Assessing the recycling potential of “unregulated” e-waste in Australia

Resources, Conservation & Recycling 152 (2020) 104526

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Full length article

Assessing the recycling potential of “unregulated” e-waste in Australia Md Tasbirul Islam, Nazmul Huda

⁎

T

School of Engineering, Macquarie University, NSW 2109, Australia

ARTICLE INFO

ABSTRACT

Keywords: Waste electrical and electronic equipment (WEEE) Dynamic material flow analysis Recycling Generation Household appliances Australia

Currently, Australian e-waste management system under the National Television and Computer Recycling Scheme (NTCRS) consider television, computer, and IT peripheral products and MobileMuster only considers mobile phones. A large proportion of E-waste from other categories is still unregulated. This study aims to estimate e-waste generation from this “unregulated” e-waste stream by a Weibull distribution-based sales-stocklifespan model from 2010 to 2030. A total of sixteen unregulated products (eleven electrical appliances and five electronic equipment) were selected for the estimation. The results of this study show that Australia will generate 342 kilo tonnes (kt) of the e-waste in 2020, which is predicted to grow to 461 kt in 2030 with an annual increase rate of around 3.7% from the 16 unregulated electrical and electronic equipment (EEE). Home laundry appliances, air treatment products, refrigeration appliances, large cooking appliances, and heating appliances are the critical items that account for more than 70% of the total e-waste generation. Base metals such as Fe, Cu, and Al will have a sharp increase by the year 2030, accounting 234.27 kt, 31.19 kt, 13.93 kt, respectively from the estimated e-waste quantities. Electronics products (e.g., home audio and visual devices, portable players, video games hardware, and others) will be a significant source of precious and rare-earth elements. By 2030, the estimated economic value of the metals will vary in between 2.74–4.60 billion US$. This study provides suggestion to policymakers in decision making for the future collection and recycling of e-waste in Australia.

1. Introduction 1.1. E-waste Rapid technological developments, urbanization and increased purchasing power among consumers fueled the consumption trends of various electrical and electronic equipment (EEE) all over the world (Islam et al., 2016; Li et al., 2019). Forti et al. (2018) mentioned that in the year 2016, global consumption of new EEE reached 60 million tons (Mt). There are nearly 900 different types of EEE items available in the market including information and communication technology (ICT) products such as computer, laptops, mobile phones and household appliances such as washing machine, refrigerators, microwave ovens, air conditioner, vacuum cleaners, etc. After the useful life of the products, it becomes a special waste stream, which is generally known as electronic waste (e-waste) or Waste Electrical and Electronic Equipment (WEEE). According to Solving the E-waste Problem (StEP), “E-waste refers to an item that has no further use and is rejected as useless or excess to the owner in its current condition”. E-waste is one of the fastest-growing waste streams in the world (Awasthi et al., 2018). In 2016, approximately 44.7 Mt of e-waste was generated, which is

⁎

expected to increase to 52.2 Mt by 2021 (Balde et al., 2017). For some developed countries, for instance, in Australia, the amount of e-waste is growing three times faster than municipal solid waste (Islam and Huda, 2019a). Managing e-waste is significantly challenging as it contains diverse range of base metals (e.g. Fe, Al, Cu), precious metals (e.g. Au, Ag, Pd), and critical and rare-earth elements (e.g. Nd, Ta, Dy etc) as well as toxic elements (e.g. Pb, Cr, As) which are detrimental for human health and environment (Cucchiella et al., 2015). However, to understand the amount of e-waste generation and amount of recoverable material present in the e-waste, a model needs to be employed that can provide an estimation of a potential generation of the e-waste (Islam and Huda, 2019a). Also, it is needed to be noted that such estimation should not only quantify past and present amount of e-waste generation but also the potential amount of generation in the future which is particularly demanded by the policymakers (Habuer et al., 2014). However, D’Adamo et al. (2019) mentioned that estimating overall ewaste generation in a specific country is not a convenient task, especially when collection systems are absent. This is particularly true for Australia despite being as one of the Organization for Economic Cooperation and Development (OECD) countries. In the OCED countries, market for EEE reached saturation level and generation of e-waste is

Corresponding author. E-mail address: [email protected] (N. Huda).

https://doi.org/10.1016/j.resconrec.2019.104526 Received 12 June 2019; Received in revised form 7 August 2019; Accepted 29 September 2019 0921-3449/ © 2019 Elsevier B.V. All rights reserved.

Resources, Conservation & Recycling 152 (2020) 104526

M.T. Islam and N. Huda

increasing at an alarmingly rate (Ongondo et al., 2011).

lognormal and others. Research performed by Wang et al. (2013) claimed that considering the fixed average lifespan of a product often underestimates the estimation of e-waste generation for a particular year and using the methods mentioned above (except the dynamic MFA) often overlook the dynamic nature of the product obsolesce that must be taken into account for accurate estimation. In this case, a method previously proposed and applied by Robinson (2009) and Araújo et al. (2012) (where fixed average lifespan was used) are found as not state-of-the-art in the topic of e-waste generation and any future research should avoid using this method to prevent possible confusion and underestimation in the overall estimation (Islam and Huda, 2019a). Wang et al. (2013) proposed a sales-stock-lifespan model where Weibull distribution-based lifespan estimation for a product was used. The model is specifically referred to as sales-stock-lifespan based model that quantitatively describe the dynamics, magnitude and interrelation of the product sales, stocks and lifespans (Ai et al., 2019; Brunner and Rechberger, 2004; Johnson et al., 2018). Overall, this approach was utilized by several researchers such as Parajuly et al. (2017), Golev et al. (2016), Ford et al. (2016), Song et al. (2017), and others for e-waste generation estimation. Stock-lifespan based e-waste generation model is another approach, which has been utilized by several researchers as well, for example, Tran et al. (2018), Gusukuma and Kahhat (2018), Nakatani and Moriguchi (2014), Zeng et al. (2016) and others. Weibull distribution based lifespan model (both sales and stock-based model) applied to e-waste generation estimation has become popular approach in dealing with the issue and at present researchers are using this method extensively. A detailed list of studies performed with Weibull distribution based Stock-lifespan or sales-stock-lifespan models is presented in Table 1.

1.2. E-waste in Australia Australia is a net importer of EEE, and per capita, EEE was estimated to be 35 kg as of 2014 (Golev et al., 2016). At present, two collection and recycling schemes exist in Australia. The first one is the National Television and Computer Recycling Scheme (NTCRS) responsible for handling waste television sets, computer and IT peripherals items (initiated in 2012 on a co-regulatory basis) and the second one is the MobileMuster that voluntarily recycle waste mobile phones from the year 2018. Waste mobile phone collection and recycling system in Australia is very similar to the UK (Ongondo and Williams, 2011). These schemes recycle products that constitute less than 10% of the total e-waste generation in the country (ABC, 2018). Rest of the products are out of scope in the current e-waste management system in the country. This means there is no specific regulation as well as no national accounting (by following a particular estimation methodology) on the amount of e-waste generation from the products. In general, these products should be called as “unregulated”. However, it is often difficult to identify all the “unregulated” products and their e-waste generation as, at the national level, no database has been developed so far, and product scope of the current e-waste management is limited (Islam et al., 2018; Morris and Metternicht, 2016). Under such circumstances, major products can be identified (by consulting data from market research organization); which were sold on the market may provide some information on the “unregulated” products. In this study, air treatment products, refrigeration appliances, dishwashers, food preparation appliances, small cooking appliances, large cooking appliances, home laundry appliances, microwave ovens, heating appliances, personal care appliances, vacuum cleaners, video players, home audio and cinema equipment, imaging devices, portable players and video games hardware are considered as “unregulated” products.

