**4. Data**

EU policies, initiatives, and assessment tools relevant to the monitoring and evaluation of the CE can be found in Europe 2020, Sustainable Development Strategy/Sustainable Development Goals, Euro indicators (PEEIS) and European Pillars of Social Right [151]. A similar set of indicators with the following focus areas of material input, eco-design, production, consumption, and waste recycling was also proposed by the European Environment Agency [152]. The indicators monitoring the CE are not unique to the CE only but are present in other UE frameworks. It is because the CE is not a closed system but directly or indirectly influences the economy, and thus the CE assessment relies both on direct and indirect indicators [153].

In this article, the EU CE monitoring framework intended to track the progress of CE implementation at the member states' level was used. The indicators' set represents 4 dimensions: production and consumption, waste management, secondary raw materials, competitiveness and innovations. According to the EU, they allow the European Commission and other policy makers to monitor the progress and evaluate the effectiveness of the EU members and inform stakeholders about current trends.

The indicators proposed by the European Commission to measure the CE development are in different units (percentage, absolute, or per capita). The first stage was to verify availability of data and their transformation to obtain comparable indicators and consistent interpretation. Indicators whose values are aggregated for the whole EU or are not collected directly (but estimated on the basis of different categories of waste) as food waste or whose interpretation without other information are problematic (e.g., about EU or non-EU exports and the dominant industry) as trade in recyclable raw materials was not included.

Table 2 includes the original data (from the EU methodology) and the 16 variables selected for further analysis (P1, P2, P3, W1, W2, W3, W4, W5, W6, W7, W8, S1, C1, C2, C3, C4) of the 27 EU counties, along with descriptive statistics. The data come from the publicly available Eurostat database (up-to-date on 17 March 2022) and cover mainly 2019 and 2018. Taking different years was possible due to the assumption that there were no radical changes in the economies of individual countries in recent years.


**Table 2.** EU CE monitoring framework data table.


## *Energies* **2022**, *15*, 3924

**Table 2.** *Cont.*

## **5. Research Results**

The introductory examination of the collected data included the substantive and statistical analysis presented in Table 2, and the attempt to group countries. Standardisation was carried out, and countries were grouped via cluster analysis to assess the countries' development (missing data were supplemented with an average value). As a result of applying the cluster analysis procedure selected in the previous stage P1, P2, P3, W1, W2, W3, W4, W5, W6, W7, W8, S1, C1, C2, C3, C4 (missing data were supplemented with an average value), two groups were obtained (Table 3).

**Table 3.** Results of cluster analysis.


What is worth noting is that it is impossible to indicate a group of leaders in terms of all variables (Figure 3). Cluster 1 has high values for P2, W1, W2, W3, W5, W7, W8, S1, C4, and, respectively, low values for P1, P3, C1, C2, C3. Furthermore, variables W4 and W6 do not differentiate clusters.

**Figure 3.** Means of CE monitoring indicators in the obtained country clusters.

Although GDP per capita was not included in the dataset, the clustering was generally conducted on the basis of it. In the second cluster, the average GDP per capita amounts to 14,486.4 euro, and the minimum value is 6840 euro. Treating Greece, the Czechia, Portugal, and Slovenia as exceptions and excluding them from the first cluster, the highest GDP in the second cluster (24,530 euro) would be even lower than the lowest value in the first cluster. In the first cluster, the mean is 36,233.3 euro per capita. Thus, it is justified to conclude that the indicators of the circular economy are primarily influenced by GDP. In Figure 4, the leaders are presented in terms of each indicator.

It is impossible to indicate obvious leaders on the basis of the presented data. Nevertheless, it is possible to point at leaders with respect to particular (sets of) indicators. The following countries can be distinguished: Romania in the case of P1 and P3, Luxembourg and Ireland in the case of P2, and Latvia in the case of P3. Similarly, for W1—Germany, W2—Slovenia, W3—Belgium, W4—Lithuania W5—Belgium, W6—Croatia, W7—Austria, and there are no pioneers in the case of W8. If one takes into account factor S1—it is the Netherlands, C1—Slovakia, C2—Lithuania and Latvia, C3—Croatia, C4—Luxemburg.

The DEA method allows for assigning ratings to the analysed countries. Its usefulness and adequacy are proven in many studies. Assuming a constant, identical level of inputs for each European country, weights for outputs can be adjusted to maximise the assessment of environmental performance. However, applying the DEA method to all variables does not differentiate the scores at all. The number of variables (16) is too high as compared to the number of countries (27).

To limit the number of units, the principal component method is often suggested in the literature [154]. PCA is a data space reduction method that is based on linear relationships and usually on standardised variables. However, as mentioned earlier, the values of the main components attain negative figures, which is not accepted in the DEA method.

Factor analysis describes variability among observed variables with a lower number of unobserved factors. The five factors have eigenvalues greater than 1 and explain almost 75% of the variance (Table 4). Nevertheless, the use of the vector of factor values as well as the vector of components is not possible due to the output of the negative values. For this reason, a non-standard approach was used. After factors were determined, the most correlated variables were selected as representatives.

Factor 1 contains W1, W2, W3 but also three more variables with factor loadings over 0.5: W4, W5, W7. It represents the recycling rate but excluding the recycling rate of e-waste (W6) and construction and demolition waste (W8). The W6 and W8 build factor 4—recycling waste of special products. Factor 2 can be named waste generation because it has the highest factor loadings for the generation of municipal waste per capita and the generation of waste per GDP, P1 and P2, respectively. The opposite signs of P1 and P2 may suggest the following relationship: the higher generation of waste per GDP the smaller generation of waste per capita. In Factor 2, C1 (gross investment in tangible goods as percentage of GDP) also has a factor loading higher than 0.5. Factor 3 represents C2 (persons employed as percentage of total employment) and C3 (value added as percentage of GDP). It is related to investments. Considering Factor 5, one notices that S1 (circular material use rate) and P3 (generation of waste per domestic material consumption) have the higher factor loadings with opposite signs. Generally, the division of variables is consistent with the area indicated by the EU methodology. Thus, the following indicators were selected as the representatives of each discovered factor: P2, W2, W6, S1, C3. Next, the DEA scores were calculated for the representatives. Results of the computation are presented in Table 5 and in Figure 5.

(**a**)

**Figure 4.** *Cont.*

**Figure 4.** Visualisation of standardised CE monitoring variables, respectively for variables: (**a**) P1, P2, P3; (**b**) W1, W2, W3, W4; (**c**) W5, W6, W7, W8; (**d**) S1, C1, C2, C3, C4.


**Table 4.** Factor loadings (Biquartimax normalised). Extract: Principal components. Numbers in red mark the indicators forming the respective meta-indicators (factors). For each factor, the indicators with the highest factor loading are marked in bold.
