Evaluating the Impact of EU Expenditures Under Agricultural Priorities on Energy Sustainability in CEE Countries

Abstract

This study examines the impact of EU agricultural expenditures on renewable energy production and energy efficiency in the agricultural sector across nine Central and Eastern European (CEE) countries over the period 2015–2022. The analysis is based on a panel dataset compiled from European Commission databases, incorporating annual expenditures under five Common Agricultural Policy (CAP) priorities, as well as indicators of renewable energy production and direct energy consumption in agriculture and forestry. Using panel regression models, the study assesses how different CAP funding priorities influence energy sustainability outcomes. The findings indicate that certain funding priorities significantly contribute to renewable energy adoption, while others have a limited effect, emphasizing the need for a more targeted policy approach. The results also highlight regional disparities in the effectiveness of CAP funding, suggesting that farm structure, institutional capacity, and climate conditions mediate the impact of EU expenditures on energy sustainability. These insights contribute to the ongoing discourse on optimizing EU funding mechanisms to support a sustainable agricultural transition in the CEE region.

1. Introduction

The interest in renewable energy in research has been stimulated by the awareness ofthe seriousness of global problems related to the limited material resources used in the energy production process and the greenhouse gas emissions generated by the use of traditional energy sources [1,2]. The depletion of fossil fuel reserves and the negative impact of their use on the environment have intensified efforts to find sustainable alternatives, particularly renewable energy sources [3]. At the European level, energy sustainability has become a major objective on the agenda of the European Union (EU), especially in the context of the European Green Deal and the Renewable Energy Action Plan, which highlight the need to reduce greenhouse gas emissions and increase the share of renewable energy [4]. The European Green Deal sets ambitious targets to achieve climate neutrality by 2050, making the transition to renewable energy a priority for both policy and research. According to the specialized literature, sustainable energy is that form of energy which, in its production and consumption process, generates a minimal impact on human health and ensures the optimal functioning of essential ecological systems, including the global environment [5,6,7,8,9]. Sustainable energy includes renewable energy as a central element, as the use of renewable sources ensures minimal environmental impact and contributes to the long-term sustainability of energy systems [10]. This transition is crucial for reducing dependence on non-renewable resources, minimizing pollution, and ensuring long-term energy security. By integrating renewable sources, sustainable energy not only ensures the reduction of greenhouse gas emissions but also facilitates an efficient energy transition, balancing economic, social, and environmental needs [11,12,13]. An essential challenge in this transition is achieving a cost-effective integration of renewable energy into existing infrastructures while maintaining energy affordability and reliability, therefore, the widespread integration of renewable sources into the energy mix directly contributes to the transition towards a sustainable energy model, supporting both energy security and the objectives of reducing the ecological impact of energy production and consumption. Renewable energy sources such as solar, wind, hydro, and biomass play a crucial role in mitigating climate change and fostering sustainable economic growth [14].

The approach to sustainable energy places particular emphasis on promoting renewable energy in the agricultural sector, considering that this sector not only consumes energy but can also become an important supplier of renewable sources. The agricultural sector offers significant potential for integrating renewable energy, including bioenergy production, solar-powered irrigation, and wind energy applications [15]. At the same time, renewable energy plays a vital role in sustainable agriculture, meeting energy needs and mitigating environmental damage related to agriculture [16]. Thus, by utilizing renewable energy, agriculture can reduce its carbon footprint, enhance energy independence, and contribute to rural development [17]. This aspect is particularly relevant for rural and isolated regions globally, where solar and biomass energy resources are abundant and can be harnessed to support local economic development and community resilience [7,18].

The increasing integration of renewable energy in agriculture raises important questions about the balance between food security and energy production. The use of agricultural land for bioenergy crops or other renewable energy initiatives may, in some cases, compete with food production, potentially affecting food availability and prices. While renewable energy can enhance the sustainability and efficiency of agricultural processes, policymakers must carefully assess the implications of land use decisions to ensure that energy goals do not undermine food security, particularly in regions facing economic or climatic vulnerabilities. In this context, agriculture and forestry play an essential role in the transition to a sustainable energy system. These sectors contribute not only to food security but also to reducing greenhouse gas emissions through the use of biomass and other renewable sources. The deployment of renewable technologies in rural areas is essential for ensuring access to clean energy, increasing productivity, and improving livelihoods. The integration of renewable energy into agricultural practices, such as the use of solar energy to generate energy for irrigation or biogas for electricity generation, represents concrete examples of how these sectors can support energy and environmental objectives [6,8]. For example, in Germany, the use of biomass has increased significantly in the last decade, thanks to government subsidies and advanced technologies [19]. Similarly, in Denmark, farms extensively use biodigesters to convert agricultural waste into biogas, contributing to both the reduction of methane emissions and energy production [20]. These successful models demonstrate how policy support and technological advancements can drive the adoption of renewable energy in agriculture.

Solar energy, considered the most abundant source of renewable energy, is increasingly used in agriculture. In Italy, photovoltaic panels are mounted on greenhouses to produce electricity, and in Spain, solar systems power irrigation pumps in arid regions [18]. In addition, wind energy can be used on farms located in areas with high wind capture potential, such as those in Poland or Romania. Expanding the use of solar and wind energy in agriculture is essential for enhancing energy efficiency and reducing operational costs.

The Common Agricultural Policy (CAP) plays an essential role in supporting the transition to a more sustainable agricultural sector. The Common Agricultural Policy (CAP) of the European Union (EU) is one of the largest agricultural policies in the world and the longest-running in the EU [21]. The funding provided through the CAP priorities aims to conserve ecosystems, use natural resources efficiently, and promote rural development [21,22]. According to a report by the European Commission [23] projects that integrate renewable energy into agricultural activities receive priority, which has led to an increase in the use of biomass and solar energy in regions such as Central and Eastern Europe (CEE). Financial incentives and subsidies play a key role in enabling farmers to adopt renewable technologies and transition to sustainable energy use.

However, the implementation of these initiatives varies considerably between member states due to differences in infrastructure, national policies, and access to funding. The CAP has evolved significantly since the 2003 reform, which decoupled direct payments from production levels, marking a major shift in agricultural policy [24,25]. In the 2007–2013 period, through the “Health Check” of 2008, the CAP consolidated these changes, establishing a new support model structured around two essential funds: the European Agricultural Guarantee Fund (EAGF) and the European Agricultural Fund for Rural Development (EAFRD) [26,27]. In the 2014–2020 financial period, the two-pillar structure was maintained, but with increased flexibility and the possibility of transfer between the two funds, as highlighted by recent research [28,29]. This period was also marked by the implementation of a set of essential priorities within the Common Agricultural Policy (CAP), aimed at supporting the sustainable development of the agricultural sector in the European Union. These priorities were structured as follows:

Priority 1: Promoting Knowledge Transfer and Innovation in Agriculture, Forestry, and Rural Areas;

Priority 2: Farm Viability and Competitiveness;

Priority 3: Organization of the Food Chain and Risk Management;

Priority 4: Restoration, Conservation, and Improvement of Ecosystems;

Priority 5: Resource-efficient and Climate-resilient Economy;

Priority 6: Social Inclusion and Economic Development.

Although these priorities were essential within the CAP, financial allocations focused mainly on priorities 2–6 [30]. Thus, a large part of the CAP’s financial resources was directed towards strengthening farm viability, efficient organization of food chains, protecting ecosystems, promoting a resource-efficient economy, and supporting economic and social development in rural areas. This type of financial allocation aimed not only to address the economic challenges of the agricultural sector but also to support the transition to a more sustainable agriculture adaptable to climate change [31].

The adoption of renewable technologies in agriculture within the CEE region is hindered by several challenges, including land fragmentation, constrained institutional capacities, and restricted access to modern technologies [32]. However, CEE countries have begun implementing pilot projects demonstrating the potential of renewable energy. For example, in Hungary, biomass use in the agricultural sector has been promoted through public-private partnerships, leading to a significant reduction in carbon emissions [33]. In Romania, European funding programs have supported the installation of photovoltaic systems in rural farms, increasing energy efficiency and reducing dependence on fossil fuels [34,35,36]. Similarly, Poland and Slovakia have invested in biogas infrastructure, utilizing agricultural waste to generate renewable energy [37,38,39,40].

Outside Europe, developing countries demonstrate how renewable energy can transform agriculture. For example, in India, solar irrigation systems have allowed farmers to increase productivity in drought-affected regions [18]. In Sub-Saharan Africa, solar energy and biodigesters are widely used to power agricultural equipment and improve living conditions in rural communities.

The adoption of renewable energy sources (RES) in agriculture has garnered significant attention in recent years, particularly within CEE countries. Understanding farmers’ perspectives on RES is crucial for effective policy formulation and implementation. A comprehensive study by White Research [41] delved into the socio-economic factors influencing RES adoption among farmers in CEE nations. The study identified key drivers such as environmental objectives and potential cost savings, while also highlighting significant barriers including high installation costs, limited access to financing, and concerns regarding long-term profitability. Interestingly, the research revealed that smaller farms exhibit a greater willingness to adopt RES technologies compared to larger operations, possibly due to their adaptability and perception of RES investments as beneficial for reducing operational costs.

