Supporting Information for:

Systematic characterization of generation and management

of e-waste in China

Huabo Duan*,a Jiukun Hub, Quanyin Tan c, Lili Liu,c Yanjie Wang, b and Jinhui Li,c

a College of Civil Engineering, Shenzhen University, 518060 Shenzhen, China. b Dongjiang Environmental Co., Ltd., 518057 Shenzhen, China.

b State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment,

Tsinghua University, 100084 Beijing, China. E-mail: Jinhui@tsinghua.edu.cn; Tel: +8610-62794143

1. Generation and collection

The Sales Obsolescence Model (SOM) approach developed by Miller (2012) and Duan

et al. (2013) is used to calculate generation quantities of e-waste. A flowchart of the life cycle

of electric and electronics is shown in Figure S1 as a guide for key definitions in this study.

The term “generation” refers to electronics coming directly out of use (retired) or post-use

storage destined for collection or disposal. Thus, “generation” is consistent with the term

“ready for end-of-life [EOL] management”. One generation pathway for items is disposal

(F), including landfills and incinerators. Another generation pathway already mentioned is

collection for processing in a working (H) or an obsolete (G) state. An assumption is made

that after two terms of use, items are obsolete. The used electronics processor, having

collected the used electronic whole unit, opts either to prepare it for reuse by a new user in

China (C), recover parts and materials from the item (I) and transfers them to downstream

vendors (some of which may be in foreign countries), or export the used electronic product as

a whole unit (J). The focus of this study is on used electronic products that are whole units.

“Whole Units” refers to intact monitors, computers, mobile phones, LHAs etc. that may or

may not have been refurbished. Thus, this excludes disassembled products that may be

Corresponding author: Tel: +86 755 86674644; fax: +86 755 2653 2850E-mail address: huabo@szu.edu.cn (H. Duan); jinhui@tsinghua.edu.cn(J. Li)

1

exported as several different commodity material or product streams.

Fig. S1 Life Cycle Flow Chart of Electronic Products (Miller, 2012 and Duan et al., 2013)

Unlike previous studies, this study includes uncertainty in input quantities and then

propagates that uncertainty into outputs using Monte Carlo simulations. Generation quantities

are modeled and then combined for the various electric and electronic types. This is done

because these types have different consumption, use, and end of use disposition habits. The

basic approach for quantifying generation and collection includes the following steps:

(1) Determine the sales of a product in a China over a time period.

(2) Determine the typical distribution of lifespan for the product over a time period

using survey-based data (but from literature).

(3) Determine the annual markets shares for each type of electronic in terms of ‘sizes’

distribution. the unit weights (divided by ‘sizes’), such as screen inches of monitor

and TVs, volume (capacity) of refrigerator and washing machine, and output power

of air conditioner.

(4) Determine median values of the material composition of products and the content of

selected common metals, precious metals, and less common metals in printed circuit

boards, CRT glass and Li-ion battery for 11 types of electrics and electronics.

(5) Calculate how many products are predicted to be generated in a given year using the

2

sales and lifespan information; calculate the weight of generated waste by

multiplying unit weights and size by the quantities; and calculate the weight of

generated waste by multiplying mass fractions of materials and metals by the

quantities.

These generation calculation steps roughly comprise a SOM (alternatively known as

market supply method). Studies cover different products, time periods, geographical regions,

and vary in complexity

1.1 Sales data

Several sources offer annual production volume, export and import data as shown in

Table S1, including ‘Yearbook of China Information Industry’ (Ministry of Industry and

Information Technology), ‘Yearbook of China Information Industry’ (National Bureau of

Statistics of China). Sales here refer to manufacturer shipments into the domestic channel

(Equation 1: Sales (S)= Production Volume (P) – Export (E)+ Import (I) ). The sales data

(Table S2) in surrounding years (from 2013 to 2015) with projection were allowed to vary

uniformly one standard deviation from the mean (uniform distribution), by given an

approximate 10% of Correlation of Variances (COV).

