0.30 24% 0% 34% 42% 0.78 22% 0% 78% 0%
Vacuum cleaner 0.05 33% 5% 54% 2% 0.04 32% 3% 15% 44% 0.09 32% 6% 53% 2%
Sewing machine 0.08 47% 19% 27% 8% 0.05 46% 8% 5% 41% 0.10 45% 24% 27% 4%
Laptop 0.24 45% 17% 19% 13% 0.08 50% 15% 7% 25% 0.57 42% 17% 17% 16%
PC 0.16 50% 16% 21% 11% 0.11 55% 12% 11% 22% 0.48 43% 17% 18% 15%
Printer 0.07 35% 8% 38% 14% 0.05 39% 5% 3% 53% 0.15 34% 10% 39% 12%
Mobile phone 1.09 46% 17% 19% 12% 0.74 51% 14% 8% 24% 2.69 43% 17% 17% 16%
Telephone 0.51 42% 10% 39% 6% 0.34 40% 5% 16% 37% 0.93 45% 9% 34% 9%
TV 0.31 36% 8% 37% 16% 0.22 39% 5% 3% 52% 0.56 35% 9% 39% 13%
DVD/Video-player 0.35 35% 9% 40% 13% 0.24 38% 6% 2% 54% 0.58 34% 10% 40% 11%
Radio 0.24 45% 4% 44% 0% 0.17 41% 4% 9% 40% 0.41 45% 3% 44% 0%
Stereo 0.17 46% 4% 43% 0% 0.13 43% 4% 9% 38% 0.24 46% 4% 43% 0%
Portable MP3/MP4-
player
0.81 44% 17% 18% 14% 0.30 47% 16% 6% 26% 1.70 42% 16% 17% 16%
Digital camera 0.18 45% 17% 19% 14% 0.07 47% 16% 6% 26% 0.36 43% 17% 18% 15%
Other
Electro-scooter 0.26 44% 25% 26% 5% 0.10 46% 17% 4% 33% 0.47 43% 28% 26% 3%
26

Results
4.3 WEEE arising for recycling
The amount of e-waste having to be recycled was calculated in kg per household and year
(see method 3.4).
4.3.1 Total flow
The development over time of the WEEE arising for recycling is compared to the other flows
also composing the total WEEE output in Fig. 15. The values for 2007 and 2060 are
recorded in Table 14.
In the worst-case scenario, the total output of obsolete appliances rises from 50 kg per
household and year in 2007 to 149 kg in 2060 with trend still rising. In the best-case
scenario, the total output decreases after a peak around 2010 to 50 kg per household and
year. The value for the base scenario is in between, reaching around 2020 a constant level
of about 80 kg per household and year.
For base and worst-case scenarios, the flow to recycling approximates the flows to second
use and storage. In the best-case scenario, the flow of WEEE to recycling makes up the
principal fraction as it grows over time to about two thirds of the total output in 2060.
The total WEEE having to be recycled is itself composed of stored appliances as well as
appliances collected formally and informally (see Fig. 17 in method 3.4). The time curves can
be seen in Fig. 16 and the values for the total flow to recycling and its percentage of formal
collection are documented in Table 15.
The informal sector in the base and worst-case scenario accounts for about two thirds of the
WEEE for recycling. In the worst-case scenario, however, the fraction of formal collection
increases over time at the expense of informal collection and especially of storage. The
percentage of formal collection in the worst-case scenario in 2060 is even bigger as in the
base scenario. This occurs because the fraction of appliances in the first age group
increases with decreasing residence time, as assumed in the worst-case scenario. In turn,
appliances in the first age group are rather disposed of by formal collection than by informal
collection or storage.
The total WEEE arising for recycling in the best-case scenario is bigger than in the base
scenario because of the large fraction of formal collection, for which the flows from all three
age groups are assumed to have to be recycled. As all three age groups of the formal
collection are considered, an adaptation of the transition ages would only have an effect on
the less important fractions “storage” and “informal collection”.
If it is assumed that the stored appliances of the third age group are disposed of in the same
proportion than assessed by either formal or informal collection, formal collection accounts
for 90% of the total WEEE arising for recycling in the best-case scenario.
By chance, the flows of WEEE to recycle in the best- and worst-case are almost the same,
the flow in the best-case being a major part of a small total output and the flow in the worstcase
being a small part in a much bigger total output.
In Table 16, a rough estimation is given for the flow to recycling of some metals and
dangerous substances for the base scenario in the time period 2030 to 2060. For example,
about 9 kg of iron arise per household and year.
27

Results
WEEE-flow (kg/household/a)
90
90
180
Base scenario Best-case scenario
80
80
160 Worst-case scenario
70
70
140
60
60
120
50
50
100
40
40
80
30
30
60
20
20
40
10
10
20
0
0
0
1980 2000 2020 2040 2060 1980 2000 2020 2040 2060 1980 2000 2020 2040 2060
Stored
To recycling
Total
Second use
Others t
t
Fig. 15 Total output for the base scenario, best- and worst-case scenarios. The scale for the
worst-case scenario is doubled.
Table 14 Total WEEE output flow in 2060 per household and percentage of WEEE arising
for recycling for the 3 scenarios, compared to the values in 2007
Value 2007
Values 2060
Base scenario Best-case Worst-case
Total output flow (kg/hh/a) 50.2 81 49.9 149
Percentage of flow to recycling 26% 32% 68% 23%
WEEE-flow (kg/household/a)
40
35
Base scenario
40
35
Best-case scenario
40
35
Worst-case scenario
30
30
30
25
25
25
20
20
20
15
15
15
10
10
10
5
5
5
0
0
0
1980 2000 2020 2040 2060 1980 2000 2020 2040 2060 1980 2000 2020 2040 2060
Formal
Informal
t
Stored
Total
Fig. 16 Total WEEE arising for recycling for the base scenario, best- and worst-case scenarios
Table 15 Total WEEE arising for recycling in 2060 per household and percentage of
formal collection for the 3 scenarios, compared to the values in 2007
Value 2007
Values 2060
Base scenario Best-case Worst-case
Total flow to recycling (kg/hh/a) 13 26 33.9 34.0
Percentage of formal collection 22% 25% 90% 31%
28

Results
Table 16 Amount of metals and hazardous substances arising for recycling for the base
scenario in 2060
Element/
substance
Amount arising for recycling
per household and year
Fe (kg/hh) 9.31
Al (kg/hh) 1.27
Cu (kg/hh) 1.06
Pb (g/hh) 75.03
Cd (g/hh) 4.66
PCB Sum (g/hh) 0.34
Hg (g/hh) 0.02
4.3.2 Flows of appliance clusters
WEEE arising for recycling for different appliance categories is represented in Fig. 17 for the
base scenario. The appliances are classified as in Table 3, except that the five appliances
listed in the Zhejiang province provisional measures (refrigerator, washing machine, air
conditioner, TV and PC) are allocated to a separate cluster. This new category clearly makes
the biggest contribution to total WEEE arising for recycling. The fraction of each appliance
category in the total WEEE arising for recycling can be found in Table 17 for the years 2007
and 2060
For best- and worst-case scenarios, similar values are obtained (see Table 32 resp. Table 33
in annexe B.6).
WEEE-flow (kg/household/a)
16
14
12
10
8
6
4
Appliances under legal
regulation
Scooter
Large household
appliances
Small household
appliances
IT and telecommunication
2
0
1980 1990 2000 2010 2020 2030 2040 2050 2060
t
Consumer equipment
Fig. 17 WEEE arising for recycling in the base scenario
Table 17
Fraction of the categories in the WEEE arising for recycling in the base scenario
Appliances
under legal
measures
Scooter
Large
household
appliances
Small
household
appliances
IT and
telecommuni
cation
Consumer
equipment
2007 54% 6% 25% 3% 4% 8%
2060 57% 14% 18% 2% 3% 5%
29

