Empirical assessment of the circular economy of selected European countries

Journal Pre-proof Empirical assessment of the circular economy of selected European countries

Tihana Škrinjarić PII:

S0959-6526(20)30293-6

DOI:

https://doi.org/10.1016/j.jclepro.2020.120246

Reference:

JCLP 120246

To appear in:

Journal of Cleaner Production

Received Date:

02 August 2019

Accepted Date:

23 January 2020

Please cite this article as: Tihana Škrinjarić, Empirical assessment of the circular economy of selected European countries, Journal of Cleaner Production (2020), https://doi.org/10.1016/j.jclepro. 2020.120246

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Journal Pre-proof Empirical assessment of the circular economy of selected European countries Tihana Škrinjarić, PhD Faculty of Economics and Business University of Zagreb, Croatia [email protected] Abstract: This research focuses on assessing the results of achieving circular economy (CE) goals in selected European countries for the period from 2010 to 2016. The European Union has imposed ambitious goals regarding sustainable development (SD) and CE as a closely linked concept; so objective comparisons of the efficiency of achieving CE should be performed. Since existing research mostly focuses on measuring the SD, this paper fills the gap in literature by: focusing on the CE goals; utilizing variables which are important according to the European Commission when constructing more complex measures and by comparing the robustness of the results (which is often ignored). Although studies exist on measuring the efficiency of achieving SD goals, there is a lack of research which focuses on CE goals in a similar manner. The methodology utilized in the study is the Grey Relational Analysis as a nonparametric approach of constructing a ranking system between different alternatives. Robust results of the empirical analysis indicate regional discrepancies between the European countries. Namely, the best performing CE countries were Germany, Netherlands, Denmark, France and Italy; with the worst performance of Romania, Greece, Cyprus, Slovakia and Bulgaria. The best performing countries were shown to be those which have greater GDP p.c. (Gross Domestic Product per capita) and have better infrastructure, education and the development of R&D (research and development). Some of the countries found to be the worst-ranked have greater corruption indices in the world rankings, as well as lower government efficiency indices, with worse PISA results in schooling. These results are in line with related research and ranking systems of different world organizations which are based on a more complex approach. Discussion is provided on policy recommendations and future work within this area. Worst performing countries are advised to raise awareness on CE topics, as well as education, minimizing the dependence on raw materials, as well as increasing the withdrawal of funds from relevant financing institutions to aid in achieving CE goals. Key words: sustainable development, Grey Relational Analysis, robust ranking, Circular Economy Action Plan, energy recovery, recycling, MCDM JEL classification: C14, Q01, Q56 Highlights:   

Dynamic rankings of achieving CE goals of 23 European countries are provided in the study. Robustness of the results is tested via several approaches. Detailed discussion on sources of (in)efficiency of worst and best performing countries is provided.

Journal Pre-proof Empirical assessment of the circular economy of selected European countries Abstract: This research focuses on assessing the results of achieving circular economy (CE) goals in selected European countries for the period from 2010 to 2016. The European Union has imposed ambitious goals regarding sustainable development (SD) and CE as a closely linked concept; so objective comparisons of the efficiency of achieving CE should be performed. Since existing research mostly focuses on measuring the SD, this paper fills the gap in literature by: focusing on the CE goals; utilizing variables which are important according to the European Commission when constructing more complex measures and by comparing the robustness of the results (which is often ignored). Although studies exist on measuring the efficiency of achieving SD goals, there is a lack of research which focuses on CE goals in a similar manner. The methodology utilized in the study is the Grey Relational Analysis as a nonparametric approach of constructing a ranking system between different alternatives. Robust results of the empirical analysis indicate regional discrepancies between the European countries. Namely, the best performing CE countries were Germany, Netherlands, Denmark, France and Italy; with the worst performance of Romania, Greece, Cyprus, Slovakia and Bulgaria. The best performing countries were shown to be those which have greater GDP p.c. (Gross Domestic Product per capita) and have better infrastructure, education and the development of R&D (research and development). Some of the countries found to be the worstranked have greater corruption indices in the world rankings, as well as lower government efficiency indices, with worse PISA results in schooling. These results are in line with related research and ranking systems of different world organizations which are based on a more complex approach. Discussion is provided on policy recommendations and future work within this area. Worst performing countries are advised to raise awareness on CE topics, as well as education, minimizing the dependence on raw materials, as well as increasing the withdrawal of funds from relevant financing institutions to aid in achieving CE goals. Key words: sustainable development, Grey Relational Analysis, robust ranking, Circular Economy Action Plan, energy recovery, recycling, MCDM JEL classification: C14, Q01, Q56 Highlights:   

Dynamic rankings of achieving CE goals of 23 European countries are provided in the study. Robustness of the results is tested via several approaches. Detailed discussion on sources of (in)efficiency of worst and best performing countries is provided.

Journal Pre-proof 1. Introduction The concept of sustainable development (SD) is not particularly new. It has been in the centre of focus of many researchers as well as the public view for over 30 years now, with greater interest being generated since the UN (United Nations) Brundtland Report (1987). However, the notion of circular economy (CE) is getting more attention over the last few years and it is closely linked to SD, with many possibilities of obtaining SD via achieving CE (Schroeder et al. 2018). The research on CE concepts has been rapidly growing in the last couple of years (Kircherr et al. 2018). The notion of CE represents reusing the products and materials within the economy for as long as possible, in order to minimise the waste and usage of new resources (European Commission 2019 a). One of the main ideas within the CE approach is to capitalize on material flow recycling, with balanced economic growth (Zhu et al. 2010). Thus, a quite opposite approach when compared to linear economic systems. However, the CE is not linked only to waste management (Ghisellini, et al. 2016). Some explanations of the foundations of CE can be found in Ashby (2016), Murray et al. (2015) or Ghisellini et al. (2016). Recent research has started to popularize the concept of CE in the last couple of years (see Stahel 2015); although only 9% of the total global economy is estimated to belong to CE (de Wit et al. 2018). As Wu et al. (2014) state: three principles of the CE concept are 3Rs: reuse, reduce and recycle, whereas Jawahir and Bradlex (2016) extend that concept to 6Rs (besides the three mentioned, authors add remanufacture, redesign and recover). Finally, Potting et al. (2017) extend the 6Rs to 9 in total (by adding refuse, refurbish and repurpose). The European Commission (2014, 2015) states that CE keeps the added value in products for as long as possible and eliminates waste; with keeping resources within the economy itself after a product has reached the end of its life, due to it being used again and creating new value. Kirchherr et al. (2017) add that CE represents an economic system based on business models that reduce, alternative reuse, recycle and recover materials in the production, distribution and consumption process (on all levels, ranging from micro, meso to macro). In March 2019, the European Commission adopted a report on implementing the Circular Economy Action Plan. It is a rather ambitious plan, due to set goals such as: a common EU (European Union) target of recycling 65% of municipal waste by 2035; 70% of packaging waste; reduce landfill to maximum of 10% of municipal waste; specific measures to deal with food and marine waste to achieve the commitments that have been imposed by the United Nations Sustainable Development Goals; etc1. This Plan is just one of many things which have been officially started over the last couple of years (such as the EU Circular Plan from 2015, European Commission 2015; The Rio+20 Summit of UN Conference on Sustainable development –UNCSD in 2012; EU Circular Economy Stakeholder Conferences since 2017, etc.). However, achieving CE demands great changes in consumer behaviour, economic policies of governments and business practices, new business models, changes in infrastructure and supply chain management, all of which can increase total costs, as well as human resources schooled for such purposes, and administrative duties, due to CE demanding greater monitoring of the whole production and reusing processes (Kok et al. 2013, Calogirou et al. 2010). In order to achieve any of the set goals, they should be able to be measured and compared unambiguously. In the last couple of years, there has been a rise in the number of papers that Some specific recycling target rates regarding types of packaging materials include: paper and cardboard 85%, ferrous metals 80%, aluminium 60%, glass 75%, plastic 55%, wood 30%; a binding landfill target to reduce landfill to maximum of 10% of municipal waste by 2035. The EU cohesion policy is included in achieving the CE goals, by allocating 150 billion Euros from 2014-2020 in innovation, small and medium enterprises competitiveness, resource efficiency and low-carbon investments (European Commission 2019b). 1