Equation 1: Sales data calculation

Sn=Pn−En+ I n

Prediction model: an association between the sales of electronics and the corresponding

variables (only time series parameter is considered in this study) is to be expected. Based on

this assumption, the Pearson product moment correlation coefficient was initially applied to

find the coefficient of determination (R2) between the independent variables. Subsequently, to

test the hypothesis of independence between the selected explanatory variables, the Student t

test was applied on the R2 coefficient of the variable. Because the analysis included one

variable, and the variable displayed a linear distribution, a multiple linear regression analysis

was applied to determine the probable shape of the relation between variable and to estimate

3

the sales quantity of electronics, which corresponds to the values of the analyzed variable.

From this, it may be ascertained that the generation of sales may be explained by a multiple

linear equation having the form of Equation 2. The results are shown in Table S3.

Equation 2: Prediction model

Y=β0+β1 X+β2 X 2…+ε

Where Y is the dependent variable; β0 is the intercept; X is the independent variable; β1

,β2 are regression parameters; and ε is residuals.

4

Table S1 Production volume (In China)

Year LCD monitorLCD TVs

PDP TVs

Laptop DesktopCRT

MonitorCRT Color

TVMobile Phone

RefrigeratorAir

ConditionerWashing Machine

1990 158 58 10,3391991 231 28 12,051 1,885 630 2,1471992 0 392 68 13,331 2,693 983 3,0671993 0 639 126 13,073 3,847 1,965 4,3811994 7 875 1,316 16,371 5,495 3,930 6,2591995 1 836 566 19,121 1,310 6,869 6,830 7,8231996 1 1,388 1,995 25,376 600 8,586 7,860 9,7791997 8 2,066 5,485 27,113 3,780 9,540 9,740 10,8661998 1 3,933 7,858 36,430 8,550 10,600 11,570 12,0731999 3 1 4,055 15,046 42,620 23,010 12,100 13,380 13,4212000 1,492 79 7,118 27,000 39,361 52,479 12,790 18,270 14,4302001 1,636 283 8,776 33,269 40,937 80,317 13,513 23,130 13,4162002 5,146 165 18 1,170 14,633 44,068 41,610 121,464 16,800 31,350 15,8712003 21,731 202 112 12,870 18,830 51,526 65,215 182,314 22,426 48,209 19,6452004 77,270 808 194 27,500 17,624 24,224 72,161 237,516 30,076 63,903 25,3342005 130,760 4,530 770 45,650 35,188 29,811 76,677 303,542 29,871 67,646 30,3552006 107,020 9,950 680 59,120 34,246 26,570 72,330 480,138 35,309 68,494 35,6052007 134,790 17,579 1,089 86,710 34,020 9,570 66,040 548,580 43,971 80,143 40,051

2008 112,510 29,425 3,509108,59

028,079 20,460 56,760 559,640 48,000 81,474 44,470

2009 127,840 67,653 1,911 150,09 31,939 2,300 29,160 681,930 59,305 80,783 49,736

5

Year LCD monitorLCD TVs

PDP TVs

Laptop DesktopCRT

MonitorCRT Color

TVMobile Phone

RefrigeratorAir

ConditionerWashing Machine

0

2010 106,570 89,375 2,141185,84

050,080 26,790 25,117 998,270 72,957 108,875 62,477

2011 119,040 104,010 3,121238,97

479,748 7,220 15,152 1,132,580 86,992 139,125 67,159

2012 111,578 114,183 2,139252,89

062,263 15,419 8,457 1,181,550 84,270 132,811 67,911

Table S2 Sales Data (In China)

YearLCD

monitorLCD TVs

PDP TVs

Laptop

DesktopCRT

MonitorCRT Color

TVMobile Phone

RefrigeratorAir

ConditionerWashing Machine

1990 126 40 6,7661991 184 19 7,886 1,188 413 1,4751992 0 313 47 8,724 1,697 644 2,1071993 0 510 87 8,555 2,424 1,288 3,0111994 2 698 911 10,713 3,462 2,576 4,3011995 0 667 392 12,513 579 4,328 4,477 5,3761996 0 1,108 1,381 16,606 265 5,410 5,152 6,7201997 2 1,649 3,797 17,743 1,672 6,011 6,384 7,4671998 0 3,139 5,440 23,839 3,781 6,679 7,584 8,2961999 1 0 3,236 10,417 27,890 10,175 7,624 8,770 9,223

