Quantifying Domestic Used Electronics Flows using a Combination of

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Quantifying Domestic Used Electronics Flows using a Combination of Material Flow Methodologies: A US Case Study T. Reed Miller, Huabo Duan, Jeremy Gregory, Ramzy Francis Kahhat, and Randolph Kirchain Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00079 • Publication Date (Web): 01 May 2016 Downloaded from http://pubs.acs.org on May 2, 2016

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Environmental Science & Technology

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Quantifying Domestic Used Electronics Flows using a

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Combination of Material Flow Methodologies:

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A US Case Study

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T. Reed Miller *1, Huabo Duan 1, Jeremy Gregory1, Ramzy Kahhat2, Randolph Kirchain1 1

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. Materials System Laboratory, Engineering Systems Division, Massachusetts Institute of Technology,

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Cambridge, Massachusetts, United States.

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Note: Huabo Duan is currently at the Department of Civil Engineering at Shenzhen University.

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. Department of Engineering, Pontificia Universidad Católica del Perú, 1801 Avenida Universitaria, San Miguel, Lima

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E32, Peru

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ABSTRACT

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This paper describes the scope, methods, data, and results of a comprehensive quantitative

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analysis of generation, stock, and collection of used computers and monitors in the United States

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(US), specifically desktops, laptops, CRT monitors and flat panel monitors in the decade leading up

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to 2010. Generation refers to used electronics coming directly out of use or post-use storage destined

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for disposal or collection, which encompasses a variety of organizations gathering used electronics

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for recycling or reuse. Given the lack of actual statistics on flows of used electronics, two separate

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approaches, the sales obsolescence method (SOM) and the survey scale-up method (SSUM), were

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used in order to compare the results attained and provide a range for estimated quantities. This study

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intentionally sought to capture the uncertainty in the estimates. To do so, uncertainty in each dataset

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was incorporated at each stage using Monte Carlo simulations for SOM and establishing scenarios

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for SSUM. Considering the average results across both methods, we estimate that in 2010 the US

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generated 130 to 164 thousand metric tons of used computers and 128 to 153 thousand tons of used

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monitors in 2010, of which 110 to 116 thousand tons of used computers and 105 to 106 thousand tons

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of used monitors were collected for further reuse, recycling, or export. While each approach has its 1

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strengths and weaknesses, both the SOM and the SSUM appear to be capable of producing reasonable

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ranges of estimates for the generation and collection of used electronics.

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INTRODUCTION

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In 2014, 134 million desktop computers, 174 million laptop computers, and 113 million computer

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monitors were shipped worldwide [1, 2]. Annual overall purchase levels are expected to slow only

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slightly in the coming years. After being used by one or more people in homes and institutions and

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being put in storage temporarily, these millions of devices become used electronics that are generated

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and are either thrown away or collected for end of use (EOU) processing. Generation is consistent

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with the term “ready for end-of-life management”[3]. In 2014, an estimated 3 million tons of small

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IT devices and 6.3 million tons of screens were generated across the world[4]. There is significant

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interest from a variety of stakeholders, such as government agencies, electronics recycling firms, and

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environmental NGOs, in the quantities of used electronic products generated and collected.

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Several studies have developed generation estimation approaches; the methods, data sources, and

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regional and temporal scope vary considerably. The United Nations Envrionment Programme applied

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simple stock models to quantify used electronics volumes[5]. “Free or cheaply available indicators

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provided by the International Telecommunication Union (ITU) and the World Bank” served as model

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data for Mueller et al. to quantify used electronics flows[6]. The application of optimization

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techniques with material flow analysis have been used, in Japan, by Yoshida and Terazono to estimate

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the flow of used computers[7]. Stock and flow models incorporating logistic technological diffusion

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were applied by Yang et al. for the US[8] and Yu et al. for world regions[9]. An estimate by Babbitt

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et al. of used electronics from higher education institutions was based on university employee

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personal computer property control data[10]. The US Environmental Protection Agency (EPA)

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updated two earlier studies and estimated generated quantities using a deterministic sales

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obsolescence method[3]. Kahhat and Williams commissioned a survey of US residential and business

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computer owners and captured their ownership and disposition behavior and then used the data to

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calculate a mass balance of those electronics flows[11]. Wang et al. conducted an input-out analysis

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linking sales, stock, and lifespan data[12]. The US International Trade Commission (ITC) surveyed 2


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the domestic used electronics product industry about export, and also captured its domestic

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output[13]. The United Nations University (UNU) launched an interactive map of e-waste

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generation[14] and then released a global monitor, both of which used trade data to estimate sales and

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inferred lifespans from stocks and apparent sales[4].

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Surveys, collection rates from other regions, and government data have been used to estimate

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US used electronics collection rates. Collection refers to a variety of organizations gathering used

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electronics for recycling or reuse. Several surveys[11, 15, 16] have been conducted to ascertain the

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used electronics management options utilized by consumers and business electronics owners; this

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data can be used to infer collection rates. Documented European collection trends were adjusted for

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other world regions’ CRT collection[17]. Reports from states with used electronics recycling

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programs were combined with low collection rate assumptions for states without programs[3].

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While these studies have been informative, there remain a few gaps in the existing literature on

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quantitative estimates of the generation and collection of computers and monitors in the United States.

