Estimation of the Unregistered Inflow of Electrical and Electronic

Article pubs.acs.org/est

Estimation of the Unregistered Inflow of Electrical and Electronic Equipment to a Domestic Market: A Case Study on Televisions in Vietnam Ha Phuong Tran,*,†,‡ Feng Wang,§,∥ Jo Dewulf,*,† Trung-Hai Huynh,‡ and Thomas Schaubroeck† †

Research Group Environmental Organic Chemistry and Technology (ENVOC), Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium ‡ School of Environmental Science and Technology, Hanoi University of Technology, 1 Dai Co Viet Road, Hanoi, Vietnam § Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft, The Netherlands ∥ Institute for the Advanced Study of Sustainability, United Nations University, Platz der Vereinten Nationen 1, 53113 Bonn, Germany S Supporting Information *

ABSTRACT: Waste electrical and electronic equipment (WEEE) constitutes one of the most problematic waste streams worldwide, and accurately estimating the scale of WEEE can assist in tackling its associated issues. However, obtaining an accurate estimation of WEEE remains a challenge because a share of the waste is difficult to calculate. This share stems from the administratively unregistered (so-called “invisible”) inflow of electrical and electronic equipment (EEE) into the domestic market. As a first attempt to qualitatively and quantitatively investigate this invisible inflow, this study discusses the nature of this flow in detail and proposes a calculation pathway for quantifying its magnitude. The size of the invisible inflow to a domestic market (assumed equal to invisible sales) is calculated by subtracting the registered, also called “visible”, sales from the total sales. The total sales are modeled, whereas the visible sales are derived from statistical data. The method is illustrated by a case study on televisions (TVs) in Vietnam. The results show that from 2002 to 2013, the invisible TV inflow contributed, on average, 15% to the total TV sales (coefficient of variation: 0.21). This average share would increase by approximately 1.0% when the maximum number of TVs used per household increased by 1.0%. However, it would decrease by 1.7% when the visible sales increased by 1.0%. Additionally, the average share of the invisible TV inflow would change from 15% to 27% when an unadjusted constant instead of an adjusted time-varying lifespan is employed. This first estimation of the invisible EEE inflow to the domestic market can be improved with additional knowledge and data in the future.

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Adequate information on the size of the waste flow and its nature is a prerequisite to this understanding.16 However, developing this information remains a challenge because of two reasons. First, a complete e-waste inventory is lacking in developing countries, in which the necessary statistical data on production, sales, and trade of EEE and the formal e-waste management system are often inadequate.4,5,14,17 Second, there is a notice on the unregistered inflow of EEE that infiltrates into the domestic market or the e-waste stream without any administrative registration (explained in the next section).18,19 This flow is referred to as invisible EEE inflow in the rest of the paper. Remarkably, although this invisible inflow has been discovered in many countries (notably in developing

INTRODUCTION Waste electrical and electronic equipment (WEEE), or e-waste, is defined by the Step Initiative1 as “items of all types of electrical and electronic equipment (EEE) and its parts that have been discarded by the owner as waste without the intention of re-use”.1 A growing concern has developed over this waste type and its associated issues.2,3 The main reasons for this concern are the ever-increasing quantities of e-waste4,5 and its inherent complex characteristics induced by the embodiment of various elements, both hazardous and valuable.6,7 The latter reason is often known as the root of the duality of e-waste, i.e., a potential resource for “urban mining”8,9 versus a potential risk to the environment and human health.10−13 This duality emphasizes the need for a careful management of the waste from both environmental and economic perspectives.5,14,15 To obtain a proper management strategy, all countries need to have a comprehensive overview of the e-waste problem. © XXXX American Chemical Society

Received: April 16, 2015 Revised: January 28, 2016 Accepted: January 28, 2016

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DOI: 10.1021/acs.est.5b01388 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

Figure 1. Invisible inflow of electrical and electronic equipment (EEE). In this figure, t is the time in the calendar year, where t0 and tn are the initial and the evaluation year, respectively. P-visible(t) and P-invisible(t) are the registered and unregistered sales of product in year t, respectively. S(t) is the quantity of the product in use at the end of year t, representing the stock in year t. W(t) is the amount of e-waste generated in year t. L(t,tn) is the lifespan profile for the products sold in historical year t, representing the probabilistic obsolescence rate in year tn. The gray color represents the contribution of the invisible EEE inflow.

