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Article

Can European Farms Cover Their Energy Costs with Revenue from Renewable Energy Production? Evidence from FSDN Data

by
Roma Ryś-Jurek
Department of Finance and Accounting, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
Energies 2026, 19(17), 4009; https://doi.org/10.3390/en19174009
Submission received: 17 July 2026 / Revised: 13 August 2026 / Accepted: 14 August 2026 / Published: 26 August 2026
(This article belongs to the Topic Energy Transition: Balancing Economic, Political, and Social Realities)
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Abstract

This study examines whether renewable energy production is capable of covering energy costs in European Union family farms and identifies the determinants of the Energy Cost Coverage Ratio across different economic size classes. The analysis was based on Farm Sustainability Data Network (FSDN) data for 2014–2023 and combined descriptive statistics with panel data models. The results indicate that the Energy Cost Coverage Ratio remained relatively stable, fluctuating between 30.9% and 39.2%, despite a substantial increase in revenue from energy production and other gainful activities from €1594 to €2999 per farm. This limited improvement resulted from a simultaneous rise in energy costs, which increased from €5162 to €8465 per farm over the analysed period. Considerable differences in the Energy Cost Coverage Ratio were observed between economic size classes, while panel data models showed that its determinants vary across farm classes, with no single factor being significant for all classes. The findings suggest that revenue from the production of renewable energy has strengthened the economic resilience of European farms, but its contribution remains insufficient to fully offset rising energy expenditures, highlighting the need for farm-size-specific policy support.

1. Introduction

The transition towards a low-carbon economy has become one of the central objectives of European Union policy [1,2]. Increasing the share of renewable energy sources is expected not only to reduce greenhouse gas emissions and dependence on fossil fuels but also to improve energy security and the long-term sustainability of economic activities [3,4,5,6,7,8]. Agriculture occupies a special position in this process because it is simultaneously a consumer and a potential producer of energy. As energy is required throughout the food production chain, fluctuations in energy prices directly affect production costs, farm profitability, and food security [9,10,11,12].
From the perspective of sustainable development, the ability of farms to generate renewable energy may contribute not only to environmental goals but also to economic resilience. Renewable energy production can reduce dependence on external energy supplies, mitigate the effects of rising energy prices, and create an additional source of income for farm households [13,14,15]. However, despite the growing importance of renewable energy in European agriculture, relatively little is known about its actual economic significance at the farm level and, in particular, about its capacity to offset energy costs incurred by agricultural producers.
Therefore, this study examines whether renewable energy production and related activities are capable of covering energy costs in European family farms. Particular attention is paid to differences between farm size classes and to the factors affecting the degree of energy cost coverage. By focusing on the economic dimension of the energy transition, the study contributes to the discussion on the sustainability and resilience of European agriculture under conditions of increasing energy market uncertainty.

2. Related Studies

Agriculture is highly dependent on energy inputs and remains one of the sectors most exposed to changes in energy availability and prices. The growing global population and the need to increase food production place additional pressure on agricultural systems, which must maintain productivity while using resources more efficiently [16]. Energy is required throughout the food supply chain, including agricultural production, processing, storage, packaging and distribution of food products [17,18]. Moreover, both direct energy inputs used on farms and indirect energy embodied in fertilisers, seeds, machinery and other production factors contribute to the overall energy requirements of food production systems [19,20,21,22].
Over recent decades, the intensification of agricultural production and the increasing complexity of food supply chains have strengthened the dependence of agriculture on external energy sources, particularly fossil fuels [23,24,25]. Energy plays a crucial role in maintaining agricultural productivity [26], while food processing is regarded as one of the most energy-intensive stages of the food chain [27]. Previous studies have also highlighted the importance of technological progress and innovation in reducing energy use and improving production efficiency [28,29,30,31,32]. Nevertheless, the adoption of modern technologies often requires substantial investments, which may create additional challenges for farms, especially in less-developed regions.
As a result, the economic consequences of energy use have become increasingly important for the long-term viability of farms. While numerous studies have examined energy consumption, energy efficiency and energy structures in agriculture and national economies [33,34,35,36,37], considerably less attention has been paid to the relationship between farm-generated energy and the energy costs incurred by agricultural producers. In particular, there is limited evidence on the extent to which renewable energy production and related on-farm activities can compensate for rising energy expenditures. This issue appears especially relevant in the European Union, where farms operate under diverse production conditions and face growing pressure to improve both their economic resilience and resource-use efficiency.