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The estimates derived from sales obsolescence methods tend to be sensitive to lifespan assumptions,

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so considerable effort should be spent on constructing accurate product lifespans. A fraction of used

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electronics is reused by another owner before and/or after generation, so reuse expectations should

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be factored in to lifespan estimates. Since used electronics collection systems vary widely across the

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US, estimated collection rates need to incorporate the governmental, non-profit, and private business

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collection options available to consumers and businesses. Finally, given that several aspects of used

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electronics flows are difficult to measure with precision, quantity and weight estimates would ideally

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include measures reflecting the underlying data and methodological uncertainties.

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The two methodologies described in this study attempt to address the existing gaps in the

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literature. In particular focus are the lack of uncertainty in quantitative estimates of used electronics

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flows and the simple treatment of product lifespans.

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comprehensive quantitative analysis of generation and collection flows of used computers and

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monitors in the United States (US) in the years 2000 to 2010.

The main objective of this study is a

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OVERVIEW OF TWO METHODS EMPLOYED

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There are tradeoffs between feasibility, accuracy, and flexibility in the selection of an approach.

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Two separate approaches, the sales obsolescence method (SOM) and the survey scale-up method

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(SSUM), were used in this paper in order to compare the results attained and provide a range for

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estimated quantities. SOM was chosen based on data availability and the ability of the model to easily

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capture uncertainty, whereas SSUM was chosen because of its streamlined approach. The increased

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rigor of the implementation of the two methodologies along with the quantification of uncertainty

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defines the contribution of this work. However, there are still data gaps that need to be resolved before

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the uncertainty in the calculations can be reduced.

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The SOM quantifies generation using a modified sales obsolescence model involving sales data

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and lifespans derived from a survey, and collection using trends in survey collection rates. A unique

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element of the SOM is the development of lifespan estimates using survival analysis techniques

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borrowed from medical sciences: each product is effectively treated as a terminally ill patient.

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Uncertainty in each dataset was incorporated at each stage in the SOM using Monte Carlo (MC)

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simulations. Each MC trial calculates results with a randomly drawn combination of values from

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within the bounds of reasonable assumptions for each variable.

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The SSUM uses survey data to quantify flows of electronics that are generated and collected by

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balancing all of the flows around intermediaries. Estimates of the national quantities are made via

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scale factors. Uncertainty is captured in the development of the scale factors and by use of three

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scenarios for the fractions of the collected electronics which are subsequently reused or exported.

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Table S2 in the SI summarizes key elements of the two methods.

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SCOPE OF STUDY AND DATA SOURCES

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Scope of Study

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Products Studied: Used, whole units of computers and computer monitors were studied. Desktop

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and laptop computers were included, but tablet computers were excluded. Computer monitors

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comprise CRT and flat panel monitors. 4


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Time Frame: Both the SOM and SSUM base their analyses on a set of surveys targeting computer

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equipment user behavior in the year 2010, and therefore it makes most sense to apply the results to

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the intended year. Also, while sales and trade data are available quickly, related analyses about used

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electronics flows for comparison typically lag several months to several years. Additional calculations

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are made beginning in 2000 in order to observe trends and to compare stock estimates to external

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sources; robust stock estimates are available in years 2001, 2005, and 2009.

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Data Sources

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Survey Data: Both approaches utilized the data from the same residential and business/public

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surveys, which were conducted focusing on computers and monitors in the year 2010, but also asked

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about previously owned devices and the EOU activities. The residential survey included questions

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about each product, while the business/public survey included questions about groups of items. The

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nationally representative surveys were designed and commissioned by Kahhat and Williams[11].

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Residential surveys conducted by others from other years were sought to create a time series trend of

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collection rates[18-21]. Additional survey details are found in the supporting information (SI)

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(Section 1.1).

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Sales Data: Sales data were utilized in both the residential and business/public steps. Anticipating

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that some used electronics are generated decades after their purchase, time series sales data is sought

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from two decades before the year of prediction (since 1990). Sales data for each product was

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purchased from the International Data Corporation (IDC) and are allowed to vary +/- 10% to capture

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error in the MC simulation. Sales data was available for computers since 1996, and monitors since

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2008. The model begins with sales in year 1990, so additional data sources were required for the

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missing information[3, 22]. If these data sources did not distinguish between residential and

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business/public, the fraction of residential sales observed in the IDC data was allowed to vary +/-

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10% to capture error and applied to the total sales quantity. Due to the proprietary nature of the

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purchased data, values are not presented here, but similar published data is presented in Figure S1.

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Unit Weight Data: The unit weight data for computers and monitors are estimated based on the

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several thousand samples of collected used products in Oregon and Washington [23]. 5


The


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distributions of these products’ unit weights formed a probability density function that was used

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within MC simulations. While desktops and laptops were differentiated in the dataset, monitors were

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combined, and thus the apparent bimodal distribution was assumed to differentiate CRT monitors and

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flat panel monitors. The Finite Mixture Models[24] package embedded in Stata® 12.1 data

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management software was employed to differentiate the underlying lognormal distributions. Figures

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S2 and S3 and Table S1 in the SI provide the unit weight distributions.

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SALES OBSOLESCENCE METHOD (SOM)

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The SOM, which has been described in a report[25], is summarized here and elaborated on in the SI

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(Section 2). The residential generation and collection SOM follows the basic overall approach to

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determine generation and collection quantities in several cited studies; an emphasis is placed here on

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lifespan estimation and factoring in informal reuse. The business/public dataset from the surveys used

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did not enable the use of SOM; future surveys could be designed with this intention.