Further insights are provided by Ember’s 2024 report [42], which emphasizes the potential of agrivoltaics (agri-PV) in enhancing both energy production and agricultural yields. The report suggests that integrating solar panels with crop cultivation can lead to increased crop yields by up to 16%, offering a synergistic approach to land use that benefits both energy generation and food production. This dual-use strategy is particularly pertinent for CEE countries, where optimizing land resources is essential.

Thus, integrating renewable energy sources into agriculture not only supports the EU’s climate neutrality objectives but also contributes to the economic development of rural areas. Studies highlight the importance of adequate financial support, modern technology transfer, and international collaboration to overcome regional barriers and accelerate the energy transition [18,32,43].

The transition to sustainable energy in agriculture is a critical component of the European Union’s broader environmental and economic policies. As the EU strives to achieve climate neutrality by 2050, the role of agricultural expenditures in promoting energy sustainability has gained increasing attention. However, the effectiveness of these expenditures in fostering renewable energy use and improving energy efficiency remains a subject of debate, particularly in the context of CEE countries. These nations exhibit significant economic and geographical diversity, which influences their capacity to implement energy sustainability policies effectively.

Against this background, the present research explores the relationship between EU agricultural expenditures and energy sustainability in nine CEE countries: Bulgaria, Czechia, Estonia, Hungary, Lithuania, Latvia, Poland, Romania, and Slovakia. The study focuses on two key dimensions: (i) renewable energy production in agriculture and (ii) energy consumption in agriculture and forestry. While previous research has examined the general impact of EU funding on agricultural development, there remains a gap in understanding how these expenditures specifically influence energy sustainability in CEE countries. This study addresses this gap by incorporating region-specific indicators that reflect both economic and environmental outcomes, offering a more comprehensive perspective on the interplay between agricultural policy and energy transition.

To achieve this objective, the study employs advanced econometric techniques, including regression models and Granger causality analyses, to assess the effectiveness of EU agricultural expenditures in driving energy sustainability. The hypotheses tested in this research are as follows:

H1. 

EU agricultural funding has a positive and significant effect on renewable energy production in CEE countries.

H2. 

EU agricultural expenditures contribute to reducing energy consumption in agriculture and forestry, improving energy efficiency.

H3. 

The impact of EU agricultural expenditures on energy sustainability varies across CEE countries due to economic and structural differences.

By testing these hypotheses, the study provides empirical evidence on the effectiveness of European funding mechanisms and offers recommendations for policy improvements. The novelty of this research lies in its examination of EU-specific agricultural spending within a region characterized by distinct economic structures and policy implementation challenges. Furthermore, it contributes to the ongoing academic discussion on energy transition by identifying key factors that influence the success of energy sustainability policies.

This study is structured into five main sections. Section 2 presents an extensive review of the relevant literature, focusing on the impact of the Common Agricultural Policy (CAP) on renewable energy use in agriculture. Section 3 details the methodological framework, describing the datasets and econometric techniques used in the analysis. Section 4 presents the key findings and discusses their implications. Finally, Section 5 synthesizes the major conclusions, emphasizing the study’s contributions to public policy and providing suggestions for future research.

By addressing these aspects, the study offers valuable insights into the role of agricultural policy in supporting the EU’s climate and energy objectives, particularly in the CEE region. The findings have implications for both policymakers and researchers, highlighting the need for targeted policy interventions and further exploration of regional disparities in energy transition efforts.

2. Literature Review

2.1. The Impact of CAP in EU Countries

The Common Agricultural Policy (CAP) of the European Union is a stable and globally significant policy, whose establishment by the European Economic Community (EEC) in 1962 had a substantial impact on agricultural production and consumption in Europe, as well as on the environment [44]. CAP has been the subject of numerous research studies addressing various aspects, ranging from its economic impact to its influence on environmental sustainability and social equity in the European agricultural sector [45,46].

Buitenhuis et al. [47] analyzed how CAP and its national implementations influence the resilience of agricultural systems. The authors concluded that CAP significantly supports the robustness of these agricultural systems but offers fewer opportunities for adaptability and limits transformability.

On the other hand, a study conducted in Spain highlights that European agricultural aid can disproportionately benefit more dynamic regions, leaving less developed areas at a disadvantage [28]. Andrés González-Moralejo and Estruch-Sanchís [48] emphasized that the CAP has led to a gradual and asymmetric liberalization of European agriculture, focusing more on eliminating intervention mechanisms rather than restructuring and modernizing the sector. In Slovakia, the CAP 2014–2020 had a positive impact through financial support and greening measures, but the structural vulnerabilities of Slovak agriculture require better-integrated policies [49].

In Romania, studies show that the CAP has played an essential role in the development of agritourism through subsidies allocated to rural regions [50]. Additionally, Severini et al. [51] highlight that the CAP provides essential support to European farmers in managing income risks through the Income Stabilization Tool, ensuring their financial sustainability.

Analyses at the level of country groups indicate that the CAP initially favored the older EU member states (EU-15), which benefited from the vast majority of Pillar I funds. However, between 2014 and 2020, there was a trend toward balancing direct payments among member states [27]. Pillar II remains a key element for rural development, supporting priorities such as sustainability and agricultural modernization.

An important dimension of the CAP is its impact on environmental sustainability. Himics et al. [52] analyzed the effects of reallocating resources from Pillar I, finding that redirecting subsidies toward measures to reduce greenhouse gas emissions could decrease non-CO₂ agricultural emissions by 21% by 2030. However, fund redistribution creates regional disparities and may affect farmers’ competitiveness unevenly.

Regarding the financial instruments available to member states, Paun and Ivașcu [53] examined the evolution of CAP funds and their impact on the main agricultural sectors. Their results indicate a positive relationship between net subsidies and agricultural production. However, an analysis with a three-year lag revealed a statistically significant negative relationship between the dynamics of subsidies and the volume of agricultural production in the EU and the Eurozone.

The Common Agricultural Policy (CAP) of the European Union represents an essential pillar of agricultural development and sustainability strategies at the European level, having a significant impact on the agricultural sector and rural economies. Although CAP is one of the most studied public policies of the European Union, it is surprising that there is no research directly addressing its impact on the use of renewable energy in agriculture. Most existing studies focus on the economic and social dimensions of CAP, but its relationship with the transition to renewable energy sources remains undervalued. In this context, this paper aims to analyze how CAP can facilitate the adoption of renewable energy technologies in the agricultural sector and to explore how specific CAP measures support the transition to greener energy sources, such as solar, wind, or biofuels, in Central and Eastern European countries.

2.2. The Role of Renewable Energy in Agriculture

Sustainable agriculture involves a delicate balance between increasing agricultural production, maintaining economic stability, reducing the consumption of limited natural resources, and minimizing the negative environmental impact. In this regard, promoting the use of renewable energy systems becomes essential for supporting sustainable agriculture [54]. Renewable energy plays an increasingly important role in meeting the energy needs of the agricultural sector, contributing to improved energy efficiency and reduced dependence on fossil fuels [55]. Furthermore, it reduces reliance on non-renewable resources while supporting the adoption of sustainable practices. For example, renewable energy aids in soil fertilization and pest control without the use of chemicals, protecting farmers from fluctuations in oil prices.

At the same time, renewable energy facilitates essential operations such as irrigation, lighting for agricultural buildings, and powering production processes [56]. Moreover, it reduces agriculture’s negative environmental impact, meeting the energy requirements of this sector while leveraging agricultural waste as an energy source [57]. In this way, renewable energy not only supports the development of sustainable agriculture but also helps reduce costs, improve farmers’ living conditions, and protect ecosystems [18].

According to Bardi et al. [58] renewable electricity produced on farms should be considered a component of a broader transition involving significant transformations and the adaptation of existing agricultural processes to develop truly sustainable agriculture. Thus, the use of renewable energy becomes a key element in transforming agriculture into a more sustainable and efficient sector.

Martinho [59] highlights that renewable energy sources such as biofuels and biomass play an essential role in energy production and reducing environmental impact. The agricultural sector can also contribute to combating climate change by adopting renewable energy, thereby promoting economic sustainability. Moreover, the study emphasizes that the involvement of farmers and institutions in energy and agricultural policies can enhance efficiency and profitability, and the use of marginal land for renewable energy production represents a significant opportunity for expanding sustainable sources in agriculture.

Rahman et al. [60] provided empirical evidence in a study analyzing renewable energy use in agriculture in both developed and developing countries. The results show that, in developed countries, integrating renewable energy in agriculture is well-implemented. In contrast, developing countries still face difficulties adopting these resources due to technical and economic challenges. The study highlights that using renewable energy in agriculture can be a critical factor for the sustainable development of the agricultural sector and outlines the associated policy implications.

Another study by Majeed et al. [61] provides a broad perspective on alternative energy sources for powering agricultural operations, with a particular focus on renewable energy technologies. The study emphasizes that the transition to alternative energy sources in managing energy consumption in agriculture has significant potential to reduce greenhouse gas emissions, improve energy efficiency, and promote sustainability in food production. However, the authors point out that overcoming technical, economic, and political barriers is essential for effective implementation, along with promoting knowledge dissemination and capacity development among farmers and other stakeholders.