Journal Pre-proof deal with the taxonomy of CE, how to measure specific variables, results, etc. A rather comprehensive study and overview on over 25 pages are given in Iacovidou et al. (2017). Comments on assessments of CE are given in Andabaka et al. (2017) as well, where it is seen that different definitions are given in the literature. Probably the most known and renowned definition is the one given by the Ellen MacArthur Foundation (EMF 2013:14): circular economy is “an industrial economy that is restorative or regenerative by intention and design”. Some authors emphasize that CE should not only be focused on production but sustainable consumption should be developed alongside it (Naustdalslid 2017). The semantic analysis was done in Park and Kremer (2017), where it is seen that CE and SD indicators could be fairly differently defined. This could make problems in empirical research on how to conduct such assessments. Maybe this is why a small number of studies are focused on direct measurement and comparisons of the results of conducting measures and steps towards the CE. Saidani et al. (2018) made a classification based on several criteria. Still, there is no consensus on the operalisation to use in measuring the circularity of an economy (Geisendorf and Pietrulla, 2017). Banaite (2016:145) explains: ”Successful evaluation of CE leads to successful and sustainable development of a circular economy.”, whereas Linder et al. (2017:122) explain that the ultimate goal of CE is SD. Geissdoerfer et al. (2017) depicted main similarities and differences between CE and SD based on a bibliometric analysis. Similarities include intra and inter-generational commitments, integration of non-economic aspects into development, necessary cooperation of different stakeholders, regulation as a core implementation tool, business model innovation, technological solutions, etc. Main differences include: SD has open-ended goals, depending on agents, whereas CE is a closed loop; SD prioritizes horizontal system for benefits of the environment, economy and society, whilst CE prioritizes hierarchical system with economic agents at the core with benefits for the economy and environment. Other detailed similarities and differences can be seen throughout the analysis of Geissdoerfer et al. (2017), in which other literature can be found which suggests that CE is the new paradigm of SD. Furthermore, Korhonen et al. (2018) explain that SD with its three dimensions (economic, environmental and social) is a concept which needs to be defined as a departure from the linear nature of the material and energy flow of production on the one hand, and on the other hand the successful adoption of CE within all three mentioned dimensions has a holistic contribution to the SD. As a result, the throughput flow will be at a level that nature can tolerate, with economic cycles respecting the natural reproduction rates. As will be seen in the literature review part, the majority of existing empirical evaluations focus on the notion of SD. Thus, there is a gap in the literature which focuses solely on the CE, in terms of quantifying the achievements of CE goals, utilizing the dynamic approach of modelling, with a nonparametric approach which enables robust rankings. Furthermore, the focus is on evaluation of CE goals at national levels, so that policymakers could obtain a clear picture on the economy as a whole, with the mentioned dynamic analysis which enables evaluation in a long-term perspective. This is why the main goal of this research is to empirically evaluate the practices in selected European countries, with respect to CE measures that are monitored by the European Commission (EC). Since the literature still does not agree on all of the measures which need to be monitored over time (detailed analysis on this is given in Moraga et al. 2019), this research aims to use official measures which are provided on the website of the European Commission’s Eurostat in the database called Circular economy indicators. Although it consists of only several variables that can be compared over countries and time, the results in this analysis can provide first insights into the effectiveness of European „The ultimate goal of a circular economy is sustainable development (Bonciu 2014; Kopnina 2014; Mathews et al. 2011; Qiao and Qiao 2013; Lowe 2015).“ 2

Journal Pre-proof countries regarding at least the basic CEs3. In that way, future work can extend the analysis and measurement of those variables which will be closely linked to the results provided here. Moreover, due to the legislation imposed on the majority of the European countries within the European Union, practices of those countries which have similar laws, agreements, rules and regulations can be objectively compared. Since the legislation is based on best practices, the results obtained here can be a starting point for specific case studies within countries with best practices in the future. Reasoning on why this research focuses on the particular measures from the EC classification is not only that such variables provide the most comparable results, but previous research which tried to classify measures of CE into specific categories and the variables used in this study falls into those important categories4. This is important so that an objective and unified comparisons can be made between countries: good and bad practices can be in a better focus to achieve the goals in a faster and better way, with the best quality. The results of this study were obtained by ranking the selected European countries based on the Grey Relational Analysis methodology. As Fernández (2007) and Fang et al. (2009:259) state: “the evaluation system of circular economy is a typical grey system“. This approach was used due to it being nonparametric and not depending on distribution assumptions. Moreover, it is a straightforward methodology which enables direct rankings and comparisons of the entities observed based on multiple criteria5. The robustness of results has been tested as well. They can be used in further research on CE and finding the best approaches to achieve the CE and SD goals. This research shows that by using fewer amounts of available data and a suitable methodology, the rankings and comparisons of the countries could be obtained as in the more complex ranking systems. Furthermore, the research shows that robust dynamic rankings can be made and utilized for future work so that worst performing countries can focus on specific questions and topics. The rest of the paper is structured as follows. The second section deals with the literature overview of related studies with the goal of a critical overview, summarizing main findings of related research so that the novelties of this research could be better observed. Methodology description is given in the third section, where the nonparametric approach of constructing a ranking system of countries based on different criteria will be explained (the Grey Relational Analysis). The empirical results are given in the fourth section, where the dynamic rankings will be given based on CE variables, the comments on the dynamic of worst and best performing countries will be made and robustness of the results will be checked via several approaches. The final, fifth section, discusses the results with conclusions in a way where recommendations for the worst performing countries are given (their policymakers), as well as notes for future related research with comments on the limits of this study. Moraga et al. (2019) have made a classification framework of measures of CE, based on the hierarchical ladder from Potting et al. (2018), in which CE indicators are classified in three types: direct CE with specific strategies (e.g. recycling rate); direct CE with non-specific strategies; and indirect CE (e.g. eco-innovation index). The classification of the European Commission in monitoring framework on the CE covers these three types of indicators via several variables which are used in this study. 4 Please see the empirical section of this paper with a detailed description. 5 As Liu and Lin (2010) explain, this methodological approach generates, excavates and extracts useful information from partially known information. “Grey” refers to uncertain data. Advantage of this approach is less quantity of data needed to evaluate or estimate behaviour (Deng, 1989). Furthermore, statistical models are based on large samples and distribution assumptions which data needs to follow in order to use models and methods within this area; whereas Grey models are based on smaller samples and poor information (Liu and Lin, 2010). Finally, Liu et al. (2016:10) state “The focus of grey system theory, on the other hand, is on the uncertainty problems of small data sets and poor information, which are different to the problems addressed by probability, fuzzy mathematics or rough set theory.” 3

Journal Pre-proof 2. Literature overview Existing related research is growing in numbers and spreading in many different countries. However, a lot of empirical assessments of the circular economy and sustainable development are focused on more developed countries and those which have big emissions of different types of pollutants in the environment. Some studies try to develop models or approaches of measuring and quantifying the CE changes over time (such as Figge et al. 2014, Li 2011, Nakamura et al. 2008); others evaluate regions or countries (Wen and Meng 2014, Tampakoudis et al. 2014); whilst some focus on particular companies or industries (Ardente and Mathieux 2004, Li and Su 200.). Some research focuses on the other side: how cleaner production which leads to CE affects the business performance (Zeng et al. 2010, Yang et al. 2010). Kircherr et al. (2018) conducted a research via desk research, semi-structured interviews and a survey all over EU (UK, Portugal, Germany, Netherlands, Sweden, Belgium) regarding the barriers to the CE. The main results indicate that the cultural barriers have the greatest effects on CE (lack of consumer interest and awareness and hesitant company culture). Other barriers include market (low virgin material prices, limited funding for CE business models), regulatory and technological ones. Although the attention to CE is growing, the authors conclude that, so far, only limited implementations are found in practice. Regional or country-specific studies can be found in the literature as well. Some of the analysis is as follows. Xu et al. (2014) observed Leshan (an industrial city in China) and proposed a sustainable low-carbon industrial restructuring model with multi-objective planning techniques. Scenario analysis was conducted in that paper, where sustainable relationships between the economy, society and the environment were modelled. Gasparatos et al. (2009 a, b), in a detailed two-part study, observe the effects of human activity on the environment in the UK and resource usage within a Multi-Scale Integrated Analysis of Societal Metabolism model (period 1981-2004). Details were estimated regarding specific industries contributing to the energy conversion efficiency. Good practices on particular products and industries can be found in the papers shown in Table A26. The table includes research and main results which are not strictly related to the approach of this study. The papers which are mostly related to this work are shown in Table 1.

This group of papers with specific focuses on a product and its parts or industries is heterogeneous in terms of the methodology used to measure specific variables of interest. Thus, more often than not, the results are not mutually comparable. However, this research is very valuable for the specific industry on which it focuses so that detailed insights can be obtained for enhancing the whole production process towards fulfilling circular economy requests. Not only that, major cost-cutting can be achieved in the long run, as new innovations can be achieved by doing such research. 6

Journal Pre-proof Table 1. Summary of related research Authors Xiong et al. (2011)

Data Jiangsu province 1999-2008

Methods Development of CE goals, DEA (Data Envelopment Analysis)

Comments Variables included: economic growth, social development and industrial discharge treatment compliance

Wu et al. (2014)

30 Chinese regions 2005-2010

DEA (Data Envelopment Analysis)

Chinese CE efficiency increased over time; however, the resource-saving and pollutant reducing (RSPR) sub-system was the worst among all others. Many regional discrepancies were found, which evoked authors to advise that the better coordination of various policies is needed for realizing sustainable development.

Avdiuschenko and Zajac (2019)

Malopolska region in Poland 2005-2016 28 EU countries 2005-2016

Grey Relational Analysis on CE indicators

Regional comparisons cannot be made based on measures which are provided on a national level. Positive effects of CE on growth.