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SOM Residential Generation Estimation Procedure

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I.

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often be purchased from firms that either gain shipment information from manufacturers or sales

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volumes from retailers. Other firms may make inferences about sales using complex algorithms. Data

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sources used were described above.

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II.

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the product over a time period. Also determine the typical distribution of time until an electronic

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product is reused, and the proportion of products that are expected to be reused. Three pathways to

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generation considered are illustrated in Figure 1: electronics that are only used once before generation

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(O), that are formally reused after a first round of generation and collection (C), and that are used by

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a first owner and then informally reused before generation (I). Informal reuse refers to small-scale

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exchanges of electronics between individuals. This contrasts with formal reuse, which occurs if the

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used electronics processor opts to prepare the used electronic whole unit for reuse by a new user in

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the US versus recovering and selling parts and materials from the item or exporting the used electronic

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product as a whole unit; these devices are generated twice. Note in Figure 1 that there are two types

Determine the residential sales of a product in a region over a time period. Sales data can

Determine the typical distribution of residential lifespans, including use and storage, for

6


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of lifespan distributions (possession spans, see [26]) represented, possession span, no informal reuse

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λ and possession span, pre-informal reuse δ. Since electronic products are more useful when younger,

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ownership of products is typically transferred sooner when informally reused, and therefore δ is

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shorter on average than λ. Also, the collected products that get formally reused are among the younger

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products collected, and their age at reuse follows δ as well.

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Figure 1: Flows of electronic products undergoing one use before generation (O), formal reuse after generation

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and collection (C), and informal reuse before generation (I). Above each owner is an illustration of the distribution

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of the possession span, no informal reuse λ or possession span, pre-informal reuse δ. The dotted line indicates that

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the product is generated at that point. Note that products in path C are generated twice.

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To compute the λ distributions for each residential product, survival analysis techniques were

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employed[27]. Survival analysis is typically employed in studies of patient survival of disease or of

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machine failure. Adapting that terminology to this study, a failure is defined as the end of a period of

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ownership, delimited either by generation or informal reuse. If an item is still with the owner at the

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time of the survey, it is censored; this presents a challenge of using this approach for new technologies 7



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since many would be censored. Weibull distributions of λ were obtained by fitting Kaplan-Meier

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survivor curves, as described in detail in SI Section 2.1. Ideally items would be separated into those

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that were purchased new and those purchased used since the two groups have different remaining

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possession spans, but there were insufficient used purchases captured by the surveys.

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Given the structure of the survey questions, the best approximation of δ is obtained by

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modeling the distribution of age of electronics that were informally reused at EOU (see Figure S11).

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This does not capture electronics that were formally reused, nor electronics still in the home which

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were purchased used. Therefore, we approximate that the estimated δ applies to formally used

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electronics as well.

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III.

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the sales and lifespan information. The goal of this step is to estimate how many residential products

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are generated in a given year. To do so, first, the EOU activities provided by survey respondents were

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categorized as either not generated (eg. given to friend, sold on eBay), or generated (eg. returned to

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retailer, put in garbage). Table S3 provides all of the categorizations. Next, to find the quantity

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generated in a given year y, the quantity of products sold in a given year s is multiplied by the

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percentage of those that are generated in year y; the sum is taken across all prior years s when the

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product was sold.

Calculate how many residential products are predicted to be generated in a given year using

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In order to determine the total generation in a given year y, the estimates of λ and δ are

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combined with probabilities that electronics follow the pathways illustrated in Figure 1. A detailed

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description of this method is found in SI Section 2.1. The generation quantity in year y is the sum of

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these three groups, as shown in Equation 1. The starting year of the reuse purchases (I and C) is

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shifted from the year of new sales s by the length of δ. The same possession span λ was applied to

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used and new products. 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦) = 𝑦

∑ 𝑆𝑎𝑙𝑒𝑠𝑂 (𝑠) ∗ 𝜆(𝑦 − 𝑠) + 𝑆𝑎𝑙𝑒𝑠𝐼 (𝑠) ∗ 𝜆(𝑦 − (𝑠 + 𝛿)) + 𝑆𝑎𝑙𝑒𝑠𝐶 (𝑠) ∗ 𝜆(𝑦 − (𝑠 + 𝛿))

(1)

𝑠

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In order to estimate the sales quantities of each group, it is assumed that reuse purchases (I and 8


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C) in a given year s are proportional to new sales in the same year s. This is based on the assumption

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that popularity of used products trends with the popularity of new products. The ratio β of used to

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new purchases in the survey data each year were modeled in order to capture this phenomenon, and

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found to be around 0.1 for all products except CRT monitors (see Figure S13). Next, the fraction of

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used purchases that occurred through I as compared to C, α, was estimated with high uncertainty.

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Lastly, all of the new purchases in a given year were assumed to pathway O, less those which are

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predicted to be informally reused in future years (I). These equations are presented in the SI.