1.4. Rationale and justification of the study Academic literature on e-waste generation estimation is also very scarce from Australia context. So far, there are only two studies that are published, which are Islam and Huda (2019a) and (Golev et al., 2016). However, none of the studies showed the amount of e-waste generation from a specific product, for example, microwave ovens. Furthermore, Islam and Huda (2019b) identified that dynamic MFA-based salesstock-lifespan e-waste generation estimation models in the previous studies focused more on screen and monitor (e.g., television, laptops, etc.) and ICT products such as mobile phones, desktop computers. Thus, large equipment and small WEEE product’s e-waste generation needs further attention, which was not previously extensively investigated. Product-level e-waste generation is particularly important as this gives an indication to the policymakers expanding the scope of products in the future e-waste management system. To the best of author’s knowledge, this study is the first attempt in estimating e-waste and amount of recoverable materials from “unregulated” products using a specific methodology. Data sources and methodological details will be discussed in Section 2. This purpose of this study is to estimate e-waste generation from “unregulated” product using Weibull distribution-based sales-stocklifespan model and quantify metals and materials available for recycling from the waste stream in Australia. Also, the economic potential was evaluated based on variable material price over the 20 years (2010–2030) for the selected products. To assess the robustness and accuracy, sensitivity, and uncertainty analysis were performed considering the variable weight of the products and its material content present in the e-waste products using the Monte Carlo simulation.

1.3. E-waste generation estimation models When it comes to e-waste generation estimation, researchers have used various methods. According to Zeng et al. (2016), there are approximately ten methods/models utilized by the academics on the topic among which market supply method, market supply method A, Stanford method, the Carnegie Mellon method, consumption and use (C&U) and time-step are notable. These methods use fixed average lifespan of a product. Another well-employed method is known as material flow analysis (MFA). According to Van Eygen et al. (2016), MFA is explained as a particular area of studies that deal with the flux of materials through an experimental system defined in space and time, and by making quantifications of inputs and outputs to and from the system. The application of MFA in e-waste management system, more specifically, in generation estimation area can be referred to as a process that starts with the sales by which products enters into a society, then accumulate in the built environment as stock and finally after a certain period (lifespan), products reach its end-of-life (EoL) (Brunner and Rechberger, 2016; De Meester et al., 2019; Parajuly et al., 2017). By considering sales, stocks, and lifespan, MFA model can quantitatively describe the dynamics, magnitude, and interconnection of several components (inputs and outputs) of a system (Agamuthu et al., 2015; Gusukuma and Kahhat, 2018; Tran et al., 2018). Islam and Huda (2019b) found that both static and dynamic MFA is used for e-waste generation estimation. Dynamic MFA refers to the issue that provides an estimate for the year on year evaluation, whereas static MFA is the snapshot of the estimation for a single year (Islam and Huda, 2018a). In all the methods, lifespan is one of the significant parameters (Tran et al., 2018). Two types of lifespan are used, the fixed average lifespan and lifespan that reflects the gradual obsolescence of a product over a number of years. The dynamic product lifespan is generally expressed by various statistical distribution such as Weibull distribution, normal,

2. Material and methods 2.1. Data sources This study used all available sources to estimate the e-waste 2

3

Large household appliances, Small household appliances, IT and telecom equipment, Consumer equipment, Lighting equipment, Electrical and electronic tools, Toys, leisure and sports equipment, Medical devices, Monitoring and control instruments, Automatic dispensers Large equipment, small equipment, small IT, lamps, screens and monitors, cooling and freezing Large household appliances, small household appliances, consumer equipment, profession equipment, lighting equipment, mobile phones, NTCRS products

Television sets, refrigerator, washing machine, air conditioner, personal computer, range hood, electric water-heater, gas water-heater, fax machine, mobile phone, single-machine telephone, printer, copier, and monitor Mobile phone

SFA: substance flow analysis; PFA: Product flow analysis.

Islam and Huda (2019a)

Golev et al. (2016)

Polák and Drápalová (2012) Wang et al. (2013)

Zeng et al. (2016)

Wang et al. (2018)

Zhang et al. (2012)

Steubing et al. (2010) Thiébaud et al. (2017)

Kalmykova et al. (2015) Parajuly et al. (2017)

Refrigerator, Washing machine, Air conditioner, Desktop computer, Television sets Television sets and monitors Consumer electronics, Information technology equipment, small household appliances, Large household appliances Desktop computer, laptops, CRT and LCD monitors Mobile phone, desktop and laptop computer, monitor, cathode ray-tube and flat-panel display television, DVD player, and headphone Refrigerators, air conditioner, washing machines, television sets, personal computers Television sets

Sale-stock-based model

Sale-stock-based model

Sale-stock-based model

Sale-stock-based model

Sales-based model

Stock-based model

Stock-based model

Sales-stock-based model Stock-based model

Stock-based model Sales-based model

Stock-based model

Stock-based model

Mobile phones

Habuer et al. (2014)

Stock-based model Stock-based model

Television sets Cathode ray tube (CRT) television sets

Tran et al. (2018) Gusukuma and Kahhat (2018) Guo and Yan (2017)

Characteristics of the model

E-waste product

Reference

Table 1 Summary of Weibull distribution-based e-waste generation estimation studies.

Dynamic MFA

Dynamic MFA

Dynamic MFA

Dynamic MFA

Bottom-up, and stock-driven dynamic MFA Dynamic MFA (sales-lifespan based model using Weibull distribution)

Dynamic MFA

Dynamic MFA Dynamic MFA

Dynamic MFA Dynamic MFA

Dynamic PFA and SFA

Dynamic MFA and SFA

Dynamic SFA and MFA Dynamic MFA

Name of the model

Australia

Australia

The Netherlands

Czech Republic

China

China

China

Chile Switzerland

Sweden Denmark

China

China

Vietnam Peru

Country

UN Comtrade database for product sales

UN Comtrade database for product sales

Direct waste sampling, online foreign trade database for sales European Prodcom (Production Statistics Database for the domestic statistics on the production of manufactured goods)

National trade statistics, literature review

Literature review, national trade statistics

Survey and interview, literature review

Literature review, survey and interview Sales statistics from market research organization, National trade statistics National trade statistics and customer survey Sales statistics from market research organization

Survey and interviews, National trade statistics, literature review National trade statistics, literature review

Surveys, experiments, and literature review National import and export trade statistics