countries), e.g., China,19 Japan,20 Nigeria,21 Ghana,22 Bangladesh,23 Cambodia, Philippines, and India,24 no clear discussion is available on the nature of this inflow and its effects on e-waste estimation. An approach to quantifying this inflow is, to the best of our knowledge, not available. The main objective of this study is therefore 3-fold: (i) To provide a detailed description of the possible subflows of the invisible EEE inflow and how this inflow affects the e-waste estimation. (ii) To propose a calculation pathway to quantify the size of the invisible EEE inflow in addition to a qualitative analysis. (iii) To improve the e-waste estimate that is an additional result of the calculation pathway. After a comprehensive review of all available e-waste estimation models (Section 2 of the Supporting Information (SI)), the newly available and advanced Input-Output Analysis (IOA) model (i.e., the Sales-Stock-Lifespan model25) was selected and adopted in the calculation. The application of this procedure in calculating the invisible inflow of EEE and improving existing limitations in e-waste estimations was demonstrated in a case study on television sets (TVs, one of the most commonly used household appliances26) in Vietnam. Although the applicability of the Sales-StockLifespan model in estimating e-waste has been shown in many case studies in developed countries (e.g., Belgium, Italy, and The Netherlands),27−29 this model has not yet been applied to developing countries. Therefore, our study is the first attempt to apply this model to a case study in a developing country. Over the past decade, the consumer electronic market in Vietnam has grown significantly, with revenues expecting to hit US$10 billion by 2016, of which more than 70% is from video appliances.30 Similar to other developing countries, the contribution of the invisible inflow of EEE to the domestic

electronic market is evident,24,31 but its share remains unknown. Except for the studies by Nguyen et al.32 and Huynh and Lee,33 all studies available so far for Vietnam34,35 quantified the e-waste generated only based on reported sales data without considering the potential contribution of the invisible EEE inflow to the total domestic consumption. Moreover, only three studies32−34 estimated e-waste generation at a national level. The other studies35−37 focused only on Hanoi and Ho-Chi-Minh City, the two most developed cities in Vietnam. Although these studies predicted an increase in ewaste in the future, divergences in the methodologies and data employed in estimating brought about different estimation results. Furthermore, additional limitations incurred through the underlying assumptions and simplifications indicate the need for further work. Noteworthy limitations that must be addressed are as follows. (1) The majority of the existing estimates are based on data and information up to around 2006; therefore, an update is required to consider the rapid changes in EEE consumption and disposal in recent years. (2) In these studies, the selected estimation models (i.e., traditional IOA variations) were directly applied without considering the quality of the input data (i.e., neither data quality screening nor data quality improvement was performed). (3) Despite variations over time in the product lifespan,38 none of the previous studies considered this.

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INVISIBLE INFLOW OF EEE To explain the invisible inflow of EEE and how it affects ewaste estimation, a material flow analysis is employed to investigate the flows in and out of the domestic EEE trade and consumption circuit (Figure 1). In this study, we use the term “invisible inflow of EEE” to refer to all trade flows of electrical and electronic equipment that enter the domestic EEE trade and consumption circuit without being administratively registered in any management B


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Figure 2. Calculation pathway to estimate the invisible inflow of electrical and electronic equipment (EEE) into the domestic consumption market and e-waste generation. In this figure, t is the time in the calendar year, where t0 and tn are the initial and the evaluation year, respectively. S(t) is the total quantity of product in use at the end of year t, therefore in the rest of this paper S(t) is referred to as the active stock. W(tn) is the total amount of e-waste generated in year tn. wa(t,tn) and sa(t,tn) are the disposal age composition and stock age composition. In fact, wa(t,tn) and sa(t,tn) represents the age composition of all products disposed after usage and in use in year tn, respectively, therefore in the rest of this paper they are referred to as the disposal-after-usage age composition and active-stock age composition. L(p)(t,tn) is the lifespan profile for the products sold in year t, reflecting their obsolescence rates in year tn. L(c)(t,tn) is the cumulative lifespan distribution from year t to tn, reflecting the total obsolescence rates of products (sold in year t) during this period. P-total(t), P-visible(t), and P-invisible(t) are the total, visible, and invisible sales of EEE in year t, respectively.

