































 
 

 
European Journal of Social Impact and Circular Economy - ISSN: 2704-9906  
DOI: 10.13135/2704-9906/7691 Published by University of Turin http://www.ojs.unito.it/index.php/ejsice/index 
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Examining the dimensionality of Circular Economy 

metrics using Hierarchical Clustering on Principal 

Components 
Collin L. Yobe1* 

1 University of South Africa, Graduate School of Business Leadership, Cnr Janadel and, 

Alexandra Ave, Midrand, 1686 (South Africa)  

collinyobe@gmail.com 

*contact author 
ORCID   0000-0001-5270-2192 

 

Received: 03/01/2024 

Accepted for publication: 26/04/2024 

Published: 30/04/2024 
 

 

Abstract

The Circular Economy (CE) indices have become a valuable tool for supporting the development of policies that provide 

information that reduces environmental pressures and impacts.  However, highly dimensional data identifying many CE 

indicators is impractical in application.  This paper aims to create a composite index of the CE indicators using the 

Hierarchical Clustering on Principal Components (HCPC) to extract the meaning of the CE indicators, as reducing 

dimensionality can improve understanding of indicators and metrics.  The advantage of the HCPC methodology over 

principal components analysis (PCA) alone involves applying objective clustering techniques to the PCA results, which 

results in a better cluster solution.  This study analysed a dataset of 61 indicators obtained from De Pascale, Arbolino, Szopik-

Depczyńska, Limosani, and Ioppolo (2021).  The composite indices revealed the dimensions of industrial symbiosis (IS), CE 

strategies, and spatial applications of the CE and IS concepts.  The bottom-up and top-down approaches for CE and IS 

strategies have been the main implementation approaches in different governments and regions. 

 

Keywords: Circular Economy, Circular Economy indicators, industrial symbiosis, Hierarchical Clustering on Principal 

Components

 

 
 



 
 

 
European Journal of Social Impact and Circular Economy - ISSN: 2704-9906  
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21 

1.  Introduction 

The circular economy (CE) is being touted as a promising solution to mitigate human activities' growing environmental and 

resource pressures (Bocken, De Pauw, Bakker, & Van Der Grinten, 2016).  Several countries have adopted CE principles to 

achieve zero waste, a crucial goal for most economies as it reduces greenhouse emissions and environmental impacts from 

current linear waste production systems, making CE a crucial part of future strategies.  A CE promotes system innovations to 

improve waste management resource efficiency and balance the economy, environment, and society (Kristensen & Mosgaard, 

2020).  The CE paradigm promotes autonomous production processes that reuse materials, generating increased interest in 

research and developing metrics for a circular shift.  The interest in circularity indicators has led to extensive literature on CE.  

Currently, there are three levels of indicators for measuring the CE, i.e., micro (companies, product), meso (industrial symbiosis 

(IS), eco-industrial parks (EIPs)), and macro (governments, global, national, regional, city) (de Oliveira, Dantas, & Soares, 

2021; De Pascale et al., 2021; Kristensen & Mosgaard, 2020; Moraga et al., 2019). 

The rise of EIPs is a significant trend in the new industrial reality, aiming to promote environmental benefits and economic 

development through collaboration between companies (Felicio, Amaral, Esposto, & Durany, 2016).  IS promotes a collective 

approach to competitive advantage across industries by integrating the physical exchange of materials, energy, water, and by-

products into their business processes (Felicio et al., 2016; Geng, Fu, Sarkis, & Xue, 2012; Geng, Zhang, Ulgiati, & Sarkis, 

2010; Jacobsen, 2006).  Moreover, as such, EIPs must be able to promote IS.  

The IS and CE are intricate subjects that require meticulous coordination due to the potential differences in preferences among 

various stakeholders.  Therefore, policymakers must take a direct lead in pushing and driving IS and the CE.  Through 

legislation, governments have managed to work towards putting CE plans into action.  The Chinese government has played a 

significant role in the implementation of IS.  They can define policy, and everyone within the country follows.  CE, adopted in 

China to promote economic growth despite material and energy limitations, has garnered significant global interest from 

governments, international bodies, industrial associations, and corporations (Franklin-Johnson, Figge, & Canning, 2016).  

China leads the ranking of countries that have contributed most to the increasing growth rate in research, implementation, and 

extensive development of CE concepts in academia and politics (De Pascale et al., 2021; Geng et al., 2012).  In 2008, China 

was the first country to adopt legislation to deploy CE strategies by enacting a specific law, confirming China's prominence 

(De Pascale et al., 2021; Geng et al., 2012; Moraga et al., 2019).  Unlike China, the European Commission and its member 

countries use self-regulatory, bottom-up strategies for implementing CE strategies, which primarily rely on external factors 

(Cayzer, Griffiths, & Beghetto, 2017; Linder, Sarasini, & van Loon, 2017).  The European Commission and member countries 

employ self-regulatory, bottom-up approaches to implement CE strategies, unlike China, which primarily relies on external 

factors. 

