SUSTAINABLE DEVELOPMENT GREEN INDEX: MEASURING PROGRESS TOWARDS SUSTAINABLE DEVELOPMENT GOALS IN THE EUROPEAN UNION

. The EU positions itself at the forefront of the global green agenda. Therefore, it is necessary to assess the development of European countries considering the "green" component of this process. Not all currently known indices cover all aspects of sustainable development. The article aims to develop a Sustainable Development Green Index (SDGI), which, on the one hand, would be used as an effective tool for measuring progress in the implementation of sustainable development goals; and, on the other hand, would take into account ecоnоmic, sоcial, educatiоnal, envirоnmental and pоlitical aspects. This study's results demonstrate effectiveness of the suggested tool. Practical application of the SDGI may be instrumental for reaching faster movement towards Sustainable Development Goals in the European Union.

The analysed indexes aggregate only some aspects of sustainable development, and their framework hasn't been used for testing the relationship with SDGs'.The present article suggests using an original Sustainable Development Green Index (SDGI), which embraces economic, social, educational, environmental and pоlitical aspects and demonstrates a strong relationship with some SDGs' progress in the European Union.
In 2015, the United Nations devised seventeen sustainable development goals (SDGs) to counter such negative consequences.SDGs included five critical areas of importance by 2030planet, people, peace, partnership, andprosperity (UNDP, 2022).United Nations motivated global economies to incorporate sustainable principles in their industrial processes (Sivageerthi et al., 2022).
The necessity to integrate ecоnоmic, sоcial, educatiоnal, envirоnmental and pоlitical factors into the analysis of SDGs predetermined by applying the Quintuple Helix model within the research.The Quintuple Helix model focuses on the transfer of knowledge and public exchange within the ecosystem of the state (Barth, 2011;Arsova et al., 2021;Purbavati et al., 2023).In addition, the innovative Quintuple Helix model explains how the natural environment, knowledge and innovations are interdepended (Carayannis et al., 2012;2021;Cai, 2022).The Quintuple Helix Model is a complex structure, with all five helices requiring knowledge in natural science, social science, and humanities (Carayannis and Campbell, 2012;Vitola et al., 2021).Fig. 1 presents the structure of the Quintuple Helix Model, where knowledge moves in a circle from the education system to the economic system, then to the political system, the public, and the natural environment (Grundel et al., 2016).These five helices work as "subsystems" (Ibid).Source: The authors' constructions according to Carayannis et al., 2009Carayannis et al., , 2010Carayannis et al., , 2011Carayannis et al., , 2012Carayannis et al., , 2021 Despite the previous attempts to relate Quintuple Helix Model to sustainable development processes, there is still a gap, in area of how this model could be used for measuring of complex green growth proceeses.

Methodology
The authors suggest to look at sustainable development process via lenses of the Quintuple Helix Innovative Model.Here it has to be noted that similar attempts already has been made by e.g.Barcellos-Paula et al. (2021).
Hence, Quintuple Helix Model has been used to make necessary calculations of SDGI.An equal number of indicators (10) were assigned to each of the subsystems (5), 50 indicators in total (see Appendix).All indicators are standardised.
The mean values Sustainable Development Green Index are obtained as arithmetic means of the corresponding indicators.The integrated SDGI was received as the arithmetic mean of the values of five subsystems (Rybalkin, 2022).