Similar findings were reported by Bolyssov et al. [62], confirming the significant potential of transitioning to renewable energy sources in agriculture. The study’s conclusions align with earlier research, highlighting the contribution of these sources to improving economic and energy sustainability. Additionally, renewable energy supports rural development, reduces dependence on fossil fuels, and encourages job creation and sustainable economic growth in the agricultural sector. These findings reinforce the idea that adopting renewable sources is a key element for sustainable transformation in agriculture. A study by Gorjian et al. [63] also highlights that renewable energy has promising potential for integration into a wide range of agricultural activities and offers a sustainable alternative solution to current practices.

The findings of Wang et al. [64] indicate significant implications for policymakers. Surprisingly, both agriculture and globalization are factors that increase CO₂ emissions. However, on a positive note, renewable energy emerges as a key element in reducing CO₂ emissions, particularly in the agricultural sector. It plays a essential role in promoting sustainable agriculture, which contributes to mitigating its negative environmental impact. In light of these empirical findings, policymakers are encouraged to actively support investments in renewable energy technologies, thereby fostering the transition to sustainable agricultural practices that can significantly reduce CO₂ emissions.

Another study conducted at the EU level [65] provides important findings. The study emphasizes that renewable energy use in agriculture plays an essential role in promoting a more sustainable and energy-efficient agricultural sector. However, it is important to note that the relationship between agriculture and renewable energy is reciprocal. On one hand, agriculture significantly benefits from implementing renewable energy technologies, reducing dependence on fossil sources and contributing to CO₂ emission reductions. On the other hand, agriculture can be a major source of raw materials for renewable energy products such as biomass and biofuels, thus positively impacting the sustainable development of the entire energy sector. Therefore, integrating renewable energy into agriculture and vice versa is fundamental to building a truly sustainable agricultural system that addresses both economic and ecological challenges.

In 2022, at the EU level, Paris et al. [19] combined the results of numerous studies investigating energy use in open-field agriculture within the EU, providing an overview of energy use and concentrations. This review highlights the significant role of energy use in EU open-field agriculture, which accounts for approximately 3.7% of the EU’s total annual energy consumption. It underscores that most of the energy used in this sector comes from non-renewable sources.

The most recent study conducted at the EU level, analyzing the relationships between renewable and non-renewable energy consumption in agriculture and production levels in this sector, was carried out by Supron and Myszczyszyn [66]. The results indicate the existence of two distinct groups of countries with significant differences in adopting sustainable agricultural practices. A limited number of seven EU countries stand out for their reduced use of pesticides, a significant share of organic farms, and intensive application of renewable energies in agriculture. In these countries, increased renewable energy consumption is associated with positive growth in agricultural production. In contrast, in countries with a less sustainable agricultural model, an increase in non-renewable energy consumption leads to a reduction in the growth rate of agricultural production. These results highlight the need to promote renewable energy development in agriculture and greater awareness of the negative effects of intensive agriculture on the environment while showcasing the positive impact of organic farming on agricultural production.

Building on these findings, our study provides a novel perspective by examining the impact of EU-specific agricultural spending on energy sustainability in an economically and geographically diverse region. While previous research has highlighted the relationship between energy use and agricultural production, our approach expands this analysis by integrating region-specific indicators that capture both economic and environmental outcomes within the framework of agricultural development policies and renewable energy transition. Additionally, by employing advanced statistical methods, we analyze the link between European agricultural policy and regional energy transition, identifying key factors influencing the success of energy sustainability policies, particularly in the context of CEE countries. These insights are especially relevant given the EU’s commitment to achieving climate neutrality by 2050, underscoring the necessity for targeted policies that align economic development with environmental sustainability.

Our findings align with previous research analyzing the impact of the Common Agricultural Policy on energy and environmental sustainability in EU agriculture under the European Green Deal [67]. This research highlights disparities between Eastern and Western Europe in policy implementation and underscores the need for a coordinated EU approach to support sustainable farming, reduce emissions, and improve resource efficiency. Additionally, our study contributes to the broader discourse on policies influencing renewable energy development [68], which identified key institutional, environmental, financial, socio-cultural, and technical factors affecting energy transition. These findings reinforce the necessity of targeted policy interventions, regulatory support, and financial transparency to ensure an inclusive and effective transition. Furthermore, our study complements reports providing policymakers with guidance on transitioning regions and cities to a climate-neutral and circular economy by 2050 [69] particularly in managing trade-offs and implementing energy and environmental policies at multiple governance levels.

By integrating these perspectives, our study offers a comprehensive view of how European agricultural policies can support energy sustainability and climate goals, emphasizing the role of policy coordination and regional adaptation.

In the introduction, we have outlined the key hypotheses that guide this research, focusing on the role of EU agricultural expenditures in promoting energy sustainability. These hypotheses explore whether CAP funding has positively influenced renewable energy production (H1), contributed to reducing energy consumption and improving energy efficiency in agriculture and forestry (H2), and whether its impact varies across CEE countries due to economic and structural differences (H3). Given that the CAP 2014–2020 period has ended and its impact can now be assessed using available data, unlike the CAP 2023–2027, whose effects remain uncertain, this study evaluates the outcomes of the previous policy framework while considering its specific characteristics and evolution.

By testing these hypotheses, this research provides empirical insights into the role of EU funding mechanisms in fostering energy sustainability. The findings contribute to the literature by addressing regional disparities in policy implementation and highlighting key factors that shape the effectiveness of sustainability measures. This study thus fills an important gap in understanding the impact of agricultural policies on energy transition and offers evidence-based recommendations for enhancing future policy frameworks.

3. Data and Methodology

3.1. Data Description

The dataset utilized in this study is designed to analyze the relationship between European Union (EU) agricultural and rural development expenditures and energy-related outcomes in the agricultural sector. The data cover nine Central and Eastern European (CEE) countries—Bulgaria, Czechia, Estonia, Hungary, Lithuania, Latvia, Poland, Romania, and Slovakia—over the period 2015 to 2022. These countries were selected based on data availability, as other CEE countries did not have sufficient historical records for a comprehensive analysis. The data were extracted from the European Commission’s official databases [70], ensuring reliability and consistency across the observed period. The dataset consists of panel data, meaning it includes observations for multiple countries over multiple years. This panel structure allows for longitudinal analysis, capturing both cross-sectional and time-series dimensions of EU agricultural expenditures and their effects on energy sustainability. By incorporating data across multiple years and regions, the study can control for unobserved heterogeneity and identify both short-term and long-term impacts of EU funding priorities. The dataset includes seven key variables, categorized into independent and dependent variables, as outlined in

Table 1. Key variables definitions.

Acronym	Variable Type	Indicator	Parameter	Unit
lnP2	Independent	total EU expenditure under Priority 2 Farm Viability and Competitiveness	total	euro
lnP3	Independent	total EU expenditure under Priority 3 Food Chain Organisation and Risk Management	total	euro
lnP4	Independent	total EU expenditure under Priority 4 Restoring, Preserving and Enhancing Ecosystems	total	euro
lnP5	Independent	total EU expenditure under Priority 5 Resource-efficient, Climate-resilient Economy	total	euro
lnP6	Independent	total EU expenditure under Priority 6 Social Inclusion and Economic Development	total	euro
lnREN	Dependent	Production of renewable energy from agriculture	agriculture	kToe
lnENUSE	Dependent	Direct use of energy in agriculture/forestry	total	kToe

Note: The prefix ‘ln’ in variable names indicates that the data have been transformed using the natural logarithm (ln). This transformation was applied to reduce skewness, improve normality, stabilize variance, and facilitate the interpretation of regression coefficients in percentage terms. The data sources for all variables are secondary and obtained from the European Commission [70]. Source: Own processing according to the data available on European Commission database [70].

.

The independent variables represent EU agricultural expenditures under five distinct priorities established within the Common Agricultural Policy (CAP) framework. These priorities are designed to support sustainable agricultural and rural development by allocating funds toward key sectors. Priority 2 (Farm Viability and Competitiveness, lnP2) offer financial support aimed at enhancing agricultural productivity, modernizing farms, and improving competitiveness within the EU agricultural sector. Investments under this priority are expected to contribute to energy efficiency improvements through the adoption of modern agricultural technologies. Priority 3 (Food Chain Organization and Risk Management, lnP3) provide funds allocated to developing resilient agricultural supply chains, improving food security, and implementing risk management strategies. This priority also includes measures to mitigate risks related to climate variability and energy use efficiency. Priority 4 (Restoring, Preserving, and Enhancing Ecosystems, lnP4) allows investments for environmental conservation, promoting renewable energy sources (e.g., biomass, solar energy in agriculture), and supporting ecosystem-based farming practices. Priority 5 (Resource-efficient, Climate-resilient Economy, lnP5) covers financial support aimed at fostering a low-carbon, climate-resilient agricultural sector, promoting sustainable land use and energy-efficient farming techniques. Priority 6 (Social Inclusion and Economic Development, lnP6) for investments targeting rural community development, including financial support for energy transition projects and local renewable energy initiatives in agriculture.

These independent variables are expressed in euros (€), representing the total EU agricultural expenditures allocated to each priority in a given year for each country. These financial allocations serve as policy instruments influencing farm-level investments, technological adoption, and environmental sustainability, thereby shaping the trajectory of energy use in agriculture. All financial values representing EU agricultural expenditures are reported in nominal terms, as sourced from official European Commission databases. Since these expenditures are used as explanatory variables in the econometric models rather than for direct temporal comparisons, no adjustments for inflation were applied. The panel regression framework inherently captures temporal effects, ensuring that inflation does not distort the estimated relationships between EU funding priorities and energy sustainability outcomes.

The dependent variables reflect energy-related outcomes in the agricultural and forestry sectors. These variables are critical for assessing the effectiveness of EU agricultural expenditures in promoting energy efficiency and renewable energy adoption. Production of renewable energy from agriculture (lnREN) measures the total renewable energy generated within the agricultural sector, expressed in kilotons of oil equivalent (kToe). This variable includes energy production from biomass, biofuels, solar panels on farms, and biogas systems used in agricultural activities. Direct use of energy in agriculture and forestry (lnENUSE) represents the total energy consumption in agricultural and forestry operations, also expressed in kToe. This includes energy used for irrigation, heating, mechanized farming operations, and post-harvest storage. By examining these dependent variables in relation to EU funding allocations, the study evaluates how agricultural investments contribute to reducing fossil fuel dependency and enhancing renewable energy integration in the sector.

This dataset is closely aligned with EU policies and funding mechanisms under the Common Agricultural Policy (CAP) and the European Green Deal, both of which emphasize the role of agriculture in achieving climate neutrality by 2050. The financial investments captured by the independent variables are expected to contribute to improvements in energy efficiency and the adoption of renewable energy practices in agriculture. The integration of energy metrics provides a valuable perspective for evaluating the broader environmental and economic impacts of CAP funding.

3.2. Methodology

The study employs a robust methodological framework to assess the relationship between EU agricultural expenditures and energy sustainability outcomes. The analysis is conducted using panel data from nine Central and Eastern European countries, covering the period 2015–2022. In this study, data were aggregated across the researched Central and Eastern European (CEE) countries rather than analyzed on a country-by-country basis. This decision was made to provide a broader and more policy-relevant perspective on the impact of EU agricultural funding on energy sustainability in the region. Given that the selected countries share common structural characteristics—including a transition from centrally planned economies to market-based systems, similar agricultural modernization challenges, and a unified framework of CAP funding—an aggregated approach allows for a more comprehensive assessment of overarching trends. Additionally, a country-level analysis would result in smaller sample sizes, reducing the statistical power of the econometric models and increasing the likelihood of biased estimates. By using a panel data approach, we account for both cross-sectional and time-related variations, improving the reliability of the estimations.

Furthermore, EU agricultural policies and CAP funding mechanisms are designed and implemented at a regional level, rather than strictly on an individual country basis. Many funding programs operate across multiple member states, making a regional-level analysis more aligned with policy evaluation objectives. To account for country-specific effects, fixed effects were included in the regression models, ensuring that structural differences between countries are controlled for while still capturing the general impact of CAP funding on energy sustainability.

The methodological approach (Figure 1) consists of four key stages: descriptive statistical analysis, correlation matrix analysis, econometric modeling, and causality testing. The regression models used in this study include robust least squares estimation to account for potential heteroscedasticity and outlier influences. Furthermore, Granger causality tests are applied to assess the temporal relationships between EU funding priorities and energy-related indicators. The selection of these methodologies is justified by their ability to address endogeneity concerns and provide statistically reliable insights into policy effectiveness.

The initial step involves a descriptive statistical analysis of the variables to provide an overview of their central tendencies, dispersion, and distributional characteristics. Key metrics such as mean, median, standard deviation, skewness, and kurtosis are employed to identify patterns and anomalies in the data. This step helps to highlight variability in EU expenditures across different priorities and assess disparities in energy-related outcomes. The descriptive statistics also establish a foundational understanding of the data’s distributional properties, including skewness and kurtosis, which are critical for determining the appropriateness of subsequent econometric analyses [71].

Following the descriptive analysis, a correlation analysis is conducted to evaluate the strength and direction of linear relationships between EU expenditures (independent variables) and energy sustainability indicators (dependent variables) [72]. The Pearson correlation coefficients provide quantitative measures of association, enabling the identification of expenditure priorities that are strongly linked to renewable energy production and energy use. Furthermore, p-values associated with these coefficients are used to determine the statistical significance of these relationships. This analysis serves as a precursor to regression modelling, helping to refine hypotheses about potential causal links [73].

The core of the framework is a robust least squares regression analysis, which assesses the direct impacts of EU expenditures under different priorities on renewable energy production (lnREN) and direct energy use (lnENUSE). Robust regression techniques are chosen to address potential issues of outliers and heteroscedasticity, ensuring the reliability of parameter estimates [74]. The regression models quantify the effect sizes of each priority’s expenditures on the dependent variables, allowing for a comparative evaluation of their relative contributions. Model diagnostics, including R-squared and Adjusted R-squared values, assess the explanatory power of the models, while statistical significance levels of individual coefficients are evaluated to identify the most impactful priorities. This step also incorporates residual analysis to validate the assumptions of the regression model and enhance its interpretative robustness [75].

Following Rousseeuw and Yohai (1984) the robust regression equation is based on the ordinary least squares (OLS) framework but modified to include a robust estimator (S-estimator) [76]. For a dataset with n observations:

$$y_i={\beta }_0+{\beta }_1{x}_{i1}+{\beta }_2{x}_{i2}+\cdots +{\beta }_p{x}_{ip}+{\epsilon }_i$$

(1)

where y is the dependent variable, x_i represents the independent variables (predictors), β is the regression coefficients to be estimated and $\epsilon$ is the error term for observation i.

The S-estimator seeks to minimize the scale of residuals ($r_i=y_i-{\widehat{y}}_i$) by solving the following equation for scale σ:

$${\displaystyle \frac{1}{n}}{\sum }_{i=1}^n\rho \left({\displaystyle \frac{r_i}{\sigma }}\right)=b$$

(2)

where: ρ represents a bounded influence function (Huber’s function) [64], σ is the scale of residuals, r_i are the residuals and b is a constant (typically related to the desired breakdown point). In the context of the robust regression estimation, sigma (σ) represents the scale parameter of the residuals, providing a robust estimate of the standard deviation. Unlike traditional standard deviation measures, sigma in robust regression accounts for down-weighting extreme observations, reducing their influence on coefficient estimates. This ensures that the model remains resistant to outliers and maintains statistical efficiency.

Once the scale σ is estimated, the coefficients β are estimated by minimizing the weighted sum of squared residuals:

$${min}_{\beta }{\sum }_{i=1}^n\omega \left({\displaystyle \frac{r_i}{\sigma }}\right)r_i^2$$

(3)

where $\omega$ is the weight function derived from $\rho$.

The S-estimator is computed through an iterative process designed to enhance robustness against outliers and heteroscedasticity in regression analysis. The procedure begins with an initial estimation of the regression coefficients. Next, the residuals (r_i) and the scale parameter (σ) are calculated using a predefined objective function that minimizes the impact of extreme values [76]. Subsequently, the regression coefficients (β) are updated by minimizing the weighted sum of squared residuals, ensuring that the influence of outliers is reduced [77]. This process is iterated multiple times until the model reaches convergence, meaning that further changes in the estimated coefficients (β) or scale (σ) fall below a specified tolerance threshold [78].

To explore temporal and directional relationships, Granger causality tests [79] are performed as a complementary analysis. These tests investigate whether past values of one variable can predict current values of another, thereby establishing potential causal linkages between EU expenditures and energy outcomes. The Granger causality framework is particularly relevant for this study, given the time-series nature of the data and the policy-driven context, where financial allocations are expected to generate delayed impacts on energy sustainability. The inclusion of lagged variables enables the examination of dynamic interactions and helps uncover dependencies that are not apparent in static regression models.

For all methodological steps, including data processing, descriptive statistics, correlation analysis, panel regression modeling, robust least squares estimation, and Granger causality tests, we used EViews 12 software. EViews 12 is a widely recognized econometric software that does not require the implementation of custom code, ensuring a standardized and replicable approach to data analysis.

The methodology employed in this study is essential due to the inherent complexity of the relationship between EU agricultural expenditures and energy sustainability. Given that the dataset consists of panel data covering multiple Central and Eastern European countries over the period 2015–2022, a rigorous econometric approach is necessary to ensure the reliability and validity of the findings. Traditional econometric techniques, such as OLS regression, are often inadequate in addressing the challenges posed by heterogeneity, outliers, and potential endogeneity issues commonly encountered in policy impact assessments [58]. To overcome these limitations, the study employs robust regression techniques, panel data models, and Granger causality tests, which provide a more accurate and comprehensive evaluation of the effects of EU funding on energy sustainability.

The use of robust regression based on the S-estimator is particularly justified given the nature of economic and energy-related datasets, which are frequently affected by heteroscedasticity and extreme values [75]. Unlike OLS regression, which assigns equal weight to all observations, the S-estimator reduces the influence of extreme values by iteratively refining the coefficient estimates until a stable solution is achieved [76]. This approach ensures that the results are not disproportionately affected by data anomalies, making the findings more generalizable and policy-relevant. Given that energy policy and funding allocations can vary significantly across regions, employing a robust estimation technique enhances the reliability of policy recommendations derived from the study.

Furthermore, the application of Granger causality tests is crucial for understanding the temporal dynamics between agricultural funding priorities and energy sustainability outcomes. Policy effects often manifest with a time lag, meaning that the impact of funding allocations may not be immediately observable [77]. The Granger causality test allows for the identification of directional relationships, helping policymakers discern whether historical funding trends are predictive of future changes in renewable energy production and energy use in agriculture. This methodological approach enhances the study’s ability to provide evidence-based policy insights, supporting the refinement of EU funding strategies for sustainable energy transitions.

The integration of panel data analysis offers several advantages over conventional cross-sectional or time-series models. By incorporating data across multiple countries and years, panel regression models allow for the control of unobserved heterogeneity, thereby improving the efficiency and accuracy of the estimates [80]. This is particularly important in the context of EU agricultural policies, where structural differences between countries—such as institutional capacities, land use patterns, and economic conditions—may influence the effectiveness of funding allocations [81]. By leveraging panel econometric techniques, the study is able to capture both country-specific and time-varying effects, providing a more nuanced understanding of how EU agricultural expenditures contribute to energy sustainability in the CEE region.

4. Results

The descriptive statistics presented in

Table 2. Descriptive Statistics of Renewable Energy, Energy Use, and EU Expenditures under Agricultural Priorities.

	REN	ENUSE	P2	P3	P4	P5	P6
Mean	338.15	714.33	113,000,000.00	47,405,007.00	183,000,000.00	19,851,265.00	78,746,446.00
Median	186.10	193.78	59,762,559.00	25,934,847.00	105,000,000.00	9,059,863.00	32,442,607.00
Maximum	1191.69	3918.94	610,000,000.00	184,000,000.00	534,000,000.00	129,000,000.00	471,000,000.00
Minimum	0.00	88.11	5,482,200.00	837,100.20	32,631,564.00	37,894.74	347,659.40
Std. Dev.	328.14	1108.38	133,000,000.00	50,949,293.00	144,000,000.00	29,220,907.00	99,415,575.00
Coef. of var.	0.97	1.55	1.18	1.07	0.79	1.47	1.26
Skewness	1.17	2.26	2.06	1.33	0.80	2.20	1.77
Kurtosis	3.21	6.45	6.73	3.49	2.31	7.26	5.76
Jarque-Bera	15.75	92.67	88.81	21.07	8.72	107.81	57.91
Probability	0.00	0.00	0.00	0.00	0.01	0.00	0.00
Sum	23,332.36	49,288.85	7,810,000,000.00	3,270,000,000.00	12,600,000,000.00	1,370,000,000.00	5,430,000,000.00
Sum Sq. Dev.	7,321,985	83,538,130	1.20 × 1018	1.77 × 1017	1.41 × 1018	5.81 × 1016	6.72 × 1017
Observations	69	69	69	69	69	69	69

Source: Own processing in Eviews12.

provide a comprehensive summary of the variables used in the study, including renewable energy production (REN), energy use in agriculture and forestry (ENUSE), and EU expenditures under Priorities 2 to 6 (P2–P6). The analysis focuses on measures of central tendency, dispersion, and distribution shape. Although the dataset theoretically includes 72 observations (9 countries over 8 years), the final sample consists of 69 observations due to missing data. Specifically, renewable energy production (REN) data for Estonia in 2022 and EU expenditure allocations under Priority 6 (P6) for Bulgaria and Slovakia in 2022 were unavailable in official sources. These missing values led to the exclusion of three observations to maintain data consistency and ensure robust econometric analysis. The mean production of renewable energy (REN) is 338.15 kToe, which is considerably higher than the median value of 186.1 kToe, indicating a positively skewed distribution. This observation suggests that while the average renewable energy production is relatively high, there are instances of significantly lower production. The standard deviation of 328.14 kToe further highlights considerable variability across the data points. Similarly, energy use in agriculture and forestry (ENUSE) has a high mean of 714.33 kToe but a much lower median of 193.78 kToe, reflecting a positively skewed distribution as well. The standard deviation of 1108.38 kToe indicates extreme variability, which is confirmed by the maximum value of 3918.94 kToe and a minimum of 88.11 kToe. This suggests that certain regions or contexts are consuming disproportionately high amounts of energy in comparison to others. In terms of EU expenditures, Priority 2 (P2) exhibits the highest average funding level, with a mean of €113 million, followed by Priority 4 (P4) at €183 million. However, Priority 5 (P5) and Priority 6 (P6) have considerably lower mean expenditures of €19.85 million and €78.75 million, respectively. The large standard deviations for all priorities, particularly P2 (€133 million) and P4 (€144 million), indicate substantial variation in how EU funds are allocated across regions or projects.

Skewness and kurtosis values indicate the non-normal distribution of most variables. For instance, the skewness of REN (1.17) and ENUSE (2.26) points to a right-skewed distribution, meaning that a small number of observations have significantly higher values than the rest. The kurtosis values of 3.21 for REN and 6.45 for ENUSE suggest moderate and extreme peaks, respectively, with ENUSE showing a leptokurtic distribution. The Jarque-Bera test confirms the non-normality of all variables, with p-values below 0.05 for all cases. This statistical significance implies that outliers or extreme values heavily influence the distribution of these variables, particularly for EU expenditures (P2–P6) and energy use (ENUSE).

The high variability and non-normal distributions of EU expenditures (P2–P6) suggest an uneven allocation of funds, possibly reflecting differences in regional priorities or project requirements. The wide range between the minimum and maximum expenditures further supports this observation. Additionally, the strong skewness in renewable energy production (REN) and energy use (ENUSE) indicates that only a few regions may be significantly advancing in energy sustainability, while the majority exhibit lower performance levels. These findings underscore the need for targeted policies to reduce disparities in both financial allocations and energy outcomes. The high variability in renewable energy production and energy use suggests potential inefficiencies in the implementation of EU-funded programs, calling for further investigation into the factors driving these disparities.

To provide a more meaningful assessment of variability across the observed variables, we include the coefficient of variation (Coef. of var.) as a relative measure of dispersion. Unlike the standard deviation, which represents absolute variability, the coefficient of variation expresses dispersion as a proportion of the mean, making it a more suitable indicator for comparing variables with different scales. This measure allows for a better understanding of the extent to which values fluctuate relative to their average, particularly in the context of renewable energy production, energy use in agriculture, and EU agricultural expenditures under different priorities. The results presented in Table 2 indicate that ENUSE exhibits the highest coefficient of variation (coefficient of variation = 1.55), followed by expenditures under Priority 5 (coefficient of variation = 1.47). This suggests a high degree of variability in energy consumption patterns across countries, likely due to differences in farm mechanization, climate conditions, and access to alternative energy sources. In contrast, expenditures under Priority 4 display the lowest relative variation (coefficient of variation = 0.79), reflecting a more uniform allocation of funds across regions. The inclusion of the coefficient of variation provides a more comprehensive statistical perspective on the distribution of values, allowing for a more precise interpretation of funding disparities and energy sustainability trends in EU agriculture.

The correlation matrix (

Table 3. Correlation matrix.

Correlation
Probability	lnREN	lnENUSE	lnP2	lnP3	lnP4	lnP5	lnP6
lnREN	1.00000
	-
lnENUSE	0.633626	1.000000
	0.0000	-
lnP2	0.215292	0.518165	1.000000
	0.0779	0.0000	-
lnP3	0.434424	0.619452	0.603364	1.000000
	0.0002	0.0000	0.0000	-
lnP4	0.582608	0.806552	0.565905	0.758365	1.000000
	0.0000	0.0000	0.0000	0.0000	-
lnP5	0.011890	0.344241	0.619359	0.331988	0.297610	1.000000
	0.9233	0.0040	0.0000	0.0057	0.0137	-
lnP6	0.126057	0.429488	0.664178	0.710138	0.442880	0.574758	1.000000
	0.3057	0.0003	0.0000	0.0000	0.0002	0.0000	-

Source: Own processing in Eviews12.

) provides insights into the relationships between renewable energy production (lnREN), energy use in agriculture and forestry (lnENUSE), and EU expenditures under the studied priorities (lnP2–lnP6). The analysis reveals varying degrees of association among the variables, highlighting both significant and negligible relationships. The strongest positive correlation is observed between lnENUSE and lnP4 (correlation coefficient: 0.807, p-value: 0.0000), indicating that increased energy use in agriculture and forestry is closely associated with higher EU expenditures on Priority 4, which focuses on restoring, preserving, and enhancing ecosystems. Similarly, lnREN is strongly correlated with lnP4 (0.583, p-value: 0.0000), suggesting that investments under Priority 4 significantly contribute to renewable energy production. These findings align with the objectives of Priority 4, which emphasize environmental sustainability and resource efficiency, potentially facilitating the adoption of renewable energy practices. Another strong correlation exists between lnP3 and lnP6 (0.710, p-value: 0.0000). This indicates a close relationship between expenditures on Priority 3 (Food Chain Organization and Risk Management) and Priority 6 (Social Inclusion and Economic Development). This relationship may reflect the interconnectedness of food security and socio-economic growth in rural areas.