6

YearLCD

monitorLCD TVs

PDP TVs

Laptop

DesktopCRT

MonitorCRT Color

TVMobile Phone

RefrigeratorAir

ConditionerWashing Machine

2000 679 17 5,680 18,693 25,758 23,207 8,059 11,976 9,9162001 745 62 7,004 23,033 26,789 35,517 8,515 15,162 9,2192002 2,343 101 11 255 11,678 30,510 27,229 53,713 10,586 20,550 10,9062003 8,289 202 95 1,649 15,070 20,848 45,232 109,037 14,130 31,600 13,4992004 64,071 808 96 3,035 12,721 24,224 49,986 104,193 18,951 41,888 17,4092005 60,686 2,354 535 12,636 28,979 10,160 51,219 87,993 18,822 44,341 20,8602006 54,445 5,024 42 11,661 26,116 14,512 42,697 123,636 22,248 44,897 24,4672007 34,411 9,948 533 14,624 25,299 3,574 42,233 82,001 24,687 48,157 26,6432008 38,621 18,161 2,249 10,395 20,269 16,714 36,455 44,135 31,915 49,552 29,7892009 49,724 50,072 1,297 40,169 25,342 1,191 15,120 123,607 43,992 57,033 36,0362010 59,355 61,174 1,649 48,981 39,893 26,089 10,816 259,028 42,387 71,878 44,5842011 58,942 71,684 2,634 70,754 70,400 7,011 4,251 266,738 54,365 95,498 46,0632012 70,213 68,358 1,944 45,714 53,540 15,394 2,237 176,667 51,115 89,151 45,091

Table S3 Sales Data (In China) (Projection, mean values)

YearLCD

monitorLCD TVs

PDP TVs

Laptop DesktopCRT

MonitorCRT Color

TVMobile Phone

RefrigeratorAir

ConditionerWashing Machine

2013 53,141 75,246 1,428 66,773 57,901 9,447 3,465 300,501 58,515 90,605 51,8322014 54,913 84,033 1,235 76,504 63,899 8,267 2,444 333,400 63,275 96,692 55,7252015 56,394 92,820 1,035 86,596 70,187 7,199 1,520 375,495 68,035 102,779 59,6172016 57,531 101,606 888 96,989 76,766 6,225 677 420,098 72,795 108,867 63,509

7

YearLCD

monitorLCD TVs

PDP TVs

Laptop DesktopCRT

MonitorCRT Color

TVMobile Phone

RefrigeratorAir

ConditionerWashing Machine

2017 58,328 110,393 795 107,622 83,636 5,328 338 467,210 77,555 114,954 67,4022018 58,845 119,180 743 118,436 90,797 4,498 169 516,829 82,315 121,042 71,2942019 59,158 127,967 716 129,372 98,249 3,726 85 568,957 87,075 127,129 75,1862020 59,335 136,753 703 140,369 105,991 3,003 42 623,593 91,835 133,216 79,079

8

1.2 Determine the distribution of lifespan for the product over a time period

This method for determining typical distributions of lifespans for the product is a

refinement of the model developed by Matthews et al. which accounts for two use stages

(initial and reused), and accounts for different fates after each stage (Matthews et al., 1997).

The primary difference is the incorporation of a distribution of lifespan lengths and path

probabilities so that both data quality uncertainty and variation are considered. The steps are

as follows:

i. Combine literature and industry estimates for the distribution of lengths of each

lifespan stage(s) (eg., B. Initial Use, E. Reuse Storage) in Figure S2 (repeated above

for convenience) to arrive at a mean estimate with uncertainty for each lifespan

stage.

ii. Define pathways to generation (Figure S3) involving combinations of lifespan

stages related to Figure S1.

This method is somewhat of an underestimate, because we do not estimate the second

round of generation of products that underwent formal domestic reuse. Initial sensitivity

analyses suggest that the result is not very sensitive to the exclusion of the second round of

generation.