world41 (Figure 1). The share of WEEE that ends up in a registered take-back system is consequently considered here as visible or registered WEEE flow. After the use phase, products belonging to the invisible EEE inflow can end up in both the invisible and visible WEEE flow and the same is valid for the ones of the visible EEE inflow. Using statistical sales data to estimate the amount of e-waste generation via a material flow analysis has become a popular approach in both developed and developing countries.27,42,43 However, the lack of data on the amount of EEE invisibly entering the market might lead to an underestimate of the actual e-waste amount. Therefore, the following sections are dedicated to investigating how the invisible inflow of (both new and second-hand) EEE into a domestic consumption market can be quantified using TVs in Vietnam as an example.

system or documented in any statistical system for production or product trade. The deficient data covering these flows likely result from their illicit nature. In general, the invisible inflow of EEE can be formed by (i) the introduction of domestic self-assembled nonbrand EEE, (ii) the illegal net import of new EEE, (iii) the illegal net import of second-hand EEE, and (iv) the domestic return of second-hand EEE. A detailed discussion of these flows is given in SI Section 1. In this study, the term “importation of second-hand EEE” is used to refer to the importation of discarded equipment that is still reusable. However, in reality, because of the unclear distinction between reusable second-hand EEE and waste that is not reusable, a fraction of waste is easily mixed and imported to a country under the label of reusable second-hand EEE. The main difference between them is the pathway, i.e., whereas a second-hand EEE is reused directly or after repair and refurbishment, WEEE directly joins the waste stream once imported. The latter is therefore not included in the sales but can be included in a direct waste monitoring system. However, data collection for this system is expensive, time-consuming, and sometimes difficult,4 and thus, the importation of WEEE is beyond the scope of this study. Because of the return loop of the waste products, secondhand products for current users are, in fact, waste from previous ones. The illegal importation of second-hand EEE (the third flow) is therefore a part of the WEEE flow from the export country and often well-known as a transboundary flow of WEEE, notably from developed to developing countries.14,39,40 Particularly, this invisible flow might originate from the unregistered (also so-called invisible or hidden) flow of WEEE, which is defined as the share of WEEE escaping from the responsible take-back system and ending up at a final disposal site, being stored or being exported to the developing

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MATERIALS AND METHODS The mass balance principle has been applied in previous studies to quantify the unknown flow.44 In this study, this principle is also applied to define the invisible sales (presents the size of invisible inflow of EEE into the market) as the difference between the total sales and the registered (or so-called “visible”) sales. The visible sales can be collected from statistics; therefore, the total sales must be defined. A general calculation pathway containing five main steps is proposed in Figure 2. Step 1: Modeling the Product Lifespan. The product lifespan is a fundamental variable, as it is of decisive importance in calculating the invisible EEE inflow and the quantity of ewaste.38,45 Therefore, it must be clearly defined and validated. Because the main purpose of this study is to calculate the invisible EEE inflow, the service lifespan (for one owner), which is defined as the period of time starting from the purchase of an EEE until it is deemed obsolete by its owner, is chosen. The defined lifespan is thus actually the in-use time of a C


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with the time-series total number of households or total population. Data on the product in-use rate can be retrieved from consumer and business surveys.49 Previous studies49−51 showed that the in-use rate (nt) of EEE could be described by a simple logistic function:

