*Article* **Evaluation of the Effective Material Use from the View of EU Environmental Policy Goals**

**Marcela Taušová , Katarína Culkov ˇ á \* , Peter Tauš , Lucia Domaracká and Andrea Se ˇnová**

Institute of Earth Resources, Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Park Komenského 19, 042 00 Košice, Slovakia; marcela.tausova@tuke.sk (M.T.); peter.taus@tuke.sk (P.T.); lucia.domaracka@tuke.sk (L.D.); andrea.senova@tuke.sk (A.S.) **\*** Correspondence: katarina.culkova@tuke.sk; Tel.: +421-556-023-116

**Abstract:** Humanity is dependent on natural resources. Use and productivity of these resources plays an important role in energy savings and circular economy. The goal of this contribution is to evaluate productivity of resources in the frame of EU countries. Single analysis deals with data from the publicly available portal database and collected data were processed in the statistical software JMP. The trend of development and analysis of variability and linear dependence helped to create cluster analysis and comparison of the EU countries. The results from the view of average value of the indicator registered the growth, and from the view of variability the statistically important differences were verified for EU member states. Some pairs of indicators recorded positive, while some pairs recorded negative linear dependence. Cluster analysis shows two groups of countries—the first one with positive results, having the lowest tax burden in the case of energy taxes and environment, and the second one with negative results, having the highest tax burden of environmental and energy policy. The results are useful for a proper setting of energy and environmental goals that can increase the effectiveness of resource productivity in the countries studied.

## **1. Introduction**

Effective use of raw materials means the sustainable use of the limited resources of the Earth. Humanity is dependent on natural resources—metals, minerals, fuels, water, soil, wood, fertile soil, clean air and biodiversity. All mentioned resources present significant input that makes possible the operation of our economy. Raw materials are the prerequisite for the production of enterprises. Therefore, the science of materials management relates to economic benefit of an enterprise from the view of reducing costs and promoting the efficiency of materials management, which further reflects the important role of the materials classification in materials procurement [1]. Proper choice of the materials also plays a significant role in energy savings and high-quality suppliers [2,3].

Increasing the effectiveness of materials use is a key approach to providing economic growth and new workplaces in Europe [4]. It brings many economic possibilities, including decreasing costs and increasing competition [5]. Therefore, we must find new tactics toward this goal in all steps of the value chain: to improve management of resource stocks; to decrease inputs; to optimize production processes, management and business methods; to improv logistics; to change the calculation of consumption and decrease waste; and to develop new products and services.

Effective use of raw materials can help to stimulate technological innovation, increase employment in the rapidly developing sector of ecological technologies, open new export markets and bring benefits for consumers through sustainable products [6]. Effective use of raw materials also means the sustainable use of limited resources while minimizing impacts to the living environment [7,8]. This enables the creation of more products with higher value, while using less materials. This approach is a result of the main initiative of Europe

**Citation:** Taušová, M.; Culková, K.; ˇ Tauš, P.; Domaracká, L.; Se ˇnová, A. Evaluation of the Effective Material Use from the View of EU Environmental Policy Goals. *Energies* **2021**, *14*, 4759. https://doi.org/ 10.3390/en14164759

Academic Editors: Wadim Strielkowski and Dalia Štreimikiene˙

Received: 17 June 2021 Accepted: 2 August 2021 Published: 5 August 2021

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to use a resource effectively as a part of Strategy Europe 2020, a strategy of EU growth for an intelligent, inclusive, and sustainable economy [9,10]. It supports the transition to sustainable growth through a low-carbon economy, effectively using the available resources. The plan for Europe to use resources effectively is one of the basic elements of the initiative of effective resources use. A road map was created that determined the framework for performing future activities. The plan also outlined structural and technological changes to be made by 2050, including milestones that should be achieved by 2020.