9

Fig. S2 Probability tree diagram of informal paths leading to generation. Letters and colors refer

to lifespan stages in Figure 1. The probabilities of a path to a lifespan stage are represented by

P( lifespan stage), or its complement P(lifespan stage’). Some probabilities are conditional on

previous pathways, P(lifespan stage| previous lifespan stage) (Miller, 2012 and Duan et al., 2013)

10

Fig. S3 Probability Tree Diagram of Informal and Formal Paths Leading to Generation (Miller,

2012 and Duan et al., 2013)

iii. Combine the lengths of the lifespan stages to calculate the lengths of each pathway

to generation and estimate the probability of each pathway to generation.

In Table S4 below, the equations for determining the mean path length and mean path

probability are found for each of the six pathways to generation.

Table S4 Equations used to calculate mean path length and mean path probability

Six Paths (ϖ) Mean Path Length μϖ Mean Path Probability

P (ϖ )

Path B, D, C, E μB+μC+μD+μE 1*P(D)*P(C|D)*P(E)

Path B, D, C, E’ μB+μC+μD 1*P(D)*P(C|D)*P(E’)

Path B, D, C’ μB+μD 1*P(D)*P(C’|D)

Path B, D’, C, E μB+μC+μE 1*P(D’)*P(C|D’)*P(E)

11

Path B, D’, C, E’ μB+μC 1*P(D’)*P(C|D’)*P(E’)

Path B, D’, C’ μB 1*P(D’)*P(C’|D’)

iv. Determine the overall mean lifespan by aggregating the paths to generation

probabilistically. Estimate the variance of the lifespan distribution from literature.

The generation model only incorporates a single mean path length, and so in Equation ,

the overall weighted mean of lifespan for all six paths ϖ is presented.

Equation 3: Overall weighted mean of lifespan for all six paths ϖ

μOverall=∑ϖ=1

6

P ( ϖ )∗¿ μϖ ¿

1.3 Market Shares and Unit Weight

In terms of the statistics data released by ZDC (2014), the market shares (historical data,

most from 2004 to 2013) divided by ‘sizes’ can be available. There is only possibility to

collect the unit weight all electric and electronics in the year of 2014 based on product’s

specification introduction from ZDC and PCONLINE (2014), we therefore assumed the unit

weight data historically keep consistent but keeps various if divided by ‘sizes’ . Since the unit

weight of mobile phone keeps decreasing, we used the data from other literature (USEPA,

2011). In addition, the unit weight of desktop always keeps consistent (Mean: 10.694 kg,

STD: 2.382kg) because we assume there is not ‘size’ difference. The market shares data are

all shown in figures S4 and S5. The unit weight data are shown in table S5-S13. The unit

weight data were allowed to vary uniformly one to two times standard deviation from the

mean (uniform distribution).