CE is extensively researched, yet its practical application in economic initiatives remains a significant challenge.  The extensive 

literature on CE necessitates rigorous analysis to ensure its relevance for functional purposes due to its disconnection.  Several 

studies have come to the fore and have identified CE indicators (Argüelles, Benavides, & Fernández, 2014; de Oliveira et al., 

2021; De Pascale et al., 2021; Parchomenko, Nelen, Gillabel, & Rechberger, 2019; Stanković, Janković-Milić, Marjanović, & 



 
 

 
European Journal of Social Impact and Circular Economy - ISSN: 2704-9906  
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22 

Janjić, 2021).  The current set of circularity metrics is criticised for failing to fully capture the multidisciplinary and systemic 

nature of the CE (Stanković et al., 2021).  These studies critically examine the dimensionality of CE indicators used in studies, 

highlighting their high dimensionality and potential limitations for practitioners.  It suggests that reducing dimensionality can 

improve understanding of indicators and metrics.  The research highlights the need for further knowledge of current research 

work.  Therefore, this paper aims to create a composite index of the CE indicators using the Hierarchical Clustering on Principal 

Components (HCPC) to extract the meaning of the CE indicators.  A dataset of 61 indicators developed in a previous paper 

identified by De Pascale et al. (2021) will be analysed using the HCPC to achieve the stated aim.  This approach reduces data 

dimensionality and enables further understanding of the already-identified CE issues—IS, spatial applications of the CE 

concept, and CE strategies.  Based on this dataset, the exercise attempts to cluster and classify indicators and papers to provide 

a structure for the problem. 

The rest of the document is structured as follows: Section 2 offers an extensive review of Circular Economy (CE) indicators 

found in previous studies, providing an understanding of the many metrics and approaches used to evaluate CE performance.  

In Section 3, the data sources and methodology of this study are explored.  The analytical frameworks used for CE indicator 

analysis and classification, such as principal component analysis (PCA) and hierarchical clustering, are explained.  Part 4 

presents the PCA analysis and hierarchical clustering findings, thoroughly studying the principal components and clusters 

found, thereby illuminating the underlying patterns and trends in the CE environment.  Ultimately, Section 5 summarises the 

results derived from the research, emphasising the critical takeaways and implications for furthering CE initiatives. 

 

2.  Review of CE indicators in research 

The growing resource demand and environmental issues drive a shift towards sustainable production and consumption 

(Gallego-Schmid, Chen, Sharmina, & Mendoza, 2020).  Contemporary sustainability literature focuses on the potential of the 

CE to disrupt the unsustainable production and consumption linear economy (Kristensen & Mosgaard, 2020).  However, 

considerable effort is required to transition toward a more CE (Parchomenko et al., 2019).  In transitioning to this CE economy, 

indices are fast becoming a valuable tool to support the development of policies in providing information and reducing 

environmental pressures and impacts (De Pascale et al., 2021). 

There three main levels of indicators for measuring CE in the literature so far are macro (global, national, regional, city, 

governments), meso (IS, EIPs), and micro (single firm, product) (de Oliveira et al., 2021; De Pascale et al., 2021; Geng et al., 

2012; Kayal et al., 2019; Kristensen & Mosgaard, 2020; Linder et al., 2017; Mazur-Wierzbicka, 2021; McCarthy, Kapetanaki, 

& Wang, 2019; Moraga et al., 2019).  de Oliveira et al. (2021) present a fourth dimension, nano (products).  The lack of detailed 

measurement and documentation of the CE's progress can hinder understanding of the subject matter, creating barriers for 

actors at the specific CE level.  Governments, policymakers, and business practitioners need more information on CE typologies 

to promote their business environment towards IS.  

Transitioning to a CE requires significant effort, but a widely accepted framework for monitoring progress is lacking due to the 

vast and diverse areas covered by different assessment methodologies (Parchomenko et al., 2019).  Despite the concept's lack 



 
 

 
European Journal of Social Impact and Circular Economy - ISSN: 2704-9906  
DOI: 10.13135/2704-9906/7691 Published by University of Turin http://www.ojs.unito.it/index.php/ejsice/index 
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23 

of clarity, CE focuses on defining action plans supported by specific indicators (Moraga et al., 2019).  Transitioning to circular 

systems may not always lead to favourable alternatives, as potential environmental, economic, or social trade-offs may arise 

(de Oliveira et al., 2021). 

Past CE research methodologies include reviews and empirical papers, with CE indicators classification, categorising and 

assessing existing work, with some articles developing a measurement method (de Oliveira et al., 2021; Kristensen & Mosgaard, 

2020).  Some of the approaches used in developing the metric for measuring CE indicators have included the (a) Principal 

Component Analysis and PROMETHEE (de Oliveira et al., 2021),(b) The Multiple Correspondence Analysis (MCA) 

(Parchomenko et al., 2019), and (c) indicators grouped by using a double classification: first according to the three spatial 

dimensions of sustainability –macro, micro and meso – then based on the 3R Core CE principles (De Pascale et al., 2021). 

 

3.  Data and methods 

3.1 Data collection 

The data for this research was obtained from De Pascale et al. (2021) through a systematic literature review, and a final list of 

indicators was extracted and grouped into three clusters: micro, meso, and macro.  The review process involved selecting 

academic databases, search terms, and screening for practical purposes.  Definitions and key concepts were determined, and 

the gathered material was summarised for future insights.  The first step involved a systematic search for published CE indicator 

implementation studies. 

The study used a methodological approach to define and select a sampling framework to review Circular Economy (CE) 

indicators.  The framework was based on a time scale of 2000-2019 and a preliminary search of existing literature in Scopus 

and Web of Science databases.  The results were refined using advanced search terms and keywords to identify the level of 

implementation of CE indicators at micro, meso, and macro spatial levels.  A combination of selected keywords was chosen 

and compared with other CE reviews to ensure high-level significance.  The keyword "Circular Economy" was selected to 

ensure coherence with the main topic.  The academic databases were searched for spatial levels (micro, meso, and macro) and 

identified keywords for each level. 