3. Materials and Methods

The empirical analysis is based on data obtained from the Farm Sustainability Data Network (FSDN), which provides harmonised economic and financial information on commercial farms across the European Union. The database enables comprehensive analyses of production, financial performance, and farm management over the period 2004–2023. Currently (as of 30 July 2026), the database does not contain complete data for 2023 and lacks information for Malta. Also, United Kingdom data is excluded after 2020.
The study focuses on Energy Cost Coverage Ratio, calculated as the ratio of SE730 to SE345 (SE numbers denote the official variable identifiers used in the FSDN database). This indicator reflects the extent to which revenues generated from renewable energy and related activities correspond to farm expenditure on motor fuels, lubricants, electricity, and heating fuels. According to the FSDN methodology, SE730 includes revenue from energy production together with other gainful activities that are not reported separately. Therefore, throughout this paper, the term “Revenue from energy production and other gainful activities (or OGA)” is used consistently. According to the FSDN methodology, the SE730 variable combines revenue from renewable energy production with other gainful activities that are not reported separately. Consequently, the database does not allow the contribution of renewable energy production alone to be isolated.
While SE730 is not limited exclusively to renewable energy production, revenues related to renewable energy constitute the core element of this category. As no separate EU-wide indicator is available within the FSDN database, SE730 represents the most suitable variable for comparative analyses across Member States. The FSDN database does not distinguish between individual renewable energy technologies, such as photovoltaic systems, biogas plants or wind turbines, which prevents technology-specific analyses.
Despite the acknowledged limitations of these indicators, they constitute the only currently available and harmonised data published by the European Commission within the FSDN database. Consequently, they were adopted for the present analysis, as the collection of primary data from FSDN farms through a separate survey is not feasible given the methodological framework and confidentiality requirements of the FSDN system. This limitation results from the structure of the FSDN database and the way in which non-agricultural activities are aggregated. Nevertheless, SE730 currently represents the only internationally comparable source of information available for all European Union Member States that allows renewable energy-related activities to be analysed at the farm level. The objective of this study was to identify general relationships at the European Union level. Therefore, differences in national regulatory frameworks and support schemes were beyond the scope of the present analysis.
This study is an attempt to address the questions listed below:
  • To what extent does revenue from renewable energy production and other gainful activities contribute to covering on-farm energy costs in European agriculture?
  • How does the Energy Cost Coverage Ratio differ across economic size classes of European farms?
  • Which farm characteristics are associated with the Energy Cost Coverage Ratio?
Average information was retrieved from the FADN in order to answer questions 1 and 2. To answer question 3, the Gretl v.2025a software was used. The panel models based on 1094 individual FSDN observations were estimated.
The panel structure already accounts for country-specific effects. Further stratification by production type or additional farm characteristics would substantially reduce the number of observations within each panel and would therefore require a different modelling framework beyond the scope of the present study.
In its general form, the panel data model can be expressed as follows [38]:
yi,t = αi + X′i,t β + ui,t + εi,t
where
−
i (i = 1, …, N) means individuals,
−
t (t = 1, …, T) means time intervals,
−
X′i,t is the observation of K explanatory variables (in country i at time t),
−
αi is a time-invariant parameter accounting for any effects that are specific to the individual concerned and are not covered by the regression equation.
Panel data models may be estimated using either Fixed Effects (FEM) or Random Effects (REM) specifications, depending on the assumptions regarding individual effects [39]. The appropriate model was selected using the Hausman test, supported by the characteristics of the analysed data and the substantive interpretation of the results, rather than relying solely on statistical significance tests [40,41,42].
The FSDN database contains aggregated observations representing groups of at least 15 farms rather than individual farm records. The results should therefore be interpreted as representative for aggregated groups of farms rather than individual units. Consequently, the appropriate panel specification (FEM or REM) was selected separately for each estimated model based on the Hausman test and the statistical characteristics of the data. As an additional diagnostic step, the estimated panel models were assessed using the Durbin–Watson statistic to examine residual autocorrelation and the Variance Inflation Factor (VIF) to assess potential multicollinearity among explanatory variables [38].
The empirical analysis was based on a set of explanatory variables describing the production, economic, and financial performance of agricultural farms. These variables were selected to capture the key characteristics that may influence renewable energy-related activities and were obtained directly from the FSDN database. The dependent and independent variables included in the analysis are listed below:
  • Y01: Energy Cost Coverage Ratio (SE730/SE345, %);
  • X01: Labour Inputs (SE010, Annual Work Units);
  • X02: Utilised Agricultural Area (SE025, ha);
  • X03: Total Output (SE131, €);
  • X04: Total Inputs (SE270, €);
  • X05: Depreciation (SE360, €);
  • X06: Taxes (SE390, €);
  • X07: Balance Subsidies and Taxes on Investments (SE405, €);
  • X08: Family Farm Income (SE420, €);
  • X09: Assets (SE436, €);
  • X10: Liabilities (SE485, €);
  • X11: Net worth (SE501, €);
  • X12: Gross Investment (SE516, €);
  • X13: Net Investment (SE521, €);
  • X14: Cash Flow (SE526, €);
  • X15: Total Subsidies without on Investments (SE605, €).