Table S5 Unit Weight for LCD Monitor

12

LCD Monitor >=27 24 23.6 22 21.5 20 19 <=17

Mean 6.0 4.8 4.1 4.1 3.0 2.8 2.6 2.5

Std 1.3 1.0 0.7 0.9 0.5 0.5 0.4 1.0

Table S6 Unit Weight for LCD TVs

LCD TVs >=55 52 47 46 43 42 40 37 32 <30

Mean 23.4 24.5 18.6 17.3 14.8 14.7 11.5 11.9 7.8 4.4

Std 4.4 3.7 2.4 2.9 1.6 2.4 3.0 2.8 2.0 0.6

Table S7 Unit Weight for PDP TVs

PDP TVs <=42 46 50 55 60 >=65

Mean 21.3 29.9 　 41.3 47.6 76.7

Std 3.3 5.9 　 15.0 17.8 20.3

Table S8 Unit Weight for Laptop

10.1 & 11.6 12.5 13.3 14 15.6 >=17.3

Mean 1.3 1.4 1.5 2.0 2.3 3.8

Std 0.4 0.1 0.1 0.3 0.3 1.0

Table S9 Unit Weight for CRT Monitor

CRT Monitor <=14 15 17 19 21 22

Mean 12.7 14.0 15.9 21.1 25.3 29.6

Std 2.3 3.4 2.7 2.6 7.4 5.9

Table S10 Unit Weight for CRT Color TVs

CRT Color TVs <=12 14 17 21 25 29 >=32

Mean 7.2 10.8 16.0 21.4 30.0 40.0 65.0

Std 1.4 1.6 3.2 1.3 6.0 8.0 13.0

Table S11 Unit Weight for Refrigerator

Ref >=280 230-280 200-230 180-200 100-180 <=100

Mean 110.2 76.8 65.7 54.3 33.1 110.2

13

Std 24.2 10.8 7.3 6.2 11.5 24.2

Table S12 Unit Weight for Washing Machine

Tumbling-box* <5kg 5kg 6kg 7kg >7kg

Mean 45.9 60.1 63.8 72.7 77.4

Std 20.9 9.4 6.7 10.7 13.4

Impeller type <5kg 5kg 6kg 7kg >7kg

Mean 21.8 27.4 31.7 34.3 38.7

Std 9.7 2.6 2.5 3.5 10.9

*, There is statistic data on the market shares based on the types of washing machine.

Table S13 Unit Weight for Air Conditioner

AC 1 1.5 2 2.5 3

Mean 41.2 44.0 85.5 108.3

Std 3.3 3.3 9.6 　 18.1

14

20042006

20082010

20120%

20%

40%

60%

80%

100%LCD Monitors (By screen size)

Other272423222120191715<15

Mar

ket S

ahre

s (%

)

20072008

20092010

20112012

0%

20%

40%

60%

80%

100%LCD TVs (By screen size)

Other>555552474642403732<30

Mar

ket S

ahre

s (%

)

20102011

0%

20%

40%

60%

80%

100%PDP TVs (By screen size)

Other

65

60

55

50

46

42

32

Mar

ket S

ahre

s (%

)

20032005

20072009

20110%

20%

40%

60%

80%

100%Laptop(By screen size)

Other

16&17.3

15.6

14

13.3

12.5

10 &11.6

Mar

ket S

ahre

s (%

)

20002002

20042006

0%

20%

40%

60%

80%

100%CRT Monitor (By screen size)

22 21

19 17

15 14

Mar

ket S

ahre

s (%

)

19992000

20012002

0%

20%

40%

60%

80%

100%CRT Color TVs (By screen size)

Other

>=34

29

25

21

<21

Mar

ket S

ahre

s (%

)

Fig. S4 Market Shares of Monitors, Laptops and TVs

15

20072009

20112013

0%

20%

40%

60%

80%

100%Refrigerator(By Volume-Capacity)

>300

300

250

200

<180Mar

ket S

ahre

s (%

)

20072009

20112013

0%

20%

40%

60%

80%

100%Air Conditioner(By Output power)

Other

3

2

1.5

1Mar

ket S

ahre

s (%

)

20102011

20122013

0%

20%

40%

60%

80%

100%Washhing Machine (By Volume-Capacity)

>7kg

7kg

6kg

5kg

<5kg

Mar

ket S

ahre

s (%

)

Fig. S5 Market Shares of LHAs

1.4 Material Composition of Products and the Content of Selected Metals

Tables S14 shows the median values of the material composition of products and Table

15 and 16 the content of selected common metals, precious metals, and less common metals

in printed circuit boards, CRT glass, Li-ion battery and flat panel screen for 11 types of

electrics and electronics.

16

Table S14 Material Composition of 11 Types of End-of-life Electric and Electronics (Oguchi et al., 2011) *