The classification system includes micro-level (company), meso-level (circular economy), and macro-level (city, country, 

region) terms, combining them to create a comprehensive understanding of the circular economy.  The categories of indicator, 

index, assessment, evaluation, and measuring were incorporated into the literature as they are frequently used in CE studies.  

The study enumerated 61 articles that were used for the analysis. 

 

3.2 Description of the Explanatory Variables 

Table 1 shows the CE indicators identified by De Pascale et al. (2021).  The study quantified these as categorical variables by 

creating lists of indicators to capture the various attributes of CE indicators.  The multivariate approach implemented the 

Hierarchical Clustering on Principal Components (HCPC).  This multivariate approach uses Principal Component Analysis 

(PCA) and Hierarchical Cluster Analysis (HCA) on these attributes. 



 
 

 
European Journal of Social Impact and Circular Economy - ISSN: 2704-9906  
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24 

 

Table 1: Description of the dimensions of circular economy indicators 

Attributes Description 

Approach: 
It includes five subdivisions: Quantitative, qualitative, VBI, analytical tool, and theoretical 
results. 

Application-level:  
Refers to Product, Materials, (embodied) Energy, Components, Resource Waste (/waste 
streams), Product families, IS districts and networks, National, Regional, Provincial, the 
European Union (EU), and World Regions. 

Core CE principles: 
This category concerns the following Core CE principles: Reduce, Reuse, Recycle, 
Recover, Remanufacture, and Redesign 

Sustainable Development 
Dimensions: The dimensions are Environmental, Economic, and Social. 

Level of CE indicators:  
The spatial level applications of the circularity vary between macro - as a city, region, or 
nation, meso – as EIPs (eco-EIPs) and IS, and micro levels – as a single company or 
products using different methods and techniques. 

Regions: 
The study considered the following regions:  the United States of America (USA), Europe, 
EU, Belgium, Netherlands, Italy, England/ United Kingdom (UK), India, Spain, Denmark, 
China, South Korea, Australia, Jordan, Japan, Switzerland, and World Regions. 

Source: De Pascale et al. (2021) 

 

3.3 Analytical techniques 

Please use Times New Roman, size 10, for the text of your manuscript, including the main body, references, and footnotes. 

 

3.3.1 Multivariate Approach for Classification 

 - Principal Components Analysis 

The study utilised Principal Component Analysis (PCA) to create typologies of CE indicators, a statistical method that reduces 

variables into smaller dimensions with minimal information loss (Jolliffe, 2002).  PCA is a feature extraction technique that 

transforms an initial dataset of variables into a new uncorrelated dataset of orthogonal linear combinations (PCs).  It aims to 

reduce the dimensionality of the data by obtaining the largest variance original variables, accounting for as much variation as 

possible (Jolliffe, 2002; Manly, 2005); this means that the first PC is the linear combination with the largest variance, while the 

second one is the linear combination with the second-largest variance, orthogonal to the first PC, etc.  According to Costello 

and Osborne (2005), Varimax rotation simplifies the data's factor structure and makes its interpretation more straightforward 

and reliable.  The authors argue that Varimax rotation, which produces uncorrelated factors, is superior to other rotation 

orthogonal methods like quartimax and equamax, as it cannot improve fundamental aspects of analysis like variance extraction. 

De Pascale et al. (2021) grouped the indicators using a double classification: first, according to the three spatial dimensions of 

sustainability – macro, micro, and meso– and then based on the 3R Core CE principles.  The study mapped out dummy variables 

for indicators' attributes, such as level, country, analytical approach, application level, core CE principles, and sustainable 

development dimensions, but this approach failed to provide a clear group structure and dimensionality of the CE indicators in 



 
 

 
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25 

the research.  Jolliffe (2002) posits that the approach to using cluster analysis is vital in cases where such a group structure is 

lacking.  Following Vyas and Kumaranayake (2006) and Achia, Wangombe, and Khadioli (2010), PCA was applied to the 

dummy variables to reduce the dimensionality of the data and categorise the CE indicators into distinct dimensions (Jolliffe, 

2002).  The PCA scores of CE indicators initially extracted and retained were followed by Varimax rotation for the above 

reasons.  The study used the Kaiser-Maier-Olkin (KMO) and Bartlett's sphericity tests to assess the suitability of the variables 

for PCA.  Hair, Black, Babin, Anderson, and Tatham (2006) suggest that researchers should consider variables suitable if their 

KMO values exceed 0.5, and Bartlett's sphericity test yields statistical significance at p<0.05.  PCs with Eigenvalues equal to 

or greater than 0.7 were retained, following Jolliffe (2002).  

 

- Hierarchical cluster analysis  

The study used Hierarchical Clustering (HCA) to classify CE indicators based on their similarity or dissimilarity.  The technique 

uses Euclidean distance as the dissimilarity metric.  The research used PCA on CE indicators' characteristics data, a hierarchical 

clustering method called Hierarchical Clustering on Principal Components (HCPC), to analyse the classification limits of CE 

indicators. 

Hierarchical cluster analysis estimates the number of clusters in a dataset using Principal Component Analysis (PCA).  The 

study uses Ward's criterion to perform hierarchical clustering on all principal components derived from PCA on the initial 

dataset of variables.  Ward's method estimates hierarchical clusters that create equal and evenly sized clusters, with solutions 

estimated based on cases and variables. 

Several studies, including Argüelles et al. (2014), have used HCPC and K-means clustering as candidate multivariate 

approaches.  HCPC offers two main advantages: it applies objective clustering techniques to PCA results, resulting in a better 

cluster solution than factorial analysis alone, and Ward's classification enhances the robustness of the final clustering results.  

