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Record W4297911093 · doi:10.51414/sei2022.027

Governing the circular economy: how urban policymakers can accelerate the agenda

2022· report· en· W4297911093 on OpenAlexaboutno aff
Shogofa Rezaie, Fedra Vanhuyse, Karin André, Maryna Henrysson

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicSustainable Industrial Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCircular economySustainabilityPopulationGross domestic productEconomyProduct (mathematics)Production (economics)World populationNatural resourceDistribution (mathematics)Dispose patternNatural resource economicsBusinessEconomicsEngineeringEconomic growthPolitical science

Abstract

fetched live from OpenAlex

We believe the climate crisis will be resolved in cities. Today, while cities occupy only 2% of the Earth's surface, 57% of the world's population lives in cities, and by 2050, it will jump to 68% (UN, 2018). Currently, cities consume over 75% of natural resources, accumulate 50% of the global waste and emit up to 80% of greenhouse gases (Ellen MacArthur Foundation, 2017). Cities generate 70% of the global gross domestic product and are significant drivers of economic growth (UN-Habitat III, 2016). At the same time, cities sit on the frontline of natural disasters such as floods, storms and droughts (De Sherbinin et al., 2007; Major et al., 2011; Rockström et al., 2021). One of the sustainability pathways to reduce the environmental consequences of the current extract-make-dispose model (or the "linear economy") is a circular economy (CE) model. A CE is defined as "an economic system that is based on business models which replace the 'end-of-life' concept with reducing, alternatively reusing, recycling and recovering materials in production/distribution and consumption processes" (Kirchherr et al., 2017, p. 224). By redesigning production processes and thereby extending the lifespan of goods and materials, researchers suggest that CE approaches reduce waste and increase employment and resource security while sustaining business competitiveness (Korhonen et al., 2018; Niskanen et al., 2020; Stahel, 2012; Winans et al., 2017). Organizations such as the Ellen MacArthur Foundation and Circle Economy help steer businesses toward CE strategies. The CE is also a political priority in countries and municipalities globally. For instance, the CE Action Plan, launched by the European Commission in 2015 and reconfirmed in 2020, is a central pillar of the European Green Deal (European Commission, 2015, 2020). Additionally, more governments are implementing national CE strategies in China (Ellen MacArthur Foundation, 2018), Colombia (Government of the Republic of Colombia, 2019), Finland (Sitra, 2016), Sweden (Government Offices of Sweden, 2020) and the US (Metabolic, 2018, 2019), to name a few. Meanwhile, more cities worldwide are adopting CE models to achieve more resource-efficient urban management systems, thereby advancing their environmental ambitions (Petit-Boix & Leipold, 2018; Turcu & Gillie, 2020; Vanhuyse, Haddaway, et al., 2021). Cities with CE ambitions include, Amsterdam, Barcelona, Paris, Toronto, Peterborough (England) and Umeå (Sweden) (OECD, 2020a). In Europe, over 60 cities signed the European Circular Cities Declaration (2020) to harmonize the transition towards a CE in the region. In this policy brief, we provide insights into common challenges local governments face in implementing their CE plans and suggest recommendations for overcoming these. It aims to answer the question: How can the CE agenda be governed in cities? It is based on the results of the Urban Circularity Assessment Framework (UCAF) project, building on findings from 25 interviews, focus group discussions and workshops held with different stakeholder groups in Umeå, as well as research on Stockholm's urban circularity potential, including findings from 11 expert interviews (Rezaie, 2021). Our findings were complemented by the Circular Economy Lab project (Rezaie et al., 2022) and experiences from working with municipal governments in Sweden, Belgium, France and the UK, on CE and environmental and social sustainability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.250
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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