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Ideally possession span estimates would be made as a time-series trend, but that was not done

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due to data limitations; more frequent surveys could enable that analysis. However, while λ and δ are

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fixed in this analysis, the mean age of a new product cohort at first round of generation lengthens

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with a larger γ, the fraction of new sales that followed pathway I versus O. While the first owner on

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pathway I tends to possess the product for a shorter span of time than the first owner on pathway O,

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since there are two owners before generation on pathway I the overall span is longer. The fraction γ

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is positively correlated with β, α, and the growth rate of future sales. If future sales are flat, γ

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approaches the product of β and α.

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SOM Residential Stock Estimation Procedure

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Since estimates of electronic ownership can be derived from large government surveys, these data

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serve as more robust validation metrics than comparisons to other modeled generation estimates. The

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stock of electronics still in households can be calculated using sales data and generation estimates if

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sales data are available since the product was first sold, s*. We effectively modify the stock and

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lifespan model in [12] to explicitly account for the electronics in stock being formal reused. Equation

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2 shows that the stock is the cumulative sales less the cumulative generation. 𝑦

𝑦

𝑆𝑡𝑜𝑐𝑘(𝑦) = ∑(𝑆𝑎𝑙𝑒𝑠𝑂 (𝑠) + 𝑆𝑎𝑙𝑒𝑠𝐼 (𝑠) + 𝑆𝑎𝑙𝑒𝑠𝐶 (𝑠)) − ∑ 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦) 𝑠∗

(2)

𝑠∗

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SOM Residential Collection Estimation Procedure

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Calculate how many of the residential generated products are predicted to be collected in a given 9



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year by applying collection rates. In order to calculate the quantity of residential used electronics

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collected for processing toward reuse or recycling versus those that are disposed of in the garbage, a

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collection rate is applied to the quantity generated that same year y as shown in Equation 3. Since

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some sources report the percentage of electronics that go to intermediate fates such as storage,

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Equation 4 presents the normalization of only those percentages that pertain to generation, collection

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and landfill. 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑒𝑑(𝑦) = 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒(𝑦) ∗ 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦)

𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒(𝑦) =

𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑒𝑑 % (𝑦) 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑒𝑑 % (𝑦) + 𝐿𝑎𝑛𝑑𝑓𝑖𝑙𝑙 %(𝑦)

(3)

(4)

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While the collection rates could have been inferred solely from the survey data used for the

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generation model, we applied a more robust approach using results from several surveys which

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sampled five different representative groups of US residential computer owners (including the survey

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used in the generation model) from 2005 to 2012[18-21].

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Figure S14 provides the estimated collection rates for monitors (not distinguished between CRT

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and flat panel monitors), laptops, and desktops across all of the surveys. To account for uncertainty

237

in the survey data and regression, the estimated collection rates for a given year were allowed to vary

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+/- 10% from the linear regression in the MC simulation.

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SURVEY SCALE-UP METHOD (SSUM)

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The survey scale-up method (SSUM) has the capacity to calculate several flows of used electronics

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while requiring relatively few data inputs. The description of the SSUM here is based on an article

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describing its development and the associated surveys[11], along with a report applying the

243

method[25]. Survey results are scaled to the national level. This is accomplished using scaling factors

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based on comparison between household size and computer owners nationally for residential

245

estimates, and reported purchases by survey respondents and sales data for business/public estimates.

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These are the same nationally representative residential survey results used in SOM as well as results

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from a nationally representative business survey. While the prior work includes three end-of-use 10


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scenarios to capture uncertainty, this version of the method additionally incorporates data uncertainty

249

in the scaling factors. As with the SOM, the weight of generated and collected products is calculated

250

by multiplying the quantities by associated unit weight distribution during the MC simulation.

251

Purchase, use, and EOU habits are assumed to differ between these households and

252

business/public users, so the flows are modeled separately. Figure 2 depicts the flows of electronics

253

from the manufacturer (M) through residential households (H) and business/public (B) users, to

254

intermediaries (I); informal reuse might occur before it reaches an intermediary. Intermediaries also

255

collect used imports (Im). The intermediaries then either redistribute what they have collected for

256

formal reuse to households and business/public users, dispose them at the landfill or incinerator (L),

257

sell them domestically for parts and materials recovery(R), or export them to a foreign country (E).

258 259

Figure 2: Material flow analysis for the selected country in SSUM. The ordering of indices is from/to, i.e. FHI

260

refers to flows from residential households (H) to intermediary (I). Adapted from [11].

261

An array of organizations act as intermediaries in this process. These organizations include used 11



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electronics management companies, brokers, resellers, donation agencies, internet sales sites, and

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municipalities. In the SOM, only the aggregate generation and collection flows depicted in Figure 1

264

are estimated, while in the SSUM, each flow can be estimated. In this study, we follow the

265

methodology established in previous work by Kahhat and Williams [11] to scale the survey data to

266

the national level, with refined data.

267

SSUM Residential Generation and Stock Estimation Procedures

268

The residential R flows from the survey are scaled to the national level by comparing the total

269

surveyed residential population SPR to the corresponding national population in the same year y [11].

