A multi-level resource circularity index based in the European Union’s circular economy monitoring framework
(2024) In Waste and Biomass Valorization 15(2). p.615-636- Abstract
Purpose: to propose two enhancements for the European Union’s Circular Material Use rate (CMU): inclusion of Preparation for Reuse (PfR) flows and enhanced reproducibility across lower levels of analysis. Methods: PfR flows are added to the material flow Sankey Diagram. The Local Circularity Rate (LCR) is based in the CMU and is broke down in three waste-related ratios: recovered-to-treated (RCV-to-TRT), treated-to-end-of-life and end-of-life-to-overall-material-use (EoL-to-OMU). LCR, CMU and CMU’, an alternate version of CMU, are computed and compared in the macro-level for EU27 member states and in the meso-level for Germany’s sixteen states. LCR is computed and broke down for regions in Belgium, The Netherlands and Greece. In the... (More)
Purpose: to propose two enhancements for the European Union’s Circular Material Use rate (CMU): inclusion of Preparation for Reuse (PfR) flows and enhanced reproducibility across lower levels of analysis. Methods: PfR flows are added to the material flow Sankey Diagram. The Local Circularity Rate (LCR) is based in the CMU and is broke down in three waste-related ratios: recovered-to-treated (RCV-to-TRT), treated-to-end-of-life and end-of-life-to-overall-material-use (EoL-to-OMU). LCR, CMU and CMU’, an alternate version of CMU, are computed and compared in the macro-level for EU27 member states and in the meso-level for Germany’s sixteen states. LCR is computed and broke down for regions in Belgium, The Netherlands and Greece. In the micro-level, LCR is computed for a network modelled around a Textile Sorting Centre (TSC) in Amsterdam. Results: LCR showed closer average results to CMU in comparison to CMU’. Considering RCV-to-TRT and EoL-to-OMU, The Netherlands and Luxembourg are the best performing countries in the EU27. Eight countries performed worse than 0.4 in both ratios. In total, twelve German regions showed negative results, either for CMU or CMU’. Saxony-Anhalt is the most circular region in Germany, while Berlin is the less circular. The Amsterdam textiles’ network features an LCR of 12%, with the TSC contributing to 63% of all textiles recovered. Conclusion: The revised circular Sankey Diagram comprehensively illustrates the circularity gap. LCR’s three ratios enhances in-depth analysis, allowing better prioritisation of public policies. Limitations remain in data availability and harmonisation across regional and national databases. Graphic Abstract: (Figure presented.)
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- author
- de Souza, Vitor Miranda LU ; Fröhling, Magnus and Pigosso, Daniela C.A.
- publishing date
- 2024-02
- type
- Contribution to journal
- publication status
- published
- keywords
- Circular material use rate, Circularity gap, Circularity metrics, Preparation for reuse, Urban metabolism, Urban resource centre
- in
- Waste and Biomass Valorization
- volume
- 15
- issue
- 2
- pages
- 22 pages
- publisher
- Springer
- external identifiers
-
- scopus:85164180891
- ISSN
- 1877-2641
- DOI
- 10.1007/s12649-023-02193-6
- language
- English
- LU publication?
- no
- id
- 77f8a6e4-1057-498a-90b8-9b20de64fb08
- date added to LUP
- 2024-03-04 15:15:48
- date last changed
- 2024-03-27 09:19:26
@article{77f8a6e4-1057-498a-90b8-9b20de64fb08,
abstract = {{<p>Purpose: to propose two enhancements for the European Union’s Circular Material Use rate (CMU): inclusion of Preparation for Reuse (PfR) flows and enhanced reproducibility across lower levels of analysis. Methods: PfR flows are added to the material flow Sankey Diagram. The Local Circularity Rate (LCR) is based in the CMU and is broke down in three waste-related ratios: recovered-to-treated (RCV-to-TRT), treated-to-end-of-life and end-of-life-to-overall-material-use (EoL-to-OMU). LCR, CMU and CMU’, an alternate version of CMU, are computed and compared in the macro-level for EU27 member states and in the meso-level for Germany’s sixteen states. LCR is computed and broke down for regions in Belgium, The Netherlands and Greece. In the micro-level, LCR is computed for a network modelled around a Textile Sorting Centre (TSC) in Amsterdam. Results: LCR showed closer average results to CMU in comparison to CMU’. Considering RCV-to-TRT and EoL-to-OMU, The Netherlands and Luxembourg are the best performing countries in the EU27. Eight countries performed worse than 0.4 in both ratios. In total, twelve German regions showed negative results, either for CMU or CMU’. Saxony-Anhalt is the most circular region in Germany, while Berlin is the less circular. The Amsterdam textiles’ network features an LCR of 12%, with the TSC contributing to 63% of all textiles recovered. Conclusion: The revised circular Sankey Diagram comprehensively illustrates the circularity gap. LCR’s three ratios enhances in-depth analysis, allowing better prioritisation of public policies. Limitations remain in data availability and harmonisation across regional and national databases. Graphic Abstract: (Figure presented.)</p>}},
author = {{de Souza, Vitor Miranda and Fröhling, Magnus and Pigosso, Daniela C.A.}},
issn = {{1877-2641}},
keywords = {{Circular material use rate; Circularity gap; Circularity metrics; Preparation for reuse; Urban metabolism; Urban resource centre}},
language = {{eng}},
number = {{2}},
pages = {{615--636}},
publisher = {{Springer}},
series = {{Waste and Biomass Valorization}},
title = {{A multi-level resource circularity index based in the European Union’s circular economy monitoring framework}},
url = {{http://dx.doi.org/10.1007/s12649-023-02193-6}},
doi = {{10.1007/s12649-023-02193-6}},
volume = {{15}},
year = {{2024}},
}