Fast and reliable evaluation of the materials' efficiency is necessary [11]. Moreover, the announcement, "towards circular economy" (also referred to as circularity, which means an economic system that tackles global challenges like climate changes, biodiversity loss, waste and pollution) supports further basic transition in EU, compared to a linear economy, where the resources are not only extracted, but not reused and returned to production. In a circular economy, the resources must be returned to the circle to be used for the longest possible time. Measurements leading to the effective use of resources and the minimization of waste are determined in this study. The circular economy is presently studied by a number of authors, throughout the world and in Europe [12,13].

The goal of this contribution is to evaluate a level of the indicator, expressing source productivity in the frame of individual EU countries during 10 analyzed years, and to define a trend of the development and discover connections with other indicators, evaluated at the EU level. The structure of the contribution consists of an investigation of the present state of problem solving followed by main research, consisting of three steps: basic analysis of resource productivity in EU, analysis of linear dependence of resource productivity indicators, and cluster analysis according to the similar behavior clusters that have been created.

#### **2. Present State of Problem Solving**

In the last decades the EU established a broad scale of legal decrees in the area of the living environment. This required focus on the Triple Bottom Line approach, measuring the impacts of business on 3P (people, planet, profit) criteria [14–16].

Due to these decrees, the aforementioned pollution of air, water and soil considerably decreased. The legal decrees were modernized, and concerned mainly chemical elements, limiting the use of various toxic or dangerous substances. Presently, inhabitants of EU have the best quality of water worldwide and more than 18% of the EU area is registered as protected areas of nature. However, there is still a number of problems needing structural solutions.

The seventh Environmental Action Program (EAP) includes European policy in the area of the living environment to the year 2020, with a goal to give a long-term vision of EU to 2050 [17]. The goal is to live in 2050 properly, in the frame of the ecological limits of the planet. The prosperity of humanity and a healthy environment will be the result of an economy focused on the sustainable use of natural resources, and, protecting, evaluating, and renewing biodiversity. This growth, paired with a decrease of CO<sup>2</sup> emissions, and responsible use of resources, will create a secure and sustainable global society.

Three key goals are determined in the frame of the program:


At the same time "four activators" can help the EU to meet these goals:


From the view of an aforementioned number of authors, evaluating materials use and their impact on the living environment from different considerations is of utmost importance. Joensuu et al. (2020) determined sectors that are most responsible for materials extractions and use [19]. This must be evaluated due to the impact it has on the protection of the living environment, as well as in reusing and recycling of materials. According to Chen et al. (2020), materials use requires safe and effective evaluation with the goal of helping to develop innovative products [20].

Rajca et al. (2020) studied materials use for refuse-derived fuel in order to use it for energy purposes [21,22]. Materials can also be used in waste. In this area, Girondi et al. (2020) studied use of biomass in ceramic materials, observing that it was possible to save costs for the ceramic industry [23]. Tian et al. (2020) studied materials use from the view of temperature [24]. According to their results, economic evaluations are significant criteria in the marketing area of any industrial product produced from raw materials.

During the economic evaluation of materials use it is necessary to also consider technical criteria and material properties [25]. Factor analysis quality and assessments of the innovations using can be used during the economic evaluation of materials from the view of innovative materials [26].

Presently, there is a drive to advance technology, such as the use of better and cheaper material [27]; increasing material efficiencies and pairing that with maintaining a clean environment for people, as well as the life cycle cost of the alternative energy proposals are compared.

Wang et al. (2019) recommend using eco-friendly materials in different industries; however, data for each eco-friendly material are managed individually, causing inefficiency, increased costs, and potentially greater environmental impacts associated with material and resource choice [28].

Long et al. (2018) studied the workability, static and dynamic mechanical properties, and environmental impact of materials from the view of thermoplastic behavior, finding through an economic and ecological evaluation a benefit to the environment [29]. Guo et al. (2018) summarized and discussed materials use based on the analysis of economic indicators, including initial cost, operating cost, revenue, subsidy, and energy cost [30]. Haider and Bhat (2020) studied the linkage between material and energy efficiency and total factor productivity, finding that not all states are equally energy intensive [31]. The increase of total factor productivity is associated with a lower level of energy per unit of output.

