The Use of Biomass in the Visegrad Group Countries and Its Determinants

Abstract

This article aims to assess the conditions and prospects for biomass utilization in the Visegrad Group (V4) countries. Additionally, the relationship between biomass energy production and greenhouse gas emissions was examined. A key component of the analysis involved identifying potential directions for the development of biomass utilization in the pursuit of the sustainable development of agricultural enterprises. In relation to these research objectives, a hypothesis was formulated regarding the causal relationship between biomass energy consumption and economic growth, the abundance of natural resources, and income in reference to the European Union economies. Both static and dynamic panel studies were applied. The conducted research revealed the complex nature of the conditions influencing biomass utilization. The study period covered the years 2004–2022. A negative correlation was found between the use of biomass and greenhouse gas emissions. At the same time, factors favoring biomass utilization included economic growth, the level of natural resource consumption per capita, and government policies aimed at increasing the share of renewable resources in the economy.

1. Introduction

Recently, interest in biomass has been growing due to the depletion of fossil fuel energy sources and the increasing emission of greenhouse gases into Earth’s atmosphere, including carbon dioxide, methane, and nitrous oxide. This contributes to rising temperatures, resulting in the greenhouse effect and, consequently, climate change. Human activity has a significant impact on climate change, as the burning of fossil fuels, animal farming, and deforestation increasingly affect global temperatures. According to the European Commission, Earth’s temperature is currently rising at a rate of 0.2 °C per decade [1], which negatively impacts the environment. The use of biomass is not without its negative environmental impacts. The question arises as to whether the use of biomass can contribute to reducing greenhouse gas emissions and mitigating global warming. If so, greater reliance on biomass and increased agricultural production capacity would be crucial. However, based on numerous previous studies, there are significant uncertainties regarding the assessment of the carbon footprint and the assessment of whether biomass usage truly benefits the natural environment or, conversely, has adverse effects. Additionally, competition among different agricultural production methods and development paths can lead to various negative consequences, such as resource wastage amid global malnutrition, rising agricultural product prices, or excessive resource exploitation, particularly land use in agriculture, which may result in land degradation [2,3]. Given agriculture’s raw material potential, the use of biomass can contribute to achieving many potential benefits. On the other hand, as some authors [4,5,6,7] point out, biomass energy consumption can lead to an increased ecological footprint and, consequently, environmental degradation. Gao and Zhang [8] hold a different view, confirming a positive relationship between biomass energy and CO₂ emissions. Research conducted by Banaszuk, Wysocka-Czubaszek, Czubaszek, and Roj-Rojewski [9] confirms that the high energy potential of biomass creates an opportunity for climate protection if used rationally, whereas its improper use may have the opposite effect and impact the natural environment. Hence, a frequently suggested solution is the sustainable intensification of agricultural production. As noted by Rosillo-Calle, Hemstock, de Groot, and Woods [10], it becomes crucial when biomass is grown sustainably and its quantity equals the amount burned within a given period, preventing CO₂ accumulation, as the amount of CO₂ released during combustion is offset by the CO₂ absorbed by the growing energy crop. Existing studies show that these issues vary in spatial distribution [11]. Currently, achieving sustainable development goals is an important element of the strategy for EU countries, covering three areas: economic, social, and environmental. The importance of the environmental aspect is highlighted by Kirikkaleli and Adebayo [12], and Kułyk, Michałowska, and Szudra [13], who point out that maintaining a balance between economic well-being and the environment has become a primary priority for governments. Biomass can be used as a renewable energy source, but its use for energy purposes should be analyzed in three aspects, i.e., economic, social, and environmental. When biomass is used for energy production, it appears in various types and forms, notably carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)-. The energy potential of biomass is significant for the economy and its development, particularly agriculture, considering increased employment and additional income. As previously noted, the issue of biomass utilization seems more complex. The energy potential of agriculture and the use of biomass for biogas production through methane fermentation appear crucial in this regard. Notably, reports show that Poland utilizes only 3% of its biogas production potential. Such a low rate calls for a closer examination of the barriers and factors influencing biomass use. The share of biomass energy is also low compared to other renewable energy sources in Visegrad Group countries, although it is increasing. On the other hand, an analysis of the bioeconomy potential in V4 countries indicates that biomass currently contributes to approximately 13% of the economic production value, 10% of the added value, and 15% of employment in the region [14].

