1
Supporting Information for:
2
Rethinking China’s Strategic Mineral Policy on Indium:
3
Implication for the Flat Screens and Photovoltaic Industries
4
Huabo Duan1, Jiayuan Wang1, Lili Liu2, Qifei Huang3, and Jinhui Li2*
5
1 College
of Civil Engineering, Shenzhen University, Shenzhen 518060, China.
6
2
7
Tsinghua University, Beijing 100084, China. E-mail: jinhui@tsinghua.edu.cn; Tel: +8610-62794143
8
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment,
3 State
Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of
9
10
Environmental Sciences, Beijing 100012, China
1. Production, Trade, Consumption and EoL Production Generation
11
The quantification of indium’s overall lifecyc flows inlcudes four phases: (1)
12
production flows; (2) trade flows ; (3) consumption flows, and; (4) end of life flows. A
13
simpified flow chart and system boundary description can be found in figure S1. The
14
approaches for the first three steps are mainly data collection and processing.
Consumption
Indium use
Domestic Market
Supply
Semi-products or
Components
Flat panel
products
Primary Indium
Indium
production
Finished product
assembly
CIGS module
Secondary
Indium
Domestic
consumption
Other uses
Manufacturing
Loss
17
Collection
Export
dominated
EoL products (Generation):
Collected for processing
Reserve
Supply Chain
15
16
Reuse or
Storage
Processing for
recovery
Export
Import
Export/Import
Kept balance
Recovery loss
Export/import
Neglected
Foreign market share
Figure S1. Flows of Indium metal and system boundary consideration
18
The Sales Obsolescence Mode (SOM) approach, which is developed in our previous
19
work, is used to calculate generation quantities of end-of-life (EoL) electronics (Flat screen
1
1
products) [1]. A flowchart of the life cycle of electronics is shown in Figure S2 as a guide
2
for key definitions in this study. The term “generation” refers to products coming directly
3
out of use (retired) or post-use storage destined for collection or disposal. Thus, “generation”
4
is consistent with the term “ready for EOL management”. One generation pathway for items
5
is disposal (F), including landfills and incinerators. Another generation pathway already
6
mentioned is collection for processing in a working (H) or an obsolete (G) state.
7
assumption is made that after two terms of use, items are obsolete. The waste processor,
8
having collected the EoL electronics whole unit, opts either to prepare it for reuse by a new
9
user in China (C), recover parts and materials from the item (I) and transfers them to
10
downstream vendors (some of which may be in foreign countries), or export the EoL
11
product as a whole unit (J). The focus of this study is on EoL electronics that are whole
12
units. “Whole Units” refers to intact monitors, TVs, mobile phones, etc. that may or may
13
not have been refurbished. Thus, this excludes disassembled products that may be exported
14
as several different commodity material or product streams.
An
15
16
Figure S2. Life cycle flow chart of electronic products[1]
17
Unlike previous studies, this study includes uncertainty in input quantities and then
18
propagates that uncertainty into outputs using Monte Carlo simulations. Generation
19
quantities are modeled and then combined for the various products types. This is done
20
because these types have different consumption, use, and end of use disposition habits. The
21
basic approach for quantifying generation and collection includes the following steps:
22
(1) Determine the sales of a product in a China over a time period.
23
(2) Determine the typical distribution of lifespan for the product over a time period
24
25
using survey-based data (but from literature).
(3) Determine the annual markets shares for each type of electronic in terms of sizes
2
1
distribution, such as screen inches of monitor and TVs.
2
(4) Determine the content of Indium in flat panel screens.
3
(5) Calculate how many flat panel products (units) are predicted to be generated in a
4
given year using the sales and lifespan information; calculate the total screen size
5
(cm2) of generated waste by multiplying unit size by the quantities; and calculate
6
the weight of generated waste by multiplying metals content (mg/cm2) by the
7
quantities.
8
These generation calculation steps roughly comprise a SOM (alternatively known as
9
market supply method). Studies cover different products, time periods, geographical
10
regions, and vary in complexity
11
1.1 Production and trade data (domestic market supply) collection and
12
processing
13
1.1.1 Production and trade data of Flat screen products
14
Several sources offer annual production volume, export and import data as shown in
15
Table S1, including ‘Yearbook of China Information Industry’ (Ministry of Industry and
16
Information Technology) [2], ‘Yearbook of China Statistics’ (National Bureau of Statistics
17
of China) [3]. Domestic supply here refer to manufacturer shipments into the domestic
18
channel (Equation 1: Domestic Supply (Sales) (S)= Production Volume (P) – Export (E)+
19
Import (I) ), see Table S2. The domestic supply data in surrounding years (from 2013 to
20
2015) with projection were allowed to vary uniformly one standard deviation from the mean
21
(uniform distribution), by given an approximate 10% of Correlation of Variances (COV).