Material and energy consumption do not have a direct causal relationship to gross domestic product [32], which is necessary for follow-up of sustainable development. A study by Belke [33] found a long-running relationship between material and energy consumption and GDP. Productivity in the area of material recycling must also be researched [34], bringing contributions to the improvement of municipal solid waste management. Moreover, material use and recycling must be regarded in the construction industry especially [35,36], affecting entrainment factors such as energy consumption, carbon footprints, and overall construction operation productivity. The productivity of material use in the rest of the production steps can be increased. For example, Dini et al. (2018) studied how wood can be used throughout its entire production so that its production can be more economical [37].

During this research, we obtained results from previous research aiming to analyze the effectiveness of resource use at the level of individual EU states in context of the goals determined in the frame of environmental policy in 2020. Consequently, the goal of this contribution is also to define the intensity of the economic transformation necessary to influence the circular economy toward meeting the EU environmental policy goals.

#### **3. Materials and Methods**

In the frame of single analysis, we obtained results from the continuously published values of chosen indicators from the portal https://ec.europa.eu/eurostat/data/database (20 June 2020) during all available years and for all available member states. Collected data had been registered, selected, and adopted in the database, and created in MS Excel editor according to the demands of the statistical software JMP, where adapted data was transmitted and then analyzed [38].

The collected data presents the results of the eight chosen indicators during 1995–2018 (Table 1). The constructed database consists of 3765 data points and any indicator is defined for concrete EU member state and concrete year. As we can see from Table 1, the extent of published data for each indicator is considerably different, while the volume of data connects with an incompleteness of countries assigned, or the publishing of data for some indicators for any second year, as in the case of measure of recycling, waste production on GDP, and measure of waste stocking. Extend and structure of obtained data had been adapted by analysis choice and results formulation.


**Table 1.** Structure of collected data.

(Source: own processing in MS Excel editor).

The process of the analysis is as follows:

	- Trend of development;
	- Graphical analysis—cartographer;
	- Analysis of the variability—nonparametric Kruskal-Wallis test.
	- Analysis of linear dependence—pair correlation analysis of indicators;
	- Cluster analysis;
	- Comparison of the countries.

Resource and material productivity represent the main indicator of the evaluation table in effectiveness of resources. It is used for the monitoring of the steps taken toward effective use of resources in the EU. The indicator is defined as gross domestic product (GDP) divided by domestic material consumption (DMC). DMC measures total volume of materials directly used in the economy, defining the annual volume of raw materials as that extracted from a domestic territory into the local economy, plus all physical import, minus all physical export. It is necessary to underline that "consumption" in DMC means net, not last consumption. DMC does not include inflows connected with import and export of raw materials and products with origin outside the local economy.

Since productivity of sources is calculated as GDP divided by DMC, measuring units are GDP units over DMC units.

$$\text{Productivity of sources} = \frac{\text{GDP}}{\text{DMC}} [\text{PPS} \cdot \text{kg}^{-1}] \tag{1}$$

$$\text{DMC} = \text{Mining} + \text{Import} - \text{Export} \, [\text{kg}] \tag{2}$$

$$\text{Productivity of sources} = \frac{\text{GDP}}{\text{Mining} + \text{Import} - \text{Export}} [\text{PPS} \cdot \text{kg}^{-1}] \tag{3}$$

PPS, standards of purchase power, present fictitious "currency" units that remove differences in purchase power, and by this way eliminate differences in price levels in individual countries used during comparison between countries. PPS, standards of purchase power, present fictitious "currency" units that remove differences in purchase power, and by this way eliminate differences in price levels in individual countries used during comparison between countries.

#### **4. Results 4. Results**

#### *4.1. Basic Analysis of Sources Productivity in EU 4.1. Basic Analysis of Sources Productivity in EU*

The indicator of resource productivity is defined as the GDP and DMC rate in a concrete year and country. Due to the achievement of environmental policy goals in the EU, the effort of any country is to increase the value of the indicator. The indicator of resource productivity is defined as the GDP and DMC rate in a concrete year and country. Due to the achievement of environmental policy goals in the EU, the effort of any country is to increase the value of the indicator.