All four V4 countries have implemented support systems for biomass energy, such as feed-in tariffs and certificate systems. However, the effectiveness of these systems varies by country. For example, in the Czech Republic and Slovakia, where financial support was higher, faster development of agricultural biogas energy production was observed. Additionally, all four countries in this group are investing in biomass utilization technologies in both the heating and energy sectors. Faced with challenges related to raw material availability and rising prices, countries aim to increase biomass utilization efficiency [15,16,17], which improves the profitability of biomass energy.

Meanwhile, Adedoyin, Abubakar, Bekun, and Sarkodie [18], along with Galán, Taifouris, Martín, and Grossmann [19], indicate the need to implement a program of decarbonization of the economy, which gives priority to the protection of the natural environment. The use of biomass is also supported by the instability on the fuel market, which makes countries strive to achieve independence from external suppliers of raw materials and to develop their own sources of energy production [20]. Moreover, as fossil fuel resources deplete and contribute to environmental pollution, renewable energy sources, including biomass, gain an advantage [21,22]. The use of biomass is supported by its wide application in various energy purposes, such as electricity production, heating buildings, powering vehicles, and providing the heat needed in industrial processes [23].

The main objective of this article is to assess the conditions and possibilities of using biomass in the countries of the Visegrad Group (V4). This main objective is accompanied by the following specific goals:

(1)

To examine the relationship between biomass energy production and greenhouse gas (GHG) emissions.

(2)

To identify potential directions for the development of biomass utilization in the context of sustainable development of agricultural enterprises.

In order to conduct an in-depth analysis of the relationship between biomass utilization and greenhouse gas emissions in the Visegrad Group (V4) countries, both static and dynamic panel data models were applied.

With socio-economic development, changes in the structure of renewable energy consumption become evident. This transformation is associated with a gradual shift from less efficient energy sources to more efficient ones, ultimately involving increased reliance on renewable energy. This progression is commonly referred to as the energy ladder hypothesis.

2. Literature Review

The assessment of biomass utilization should adopt a multidimensional perspective. The literature contains numerous studies on the impact of biomass energy consumption on greenhouse gas emissions, revealing noticeable discrepancies regarding the environmental impact of biomass use. Some researchers identify negative effects of biomass use [5,7,24,25]. Others indicate a positive or neutral effect of biomass, drawing attention to its potential in mitigating climate change [26,27,28,29,30].

Karmaker, Hosan, Rahman, Sen, Saha [7] focused on such studies, examining the relationship between biomass energy use and the ecological footprint in BRICS countries, finding that biomass energy consumption increases the ecological footprint in these nations. The main independent variables they considered were ecological footprint, economic growth, natural resources, and globalization index. Other researchers, including Solarin, Al-Mulali, Gerald, and Shahbaz [5] investigated the impact of biomass energy consumption on CO₂ emissions in 80 developed and developing countries, concluding that biofuel energy consumption plays a significant role in increasing CO₂ emissions. The key control variables in this study included real GDP, fossil fuel consumption, hydropower generation, urbanization, population, foreign direct investment, financial development, and institutional quality.

Shahbaz, Balsalobre, and Shahzad [24] examined the impact of biomass energy consumption on CO₂ emissions in 80 developed and developing countries. Alongside biomass energy as an independent variable, they considered, like previous researchers, dependent variables such as carbon dioxide emissions, ecological footprint, carbon footprint, biomass energy, trade globalization, natural resources, economic development, institutional quality, and an additional variable—economic complexity. Their findings indicated that both biomass energy consumption and economic complexity negatively affects the ecological footprint and carbon footprint. The study by Karmaker, Hosan, Rahman, Sen, and Saha [7] also showed that since biomass energy consumption increases the ecological footprint, national policies should focus on other renewable energy sources, such as wind and solar energy, which have a less adverse environmental impact. The production of biomass, especially when it involves large-scale energy crops, can lead to various negative consequences, including biodiversity loss. Moreover, the excessive exploitation of forest biomass may disrupt natural forest regeneration processes. Bryce [25] reached similar conclusions, stating that policies promoting biofuels for climate reasons are scientifically flawed. In his view, promoting biomass energy to reduce pollution is inappropriate, as it does not contribute to this goal.

However, other researchers, including Awosusi, Adebayo, Altuntaş, Agyekum, Zawbaa, and Kamel [26]; Abbasi, Adedoyin, Abbas, and Hussain [27]; Kim, Choi, and Seok [28]; and Wang, Bui, Zhang, and Pham [29], reached different conclusions, observing that biomass energy use helps mitigate environmental degradation. The control variables considered in their study included ecological footprint, economic growth, natural resource abundance, globalization, and gross capital accumulation.