Vuță et al. (2018)

Panel regression, effects of CE on economic growth

The final group of research which will be shortly mentioned here is the one which utilizes variables of circular economy and sustainable development, as brought and defined by the European Commission, United Nations, OECD (Organization for Economic Co-operation) and other relevant organizations. 11 indicators of sustainability are observed in Tampakoudis et al. (2014). Authors applied panel regression on countries of the Eurozone and found that sustainable GDP growth is mostly dependent on resource productivity, gas emissions, total renewable electricity net generation and employment rate. Lopez-Menendez et al. (2014) have focused on the environmental Kuznets curve for EU-27, for CO2 (carbon dioxide) air emissions. Although this study does not focus on the aforementioned topics of this research, it gave good insights into the relationship between the environment pollution and economic development. This research has shown that there exist disparities between EU countries with regards to the quality of the income distribution and pollution reduction. Thus, economic growth is closely linked to environmental pollution. Eurozone countries were also observed in Fotis and Pekka (2017), a study in which authors applied GMM (generalized method of moments) panel estimation and found that lower pollution levels in countries were caused by greater usage of renewable energy sources. This means that moving towards the circular economy overall leads to lower pollution. Armenau et al. (2017) also looked at EU countries (all 28, period 1977-2014) in order to find what drives sustainable economic growth. The findings were as follows. Variables which negatively affect

Journal Pre-proof SD growth are infrastructure, technology and demographic changes, whilst positive influence is found in expenditures on higher education, adult literacy rate and expenditures on R&D. Thus, it could be said that better education, investment into R&D and innovations are very important in obtaining CEs. A bulk of papers focuses more on the OECD and BRICS (Brazil, Russia, India, China and South Africa) countries to compare country-level performances: Camioto et al. (2016) look at BRICS countries, period: 1993-2010, with the DEA model (Slack-based measure, SBM) and a window analysis. Here, based on the variables such as workforce, energy consumption, GDP and CO2 emissions, authors found that the most efficient was Brazil, with Russia and India the most inefficient countries regarding energy efficiency. Tsai et al. (2016) looked at 37 European and 26 Asian countries (period: 2006-2010), and based on a meta-frontier slacks-based measure (DEA methodology), authors analysed the efficiency of countries based on GDP, labour force, energy consumption and CO2 emissions. There are, of course, differences in the results based on the level of country development. Authors concluded that developing countries should establish their own climate change governance, whereas developed countries exhibit greater carbon emissions, and whose aim should be towards lowering them. This final group of papers helps obtain an overall overview of the state of different economies, their social, economic and environmental connectedness and which countries should be looked upon as good examples. Such analyses do not provide detailed insights as do the case-study ones. However, all types of analysed studies are important for achieving better results. An increasing number of papers are also found in the area of life cycle approach (LCA). Hoekstra and Wiedmann (2014) gave an overview of the environment footprint of humankind, their types, characteristics and limits. Authors encourage that future work gives equal weights to all footprints (water, land, etc.), alongside being used to measure environmental performance by both companies and governments. The LCA is, in essence, an approach which should be applied from the beginning of the “life” of every product and system (since LCA borrows metaphors from biology, Bjørn et al. 2018); and it has a quantitative nature which means that LCA can be used for comparisons purposes in terms of environmental impacts of different product systems. Thus, it is important to obtain objective measures that are important within the LCA. However, it is not only a methodology of analysis, as Mazzi (2019) states. Namely, it can be considered as a philosophy (observing, reflecting, finding effective solutions for improvement of sustainability). The last couple of years are experiencing an increase of not only the environmental impacts generated by products or systems (Toniolo et al. 2019), but coverage is extending to the social and economic sustainability dimensions (Hellweg and Milà i Canals 2014, Ren et al. 2013, Gundes 2016). Toniolo et al. (2019) agree that life cycle thinking needs quantitative instruments to conduct sound evaluations. These are some of the additional reasons why analysis, as provided in this research, is needed for better decision making in the future, on all important levels. By observing the mentioned existing literature in the last paragraph, it was found that not many studies exist which compare the European countries with respect to the EU Circular Plans. Majority of work utilizes simpler methods in data processing, due to the nature of the research, as it is mostly survey data on opinions and perceptions. The European Commission provides the results of measuring specific variables of interest regarding the CE. Thus, this paper aims to fill the gap in the literature by using those variables in an objective comparison of the efficiency in obtaining CE goals. 3. Methodology description

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The Grey Systems Theory is being developed since the 1980s in the Far East. However, it is still relatively unknown in Europe and western countries (see Liu et al. 2016). The Grey Relative Coefficient is the part of the Grey Relational, i.e. Incidence Analysis (GRA), the methodology developed for usage in the decision-making process when data is uncertain, scarce and/or incomplete. Thus, the term “Grey” refers to the incomplete or partially available information. The GRA methodology can be used to rank different alternatives based on multiple criteria, which could be conflicting. Although other approaches can be used to rank alternatives, such as Data Envelopment Analysis, Artificial Hierarchical Process, Multiple Criteria Decision Models, etc., the GRA methodology is simpler compared to the mentioned alternative approaches. Moreover, less subjectivity needs to be involved in the whole modelling process. The GRA approach in ranking alternatives does not need to impose assumptions on the distribution of the data, no translation of data is required (such as in some DEA models), missing and grey data do not impose problems within this methodology, small and large samples can be observed, etc. This section follows Liu and Lin (2006, 2010) and Kuo et al. (2008a) in describing the GRA methodology. M alternatives have to be observed and ranked, by observing their K behavioural sequences, where each alternative m with its sequence data k is compared one to another, k ∈ {1, 2, ..., K}, m ∈ {1, 2, ..., M}. The behavioural sequence data of each alternative consists of criteria upon which the alternatives are being ranked. All of the data in time period/moment t can be formatted in one matrix as follows:  x1 (1)t   x (1) Xt   2 t    xM (1)t

x1 (2)t x2 (2)t  xM (2)t

x1 ( K )t   x2 ( K )t  ,      xM ( K )t  

(1)

where each row denotes the m-th alternative, columns the k-th criteria and e.g. (xm(1), xm(2), ... , xm(K)) is interpreted as the behavioural sequence for the m-th alternative. Firstly, data normalization is performed, so that comparisons over all criteria can be made. This is important so that the results of the analysis are based on comparable inputs; see Huang and Liao (2003). There are three approaches to normalize the data here, based on the criteria themselves. If a criterion is of better value if it is greater, the normalization is performed for every period as follows: xm (k )t  min xm (k )t m ym ( k ) t  , (2) max xm (k )t  min xm (k )t m

m

if the smaller the value, the better is the criterion, then the following normalization is done:

ym ( k ) t 

max xm (k )t  xm (k )t m

max xm (k )t  min xm (k )t m

.

(3)

m

Finally, if a criterion is better if its value is closer to the desired value x*(k), determined by the researcher, the legislation, agreement, etc., then the following normalization is performed:

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ym ( k ) t 

xm (k )t  x* (k )t

.

max xm (k )t  x* (k )t

(4)

m

If the index t is removed from formulas (1)-(4), then the analysis is the same, just for one period or for an average of a time span. This research will compare results for several years in a row, thus the GRA approach will be done by adding an index t to the analysis. The normalization in (2)-(4) forces the variables to be within the range [0,1], with values of criteria closer to the unit value being better-performing ones. The next step includes comparisons of new sequences ym(k)t to a referent sequence y*(k). This referent sequence could be the one which is determined by the researcher based on previous knowledge and experience, thus including some subjectivity in the analysis. Moreover, the values could be determined by the industry average if observing companies, legislative limitations, etc. Due to data here being normalized in a way that the closer to unit value the better, referent sequence here consists of unit values for every criterion. For details, please refer to Kuo et al. (2008a). In the third step, the differences between each behavioural sequence by * each criterion are calculated as Δym (k )t  ym (k )t  y (k ) .

The fourth step includes calculating the Grey Relational Coefficient (GRC) for the comparing alternatives via the formula: Gm (k )t 

Δ min,t  pΔ max,t

Δym (k )t  pΔ min t

,

(5)

where p denotes the distinguishing coefficient, p ∈ [0,1]; and the values Δ min,t and Δ max,t defined as Δ min,t  min Δy1 (k )t , ..., ΔyM (k )t  k and Δ max,t  max Δy1 (k )t , ..., ΔyM (k )t  k . Value p can expand or compress the values of G in (5), but the rankings based on (5) do not change7. Majority of existing research uses the value p of 0.5. Finally, the last step is the Grey Relational Degree (Grade, GRD), calculated as a weighted average from all GRCs in (5) for all criteria: K

GRDm,t   wk Gm ( k )t m .

(6)

k 1

Weights wk are given to each of the criteria k; with

K

 wk

 1 . Now, the values in (6) for

k 1

every alternative can be interpreted as the degree of similarity between that alternative and the referent sequence y*(k). Greater values of similarity between the m-th alternative and the referent sequence mean that this observed alternative is better performing with respect to its criteria. Values in (6) will be calculated for every alternative for every year in the empirical part of the research. Thus, these values will be comparable within a year. Besides comparisons of alternatives with the reference sequence, comparisons can be made to any selected reference sequence if needed.

For details see, e.g. Kuo et al. (2008b), in which suggestions can be found on using the Grey approach in solving other mathematical models. 7

Journal Pre-proof 4. Empirical findings This section deals with the description of data used in the study, with the main results of ranking the selected European countries via goals of achieving a circular economy. 4.1.