Analysis of the published results in the database Eurostat of the indicator during 2000–2018 in EU member states had been evaluated from the view of success of each country orientation in an effort to use the resources effectively. With the use of a cartographer it is possible to make visual comparisons of the countries. In 2006, Switzerland and the Netherlands belonged to the countries with the highest values of the indicator. Ten years later there is considerable change in the color scale of the countries, but still the best results are recorded in Switzerland and Netherlands. However, some additional countries are entering to this group, such as Italy, England, Spain and France (Figure 1). Analysis of the published results in the database Eurostat of the indicator during 2000– 2018 in EU member states had been evaluated from the view of success of each country orientation in an effort to use the resources effectively. With the use of a cartographer it is possible to make visual comparisons of the countries. In 2006, Switzerland and the Netherlands belonged to the countries with the highest values of the indicator. Ten years later there is considerable change in the color scale of the countries, but still the best results are recorded in Switzerland and Netherlands. However, some additional countries are entering to this group, such as Italy, England, Spain and France (Figure 1).

**Figure 1.** Cartographer—resources productivity in 2006 and 2016. **Figure 1.** Cartographer—resources productivity in 2006 and 2016.

At the same time, the average value of the indicator moved from 1.17 (in 2000) to 2.35 (in 2018), which presents 200% growth during 18 years (see Figure 2). EU countries are then on average able to produce double the volume of production compared to in 2000, with the same inputs. At the same time, the average value of the indicator moved from 1.17 (in 2000) to 2.35 (in 2018), which presents 200% growth during 18 years (see Figure 2). EU countries are then on average able to produce double the volume of production compared to in 2000,with the same inputs. *Energies* **2021**, *14*, x FOR PEER REVIEW 6 of 15

It is also illustrated by Figure 4, that the results of individual EU states are considerably different. The EU average in 2017 is at the level 2.2; significantly over-average values were registered in the Netherlands, Switzerland, United Kingdom, Italy, Luxembourg. Luxembourg at the same time belongs among countries where during the last years there is a registered undesirable trend of the indicator decrease. Moreover, Hungary, Albania, Norway and Cyprus belong here (red ellipse). We see here countries, where results of the indicator are under EU average, but the trend is significantly growing. Here belongs, for example,

**Figure 2.** Average resources policy in EU in 2000–2018. **Figure 2.** Average resources policy in EU in 2000–2018.

Slovakia, Greece, Czech Republic, Croatia and North Macedonia.

sults of the resources productivity (see Figure 3).

**1-Way Test, ChiSquare Approximation ChiSquare DF Prob>ChiSq**  486.3820 35 <0.0001

**Figure 3.** Results of Kruskal-Wallis test.

According to the analysis of variability through the Kruskal-Wallis test, the statistically important differences were verified for EU member states according to achieved results of the resources productivity (see Figure 3). According to the analysis of variability through the Kruskal-Wallis test, the statistically important differences were verified for EU member states according to achieved results of the resources productivity (see Figure 3).


**Figure 2.** Average resources policy in EU in 2000–2018.

*Energies* **2021**, *14*, x FOR PEER REVIEW 6 of 15

**Figure 3.** Results of Kruskal-Wallis test. **Figure 3.** Results of Kruskal-Wallis test.

It is also illustrated by Figure 4, that the results of individual EU states are considerably different. The EU average in 2017 is at the level 2.2; significantly over-average values were registered in the Netherlands, Switzerland, United Kingdom, Italy, Luxembourg. Luxembourg at the same time belongs among countries where during the last years there is a registered undesirable trend of the indicator decrease. Moreover, Hungary, Albania, Norway and Cyprus belong here (red ellipse). We see here countries, where results of the indicator are under EU average, but the trend is significantly growing. Here belongs, for example, Slovakia, Greece, Czech Republic, Croatia and North Macedonia. It is also illustrated by Figure 4, that the results of individual EU states are considerably different. The EU average in 2017 is at the level 2.2; significantly over-average values were registered in the Netherlands, Switzerland, United Kingdom, Italy, Luxembourg. Luxembourg at the same time belongs among countries where during the last years there is a registered undesirable trend of the indicator decrease. Moreover, Hungary, Albania, Norway and Cyprus belong here (red ellipse). We see here countries, where results of the indicator are under EU average, but the trend is significantly growing. Here belongs, for example, Slovakia, Greece, Czech Republic, Croatia and North Macedonia. *Energies* **2021**, *14*, x FOR PEER REVIEW 7 of 15