Data source

M.T. Islam and N. Huda

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generation (from the “unregulated” products) in Australia. The first data required to conduct a dynamic MFA based on a sales-stock-lifespan model for e-waste generation estimation is sales data. The data were collected from the Euromonitor database (known as the passport database) which was not utilized previously by Golev et al. (2016) and Islam and Huda (2019a). The market research organization collects product sales related data from various sources such as the Australian Bureau of Statistics (ABS), UN Comtrade, Departments of Foreign Affairs & Trade, Department of Fair Trading and others. Sales data were collected for the selected “unregulated” products from the year 2004 to 2018 (Supplementary information (SI) Table S1). In this study, sixteen (16) EEE were considered as mentioned in Section 1. Details are given in the SI in Table S5. Besides, using sales statistics, field visit was conducted to understand how and where unregulated products are disposed of in the Australian context. From Section 1.3, it is seen that Weibull distribution based models are the state-of-the-art that considers the dynamic product obsolesce over the years for a product, which is a widely employed the method (seen in Table 1). Within the scope and data availability, this research focused on the sales-stock-lifespan based model as there is no survey data currently available that suits the product under consideration conducting the investigation. Global E-waste Monitor Report 2017 reported by United Nations University (UNU) used the same methodology proposed by Wang et al. (2013). The detailed guideline and processes of calculation are available by Balde et al. (2017). Balde et al. (2017) mentioned that baseline sales data needs to be extended into past years as well as the sales forecasting for e-waste generation estimation. The method is now widely applied at EU-level and for other countries (even in Chinese e-waste generation estimation). We considered baseline years from 2004 to 2018 as per data availability, and then data were extrapolated until 1989 using the linear regression method. This was also done for the future years until the year 2030. The selection of the linear regression is widespread, particularly in e-waste generation estimation-based studies (Ikhlayel, 2016; Kahhat and Williams, 2012; Zeng et al., 2016). Also, it is found that the sales data used from the source (passport database) also follows the same pattern of the selected product sales. Detailed representation of the sales data that fits with the model is shown in the SI (Fig. S2) for the products considered in this study (R-value for all products showed close to 0.9–0.8). The linear fitting was adopted in this study to determine the sales of the products from 2010 to 2030.

Table 3 Estimated market price (in US$) of various common metals, precious metals, and rare-earth elements.

Stand. dev.

Distribution

Air treatment products Refrigeration appliances Dishwashers Food preparation appliances Small cooking appliances Large cooking appliances Home laundry appliances Microwave ovens Heating appliances Personal care appliances Vacuum cleaners Video players Home audio and cinema Imaging devices Portable players Video games hardware

34.747 38.495 44.346 3.394 1.971 47.157 68.147 17.024 12.326 0.559 4.915 3.430 3.886 0.299 0.227 0.493

8.614 10.057 8.068 0.622 0.319 16.298 10.160 4.658 5.332 0.137 0.938 0.544 0.513 0.099 0.057 0.040

Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal

Error

Ag (in gram) Al (in tons) Au (in gram) Cu (in tons) Pb (in tons) Pd (in gram) Sn (in tons) Ta (in kg) Zn (in tons) Plastic (in tons)* Fe (in tons)* Nd (in tons)* Dy (in kg)#

0.99 2348.5 48 8134.5 2164 32.5 23 178 2539.5 890 120 176 499

± 0.49 ± 395.5 ± 10 ± 1718.5 ± 531 ± 16.5 ±9 ± 96 ± 992.5 ± 140 ± 68 ± 166 ± 82

distribution of the data in Oracle’s Crystal Ball simulation package, it is found that the product weight distributions follow a normal distribution. Process of line fitting in the software package is shown in Fig. S1 (in SI). Complete weight data series is given in Table S2 (in the SI). 2.1.2. Material market price The market prices of several metals and plastics are estimated from the year 2010 until 2019. Mean, and error of the prices are presented in Table 3. In SI, the historical and present market prices of the common metals, precious metals, and rare earth metals are shown in Table S3. 2.1.3. Material content in e-waste products The average material content of the selected “unregulated” e-waste was aggregated from various sources (Table 4). Uncertainties of the metal content of the product do not exceed any more than 20% (Chen and Graedel, 2015; Løvik et al., 2015). However, as all the sources are more recent, it is assumed that material composition will not vary substantially across the product ranges. 2.1.4. Required formulas for estimating e-waste generation As mentioned earlier, Australia is a net importing country of EEE, and there is no domestic production of EEE in the country (Golev et al., 2016). According to apparent consumption method represented by Eq. (1) which is widely used at EU-level for measuring national-level product put-on-market (PoM) in a given year (Kalmykova et al., 2017), we can compute the net sales as S(t) of a product in the year t. However, as there is no production, P(t) and export, E(t) of EEE in Australia (nationally produced and then exported) (Golev et al., 2016), Eq. (1) can be simplified by Eq. (2) where only the import term, I(t) is present, and that can be implicated as sales. Later on, by employing a time-step method based on linear regression, the past and future quantities of sales could be determined.

Table 2 Weight (in kg/unit) of selected unregulated e-waste used for Monte Carlo Simulation in this study. Mean

Mean

*These prices are directly taken from the reference Zeng et al. (2016) and #price data was taken from Statista (2019).

2.1.1. Weight of e-waste products The weight ranges of the “unregulated” e-waste were determined from large samples, which were collected by field survey by visiting local scrap metal enterprises (Table 2). After conducting the fitted

E-waste

Resource

S(t) = P(t) + I(t) + E(t)

(1)

S(t) = I(t)

(2)

The probability density function (PDF) of the Weibull distribution function for dynamic lifespan modeling of a product can be represented by the Eq. (3) (Forti et al., 2018; Wang et al., 2013) 1

x

f(x) =

e

0x < 0

(x)

x

0 (3)

The cumulative density function (CDF) for the Weibull distribution function can be expressed by Eq. (4)

F (x ) = 1 4

e

(x)

(4)

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M.T. Islam and N. Huda

Table 4 Material content (%) in various e-waste products used in this study, compiled from Cucchiella et al. (2015), Parajuly et al. (2017), Reuter et al. (2013) and Parajuly and Wenzel (2017). Common metals

Refrigeration appliances Home laundry appliances Air treatment equipment Dishwashers Large cooking appliances Microwave Oven Food preparation appliances Heating appliances Personal care appliances Small cooking appliances Vacuum cleaners Portable players Imaging devices* Home audio and cinema* Video players* Video games hardware*

Valuable metals

Fe

Cu

Al

Plastic

0.505 0.53 0.566 0.51 0.51 0.744 0.6035 0.461 0.258 0.21925 0.232 0.397 0.43 0.386333333 0.654 0.43

0.037 0.04 0.137 0.04 0.04 0.023 0.1415 0.07 0.1305 0.0365 0.067 0.029 0.01 0.037333 0.017 0.01

0.0155 0.03 0.035 0.02 0.02 0.026 0.0075 0 0.003 0.15 0.019 0.006 0.03 0.016 0.09 0.03

0.4065 0.26 0.2025 0.38 0.38 0.059 0.2275 0.47 0.552 0.56475 0.637 0.434 0.28 0.418 0.081 0.38

Ag

Au

0.000132 0.001 0.023 0.000488 0.054 0.017

1.68E-05 0.002 8.66E-05 0.004 0.0025

Less common metals Pd

2.03E-05 0.0001 0.0014 1.57E-05 0.0009 0.0004

Rare-earth elements

Pb

Sn

Zn

0.019762

0.016198

0.010281

0.015 0.019762

0.024

0.032

0.007992 0.019762 0.019762 0.004 0.0625 0.007992 0.392 0.1505

0.00433 0.016198 0.016198

0.007519 0.010281 0.010281 0.005 0.034 0.007519 0.55 0.02205

0.00433 0.00433

Nd

Dy

Ta

0.0001 0.00165

0.0002

0.0018 0.0252

0.0044 0.00425

0.0011 0.00015

0.0251 0.0059

*Material composition of the products were collected from Torihara et al. (2015).

Simulation was utilized with 105 iterations to obtain a final estimation of the e-waste flows. For the simulation, Oracle’s Crystal Ball as Microsoft Excel’s add-in was employed performing the uncertainty analysis.

