**5. Discussion and Conclusions**

The results of the EU countries' classification for Goal 7 SD Strategy and Environmental production efficiency of GG Strategy show how different countries' development paths within a single economic community can be. Despite continued attempts to equalize the development levels between European Union countries in many strategic areas, this level remains highly diversified.

Should we expect that the action taken by the European Commission will eliminate these differences in every possible area? In our opinion, this is not possible, especially that this view is also confirmed by the research results presented in this paper. It turns out that even the Scandinavian countries, which in the EU are among the few countries that have managed to separate economic growth from negative environmental impacts, are unable to predict all the pitfalls of their growth and economic development. Problems of this kind are also experienced in the highly developed countries of Northern and Western Europe.

The literature of the subject [88–90] points out that in the initial stage of economic development, environmental pollution increases with economic growth. The higher the level of economic development a society achieves, the more attention it pays to the environment. Less developed countries should therefore exert less pressure on the environment. What should these relationships look like for the areas analyzed in the paper? Given the computational procedure used, where one of the steps is transforming the destimulant into stimulants, we expect that with economic growth, there will be an improvement in the areas of sustainable development and a green economy. The higher the GDP per capita, the higher the results in terms of implementation of Goal 7 SD Strategy and environmental production efficiency under the GG Strategy should be. However, the results of the studies presented in the paper show that these relationships are not so obvious. It was noted that among the countries with the highest GDP values per capita (Austria, Belgium, Denmark, Finland, Germany, Ireland, Luxembourg, the Netherlands, Sweden, and the United Kingdom), there are also those for which the designated values of synthetic measures were lower than the average in the group. Countries such as Belgium, the Netherlands, despite their relatively high GDP per capita, are successful in achieving Goal 7 of the SD Strategy.

Their environmental production efficiency is also lower than that of other less developed EU countries, hence their qualification for Group III or IV.

On the other hand, the opposite was observed for Croatia, Latvia, Romania, and Spain. Despite being lower than the EU average GDP per capita, these countries achieved relatively high results in the analyzed areas, allowing them to qualify for typological groups I or II. These observations are also confirmed by the results of studies by other authors [91–95], which show, among other things, that the relationship between environmental pollution and the wealth of a country has the shape of an inverted letter "U"—with economic growth, the pollution increases, but only to a particular level beyond which environmental pollution decreases with economic growth. In recent years, the above-described curve has flattened, and the peaks at ever-lower levels signify that even poorer countries are beginning to pay more attention to the environment. This observation is fundamental in the analyzed area of environmental production efficiency. It seems that a large group of highly developed countries may not have reached the so-called tipping point yet—hence their worse position than in the case of other countries in terms of indicators showing, for example, CO2 production [96].

However, it is worth noting that investments, which often rely on costly and durable infrastructure, play an essential role in achieving the objectives of both strategies. Vast amounts of capital are needed to finance infrastructure such as smart grids, renewable energy sources, resource efficiency. The analysis of EU Community Innovation Survey (CIS) data for eco-innovation adoption by EU firms—for energy efficiency and carbon dioxide abatement—suggests that adoption is positively correlated to the emission efficiency of the countries where the companies are based. There are structural differences in this correlation across the EU Member States, with leaders and laggards.

This also means that we need reliable data that will allow us to assess accurately at which development stage the countries currently are and how their development paths may proceed [97]. The choice of computational method is also important. In the literature, there are many different proposals for determining the level of development of the analyzed objects (in this paper, these are the EU countries). These methods focus on determining the average level of development of these objects. In this paper, however, it is proposed to examine the distance of the EU countries in relation to the so-called development pattern and, at the same time, the so-called anti-pattern. On this basis, in the next step, the objects are grouped in order to recognize their current level of development more accurately. It is essential for the evaluation of the studied phenomena because it allows illustrating complex relationships between them. It also enables assessing whether the high level of development of one phenomenon (in this case, within the selected goal of the green economy) influences the development of another one (within the selected goal of sustainable development). The results presented in this paper showed that these relationships are not straightforward. The high level of development in both examined areas concerns only a few countries. Economically less developed countries pollute the environment to a lesser extent, but it can be expected that the environment will be increasingly polluted as the rate and level of economic development increases.

The key question can be formulated as follows: What can be done? What instruments should be applied in order to make this development in the less economically developed EU countries progress in a different way than in the case of the currently most developed EU countries? How to make the transition from one stage of development to another (high economic development and low environmental pressure) as fast as possible?

It is also worth emphasizing that the unique value of the study lies in the research approach that focuses on the relationships between the areas selected for the analysis. In the literature on the subject, there are no works examining the relationships between different areas of development, especially conducted in the manner proposed by the authors (the similarity versus dissimilarity of development). The main concern is to determine the average level of the studied phenomena. The authors of the paper propose a more advanced approach to this issue. The final goal is the degree of correlation between the two areas and the indication of the different development stages currently faced by the EU countries.

In the subsequent research, the authors also plan to extend the range of research methods used to study the relationship between these areas to qualitative and quantitative techniques using, for example, cognitive mapping. The ability to anticipate changes in the relationships that connect the analyzed areas is the advantage of this approach. Such attempts in the study of dependencies can be found in the earlier work of the authors of this article [3–5] and the studies of other authors [98,99]. The authors plan to concentrate on the relationship between the different goals of sustainable development and the green economy in their future studies. In this way, the authors will be able to examine more broadly the relationship between these two areas.

**Author Contributions:** Conceptualization, K.C. and I.B.; data curation, K.C. and I.B.; formal analysis, K.C. and I.B.; funding acquisition, K.C. and I.B.; investigation, K.C. and I.B.; methodology, K.C. and I.B.; project administration, K.C. and I.B.; resources, K.C. and I.B.; software, K.C. and I.B.; supervision, K.C. and I.B.; validation, K.C. and I.B.; visualization, K.C. and I.B.; writing—original draft, K.C. and I.B.; writing—review and editing, K.C. and I.B. Both authors have read and agreed to the published version of the manuscript.

**Funding:** The APC was funded by the West Pomeranian University of Technology.

**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: https://ec.europaJru/eurostat/web/energy/data/shares (accessed on 30 January 2021) and https://stats.oecd.org/ (accessed on 30 January 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.