Discussion & conclusion
5 Discussion & conclusion
In this chapter, results concerning EEE-stocks, WEEE output flows and WEEE arising for
recycling are discussed. In a fourth part, the limitations of the study are pointed out and
directions for further research presented.
5.1 EEE-stock
The development of the stock of refrigerators and mobile phones in high-income households
from 1980 to 2060 is represented in Fig. 8. The household stocks attain a constant saturation
level after some years. The diffusion of a new type of appliance can take place at different
speed. As trendy goods, mobile phones have a much faster diffusion as refrigerators.
The actual stock and a possible range for the future development given through the
scenarios are listed in results 4.2.1 for all the 24 appliance types.
It can be said that the method used for the simulation of the stocks fits well the collected data
and gives reasonable results. Since a stock-driven model was used, the stocks in function of
time are essential for the calculation of resulting output flows. A solid assessment of stocks is
an important prerequisite for the accuracy of WEEE output flows.
5.2 WEEE output flows
The development over time of the output flows is discussed with the example of the two
chosen exemplary appliance types. The flows into the different disposal channels are
discussed generally for all appliance types.
5.2.1 Exemplary appliance types
In the base scenario, all calculated output flows reach a constant level (cp results 4.1.2). This
happens before 2020 for mobile phones and after 2040 for refrigerators. In the best- and
worst-case scenarios, however, the flows are varying over the whole calculated period of
time following the underlying assumptions (see section 3.3.4).
The total WEEE output flow for both appliance types are in the same scale for the base and
best-case scenarios, whereas the one for the worst-case scenario turns out twice as high in
2060 as a consequence of the increased stock and decreased residence time.
In the worst-case scenario, the growth of the total output flow flattens somewhat after the
WEEE comes out of saturated stock.
In the best-case scenario, the total output for both appliance types reaches a peak in 2010,
the year when the residence time of appliances was set to begin increasing. For the mobile
phone, the decrease of the output flow after that peak follows the defined increase in
residence time: steep in the first ten years, then flatter until 2060. As in the base scenario,
the total output for refrigerators has a quite steep increase until 2030, the decrease in the
best-case scenario after 2010 is not so steep and a very small increase is even predicted
around 2030.
The partition of the total WEEE output flow into the three age groups is similar for the two
appliance types in the base scenario (see Fig. 9 and Fig. 10). The major fraction of the
appliances is belonging to the second age group, that is the appliances that are partly
reusable. For the refrigerator, a bigger part of the remaining fraction is composed of
appliances in the third age group (i. e. not reusable appliances), whereas for the mobile
phone the fraction of first (i. e. reusable appliances) and third age group are almost the
same.
30

Discussion & conclusion
In the best-case scenario, the fractions of the age groups change after 2010 in consequence
to the increasing residence time. For both appliance types, third age group appliances have
replaced the second age group as major fraction in 2060 and the fraction of reusable
appliances has become irrelevant.
As the transition ages were not increased over time, the increase of effective product lifetime
is not considered. The fractions for the first and second age group are therefore smaller than
if it had been considered. The implications of it on all the different disposal channel fractions
have not been studied in detail.
In the worst-case scenario, it is the fraction of the first age group that increases. Whereas for
refrigerators it surpasses the second age group and approaches the second age group, for
the mobile phone it even replaces the second age group as major fraction.
A fine tuning of the system would be possible by adjusting the transition ages between the
age groups. As no data about ages of second-hand appliances was available, this was not
considered in this study.
As is illustrated in Fig. 11 for the base scenario, informal collection is the principal disposal
channel of refrigerators with a fraction of over 50%. Not only informal recycling, but also
incautious transportation of old refrigerators can cause harm to human health and
environment (Florence and Price 2005). If components like the compressor unit are
damaged, leakages of chlorofluorocarbons (CFCs) may happen. CFCs were used for
refrigerant and in the insulating foam of refrigerators before 1995 and are ozone-depleting
substances. For the refrigerator, it would therefore be particularly important to establish a
formal collection system or to train informal collectors.
The principal disposal channel for mobile phones (see Fig. 12 for the base scenario) is
storage. It is not known how long appliances are stored.
In the worst-case scenario, the same transfer coefficients than in the base scenario are used
for the calculation. The difference resides in the changing age group fractions. However, the
fractions of the disposal channels in the worst-case are almost the same as in the base
scenario, as the disposal transfer coefficients do not vary much between the first and second
age group (see Table 9 and Table 10).
In the best-case scenario, the biggest change in the disposal channel fractions occurs
through the replacement of informal collection by formal collection between 2010 and 2020.
For the refrigerator, for which informal collection is the major disposal channel until 2010,
formal collection becomes the major fraction after 2020. As the fraction of informal collection
is not so important for the mobile phone, the change in the fraction of formal recycling is not
as important, and storage remains the major disposal channel fraction.
31

Discussion & conclusion
5.2.2 All appliance types
The development over time of total output flow, of age group fractions and disposal channel
fractions of all appliance types lies within the range of results described for refrigerators and
mobile phones.
There is a difference in the total output of appliance types with an estimated saturation value
like the laptop. For these appliances, the total output in 2060 in the best-case scenario is
much smaller than in the base scenario (e.g. one third for the laptop).
As can be seen in Table 12, results 4.2.2, informal collection is actually a main disposal
channel for WEEE. Alike refrigerators, washing machines and TVs are mainly going into the
informal collection, as well as more than 20% of the air conditioners and PCs. These results
show very clearly that the Zhejiang province measures have not yet been implemented for
the five defined appliance types.
More than 10% of rather cheap appliances as electric kettle, electric iron, hairdryer, vacuum
cleaner, telephone and electric fan are almost no being reused and still informally collected
even in the best-case scenario. These are appliances that are often discarded together with
the domestic waste and therefore lost or damageable for the environment.
The reuse of most of the other appliance types is respectable with fractions between 10 and
30%.
Unfortunately, formal collection does not exist at all or is yet not much developed. A maximal
value of 17% is assessed for the digital camera. Generally it can be said that the formal
collection attains values over 10% for the appliances of the IT and telecommunication and
the consumer equipment categories. These are appliances with a higher content of precious
metals, for which the benefit of recycling may be quite interesting. This is probably the
reason why the fraction of formal collection is the highest for these appliances, the trade
being interested in giving better discounts as for other appliance types.
5.3 WEEE arising for recycling
5.3.1 Total flow
The total annual WEEE output flow from consumption in 2060 varies from about 50 kg per
household in the best-case scenario, which corresponds to the actual value for 2007, to the
triple in the worst-case scenario (see Table 14 in results 4.3.1). However, the flow having to
be recycled, actually estimated at 13 kg per household and year, increases from 2007 to
2060 in each of the three scenarios, doubling in the base scenario and almost tripling in the -
and worst-case scenario.
For base scenario and worst-case scenario, the fraction of appliances going to be reused is
slightly bigger than the one having to be recycled. It should not be forgotten that after having
been used for some years in households of lower social strata, these appliances may of
course also need to be recycled. However, this is not part of this study.
In the best-case scenario, the future amount of WEEE arising for recycling is as important as
in the worst-case scenario because of the high percentage of formal collection and the
assumption, that formally collected appliances from the three age groups would all be
recycled. It would make sense to reuse appliances or components collected formally as
much as possible. This would implicate a collaboration of the formal recycling sector with the
second-hand trade. A combination with the elaboration of quality standards for second-hand
32