22
Equation 1: Supply data calculation
𝑆𝑦 = 𝑃𝑦 − 𝐸𝑦 + 𝐼𝑦
23
24
Prediction model: an association between the domestic supply of electronics and the
25
corresponding variables (only time series parameter is considered in this study) is to be
26
expected. Based on this assumption, the Pearson product moment correlation coefficient
27
was initially applied to find the coefficient of determination (𝑅 2) between the independent
28
variables. Subsequently, to test the hypothesis of independence between the selected
29
explanatory variables, the Student t test was applied on the 𝑅 2 coefficient of the variable.
30
Because the analysis included one variable, and the variable displayed a linear distribution,
31
a multiple linear regression analysis was applied to determine the probable shape of the
32
relation between variable and to estimate the sales quantity of electronics, which
33
corresponds to the values of the analyzed variable. From this, it may be ascertained that the
34
generation of sales may be explained by a multiple linear equation having the form of
35
Equation 2 (Prediction model).
3
𝑌 = 𝛽0 + 𝛽1 𝑋 + 𝛽2 𝑋 2 … + 𝜀
1
2
Where Y is the dependent variable; 𝛽0 is the intercept; 𝑋 is the independent variable;
3
𝛽1 , 𝛽2 are regression parameters; and ε is residuals. The data projection can be found in
4
Table S3.
5
Table S1 Production volume (In China)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
6
LCD
TVs
PDP
TVs
Laptop
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0
0
7
1
1
8
1
1
79
283
3
1,492
1,636
Year
LCD
monitor
5,146
21,731
77,270
130,760
107,020
134,790
112,510
127,840
106,570
119,040
111,578
LCD
TVs
165
202
808
4,530
9,950
17,579
29,425
67,653
89,375
104,010
114,183
PDP
TVs
18
112
194
770
680
1,089
3,509
1,911
2,141
3,121
2,139
LCD
TVs
101
202
808
2,354
5,024
9,948
18,161
50,072
61,174
71,684
68,358
PDP
TVs
11
95
96
535
42
533
2,249
1,297
1,649
2,634
1,944
Laptop
1,170
12,870
27,500
45,650
59,120
86,710
108,590
150,090
185,840
238,974
252,890
Table S2 Domestic Supply Data (In China)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
7
LCD monitor
LCD monitor
LCD
TVs
1
679
745
PDP
TVs
Laptop
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0
0
2
0
0
2
0
0
17
62
LCD
monitor
2,343
8,289
64,071
60,686
54,445
34,411
38,621
49,724
59,355
58,942
70,213
Laptop
255
1,649
3,035
12,636
11,661
14,624
10,395
40,169
48,981
70,754
45,714
Table S3 Domestic Supply Data (In China) (Projection, mean values)
Year
2013
2014
2015
2016
2017
2018
LCD monitor
53,141
54,913
56,394
57,531
58,328
58,845
LCD TVs
75,246
84,033
92,820
101,606
110,393
119,180
PDP TVs
1,428
1,235
1,035
888
795
743
4
Laptop
66,773
76,504
86,596
96,989
107,622
118,436
Mobile phone
300,501
333,400
375,495
420,098
467,210
516,829
2019
2020
59,158
59,335
127,967
136,753
716
703
129,372
140,369
568,957
623,593
100%
Zhejiang
Shannxi
80%
Qinghai
Guangdong
60%
Liaoning
Henan
Jiangsu
40%
Guangxi
Yunnan
20%
Hunan
0%
2010
1
2
2011
2012
2013e
2014e
2015e
Million units
Figure S3. Geographical distribution of indium production in China [4]
4.0
3.5
3.0
Export
Import
Outputs
2.5
2.0
1.5
1.0
0.5
0.0
3
4
5
2005 2006 2007 2008 2009 2010 2011 2012
Year
Figure S4. Export and import of LCD screens (as a component of display panel)
1.1.2 PV CIGS thin film modules
6
Based on GBI Research estimates and projection, from a mere 14 MW production in
7
2001, the market has grown to reach 2141 MW in 2009, at a Compound Average Growth
8
Rate (CAGR) of 58%. The market outlook for the coming decade appears promising as the
9
major thin film producing countries - Japan, China, and the US - are announcing aggressive
10
support for renewable energy expansion through incentives and regulations. In the
5
1
retrospect, the thin film module production is projected to grow at the rate of 24% from
2
2009 to reach 22,214 MW of production by 2020 (Table S4).