Data description and variable justification

From the database Eurostat (2019a), yearly data was collected on the following variables from the base Circular Economy shown in Table 2, with a short description and the full description of each variable given in Table A1 in the Appendix. These variables were selected so that as many different criteria as possible can be included for comparisons/rankings, and the time span included is 2010-2016. 2010 is the first year in this analysis, due to many countries not having any earlier data available. 2016 is the last, due to it being the last publicly available on Eurostat. The countries in the analysis were included, again, based on the criteria to include as many as possible but to have sufficient data to be comparable. These are Austria, Belgium, Bulgaria, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Netherlands, Poland, Romania, Slovakia, Slovenia, Spain and Sweden. The variables in Table 2 were chosen based on the European Commission (Eurostat 2019 e) monitoring framework on the circular economy, which is based on self-sufficiency for raw materials, green public procurement, waste generation, contribution of recycled materials to raw materials demand and trade in recyclable raw materials, with overall recycling rates, private investments, jobs and gross value added in the CE sectors, with number of patents related to waste management and recycling. All these indicators are divided into four thematic areas: production and consumption, waste management, secondary raw materials, and competitiveness and innovation (European Commission 2018 b). The European Commission (2018 b: 6)8 gave a list of 26 indicators (of which 2 are currently under development) classified in the aforementioned four thematic areas. Moraga et al. (2019) have made a classification framework of measures of CE, based on the hierarchical ladder from Potting et al. (2018), in which CE indicators are classified in three types: direct CE with specific strategies (e.g. recycling rate); direct CE with non-specific strategies; and indirect CE (e.g. eco-innovation index). These indicators overlap with the EC indicator list. The main idea in this research was to include variables which were defined by the EC, as the member states would have to provide yearly information on those indicators, which would enable the best comparability. Due to data availability, the approach of this paper was to collect as much data possible to cover all the areas. Moreover, by observing the previous related research (based on the literature review section), the summarization of the used variables is as follows. Orlov et al. (2019) used recycling rates as one of main indicators of CE to assess the impacts on the gross domestic energy consumption in EU. Steliac and Steliac (2019) also utilized recycling rates, number of CE related patents and CE sector related investments in constructing their own aggregate indicators to assess the progress of CE within EU countries. Authors followed the official classification of variables of EC; and the main results indicated that northern and western European countries were ranked higher compared to south and eastern ones. Another study which used recycling rates (in this instance only of municipal waste) and number of patents regarding the CE concept was Vuță et al. (2018). Based on empirical analysis on the panel data set of all EU countries, positive effects of CE measures on the productivity within a country were found. Haas et al. (2015) used 8

For the detailed interpretations and definitions please refer to the mentioned document.

Journal Pre-proof recycling rates as an indicator of circularity of the economy of EU-27. Based on several rates of recycling of different types of materials, this study found that those rates are good indicators of being or not being close to achieving CE. Employment variable also has a great significance in the literature and empirical findings. International Labour Organization (ILO 2018) has estimated that keeping the 2015 Paris Agreement goal of increasing the global temperature less than 2°C compared to pre-industrial levels could open 18 million new jobs all over the world by 2030. Some of the specific estimations of the growth of the number of new jobs due to CE are as follows: Morgan and Mitchell (2015) focused on Great Britain and found that CE activities could open more than 200.000 new positions by 2030; Bastein et al. (2013) looked at the Dutch economy, where 54.000 new jobs are estimated; Lehr et al. (2012) estimated economic impacts of renewable energy in Germany, where the results showed that new employment will reach 150.000 by 2030. Number of patents related to CE is also an important variable in studies. The European Commission (2018c) defines the number of patents as a proxy for technological progress. Horbach et al. (2015) emphasize the role of patents in the development of a country towards CE. Gagliardi et al. (2014) are focused on the Italian companies and the empirical analysis has shown that there is a strong positive impact of eco-innovation on creating long-term jobs, with the effects being significantly greater than other types of innovations. CE related innovations were found to be significant in increasing total employment in European countries in Licht and Peters (2013, 2014). Mitrović and Veselinov (2018) used several of the same variables as in this study (recycling rates and circular material use rate) to construct indices of CE efficiency within the Data Envelopment Analysis (DEA) approach for 23 EU countries. Rankings based on the results showed that the top performing countries are in the north and east of Europe, whereas the lowest ranking ones were at the periphery. Several CE variables were utilized in the Nedelea et al. (2018) study to assess the effects of recycling rates and employment in CE businesses on the added value created in CE in the EU-28. Positive effects of both variables were found on the added value of CE. Table 2. Variables used in the empirical analysis, with description Thematic area of the European Abbreviation Full name Commission Energy recovery in kilograms per ER p.c. Production and consumption capita. Recycling material in kilograms per Waste management capita. Gross investment in tangible goods, GI % GDP Competitiveness and innovation percentage of GDP. Employed in contributors to circular Emp % economy, percentage of total Competitiveness and innovation employed persons. CMR Circular material use rate. Secondary raw materials Number of patents related to recycling Pat Competitiveness and innovation and secondary raw materials. Source: Eurostat (2019a), see Table A1 in Appendix for details. REC p.c.

Some of the countries did not have all the data available for every year, and the number of patents (variable Pat) was available only until 2014. Thus, the analysis will be performed with several missing data entries and, for the years 2015 and 2016, based on the first five variables

Journal Pre-proof listed in Table 2. The overall average for every variable by country was calculated for the entire time span and is shown in Table 3. The greater each value is, the better the country is in observing that criteria. E.g., Germany was the best in terms of REC p.c. and Pat, Denmark regarding ER p.c., Latvia for GI %GDP and Emp%, while Netherlands was the best in the column CMR. However, these are just the averages for the entire time span. Thus, the GRA analysis in the next section will make comparisons over the years as well. Table 3. Averages of every variable in the model, by country Country Belgium Bulgaria Denmark Germany Estonia Greece Spain France Croatia Italy Cyprus Latvia Lithuania Hungary Netherlands Austria Poland Portugal Romania Slovenia Slovakia Finland Sweden

4.2.

ER p.c. 177.43 5.71 426.17 130.14 105.14 2.00 51.14 179.57 0.14 83.86 0.71 4.29 28.57 42.86 233.86 204.00 19.57 96.00 5.14 24.86 33.29 192.00 231.14

REC p.c.

144.43 105.00 211.17 294.14 61.57 67.71 82.43 113.29 49.00 121.57 82.00 58.43 81.86 84.14 131.57 143.14 52.43 61.57 11.43 141.43 25.43 111.43 147.86

GI %GDP 0.16 0.23 0.08 0.09 0.20 0.03 0.08 0.11 0.16 0.12 0.07 0.25 0.14 0.13 0.13 0.09 0.17 0.11 0.21 0.23 0.17 0.09 0.12

Emp% 1.15 1.77 1.32 1.43 1.91 1.46 1.84 1.63 2.16 2.08 1.69 2.70 2.64 1.85 1.20 1.49 2.15 1.78 1.51 2.08 1.88 1.69 1.57

CMR

16.49 2.63 7.87 10.89 12.99 1.84 8.91 17.79 3.51 14.99 2.17 2.23 3.84 5.79 26.57 8.26 10.96 2.10 1.93 8.21 4.76 10.29 7.40

Pat 10.04 0.87 4.28 84.23 0.93 0.50 18.77 58.20 0.34 28.75 0.35 1.17 1.27 3.72 15.05 10.72 37.56 2.35 4.27 0.96 0.65 11.09 4.44

Main results

As described in the methodology section, all of the criteria listed in Table 2 were normalized in each year for every country via formula (2). The reasoning is that every criterion is defined in such a way that the greater its value is, the better the country performs in that field. The Grey Relational Degrees for the year 2016 are shown in Figure 1. It is obvious that the best performing countries in that year were Germany, Denmark, Netherlands and France; while the worst performing were Romania, Greece, Slovakia and Portugal. However, this is just one year of performance and it is always better to observe dynamics over time. Thus, Table 4 depicts all GRDs for all countries over the years. Now, policymakers can make full comparisons and focus on further analysis of specific countries to find good and bad practices. Those with the greatest values of GRDs, i.e. the best performing CE countries were Germany, Netherlands, Denmark, France and Italy; with the worst performance of Romania, Greece, Cyprus, Slovakia and Bulgaria. This is in line with findings in Mitrović and Veselinov (2018) where the authors constructed their own CE composite indicators via DEA analysis. Furthermore, these results confirm the report of the European Commission (2018d) in which the analysis identified 149 member states at risk of missing the 2020 targets regarding recycling and waste policies. The 14 member states are: Bulgaria, Croatia, Cyprus, Estonia, Finland, Greece, Hungary, Latvia, Malta, Poland, Portugal, Romania, Slovakia and Spain. 9

Journal Pre-proof Figure 1. Grey Relational Degrees for the year 2016 Germany Denmark Netherlands France Lithuania Finland Italy Belgium Latvia Austria Sweden Estonia Slovenia Poland Spain Hungary Croatia Cyprus Bulgaria Portugal Slovakia Greece Romania 0,00

0,10

0,20

0,30

0,40

Table 4. Grey Relational Degrees results for every country and every year Country Austria Belgium Bulgaria Croatia Cyprus Denmark Estonia Finland France Germany Greece Hungary Italy Latvia Lithuania Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden

2010 0.3298 0.3271 0.2929 0.2774 0.2722 0.2603 0.2671 0.3043 0.3511 0.4066 0.2646 0.2888 0.3261 0.2979 0.2907 0.3524 0.3030 0.2873 0.2591 0.2858 0.2913 0.2962 0.3396

2011 0.2998 0.3113 0.2665 0.2742 0.2685 0.3467 0.2827 0.2991 0.3195 0.3934 0.2661 0.2803 0.3159 0.2908 0.3063 0.3357 0.2929 0.2751 0.2553 0.2761 0.2960 0.2855 0.3082

2012 0.2998 0.3066 0.2642 0.2785 0.2692 0.3430 0.2912 0.3069 0.3407 0.3943 0.2590 0.2828 0.3190 0.2972 0.3054 0.3379 0.3027 0.2733 0.2584 0.2689 0.2801 0.2895 0.3076

2013 0.3028 0.3080 0.2646 0.2774 0.2597 0.3467 0.2885 0.2978 0.3240 0.3956 0.2610 0.2786 0.3206 0.3003 0.3081 0.3350 0.2950 0.2743 0.2575 0.2665 0.2761 0.2902 0.3068

2014 0.3033 0.3083 0.2748 0.2805 0.2598 0.3486 0.2922 0.2973 0.3507 0.3903 0.2613 0.2790 0.3137 0.3053 0.3064 0.3340 0.2961 0.2761 0.2574 0.2646 0.2764 0.2876 0.3016