**Figure 4.** Graphical comparison of resources productivity in EU in 2000–2018. **Figure 4.** Graphical comparison of resources productivity in EU in 2000–2018.

Overall, we can conclude that productivity of resources has, in most countries, an increasing trend. However, the level of the indicator proportionally different in comparing the countries. We tried to find out what the reason is for such differences by analysis of other indicators and the relations between them, which could significantly influence productivity of resources in these countries. It means choosing an indicator that defines the transformation of the economy toward a circular economy, such as: Overall, we can conclude that productivity of resources has, in most countries, an increasing trend. However, the level of the indicator proportionally different in comparing the countries. We tried to find out what the reason is for such differences by analysis of other indicators and the relations between them, which could significantly influence productivity of resources in these countries. It means choosing an indicator that defines the transformation of the economy toward a circular economy, such as:


**Table 2.** Descriptive statistics (Univariate simple statistics) of the analyzed indicators.

Waste production without main mineral waste on GDP (kg/1000€) 107.317 134.378 12878.0 20.0000 779.000 Measure of material using in circulation (%) 8.5232 6.4263 954.600 0.7000 29.0000 Taxes of energy/GDP 0.0193 0.0052 2.3186 0.0095 0.0329 Environmental taxes % (rate of incomes from total taxes) 7.3443 1.7936 910.690 2.4100 11.6600 Index eco-innovation (%) 89.2545 30.2192 9818.00 29.0000 149.000 Measure of waste stocking % 32.9712 23.4796 3429.00 1.0000 85.0000 Measure of recycling % 49.5922 16.5259 5108.00 10.0000 87.0000 Productivity of sources 1.8827 0.8331 225.927 0.6607 3.9902 (Source: own processing in software JMP).


lowing analysis (Table 2).


**Column Mean Std Dev Sum Minimum Maximum** 

The following descriptive statistics give basic statistical characteristics of the indicators (average, standard deviation, summary, minimum, maximum), included in the fol-


The following descriptive statistics give basic statistical characteristics of the indicators (average, standard deviation, summary, minimum, maximum), included in the following analysis (Table 2).


**Table 2.** Descriptive statistics (Univariate simple statistics) of the analyzed indicators.

(Source: own processing in software JMP).

#### *4.2. Analysis of Linear Dependence of Indicators*

With the use of correlation analysis, we searched the existence of the relationships between indicators and looked for possible reasons for the achieved values in the EU and at the level of individual states. The aim is to limit indicators that could participate at the Europe orientation to achieve environmental policy goals in area of increasing effective resource use. Analysis of linear dependence of indicators was performed in the software JMP by using pair correlation analysis. We analyzed all indicators mutually, and obtained statistically important correlations. Results of the analysis went through further searching, while finding important correlations with the coefficient of correlations r > 0.5 (positive linear dependence = red area) and r < −0.5 (negative linear dependence = blue area), which is illustrated by Table 3.

Results of the analysis showed in some pairs strong positive linear dependence between indicators with the coefficient of correlation at the level max 0.64. It means the following pairs of indicators:



#### **Table 3.** Results of pair correlation of indicators.

(Own processing according to the ES data from software JMP. Signif Prob: Significance probabilities correlation; \* draws attention to a statistically significant correlation).

