Summary Product lifetime is an essential aspect of dynamic material flow analyses and has been modeled using lifetime distribution functions, mostly average lifetimes. Existing data regarding the lifetime of electronic equipment (EE) are based on diverging definitions of lifetime as well as different temporal and regional scopes. After its active use, EE is often not disposed of immediately, but remains in storage for some time. Specific data on the share of EE that is stored and the time they remain in storage are scarce. This article investigates the service lifetime, storage time, and disposal pathways of ten electronic device types, based on data from an online survey complemented by structured interviews. We distinguish between new and secondhand devices and compute histograms, averages, and medians of the different lifetimes and their change over time. The average service lifetime varies from 3.3 years for mobile phones to 10.8 years for large loudspeakers, the average storage time from 0.8 years for flat panel display televisions to 3.6 years for large loudspeakers. Most service lifetime histograms are positively skewed and show substantial differences among device types. The storage time histograms, being more similar to one another, indicate that the storage behavior is similar for most device types. The data on disposal pathways show that a large proportion of devices are stored and reused before they reach the collection scheme.

Electronic devices contain important resources, including precious and critical raw materials. For an efficient management of these resources, it is important to know where the devices are located, how long they are used and when and how they are disposed of. In this article, we explore the past and current quantities of electronic devices in the in-use stock and storage stock in Switzerland and quantify the flows between the use, storage and disposal phase with dynamic material flow analysis (MFA). Devices included are mobile phones, desktop and laptop computers, monitors, cathode ray tube and flat panel display televisions, DVD players, and headphones. The system for the dynamic MFA was developed as a cascade model dividing the use phase in first, second and further use, with each of these steps consisting of an in-use stock and a storage stock for devices. Using a customized software tool, we apply Monte Carlo simulation to systematically consider data uncertainty. The results highlight the importance of the storage stock, which accounts for 25% (in terms of mass) or 40% (in terms of pieces) of the total stock of electronic devices in 2014. Reuse and storage significantly influence the total lifetime of devices and lead to wide and positively skewed lifetime distributions.

The increased use of digital information and communications technologies (ICT) is giving rise to fast-growing waste streams that contain important material resources. In contrast to bulk materials and precious metals, the recovery of most critical metals has not yet been commercially established, and they are thus lost within the recycling process. In this article, we used dynamic material flow analysis to explore the stocks and flows of indium, neodymium, and gold incorporated in end-user devices in Switzerland. Our analysis covered the use, collection, recycling, and disposal phases. This enabled us to track the three metals from their entry into Switzerland as components of new devices until their recovery, disposal in landfills, or dissipation to the environment. Using statistical entropy analysis (SEA), we further analyzed the dilution or concentration of the metals during their route through the current system. The data uncertainty was addressed employing a probabilistic approach. The largest quantities of all three metals are found in the devices currently in use. The second-largest stocks are slags disposed in landfills for indium, slags used for construction for neodymium, and the output of metal recovery processes for gold. The SEA illustrates how the current collection and recycling system successfully concentrates all three metals. While 70% of gold leaving the use phase is recovered, indium and neodymium are dissipated to slags after smelting and incineration processes due to the lack of economic incentives and lacking recovery processes on a commercial scale.

Product lifetimes and disposal pathways are essential aspects of dynamic material flow analyses (MFAs), which have often been used to model stocks and flows of electronic devices and the resources they contain. Existing studies mainly focus on the use and disposal of electronic devices by private consumers. The specific handling of business devices has rarely been assessed. This article presents the results of a study conducted in Switzerland in 2015, comprising interviews with 28 companies. Devices included are desktop and laptop and computers, mobile phones, monitors, televisions, external hard disk drives and servers. Results are compared to data collected from Swiss private consumers. The service lifetime and disposal pathways are fed into a dynamic MFA model to cal-culate the stocks and flows of business devices. With the example of indium, neodymium and gold, the material resources contained in these stocks and flows are illustrated. Service Lifetime and Disposal Pathways of Business Devices AbstractProduct lifetimes and disposal pathways are essential aspects of dynamic material flow analyses (MFAs), which have often been used to model stocks and flows of electronic devices and the resources they contain. Existing studies mainly focus on the use and disposal of electronic devices by private consumers. The specific handling of business devices has rarely been assessed. This article presents the results of a study conducted in Switzerland in 2015, comprising interviews with 28 companies. Devices included are desktop and laptop computers, mobile phones, monitors, televisions, external hard disk drives and servers. Results are compared to data collected from Swiss private consumers. The service lifetime and disposal pathways are fed into a dynamic MFA model to calculate the stocks and flows of business devices. With the example of indium, neodymium and gold, the material resources contained in these stocks and flows are illustrated.