The impact of the transition to a green economy on Romania’s economic growth

Abstract

Background

Globally and regionally, nations are going through a period of important changes determined by the climate and environmental challenges in the context of the transition towards green economy, by the energy crisis caused by the Russian–Ukrainian war started in 2022, as well as the economic and social effects of the COVID-19 pandemics, which all affect economic growth. Providing a sustainable development capable of contributing to the increase of welfare and life longevity requires high rates of economic growth as well as a healthy living environment. At present, boosting the transition to the green economy is considered as an alternative. Based on a multivariable linear regression model, this study aims to analyze the connection and influence of five macroeconomic indicators, on Romania’s economic growth over a period of 16 years (2006–2021), where the indicators are considered to be representative for green economy.

Results

The results pinpoint both the existence of a positive and long-term relation among the total greenhouse gas emissions, the value of the production of environmental goods and services, the total environmental taxes and real GDP, and the negative impact of the total generation of renewable electricity and investments for environmental protection upon real GDP. These results provide a relevant picture of the complex interdependences between the environmental indicators and economic growth, amid the significant challenges determined by the implementation of sustainability strategies.

Conclusions

Romania’s transition towards green economy not only represents an initiative based on the obligations resulting from joining the European Union’s Green Agenda, but also results from acknowledging the consequences of climate change; in accordance, our analysis intends to empower policy makers in intensifying the current levels of the total generation of renewable energy and the investments for environmental protection, so that these might reach the thresholds required to transform them into decisive factors of economic growth.

Background

Globalization has determined economic growth in all fields, from industrial processes, agriculture, international trade, energy and transportation to technology exchanges, international financial fluxes and foreign investments. Nevertheless, it has also led to negative consequences such as the increase in economic inequality, financial crises, and air and water pollution caused by the increase in greenhouse gasses.

Globally, during the last decades, environmental issues have become more complex and required a rapid transition to the green economy with a view to separate economic growth from the excessive use of resources, to increase quality of life and population welfare together with decreasing environmental risks and social deficit. The concept of green economy designates an all-encompassing approach of the principles of a sustainable economy, relying on policies and investments able to interconnect economic growth, the ecosystem, climate changes and population health and welfare on a medium and long term [1].

Considering that “sustainable development can help reduce poverty, improve health, and increase access to resources” [2], the environment, economy and society should be regarded as inseparable parts of the same whole. To do this, a green economy should represent a model of social and economic development that prioritizes sustainable economic growth, the improvement of welfare, equity and social inclusion, based on the decrease of carbon emissions, efficiency of using natural resources, reliable administration and waste prevention. In fact, protecting the environment and improving economic growth has now become necessary conditions for economic development [3]. Xu et al. [4] support the idea that “the deep integration of the green economy and the digital economy is a trend for future development. The synergistic development of these two economic subsystems will strongly drive sustainable economic growth”, while Davidenko et al. [5] consider that “a sustainable economy is directly linked to the growth of consumer welfare and the environmental culture”.

The transition to green economy, efficient from the perspective of resource use and intending to attain climate neutrality by 2050, represents an ambitious objective of the EU; under such circumstances, most of the actions have focused on decreasing the greenhouse gas emissions, on increasing the share of energy from renewable sources within energy consumption, on increasing energy efficiency and on fostering investments which protect the environment. As Directive (EU) 2023/1791 [6] asserts, the “level of climate ambition should increase so that it stimulates economic growth, creates jobs, provides health and environmental benefits for the citizens of the European Union and contributes to the general long-term competitiveness of the EU’s economy.” In this context, Chou et al. [7] show a positive correlation between the adoption of renewable energy and key economic indicators, including Gross Domestic Product (GDP) growth, industrial productivity, and technological innovation and pinpoint the potential of renewable energy to stimulate investment, economic growth, and promote equitable development; Phiri and Nyoni [8] assert that “clean energy sources (solar and wind, biomass, hydro) show the greatest potential for growth”. In addition, several authors consider that industrial production relying on renewable energy sources and advanced technologies has an important contribution to the growth of GDP [9] and that economic growth has a positive effect on reducing CO2 emissions, which confirms the separation of economic development from the impact on the environment [10].

The economic and energy crisis has increased the role of energy as a factor for growing the global economy [11]. In accordance with this idea, a series of authors support the four production factors model: capital–labour–energy–creativity (KLEC) [12], which states that in modern economies productivity is not driven solely by traditional factors, such as labor and capital, but also by energy efficiency and human innovation [13, 14], with energy being a priority among these factors. At present, the majority of our energy comes from traditional fossil fuels, despite the fact that the supply of such fuels is limited and their extraction, processing, and transport have negative effects on the environment. Hook and Hume [15] and Tollefson [16] assert that, regardless of the magnitude of such effects, The Ukraine war has increased their negative effect due to the sanctions adopted against fuel imports from Russia, which affected the price of energy and the security of the member states [17] and temporarily “derailed” the transition to a green economy while increasing the need for implementing long-term energy plans and energy independence scenarios relying on renewable sources [18]. The Glasgow conference on climate changes (COP26), at the end of 2021, highlighted the importance of an accelerated energy transition from fossil fuels to renewable resources through which the risk of climate change is decreased [19]. To soften energy shortage and the issues of a continually degrading environment, producing energy from renewable sources becomes an efficient substitute for the global energy demand [20] and for the decrease of carbon dioxide emissions and other greenhouse releases [21].

If, in the past, certain European countries faced major problems determined by the process of transition to the market economy [22], at present, the main challenges all the EU economies face are: energy crisis, the efficient administration of natural resources, the decrease of the negative effects of environmental pollution, which have been amplified by the economic and geopolitical crises in Europe, mainly after year 2022. The post-pandemic crisis and the Russian–Ukrainian war put EU energy security at risk, which required the reconfiguration of the energy resources portfolio complying with the sustainable development models for reaching energy independence [23] and for observing the principles of green economy. In this regard “the energy transition on both global and regional scales has started to take the connotation of urgency” [24] and relies on a complex legislative package to which Romania, as a Member State, has aligned its own strategy. Furthermore, “Romania’s 2025–2035 energy strategy, with 2050 prospects”, which is, at present, awaiting approval by the Romanian Government, aims to build a resilient energy sector “under conditions of safety, sustainability, economic growth and accessibility” [25].

Romania’s energy independence and security prospects might appear optimistic in the case we consider the fact that “Romania was the first country to achieve the EU targets regarding the share of renewables, being ahead of other EU states” and that “it is in a state of energy independence that most EU members are not” [26]. In addition, “Romania is determined to transform itself into a regional energy provider” [26] which will impact positively the whole national economy.

Literature review

Although there are many studies analyzing the possible factors influencing economic growth in the context of maintaining a global clean environment [27], we have discerned some works dealing with the research topic of this study. Below, we detail our findings and present some of the existing literature.

Relation between economic growth and renewable energy

During the last decades, a lot of studies have mainly focused on the relationship between economic development, energy, consumption, and environmental sustainability [28]. As energy is fundamental for all fields of the modern economy and represents the foundation of economic growth, various authors base their analyses on the need to produce energy in correlation with a demand that is constantly growing (prognoses showing the doubling of the demand until 2050) [29]. As a result, a large part of the existing studies has focused on the impact of electrical energy production on economic growth. The transition to a green economy and the compliance with environmental protection regulations, corroborated with technological progress, have created a fundamental change in the global energy system [30] as well as the diversifying of the energy matrix in accordance with a trend directed to producing energy from renewable sources. [31], even in countries where coal heating systems are dominant but which own renewable energy resources that are not being sufficiently used [32]. In accordance, the study of the interdependence between economic growth and electrical energy production is complemented by more and more exhaustive studies on the effects of renewable energy production [29] on economic growth while considering the separating of the impact of the total production of electrical energy from renewable sources from the production of electrical energy from non-renewable sources.

Specialised studies regarding the influence of the production of energy from renewable sources on GDP show that, irrespective of the region, country or period analysed [33–37], the connection between the two indicators displays several types of relations [19, 38, 39]:

• The one-way relation, most frequently found in specialised works, is able to show the effect determined by the production of renewable energy on GDP. For instance, a growth relation between the two variables is displayed by Haldar et al. [40], who, by means of the data from the period 2000–2018, regarding 16 emerging economies, show that generating renewable energy represents the determining factor of economic growth, besides ICT, innovation and electrical energy consumption within the growth equation. Rehman et al. [41] also show that, in the case of Pakistan, the analysis of several factors, including the production of renewable energy, positively influence the increase of GDP and notice that, on a long term, this relation among variables has a more powerful effect on GDP per capita than the short-term dynamics. The case of the Netherlands is analysed both by Bulavskaya and Reynès [42], who consider the positive impact of an energy system relying only upon renewable sources might have upon the Dutch economy, and by Atems and Hotaling [29], who show that a transition to renewable energy might create 50,000 new jobs until 2030 and determine the increase of the GDP by 1%. The decrease relation, based on the one-way causality determined by the production of renewable energy on economic growth, has been studied by Khanniba et al. [43], for Morocco. When analysing the effect of economic growth on the production of renewable energy, the relation is considered to be a preservation relation; one such example is the study carried out by Aydin [44], who, having analysed a period of 35 years and 26 OECD countries, shows a one-way growth relation between GDP and the production of renewable energy, and pinpoints that the effect of the policies for economic growth should promote the production of renewable energy and the increase of the countries’ energy independence;

• The two-way relation between renewable energy production and economic growth was set forth by Temiz Dinç and Akdoğan [31], who support the hypothesis of feedback and show that the increase of renewable energy production and the decrease of energy consumption are vital for the sustainable development of Turkey;

• The neutral, no influence relation between the production of renewable energy and GDP is analysed by Simionescu et al. [45], who do not detect a causal relation between the two variables for the period 2007–2017, as there is a very low positive impact of the GDP per capita upon the share of renewable energy resources within the total electrical energy produced in the EU countries, which the authors consider to be insignificant.

As seen, most studies set forth causal relations, in at least one direction, between the production of renewable energy and economic growth, while the hypothesis of neutrality is the least demonstrated. The preoccupations dealing with such hypotheses started in the '70 s [19] and, at present, the numberless researches are more extensive and complex, both from the point of view of the econometric models employed and from the environmental variables and macroeconomic indicators analysed for various periods of time.

Certain authors, such as Temiz Dinç and Akdoğan [31] or Atems and Hotaling [29], consider that the effects of the production of renewable energy on economic growth should be considered in a larger context, as renewable energy production has become, in time, an instrument of energy safety and a buffer for price volatility on energy markets, also is able to support economic growth and sustainable development on a long term. Bogdanov et al. [30] as well Dura and Isac [46] supplement these claims and consider that renewable energy might also determine additional positive effects in a country through changing the mentality of producers and consumers, creating a positive experience regarding the production and consumption of renewable energy, decreasing social costs and creating additional jobs, which, in the end, indirectly determine economic growth.

In conclusion, producing renewable energy is an essential factor for providing sustainability of economic growth of countries, and for enabling a full transition to a circular economy.

Relation between economic growth and greenhouse gas emissions

Globally, economic growth relies on high energy consumption provided by means of less efficient methods, so that the energy mix is still strongly influenced by non-renewable energy sources [47] and causes an increased level of greenhouse gas emissions [48]. These emissions are represented by atmosphere gases that absorb and emit infrared radiations and cause the deterioration of the environment and climate changes. As a result, the decarbonisation process should be approached as a global strategic process and not only as a national one, owing to the significant impact on the environment, on economic growth and on the relations with the neighboring countries [49].

The ideal situation is when the growth of GDP occurs together with the decrease of environmental pollution [50]. From this perspective, there are opinions that support the idea that GDP might be both a cause and a remedy for environmental degradation [51, 52] or that early GDP harms the environment but later restores it [53]. Li and Zhang [54], notice an inverted “U” relationship between per capita real gross domestic product (GDP) and environmental pollution indicators.

A lot of studies about the Environmental Kuznets Curve (EKC) hypothesis show that the common element of all the studies is the assertion that environmental quality deteriorates in the incipient stages of economic development and improves in the subsequent stages [55, 56]. As a consequence, although social welfare improves owing to economic growth, there is a constant process of environmental pollution [57], whose fixing has become a challenge for the international community and a hotspot for researchers [58].

In general, the negative effects of greenhouse gas emissions are higher in economies under development compared to developed economies [59]; in addition, there are certain countries that appear to neglect this serious issue of mankind [60]. Nonetheless, we cannot allow this phenomenon to become general or permanent. A study by Romero and Gramkow[61], conducted during 37 years and for 67 countries, shows that in the countries under development, at an optimal growth of GDP, a decrease of greenhouse gas emissions occur owing to a series of institutional factors that exert their control upon such issues, while economic growth heads for complex goods production, associated with decreased emissions. Li et al. [62] get different results through applying autoregressive distributed lag (ARDL) for controlling co-integration between the sequences, and show that the short- and long-term effects of economic expansion have a positive incremental impact on CO₂ emissions, their findings being similar for countries under development and for developed countries.

Without being able to structure the large number of approaches regarding the effects of economic development on greenhouse gas emissions in the specialized literature, we nonetheless are able to assert that most studies and authors [63–65] display a direct positive relation between GDP and greenhouse gas emissions. The cases showing insignificant linear relationships, as, for instance, the study carried out by Acheampong [66] are only a few. Part of the analyses focuses on the strong relation between energy consumption and economic growth, on the one hand, and CO2 emissions, on the other hand [67], the well-known environmental U-inverted Kuznets curve being relevant in this respect. According to it, environmental pollutants begin to decrease as economic growth increases or a “relative uncoupling” occurs, in which the use of resources or emissions increases less than the GDP [68] and incomes go towards cleaner technologies [28, 69].

Although the fundamental objective of all nations is to increase their rate of economic progress to promote social welfare [53], Peng et al. [70] raise awareness of the fact that economic and social development might determine the increase of the intensity of carbon emissions, with a negative effect on the environment; the assertion is also supported by the results obtained by Thu et al. [71], who show that, at least in the case of Vietnam, renewable consumption, economic growth, and the flux of direct foreign investments positively affect environmental pollution. Xie et al. [3], while studying the impact of carbon emission control on economic growth under a green economy to improve the economic development level of Hebei Province, underline that it is necessary to control carbon emissions while achieving economic growth. Duong and Flaherty [72] also show that although economic development decreases poverty, carbon emissions (from carbon-intensive growth) together with incomes inequity exacerbate poverty.

Relation between economic growth and environmental taxes

As a result of the preoccupation for decreasing the negative effects of global warming, environmental taxes are considered to be an efficient instrument for fighting environmental pollution [73]. Environmental taxation is a tax system to charge polluters for negative externalities in the same amount as the social cost incurred by society [74].

A series of studies show that environmental taxes determine a two-way effect [74] and are implemented with a view to create a “win–win” framework both for the environment and for economic growth [75]. Still topical is the “double dividend” hypothesis proposed by Pearce [76], which is also called the hypothesis of the “green dividend”, which supports the idea that environmental taxes might improve environmental quality while making the polluters pay; we also notice the hypothesis of the “blue dividend”, according to which the income from environmental taxes might be used to correct environmental externalities. In accordance with this, a legal framework may be established, through which taxes may be applied upon the activities having a negative impact on the environment [74] while encouraging the replacement of fossil fuels and other non-renewable energy sources with renewable ones. An environmental taxation policy might discourage the companies that use environmentally unfriendly technologies [77], and determine the implementation of the principle according to which the polluter pays, which asserts that the cost of pollution should not be paid by society but by those who generate pollution [78]. The implementation of environmental taxes has the goal of directing the funds towards environmental investments, the provision of grants to technologies capable of reducing carbon emissions, and for activities in agriculture, tourism, health, and education, determining a unified result of social, economic and environmental benefits [79]. In accordance, the Green Tax Reform shows that environmental taxes are not only an instrument for generating budget income [80], but they should also be socially tolerated and politically accepted [81], so that their effect enables behavioral changes through the imposition of liability for the negative environmental externalities [82, 83].

A lot of points of view and studies show that environmental regulations and policies, including the increase of specific taxes, limit production due to the high price of energy [84], and might affect economic growth while causing a series of economic issues [85]. Although the role of environmental taxes is still ambiguous and requires a deep investigation [86], such taxes represent a requirement for sustainable development [87], owing to the fact that, in the long run, environmental taxation will result in a decrease of greenhouse gas emissions and will positively impact renewable energy consumption [75] and stimulate companies to use proper technologies for environmental protection, and for the production of ecological goods [84, 85].

Starting from the idea that all taxation policies have a negative effect upon economic growth indicators, there are various opinions, in connection to environmental taxation [74, 88, 89], which support the idea that a higher taxation would influence the development of the green economy, although causing a decrease in GDP [90]. The conclusions reached by Hu et al. [91] and by Elgie and McClay [92], showing that the amount of these taxes does not determine significant changes in GDP, are also relevant. In addition, the idea that environmental over-taxation would negatively influence economic growth cannot be generalised due to the fact that, for instance, countries in Northern Europe, Ireland, and Great Britain, which have the highest environmental taxes, are more competitive than the countries in southern Europe [93], which have lower environmental taxes. Following the strategy of developed countries, emerging economies such as China, India, and Singapore strive to implement proper ecological taxation [86, 94, 95]. Although negative consequences upon economic growth might come out, especially on a short term, Wei et al. [96] show that in China, lower environmental taxes might support, on the long term, a cleaner energy system, as well as economic growth: a 5% shift away from burning coal would reduce emissions by 9%, while GDP would increase by 1.3%. Irrespective of these opinions, environmental taxes exert different effects upon economies and welfare [86, 97].

In the European Union, through the European Green Deal, all member countries apply environmental taxes. The analysis of Famulska et al. [98] shows that there is a slight tendency towards the decrease of the share of environmental taxation within GDP and of the share of the incomes from environmental taxation within total tax revenue, while Dogan et al. [89] pinpoint that a potential increase of these taxes will require supplemental adjustments of the EU’s policies.

Relation between economic growth and environmental investments

Transitioning towards a climate-neutral economy requires the implementation of energy policies focusing on stimulating technological innovation for increasing the efficiency of energy consumption, and important investments for the survival of the natural environment [99, 100]. Misik and Nosko [101] consider that the pandemic represents an innovating factor for the energy transition, far from the carbon-relying old system.

The funds collected from environmental taxes play a significant role in designing a financial structure capable of financing environmental investments [102] for technological innovation and the creation of new industries to facilitate the transition towards an economy with decreased carbon emissions and increased resilience to climate changes [73].

Although the goal of using the collected environmental taxes is to stimulate environmental protection investments, their effects are assessed differently. In accordance, Liu et al. [103] show in their studies that, in China, the implementation of environmental taxes have determined a significant increase in environmental protection investments, while Karmaker et al. [83] consider that the tightening of environmental policies, including the increase of specific taxes, might decrease investments, determining negative economic growth. Nonetheless, Radulescu et al. [73] assert that measures such as granting subsidies for environmental protection investments are required, despite the fact that the relation among investments, economic growth and environmental taxes is not significant during certain periods of time or in certain countries. Dogan et al. [89] consider that renewable energy investments, both public and private, should be supported by governments to get to an optimal level that enables the attaining of Sustainable Development Goal (SDG) 7—Providing access to accessible, reliable, sustainable and modern energy for all—as the transition to a green economy is achieved by means of significant investments, and represents a great challenge for many economically disadvantaged countries [32].

Irrespective of the amount of environmental taxes applied, such taxes might create attractive incentives for investments in new technologies [83], which, from a financial perspective, might result in tax credits for investments [104], subsidies for green technologies [77] or deductions from the calculation base of the profit tax for certain expenditures connected to environmental protection, both for companies and for the local authorities that implement good ecological practices [105].

Irrespective of the financing resources, governmental institutions might use efficient and interdisciplinary mechanisms for the risk evaluation of high-value ecological investments developed under increased risk situations, through a strategic approach; according to Stevanović et al. [106], such institutions might support the implementation of certain models for sustainable development risk evaluation, designed in accordance with the specific features of each country.

Data and methodology

This section analyses the connection between, and influence of, five macroeconomic indicators representative of the green economy and their effect on Romania's economic growth, based on a multivariable linear regression model, where the dependent variable is Romania’s GDP, and the independent variables are: the greenhouse gas emissions, the total production of electrical energy from renewable sources, the value of the production of environmental goods and services, the investments for environmental protection, and environmental taxes.

Sources of data

As part of the modelling, the five independent variables on the basis of which the analysis is carried out are as follows: total greenhouse gas emissions (TGGE), total generation of renewable electricity (TGRE), value of the production of environmental goods and services (PVE), investments for environmental protection (IEP) and total environmental taxes (TET); the dependent variable is real GDP (RGDP). Table 1 displays the variables under analysis, the units of measurement, as well as the sources from which the data series were retrieved. The period under analysis includes 16 years, between 2006 and 2021.

Table 1 Variable description and data sources

The choice of the relevant indicators for assessing the transition to a green economy in Romania and its impact on economic growth takes into account considerations such as: their nomination as indicators of the green economy in specialized works (which enables making pertinent comparisons between the results obtained by us and those obtained by other authors), access to the databases whose available time horizon proves to be valid for the use of econometric models, and the opportunity to analyse the impact of the green economy on Romania’s economic growth through a combination of indicators that transpose into value form the principles of green economy: sustainable production and consumption, requirements for providing well-being, stopping and preventing environmental degradation, all related to Romania's national program for sustainable development ORIZONT 2030, which was not analysed by other studies.

Trend analysis

To track the trend of the variables analysed, Fig. 1 shows their evolution: real GDP (RGDP), total greenhouse gas emissions (TGGE), total generation of renewable electricity (TGRE), value of the production of environmental goods and services (PVE), investments for environmental protection (IEP) and total environmental taxes (TET).

Fig. 1

(Source: authors’ elaboration)

Evolution of the variables analysed during the period 2006–2021.

The analysis of the data series shows an ascendant trend both for Real GDP during the entire period under review (except for the years 2009, 2010 and 2020, when RGDP value was lower than the value in the previous year) and for environmental taxes (except for the years 2017 and 2020, when TET value was lower than the value in the previous year). TGGE followed a decreasing trend during the period analysed, with slight increases of its value in the years 2011, 2015, 2017, 2018 and 2021.

As far as the production of electrical energy from renewable sources is concerned, significant increases of over 30% in 2010, 2013 and 2014, compared to the previous years, should be noticed.

According to the data provided by the U.S. Energy Information Administration [109], during the period 2010–2021, electricity production from renewable resources within the total electricity production recorded the lowest shares in 2011 and 2012 (26.33% and 25.86%); during the rest of the years, electricity shares exceeded 30% and even 40%, and reached a maximum of 45.45% in 2020, as shown in Fig. 2. The highest share of RES within the total production of electricity belongs to hydroelectricity; nonetheless, it is worth mentioning the increase of wind energy contribution, which, from 0.5% within the total generation of electricity in 2010, reached a maximal value of 11.74% in 2017, while in 2021, it reached a contribution of 11.38% within the total generation of electricity.

Fig. 2

(Source: authors’ elaboration based on data from U.S. Energy Information Administration)

Share of renewable resources and types of renewable energy within the total generation of electricity

In the case of environmental protection investments, oscillating values were noticed, with the year 2015 displaying a significant increase, which was 58% higher than the value of the previous year; that increase was, nonetheless, followed by an important decrease during the next 2 years; the growth of the value was only resumed in 2018. It is estimated that the significant increase of the investments for environmental protection in 2015, in accordance with the report of the National Statistics Institute of Romania [112], was mainly determined by an increase of investments in water and air protection made by non-specialised producers. Investments were determined by the implementation of the environmental management system ISO14001:2015 at a national level.

A similar situation could be noticed in the case of the value of the production of environmental goods and services, where a 125% increase in 2008, compared to the previous year, was followed by a decrease of 51.6% in 2009. The most important growth of the indicator during the analysed period was caused by an increase of the internal demand for basic services, such as: water collection and wastewater treatment, collection and treatment of waste, prevention of air pollution, decrease of noise etc., all of these relying on aligning Romanian legislation with the European directives which concern sustainable consumption and production.

Descriptive statistics

Table 2 displays the values of the specific descriptive indicators for the variables analysed. In accordance, the table pinpoints both the values of the central tendency measures and those of the dispersion and distribution of the six variables.

Table 2 Descriptive statistics of variables.

As shown in Table 2, Romania’s RDGP reached its maximal value (47,253.3 million Lei) in 2021, the last year included in the analysis. The production of electrical energy from renewable sources reached its maximal value (27 billion kWh) in the years 2014 and 2016, while investments for environmental protection recorded their maximal value in 2015. The lowest level of greenhouse gas emissions (95,744.4 thousand kt CO2 equivalent) was recorded in 2020, and their highest level in 2006.

Analysing the values of the indicators based on the form of the data series distribution for Romania’s RGDP shows that, during the 16 years under analysis, it displayed a positive asymmetry (asymmetry coefficient = 0.52), slightly platykurtic (arching coefficient = 1.89), Jarque Bera indicator showing a normal type distribution for a materiality threshold $\alpha =10\%$. The same is true for the other variables, with the difference being that variables TGRE and PVE display a negative asymmetry of the distribution at the level of the data series.

Econometric analysis of the model

When tracking the evolution of the variables and comparing the various models applied and selected for pinpointing the correlation between the RGDP dependent variable and the independent variables previously mentioned, it has been concluded that the model that generates the best results from the point of view of performance criteria (comparison of the statistic measures that are based on information theory), though not exclusively, is the multivariable linear regression model. As measuring scales are different, the interpretation of the coefficients might distort the image of the importance of the independent variables within the model. For this reason, a logarithm of the variables analysed within the model is used from this point on. Under these circumstances, the multivariable linear regression model has the following representation:

$${\text{lnRGDP}}_{i} = \beta_{0} + \beta_{1} {\text{lnTGGE}}_{i} + \beta_{2} {\text{lnTGRE}}_{i} + \beta_{3} {\text{lnPVE}}_{i} + \beta_{4} {\text{lnIEP}}_{i} + \beta_{5} {\text{lnTET}}_{i} + \varepsilon_{i}$$

(1)

where ${\beta }_{0}, {\beta }_{1},\dots ,{\beta }_{5}$ are the parameters of the model and ${\varepsilon }_{i}$ is the residual variable.

The six parameters of the model are determined through the method of the least squares. Their values as well as the values of the Student test and the probabilities of achieving them are determined by means of the Eviews 10.1 program package and displayed in Table 3.

Table 3 Values of the parameters and indicators analysed.

With a view to test whether the explicative variables exert a significant influence on the behavior of the LNRGDP variable, t-Student testing is employed. In accordance, the above table shows that all the estimators of the parameters may be considered as being significantly different from zero. In the last column of Table 3, the probabilities of achieving the parameters are calculated for each test.

For multivariable regression models to be valid, they should observe a series of hypotheses. Various statistic tests are applied with a view to check these hypotheses. Therefore, Table 4 includes the values of the tests applied in the case of the econometric model analysed.

Table 4 Values of the statistic indicators and of the tests applied.

Figure 3 displays the real values of Romania’s GDP as well as the values adjusted through the multivariable linear model.

Fig. 3

Evolution of RGDP in logarithm terms (LNRGDP real values), and the values approximated through the multivariable linear regression model (LNRGDP adjusted values)

When tracking the evolution of the two graphs, one may notice that the multivariable linear regression model approximates well enough the data series of the LNRGDP. The statistical analysis of the model introduced above shows as follows:

• The value of the R-Squared coefficient of determination and the value of the Adjusted R-Squared coefficient of determination show that the variables in the model are able to explain together 93.16% of the total variation of the economic growth during the period analysed. In addition, the coefficient of determination shows that there is a powerful direct linear dependence between the variables analysed;

• The values of the criteria based on information theory (Akaike, Schwartz and Hannan–Quinn), measuring the performance of a model, are very close to zero, showing that the model analysed is a model that provides good results when predictions are intended;

• With a view to detect the phenomenon of self-correlation on the order of one of the calculation errors for the model analysed, Durbin–Watson statistics are used. Accordingly, the value calculated is within the limits ${d}_{2}=1.901<DW=2.156<4-{d}_{2}=2.199,$ where ${d}_{1}$ and ${d}_{2}$ are tabular values for 16 observations, materiality threshold $\alpha =0.05$ and $k=5$ degrees of liberty;

• With a view to check whether the residues are distributed normally, Jarque–Bera test is applied, whose value is 0.56. When comparing the value calculated for this test with the critical value ${\chi }_{\text{5,0.05}}^{2}=11.07$, for a materiality threshold $\alpha =5\%$ and five degrees of liberty, the inequality $JB<{x}^{2}$ holds;

• To detect heteroscedasticity, Breush–Pagan–Godfrey test is applied through statistics $\text{LM}=n\cdot {R}^{2}$, whose value is 2.99. When checking inequality $\text{LM}<{\chi }^{2}$ for a materiality threshold of 5% and five degrees of liberty, the variance of the residue is constant;

• One of the most difficult issues multivariable models have to face is the presence of multicollinearity at the level of exogenous variables. To check whether among the variables analysed by this study the effect of multicollinearity appears, an analysis of variance inflation factor is applied. It represents an important step in detecting the presence of this phenomenon. Table 5 shows the values of the variance inflation factor for the variables analysed (in terms of logarithms).

Table 5 Variance inflation factors.

It is estimated that a value higher than five shows the presence of the phenomenon of multicollinearity. In this case, as all the values are lower than five, it is possible to assert that the model does not display the phenomenon of multicollinearity.

Interpretation of results

Interpretation of the significance of the model parameters

Table 3 shows that TGRE and IEP exert a negative effect on RGDP during the period analysed. Accordingly, while the estimated value of the parameter of LNTGRE variable determines a decrease by 0.155% of LNRGDP, the decrease effect of LNIEP on LNRGDP is less significant, displaying an inverse linear connection. On the other hand, the greenhouse gas emissions (LNTGGE) and the value of the production of environmental goods and services (LNPVE) have a positive and significant effect on LNRGDP. Therefore, while a growth by one unit of the changes in TGGE (in terms of logarithms) is going to determine an increase of 0.77% of LNRGDP; a growth by one unit of the changes in LNPVE will cause an increase of 0.014% of LNRGDP, given that the other variables do not change. Considering that this coefficient has a low value, we may reach the conclusion that the dynamics of RGDP respond slowly to a change of this macroeconomic indicator. TET exerts a positive and significant effect upon RGDP, the estimated value of parameter ${\beta }_{5}$ (in terms of logarithms) determining an LNRGDP increase by 0.558%.

Analysis of variance

To check whether, at the level of the model analysed above, there is at least one explicative variable able to justify the behavior of the dependent variable, F test is employed. The value of this test (F-Statistic) is displayed in Table 4. In addition, a probability higher than 5% of guaranteeing the results recommends a rejection of null hypothesis (H₀).

• H₀. $\beta = 0$, all the parameters of the input variables are null, meaning that no input variable is able to explain the evolution of the output variable.

• H₁. $\exists \beta_{i} \ne 0$, meaning that there is at least one input variable able to explain the variation of the output variable.

The calculated value of this statistic is compared with its critical value, defined for a materiality threshold of 5%, k−1 and n−k degrees of liberty. Accordingly, in the case of the model analysed by this study,${F}_{0.05;(4;12)}=3.259$. If $F_{\text{calc}}> F_{0.05;(4;12)}$, then the null hypothesis is rejected. When comparing these values, we may assert that all explicative variables exert a significant influence on the dependent variable.

Correlation analysis

Correlation analysis is carried out with a view to settle the direction of association among the variables used as part of the model. For emphasis, correlation analysis is discussed based on the key variables of interest, together with the interaction terms. Table 6 displays the values of the Pearson correlation coefficient calculated for the variables analysed.

Table 6 Correlation matrix of the variables.

Table 6 shows that the values of the generation of electricity from renewable sources (LNTGRE), of the production of environmental goods and services (LNPVE), and of environmental taxes are correlated positively with LNRGDP, and that these correlations are strong. Accordingly, the correlation between LNTGRE and LNRGDP is + 0.71, the correlation between LNPVE and LNRGDP is + 0.603, and the correlation between LNTET and LNRGDP is + 0.903. On the other hand, investments in environmental protection (LNIEP) and greenhouse gas emissions (LNTGGE) are correlated negatively with LNRGDP. These correlations do not display any cause–effect relation, so supplemental analyses should be carried out to determine whether these correlations exert a significant effect on Romania’s economic growth.

Stability test

To check the stability of the parameters of the model under analysis, the Cumulative Sum of Squares (CUSUMSQ) test is employed. The test reports the presence of outliers within the data series and pinpoint the structural fractures within the series (Fig. 4).

Fig. 4

CUSUMSQ test for checking the stability of the parameters of the model

As the 5% line is located between the upper and the lower limits, the estimations of the parameters of the multivariable linear regression model are stable.

Finally, with a view to measure the performance of this model, by means of the Eviews 10.1 program, a series of specific indicators were calculated, as follows: the values of the standard deviation, average linear deviation, Theil coefficient, Bias proportion, and variance coefficient (Table 7).

Table 7 Descriptive indicators.

The analysis of the values obtained (close to zero) leads to the conclusion that the multivariable linear regression model gives very good results both statistically and from the perspective of making predictions. Although this research does not aim at making predictions, nonetheless the model employed, which is a performing and valid one, is also suitable for this purpose.

Discussion

The current study aims to analyse the influence of five macroeconomic indicators, considered to be representative for the green economy, on Romania’s economic growth during 16 years (2006–2021), to show the progress of the transition towards a green economy in the country, as well as the extent to which this transition might be shaped and accelerated. With this in view, the entire approach of the investigation consisted in using a multivariable linear regression model that enables the pinpointing both of the main features of Romania’s economic growth dynamics, and of the influence exerted on it by the transition to a green economy.

The obtained results consist in highlighting the positive effect of total greenhouse gas emissions (TGGE), the value for production of environmental goods and services (PVE) and total environmental taxes (TET) on economic growth, measured through RGDP.

As far as the positive connection between the total greenhouse gas emissions (TGGE) and RGDP is concerned, the findings are similar to those reached by Mehmood et al. [48], who proved the positive significant impact of CO₂ on GDP in the long-run. The fact that a decrease of greenhouse gas emissions determine an increase in RGDP is likely to determine policymakers to build a legal and institutional framework able to promote the large-scale use of renewable sources, with a view to improve the chances for a sustainable economic growth.

According to the empirical evidence obtained, although the value of the production of environmental goods and services (PVE) contributes to the increase of RGDP, its contribution is insignificant; consequently, this requires the boosting of investments in environmental protection and of those businesses that have, as a result, produced goods and services that do not harm the health of the environment.

The positive influence determined by environmental taxes on the RGDP pinpoints the fiscal effect of all taxes and fees, namely, the effect of generating budget revenues. Nonetheless, Radulescu et al. [73] assert that “Romania cannot increase the level of the environmental tax for supporting economic growth, but it can grant environmental subsidies for decreasing the emissions and supporting the economic growth”. As far as the increasing trend of environmental taxes is concerned, interpretation should be done with caution due to the fact that, in the case when environmental taxes represent a fee that mirrors a proven specific negative impact on the environment, the recording of an upward trend during the entire period analysed might show that the economy continues to rely on pollutant economic activities, given that the growth is not due to introducing new taxes, nor to increasing the taxable amount or the level of taxation. In addition, the recording of an upward trend of environmental taxes in conjunction with the constant decrease of greenhouse gas emissions might be considered a manifestation of an incentive effect [113], strengthening the belief that environmental taxes represent an efficient policy tool for decreasing pollutant emissions [114].

Investments are considered to be the fuel for sustainable development, and the studies that analysed the role of green investments or renewable energy investments support the positive contribution determined by both of them on economic growth [115] and on the decrease of CO₂ emissions [116–118].

To explain the results of this study, which display the negative effect determined by environmental investments on economic growth, we relate to their impact, which differs according to a country's developmental status, or to the assertion made by Liu and Xia [119], according to which “there is a complex and changing interrelationship among investment and economic growth owing to different environments and their own efficiencies”. In this particular case, the data series associated with the investments in environmental protection show that in the year 2016, a drop of 44.5% occurred compared to the previous year, which continued during the following year. Although beginning with 2017 environmental protection investments recorded a growth from 1 year to another, at the end of 2021 their amount could not reach the level of those recorded in 2015; as a result, their evolution and low amount proved to be unable to determine a positive contribution on economic growth during the period analysed.