> Results of the analysis also pointed in some pairs to the strong negative linear dependence between indicators with the coefficient correlation at the level max−0.86. It means the following pairs of indicators:


#### *4.3. Cluster Analysis*

Through cluster analysis we searched common characteristics of each state, while according to similar behavior clusters had been created. The principle of the cluster analysis is to group indicators, in our case by state, by the way that inside the cluster there was

achieved maximal homogeneity of indicators and between clusters maximal variabilities were recorded.

Analysis was performed by method of hierarchical clustering. The method begins with every observation by its own cluster. In any step of the clustering process the distance between all cluster pairs are calculated and the two closest clusters are determined. The process continues until all points are included in the one cluster. Hierarchical clustering is known also as agglomerate clustering due to the use of a combined approach.

Results of the analysis in our case mean the creation of four clusters that are presented in Figure 5. In our analysis we evaluated each cluster individually. *Energies* **2021**, *14*, x FOR PEER REVIEW 10 of 15


**Figure 5.** Results of cluster analysis in EU states according to the effectiveness of resource use in 2016. (Own processing according to ES data in software JMP, Excel). **Figure 5.** Results of cluster analysis in EU states according to the effectiveness of resource use in 2016. (Own processing according to ES data in software JMP, Excel).

> Individual clusters were analyzed through the analysis of average cluster values of indicators. In the frame of any indicator, average achieved values were ranked from the best to the worst value according to the target values in the EU. Individual clusters were analyzed through the analysis of average cluster values of indicators. In the frame of any indicator, average achieved values were ranked from the best to the worst value according to the target values in the EU.

Indicators with the goals to growth are (Table 4): Indicators with the goals to growth are (Table 4):


and energy policy.

Indicators with the goal to decrease are: Indicators with the goal to decrease are:


Finally, ranked values were scored, while the first place means 1 point, second place 0.5, and third place 0.3. According to the summary of obtained points, we constructed a chart of clusters according to the ability to use resources effectively. The best results were achieved in the first cluster, which achieved the best average values in the EU, in the case of Finally, ranked values were scored, while the first place means 1 point, second place 0.5, and third place 0.3. According to the summary of obtained points, we constructed a chart of clusters according to the ability to use resources effectively. The best results were achieved in the first cluster, which achieved the best average values in the EU, in the

case of energy taxes and the environment. The worst results were recorded in countries from the second cluster with 0.5 points—Bulgaria and Estonia. These countries produce almost eight times more waste and they produce waste stocks five times higher, as compared to the first cluster. At the same time, there are countries that in comparing with the first cluster achieve only one-third level of indicators, a shown in the measure of recycling, index of ecoinnovation, and measure of material use in a cycle. The productivity of resources is at the level 0.79 and these countries have the highest tax burden in the frame of environmental

case of five indicators—5.5 points. The cluster is presented by Belgium, the Netherlands, France, Italy, and England. The countries have at the same time one of the lowest tax burdens in case of energy taxes and the environment. The worst results were recorded in countries from the second cluster with 0.5 points—Bulgaria and Estonia. These countries produce almost eight times more waste and they produce waste stocks five times higher, as compared to the first cluster. At the same time, there are countries that in comparing with the first cluster achieve only one-third level of indicators, a shown in the measure of recycling, index of eco-innovation, and measure of material use in a cycle. The productivity of resources is at the level 0.79 and these countries have the highest tax burden in the frame of environmental and energy policy. *Energies* **2021**, *14*, x FOR PEER REVIEW 11 of 15

#### **Table 4.** Average values of indicators in each cluster-year. **Table 4.** Average values of indicators in each cluster-year.


(Own processing according to ES data in software Excel). (Note: red color—indicators with goal to growth, blue color indicators with a goal to decrease. Note: Sample of cluster 1 scoring: productivity of sources—1 place/1 points + measure of material use in cycle (%)—1 place/1 points + index eco-innovation (%)—2 place/0.5 points + measure of recycling (%)— (Own processing according to ES data in software Excel). (Note: red color—indicators with goal to growth, blue color—indicators with a goal to decrease. Note: Sample of cluster 1 scoring: productivity of sources—1 place/1 points + measure of material use in cycle (%)—1 place/1 points + index eco-innovation (%)—2 place/0.5 points + measure of recycling (%)—1 place/1 points + measure of waste stocking (%)—1 place/1 points + waste productivity without main mineral waste on GDP (kg/1000€)—1 place/1 points = 1 + 1 + 0.5 + 1 + 1 + 1 = 5.5).