product, i.e., the time in storage or hibernation is not included in the lifespan. Moreover, the starting point for a new product is the time it is (first) purchased; whereas, the starting point for a second-hand product (both domestically generated and imported) is the moment it is sold as an used product. The lifespan of the second-hand product therefore only covers the service time for one owner, without considering the time it was used by the previous owners. A system boundary is drawn to clearly define the lifespan, the inflow, and the outflow of the studied system (Figure S1). On the basis of the above definition of the lifespan, the product stock actually represents the total quantity of products in use and hereafter is called the in-use stock or active stock; the age of the products in stock is called the active-stock age. Moreover, a product is considered to be disposed of when it is no longer used by its owner. Thus, the disposal age is called the disposal-after-usage age in the rest of the paper. The service lifespan is chosen to estimate the invisible EEE inflow because it has a better link with the consumer’s purchase behavior than the possession lifetime (i.e., the time interval that a product is kept in the household, including both service and storage time) and the domestic service lifespan (i.e., the period from the product shipment until it is disposed of by the final owner) (Figure S1).46,47 It, therefore, delivers a better estimation of the total consumption or total product sales and the invisible inflow. This judgment comes from the fact that if people have a demand for a certain product, they normally intend to buy a new one (to replace the old one) once the old appliance is not or cannot be used anymore. They will not wait until the old device is disposed of from their house. To construct the lifespan, the approach of Wang et al.25 is applied, i.e., (i) define the most suitable distribution and model the initial lifespan distribution using the disposal-after-usage age composition wa(t,tn) (the age composition of all products disposed after usage in year tn); (ii) consolidate the quality of the initial lifespan by active-stock age composition sa(t,tn) (the age composition of all products in use in year tn); and (iii) define the evolution of the lifespan distribution over time (Figure 2). Data on disposal-after-usage and active-stock age composition can be obtained through a consumer survey (i.e., by asking consumers about the service time of the discarded products and the purchase year of the product currently in use) or by directly monitoring the stock level or the waste stream.25 The survey results are then used to construct the disposal-afterusage age composition and the active-stock age composition without any adjustment for the age of second-hand products (i.e., adding the time they were in use in the past). Lifespan distribution can take the form of different statistical distributions (such as Normal, Gaussian Lognormal, Weilbull),25,48 in which the Weibull distribution has been shown to produce the best simulation of the lifespan for most EEE.25,45 The Weibull function, defined by the time-varying shape parameter α(t) and scale parameter β(t), is written as follows25 L(p)(t , tn) =

α (t ) α(t )

β (t )

nt =

1 + Be−k(t − t0)

(2)

where t is the time in the calendar year, t0 is the initial year, A is the carrying capacity representing the saturation value of nt , k denotes the growing speed of nt , and B is determined by the saturation value A and the initial value of nt.51 Steps 3 and 4: Calculating the (Total) Product Sales and E-Waste Generation. The lifespan is combined with the time-series stock to calculate the time-series product sales and the waste generation W(tn) based on eq 3 and eq 4.25,52,53 For the initial year t0, the following can be written: W (t0) = P(t0) − S(t0) = P(t0) × L(p)(t0 , t0)

(3)

For the evaluation year tn, the following can be written: W (tn) = P(tn) − [S(tn) − S(tn − 1)] tn

=

∑ P(t ) × L(p)(t , tn) (4)

t = t0

where t is the time in the calendar year; t0 and tn are the initial year that the product entered the market and the evaluation year, respectively; P(t) and W(t) are the quantity of the product sold and disposed in year t, respectively. S(t) is the quantity of product in use at the end of year t. The function L(p)(t,tn) represents the lifespan profile for the products sold at a certain historical year t, reflecting their probabilistic obsolescence rate in the evaluation year tn. For example, L(p)(t0,t0) is the share of the EEE put on the market in year t0 that is discarded in year t0. On the basis of the above definition of the lifespan, a product is considered obsolete when it is no longer used by its owner. Consequently, end-of-life treatment options include: reuse, storage, recycle, and final disposal (e.g., incineration or landfill). Therefore, to estimate the actual waste amount entering the waste management system, an adjustment of the lifespan to cover the dead-storage time or a follow-up material flow analysis for the waste stream after usage is recommended. Step 5: Estimating the Invisible Product Inflow. The quantity of TVs invisibly sold on the market is derived from its total sales (P-total(t) calculated in step 4) and its visible sales via eq 6. Data on the visible sales can be obtained from national statistics, statistics compiled by producer associations, and/or marketing research. When the sales data cannot be retrieved or is unreliable, the sales can be assumed to equal the total quantity of product put on the market (POM), which is compiled from the domestic commodity production, product import, and product export (eq 5).

α(t )

(tn − t )α(t ) − 1e−[(tn− t )/ β(t )]

A

P‐ visible(t ) = POM(t ) (1)

= production(t ) + import(t ) − export(t ) (5)

where t is the time in historical year and tn is the evaluation year. Step 2: Constructing the (Time-Series) Product (Active) Stock. The (time-series) active stock of a product can be obtained by multiplying the time-series in-use rate (quantity of equipment per household/enterprise or capita)

P‐invisible(t ) = P‐total(t ) − P‐ visible(t )

(6)

Case Study on TVs in Vietnam. The current TV market in Vietnam is characterized by two features. First, the TV market is shared by both flat panel display (FPD) and cathode D