4. Results

Table 1 presents the evolution of renewable energy-related activities and selected economic indicators of European Union farms during the period 2014–2023. Over the study period, the average value of revenues from renewable energy production and other gainful activities increased from €1594 to €2999 per farm, while average energy costs rose from €5162 to €8465 per farm. Despite these substantial increases in absolute values, the Energy Cost Coverage Ratio remained relatively stable, fluctuating from 30.9% to 35.4%, with the highest value recorded in 2015 (39.2%) (Table 1; Figure 1).
The share of revenue from renewable energy production and other gainful activities in total farm output remained remarkably stable throughout the analysed period, varying between 2.0% and 2.6%. Similarly, the share of energy costs in total farm inputs fluctuated around 6.7–8.4%, reaching its highest level (8.42%) in 2022, when energy prices increased considerably across Europe (Figure 2). At the same time, the economic performance of European farms improved substantially. Average total output increased from €70,960 to €126,838 per farm, while farm net income rose from €17,452 in 2014 to €30,780 in 2023, despite reaching a temporary peak of €41,160 in 2022. The average utilised agricultural area also expanded from 33.9 to 41.8 hectares per farm, indicating the continuing structural consolidation of farms within the European Union (Table 1; Figure 2).
The annual EU-level values presented in Table 1 and Figure 1 and Figure 2 are aggregated averages for the respective years and are therefore used to illustrate the general dynamics of revenue from energy production, energy costs and related indicators over the 2014–2023 period. As these aggregated values do not contain farm-level observations or measures of statistical error, the observed changes over time should be interpreted as descriptive rather than as statistically estimated trends.
Overall, the results suggest that although renewable energy production and OGA generated progressively higher revenues in absolute terms, their relative importance within total farm production changed only marginally. Likewise, the increase in energy production revenues was accompanied by a comparable rise in energy costs, resulting in a relatively stable level of energy cost coverage throughout the analysed period.
In the following section, the Energy Cost Coverage Ratio, revenue from energy production and other gainful activities, energy costs and selected additional farm characteristics are analysed according to the economic size of European Union farms (Table 2 and Table 3). Due to the extensive amount of available data, four reference years were selected to provide equal intervals and ensure a clear presentation of long-term trends: 2014, 2017, 2020 and 2023 (Table 2 and Table 3). This approach allows for a consistent comparison of changes over time while limiting the influence of short-term market disturbances. In particular, the exclusion of 2022 reduces the impact of exceptional energy price fluctuations and market instability associated with the geopolitical crisis following the outbreak of the war in Ukraine. Although 2022 was characterised by exceptional energy price disturbances, all panel models were estimated using the complete annual dataset covering 2014–2023. The descriptive analysis presents selected years only to improve readability and to reduce the visual influence of this exceptional market shock.
Table 2 presents the Energy Cost Coverage Ratio, revenue from energy production and other gainful activities, and energy costs of the European Union farms according to economic size classes in 2023, with reference values for the years 2014, 2017, and 2020. The results indicate substantial differences between economic size classes, confirming that the scale of the farm is an important factor differentiating both the level of revenue from energy production and OGA and the ability to cover energy costs.
The highest values of revenue from energy production and other gainful activities were consistently observed in the largest farms (class 6, very large). In this group, revenue from energy production and other gainful activities increased from €33,279 per farm in 2014 to €57,446 per farm in 2023. At the same time, these farms also recorded the highest energy costs, which increased from €74,274 to €88,698 per farm over the analysed period. Despite the high level of energy expenditure, the Energy Cost Coverage Ratio in this group was the highest among all economic size classes, reaching 64.77% in 2023 (Table 2). Also, large farms (class 5) showed higher levels of revenue from energy production and OGA compared with smaller farms, increasing from €3768 per farm in 2014 to €4497 in 2023. However, the Energy Cost Coverage Ratio remained considerably lower than in very large farms, reaching 24.14% in 2023 (Table 2).
Medium-sized farms (classes 3 and 4) showed moderate levels of revenue from energy production and other gainful activities and energy costs, with a gradual decline or limited changes in the Energy Cost Coverage Ratio during the analysed period (Table 2).
The smallest farms demonstrated substantially lower values of revenue from energy production and other gainful activities and energy costs. In very small farms (class 1), revenue from energy production and other gainful activities increased from €17 per farm in 2014 to €159 per farm in 2023. However, energy costs also increased, resulting in a relatively low Energy Cost Coverage Ratio of 16.79% in 2023 (Table 2). Small farms (class 2) recorded the lowest Energy Cost Coverage Ratio in 2023 (5.64%), despite higher revenue from energy production and other gainful activities compared with the smallest farms (Table 2).
Overall, the results show that revenue from energy production and OGA increases with farm economic size, but the relationship between revenue from production and the ability to cover energy costs is not proportional across all classes. The highest Energy Cost Coverage Ratio was observed in very large farms, suggesting that larger farms benefit from greater capacity to generate energy in relation to their energy expenditure.
