**4. Study Results**

### *4.1. The Results of the TOPSIS Method*

The indicators described in the previous parts of the article are now used to construct synthetic measures describing the degree of the implementation of Goal 7, the 2030 Agenda, and the Green Growth Strategy within the framework of environmental production efficiency. The results obtained from the classification and grouping of EU countries due to the examined areas are shown in Table 3.


**Table 3.** Ranking of European Union countries in 2018 due to Goal 7, 2030 Agenda, and the environmental and resource productivity of the economy of Green Growth strategy.

> As it has already been mentioned, many of the papers published so far point out that the Scandinavian countries are basically the only countries in Europe that have managed to separate economic growth from the negative environmental pressures permanently. Similar patterns are also visible in the presented list. Scandinavian countries that are members of the EU: Denmark and Sweden, and additionally Croatia, are the only countries in the top five countries with the highest scores in both areas of the survey. For the other countries, two patterns are visible. According to the first one, EU countries ranking high on Sustainable Development Goal 7 have lower rankings on environmental productivity and vice versa. That applies to countries located in different parts of Europe, although more often those located in:


In Western Europe, this is the case for only one country (Luxembourg: 28 and 15). The results of other countries located in this part of Europe are more often at a similar level in

both analyzed areas. This similarity of the results, that is, the second type of regularity, is visible regarding other countries located in different parts of Europe, apart from the already indicated Scandinavian countries and Croatia. The most considerable differences, not exceeding five positions in the rankings, were noted for: Austria (7 and 2), the Netherlands (21 and 26), and the United Kingdom (8 and 13). Several different development models of EU countries (Table 4) can be identified, based on the division into typological groups (I–IV).

**Table 4.** EU countries' development model in the areas of Goal 7 of SDS and the environmental and resource productivity of the economy of GGS.


It is clear from the information provided in Table 4 that countries classified in the first typological group in the case of the first ranking (Goal 7, SDS) were also classified in the first two groups in the case of the second ranking (Environmental and resource productivity of the economy of GGS). The first and second typological groups include countries that have above-average values for taxonomic measures of development. However, this division is no longer evident for other typological groups. The countries representing the second typological group in the case of Goal 7 were classified into all groups in the case of the second analyzed area. In the prepared set, however, no country was identified that was classified in group IV (with the lowest scores) in the case of both analyzed areas. These regularities are also confirmed by the assessments of correlation coefficients *r* Pearson (for taxonomic measures) and *τ* Kendall (for positions occupied; Table 5).

**Table 5.** Correlation coefficient matrix *r* Pearson and *τ* Kendall, respectively, for the values of the synthetic measures determined and the positions held in the built rankings.


Their analysis indicates a moderate correlation between both the values of the determined synthetic measures and the positions taken by EU countries in the case of Goal 7 and GGS.

Of course, the reasons for the differences in performance between countries vary. As we have already mentioned in the case of the Scandinavian countries, good results in both areas analyzed result from economic development achieved with care for the environment. Countries such as Romania or Croatia owe their high places in the rankings primarily to the lower economic development level compared with other countries. Their GDP per capita (USD 25,805 and USD 26,018, respectively) is well below the EU average (USD 40,192), which results in lower than in other EU countries, environmental interference at this stage of development. The opposite situation can be observed in the case of much more economically developed countries: the United Kingdom (second place in both rankings with a GDP of per capita above average (USD 43,720) or France (USD 42,543). The observed

differences in development directions are confirmed by low Pearson's *r* and Kendall's τ correlation coefficients for the calculated taxonomic measures and GDP per capita not exceeding the level of 0.3, indicating only a moderate correlation.

