Sustainable Materials Management for the Evolving Consumer Technology Ecosystem Summary Report of Phase 1 Research: Development of a Sustainable Materials Management Modeling Framework and Baseline Model Results July 2017 Authors Callie W. Babbitt* Shahana Althaf Roger Chen *Corresponding Author cwbgis@rit.edu Golisano Institute for Sustainability Rochester Institute of Technology

2

Executive Summary In a collaboration between Rochester Institute of Technology, Staples Sustainable Innovation Lab, and the Consumer Technology Association, a comprehensive analysis was carried out to create a baseline “material footprint” of consumer technology used in U.S. households from 1990-2015. This study focused on 21 of the most common consumer technologies used during this time, including TVs, phones, computers, monitors, and entertainment devices. The materials consumed, held in stock, and entering the waste stream annually were quantified using a material flow analysis (MFA) approach, which took into account the number and type of products being sold, adoption and ownership rates for an average U.S. household, and product weight and material content. Data came from a wide variety of sources, including CTA, the U.S. EPA, scientific literature, and laboratory studies of material content in electronics. A key finding was that across the household “consumer technology ecosystem,” the number and type of products sold have increased, but the net material consumption has declined to levels not seen since the early 1990s. Most of this material reduction is due to phasing out heavy products like cathode ray tube TVs and substitution with light-weight flat panel displays. In addition, many products are being designed with lighter materials, like aluminum instead of plastic or steel. “Device convergence” was also observed, particularly in the last 10 years of the study scope, wherein multi-functional mobile devices powered by lightweight lithium-ion batteries have replaced many products consumers would have owned separately before, as seen in smart phone displacement of MP3 players or digital cameras. When considering specific materials, a key finding was that major materials of concern used historically to enable consumer technology products, like lead and mercury, have declined in parallel with product substitution due to technological progress. However, there are new opportunities for study and green innovation. For example, increased demand for mobile products resulted in greater use and disposal of lithium-ion batteries, and there is a clear opportunity to proactively develop recycling systems to target these emerging waste streams. On the other hand, reducing the mass of electronics owned and disposed also led to proportional reduction of precious metals in the waste stream, which can decrease economic recycling incentives. Ultimately, this study is intended to serve as a “materials baseline” by which emerging technology can be compared, such as smart home appliances and internet of things (IoT), which may introduce new material challenges and new opportunities for integrating sustainable materials management and circular economy approaches. An ongoing Phase 2 research initiative will expand this model and the generated baseline to include additional emerging products and evaluate strategies for sustainable innovations

3

1. Background Innovations in the consumer technology sector offer the potential to improve quality of life, broaden education and information access, and enable industrial resource efficiency, thereby reducing global environmental impacts and energy and material demands [1]. Technology products themselves have undergone rapid transformation as part of widespread efforts to minimize material usage, decrease power consumption, and improve reuse and recycling potential at end-of-life. However, consumers continue to adopt an increasing number and variety of electronic devices to meet ever-changing information and communication needs. As such, it is not yet clear if product-level innovations can offset the associated cumulative growth of electronic product consumption and the resulting environmental impacts associated with greenhouse gas emissions, electricity usage, and material demand across product life cycles [2-4]. Furthermore, the rapidly evolving nature of electronics has resulted in diversification of material inflows [5-8]. In fact, the average electronic product now contains more than 60 different materials, which increasingly include critical and rare earth metals [9-12]. While past research has examined the material and energy impacts of consumer technology for a single product at a time, the reality is that consumers purchase and use electronic devices in increasingly interactive and interconnecting ways [13-14]. Therefore, it is necessary to understand how these consumption trends, when accompanied by technological evolution at the product level, change the overall material impact of the consumer technology “ecosystem.” Therefore, the research described here aimed to create and apply an analytical material modeling framework to estimate baseline material implications associated with purchase, use, and disposal of consumer technology products at a systems level. Ultimately, this baseline can be applied for future evaluation of material consumption in emerging technologies as part of a proactive sustainable materials management approach. While not within the study scope, research outcomes can help guide development of sustainability initiatives, such as green procurement, product labeling, industry level improvements, and electronic waste management. 2. Approach This study was carried out in five steps, conducted between August 2016 and January 2017:

1. Characterize the baseline consumer technology “ecosystem” in terms of products owned within the average U.S. household from 1990-2015;

2. Create a material flow analysis (MFA) model to analyze consumer technology product inflows to, stock held within, and outflows from the average U.S. household;

3. Collect data characterizing the sales and ownership of these consumer technologies; 4. Collect and create data characterizing the mass and material composition of these

consumer technologies; and 5. Analyze data using the material flow model and synthesize findings into a final, peer-

reviewed report for CTA, the Staples Sustainable Innovation Lab, and public audiences.

4

2.1 Characterizing the consumer technology ecosystem The products that were included in this baseline analysis were those consumer technologies with the greatest prevalence within U.S. households over the 25-year period analyzed (1990-2015). These products were selected based on review of U.S. trade and industry reports, scientific literature, and past studies on electronic waste [15]. Products were excluded if there was not sufficient sales or household adoption data and/or if products were present at low ownership rates (as is common when new products emerge on the market). Automotive electronics and most analog (non-digital) products were excluded due to lack of data and/or scope of the study, which focuses on digital technology, although we acknowledge that such analog devices may have comprised a larger fraction of the product ecosystem in the early 1990s. Camcorder and camera data included only digital devices, but some analog products were included in this analysis because of high ownership concentrations (e.g., VCR) and/or environmental significance (e.g., CRT television). While exclusion of products due to scope or data may result in an incomplete inventory, the methods created and applied here can easily be updated as more data become available or as the product ecosystem continues to evolve in the future. The list of the 21 products included is shown in Table 1. Table 1: Consumer Technologies Included in this Study

2.2 Creating a model of consumer technology inputs, stocks, and outputs The methodology of material flow analysis (MFA) uses mass balance principles to quantify stocks and flows of materials or final goods of interest through a system defined in time and space. A more detailed explanation of this methodology is provided in Appendix A, with a summary provided here. In this study, the flows of interest are the annual inputs of new consumer electronics to U.S. households and the annual outflows of used consumer electronics from U.S. households into the waste stream. The stock is considered to be the amount held in the household from year-to-year, which includes both products in use and post-use products in storage. The relationship between these terms is expressed as:

