Service lifetime and disposal pathways of business devices

Thiébaud, Esther; Pešková, Marie Brechbühler; Hilty, Lorenz M.; Schluep, Mathias; Faulstich, Martin

doi:10.1109/egg.2016.7829851

Cited by 4 publications

(1 citation statement)

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“…[105] assess the global end-of-life treatments of paper products, while Taulo and Sebitosi [119] evaluate tea production in Malawi. The models assess material flows in the product categories of general construction (e.g., construction sector in Japan [120] or housing in China [121]), infrastructure (e.g., cement for infrastructure in China [122]), transportation (e.g., aluminum use in future vehicles [123]), agriculture (e.g., phosphorous application in Denmark [124]), clothing (e.g., plastics in textiles [125]) and electronic equipment (e.g., end-of-life treatment of business devices [126]). In principle, all MFA models follow the generic systematic approach as seen in Fig.…”

Section: System Boundarymentioning

confidence: 99%

Combining the worlds of energy systems and material flow analysis: a review

et al. 2021

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Recent studies focusing on greenhouse gas emission reduction strategies indicate that material recycling has a significant impact on energy consumption and greenhouse gas emissions. The question arises how these effects can be quantified. Material recycling is not at all or insufficiently considered in energy system models, which are used today to derive climate gas mitigation strategies. To better assess and quantify the effects one option would be to couple energy system models and material flow models. The barriers and challenges of a successful coupling are addressed in this article. The greatest obstacles are diverging temporal horizons, the mismatching of system boundaries, data quality and availability, and the underrepresentation of industrial processes. A coupled model would enable access to more robust and significant results, a response to a greater variety of research questions and useful analyses. Further to this, collaborative models developed jointly by the energy system and material analysis communities are required for more cohesive and interdisciplinary assessments.

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Section: System Boundarymentioning

confidence: 99%

Combining the worlds of energy systems and material flow analysis: a review

et al. 2021

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New Business Models and Logistical Considerations for Composites Re-use

Copani

Mirpourian

Trivyza

et al. 2022

Digital Innovations in Architecture, Engineering and Construction

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The growing use of composites in various industries such as aerospace, automotive and wind turbine has increased environmental concerns regarding their waste disposal methods. Deploying circular economy practices to reuse composites could play a crucial role in the future. In this regard, this chapter addresses the development and implementation of new business models for composites re-use, as fundamental enabler for the industrial exploitation and diffusion of technological and methodological innovations developed in the FiberEUse project. Seven products were chosen as representatives for composites reuse application in four industrial sectors: sanitary, sports equipment, furniture and automotive. Re-use business models are presented describing their value proposition, with particular reference to the provision of advanced product-service bundles, the revenue models (including schemes such as leasing), as well as new supply chain configurations entailing new partnership between producers and recyclers to access post-use composites to re-use. Given the importance of reverse supply networks, the potential reverse logistics pathways for mechanical recycling of Glass Fiber Reinforced Plastic (GFRP), thermal recycling of Carbon Fiber Reinforced Plastic (CFRP) and remanufacturing of CF composites waste in Europe for 2020 and 2050 have been investigated. We concluded that the optimal reverse logistics network needs to be decentralized in more than one country in Europe. Therefore, it is suggested that policy makers address regulation to allow the transportation of waste between European countries to facilitate the development of recycling networks for composites reuse.

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Can analytics software measure end user computing electricity consumption?

Sutton-Parker

2022

Clean Techn Environ Policy

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The purpose of this research is to answer the question, ‘can analytics software measure end user computing electricity consumption?’ The rationale being that the success of traditional methodologies, such as watt metres, is limited by newly evolved barriers such as mobility and scale (Greenblatt et al., in Field data collection of miscellaneous electrical loads in Northern California: initial results. Ernest Orlando Lawrence Berkeley National Laboratory research paper, pp 4–5, 2013). Such limitations significantly reduce the availability of end user computing use phase energy consumption field data (Karpagam and Yung, in J Clean Prod 156:828, 2017). This causes computer manufacturers to instead rely upon no-user present energy efficiency benchmarks (Energy Star, in Product finder, product, certified computers, results. Washington, D.C.: United States Department of Energy. https://www.energystar.gov/productfinder/product/certified-computers/results, 2021) to act as baseline data for product carbon footprint reports. As the benchmark approach is previously tested to cause scope 2 greenhouse gas emissions quantification to be inaccurate by − 48% to + 107% (Sutton-Parker, in Determining end user computing device Scope 2 GHG emissions with accurate use phase energy consumption measurement, 1877-0509. Amsterdam: Science Direct, Elsevier B.V., 2020), testing a new methodology that includes the impact of human–computer interaction is arguably of value. As such, the proposed method is tested using a distributed node based analytics software to capture both computer asset and human use profile data sets from one hundred and eleven computer users operating in a subject organisation for 30-days. The simple rationale is that the node, unlike a watt metre, is not restricted by location, can be deployed and monitored globally from a centralised location and can move with the computer to ensure constant measurement. The resulting data sets are used to populate a current use phase electricity consumption calculation data flow (Kawamoto et al., in Energy 27:255, 2001; Roth et al., in Energy consumption by office and telecommunications equipment in commercial buildings: energy consumption baseline, 2002) in order to examine for omissions. Additionally, to test for data accuracy, one computer user acts as a control subject, measuring electricity consumption with both a watt-metre and the analytics software. The rationale being that the watt-metre data is extensively proven to be accurate (Energy Star, in Energy star computers final version 8.0 Specification, Washington D.C., United States Department of Energy. https://www.energystar.gov/products/spec/computers_version_8_0_pd, 2020) and will therefore expose errors produced by the software in relation to power draw, on-time and resulting kilo-watt hours (kWh) values. Further to the data capture period, the findings are mixed. Positively, the new method overcomes the barriers of numerous, assorted devices (scale) operating in ever changing locations (mobility). This is achieved by the node reporting in real-time make and model asset data together with device specific electricity consumption and location data via internet technologies. Negatively, the control subject identifies that the electricity consumption values produced by the software are inaccurate by a relatively constant 48%. Furthermore, data omissions are experienced including the exclusion of computer displays caused by the node requiring an operating system to collect data. This latter point would exclude the energy consumption measurement and therefore concomitant greenhouse gas emissions of any displays connected to desktop or mobile computers. Consequently, whilst the research question is answered, the identification of the software exaggerating use phase energy consumption by 48% and excluding peripheral devices, determines the analytics methodology to be in need of further development. The rationale being that use phase consumption quantification is key to lifecycle assessment and greenhouse gas accounting protocol and both require high levels of accuracy (WBCSD and WRI, in The greenhouse gas protocol. A corporate accounting and reporting standard, Geneva, Switzerland and New York, USA. https://ghgprotocol.org/corporate-standard, 2004). It is therefore recommended that further research be undertaken to specifically address omissions and to reduce the over reporting aspect identified as caused by algorithms in the software used to calculate hardware power draw. Graphical abstract

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Service lifetime and disposal pathways of business devices

Cited by 4 publications

References 9 publications

Combining the worlds of energy systems and material flow analysis: a review

Combining the worlds of energy systems and material flow analysis: a review

New Business Models and Logistical Considerations for Composites Re-use

Can analytics software measure end user computing electricity consumption?

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