Moderate positive correlations are observed between lnENUSE and lnP3 (0.619, p-value: 0.0000), as well as between lnREN and lnP3 (0.434, p-value: 0.0002). These results suggest that investments in food chain organization and risk management moderately influence both energy use and renewable energy production. Similarly, lnP6 shows moderate correlations with lnREN (0.126, p-value: 0.3057) and lnENUSE (0.429, p-value: 0.0003), pointing to a potential but less pronounced effect of socio-economic expenditures on energy outcomes.

Notably, lnREN exhibits a negligible correlation with lnP5 (0.012, p-value: 0.9233). This implies that expenditures under Priority 5 (Resource-efficient, Climate-resilient Economy) have minimal impact on renewable energy production. Similarly, the correlation between lnENUSE and lnP5 is relatively weak (0.344, p-value: 0.0040). These results may suggest inefficiencies in how Priority 5 funding is translating into measurable energy-related outcomes.

The results mark the critical role of Priority 4 in driving both renewable energy production and energy use in agriculture, as evidenced by its strong correlations with lnREN and lnENUSE. In contrast, the minimal correlation between lnREN and lnP5 raises concerns about the effectiveness of resource-efficient investments in promoting renewable energy adoption. The moderate to strong correlations between expenditures under Priorities 3 and 6 and energy variables further highlight the interconnected nature of food security, socio-economic development, and energy sustainability. These findings suggest that a more integrated approach to EU funding under these priorities could yield synergistic benefits in terms of energy efficiency and rural development. Overall, the correlation analysis identifies Priority 4 as a key driver of energy sustainability outcomes, while highlighting areas where policy adjustments may be necessary to maximize the impact of EU expenditures.

The robust least squares regression analysis evaluates the relationship between renewable energy production (lnREN) and EU expenditures under Priorities 2 to 6 (lnP2–lnP6). The results presented in

Table 4. Robust Least Squares Regression Results for Renewable Energy Production.

Dependent Variable: lnREN
Method: Robust Least Squares S-estimation
Sample: 2015 2022
S settings: tuning = 1.547645, breakdown = 0.5, trials = 200, subsmpl = 6, refine = 2, compare = 5
Random number generator: rng = kn, seed = 148,189,186
Huber Type I Standard Errors & Covariance
Variable	Coefficient	Std. Error	z-Statistic	Prob.
lnP2	−0.037653	0.074806	−0.503340	0.6147
lnP3	0.170416	0.089107	1.912499	0.0558
lnP4	0.934766	0.097033	9.633474	0.0000
lnP5	−0.024720	0.034780	−0.710744	0.4772
lnP6	−0.032225	0.056872	−0.566628	0.5710
C	−12.93057	1.156267	−11.18303	0.0000
Robust Statistics
R-squared	0.403931	Adjusted R-squared		0.355861
Scale	0.660478	Deviance		0.436231
Rn-squared statistic	325.6878	Prob(Rn-squared stat.)		0.000000
Non-robust Statistics
Mean dependent var	5.199691	S.D. dependent var		1.413700
S.E. of regression	1.361869	Sum squared resid		114.9906

Source: Own processing in Eviews12.

indicate varying levels of significance and impact of the independent variables on renewable energy production, providing insights into which priorities are most effective in driving renewable energy adoption in agriculture.

The expenditure under Priority 4 (lnP4), which focuses on restoring, preserving, and enhancing ecosystems, emerges as the most significant predictor of renewable energy production. The coefficient for lnP4 is 0.935 (p-value: 0.0000), indicating a strong and statistically significant positive effect. This suggests that investments under Priority 4 play a critical role in fostering renewable energy production, aligning with its environmental objectives that promote sustainability and resource efficiency. The variable lnP3 (Food Chain Organization and Risk Management) has a coefficient of 0.170 (p-value: 0.0558), which is marginally significant at the 10% level. This result indicates a potential positive relationship between expenditures under Priority 3 and renewable energy production, though the effect is relatively weaker compared to lnP4. The association may reflect efforts to integrate energy sustainability into food supply chains and risk management practices.

Expenditures under Priority 2 (lnP2), Priority 5 (lnP5), and Priority 6 (lnP6) exhibit no statistically significant relationship with renewable energy production. Their coefficients are small and accompanied by high p-values. These results suggest that funding allocated to farm viability and competitiveness (lnP2), resource efficiency and climate resilience (lnP5), and social inclusion and economic development (lnP6) has minimal direct impact on renewable energy production. The negative coefficients for lnP2, lnP5, and lnP6—though statistically insignificant—may indicate inefficiencies in how these expenditures are translated into renewable energy outcomes. This warrants further investigation into whether these priorities indirectly contribute to energy sustainability or are misaligned with renewable energy objectives.

The R-squared value of 0.404 and the adjusted R-squared of 0.356 indicate that approximately 36–40% of the variability in renewable energy production is explained by the independent variables included in the model. While this suggests moderate explanatory power, it also indicates that other unobserved factors significantly influence renewable energy production. The robust regression diagnostics, including the Rn-squared statistic (325.688, p-value: 0.0000), confirm the overall significance of the model. This highlights that the chosen variables collectively explain renewable energy production trends, even though some variables do not have individual significance.

The findings underscore the pivotal role of Priority 4 in driving renewable energy production. Policymakers should prioritize and potentially increase funding allocations to initiatives under this priority, given its substantial and statistically significant impact. Conversely, the lack of significance for lnP2, lnP5, and lnP6 suggests that these expenditures may require a reevaluation to better align with renewable energy goals.

According to the results presented in the

Table 5. Robust Least Squares Regression Results for Energy Use.

Dependent Variable: lnENUSE
Method: Robust Least Squares S-estimation
Sample: 2015 2022
S settings: tuning = 1.547645, breakdown = 0.5, trials = 200, subsmpl = 6, refine = 2, compare = 5
Random number generator: rng = kn, seed = 920,021,897
Huber Type I Standard Errors & Covariance
Variable	Coefficient	Std. Error	z-Statistic	Prob.
lnP2	0.061119	0.070006	0.873049	0.3826
lnP3	0.381304	0.083383	4.572912	0.0000
lnP4	0.090846	0.089720	1.012558	0.3113
lnP5	0.154501	0.032476	4.757433	0.0000
lnP6	−0.155583	0.053313	−2.918294	0.0035
C	−3.622358	1.053763	−3.437544	0.0006
Robust Statistics
R-squared	0.591492	Adjusted R-squared		0.559577
Scale	0.435850	Deviance		0.189966
Rn-squared statistic	143.8870	Prob(Rn-squared stat.)		0.000000
Non-robust Statistics
Mean dependent var	5.817350	S.D. dependent var		1.106668
S.E. of regression	0.969606	Sum squared resid		60.16874

Source: Own processing in Eviews12.

, lnP3 and lnP5 exhibit statistically significant relationships with lnENUSE, as indicated by their respective p-values of 0.0000, which are well below the 0.05 significance threshold. Specifically, the coefficient for lnP3 is 0.381304, suggesting that a 1% increase in lnP3 is associated with a 0.38% increase in environmental use, while the coefficient for lnP5 is 0.154501, implying a positive but smaller effect on the dependent variable.

On the other hand, the variables lnP2 and lnP4 do not show statistically significant effects, with p-values of 0.3826 and 0.3113, respectively. These findings suggest that changes in lnP2 and lnP4 do not have a robust influence on environmental use within the context of this model. The variable lnP6 has a statistically significant negative coefficient of −0.155583 (p-value = 0.0035), implying that higher values of lnP6 are associated with a decrease in lnENUSE. This suggests that the impact of lnP6 on environmental use is inverse, and this relationship is robust given the low p-value. The constant term (C) is also statistically significant, with a coefficient of −3.622358 and a p-value of 0.0006. This indicates that, holding all other variables constant, the baseline level of lnENUSE is negative, further suggesting a need for consideration of other factors influencing environmental use beyond those included in the model.

The R-squared value is 0.591492, indicating that the model explains about 59.15% of the variance in the dependent variable. While this is a decent fit, there is still a significant portion of the variability in lnENUSE that remains unexplained by the independent variables in the model. The Adjusted R-squared value of 0.559577, which accounts for the number of predictors used, further supports this moderate explanatory power. Robust statistics indicate the model’s robustness against outliers and heteroscedasticity. The Rn-squared statistic of 143.8870 with a p-value of 0.0000 suggests that the model fits well and the overall regression is statistically significant. The scale of 0.435850 and deviance of 0.189966 indicate a reasonably low level of residual variance, suggesting that the model does not suffer from large unexplained errors. The sum of squared residuals (60.16874) is relatively modest, reinforcing the idea that the model fits the data well without significant residuals that would indicate model misfit.

The results from the pairwise Granger causality tests (

Table 6. Pairwise Granger causality tests results.