(Eqn. 1) In other words, if more products are being purchased than disposed, the stock increases, while if outflows (or waste) exceed inflows (or sales), then the stock declines over time. Each of the parameters in Equation 1 can be calculated or estimated in various ways, depending on data available. In this study, the change in stock from one year to the next was determined by

5

ownership rates of products in consecutive years. The sum of inflows was quantified using data about new product sales and shipments. Direct quantification of waste outflows is rare, and therefore, this term was estimated by rearranging Equation 1 to solve for the sum of outflows. The outflow term represents the total volume of products expected to enter the waste stream in a given year, and does not differentiate between products destined for reuse, recycling, or disposal, or between products that are disposed directly after they reach obsolescence or after a period of storage within the household. This modeling approach was implemented using data provided by project partners, as described in Section 2.3 below. The model described above estimates stocks and flows in terms of units (or number of technology products). However, the net mass of these products and the materials contained within are also of great interest, as they strongly influence life cycle environmental impact, electronic waste management and sustainable product design and materials management decisions. Therefore, the model also translated the estimated stocks and flows into cumulative mass flows (using data on average product mass per product) and disaggregated material flows (using data on average product bill of material). Data collection for these parameters and those mentioned above are described in sections 2.3 and 2.4 and Appendix B and C. The overall modeling framework is shown graphically in Figure 1.

Figure 1: Modeling framework applied to quantify the inputs, stocks, and outputs for the average U.S. household consumer technology ecosystem 2.3 Collecting data on consumer technology sales and ownership This modeling approach was implemented using data provided by project partners. Data that describe consumer technology sales and ownership rates were primarily obtained from CTA (e.g., Annual Consumer Electronics Sales and Market Potential Studies) and collaborators (e.g.,

6

IDC product sales data provided courtesy of the National Center for Electronics Recycling (NCER)). Some supplemental data were obtained from past versions of the U.S. EPA Electronic Waste Management report [15]. These data were analyzed, aggregated, and reported in tables (Appendix B) as rounded estimates, to represent recognized uncertainty and to avoid replication of exact data from any one source concerned about proprietary information. Statistical tools like curve fitting and regression were used to harmonize inconsistencies and fill data gaps by interpolating or extrapolating from available data as described in Appendix B. We recognize that potential uncertainty may inherently arise from the collection of such data. For example, provided product sales data came from a combination of manufacturer reported sales and shipment data and consumer purchase surveys. The provided product ownership data was collected from CTA telephone surveys (sample size of about 2,000 households per year) and weighted to reflect the known demographics of the population under study. Even using this well-tested survey instrument, uncertainty may arise from consumer responses and self-reporting errors. However, these data sources are compiled using consistent collection methods from year-to-year, which minimizes uncertainty when observing trends over time. 2.4 Collecting data on consumer technology mass and material composition To translate product flows into cumulative mass flows, average mass data was collected from literature, direct weighing of sample products in the lab, and data compilations provided by NCER, who monitor product weight as part of recent e-waste compliance efforts in several U.S. states. Since product mass may change dramatically over time for some products, where, possible, we applied a dynamic mass estimate, particularly for key products like CRT, LCD, and LED display technologies, which are not only major contributors by weight to the product ecosystem but are also undergoing changes due to expanding screen sizes or technology driven light weighting. In some cases, no significant changes were observed in certain product masses or no dynamic data were available, and so a static mass estimate was applied. This is a key point of uncertainty in this study, but the modeling framework can certainly be expanded and re-analyzed in future phases of work as more data become available. Appendix C provides detailed parameters for product mass and material composition. Collecting comprehensive, publicly-available material composition data is well understood to be a major research challenge when analyzing consumer technology products. For some products, a wide array of material data is available, particularly for products like cathode ray tube displays, which have been the subject of extensive testing due to concern about lead content and potential exposure risks [24]. On the other hand, newer products, like LED TVs or tablet computers have yet to be comprehensively studied. Further, where such data are published in the peer-reviewed literature, inconsistencies are common, particularly in the granularity by which materials are classified or the methods by which materials are quantified. Therefore, material composition data were compiled through a combination of peer-reviewed technical sources and empirical research led by RIT using published methods [20]. Specifically, a breakdown of bulk materials (e.g., copper, plastics, steel) and components (e.g., battery cell, printed circuit board, display module) were determined via product disassembly by

7

the authors and collaborators at RIT. Representative products were weighed and disassembled to a level where each piece was comprised of a single material (if feasible). Materials were identified by visual inspection, basic physical properties, labels, recycling codes and product heuristics. Metals were separated into ferrous and non-ferrous components using a magnet and verified by handheld X-ray fluorescence (XRF). Non-ferrous metals were identified based on visual inspection and common knowledge of material composition (e.g. copper wiring). Composite material groups included LCD materials (the LCD module only), battery cell, and printed circuit board (PCB). Where primary disassembly was not possible, either due to lack of product availability or safety concerns (e.g., exposure to lead during CRT disassembly), published product data was used to establish a representative suite of material compositions for the remaining products [16, 17]. Using published data for components, the composition of PCB, LCD and CRT modules and battery cells was further disaggregated to estimate the contribution of key materials of value and concern, like gold, lead, mercury, indium, and cobalt. One limitation of this approach is exclusion of materials that were once common (e.g., wood used in TV and stereo cabinetry in the 1990s) but no longer present in products studied via disassembly. In addition, materials are reported with relatively low granularity (i.e., no distinction between metallic alloys or plastic resins), a necessary simplification to accommodate the use of literature-reported data (which commonly groups bulk materials) and the resolution possible with empirical characterization techniques employed. However, creating and validating additional material sets is an important challenge for the industry, and this model can be updated with new data as they become available. 3. Key Findings The primary goal of this study was to 1) develop a material modeling framework that can be applied holistically to the consumer technology ecosystem and then to 2) apply this framework to establish a data-driven baseline of consumer technology over the last 25 years, a topical area characterized by experience and practitioner knowledge, but historically lacking rigorous quantification. The quantitative results derived from this baseline highlight three key findings, each discussed in greater detail below. Finding 1: Product consumption has grown significantly over the last 25 years, but the net material footprint of the consumer technology ecosystem is declining As shown below, the total consumption of technology products by unit increased approximately nine-fold from 1990 until 2015, particularly dominated by the emergence of small mobile devices in the last 10 years (Figure 2). Only in the past 3-5 years has this consumption begun to slow or decline, particularly for larger, stationary products. However, within the same time horizon, the fundamental composition of the product ecosystem has undergone significant changes. Most notably, the phase-out of CRT TVs and monitors in the late 2000s (particularly due to the digital television transition), and subsequent replacement with LCD and LED displays has resulted in dramatic dematerialization of the overall system (Figure 3).