3
4
Table S4 Global and China CIGS thin film modules production and projection
Year
Global Thin Film
Market shares
Global CIGS
China
Production
of CIGS
Production
Production*
(GM)
(%)
(GM)
(GM)
2005
0.15
13.0%
0.02
0.01
2006
0.3
15.4%
0.05
0.02
2007
0.5
17.8%
0.09
0.04
2008
1.1
20.2%
0.22
0.11
2009
2.25
22.6%
0.51
0.25
2010
2.4
25.0%
0.60
0.30
2011
2.8
27.4%
0.77
0.38
2012
3.75
29.8%
1.12
0.56
2013
5.1
32.2%
1.64
0.82
2014
6.1
34.6%
2.11
1.06
2015
7.1
37.0%
2.63
1.31
2016
8.65
37.6%
3.25
1.63
2017
10.6
38.2%
4.05
2.02
2018
13.1
38.8%
5.08
2.54
2019
17.2
39.4%
6.78
3.39
2020
22.1
40.0%
8.84
4.42
5
*, It is estimated the CIGS thin film modules production volume in China accounts for 50% of the
6
global market (conservative estimates): in the years between 2007 and 2010, China PV
7
manufacturing outputs account for 27%, 33%, 38% and 48% of Global total outputs respectively,
8
and the market shares kept increasing [5].
9
1.2 Determine the distribution of lifespan for the product over a time period
10
This method for determining typical distributions of lifespans for the product is a
11
refinement of the model developed by Matthews et al. which accounts for two use stages
12
(initial and reused), and accounts for different fates after each stage [6].
13
The primary difference is the incorporation of a distribution of lifespan lengths and
14
path probabilities so that both data quality uncertainty and variation are considered. The
15
steps are as follows:
16
i.
Combine literature and industry estimates for the distribution of lengths of each
6
1
lifespan stage(s) (eg., B. Initial Use, E. Reuse Storage) in Figure S5 (repeated
2
above for convenience) to arrive at a mean estimate with uncertainty for each
3
lifespan stage.
4
5
ii.
Define pathways to generation (Figure S6) involving combinations of lifespan
stages related to Figure S2.
6
This method is somewhat of an underestimate, because we do not estimate the second
7
round of generation of products that underwent formal domestic reuse. A full model
8
inclusive of the second round of generation is presented in Figure S6; initial sensitivity
9
analyses suggest that the result is not very sensitive to the exclusion of the second round of
10
generation.
11
12
Figure S5. Probability tree diagram of informal paths leading to generation. Letters and colors
13
refer to lifespan stages in Figure S2. The probabilities of a path to a lifespan stage are
14
represented by P ( lifespan stage), or its complement P(lifespan stage’). Some probabilities are
15
conditional on previous pathways, P (lifespan stage| previous lifespan stage) [1]
16
7
1
2
3
Figure S6. Probability tree diagram of informal and formal paths leading to generation [1]
iii.
Combine the lengths of the lifespan stages to calculate the lengths of each
4
pathway to generation and estimate the probability of each pathway to generation.
5
In Table S5 below, the equations for determining the mean path length and mean path
6
probability are found for each of the six pathways to generation.
7
Table S5: Equations used to calculate mean path length and mean path probability
8
9
10
11
12
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)
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
2, the overall weighted mean of lifespan for all six paths 𝝕 is presented.
8
1
2
Equation 2: Overall weighted mean of lifespan for all six paths 𝝕
6
𝜇𝑂𝑣𝑒𝑟𝑎𝑙𝑙 = ∑ 𝑃(𝜛) ∗ 𝜇𝜛
3
𝜛=1
4
1.3 Market shares by product sizes
5
In terms of the statistics data released by ZDC [7], the market shares (historical data,
6
most from 2004 to 2013) divided by ‘sizes’ can be available. The market shares data are all
7
shown in Figure S7. The typical size of a panel in mobile phone is set as 1.3 to 2.9 inch [8],
8
with a uniform distribution assumption in this study.