The findings of Danish and Ulucak [30] suggest that biomass energy consumption aids in pollution reduction, while biomass energy production decreases CO₂ emissions, making it, according to the authors, a viable alternative to fossil fuels.

In light of the above, significant discrepancies emerge in the assessment of biomass as an effective energy source. On the one hand, it is evident that biomass contributes to environmental degradation, particularly in industrialized economies with low energy efficiency, where its processing results in additional emissions. This view is supported by researchers such as Shahbaz, Balsalobre, and Shahzad [24] and Karmaker, Hosan, Rahman, Sen, and Saha [7], who highlight the negative side effects of indiscriminate promotion of biomass. On the other hand, studies by Awosusi, Adebayo, Altuntaş, Agyekum, Zawbaa, and Kamel [26], as well as Danish and Ulucak [30], emphasize that, within an appropriate institutional and technological context, biomass can contribute to emission reductions—especially when sourced from agricultural or forestry waste and when it does not compete with food production. Therefore, further research on this topic is highly desirable to address the existing gap.

In the opinion of the authors of this study, the considerations presented in the earlier literature are outdated and underscore the need for further research on the impact of biomass energy consumption on greenhouse gas emissions in European Union countries. The review of previous studies indicates that a number of factors may influence the relationship between biomass energy use and greenhouse gas emissions, such as economic growth, urbanization rate, investments in the energy sector, trade openness, and others.

Table A1. Review of studies on factors influencing energy consumption from biomass and explanatory variables.

Author	Analysis Period	Region	Area Analysis	Method	Variables	Main Results
Solarin, Al-Mulali, Gerald, Shahbaz [5]	1980–2010	80 developed and developing countries	Examination of the impact of biomass energy consumption on CO2 emissions	Generalized Method of Moments (GMM)	Biomass energy consumption, real GDP, fossil fuel consumption, hydropower production, urbanization, population, foreign direct investment, financial development, institutional quality, Kyoto Protocol	Biomass energy consumption plays a significant role in increasing CO2 emissions
Awosusi, Adebayo, Altuntaş, Agyekum, Zawbaa, Kamel [24]	1992–2018	BRICS (Brazil, Russia, India, China, South Africa)	Examination of the impact of biomass energy use on ecological footprint	Panel FMOLS and DOLS, FE-OLS	Globalization, biomass energy, economic growth, natural resources, gross capital accumulation	Biomass energy use helps mitigate environmental degradation
Shahzad, Elheddad, Swart, Ghosh [34]	1990–2019	G7 (Canada, France, Germany, Italy, Japan, United Kingdom, United States)	Examination of the impact of biomass energy consumption and economic complexity on greenhouse gas emissions	Panel cointegration tests	Carbon emissions, ecological footprint, carbon footprint, biomass energy, economic complexity, trade globalization, bureaucratic quality, NAT resources, economic growth	Both biomass energy consumption and economic complexity have a harmful impact on the ecological and carbon footprint
Bilgili, Ozturk [56]	1980–2009	G7 (Canada, France, Germany, Italy, Japan, United Kingdom, United States)	Examination of the impact of biomass energy consumption on economic growth	Panel cointegration tests	Biomass energy consumption, GDP	Biomass energy consumption positively influences economic growth
Karmaker, Hosan, Rahman, Sen, Saha [7]	1990–2017	BRICS (Brazil, Russia, India, China, South Africa)	Examination of the impact of biomass energy use on the ecological footprint	Panel cointegration tests	Ecological footprint, biomass energy consumption, economic growth, natural resources, globalization index	Biomass energy consumption increases the ecological footprint
Danish, Ulucak [30]	1982–2017	China	Investigation of the relationship between biomass energy and real income with CO2 emissions	Econometric tool of Jordan and Philips, dynamic Autoregressive–Distributed Lag (DARDL) simulation	Biomass energy consumption, real GDP	Biomass energy production reduces carbon dioxide emissions and may be the best alternative to fossil fuels

Source: own study based on [5,7,24,30,32,55].

(Appendix A) presents an overview of factors influencing biomass energy consumption and explanatory variables. These can be grouped into three areas: social, economic, and environmental. Biomass production and its potential for renewable bioenergy vary by country, geographical location, resource availability, biodiversity, technology, and economy. This necessitates regional or national studies, as undertaken in this research. This is particularly relevant because CO₂ emissions in Visegrad Group countries are relatively high [31]. At the same time, these countries rely heavily on imported non-renewable energy sources such as crude oil and natural gas. Therefore, the use of biomass represents an interesting direction for transforming the energy mix in this group of nations.