8

Figure 2. Inflows of consumer technology into U.S. households from 1990-2015 as measured in units (or number of products). Note that the greatest unit sales, particularly in the last 10 years, have been seen in small, mobile devices like basic and smart phones, MP3 players, tablets, and cameras. However, these products are lightweight and only contribute a small fraction to the net inflow mass. Data sources include IDC and CTA sales and shipments data. Figure 3 shows an interesting transition during the late 2000s associated with material intensity of consumer electronics. In 2007 and 2008, sales of CRT TVs and monitors had all but ceased, due to emergence of new flat panel technology and the planned digital television transition, which called for ending analog TV broadcasts by June 2009. Within this time, sales of flat panel LCD technology were increasing, but had not yet caught up to that of the CRT. Further, flat panels sold during this time had masses between one-half and one-third of a CRT mass, resulting in a noticeable mass decline around 2007. Consumption by mass ramped back up as consumers quickly purchased digital TV replacements and as flat panel screen size grew, but again declined in the transition from LCD to LED TVs due to light weighting within that product category. Overall, the direct material consumption in 2015 was comparable to that of the 1990s.

9

Figure 3. Inflows of consumer technology into U.S. households from 1990-2015 by mass (metric tons). Note that the dominant contributor is the mass of display technologies, particularly the CRT TV, followed by large computing devices. Results are from product mass data collected from empirical and literature sources (see Appendix C). Because the mass breakdown in Figure 3 is heavily dominated by display technology, trends within other product categories are harder to distinguish. Figure 4 presents the same findings with all display technologies removed and the scale adjusted commensurately. At this scale, more subtle shifts can be noted, such as the recent contributions of smart phones and tablets and the stabilization or shrinking of contributions from larger products (e.g., desktop computer, printer) or “few function” products (e.g., DVD players, digital cameras, MP3 players). We recognize that the products included in the 2015 estimate may omit some emerging categories, like home automation and digital streaming devices, but suggest that given the dominant contribution by display technologies, the above estimates effectively capture the key trends in product consumption. We also anticipate that future phases of research will explicitly focus on these emerging technologies.

10

Figure 4. Expanded view of inflows of consumer technology into U.S. households from 1990-2015 by mass (metric tons), excluding display technology. Note the expanded scale on the y-axis compared to Figure 3. Results are from data taken primarily from empirical and literature sources (see Appendix C). Similar trends are seen when examining products held in stock in the household (Figures 5 and 6) in this same time frame. The product stock represents the number of products owned across all U.S. households, which reflects both the annual penetration rate of each product (how many households own at least one of the product) and the product density (number of products per owning household), parameters which were provided by project partner CTA. The product mass in stock reflects the likelihood that products owned at any given time will be of variable vintages, due to household-level differences in purchasing, storage, and disposal behaviors. Stock, by unit and by mass, both show recent declines, again attributed primarily to the shift in display technologies and the phase-out of CRT TVs.

11

Figure 5: Stock of consumer technology in U.S. households, 1990-2015, by unit. Data are from CTA ownership surveys.

Figure 6: Stock of consumer technology in U.S. households, 1990-2015, by mass (metric tons). Product mass data collected from empirical and literature sources (see Appendix C).

12

Product outflows from the household during this time frame are shown in Figures 7 and 8. When considering individual units (Figure 7), significant expansion and diversification of the waste stream is apparent, signaling a potential impact to reuse and recycling strategies that have historically been based on deep disassembly of large end-of-life products and/or component resale or recycling. Greater presence of small, mobile devices may require new e-scrap management models, recycling technologies, or eco-design approaches. It should be noted that these outflows are not estimated by direct sampling of the electronic waste stream, but rather indirectly quantified using the change in reported product sales and stocks. For example, if household stock of a specific product decreases from one year to the next but sales stay relatively constant, then the number of these products entering the waste stream would increase because fewer devices are being accumulated. Because the outflows are a mathematical function of inflows and stocks, any uncertainties in these estimates will similarly propagate into the resulting calculation. It is anticipated that future phases of work will validate these results using methods that account for product lifespan and/or direct data collected in partnership with e-scrap management firms.

Figure 7: Outflows of consumer technology from U.S. households, 1990-2015, by unit. Results are obtained by calculation of Equation 1 (above) using previously described inflow and stock results. These outflows represent the cumulative expected discard rate of products from all U.S. households and do not differentiate between products discarded or collected for reuse or recycling. The final fraction of products entering e-scrap management systems in the U.S. would be smaller, but variable across regions depending on state-level policy or local collection infrastructure.

13

Figure 8 presents the outflow results in terms of mass, which is observed to begin a decline in the past few years. Most of the decline is due to continued slowing in CRT display dispositions as consumers remove older TVs and monitors from stock and gradually replace them with flat panel technology. Data collected from CTA Sales and Ownership studies indicates that there are still about 0.5 CRTs per average U.S. household that haven’t yet entered the waste stream.

Figure 8: Outflow of consumer technology from U.S. households, by mass (metric tons). Results are obtained by calculation of Equation 1 (above) using previously described inflow and stock results and product mass data as described in Appendix C. To examine the waste flows of other technology, Figure 9 shows the same results, at an expanded scale, but with CRT TVs and monitors removed. These results suggest that the outflow of remaining products is still slightly increasing into the waste stream, and the reductions promised by reduced product consumption (Figure 3) are yet to be realized, due to the time delay during which products are held in stock from year to year.