Null Hypothesis:	F-Statistic	Prob.
lnP2 does not Granger Cause lnREN	2.78033	0.0550
lnREN does not Granger Cause lnP2	1.34019	0.2766
lnP3 does not Granger Cause lnREN	1.48508	0.2350
lnREN does not Granger Cause lnP3	1.10086	0.3614
lnP4 does not Granger Cause lnREN	2.59268	0.0677
lnREN does not Granger Cause lnP4	0.82583	0.4883
lnP5 does not Granger Cause lnREN	2.67382	0.0618
lnREN does not Granger Cause lnP5	0.32252	0.8090
lnP6 does not Granger Cause lnREN	2.90352	0.0489
lnREN does not Granger Cause lnP6	0.75372	0.5279
lnP2 does not Granger Cause lnENUSE	2.81048	0.0699
lnENUSE does not Granger Cause lnP2	13.5275	2 × 10−5
lnP3 does not Granger Cause lnENUSE	0.03979	0.9610
lnENUSE does not Granger Cause lnP3	3.64386	0.0335
lnP4 does not Granger Cause lnENUSE	1.21336	0.3060
lnENUSE does not Granger Cause lnP4	1.14774	0.3257
lnP5 does not Granger Cause lnENUSE	3.80746	0.0290
lnENUSE does not Granger Cause lnP5	0.22139	0.8022
lnP6 does not Granger Cause lnENUSE	2.16745	0.1258
lnENUSE does not Granger Cause lnP6	10.0915	0.0002

Source: Own processing in Eviews12.

), conducted on the sample from 2015 to 2022 with a lag length of 2, reveal significant insights into the potential predictive relationships between the variables. The null hypothesis for each test assumes that one variable does not Granger cause another, and the p-values indicate whether these assumptions hold true. First, the analysis of lnP2 and lnREN suggests a weak potential causality. The p-value for lnP2 Granger causing lnREN is 0.0550, which is slightly above the 0.05 threshold, indicating that there is marginal evidence that lnP2 can influence lnREN. However, the reverse relationship, lnREN → lnP2, is not significant, with a p-value of 0.2766. This suggests that while lnP2 might have some predictive power over lnREN, the reverse is not true. For lnP3 and lnREN, no significant Granger causality is found in either direction. The p-values for both directions (lnP3 → lnREN and lnREN → lnP3) are well above 0.05, at 0.2350 and 0.3614, respectively. This indicates that there is no evidence of a predictive relationship between these two variables within the given time frame. In the case of lnP4 and lnREN, there is some weak evidence of causality in the direction of lnP4 → lnREN, with a p-value of 0.0677. This is just above the conventional 0.05 significance level but suggests a possible relationship at the 0.10 level. On the other hand, the reverse causality (lnREN → lnP4) shows a p-value of 0.4883, suggesting no significant predictive power from lnREN to lnP4. For lnP5 and lnREN, the p-value for lnP5 → lnREN is 0.0618, indicating weak evidence of a Granger causality, but it is not quite significant at the 0.05 threshold. In contrast, the reverse relationship (lnREN → lnP5) shows no significant causality, with a p-value of 0.8090. This suggests that while lnP5 might have some influence on lnREN, this relationship is not particularly strong or reliable. The test for lnP6 and lnREN reveals a significant result in the direction of lnP6 → lnREN, with a p-value of 0.0489, indicating that lnP6 has a statistically significant predictive power over lnREN. However, the reverse relationship (lnREN → lnP6) is not significant, as indicated by the p-value of 0.5279.

In the case of energy use, the test for lnP2 and lnENUSE shows weak evidence of causality in the direction of lnP2 → lnENUSE, with a p-value of 0.0699, which is slightly above the conventional 0.05 threshold, suggesting marginal causality. In contrast, the reverse direction, lnENUSE → lnP2, shows strong evidence of causality with a p-value of 2 × 10⁻⁵, indicating that lnENUSE significantly influences lnP2. For lnP3 and lnENUSE, the results suggest no significant causal relationship in the direction of lnP3 → lnENUSE, with a very high p-value of 0.9610, suggesting that lnP3 does not predict lnENUSE. However, the reverse relationship (lnENUSE → lnP3) shows a statistically significant result with a p-value of 0.0335, indicating that lnENUSE has a predictive effect on lnP3. In the pair of lnP4 and lnENUSE, neither direction shows significant causality. The p-values for lnP4 → lnENUSE (0.3060) and lnENUSE → lnP4 (0.3257) are both above the 0.05 threshold, indicating that there is no evidence of Granger causality between these two variables within the given time frame. For lnP5 and lnENUSE, the test for lnP5 → lnENUSE shows significant causality with a p-value of 0.0290, indicating that lnP5 can predict lnENUSE. However, the reverse relationship (lnENUSE → lnP5) is not significant, as the p-value is 0.8022, suggesting no causality from lnENUSE to lnP5. The analysis for lnP6 and lnENUSE reveals mixed results. The test for lnP6 → lnENUSE shows no significant causality with a p-value of 0.1258, while the reverse direction (lnENUSE → lnP6) shows strong causality with a p-value of 0.0002, indicating that lnENUSE significantly influences lnP6.

5. Discussions

The historical trajectory of agricultural development in Central and Eastern European countries has played a crucial role in shaping farm structures, funding allocations, and policy adoption in the post-socialist era. Prior to 1990, these countries operated under state-controlled agricultural systems, where land was largely collectivized, and farming was managed through large-scale cooperative structures [82]. The transition to market-oriented economies following the collapse of socialism led to significant agricultural restructuring, affecting farm sizes, land ownership patterns, and access to financial support, including EU funding mechanisms [83]. These structural transformations continue to have implications for agricultural investment patterns, energy use, and the effectiveness of EU agricultural policies in fostering energy sustainability.

A key factor that differentiates CEE countries is farm size distribution, which influences both the allocation of EU funds and the capacity of farmers to implement energy-efficient agricultural practices. Countries such as Romania and Poland maintain a high number of small-scale farms, with average farm sizes of 3.6 hectares and 11 hectares, respectively [84]. In contrast, countries like Slovakia and the Czech Republic have significantly larger farms, with average acreages of 73 and 130 hectares, respectively [85]. This disparity may affect the ability of farms to access and utilize EU funding effectively, as larger farms may have greater financial capacity and technical expertise to invest in renewable energy production and energy-efficient technologies, while smaller farms may face financial and bureaucratic constraints [86,87].

Additionally, climate variation across CEE countries may play a role in shaping agricultural energy needs and policy implementation. Southern regions, which experience higher average temperatures, may have higher demand for irrigation systems, cooling technologies, and water-efficient agricultural practices [88]. In contrast, colder regions in Central and Northern Europe may require increased energy inputs for heating, greenhouse cultivation, and mechanized farming. These regional climatic factors could influence the effectiveness of EU funding allocations, particularly under Priority 5 (Resource-efficient, Climate-resilient Economy), which targets sustainability and climate adaptation strategies in agriculture.

The findings of this study offer important insights into the relationship between EU agricultural expenditures and energy sustainability in nine Central and Eastern European (CEE) countries: Bulgaria, Czechia, Estonia, Hungary, Lithuania, Latvia, Poland, Romania, and Slovakia. These countries, characterized by their diverse agricultural practices and socio-economic contexts, present unique opportunities and challenges for the effective use of EU funds to promote renewable energy production and optimize energy use in agriculture.

One of the key findings is the strong positive relationship between Priority 4 expenditures, which focus on restoring, preserving, and enhancing ecosystems, and renewable energy production. This suggests that ecosystem-focused investments play a critical role in fostering the adoption of renewable energy technologies. In the CEE context, these expenditures likely target activities such as biomass energy production from agricultural and forestry residues, small-scale solar installations on farms, and wind energy projects in rural areas. The natural resources and land availability in these countries provide a solid foundation for leveraging such investments.

However, the results also highlight the variability in the effectiveness of different funding priorities. For instance, Priority 5, which emphasizes resource-efficient and climate-resilient economies, did not demonstrate a significant impact on renewable energy production. This may indicate that the implementation of resource-efficiency programs in the CEE region does not directly address renewable energy objectives or that these programs are still in the early stages of development. Such findings point to the need for a more strategic alignment between funding objectives and regional energy goals.

The analysis of energy use in agriculture and forestry reveals important insights into how different funding priorities influence energy consumption patterns. Priority 3 expenditures, which focus on food chain organization and risk management, show a strong positive relationship with energy use. This likely reflects the adoption of energy-intensive technologies and infrastructure improvements within agricultural supply chains, such as refrigeration, transportation, and storage systems. While these investments are essential for modernizing agriculture and reducing post-harvest losses, they also underscore the need for integrating energy efficiency measures into such initiatives.

In contrast, Priority 6 expenditures, aimed at social inclusion and economic development, exhibit a negative relationship with energy use. This finding suggests that socio-economic investments may promote more sustainable energy practices, potentially through the empowerment of rural communities, the development of local renewable energy projects, or the encouragement of energy conservation behaviors. This dynamic is particularly relevant in the CEE context, where rural areas often face challenges such as energy poverty and limited access to modern energy infrastructure.