Garson (2009), cited in Yobe, Mudhara, and Mafongoya (2019), suggests that hierarchical clustering is the most suitable 

technique for data sets with less than 250 observations, indicating that datasets below this threshold are unsuitable.  According 

to Kaur and Kaur (2013), the K-means algorithm outperforms the hierarchical algorithm on data sets with over 250 

observations.  However, preliminary analyses with a smaller sample size confirmed this, as the K-means clustering technique 

failed to adequately classify cases, leading to using the HCPC technique for multivariate classification. 

 

4.  Results 

4.1 Descriptive Statistics of the Circular Economy Indicators 

The dummy variables were created to measure the variation of the CE indicators accurately, enabling a quantitative analysis of 

their performance.  Table 2 shows the descriptive statistics that measure the mean, standard deviation, minimum, and maximum.  

On average, the Quantitative Approach was extensively used, with a mean of 0.7458.  The dominant application level is for 

product, and the mean value is 0.4068.  The dummy variables were created to accurately measure the variation of the CE 

indicators, enabling a quantitative analysis of their performance. 



 
 

 
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Table 2: Descriptive statistics of the circular economy indicators 

Variable Mean Std. Dev. Min Max 
Approach:     
Quantitative 0.7458 0.4392 0 1 
Qualitative 0.0847 0.2809 0 1 
VBI 0.0678 0.2536 0 1 
Analytical tool 0.0170 0.1302 0 1 
Theoretical results 0.0169 0.1302 0 1 
Application-level:      
Product 0.4068 0.4954 0 1 
Materials 0.1695 0.3784 0 1 
(embodied) Energy 0.0678 0.2536 0 1 
Components 0.0678 0.2536 0 1 
Resource Waste (/ waste streams) 0.0508 0.2216 0 1 
Product families 0.0169 0.1302 0 1 
Industrial symbiosis districts and networks 0.1017 0.3048 0 1 
National 0.0508 0.2216 0 1 
Regional 0.0847 0.2809 0 1 
Provincial 0.0339 0.1825 0 1 
EU 0.0678 0.2536 0 1 
World Regions 0.0170 0.1302 0 1 
Core CE principles:     
Reduce 0.5593 0.5007 0 1 
Reuse 0.6610 0.4774 0 1 
Recycle  0.8136 0.3928 0 1 
Recover 0.1356 0.3456 0 1 
Remanufacture 0.1864 0.3928 0 1 
Redesign 0.1017 0.3048 0 1 
Sustainable Development Dimensions:     
Environmental 0.8983 0.3048 0 1 
Economic 0.9153 0.2803 0 1 
Social 0.4746 0.5036 0 1 
Level of CE indicators:      
Micro-level 0.5085 0.5042 0 1 
Macro-level 0.2373 0.4291 0 1 
Regions:     
USA 0.0508 0.2216 0 1 
Europe 0.0169 0.1302 0 1 
EU 0.1017 0.3048 0 1 
Belgium 0.0508 0.2216 0 1 
Netherlands 0.0339 0.1825 0 1 
Italy 0.0169 0.1302 0 1 
England/UK 0.0339 0.1825 0 1 



 
 

 
European Journal of Social Impact and Circular Economy - ISSN: 2704-9906  
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Variable Mean Std. Dev. Min Max 
Approach:     
India 0.0169 0.1302 0 1 
Spain 0.0169 0.1302 0 1 
Denmark 0.0169 0.1302 0 1 
China 0.0169 0.1302 0 1 
South Korea 0.0169 0.1302 0 1 
Australia 0.0169 0.1302 0 1 
Jordan 0.0169 0.1302 0 1 
Japan 0.0169 0.1302 0 1 
Switzerland 0.0169 0.1302 0 1 
World Regions 0.0169 0.1302 0 1 

Source: De Pascale et al. (2021) 

Observations = 58.  VBI is a Value-based indicator/Evaluation indicator system. 

 

4.2 Multivariate analysis results 

The results of the multivariate analysis, which employed the PCA, are presented below.  Table 3 shows the level of the scree 

plot of the estimated PCs of CE indicators. 



 

 28 

Table 3: Estimated PCs of circular economy indicators 

Component 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 
Initial eigenvalues:                

% of Variance 13.014 7.314 5.636 5.438 5.388 4.722 4.252 3.760 3.540 3.471 3.267 3.034 2.859 2.643 2.582 
Cumulative % 13.014 20.328 25.964 31.402 36.790 41.512 45.764 49.524 53.064 56.535 59.801 62.835 65.694 68.337 70.980 
Approach:                

Quantitative      -0.464   -0.426       

Qualitative      0.887          

Value-based 
indicator/Evaluation indicator 
system 

        0.824       

Analytical tool                

Theoretical results            0.910    

Core CE principles:                

Reduce 0.694             0.316  

Reuse             0.480   

Recycle         0.706        

Recover   0.731             

Remanufacture -0.350     0.530  -0.499        

Redesign      0.604 -0.320    0.568     

Sustain.  Develop.  
Dimensions: 

               

Environmental 0.448          0.356   0.358  

Economic       0.791         

Social 0.632               

Level of CE indicators:                 

Micro-level -0.857               

Macro-level 0.637               

Application-level:                 

Product -0.811               

Materials -0.445 0.351      0.446        

Components     0.538   -0.601        

Resource Waste (/ waste 
streams) 

    0.836           

Product families     0.843           

Industrial symbiosis districts 
and networks 

            -0.524  0.453 



 
 

 
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Component 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 
National    0.547      0.734      

Regional 0.549        -0.375       

Provincial         0.671       

(embodied) Energy   0.868             

Regions:                

USA  0.513  0.743            

Europe   0.769             

EU  0.342  0.441        0.636    

Belgium -0.377             -0.309  

Netherlands              0.826  

Italy           0.847     

England/UK  0.889              

India  0.888              

Spain       -0.808         

Denmark                

China 0.694        0.315       

South Korea                

Australia             -0.846   

Jordan               0.858 
Japan    0.897            

World Regions          0.911      
Source: De Pascale et al. (2021) 
Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization. 