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and sensitivity analysis were applied to evaluate the sensitivity and uncertainty of the outcomes (i.e., the quantity of waste TVs and the size of the invisible TV inflow into the market) to the variations of the model variables and the most uncertain data inputs. Particularly, three main model variables, which are (i) the lifespan, (ii) the stock and, (iii) the P-visible of the TVs, and five data inputs (see Table S4) were investigated. All the variables and data inputs were assumed to be independent, and their variation ranges were defined based on the literature and the reliability of the collected data (see SI Section 3.3). The possible ranges of the outcomes were derived using a Monte Carlo analysis with 1000 runs. The 10% and 90% percentiles of the probability distribution were calculated to define the range in which 80% of the observations of the main outcomes (i.e., invisible inflow and waste amount) can be found. To perform the sensitivity analysis, the sensitivity index method was employed; the sensitivity index and elasticity were calculated to present the analysis results.57,58 Details of the uncertainty and sensitivity analysis are described in SI Section 3.3.

ray tube (CRT) TVs; FPD TVs are growing in prevalence and are phasing out CRTs.54 Second, similar to other developing countries, the TV market is a mixture of both new and secondhand TVs. Data on TV sales, quantity in domestic use, and the TV lifespan profile were compiled from different sources (i.e., the statistics made available by the Vietnamese Customs,55 the national household living standard survey of the General Statistics Office (GSO),26,56 publications citing data from market research, and previous studies on e-waste in Vietnam). The data quality was then screened and evaluated based on select criteria. The following criteria were adapted from the work of Wang et al.25: (i) the time sequence: time-series (e.g., 2002−2012) or discrete historic data (e.g., 2004, 2006 and 2008); (ii) the data source and transparency of the data profile; and (iii) the representativeness and reliability of the data. Additional details can be found in SI Section 3.2. In this case study, data on the total TV sales and total number of TVs in stock are available, but there are no data on the market share of either new and second-hand TVs or CRT and FPD TVs. The survey data on the disposal-after-usage age composition and active-stock age composition were obtained without clearly distinguishing between new and second-hand TVs and different TV models.26,32,56 This, therefore, did not allow us to model their lifespan, sales, and stock separately. Particularly, a “TV” in this case denotes an “average” TV, representing the mixed Vietnamese market of new and secondhand TVs and different TV models. An integrated TV lifespan distribution was constructed for all kinds of TVs by assuming that their market shares are integrated in the survey data on disposal-after-usage age composition, active-stock age composition, and stock level. To construct the integrated TV lifespan, a Weibull function was employed because it also showed a better data fit (higher sum of r-square) (SI Section 3.3) than the normal distribution. Because the change of the shape parameter (α) over time was observed to be insignificant,25,53 this parameter was assumed to stay constant in this study, and only the change of the scale parameter (β) was modeled. This assumption also assists in maintaining the simplicity and transparency of the simulation of the dynamic parameters given the data limitations. Moreover, because of the data limitation, the number of TVs used in offices and governmental institutions were not included, and the total quantity of TVs in use in households was assumed to represent the entire national consumption of TVs; the active stock size was therefore calculated from the TV in-use rate in household and the total number of households. The sales calculated based on active stock were thus considered to represent the total number of TVs sold on the Vietnamese market (total sales) and was denoted as P-total(t). Because of the data limitation (see SI Section 3.2), the visible sales of TVs in this case study were assumed to equal the statistical POM, which was calculated by eq 5 using statistics from the GSO and Vietnamese Customs on TV production, import, and export. The quantity of waste TVs from 1966 to 2035 and the size of the invisible inflow of TVs in the period of 2002 and 2013 were then calculated based on eq 4 and eq 6. More details on the calculation steps of the case study are presented in SI Section 3.3. Uncertainty Analysis and Sensitivity Analysis. Uncertainty analysis is an important aspect of the study on waste estimation and prediction, notably whenever no empirical data are available to validate the results. In this study, both uncertainty

