**1. Introduction**

The current tendency of greening the economic development contributes to analysing the most significant drivers that boost this process. All EU countries signed the agreemen<sup>t</sup> on achieving Sustainable Development Goals 2030 (SDGs 2030). According to this agreement, the EU countries, on a voluntary basis, try to reduce their negative impact on the environment and harmonise their economic, social, and environmental development. The results of many studies [1–5] indicate that one of the main drivers which provide the financial base for sustainable development is green investment. Several studies [6,7] have found that the biggest share of green investments was spent on spreading and implementing renewable energy which could reduce greenhouse gas emissions (GHG emissions) [8,9].

Noteworthy here, the experts have created the Sustainable development goal index to demonstrate countries' success on the way to achieving SDGs 2030. Thus, according to the o fficial report [10], in 2018, the first five places were occupied by Sweden, Denmark, Finland, Germany. All these countries belong to the group of high-income countries in which 2016 Gross National Income (GNI) per capita was \$12,235 or higher. For these countries, allocation of additional capital for achieving SDGs is not a huge issue.

At the same time, such countries as Slovak Republic, Hungary, Portugal, Poland, Bulgaria, Serbia, Romania, Greece occupied the ranks from 25 to 48. Among these countries, three are high-income countries (Slovak Republic, Hungary and Greece) and upper-middle-income countries with 2016 GNI per capita being between \$3,956 and \$12,235 (Bulgaria, Serbia, Romania), only Moldova belongs to low middle-income countries in this group.

In this case, for upper and low middle-income countries allocation of additional financial resources is a big issue due to their unstable economic situation. As green investments can help in achieving important sustainable development goals: GDP per capita growth, increase of renewable energy utilisation and GHG emission reduction, the main input of this paper is to test this hypothesis and to assess the impact of green investments on GDP per capita, the share of renewables in the final energy consumption and GHG emissions in the EU member states and to develop policy recommendations stemming from the results of this empirical study.

As indicated above, increasing the share of renewable energy in the total energy consumption requires additional financial resources. In this case, it would be appropriate to analyse the statistical significance between GDP per capita, GHG emissions, renewable energy consumption and the volume of green investments. The authors of this paper have checked the following hypothesis:

H1: There is a linking between green investment, GDP per capita, GHG emissions and the share of renewable energy in the total energy consumption.

In the next section of this paper, data and methods used are presented. Note that our previous investigation has been focused on a more general analysis of green investment development. In this case, the object of our analysis is the EU countries during the 2008–2016 period while our purpose has been to explain that green investment is one of the ways to attract financial resources.

The reminder of the paper is structured in the following way: Section 2 provides a literature review on the subject and formulation of the hypothesis; Section 3 delivers data and methods applied in the study; Section 4 presents discussions of the results and Section 5 concludes.

#### **2. Literature Review**

The main dimensions of sustainable development then GDP per capita is growing selected for this study are as follows: use of renewable energy sources and GHG emission reduction. These are also the main EU energy policy targets including energy e fficiency increase. In addition, increase of utilisation of renewable energy sources also provides for energy e fficiency improvements [2].

According to the dataset, the EU as the whole tries to decrease the GHG emissions compare to the 1990 year (Figure 1).

**Figure 1.** GDP per capita and greenhouse gas emissions (GHG) emissions for EU countries (1990–2016) [11,12].

In this case, the snowballing effect on decreasing GHG emissions could be achieved through increasing the share of renewable energy in total energy consumption (Figure 2).

**Figure 2.** GDP per capita and renewable energy consumption for EU countries (1990–2015) [11,12].

The findings in Figures 1 and 2 showed that increasing the share of renewable energy in total energy consumption leads to decreasing GHG emissions.

The huge range of investigation deals with the analysis of preconditions to develop green investment market as a financial driver of sustainable development. However, according to the results of the previous investigations [13–23] the main factor which restricts the developing of green investment market is misunderstanding the meaning and goals of green investment among scientists, experts and investors. Thus, Martinez-Oviedo and authors in the papers [24–31] highlighted that green investment is a capital to low carbon and climate resilient initiatives, clean technologies, renewable energy, or natural capital that can be considered environmentally beneficial. The one group of scientists proved that the main goal of green investment is declining of CO2 emission [32–45]. The third group of scientists defined green investment as investment into renewable energy [46–63]. In this case, it is necessary to investigate and develop the universal approach to define and classify green investment which will be based on the classification of green assets.