14

Figure 9: Outflows of all consumer technology products excluding CRT TVs and monitors, 1990-2015 by mass (metric tons). Note the expanded scale on the y-axis as compared to Figure 8. Outflow results are still dominated by display technology and are still gradually increasing. Finding 2: The overall material profile of the consumer technology ecosystem has remained steady, but some key materials of concern have declined. Recasting results above in terms of their material breakdown, we can again consider inflows (Figure 10), stock (Figure 11) and outflows (Figure 12) in terms of specific materials, resolved to the level of granularity possible with available material composition data. Over time, the material profile has stayed relatively constant, and proportional to net mass flows. Aluminum emerged as a more common casing material for products introduced while plastic use declined. Battery cells (particularly lithium-ion batteries after about 2000) have emerged with the introduction of more mobile devices.

15

Figure 10: Material composition of product inflows to U.S. households, 1990-2015, by mass (metric tons). *Note that CRT glass refers to the glass fraction of CRT panel and funnel glass. The Lead fraction contained in those CRT components is calculated as a separate material category. In each of these sets of results, trends mirror the product-level shifts observed in results shown above. Displacement of CRT technology not only reduced the total amount of CRT glass entering the product ecosystem, but also the lead contained within CRT panel and funnel glass. These results reflect not only the changing consumption of CRT displays but also the change in mass, screen size, and lead content over time [23]. Newer flat panel display technology is differentiated by those containing cold cathode fluorescent lamp (CCFL) lighting systems and those using newer light emitting diode (LED) lighting systems. This differentiation is largely due to the presence of mercury in CCFL systems and the interest in tracking its stocks and flows over time (discussed in greater detail below).

16

Figure 11: Material composition of product stock U.S. households, 1990-2015, in metric tons.

Figure 12: Material composition of outflows from U.S. households, 1990-2015 in metric tons.

17

The waste flows show similar material breakdown over time, although CRT glass (containing lead) continues to flow out of U.S. households as consumers slowly get rid of legacy televisions. These results also show initial declines in bulk material waste flows, including plastic and ferrous metal (steel) as well as stabilization and slight decline in printed circuit boards (PCBs) which contain precious metals targeted by current recycling systems. Many of the changes observed in these figures demonstrate the effect of technological progress and consumer demand, particularly in the demand for widescreen but lightweight TV display technology. Results presented above include the caveat that within these broader material categories, other shifts may be occurring that are not seen at this level of resolution. For example, the broad category of “plastic” does not elaborate on specific resins used or areas of concern, such as the use of halogenated flame retardant additives within these plastics. The material set analyzed is the most disaggregated level at which empirical data could be collected and harmonized with peer-reviewed published material composition data, particularly within this initial product scope. However, for several key materials, a deeper analysis is possible due to historic study on focal materials of interest, three of which – lead, mercury, and gold – will be discussed in more detail below. Lead and mercury have been key enablers of CRT and LCD (with cold cathode fluorescent lamp or CCFL lighting technology) displays, respectively. However, these metals also pose significant risk to human and environmental health, particularly if released in an uncontrolled manner during e-waste management. On the other hand, gold is one of the key constituents driving the economic basis for e-scrap recycling, due to its high value about ability to be extracted using common recycling techniques (e.g., precious metal smelting). We investigated how these material flows changed over time, particularly when considering the combination of regulations (e.g., RoHS and the elimination of lead-based solder), consumer demand (e.g., light weighting products), and technological progress (e.g., transition from LCD-CCFL to LCD with light-emitting diode or LED lighting technology). This initial set of materials is quantifiable due to data availability because of economic or regulatory interest in these materials. For many areas of emerging concerns (e.g., flame retardants, rare earth elements), widespread composition data are not yet available, but we anticipate adding these to the model as information is published. For the results shown below (Figures 13-15), note that each graph is presented with a different y-axis, scaled according to the relative volume of each material’s stocks and flows. In many cases, these results follow similar trends as seen earlier, time adjusted for when products in which they are contained have peak ownership. Because each material is present in a product in a fraction that typically scales with overall product or component mass, trends towards dematerialization similarly affect each material individually.

18

Figure 13: Lead contained in consumer technology inflows, stock and outflows, 1990-2015, by mass (metric tons). Between 96-100% of this lead is attributed to leaded CRT glass; the remaining fraction is attributed to lead solder in PCBs (prior to RoHS regulations).

Figure 14: Mercury contained in consumer technology inflows, stock and outflows, 1990-2015, by mass (metric tons). In these results, mercury is associated with the mercury containing CCFL bulbs used in LCD display technology.

19

Figure 15: Gold contained in consumer technology inflows, stock and outflows, 1990-2015, by mass (metric tons). In these results, gold is associated with the printed circuit boards (PCBs) used in all products. Finding 3: Emerging product trends suggest new material management challenges and opportunities Results presented so far demonstrate how evolution of the consumer technology ecosystem, as influenced by both consumer demand and external drivers like policy, has resulted in positive material outcomes, namely net dematerialization and reduction of materials of concern. Our results also suggest areas where future materials challenges may emerge and greater study should be focused. For example, the recent, rapid increases in mobile device ownership have led to concurrently increased demand for the lithium-ion battery systems that power these devices. Producing these batteries requires lithium, graphite, cobalt and a host of other metals and resources, which subsequently emerge in product outflows end-of-life, potentially requiring new recycling system design and implementation. Considering batteries as a sub-system within consumer technology, the net consumption has slowed for current products, as phones and tablets begin to reach saturation across U.S. households and as these convergent devices replace other battery-powered handhelds, like MP3 players and digital cameras and camcorders. Figure 16 illustrates these trends for lithium-ion batteries contained in the most common products currently. Within each of these battery flows, materials such as lithium, cobalt, manganese, and graphite are contained, which are potential targets for future studies due to concerns about scarcity, embodied energy, or recyclability. These material issues may also be compounded when considering emerging

20

technologies, like home assistants (Amazon Alexa), virtual reality (VR) headsets, back-up power supplies, and other “smart” devices deployed as part of the Internet of Things (IoT).