As noted by Tugcu and Menegaki [32], implementing energy policy requires a departure from the horizontal approach, i.e., the so-called equal treatment of all sectors of the economy, in favor of a sectoral analytical approach, taking into account the diversity of responses and the level of flexibility of individual industries towards the energy transformation. Moreover, as Tugcu and Menegaki emphasize [33], the diverse implications resulting from the research take on particular importance in the face of the current energy crisis in Europe. In this context, there is a clear need to increase the share of renewable energy, which is a key direction for mitigating energy security risks. The variables used by Shahzad, Elheddad, Swart, Ghosh [34] to analyze the impact of biomass on greenhouse gas emissions and economic complexity included nine factors. This study identifies those that appeared relatively frequently and helped explain the observed changes during the research.

3. Data and Methods

The research process used quantitative methods in the form of panel studies. The selection of these models enabled the examination of cross-country differences as well as temporal evolution. By using two models, the reliability of the results was increased by triangulating static and dynamic approaches. The study considered four countries of the Visegrad Group: the Czech Republic, Hungary, Poland, and Slovakia. The selection of these countries was based on their similar experiences with systemic transformation, a relatively high share of fossil fuels in energy production, and high CO₂ emissions compared to other EU countries. Their production structures were also relatively similar to those of other EU nations [35].

The study applied both static and dynamic panel analysis to confirm the obtained research results, particularly the directions of influence of the factors under consideration. Panel analysis is a method that allows for the examination of data containing both cross-sectional variables and time-series variables. This approach enables a more comprehensive study of changes over time and across units compared to traditional cross-sectional or time-series analyses.

In the context of panel analysis, two main approaches are distinguished: static and dynamic analysis. Initially, a static panel analysis with random effects was applied, and the selection method is presented later in the article. Additionally, a dynamic approach was employed, considering the time-dependent variations in the impact of individual factors. Dynamic panel analysis differs from static analysis by accounting for variability over time and delays, analyzing how changes in explanatory variables affect the dependent variable in the future. A key aspect here is the study of dynamic effects, which may be dependent on previous states of individual variables.

In this case, the Nerlove model was used, and calculations were performed using Gretl software version 2024a. The random-effects panel model takes the following form [36]:

y_it = α + X_itβ + u_iu + ϵ_it

(1)

where

• y_it—dependent variable for unit i at time t;
• α—constant;
• X_it—vector of explanatory variables for unit i at time t;
• β—vector of regression parameters;
• u_iu—random effect for unit i, which is specific to this unit and independent of t (i.e., changes between units but not over time within the unit);
• ϵ_it—random component (error) for unit i at time t, which is independent and identically distributed over time and between units.

In the literature, there are examples of the application of the Nerlove model as an analytical tool for studying the dynamics of behavior in the agricultural sector [37,38]. This model is used, among others, to analyze the relationship between factors determining agricultural activity, especially between the expected price and supply of agricultural products. Okou, Keita, N’Dri, and Kouakou [39] divided their study into two parts, where in the first they estimated the price elasticity of the agricultural products cocoa and cashew nuts and analyzed their impact on forecasting using the Nerlove model. In the second part, they developed a technique for estimating the parameters of the Nerlove model based on the maximum likelihood method. As can be seen, this model has been used in structural and political analyses aimed at forecasting changes in agricultural markets and assessing the effects of price policy and market interventions.

To determine whether to use a fixed-effects or random-effects model, the Breusch–Pagan and Hausman tests were applied. In the first stage, heteroscedasticity was assessed (the Breusch–Pagan test result indicates its presence), meaning that the variance of errors is not constant over time or across units. This can affect the accuracy of estimations in random-effects models, as these models assume homoscedasticity (constant variance). When heteroscedasticity is detected, a fixed-effects model is usually the preferred choice, as it does not require the assumption of homoscedasticity. If heteroscedasticity is present, the fixed-effects model is more resilient to its influence.

The Hausman test, on the other hand, allows for a more precise selection between fixed-effects and random-effects panel models by considering the correlation between individual effects and explanatory variables. Through this test, we assess whether the assumption of random effects (i.e., no correlation between individual effects and explanatory variables) is appropriate. If the Hausman test results indicate that fixed effects are more suitable, then despite the presence of heteroscedasticity, the fixed-effects model will be more justified (and vice versa).