The coefficients with a value of 0.3 and less were suppressed and not displayed in the estimated PC scores results. 

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.291; Bartlett’s Test of Sphericity Approx.  Chi-Square = 1301.698; df = 903; Sig. = 0.000. 

PC 14, which explained 2.77% of the variance, was dropped from the analysis because the variation explained by the factor loadings did not make economic sense. 



 

 30 

The application of PCA on the attributes of the CE indicators produced initial Eigenvalues 1.015, 19 PCs that explained a 

cumulative 80.941% of the variance in the dummy variables but only retained 15 PCs for the analysis Table 4.  This study 

dropped PC 14 – which accounts for 2.77% – from the analysis because the variation it explained did not make economic sense.  

Therefore, the total variance explained by the PCs used in this analysis was 70.92%.  The study dropped two variables – the 

meso-level of CE indicators and EIPs – because the rotation failed to converge in estimating the PCA's rotated component 

matrix. 

The study utilised KMO and Bartlett's sphericity measures to assess the feasibility of incorporating 58 cases of indicators into 

a dataset.  The KMO measure showed a 0.291 value, indicating a significant relationship between variables.  Bartlett's test 

showed a p<0.001 correlation, indicating variables could be factored in.  Varimax with the Kaiser normalisation rotation method 

improved PC interpretation.  

The scree plot below shows the estimated PCs (Figure 1).  The study plots Eigenvalue against PCs, with PC1 having the highest 

score loadings and a high Eigenvalue.  PC-2 explains less variation and has a lesser Eigenvalue.  The study estimates 

diminishing declines of Eigenvalue with other PCs, using a cut-off of PCs with an Eigenvalue of 1 and considering PCs that 

met this criterion. 

 

Figure 1: Scree plot of the estimated PCs of CE indicators 

 
Source: Author's elaboration 

 

The first principal component (PC–1) explains 13.01% of the variance in the indicators of CE, with the estimated component 

loadings expressed in the following equation: 

𝑃𝐶–1	 = 	0.694	𝑅𝑒𝑑𝑢𝑐𝑒	𝐶𝐸	𝑝𝑟𝑖𝑛𝑐𝑖𝑝𝑙𝑒𝑠	 − 	0.350	𝑅𝑒𝑚𝑎𝑛𝑢𝑓𝑎𝑐𝑡𝑢𝑟𝑒	𝐶𝐸	𝑝𝑟𝑖𝑛𝑐𝑖𝑝𝑙𝑒𝑠	

+ 	0.448	𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙	𝑆𝑢𝑠𝑡𝑎𝑖𝑛𝑎𝑏𝑙𝑒	𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡	𝐷𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑠	

+ 	0.632	𝑆𝑜𝑐𝑖𝑎𝑙	𝑆𝑢𝑠𝑡𝑎𝑖𝑛𝑎𝑏𝑙𝑒	𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡	𝐷𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑠	 − 	0.857	𝑀𝑖𝑐𝑟𝑜 − 𝑙𝑒𝑣𝑒𝑙	𝑜𝑓	𝐶𝐸	𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠	

+ 	0.637	𝑀𝑎𝑐𝑟𝑜 − 𝑙𝑒𝑣𝑒𝑙	𝑜𝑓	𝐶𝐸	𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠	 − 	0.811	𝑃𝑟𝑜𝑑𝑢𝑐𝑡	𝐴𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 − 𝑙𝑒𝑣𝑒𝑙	

− 	0.445	𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙𝑠	𝐴𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 − 𝑙𝑒𝑣𝑒𝑙	 + 	0.549	𝑅𝑒𝑔𝑖𝑜𝑛𝑎𝑙	𝐴𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛 − 𝑙𝑒𝑣𝑒𝑙	 − 	0.377	𝐵𝑒𝑙𝑔𝑖𝑢𝑚	

+ 	0.694	𝐶ℎ𝑖𝑛𝑎.	 

0

1

2

3

4

5

6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Ei
ge

nv
al

ue

Component Number



 
 

 
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In PC–1, Reduce CE principles, Social Sustainable Development Dimensions, and Macro-level CE indicators were the most 

dominant loadings variables.  Therefore, this PC was named Reduce CE principles.  PC–2 explains 7.314% of the variance, 

and the most dominant loadings were on the Materials Application-level and in the USA, EU, England/UK and India regions; 

this PC was thus called Materials Application-level. PC–3 accounts for 5.64% of the variance with dominant loadings on 

Recover CE principles, Energy (embodied) Application-level and the EU region and was intuitively named Energy (embodied) 

Application level.  PC–4 was called National Application-level because the most significant loading for this component was 

the National Application Level; the other loading for this coefficient was the USA and EU regions.  