270

(In the SOM, sales year s was distinguished from generation year y, but in these SSUM equations the

271

distinction is not relevant.) The survey respondents were limited to adults with a computer at home,

272

and the responses referred to all computers in the home. Therefore, the corresponding national

273

population was estimated by multiplying the Census civilian noninstitutionalized population NPR [28]

274

by estimates of the percentage of computer-owning households CO[29]. Note that since the surveys

275

were nationally representative of computer owners in the year 2010, estimates for y=2010 are likely

276

to be more accurate than prior years. Scale factors SF from Equation (5) for years 2000 to 2010,

277

incorporating underlying data uncertainty, are presented in SI Section 3.1. 𝑆𝐹𝑅 (𝑦) =

𝑁𝑃𝑅 (𝑦) × 𝐶𝑂(𝑦) 𝑆𝑃𝑅 (𝑦)

(5)

278

Referring back to Figure 2, residential generation is equivalent to the flow FHI. As with the

279

SOM, survey responses suggesting EOU disposition of used electronics such as “storage”, “donation

280

to a friend/family”, and “did not discard”, described in Figure S15 were not considered to be

281

generation pathways and were not included. The scale factors are multiplied by the generated quantity

282

obtained from the survey SG in (6); this equation also applied to business/public flows. 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦) = 𝑆𝐹(𝑦) ∗ 𝑆𝐺(𝑦)

(6)

283

A similar approach is taken to estimating the electronics stock in households (H). The stock

284

in survey respondents’ households SH in year y is found by counting those that were bought in or 12


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before y and were not generated by y. Scale factors are applied in Equation 7. This approach leads to

286

results with less uncertainty compared to the SOM. 𝑆𝑡𝑜𝑐𝑘(𝑦) = 𝑆𝐹(𝑦) ∗ 𝑆𝐻(𝑦)

(7)

287

SSUM Business/Public Generation Estimation Procedure

288

Sales to the business/public sector is represented by flow FMB, and business/public B generation is

289

approximately equivalent to the flow FBI. The residential survey asked about the fate of each item

290

over time while the business/public survey asked about groups of items in the recent year. In this

291

approach, the responses to survey questions about most recent purchases in year y were tabulated.

292

Future surveys could involve questions about historical purchases to develop a time series. Equation

293

6 demonstrates that to arrive at generation estimates in year y, the reported generated products in year

294

y tabulated from survey (FBI) were multiplied by the scale factors from Equation 8. Uncertainty in the

295

sales data and survey data were accounted for. 𝑆𝐹𝐵 (𝑦) =

296

𝑆𝑎𝑙𝑒𝑠𝐵 (𝑦) 𝐹𝑀𝐵 (𝑦)

(8)

SSUM Collection Estimation Procedure

297

All flows originating from the intermediaries except for landfill are considered collection.

298

Collection is calculated by subtracting the landfill flow from the estimated generation quantity in

299

Equation 9. In order to determine the quantity of used electronics from intermediaries that go to the

300

landfill, FIL, survey responses about disposition of end-of-use products are analyzed. This landfill

301

flow can be calculated separately for residential and business/public surveys, so collection can also

302

be calculated separately for each sector. To capture uncertainty, three scenarios were developed about

303

the actual destination of products. Figure S15 presents the end-of-use (EoU) path specified by the

304

survey respondent with the assigned end-of-use path for each scenario and associated explanations.

305

The key assumptions were that if a survey respondent stated the electronics underwent “Disposal via

306

curbside garbage collection”, 100% went to landfill in both the Intended EoU and Higher Export

307

scenarios, while 80% went to landfill and 20% went to recycling in the Lower Reuse scenario. 13



𝑆𝑢𝑟𝑣𝑒𝑦 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑒𝑑(𝑦) = 𝑆𝑢𝑟𝑣𝑒𝑦 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑(𝑦) − 𝐹𝐼𝐿

Page 14 of 23

(9)

308

To arrive at collection estimates for the country, survey results are amplified with the same scale

309

factors used for generation estimates, shown in Equation 10. The collected products in year y from

310

residential and business/public surveys found above are scaled to each country’s residential and

311

business/public sectors using Equation 7. 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑒𝑑(𝑦) = 𝑆𝑐𝑎𝑙𝑒 𝐹𝑎𝑐𝑡𝑜𝑟(𝑦) ∗ 𝑆𝑢𝑟𝑣𝑒𝑦 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑒𝑑(𝑦)

(10)

312

RESULTS AND DISCUSSION

313

Lifespan Distributions

314

Lifespan distributions estimated in this model were compared to other published distributions. Figure

315

3 shows estimated distribution of possession span, no informal reuse λ for desktops, laptops, CRT

316

and flat panel monitors with a solid line. Since the fraction γ of products that followed pathway I was

317

modeled to be small in the decade of interest, using λ as a proxy for the lifespan from purchase until

318

first round of generation is only a slight underestimate. The dashed line shows Weibull distributions

319

representing the lifespan until generation developed by UNU for these products in the Netherlands,

320

France, and Belgium (NL, FR, BE). Note that tablets are included in the same category as laptops in

321

the UNU lifespans[30]. The dash-dot line shows the same developed for Germany (DE) in 2010 [31];

322

the parameters of the Weibull distributions for those studies are available in Table S7 of the SI. The

323

dotted line shows empirical distributions of the age of used electronics collected over 23 months at a

324

no-fee drop-off center in Chicago; monitors were not distinguished by type. The computer age at

325

drop-off tended to be longer than the expected wear out lifespan[32]. The descriptive statistics are

326

available in Table S8 of the SI.