Discussion & conclusion
appliances would be important, as these appliances may be of poor quality or even
dangerous.
The current draft for a national Chinese WEEE regulation (EPRC 2007) takes that direction,
as it foresees mechanisms for reuse and quality certification standards for second hand
appliances.
As households average three people, the annual total WEEE output per inhabitant develops
from currently 17 kg to 17- 50 kg in 2060, depending on the scenario. The value for 2007 is
quite near to the theoretically expected annual potential of about 20 kg for the European
Union ten years ago (Lohse et al. 1998).
The WEEE having to be recycled is estimated to 4 kg per inhabitant and year actually and is
supposed to increase to 9- 11 kg until 2060. In Europe, an average of 4- 5 kg were assumed
to be collected by public authorities. We can conclude that high income households in
Taizhou are not so different from the European average with regard to their WEEE disposal.
The big difference between the collection systems is that in Taizhou the most important part
of the appliances is going to informal recycling.
The rough estimation of valuable (e. g. Iron, aluminium, copper) and hazardous substances
(e. g. Lead, cadmium) results in quite impressive flows (see Table 16). A formal recycling
would recover the secondary resources as well as make sure that the hazardous substances
are disposed of securely.
5.3.2 Flow of appliance clusters
If the legal measures would be implemented for the five appliances refrigerator, washing
machine, air conditioner, TV and PC, it would cover between 50-60% of the present and
future WEEE to recycle.
With a present fraction of 6% increasing to nearly 15% around 2020, the electric scooter is
identified as a potential candidate to be listed in the legal measures. Over 95% of electric
scooters sold in China use lead-acid batteries and about 25% of the total weight of an
electric scooters is lead (Cherry et al. 2007). According to Weinert (Weinert et al. 2007), the
lead emissions from battery production and recycling causes serious health and
environmental problems in China. Poor production and recycling practices are responsible
for 30-70% lead loss. By including only this one appliance type in the legal measures, the
coverage would increase to 70% of the total amount.
According to the calculation, a 90% coverage could be reached if large household appliances
not yet included were added (Microwave, electric boiler and electric fan).
Small ICT and consumer equipment appliances such as laptop or digital camera do not
represent important mass flows but could be quite interesting for another reason. These
appliances have high concentrations of precious metals and could therefore contribute to
finance the collection and recycling system.
Small household appliances do not account for a substantial WEEE fraction and volume.
Attention should be paid to the material content such as valuable material to be recovered or
hazardous components to be safely deposed of. This has not been investigated in this study.
To conclude this section, it can be said that a big step could already be done by just
implementing the legal measures as they are. If the five main appliance types would be
directed to formal recycling, the informal recycling sector would certainly regress drastically,
provided that illegal imports are stopped.
33

Discussion & conclusion
5.4 Limitations of the study and directions for further research
Of course, predictions about future developments are more uncertain than estimations
concerning the present or immediate future (see results 4.1.3). In this study, the three
scenarios were defined to cover the range of potential evolutions. Even though the best-case
scenario was defined conservatively, the curve for the total output passes through or very
near the uncertainty range of the base scenario. This shows that the base scenario is also
rather conservative.
It has not been considered that new kinds of appliances may be developed and thrown on
the market in the future, adding to the number of appliances in the households or replacing
some of them. In fact, modelling results of this study cannot be taken for granted. In the next
years or decades, new types of appliances will most certainly be produced, but the moment
of their introduction and the speed of their diffusion in the household are, as of yet,
unpredictable.
The appliances taken back at the stores were assumed to go to a formal sector, but this may
actually not be the case.
A big part of obsolete appliances are stored in the households. These appliances will surely
leave the households sometime by another of the assessed disposal channels, since the
stock of stored appliances cannot increase indefinitely. To consider this, data about mean
storage time of appliances should be collected.
As the research covers only high income households, the exact total WEEE amounts arising
for recycling for the Taizhou region cannot be predicted. This could be achieved by extending
the system to middle and low income households, also modelling the use of second-hand or
even third-hand appliances.
With increasing income, even the socially lower households may stop or lessen the use of
second-hand appliances in the future. This implies that more e-waste would accumulate or
that second-hand appliances would be exported to poorer regions, where it might be even
more difficult to set up a formal collection and recycling system. Socio-scientific studies about
changes in the behaviour of households and studies about actual and potential e-waste
export could be of interest.
This study is designed at the household level. For planning a WEEE management system,
population dynamics should be taken into account. A combination with a dynamic modelling
could be very interesting, as the Chinese population growth is assumed to stagnate around
2040 with following decrease in population (UN 2006), (SFPC 2000).
Another direction that should be investigated is the financing of a formal collection and
recycling system.
Streicher and Yang report that out of the five appliances refrigerator, washing machine, air
conditioner, TV and PC processed in a formal recycling pilot project, only the recycling of
PCs was profitable after deduction of the payment to the former owner and the recycling and
transport costs (Streicher and Yang 2007). As already mentioned in the previous section, an
extension of the legal WEEE management measures to ICT or consumer equipment
appliances could generate profit from precious metals. It should be investigated if the
recycling of such appliances would effectively pay for itself, alike the recycling of PCs, and
contribute to finance the collection and treatment of non-profitable appliances.
The China WEEE draft regulation provides special funds for a formal collection, recycling and
treatment system, but the functioning and modalities of these funds are not yet defined
(Kummer 2007). This issue should be tackled, because as long as appropriate financing is
not provided, the implementation of a formal recycling system will most probably not
materialize.
34