### *4.2. The Results of the Multicriteria Taxonomy Method*

The wide diversity of EU countries due to the two areas analyzed is one of the many reasons that make it difficult, for example, to develop acceptable by all countries assumptions for various EU policies. Therefore, it is important to check more precisely to what extent (in terms of which indicators) the analyzed countries are similar to each other or which ones make the biggest differences between them. However, the aim is to compare individual indicators from both analyzed areas at the same time. In the literature of the subject [59], the average level of analyzed phenomena is most often used in this case. However, in the approach proposed by the authors, the starting point to more advanced analyses are distance matrices calculated based on the distance between the individual indicators and the adopted pattern (variant V1) and the anti-pattern of development (variant V2). The result is the division of the studied EU countries into groups, as shown in Table 6 and Figure 1a,b.

**Table 6.** Division of EU countries into typological groups in 2018—variants: V1 and V2.


**Figure 1.** *Cont*.

**Figure 1.** Division of EU countries into typological groups: (**a**) variants V1; (**b**) variants V2.

The compositions of the different groups differ quite significantly, but their comparison allows drawing some interesting conclusions. It is worth noting that the computational algorithm used does not allow to determine the order of groups due to their level of development. The order in which groups are created is conditioned by the number of objects classified into them. The first group is always the most numerous, while singleelement groups are distinguished last. Despite the different number of EU countries classified in the first typological group, each of the analyzed cases included countries such as Croatia, Czechia, Estonia, France, Germany, Hungary, Italy, Latvia, Poland, Romania, Slovakia, Slovenia, and the United Kingdom. In their case, there is a substantial similarity of development regarding the achievement of SDG 7. With the exception of Italy, all countries are above the EU average in terms of reaching Sustainable Development Goal 7 (cf. Table 2). The situation in terms of green growth is more diverse, as for six out of thirteen countries (Czechia, Estonia, Germany, Hungary, Poland and Slovakia, and Slovenia), the indicators representing this area often reach unfavorable values compared to other EU countries (below average). However, the fact that these countries are in the same group does not raise any doubts when the distributions of the values of the individual diagnostic characteristics are analyzed in detail. For SDG 7, feature distributions vary distinctively (101.81% to 318.45%), with a strong asymmetry of the vast majority of all indicators. On the other hand, the distributions of individual indicators for environmental production efficiency have slightly different characteristics: variation level from 20.11% to 58.35% and weaker asymmetry. The typological groups presented in Table 4 are the consequence of applying a multicriteria analysis that considers both research areas simultaneously, and the characteristics of the distributions of indicators within each area affect the final result.

Regardless of the adopted variant of grouping, one can clearly see the countries that differ in plus or minus from the rest of European countries, reaching maximum or minimum values. They include:

1. Malta—in the area of achievement of Goal 7 the best in terms of *X*1.3*D*—final energy consumption in households per capita, kg of oil equivalent, and worst in terms of implementation of indicators: energy productivity, purchasing power standard (PPS) per kilogram of oil equivalent (*X*14*S*), energy import dependency, % of imports in total gross available energy (*X*1.6*D*). In the area of green growth, the country stands out positively in terms of the following indicators: energy productivity, GDP per unit of TPES, US Dollar, 2015 (*X*2.6*S*), energy consumption in industry, % total energy consumption (X2.12*D*); it stands out negatively for the following: energy intensity, TPES per capita, tonnes of oil equivalent (TOE; *X*2.7*S*), total primary energy supply, tonnes of oil equivalent (TOE), millions per capita (X2.8*S*), renewable energy supply, % total energy supply (*X*2.9*S*);


It would seem that the grouping of countries in two variants, using distances from the pattern (*z*+0*j*) and the anti-pattern (*z*<sup>−</sup>0*j*) determined in the TOPSIS method, should produce similar results, i.e., groups of countries similar to each other due to both their similarity and dissimilarity should be distinguished. This situation occurs only in some cases when most of the characteristics adopted for the study show similar direction and values, which happens especially in the case of single-element groups. It should be noted that most EU countries, due to the characteristics adopted for the study, cannot be unambiguously assigned to the group of those that achieve only desirable or undesirable values. Their situation varies greatly, and this has an impact on the obtained results.