In the second stage, dynamic panel analysis was applied. The general assumptions of the dynamic panel model are as follows [40]:

y_it = α + ρ_yit−1 + X_itβ + u_iu + ϵ_it

(2)

where

• y_it—dependent variable for unit i at time t;
• y_it−1—lagged value of the dependent variable, i.e., the value of the dependent variable from period t − 1 (in dynamic models, it is assumed that past values of the dependent variable affect its future values);
• α—constant;
• X_it—vector of explanatory variables for unit i at time t;
• β—vector of regression parameters for explanatory variables;
• ρ—coefficient for lagged dependent variable y_it−1;
• u_iu—unit effect, which may be fixed or random, depending on the model;
• ϵ_it—random component (error) for unit i at time t, which is independent and identically distributed.

The assessment utilized variables identified based on a previously analyzed body of literature. These variables include biomass energy (dependent variable), GDP, total natural resources rent, ecological footprint per capita, share of energy from renewable sources, gross electricity production, final energy consumption, and CO₂ emissions. The study period covered the years 2004–2022. The results were generated using the econometric software STATA 17.0. A preliminary transformation was performed using the natural logarithm. The model hypothesis is formulated as follows:

l_BiomassE = α + al_GDP + bl_TotalNaturalResourcesRents + cl_Shareofenergyfromrenewable + dl_EcologicalFootprintpercapit + el_Finalconsumptionenergy + fl_Grosselectricityproduction + hl_CO + u_iu + ϵ_it

(3)

Additionally, to verify the model, the Wooldridge test was applied. This test is used in panel data analysis to detect autocorrelation in the residuals of a panel model, specifically first-order autocorrelation (rho = −0.5). Autocorrelation in panel models can be problematic, because its presence suggests that errors in the model are correlated over time, which may lead to inaccurate estimations and conclusions.

In light of the above, this issue may constitute a significant limitation in the use of panel data, requiring the application of appropriate estimation methods. Another limitation associated with using panel data for the V4 countries over the period 2004–2022 stems from differences among these countries in terms of, among others, the size of their economies, energy mix, and national policies. To avoid obscuring regional disparities, it is advisable to consider estimating the model with the inclusion of fixed or random effects for the countries studied. It should also be noted that some factors, such as the COVID-19 pandemic, are difficult to quantify as variables, yet have had a substantial impact on the dynamics observed in individual economic sectors.

However, “the use of panel data enables, in comparison with time series or cross-sectional data, more in-depth, detailed economic analyses. It is then possible to explain differences in the behavior of different objects in a given period and differences in the behavior of a selected object in individual sample periods” [41]. The results of static and dynamic models are complementary tests, with a noticeable difference in the purposes of application in terms of the scale or strength of the dependence.

4. Energy, Climate, and Economic Indicators of the Visegrad Group in 2022

Considering greenhouse gas emissions, it is worth noting that in 2022, Poland emitted 315,042,270.00 tonnes of GHGs. Within the Visegrad Group, Poland was the largest emitter—four times more than the Czech Republic, which ranked second with 95,107,760.00 tonnes. This was largely due to, on the one hand, the scale of Poland’s agricultural sector—the largest among the V4 countries—and on the other, the dominance of fossil fuels in its energy mix. Hungary ranked third with 44,842,524.00 tonnes, while Slovakia was the country with the lowest emissions, reporting 31,550,242.00 tonnes of agricultural-origin GHGs in 2022. In terms of ecological footprint in 2022, the highest value was recorded in the Czech Republic (5.1 gha), largely resulting from its high level of industrialization. Poland ranked second, reflecting its continued dependence on fossil fuels, particularly coal. A slightly lower footprint than Poland was observed in Slovakia (4.2 gha), while the lowest was in Hungary (3.8 gha), potentially indicating a more energy-efficient economy. When it comes to the share of energy from renewable sources in 2022, the Czech Republic reached 18%. Both Poland and Slovakia maintained a moderate share of renewables—around 16–17%. The lowest share among V4 countries was observed in Hungary at 15%. As for biomass energy use in 2022, Hungary had the lowest level at 90,055 TJ, while Slovakia recorded the highest value—478,940 TJ. The Czech Republic placed third with 211,634 TJ, and Poland ranked fourth with 114,924 TJ. In terms of GDP, Poland held the top position among V4 countries, followed by the Czech Republic, then Hungary and Slovakia. With regard to the share of natural resource rents (% of GDP), Poland had the highest proportion in 2022, primarily due to the extraction of hard and brown coal, as well as forest resources. The Czech Republic and Hungary had low levels, while Slovakia recorded the lowest (0.08%), suggesting very limited exploitation of natural resources. The energy, climate, and economic indicators of the V4 in 2022 are shown in

Table 1. The indicators of the Visegrad Group in 2022 [42,43,44,45,46].