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RESULTS AND DISCUSSION Waste TV Generation and Other Intermediate Results. Estimated TV Stock. From the first calculation step, a best-fit logistic function was obtained using the following parameters: A = 1.3 unit/household, B = 69.87, k = 0.12, and t0 = 1966. Compared to the previous study of Nguyen et al.,32 our curve predicts a slower evolution in TV use and finally reaches a lower saturation level (a maximum of 1.3 TVs/household, compared to 2.4 TVs/household32) (Figure S4). Our model displays a better fit to the actual stock data (sum of r-square of 0.008 vs 0.063), likely because Nguyen et al.32 assumed that the TV demand in Vietnam followed the identical trend of the one observed in Japan approximately 36 years ago (i.e., the logistic curve has the same A, B, and k, and only t0 was adapted based on actual stock data) because of limited data. Although the time factor was considered, the validity of this assumption is questionable because of the significant differences in the socioeconomic conditions and policies in place between these two countries. Modeled TV Lifespan. The results show that after adjustment and validation, the consolidated lifespan distribution changes significantly compared to the original lifespan directly obtained from the survey. The active-stock age composition and stock level of TVs derived from the adjusted lifespan fit better to the actual active-stock age composition and stock level in 2006 and 2012 (Figure S3). The adjusted (constant) lifespan has a higher shape and scale parameter compared to the (initial) unadjusted lifespan (α = 3.3 vs 2.5 and β = 9.6 years vs 8.4 years), representing a longer product life. In this study, the TV lifespan distribution is normalized to the size of shipments in the corresponding shipment years. Its scale parameter is therefore not affected by the product shipment, and the temporal changes in lifespan can be predicted by comparing the lifespan distributions of different years.46 Simulating the evolution in the active-stock age composition in 2006 and 2012 indicates an annual decrease by approximately 0.097 years in the scale parameter when the shape parameter remains constant. This might be explained by recent innovations in image technology, leading to the significant transition from CRT TVs to FPD TVs and economic development inducing an increase in living standards in Vietnam.30 Because of the data limitation, this trend is assumed to be constant for the entire studied period (i.e., from E


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Vietnamese households, confirmed by our estimations shown in Figure 3a. Before 2000, the number of TVs annually disposed was relatively low. However, a rise of about 3.6 times in the annual amount of TV waste, from 700 thousand units to approximately 2.6 million units, was observed in the period from 2000 to 2014. From 1966 to 2014, a total of 30.5 million TVs were discarded in Vietnam. This upward trend is expected to continue in the future. In 2035, about 9.5 million TVs are predicted to be discarded (Figure 3a). Estimated Invisible TV Inflow. The results show that the total quantity of TVs sold on the market in 2013 is approximately 3.9 million TVs, of which 3.1 million are officially recorded in statistics,55 leaving a gap of 0.8 million TVs undocumented. The share of the invisible TV inflow in the TV market in 2013 is thus approximately 20% (Figure 3b). During the whole period of 2002 to 2013, a total of 4.6 million TV sets were estimated to be invisibly introduced into the Vietnamese TV market. On average, this flow contributed approximately 15% to the total number of TVs available and sold on the market during that time (Figure 3b). As discussed above, this 15% of the total TV sales because of the invisibility of the sale might lead to the omission of the identical fraction from the e-waste estimation based on market data, equivalent to an average of approximately 384 000 waste TVs per year (mix of CRT and FPD TVs). It is assumed that the average weights of CRT and FPD (LCD) TVs are constant over the studied period and are approximately 26.7 and 28.3 kg, respectively.6 Considering the presence of many valuable elements (e.g., the content of Cu, Au, and Ag in an average CRT TV is 3.6%, 6.4 ppm, and 99.4 ppm, respectively, and in an average LCD TV is 2.9%, 3.9 ppm, and 15.9 ppm, respectively)6 and hazardous elements (e.g., the content of deca-PBDEs in CRT TV is 40 000 ppm and in FPD TV is 7000 ppm),60 this invisibility likely results in a failure to properly manage these valuable secondary materials (e.g., 316−373 t Cu, 42−65 kg Au, 173− 1018 kg Ag) and prevent the leakage of toxic compounds (e.g., 76−409 t deca-PBDEs) by official/legal e-waste management system. This result suggests that quantifying the invisible inflow of a product is important from both the economic and environmental perspectives. As this is the first estimation of the invisible EEE inflow, there are no such data from existing literature to compare. Additionally, the illegal nature of the invisible inflow complicates or precludes empirically judging the accuracy of this estimation. Therefore, in the next section, the uncertainties associated with the applied model and assumptions are discussed in detail. Uncertainty and Sensitivity Analysis. The uncertainty analysis defines the possible uncertainty ranges for both the invisible inflow of EEE and the waste estimation due to the uncertainty of the data inputs (Figure 4). The probabilistic mean defined for the average share of the invisible sales in the period 2002−2013 is 15% (coefficient of variation (CV) is 0.21) and the total amount of TV waste from 1966 to 2014 is 29.3 million (CV is 0.07), matching well with our original estimations (15% for the average share of the invisible sales and 30.5 million for waste TVs). Their 10% and 90% percentile are defined at 11% and 20%; and 26.8 million and 32.1 million, respectively. About 7% of the values obtained in the uncertainty analysis have a value below 0. These values should be ignored because they are not realistic. The negative values in the uncertainty

