Regional monitoring frameworks for the circular economy: implications from a territorial perspective

Figures & data

Figure 1. Overview of the three regional case studies analysed in this article. Note: GEO NUTS 2 code: Innlandet (NO02), Viken (NO03), West Sweden (SE23), North Middle Sweden (SE31), Luxembourg (LU00), Switzerland (CH00), Lake Geneve region (CH01), Espace Mitteland (CH02), Northwestern Switzerland (CH03), Zurich (CH04), Eastern Switzerland (CH05), Central Switzerland (CH06), Ticino (CH07), Liechtenstein (LI00).

Table 1. Regional (NUTS-2) indicators used in this work for the CE territorial analysis.

CE Focus areas	Regional indicators	Studies or monitoring frameworks endorsing the CE indicators	Data source
Socioeconomic characteristics	GDP per capita (€/hab)	Avdiushchenko (2018)	Eurostat
	GDP growth (%)	Avdiushchenko (2018)	Own estimation based on Eurostat data
	Employment (%)	Avdiushchenko (2018)	Eurostat
	Economic specialization (location quotient)		Own estimation based on Gross Value Added (GVA) by economic activities (NACE)
	Population density (hab/km2)		Eurostat
	Innovation performance	Avdiushchenko (2018); Smol, Kulczycka, and Avdiushchenko (2017)	RIS scoreboard
Resource efficiency	DMC per capita (t/hab)	ESPON CIRCTER (2019); Avdiushchenko (2018)	ESPON
	Material intensity (kg/€)	ESPON CIRCTER (2019); Avdiushchenko (2018); Smol, Kulczycka, and Avdiushchenko (2017)	ESPON
	Domestic extraction per capita (t/hab)	ESPON CIRCTER (2019)	ESPON
	Domestic extraction per DMC (%)	ESPON CIRCTER (2019)	ESPON
	Change in DMC per capita (%)	ESPON CIRCTER (2019); Avdiushchenko (2018)	ESPON
Waste generation and management	Total waste per capita (kg/hab)	ESPON CIRCTER (2019); European Commission (2018); Avdiushchenko (2018); Smol, Kulczycka, and Avdiushchenko (2017)	ESPON
	Waste intensity (Kg/ €)	ESPON CIRCTER (2019); European Commission (2018)	ESPON
	Waste per DMC (%)	ESPON CIRCTER (2019); European Commission (2018)	ESPON
	Municipal waste per capita (kg/hab)	ESPON CIRCTER (2019); European Commission (2018); Avdiushchenko (2018); Smol, Kulczycka, and Avdiushchenko (2017)	National statistical databases
	Municipal waste treatment (%)	ESPON CIRCTER (2019); European Commission (2018); Avdiushchenko (2018); Smol, Kulczycka, and Avdiushchenko (2017)	National statistical databases
Competitiveness and innovation around CE businesses	Employment in CE related economic activities (nr. employees)	ESPON CIRCTER (2019); European Commission (2018); Avdiushchenko (2018)	ESPON
	Turnover generated by CE related economic activities (€)	ESPON CIRCTER (2019); European Commission (2018); Avdiushchenko (2018)	ESPON

Table 2. Indicator results of the CE monitoring framework for selected regions (summary).

Note: Data refer to 2014, while trends refer to the period 2006–2014. Green arrows indicate desirable trends, while red arrows undesirable trends.

Table 3: Overview of key results of the territorial analysis

CE policy implications	LU	NO-SE	CH-LI
Economic sectors with the greatest potential to contribute to the CE generally depend on the economic specializations of a region and its material-waste flows.	**, ***	*, **	***
Urban/land planning can effectively contribute to the CE transition (e.g., through brownfield reconversion, allocation of spaces for CE activities and mixed-use zoning), especially in agglomerated areas.	V		(V)
Preventing waste generation should be prioritized over recycling targets, and waste incineration should be avoided (as it could lead to lock-in effects)	(V)	V	V
Mitigating the dependence on raw materials (e.g., via technical cycles optimization for the recovery of materials) is fundamental in areas with scarce availability of natural resources.	V		V
Biological cycles optimization should be a priority intervention for the deployment of circular biomass-based economies, especially in areas rich in biomass resources		V	(V)
Cooperation networks and strategic alliances in R&D are fundamental enablers for the implementation of circular solutions.	(V)	V	V
Recommendations for regional CE monitoring frameworks
For territories affected by significant cross-border commuting flows: either intensity indicators should be preferred over per capita indicators, or cross-border commuting flows should be included in the indicators.	V		V
When a region is a net importer of waste, the circular material use indicator (CMU) does not reflect well the real circularity performance of the region. The alternative version of CMU, which accredits countries efforts to reintroduce secondary raw material into the economy, should be preferred.	V
The Waste-to-DMC ratio is a better indicator to inform about the real efficiency of consumption-production systems for material-intensive economies specialised on the processing of raw materials.	(V)	V	(V)

Note: *Primary sectors: agriculture and forestry, mineral ores mining; **Secondary sectors: wood and food industry (especially in NO-SE) and metal manufacturing and construction (especially in LU); ***Tertiary sector: service & retail sector; V: direct findings based on territorial evidence of case studies; (V): inferred findings based on similarities of territorial characteristics between case studies.

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