It should be underlined, that a lot of the scientists investigate the relationship between economic development and efficiency of SDGs 2030 achieving through the analysis of linking between: Environmental Performance Index and countries GDP [31,64–67], Environmental Performance Index and Institutions Quality [68–72]; economic, social and ecological indicators of the countries development [73–84], socioeconomic development and economic growth [85], economic growth, environmental pollution and social development which measured by the level of morbidity [86–89].

The author Greco in the paper [89] analysed the sustainable economic development from the point of view of behavioural economics theory. Greco [89] highlighted that the economic growth related to the solving of social conflict and improving the quality of life and work conditions.

The scientist [90–95] proved the hypotheses on linking between ecological, social and economic indicators which influence on country's GDP. The scientists Zaj ˛aczkowska M. [95], Kisiała W. [96] and Malkina, M. [97] proved the relationship between social indicators, ecological indicators which include efficiency of renewable energy, macroeconomic stability in low-middle income countries [79–99]. Besides, the range of scientists [99] paid attention to analyse the linking between political parameters on economic growth (eliminating of the ecological factors).

Some group of authors in the paper [100–112] tried to prove the relationship between renewable energy, economic growth and volume of foreign direct investment in energy efficient projects and country's brand. In this case, the foreign direct investment analysed as the green investment by the authors. Noted, that using such types of green investment limited the complexity of impact analysis of green investment in the efficiency of sustainable economic development.

The findings allowed making conclusions that most investigation analysed the correlation between CO2 emissions, renewable energy and investment in renewable energy. Under this investigation, the authors analysed instead of CO2 emissions – GHG emission as it was indicated the goal of SDGs

2030. Therefore, green investment was defined as the private investments, jobs, and gross value added related to circular economy sectors which are an integrated indicator which involves the social, ecological and economic impact of the investment.

#### **3. Data and Methods**

For checking H1 hypothesis and further analysis of the main drivers of sustainable development, the authors used the modified model of economic growth as follows:

$$\text{GDP} = \text{F} \text{(PICE, GHG, RE)} \tag{1}$$

where GDP – GDP per capita, PICE – private investments, jobs and gross value added related to circular economy sectors; GHG – GHG emission; RE – share of renewable energy in the total energy consumption.

Modified function (2) can be demonstrated as panel cointegration equation:

$$
\ln \text{GDP}\_{\text{it}} = \phi \, + \, \text{or} \, \ln \text{PICE}\_{\text{it}} + \beta \, \ln \text{GHG}\_{\text{it}} + \gamma \, \ln \text{RE}\_{\text{it}} + \mu\_{\text{it}} \tag{2}
$$

where α, β, γ – regression's parameters which are evaluated and explain the elastic of output relate on PICE, GHG, RE; μ is the error term; i = 1, ... , N; t = 1, ... , T.

For checking above-mentioned hypotheses, the authors used the databases as follows: World Data Bank, Eurostat, European Environmental Agency. For the analysis, the EU countries during the 2008-2016 period were chosen (Table 1).


**Table 1.** Indicators, meaning and sources for analysis

Sources: compiled by the authors.

Under the research, the following methods were used: panel unit root tests using the Im, Pesaran, and Shin's (IPS); Levin, Lin, and Chu test (LLC); and the Fisher-type tests (Augmented Dickey–Fuller test (ADF) Fisher and Phillips–Perron test (PP) Fisher). In the basis of the abovementioned tests is the checking of the hypothesis, which assumed the existing a unit root in the panel data on the time series and alternative absence in the unit root.

With the purpose to check the long-term correlation, the authors used Pedroni test. If the cointegration exists, the long-run equilibrium relationship will estimate using the Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) panel cointegration techniques.