LCD Monitors (By screen size)
60%
40%
20%
0%
Other
>55
55
52
47
46
42
40
37
32
<30
100%
80%
Market Sahres (%)
80%
Market Sahres (%)
LCD TVs (By screen size)
Other
27
24
23
22
21
20
19
17
15
<15
100%
60%
40%
20%
0%
PDP TVs (By screen size)
Laptop(By screen size)
100%
100%
Other
Other
80%
65
Market Sahres (%)
Market Sahres (%)
80%
60
60%
55
50
40%
46
42
20%
60%
20%
11
12
13.3
12.5
10 &11.6
0%
10
15.6
14
40%
32
9
16&17.3
0%
Figure S7. Market shares of monitors, laptops and TVs
1.4 Content of indium and its consumption (use) estimates
Tables S6 and S7 show the content of indium in flat screen products and PV CIGS
solar cell respectively.
9
1
2
3
Table S6 Indium content in flat screens
Sources
mg In/m2
Sources
mg In/m2
Martin
2009
Becker,
Simon-Hettich
et al. 2003
Becker,
Simon-Hettich
et al. 2003
Bogdanski,
2009
Bogdanski,
2009
546
Böni et al.,
2012
234
150
Takahashi et al.,
2009
4408
187
Wang,
2009
408
50
IUTA & FEM,
2011
696
150
Götze & Rotter,
2012
780
4
*, This table is an updated version of Böni and Widmer’ s work [9] (not only cited the previous work,
5
including their own), which inlcudes the work by Gotze, R.; Rotter (2012) [10] and Buchert et al.,
6
2013[11].
7
Table S7 Indium content in PV CIGS solar cell [12]
Sources
mg In/WP
Fthenakis
(2009)
Conservative
(Max)
Fthenakis 2009)
Most ikely
(Mean)
Fthenakis(2009)
Optimistic
(min)
11.0
8.1
6.3
8
A range of estimates of the metal use of for indium exist in the literature. However, as
9
we discuss in detail below, assumptions about efficiency, material thickness or utilisation
10
differ between studies. The key factors are as follows: Quantity of material per Wp,
11
expressed in g/Wp and a function of [13]:
12
– Density of active material of CIGS.
13
– Thickness of active layer, measured in microns (μm).
14
– % of material in layer, in this case measuring the share of Indium in CIGS and
15
16
17
calculated by formula weight.
– Efficiency, a measure of the amount of energy captured per square meter under
standard test conditions (STC), being an energy intensity of 1000W/m2.
18
– Utilisation, a measure of efficiency of material use in the manufacturing process.
19
These factors can be combined in the following mathematical relationship:
20
21
Equation 3: Metal demand of indium contained in CIGS thin film modules for
per Wp production.
22
𝑀𝐷−𝐶𝐼𝐺𝑆−𝑊𝑝 =
10
𝜌𝐹𝑡
𝑈𝐼𝑆𝐶 𝜂
1
Where 𝑀𝐷−𝐶𝐼𝐺𝑆−𝑊𝑝 is the indium material requirement in mg∙ 𝑊𝑝, ρ is the density of
2
the active layer material, 𝐹 is the % of material in layer, 𝑡 is the thickness of the layer in
3
microns (μm), 𝑈 is the utilisation factor, 𝐼𝑆𝐶 is solar insolation under standard conditions
4
(1000W per m2) and 𝜂 is the electrical conversion efficiency of the PV CIGS thin film
5
modules [12].
6
Differently, the quantity of indium generated in year y is based on the sales
7
(production volume in China) in year y and the indium content (an unchangable value with
8
a uniform distribution) in the product that sold in year y, see Equation 4. Here, 𝑦 is the
9
year, and 𝑐 is th content of indium in each products (𝑖). Accordingly,The total demand of
10
11
12
Indium is shown in Equation 5.
Equation 4: Indium use calculated in Flat Screens (FS)
𝑀𝐷−𝐹𝑆 = ∑ 𝑆𝑦𝐹𝑆 ∗ 𝑐𝑖
𝑖
13
14
15
Equation 5: Indium use in a given year (𝐲) calculated
𝑀𝐷−𝑇 (𝑦) = 𝑀𝐷−𝐹𝑆 (𝑦)+𝑀𝐷−𝐶𝐼𝐺𝑆 (𝑦)
1.5 Prediction of EoL products and scrap indium metals generation
16
The quantity of e-waste generated in year y is based on the sales in year s and the
17
probability µ(y − s)that a product sold in year s is generated in year y. The probability
18
distribution µ(y − s) is created using parameters from the lifespan estimates. Here, a
19
lognormal distribution was assumed. Equation 5 shows the how the quantity is calculated.