Figure 16: Lithium-ion battery outflows, disaggregated by products in which the batteries are contained, 2010-2015, by mass (metric tons of battery materials). These results are derived from battery content for specific products as described in Appendix C. Another potential emerging materials management challenge is likely to be associated with recent increases in consumption of flat panel display technology, and the attendant material composition, which include critical minerals (e.g., indium) and rare earth elements (e.g., europium, terbium, yttrium). Figure 17 illustrates recent trends in inflows of flat panel displays associated with consumption of specific products. Estimates are beginning to emerge in the peer-reviewed literature [11] regarding intensity of specific materials within these products and the extent which these materials can be recovered at end-of-life, but it is clear that more data collection and research is required before this materials management issue is fully understood. In addition, results presented here show a clear opportunity for creating circular economy strategies to close the loop on critical and valuable materials, like cobalt, gold, indium, and rare earth elements. While not within the scope of this study, these strategies would require new approaches such as greater integration of sustainable product design, new product delivery and ownership models, new forms of producer responsibility, and expanded recycling capabilities to recover valuable materials and put them back into productive use.

21

Figure 17: Flat panel display inflows, disaggregated by products in which the display technology is contained, by mass (metric tons of display module materials). These results are derived from display contribution to product mass for specific products as described in Appendix C. Conclusions and Future Work Results presented here demonstrate that the material flow modeling framework can capture the material footprint of the entire consumer technology “ecosystem”, accounting for both changing product mass and material composition as well as shifts in technology and consumer ownership trends. The modeling framework was applied to generate an initial materials baseline, which demonstrates promising signs of reduced material demand in the consumer technology sector, even while product ownership and functionality continues to expand. These material reductions include historic materials of concern, like lead and mercury. However, these findings are not without uncertainty, particularly in two key areas: data quality and product inclusion. While we have attempted to capture broad trends using best available data from reliable, peer-reviewed sources, we anticipate that more refinements will be required in the future as additional data are collected by our ongoing work and by the many others working in this field. Secondly, this study represents the broad swath of products that are widely sold and used in U.S. households. However, the rate of innovation in the consumer electronics market is unprecedented, and new products emerge at a steadily increasing rate. In addition, electronics are increasingly integrated into new products and systems, as seen in “wearable” electronics, “smart” home systems, and the “Internet of Things.” To this end, the next phase of this research initiative will build additional modeling capacity to expand this baseline model in order to reflect emerging trends in consumer technology, particularly scenarios of widespread consumer adoption of smart products. The MATLAB code

22

with which the current model was analyzed will be updated with the ability to validate the calculation method used here (Equation 1) using a second estimate of outflows as a function of product lifespan and residence time within an average U.S. household. While the current model relies on hardcoded historical data of sales and ownership for material flow calculations, the next phase will equip the model with ability to use projected adoption curves for emerging products to generate future material results for emerging products. In addition, expanded research efforts will couple material flows with other relevant sustainability metrics, included embodied energy, scarcity, price, and recyclability of materials. Acknowledgements The authors gratefully acknowledge and thank Walter Alcorn (CTA), Mark Buckley (Staples), David Refkin (GreenPath Sustainability Consultants), and Jason Linnell (National Center for Electronics Recycling) for feedback, data collection support, and guidance in the development and implementation of this study. We are grateful for the helpful feedback, questions, and comments provided by the academic and industry professionals who peer-reviewed the first draft of this report. Collaborators at RIT who have assisted with parts of the data collection and literature review include Barbara Kasulaitis, Erinn Ryen, Matthew Koskinen, Jackson Haskell, Mona Komeijani, Gabrielle Thurston, and Gabrielle Gaustad. This research was primarily supported by the Staples Sustainable Innovation Lab at RIT with additional funding from the National Science Foundation (Grant #CBET-1236447).

23

References [1] Erdmann, L. and L. Hilty. 2010. Scenario analysis: exploring the macroeconomic impacts of

information and communication technologies on greenhouse gas emissions. Journal of Industrial Ecology 14(5)826 – 843.

[2] Arushanyan, Y., E. Ekener-Petersen, and G. Finnveden, 2013. Lessons learned – review of LCAs for ICT products and services. Computers in Industry 65(2)211 – 234.

[3] Hageluken, C. 2007. The challenge of open cycles. In R’07, 8th world congress, edited by L. M. Hilty et al. CD-ROM. Stuttgart, Germany: Fraunhofer Institut Zuverlassinkeit und Mikrointegration.

[4] Malmodin J., Å. Moberg, D. Lundén, G. Finnveden, and N. Lövehagen. 2010. Greenhouse gas emissions and operational electricity use in the ICT and entertainment and media sectors. Journal of Industrial Ecology 14(5)770–90.

[5] McKinsey and Company. 2012. Materials roadmaps to meet energy challenges: a report on the results and recommendations of the international summit world materials perspectives.

[6] Friege, H. 2012. Review of material recovery from used electric and electronic equipment – alternative options for resource conservation. Waste Management & Research 30(9) Supplement 3 – 16.

[7] Sthiannopkao, S. and M. Wong. 2013. Handling e-waste in developed and developing countries: initiatives, practices, and consequences. Science of the Total Environment 463:1147-1153.

[8] Wäger P. 2011. Scarce metals: applications, supply risks and need for action. Not Polit 2011;XXVII(104):57–66.

[9] Schluep, M., C. Hageluken, R. Kuehr, F. Magalini, C. Maurer, C. Meskers, E. Mueller, and F. Weng. 2009. Recycling – from e-waste to resources. United Nations Environmental Programme (UNEP). 2009.

[10] Hageluken, C. and C. Corti. 2010. Recycling of gold from electronics: cost effective use through ‘design for recycling’. Gold Bulletin 43(3) 209-220.

[11] Li, J., S. Gao, H. Duan, and L. Liu. 2009. Recovery of valuable materials from waste liquid crystal display panel. Waste Management 29: 2033 – 2039.

[12] Dahmus J. and T. Gutowski. 2007. What gets recycled: an information theory based model for product recycling. Environmental Science and Technology 41:7543–7550.