Where is the shape parameter ( > 0) , and is the scale parameter ( > 0) . According to Forti et al. (2018) and Wang et al. (2013), EoL units for a particular year x can be expressed mathematically based on the net sales and lifespan function. Then total e-waste generation can be determined by the Eq. (5)

D (x ) =

f (x ) P (x ) dx

3. Results

[Pi(1990) × fi(x 1990) + pi(1991) × fi(x 1991) +…

3.1. Data availability for e-waste generation estimation

16

= i= 1

+pi(x 1) × fi(1)]

As seen in Table S1 and Fig. S2 (in the SI), linear regression of the sales data (from 2004 to 2018) of various EEE considered in this study showed excellent linearity which also found with the correlation value close to 0.90 for many of the products under consideration. Euromonitor predicts that for all the products, year on year demand growth starting from the year 2004 until 2023 is assumed to be in between 2.2 and 3%. For all the products considered in this study, we used the scale and shape parameters in the Weibull distribution function prescribed by Forti et al. (2018). Detailed of the values used for each of the products can be found in Table S4 (in the SI). This approach was widely used by many of the authors among whom the work of Parajuly et al. (2017) from Denmark and Zeng et al. (2016) from China are notable. We believe that taking a similar approach is rational for the case of Australia. Figs. 1 and 2 show the cumulative and annual failure rate of various EEE, respectively. Analyzing Fig. 1, it is found that it has two main parts to be considered. The upper left part (above the curves) represents inuse stocks of EEE, and the lower section provides information on the cumulative obsolete amount of waste. Fig. 2 represents the yearly obsolescence amount of the EEE considered in this study.

(5)

15

Sj =

Fi (x ) × cij i=1

(6)

Where x is the year; D(x) is the total weight of e-waste generation in the year x (in tons); i is the ith category of EEE; n is the total number of items in the e-waste category; pi(1990) is the net weight of the ith EEE in the year 1990 (in tons), fi(x-1990) is the failure rate of the ith EEE since the year 1990; j is the jth stock of resources with the e-waste; Sj is the jth cumulative resource stock with the e-waste (in tons), Fi(x) is the ith cumulative generated weight of the e-waste in the year x (in tons); and finally, cij is the content of the jth material resource in the ith category of the e-waste. 2.2. Sensitivity and uncertainty analysis The sensitivity and uncertainty analysis are the instruments allowing to evaluate and validate how a final index is influenced by variations of some input variables (Cao et al., 2019; Peeters et al., 2017). More specifically, the purpose of using sensitivity analysis is to investigate how the model outputs react with the variations of the model’s inputs. The most usual approach for sensitivity analysis is modifying one input parameter at a time observing the way, how the change of model input is affecting the model output. On the other hand, uncertainty analysis signifies a model’s level of precision and accuracy of the outputs (or predictions) due to the variation of one or more input data (Do et al., 2014). In such context, the market price of various metals (common, precious and less-common) will be evaluated using sensitivity analysis due to the data limitations, as we only can observe the minimum and maximum values of the market prices of the metals (Table 3). Subsequently, for uncertainty analysis, weight variations (Table 1) and average metal content (Table 2) in each of the EEE item will be taken into account. In the uncertainty analysis, Monte Carlo

3.2. E-waste generation estimation By using Eq. (5), the weights of the 16 types of e-waste generation were estimated and shown in Fig. 3. Considering the total amount of sales of the selected EEE in Australia in the year 2010 and the previous years (starting from 1989), the estimated e-waste generation in 2010 was approximately 223 kt. This amount will reach 342 kt in 2020 and 461 kt in 2030 with an annual increase rate of around 3.7%. According to the recent study by Blue Environment (2018), a consultation report on National Waste generation in 2018, prepared for Department of the Energy and Environment (DOEE), Government of Australia, in the year 2016–2017, approximately 485 kt of e-waste was generated in Australia which was an increase of about 3.8% on the previous year. In this case, 5

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Fig. 1. Weibull distribution of EEEs: Cumulative density function (CDF).

our study found a similar pattern of the increasing growth rate of ewaste from the selected products and the products under consideration share around 63% of the generated e-waste, which is much higher than e-waste generated under the NTCRS scheme (around 25%). This also signifies the importance of expansion of the e-waste product scope in the current e-waste management system in Australia. When this growth rate is compared to general municipal solid waste (MSW) which was about 6%, considering the last 11 years of available data, it is easily understood that e-waste is one of the fastest-growing waste streams in the overall solid waste generation scenario, if only these items are considered. Both in terms of total e-waste generation and growth rate of e-waste products covered under NTCRS, the selected products (discussed in this study) possess a significant share in the overall e-waste generation scenario that needs to be taken care of in the future. The distribution of e-waste generation in Australia, considering the unregulated products from the year 2010 to 2030 is shown in Fig. 4. In 2010, the four highest-ranking types of e-waste in terms of weight were air treatment products, home laundry appliances, heating appliances,

and refrigeration appliances, large cooking appliances those accounted for 71% of the total e-waste generation. In 2015, air treatment products, heating appliances, home laundry appliances, large cooking appliances, home laundry appliances, large cooking appliances, and refrigeration appliances accounted for 73% of the total e-waste. However, in 2020, air treatment products, heating appliances, home laundry appliances, large cooking, and refrigeration appliances will be accounted for 72% of the total e-waste generation. In 2030, the products mentioned above will cover approximately 74% of the entire e-waste generation. In these four different years, e-waste generation from microwave ovens and video games hardware was and will remain around 5% and 4%, respectively, while, air treatment products will be the dominant product category that will be increased (by weight share) over the years. The 16 products considered in this study can be categorized into electrical and electronic equipment where electrical appliances consist of air treatment products, dishwashers, food preparation appliances, heating appliances, home laundry appliances, large cooking appliances,

Fig. 2. Weibull distribution of EEEs: Probability density function (PDF). 6

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M.T. Islam and N. Huda

Fig. 3. Estimated e-waste generation from 2010 to 2030.

microwave ovens, personal care appliances, refrigeration appliances, small cooking appliances, and vacuum cleaners. On the other hand, video players, home audio and cinema equipment, imaging devices, portable players, and video games hardware fall under the category of electronic equipment (Fig. 5). Although electrical appliances and electronics equipment continue to rise together, the weight share of

electrical appliances will increase from 88% (in 2010) to 94.44% (in the year 2030). On the other hand, electronic equipment’s weight share will decrease from 11% (in the year 2010) to 5% (in 2030). Among the electronic items, video games hardware (gaming console) will have the largest share 74% in 2030, which was 36% in the year 2010, with a 51% increase in between the years. Other electronic products will

Fig. 4. Estimated e-waste generation under various EEE in 2010, 2015, 2020 and 2030. 7

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Fig. 5. E-waste generation in electrical and electronic product categories from 2010 to 2030.

sharply decrease in the years, probably due to the increased use of mobile phones (for high-quality image capture replacing imaging devices). Similarly, digital television (TV) sets that can use on-line streaming via the internet would likely to capture usability of the other equipment incorporating enhanced features, portability and convenience of the other equipment. For example, DVD players will be replaced by internet TV sets with premium channel subscription such as Netflix, YouTube, and Amazon. However, this is often a challenging task in predicting product replacement considering integrated innovative features, consumer affordability, and market share of a product.