2015 0.3117 0.3173 0.2750 0.2848 0.2792 0.3637 0.3069 0.3141 0.3186 0.3777 0.2640 0.2871 0.3177 0.3178 0.3170 0.3444 0.2993 0.2782 0.2567 0.2692 0.3136 0.2907 0.3115

2016 0.3113 0.3140 0.2796 0.2861 0.2806 0.3636 0.3085 0.3171 0.3322 0.3801 0.2688 0.2906 0.3168 0.3114 0.3177 0.3453 0.2987 0.2790 0.2583 0.2736 0.3051 0.2910 0.3104

Average 0.3084 0.3132 0.2739 0.2798 0.2699 0.3390 0.2910 0.3052 0.3338 0.3911 0.2636 0.2839 0.3185 0.3029 0.3074 0.3407 0.2982 0.2776 0.2575 0.2721 0.2912 0.2901 0.3122

Rankings

8 6 19 17 21 3 14 10 4 1 22 16 5 11 9 2 12 18 23 20 13 15 7

Figures 2 and 3 show the dynamics of GRDs of the worst and best 5 countries in the observed period. Figure 2 is focusing on the worst-performing countries (Bulgaria, Slovakia, Cyprus,

Journal Pre-proof Greece and Romania). Although being the worst, some countries are showing an increase in the GRD values in the last few years, which indicates that they are improving their circular economy measures with changes in the whole system. Some of the countries found to be the worst-ranked have greater corruption indices in the world rankings, as well as lower government efficiency indices, with worse PISA results in schooling. Thus, some correlating factors between the best and worst-performing ones exist which surely influence their results. Analysis in Mauro (1995) indicates that corruption within a country lowers economic growth, which is a common factor of worst performing countries in this study. Problems with the worst ranking countries include low environmental awareness in Romanian companies, a still not stabilized legal framework, incomplete projects which have to establish integrated waste management systems, lack of adequate infrastructure, etc. (European Commission 2019c). The decrease of the GRD value for Bulgaria in 2011 can be explained by the reduction of the total factor productivity (World Bank 2015); increase of the GRD for Cyprus in 2015 is due to increase of the recycling rate of municipal waste in that year, which was the highest since 2010 (EC 2019g), with municipal solid waste management plan prioritized landfill tax and separate collection of waste (EC 2019h). The problem of waste management in Romania remains a key challenge, as well as the secondary use of material, which is around 1.5% in 2015, compared to the EU-28 average of 11.7% (EIR 2019), with the highest waste per person generated in the EU. Botezat et al. (2018) conducted a survey of Romanian producers and found that the differences between them and other EU producers, with respect to CE and green economy, included a low degree of cooperation between the Romanian firms and lack of trust in collaborations alongside lack of specialization. Problems with Slovakia are related to the low rates of CE innovation and low R&D rates related to environment (EC 2019d), high dependency on importing raw materials for production, which is experiencing great rates of increase over the last decade (Ministry of Environment of Slovak Republic 2019; OECD 2017). Greece has shown resistance to proposals by the EU Commission with malpractices of local authorities (Eco-innovation Observatory 2018); with research within this area heavily depending on EU funds (EIO 2019). Additional problems are found in the poor economic situation, with problems in government funding R&D related with CE innovations and Greece heavily depending upon EU Structural Funds (EC, 2019d); as well as the low awareness of consumers on the CE concept and economic model (Trigkas and Lazardiou, 2018); and not only of consumers but among authorities and other stakeholders in the economy (EC, 2019f), as well as difficulties with enforcing the related legislation. Bulgaria also has problems with funding of modernising the equipment for CE, as well as low levels of foreign capital investments and government incentives (Ecopreneur 2019). Cyprus has some of the similar issues, and some specific problems: poor legislative framework regarding eco-innovation, the R&D sector is a relatively new one in the economy, location of the country (being remote from other EU countries) which does not induce companies to invest and settle in Cyprus (EC, 2019g). Figure 3 is focusing on the top 5 countries: Germany, Netherlands, Denmark, France and Italy. These countries are overall showing more stable GRDs over time. This could indicate that mentioned countries have already achieved some good practices and goals and are now maintaining good results. The reasoning on why they are better ranked compared to others in the analysis is the implementation of successful policies related to renewable energy (see Fotis and Pekka, 2017); increasing access to clean fuels; and the highest net enrolment rates in education with the highest indices of corruption perception. This is all in line with Romer’s (1986) and Todaro and Smith’s (2003) new growth theory; where the state of the economy is highly dependent on investments in human capital and R&D; as well as in Trica et al. (2019),

Journal Pre-proof in which the modification of the Mankiw-Romer-Weil model was made on empirical data on EU 28, where the CE variables added in the model were found to have significant impacts on the economic growth; as well as the CE infrastructure and innovations which were found to be essential for sustainable economic growth. Tsai et al. (2016) explain, for example, that the French government provides subsidies for technologies and industries overall linked to lowcarbon emissions and with a low environmental impact. Moreover, it is not surprising that Germany is leading in the majority of the years (Table 4), due to it having impressive recycling rates for almost all types of waste; especially household waste being recycled in amount of almost 87%, compared to the European average of 37% (in 2012, Wilts 2016); employing almost 200.000 people in the waste management with an annual turnover of around 40 billion Euros (Wilts 2016). Furthermore, Germany has more than 1200 CE patents in the period 20002018, where the next EU country according to number of CE patents, France, has a little over 540 (Politico 2018); which is more than 2.2 times less. Thus, investments into CE patents, and R&D in general, affect the circularity of the economy in a great manner. Netherlands and Italy have high recycling rates in total waste treatment (Eurostat 2019), with good practice examples such as the Green Deal Circular Procurement in the Netherlands, which invoked over 100 million Euros in procurement done circular, and Italy increasing municipal waste recycling rate from 17% in 2001 to 45% in 2018 (Ecopreneur 2019). Moreover, other good CE practices in Netherlands are found in establishing a collaboration of suppliers and partners of a business, with sustainable packaging being used, chemicals and additives being removed from the production process, etc. in the brewery group Carlsberg (Hower 2014). The increase of Denmark’s GRD value in 2011 is due to the green economy made priority for the new Government which was appointed that year, which has induced more eco-innovation and cleantech industries (Eco-innovation Observatory 2012). Other countries which are not included in the best or worst ranked have their specific characteristics, advances and issues. For example, in Portugal, the CE concept is mostly applied in the area of waste management (Winans et al. 2017), there is a lack of CE awareness among Portuguese companies and great dependence on importing raw materials for production (Fonseca et al. 2018). Good practices can be found in Austria, where a “Green Tech” cluster is found near Graz and “Clean Tech” in upper Austria (Austrian Business Agency 2019); whereas problems toward achieving CE goals in Spain include lack of public organizations support, insufficient financing and lack of consumer interests towards the environment (Ormazabal et al. 2018). Similar problems with the lack of awareness are found in Croatia and Czech Republic as well (Ecopreneur 2019).

Journal Pre-proof Figure 2. Dynamics of Grey Relational Degrees for the worst performing countries 0,30 0,29 0,28 0,27 0,26 0,25 2010

2011

Bulgaria

2012 Slovakia

2013

2014

Cyprus

2015

Greece

2016 Romania

Figure 3. Dynamics of Grey Relational Degrees for the best performing countries 0,43 0,40 0,37 0,34 0,31 0,28 0,25 2010 Germany

4.3.

2011

2012 Netherlands

2013

2014

Denmark

2015 France

2016 Italy

Robustness checking

4.3.1. Other methodology in ranking the countries In this subsection, the robustness checking of previous results is given. Since countries are ranked based on several criteria, the Multiple Criteria Decision Models (MCDM) can be applied as well. These models fall within the area of Operations Research, in order to compare alternatives, based on often conflicting criteria. Some subjectivity is included in this approach, due to the decision-maker assigning weights to the criteria. Based on the application area, many different models have been developed. For details in environmental applications please see Lahdelma et al. (2014). Here, a basic approach is used so that comparisons can be obtained: the MOORA (Multi-Objective Optimization by Ratio Analysis) and the MultiMOORA. The reasoning lies upon this analysis being robust with respect to 7 criteria analysed in Brauers and Zavadkas (2010). A brief summary is given as follows. A matrix of responses is constructed as Xij = [xij], where i denotes an objective and j denotes an alternative. In the case of this research, i refers to the CE variables, i.e. criteria and j to the countries. The MOORA calculates the ratio between response xij and all alternatives regarding an objective i:

Journal Pre-proof xij* 

,

xij

(7)

m

 xij2 j 1

so that the obtained value xij* is normalized in the range [0,1]. Now, the values xij* are being added for the case of maximisation of the objective/criteria; with subtracting those which are observed to be minimised: g

y*j   xij*  i 1

n

 xij* .

(8)

i  g 1

Finally, values y*j can be ranked from best to worst. However, values in (8) are based on equal weights given to each criterion. The decision maker can give different weights so that the new values are defined as vij*  wij  xij* and are used in (8). For more details, please see Xidonas et al. (2009) or Blažentis et al. (2012). This procedure will be repeated for every country in every year so that rankings can be obtained. As in the Grey Relational Analysis approach, equal weights will be given to all criteria here as well. In that way, comparable results can be obtained and commented. Moreover, another mentioned approach for robustness checking will be MultiMOORA (based on MOORA and Full Multiplicative Form of Multiple Objectives, FMFMO), based on utility theory (Miller and Starr 1969) where the utility function used to rank n

the alternatives can be in the form U j   xij . In the case of criteria which have to be i 1

simultaneously minimised and/or maximised, the functional form of utility can be expressed as: g

U j   xij* i 1

n



i  g 1

xij* .