327

14


Page 15 of 23


328 Desktop

Frequency

20% 16%

16%

12%

12%

8%

8%

4%

4%

0%

0%

0

10

20

30

0

CRT Monitor

20%

Frequency

Laptop

20%

16%

12%

12%

8%

8%

4%

4%

20

30

Flat Panel Monitor

20%

16%

10

0%

0% 0

10 20 Lifespan (years)

This study

0

30

NL, FR, BE [28]

DE [29]

10 20 Lifespan (years)

30

US collection [30]

329

Figure 3: Comparison of this study’s lifespan distributions (possession span, no informal reuse λ) for desktops,

330

laptops, CRT and flat panel monitors with those modeled by UNU (2015)[30] and empirically derived by Kwak et.

331

al (2011)[32]. Note that tablets are incorporated into the laptop category in the UNU study.

332

Considering that very different methods and geographies were involved, it is remarkable that

333

our desktop and CRT monitor distributions align rather well with those of NL, FR, BE [30]. Since

334

monitor type was not distinguished in the empirical collection data, and CRT and flat panel monitors

335

have different possession spans, it is difficult to make a direct comparison, but both represent

336

somewhat shorter lifespans than the empirical distributions. One explanation is that the empirical

337

distributions are longer since they do not include items put in the trash, which may have shorter

338

possession spans than with an owner is storing the product awaiting an opportunity to recycle it.

339

The UNU laptop and flat panel monitor distributions tend to be shorter than the empirical

340

distributions, and this study’s estimates are considerably longer than both of them. Tablet computers 15



Page 16 of 23

341

are incorporated into the UNU laptop category, which likely drives the shorter lifespan. Also, desktops

342

and CRT monitors had been on the market for several decades compared to laptops and flat panel

343

monitors, which are newer technologies that grew market share rapidly. The recent, rapid growth may

344

have influenced the survival analysis process for the newer technologies, since at the time of the

345

survey there was a shorter history of products generated (failures) and more that were still in homes

346

(censored).

347

Comparison of SOM and SSUM Generation and Collection Estimates

348

Figure 4 presents the quantity in millions of computers and monitors estimated to be generated and

349

collected by using SOM and SSUM in the year 2010. Columns represent mean values and error bars

350

represent 95% confidence interval for SOM and range for SSUM. SOM estimates were slightly

351

higher than SSUM estimates for all products except for laptops and CRT monitor collection. For

352

residential used computers, the SOM calculated a mean of 18.1M units (weighing 164.2KT)

353

generated and 12.9M units (116.5KT) collected, while the SSUM calculated corresponding means of

354

15.9M units (130.2KT) and 13.7M units (110.0KT). Similarly, for residential used monitors, the

355

SOM calculated a mean 10.4M units (153.4KT) generated and 7.1M units (105.0KT) collected, while

356

the SSUM calculated means of 8.7M units (127.7KT) and similarly 7.1M units (105.7KT). Detailed

357

numbers can be found in Table S9 to S13 of the SI; Figure S19 of the SI presents the results for weight

358

in kilotons. Comparison of these methods’ results to those from related studies are also available in

359

SI Section 4.2.4. Our average generation estimates are somewhat lower than those of the EPA[3], but

360

in between the EPA and Daoud[33] collection estimates.

16


Page 17 of 23


Quantity (Millions of Units)

25

20

15

10

5

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

Collection

Generation

0

Res Bus/Pub Res Bus/Pub Res Bus/Pub Res Bus/Pub Res Bus/Pub Res Bus/Pub Desktops

Laptops

Total Computers CRT Monitors SOM

Flat Panel Monitors

Total Monitors

SSUM

361 362

Figure 4: Comparison of 2010 US generation and collection quantity in millions by product, residential sector (Res)

363

and business/public sector (Bus/Pub), and method (SOM or SSUM). Columns represent mean values and error

364

bars represent 95% confidence interval for SOM and range for SSUM.

365

The SSUM calculated higher business/public used computer and monitor 2010 generation and

366

collection quantities as compared to residential quantities. The business/public used computers

367

generated and collected means were estimated at 23.0M units (178.3KT) and 17.0M units (129.0KT),

368

137% and 117% of residential estimates, respectively. Turning to used monitors, the business/public

369

generated and collected means 15.9M units (229.4KT) and 12.1M units (179.9KT), representing

370

180% and 170% of the residential estimates, respectively.

371

Comparing the uncertainty associated with residential generation and collection 2010 quantity

372

results, the SSUM results across products, scenarios, and scale factors have a lower coefficient of

373

variation (CoV, 3% to 6%) than SOM results (CoV 6% to 28%). The SOM results embed the

374

considerable variability assigned to lifespan distribution parameters, reuse pathway likelihoods,

375

future sales, and collection rates in the MC simulations. To be conservative, wide distributions of 17



Page 18 of 23

376

product unit weights were incorporated, and thus the CoV of the weight estimates is quite high for

377

both methods: 30% to 75% for the SOM and 35% to 88% for the SSUM. In order to improve the

378

accuracy of quantity estimates and reduce uncertainty, better historical sales data and more complex

379

business/public surveys from a broader swath of businesses and institutions are needed. To reduce

380

uncertainty in the weight estimates, more widespread tracking of unit weights of product weights of

381

electronics sold and/or collected would allow for the creation of a credible time series of quantity-

382

weighted average unit weights for each product. Such values could be applied with a much smaller

383

variance.