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mountains or mole hills? Applied Energy. 80 (2), pp. 125-140, 2005
(FSO 2004) Swiss Federal Statistical Office (FSO). Available from http://www.bfs.admin.ch.
Retrieved on 25.07.2007
(Hicks 2005) Hicks C., Dietmar R.; Eugster M. : The recycling and disposal of electrical and
electronic waste in China-legislative and market responses. Environmental Impact
Assessment Review 25 (5), pp. 459-471, 2005
(Hug et al. 2004) Hug F., Bader H.-P., Scheidegger R., Baccini P. : A dynamic model to
illustrate the development of an interregional energy household to a sustainable status.
Clean Technol Environ Policy 6, pp. 138–148, 2004
(Kummer 2007) Kummer Peiry K. : E-waste legislation in China. End-of-Mission Report, 24th
July 2007
(Li et al. 2006) Li J., Tian B., Liu T., Liu H., Wen X., Honda S. : Status quo of e-waste
management in mainland China. Journal of Material Cycles & Waste Management. 8, pp. 13-
20, 2006
(Liu et al. 2006 a) Liu X., Tanaka M., Matsui Y. : Electrical and electronic waste management
in China: progress and the barriers to overcome. Waste Management Research. 24, pp. 92-
101, 2006
(Liu et al. 2006 b) Liu X., Tanaka M., Matsui Y. : Generation amount prediction and material
flow analysis of electronic waste: a case study in Beijing, China. Waste Management &
Research. 24, pp. 434-445, 2006
(Lohse et al. 1998) Lohse J., Winteler S., Wulf-Schnabel J. : Collection targets for waste from
electrical and electronic equipment (WEEE). The directorate general (DG XI) environment,
Nuclear safety and civil protection of the Commission of the European Communities, 1998
(Mieg and Brunner 2001) Mieg H.A. , Brunner B. : Experteninterviews (MUB Working Paper
6). Professur für Mensch-Umwelt-Beziehungen, ETH Zürich, 2001
(Morf et al. 2006) Morf L., Tremp J., Gloor R., Schuppisser F., Stengele M., Taverna R. :
Metals, non-metals and PCB in electrical and electronic waste – Actual levels in Switzerland.
Waste Management 27 (10), pp.1306-1316, 2007
(OECD 2001) OECD. Extended producer responsibility: a guidance manual for governments.
Paris : OECD, 2001
(Puckett and Smith 2002) Puckett J., Smith T. : Exporting Harm: The High-Tech Trashing Of
Asia. The Basel Action Network (BAN), The Silicon Valley Toxics Coalition (SVTC), Toxics
Link India, SCOPE (Pakistan), Greenpeace China. Seattle,WA, and San Jose, CA; February
25th, 2002
(Schäfer and Pretz 2002) Schäfer T., Pretz Th. : Access to secondary copper from WEEE.
TMS Fall 2002. Extraction and Processing Division Meeting on Recycling and Waste
Treatment in Mineral and Metal Processing: Technical and Economic Aspects, 16.-
20.06.2002, Lulea
36

References
(SFPC 2000) The Projection of China's Population by State Family Planning Commission of
China (2000-2050). Available from http://www.npfpc.gov.cn/en/index.htm. Retrieved
03.08.2007
(Sinha 2004) Sinha D. : The management of electronic waste: a comparative study on India
and Switzerland. St. Gallen, University of St. Gallen. Master Thesis, 2004
(StEP 2005) StEP. Solving the e-waste problem: a synthetic approach (StEP), Draft Project
Document; 2005. Available from http://step.ewaste.ch
(Streicher et al. 2007) Streicher-Porte M., Bader H.-P., Scheidegger R., Kytzia S. : Material
flow and economic analysis as a suitable tool for system analysis under the constraints of
poor data availability and quality in emerging economies. Handed in for publication to the
Journal of Clean Technology and Environmental Policy 2007
(Streicher and Yang 2007) Streicher-Porte M., Yang J. : WEEE recycling in China. Present
situation and main obstacles for improvement. Proceedings of the 2007 IEEE International
Symposium on Electronics & the Environment in Orlando, FL on 7-10 May 2007, pp. 40-45
(SWICO 2007) Unpublished data from SWICO, 2007
(UN 2006) Population Division of the Department of Economic and Social Affairs of the
United Nations Secretariat, World Population Prospects: The 2006 Revision. Available from
http://esa.un.org/unpp. Retrieved 03.08.2007
(Weinert et al. 2007) Weinert J., Ma C., Cherry C. : The transition to electric bikes in China:
history and key reasons for rapid growth. Transportation 34, pp. 301–318, 2007
(Widmer et al. 2005) Widmer R., Oswald-Krapf H., Sinha-Khetriwal D., Schnellmann M., Böni
H. : Global perspectives on e-waste. Environmental Impact Assessment Review 25, pp. 436–
458, 2005
(Zeltner et al. 1999) Zeltner C., Bader H.-P., Scheidegger R., Baccini P. : Sustainable metal
management exemplified by copper in the USA. Regional Environmental changes (1).
Springer, Berlin Heidelberg New York, pp 31–46, 1999
(ZETC 2004) Zhejiang Province Economic and Trade Commission. Zhejiang Province Pilot
Provisional Measures for the Recycling and Treatment of Waste Home Appliances and
Electronic Products, 2004
(Zhang et al 2000) Zhang S., Forssberg E., van Houwelingen J., Rem P., Wei L.-Y. : End-oflife
electric and electronic equipment management towards the 21st century. Waste Manage
Res 18, pp. 73-85, 2000
(Zhejiang Yearbook 2005) Zhejiang Statistical Yearbook 2005. Available from
http://www.zj.stats.gov.cn/
37

Annexe A
Annexe A
38

Annexe A
Questionnaire #1
39

Interview outline
Introduction
This interview is part of a swiss Master’s thesis that runs under the project ‘knowledge
partnership in e-waste recycling’ of the Swiss Federal Laboratories for Materials Testing and
Research (EMPA). The goal of the thesis is to analyse the streams of electronic waste in a
chinese city at the example of the Taizhou. It will contribute to understand how the actual
recycling system works and enhance the planning reliability of emerging pilot projects.
Eventually warrant anonymity
General information
Interviewer-team:
Date:
Place:
Duration:
Interviewed person:
Household:
Number of persons:
Profession:
Income:
List of questions
1. Questions about the quantity of electrical and electronic equipment
Which and how many electrical and electronic appliances are present in the household?
Do you have appliances for ...?
Has this amount increased or decreased over the last five years?
When have you bought the devices? Where they new when you bought them ?
How many of these devices have you had before?
2. Questions about the e-waste production
Which appliances became obsolete during the last year?
What was the reason for disposal?
How old were the appliances when they became obsolete?
Were the appliances technical in order or were they broken?
How were the appliances disposed of?
Did you discard the appliance with the normal waste?
If not, to whom the appliances were given?
Did you receive any money for the appliances?
For sold appliances: how much money did you receive?
Is there a price difference if the appliance is broken or not?
What about the appliances that became obsolete more than one year ago?
Are certain appliances allways disposed of in one way and other appliances in another way?
Have you allready sold more than one device of the same type? Has the price increased or
decreased over time?

3. Questions about the opinion of the households
Is the form of collection and waste disposal convenient?
Would you do without the reimbursement from waste collectors if you would know that waste
is processed in an appropriate way?
Closing
Thanks
Does the interviewed person have questions?

Annexe A
Questionnaire #2
40

Questionnaire
This questionnaire is part of a Swiss Master thesis running under the project ‘Knowledge partnership in
e-waste recycling’ of the Swiss Federal Laboratories for Materials Testing and Research and the Swiss
State secretariat for Economic Affairs (seco). The goal of the thesis is to analyse the streams of
electronic waste in a Chinese city at the example of Taizhou. This study will contribute to understand and
evaluate the actual recycling system and to enhance the planning reliability of emerging pilot projects.
This questionnaire is anonymous, so there will be no connection of your data and name in any
publication. Please fill out the questionnaire as completly and exactly as possible.
Thank you for helping me. If you have any questions or if you are interessted in the results of this study
you can contact me: geering_china@hotmail.com
List of questions
Please fill out these tables and the table and questions on the other side of the sheet. If you have more
than one appliance of a certain type, please make a check mark for each appliance. If you have big
electric or electronic appliances (excepted lighting) that are not in the list, please complete the list.
Electrical &
electronic equipment
Refrigerator
Microwave
Rice cooker
Electric kettle
Vacuum cleaner
Sewing machine
Electric iron
Washing machine
Electric boiler
Air conditioner
Electric fan
Hairdryer
TV
DVD-/Video-player
Stereo
Radio
Portable MP3/MP4 player
Digital camera
Laptop
PC
Mobile phone
Telephone
Printer
Electro-scooter
Quantity
in your
household
that you
own
2005-
now
2000-
2004
Bought between
1995-
1999
1990-
1994
1985-
1989
older
Encircle the check if you bought an appliance
second-hand
Quantity
in your
household
that you
rent
Quantity
that you
left in
appartme
nt that
you rent
How
many of
these
appl. have
you
owned
before?
(rented
ones not
included)

This table is about the electrical and electronic equipment that became obsolete
Condition of the
appliance
Equipment that
became obsolete
Quantity
In which year did the appliance become
obsolete?
For how many years have you had the
appliance ?
(encircle for second hand appliances)
Broken/defective, unfixable
Broken/defective, maybe fixable
In working order
Given to family/ friends
Left in rented house
Discarded with domestic waste
Stored at home
(For how many years?)
Disposal
Exchanged in store when buying
new
To informal waste collector
Sold
To second-hand store
Approximative price
Other disposal
Refrigerator
Microwave
Rice cooker
Electric kettle
Vacuum cleaner
Sewing machine
Electric iron
Washing machine
Electric boiler
Air conditioner
Electric fan
Hairdryer
TV
DVD-/Video-player
Stereo
Radio
Portable MP3/MP4
player
Digital camera
Laptop
PC
Mobile phone
Telephone
Printer
Electro-scooter
Are informal collectors coming to your house to ask for obsolete appliances? Yes No
Is the form of collection and waste disposal convenient? Yes No
If No: Why not? How could it be better?
Would you renounce to get money from waste collectors if you could be sure that the waste is taken care
of in a way that is useful and that does not cause pollute the environment? Yes No

Date: year month day
Number of persons living in the household:
men women children/students
Professions of household members:
Total anual income:
Place of living:
Jiaojiang Luqiao Huangyan Linhai Wenlin Yuhuan Tiantai Xianju Sanmen
Other:
Did you move from another place? If yes and from outside Taizhou, when? Year

问 卷 调 查
我 是 来 自 瑞 士 苏 黎 士 联 邦 理 工 大 学 在 校 硕 士 研 究 生 Anne Catherine, 因 撰 写 毕 业 论 文 的 需 求 , 需 要
进 行 下 列 的 问 卷 调 查 , 首 先 向 各 位 的 支 持 与 配 合 表 示 最 诚 挚 的 谢 意 。
本 问 卷 调 查 为 “ 瑞 士 材 料 检 测 与 研 究 联 邦 实 验 室 ” 及 “ 瑞 士 国 家 经 济 事 务 处 ” 下 设 “ 电 子 废 弃 物 回 收 信
息 合 作 ” 项 目 中 硕 士 论 文 的 组 成 部 分 。 论 文 旨 在 以 台 州 为 例 , 分 析 中 国 城 市 电 子 废 弃 物 的 流 通 渠 道 。 本
研 究 将 有 利 于 对 实 际 回 收 系 统 的 了 解 和 评 估 , 并 有 利 于 加 强 电 子 废 弃 物 试 点 项 目 策 划 的 可 信 度 。
我 们 将 匿 名 进 行 本 次 调 查 问 卷 , 请 您 尽 可 能 完 整 、 正 确 地 填 写 本 问 卷 。
如 果 您 有 任 何 疑 问 或 对 本 调 查 感 兴 趣 的 话 , 请 联 系 我 们 。
我 们 的 联 系 方 式 是 :geering_china@hotmail.com 谢 谢 !
1、 请 在 下 列 表 格 相 应 的 空 格 中 打 勾 , 如 果 您 有 其 它 大 型 电 子 电 气 设 备 也 可 添 加 在 空 格 处 ( 除 照 明 设 施
外 ), 以 便 于 我 们 统 计 。 请 仔 细 填 写 , 谢 谢 !
电 子 电 气 产 品
冰 箱
微 波 炉
电 饭 煲 / 电 饭 煲
小 型 热 水 器 / 煮 水 器
真 空 吸 尘 器
缝 纫 机
电 熨 斗
洗 衣 机
电 热 水 器
空 调
电 风 扇
吹 风 机
电 视 机
DVD/VCD 机
音 箱
收 音 机
便 携 式 音 乐 播 放 器
(DVD/VCD 机 , MP3, MP4)
数 码 相 机
手 提 电 脑
台 式 电 脑
手 机
电 话 机
打 印 机
电 动 车
在 使 用 中 的
家 用 电 器 数
目
( 包 括 个 人
购 买 或 别 人
赠 送 )
2005
至 今
购 买 日 期
如 购 买 的 是 新 产 品 , 请 打 “√”,
如 购 买 的 是 二 手 产 品 , 请 画 “○”
2000­ 1995­ 1990­ 1985­
2004 1999 1994 1989
早 于
1985
如 果 你 是
房 客 , 所
租 用 的 房
里 , 房 东
留 下 的 电
器 数 目
如 果 你 是
房 东 , 要
出 租 的 房
里 , 留 下
电 器 的 数
目
曾 经 使 用 过
的 家 用 电 器
数 目

2、 此 表 格 涉 及 废 弃 电 子 电 气 产 品 的 相 关 信 息
废 弃 电 子 电 气
产 品
数
量
哪 一
年 报
废
报 废 前
已 使 用
过 多 少
年 ?
( 如 果
是 二 手
产 品 请
用 ” ○ ”
表 示 )
电 器 使 用 状 况
已 损
坏 ,
不 能
修 理
已 损
坏 ,
但 可
修 理
在 使
用 中
送 给
家 人
或 朋
友
( 可 多 选 , 在 空 格 中 打 “√”)
与 其
他 家
用 电
器 一
起 丢
弃
搁 置
在 家
( 放 置
时 限 )
以 旧
换 新
处 理 方 法
售 于
非 正
规 回
收 点
售 于
二 手
市 场
转 卖
大 概 的 价 格
其 他 处
理 方 法
冰 箱
微 波 炉
电 饭 煲 / 电 饭 煲
小 型 热 水 器 / 煮 水 器
真 空 吸 尘 器
缝 纫 机
电 熨 斗
洗 衣 机
电 热 水 器
空 调
电 风 扇
吹 风 机
电 视 机
DVD/VCD 机
音 箱
收 音 机
便 携 式 音 乐 播 放 器
(DVD/VCD 机 , MP3, MP4)
数 码 相 机
手 提 电 脑
台 式 电 脑
手 机
电 话 机
打 印 机
电 动 车
3、
是 否 有 非 正 规 回 收 的 个 体 户 上 门 寻 求 废 弃 电 器 ? 是 否
电 器 丢 弃 和 回 收 的 方 式 是 否 便 利 ? 是 否
如 果 不 便 利 ? 如 何 改 善 ?
如 果 废 旧 电 器 能 被 正 当 地 处 理 回 收 并 不 会 对 环 境 造 成 污 染 , 您 是 否 可 以 免 费 将 废 旧 电 器 赠 给 回 收 者 ?
是
否
日 期 : 年 月 日
家 庭 居 住 成 员 : 成 年 男 性 : 成 年 女 性 : 小 孩 / 学 生 :
家 庭 居 住 成 员 的 职 业 :
家 庭 年 总 收 入 :
居 住 地 ( 可 在 下 列 选 项 中 打 勾 ):
椒 江 路 桥 黄 岩 临 海 温 岭 玉 环 天 台 仙 居 三 门
其 他 :
如 果 您 是 外 省 市 户 口 , 请 问 何 时 迁 入 台 州 ?
年

Annexe A
Questionnaire #3
41

Questionnaire year month day
This questionnaire is part of a Swiss Master thesis running under the project ‘Knowledge partnership in
e-waste recycling’ of the Swiss Federal Laboratories for Materials Testing and Research and the Swiss
State secretariat for Economic Affairs (seco). The goal of the thesis is to analyse the streams of
electronic waste in a Chinese city at the example of Taizhou. This study will contribute to understand and
evaluate the actual recycling system and to enhance the planning reliability of emerging pilot projects.
This questionnaire is anonymous, so there will be no connection of your data and name in any
publication. Please fill out the questionnaire as completly and exactly as possible.
Thank you for helping me. If you have any questions or if you are interessted in the results of this study
you can contact me: geering_china@hotmail.com
List of questions
Please fill out these tables and the table and questions on the other side of the sheet. If you have big
electric or electronic appliances (excepted lighting) that are not in the list, please complete the list.
Electrical &
electronic equipment
Quantity in
your
household
that you
own
Example 3 √ √ X
Refrigerator
Microwave
Rice cooker
Electric kettle
Vacuum cleaner
Sewing machine
Electric iron
Washing machine
Electric boiler
Air conditioner
Electric fan
Hairdryer
TV
DVD-/Video-player
Stereo
Radio
Portable MP3/MP4 player
Digital camera
Laptop
PC
Mobile phone
Telephone
Printer
Electro-scooter
Bought between
If you bought an appliance new, make a “√”
If you bought an appliance second-hand, make a “X”
You can mark more than one
2005-now 2000-2004 1995-1999 1990-1994 1985-1989 Before 1985

This table is about the electrical and electronic equipment that became obsolete
Condition of the
appliance
Equipment that
became obsolete
Quantity
In which year did the appliance become
obsolete?
For how many years have you had the
appliance ?
(make a “X” for 2. - hand appliances)
Broken/defective, unfixable
Broken/defective, maybe fixable
In working order
Given to family/ friends
Disassembled to reuse parts
Discarded with domestic waste
Stored at home
(For how many years?)
Disposal
Exchanged in store when buying
new
To informal waste collector
Sold
To second-hand store
Approximative price
Other disposal
Example 2 98/? 6 /5~10 √ √ √ √
Refrigerator
20
RMB
Microwave
Rice cooker
Electric kettle
Vacuum cleaner
Sewing machine
Electric iron
Washing machine
Electric boiler
Air conditioner
Electric fan
Hairdryer
TV
DVD-/Video-player
Stereo
Radio
Portable MP3/MP4
player
Digital camera
Laptop
PC
Mobile phone
Telephone
Printer
Electro-scooter

Are informal collectors coming to your house to ask for obsolete appliances? Yes No
Is the form of collection and waste disposal convenient? Yes No
If No: Why not? How could it be better?
Would you renounce to get money from waste collectors if you could be sure that the waste is taken care
of in a way that is useful and that does not cause pollute the environment? Yes No
Number of persons living in the household:
men women children/students
Total anual income: 15 (in 10’000 RMB)
Place of living:
Jiaojiang Luqiao Huangyan Linhai Wenlin Yuhuan Tiantai Xianju Sanmen
Other:
Did you move from another place? If yes and from outside Taizhou, when? Year

问 卷 调 查 2007 年 ___ 月 ____ 日
我 是 来 自 瑞 士 苏 黎 士 联 邦 理 工 大 学 在 校 硕 士 研 究 生 Anne Catherine, 因 撰 写 毕 业 论 文 的 需 求 , 需 要
进 行 下 列 的 问 卷 调 查 , 首 先 向 各 位 的 支 持 与 配 合 表 示 最 诚 挚 的 谢 意 。
本 问 卷 调 查 为 “ 瑞 士 材 料 检 测 与 研 究 联 邦 实 验 室 ” 及 “ 瑞 士 国 家 经 济 事 务 处 ” 下 设 “ 电 子 废 弃 物 回 收 信
息 合 作 ” 项 目 中 硕 士 论 文 的 组 成 部 分 。 论 文 旨 在 以 台 州 为 例 , 分 析 中 国 城 市 电 子 废 弃 物 的 流 通 渠 道 。 本
研 究 将 有 利 于 对 实 际 回 收 系 统 的 了 解 和 评 估 , 并 有 利 于 加 强 电 子 废 弃 物 试 点 项 目 策 划 的 可 信 度 。
我 们 将 匿 名 进 行 本 次 调 查 问 卷 , 请 您 尽 可 能 完 整 、 正 确 地 填 写 本 问 卷 。
如 果 您 有 任 何 疑 问 或 对 本 调 查 感 兴 趣 的 话 , 请 联 系 我 们 。
我 们 的 联 系 方 式 是 :geering_china@hotmail.com 谢 谢 !
1、 请 在 下 列 表 格 相 应 的 空 格 中 打 勾 , 如 果 您 有 其 它 大 型 电 子 电 气 设 备 也 可 添 加 在 空 格 处 ( 除 照 明 设 施
外 ), 以 便 于 我 们 统 计 。 请 仔 细 填 写 , 谢 谢 !
在 使 用 中 的 家 用
电 器 数 目
电 子 电 气 产 品
( 请 填 写 具 体 数
字 , 包 括 个 人 购
买 或 别 人 赠 送 )
2005
至 今
2000­
2004
例 子 3 √ √ X
冰 箱
微 波 炉
电 饭 煲
煮 水 器
真 空 吸 尘 器
缝 纫 机
电 熨 斗
洗 衣 机
电 热 水 器
空 调
电 风 扇
吹 风 机
电 视 机
DVD/VCD 机
音 箱
收 音 机
便 携 式 音 乐 播 放 器
(DVD/VCD 机 , MP3, MP4)
数 码 相 机
手 提 电 脑
台 式 电 脑
手 机
电 话 机
打 印 机
电 动 车 ( 电 瓶 车 )
购 买 日 期
如 购 买 的 是 新 产 品 , 请 打 “√”,
如 购 买 的 是 二 手 产 品 , 请 画 “X”
可 多 选
1995­
1999
1990­
1994
1985­
1989
早 于
1985
请 转 阅 反 面