Equipment typeLCD

Monitor

LCD

TVs

PDP

TVsLaptop

Deskto

p

CRT

Monitor

CRT Color

TVs

Mobile

Phone

Refrigerato

r

Air

Conditioner

Washing

Machine

Number of data 192 66 5 130 7 15 15 16 2 2 3

Ferrous material (%) 43.2 50.9 28.5 23.2 42.1 12.7 12.7 0.8 47.6 45.9 51.7

Aluminum material (%) 8.9 3.1 16.4 3.1 4.0 0.1 0.1 – 1.3 9.3 2.0

Copper cable and material

(%)1.5 1.3 1.6 1.0 1.0 3.9 3.9 0.3 3.4 17.8 3.1

Plastic (%) 29.3 28.3 7.7 28.9 20.4 17.9 17.9 37.6 43.7 17.7 35.3

Printed circuit board (%) 6.6 7.1 7.9 9.4 7.7 8.7 8.7 30.3 0.5 2.7 1.7

CRT glass

(%)

Panel glass 22.9 22.9 – – – –

Funnel glass 12.9 12.9 – – – –

Glass (%) 9.0 28 13.7

Battery (%) 15.7 – – 20.4 – – –

Unidentified material (%) 10.5 0.3 9.9 5.1 24.9 20.9 20.9 10.6 3.5 6.6 6.2

*, This table is the updated version of Oguchi’s work (not only cited the previous work, including their own), which includes the analysis from more other work: USEPA,

2008; Buzatu & Milea, 2008;Cryan, et al., 2010; Salhofer et al., 2011; Boni & Widmer, 2011;Petters et al., 2012; Fan et al., 2013.

Table S15 Metals Composition of Printed Circuit Board of 11 Types of End-of-life Electric and Electronics (mg/kg) (Oguchi et al, 2011)*

17

Equipment type Number of dataCommon metal Precious metal Less common metal

Al Cu Fe Pb Sn Zn Ag Au Pd Ba Bi Co Ga Sr Ta

LCD Monitor 1 63,000 180,000 49,000 17,000 29,000 20,000 600 200 – 3000 – – – 300 –

LCD TVs 1 63,000 180,000 49,000 17,000 29,000 20,000 600 200 – 3000 – – – 300 –

PDP TVs 2 38,000 210,000 20,000 7100 15,000 12,000 400 300 – 3900 100 – – 650 100

Laptop 2 18,000 190,000 37,000 9800 16,000 16,000 1100 630 200 5600 120 80 10 380 5800

Desktop 8 18,000 200,000 13,000 23,000 18,000 2700 570 240 150 1900 50 48 11 380 7

CRT Monitor 5 62,000 72,000 34,000 14,000 18,000 5300 120 5 20 2400 280 36 – 550 –

CRT Color TVs 5 62,000 72,000 34,000 14,000 18,000 5300 120 5 20 2400 280 36 – 550 –

Mobile Phone 19 15,000 330,000 18,000 13,000 35,000 5000 3800 1500 300 19,000 440 280 140 430 2600

Refrigerator 1 16,000 170,000 21,000 21,000 83,000 17,000 42 44 – 82 480 120 – 51 –

Air Conditioner 1 6900 75,000 20,000 5800 19,000 4900 58 15 – 320 – 29 – 26 –

Washing Machine 1 1000 70,000 95,000 2200 9100 2400 51 17 – 65 51 16 – 9 –

*, This table is the original version of Oguchi’s work ( not only cited the previous work, including their own).

Table S16 Metals Composition of CRT Glass, Li-ion Battery and flat panel screen (Oguchi et al., 2011)*

Glass types Common metal Less common metal

18

Al Fe Pb Zn Cu Co Ba Sr In

CRT panel glass (mg/kg) 14,000 1,100 140 3,400 78,667 74,333

CRT funnel glass (mg/kg) 19,000 805 216,250 1,416 5,333 5,600

Li-ion Battery (Laptop and Mobile phone) (mg/kg) 69,750 222,125 96,500 167,250

In content in flat panel screen (mg/cm2)# 356

*, This table (except Indium content value) is the simplified version of Oguchi’s work (  not only cited the previous work, including their own). #, an updated version of Böni

and Widmer’ s work (Boni & Widmer, 2011)( not only cited the previous work, including their own), which inlcudes the work by Gotze and Rotter,2012; Buchert et al., 2013.

19

1.5 Prediction of E-Waste and Scrap Metals Generation

The quantity of e-waste generated in year y is based on the sales in year s and the

probability λ ( y−s )that a product sold in year s is generated in year y. The probability

distribution λ ( y−s ) is created using parameters from the lifespan estimates. Here, a

lognormal distribution was assumed. Equation 4 shows the how the quantity is calculated.