The fifth component, PC–5, which accounted for 5.34% of the variation in the PC estimation, was called Diverse Application-

level because it had loading for Application-Level variables, namely Components, Resource Waste and Product families.  The 

sixth component, PC–6, Recover and Redesign CE principles Qualitative Approach, accounted for 4.72% of the variation and 

had significant loadings on qualitative – and negatively so on quantitative – approach, as well as Recover and Redesign CE 

principles.  The seventh component, PC–7, was named Sustainable Development Economic Dimension because this PC had 

dominant positive loadings on the Sustainable Development Economic.  The study revealed that Redesign CE principles, 

specifically PC-8, accounted for 3.76% of the study's variation.  PC-8 had positive coefficients for Recycle and Remanufacture 

CE principles, positive coefficients for Materials and Components Application-level, and negative coefficients for the research 

approach.  PC-9 had significant coefficient loadings on regional and provincial variables and China region.  Hence PC–9 was 

called Approach Value-based indicator/ Evaluation indicator system and accounted for 3.54% of the variance.  

PC–10, named Application Level National and World Regions, had dominant coefficient loadings of 0.734 Application Level 

National and 0.911 for the World regions, accounting for 3.47% of the variation.  The next component, PC–11, accounted for 

3.26% of the variation.  The variables Redesign CE principles and Environmental Sustainable Development Dimensions had 

positive coefficient loadings of 0.568 and 0.356, respectively; this PC was called Redesign CE principles Environmental 

Sustainable Development Dimensions.  PC–12 is accounted for by 3.034% of the variation and represents the dimension of the 

CE indicators with significant coefficient loadings on the variables for the Theoretical results approach and the EU region; this 

PC was thus identified as the Theoretical results approach.  PC-13, accounting for 2.86%, showed positive coefficient loadings 

on Reuse CE principles, while negative coefficient loadings were observed on IS districts, networks Application-level, and 

Australia. 

The next component, Reduce CE principle and Environmental Sustainability Development Dimensions, accounted for 2.64% 

of the variation.  The component loadings of the coefficients on the variables are as follows: 

𝑃𝐶–14	 = 	0.316	𝑅𝑒𝑑𝑢𝑐𝑒	𝐶𝐸	𝑝𝑟𝑖𝑛𝑐𝑖𝑝𝑙𝑒𝑠	

+ 	0.358	𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙	𝑆𝑢𝑠𝑡𝑎𝑖𝑛𝑎𝑏𝑖𝑙𝑖𝑡𝑦	𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡	𝐷𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑠	– 	0.309	𝐵𝑒𝑙𝑔𝑖𝑢𝑚	

+ 	0.826	𝑁𝑒𝑡ℎ𝑒𝑟𝑙𝑎𝑛𝑑𝑠.	 

The PC-16, the Application-level PC, represents coefficient loadings on IS districts and networks in the Jordan region, 

exhibiting minimal variation in CE indicator scores. 

 



 
 

 
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4.2.1 Hierarchical clustering 

The HCPC analysis uses the HCA to analyse the PCA outcome.  Figure 2 displays a dendrogram representing the HCA result, 

with the vertical axis representing observations and clusters and the horizontal axis representing the distance between them.  

Similar observations are grouped based on their dissimilarity, allowing for better understanding and classification. 

Figure 2 shows a hierarchical dendrogram, where PCs are extracted and arranged into new clusters along the y-axis.  Each 

branch represents connections, and the closer they are to each other, the more related they are.  For instance, PC-6 and 15 are 

members of one cluster from a branch that splits into two, similar to the next group comprising PCs 13, 16, 9, and 8.  However, 

the group composed of PC-2 and PC-4 is far apart in the chart. 

An imaginary cut-off point in a dendrogram creates a cluster, which becomes larger and more heterogeneous as the branching 

diagram moves up.  This results in more variation within the cluster.  For example, two variables next to each other, like PC-6 

and PC-15, will be similar.  Expanding the group, like one large group with PCs 6, 15, 13, 16, 9, 8, 5, 7, and 1, will result in 

more similar observations than grouped observations in another branch.  However, there is still much variation in each group, 

necessitating a trade-off decision on where to make the cut-off. 

The study identifies two clusters in a dendrogram, with each observation in the clusters being similar.  Large clusters, with 

more observations, are more heterogeneous.  The choice of meaningful clusters depends on the degree of change in grouping 

due to slight deviations.  The study suggests a two-cluster solution on the dendrogram, while Figure 3 has a four-cluster solution.  

The ideal variation is minimally affecting solutions.  The study's findings align with this rationale. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 
 

 
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Clustering solutions by variables 

 

Figure 2: Hierarchical clustering and initial partition solutions by variables 

 
Source: Author's elaboration on Linkage method—Ward's method.  Euclidean distance of all elements 



 
 

 
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34 

Table 4 shows references with their title and country for each cluster that result from the dendrogram in Figure 2.  The PC-16, 

also known as the Application-level PC, represents coefficient loadings on IS districts and networks in the Jordan region, 

exhibiting minimal variation in CE indicator score (Guo-gang, 2011; Li & Su, 2012; Xiong, Dang, & Qian, 2011).  Other 

studies in this set of results focus on industrial parks (Felicio et al., 2016; Geng, Zhang, Côté, & Fujita, 2009; Geng et al., 

2010; Tiejun, 2010; Wenbo, 2011; Zhao, Guo, & Zhao, 2018), while the case studies included (Adibi, Lafhaj, Yehya, & 

Payet, 2017; Bovea & Pérez-Belis, 2018; Huysman, De Schaepmeester, Ragaert, Dewulf, & De Meester, 2017; Huysman et 

al., 2015; Sałabun, Palczewski, & Wątróbski, 2019).  The countries that dominated the studies included China, EU members, 

Belgium, and the USA.  