1966 to 2035). The specific values for the alpha and beta parameters of each year are listed in Table S5. Compared to the results from a case study in The Netherlands,25 the evolution of the TV lifespan in Vietnam actually occurs at a lower rate. Over 10 years (from 1995 to 2005), the TV lifespan in The Netherlands decreased by 1.9 years, equivalent to an average loss of 0.19 years every year.25 This difference in the rate of decrease can be explained by the higher replacement rate of electronic products in countries with higher income. A comparison between the lifespan results obtained in this study and previous e-waste studies in Vietnam is presented in SI Section 3.4.1. Estimated (Total) TV Sales and Waste TV Generation. The results of the annual (total) TV sales (P-total(t)) show a continuous growth of the TV market in Vietnam (Figure 3a).

Figure 3. Annual total TV sales and the number of waste TVs annually generated in Vietnam from 1966 to 2035 (a) and the share of the invisible sales in the annual total TV sales on the market in the period of 2002 to 2013 (b).

From 2000 to 2014, the total number of TV sets sold on the Vietnamese market increased by a factor of 2.6, equivalent to an average annual growth rate of approximately 6.9% for the entire period and 5.7% for the specific period from 2002 to 2012, which is in the estimated range (5.5−6.4% for the period 2002−2012) of RNCOS.59 In the upcoming period of 2015 to 2035, TV sales are expected to continue to increase, though at an average growth rate of 6.4%. Additional discussion of total TV sales (including the explanation for the wiggles around 2010 in the curve of the total TV sales) can be found in SI Section 3.4.2. The development of the TV market along with shorter lifespan of the equipment has led to a gradual increase in the number of waste televisions annually resulting from the F


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Additional results on the uncertainty analysis and the sensitivity analysis for the quantity of waste TVs generated are shown in SI Section 3.4.3. Remaining Limitations and Future Perspectives. Besides the uncertainties associated with the limitations in the model, another source of uncertainty is from the assumptions and simplifications used in the estimation (see Table S2). One of the most important assumptions used in this study is that a TV is considered to be waste when it is no longer used by its owner. Therefore, the service lifespan is used in calculating the invisible flow and the number of waste TVs. Although this selection is mainly based on the available data for the lifespan, it is, in our opinion, also suitable for estimating the invisible inflow. However, the use of the service lifespan in estimating waste TVs might be questionable because it might result in an overestimation of the waste amount entering the waste management system. In this case study on TVs in Vietnam, the effect of using the service lifespan for waste estimation is assumed not to be substantial because of the tendency of people to quickly sell large-sized and valuable obsolete devices (such as waste TVs) for both cash and to save storage space. Additionally, the survey results showed that, on average, only 6.8% to 16.9% of disposal TVs are put in storage after use.32,36,37 However, further study on the lifetime or a follow-up material flow analysis of the waste stream from households is always recommended, notably whenever the storage time is significant (e.g., in the case of mobile phones).61 The assumption is that the quantity of TVs sold on the market (the visible sales, P-visible(t)) is considered equal to the amount of TVs available on the market. In fact, not all products put on the domestic market will be sold. Consequently, by maintaining this assumption, we could overestimate the actual scale of the visible TV inflow and, hence, underestimate the size of the invisible TV inflow. Another assumption is that the number of TVs used in households represented the entire TV stock, and the number of TVs in offices or governmental institutions was not included. Because the share of the latter has been witnessed to be insignificant in some case studies in developing countries (e.g., 2% in Ghana22 and 1% in Morocco62), the effect of this assumption on the result is expected to be negligible. Several simplifications in the calculations may also cause a certain deviation for the estimation results, particularly for future prediction. For example, the market share of CRT and FPD TVs, of new and second-hand TVs, and their potential effect on the future e-waste stream are not considered. Considering the differences in the lifespan of new and second-hand TVs and the gradually reducing demand for used products when the price of new products fall,50 it is more accurate to separate their mass flow and lifespan. However, as data are lacking, we consider this assumption as one of the acceptable limitations of the current paper that will be addressed in future work. Moreover, the lifespan is a very dynamic factor that varies sensitively according to the effect of many socioeconomic factors (e.g., technical change, environmental policy, economic conditions, and consumer’s awareness). The mixed effect of these factors on the lifespan is impossible to predict, particularly in the long term. In this case study, because of data limitations, the dynamic lifespan of TVs was constructed only based on data of 2006 and 2012; the estimated changes of the lifespan therefore only best represents the trend of the period 2006−2012, and extrapolation to the