Indicators	Czech Republic	Poland	Slovak Republic	Hungary
Annual CO2 emissions (t)	95,107,760.00	315,042,270.00	31,550,242.00	44,842,524.00
Ecological footprint per person (gha)	5.1	4.6	4.2	3.8
Share of energy from renewable sources (%)	18.123	16.629	17.481	15.128
GDP billion USD	216.21	627.46	102.53	157.73
Total natural resources rents (% of GDP)	0.14	0.38	0.08	0.20
Energy from biomass (TJ)	211,634	114,924	478,940	90,055

. The data used in the study were obtained from the resources of Our World in Data [42], Global Footprint Network [43], World Bank Group [44], World Integrated Trade Solution [45], and Fao [46].

5. Research Results and Discussion

Based on the results of the Breusch–Pagan test, where p is less than 0.05, we reject the null hypothesis (H₀) at the 5% significance level. This indicates that the variance of errors is not constant, meaning heteroscedasticity is present in the model. Consequently, the fixed-effects model is preferable to a classical regression analysis.

Considering the results of the Hausman test, where the parameter p = 0.68569, we conclude that there is no reason to reject the null hypothesis (H₀). As a result, we infer that individual effects are not correlated with explanatory variables. This model is not affected by significant errors. Therefore, a random-effects model can be applied, as there is no need to account for fixed effects that could be correlated with explanatory variables.

Table 2. Estimated model coefficients.

Variable	Coefficient	Standard Error	z	p-Value	Significance
Constant	14.1883	8.79579	1.613	0.1067
l_GDP	0.610800	0.296670	2.059	0.0395	**
l_TotalNaturalResourcesRents	0.141633	0.0522258	2.712	0.0067	***
l_Shareofenergyfromrenewable	0.443843	0.191535	2.317	0.0205	**
l_EcologicalFootprintpercapit	0.391582	0.354256	1.105	0.2690
l_Finalconsumptionenergy	−0.266476	0.133116	−2.002	0.0453	**
l_Grosselectricityproduction	0.0565151	0.225384	0.2508	0.8020
l_CO	−1.00588	0.479955	−2.096	0.0361	**

Significance levels: ** p < 0.05; *** p < 0.01.

shows the regression coefficients and statistical significance.

The descriptive statistics for the model are shown in

Table 3. Summary of model statistics.

Specification	Value
Mean of dependent variable	11.62170
Standard deviation of dependent variable	0.804263
Sum of squared residuals	76.57866
Standard error of residuals	1.053487
Log-likelihood	−108.1276
Akaike information criterion	232.2551
Bayesian (Schwarz) criterion	250.9010
Hannan–Quinn criterion	239.7069
Residual autocorrelation (rho1)	0.486883
Durbin–Watson statistic	0.772032

.

The results of the variance analysis present key model parameters, including between-group and within-group variance, as well as the theta value calculated using quasi-demeaning:

‘Between’ variance = 1.73558; ‘Within’ variance = 0.0229609; theta using quasi-demeaning = 0.973622

To further assess the model’s validity and statistical properties, several tests were conducted:

Joint test on named regressors

• Asymptotic test statistic: Chi-square(7) = 996.474;
• p-value = 6.96035 × 10⁻²¹¹.

Breusch–Pagan test

• Null hypothesis: Unit error variance = 0;
• Asymptotic test statistic: Chi-square(1) = 8.20645;
• p-value = 0.00417417.

Hausman test

• Null hypothesis: The GLS (UMNK) estimator is consistent;
• Asymptotic test statistic: Chi-square(3) = 1.4852;
• p-value = 0.68569.

Wooldridge test for autocorrelation in panel data

• Null hypothesis: No first-order autocorrelation (rho = −0.5);
• Test statistic: F(1,3) = 26.8262;
• p-value = P(F(1,3) > 26.8262) = 0.0139707.