and it is expected to increase over the next decades (considering the linear growth in product sales). However, the number of precious metals and rare-earth elements will drop (in the e-waste) starting from the estimated the year 2010, especially Ag and Ta (Fig. 7). On the other hand, Nd and Au will have a stable supply in the e-waste quantities due to the large hibernation as stock. Another reason is that the electronic products that are considered in this study will experience a slow decrease in the future due to products being replaced by other high performing products, as mentioned earlier. It is to be mentioned that electronic products under consideration in this study are important due to the presence of precious metals compared to electrical appliances. If other EEE such as computer and mobile phones are included, then this figure would have changed dramatically. At present, there are about 25 million mobile phones stored in Australian households (UTS, 2019), which is a tremendous source of precious and rare-earth metals in that case. In 2010, the amount of estimated Fe, Cu, Al, and plastics were 114 kt, 13.42 kt, 7.18 kt and 70.40 kt, respectively which are expected to increase to 234.27 kt, 31.19 kt, 13.93 kt, and 147.72 kt, respectively in the year 2030 (Fig. 6). On the other hand, the total amount of Ag, Au, and Pd were estimated to be 0.76 kt, 0.06 kt, and 0.014 kt, respectively in 2010 which will then decreased to 0.39, 0.05 and 0.009 kt in the year 2030. The

3.3. Estimation of valuable material resources in e-waste After the useful life of EEE, a tremendous amount of secondary raw materials are found present in the e-waste, regardless collected and recycled appropriately or not. From the Fig. 3, and material content in products presented in Table 2, the amount of recyclable Fe, Al, Cu as common metals and plastics, precious metals such as Ag, Au, Pd and rare-earth elements (e.g. Nd, Ta, Dy) and less-common metals such as Zn, Pb, Sn can be quantified which is shown in Figs. 6 and 7. The amount of base metals in e-waste constantly grows from the year 2010,

Fig. 6. Common metals resource stock in e-waste from 2010 to 2030. 8

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Fig. 7. Precious and rare earth metal resources in e-waste from 2010 to 2030.

reason and case of having probable decreasing trend are discussed earlier. In the face of having a decreasing trend, over the years, the economic share of the precious metals was and will be dominant. For instance, in Fig. 8, the share of Au remained 68% starting from the year 2010 to 2030 while the share of Ag and Pd will be around 10% and 8%, respectively in the year 2030. For Cu, the percentage will increase from 2% in 2010 to 7% in 2030. Overall, precious metals will represent more than half of the total economic potential of the metals involved in the recycling process. Over the years, the full economic potential from all the metals will show almost a constant figure. The average potential revenue earning from the e-waste recycling (translated in metal value) has been evolving from 4.88 billion US$, which will remain steady at around 3.7 billion US$ starting from 2022. In 2030, the estimated recycling potential from all types of metals and plastics will be approximately 3.73 billion US$. When it comes to base metals and plastics, home laundry

appliances, air treatment products, refrigeration appliances, large cooking appliances, and heating appliances will be the important items that will generate most of the metals in the year 2020 (Fig. 9). These products need to be collected under a formal collection and recycling channels for commercial metal recovery. It is to be noted that currently these products are primarily disposed of by the households during the council clean-up days and household bulky waste collection along with other materials that eventually go to landfill. Besides, even though a fraction of the amounts are disposed of at the resource recovery facilities (at a high disposal fees around AU$ 25/unit), there is no specific and official material flow accounting and revenue generation statement available under the current e-waste management system in Australia. There must be a differentiation of scrap metals from other sources and e-waste. Fig. 10 shows that waste air conditioning unit and washing machine disposed of along with other scrap metals in a resource recovery facility in the State of New South Wales (NSW). Specific material availability and quantification of the material only from e-waste (and

Fig. 8. Economic share of various material in e-waste quantities in the year 2010, 2015, 2020 and 2030. 9

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Fig. 9. Estimated common metals, plastics, and the less common metal flows from various products in 2020.

With such massive amount of stock, amount of base metals will also be encased in the stock amount in which Fe, Cu, Al and plastic quantities were estimated to be 806 kt, 91 kt, 59 kt, and 506 kt, respectively for the year 2010. This amount is expected to grow by 3101 kt, 429 kt, 199 kt, and 2082 kt, respectively in the year 2030 (Fig. 12). Besides the base metals, precious metals and rare earth element stock will increase sharply from 2010, and will reach its peak in 2024 and will slowly decrease until 2030. The reason for such decrease would be most likely to the fact that the majority of the electronic products under the current stock quantities will reach its EoL as shown by the PDF (in Fig. 2). For Ag, Au, Pd, Nd, Dy and Ta, stock quantities will be varied (14–18) kt, (1.8–1.79) kt, (0.26–0.36) kt, (1.4–2.4) kt, (0.26–0.29) kt and (6.7–7.8) kt, respectively from the year 2010 to 2030 (Fig. 13). Definitely, in-use inventory of metals are the building blocks of modern society, enhancing productivity of contemporary living, however, long-term strategic e-waste management planning should understand this trend and accumulation to capture material value from the future quantities of e-waste generation from the viewpoint of circular economy and urban mining (Li et al., 2018; Zhang et al., 2019). These unregulated products need special attention in terms of developing reverse logistics capabilities considering the local collection and recovery activities in Australia together with formal regulatory measure and expanding product scope.

Fig. 10. Air conditioning unit, washing machine, and heating appliances disposed of with other scrap metals (Source: Author’s field trip to a resource recovery facility in NSW).

particularly from a product stream) cannot be understood if this specific waste stream mixed with other scrap metal sources. 3.4. Stock estimation Hibernation or building in-use stock is one of the crucial characteristics of e-waste in developed countries (Kahhat et al., 2008), and Australia is no exception. As disposal fees are charged (for appropriate channels) for the unregulated e-waste at the local collection points, it is assumed that a large amount of e-waste is being stored at Australian households. Documentary evidence from a recent investigation of the Australian Broadcasting Corporation (ABC) found a similar trend (ABC, 2018). However, as there is no large-scale household survey (in the academic arena) has been performed in Australia for products (that are kept in the household storage regardless of in-use or out-of-use), it is difficult to confirm such statement. The estimation of the stock using the difference between net sales and e-waste generation of the selected products, it is found that total amount of stock quantities was around 2088 kt in 2010 which will rise to 7901 kt in 2030, a net 73% increase in between the years (Fig. 11).

3.5. Sensitivity and uncertainty analysis Due to data constraints of present e-waste generation estimation effort from an Australian context, (only one paper has been published in this issue), sensitivity analysis is performed based on the variable material price that dictates the recycling potential (in terms of economic benefits) of the selected products. Zeng et al. (2016) also mentioned that the fluctuating metal market price is the single most influential factor that should be taken care of in conducting a sensitivity analysis in the e-waste generation estimation study. By considering the variable market price, it is seen from Fig. 14 that in 2010, overall revenue earning potential varies in between 3.4–6.1 billion US$ which will drop in between 2.75–4.73 billion US$ in the year 2022 and then stabilized until 2030 with an estimated value that lies in between 2.74 and 4.60 billion US$. 10

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Fig. 11. Estimated stock quantities of EEE in Australia.