384

The SSUM residential collection rates are higher than those modeled in SOM. For used

385

computers and monitors, the SSUM estimated an average 86% and 82% collected quantity out of

386

generated quantity, respectively, while the SOM estimated an average 71% and 68%, respectively.

387

The SOM based collection rates on the fitted trends of across multiple surveys, while the SSUM used

388

the 2010 values directly. As shown in Figure S14, the products’ 2010 collection rates in the primary

389

survey are slightly higher than predicted by the trend line, which explains why the SSUM rates are

390

higher than the SOM rates.

391

In order to compare the results with methods with fairly accurate external data sources, the stock

392

of electronics in households was calculated for the years 2000 to 2010. Figure 5 provides desktop

393

stock and generation estimates for the SOM and SSUM; results for all products are provided in

394

Figures S21 to S26. The Energy Information Administration’s Residential Energy Consumption

395

Surveys (RECS) periodically ask householders about their ownership of desktops, laptops, and

396

monitors[34]. While some Census surveys ask whether or not these devices are owned, RECS

397

quantifies the number in each household, and therefore a stock estimate can be calculated using RECS

398

microdata.

399

There is reasonable agreement among the stock estimates across all products. For all products

400

aside from laptops, the SOM stock and generation results tend to be slightly higher than the SSUM

401

results and RECS stock estimates. Since SSUM and RECS data are scaled to intercensal Census

402

population and household estimates while SOM uses sales data, it may be that the sales data are

403

overestimates and/or the Census data are underestimates; the Census data is more statistically robust, 18


Page 19 of 23


405

suggesting an underestimate of sales since the SOM generation estimate is lower than the SSUM as

406

well, meaning that excessive generation in early years does not account for lower stock. 140 120 100 80 60 40 20 0 -20 -40 -60 -80 2000

2002

2004

2006

2008

48 44 40 36 32 28 24 20 16 12 8 4 0 2010

Generation (millions)

though. Both the SSUM and RECS stock slightly exceed SOM stock for laptops in the early 2000s,

Stock (millions)

404

Year

407

RECS Stock

SOM, mean

SOM, range

Stock 0.975

SSUM, mean

SSUM, range

408

Figure 5: Desktop residential stock (upper) and generation (lower) estimates from 2000 to 2010 as calculated by

409

the SOM and SSUM. Range represents 95th CI for SOM and minimum and maximum for SSUM. Comparison stock

410

data calculated from EIA RECS microdata[34].

411

Both the SOM and the SSUM are capable of calculating a range of estimates for the generation

412

and collection of used electronics. The SOM for generation requires a more complex model and time

413

series survey dataset in order to model lifespans in a sophisticated model incorporating reuse.

414

SSUM requires census data or sales data and survey data from a single year, and therefore is a more

415

streamlined approach for a snapshot of flows. Provided forecast sales data, the SOM can predict future

416

generation and collection; the SSUM would require forecast survey responses to predict future flows.

417

Recommendations

The

418

There are several recommendations that arise from this work in order to improve on the

419

generation and collection estimates made and to reduce the associated uncertainty. Recognizing the

420

uncertainty in this space, it is useful to arrive at used electronics flows estimates with multiple

421

methods, each with sets of reasonable assumptions and incorporation of underlying uncertainty. 19



Page 20 of 23

422

Additional annual, detailed surveys of business/public organizations designed with the intention of

423

SOM could enhance the accuracy of business/public generation and collection estimates. Additional

424

survey questions on whether used items were purchased from formal or informal sources would

425

reduce uncertainty in the SOM pathway likelihoods. More certain sales and unit weight data would

426

enable more precise generation quantity and weight estimates.

427

When employing the SOM, whether in complex form as in this study or more simplistically,

428

considerable attention should be paid to the creation of lifespan distributions. The lifespan estimates

429

should be consistent with the definition of generation with regards to assumptions about post-use

430

storage and informal versus formal reuse. The use of average lifespan values without associated

431

variation and uncertainty is not recommended. Comparing derived stock estimates with published

432

values serves as an effective validation of the SOM model parameters.

433

SUPPORTING INFORMATION

434

Additional information as noted in the text. This information is available free of charge via the Internet

435

at http://pubs.acs.org/.

436

AUTHOR INFORMATION

437

*T. Reed Miller, Corresponding Author

438

Phone: 617-715-5473; E-mail: [email protected]

439

ACKNOWLEDGMENT

440

This study was supported by the Solving the E-Waste Problem (StEP) initiative with a grant from the

441

US EPA and later the Commission for Environmental Cooperation of North America. Assistance from

442

Jason Linnell at the National Center for Electronics Recycling on this effort is greatly appreciated.

443

The provision of the survey data and the creation of the Survey Scale-Up Method by Ramzy Kahhat

444

and Eric Williams were crucial and valued contribution to this effort.