Results and discussion
The Sustainable Development Green Index (along with its subsystems) has been calculated for the European Union countries (plus the United Kingdom) data collected in 2020.The results of the calculations were analysed and visualised with the help of SPSS software; specifically, cluster analysis was performed.Being a quantitative method of data analysis aimed at discovering groups in data (in the case of the present articleclusters of the EU countries), the value of such research is that it suggests groupings that might form the basis of future hypotheses to be investigated (Landau et al., 2010).
The results of the leading and outsider countries in terms of the Sustainable Development Green Index in 2020 are presented in Fig. 2. Cluster analysis allowed to group EU countries into two homogeneous clusters (Figure 2) by their SDGI.The first cluster (Cluster 1) included countries with higher mean values of all five subsystems; a lower level of these mean values characterised other countries (Cluster 2).See Figure 3   Source: The authors' calculations in SPSS according to statistical data; were elaborated using mapchart.net.
Considering the mean values of the subsystems in the two clusters, it can be concluded that all mean values of subsystems in the Cluster 1 exceed those of subsystems in Cluster 2. Notably, the mean value of the educational subsystemby 27%, the mean value of the political subsystemby 18.5%, the mean value of the societal subsystemby 14.3%, the economic subsystemby 14.2%, the environmental subsystemby 11.3% (Figure 4).The analysis of the multicollinearity of the unified statistical indicators was performed.To that end, the coefficients of determination R 2 = r 2 of each of the primary statistical indicators of the analysed set were found (Ajvazyan, 2005).
Next, selecting the most informative criteria among the indicators of each Sustainable Development Green Index category was conducted.The most informative set is the one in which the sum of the coefficients of determination of the dependent variable by the explanatory variables is maximum.
I.e., the set of indicators is considered to be the most informative, if , where R 2 (y;(x (1) ,K,x (s) )) -coefficient of determination of the dependent variable by the explanatory variables x(1), K, x(s).
The quantitative composition of a limited set of indicators is chosen in each specific case based on a combination of theoretical (substantial) considerations and requirements for the minimum allowable values of R 2 min of the coefficients of determination.
After that, it was decided to take three indicators with the most significant sum of the coefficients of determination within each of the subsystems (to ensure equal representation of indicators, just like in the Sustainable Development Green Index itself).The average of their sum constituted the simplified Sustainable Development Green Index (Table 1).As seen from Table 3, the multicollinearity analysis has allowed us to define the most relevant indicators within each of the subsystems of the Sustainable Development Green Index and construct its simplified version, which consists of 15 instead of 50 indicators.Now that the simplified version of SDGI has been presented, it is suggested to test empirically its interrelation with indicators connected with some of the Sustainable Development Goals in the European Union by correlation analysis, with a focus on society-related SDGs because of the high demand for such studies (Sianes et al., 2022).
To determine if there is an interrelation between the Sustainable Development Green Index and SDG 3, 'Good health and well-being', SDG 4 ', Quality education', SDG 9 'Industry, innovation and infrastructure' correlation analysis were performed.The 'Smoking Prevalence' indicator reflects progress towards Sustainable Development Goal 3 (Eurostat, 2022a).For Sustainable Development Goal 4the indicator 'Share of individuals having at least basic digital skills' (Eurostat, 2022b).Finally, for Sustainable Development Goal 9, the indicator of the market share of plug-in electric vehicles in the EU countries in 2020 (European Automobile Manufacturers Association (ACEA, 2021) was analysed.
The results upon analysing the relationship between progress towards the abovementioned SDGs and SDGI and its subsystems were as follows (Table 2).They are also compared to the correlation with GDP per capita in the EU countries.

ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES
ISSN 2345-0282 (online) http://jssidoi.org/jesi/2023 Volume 10 Number 4 (June) http://doi.org/10.9770/jesi.2023.10.4(17)As can be seen from the table, the correlation between the indicators corresponding to SDGs 3, 4, 9 and the simplified version of the Sustainable Development Green Index was significant and can be characterised as very strong according to Quinnipiac University's interpretation (as quoted from Akoglu, 2018).Also, it can be observed that all analysed SDGs interrelated with the simplified SDGI are more robust than with such conventional metrics as GDP per capita, which covers only the economy.
These findings substantiate the statement that Sustainable Development Green Index is more suitable for measuring progress towards SDGs in Europe than conventional metrics, since it is more consistent with sustainable development and considers all subsystems: educational, economic, political, societal and environmental.
It can also be seen that the correlation coefficients of various subsystems of simplified SDGI are different (Fig. 5):

Conclusions
The article revealed strong interrelations between the simplified version of the Sustainable Development Green Index and SDGs 3, 4, and 9 progress in the European Union.
The correlation was higher than with GDP per capita.It makes the newly elaborated SDGI more relevant to measuring progress towards SDGs than conventional metrics.The created SDGI considers all subsystems of the phenomenon: educational, economic, political, societal and environmental, and thus more consistent with the context of sustainable development.
It also underlines the fact that a market-centric approach seems to be entirely one-sided, overestimating the influence of economic prosperity on sustainable development.
That SDGI provides a better-balanced and multidimensional view of this phenomenon, considering all related factors.
Sustainable Development Green Index proposed within the present study offers academia, society, business and governments a tool to measure a country's performance in terms of sustainable development and progress towards some of the SDGs.The suggested tool can be helpful for different stakeholders.The study opens up new research opportunities regarding the further applicability of the index towards sustainable development issues in EU countries and globally.
Analysing the interrelation between the SDGI and other SDGs by performing correlation analysis will still need to be the purpose of subsequent research.

Figure 2 .
Figure 2. The Sustainable Development Green Index 2020 Source: The authors' calculations in SPSS according to statistical data; were elaborated using mapchart.net. below.

Figure 3 .
Figure 3.European Union countries are divided into Cluster 1 and Cluster 2 by the Sustainable Development Green Index, 2020Source: The authors' calculations in SPSS according to statistical data; were elaborated using mapchart.net.

Figure 4 .
Figure 4. Comparison of Cluster 1 and Cluster 2 according to the Sustainable Development Green Index subsystems, 2020Source: The authors` calculations in SPSS according to statistical data.

Figure 5 .
Figure 5. SDG-weighted subsystems of SDGI simplified Source: The authors' calculations according to statistical data

Table 1 .
Simplified SDGI and its indicators, 2020This indicator is a part of the Global Innovation Index and measures environmental performance on a state level; it is not the same as the Environmental Performance Index (environmental subsystem) from the Environmental Performance Index Report (which deals exclusively with the quality of the environment).Source: the author's calculations in SPSS according to statistical data. *

Table 2 .
Correlation analysis between SDGI and some SDGs' progress in the EU The authors' calculations in SPSS according to statistical data.