Table 3 presents selected economic characteristics of European Union farms according to economic size classes, including the share of revenue from energy production and OGA in total output, the share of energy costs in total inputs, total output, total inputs, total utilised agricultural area, and farm net income. The results demonstrate clear differences between farm size classes and confirm the strong relationship between economic size and the overall scale of farm activity.
The smallest farms (class 1, very small) recorded the lowest values of total output, total inputs, utilised agricultural area, and farm net income. Between 2014 and 2023, total output increased from €6881 to €9602 per farm, while farm net income remained relatively stable, changing from €2756 to €2468. Although revenue from energy production and other gainful activities increased considerably in relative terms, the share of revenue from energy production and OGA in total output remained limited, reaching 1.66% in 2023. At the same time, the share of energy costs in total inputs remained relatively high (10.69%), indicating the significant importance of energy expenditure for the smallest farms (Table 3).
Small farms (class 2) showed moderate growth in economic indicators, with total output increasing from €19,623 to €21,969 per farm between 2014 and 2023. However, the share of revenue from energy production and other gainful activities in total output declined from 2.05% to 0.54%, while the share of energy costs in total inputs remained above 11% in 2023. Farm net income also remained relatively stable over the analysed period (Table 3).
Medium-small and Medium-large farms (classes 3 and 4) represented intermediate levels of economic activity. In both groups, total output and farm net income increased between 2014 and 2023. For medium-small farms, total output rose from €42,898 to €49,883 per farm, while farm net income increased from €15,305 to €17,396. Medium-large farms recorded a stronger increase, with total output growing from €81,858 to €94,746 and farm net income from €26,084 to €31,153. The contribution of revenue from energy production and other gainful activities to total output decreased slightly in these classes, reaching 1.88% and 1.47% in 2023, respectively (Table 3).
Large farms (class 5) demonstrated substantially higher levels of production and income. Between 2014 and 2023, total output increased from €237,057 to €285,777 per farm, while farm net income increased from €54,928 to €69,867. Despite the increase in revenue from energy production and other gainful activities, its share in total output remained stable at approximately 1.5–1.6%. The share of energy costs in total inputs also remained relatively low compared with smaller farms, reaching 7.38% in 2023 (Table 3).
The largest farms (class 6, very large) differed markedly from all other groups. In 2023, these farms achieved a total output of €1,568,367 per farm and farm net income of €282,736, accompanied by the largest utilised agricultural area (261.8 ha/farm). They also recorded the highest share of revenue from energy production and other gainful activities in total output, increasing from 3.00% in 2014 to 3.66% in 2023. At the same time, the share of energy costs in total inputs remained the lowest among all classes (6.46% in 2023) (Table 3).
Overall, the results indicate that larger farms operate at a substantially larger economic scale and generate higher absolute values of revenue from energy production and other gainful activities. However, the relative importance of revenue from energy production and other gainful activities differs between farm size classes. While very large farms achieved the highest contribution of this revenue to total output, smaller farms faced a considerably higher relative burden of energy costs within their input structure (Table 3).
In contrast, the panel data models were estimated using multiple farm-year observations within each economic size class rather than aggregated annual averages, allowing the estimated coefficients, standard errors and significance tests to be evaluated on the basis of the underlying panel structure.
The panel data models estimated for the Energy Cost Coverage Ratio indicate differences in the determinants of this indicator across farm size classes (Table 4). The final specifications were selected separately for each economic size class on the basis of the Hausman test. Random-effects models were selected for the first and fifth economic size classes, whereas fixed-effects models were estimated for the second, third, fourth and sixth classes. The final models included one explanatory variable in the first and fifth classes and two explanatory variables in each of the remaining four classes. Thus, the final specifications remained relatively parsimonious while allowing for differences in the determinants of energy cost coverage across farm size classes (Table 4).
The overall explanatory power of the estimated models was relatively high for empirical farm-level data, with the reported R2 values exceeding 0.63 in all six models. The within R2 values were lower, reaching up to approximately 0.31, which reflects the more limited variation explained within individual panel units. The Durbin–Watson statistics exceeded 1 in all estimated models, providing no indication of severe positive residual autocorrelation. In addition, multicollinearity diagnostics based on the Variance Inflation Factor (VIF) did not indicate problematic multicollinearity in the final model specifications (Table 4).
The estimated coefficients further demonstrate that the determinants of the Energy Cost Coverage Ratio differed considerably between economic size classes. Total assets had a significant impact in most farm classes and generally showed a negative relationship with the Energy Cost Coverage Ratio. The exception was very large farms, where the relationship was positive. In large and very large farms, an increase in utilised agricultural area reduced the level of energy cost coverage. Total inputs had a positive effect in very small and medium-small farms, while in medium-large farms a significant positive impact of total liabilities was identified (Table 4).
The results indicate that the ability of farms to cover energy costs is determined by different factors depending on their economic size. At the same time, the absence of a single variable that was statistically significant across all farm classes suggests that the development of energy-related activities is highly differentiated and depends on the specific characteristics of individual farm groups (Table 4).