The materials composition and metals contents are further estimated when the mass fractions

and metals contents (indicated in section 1.4) are multiplied, see Equaiton 5 and 6. Here, f is

fraction of each mateiral, a is the type of materials; C is conent of each mateiral, and b is the

type of metals

Equation 4: Quantity of e-waste generated in year y

Generated ( ye−waste )=∑s

y

Sales ( s )∗¿ λ ( y−s ) ¿

Equation 5 and 6: Quantity of materials and scrap metal generated in year y

Generated ( ymaterials )=∑s

y

Sales (s )∗¿ λ ( y−s )∗f a ¿

Generated ( ymetal )=∑s

y

Sales (s )∗¿ λ ( y−s )∗∑a

m

f ∗Cb ¿

1.6 Data and Intermediate Results

1.6.1 Life span

Ideally, lifespan stage assumptions would be disaggregated by electric and electronic

type, owner type, and purchase year and distinguishing first use, reuse, and storage.

However, lifespans were modeled separately for the following types: TVs and monitors;

laptop, desktop, mobile phone and LHAs. The relevant estimates for each electric and

electronic type from Table S17 were included in the development of lifespan stage length

estimates.

20

Table S17 Modeled Lifespan Stage Lengths (Years)

B. Initial

Use

D. Initial

Storage

C.

Reuse

E. Reuse

Storage

TVs Huang et al.,

2006

µ 6.94 3.47 3.47 1.74

σ 2.31 1.16 1.16 0.58

Laptop Zheng, 2009 µ 4.31 2.16 2.16 1.08

σ 1.67 0.84 0.84 0.42

Desktop Zheng, 2009 µ 5.63 2.82 2.82 1.41

σ 1.55 0.78 0.78 0.39

Mobile

phone

Huang et al,

2006

Yin et al.,

2014

µ 2.78 1.39 1.39 0.70

σ

1.45 0.73 0.73 0.36

Refrigerato

r

Huang et al.,

2006

µ 7.42 3.71 3.71 1.86

σ 4.06 2.03 2.03 1.02

Air

Conditioner

Huang et al.,

2006

µ 5.98 2.99 2.99 1.50

σ 3.21 1.61 1.61 0.80

Washing

Machine*

µ 5.98 2.99 2.99 1.50

σ 3.21 1.61 1.61 0.80

*, Assumed to the same to Air conditioner.

1.6.2 Probability of Paths Leading to Generation

With a goal of modeling generation in two decades (2005 to 2025), the analysis included

lifespan stage estimates from twenty one years prior in 1989, which allows for a generous

total lifespan of electics (such as TVs) purchased in 1989. Because most of the data sources

only reported the total life span and do not differentiate the electric and electronic type,

lifespan stage estimates for each type were only included the survey data provided by Huang,

et al., 2006 and Yin, et al., 2014. The mean µ and standard deviation σ for each lifespan stage

for each type are shown in Table S18.

Table S18 Probability of Paths Leading to Generation

21

Types Source# Storage rate Reuse

rate

Reuse Storage rate* Collected

processing

rate+

Reuse rate

after

processing+

P(D) P(D’) P(C) P(C’) P(E) P(E’

)

P(F) P(F’) P(H) P(H’)

TVs &

Monitor

Mean (1)(2) 35% 65% 52% 48% 9% 91% 90% 10% 10% 90%

Std 4% 　 8% 　 1% 　 9% 　 1% 　Laptop Mean (1)(3) 22% 78% 46% 54% 6% 94% 90% 10% 10% 90%

Std 9% 　 5% 　 2% 　 9% 　 1% 　Desktop Mean (1) 47% 53% 46% 54% 12% 88% 90% 10% 10% 90%