2、 此 表 格 涉 及 废 弃 电 子 电 气 产 品 的 相 关 信 息 . 请 完 整 填 写 下 列 表 格 中 的 各 个 项 目
曾 哪
报 废
经 一
电 器 使 用 状 况
前 已
使 年
使 用 可 多 选 , 在 空 格
用 报
过 多 中 打 “√”
过 废
少 年 ?
废 弃 电 子 电 的 ( 如
送
气 产 品
家 不 ( 如 果
给 拆 与 其 搁 置
用
知 是 二 已 损 已 损
家 卸 他 家 在 家 以
道 手 产 坏 , 坏 , 在 使
电
人
有 用 电
( 放 旧
可 品 请 不 能 但 可 用 中 用 器 一 换
器
或 置 时
不 用 ”X” 修 理 修 理
零 起 丢 新
数
朋
填
件
目
表 示 )
弃
限 )
友
写 )
处 理 方 法
可 多 选 , 在 空 格 中 打 “√”
当
废
品
卖
转 卖
售
于
二
手
市
场
大 概
的 价
格
例 子 2 98/? 6 /5~10 √ √ √ √ 20 元
冰 箱
微 波 炉
电 饭 煲
煮 水 器
真 空 吸 尘 器
缝 纫 机
电 熨 斗
洗 衣 机
电 热 水 器
空 调
电 风 扇
吹 风 机
电 视 机
DVD/VCD 机
音 箱
收 音 机
便 携 式 音 乐 播 放 器
(DVD/VCD 机 , MP3/4)
数 码 相 机
手 提 电 脑
台 式 电 脑
手 机
电 话 机
打 印 机
电 动 车 ( 电 瓶 车 )
其 他 处
理 方 法
3、 是 否 有 个 体 户 上 门 购 买 废 弃 电 器 ? 是 否
电 器 丢 弃 和 回 收 的 方 式 是 否 便 利 ? 是 否
如 果 不 便 利 ? 如 何 改 善 ?
如 果 废 旧 电 器 能 被 正 当 地 处 理 回 收 并 不 会 对 环 境 造 成 污 染 , 您 是 否 可 以 免 费 将 废 旧 电 器 赠 给 回 收 者 ? 是 否
家 庭 成 员 共 人 , 其 中 成 年 男 性 人 , 成 年 女 性 人 , 未 成 年 小 孩 / 学 生
家 庭 年 总 收 入 约 为 : < 2 万 2­5 万 5­10 万 10­15 万 > 15 万
居 住 地 ( 可 在 下 列 选 项 中 打 勾 ):
椒 江 路 桥 黄 岩 临 海 温 岭 玉 环 天 台 仙 居 三 门
其 他 : 如 果 您 是 外 省 市 户 口 , 请 问 何 时 来 台 州 ? 年
人

Annexe B
Annexe B
42

Annexe B
B.1 Household income distribution
The income distribution assessed in the questionnaires is represented in Fig. 18. This is the
distribution of 55% of all questionnaires, as in 45% the income was not stated.
The mean incomes for the different social strata in the Zhejiang province can be seen in
Table 18.
Combining the two informations, it results that only 11% of the surveyed households are not
in the high or highest income strata. Therefore, it was assumed that the survey is
representative for high income households only.
30%
Percentage of households
25%
20%
15%
10%
5%
0%
< 20 20-50 50-100 100-150 > 150
Household income in thousands RMB
Fig. 18 Income distribution of surveyed households
Table 18 Mean income for social strata (Zhejiang Yearbook 2005)
Social stratum
Average
lowest
low
lower
middle
middle
upper
middle
high
highest
Percentage of households 100% 10% 10% 20% 20% 20% 10% 10%
Income of urban households
(in thousands RMB)
15.9 5.4 7.8 10.1 13.8 19.5 25.7 37.8
43

Annexe B
B.2 Disposal transfer coefficients
An over all disposal coefficient was assessed as percentage of all disposed appliances that
were indicated to be disposed by this disposal channel.
The disposal coefficient for the age group of a disposal channel was assessed as percentage
of all the disposed appliances belonging to the age group that were indicated to be disposed
by this disposal channel.
In the questionnaires, if an obsolete appliance had been disposed, the way of disposal as
well as the age at disposal had to be indicated. Often, households didn’t indicate the age. In
Table 19, the total quantity (including disposed appliances without age indication) and the
quantities per age group are listed.
For appliance types for which the total quantity of disposed appliances was inferior to 20
(coloured red in column “Total”), the disposal coefficients of the three age groups of another
appliance type, for which a similar disposal behaviour was assumed, were adopted. For
appliance types with a total quantity inferior to 40 (orange), the disposal coefficients of an
appliance type with similar over all disposal coefficients were adopted.
According to the same principle, the disposal coefficient for the third age group was assumed
if the quantity was inferior to 5.
The calculated coefficients are listed in Table 20 to Table 29. The coefficients for refrigerator
and mobile phone are illustrated in Table 9 resp. Table 10 in section 4.1.2.
Table 19
Appliance type
Quantities of disposed appliances and disposal assumptions
Total
Quantity of disposed appliances
First age
group
Second
age
group
Third age
group
Disposal like
Assumptions
Disposal of
third age
group like
Refrigerator 77 19 36 8
Microwave 12 4 1 1 Refrigerator
Rice cooker 137 28 27 14
Electric kettle 64 18 10 3 Hairdryer
Vacuum cleaner 12 4 1 1 Electric fan
Sewing machine 47 8 14 2 TV
Electric iron 56 10 12 6
Washing machine 66 9 16 13
Electric boiler 30 6 8 3 Washing machine
Air conditioner 26 6 4 1 Sewing machine
Electric fan 156 18 19 10
Hairdryer 70 13 10 6
TV 104 16 23 9
DVD/Video-player 38 6 9 0 TV
Stereo 22 5 0 1 Radio
Radio 52 12 13 1 Electric fan
Portable
MP3/MP4-player
18 5 2 1 Mobile phone
Digital camera 8 2 1 1 Mobile phone
Laptop 8 1 0 0 Mobile phone
PC 23 1 5 4 Mobile phone
Mobile phone 166 20 20 9
Telephone 124 9 22 5
Printer 12 2 0 0 TV
Electro-scooter 27 8 5 0 Sewing machine
44

Annexe B
Table 20
cooker
Transfer coefficients of disposal channels for the 3 age groups for the rice
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 25% 0% 0% 0% 50% 18% 68% 4% 0%
2. age group 30% 0% 7% 7% 22% 26% 48% 11% 0%
3. age group 21% 7% 7% 14% 50% 7% 57% 7% 0%
Table 21
Transfer coefficients for the electric kettle
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 28% 0% 0% 0% 39% 17% 56% 0% 0%
2. age group 40% 0% 0% 0% 10% 50% 60% 0% 0%
Table 22
Transfer coefficients for the sewing machine
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 38% 13% 25% 38% 25% 0% 25% 0% 0%
2. age group 50% 0% 21% 21% 21% 7% 29% 0% 0%
Table 23
Transfer coefficients for the electric iron
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 20% 0% 0% 0% 10% 50% 60% 10% 0%
2. age group 25% 0% 0% 0% 42% 25% 67% 8% 0%
3. age group 17% 17% 0% 17% 17% 50% 67% 0% 0%
45