A comprehensive assessment of the obtained results reveals that a high “Between” variance of 1.0652 indicates significant differences between units in the context of the adopted model. In contrast, the low variability of the dependent variable within a single unit (in this case, the group of countries) over time suggests that these differences are less significant (Within = 0.0246308). Furthermore, the theta coefficient of 0.966032 suggests that individual effects have a considerable influence on the model, but quasi-demeaning allows for some smoothing of these effects [47], enabling a better estimation of the impact of explanatory variables while accounting for individual effects.

The results indicate a strong negative correlation between biomass energy production and CO₂ emissions. Thus, the development of the biomass market contributes to reducing greenhouse gas emissions, and among other factors, the coefficient value is relatively high for the Visegrad Group countries. This confirms the importance of the biomass market for improving environmental conditions in these nations. This correlation is undoubtedly tied to the high level of greenhouse gas emissions, implying significant potential for their reduction.

On the other hand, strong positive correlations were mainly observed with economic growth (GDP), ecological footprint per capita, and the share of electricity generated from renewable sources. It can be inferred that improving economic conditions, as measured by economic growth, encourages the development of the biomass market. On one hand, energy needs increase, while on the other, social awareness evolves [48]. Additionally, the increase in the consumption of natural resources (measured by the ecological footprint per capita) also drives the expansion of the biomass market. This serves as a corrective factor in relation to economic growth, which generally leads to higher natural resource consumption.

Moreover, economic growth supports the development of the biomass sector, while simultaneously posing challenges related to its sustainable use. As noted by Banaszuk, Wysocka-Czubaszek, Czubaszek, and Roj-Rojewski, “the energy use of biomass should primarily be associated with the management of agricultural production waste, biomass from the maintenance of protected ecosystems, and surplus biomass from forestry [9]”.

Some studies confirm that biomass energy production increases the ecological footprint of G7 countries [29] and other country groups [49]. Similarly, the overall increase in energy consumption negatively affects biomass utilization and stimulates the search for alternative energy sources to rapidly improve the energy balance, often at the expense of the environment. Likewise, the share of renewable energy sources should be seen as a reflection of a country’s policy aimed at fostering the development of this market. It is also an expression of societal expectations and acceptance of such changes in the energy mix [50].

The presented results of the random-effects panel analysis were further compared with those obtained through dynamic panel analysis (

Table 4. Dynamic panel analysis for four cross-sectional units and dependent variable (Y) biomass.

Variable	Coefficient	Standard Error/Asymptotic Standard Errors	z-Value	p-Value	Significance
l_BiomassE(−1)	0.715548	0.143097	5.000	<0.0001	***
l_BiomassE(−2)	0.0997616	0.100107	0.9965	0.3190
Constant	0.0159315	0.0169716	0.9387	0.3479
l_GDP	−0.120210	0.380575	−0.3159	0.7521
l_TotalNatural~	0.0285921	0.0296081	0.9657	0.3342
l_Shareofenergy~	−0.225940	0.113110	−1.998	0.0458	**
l_EcologicalFo~	−0.249974	0.197940	−1.263	0.2066
l_Finalconsumption~	−0.0772541	0.0774186	−0.9979	0.3183
l_Grosselectri~	−0.0475092	0.178211	−0.2666	0.7898
l_CO	−0.0244868	0.338180	−0.07241	0.9423

Significance levels: ** p < 0.05; *** p < 0.001.

).

The sum of squared residuals was 0.662157, with a residual standard error of 0.110735. The number of instruments used in the estimation is 64.

The

Table 5. Model tests.

Test	Test Statistic	p-Value
AR(1) test for error	z = −3.52415	0.0004
AR(2) test for error	z = −1.40402	0.1603
Sargan test (over-identification)	Chi-square(54) = 57.6348	0.3424
Wald joint test	Chi-square(9) = 150.241	<0.0001
Joint test on named regressors	Chi-square(5) = 824.065	7.1945 × 10−176

shows statistical tests for model diagnostics.

To assess the validity of the model specification, several statistical tests were performed:

• The AR(1) test for error yielded a z-value of −3.52415 with a p-value of 0.0004.
• The AR(2) test for error resulted in a z-value of −1.40402 with a p-value of 0.1603.
• The Sargan test for over-identification produced a Chi-square statistic of 57.6348 with 54 degrees of freedom, yielding a p-value of 0.3424.
• The Wald joint test returned a Chi-square statistic of 150.241 with nine degrees of freedom and a p-value of <0.0001.
• The joint test on named regressors produced a Chi-square statistic of 824.065 with five degrees of freedom, with an extremely low p-value of 7.1945 × 10⁻¹⁷⁶.