The Granger causality analysis further enriches the understanding of the temporal relationships between expenditures and energy outcomes. The delayed but significant impact of Priority 6 expenditures on renewable energy production highlights the transformative potential of socio-economic development in creating enabling conditions for energy transitions. Investments in education, infrastructure, and community engagement can foster long-term changes in energy practices, particularly in underdeveloped rural areas.

The weak evidence of causality for Priority 2 (farm viability and competitiveness) suggests that this funding priority primarily focuses on enhancing agricultural productivity rather than energy sustainability. However, the broader implications of such investments, including their potential to drive technological innovation and efficiency improvements, warrant further exploration.

The CEE countries included in this study share several historical and structural characteristics that shape the impact of EU agricultural expenditures. Many of these nations transitioned from centrally planned economies to market-based systems in the late 20th century, resulting in fragmented land ownership, outdated infrastructure, and varying levels of institutional capacity. These factors influence the effectiveness of funding priorities and may explain some of the observed variability in outcomes. For example, larger countries like Poland and Romania, with extensive agricultural and forestry resources, may have greater potential to benefit from ecosystem-focused investments compared to smaller states such as Estonia or Latvia. Similarly, differences in administrative capacity and policy implementation across the CEE region can affect how effectively EU funds are utilized.

The findings of our study align with existing literature on the challenges and motivations associated with RES adoption in agriculture. The socio-economic barriers identified, such as financial constraints and regulatory complexities, are consistent with those reported by White Research [41]. Addressing these challenges through targeted policies and support mechanisms could enhance the uptake of RES among farmers in the CEE region. Furthermore, the observed willingness of smaller farms to engage with RES technologies suggests that tailored interventions considering farm size and capacity may be effective in promoting sustainable energy practices within the agricultural sector.

Moreover, the concept of agrivoltaics presents a promising avenue for RES integration in agriculture. By allowing the simultaneous use of land for both crop production and solar energy generation, agrivoltaics can improve land use efficiency and provide additional income streams for farmers. Ember’s 2024 report [42] highlights that Central European countries could deploy up to 180 GW of agri-PV, potentially tripling the region’s current renewable electricity generation. This approach not only contributes to energy sustainability but also supports agricultural productivity, making it a compelling strategy for RES adoption in the CEE context.

The findings of this study provide partial support for the formulated hypotheses, indicating that the impact of EU agricultural expenditures on energy sustainability varies depending on the funding priority and regional characteristics.

The results partially support Hypothesis 1 (H1), which posited that EU agricultural funding positively influences renewable energy production. The analysis confirms that expenditures under Priority 4 (Restoring, Preserving, and Enhancing Ecosystems) exhibit a strong and significant positive relationship with renewable energy production, suggesting that ecosystem-focused investments play a crucial role in fostering the adoption of biomass, solar, and other renewable energy technologies in agriculture. However, expenditures under Priority 2 (Farm Viability), Priority 5 (Resource Efficiency), and Priority 6 (Social Inclusion) do not demonstrate a significant impact, implying that these funding priorities may not effectively drive RES adoption, or that additional mediating factors influence their effectiveness.

Hypothesis 2 (H2), which proposed that EU agricultural expenditures contribute to reducing energy consumption and improving energy efficiency, is also partially supported. The results indicate a complex relationship between EU funding and direct energy use in agriculture. While Priority 6 expenditures (Social Inclusion and Economic Development) are negatively correlated with energy consumption, suggesting that socio-economic investments may contribute to long-term energy efficiency improvements, expenditures under Priority 3 (Food Chain Organization and Risk Management) are positively associated with higher energy use. This finding suggests that certain agricultural investments, particularly those aimed at improving supply chain resilience and risk management, may inadvertently lead to increased energy demand due to mechanization, storage, and processing requirements. These mixed results highlight the need for targeted policy interventions that align agricultural funding priorities with broader energy sustainability goals.

Hypothesis 3 (H3), which states that the impact of EU agricultural expenditures on energy sustainability varies across CEE countries due to economic and structural differences, is strongly supported. The study underscores that regional disparities in farm size, institutional capacity, and climate conditions shape the effectiveness of EU funding in promoting renewable energy adoption and improving energy efficiency. For instance, larger farms in Slovakia and the Czech Republic may have better access to EU funds and technical expertise, allowing for more efficient implementation of RES projects, whereas fragmented farm structures in Romania and Poland may limit the scalability of such investments. Furthermore, climate variations across CEE countries may influence how agricultural energy policies are adopted, with warmer southern regions facing higher energy demands for irrigation and cooling systems.

6. Conclusions

This study provides a detailed analysis of the relationship between European Union (EU) agricultural expenditures and energy sustainability outcomes in nine Central and Eastern European (CEE) countries: Bulgaria, Czechia, Estonia, Hungary, Lithuania, Latvia, Poland, Romania, and Slovakia. By focusing on two key metrics—renewable energy production and energy use in agriculture and forestry—this research offers critical insights into how different funding priorities influence energy-related outcomes in these countries. The findings highlight the pivotal role of expenditures under Priority 4, which focuses on restoring, preserving, and enhancing ecosystems. These investments have a significant positive impact on renewable energy production, suggesting that ecosystem restoration and conservation efforts create enabling conditions for adopting renewable energy technologies, such as biomass and solar energy. This outcome underscores the importance of targeted funding to ecosystem-based initiatives in regions with substantial natural resources and agricultural potential, such as the CEE countries. In contrast, expenditures under Priority 5, targeting resource-efficient and climate-resilient economies, show no statistically significant relationship with renewable energy production. This finding points to potential misalignment between the implementation of resource-efficiency programs and the energy objectives of the CEE region. It also highlights the need for more strategic integration of renewable energy goals into such programs, ensuring that resource-efficiency investments contribute directly to energy sustainability. Expenditures under Priority 6, which aim to promote social inclusion and economic development, are negatively associated with energy use in agriculture and forestry. This indicates that socio-economic investments can contribute to energy efficiency by fostering community-led initiatives, promoting energy conservation behaviors, and reducing energy-intensive practices. These findings demonstrate the broader role of socio-economic development in shaping sustainable energy transitions, particularly in rural and underdeveloped areas of the CEE region. The analysis also reveals that expenditures under Priority 3, focused on food chain organization and risk management, are positively correlated with energy use in agriculture and forestry. This reflects the energy demands associated with modernizing agricultural supply chains, such as improved logistics, transportation, and storage systems. While these investments enhance supply chain efficiency, they also emphasize the need for integrating energy efficiency measures into such modernization efforts to mitigate the potential for increased energy consumption.

The findings of this study suggest several important policy recommendations for enhancing the impact of EU agricultural expenditures on energy sustainability in the Central and Eastern European (CEE) region. First, policymakers should prioritize ecosystem-focused investments under Priority 4, as these expenditures have demonstrated a significant positive impact on renewable energy production. Strengthening these investments can maximize the use of the region’s abundant natural resources and accelerate the transition toward renewable energy. Additionally, resource-efficiency initiatives under Priority 5 need to be better aligned with energy sustainability goals. Incorporating explicit renewable energy objectives into resource-efficiency programs can enhance their effectiveness in driving energy transitions. Lastly, the transformative potential of socio-economic development programs under Priority 6 should be leveraged by integrating energy-related goals, such as supporting rural energy autonomy and promoting community-driven renewable energy projects, to achieve both social and environmental benefits.

The practical implications of this study are particularly relevant for stakeholders involved in agricultural policy, rural development, and energy planning. For policymakers, the results highlight the importance of designing funding programs that consider regional specificities and align with the unique socio-economic and environmental contexts of CEE countries. Development agencies and local governments can use these findings to prioritize ecosystem restoration projects and socio-economic initiatives that simultaneously address energy sustainability and rural development goals. Additionally, private sector stakeholders, such as renewable energy developers and agricultural enterprises, can capitalize on the opportunities created by EU funding to implement innovative energy solutions in the agricultural sector.

While this study provides valuable insights, several limitations must be acknowledged. The analysis focuses solely on nine CEE countries, which, although diverse, may not fully represent the broader dynamics of EU agricultural expenditures. Additionally, the study relies on aggregated expenditure data, which may obscure variations in the implementation and impact of funding at the regional or project level. The moderate explanatory power of the regression models suggests that other factors, such as governance quality, energy infrastructure, and technological adoption, may also influence energy outcomes but were not explicitly included in this study. Furthermore, temporal dynamics, while partially explored through Granger causality analysis, may require more detailed longitudinal studies to capture the full impact of funding over time.

Building on the findings and limitations of this study, future research should explore several key areas. First, extending the analysis to include a broader range of EU countries or focusing on cross-regional comparisons within the EU could provide deeper insights into the varying effectiveness of agricultural expenditures. Second, incorporating additional variables, such as technological innovation, policy incentives, and regional governance quality, would enhance the understanding of the factors driving energy sustainability. Third, qualitative studies investigating the implementation and management of EU-funded projects at the local level could provide a more nuanced perspective on the mechanisms through which funding influences energy outcomes. Finally, exploring the interplay between agricultural expenditures and other sustainability metrics, such as carbon emissions and biodiversity conservation, would offer a more holistic view of the impact of EU agricultural policies. These research directions can contribute to the development of more effective and comprehensive policy frameworks for achieving energy and environmental sustainability in the EU.