5. Discussion

The results obtained in this study indicate that although revenue from energy production and other gainful activities in European Union farms increased during the analysed period, energy costs also rose substantially. Consequently, improvements in the Energy Cost Coverage Ratio were relatively limited, suggesting that the expansion of on-farm energy production has not been sufficient to offset the growing costs of energy consumption. These findings confirm that increasing energy production alone does not automatically improve the economic resilience of farms.
The growing importance of renewable energy in agriculture has been emphasised in numerous studies. Modern farming requires considerable amounts of energy for crop production, livestock management, irrigation, machinery operation and post-harvest processing. Therefore, the transition towards renewable energy sources is increasingly recognised as an important element of sustainable agricultural development and climate policy. However, this transition remains a complex process requiring technological progress, financial support and long-term policy commitment [43].
The present results are consistent with previous studies showing that the development of renewable energy on farms depends not only on the willingness of farmers to invest but also on institutional conditions and regulatory frameworks. In many European countries, administrative procedures, investment costs and different support schemes continue to influence the pace of renewable energy adoption [44]. Consequently, the economic benefits of Energy Production may differ considerably between farms and regions.
Agriculture possesses substantial potential for renewable energy generation because agricultural land and biomass resources can contribute to diversified energy production systems. Nevertheless, renewable energy generated on farms should be regarded as a complementary component of agricultural activity rather than a complete substitute for conventional energy sources. As pointed out in earlier studies, the technical and biological limitations of agricultural production set natural boundaries for the amount of energy that can realistically be produced by farms [45].
The observed increase in Energy Production is also consistent with the long-term priorities of the European Union. Financial instruments implemented under the Common Agricultural Policy and Horizon 2020 have supported investments in innovation, renewable energy technologies and sustainable rural development [46]. Such support has contributed to improving investment opportunities for farms, although access to these instruments remains uneven across regions and farm types.
The results further demonstrate that the role of energy production differs substantially according to the economic size of farms. Larger farms generally generated considerably higher energy production values and achieved higher Energy Cost Coverage Ratios than smaller farms. One possible explanation for the higher Energy Cost Coverage Ratio observed in larger farms is their greater investment capacity and the economies of scale they achieve. Larger farms generally generate higher revenues from energy production while being better able to spread investment and operating costs over a larger production scale. They may also have easier access to external financing, modern technologies and advisory services, making investments in renewable energy more economically feasible. Consequently, the monetary value of energy production may increase faster than energy-related costs. However, this interpretation should be treated with caution because the FSDN database does not provide information on specific renewable energy technologies or individual investment projects.
The panel data models presented in this study complement these descriptive findings by demonstrating that the determinants of the Energy Cost Coverage Ratio vary across economic size classes. No single explanatory variable remained statistically significant for all farm groups, suggesting that the ability to cover energy costs depends on different combinations of production, financial and structural characteristics. This heterogeneity indicates that the mechanisms shaping farm energy performance cannot be explained by one universal model.
At the farm level, investments in renewable energy frequently require changes in production organisation and long-term planning. Introducing new technologies often involves adjustments in farm management, financial decision-making and investment strategies [47]. Therefore, improvements in Energy Cost Coverage Ratio should be considered as a gradual process rather than an immediate outcome of energy investments.
Previous studies also indicate that the transition towards more sustainable production systems requires not only technological innovation but also changes in business models and resource management. Circular economy concepts, resource efficiency and environmentally oriented innovation are increasingly recognised as important directions for agricultural development [48,49,50]. The relatively modest improvements observed in the Energy Cost Coverage Ratio suggest that these broader transformations are still underway in many European farms.
Beyond the farm-level results presented in this study, the broader context of energy use and renewable energy development in agriculture also deserves consideration. Agriculture has become an increasingly important sector in the global energy transition. According to recent international reports, agriculture accounted for approximately 2.5% of global total final energy consumption in 2022, while the share of renewable energy in the sector increased from 11.4% to 17.8% over the previous decade. At the same time, energy use on farms remains responsible for approximately 12% of total on-farm greenhouse gas emissions, highlighting the need to improve energy efficiency and expand renewable energy production [51]. In the European Union, agriculture and forestry consume energy both directly, through machinery operation, heating and electricity use, and indirectly through the production of fertilisers, machinery and farm infrastructure. Although direct energy consumption in the agricultural sector declined in 2023 compared with the previous year, energy remains an essential production input for European farms [52].
Previous empirical studies have also demonstrated that energy consumption is positively associated with agricultural production and broader economic performance. Panel analyses conducted for G20 countries indicate that both renewable and non-renewable energy contribute positively to agricultural activity, while the economic effects of renewable energy appear to be even stronger. These findings further support policies encouraging investments in renewable energy technologies and improvements in farm energy efficiency [53,54].
Overall, the results indicate that revenue from renewable energy production and other gainful activities has become an increasingly important element of farm development across the European Union. Nevertheless, its economic effectiveness remains strongly differentiated between economic size classes, while rising energy costs continue to limit improvements in energy self-sufficiency. These findings suggest that future support measures should not rely on a uniform approach. Instead, policies promoting renewable energy in agriculture should account for differences in farm economic size, as the determinants of Energy Cost Coverage Ratio vary considerably between farm classes.
In light of the obtained results, renewable energy support measures should be better tailored to the economic size of farms. Smaller farms may require greater investment support and easier access to financing renewable energy technologies, whereas larger farms may benefit more from incentives promoting further technological development and energy efficiency improvements. Because the determinants of the Energy Cost Coverage Ratio differ across farm classes, a uniform policy approach may be less effective than farm-size-specific support measures.