Based on the variable weight distribution presented in Table 1 and average metal content in the EEE products under consideration (Table 2), uncertainties of total e-waste generation by weight is shown in Fig. 15(A) for the year 2010 and Fig. 15(B) 2020. Total e-waste generation in 2010 was estimated to be 223 kt and 342 kt in 2020 (shown in Fig. 3) which matched with the Monte Carlo simulation result (with 100% certainty). With the variable weight of the e-waste items and content of base metals, uncertainty analysis showed that Fe amount varied from 110 to 180 kt (Fig. 16(A)), Cu amount varied from 12 to 24 kt (Fig. 16(B)) and Al amount varied from 7.20 to 11.20 kt (Fig. 16(C)). The mean recoverable amount of Fe (144 kt), Cu (17.89 kt) and Al (8.9 kt) (with 95% certainty) in the estimation results for the year 2015 directly matched with the simulated results as well (Fig. 16). This confirms the maximum probability of occurring the values identified in the forecasting results. Through this, all the estimated values (in terms of the ewaste weight and material content encased in the selected products)

substantially verify the level of accuracy and the strength of our study. 4. Discussion The purpose of the e-waste generation estimation and subsequent evaluation of material recycling potential is to provide decision supports for future inclusion of unregulated products in the e-waste management system and to develop local recycling infrastructure. Furthermore, as there are no specific methods currently considered by the waste management authorities in estimating “unregulated” products in Australia. This study provides a preliminary assessment model that can be further utilized and modified by the policymakers in Australia in the issue. The authors believe that the findings of this study are adequate to provide informed decision to the decision-makers in both government and e-waste management stakeholders for considering recycling infrastructure meeting the demands of massive ewaste generation in Australia.

Fig. 12. The trend of common metal resources in stock of EEE. 11

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Fig. 13. Precious and rare-earth elements in available stocks.

4.1. Generation amount of unregulated products

utilized by previous studies, as shown by Islam and Huda (2019a). This kind of estimation can also be performed considering stock-driven model by conducting a survey, and in that case, logistic regression can be used to predict future possession amount of the products by household. With the projected growth rate of 2.2%–3% of the selected product sales, on year on year basis, starting from the year 2004, “unregulated” products will dominate 63% of the total e-waste generation in the year 2030, by weight. According to the findings of the study, e-waste generation from the “unregulated” product will be 461 kt in 2030 with an annual increase rate of around 3.7%. Also, electrical household appliances such as home laundry appliances, air treatment products, refrigeration appliances, large cooking appliances, and heating appliances, and electronic product such as video games hardware (e.g., gaming console) are the products that will be generated the most. In the future expansion of e-waste collection and recycling scheme, these products should be considered as the priority due to both amount of generation and high base metal content. Our analysis also showed that considering the “unregulated”

Product lifespan is the most significant factor in predicting e-waste generation from household appliances (e.g., refrigeration equipment, air conditioners, washing machine, etc.) (Habuer et al., 2014). Instead of a fixed average lifespan of EEE, Weibull distribution based lifespan modeling is more justified and implemented by several researchers (Zhang et al., 2011). As because Weibull distribution based model requires two lifespan distribution parameters, namely scale parameter (α) and shape parameter (β), further studies can be carried out to estimate the parameters by conducting consumer survey from an Australian context. Within the scope of this study and performing a baseline study on e-waste generation from a specific product, we have utilized the parameters prescribed by Balde et al. (2017). This approach was implemented by other studies, for instance, Zeng et al. (2018) who estimated 14 new WEEE in China and Parajuly et al. (2017) in Denmark. Both the researchers used the prescribed parameters mentioned by Balde et al. (2017). For the sales-driven model, future prediction of sales amount was estimated with linear regression, which is widely

Fig. 14. Sensitivity analysis of metal price from 2010 to 2030. 12

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Fig. 15. Uncertainty analysis of e-waste generation for (A) 2010 and (B) 2020.

products, e-waste generation would be 1.54 times in 2020 and almost two times in 2030 compared to 2010. If these additional e-waste items are included in the present management system, then local recyclers would have gained high economies of scale and achieve cost efficiency. Lack of economies of scale is found as one of the single most significant barriers in the Australian e-waste management system (ANZRP, 2018), which can be overcome by including these products in the e-waste stream. Long term planning (local recycling infrastructure development) for electrical appliances and short term planning (extending product scope and collection system development) until the year of 2024 for the electronics products should be taken to capture the full economic potential. Field visit to the local resource recovery facilities (as seen in Fig. 10) also suggests that separate collection mechanism also should be in place considering only e-waste. From Fig. 11, it is seen that total amount of stock quantities (of the selected “unregulated” products) was around 2088 kt in 2010 which will rise to 7901 kt in 2030, a net 73% increase in between the years among which laundry appliances, heating and refrigeration equipment

will be the significant portion of the stock quantities. To collect and recover this significant amount of stock (which will eventually turn into e-waste in the future) stakeholder involvement is necessary for developing an appropriate reverse logistics network and material recovery facilities. Local government councils (LGCs) play a crucial role in ewaste management (Davis and Herat, 2008), which need to be strengthened. Under the NTCRS, LGCs were not given any mandatory responsibility in collecting e-waste from households and small businesses (Dias et al., 2018), which should be improved by introducing extended product scope and make the councils responsible for waste electrical items collection. If additional reverse logistics services are needed for the expansion, this will also create job opportunities in the sector (Islam and Huda, 2018b). Considering more than 80% of the population in Australia living in the urban (city) areas within 100 km of the coast (Herat and Panikkar, 2019), there is a substantial opportunity for LGCs implementing the urban mining (UM) concept for e-waste items. For sustainability and sustainable development goals viewpoint, UM mining is an increasing concern in recent times (Zhang et al.,

Fig. 16. Uncertainty analysis of recyclable (A) Fe, (B) Cu and (C) Al with 95% certainty for the year 2015. 13

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2019). To have Australian cities more sustainable, policymakers and actors in the LGCs should consider the “unregulated” e-waste as an excellent resource of material and employment creation.

Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.resconrec.2019. 104526.

4.2. Encased metal and material contains in unregulated products As seen from Figs. 6 and 7, base metal (Fe, Cu, Al), plastics amount in e-waste will tend to increase linearly whereas precious metals (e.g. Ag, Au, and Pd) and rare-earth elements (e.g. Nd, Ta, Dy) will be stable until 2030 which showed an excellent economic opportunity. Among the base metals, Cu will remain one of the attractive metals, and with the estimation, it was found that the overall content will be increased from 2% in 2010 to 7% in 2030. Zhang et al. (2011) mentioned that Cu stock in e-waste would remain constant in the next 40 years in China. By quantity, Fe, Cu, Al, and plastics will be around 234.27 kt, 31.19 kt, 13.93 kt, and 147.72 kt, respectively in the year 2030. Stock amount of the base metals and precious metals will also continue to rise. Overall revenue earning potential (of the recoverable metals and plastics) will vary between 2.74 and 4.60 billion US$ in 2030 only from the selected “unregulated” products. In the coming years, Australia has the opportunity to recycle specifically Fe, Cu and plastic and products such as home laundry appliances, air treatment products, refrigeration appliances, large cooking appliances, and heating appliances will be the significant source from where such e-waste could be collected. In our estimation, products that contain precious metals and rare-earth elements may decrease in terms of sales as it often hard to predict future technological innovations in small-sized WEEE.