#### 1 place/1 points + measure of waste stocking (%)—1 place/1 points + waste productivity without main mineral waste on GDP (kg/1000€)—1 place/1 points = 1 + 1 + 0.5 + 1 + 1 + 1 = 5.5). **5. Discussion**

**5. Discussion** As for the comparison of the results with other studies, Haider and Bhat (2020) studied a connection between material and energetic effectiveness and total productivity of factors, As for the comparison of the results with other studies, Haider and Bhat (2020) studied a connection between material and energetic effectiveness and total productivity of factors, finding that not all states are equal from the view of energy demands [31]. Increasing of total productivity of factors is connected with lower level of energy per output unit.

finding that not all states are equal from the view of energy demands [31]. Increasing of total productivity of factors is connected with lower level of energy per output unit. In Europe annually 18 million tons of waste could be collected in the case of installing a strategy of collection according to proven processes, which could lead to a 13% decrease of greenhouse gas production in connection with packages and package wastes. In spite of the high performance of the collection being an effective use of resources, a single improvement of the system of waste collection, separate from the resources, is not enough for recycling goals achievement or decreasing emissions production. Material loss has to be decreasing in the whole value chain, which means from selection and collection to recycling [38,39]. Kuhl et al. (2018) considered business models for circular economy (CE) that have potential environmental contributions and productivity of resources [40]. Circular business models, based on refurbishment and repeated use of materials could bring significant cost savings, as well as radically decreasing negative influences on the living environment [41]. The processing of resources has to be radically changed from the model of linear use to the sustainable, circular model. In this area, Velenturf et al. (2020) created a model that considered the In Europe annually 18 million tons of waste could be collected in the case of installing a strategy of collection according to proven processes, which could lead to a 13% decrease of greenhouse gas production in connection with packages and package wastes. In spite of the high performance of the collection being an effective use of resources, a single improvement of the system of waste collection, separate from the resources, is not enough for recycling goals achievement or decreasing emissions production. Material loss has to be decreasing in the whole value chain, which means from selection and collection to recycling [38,39]. Kuhl et al. (2018) considered business models for circular economy (CE) that have potential environmental contributions and productivity of resources [40]. Circular business models, based on refurbishment and repeated use of materials could bring significant cost savings, as well as radically decreasing negative influences on the living environment [41]. The processing of resources has to be radically changed from the model of linear use to the sustainable, circular model. In this area, Velenturf et al. (2020) created a model that considered the complex character of resource flows [42]. Environmental sustainability has to be connected with conceptions of an ecological economy, circular economy and bio-economy [43,44].

complex character of resource flows [42]. Environmental sustainability has to be connected with conceptions of an ecological economy, circular economy and bio-economy [43,44]. However, the connection with sustainability is not, according to Loiseau et al. (2016), always clear [45], since there are various levels of substitution and compromises are allowed However, the connection with sustainability is not, according to Loiseau et al. (2016), always clear [45], since there are various levels of substitution and compromises are allowed between environmental and economic advantages. Additionally, structuralized changes of

the human way of life are demanded [46]. It is necessary to combine the circular economy with an approach regarding human development (HD), since one of the discussions is connecting the missing social or human dimension of the circular economy [47]. Schroder et al. (2020) included social and economic elements in the transformation from linear to circular economic models in combination with HD from studies of social science and development [48]. This could be connected with application of analysis results from area of tax burden influence for inhabitants and businessmen. Setting energy and environmental taxes at manageable levels can also increase effectiveness of productivity of resources in the countries. This approach can also be linked to the possibilities of effective use of spatial data models from

GIS environment [49], that are commonly used in such research.

the human way of life are demanded [46]. It is necessary to combine the circular economy with an approach regarding human development (HD), since one of the discussions is connecting the missing social or human dimension of the circular economy [47]. Schroder et al. (2020) included social and economic elements in the transformation from linear to circular economic models in combination with HD from studies of social science and development [48]. This could be connected with application of analysis results from area of tax burden influence for inhabitants and businessmen. Setting energy and environmental taxes at manageable levels can also increase effectiveness of productivity of resources in the countries. This approach can also be linked to the possibilities of effective use of spatial data models from GIS environment [49], that are commonly used in such research.