[13] Ryen, E., C. Babbitt, A. Tyler, and G. Babbitt. 2014. Community ecology perspectives on the structural and functional evolution of consumer electronics. Journal of Industrial Ecology 18(5) 708-721.

[14] Ryen, E., C. Babbitt, and E. Williams. 2015. Consumption-weighted life cycle assessment of a consumer electronic product community. Environmental science & technology 49(4) 2549-2559.

[15] U.S. Environmental Protection Agency (EPA). 2011. Electronic waste management in the United States through 2009. Office of Resource Conservation and Recovery, Washington, D.C.

[16] Oguchi, M., S. Murakami, H. Sakanakura, A. Kida, and T. Kameya. 2011. A preliminary categorization of end-of-life electrical and electronic equipment as secondary metal resources. Waste Management 31 (9-10) 2150-2160.

24

[17] Teehan, P. and M. Kandlikar. 2013. Comparing embodied greenhouse gas emissions of modern computing and electronics products. Environmental Science and Technology (47) 3997-4003.

[18] Hikwama, B. 2005. Life cycle assessment of a personal computer. Ph. D. University of Southern Queensland.

[19] Eugster, M., R. Hischier, and H. Duan. 2007. Key environmental impacts of the Chinese EEE industry: a life cycle assessment study. St. Gallen, Switzerland and Beijing, China.

[20] Kasulaitis, B., C. Babbitt, R. Kahhat, E. Williams, and E. Ryen. 2015. Evolving materials, attributes, and functionality in consumer electronics: case study of laptop computers. Resources, Conservation and Recycling. 100: 1-10.

[21] Kozak, G. 2003. Printed scholarly books and e-book reading devices: a comparative life cycle assessment of two book options. Ph.D. dissertation. University of Michigan.

[22] SB20 Report. 2004. Determination of regulated elements in discarded laptop computers, LCD monitors, plasma TVs and LCD TVs. Hazardous Material Laboratory. California Department of Toxic Substances Control (CDTSC).

[23] Monchamp A., et al. 2001. “Cathode ray tube manufacturing and recycling: analysis of industry survey.” Proceedings of the 2001 IEEE International Symposium on Electronics and the Environment.

[24] Townsend T., et al. 2004. RCRA Toxicity Characterization of Computer CPUs and Other Discarded Electronic Devices. A report prepared for the United States Environmental Protection Agency, Region 4 and Region 5. https://dnr.mo.gov/env/hwp/escrap/docs/cputoxic04.pdf

25

Appendix A: Detailed Material Flow Analysis Method The methodology used for the study is material flow analysis (MFA), which applies mass balance principles to quantify stocks and flows of materials or final goods of interest through a system defined in time and space. In this study, the flows of interest are the annual inputs of new consumer electronics to U.S. households and the annual outflows of used consumer electronics from U.S. households into the waste stream. The stock is considered to be the amount held in the household from year-to-year, which includes both products in use and post-use products in storage. The relationship between these terms is expressed as: ∆Stock = ∑Inflow - ∑Outflow (Eqn. 1) Material flow analysis can be performed in two ways:

(i) “Market-lifespan” MFA quantifies inflow and outflow directly, and then the change in stock is inferred as the difference, as expressed in Equation 1. The sum of inflows is typically determined based on the sum of sales of all new electronics in the year of interest (n). The sum of outflows is typically determined indirectly, as very few directly measured estimates of waste flow exist. Outflows are based on product lifespan, wherein a product sold into a household in year n-L (where L is the product lifespan) ultimately leaves the household in year n. This approach requires a known or assumed lifespan distribution.

(ii) “Time-step” MFA quantifies the change in stock directly, and then using either a

known inflow or outflow, determines the unknown variable by rearranging Equation 1. ∑Outflow = ∑Inflow -∆Stock (Eqn. 2) Or ∑Inflow = ∆Stock -∑Outflow (Eqn. 3) Typically, the known variable is inflow (again, usually quantified by sales). The change in stock is calculated as the difference between the number of products owned per household in year n and year n-1. The present study has utilized the time-step approach to quantify the outflows using the equation, 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜, 𝑖𝑖, 𝑜𝑜 is the total outflow of each product i in year t, while 𝑃𝑃𝑖𝑖𝑖𝑖, 𝑖𝑖, 𝑜𝑜 is the inflow of the product and ∆𝑃𝑃𝑖𝑖,𝑜𝑜 is the change in stock of the product in year t. The equation implies that the annual

(Eqn. 4) 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜, 𝑖𝑖, 𝑜𝑜 = 𝑃𝑃𝑖𝑖𝑖𝑖, 𝑖𝑖, 𝑜𝑜 − ∆𝑃𝑃𝑖𝑖,𝑜𝑜

26

product (i) outflow from a HH in year t is equal to the difference between the inflow of product (i) to the household in year t and the change in product (i) stock in the household in year t. Model Inputs In Eqn.4, inflow of product i in year t, 𝑷𝑷𝒊𝒊𝒊𝒊, 𝒊𝒊, 𝒕𝒕 = 𝑼𝑼𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔, 𝒊𝒊, 𝒕𝒕

𝒙𝒙𝒕𝒕

where 𝒙𝒙𝒕𝒕 is the household population in US in year t And change in stock of product i, ∆𝑷𝑷𝒔𝒔𝒕𝒕𝒔𝒔,𝒊𝒊,𝒕𝒕 = 𝑷𝑷𝒔𝒔𝒕𝒕𝒔𝒔,𝒊𝒊,𝒕𝒕 − 𝑷𝑷𝒔𝒔𝒕𝒕𝒔𝒔,𝒊𝒊,𝒕𝒕−𝟏𝟏 Therefore, the following inputs are used to calculate the product outflow per average US household

• For product inflow Pin, i, t o Product sales data in US o Number of households in US o Average mass of product (to convert inputs by unit to inputs by mass) o Product material composition (to convert inputs by mass to individual material

flows)

• For change in stock, ∆Pi, t o Ownership rate of products in US households o The ownership rate was calculated directly from data and reports provided by

CTA (namely the Annual Consumer Electronics Ownership and Market Potential Study and data collected therein).

o The CTA report collects two key pieces of data: • Household Penetration Rate (percent of surveyed households that own

the technology) • Density per Owning Household (the average number of products per

households who own the technology). o The product of these two data points (Penetration rate x Density per owning

household) gives the Average ownership rate for all US Households (taking into account households that do and do not own the technology). This conversion was necessary to put the data into the same format as Sales data, which are normalized per all U.S. households using census data.