Annexe B
Table 24
Transfer coefficients for the washing machine
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 33% 0% 22% 22% 0% 11% 11% 11% 0%
2. age group 25% 0% 19% 19% 38% 19% 56% 0% 0%
3. age group 31% 8% 8% 15% 38% 15% 54% 0% 0%
Table 25
Transfer coefficients for the electric fan
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 28% 0% 11% 11% 22% 33% 56% 0% 0%
2. age group 42% 0% 0% 0% 16% 26% 42% 5% 0%
3. age group 20% 0% 0% 0% 50% 30% 80% 0% 0%
Table 26
Transfer coefficients for the hairdryer
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 23% 0% 0% 0% 38% 38% 77% 0% 0%
2. age group 20% 0% 0% 0% 0% 80% 80% 0% 0%
3. age group 33% 0% 0% 0% 17% 50% 67% 0% 0%
Table 27
Transfer coefficients for the TV
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 31% 6% 6% 13% 38% 0% 38% 6% 0%
2. age group 35% 4% 4% 9% 43% 0% 43% 13% 0%
3. age group 44% 0% 0% 0% 11% 11% 22% 33% 0%
46

Annexe B
Table 28
Transfer coefficients for the radio
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 42% 0% 0% 0% 17% 33% 50% 0% 0%
2. age group 54% 0% 8% 8% 23% 8% 31% 0% 0%
Table 29
Transfer coefficients for the telephone
Disposal channels
Storage
Sold secondhand
Given
Second use
Sold informal
Discarded
Informal
collection
Formal
collection
Others
1. age group 56% 0% 0% 0% 11% 11% 22% 22% 0%
2. age group 41% 0% 14% 14% 18% 18% 36% 5% 0%
3. age group 40% 0% 0% 0% 20% 40% 60% 0% 0%
47

Annexe B
B.3 Household stock assessment
For the stock of appliances still in use in the households, the time period (before 1985, 1985-
1989, ... , 2000-2004, 2005-2007) of acquisition of each appliance had to be stated (see
questionnaire #2 and #3 in annexe A). As six time periods had been defined, the stocks in six
different years could be calculated.
To calculate the past stocks of an appliance type, only certain questionnaires were retained:
1) Questionnaires in which was stated, that no appliance of that type had been disposed of
2) Questionnaires in which the age at disposal and the year of disposal were stated for all
appliances of that type that were disposed of
The stock in 2007 for the appliance type was calculated by summing up all appliances still in
use in the household.
The stock in 2004 for the appliance type was calculated by summing up the appliances still in
use in the household that were bought before 2005, as well as the appliances that were
bought before 2005 and disposed of after 2004. The year of acquisition for appliances that
were disposed of was calculated by subtracting the age at disposal from the year at disposal.
The stocks in 1999, 1995, 1989 and 1984 for the appliance type were calculated basing on
the same method as the stock in 2004.
To obtain a stock in quantity of appliances per household, the calculated stocks were divided
through the number of questionnaires used for the calculation.
This procedure was applied to calculate the stocks in the six defined years for each of the 24
appliance types.
These six data points were then used for the calibration of the logistic growth curve
parameters. The resulting parameters in the base scenario are listed in Table 30.
48

Annexe B
Table 30
Parameters for the logistic growth curve in the base scenario
Parameters for logistic growth curve
Appliance type p init p sat α t turn
Mobile phone 0.01 3.3 0.44 2004
Electric fan 0.03 2.6 0.31 2002
Telephone 0.01 1.9 0.55 2000
Air conditioner 0.00 1.8 0.46 2003
Refrigerator -0.15 1.6 0.14 2000
Hairdryer -0.03 1.3 0.23 2002
Washing machine -0.04 1.3 0.20 2001
Electric iron -0.10 1.2 0.16 1999
DVD/Video-player -0.01 1.1 0.46 2000
Electric boiler 0.00 1.0 0.32 2002
Radio -0.11 1.0 0.14 2000
PC -0.01 0.9 0.41 2003
Stereo -0.01 0.9 0.37 2002
Microwave 0.00 0.7 0.61 2002
Sewing machine -0.33 0.7 0.22 1987
Vacuum cleaner 0.01 0.3 0.38 2002
Printer 0.00 0.3 0.54 2003
Estimated p sat
TV -0.03 3 0.19 2002
Rice cooker -0.12 2 0.13 2004
Electric kettle 0.01 2 0.22 2008
Portable MP3/MP4-
player
-0.01 2 0.40 2008
Laptop -0.01 1 0.31 2010
Digital camera 0.00 1 0.44 2008
Electro-scooter -0.01 1 0.33 2007
49

Annexe B
B.4 Residence time
The residence time frequency distribution of the mobile phone was calculated for three time
periods of disposal. These three distributions are illustrated in Fig. 19. It can be seen that in
the last two years, younger appliances than before have been disposed of.
k(t, t')
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0 1 2 3 4 5 6 7 8 9 10
2005-now 2000-2004 1995-1999
t-t'
Fig. 19 Residence time frequency distribution for three time periods for the mobile phone
50

Annexe B
B.5 Assumed errors
The errors of the parameters were calculated by multiplying the parameter with the assumed
error coefficient or a value for the error was directly assumed (see Table 31). The range of
error coefficient is defined by a coefficient of 0.1 for confident data and 0.4 for unsure data.
For example, the present and past stock data, assessed with the questionnaires, was
assumed to be quite reliable, so that an error coefficient of 0.1 was used. Future stock values
predicted by S-curve fit are assumed to be less reliable and future values for a fit with en
estimated saturation value even less.
For the mean residence time, an error value of 1 was chosen, as it was assumed that
households stated the ages with a marge of 1 year. For appliance types with few age values,
the error value was defined at 1.5 years. Transfer coefficients being equal to zero were
assumed to have an error of 5%.
Table 31
Assumed error coefficients and values
Error
coefficient
Error value
Stock
Past and present values 0.1 -
Future values 0.2 -
Future values for estimated saturation 0.3 -
Residence time distribution
Mean residence time - 1
Mean residence time
(less than 15 age values assessed)
- 1.5
Standard deviation 0.2 -
Disposal transfer coefficient
Calculated 0.2 -
Adopted from other appliance type 0.4 -
Transfer coeff = 0% - 0.05
B.6 Flows of appliance clusters
Table 32
E-waste arising for recycling in the best-case scenario
Appliances
under legal
measures
Scooter
Large
household
appliances
Small
household
appliances
IT and
telecommunication
Consumer
equipment
2007 54% 6% 25% 3% 4% 8%
2060 58% 10% 20% 2% 3% 7%
Table 33
E-waste arising for recycling in the worst-case scenario
Appliances
under legal
measures
Scooter
Large
household
appliances
Small
household
appliances
IT and
telecommunication
Consumer
equipment
2007 53% 7% 26% 3% 4% 8%
2060 58% 13% 18% 2% 4% 5%
51