Figure 4. Uncertainty distribution of the share of the invisible sales in the total sales (a) and the waste generation (b). In these figures, the red line represents the deterministic estimation result (the one presented in Figure 3b), the blue line is the “best probabilistic estimate result” representing the mean from 1000 runs, and the shaded area represents the range defined by the 10% and 90% percentiles.

analysis results are explained by two reasons. First, in the uncertainty analysis, the (possible) correlations between the concerned variables and the data inputs are not included. This might lead to an overestimation of the uncertainty analysis results. Second, whereas the data on visible sales are retrieved through statistics and assumed to represent the actual fluctuation of the market, the total sales are modeled. The calculated results of the annual total sales are therefore actually smoothed out and cannot reflect the actual variations of the annual sales. Hence, the calculation approach proposed in this study should more likely be used to estimate the invisible inflows for a certain period than that of a certain year. Regarding the sensitivity analysis, variations of P-invisible(t) when the carrying capacity (A) and the visible sales (P-visible) change from the lower bound to the upper bound are shown in Figures S5a and S6. The result shows that the P-invisible is more sensitive to the changes of the P-visible than the carrying capacity. On average, the average share of the invisible inflow in the period of 2002 to 2013 increases by approximately 1.0% when the carrying capacity parameter (A) increases by 1.0%, and decreases by 1.7% when the P-visible increases by 1.0%. The calculated sensitivity index for this output with respect to the change of A and P-visible are 0.5 and 1.0, respectively, also confirming the above observation. Additionally, when the carrying capacity is kept constant, the adjustment of the initial constant lifespan by the active-stock age composition has a substantial effect on the scale of the invisible inflow (Figure S5b). Specifically, the average market share of the invisible inflow within the period of 2002 to 2013 decreases by 10% (from 27% to 17%) when the initial constant lifespan is replaced by the adjusted constant lifespan. However, this value only decreases by 2% when this adjusted constant lifespan is substituted by the time-varying lifespan (Figure S5b). G


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past and the future has a higher uncertainty. These errors demand exploiting and integrating additional data to validate and adapt current estimations and predict future trends. In short, additional data, better data, and improving the model employed (e.g., using the extended logistic model, which is more advanced and more suitable when data are lacking63) can further enhance our first estimation results. Despite the remaining limitations, this study is the first attempt to quantify the invisible flow of products penetrating into a market for domestic consumption. Although only TVs in Vietnam are used as an example, the proposed approach is suitable for other countries to define the invisible inflows of other appliances.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b01388. Additional information on the invisible inflows of EEE; a review on available e-waste estimation models, focusing on their application in developing countries; assumptions and simplifications used in the case study; assessment of the data availability and quality; and additional results and discussion on the modeled TV lifespan, total TV sales, and uncertainty and sensitivity analysis (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*Tel: +32 (0)9 264 99 27; fax: +32 (0)9 264 62 43; e-mail: ha. [email protected]. *Tel: +32 (0)9 264 59 49; fax: + 32 (0)9 264 62 43; e-mail: jo. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS H.P.T. is a PhD research fellow supported by the Special Research Fund (BOF) of Ghent University. T.S. was granted by a research project (number 3G092310) of the Research Foundation - Flanders (FWO-Vlaanderen). F.W. was funded by the EU H2020 PROSUM project. We sincerely thank DucQuang Nguyen for sharing data, Duc-Anh Luong for his kind support and useful discussion on the logistic model, Minh-Tu Pham for his help with Matlab, and Rudi Kohnert for checking and editing the English.

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REFERENCES

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Estimation of the Unregistered Inflow of Electrical and Electronic

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