Novel indicators to better monitor the collection and recovery of (critical) raw materials in WEEE: Focus on screens.

Currently, in the European Union (EU), e-waste chain performance is assessed by technical indicators that aim to ensure system compliance with collection and recovery targets set by the WEEE Directive. This study proposes indicators to improve WEEE flow monitoring beyond the current overall weight-based approach, including complementary flows and treatment performance. A case study focused on the screen category in France is presented. In 2017, the collection rate of cathode-ray tube screens (CRT) was 68%, while for flat panel display (FPD) generated only 14% was collected. CRT screens have less precious and critical materials than FDP. Thus, elements like cobalt and gold highly concentrated in FPD, have a collection rate two to four times lower than elements such as copper (37%) which represents a high proportion in CRTs. Recycling is the main treatment in France. Nevertheless, the recycling rate per element varies significantly due to the low collection, and also the lack of technology and/or secondary raw materials market. The elements with higher recycling rates are base metals such as copper (28%), followed by precious metals like silver (23%), and gold (13%). Except for palladium, the recycling rate of the critical raw materials targeted in the study ranged from 6% (cobalt) to 0% (e.g. neodymium and indium). The results stress the need for indicators to support the development of WEEE chain from waste management to secondary (critical) raw materials suppliers.

A high-resolution dynamic probabilistic material flow analysis of seven plastic polymers; A case study of Norway.

Plastic pollution has long been identified as one of the biggest challenges of the 21st century. To tackle this problem, governments are setting stringent recycling targets to keep plastics in a closed loop. Yet, knowledge of the stocks and flows of plastic has not been well integrated into policies. This study presents a dynamic probabilistic economy-wide material flow analysis (MFA) of seven plastic polymers (HDPE, LDPE, PP, PS, PVC, EPS, and PET) in Norway from 2000 to 2050. A total of 40 individual product categories aggregated into nine industrial sectors were examined. An estimated 620 ± 23 kt or 114 kg/capita of these seven plastic polymers was put on the Norwegian market in 2020. Packaging products contributed to the largest share of plastic put on the market (∼40%). The accumulated in-use stock in 2020 was about 3400 ± 56 kt with ∼60% remaining in buildings and construction sector. In 2020, about 460 ± 22 kt of plastic waste was generated in Norway, with half originating from packaging. Although ∼50% of all plastic waste is collected separately from the waste stream, only around 25% is sorted for recycling. Overall, ∼50% of plastic waste is incinerated, ∼15% exported, and ∼10% landfilled. Under a business-as-usual scenario, the plastic put on the market, in-use stock, and waste generation will increase by 65%, 140%, and 90%, respectively by 2050. The outcomes of this work can be used as a guideline for other countries to establish the stocks and flows of plastic polymers from various industrial sectors which is needed for the implementation of necessary regulatory actions and circular strategies. The systematic classification of products suitable for recycling or be made of recyclate will facilitate the safe and sustainable recycling of plastic waste into new products, cap production, lower consumption, and prevent waste generation.

Enhancing e-waste estimates: improving data quality by multivariate Input-Output Analysis.

Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input-Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies.