How the Use of Biomass for Green Energy and Waste Incineration Practice Will Affect GDP Growth in the Less Developed Countries of the EU (A Case Study with Visegrad and Balkan Countries)

Abstract

Combustible renewable energy can be an effective instrument to confirm sustainable development in reducing CO₂ emissions to Gross Domestic Product (GDP) per capita in developing countries. However, connecting to some developing regions, the main research question is to what extent, in EU post-communist fast-developing countries (Visegrad Countries/Czech Republic, Slovakia, Hungary, Poland), will meeting the climate change preferences affect the use of biomass for energy and waste incineration, and how will this affect GDP growth? In addition, of course, what the Balkan countries can learn from this is also very important. The study investigates the relationship between GDP per capita, CO₂ emissions, and Combustible Energy and Waste Consumption (CEWC). According to the Hausman test, the regression model along with random effect is the appropriate method for panel-balanced data as of 2008 to 2020 concerning Balkan countries. The data was divided into three categories: 10 Balkan countries, 4 countries without access to the sea (Kosovo, Bosnia and Herzegovina, Serbia, and Macedonia), and Visegrad countries. The study discovered a substantial positive influence of CEWC on GDP per capita and a significant negative influence of CO₂ emissions. The cointegration test confirms the cointegration of all three variables. This means that all three variables have a long-term relationship concerning the sense of each three forms of the chosen panel. The Granger causality findings shows the variables have a two-way causative relationship. The biomass energy use can dramatically hamper GDP growth in Visegrad and less developed Balkan countries without sea water, due to low energy productivity and a lack of technical innovation. The study recommended that instead of using energy production from simple biomass, these countries can use other circular, platform-based models to prevent unexpected rises in CO₂ emissions and achieve Green House Gas (GHG) reductions. Therefore, this should be given more attention when setting climate and renewable energy policy targets, because they can significantly slow down economic growth.

1. Introduction

Biomass for energy (bioenergy) remains the primary source of renewable energy in the European Union, accounting for about 60% of total renewable energy. The heating and cooling industry is the largest end-user, accounting for over 75% of worldwide bioenergy use. Bioenergy helps the energy security of the EU because domestically produced biomass met 96% of the demand in 2016. Forestry is the most important source of biomass for energy production (wood-processing residues, logging residues, fuelwood, etc.). Wood pellets have grown in popularity as a source of energy, mostly for heating and electricity generation. In terms of absolute consumption, Sweden, France, Italy, Germany, and the United Kingdom are the biggest bioenergy consumers, whereas the Baltic and Scandinavian countries, along with Austria, consume the most bioenergy per capita [1]. The main elements of renewable energy are combustible renewables along with waste, including solid biomass, urban waste, industrial waste, liquid biomass, and biogas. Biofuels have become a top priority for many countries worried about GHG pollution and energy stability. They agree to give the biofuels industry tax breaks. Livestock feed, fiber, and fruit are all examples of biomass sources [2]. Biomass is the world’s fourth primary energy resource, following oil, coal, and natural gas, accounting for over 10% of the worldwide primary energy allocation. Raw materials of biomass can be transformed into heat, transportation fuels, and electricity using various technologies [3]. Agriculture crop residues are also one type of biomass and are widely used in the combustion process to produce energy and useful end-products [4,5]. The global average annual percentage share of liquid biofuel production is 17%. Between 2007 and 2013, the rate of ethanol production was 17%, and the rate of biodiesel production was 27% [6]. Agricultural leftovers and by-products, as well as waste, offer great potential for development. Biogas is made from a variety of waste products and residues, landfill gas, and energy crops (silage maize, energy grasses, etc.). The EU leads the world in biogas power production, and biomethane production for automobile fuel or natural gas grid injection (459 plants producing 1.2 billion m³) [7].

When it comes to Balkan countries (part of developing countries) wood from forests is the principal source of heating energy; nevertheless, farm leftovers are not used for heating or any other type of energy generation (in some cases with a very low amount). Due to the high costs of collection and baling, leftover straw is generally burned in fields [8]. In Kosovo, the main source of energy is in the form of electricity which comes from coal (97%); wood is used as the primary source of heat in 85% and 100% of urban and rural houses [9]. There are reports on illegal forest cutting which is caused by the population’s poor financial status, particularly in rural areas, as well as a lack of economical and reliable alternative energy sources [10]. Electricity production in Serbia relies around 70 per cent on coal [11]. For heating, most households (40.9%) use solid fuels such as fuelwood, coal, briquettes, pellets, agricultural residues and combinations of solid and other fuels, while the other part is covered by the district heating system, electricity, and gas [12]. In the country of Bosnia and Herzegovina, the results of the research showed that wood fuels were used by 71.59% of the total number of households [13], while coal-fired power plants accounted for about 60% of total generation [14]. In North Macedonia, 70% of the country’s total electricity comes from coal [15]. Concerning individual heating, an estimated 62% of households in North Macedonia are using wood. In the Western Balkans, district heating is used in Bosnia and Herzegovina, Kosovo, North Macedonia, and Serbia. Indoor heating and hot water supply account for 43% of energy consumption. Additionally, of all district heating in the region, a staggering 97% is based on fossil fuels and only 3% on renewable energy. Even this renewable energy is mostly biomass, the sustainability of which is currently subject to vigorous debate [16]. Another reason for using a high amount of coal is that households’ electricity prices in four Balkan countries without sea are the lowest in Europe. A major problem with coal is that its full costs are not reflected in its market price; thus, while they may seemingly purchase and burn coal cheaply, these countries are paying a much higher cost in the long run, if we look at the big picture. The price of electricity is the lowest due to low GDP compared with EU countries [17]. The four countries have reported old power plants, a high use of coal for electricity, and biomass for heating purposes [11,13,14,15,16]. We cannot escape a situation which is also present in developing countries, that low-income households are especially affected by energy poverty since they cannot afford newer, more efficient appliances [18] and often live in older non-refurbished buildings [19], which also leads to higher emissions for the same amount of heat. In other developing regions of the world, there is also a dilemma about how to find the right way to use biomass in a climate-friendly way.

Brazil, for example, has undergone steady, substantial economic growth over the last few decades, reorganizing the agricultural products and poverty reductions for millions of people in Brazil. The agricultural bioethanol sector was instrumental in this transition, as were Brazilian government policies. These actions have resulted in Brazil’s agricultural stability [20]. The development of the EU’s bioenergy sector needs a dialogue about in what way to encourage the use of crops cultivated on agricultural lands for energy purposes. As a result, a comparison of economic, social, and environmental goals should be made. Environmental and economic hazards should be balanced to satisfy local community concerns [21]. Years ago, the government took an active role in rural development and agriculture, investing heavily in infrastructure and research and development. Brazil’s agricultural sector productivity has skyrocketed, as has its GDP, transforming the country from a net food importer to one of the world’s leading exporters of agricultural products [22]. Brazil is the fourth-largest emitter of greenhouse gases in the world. However, it is one of the five countries with the most critical pollution reduction plans. Agronomy delivers about half of Brazil’s total energy resources. Sugarcane biomass accounts for 42% of agricultural renewable energy, followed by hydraulic power at 28%, firewood at 20%, and other sources at 10% [23]. Energy demand is projected to rise dramatically because of economic and population growth. CO₂ emissions are increasing, according to many sources, because of improved fossil energy management and inadequacy (coal, natural gas, and petroleum). It is essential to use compatible energies to promote their use [24,25,26,27,28,29,30]. The Brazilian example shows that it is very important for fast-growing countries to have the right balance between climate targets and energy demand, because the technological gap is causing serious economic problems for bad developments, and this could have an impact on GDP growth. Simultaneously, the present political and economic problem is: which form of renewable energy can a nation utilize to deal with the diminishing utilization of fossil energy reserves while avoiding the serious challenges affected by CO₂ emissions? It is unclear whether the usage of renewable biomass energy or other crisis management strategies, such as wrong ways, will have an adverse effect on the growth of GDP. The main research question is to what extent, in developing countries, will meeting the climate change preferences affect the use of biomass for energy and waste incineration, and how will this affect GDP growth?

2. Review of the Literature

Because of political and environmental issues, renewable energy sources are becoming more commonly used. Projections for renewable energy are vital for boosting GDP per capita and enhancing air quality. Biomass is made up of waste and combustible renewables [31,32,33]. Biomass consists of urban and industrial waste, liquid gas, and solid biogas. These renewable energy resources are not as pure as other types of renewable energy (solar, geothermal, wind, etc.) As far as renewable energy consumption goes, solid biomass accounts for the majority (90.8 Mtoe), followed by liquid biomass (14.4 Mtoe), biogas (13.5 Mtoe), and recycled municipal solid waste (9.1 Mtoe) at the European Union level. Although, they did find that it pollutes the atmosphere less than fossil fuels [34,35,36].

2.1. In Developing Countries, Relationships among GDP Growth and Combustible Renewables

The underlying correlation between economic development and renewable energy use has been studied in numerous scientific studies [37,38]. The outcome of the cause–effect relationship path depends on the countries’ trial methods, country selection, time counted, and the empirical testing methodology used in the models listed. Because of the relevant relationship between renewable energy use and GDP growth, four hypotheses emerge: (1) The response statement asserts that GDP growth and renewable energy use have a two-way cause-and-effect relationship [39,40,41]. GDP promotes the utilization of renewable energy sources. (2) The neutrality hypothesis states that the two variables have no cause-and-effect relationship in either direction. (3) The growth hypothesis implies that renewable energy consumption is necessary for GDP growth to be justified; (4) the hypothesis implies that energy utilization is necessary for GDP growth to be clarified. Raising the use of renewable energy has no effect on GDP in this situation [42]. Al-Mulali investigates the effect of renewable and non-renewable energy consumption on the rate of GDP changes in eighteen Latin American countries in this article. Renewable electricity consumption has a more significant influence on short- and long-term GDP growth in these Latin American countries than non-renewable electricity consumption. According to the Granger causality test findings, green energy and economic development have both short- and long-term two-way associations [43,44]. For a panel of 18 emerging market countries, Sadorsky used a cointegration model to demonstrate a long-term one-way connection between GDP growth and consumption of renewable energy. Long-term research also indicates that economic growth has a major influence on resources of renewable energy [39]. An inquiry of cointegration tactics in 11 African countries revealed the causal link between trade, combustible energy, and per-capita growth of GDP. The findings of the panel of error correction models (ECM) show that trade, economic growth and net export have a bidirectional causative relationship. Renewable energy consumption and GDP growth have a one-way causative connection in both the short and long run. There is a relationship between renewable energy consumption and the growth of GDP [45]. Their findings indicate that consumption of renewable energy has a negative influence on the growth rate. There is a one-way causative link between GDP growth rate and renewable energy use.

2.2. Relationship between CO₂, Renewable Energy, and GDP Growth

As one of the most significant implications of renewable energy is its effect on CO₂ emissions, it is crucial to investigate the complicated cause-and-effect relationship between CO₂ emissions, GDP growth, and renewable energy use [46]. Panel data from 19 developed countries was examined by Apergis et al., (2010). According to the researchers, renewable energy, CO₂ emissions, nuclear power, and GDP growth all have a cause-and-effect relationship. According to long-term projections, nuclear energy production emits greenhouse gases. Renewable energy use, on the other hand, has a significant positive influence on CO₂ emissions. Bidirectional links between renewable energy usage, growth of GDP, and CO₂ emissions were found in the short-run and long-run [47]. Apergis and Payne analyzed data from seven Central American countries as part of a panel. The consumption determinants involving per capita renewable energy were discovered during the study. They also looked at how GDP, CO₂ emissions, coal prices, real oil, and per-capita renewable energy consumption changed over time [48]. Ben Jebli and his colleagues analyzed data from 24 African countries in sub-Saharan Africa. Economic growth, CO₂ emissions, trade, and consumption of renewable energy were all explored in this panel data analysis, and the researchers discovered a multifaceted correlative link between them. Emission of CO₂ and growth rate of GDP have been shown to have a short-term bidirectional correlation. There is a long-term two-way relationship between the growth rate of GDP and emission of CO₂. This contradicts the hypothesis of the EKC (Environmental Kuznets Curve) [49]. Shahbaz and Farhani glanced at panel data from ten Middle Eastern and North African countries in a separate paper. Researchers have discovered a cause-and-effect relationship between non-renewable and renewable energy sources’ use, generation, and CO₂ emissions. CO₂ emissions have increased over time because of non-renewable and renewable energy use, according to EKC’s hypothesis. The study also discovered a long-term connection between CO₂ emissions and the use of non-renewable and renewable energy [50].

2.3. Agricultural Value-Added (AVA)

The link between Agricultural Value-Added (AVA) and renewable energy has been investigated in numerous studies. The study looks at the short- as well as long-term connections between CO₂ emissions, GDP growth, AVA, renewable and nonrenewable energy resources, and access of foreign trade [51]. According to Granger causality checks, CO₂ emissions, AVA, and openness of trade have a short-term two-way relationship. Long-term bidirectional associations were found in many of the variables analyzed. Long-term forecasts indicate that non-renewable energy, trade, and agriculture boost CO₂ emissions, while renewable energy decreases them. Subsidizing the use of green resources in agriculture, according to these researchers, will facilitate the industry to turn out to be more competitive in trade markets while also becoming less contaminating. Jebli and Youssef highlighted panel data from five North African nations. Using cointegration panel schemes, they assess the complicated cause–effect relationships among renewable energy usage, CO₂ emissions, AVA, and GDP growth.

The study discovered a short- in addition to a long-term connection between CO₂ emissions and AVA pollution. A one-manner cause–effect relationship between renewable energy usage and AVA was also discovered (Agricultural Value-Added). According to the analysis, north African nations should promote wind or solar power as a tidy, renewable energy source since it boosts agricultural productivity while also lowering CO₂ emissions [52]. Between 1981 and 2000, the usage of energy in the Middle East and North African countries (MENA) had a major effect on CO₂ emissions. Furthermore, real GDP in the area has a quadratic relationship with CO₂ emissions. Although the EKC hypothesis is supported by the long-run income coefficients and their square in most of the nations assessed, the turning points are insufficient in several circumstances and tremendously bulky in others, suggesting that there is insufficient evidence to support the EKC hypothesis [53].

Renewable energy is predominantly obtained from the combustion of traditional biomass (wood) in the Visegrad (V4) countries, with little or no technological innovation. This technique of energy consumption has little positive impact on energy efficiency or GDP growth since it lacks significant technological innovation. Among the findings, it should be highlighted that in countries with a fundamentally inverted U-shape on the Kuznets curve, economic expansion enters a new, energy-intensive phase after a long period, resulting in a dramatic increase in pollutant emission indices, including CO₂ emissions. When GDP is lower, short-term economic growth is strong, and additional high energy demands must be considered; low biomass and waste incineration efficiency, as well as a moderately advanced technology level, have a negative influence on CO₂ emissions and GDP growth in the Visegrad countries. Another GHG reduction approach, rather than biomass and waste energy recovery, is more applicable in the post-crisis period of rapid economic development. Circular business solutions (where energy consumption can be significantly reduced due to the residual energy content within the circular processes) may provide a feasible foundation for solving problems [54].

2.4. CO₂ Emission Correlation and Kuznets Hypothesis in Different Countries

Energy utilization has a major impact on GDP growth. There is a bidirectional cause-and-effect relationship between economic development and energy use. Energy utilization is a crucial issue in growth of GDP in MENA (Middle East/North Africa) nations, and thus the trend of high GDP growth demands higher energy. As a result of increased demand, countries use more oil, placing pressure on the environment and increasing pollution, according to the report [55]. The Hypothesis of Environmental Kuznets Curve (EKC) states that the connection between CO₂ emission and GDP growth has a reversed U form. This signifies that, after a certain point, economic development will help the world [56]. Richard spent 57 years looking into the possibility of a CO₂ EKC in Canada. According to the analysis, the function’s slope changes monotonically over time (He and Richard, 2010). From year 1857 to 2007, there was a long-term correlation between CO₂ and per capita GDP. The findings also support the EKC hypothesis [57]. The EKC hypothesis is supported by a 40-year time series data collected from Turkey [58]. However, the hypothesis of EKC was not verified in Cambodia from 1996 to 2012. According to the report, GDP, energy use, and urbanization all lead to CO₂ emissions, whereas government policy as well as corruption control may help lessen CO₂ emissions [59]. From 1971 to 2009, there was a long-term correlation in Pakistan among energy usage, trade openness, CO₂ emissions, as well as economic development. According to the EKC hypothesis, the nation has made considerable efforts to reduce CO₂ emissions. The findings recommend that research be undertaken at the regional level to assess the influence of emission of CO₂ on economic growth [60,61]. The EU-27 panel’s EKC hypothesis discovered a relationship between GDP per capita and CO₂ emissions. According to the study, only four countries (Slovenia, Spain, Greece, and Cyprus) have a reversed U-shaped curve. According to the results [62], further studies should involve econometric study to assess the long-term correlation between GDP and CO₂ emissions.

The demand for energy in North Africa is growing at a rate of 6–8% per year. Fossil fuels power energy fusion by contributing more to natural gas, according to a report conducted by the United Nations Economic Commission for Europe. In view of the unprecedented instability of gas and oil prices, these countries’ energy policies have been revised. When it comes to diversifying their energy mix, renewable energy takes precedence. Renewable energy also has the benefit of being able to supply remote areas that are not linked to the national grid. Renewable energy and energy-saving power are still underutilized to a large extent. Natural energy conservation holds promise for these countries, as improved energy programs have the potential to achieve a 10% rise in energy utilization in the area by 2030, which is a Sustainable Development Goal (UN Sustainable Development Goals, 2015). Renewable energy sources do not make up a significant portion of the energy mix. It accounted for just 8% of overall energy usage in 2006, with the remainder coming from oil (19%), gas (67%), and coal (19%). Regulatory changes have been made to stimulate more involvement of the private sector in the production of renewable sources of energy [63]. These nations have set themselves lofty and well-defined objectives. They have applied large-scale, coordinated policies to lessen GHG emissions, economic development, industrial growth, and human capital, all of which have resulted in increased indirect and direct job creation. Numerous current ventures, such as the Mediterranean Solar Plan (MSP) or current agreements between the EU and individual North African nations, will extend regional renewable energy markets while also boosting financial and technical cooperation [64,65,66]. Estimations of biofuel output effects often depend on models that are unable to account for economic and environmental consequences and ignore co-product generation. Early biofuel assessments failed to account for co-product generation, resulting in exaggerated land requirements and GHG emissions. Due to the larger share of grains utilized in the production of ethanol with elevated production of food, the productivity of feed co-goods in the European Union, the United States, and China is relatively high [67].

2.5. Developed and Developing Countries within the EU

We must also distinguish between developed and developing countries within the EU. For Eastern and Central European nations, the EKC curve demonstrates a diverse correlation between emissions and GDP with GHG emissions [68]. The EKC showed a turning point in GDP growth and CO₂ emission in Eastern and Central Europe by USD 21,000 GDP in their report. In a survey of 15 European countries, Kasman and Duman (2015) discovered a long-term cause–effect correlation between energy use, economic growth, trade openness, and carbon emissions. In addition, economic development and energy usage confirm the EKC hypothesis for sulfur dioxide emissions and greenhouse gases. There was a short-term relationship among the variables but no cause-and-effect relationship, showing that economic development impacts emissions evolution [69]. The relationship between economic growth and CO₂ emissions in Hungary, Slovakia, and Poland does not follow a reversed U-shaped curve but rather a reversed N-shaped curve. Early in the economy, CO₂ emissions began to fall, but as soon as they attained the first income spinning point, they began to rise before the second spinning point, after which they began to fall again. As a result, the rise in CO₂ release was just a short-term phenomenon. On the other hand, the global renewable energy industry is expanding faster, and CO₂ emissions must be reduced [70,71,72]. Business solutions that promote modern financial growth should be prioritized over energy-storing and proficiency-oriented technological solutions. There is a cause-and-effect relationship between CRW (Combustible Renewables and Waste) spending, GDP per capita growth, and CO₂ emissions, as far as we know. This study’s main aim is to investigate the relationship between per-capita GDP, waste consumption, combustible energy, and CO₂ emissions [66].

Based on a review of the literature, further research objectives are:

(1)

In 10 Balkans countries, determine the relationship between combustible energy, GDP per capita, CO₂ emissions, and waste consumption. The Visegrad Four nations (Slovakia, Hungary, Poland and Czech Republic,) are different from the four Balkan countries (Kosovo, Bosnia and Herzegovina, Serbia, and Macedonia).

(2)

Investigate the relationship among combustible energy, GDP per capita, CO₂ emissions, and waste consumption, in Balkan countries. The 10 Balkan countries have different correlations between the four Balkan nations and the Visegrad Four (V4) nations.

There has been a considerable beneficial influence of combustible energy and trash consumption and a negative impact of CO₂ emission on GDP per capita in Visegrad nations. Based on the publications, the cointegration test verifies that all three variables are cointegrated. In all three kinds of the panel chosen, this suggests a long-term connection among all three variables. The causation conclusions of the variables are two-way causal effects [67,68].

3. Methods and Materials

To test the hypotheses, the subsequent methodological procedure was utilized. The stationary test’s research techniques, such as the Hausman test for fixed and random effects, were used to determine the regression models were suitable. After that, panel cointegration was to take place. Granger causality also looked for tests of cause-and-effect relationships. This research is organized as follows: The materials and the process will be covered in the third section. In the fourth segment, the results and discussion will be given. The conclusion and policy recommendations are found in the fifth section. The analysis relies on secondary data from 10 Balkan countries and 4 Visegrad. The panel is balanced on three key variables: per-capita GDP in constant USD, CO₂ emissions, and combustible energy and waste consumption. Per capita GDP in real term, as a proxy for economic development, was the dependent variable. The World Bank, 2020, is the primary source of results. Combustible energy as well as waste consumption (CEWC) and CO₂ emissions were the independent variables. Stata 14 and Eviews 10 were used in the study.

3.1. Data and Sample

The research area is 10 Balkan and 4 Visegrad countries. The ten countries that comprise this sub-region are Bulgaria, Croatia, Albania, Bosnia and Herzegovina, Kosovo, Romania, Serbia, Slovenia, Montenegro, and North Macedonia. We divided our analysis into three categories based on regions: (1) 10 Balkan countries as a whole; (2) 4 Balkan countries without sea, and 4 Visegrad countries. All the data were accumulated from the World Development Indicator for the period 2008–2020 [32,68].

3.2. Model and Econometric Specification

The aim of the analysis is to see how CO₂ emissions and CEWC affect GDP per capita. Three models will be investigated in this report. There are three types of regression models: (1) panel regression (pooled OLS), (2) regression with permanent effect, and (3) regression with arbitrary effect. Based on the data structure, the fixed effect is sufficient. All the following models are described below:

$$GD{P}_{it}={\alpha }_0+{\beta }_1{C}_{it}+{\beta }_{2}CEW{C}_{it}+{\epsilon }_{it}$$

(1)

$$GD{P}_{it}=\left({\alpha }_0+{\upsilon }_i\right)+{\beta }_i{C}_{it}+{\beta }_{i}CEW{C}_{it}+{\mu }_{it}$$

(2)

$${\epsilon }_{it}={\upsilon }_i+{\mu }_{it}$$

(3)

$$GD{P}_{it}={\alpha }_0+{\beta }_i{C}_{it}+{\beta }_{i}CEW{C}_{it}+\left({\upsilon }_i+{\mu }_{it}\right)$$

(4)

where $GD{P}_{it}$ is the dependent variable of country i at time t.

$C_{it}$ is the CO₂ emission of country i at time, t.

$CEW{C}_{it}$ is the combustible energy and waste consumption of country i at time t.

${\alpha }_0$ is the constant term and intercept.

${\beta }_1$ and ${\beta }_2$ are the coefficients of the variables $C_{it}$ and $CEW{C}_{it}$.

${\epsilon }_{it}$ is the error term, ${\upsilon }_i$ is the individual effect and ${\mu }_{it}$ is the time effect.

4. Results and Discussion

4.1. Descriptive Statistics

For the selected 10 Balkans, 4 Balkan countries without sea and Visegrad countries were analyzed. The average per-capita GDP is USD 10,379.50, with a standard deviation of USD 7399.98 from 2008 to 2020 in the selected 10 Balkan countries. In the 4 Balkan countries without sea, the average per-capita GDP is USD 5140.16 from 2008 to 2020 with a standard deviation of 1081.27.

The average CEWC in the 10 Balkan countries is 7.75%. In the four selected Balkan countries without sea, the average CEWC is 7.75%. In the context of Visegrad countries, it is 6.24 lower than the four selected Balkan countries. The average CO₂ emission in the 10 selected Balkan countries is 5.11%. The four selected Balkan countries’ total GDP per capita is two times lower than for the 10 selected Balkan countries. Compared to the Visegrad countries, the per capita GDP of the four selected nations is three times lower, shown in

Table 1. Descriptive statistics.

Variable	Observation	Mean	Std. Dev.	Min	Max
(a) For Selected 10 Balkan Countries
Per-capita GDP	120	10,379.5	7399.98	3209.69	31,997.28
Combustible Energy and Waste Consumption (CEWC)	120	8.52	3.18	2.50	17.82
CO2 Emission	120	4.92	1.63	1.48	8.66
(b) For Selected 4 Balkan Countries without Sea
Per-capita GDP	48	5140.16	1081.27	3209.69	7411.83
Combustible Energy and Waste Consumption (CEWC)	48	7.75	2.71	2.50	17.82
CO2 Emission	48	5.11	1.08	2.88	7.04
(c) For Visegrad (V4) Countries of EU
Per-capita GDP	48	16,581.16	32.7.41	11,516.06	23,494.60
Combustible Energy and Waste Consumption (CEWC)	48	6.24	2.09	2.63	11.24
CO2 Emission	48	7.09	2.02	4.19	11.23

.

4.2. Correlations and Relationships

There is a weak positive relationship between combustible energy and GDP per capita and waste consumption in the 10 Balkan countries. CO₂ emissions and GDP per capita have a fragile positive relationship as well. The most compelling point is that there is a negative relationship between CO₂ emissions and CEWC in the 10 Balkan countries. The GDP per capita and CEWC have a weak negative relationship in the four selected Balkan countries without sea. There is also a negative relationship in Visegrad countries (

Table 2. Correlation matrix.

(a) For Selected 10 Balkan Countries
	GDP	CEWC	CO2
GDP	1
CEWC	0.10	1
CO2	0.31	−0.37	1
(b) For Selected 4 Balkan Countries without sea
	GDP	CEWC	CO2
GDP	1
CEWC	−0.22	1
CO2	0.42	−0.40	1
(c) For Visegrad countries of EU
	GDP	CEWC	CO2
GDP	1
CEWC	−0.37	1
CO2	0.51	−0.37	1

). The relationship is clarified because the 10 Balkan countries’ energy efficiency is significantly higher than that of the V4 countries. It is important to emphasize the differences in the associated technological context and the usage of biomass forms (energy density). The other explanation is that the Kuznets curve in the V4 countries has a fundamentally reversed N-shape, indicating that the financial system is entering a new phase of development (following the financial crisis) with very high energy demand, causing pollution indicators (CO₂) to jump [73,74,75].

4.3. Stationary Test

The most conventional models of the panel data, the Levin–Lin–Chu (LLC) unit root test and the Hadri LM test, were applied to examine whether the panel data was stationary or not. To do so, we performed:

Hadri LM Test	LLC unit root test
Ho: All panels are stationary	Ho: Panels contain unit roots
Ha: Some panels contain unit roots	Ha: Panels are stationary

In the context of the Hadri LM, the null hypothesis is that all the data are stationary. At the level of all the values of the Hadri LM test, statistics are significant at 5% level of significance. It means that we can reject the null hypothesis that all the data are not stationary at this level (

Table 3. Results of stationary test.

Hadri LM Test					Levin–Lin–Chu (LLC) Unit Root Test
	Level		First Difference		Level		First Difference
Variables	Statistic	p-Value	Statistic	p-Value	Statistic	p-Value	Statistic	p-Value
(a) For Selected Balkan Countries
GDP	13.00	0.00	0.75 *	0.29	0.79	0.78	−2.26 *	0.00
CO2	16.28	0.00	−1.97 *	0.97	−1.15	0.12	−12.66 *	0.00
CEWC	7.77	0.00	−2.62 *	0.99	−2.02 *	0.02	−4.59 *	0.00
(b) For Selected 4 Balkans Countries without Sea
GDP	4.89	0.00	−0.24 *	0.59	0.71	0.76	−4.47 *	0.00
CO2	6.03	0.00	−1.37 *	0.91	−1.08	0.13	−4.81 *	0.00
CEWC	3.39	0.00	−1.72 *	0.95	−0.14 *	0.44	−3.40 *	0.00
(c) For Visegrad Countries of EU
GDP	1.13 *	0.13	0.70 *	0.24	0.61	0.72	−4.15 *	0.00
CO2	9.31	0.00	−0.96 *	0.83	−0.36	0.35	−2.95 *	0.00
CEWC	6.42	0.00	2.86 *	0.22	−1.95 *	0.02	−2.52 *	0.00

* All panels are stationary at the first difference at a 1% level of significance.

). However, we cannot reject the null hypothesis at first difference in the context of the Hadri LM test; it means that all the data are stationary at first difference. The hypothesis of the Levin–Lin–Chu (LLC) test are opposite from the Hadri LM test. Furthermore, based on the LLC test, we can reject the null hypothesis at first difference, which means that all the data are stationary at first difference.

4.4. Regression Analysis

We can use regression because at the first difference all the data are stationary. According to the Hausman test, the random impact regression model is the best fit for the panel data in this analysis. We did not reject the null hypothesis since (Prob > chi2 ≠ 0.0000), and the random impact is sufficient for conversation. According to the regression results with the random consequence, in all 10 Balkan countries, the 4 Balkan countries without sea (for the V4 countries there is no sea, so the Balkan countries without sea were examined for better comparability), and the Visegrad countries, there was a positive association between CEWC and GDP per capita in the 10 selected Balkan nations and 4 selected Balkan countries separately. However, there was a negative relationship between GDP and CEWC in the Visegrad countries. The coefficients are not statistically significant. For the 10 selected Balkans and 4 selected Balkan countries, the coefficient of CEWC is 205.33 and 59.53, respectively. This implies that if CEWC consumption rises by one unit, GDP per capita rises by USD 205.33 and USD 59.53 in the 10 selected Balkan and 4 selected Balkan nations, respectively. These coefficients are statistically significant. The Visegrad countries’ coefficient is −332.25. There was a negative connection between CO₂ emissions and GDP per capita in the four selected Balkan countries for all three categories of country, but the coefficient is not statistically significant (

Table 4. Regression results.

Dependent Variables: GDP per Capita
Independent Variables/Constant	Selected 10 Balkan Countries	Selected 4 Balkan Countries without Sea Water	Selected 4 Visegrad Countries of Central Europe
Appropriate Panel Method	Random Effect	Random Effect	Random Effect
CEWC	205.33 * (2.20)	59.53 ** (1.76)	−332.25 (−1.62)
CO2	1554.63 * (6.33)	−100.89 (−0.58)	695.39 * (3.29)
Constant	970.60 (0.35)	5194.12 * (4.34)	13,720.47 * (5.89)
Wald	40.33	5.07	10.12
Probability>	0.0000	0.0000	0.0000
Hausman Test	−0.74	1.38	446.20
Probability>	0.7465	0.50	0.0000

* all values are significant at 5% level of significance. ** all values are significant at 5% level of significance.

).

For the 10 Balkan and Visegrad countries, there was a positive relationship between CO₂ emission and GDP growth. The coefficients of CO₂ emission are 1554.63 (for the 10 Balkan countries), 695.39 (for the Visegrad countries), and −100.89 for the 4 Balkan countries. This means that a one-unit rise in CO₂ emissions would result in a USD 1554.64 increase in GDP per capita. According to regression analysis, increased CO₂ emissions are significant to GDP growth in the 10 Balkan and Visegrad countries. For the four selected Balkan countries (without sea), there was a negative effect of CO₂ on GDP per capita. The key reason for this is traditional straw or biomass. The combustion of straw or biomass accounts for most renewable energy in the four selected Balkan countries’ energy mix. There has not been any technical advancement [70,76,77]. At a 1% level of significance, this coefficient is not significant. The F statistic’s value indicates that the model is suitable for the study.

4.5. Panel Cointegration Test

Pedroni suggested a method for testing panel data cointegration. He provided two tools for evaluating panel data cointegration. Seven test statistics are provided for the null hypothesis of no cointegration in nonstationary heterogeneous panels with one or more regressors. The second tool is a panel-dynamic ordinary least squares (PDOLS) estimator that operates between dimensions (that is, group–mean). He presented seven test statistics for nonstationary panels to test the null hypothesis of no cointegration. The seven-test statistics account for panel heterogeneity, both in terms of short-run dynamics and long-run slope and intercept coefficients. For the chosen panel, the test examines the long-term connection between the variables. In order to run a cointegration test, variables must have a unit root on the label but be stationary at the first difference. At the level of the findings, all of the data in a unit root analysis is not stationary. However, at first glance, they are all immobile. At a 1% level of significance, the test of panel cointegration reveals that four statistics (v-statistic, panel PP-statistics, ADF-statistic, and group PP-statistic) with intercept deny the null hypothesis for 10 Balkan nations. At the 1% significance level, four statistics (panel v-statistics, panel ADF statistic, panel, and group rho-statistic) with intercept and trend are significant. As a result, for the selected panel of 10 Balkan nations and 4 Balkan countries without sea water, GDP per capita, CEWC, and CO₂ are cointegrated. The panel cointegration test reveals that four statistics (panel rho-statistic, panel PP-statistic, panel ADF-statistic and group ADF statistics) are significant for the Visegrad countries. This entails rejecting the null hypothesis at a 1% level of significance for the Visegrad nations. As a result, the Visegrad nations designated panel, CEWC, GDP per capita, and CO₂ are cointegrated. As a result, the analysis indicates that GDP per capita, three variables, CO₂ emission, and CEWC, have a long-term connection.

The results of FMOLS and DOLS of three different categories of country at group level are given in

Table 5. Fully modified ordinary least squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS).

Dependent Variables: GDP per Capita
	FMOLS		DOLS
Variables	Coefficient	p-Value	Coefficient	p-Value
Selected 10 Balkan Countries
CEWC	291.66	0.0219	330.15	0.0000
CO2	1420.54	0.0002	525.31	0.0000
Selected Four Balkan Countries without Sea Water
CEWC	79.11	0.0047	48.66	0.0074
CO2	−340.71	0.0017	−510.41	0.5338
Selected Visegrad 4 Countries of Central Europe
CEWC	44.45	0.0013	−529.45	0.0448
CO2	−1168.42	0.0015	−3319.79	0.3152

. For the 10 Balkan countries, the coefficients are significant at 5% level of significance. For the four selected Balkan and Visegrad countries without sea water, all the coefficients are significant except CO₂ in the context of DOLS. For the Balkan countries without sea water, the coefficient of CO₂ confirms that there is a negative effect of CO₂ on GDP per capita in the context of FMOLS. The coefficient is significant. For the Visegrad countries, DOLS confirms that there is a negative effect of CEWC on economic growth. FMOLS also verifies that there is a negative effect of CO₂ emission on GDP per capita. The coefficient is statistically significant at 55 level of significance (

Table 6. The results of Pedroni Cointegration Test.

Test Statistics	Intercept	p-Value	Intercept and Trend	p-Value
(a) For Selected 10 Balkan Countries
Panel
v-statistic	1.20	0.11	9.13 *	0.02
Rho-statistic	0.73	0.76	2.98 *	0.04
PP-statistic	1.49 *	0.00	−1.78	0.13
ADF-statistic	0.42	0.33	−2.38 *	0.00
Group
Rho-statistic	2.32	0.98	3.40	0.99
PP-statistic	1.33 *	0.04	1.63 *	0.03
ADF-statistic	1.20	0.88	1.50	0.93
(b) For 4 Balkan Countries without Sea Water
Panel
v-statistic	0.22	0.41	1.16 *	0.04
Rho-statistic	2.13 *	0.00	1.76 *	0.01
PP-statistic	−0.36	0.35	1.20	0.54
ADF-statistic	0.05	0.52	1.28 *	0.01
Group
Rho-statistic	1.41	0.92	1.69	0.03
PP-statistic	1.74 *	0.02	−0.58	0.27
ADF-statistic	1.16	0.87	1.12	0.87
(c) For Visegrad Countries of EU
Panel
v-statistic	0.21	0.41	−0.96	0.83
Rho-statistic	−0.20	0.41	1.75	0.00
PP-statistic	−1.76	0.03	−1.46	0.03
ADF-statistic	−1.15	0.12	−1.89	0.02
Group
Rho-statistic	0.70	0.75	2.55	0.99
PP-statistic	−1.63	0.04	−0.10	0.45
ADF-statistic	−0.80	0.20	−1.63	0.04

* all values are significant at 5% level of significance.

).

Table 7. Result of Vector Error Correction Mechanism (VECM).

	Independent Variables
For 10 Balkan Countries
Dependent Variables	∆GDP	∆CEWC	∆CO2	Coefficient of Error Correction Term (ECT)
For 10 Balkan Countries
∆GDP		3.36 (0.94)	299.005 (0.00)	−0.04 (0.00)
∆CEWC	1.13 (0.00)		−191.18 (0.00)	−0.036 (0.00)
∆CO2	3.67 (0.00)	4.34 (0.02)		−0.014 (0.04)
For Four Balkan Countries without Sea Water
	∆GDP	∆CEWC	∆CO2	Coefficient of Error Correction Term (ECT)
∆GDP		30.08 (0.03)	153.03 (0.03)	−0.09 (0.01)
∆CEWC	0.01 (0.23)		−2.02 (0.04)	−0.002 (0.00)
∆CO2	0.04 (0.39)	−0.04 (0.00)		−0.07 (0.00)
For Visegrad Countries of EU
	∆GDP	∆CEWC	∆CO2	Coefficient of Error Correction Term (ECT)
∆GDP		−364.28 (0.02)	729.68 (0.00)	−0.08 (0.03)
∆CEWC	−8.03 (0.27)		−0.43 (0.21)	−0.05 (0.02)
∆CO2	3.47 (0.31)		−0.09 (0.21)	−0.02 (0.01)

shows the results of Vector Error Mechanism (VECM). The negative sign of error correction term for the 10 Balkan countries confirms the long-run causality running from GDP per capita, CEWC, and CO₂ emission in the long run with 4% speed of adjustment to equilibrium. For the four selected Balkan countries, there is a long-run causation among the GDP per capita, CEWC, and CO₂ emission. The sign of ECT is negative. It confirms the long run equilibrium with the speed of 9% adjustment. For the Visegrad countries, there is a long run causality among the GDP per capita, CO₂ emission, and CEWC. The sign of ECT proves that the long run equilibrium adjustment is 8%.

4.6. Granger Causality Test

Table 8. Results of the Granger causality test.

Null Hypothesis:	Observation	F-Statistic	Prob.
Selected 10 Balkan Countries
CEWC does not Granger cause GDP	100	1.63	0.20
GDP does not Granger cause CEWC		0.32	0.72
CO2 does not Granger cause GDP	100	0.18	0.83
GDP does not Granger cause CO2		5.56	0.01
CO2 does not Granger cause CEWC	100	0.10	0.90
CEWC does not Granger cause CO2		3.34	0.04
Selected Four Balkan Countries without Sea Water
CEWC does not Granger cause GDP	40	1.05	0.36
GDP does not Granger cause CEWC		1.75	0.19
CO2 does not Granger cause GDP	40	0.14	0.87
GDP does not Granger cause CO2		6.05	0.01
CO2 does not Granger cause CEWC	40	0.14	0.87
CEWC does not Granger cause CO2		3.24	0.05
Selected Visegrad 4 Countries of Central Europe
CEWC does not Granger cause GDP	40	0.13	0.87
GDP does not Granger cause CEWC		0.69	0.50
CO2 does not Granger cause GDP	40	3.00	0.04
GDP does not Granger cause CO2		9.77	0.00
CO2 does not Granger cause CEWC	40	0.83	0.44
CEWC does not Granger cause CO2		0.00	0.99

shows that two null hypotheses for all 10 Balkan countries fail at a significance level of 5%. There is a one-way causal link between GDP and CO₂ emissions. There is also a one-way causal relationship between CEWC and CO₂. In the context of the selected four Balkan countries, there is a one-way causality between GDP and CO₂. There is also a one-way causality between CEWC and CO₂. There is a two-way bidirectional causality between GDP per capita and CO₂ emission.

5. Discussion and Conclusions

We determine that the panel-balanced data regression with the random model is sufficient at the end of the discussion. Regression and Granger causality were investigated. There was a connection between GDP per capita and combustible energy and waste in selected Balkan countries. This is statistically significant. The relationship between CO₂ emissions and GDP per capita is negative in four selected Balkan countries without sea. However, coefficient is not statistically significant at 1%. This negative relationship (higher CO₂, less GDP) is true in Balkan countries; because of low GDP, people use more wood for heating purposes (sometimes 100%); because of low GDP farmers, burn agricultural residues on land; and people pay very little tax for the energy we use, also because of low GDP. Power plants in Balkan countries are very old, the maintenance cost is high, and they emit more CO₂ into the air. In the context of the selected 10 Balkan countries, the Granger causality test confirms that CO₂ emissions and GDP have a one-way causal link. It also ensures that CEWC and CO₂ have a one-way causative connection. In the context of the selected four Balkan countries, there is a one-way causality between GDP and CO₂. There is also a one-way causality between CEWC and CO₂. For the Visegrad countries, CO₂ emission and GDP per capita have two-way causality.

According to the report, encouraging combustible energy and waste in the chosen country can be an effective policy tool. However, this strategy would benefit the 10 Balkan countries more than the Visegrad countries. The top 9 countries had 3.4 times higher GDP growth than the V4 countries per unit of CEWC growth. Based on the findings, it is also clear that if economic growth and CO₂ reduction are joint economic advancement goals, the Visegrad nations’ GHG reduction strategy should not expand the CEWC region. The combustion of conventional biomass accounts for the most renewable energy in the V4 countries’ energy mix (wood). There has not been any technological advancement. Since this method of energy utilization lacks substantial technological advancement, it has no positive impact on energy proficiency or GDP development. According to the findings, in countries where the Kuznets curve has a fundamentally inverted N-shape, economic growth enters a new, energy-intensive phase after a long time, causing a sharp increase in pollution discharge indicators as CO₂ emissions. According to research in Central and Eastern European countries, GHG reduction market solutions that recognize new financial instruments and networks should be favored so that green investment schemes can be preferred for investments that achieve the highest GHG reductions.

As a result of the study, policies that do not favor the energy usage of solid biomass, liquid biomass, biogas, industrial waste, and urban waste can be efficiently reduced using circular economic models and increasing the GDP of the Visegrad countries. Compared to the V4 countries, in the Balkan countries the waste for energy and combustible renewable use may be effective methods for CO₂ reduction, but biomass burning can strongly impede the growth of GDP in less advanced Balkan nations due to low energy productivity and a lack of technical innovation. Our analysis shows that, due to the inverted U-shaped Kuznets curve for the Balkan countries, biomass use for energy without major technological innovation causes harmful environmental emissions and may cause a slowdown in growth. Bioenergy has the potential to help the EU meet its renewable energy targets for 2030 and beyond. Investment is necessary to increase per capita the GDP. To boost GDP per capita, investment is required. There is a link between growth and the amount of public investment [78]. The western part of the EU has enormous public and private investment in biomass-based renewable energy systems. Bioenergy could be viewed as a component of a sustainable energy supply system.

The availability of a variety of biomass, and the low cost of agricultural crop leftovers, have encouraged the replacement of fossil fuels with alternative energy resources. It provides energy in an environmentally beneficial, long-term, and cost-effective manner in Balkan and developing countries. Biomass for energy must be grown, processed, and used in a sustainable and efficient manner to maximize greenhouse gas reductions while maintaining ecosystem services, all while avoiding deforestation, habitat degradation, and biodiversity loss in Balkan countries to maintain the EU standard. Bioenergy corridors can reduce greenhouse gas emissions while also maintaining food security and safeguarding ecosystems and the services they provide from deforestation, habitat degradation, and biodiversity loss. Bioenergy generation has the potential to provide enormous social, environmental, and economic advantages, as well as contribute to regional development. Alternative applications of biomass (e.g., for feed, food, wood products, etc.) must also be considered in order to assure feedstock supply sustainability from a bioeconomy perspective.