20
The materials composition and metals contents are further estimated when the mass
21
fractions and metals contents are multiplied, see Equaiton 6 and 7. Here, 𝑓 is screen size of
22
each electronic (cm2), 𝑎 is the type of electronics; 𝑐 is conent of indium (mg/cm2).
23
Equation 5: Quantity of flat panel e-waste generated in year y
𝑦
24
𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦𝑒−𝑤𝑎𝑠𝑡𝑒 ) = ∑ 𝑆𝑎𝑙𝑒𝑠(𝑠) ∗ µ(𝑦 − 𝑠)
𝑠
25
26
Equation 6 and 7: Quantity of flat panel screen area and scrap indium generated
in year y
𝑦
27
𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦𝑠𝑐𝑟𝑒𝑒𝑛 𝑎𝑟𝑒𝑎 ) = ∑ 𝑆𝑎𝑙𝑒𝑠(𝑠) ∗ µ(𝑦 − 𝑠) ∗ ∑ 𝑓𝑎
𝑠
𝑦
28
𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦𝑖𝑛𝑑𝑖𝑢𝑚 ) = ∑ 𝑆𝑎𝑙𝑒𝑠(𝑠) ∗ µ(𝑦 − 𝑠) ∗ ∑ 𝑓𝑎 ∗ 𝑐
𝑠
11
1
1.6 Data and intermediate results
2
1.6.1 Life span
3
Ideally, lifespan stage assumptions would be disaggregated by electric and electronic
4
type, owner type, and purchase year and distinguishing first use, reuse, and storage.
5
However, lifespans were modeled separately for the following types: TVs, monitors, laptop
6
and mobile phone. The relevant estimates for each electronic from Table S8 were included
7
in the development of lifespan stage length estimates.
8
Table S8 Modeled Lifespan Stage Lengths (Years)
Sources
D. Initial
C.
E. Reuse
Use
Storage
Reuse
Storage
TVs (LCD
Huang et al, 2006
µ
6.94
3.47
3.47
1.74
screen)
[14]
σ
2.31
1.16
1.16
0.58
Laptop
Zheng, 2009 [15]
µ
4.31
2.16
2.16
1.08
σ
1.67
0.84
0.84
0.42
Huang et al, 2006
µ
2.78
1.39
1.39
0.70
Yin, 2014 [16]
σ
1.45
0.73
0.73
0.36
Mobile phone
9
B. Initial
1.6.2 Probability of Paths Leading to Generation
10
With a goal of modeling generation in two decades (2005 to 2025), the analysis
11
included lifespan stage estimates from twenty one years prior in 1989, which allows for a
12
generous total lifespan of electics (such as TVs) purchased in 1989. Because most of the
13
data sources only reported the total life span and do not differentiate the electric and
14
electronic type, lifespan stage estimates for each type were only included the survey data
15
provided by Huang, et al., 2006 [14], Streicher et al., 2011 [17] and Song et al., 2012 [18].
16
The mean µ and standard deviation σ for each lifespan stage for each type are shown in
17
Table S9.
18
Table S9 Probability of Paths Leading to Generation
Reuse
Storage rate
Types
Mean
Monitor
Std
Laptop
Mean
(1)(2)
Std
Mobile
Mean
after
rate+
processing+
P(D’)
P(C)
P(C’)
P(E)
P(E’)
P(F)
P(F’)
P(H)
P(H’)
35%
65%
52%
48%
9%
91%
90%
10%
10%
90%
22%
8%
78%
9%
(1)(2)(4)
processing
P(D)
4%
(1)(3)
Reuse rate
rate
Source#
TVs &
Reuse Storage rate*
Collected
35%
46%
1%
54%
5%
65%
55%
6%
9%
94%
2%
45%
12
9%
90%
1%
10%
9%
91%
90%
10%
90%
1%
10%
10%
90%
Std
phone
5%
27%
1%
9%
1%
1
*, Reuse Storage rate is assumed half of Initial Storage rate. #; (1) Streicher et al., 2011[17]; (2)
2
Huang et al., 2006 [14]; (3) Song et al., 2012 [18]; (4) Li et al., 2012[19]. +, We assumed roughly
3
90% of collected after processing rate and 10% of Reuse rate after processing in this study based on
4
interview with recyclers and experts.