 

Table 4: List of clusters by references, article and countries 

Cluster Reference Country 

1 

Das, Yedlarajiah, and Narendra (2000)  

Lee, Lu, and Song (2014)  

MacArthur (2015) Europe 

Huysman et al. (2017) Belgium; England 

Mohamed Sultan, Lou, and Mativenga (2017) UK; EU;  USA; India 

Azevedo, Godina, and Matias (2017)  
Mesa, Esparragoza, and Maury (2018)                  
Vanegas et al. (2018) Belgium 

2 

  
Nelen et al. (2014)  
J. Y. Park and Chertow (2014) USA 
 EU 
Huysman et al. (2015) Belgium 
Scheepens, Vogtländer, and Brezet (2016) Netherlands 
Cayzer et al. (2017)  
Figge, Thorpe, Givry, Canning, and Franklin-Johnson (2018); 
Franklin-Johnson et al. (2016) 

 

van Schaik and Reuter (2016)  

Linder et al. (2017)  

Di Maio, Rem, Baldé, and Polder (2017) Netherlands 

Favi, Germani, Luzi, Mandolini, and Marconi (2017)  
Adibi et al. (2017)  
Figge et al. (2018)  
Marconi, Germani, Mandolini, and Favi (2019)  
Mandolini, Favi, Germani, and Marconi (2018)  



 
 

 
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Cluster Reference Country 
Cong, Zhao, and Sutherland (2019)  
Zwolinski, Lopez-Ontiveros, and Brissaud (2006)  
Jacobsen (2006) Denmark 
Karlsson and Wolf (2008)  
Geng et al. (2010) China 
Sałabun et al. (2019)  

Wen and Li (2010) China 

Geng et al. (2009) China 
Su, Heshmati, Geng, and Yu (2013) China 
Geng et al. (2012) China 
Wenbo (2011) China 
Li and Su (2012) Beijing - China 
Wen and Li (2010) China 

H.-S. Park and Behera (2014) South Korea 

Pagotto and Halog (2016) Australia 
Felicio et al. (2016)  
Zhao et al. (2018) China 
Tiejun (2010) China 
Geng, Liu, Liu, Zhao, and Xue (2011) China 
Guo-gang (2011) China 
Faizi, Rashid, Sałabun, Zafar, and Wątróbski (2018) China 
Chun-rong and Jun (2011)  
Qing, Qiongqiong, and Mingyue (2011) China 
Xiong et al. (2011) China 
  
Wu, Shi, Xia, and Zhu (2014) China 
Haas, Krausmann, Wiedenhofer, and Heinz (2015) EU Members 
Haupt, Vadenbo, and Hellweg (2017) Switzerland 
Tisserant et al. (2017) World Regions 
Moraga et al. (2019)  

Mayer et al. (2019)  EU Members 

Fregonara, Giordano, Ferrando, and Pattono (2017) Italy 

Moriguchi (2007) EU; USA; Japan 

3 
Bovea and Pérez-Belis (2018) Spain 

Kayal et al. (2019) Jordan 

4 Smol, Kulczycka, and Avdiushchenko (2017) EU Members 
 

Source: Author's elaboration 
 



 
 

 
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36 

5.  Discussion 

Firms, governments, and regional authorities have started recognising environmental benefits and economic growth through 

collaboration between companies that flow directly from IS—generating better collective benefits than could have been 

achieved from the sum of all individual benefits combined (Bocken et al., 2016; Felicio et al., 2016; Jacobsen, 2006; Karlsson 

& Wolf, 2008).  IS offers several benefits, including reducing waste disposal costs by converting waste into by-products for 

other industries, enabling innovation and process development, leading to increased profitability, and benefiting from the 

geographical proximity of businesses (Bocken et al., 2016; Karlsson & Wolf, 2008). 

Creating a composite index can help better understand the CE indicators and measure the development of the CE guides.  

Applying PCA based on the above data reduced the dimensionality of the 58 – previously 61 – CE indicators in De Pascale et 

al. (2021) to 15 PCs, which could be classified.  Table 4 shows the list of the HCPC composite indices based on the title of the 

articles and the regions where these studies took place.  This technique provides a structural approach to navigating the 

multifaceted realm of circular economy indicators. 

The HCPC reduced estimated PCs, resulting in terminal elements of the dendrogram from hierarchical classification.  The 

dendrogram yielded four clusters of CE indicators, determining the group's CE performance based on specific characteristics, 

like countries with similar features (Stanković et al., 2021).  The understanding of regional and global trends is improved by 

this hierarchical classification, which provides a systematic framework for identifying distinctive groupings within the CE 

landscape. 

The areas in this first cluster include European countries (Huysman et al., 2017; McCarthy et al., 2019; Mohamed Sultan et al., 

2017; Vanegas et al., 2018), the USA (Mohamed Sultan et al., 2017) and India (Mohamed Sultan et al., 2017).  This first cluster 

displays a variation of approaches within different national contexts, spotlighting geographical concentrations of CE initiatives.  

Due to the need to respond to the impacts of unsustainable linear production methods, CE approaches will likely provide a 

compelling driver across many different governments, countries and regions.  Das et al. (2000), Lee et al. (2014), and Mesa et 

al. (2018)  in the first cluster did not focus on a particular region in their research.  The first two of these studies focused on 

estimating the end-of-life product disassembly effort and cost and assessing product End-Of-Life performance.  The last of the 

two studies focused on developing a sustainable circular index.  The diverse research focus within this cluster illustrates the 

breadth of CE initiatives, spanning multiple geographical contexts and ranging from index development to product lifecycle 

analysis.   