The result of the joint test with a p-value of 7.1945 × 10⁻¹⁷⁶ suggests rejecting the null hypothesis, as the p-value is extremely small. This indicates that all selected explanatory variables used in the dynamic panel analysis model have a statistically significant impact on the dependent variable. The test confirms that the explanatory variables (regressors) in the dynamic panel model are statistically significant and influence the dependent variable. Therefore, it can be inferred that the explanatory variables have a real effect on the dependent variable, and the model is well-specified in terms of variable selection.

The AR(2) test does not indicate second-order autocorrelation. Meanwhile, the Sargan test suggests that the model is well-identified, meaning that the instruments used are appropriate. Similarly, according to the Wald test for regressors, the explanatory variables in the model have a significant impact on the dependent variable (they are statistically significant). Based on the presented results, it can be concluded that the model is well-constructed, includes relevant explanatory variables, but requires consideration of autocorrelation in residuals, particularly at the first-order level.

Introducing time lags did not cause significant changes in the direction of the influence of the analyzed explanatory variables on the dependent variable. The differences are primarily related to the values of the coefficients characterizing the strength of the impact. A directional difference is only evident in the ecological footprint per capita. In the time-shifted, dynamic approach, an increase in the ecological footprint negatively affects biomass energy production. These findings are also confirmed in the opposite direction in other studies [51].

Based on previous research results from other authors, there is no certainty about the role that biomass energy plays in environmental pollution. The research confirmed the initial expectations that the relationship between CO₂ emissions and biomass utilization for energy production in this case is negative. Thus, increasing the use of biomass in Visegrad Group countries positively impacted greenhouse gas emissions, reducing their levels. The negative relationship has been emphasized in the literature [52,53,54].

In Visegrad Group countries during the studied period, increased biomass consumption contributed to limiting CO₂ emissions. This was a factor that improved environmental conditions while also allowing for better utilization of agricultural production and economic resources. This trend is linked to changing societal expectations in these countries [55].

6. Conclusions

Biomass plays a crucial role in the energy strategies of Visegrad Group countries, contributing to the diversification of energy sources, the reduction in greenhouse gas emissions, and the more efficient use of resources. However, in order to fully exploit its potential, it is necessary to continue investing in biomass-processing technologies, develop infrastructure, and harmonize support policies in the region. In light of the considerations carried out, several important conclusions can be drawn:

• Based on the analysis presented, it can be stated that the production of energy from biomass in the countries studied reduces greenhouse gas emissions. It was found that energy from biomass is a source that contributes to decarbonizing the economy. This is particularly important for countries that are more heavily burdened with such emissions, and these include the Visegrad Group countries. This underscores the need for appropriate policies in these countries, leading to an increase in the use of biomass. In Poland, it is recommended to pursue a policy supporting the development of cogeneration based on forest biomass and agricultural residues. In the Czech Republic, investments in the modernization of biomass combustion technologies are advised. Similarly, in Hungary, investments in biomass combustion infrastructure are recommended, as in the Czech case. Data analysis indicates that Slovakia stands out as the V4 leader in terms of biomass utilization.
• The study’s findings, suggesting a positive correlation between economic growth and the share of biomass, align with the energy ladder hypothesis. In the Visegrad Group countries, especially in the context of post-2004 changes following accession to the European Union, there is clear evidence of a shift toward decarbonizing the energy mix and increasing the share of renewable energy sources.
• An important factor influencing the biomass market is the state policy pursued. In accordance with the concept of sustainable development implemented to a varying extent, the importance of renewable energy sources is increasing. Such a policy also promotes increased biomass production, which is an expected outcome under such a policy framework. It can be expected that biomass, like other renewable resources, will be used more in such a mechanism. However, in the long term, this energy source may also encounter an environmental barrier due to the negative effects associated with biomass combustion.
• In the face of growing energy demand, as well as the need to reduce greenhouse gas emissions and diversify energy sources, biomass is becoming a key element of the energy policy and sustainable development of these countries. Although the process of biomass combustion is also associated with CO₂ emissions, in the long term its use may contribute to reducing the impact on climate change, because the plants used to produce biomass absorb CO₂ during growth.
• In the Visegrad countries, where agricultural production is still relatively important, biomass is an opportunity to strengthen sustainable development and ensure the development of agriculture and peripheral areas in general. Processing agricultural waste, such as straw, cereal husks, or energy willow, into biomass helps to use waste economically and stimulates the development of the green economy. It can also lead to the creation of new jobs in the biomass-processing and renewable energy sector.