References ABC, 2018. War on Waste. (Last accessed on 28 October 2018). https://www.abc.net. au/ourfocus/waronwaste/. Agamuthu, P., Kasapo, P., Mohd Nordin, N.A., 2015. E-waste flow among selected institutions of higher learning using material flow analysis model. Resour. Conserv. Recycl. 105, 177–185. Ai, N., Zheng, J., Chen, W.-Q., 2019. U.S. end-of-life electric vehicle batteries: dynamic inventory modeling and spatial analysis for regional solutions. Resour. Conserv. Recycl. 145, 208–219. ANZRP, 2018. Review of the Product Stewardship Act 2011, Including the National Television and Computer Recycling Scheme - Coversheet for Submissions. https:// www.anzrp.com.au/wp/wp-content/uploads/ANZRP-Act-and-NTCRS-ConsultationPaper-Response.pdf. Araújo, M.G., Magrini, A., Mahler, C.F., Bilitewski, B., 2012. A model for estimation of potential generation of waste electrical and electronic equipment in Brazil. Waste Manag. 32 (2), 335–342. Awasthi, A.K., Cucchiella, F., D’Adamo, I., Li, J., Rosa, P., Terzi, S., Wei, G., Zeng, X., 2018. Modelling the correlations of e-waste quantity with economic increase. Sci. Total Environ. 613–614, 46–53. Balde, C.P., Forti, V., Gray, V., Kuehr, R., Stegmann, P., 2017. The Global e-waste Monitor 2017: Quantities, Flows and Resources. https://www.itu.int/en/ITU-D/ClimateChange/Documents/GEM%202017/Global-E-waste%20Monitor%202017%20.pdf. Brunner, P.H., Rechberger, H., 2004. Practical handbook of material flow analysis. Int. J. Life Cycle Assess. 9 (5), 337–338. Brunner, P.H., Rechberger, H., 2016. Practical Handbook of Material Flow Analysis. CRC Press. Cao, R., Leng, Z., Yu, H., Hsu, S.-C., 2019. Comparative life cycle assessment of warm mix technologies in asphalt rubber pavements with uncertainty analysis. Resour. Conserv. Recycl. 147, 137–144. Chen, W.-Q., Graedel, T.E., 2015. In-use product stocks link manufactured capital to natural capital. Proc. Natl. Acad. Sci. 112 (20), 6265–6270. Cucchiella, F., D’Adamo, I., Koh, S.L., Rosa, P., 2015. Recycling of WEEEs: an economic assessment of present and future e-waste streams. Renew. Sustain. Energy Rev. 51, 263–272. D’Adamo, I., Ferella, F., Gastaldi, M., Maggiore, F., Rosa, P., Terzi, S., 2019. Towards sustainable recycling processes: wasted printed circuit boards as a source of economic opportunities. Resour. Conserv. Recycl. 149, 455–467. Davis, G., Herat, S., 2008. Electronic waste: the local government perspective in Queensland, Australia. Resour. Conserv. Recycl. 52 (8), 1031–1039. De Meester, S., Nachtergaele, P., Debaveye, S., Vos, P., Dewulf, J., 2019. Using material flow analysis and life cycle assessment in decision support: a case study on WEEE valorization in Belgium. Resour. Conserv. Recycl. 142, 1–9. Dias, P., Bernardes, A.M., Huda, N., 2018. Waste electrical and electronic equipment (WEEE) management: an analysis on the Australian e-waste recycling scheme. J. Clean. Prod. 197, 750–764. Do, N.T., Trinh, D.A., Nishida, K., 2014. Modification of uncertainty analysis in adapted material flow analysis: case study of nitrogen flows in the Day-Nhue River Basin, Vietnam. Resour. Conserv. Recycl. 88, 67–75. Environment, B., 2018. National Waste Report 2018. (Last accessed on 29 May 2019). https://www.environment.gov.au/system/files/resources/7381c1de-31d0-429b912c-91a6dbc83af7/files/national-waste-report-2018.pdf. Ford, P., Santos, E., Ferrão, P., Margarido, F., Van Vliet, K.J., Olivetti, E., 2016. Economics of end-of-life materials recovery: a study of small appliances and computer devices in Portugal. Environ. Sci. Technol. 50 (9), 4854–4862. Forti, V., Baldé, K., Kuehr, R., 2018. E-waste Statistics: Guidelines on Classifications. Reporting and Indicators. Golev, A., Schmeda-Lopez, D.R., Smart, S.K., Corder, G.D., McFarland, E.W., 2016. Where next on e-waste in Australia? Waste Manag. 58, 348–358. Guo, X., Yan, K., 2017. Estimation of obsolete cellular phones generation: a case study of China. Sci. Total Environ. 575, 321–329. Gusukuma, M., Kahhat, R., 2018. Electronic waste after a digital TV transition: material flows and stocks. Resour. Conserv. Recycl. 138, 142–150. Habuer, Nakatani, J., Moriguchi, Y., 2014. Time-series product and substance flow analyses of end-of-life electrical and electronic equipment in China. Waste Manag. 34 (2), 489–497. Herat, S., Panikkar, A.K., 2019. Chapter 12–E-waste management in Australia: current Status. In: Prasad, M.N.V., Vithanage, M. (Eds.), Electronic Waste Management and Treatment Technology. Butterworth-Heinemann, pp. 269–284. Ikhlayel, M., 2016. Differences of methods to estimate generation of waste electrical and electronic equipment for developing countries: Jordan as a case study. Resour. Conserv. Recycl. 108, 134–139. Islam, M.T., Abdullah, A.B., Shahir, S.A., Kalam, M.A., Masjuki, H.H., Shumon, R., Rashid, M.H., 2016. A public survey on knowledge, awareness, attitude and willingness to pay for WEEE management: case study in Bangladesh. J. Clean. Prod. 137, 728–740. Islam, M.T., Dias, P., Huda, N., 2018. Comparison of E-waste management in Switzerland and in Australia: a qualitative content analysis. Int. J. Environ. Ecol. Eng. 12 (10),

5. Conclusion, limitations and future work In this study, a sales-stock-lifespan based e-waste generation estimation method has been implemented to estimate e-waste generation from “unregulated” products in Australia from the year 2010 to 2030. On the top of that total amount of various base metals (Fe, Cu, Al), precious metals (Ag, Au, Pd) and rare-earth elements were estimated which highlighted that there is a billion-dollar economic opportunity in the sector in Australia. Policymakers can identify various products under the electrical appliances category that need to have attention in an urgent basis by expanding the product scope in the current e-waste management system and by embossing the concept of extended producer responsibility, urban mining, and circular economy. There are substantial opportunities for the Australian recycling industry by expanding product scope, introducing the regulatory measure, and subsidizing local recyclers. Customer survey provides crucial information on the average age distribution of the product currently in use and number of items used by the households. As such, large-scale study has not been performed in Australia. Such a survey can help to identify the scale and shape parameters of various products from the Australian context. In this study, only the “unregulated” products from the Euromonitor database are considered, future research should explore another database such as UN Comtrade (UNCOMTRADE, 2018). Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors would like to thank two anonymous reviewers for their constructive comments on the manuscript. The first author acknowledges the financial support from Macquarie University under the scholarship scheme “International Macquarie University Research Training Program (iMQRTP)” for conducting this research. 14

