A comprehensive review of the end-of-life modeling in LCAs of digital equipment

Abstract

Purpose: In the context of the Anthropocene and the widespread use of ICT, the growth of digital e-waste should be tackled. In this paper, we examine the role of the of end-of-life (EoL) in relation to the other life cycle stages in life cycle assessments (LCA) of digital equipment. We investigate how LCAs of digital equipment model the EoL, what the results are for the environmental impacts of EoL treatments, and if the LCAs published after the ETSI standard and the ITU-T 1410 recommendation follow EoL modeling criteria. Method: We did a systematic literature review with three main research criteria. The LCA must (1) concern digital equipment or compare digital devices, (2) consider several impact categories, and (3) be from cradle-to-grave. As the number of scientific papers found was relatively small, we included LCAs of manufacturers, master thesis, and technical reports when they were appropriated. We found twenty-six references from academic and industrial sources corresponding to our research criteria. Results and discussions: Our review is structured according to the ISO 14040-14044 standards. EoL modeling is mainly in the scenario description, which is the first step of the LCA. For EoL modeling, we examine the allocation procedure, scenarios, distribution of scenarios, data source, transport modeling, EoL treatment rates, purity and quality of recycled materials, treatment of specific and hazardous components, and informal flow modeling. We identify the missing elements in the studies and compare them to ETSI and ITU-T 1410. We find it unclear whether ETSI and ITU-T 1410 are being followed in LCAs, as there are no noticeable changes in the EoL modeling. EoL often generates low environmental impacts compared to the other life cycle stages, except for some impact categories. Choices made in EoL modeling, such as using substitution with avoided impact approach or unspecified allocation, can result in low-impact estimates. We underline the need for more transparency in EoL allocation modeling to enable comparisons and interpretation of results from clear reporting. Conclusion: There is no clear consensus on how to model EoL in an LCA. As the EoL of a digital device is highly uncertain, modeling needs to be more consistent and detailed. LCA practitioners should go beyond these guidelines. We identify several missing elements in current LCAs. We provide recommendations for future LCAs, including providing more detail on the substitution approach, considering informal flows, using primary data, and implementing a hybrid methodology.