In this study, the total generation of renewable electricity (TGRE) has a negative impact on Romania’s RGDP, Simionescu et al. [45] previously revealing a lack of causality between the share of renewable energy in electricity and real GDP per capita due to the low levels of energy production from RES unable to ensure economic welfare in the long-run. The empirical results are similar to those got by Khanniba et al. [43], for Morocco, who, using time series data from the period 1990 to 2015, reveal that electricity production from renewable sources have a negative impact on GDP. In addition, the investigation of the relation between renewable and non-renewable energy production and economic growth across 12 sub-Saharan African countries, between 1971 and 2011, carried out by Tiwari et al. [120] concluded that, in the case of Zimbabwe and Sudan, there is a significant and negative relation between the production of renewable energy and GDP per capita. Those findings are in contradiction with the results obtained by Mecu et al. [121], who demonstrate that, in the case of the 27 EU member-states, over the period 2012–2021, renewable energy production positively influenced economic growth, but the impact was limited or by Oliveira Noronha et al. [122], who show that “the electric energy generation in Brazil by renewable sources has a positive impact on the economic growth”.

It is obvious that choosing a relatively small number of indicators for pinpointing the transition to a green economy and its impact on the economic growth are limitations of this study. As far as the unit of measurement of the indicators employed is concerned, instead of expressing them per capita, we agreed upon expressing them as total values, an inconvenience due to the deformation caused by the removal of important independent variables through the use of logarithms and normalisation on the data.

Conclusions

During the last decades, worldwide economic development put pressure on the environment, so that specialised studies frequently include analyses of the connection between economic growth and the indicators specific for a green economy transition.

The implementation of the environmental and sustainable development policies represents a priority and set up a sustainable framework for economic development; in the case of Romania, the period of transition towards a green economy was also characterised by economic growth, which is considered to be one of the most important in the European Union, during the period 2010–2020. Thus, a proper background enabling the shift to an ecologic and competitive economy, with decreased carbon emissions, a more efficient use of resources and an increase of renewable energy production has been created. Today, this places Romania among the European countries with the smallest ecological deficit.

The increase the energy production and supply from renewable sources represents an opportunity for Romania, especially when we consider that after 2022 a series of actions were adopted to eliminate the EU’s dependence on energy resources from the Russian Federation. The diversity and potential of the renewable resources Romania owns will provide energy independence and security of the country and, implicitly of the EU, as long as the fundamental goals included in the strategies elaborated by the policymakers are attained through concrete projects and actions. In addition, we should not neglect the transition to a green economy as a whole, as well as the degree according to which it contributes to economic growth.

Under such circumstances, the goal of our work is to supplement the previous studies that mainly investigated the dynamic relationship between economic growth, carbon emissions, and total energy consumption or renewable energy consumption, as well as the analysis of a portfolio of various indicators, considered by the authors to be representative for Romania’s green economy. As a result, the indicators of greenhouse gas emissions and total generation of renewable electricity are supplemented with the investments for environmental protection, the environmental taxes, and the value of the production of environmental goods and services, as proper indicators able to revise the significant influence they exerted on GDP during the period 2006–2021. The results obtained through implementing the multivariable linear regression model shows that Romania’s economic growth during the period under analysis was positively influenced by the production of environmental goods and services, as well as by the greenhouse gas emissions; meanwhile, the positive influence of environmental taxes upon economic growth shows that, at a national level, a proper policy for implementing environmental taxes discouraging companies using environmentally unfriendly technologies, is being promoted. In addition, the production process of the goods and services targeting the decrease and control of greenhouse gas emissions and of those that negatively influence the environment exert a positive influence on GDP, while creating the prerequisites for maintaining this relation along a medium and long term through accessing a series of financing sources for developing this field, such as Pillar 1. The negative relation between the total generation of renewable electricity and GDP shows that, for the period under analysis, although an increase of the total generation of renewable electricity occurred, such an increase did not reach the threshold required for positively influencing economic growth. Significant investments in the domain of renewable energy production might be able to increase the important part played by this type of energy, and change the influence on GDP into a positive one. Accordingly, positive effects both upon sustainable economic growth and upon the rapid transition to a green economy, based on the sustainable development goals (SDG) of the 2030 Agenda, might occur through improving the energy mix and implementing consistent policies in the field, on a medium and long term.

Although, at present, measuring progress in achieving sustainability targets at the level of the European Union or of the Global Green Growth Institute (GGGI) relies on calculating a series of aggregated indices, such as Transition Performance Index (TPI) and Green Growth Index (GGI), to which proposals for composite indices made by other specialists might be added, the use of as many methods, models and combinations of green economy indices is considered to benefit the approach that quantifies and predicts the scale of green economy, that compares the results. And finally contributes to the improvement of the methodology for calculating the impact of the green transition on the economy.

To conclude, although the goal of this work, that of giving answers to the manner through which Romania’s economic growth is influenced by the variables of the green economy, has been reached, and is able to provide a relevant image on the complex existing interdependences. Nonetheless, the authors are aware of the fact that research might be extended so as to include a larger number of indicators, to carrying out predictions, or to comparative analyses by means of the data provided by other countries.