Resources, Conservation & Recycling 152 (2020) 104526

M.T. Islam and N. Huda 610–616. Islam, M.T., Huda, N., 2018a. Application of Material Flow Analysis (MFA) in electronic waste (E-waste) management: a review. Paper Presented at the Multidisciplinary Digital Publishing Institute Proceedings. Islam, M.T., Huda, N., 2018b. Reverse logistics and closed-loop supply chain of Waste Electrical and Electronic Equipment (WEEE)/E-waste: a comprehensive literature review. Resour. Conserv. Recycl. 137, 48–75. Islam, M.T., Huda, N., 2019a. E-waste in Australia: generation estimation and untapped material recovery and revenue potential. J. Clean. Prod., 117787. Islam, M.T., Huda, N., 2019b. Material flow analysis (MFA) as a strategic tool in E-waste management: applications, trends and future directions. J. Environ. Manage. 244, 344–361. Johnson, M., Fitzpatrick, C., Wagner, M., Huisman, J., 2018. Modelling the levels of historic waste electrical and electronic equipment in Ireland. Resour. Conserv. Recycl. 131, 1–16. Kahhat, R., Kim, J., Xu, M., Allenby, B., Williams, E., Zhang, P., 2008. Exploring e-waste management systems in the United States. Resour. Conserv. Recycl. 52 (7), 955–964. Kahhat, R., Williams, E., 2012. Materials flow analysis of e-waste: domestic flows and exports of used computers from the United States. Resour. Conserv. Recycl. 67, 67–74. Kalmykova, Y., Berg, P.E.-O., Patrício, J., Lisovskaja, V., 2017. Portable battery lifespans and new estimation method for battery collection rate based on a lifespan modeling approach. Resour. Conserv. Recycl. 120, 65–74. Kalmykova, Y., Patrício, J., Rosado, L., Berg, P.E., 2015. Out with the old, out with the new–the effect of transitions in TVs and monitors technology on consumption and WEEE generation in Sweden 1996–2014. Waste Manag. 46, 511–522. Li, N., Han, R., Lu, X., 2018. Bibliometric analysis of research trends on solid waste reuse and recycling during 1992–2016. Resour. Conserv. Recycl. 130, 109–117. Li, Z., Diaz, L.A., Yang, Z., Jin, H., Lister, T.E., Vahidi, E., Zhao, F., 2019. Comparative life cycle analysis for value recovery of precious metals and rare earth elements from electronic waste. Resour. Conserv. Recycl. 149, 20–30. Løvik, A.N., Restrepo, E., Müller, D.B., 2015. The global anthropogenic gallium system: determinants of demand, supply and efficiency improvements. Environ. Sci. Technol. 49 (9), 5704–5712. Morris, A., Metternicht, G., 2016. Assessing effectiveness of WEEE management policy in Australia. J. Environ. Manage. 181, 218–230. Nakatani, J., Moriguchi, Y., 2014. Time-series product and substance flow analyses of end-of-life electrical and electronic equipment in China. Waste Manag. 34 (2), 489–497. Ongondo, F.O., Williams, I.D., 2011. Mobile phone collection, reuse and recycling in the UK. Waste Manag. 31 (6), 1307–1315. Ongondo, F.O., Williams, I.D., Keynes, S., 2011. Estimating the impact of the “digital switchover” on disposal of WEEE at household waste recycling centres in England. Waste Manag. 31 (4), 743–753. Parajuly, K., Habib, K., Liu, G., 2017. Waste electrical and electronic equipment (WEEE) in Denmark: flows, quantities and management. Resour. Conserv. Recycl. 123, 85–92. Parajuly, K., Wenzel, H., 2017. Potential for circular economy in household WEEE management. J. Clean. Prod. 151, 272–285. Peeters, J.R., Altamirano, D., Dewulf, W., Duflou, J.R., 2017. Forecasting the composition

of emerging waste streams with sensitivity analysis: a case study for photovoltaic (PV) panels in Flanders. Resour. Conserv. Recycl. 120, 14–26. Polák, M., Drápalová, L., 2012. Estimation of end of life mobile phones generation: the case study of the Czech Republic. Waste Manag. 32 (8), 1583–1591. Reuter, M., Hudson, C., Van Schaik, A., Heiskanen, K., Meskers, C., Hagelüken, C., 2013. Metal recycling: Opportunities, limits, infrastructure. A Report of the Working Group on the Global Metal Flows to the International Resource Panel. Robinson, B.H., 2009. E-waste: an assessment of global production and environmental impacts. Sci. Total Environ. 408 (2), 183–191. Song, X., Hu, S., Chen, D., Zhu, B., 2017. Estimation of waste battery generation and analysis of the waste battery recycling system in China. J. Ind. Ecol. 21 (1), 57–69. Statista, 2019. Dysprosium Oxide Price Worldwide From 2009 to 2025 (in U.S. Dollars Per Kilogram). https://www.statista.com/statistics/450164/global-reo-dysprosiumoxide-price-forecast/. Steubing, B., Böni, H., Schluep, M., Silva, U., Ludwig, C., 2010. Assessing computer waste generation in Chile using material flow analysis. Waste Manag. 30 (3), 473–482. Thiébaud, E., Hilty, L.M., Schluep, M., Faulstich, M., 2017. Use, storage, and disposal of electronic equipment in Switzerland. Environ. Sci. Technol. 51 (8), 4494–4502. Torihara, K., Kitajima, T., Mishima, N., 2015. Design of a proper recycling process for small-sized E-waste. Procedia CIRP 26, 746–751. Tran, H.P., Schaubroeck, T., Nguyen, D.Q., Ha, V.H., Huynh, T.H., Dewulf, J., 2018. Material flow analysis for management of waste TVs from households in urban areas of Vietnam. Resour. Conserv. Recycl. 139, 78–89. UNCOMTRADE, 2018. UN Comtrade Database. (Last accessed on 28 October 2018). https://comtrade.un.org/. UTS, 2019. ISF Hosts Launch of Ewaste Watch. https://www.uts.edu.au/research-andteaching/our-research/institute-sustainable-futures/news/isf-hosts-launch-ewastewatch. Van Eygen, E., De Meester, S., Tran, H.P., Dewulf, J., 2016. Resource savings by urban mining: the case of desktop and laptop computers in Belgium. Resour. Conserv. Recycl. 107, 53–64. Wang, F., Huisman, J., Stevels, A., Baldé, C.P., 2013. Enhancing e-waste estimates: improving data quality by multivariate input–output analysis. Waste Manag. 33 (11), 2397–2407. Wang, M.X., You, X.L., Li, X., Liu, G., 2018. Watch more, waste more? A stock-driven dynamic material flow analysis of metals and plastics in TV sets in China. J. Clean. Prod. 187, 730–739. Zeng, X., Gong, R., Chen, W.-Q., Li, J., 2016. Uncovering the recycling potential of “New” WEEE in China. Environ. Sci. Technol. 50 (3), 1347–1358. Zeng, X., Mathews, J.A., Li, J., 2018. Urban mining of e-waste is becoming more costeffective than virgin mining. Environ. Sci. Technol. 52 (8), 4835–4841. Zhang, L., Qu, J., Sheng, H., Yang, J., Wu, H., Yuan, Z., 2019. Urban mining potentials of university: in-use and hibernating stocks of personal electronics and students’ disposal behaviors. Resour. Conserv. Recycl. 143, 210–217. Zhang, L., Yuan, Z., Bi, J., 2011. Predicting future quantities of obsolete household appliances in Nanjing by a stock-based model. Resour. Conserv. Recycl. 55 (11), 1087–1094. Zhang, L., Yuan, Z., Bi, J., Huang, L., 2012. Estimating future generation of obsolete household appliances in China. Waste Manag. Res. 30 (11), 1160–1168.

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