5
6
1.6.3 Production of FS products and generation of E-waste estimates and prediction
7
Figure S8 shows the production forecasting curves for production volume of Chinese
8
LCD TVs, monitors and laptops. It is predicted that the market demand volume of LCD
9
monitors, LCD TVs, and laptops will continuously increase in next few years while the
11
demand volume for LCD monitors, LCD TVs, PDP TVs, and laptops will reach 130, 222,
12
1.1 and 643 million pieces, respectively.
Million units
Plasma Display Panel (PDP) TVs will keep decreasing slightly. By 2020, China’s market
Production volume
10
700
600
LCD monitor
500
LCD TV
400
Laptop
300
PDP TVs
200
100
0
Year
13
14
Note: Several sources offer annual production volumes of FS (1992 to 2012) , including the
15
Yearbook of China Industry Information from the Ministry of Industry and Information
16
Technology, the Yearbook of China Statistics from the National Bureau of Statistics of
17
China. The data after 2012 are predicted (see the projection method in SI). The volume
18
prior to 2000 seems quite small, as listed in Table S3.
19
Figure S8.
20
are not shown here but listed as table in SI , but listed as table in SI as the production volume are
21
much greater than other products.
22
thin film modules are aslo provided in SI)
Production and Prediction of FS in China (𝑃𝑦𝐹𝑆 ) (1990 to 2020) (Note: Mobile phones
The numbers of production capacity and projection of PV CIGS
13
1
Figure S9 shows that approximately 468 thousands (stdev: 195 thousands) of used FS
and 360 thousand (stdev: 191 thousand) tons of monitors and TVs were domestically
3
manufactured in 2015, accounting for four-fifths of the total manufactured quantity across
4
indium-containing consumer electronics. Uncertainty parameters were modelled for each
5
estimate and error bars represent one standard deviation from the mean (67% confidence
6
interval). It is estimated that in 2020, 1,252 thousand (stdev: 353 thousand) tons of EoL FS
7
products will be manufactured domestically.
Quantity
Million tons
2
1.6
1.4
Mobile Phone
1.2
Laptop
1.0
PDP TVs
LCD TV
0.8
LCD monitor
0.6
0.4
0.2
0.0
2005
8
9
10
11
Figure S9.
2010
Year
2015
2020
Estimates of FS E-waste Generation -𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦𝑒−𝑤𝑎𝑠𝑡𝑒 ) - in China (2005
to 2025).
Error bars represent one time standard deviation with 67% confidence.
1.6.3 Indium consumption
12
14
Tons
Indium Use
(Theory demant)
500
450
400
350
300
250
200
150
100
50
-
LCD monitor
LCD TV
PDP TVs
Laptop
Mobile Phone
PV (CIGS)
Year
1
2
Figure S10. Indium use (consumption, theory demand) by various flat screens and PV (CIGS)
12,000
Quantity (Tons)
10,000
8,000
2020
2018
2016
2014
2012
2019
2017
2015
2013
2011
6,000
4,000
2,000
0
Indium use
Indium use
(0% loss,
(10% loss)
theoertical use)
Indium use
(30% loss)
Indium use
(50% loss)
Reserves
(China)
Reserves
(World)
Scenarios
3
4
Figure S11. Accumulated uses of indium use (between 2011 and 2020) for all purposes in
5
China, with comparisons to indium reserves estimates in China and worldwide (uncertainty is
6
not embedded here)
7
8
Appendix
(1) A comparison of annual sales numbers between Laptop and tablet PCs in China
15
300,000
Thousand units
250,000
Laptop
200,000
Tablet
150,000
100,000
50,000
1990
1995
2000
2005
1
2
2010
Year
Figure S12. Annual sales numbers of Laptop and tablet PCs in China
3
16
2015
1
(2) Terms and Abbreviations
Abbr.
Terms
FS
Flat Screens
PV
Photovoltaic
LCD
Liquid Crystal Displays
CIGS
Copper Indium Gallium (Di) Selenide
EoL
End of Life
ITO
Indium Tin Oxide
mft
Manufacturing
SOM
Sales Obsolescence Model
PDP
Plasma Display Panel
COV
Coefficient of Variation
stdev
Standard Deviation
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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