445

REFERENCES

446 447 448

1. IDC IDC Lowers PC Outlook for 2015, While the Long-Term Outlook Improves Slightly; 12 Mar 2015 2015. 2. IDC Dell, Samsung, and Lenovo See Positive Growth in Worldwide PC Monitor Market Despite Overall Decline in Fourth Quarter of 2014, According to IDC; 31-Mar-15, 2015. 20


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3. US EPA ORCR, Electronics Waste Management in the United States Through 2009. In 2011. 4. Baldé, C. P.; Wang, F.; Kuehr, R.; Huisman, J. The global e-waste monitor – 2014; United Nations University, IAS – SCYCLE: Bonn, Germany, 2015. 5. UNEP E-Waste Volume I: Inventory Assessment Manual; Division of Technology, Industry and Economics, International Environmental Technology Centre: Osaka/Shiga, 2007. 6. Müller, E.; Schluep, M.; Widmer, R.; Gottschalk, F.; Böni, H., Assessment of e-waste flows: a probabilistic approach to quantify e-waste based on world ICT and development indicators. In EMPA: Swiss Federal Laboratories for Materials Testing and Research.: 2009. 7. Yoshida, A.; Tasaki, T.; Zollo, A., Material flow analysis of used personal computers in Japan. Waste Management 2009, 29, (5), 1602-1614. 8. Yang, Y.; Williams, E., Logistic model-based forecast of sales and generation of obsolete computers in the US. Technological Forecasting and Social Change 2009, 76, (8), 1105-1114. 9. Yu, J.; Williams, E.; Ju, M.; Yang, Y., Forecasting Global Generation of Obsolete Personal Computers. Environmental Science & Technology 2010, 44, (9), 3232-3237. 10. Babbitt, C.; Kahhat, R.; Williams, E.; Babbitt, G., Evolution of Product Lifespan and Implications for Environmental Assessment and Management: A Case Study of Personal Computers in Higher Education. Environmental Science & Technology 2009, 43, (13), 5106-5112. 11. Kahhat, R.; Williams, E., Materials flow analysis of e-waste: Domestic flows and exports of used computers from the United States. Resources, Conservation and Recycling 2012, 67, (0), 67-74. 12. Wang, F.; Huisman, J.; Stevels, A.; Baldé, C. P., Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis. Waste Management 2013, Available on line. 13. US International Trade Commission Used Electronic Products: An Examination of U.S. Exports; 2013. 14. UNU StEP Launches Interactive World E-Waste Map; Tokyo, 12/16/2013, 2013. 15. Consumer Reports, E-waste Survey 2006. In 2006. 16. Saphores, J.; Nixon, H.; Ogunseitan, O.; Shapiro, A., How much e-waste is there in US basements and attics? Results from a national survey. Journal of Environmental Management 2009, 90, (11), 3322-3331. 17. Gregory, J.; Nadeau, M.; Kirchain, R., Evaluating the Economic Viability of a Material Recovery System: The Case of Cathode Ray Tube Glass. Environmental Science & Technology 2009, 43, (24), 9245-9251. 18. Alcorn, W. 2012 CE Recycling and Reuse Survey; Consumer Electronics Association: 2012. 19. Brugge, P. Trends in CE Reuse, Recycle and Removal; Consumer Electronics Association: 2008. 20. E. Williams, R. K., C. Mattick, , Survey of Consumer Purchases and Use of Electronics. In 2009. 21. CR E-waste Survey 2006; Consumer Reports National Research Center (CR): 2006. 22. Global Industry Analysts, I. Monitors (Computer): A Global Strategic Business Report; 2008. 23. NCER. NCER Brand Data Management System, sampling share from computer and monitors (weight )Oregon and Washington Sampling Data. Data: http://www.electronicsrecycling.org/BDMS/AlphaList.aspx?sort=All 24. Deb, P., FMM: Stata module to estimate finite mixture models. Statistical Software Components 2012. 25. Duan, H.; Miller, T. R.; Gregory, J.; Kirchain, R.; Linnell, J. Quantitative Characterization of Domestic and Transboundary Flows of Used Electronics: Analysis of Generation, Collection, and Export in the United States; Solving the E-waste Problem Initiative: 2013. 26. Murakami, S.; Oguchi, M.; Tasaki, T.; Daigo, I.; Hashimoto, S., Lifespan of Commodities, Part I. Journal of Industrial Ecology 2010, 14, (4), 598-612. 27. Singh, R.; Mukhopadhyay, K., Survival analysis in clinical trials: Basics and must know areas. Perspectives in 21



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clinical research 2011, 2, (4), 145-8. 28. Census Table 1. Monthly Population Estimates for the United States: April 1, 2010 to November 1, 2012 (NAEST2011-01-11); 2012. 29. Census Table 1C. Computer and Internet Use in the United States: 2010. ; 2012. 30. Baldé, C. P., R. Kuehr, K. Blumenthal, S. F. Gill, J. Huisman, M. Kern, P. Micheli, and E. Magpantay E-waste statistics: Guidelines on classifications, reporting and indicators.; United Nations University, IAS-SCYCLE: Bonn, Germany, 2015. 31. Magalini, F.; Wang, F.; Huisman, J.; Kuehr, R.; Baldé, K.; Straalen, V. v.; Hestin, M.; Lecerf, L.; Sayman, U.; Akpulat, O. Study on Collectoin Rates of Waste Electrical and Electronic Equipment (WEEE); European Commission: 2016. 32. Kwak, M.; Behdad, S.; Zhao, Y.; Kim, H.; Thurston, D., E-Waste Stream Analysis and Design Implications. Journal of Mechanical Design 2011, 133, (10). 33. Daoud, D., Survey: Inside the U.S. Electronics Recycling Industry. In IDC: 2011. 34. US EIA, Residential Energy Consumption Survey: 2009, 2005, and 2001 Public use microdata files. In 2013.

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