Due to the pandemic situation the European Union is buffeted by a range of crises since 2007, not least the economic and financial crisis. One potential victim of the economic crisis is environmental policy ambition, since during economic crises environmental policy slips down the agenda with long term consequences for environmental quality. The future environmental policy trajectory and leadership of the EU may be under threat as the Union struggles to emerge from the economic crisis. Productivity of resources could be helpful for the overcoming of such struggles.

To address the challenges of reducing impacts on the environment and of resource scarcity, it must go hand in hand with sustainable development and state policy, when material efficiency is a key element of new thinking [50,51]. Directly related to material efficiency is the concept of the circular economy, which is based on the principle of optimizing the utility embodied in materials and products throughout their life cycle.

#### **6. Conclusions**

In the EU, basic strategy results from the need to provide intelligent, sustainable, and inclusive growth, along with respect to social and economic impacts and issues surrounding the living environment and its protection, all often-discussed topics. Experts on this global theme most often refer to sustainability, considering all elements of the environment. Therefore, the analysis realized in the frame of this research states that presently, the common effort is to increase the effectiveness of Earth's resource use and protect the living environment, going hand in hand with the use of renewable energy sources [52]. It demands a follow-up of determined goals not only at the level of absolute figures for European Union and individual member states, but also at the level of each sector. The undeniable fact is that there are dependences between all sectors and processes that need higher evaluation.

Additionally, the findings of this research point to the necessity to be oriented not only to the resulting goal, but also to the secondary factors, influencing the goal achievement. It means that obtained dependencies can help countries with significant differences to orient their means to the areas that are seemingly not connected with achievement of the goal, but which finally could help speed up the goal achievement. An example could be the increasing of recycling measures that contribute to the goal achievement—a *decrease of waste stocking* can also considerably support a decrease of *domestic consumption of material on the inhabitant* or to increase *energetic productivity of the state*. An important finding of this research is that environmental tax policy has a positive influence on the meeting of environmental goals of the individual countries. Countries with high taxes on energy and environmental taxes are at the same time countries with the lowest ability to meet determined goals in the frame of Strategy Europe 2020.

Results of the analyses point to the fact that countries, oriented only to the decreasing of emission production connected with fossil fuel energy and transport, cannot meet the demanded progress if they will not intensively support innovation processes and research and development activities oriented to ecological processes. Due to the limitation of the research to the chosen indicators, the subject of further research would be single analysis of key indicators of productivity of resources, for example productivity of water, soil and energy in relation to the main indicator—productivity of resources. Each indicator should be searched in context of chosen indicators, defining standard of living, economic and financial self-sufficiency, level of education, etc.

Industry-level productivity analysis can be a useful diagnostic tool to better understand why some sectors and countries show faster overall productivity growth, and to direct research attention to parts of the economy that warrant more detailed scrutiny. A better understanding of productivity growth (or lack thereof) in industries should still be an important goal of researchers aiming to understand cross-country growth differences.

**Author Contributions:** Conceptualization, M.T. and K.C.; methodology, M.T. and K. ˇ C.; software, ˇ M.T.; validation, P.T. and L.D.; formal analysis, M.T.; resources, K.C.; data curation, P.T.; writing— ˇ original draft preparation, M.T. and K.C.; writing—review and editing, A.S.; visualization, P.T.; ˇ supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded of grant project VEGA 1/0797/20.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Contribution presents partial result of grant project VEGA 1/0797/20 and KEGA 006 TUKE-4/2019.

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

#### **References**