6. Conclusions

The main objective of this study was to examine whether revenue from renewable energy production and related activities is capable of covering energy costs in European family farms and to identify the factors influencing this relationship across different economic size classes.
The first research question asked to what extent revenue from renewable energy production and OGA contributes to covering on-farm energy costs in European agriculture. The results indicate that renewable energy revenues covered approximately one-third of average energy costs over the analysed period. Although revenue from renewable energy production and other gainful activities increased considerably in absolute terms, rising energy expenditures limited improvements in the Energy Cost Coverage Ratio. The descriptive trends for 2014–2023 should be interpreted with caution, as the annual EU-level values are aggregated averages and are intended primarily to illustrate changes over time.
The second research question concerned differences in the Energy Cost Coverage Ratio across economic size classes. The findings show substantial variation between farm groups. Very large farms achieved by far the highest Energy Cost Coverage Ratio and generated the greatest renewable energy revenues, whereas small and very small farms exhibited much lower coverage levels and faced a relatively greater burden of energy costs.
The third research question addressed the determinants of the Energy Cost Coverage Ratio. The panel data models demonstrated that the factors influencing energy cost coverage differ across farm size classes. No single explanatory variable was statistically significant for all groups, suggesting that the mechanisms shaping renewable energy performance are heterogeneous and depend on the structural and economic characteristics of farms. The panel model results, in contrast, are based on multiple farm-year observations within economic size classes and therefore provide the statistical basis for assessing the determinants of the Energy Cost Coverage Ratio.
From an economic perspective, the obtained results indicate that the ability of farms to cover energy costs depends not only on the scale of renewable energy-related revenues, but also on the overall economic structure and financial capacity of farms. Higher Energy Cost Coverage Ratios observed in larger farms may reflect their greater ability to allocate resources to investments, adopt new technologies and benefit from economies of scale. In contrast, smaller farms may face stronger financial constraints, which can limit the development of renewable energy-related activities despite their potential importance for reducing production costs. The differences identified between farm size classes confirm that energy cost coverage is not determined by a single factor, but rather results from the interaction of farm resources, production scale and investment capacity.
The obtained results provide practical guidance for policymakers responsible for agricultural and energy policy. Since the determinants of the Energy Cost Coverage Ratio differ across economic size classes, a uniform support scheme may not produce the expected outcomes for all farms. Smaller farms may require greater assistance in overcoming financial barriers to renewable energy investments through investment grants, preferential loans or advisory services, whereas larger farms may benefit more from measures supporting the further expansion and efficiency of existing renewable energy systems. Therefore, future policy instruments should better reflect the economic diversity of European farms rather than apply identical support mechanisms to all farm categories.
This study has several limitations. First, the analysis relies on aggregated FSDN data rather than individual farm observations. Second, the SE730 indicator combines revenue from renewable energy production with other gainful activities, making it impossible to isolate revenues generated exclusively from renewable energy technologies. Although the SE730 variable includes revenues from other gainful activities, renewable energy production accounts for the vast majority of this category, whereas the remaining activities constitute only a negligible proportion. Finally, the study focuses on the European Union as a whole and does not account for country-specific institutional, regulatory, or technological differences.
Future research should use farm-level microdata where available and distinguish between different renewable energy technologies, such as biogas, photovoltaics, and wind energy. Further studies could also investigate the effects of national policy instruments, investment support schemes, and technological innovations on farm energy self-sufficiency and economic resilience.

Funding

The publication was financed by Poznań University of Life Sciences, Poland within statutory activities.

Data Availability Statement

FSDN—Farm Sustainability Data Network (Public Database SO) at https://agridata.ec.europa.eu/extensions/FSDNPublicDatabase/FSDNPublicDatabase.html (accessed on 22 June 2026).