(9)

Again, weighted values vij* can be used here as well. Thus, (8) and (9) were calculated for every country in every year and rankings were obtained. These rankings were then compared to the rankings from the Grey Relational Degrees rankings. Detailed results are shown in Table 5 where we are given a confidence boost due to very similar rankings of the MMORA approach compared to the GRD approach. Best ranked countries stay at the top every year, and the worst-ranked ones stay at the bottom. This means that such results from this analysis could be used as a stepping stone for future detailed analysis; in which focus can be put on those countries for which the researcher feels that a greater focus is needed. Next, the rankings from GRD analysis were compared to the eco-innovation resource efficiency indices (EIRFI) made by the EU Commission (Eurostat 2017). Similar rankings could be observed as those obtained via GRD analysis, although the EIRFI is mostly focused on the innovation part of the CE. This is an indication that education, R&D investment and innovations surely present one of the major conditions for a greater CE of any of the economies in the future. Rankings found in this study are in line with Škrinjarić (2019), in which 36 European countries were ranked on the basis of 3 pillars of sustainable development, which is closely linked to CE. Furthermore, the results are in line with those in Mitrović and Veselinov (2018), especially regarding the lowest ranking countries which belong to the South and Eastern Europe; and Căutișanu et al. (2018) research, where authors found that GDP per capita and years of schooling have a direct positive effect on R&D expenditures regarding CE; as well as with Garcés-Ayerbe et al. (2019). In this last study authors focused on SME (small and medium

Journal Pre-proof enterprises) in Europe, clustering them based on the R&D investment and distribution to CE goals. The results showed that countries such as Germany, Italy, and Denmark clustered together, and Hungary, Poland, Slovakia and Romania together. Finally, Rizos et al. (2016) investigated barriers of implementing the CE concepts in Europe and found that SMEs, being the major generators of the countries’ economies, face problems regarding lack of support from the supply and demand network, lack of financial capacity for transitioning to the CE concept, which is, again, a characteristic of countries that are ranked lower in this study.

Table 5. Robustness checking of countries' rankings MOORA

2010 2011 2012 2013 2014 2015 2016 MMOORA 2010 2011 2012 2013 2014 2015 2016

GRD

Austria 8 8 10 9 8 9 11 Austria 6 7 9 8 8 9 10 Austria Belgium 6 6 6 6 5 4 4 Belgium 5 5 5 5 5 3 4 Belgium Bulgaria 12 13 14 14 15 18 17 Bulgaria 15 17 19 20 15 19 19 Bulgaria Croatia 20 20 18 18 19 19 20 Croatia 20 20 20 22 19 22 22 Croatia Cyprus 22 22 22 22 22 22 22 Cyprus 21 19 22 23 20 23 23 Cyprus Denmark 3 4 4 4 4 1 2 Denmark 9 8 7 6 4 2 2 Denmark Estonia 14 11 11 8 9 7 7 Estonia 16 18 14 21 10 7 6 Estonia Finland 10 10 9 11 10 10 10 Finland 7 10 6 7 9 10 11 Finland France 2 3 2 2 2 6 5 France 1 2 1 2 1 4 5 France Germany 1 1 1 1 1 3 3 Germany 2 1 2 1 2 5 3 Germany Greece 23 23 23 23 23 23 23 Greece 19 21 21 19 18 21 21 Greece Hungary 16 18 17 17 17 15 15 Hungary 11 13 12 14 14 14 14 Hungary Italy 5 5 5 5 6 11 9 Italy 3 4 4 4 6 11 9 Italy Latvia 17 15 16 16 14 12 12 Latvia 18 22 18 12 21 17 18 Latvia Lithuania 21 16 15 15 16 14 14 Lithuania 17 14 17 15 22 13 13 Lithuania Netherlands 4 2 3 3 3 2 1 Netherlands 4 3 3 3 3 1 1 Netherlands Poland 9 9 7 10 7 13 13 Poland 12 9 10 11 7 12 12 Poland Portugal 18 17 19 19 18 20 19 Portugal 13 12 13 16 23 16 17 Portugal Romania 19 21 21 21 21 21 21 Romania 14 16 16 18 17 20 20 Romania Slovakia 15 19 20 20 20 17 18 Slovakia 23 23 15 17 16 18 16 Slovakia Slovenia 13 12 12 13 12 5 6 Slovenia 22 15 23 13 13 6 7 Slovenia Spain 11 14 13 12 13 16 16 Spain 8 11 8 10 12 15 15 Spain Sweden 7 7 8 7 11 8 8 Sweden 10 6 11 9 11 8 8 Sweden Correlation 0.605 0.909 0.916 0.882 0.885 0.872 0.869 Correlation 0.468 0.824 0.808 0.863 0.710 0.807 0.811 Note: Correlation denotes the correlation between rankings of MOORA and MMOORA with the GRD rankings

2010 2011 2012 2013 2014 2015 2016 6 7 13 19 20 2 21 10 4 1 16 8 12 3 9 11 17 23 18 14 5 6 7

9 6 21 19 20 2 15 10 4 1 22 16 5 13 8 3 12 18 23 17 11 14 7

11 8 21 17 19 2 13 7 3 1 22 15 5 12 9 4 10 18 23 20 16 14 6 -

9 7 20 16 22 2 14 11 4 1 21 15 5 10 6 3 12 18 23 19 17 13 8

9 6 19 15 22 3 13 11 2 1 21 16 5 8 7 4 12 18 23 20 17 14 10

11 7 20 17 18 2 13 9 4 1 22 16 6 5 8 3 14 19 23 21 10 15 12

10 8 19 17 18 2 12 6 4 1 22 16 7 9 5 3 14 20 23 21 13 15 11

Journal Pre-proof 4.3.2. Other measures of recycling waste in ranking the countries Since there are some problems regarding measuring recycling waste in the EU countries10, due to them not having identical approaches to defining when a material is considered recycled or not, another measure of recycling rate was used. It is called recycling material excluding major mineral wastes, and as European Commission (2019c) explains, it is more comparable over EU countries. Furthermore, the metadata explanation on the statistics of this variable states that the dataset is fully comparable (Eurostat 2019 g). This indicator is available only for 2010, 2012, 2014 and 2016 for all countries analysed in this study with the exception of Greece and Latvia. The same Grey analysis was made with all other variables in the model, now with the other measure of recycling waste and the rankings were compared to the old rankings in Table 5. Results are shown in Table 6, where it is visible that the rankings based on the two different measures are very similar, with correlations of rankings in every year being greater than 98%. Thus, this indicates that the previous results are fairly robust. Moreover, these rankings are similar to the study of Castillo-Giménez et al. (2019), in which the authors compared the treatment of waste and recycling in the EU, where the recycling material excluding major mineral wastes rate was used. Table 6. Comparison of country rankings with the original REC p.c. variable, and new one GRD Austria Belgium Bulgaria Croatia Cyprus Denmark Estonia Finland France Germany Hungary Italy Lithuania Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden

10

2010 6 7 13 19 20 2 21 10 4 1 16 8 12 3 9 11 17 23 18 14 5

2012 11 8 21 17 19 2 13 7 3 1 15 5 9 4 10 18 23 20 16 14 6

2014 9 6 19 15 22 3 13 11 2 1 16 5 7 4 12 18 23 20 17 14 10

2016 10 8 19 17 18 2 12 6 4 1 16 7 5 3 14 20 23 21 13 15 11

New GRD Austria Belgium Bulgaria Croatia Cyprus Denmark Estonia Finland France Germany Hungary Italy Lithuania Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden

The author is grateful to the reviewer for pointing this out.

2010 7 8 13 18 19 2 20 10 3 1 15 6 11 4 9 17 21 16 14 12 5

2012 11 8 20 16 19 2 12 9 3 1 14 5 6 4 10 17 21 18 15 13 7

2014 8 7 18 14 20 3 12 10 2 1 15 5 6 4 11 16 21 19 17 13 9

2016 9 8 18 16 17 2 11 7 4 1 15 6 5 3 13 19 21 20 12 14 10

Journal Pre-proof 4.4.