For analysis, the EU countries were chosen for period 2000–2016 years. Such countries were chosen as an example for the countries' potential candidate to EU membership to prove the efficiency of green investment and stimulate the attractiveness of green investment as a part of the direct foreign investment. Such investigation could be a base for the further analysis of options to minimize the gaps between EU policy and the countries' potential candidate to EU membership.

#### **4. Discussion of Results**

At the first stage, the panel unit root tests for parameters GDP, GHG, PICE, RE were done. The results of the panel unit root tests for GDP, GHG, PICE, RE were presented in Table 2.


**Table 2.** Panel unit root results for GDP, GHG, green investment (PICE), renewable energy (RE).

Sources: calculated by the authors.

The findings (Table 2) of using Levin Lin and Chu (LLC), Im Pesaran and Shin (IPS), ADF Fisher Chi-square and PP Fisher Chi-square tests confirmed that all variables were non-stationary at levels and after the first difference, all variable had become stationary. The obtained results allowed indicating the character of stationary of GDP, GHG, PICE, RE, for EU countries but also established the basis for panel cointegration analysis as the using of the regressions on non-stationary variables can give misleading parameter estimates in the economic relationship among variables. All findings are statistically significant at the level – 1% and 5%. The findings allowed realising the test for panel cointegration between GDP, GHG, PICE, RE. The cointegration test was conducted using by the Pedroni panel cointegration tests (Table 3).

**Table 3.** Pedroni panel cointegration tests.


Sources: calculated by the authors.

Thus, the findings in Table 3 showed that six from eleven results of the test exclude null hypothesis (no cointegration of time series). Therefore, the obtained results a ffirmed the cointegration between GDP, GHG, PICE, RE for the EU countries existed on significance at the 1% and 5% levels. It allows making the conclusion, about the long-term relationship among GDP, GHG, PICE, RE which could be checked by using the FMOLS and DOLS panel cointegration techniques. For that purpose, four assumptions were checked:


The obtained results of using FMOLS and DOLS panel cointegration techniques were presented in Table 4.


**Table 4.** The findings of the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DMOLS) panel cointegration techniques for four assumptions.

 Sources: calculated by the authors.

The findings showed that FMOLS and DOLS demonstrated the same results as GHG, PICE, RE influence on GDP for EU countries. The obtained results were statistical significance –1% for all three parameters. Thus, the increasing by 1% of PICE leads to increasing of GDP by 6.4% (FMOLS) and 5.6% (DMOLS), the increasing by 1% of GHG lead to increasing of GDP by 0.39% (FMOLS) and 0.41% (DMOLS), the increasing by 1% of RE provoked the increasing of GDP by 38.18% (FMOLS) and 37.24% (DMOLS).

It should be noted, that increasing of RE by 1% lead to decreasing of GHG by −5139.85%. Therefore, increasing by 1% of PICE provoked the increasing of RE by 5.6% (FMOLS) and 7% (DOLS). At the same time, increasing of RE by 1% provoked the increasing of PICE by 99.44% (FMOLS).

The obtained results proved the hypothesis of linking between green investment, GDP, GHG emissions and share of renewable energy in the total energy consumption for EU countries. In this case, the findings proved that green investment could provoke the increase of the share of renewable energy in the total energy consumption and decreasing of GHG emissions. At the same time, the spreading of RE leads to declining of GHG.

Besides, all indicators green investment (PICE), GHG emission and share of renewable energy in the total energy consumption had a statistically significant impact on GDP. Thus, the increasing of green investment (PICE) could provoke the growth of GDP by 6.4%, the declining of GHG by 3.08% and the increasing of renewable energy in the total energy consumption by 5.6% (FMOLS).

The traditional investment market in the countries' potential candidate to EU membership should be "greening" (Figure 3).

**Figure 3.** The framework concept of transformation from traditional to green investment market.

Source: developed by the authors on the basis [108–112,114,115].

The same analysis should be done for the countries' potential candidate to EU membership with the purpose to allocate the mechanism to minimize the negative imbalances in the EU on the way on achieving SDGs 2030. In this case, for further investigation, it is necessary to analyse the EU incentive mechanism to stimulate the attractiveness of green investment as a part of the direct foreign investment.