Eqn.5 Eqn.5

Eqn.6

27

Model Framework The MFA model to produce material flows of the 21 consumer technology product was built using MATLAB software. The data collected on the product sales, material and subcomponent composition was organized into 3 separate data sheets to be used as inputs in the model. The model carried out the calculations in four separate steps to produce inflow, stock and outflow of products by number, mass and material. Model Framework step1: Step1 of the model generated the inflow, outflow and stock of all 21 products by number of products per average US household. The calculations were carried out as per equations, 4, 5 and 6 by using the household data sheet and the product data sheet as the inputs. Fig.A-1 presents the step1 of model framework together with the pseudocode used for the calculations.

Figure A-1

28

Model Framework step2: Step2 utilized the average mass of the products in each year to calculate the product flows by mass; Fig A-2.

Model Framework step3: Step 3 merged the product sheet with the bill of materials sheet, which contained the mass ratio of all materials and sub components of all products in any given year. The current model included basic material and component compositions of each product, broken down by: ferrous metal, aluminum, copper, other metals, plastics, printed circuit boards, LCD module_CCFL, LCD module_LED, CRT glass, CRT lead and battery.

Figure A-2

29

The mass ratio of materials/subcomponents per product was multiplied by the outflow, inflow and stock by mass to produce the flows of each material. Figure A-3. shows the model as well as the pseudocode used to generate material flows from step2 results.

Figure A-3

30

Model Framework step4: The 4th and final step involved the merging of the latest data sheet generated with the subcomponent composition sheet. The pseudo code used to calculate flows of target materials in the subcomponent sheet is presented in Figure A-4.

Figure A-4

31

Figure A-5 presents a comprehensive picture of the model framework. The current model accounts for changes in product masses as well as changes in materials masses in product.

Figure A-5

32

Appendix B: Data collection on consumer technology product sales and ownership. Product sales and ownership data were provided by partners, including CTA (Annual CE Ownership and Market Potential Reports, sales and shipment data), IDC sales data (primarily applied to sales of computing devices), and augmented where needed with publicly available U.S. EPA electronic waste reports (which also use some of the data from the other sources listed here), the United States Geological Survey (metal and mineral data use in products), and the peer reviewed scholarly literature. These data were analyzed, aggregated, and shown in tables here as rounded estimates, to represent recognized uncertainty and to avoid presenting exact data from any one source used. In some cases, data from these sources did not fully cover the entire 25-year time series, and therefore, statistical tools like curve fitting and regression were used to harmonize inconsistencies and fill data gaps by interpolating or extrapolating from available data. Methods of extrapolating the sales or ownership data largely depended on what phase of the product lifecycle was represented by available data (Figure B-1). For example, if sales data were only available to characterize the maturation and decline of an established product, then an exponential fit could be applied to estimate early adoption and growth. This approach was selected, as opposed to curve fitting data to a full logistic curve, in order to leverage the rich data sets available from the sources, collected using well-tested survey instruments and other established methodologies.

Figure B-1: Potential methods for data extrapolation depended on which phase of the product lifecycle required missing data.

33

Table B-1: Sales of Consumer Technology into U.S. Households, 1990-2015 (part 1 of 3)

34

Table B-1: Sales of Consumer Technology into U.S. Households, 1990-2015 (continued, part 2 of 3)

35

Table B-1: Sales of Consumer Technology into U.S. Households, 1990-2015 (continued, part 3 of 3)

36

Table B-2: Stock of Consumer Technology in U.S. Households (products per average household, 1990-2015 (part 1 of 2)

37

Table B-2: Stock of Consumer Technology in U.S. Households (products per average household, 1990-2015 (part 2 of 2)

38

Appendix C: Data collection on consumer technology product mass and material composition Product mass and material composition data were collected from literature, direct analysis of sample products in the lab, and data compilations provided by NCER, who monitor product weight as part of ongoing e-waste compliance efforts in several U.S. states. Since product mass may change dramatically over time for some products, where possible, we applied a dynamic mass estimate, particularly for key products like CRT, LCD, and LED display technologies, which are not only major contributors by weight to the product ecosystem but are also undergoing changes due to expanding screen sizes or technology driven light weighting. In some cases, no significant changes were observed in certain product masses or no data were available to characterize temporal trends. In these cases, a static mass estimate was applied, which is recognized as a source of uncertainty. However, past work (Kasulaitis et al. 2015) demonstrated through multiple product characteristics that there is greater potential variability for product mass and material composition among multiple makes and models (even within a single year) than there is over time, particularly for mature technologies. In some cases, dynamic mass data were determined by product sales and mass information that were delineated along lines of easily-characterized product attributes. For instance, TV sales are frequently reported in size categories: Sales of TVs smaller than 40” and 40” and greater. In addition, average mass data were provided for each of these categories. In these cases, a weighted average was taken to account for both the product size and sales in a given year, and therefore, data could reflect changing consumption trends. The masses reported in Table C-2 represent average mass of products in the household stock. Because households purchase, store, and replace products at different rates, this stock is therefore made up of multiple vintages which can theoretically have different masses. Therefore, stock mass is calculated as a rolling average of products introduced in the preceding years. Material composition data included a breakdown of bulk materials (e.g., copper, plastics, steel) and components (e.g., battery cell, printed circuit board, display module), all of which was primarily collected through product disassembly by the authors and collaborators at RIT. Each product was weighed and disassembled to a level where each piece was comprised of a single material (if feasible). Materials were identified by visual inspection, basic physical properties, labels, recycling codes and product heuristics. Metals were separated into ferrous and non-ferrous components using a magnet and verified by handheld X-ray fluorescence (XRF). Non-ferrous metals were identified based on visual inspection and common knowledge of material composition (e.g. copper wiring). Composite material groups included LCD materials (the LCD module only), battery cell, and printed circuit board (PCB). Where primary disassembly was not possible, either due to lack of product availability or safety concerns (e.g., exposure to lead during CRT disassembly), published product data was used to establish a representative suite of material compositions for the remaining products [16, 17]. Using published data for composite components the composition of PWB, LCD and CRT modules and battery cells was further disaggregated to estimate the contribution of key materials of value and concern, like gold, lead, mercury, indium, and cobalt.