Conflicts of Interest

The author declares no conflicts of interest.

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Energies 19 04009 g001
Figure 1. Energy Cost Coverage Ratio (%), Revenue from Energy Production and OGA and Energy Costs (€/farm) of the European Union * farms in 2014–2023. Source: own compilation based on Table 1. * Without Malta in 2023; without the United Kingdom in 2021–2023.
Figure 1. Energy Cost Coverage Ratio (%), Revenue from Energy Production and OGA and Energy Costs (€/farm) of the European Union * farms in 2014–2023. Source: own compilation based on Table 1. * Without Malta in 2023; without the United Kingdom in 2021–2023.
Energies 19 04009 g001
Energies 19 04009 g002
Figure 2. Share of Revenue from Energy Production and OGA in Total Output (%), Share of Energy Costs in Total Inputs (%), Total Output (€/farm) and Total Inputs (€/farm) in the European Union * in 2014–2023. Source: own compilation based on Table 1. * Without Malta in 2023; without the United Kingdom in 2021–2023.
Figure 2. Share of Revenue from Energy Production and OGA in Total Output (%), Share of Energy Costs in Total Inputs (%), Total Output (€/farm) and Total Inputs (€/farm) in the European Union * in 2014–2023. Source: own compilation based on Table 1. * Without Malta in 2023; without the United Kingdom in 2021–2023.
Energies 19 04009 g002
Table 1. Revenue from Energy Production and Other Gainful Activities, Energy Costs and other financial details about European Union * farms in 2014–2023.
Table 1. Revenue from Energy Production and Other Gainful Activities, Energy Costs and other financial details about European Union * farms in 2014–2023.
YearEnergy Cost
Coverage Ratio (%)		Revenue from
Renewable Energy Production and OGA (€/Farm)		Energy Costs (€/Farm)Share of Revenue from
Energy Production and OGA in Total Output (%)		Share of Energy Costs
in Total
Inputs (%)		Total
Output (€/Farm)		Total
Inputs (€/Farm)		Total
Utilised Agricultural
Area (ha/Farm)		Farm Net Income (€/Farm)
201430.88159451622.258.0270,96064,36233.917,452
201539.19189448332.617.3472,54565,84934.417,619
201635.94163745552.276.9872,21665,27134.618,362
201734.97169448442.217.2776,53966,66135.121,613
201831.95204263912.117.4096,86286,31643.425,373
201932.85211464352.097.21101,35889,26343.427,234
202035.80216660512.136.69101,81090,46843.526,968
202132.23217567492.017.39108,18391,27140.432,229
202232.82294889812.248.42131,854106,69341.241,160
202335.43299984652.367.61126,838111,27741.830,780
* Without Malta in 2023; without the United Kingdom in 2021–2023. Source: own calculations based on 2026 FSDN data.
Table 2. Energy Cost Coverage Ratio (%), Revenue from Energy Production and OGA and Energy Costs (€/farm) of the European Union * farms by economic size class in 2023.
Table 2. Energy Cost Coverage Ratio (%), Revenue from Energy Production and OGA and Energy Costs (€/farm) of the European Union * farms by economic size class in 2023.
DetailsEconomic Size Classes
1. €2000 ≤
€8000
Very Small		2. €8000 ≤
€25,000
Small		3. €25,000 ≤
€50,000
Medium-Small		4. €50,000 ≤
€100,000
Medium-Large		5. €100,000 ≤
€500,000
Large		6. ≥ €500,000
Very Large
Energy Cost Coverage Ratio (%)20142.6422.1441.4130.4724.0244.81
20172.1912.1431.3534.7427.0157.68
20203.559.1331.0326.5127.7456.54
202316.795.6421.3218.7224.1464.77
Revenue from Energy Production and OGA (€/farm) 20141740215761981376833,279
20171419110151885353834,131
2020251389431375358634,349
20231591199401395449757,446
Energy Costs (€/farm) 20146451 8163806650215,68774,274
20176381 5733238542613,09859,170
20207051 5123039518712,92960,750
20239472 1114409745318,63188,698
* Excluding Malta. Source: own calculations based on 2026 FADN data.
Table 3. Revenue from Energy Production and Other Gainful Activities, Energy Costs and other financial details about European Union * farms by economic size class in 2023.
Table 3. Revenue from Energy Production and Other Gainful Activities, Energy Costs and other financial details about European Union * farms by economic size class in 2023.
DetailsEconomic Size Classes
1. €2000 ≤
€8000
Very Small		2. €8000 ≤
€25,000
Small		3. €25,000 ≤
€50,000
Medium-Small		4. €50,000 ≤