The following aggregation obtained after applying the HCPC, i.e. Cluster 2, shows studies in Europe (Di Maio et al., 2017; 

Haas et al., 2015; Huysman et al., 2015; Jacobsen, 2006; Mayer et al., 2019; Scheepens et al., 2016),  the USA (J. Y. Park & 

Chertow, 2014), South Korea (H.-S. Park & Behera, 2014), China (Faizi et al., 2018; Geng et al., 2012; Geng et al., 2011; Geng 

et al., 2009; Geng et al., 2010; Guo-gang, 2011; Li & Su, 2012; Su et al., 2013; Tiejun, 2010; Wen & Li, 2010; Wenbo, 2011; 

Wu et al., 2014; Xiong et al., 2011; Zhao et al., 2018), Australia (Pagotto & Halog, 2016) and the World Regions (Tisserant et 

al., 2017).  This second cluster has a broad geographical scope and implicates the variable approaches adopted by different 

countries and regions and the global reach of CE initiatives.  Most of these primarily focused on industrial parks (Felicio et al., 



 
 

 
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37 

2016; Geng et al., 2009; Geng et al., 2010; Tiejun, 2010; Wenbo, 2011; Zhao et al., 2018).  Industrial parks in China are for 

environmental and economic growth benefits, and collaboration between companies is critical in realising this achievement; 

thus, it is crucial to promoting IS.  According to Felicio et al. (2016), the IS in EIPs requires the implementation of intense 

broker involvement.  Impediments to the level of symbiosis during operations usually are market changes and technological 

advancement.  Wenbo (2011) further posits that developing CE is the only way to realise the new industrialisation, and building 

EIPs is an essential means of promoting CE performance.  The next area of focus in this cluster that received attention from 

researchers is that of IS, which includes research done in China (Geng et al., 2009), Denmark (Jacobsen, 2006), South Korea 

(H.-S. Park & Behera, 2014) and Australia (Pagotto & Halog, 2016).  The importance of collaborative efforts in promoting 

circular economy performance—particularly in industrialised nations—is highlighted by the focus on industrial parks as hubs 

for sustainability initiatives. 

The cluster solution suggests a lack of coverage of CE approaches, leading to a lack of studies.  These approaches may have 

been developed to address a specific CE problem but were never expanded to other areas.  They may also be challenging to set 

up in regions with similar CE issues, becoming unsupported and ineffective; this could be the case with two clusters.  

Nonetheless, the research gaps identified in CE indicate potential areas for further research and strategic intervention to advance 

more comprehensive and successful circular economy initiatives. 

Cluster 3 comprises two studies (Fregonara et al., 2017; Moriguchi, 2007) of the HCPC aggregates.  Studies conducted in Italy, 

the EU, the USA, and Japan examined resource productivity indicators and environmental impact.  The need to address 

environmental issues, such as waste reduction and pollution, is a common response to widely accepted action plans.  This third 

cluster explores resource productivity and environmental impacts, highlighting the ongoing need to address pressing 

environmental issues across geographic boundaries through sustained research and policy interventions. 

Cluster 4 classifies the studies focusing on circular design guidelines in Spain (Bovea & Pérez-Belis, 2018), measuring a firm's 

circularity in the water industry in Jordan (Kayal et al., 2019), and CE indicators concerning eco-innovation in the EU member 

countries (Smol et al., 2017).  This final cluster focuses on particular CE initiatives, such as industrial circularity, design 

guidelines, and eco-innovation, and illustrates the various strategic approaches used in multiple industries and regions.Our 

journal adheres to the APA 7th edition style for citations and references.  Please ensure that all citations in the text and the 

reference list comply with this format. 

 

6.  Conclusions 

The study used HCPC to identify clusters in a dataset with 61 CE indicators, retaining 58 after data cleaning, utilising 

complementarities between clustering and principal components methods.  The first stage estimation of PCA only allowed for 

15 of the 16 PCs to be meaningfully interpreted.  The HCPC clustering technique from four clusters provided a better cluster 

solution than the principal components method, identifying research dimensions such as IS, CE strategies, and spatial 

arrangement.  



 
 

 
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Policy recommendations for IS and CE subjects require coordination among stakeholders with diverse agendas.  

Policymakers must lead the implementation of policies related to these subjects.  Despite different approaches, governments 

and regions are adopting actions that drive the agenda forward.  China has successfully used a top-down approach to policy 

implementation, allowing the government to prescribe policies and have everyone adopt them.  This top-down approach could 

be a crucial lead for other countries seeking similar practices to follow.  China's top-down approach could serve as a model for 

other countries. 

The bottom-up approach to policy implementation has been successful in European countries, with governments, regions, and 

businesses realising the benefits of adopting such policies.  These systems receive support and spread quickly, often guided by 

an overarching goal.  The central role of government is critical in implementing regulations that fit the purpose, ensuring that 

well-established and beneficial systems receive support and spread quickly. 

The specific impacts of top-down and bottom-up policy approaches on IS and CE projects in many geographic and cultural 

situations may be the subject of future research.  Furthermore, examining how well various stakeholder coordination 

mechanisms work to implement policies will shed light on how to improve cooperation and alignment in IS and CE to achieve 

shared objectives.  Furthermore, looking into possible synergies between top-down and bottom-up techniques may provide 

insightful information about optimising policy frameworks for scalability and maximum efficacy. 

Understanding how to successfully negotiate the complicated terrain of corporate environmentalism can be improved by 

looking into these areas further, which will ultimately lead to better-informed decision-making and sustainable worldwide 

practices. 

 

7.  Conflict of Interest 

The author has no conflict of interest to declare. 

 

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