39

A summary of the data sources used to parameterize mass and material composition data are shown in Table C-1, and the values used in the model are reported in Table C-2 and C-3. The values selected represented the average value of the available data, calculated as the mean of literature and disassembly data. Some fractions may not sum to 100% due to rounding. Table C-1: Summary of data sources from which product-specific mass and material composition information was collected Product Data Sources

Desktop US EPA 2011, Hikwama 2005, Eugster 2007

Laptop Disassembled by authors, Kasulaitis et al. 2015

Tablet Disassembled by authors, Kozak 2003, Teehan and Kandlikar 2013

Netbook Disassembled by authors

E-Reader Disassembled by authors, Kozak 2003, Teehan and Kandlikar 2013

LCD, LED Monitors Teehan and Kandlikar 2013, Huisman 2007, SB20 2004

CRT Monitor Oguchi et al. 2011, Huisman 2007, Huisman 2004, Hikwama 2005

CRT TV Oguchi et al. 2011, Huisman 2007, Huisman 2004, Hikwama 2005

Plasma TV Oguchi et al. 2011, SB20 2004

LED TV Disassembled by authors

LCD TV Disassembled by authors, and Oguchi et al. 2011, SB20 2004, Huisman 2007

DVD Disassembled by authors

VCR Huisman et al. 2007

Blu-Ray Disassembled by authors

MP3 Player Disassembled by authors

Gaming Disassembled by authors, Huisman et al. 2007

Printer Disassembled by authors

Digital Camera Disassembled by authors

Digital Camcorder Disassembled by authors

Basic Cell Disassembled by authors

Smart Phone Disassembled by authors

40

Table C-2: Mass of consumer technologies in U.S. Households (in kg), 1990-2015 (part 1 of 2)

41

Table C-2: Mass of consumer technologies in U.S. Households (in kg), 1990-2015 (part 2 of 2)

42

Table C-3: Average material composition of consumer technology products in U.S. households, 1990-2015 (in mass percent)

43

Data was collected to characterize lead content in CRT displays using data from Monchamp et al. 2001, which characterized the fraction of each type of CRT glass (funnel, panel, neck, and frit) per a CRT TV or monitor of a specified screen size. These data were combined with characterizations of the average screen size in displays (TV-specific, an average value was used for monitors) over time.

Figure C-1: Average screen size for CRT TVs over time estimated from CTA reports and Monchamp et al. 2001. Note that this estimate accounts for the mass of CRT TVs within a specific size category and the number of TVs sold within each size category, resulting in this weighted average estimate. The amount of leaded glass contained in CRTs of given sizes was provided by Monchamp et al. 2001 and ultimately averaged to create a generalized model of percent contribution of specific types of CRT glass to the total CRT glass mass on a per-inch basis (Table C-4). This source also reported average lead content per type of glass (Table C-5), which when combined with screen sizes over time (Figure C-1) and dynamic masses determined by these screen sizes (Table C-1), and knowledge of the total CRT glass contribution (glass + lead, Townsend et al. 2004) ultimately enabled calculation of glass and lead mass fractions over time (Table C-3). Table C-4: Average glass composition (percent contribution of each glass type to total glass) per inch of CRT screen size adapted from Monchamp et al. 2001. Funnel Panel Neck Frit 33.5% 65.7% 0.423% 0.456%

Table C-5: Average lead content (kg lead / kg CRT glass mass fraction) in CRT glass fractions adapted from Monchamp et al. 2001. Panel glass was a minimal contribution, particularly in later years of analysis after removal of lead oxide from this component.

Funnel Panel Neck Frit 0.23 0.03 0.27 0.65

44

The mercury content per LCD display was determined based on literature values which reported mercury content (Table C-6) as a function of number of lamps per display, total weight of lamps, and screen size of the display. The average mercury content based on these data was estimated to be 1.6 mg/kg/inch of screen size. This estimate was combined with dynamic screen size data (Figure C-2) and dynamic LCD mass data (Table C-1), which also accounted for variations in both screen size and weight over time to calculate the mercury content in product inflows, stock, and outflows as reported in the main text of the report. Table C-6: Mercury content of LCD displays of various sizes and configurations

Screen Size Weight of Lamp (kg)

Number of Lamps

Mercury Content (mg)

Wrap 2010 EuP Prep Study 2007 20 Inch 0.009 6 21 24

26 Inch 0.072 13 45.5 52

32 Inch 0.115 16 56 64

42 Inch 0.17 18 63 72

Wrap 2010: http://www.alr.ie/Download/Mercury_in_waste_LCD_backlights_summary_research_report.63ad9220.11115.pdf EuP Prep Study 2007: https://www.ebpg.bam.de/de/ebpg_medien/005_studyf_07-08_complete.pdf

Figure C-2: Estimated average screen size for LCD TVs over time adapted from CTA and NCER data. Note that this estimate accounts for the mass of LCD TVs within a specific size category and the number of TVs sold within each size category, resulting in this weighted average estimate.

45

The material content of lithium-ion batteries contained in mobile devices was determined by disassembly and lab determination of material composition for multiple form factors, including 18650 cells (used in laptops), small pouch cells (used in mobile phones), and medium pouch/prismatic cells (used in tablets). The complete material breakdown, calculated as the average of multiple data points (n>5) obtained during disassembly, is shown in Table C-7. Table C-7: Average material composition of lithium-ion batteries used in consumer technology Materials Average Mass % Aluminum 19% Cobalt 13% Copper 9.4% Lithium 1.9% Nickel 2.9% Steel 12% Graphite 15% Carbon black 4.2% Electrolyte 4.1% Solvents 3.2% Binders 2.4% Plastics 10% Other 3.8%