€100,000
Medium-Large		5. €100,000 ≤
€500,000
Large		6. ≥ €500,000
Very Large
Share of Revenue from Energy Production and OGA in Total Output (%)20140.252.053.672.421.593.00
20170.221.032.472.471.613.16
20200.350.752.351.801.582.96
20231.660.541.881.471.573.66
Share of Energy Costs in Total Inputs (%)201411.9311.239.938.847.297.04
201712.1810.849.198.196.786.15
202010.7710.328.727.846.385.78
202310.6911.399.969.007.386.46
Total Output (€/farm)2014688119,62342,89881,858237,0571,109,612
2017640318,61941,02976,470220,2881,078,852
2020707018,31740,09976,470226,8251,161,919
2023960221,96949,88394,746285,7771,568,367
Total Inputs (€/farm)2014540816,16938,33573,561215,2221,054,464
2017523914,51235,23966,291193,166961,667
2020654714,65434,85066,156202,8071,051,452
2023885918,53544,24882,787252,3311,372,635
Total Utilised Agricultural Area (ha/farm)20145.215.430.556.7103.6295.7
20174.814.328.253.6100.8269.6
20206.214.028.452.6103.7258.0
20236.214.028.550.2100.7261.8
Farm Net Income (€/farm)20142756858315,30526,08454,928149,566
20172424925816,40928,03960,203205,131
20202401886916,72529,44459,947203,581
20232468876317,39631,15369,867282,736
* Excluding Malta. Source: own calculations based on 2026 FADN data.
Table 4. Panel data models for the Energy Cost Coverage Ratio of European Union * farms by economic size class in 2014–2023.
Table 4. Panel data models for the Energy Cost Coverage Ratio of European Union * farms by economic size class in 2014–2023.
DetailsEconomic Size Classes
1. Very Small2. Small3. Medium-Small4. Medium-Large5. Large6. Very Large
Number of observations128220267275275227
Type of modelREMFEMFEMFEMREMFEM
LSDV R2/Theta0.63130.86780.85260.71340.89440.8815
Within R2/corr(y.yhat)20.14530.14430.31240.09300.00000.1193
Durbin-Watson Statistic1.55471.16161.11311.14871.65041.3886
Hausman Testχ2 (1) = 0.7670
(p = 0.3812)		χ2 (2) = 12.7478 (p = 0.0017)χ2 (2) = 18.0835
(p = 0.0001)		χ2 (2) = 7.5746
(p = 0.0227)		χ2 (1) = 3.7867
(p = 0.0517)		χ2 (2) = 7.1654
(p = 0.0278)
constCoefficient−0.08461.21980.98671.66140.59450.8691
Standard Error0.09240.36970.46620.66490.20370.1643
t-statistic/z-statistic **−0.91573.29902.11602.49902.91905.2890
p-value0.35980.00120.03540.01310.00350.0000
X02: Utilised Agricultural AreaCoefficient----−0.0019−0.0012
Standard Error----0.00100.0002
t-statistic/z-statistic **----−1.8690−4.7410
p-value----0.06160.0000
X03: Total OutputCoefficient-0.00004----
Standard Error-0.00001 ----
t-statistic/z-statistic **-4.1860----
p-value-0.0000----
X04: Total InputsCoefficient0.00002-0.00005---
Standard Error0.00001-0.00001---
t-statistic/z-statistic **2.1920-6.7660---
p-value0.0283-0.0000---
X09: AssetsCoefficient-−0.00001−0.000001−0.00001-0.0000001
Standard Error-0.0000010.0000010.000001-0.00000001
t-statistic/z-statistic **-−4.8590−9.3710−3.4700-2.3770
p-value-0.00000.00000.0006-0.0184
X10: LiabilitiesCoefficient---0.00002--
Standard Error---0.000004--
t-statistic/z-statistic **---4.9780--
p-value---0.0000--
* Without Malta in 2023; without the United Kingdom in 2021–2023. ** The t-statistic is reported for FEM, whereas the z-statistic is reported for REM. Source: own compilation based on 2026 FADN data.
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Ryś-Jurek, R. Can European Farms Cover Their Energy Costs with Revenue from Renewable Energy Production? Evidence from FSDN Data. Energies 2026, 19, 4009. https://doi.org/10.3390/en19174009

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Ryś-Jurek R. Can European Farms Cover Their Energy Costs with Revenue from Renewable Energy Production? Evidence from FSDN Data. Energies. 2026; 19(17):4009. https://doi.org/10.3390/en19174009

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Ryś-Jurek, Roma. 2026. "Can European Farms Cover Their Energy Costs with Revenue from Renewable Energy Production? Evidence from FSDN Data" Energies 19, no. 17: 4009. https://doi.org/10.3390/en19174009

APA Style

Ryś-Jurek, R. (2026). Can European Farms Cover Their Energy Costs with Revenue from Renewable Energy Production? Evidence from FSDN Data. Energies, 19(17), 4009. https://doi.org/10.3390/en19174009

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