Additional analysis

Based on the previous discussion, we compare results of the GRDs to the GINI11 coefficients and GDP per capita to see if some patterns can be observed. The GINI coefficient is used since it incorporates the social factor in the whole sustainable development approach metrics, whilst GDP per capita is the basic measure of economic development. Scatter plot between the average GRDs for each country and the 2016 value of the GINI coefficients is shown in Figure 5. It can be seen that the SEE (Southern and Eastern Europe) countries are clustered more closely compared to other countries, while northern and western countries are in the second cluster. Similar conclusions arise when looking at the scatter plot in Figure 6, where the GRDs are compared to the GDP per capita, although a greater positive correlation is seen here. Figure 5. Comparison of Grey Relational Degrees (x-axis) and 2016 GINI coefficients (y-axis) 78 Slovakia

76 74 72 70 68

Slovenia Finland Belgium Netherlands Austria Denmark Hungary Sweden France Poland

Germany

Cyprus

Estonia Italy Portugal Greece Latvia Spain Romania Croatia Lithuania Bulgaria

66 64 62 60 0,22

0,24

0,26

0,28

0,30

0,32

0,34

0,36

0,38

0,40

Thousands

Figure 6. Comparison of Grey Relational Degrees (x-axis) and 2016 GDP per capita (y-axis) 50 Sweden

45

Austria

40

Finland

35

Germany

Italy

25 Cyprus

20

Greece

15

Romania

5 0 0,22

Netherlands Belgium France

30

10

Denmark

0,24

0,26

Spain Slovenia Estonia Slovakia Lithuania Hungary Latvia Croatia Poland Bulgaria

Portugal

0,28

0,30

0,32

0,34

0,36

0,38

0,40

The GINI (last name of the statistician who developed this measure) coefficient is defined as the relationship of cumulative shares of the population arranged according to the level of equivalised disposable income, to the cumulative share of the equivalised total disposable income received by them (Eurostat 2019 f). 11

Journal Pre-proof Besides observing the scatter plots on the previous figures, additional formal analysis was made by collecting the data on GINI indices and GDP per capita for all analysed years and countries in the study. Thus, a panel data set was obtained in which the GRDs for every country were observed as dependent variables. The following model was estimated in order to obtain the effects of GINI and GDP per capita on the GRDs: GRDi ,t   i  1GINI i ,t   2GDPpci ,t   i ,t 12. The results are shown in Table 7, where it can be seen that the greater effects on GRDs are found for the GINI variable compared to the GDP per capita. This means that variables related to the inequality within a society could affect the CE achievements and goals in a greater manner compared to the growth of the economy. However, positive effects of both variables are found on the rankings, which is in accordance with expectations. Table 7. Estimated results of effects of GINI and GDP per capita on Grey Relational Degrees Variable/measure Estimated values 0.315 A i (0.028)*** 0.001 A  1 (0.0004)* 0.00002 A  2 (1.63∙10-6)*** R2 0.483 Note: panel corrected standard errors are given in parenthesis. * and *** denote statistical significance on 10% and 1%. Moreover, a cluster analysis was performed over the GRDs, GINI coefficients and GDP per capita to obtain cluster membership. The Euclidean distance was firstly calculated for every three index points and the Wards distance was used for linkages in order to obtain the values of silhouette values (how well is an entity clustered within a cluster). Basic two clusters are shown in Table 8. These results do confirm the clustering of more western and northern European countries. However, some of the countries which were closer to them in Figure 5 (Slovenia, Slovakia) were put in the second cluster, in which worst performances were found. Thus, a dendrogram was observed in order to manually define the number of clusters. It is shown in Figure A1, where 3 was selected to be the final number of clusters.

Firstly, the pooled model and the model with fixed effects were estimated in order to conduct the F-test of model adequacy. Results indicated that the model with fixed effects was appropriate (on 1% of statistical significance). Then, the model with random effects was estimated and the Hausman test was conducted to compare the models with fixed and random effects. Here, the results of the test indicated that the period effects should be fixed, whilst the cross-section effects should be random. Thus, the final model which was chosen was the one with period fixed effects and the random cross-section effects. For details on panel models, please see Greene (2003), Wooldridge (2002) or Verbeek (2005). 12

Journal Pre-proof Table 8. Cluster analysis results, criteria: GRDs, GINI and GDP per capita, 2 clusters Cluster 1 Silhouette Austria 0.714 Belgium 0.705 Denmark 0.693 Finland 0.713 France 0.663 Germany 0.710 Italy 0.497 Netherlands 0.712 Sweden 0.698

Cluster 2 Bulgaria Croatia Cyprus Estonia Greece Hungary Latvia Lithuania Poland Portugal Romania Slovakia Slovenia Spain

Silhouette 0.702 0.720 0.665 0.717 0.717 0.721 0.722 0.722 0.720 0.706 0.711 0.720 0.691 0.604

Based on the dendrogram, the final clustering results are given in Table 9. Now the grouping has a somewhat economic, social and environmental meaning13. Such analysis provides more evidence in favour of other non-related studies in which countries are grouped via economic, developing and other factors, which obviously affect other aspects of a country, such as the circular economy and moving towards it. Main differences between these 3 clusters are shown in Figures 7, 8 and 9 which depict box plots for the GRD results, as well as GINI coefficients and GDP per capita. Although there are smaller differences between average GRDs and GDP per capita for clusters C2 and C3, the greater difference is found when comparing them to C1 (Figure 7 and 8 respectively). However, by looking at differences between the GINI coefficients, a smaller difference is observed between clusters C1 and C2 with a great gap between them and C3. Thus, great social inequalities surely affect the state of a (non)circular economy; due to poor education and awareness of a part of the nation. This is in line with Armenau et al. (2017) in which authors focused on a panel dataset of 28 EU countries (from 1977 to 2014) where higher education, adult literacy rate, greater investment in R&D and better infrastructure of an economy lead to a greater sustainable GDP growth, as well as clustering in Castillo-Giménez et al. (2019). Table 9. Cluster analysis results, criteria: GRDs, GINI and GDP per capita, 3 clusters Cluster 1 Austria Belgium Denmark Finland France Germany Netherlands Sweden

Silhouette 0.709 0.687 0.687 0.706 0.576 0.700 0.708 0.693

Cluster 2 Cyprus Italy Portugal Slovenia Spain

Silhouette 0.675 0.640 0.523 0.634 0.682

Cluster 3 Bulgaria Croatia Estonia Greece Hungary Latvia Lithuania Poland Romania Slovakia

Silhouette 0.673 0.705 0.595 0.586 0.705 0.699 0.690 0.705 0.688 0.651

Besides, the statistical meaning is also found, due to silhouette measures which fall within the interval [-1,1], the closer to unit value this measure is, the better the classification to a cluster is made. Average values in table 6 are greater compared to average values in table 5. A formal t-test was performed to see if differences between average silhouette measures exist in Tables 7 and 8. The result indicates that on 5% level significance, the average value of silhouettes in Table 8 is greater compared to values in Table 7. 13

Journal Pre-proof Figure 7. Box-plot of GRDs for clusters C1, C2 and C3

Figure 8. Box-plot of GDP p.c. for clusters C1, C2 and C3

Figure 8. Box-plot of GINI coefficients for clusters C1, C2 and C3

Journal Pre-proof 5. Discussion and conclusion Political interest, academic research and public interest have increased over the last decade regarding the concepts of circular economy and especially sustainable development. Although much research exists on specific case-studies of CE practices, there is still a lack of research which utilizes the formally defined variables of achieving CE in an economy. Such analyses are important, so that first insight can be obtained on whether the CE plans and actions are sustainable in the long run. This research empirically evaluated the CE achievements of selected European countries by applying the Grey Relational Analysis, with robustness checking via the MCDM approach. European countries were selected, as they are not often found in related CE empirical literature as in this research. Moreover, since the majority of European countries comply with EU recommendations and legislation regarding the CE concepts and goals; this makes them more comparable in contrast to some other studies which observe very different countries. This study shows that a dynamic approach should be made, due to structural and other changes in economies which occur over time. Thus, the period from 2010 to 2016 was observed. In that way, changes could have been observed, if they existed. This was visible in Figure 2, where the worst performing countries showed some improvement over the years. The best performing countries were shown to be those which have greater GDP p.c. and have better infrastructure, education and the development of R&D. Of course, each country has its own specific culture, economic structure, politics, etc.; which have combined into a specific blend of factors which have contributed to its current state. As for the worst performing countries, some policy recommendations are as follows. Besides legislation and action plans which set goals; there should be a reinforcement of actions and players which measure and monitor if these set goals are being achieved. Public awareness and education regarding CE should be increased, as this would also increase the pressure on the government and industries to work on achieving CE and SD goals. Today, this is more easily achievable due to greater availability of data and information, which are easily accessible online. As mentioned in the main results part, previous research unfortunately finds that many countries which have the most problems with CE are characterized with lower awareness on these topics not only among consumers, but businesses and authorities as well (Kircherr et al., 2018; Liakos et al., 2019; Trigkas and Lazardiou, 2018, Smol et al., 2018). Furthermore, specialized actions need to be tailored in accordance with the possibilities of SMEs in Europe, as they represent 99% of all businesses in Europe (Ormazabal et al. 2018). Better education on the CE topics should be implemented in countries characterised by the lower awareness, so that on the one side consumers become more conscious and mindful in using, reusing and repairing the products on the one hand, which will reduce the total waste generated and increase recycling rates of all kinds of materials and waste, and better energy recovery rates. On the other hand, better CE qualified workforce within all types of businesses would lead to faster achieving CE as well. Other recommendations for all of the countries and industries aiming towards CE goals are as follows. The dependence on raw materials from other countries should be minimised (this dependency was mentioned in the main results section). Not only due to possible geopolitical tensions as those materials reduce over time; but due to reusing the same materials in a CE approach, which can also reduce the price of using such materials. Next, since the focus was made on the European countries, of which many are EU members; possibilities exist of utilizing the sources and money from EU funds (European Investment Fund, European Fund for