Std 7% 　 7% 　 2% 　 9% 　 1% 　Mobile

phone

Mean (1)(2)(4) 35% 65% 55% 45% 9% 91% 90% 10% 10% 90%

Std& 5% 　 27% 　 1% 　 9% 　 1% 　Refri-

gerator

Mean (1)(2) 22% 78% 55% 45% 5% 95% 90% 10% 10% 90%

Std 6% 　 3% 　 0% 　 9% 　 1% 　Air Con-

ditioner

Mean (1)(2) 15% 85% 50% 50% 4% 96% 90% 10% 10% 90%

Std 5% 　 8% 　 1% 　 9% 　 1% 　Washing

Machine

Mean (1) 27% 73% 56% 44% 7% 93% 90% 10% 10% 90%

Std& 4% 　 8% 　 1% 　 9% 　 1% 　*, Reuse Storage rate is assumed half of Initial Storage rate. #; (1) Streicher et al., 2011; (2) Huang et

al., 2006; (3) Song et al., 2012; (4) Li et al., 2012. &, The paths data for desktop with projection were

allowed to vary uniformly one standard deviation from the mean (uniform distribution), by given an

approximate 15% of Correlation of Variances (COV). +, We assumed roughly 90% of collected after

processing rate and 10% of Reuse rate after processing in this study based on interview with recyclers

and experts.

1.6.3 Results

(1) Metals used contented in PCBs, CRT glass, flat panel screen and Li-ion battery

(manufacturing process)

22

- 50,000

100,000 150,000 200,000 250,000 300,000 350,000 400,000

(A) Common Metals

Zn Sn Pb

Fe Cu Al

1,0

00 T

ons

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

-

500

1,000

1,500

2,000

2,500 (B) Precious Metals and Less Common Metals (1)

Ta Ga Pd InAu Ag

Tons

-

20,000

40,000

60,000

80,000

100,000

120,000 (C) Less Common Metals (2)

Co Sr Ba

Year

Tons

Fig. S6 Metals used contented in PCBs, CRT glass, flat panel screen and Li-ion battery (manufacturing

process)

(2) E-waste generation and uncertain capture.

23

2005 2010 2015 2020 20250

5,000,000

10,000,000

15,000,000

20,000,000 (A) Generation of e-waste (by types)

LHAs Mobile Phone Computers

TVs Monitors

Qua

ntity

2005 2010 2015 2020 20250

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000 (B) Generation of e-waste (by materials composition)

Unidentified Li-Battary Non-Lead Glass

Lead Glass PCBs Plastic

Copper cable Aluminum Ferrous

Year

Qua

ntity

Fig. S7 E-waste generation and uncertain capture

24

2005 2010 2015 2020 20250

20,000 40,000 60,000 80,000

100,000 120,000 140,000

(A) Common and less common metalsCo Sr BaZn Sn PbCu

Qua

ntity

2005 2010 2015 2020 20250

200

400

600

800 (B) Less common metals and precious metalsIn TaGa BiPd AuAg

Year

Qua

ntity

(ton

s)

Fig. S8 Scrap Metals generation and uncertain capture.

25

Appendix

Abbreviation Full title

MIIT Ministry of Industry and Information Technology

NBSC National Bureau of Statistics of China

NGOs Non-Governmental Organizations

BAN Basel Action Network

UNEP United Nations Environment Programme

ITU International Telecommunication Union

EPA Environmental Protection Agency

EPB Environmental Protection Bureaus

ZDC Zhongguancun Data Center

WHO-TEQ World Health Organization toxic equivalent (WHO-TEQ)

SOM Sales Obsolescence Model

WEEE Waste Electrical and Electronic Equipment Directive

EoL End-Of-Life

Ee-waste Electronic Waste

BFR Brominated Flame Retardants

FPDs Flat Panel Displays

NMMs Nonmetallic Materials

LCDs Liquid Crystal Displays

PCBs Printed Circuit Boards

CRT Cathode Ray Tube

LHAs Large Home Appliances

TBBPA Tetrabromobisphenol A

PBDEs Polybrominated Diphenyl Ethers

PCDDs, PCDFs Polychlorinated dibenzo-p-dioxins (PCDDs), Polychlorinated dibenzofurans (PCDFs)

STD Standard Deviation

COV Correlation of Variances

26

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