Journal Pre-proof Sustainable Development, etc.). This could open new jobs, especially in those countries which have problems with unemployment. Such considerations have been estimated in the literature (as mentioned previously), which has shown the positive effects on opening new jobs by 2030. Moreover, the fund resources can be used to modernise the infrastructure needed so that the energy efficiency is increased, as well as waste management and material re-usage. There is still room to improve the tax-related legislative so that the major “problem-makers” with respect to noncompliance with SD and CE. In that way, the taxes can be shifted from labour to materials. Some other special recommendations can be made in future work, where based on the results here, special case studies can be performed to see why a country is particularly good or bad in its practices. This includes financial incentives for regional enforcement of municipal targets for Poland (EC 2018e); EPR (extended producer responsibility) schemes, better separate waste collection and economic incentives and spending of EU funds for Romania (EC 2018f) and Bulgaria (EC 2018g), etc. Some of the shortfalls of this study include a relatively short time span, due to unavailability of data from the European Commission; a smaller group of countries (not all EU at least), again due to data unavailability. Moreover, the regional performance could not be observed as well (no data available). That is why as a part of CE goals, plans and actions should include raising the quality of official statistics which measures what was (or was not) achieved. Furthermore, several variables were included in the study, as opposed to many EC variables and concepts defined in previous literature. Theoretically, previous research has discussed many CE concepts, but in practice, this is often difficult to measure. That is why there is hope that future work will extend on this research with greater data availability. Finally, since the approach made in this study aimed towards an objective14 evaluation of (in)efficiencies, future work can utilize such an approach in further investigation of CE achievability. References 1. Andabaka, A., Beg, M., Gelo, T., 2017. Challenges of circular economy in Croatia. International journal of multidisciplinarity in business and science, 4(5), 115-126. 2. Ardente, F., Mathieux, F., 2014. Identification and assessment of product’s measures to improve resource efficiency: the case-study of an Energy using Product. Journal of Cleaner Production. 83, 126–141. https://doi.org/10.1016/j.jclepro.2014.07.058. 3. Armeanu, D. S., Vintila, G., Gherghina, S. C., 2017. Empirical Study towards the drivers of sustainable economic growth in EU-28 Countries. Sustainability, 10(1), 1– 22. https://doi.org/10.3390/su10010004. 4. Ashby, M. F., 2016. Chapter 14-the vision: a circular materials economy. In: Ashby, M.F. (Ed.), Materials and Sustainable Development. Butterworth-Heinemann, Boston, 211-239. 5. Austrian Business Agency, 2019. Environmental Clusters in Austria. Available at: https://investinaustria.at/en/sectors/environmental-technologies/clusters.php (accessed on 12 October 2019). The term objective refers to the exclusion of researcher’s subjectivity in the process of the modelling and estimating the results. When compared to other similar approaches, the approach in this study (the Grey methodology) can be considered as very objective, as it does not ask the researcher to insert own judgements regarding the variables, weights and other possible matters. The objectivity is also included in following the definitions and variables which measure CE goals, especially with respect to the European Commission’s guidance and methodology. Finally, the Grey methodology used in this study is such that the researcher cannot include subjectivity as he coud in other approaches (e.g. Data Envelopment Analysis where researcher can choose the model which will result in rankings which make sense when interpreting them). 14

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Journal Pre-proof APPENDIX Abbreviation

Table A1. Data description of variables used in the study Full name Description (retrieved from Eurostat descriptions)

ER p.c.

Energy recovery in kilograms per capita.

REC p.c.

Recycling material in kilograms per capita.

GI % GDP

Gross investment in tangible goods, percentage of GDP.

Emp %

Employed in contributors to circular economy, percentage of total employed persons.

CMR

Circular material use rate.

Pat

Number of patents related to recycling and secondary raw materials.

Energy recovery is defined as the incineration that fulfils the energy efficiency criteria laid down in the Waste Framework Directive (2008/98/EC), Annex II (recovery operation R1). Recycling means any recovery operation by which waste materials are reprocessed into products, materials or substances whether for the original or other purposes. It includes the reprocessing of organic material but does not include energy recovery and the reprocessing into materials that are to be used as fuels or for backfilling operations. (Waste Framework Directive, 2008/98/EC). Gross investment in tangible goods is defined as investment during the reference year in all tangible goods. Included are new and existing tangible capital goods, whether bought from third parties or produced for own use (i.e. capitalised production of tangible capital goods), having a useful life of more than one year including non-produced tangible goods such as land. Investments in intangible and financial assets are excluded. Jobs are expressed in number of persons employed and as a percentage of total employment. Number of persons employed is defined as the total number of persons who work in the observation unit, i.e. the firm (inclusive of working proprietors, partners working regularly in the unit and unpaid family workers), as well as persons who work outside the unit who belong to it and are paid by it - e.g. sales representatives, delivery personnel, repair and maintenance teams. It excludes manpower supplied to the unit by other enterprises, persons carrying out repair and maintenance work in the enquiry unit on behalf of other enterprises, as well as those on compulsory military service. The circular material use rate (CMU rate) measures, in percentage, the share of material recovered and fed back into the economy - thus saving extraction of primary raw materials - in overall material use. The CMU rate is thus defined as the ratio of the circular use of materials (U) to the overall material use (M). The indicator measures the number of patents related to recycling and secondary raw materials. The attribution to recycling and secondary raw materials was done using the relevant codes in the Cooperative Patent Classification (CPC).

Note: full names in second column refer to every variable on the yearly basis for every country. Source: Eurostat (2019a, b, c, d)

Journal Pre-proof Figure A1. Dendrogram of clustering based on average Relational Degrees, GINI coefficients and GDP per capita in 2016

Journal Pre-proof Table A2. Literature overview Authors

Subject in focus Authors present method called Resource Efficiency Assessment of Products which enables that the resource efficiency of LCD-TVs Ardente and (liquid crystal display television) can be measured via five steps of Mahtieux (2014) the model in which a detailed analysis of particular product parts is performed, so that specific results on each resource can be obtained. Wen and Meng Case study of the printed circuit boards (in a China’s district), (2015) combined the substance flow analysis approach with the resource productivity indicator to evaluate the contribution of industrial symbiosis to the development of the circular economy Li and Su (2012) Chinese chemical enterprises were in focus in this research. Authors developed their own model with weighted measures of contributions to CE within the chemical enterprises Heeres et al. (2004) Eco-industrial park15 (EIP) initiatives in the USA and Netherlands were observed; in which 6 development projects have been in the centre of focus. Authors followed the available EIP literature and derived their own way of comparing the effectiveness of those projects and found that the Dutch EIPs were more successful than US counterparts (due to greater government involvement in the USA). Küçüksayraç et al. Authors conducted research and interviews with 14 intermediaries (2015) in designing and achieving sustainability of companies in the Netherlands, UK and Turkey. Since companies often cannot do every step on their own, intermediaries help in providing innovation support. Thus, authors were motivated by how much do such organizations help in achieving SD. Roberts (2004) Eco-industrial parks in Australia were observed in this research. A case study of Australia’s first planned eco-industrial park has been in focus. Based on the results of this research, future work could have been more informed with useful findings so that the development of EIPs in other cities or countries can be such that CE goals can be obtained faster. Sokka et al. (2011) Case study of the forest industry in Kymenlaakso (Finland). This research utilized total fuel and energy use, and greenhouse gas emissions to assess the industrial symbiosis of such production. Veleva et al. (2015) Authors conducted 29 interviews with local organizations regarding the development of the EIP in Devens (USA). Based on obtained answers, it was concluded that for successful development, there is a need for quality partnerships, joint sourcing and knowledge sharing. Fonseca et al. (2018) Focus on the Portuguese organizations with an online survey regarding their attitudes towards CE. The survey was conducted on a sample of 99 organizations, and it details the results of development activities of CE within the organization, inclusion of CE within business models, promoting CE, questions regarding the Eco industrial park is a community of businesses which are located on the same property in order to achieve enhanced social, economic and environmental performance due to collaborating together with respect to environmental and resource issues (UNIDO 2019; United Nations Industrial Development Organization). 15

Journal Pre-proof 3R, 6R and 9Rs (mentioned in the introduction), etc. The main results showed that CE is a strategic issue of relevance for value creation and profits; that the awareness of the importance of CE is growing among organizations and there exists a positive relationship between the Environmental Management System certification of an organization and the willingness to improve its CE performance Table A3. Grey Relational Degrees of every country compared to Germany's performance Country Austria Belgium Bulgaria Croatia Cyprus Denmark Estonia Finland France Greece Hungary Italy Latvia Lithuania Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden

2010 0.300 0.300 0.279 0.242 0.272 0.263 0.248 0.296 0.297 0.243 0.268 0.294 0.237 0.240 0.284 0.272 0.273 0.263 0.250 0.269 0.290 0.293

2011 0.320 0.302 0.278 0.267 0.297 0.320 0.282 0.301 0.313 0.296 0.288 0.314 0.258 0.269 0.295 0.288 0.293 0.287 0.272 0.296 0.310 0.313

2012 0.317 0.311 0.270 0.270 0.290 0.315 0.267 0.290 0.314 0.265 0.289 0.308 0.256 0.268 0.294 0.298 0.288 0.280 0.277 0.291 0.306 0.312

2013 0.317 0.310 0.263 0.264 0.258 0.321 0.279 0.292 0.317 0.284 0.284 0.301 0.258 0.263 0.291 0.281 0.282 0.270 0.270 0.280 0.293 0.313

2014 0.313 0.295 0.276 0.259 0.252 0.315 0.290 0.287 0.319 0.274 0.282 0.288 0.254 0.259 0.281 0.279 0.278 0.269 0.268 0.273 0.284 0.304

2015 0.322 0.306 0.278 0.261 0.271 0.327 0.294 0.301 0.306 0.284 0.287 0.287 0.254 0.264 0.287 0.279 0.279 0.270 0.270 0.306 0.282 0.310

2016 0.321 0.300 0.276 0.260 0.264 0.322 0.296 0.299 0.277 0.270 0.279 0.283 0.244 0.263 0.283 0.275 0.272 0.264 0.270 0.291 0.276 0.310

Average 0.316 0.303 0.274 0.260 0.272 0.312 0.279 0.295 0.306 0.274 0.282 0.297 0.252 0.261 0.288 0.282 0.281 0.272 0.268 0.286 0.292 0.308

Journal Pre-proof Credit Author Statement Tihana Škrinjarić: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing.

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: